diff --git a/src/evaluation/figure_notebooks/figure_4/4d_marginal_footprinting_motifs.ipynb b/src/evaluation/figure_notebooks/figure_4/4d_marginal_footprinting_motifs.ipynb
index 3f609911..9b3577b6 100755
--- a/src/evaluation/figure_notebooks/figure_4/4d_marginal_footprinting_motifs.ipynb
+++ b/src/evaluation/figure_notebooks/figure_4/4d_marginal_footprinting_motifs.ipynb
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -147,7 +147,7 @@
        "dict_keys(['GM12878_COUNTS_metacluster_0_pattern_0', 'GM12878_COUNTS_metacluster_0_pattern_1', 'GM12878_COUNTS_metacluster_0_pattern_10', 'GM12878_COUNTS_metacluster_0_pattern_11', 'GM12878_COUNTS_metacluster_0_pattern_12', 'GM12878_COUNTS_metacluster_0_pattern_13', 'GM12878_COUNTS_metacluster_0_pattern_14', 'GM12878_COUNTS_metacluster_0_pattern_15', 'GM12878_COUNTS_metacluster_0_pattern_16', 'GM12878_COUNTS_metacluster_0_pattern_17', 'GM12878_COUNTS_metacluster_0_pattern_18', 'GM12878_COUNTS_metacluster_0_pattern_19', 'GM12878_COUNTS_metacluster_0_pattern_2', 'GM12878_COUNTS_metacluster_0_pattern_20', 'GM12878_COUNTS_metacluster_0_pattern_21', 'GM12878_COUNTS_metacluster_0_pattern_22', 'GM12878_COUNTS_metacluster_0_pattern_23', 'GM12878_COUNTS_metacluster_0_pattern_24', 'GM12878_COUNTS_metacluster_0_pattern_25', 'GM12878_COUNTS_metacluster_0_pattern_26', 'GM12878_COUNTS_metacluster_0_pattern_27', 'GM12878_COUNTS_metacluster_0_pattern_28', 'GM12878_COUNTS_metacluster_0_pattern_29', 'GM12878_COUNTS_metacluster_0_pattern_3', 'GM12878_COUNTS_metacluster_0_pattern_30', 'GM12878_COUNTS_metacluster_0_pattern_31', 'GM12878_COUNTS_metacluster_0_pattern_32', 'GM12878_COUNTS_metacluster_0_pattern_33', 'GM12878_COUNTS_metacluster_0_pattern_34', 'GM12878_COUNTS_metacluster_0_pattern_35', 'GM12878_COUNTS_metacluster_0_pattern_36', 'GM12878_COUNTS_metacluster_0_pattern_4', 'GM12878_COUNTS_metacluster_0_pattern_5', 'GM12878_COUNTS_metacluster_0_pattern_6', 'GM12878_COUNTS_metacluster_0_pattern_7', 'GM12878_COUNTS_metacluster_0_pattern_8', 'GM12878_COUNTS_metacluster_0_pattern_9', 'control'])"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,12 +177,14 @@
     "    width = end - start\n",
     "    ax.plot(range(width), uncorrected[start:end]*1000, 'r', linewidth=1, alpha=0.3, label=\"uncorrected\")\n",
     "    ax.plot(range(width), corrected[start:end]*1000, 'k', linewidth=1, label=\"corrected\")\n",
+    "    ax.spines[['top', 'right']].set_visible(False)\n",
+    "\n",
     "    #ax.set_ylim(0,ylim) \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -202,18 +204,11 @@
       "GM12878_COUNTS_metacluster_0_pattern_12\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:65: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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Ewlhs6kZiESnGgm5KeFxs+dOGtTZcu11GZ5DQGlRdXoPQ6wmwbaW9S79TcGjicbnwtDFLsrbcy1t3lXD/7NzIcyY8LwuPoei1TawEwuj1TWduTAmB8BDEf5AIhO1NMf7i+UpeuamIXRucrF7YuDGLvc4fuQEEm5TEJtUrHIPBrBIpxoJOoyMa/EQTnmAa44KPlFg4CBuqfcQlqzGYVUIg7I7k50Np6SHvIMxdG+zwHd6l9rjkRgJhTGrfhsZr027kAkGs2LHawUePl0dee9wBrj9iWyRNUpbl4D0/SY06BgKhyxHg6Tn5FGwN3s+cQiAUdFNsdcHfTYc1wBt3FAPBOZvOoEKrl7q8BuGCV6t49uoC3rizWDRIEXQqHrcbr9/fpvopm/8MitY+r0x9VXAiFp6XhTeAoseKvT72AmHBFmenfY8QCA9BDhoHYajIfVsFwsLQRO6jx8so3tnYvWKr6yYOwtAENzlTKxyEgk4jPHb2J8W4YKur2e5vWFjQGVRodVJMHIQN1T7iUzRBgVDUIOx+WK1gs+FyBAWywCHYpESWZTYuCaZLhhvp2Or8mOLVqNRhgTB2KcZisSboLjw9J5/f/1cbeR3e1P3zq3oArDV+XI4A6b11MUkx3rXByY7Vjsjrpg1TBILugq12z7O2rtzHVy9V4LIH0BkkdHqpy2sQhjfH/vqxge/eqOrS7xYcWnjcbnx+P3Ibal7nrd9zP98jEAZfh8eQt5s4COOS1KT00JK3TgiEgg4kugahUa9X7EKtvQ7CsIhRsCUoFJ5wWUrknL1+j4NQExIIY9JNMnTzSc7SUl/la5ROIxB0FBEH4X6kGD964S6evboAl6N5oVy9MXb1O63VfuJTNcQla2ioVuY97aDG7we/PyIQKnVj6kDYvsoRef5UFQXHXl2Fl4Q0TWQiGguB0OMOOwjF80bQ/dDqJTb8HhTWU3poASjPD46f9BwdGl3XOwjDNUPDlO8+NEsmCLo/1po9z9r4VA3fv1kNhDZ09aoudxDGJWsiP5ft6pgyNwJBS4RNEN42mCGia3SGfy/DDsKwC9cX1Qk8VptCLrsfg1lF9kB95DnYGQiB8BAkLA5oNRq0h4CD0O+XefG6gkgqCEBcsppTr0nnX98OYMhEM7Y6PyU7g+ctSeqYOQjtdcF/ix799fi8Mg6xKy3oBDoixfimqdsIBIJjxGH1o1IFF3JBB2HX/t5Wl3qxN/iJT9GQnKltVGRY0D1wOBy4XS5c9qD7NHAIbn5s/tOOWiMx4YR4Kos8yLJMfaWPpAztnhqEsWhSEnIOxiplRtBG7IdG3a6mjXp8HpntfwU3Fuoqgs+uspAgl5Gj31ODsAud69FpXmo1lOx0x2TOKBDsi2iB0JK4R9n2uAJo9cEN3fBcrivQ6qXIz3nrHdRVivlat8Pvh8rKfV/XzQnrA20xEtVVeMkZZgBg/r0l/PJ+TcT8YIuq3Rlmxbf1MWkm6rIHMJjVmBPUnVr7VgiEhyBhQVCv1aLWahUrEEYchPsoQNpQ5WPT0sYT60e+HwhAUroWc6Iae52fP76oo89wA9kDghPOrnZB7VwbrLmj1Uv0GhK8SYkHp6AzaE+TElmWkWWZ/M1OXryuoNG5qqLg72ew/p8GlUqKiYPw1ZuDRezjktUkZWqoLfMK920348FXX+XVDz/E7QymRCj1uXMgbF9lp88IA+m9dVhr/DRU+/F6ZBLSgmNHkrq+BuHSL+vYtSH4b2Jv8AuRo5vi93o55ogjFJvx0R4qQq4IU5yKsTPikOWgeJ2YrqGmzMuij2uoyPeg1UskZWhi0qTEZdsjEM66Kg2vR6YkV7gIBd0Lv19m90YXemOoPnRUOnFNqRedIXi8KzsZu6OyT+x1fv74vK7LvlvQRhoaYPPmWEdxwIQdhG0xQ9SV++g91BB5/clT5WxdEdQOrLVhB2HjcfK//5TT1QQFQhVGi1o0KRF0LGFxQK/RoNbpFLtQC8e9rzpq4R3nR38YGDmmUu3ZwbIkqmmo9lG+28OAcSYkKTY1CF+7NdiKXQ5AYnrQgr98QX2XxiDYB1VVsGlTrKM4YHx+PzqNZp8pxkH3bSE3TNnGYxftbia0hxdEtlo/ccnBnelY1CAs3x38e4w7Np7kTC1ej4y11i8Ej26E1W6nrr7+kKpBWLzTxYvXFdBQ46N0l5v8zS5GHhVHQlrw/h4uMB2+36vUUjP3VGfi9QR496HSyOQXiHS5FHQvnDYbv6xfT111daxD6XTCtf3+752+jJoWFzk+/W/JAHz0eDllu9xk5Ogim1LQten5zlCd26PPTeKw4+MByFvn2NtbBLHC6YRt22IdRZdhr/fzxh3F1Ff52LXBib3Bz0UPZDFkormR83Xk1LiIm68r6xC6HQHUanj0x4Fk9dNHNqgE3QhZDroIFU5bU4y9ngAuR4DUnrrIsfCaBoJCdvi6aHQGia4mLBCa4tW4nYFOe+4JgfAQxNfUQajQm0AkxXgfDsLaCi/JmVriUzRc+3wvrn+5d6PzyVlaXI4AXo9MWujmoNYE749dabuPrqMTXkD+9E4Nr91WxC/v13RZHIK94HIFmy0oHJ/Ph1Gv36e4XprrZvOf9mZdicP1O4t3BHflwg1CADQ6VdemeoXG7qnXpGGKU5OUGaxRtXphA7fO2M6CecpPkzgY8Hq9uNxuXGEHoUKfO+3hp3dq2LTUzu3H7eChs/PQaCUmn5pAYlrwdzRvffD/RVJG8LVaA4EuFDmi6z+FHSbWmoP/30WJOG3BGnwVpaUxjqRzcVj9fPhoGYlpGlKztRjjgr+XI460MO3cJNJ76YhLVlO220NGHz1ATFKMw02GZl+fTkqWlqQMTaOmJYJuhN0O5V3v9IkVP7xVxV8LG1gwr5Li7cE5Wp8RRgxmFU5bAJUKjrskhfEz4yMOwq6sQ+h2BEjP0ROfrKHPCCP5m1wi46O7EQgE/yicsAliX2ud4u3B8+m9dAyZaOboc5M44rTEyPnwxmlYYD/7lgwA9Kaul9HCAqE5PvjdnZVm3C0FQkmS3pQkqUKSpI1Rx+6XJKlYkqS1oT8nxTJGJdNIINRoFOsgbGuKsbXaR8/BwYnksMkWBk8wNzqfmq3d83NYIIxBykq4i+W0c5IiC0iANb9Y+eSpcvEA7Q4EAqDQ8RKNz+fDpNfv00FYnu8hMU3Dlf/pGTl2/KUpnHBZKjnDDPz8bg31VT6sNf5I4emuTs+vLNxTrB4gs0/wvx8/EVwQrP7JitPmp7ZCpOvHEo/Xi9PpjAiEB7uDUJZltq1o7LjNGWYgLkkTcQxuXGxDpYLsAcHnU1c718MCP8B5d2YCjetVCboPzlD9wfKDXCCsDDXvmX5eMpIkMXSSmbNuzuAfj2Sj06uYdEoC1ho/1SXeyL0+JinG9gBanYRWp0KSJPqPMbH6Jytv31sSSUsTdBNCDbIOFfI3B+/rSz6r46PHy1GrISFVg96koiTXTSAAprhQxkcXOwgXf1bLml+sGMxB+aH3MAP2Bj/VpWJ+1p3wuN38sX59rMM4YNpag7Bwm4s+I4yMOMrCdS/15pxbMzn2wj2NTMMbp+FuxpNPTSQxXYPb2fUiqjPUpMRgCY7hzkoz7pYCIfA2cEILx5+WZXlM6M+3XRzTQYPf58NiNGLQ6xUtELbVQWiv95M90NDq+ZQeeyzFaT3DTo6uTVnxeWVqy7yccFkK59yagUYr8fTiwY2uCXcrqi33Up4vat3EBFk+pATC6hIvw46wRGpiApw2Nx2dQcURpyficgT47/0lWGt8ETu+Ric1cxx2JhUFIYGwd3Acp/XSYYja1asu8XL/GbncddJO1v+ufPenUvF4vThdrj01CA/yBVt1iZe6Sh+X/qsHVz/dkxnnJ3PenVlAsJMkQPFON5l99REXRzDFuOtiLMl1o9VJPL98CH2GGwHhIOyuhAXCioPcCeWo96PVSRxzYTCdWKtTMeO85IjDNdzFGCCrf0hYj4FA6LT5MVj2PGfCtatWfFvP4k9quywOwT6w2ykpLuax996LdSRdgtPmb5bqnpihRaWSIqIcgDH0u6vVd62D8IN/lwF73L7hzbGSnWJN051Ys349c198MdZhHDCRGoT7cBA6GvzkDDOgVu9JGTYnqHnq98Ecd0kKtlofgYCMrS74fNIbJQwmFR5n1xt3XLZQk5L44Jrr7XtLmH9fSaP1TWWRh+smb2Xbqv3frOqWAqEsy78DIqeyk/D5fMQbjXschApdqEUchPsQOWR5z0OoJaInnMlZjQXCQBdpQQVbnAQC0HOQAUkKfrfeqOK0a9MiVuYHz8pj3W9W7pq1kwfOzOvS9GdBkO07d/LBjz/GOowDxu/3Y9Tr9zl2vO4AOcMMJGVoGDXVwlVP7XESTjk9kb4jjWxZFkxBTgiJHlqdhD8GDsJweQCVSoosHAeOM+H3yRHR45Wbiti1UdS7iQUejweny3XIpBiX7fKgN6oYPS2OkUfFcdZNGfQI/V5aEtVoQyUl+o40Rt7T1Q7Csl0e0nvrUKsl4lM1SFJQcPd6Aiz9qg6PS/kpRgcL4RTj8rKyGEfSuVhr/aT21DWqEx1NeCMIoGdo4zc8lrryueO0BTBa9tSo6jlozyba7k1Ofv2wBnuDn5U/1FO0fd8F8gWdxG+/sXXbNj749ddYR9IlbFlmb7bJJIdu49Fd6g0RgTA4drr6Xh9O2ewxIDhuira7qa9S/ub7wYIvVBJG6bS1BqHPu6fEWDQGk4r4FA2BQNCpZ6vzYU5UI0kSepMKl6Nrx43bGSypZIpXYQqlGO/e6GT5gnpeuamIoh0u3n2olPtOz8Xnlfnm5f0vsdQtBcK9cK0kSetDKchJsQ5Gqfi8XuLNZvQ6naK7GIfj3peDECBnuLHVc+Z4NTPOT+am13Iik9KuTlnZtjK44zfoMFOj48dfksr0vyUz4/zgbvqrtxRFzonCvl3PqvXref/nn2MdxgHj8/sxGQz7dBDCHtH6qqd6MWrqnoLxKpXE4bMS9lw3ODjR0+ikLi14XVnkCabPGPc8zk69Jo0jTkvk7/dl8Y9Hshtdv+DVShqqlXnPUzIerxen240r1E1OqSnG1dXVFBYW7vO6igIPgyeaIu7AaFQqiYxQeuTYY6LGlBoCXdikpHx30MEIwYnwgLEmVnxbz7NXFfDug6Us+ayuy2IR7J1DxUFYWeiJZHK0RLSbPTWc8RELB6HVH3FhQVDoHzjOxIgjLdSW+/jff8q5//Rc3rqrhH+fv4vtf4m04y4nWEgch9VKvf3Q+P+ft96JVidx4b1ZjJ8ZbJ4TbqzgjKpVpgs5B2PRxRj2ONUNJhUpPbSsXtjAHSfs4PeQ+1aWZX7/pJbl39aL8jAxwOv14vR4gmNIwYTXOG3RCVJbee6Es6OstT5stX4sicHXOoMKTxenGEcbIqI3qML8+7xdLP2yLvK6aLt7vzedlSQQvgz0B8YApcCTLV0kSdIcSZJWSZK06q+//urC8JSDz+djTP/+/P2445TtIGyjQGgwqUjO1Oz1mrNuymDA2D3iXNhB2FUTzrz1Tnr012NJbDnOM29M55Y3c9AZJFKztegMEp89U9Gso9KBIMbOvvF4PDTY7Yp/aPp8vjYJhJIE2QNbd9/2G7VHeM8ZFvy562sQeknr1Xjnb/AEMxfek0Vqto7xM+N5YcUQTrgshZFHWdj8p53bj9/B/PtKsDd0zL1PjJ19E04xjjgIFbox9e4rr/D4vffu8zqnzc/oo+NaPX/pw9lM/1sSgyfuqYkbdBB2SJj7xOsJUF3ijQiVAIcdH091qTfSPGXLcjufPVvOg2fn8uJ1BZ1S2kKMnbbhDHX/rqg8uJsu5Qw3Mv285FbPq1QSlzzUg5OvTG2+oduB86G9IcsyhdtcZOTsGTt6o4ob5+Vwxg3pkWPRz5fnrwl2M5dluUPqVolx0wZCTRYcVisNjoO/gUzhNherf2qg1xADR5yayD8eyebCe7O45tleAJxzW2az94RF7nBX7s6mpfJNmX31lOQGny0fPlrGiu/qyVvv5MNHy5h/bwkvXLvvDbn2IMbOvvF6PLg8HsXX7gzrA/tKMQbI7NPyWicuKbgub6j2YavzR9bpepMKdxc7CKNLKqX21DbaYG7KMRck43YGyN+8f2YixQiEsiyXy7Lsl2U5ALwGTGzlunmyLB8my/Jh48eP79ogFYLf76dXZiZzzzzzkKhBmJihjaTttpVwZ6Ku2h0oy3PTYy9p0JIk0W+UiQe/HMCdH/Tjovt7sGuDkzduL2bnWkeHpBuLsbNvPG53cKKp8Iemz+/H2AaBMClT26IDKkzPQQZmXpTC+OPiI3X/NDqpy7pJyrJMaZ67UdpZS6hUEqdek84Fd2cx4/xksvrpWb6gnvce6piC/2Ls7Buvz9fYQajQDnnuhgbcbehknpCqaeSwbUpWPz1n35LZqOZNV6YYb1hsIxBonOIcLWjOOD+ZTX/Y+OmdGsp2edi01M5Tl+ezeZmtQ2MUY6dthAXC8oqKGEfSuYyY0ryRXFMmnpjASVekRV6HBcKuEtfLdnmw1vgZdFjzODP76PnXtwN4bOFA5j7Xi4knJXD2LRn4/XD7cTu4aep27p6184BjEOOmDYSeMU6bjQaHA1nh87Z9sX6RlboKX6NMpCNOTSRnaPAen9ZTx3N/DuGi+7MYOdUC7Ek1Dnfl7kxcjkDk2TH3uV6R41l9G8/fPnmyvFFH8NI8N7s2ODusUaMYO/vG5/MFHYQKHzNtTTFOSNW0uo4IOwsrC73Yan1YkoLOvVikGIcFwrReOrQ6FVc81pMnfh7E327P5KgzEwE4fFYC93/en+MvTUGlgpdvKOLWGdt55eb2Ce17t1V1IyRJypJlObyamw1s3Nv1gtbx+XxoTCbo0wf11q2KdhCqVap9CoTDp1ja/dnmhOANILpmR2fhcgSoLvVyxOmJ+7w2PiU4ZMcdG8/s6718/mwF63+3Menk1heigo7D4/XS4HSC1wsaxdw+m+Hz+Ugwm/cpELZUk6Mps69Lb/Rao1N1WZOSst0e7PV++o1uvYRANPEpGs66KQOXIxDcqf62nvSTOzlIARAcO4FAAHdIIFTqxpTP52tTan6/0aZW66i1hlojEegigfCvHxtITNMwdNIekSM+RcPs69LJHqgnZ7iR/E1OLEkaZFnm6HOT+fiJsoibQ62GlBO7JFQBwRTj9IQEKqqqYh1Kt0MTqkHo6aLSFl88X4FWJzF0cstCZlJ6cEE5/AgLw48Izj/rq3z8+HY1bmeAwRMsHNyVJLsJssya3Fwcdjs+vx+X3Y4xPj7WUXUqx1+awgn/SG31vEYrMenkxMjrcJqiqwsEwsKtwWf/VU/1jIwLIFLmIq2nlgvv7cFz1xTw1YuNndJPXLqbfzySHUmbFnQuXo8nKBAqdCM3THiutq85297KkCVnatHqJZZ9XUd1iZfxxwV/B/XGrk0x9vtl/vqxgYwcXaOSSuYENVPPSkKWZc64IaPRuUGHmdm6wk6P/nr6jDBSlt/27+uWK1xJkj4AjgZSJUkqAu4DjpYkaQwgA7uBK2MVn9Lx+XyodTro1SvoIFToDSDSaKENKcbtxRgXfE9HpSDujdKQtT6rX+sOwpaY+fcU+gw38ttHNbjsAdj7prugA4g4CBUqboTx+f2YjMZ9jp1wXcH2oNF2XZOSnWuCu8wDxpj2cWVjDCYVF92fhdGiYsvBn3nULfB4vXh9vj0OQoWm6Xu93jYJhJl99i2uN0Wj7boO4MU73PQdaWwmYs68KCXy881v9Gl07p8v9uazpyv4a2GD0o0FysLhwOV00jstjYrq6lhH0+3QaCW0eglHF2zoFu90sWGxjVOvSYsIgW3h9GvTOeGyVHZvdDJ4gokHHujEIAUAyH4/E26+mfsvvRSAhtrag1ogzB5o4LS56fu+MIpwirHD2vljZ/sqOyoVjco5QbAOb12Fl5FT4+g12MD1L/fmuzeqyB6gJ7OfnlU/NLB1uZ037ihm64pDo5ZkrPH5fPj8fnxuNxpj2zbguyOeNjYz7bWXtY5KJSEHIHedk8R0DcdcEJwjdbWDcMdfDop3urn04R4tnpekYHflaP52eyYrv6/nuEtS0OpULLu/7d/XLQVCWZbPa+HwG10eyEGKz+dDE3I/qdVqxToI/T4fBq0WbyeINWEHoaOTBUJZlln0v1pUKug/pv034YHjTAwcZyIQkHnwwU4IUNAIj8dzUAmE7k4Q103xKuz1fmRZbndqf3vJXeMgPkVDWq+2L9TCqFQS596Wyf33d3xcguZ4fD5cbnekM55SHYRerzcy6dwbKnX7f/fjktVYazr//8vrtxdRUeBhwgntWywnpWv5xyPZnDQnlYZqHx9800kBChqzejXOykqSLJaDvknJ/iBJEvEpGhpqOn8uu+SzOjRaiaPObH+fRINJxZCJYie3q/B7vfgDgYjrtr62loycnBhH1XmMmd56PbLWUKkkDCZVp6cYr/y+ngXzqug/2ogprnFzBaNF3ahkwICxJv75Qu/I6yNOTWT1Tw38/kktO/5ywKhODVXAHkHN5XBgSUyMbTAHQPjvsS+BcF9MmZ3I8gX1/N9/+0b0Ab1RwuMMdMlaB2DzUhsarcSoaW0f5+m9dcyak7bvC1tAMTUIBR2H3+dDow11f1OwQOjz+TDq9W1yc7QXc3zXCIS//6+WFd/W02+0KVIIdX9obyqbYP/wer14fD7cNlusQzkg/H4/JpOpU8ZOYroWr0fG0dC5E86F71Sz4rsG+o82dsnDWXBgeLxeXB4PLpcLSZIUW4OwrQLh/pCQqqG+qnMFQo8rwOqfgjUUmzb3aStZffUMbqH+mqCT8Plw2mxYzOZIczZBY+KSNZ3enX7TUhuLPq5lzIy4yCJR0H0J1x+rqKkBoKGuLobRdF+McapGHY47mkBA5q27SwA48fLW05/3xrhj47nhlRzu+7RfR4YmaIXwc8al8OY+bU0x3hfn3JrB4z8NIiF1zzrdYFYTCHRdB/CtK+z0H2NslELcmQiB8BDE5/ejUQcnN0rsYvz9998zePBgHnnxRRxud6c4CPUmFSpV59Yg9PtlfnqnBkuimoseyOq07xF0HOGHTEN1taJrc/hCAqGrDZ292ktSRvABWlveOSIKBJ23P74dTLU7XNTfVAQerxenx4PL7cak1x/0DsL9ISFNQ0OVr0OaTrVGuENx7yGGdu1EC2JISCCMs1gUWxKms4nvAvftly9UYElUc+o1++fIEHQt4cYE5SFhsKGhIYbRdF+MFnWnOggrC4Pz5tnXpzNscvtrwkcjNoO7hnD5Iadd2SndbU0x3heSJEWaYYVJ6RFuXtLxRoto7A1+Kgo9FO9wM3B8123MCoHwEMTn86EOpxgrrAah3+9n7ty5fPfdd1x3ySXYXS5q6us7/HtUKgljnBqHtXP+38iyzLevVVFd6uWCu7NI7bF/Tg5B1xJ+2NSvXAkFBTGOZv/x+XxYzGbcneQgBKir6JzFWm25l1duKsJe7+eCu7MYNVWIHErA6/fj9Hhwu1yYDQZlOwg7YdxA0EEYCICtruM3phqqfSz/tp7/3leCOUHN9a/23q8SAoKuZ9vu3dgcDuEg3Aud6SD0+2XWLbJStN3NCZelkpot5mtKwNNUIOyEtcLBgDFOhdPW8c8cryfAVy9V8OULwYYjI/ajYaQgNoQFNadwELZKuG9A6a6ON1qEqSr2cM8pO7l/di6yHCwr1lV0yxqEgs5FyTUIV6xYwYABA+jXrx8SkJqQQGkndfUzJ6jZ8qeN3HUONFoJWQZJCnaalFQgR69vpeC5MCqVRCAgI8sE2+oAfp+Mrc7PtpV2Fn1Ui9cj03+0kZFTxUNTKUQchHY7hJotKBGf348lLq6TBMLgvWX979bgz6FxEZ0Gv9dNYInImIHg4izgB2uNj41LbCz5tBZJJTFrTiqTThHuQUUQCODx+fY4CA0G5ToIfb5OdBAGxfU1PzXQP9R4R1LtcU3Isty8nET0y6hxI8sQ8Ms4bQFqyrx89kw51ho/KVla5j7fC6NZpEgqhUv+8x8Cssz0I45Q1IZuVxKs3+ln5xoHWn1wvhZxfITmZ/sqxRI9Z/P7ZVy2ABWFHr55pRJrjZ+eg/TCsa4goh2EKpVKCIStYLSo2bXeybaVdnQGFSp18Pmh1khIUvAZFB47YXd707Hk98mRuZvXLVNb4WXFgnrW/x4sxzP51AQy+wphXSkcLCnG3g5yELZEWi8tKhX89UMDiWladEYJtTr47FGp92gE4TmcLAefL+Fr5CaN+oJzNvC6A1QVeVn2TR2leR5c9gBancTQyeb96lWwvwiB8BDE7/fvEQgVlmJcXFxMr169gJALymgMNo3oBPoMN7Diuwae/Ec7+oK3kax+eqafl8SU0xOFZV5BhIWB9377DV9aGhOHDIlxRPuHz+/HYrF0SopxfIqGtJ5alnxWx5LP6jr88yeelMCp16SRnNn+xiSCGCHLeHw+vD4f9TYb8RaLsrsYd5JAmNlXh1oNHz3e8Y0o1Go4+twkZs1JE/XTlIQsY3O5qKyvJy4+XtEOwnfeeYcxY8YwcuTIDv/sgePN/Ph2NU9d0fHztcR0Dadek8bRf0sWrlsFEd7QrbFayUpOpl7UIGyRMdPj2PSHjWev7vismIHjTFz+WPYB1VgXdD2RFGOFC4QejwdJkjplzqbVqRg6ycz6320RIbwjMcer6TfayDEXJtN/tAmVumtT7Ns8YiVJ6gFUyLKs3NmJAGjBQajQHWm/349Wq20xVW3evHnMmzcPAIOh9fble0UN6ScfSISt4wd+Whf801EUFRV13IcJWiT80Hx+wQKsksTECy6IcUT7h8/vx2yx4GphV60jxo40BtLHHECAe2E38NwrHfd5Ytx0AYFAZIK2q7ycqePGKddB6PW22v27I8ZOyon7Hdo+2WyHzU933OeJsdMF+Hw43G7K6+qwxMcrdr4G8OWXX+Lz+ZoJhB0yXwNSTzqg8PbKsgJY9njHfZ4YO52PN2oDNDMl5aCsQdhRYydtVkdF1Jh64MlnO/YzxdjpfCIOQqczxpEcGB6vF5Ne36KDsEPGTnLn6QQA5cD7XwNfd8zntWfstEfSLwQmAysApKCMOR+4R5bljt+yE3Qa0TUIVQpLMc7OzqawsBAI/j38gQBqdXM3xJw5c5gzZ05Xhyc4yIkUvPX5+GXlyhhHs/+EU4xbchCKsSPocEIpxhajEYfbTWZqqqKeO9H49pJiLMaOoMPx+7GHylnExcfjU+i4AXBVVeGsqGh2XIwbQWfgiZrfZBykAqEYO4LO4GByEFqMxsjfJxoxdvZOe7zyTX2NKuBCIKXjwhF0Bb7oFGO1WlHF4idMmMCOHTvYtWsXHo+HkspKLAewayYQtAePx0N8XBxGo5EGu538fGXujfj8fizx8S06CAWCjkb2+/H4fCRYLKQlJqLTaBT13ImmM2sQCgTN8PtxhIQOi9IFQocDp9Ua6zAEhwjRAmFmaioN4ndPIGgTEYHwIHAQmo3GTmssdzAjigIcgjRKMVZYF2ONRsMLL7zA8ccfT0VpKf179sSt4GYRAmXh8XpJTUkhOSWFNJWK9evXk5OTE+uw2o0/7CAUD01BF+D3epEkCbPRiMlkQqVSKTrFWAiEgq5C9nojAqE5Lg5ZlgkEAqhUyquF53K7Fb/gFCiHaNdQZno6BZWVMYxGIFAOB0uKsdfnw9yKg1Cwd5Q3wxC0mfBEsil+vx+NNljgX2ldjAFOOukktm/fzkWnncak0aPxKnShKVAeYYFw5MiRGDQafAp96Pj8fiwJCUIgFHQJXrcbnUaDUa8nKyMj6FxXapMSnw+PeOYIugiv0xnZxDWazWgUOGcLIwRCQVcS7SBMSkrC3QlN2QSCgxHhIBS0VyA8QpKkkyRJOgk4EZCBKeFj0X86PlRBe/njjz84/fTTmx33Ne1irCAHYTR+vx+DwSDcHIIuw+vzMefSS7n7nnvQaDT4FDrh9Pn9mIWDUNBFeFwudFotBrOZrF69lO0gFAKhoAtx2PZ0RzTEx6NWqZQrEHo8iq9pJVAO0Y0JEhIThYtIIGgj4VIWLoVn6Hm8Xswmkxj7+0F7U4yfauFYS/2JZKB55whBl1JeXs727dubHff5fJHGHkp0EIbx+f1YWunEKhB0Bh6vlz45OfTr1w+NVotPoQ9PXyAgahAKugxP2EGYmEhm79448vNFDUKBoA3YGxrQajR4fT6MFgsatTqS/qU0XG43DoU7UgTKIdo1FB8fL0QCgaCNHDRNSrxekuPihINwP2iPQNi306IQdAp2u52ioiJkWSbYdDqILzrFWOEOwsTERKxOJ8gySE376AgEHYvH60Wn1wMoWyD0+zFYLMg0rkkqEHQGHrcbnVaL0WgkKyuLXYWFit2Y8nq9wkEo6DIcdjvZ6enkl5YqP8XY41F8yppAOXg9HnRaLR6vl4SEBCEQCgRtxOfzYTGZcCk0SyqMVzgI95s2rwplWVZmu85DGLvdjt1up76+nsTExMhxf1OBUKGTTZ/PR0J8PE6PB7/Xi1qni3VIgoOcZgKhQh+ePr8frV6PQavF7XKhsVhiHZLgICacYjxo0CCGDx9OwV9/EVDoc8fr8+Hz+wn4/ajUIlFC0Lk4bDbizGbS0tIwWyyoVSrlOgg9HpwK3VQTKA+Px0NyQgLV9fUYRaMCgaDNeL1e4kwmxW/oeLxeLGaz6FWwH7S5BqEkSQmSJD0pSdL0vVwzPXRNXMeEJzgQ7HY7AEVFRY2OK7mLcTR+vx+tTofZYMBWXx/rcASHAB6vF21YINRoFLlQCwQCyLKMSq3GoNPhUngKgaD7E3YQPvPMM0yfPj1Yg1DBAiEEG68IBJ2Nw27HbDKxaNEievXujUYIhAJBm/B6vaQkJmIymdDqdEIkEAjaiNfnI85sVvz92uPzYTabRYrxftCeJiU3AqcDf+zlmqXAqcD1BxCToINoVSD0+1GHBUK1WtECoUajId5koqGuLtbhCA4BvH7/HgehWq3ILsY+rxeNWg2SJARCQZfg9XrRhVzrEHzuKLkGIQRdkQJBZ2O3WjEZjQwZMgQIzdkUKnQIgVDQlXjcbjJSU0lMTBQCoUDQDnwhgfCgaFIiHIT7RXsEwjOB52VZblWGlWXZDbwInH2ggQkOnL0JhBEHoVarWCeHz+9HrVYTZzJhFQ5CQRfg8XrRGQyAch2Efp8vKBCCEAgFXYLH7UYbVedSpVIpdmMq3N1PCISCrsDhcGAymSKvFbsx5fPhDwSEQCjoUHw+HzNmzGjxnNfrJadHD/7666+gQKjAcSMQxAKvz0d8XJzi79de4SDcb9ojEA4A1rThurXAwP2KRtCh2O12kpKSmgmEjWoQHgwOQrNZOAgFXYLH59vjIFSoQOjzeBoLhAqvMSLo/oRTjMOo1WpF1yAEIRAKugaH3Y7JaIy8VqtUinQQhp8zSl9wCroXVquVX3/9tUUBwOPxoNVqSUlJQavVCheRQBCFy+lk5R8tJ4X6fD7iLBbFNynx+HyYLRYx9veD9giEHkDfhut0gPiXiCX5+eDzYbfbGThwICUlJY1OHyxdjH0+H+qQQCgchIJOJxBoLhAqcEc6kmKMEAgFXYPH7UYX1URKJUmKda6HJ5puIXQIugCHw4HJbI68VqqDMOxUd4hxI+hAwvXHbTZbs3Nerzfy3NHq9UIkEAii+PPHH/nnnDktnvP6fMTFxSn+fu3xerFYLHgU+MyMNe0RCDcCx7bhupmhawWxIBDAsXIl555yCna7naysrGYPTp/PhzokECi5i7E/VEsxzmymQQiEgs5GlvH4fGhD4rpGrVamgzBKINSLFGNBF9CSg1Cpzx2vz4dJr8ej8J11gTJw2O2YowRCxToIQyVvnGLcCDoQWyh7qKVGhWEHISBqEAoETXDa7dhbMQh4fT4SEhIU7/j2+HyY4+JEeYH9oD0C4VvAtfvoYnw0cA3w+oEEJUnSm5IkVUiStDHqWLIkSQslSdoR+m/SgXzHQYvdzu/bt/Px999TXV1NWlJSpBZhmIPGQRhOMbZYhEAo6HwCAbw+X2RHWq3QFGOPy4VWOAgFXUhTB6Fao1FukxK/H7PBIARCwYHRxmeH3W7HZLFEXivWQeh0YjYYhEAo6FCstbUA2Bsamp3zeDx7HIQixVggaITL6cTWyvzf5/MRHx+PQ8n365CpwxwXJ8b+ftAegfANYAHwoyRJH0uS9A9JkmZKknSsJEmXSZL0EbAQ+EaW5TcPMK63gROaHLsd+FmW5YHAz6HXgqbYbPywYQMA+Xl5pPv9zQRCfyBwUAiEEQehxYLVao11OIKDnXCKcWjCqdQahNa6OiyhovcGvV4IhIIORZblZse8TVOMVSpFOwjNRqMQCAX7zdaVKzll2rQ2XXvQpBjb7SRZLDhFsXhBBxJJMW5BIPR6vXschHo9HgXO1wSCA6GhoYElS5a0eM7pcLQqEHp9PhISExWdYiz7/bi9XuKSkkSTkv2gzQKhHJz1nwPcBIwHXgN+AH4k6BgcD9wI/O1Ag5Jl+Xegpsnh04D5oZ/nA6cf6PcclNhs/LhyJXqtlsLiYtLN5r06CFVqNbIst7io6+40chC2MDkQCDqUpgKhQlOMrXV1xIUFQuEgFHQws2fP5tdff210rJmDUK1WroPQ5ws6CBU8cRbElm0bN7KjsLBN1zocjkYOQrVarcwUY6eTpLg44SAUdChhYdDWgkkg2kGoEzUIBYcgCxYs4JxzzmlxQ9bldGJvZR7j8/sVLxB63W7UKhV60cV4v2izQChJ0i/AYFmWn5dluT/QG5gc+tNbluUBsiy/IHee0pQhy3Jp6OcyIKOVOOdIkrRKkqRVf/31VyeF0n2xV1aSV1TE4cOG4fX5ggJhSzUINRoAJJVKsW4Ov9+PWq0mPj5eOAg7gEN97OwLv9eLLMuR+p0arVaZAmF9PfGhBadwEHYMYuzsYd26dRQUFDQ61qxJiUKfORBMMTYJB2GHcSiOndLi4jYvvBxOZ/MUYwU+d1wOB3FmM5Ik4RWLtQPmUBw3LRERCFsoM9TIQShqEApCHEpjZ+vWrZSWlrJ48eJm55wOB063u8W5mNfnIz4xUdEpxi67HYNOR3JaGjU2W5vLegiCtCfF+GggPvxCluUiWZaXh/4UdXhkeyEkQrYoRMqyPE+W5cNkWT5s/PjxXRlWTKioqGjk1tiyaRODBgwgMzUVgPTERBx7cRAiScGi1wpcrIVTpeMsFhqEQHjAHGpjpxleL2za1Oppj9uNLiSsg4JTjOvriQulrBkMBtwKngB0Fw75sRPC7XZTUFBAVVVVo+MelwvtQeAglGVZpBh3MIfi2CkpLm7VudGIQACHy4U5PjL1Vm6TEqcTg16PUafDIeZrB8yhOG5aIiwQ5u3axf/+979G5zweDzq9HlBmF+OCgoJmm22CA+dQGjtb169nRP/+fPHFF83OuULPoKYmIgg5CBMSggKhAvUBALfTiUGno3fv3hRWVeEXZoh20R6BMNaUS5KUBRD6b0WM4+kWLFy4kEcffTTyetP27YwYMYLU5GQA0hISWq5BGBY6FCwQhrsxx8fHY23hBicQtAunE/aS9uVt0olVo9AO4Nb6euKEg1DQCeTl5REIBKiurm50PHqhBsp1EAb8fiRJwmgwiJQVwX5TUlrazJnx0ksvURtquBDB56PB5cJykDQpMej1GPV6nGK+JuggrCGB8IdffuGFF15odM7r8zV2ECrsmTNv3jxeeumlWIchUDDbtm7l+JEjKS8ubnbO6XAArbhvfT7iwwKhAjdzIeha12u1GAwGUuLjKc3Pj3VIiqK9AmEsC9V9BVwc+vli4MsYxtJtqKqq2lN/z+9nY14ew0eOJDXkIEzLzm65BmHYzaFSKVYg9AcCaHQ64uLihINQcOD4fEEXYStVEjwtCISKdBBarY0FQgXXGBF0L3bs2AFAVXl5o+Met/ugcBB63W60ajU6rVY4CAXt5uuvv8btdlNaXo7L48EfEvp81dXcdtttbNy4sfEbvF4q6uvJyNhTUUdpNQhXr17NjTfeGBQIdbqgQNhkTioQ7C+2kNict3t347lMdXVwzhbamFJrtQQCAUU9d1wuF9XV1YpcnwliTyAQYPuuXRw2bBgNFc09Vc6QOcDewvo5LBA6PR5kBT1vogk/cwD69OjB7ry8GEekLNorEH4hSVJeW/4cSFCSJH0A/AkMliSpSJKkfwCPAjMlSdoBHBt6fchTWVlJfX19UNyw29lUVMSIkEAoSRIpOTnYnc5GTUh8oe6/gLIdhFE1CIVAKDhgwg/BVtwZHpfroEgxbqivJy4uDhACoaADcTrZsWED2SkpVJeWNjpls9sbuaBUKhV+BS3Uwng9HrQajRAIBfvFNddcw5o1ayiprASIOOk2fP45drudurq6xm/w+aioryctLS1ySGk1CPPz81m6dGlwsWYwYDIYhEAo6DBsNhs6jYbdRUWN5zJbt+J1OCK1byWVCq1Gg1dB7lt3SCCcddxxLP7tt1iHI1AYhYWFJMXF0WPIkBbr9IfHS0sOQp/fj06nCzYyVOj92h1yrQP0yc5m965dMY5IWWj2fUkjfgVK93nVASLL8nmtnDqms79baVRWVAQFwm3boLaWjfn5DB8+HHt+Pia9Hl1SEmqVCrfbjcFgAJrXIFRJkqJ21cL4Q0JnfEICVoXewATdiPCiy+OBKLdTGI/TGUlXAeUKhFardY9AaDAIgVDQMeTmsmnRIqYMHUpZkxTj+oYGEgYOjLxWazSKfOZ43e6gQKjTCYFQ0C5kWaa8vJzioiJKqqtRqVTYGxqwxMezdPVqAOqaOG/xeqmoqyM9PT1ySKPRKCrF2OVyUVVV1TjFWMzXBB2EzWYjMzmZgooK3FGp61vz8rBH175VqdCq1Xi9XvRR5S66M+6qKqoKCykrK2Pz8uUcdfTRsQ5JoCCqKirITEggLiuLhhbKOoQdhC11APf6fGh1Okx6PXabDWMoK1FJuJxO9GEHYa9e7N69O7YBKYz2CoTPybK8olMiEewXVcXF1NfW4rZaseXnU11fT9++fclLT8es14PJhNloxG63RwTCcHMPIOggVKsV6yDUaLXo4+NpEBNOwYHi81FQWUnvVmqLeVyugybFuG8oZc1gMFDRwu6hQNBe5Lo6Fq5bx9Nz53L/O+80Oldvs9E7MTHyWiVJinzmNEoxFjUIBe2gtrYWr9fL7p07qbXZ6JGSEmzWUV/Pnzt3Em82UxtyFobx2O3YHA6SkpIix5SW8eFsaKCqshKXy4XBYMBiMtHQtNaiQLCf2Ox2slJSKKiowBWqqQZw1ZNP8tf27Zx12WXBA0p0EDqdVNfUUFZdza6dO2MdjkBh1JWVkRgfT3xKSosmGldok7OlFGOf349Wr8dkMCi2qVSjFOPevVmxdm1sA1IYSmpSImiByqoq7E4ndz73HBc8+SRD+/dHpVKRmpGB2WgErRazwYAj6sF50HQxDqcYJyYKB6HggNm5cydT/u//Wk0xbtakRKvFp8BxY7XZiAt1xUwQ9TsFHYEss3nDBjQmE0eceCJVTQSAepuNhCiBULEOwnCKsU4nBEJBuygPuQOXLllCZnIy8WZzcGFWU8PW4mIOHz6cuibjprK8nNSkJFSqPVN1xTkIa2tpsFppaGjAoNeTnZFBSVFRrMMSHCRYbTYyQyn4rqh7ssPlwuZ07nEQSlLQQaig+7bb46GgtBSr08kukR4paCd11dUkxMURl5TUoonGGU4xbsVBqNFqgwKhQtfXbpcrkmKckZVFZVVVjCNSFkpqUiJogapQKtem3Fx+WLOGEUOHAjBywgTefOAB0GgwGwx7GpW43fgCgYOiBmHYCRmXkCAchIIDprKyktKaGvytdPU9aByEdjtxCQkAJCYkUCschIIDxWbjp40bOe6440jJyKC6rq5R3dt6m42E5OTIa6V2MY4WCN0iNV/QDsrKygBY+NtvjBo4EJPBEBQIbTbyiosZP3w4tTU1jd5TUV5OetS4gZCDUEHPHVfoeVpcVobBYKBnjx4UFRbGOCrBwYLN4dgjEEaVfQiLhbqodGKtRqMsgdDtxhYaP3lizAjaSV1NzR4HocMB1dUQJQa63G7iTabmAqEs4/X7gynGChYIo1OMjWZzRBAVtI32CISvAY9KknR8axdIknS8JEmfSpKU3to1go6lsqYGo17PtoICAIaPHw+Axmzm6DlzggKhXr9HILTbgw7CqGYLSptwhvH4fEELdHw8bq9XkWKNoPtQW1ODPxCguoVuXxDcjdJF1SZUqkDYYLVGBMKkxEQhEAoOnIYGtpSVMWrUKPTx8Rh0OhoaGiKn6202EqLTJJXaxdjjQaNWYzAYIvV7BIK2UF5aSlqoXvKooUMxG4047HZqKyrw+f0M7NOnWZOSivJy0pvUflLacydc47aopCQoEGZnU1RcHOOoBAcFsozNbiczMxMAd5SzNiwWaqMFQrUar4Jqx7pDYma/7Gx2lZUF62MLBG2krraWxIQE9GYz/kAA96ZNsHQphOYuTpeL1IQE7E3rEwYCEZ3AZDDgaKF+oRIIN8aCoEDoEHO2dtEegdAGJAM/7uWaH4G+wM0HEpSgbfj9fmobGuiTnk5+eTnXXHklJ8ya1fgitTpYZDQkENYUF2PU6xulrChVIKxpaCApLQ1JpyPeZGqxS5NA0FZqQuldq9etY+XKlc3O2202zCZT5LViU4wdjj0CYXIydVFCjkCwXzgc7CwpYcCAAaDXkxofT1U4ncPvp97haJRirFKrFd3FOC4ubs+mm0DQBsqLihjXrx8AI4cPx2Q0Yrdayd21i/59+5KUlERtSwJhVAdjCIrrSpqvhR2EazdvJiM9nZ69elFU2um9DgWHAlYrNrebzB49gKBAGHauO8MOQoslcrniHIShWEcNGoTT46Fh27YYRyRQEvV1dSQmJiKpVMSbTPyyeDFFHg+E6lm63G5SExOxNRUAww5CrRaT0dioRJmScLvdkRRjo8kkHITtpD0C4cnAK3J03lATQudeBU470MAEe+fxxx/ngw8+IMFsJtliQZZl7n/wQUaOHNn4wiYOwkW//cZRhx2GJEmRS5QoELpCTkhzQgJoNMQZjY0cKwJBewkLhE++8gqP3nVXs/NWq5W4qMmmRqtVlJMjjNVuJz7k5kpKSxMOQsGB43SSW1RE//79QacjyWLZ44YKC4QhURpQbmMsrxetRoPFYhEbUoJ2UV5ayvgBAwAYNWpUxEGYt3s3/QcMIDEpqdlmTUV5OemhhlJhNGq1op47YQdhQXk5oydNomfv3hQKgVDQEVRVYfN4yMzIINFsRqNWB2vDynIkxVjbJMVYSd3nw3Vue2Rlcfhhh/Hlhx/GOCKBkqirq4tszMaZTNw6bx43vPEGFBeD04nT7SY1KanFFGNftECo0M3Q6BRjk8WCU0FjvzvQHoEwB9jchuu2AH32KxpBm3C5XNxzzz08+OCDpCYkkGA2o1apSGmpDXlYIAztEPzyxx/MmDq10SVqlQq/gopeA1SXlpISHx8UOjUa4o1GrEIgFOwPNhvIckQgXLJxI2WhgvLRNBMIFZbqBVBfXx90EIYmDYmpqdSKcSM4QNz19ZRUVJCTkwNqNfEmE/Wh+rj4fNTb7c0EQqWmGGs1GixxcdgUOmkWxIay0lL69urFA+efz+ARIzCbzdgbGsgtLaXfgAEkJSc326ypqKwkPZQ+GUatMIEwnIqvUqkYOWkSPfv0oaiVMh4CQXvwlZVRXlPD2NGjOWHcOAxabVCQ9vtxhdY00WVhdFqt4hyEyfHxZObkcPf99/Pg228jK2ytJogddXV1JIbMAPFmM9uKi/n6u+/YGAjAqlW43G7SkpObOwgDgWCTEo0Gk9HIu599xtKlS2PwNzgwXC5XoxRjIRC2j/YIhE4gvg3XWULXCjqJwsJCemZns3z5cl676SYSLBbSEhMbpQ1HY45qU/7bihUcPX16o/NKdHNUl5WREl5wShJxJhMNTToACgRtYs0aqKigpr4eo8GAy+OhtLKy2WU2mw2L2Rx5rcQU44kTJpBfUUFcaNKQmJ5Og92uSLFG0H3YnZdHzx490Iaa+CTExdEQEggDXi82l4u4uLjI9YptUhJyEMbFx2MVAqGgHZSVlZGRk8O9f/sbGosl6Myoq2NzcTGDBw8msYVyD5XV1aRnZzc6plHYfM3lcpGemMjAXr0wmc1k9OxJTUOD6AIuOGByc3PJysggp08fPrj1Vgw6HW63GwKBPQ7CqMZyiuti7PXSp08feg8ezPQZM6i12agUHcAFbaSuvj4iEMaZzfj8fm6++WYemj8fXK6ggzAlBXuTFGLZ5ws2AQ0JhJ9+9x1//vlnLP4KB4Tb7W5cg1CkGLeL9giEq4FT23DdaaFrBZ1E/u7d9LZYSNLpmDpsGAkJCaRHFYBvitlkCnbLAworKhgwfHij80pLMf7www/5bdEiUqJqWsWbzUIgFOwXcx59lM2rV1NTV8fQwYMBKKupoWk1BavN1kjk0Gg0ihMIi0OTy/iQuK4xmzHp9SJdUnBA7Ny1iwEDB0Zex1ss1Ifux9a6OswGA2q1OnJeiQ7CsrIyvG63cBAK9otdBQX0GTgQ+vQBgyHiIFyxfTsTJ04kKTWVytparrzyyuAbfD4q6upIz8pq9DkajQafglxELpeL3j17MmbiRADUJhNZSUmik7HggNmcl8fwYcMg9GwxaLW4HA58bjcBWUatVjdyEGo1GsU1KZn39NNccMEFSJJE78xM8nNzYx2WQCHUW60kpqQAEB8XR5zJxJ133snPP/9MocOBy+slNTW12VzG53ajVqmQJAmT0QiAW4F1CBt1MbZYInVJBW2jPQLhS8A/JEm6uLULJEm6CLgUeOFAAxO0Tv6OHeSkpUFlJXg8JCQlkRG6CbSE2WTCbrPhdbuxu1wkNC16rTCB8IMPPuC9Tz4hJUoUjbdYsDYp8C0QtIXFGzawbNkyahoaGDZ0KBkZGahVqmY1La02G3Hxe0zUaoWlGDudTrw+H4GGhj2TZo2GJIuF2nA6qEDQXjwe1uzaxchRoyKHEuLiaAjdj+tqakiISs0H5TUpqaioYNCgQbhDAmFcfDw2BU6YBbEhEAiwu6SEfoMGwciRoFJhMpkoLimhtLqaYcOGEZeUhMvjYd68ecHnitNJRUMD6enpjT5LaRkfLrebqy68kKeffTZ4QKWiX1YWedu3xzYwgeLZlJfHsLBAqNOh1+lw2e247HaMOh0ZGRnoo2sQarV4FSSuuz0eklJS0Gg0AOT06EHBrl0xjkqgFOqsVhKSkwGIs1jI6dEDi8XCUUcdxZLt23F6PC0KhF6XC23ody7cmNGtwM7zLpcLQ0jgNIoahO2mzQKhLMufAs8Cb0mStFKSpIckSbpCkqTLJUl6UJKk5cBbwHOyLH/eWQELIH/nTnIyMqCkBFQq4hMS9ioQxpnNNNTVUVNWRpLFgirKyQHK64pXVVXF2k2bSAnd+GDP31Eg2Bd5eXm89dZbwReBANUNDWzeto3ahgaOPPJITjrxRDITEylrUkjdZrdjaVqDUEELtaqqKtLi45GiOjEjSSTGxVErakIJ2kggEGjsrnU6WbZ9O5MnT44cio+PpyFUT62+traZQKg0B2FJYSFWq5W8XbvQarVY4uOxCoFQ0EZKSkpINJsxR23Oms1mflu7lnHDh6NWq1HpdPTJzESr1QYb/DidVNTXNxMIFecgdLtJT0sjK8oJ2b9XL3K3bo1hVIKDgc35+QwfMQKMRujdO5hi7HTicjox6vUsXLiQfqHO4aDALsZeL/pQiiRA7x49KMjPj2FEAiVRZ7WSGOpNEJ+WRk7fvgBMnjyZZVu34vJ4SEtNbZZi7PN40IR0AlNIKPS03p+22xKdYqzV65FlWVHPzljTHgchsizfTDCFuAG4hWDH4nnArYAVOE2W5Vs6OkhBY/J37yZn4ECorwedjhlTp3Lm8ce3en1yYiK1tbWN6/ZFoVZYPaiqqio8Xi8pUaJofFycaFIiaBN33nknc+bM4ZxzzmH+m29SbbWyJTeXGquVadOn8+Zbb5GVkkJZkxQoq93eyEGotC7GlaWlpCYkRNJxwiTFxVHXQs1FgaAlLr/8cl566aXIa7mujmVbtjQSCBPi46kPC4QVFSTENy5frFKpFOUgLC8oAGDDX3+h0WiIS0gQDkJBm8ndvp3+mZkQJZSbzWbW797NlHDTOK2WHW++SZ+cHKqrq5GdzmCKscIdhM4oF0eYfn36CAehoF3s2rWLs846a8+BQICdJSUMHDwYtFoYOhSDTofL6cRlt2PQ6xk2bFiwkWEIxQmEHk9jgbBXLwpEar6gLQQC1NlsEYEwLi2N3gMGAEGB8M+1a1t3EIYyJQCSBg5Er9PhVtC4CeNyuyMOYkmlwqjX4xSlYdqMpr1vkGX5a+BrSZI0QFihqZZlWTkrZYVTUFhI7zPOgHHjoKSESTNmwF4WW8lJSdTk5wcFwqi6fWEU5yAMiRnRAmGcxcIz8+ahTU5m7ty5sQpN0M0p2LaNn7//ngeuu46n5s9HH3IybS4uxuZykRx6mGampFDapBi0rSWBUEELtaqyMtJaGP9JCQnUVlUBQXfYimXLmHTEEV0cnUApLF++nLVr1zL3mmsgN5edmzdjMpno0aNH5Jr4+Hh2hTqB11dUkNDk906t0SjKQVgWuhf8uGQJR02bhiUhAatT9GITtIHKSnKXLqV/r14Q1UjOEHJy/+2ii4IHNBrUPh/JajU1NTXYTSYkScIc1RgLgk1KlLQx5WpJIBw4kE8WLIhRRAIlsvzPP1m6ZMmeAz4ftXZ7o3WAXqfD5XDg1OkwRNUeDKM4gdDrRR+V8ZGTk8MfK1fGMCKBUvAvXIjd5SI+lGk3derUSKr6YYcdxrqNGwkEAiQkJjbb7HRH1e6bM3curvJyditQmHa53Y2ePUadDofVSlwL6yBBc9rlIIxGlmWfLMvloT/Kma0cBBSWltJrwADIyICxY4OTTk3rWm9yYiI1tbVUlZc3SssNo6QahD6fj/qGBlQqFSlR6Trx8fHkFxWxbNkyIJjSIxA05a/ffmPSiBHcOXMmLz3+OBvWrycnM5Oy2lrqrFaSQnUts9LSKGvyO2R1OLAouElJZWkpqS00M0pMSIjUIPzonXc48qijFOVQEXQdTqeTvNxcKkpL2fDrr1QuW8afv/3G5MMPb3RdQmIi9SFHd1lxMZlR4iEor4txWUkJFpOJHSUlnPa3v2FJSMAmBEJBG2jYtYvffvmF/lGpjgB1od//kSNHBg+E5nDJFgs1FRVUlJeT3kLpGLXSBEKPJyKGhuk/dCh5IlVS0A42rlpFVXX1nvIWPh+1NltkzgZg0OuDDkKnE0NU7cEwihQIowSO3n36UCDWNoJ9Ict88OOPTDzssEhJsdmzZ3PKKacAYDQaGTNmDACWhATsTeYydqsVc+j3TqfTkRAXF+wOrjCiU4wB4SBsJ/stEApigyzLlFRV0aN//za/Jzk5meqaGqorK0ltSSBUUMpKbW0tSXFx9MrIIKVXr8jx+JCzKz8/nwULFtCnTx/Wr18fqzAF3ZRN69czfNQoSEujR2UlW3bsID05mZdeeokFCxag1WoByMnOJjcvr9F7rXY7cVEp+hqdTjHjBqCqooK0kEMymrTUVCrKygC4/8EHkSSJUlEIW9ACmzZtYlB2NhdNm8Y999xDj0su4YNFi5h81FGNrotPSKAh1Bm7sKiIXqHaN2HUGg0BBdW0KS8rY8q4cVgsFo477jjM8fE43G4CChr/gtjw7Ntvszovj7POOKPR8auvvZa8Js8Y4uNJSUigei8CoUarVdRzx+V2Y2wiEPYbPpzcoqLGtUwFgr2wceNGvD4f1vx8KCpC9nqDKZRRbiCDXh+sQehwYGxJIFRQk5JAIIA/EEAb9ffo3b8/BaG5mkDQKl4v//rf//j3Y4+1eskxxxwD0OJmp91miwiEAHqDQZECocvtbpSibzIYcNpsMYxIWQiBUGE0VFejVqmIa9KJeG8kJydTU1dHdVUVKS0IBEpyEFZVVZEaH885557LsNGjI8fHjRrFDaedRn5uLvfddx/Jycns3LkzhpEKuiObt2xh2JgxMGgQPUaNwuP1kpqUxKWXXspxxx0XuW78yJGsWrOm0XttTmdjgVBpDsLyclJbWHD27NmT4uJinE4n+UVFjO3Xj4IdO4InCwpATEgFIdb+9Rdj+vblossv58ulSzEYjXy/enWzlPSEpKSgQOh0UlRdTc+cnEbnFecgLC/n7Fmz+PTTTzEYDKg1GgxaLQ4x2RTsg/LKSq684gqGHXtso+Mmk4m+TYRzpk4NztcqK6moqCC9hXneweAgTO7RA38gQENtbYyiEiiNjVu3olap+OL993nv9dex1dVh0Osjm7oQSjF2OnE6HIp3ELrdbnQaDVJUzei07GzqbDbFiJyC2CC73eSWljaqC92Uc889FwgJhE1SjG1WK5ao0hY6vR6PAn/nmqUYCwdhuxACocIo2bmTrL10LG6JlPT0oEBYXd2yQKggB2FVVRWpcXE8/vjjDAgVXAWYMH48j110EaUVFWzevJlTTz21+e684NDG42HT7t0MHz8e4uLICqVFprSQdjt+9GjWb92KJ2oyaXU4sEQLhEqrQVhVRVoLC86evXtTVFpKYWEhPVNT6dOjB/m5ucGTZWUQSj8WCNatXMmYYcMYcuyxPPTQQ8ybNw+dTsfYsWMbXRefmEi91QoOB4U1NfSKcnuDAmsQVlTQp3//RpsIcSYTtrq62AUlUASV1dWkZmS0+frkpCRqqqqoaOV+rVHQfA3A6XY3EwglSaJnWhrFeXkUFhayYcOGGEUnUAJ2u53i8nJG5OTw3hdf8O6CBdTV1JAY1fQHQinGDgeu1gRChTgI165dy0UXXYReq21Ut1RtMpGekNCs/I1AEI2tpgadVhtp0NESo0ePRpZl9GYzXr+/0aaT3W7HHHXP1uv1imxS4vZ4GguEBgMOIRC2GSEQKozS/Hx6tGOyCZCYkUGD3U5FZSUpTTriQchBqICHJgTTJFPj4oJdy6JJTESXk0N6UhK9e/dm1KhRQiAUNMJvs7G9uJghQ4YAYExOJsliaVTkOowlI4O+WVn89ttvwTQoWcba1EGo0ylKIKysqiK1hfHfMyeHorIyCgsK6JWSQu9+/SgIpxg3NICotSYIsXbtWkaHatfcfffdnHPOOfzwww/NJqLxSUk02O3gclFUVUXPnj0bnVdaF+OyqioymtRRtBiNWEOdmgWC1qiqrSUtM7PN1ycnJ1NdVUVpRQU9srObnddoNMpyEDZxcYTpmZ7O6lWrmDJlCvfdd18MIhMohS1btjC4Z08yU1JYtXUrm/PzqS0pISmqaRyEBML8fFx5eY1qj4XRajR4FJAqmZubyx9//BEUCKORJLJSUigV9TsFe6GqvJyUqLXK3pC0WiwGA/Yo4cxms2GJEgh1en0js4RScLndjWp4Cgdh+xACocIoLSggqx2TTQC1xUKc0UhuYSGpTRY5oDAHYVlZi40WSEiAwYPJSU9nTN++9K2uZteOHeDzgdhtExAU15Pi4xt1heyRlkZqC65aUlM5fswYZs2axc8//wx+PzaXizgFNympqqlpcaHas18/iisrKcjNpXdGBjl9+1JQWAheL3e++irPv/tuDKIVdDcCgQDrtmxh9JQpkWNqtZqjjz662bUJycnU22zITieF5eXNHYQKeuYAlNfUkNkkTTrOZMImBELBPqisqyM1K6vN16ekplJTU0NJRQVZCp+vQSjFuEknZoDszEzefu89ZFmmuLg4BpEJlMLGdesY0bs3qamp1FitFFRWUrhtWzOBUK/X8/B//8u/P/gAYwsCoU6nU4SDsL6mhvLy8uYCIdAjPZ0SIRAKovH5IFTzGaC6ooKUtnbqVauxGAzYot7fzEFoMCjSQehqwUHobJJO3RpLlixRVJZLZyAEQoVRUlTU4qRxrxiNJFssbMzPJ6t372anFVWDsLy8xUYrABgM9ElLY0yPHvQbPpy8nTuhogJE+ooAKN69m+wm7tsePXuS0tJ4Mhp5cu5c/j5zJrs2bybg8eD0eDBFPTSVlmJcWVPTYqpbRu/eVDc0kLdjB7169AgWwi4shPJylu3YwcZwPULBIc3u7dtJNJtJGThwn9fGJSVhdTioq6hAkiQSmuxmK6lJidvlwup0ktxEXLeYTI0m1QJBM3w+Kuvr2+cgTEmhprY22IyuifMWQhtTChA5INhUz+X1tigQ9uzRg9+XL+eEI4+kWAgegr2wcc0aRgweHNnMTU1KYumKFSQ2ea4Y9HryKypYt3t3yw5ChaQY15WUEAgE0Ot0zc71yMigpLAwBlEJui3l5bBpU+Rlaw1JW8NsMDQql2K327FEpe/rDQZF1iB0NylvYTIacToczJ8/nx07dvDnF18gh9dwbjeEakoHAgGOP/541jSpQ3+ooTiBUJKk3ZIkbZAkaa0kSatiHU9XU1paSo8mbox9oteTbLHgDwQYN25cs9NKKhhfWFDQeoq1RsMjl1zCVccfT5+pU8kvLsZfWQkeD4RsxQsWLBC71YcoRQUFZDdZqJ114YUc3oIDCoBeveiZnExxXh62igpMej2qqHowShMIq+rrSWvByaLWaslITGT5smX0ys5mwOjRrNm1C+eqVazPzyevpIRbLrmE3I0bYxC1oLuwdvFiRg8e3KgmUmtoTSaMej0bN2+mZwsCvJKeORXFxaQlJKDSaBodt5hMIsVYsFdkl4tqq7Vll3orJKelUVNbS2l1dYtzPbVarZjnjs/nQyVJaFqohdUzOxuv18tx/fpRUV2tqLRpQdeyacMGhg8fTmpqKmqVimOnTWPJxo0kNRUIQ6Kgy+NRdJOS+pBY06KDMCuLUpEVJYjit99+48p//zvyurqqqsXa6q1hMZmwR2122my2RplWOoXWIHR5POij/h5hB+Gzzz7Lr7/8wvRzzmHTkiXBkwUFsG0bAEVFRTgcDlYuWxaLsLsNihMIQ0yXZXmMLMuHxTqQrqakooKs9gqEkkRKYiIzJkxo1PErjFqtJqCQCeeO3FwG9uvX6vnevXuTmJWFMT2doX36sPjbb7HLMoS65d166618/vnnXRWuoBtRXFREdpOaTnPmzGlRNAdg4ECyhwyhqLCQuuJiEqLSi0FZNQgDgQA1DQ0ktyKu90xPZ9natfTOyWH48OFMPuoorv36a2obGsgtK2Pexx/z2fvvd3HUgu7EpnXrGDF8eNsu1mgY2rMnHyxYwIhhw5qdVpKDsKyoiMwWduPTkpIoEx2+BSEeeeQR7rzzzj1CVyBA/a5dGHS6vRaLb0pO377kFhVRXFPTcg1CrVYx4rrT4cCg07W4qdAzNI8dO3IkKfHxlJeXBxtiic7ggiZs3LaN4WPHkpqWRs/0dCZMmsSybduaOQijx1lLdS8V4yDci0CY1aMHJUIgFESxZt061kVl+lRVVbVYW701LEZjo3IpdoejkUCoyBTjQIBam43EqM05o9GI1Wply+bNrFq6FLfXy58LFwZP2u1gs+F0ONgcyjpc9eefsYi826BUgfDQxO+nqLKSnn36tPut2RkZnHzMMS2eUyvIzbFj1y4GRnUvbobBAKG6JOedey5/f+opxt9wA3JNDVVVVWzZsoV1a9d2TbCC2OJyRZyjAMWlpWS3kLK1N3r26UNxSQkFO3fSq8liTUkOwtqaGuJNJrRNukmGufS00+iXkcHwESMAePbZZ/nkk0+YPHkyu8vLsTqdfP/zz10ZsqCbsWXbNoaOGtXm68cMGsT7Cxc263AMoFJQHbXy4mIyW3CAjRw6lPXr1kVey4EADVVVXRmaoBvx6Ucf8fLzz7Pwyy+DB4qKqFy6lLR2ODkgWPLBqNVSWlNDZgupyWq1WjFuO5fDgaEFkQOCzbGMOh39pkyhZ0oKRQUFbP3mG5w//QRVVXi9XtaLudohj9vtpqKmhpwRI0jr3Zs+vXszeepUPD4fSU3GVnRasVHBAmF9SKxpMcW4Vy9KxMbUoYvHA03mTrsKCqgImWAAqmtqSGmHa91iMmFraIi8tjVJMdbp9YpLMZY9HqoaGhq5941GI5u2bcPldvNHyDn454oVIMvs3rmTVWvWcMlpp3HTddcxYeBAVq465JJUG6FEgVAGfpQk6S9JkuY0PSlJ0hxJklZJkrTqr7/+ikF4nYjTye6KCvruxUHXGq88/DCX/eMfLZ5TStFrl8tFWWUlOf37t36RyRQRCP92+eVYkpOpsVrZsXo1Sz75hNT4eNYd4oO+NQ62sRPYuZPA9u2R18VlZWQ3aTSwL7L796eorIz8XbvIafJeJQmElSUlpCYkgCS1eH7ODTew9pln6B0S33v06MHLL7/MhRdeSI/0dI478khWbNjQqNOZYA8H29hphiyzOTeXYa25bVtg7IQJ1NvtjJswodk5tVqtHAdhSQkZLUy2x0yYwNr16yOvH7/7bmafcEJXhnZQcDCMHZ/Px+atW5k9ZQpbVq8OHiwupjI+nrTWSqK0Rlwchw0ZQlpiYosZHxqNRhHzNQCX3R50ELbA8NGjefvGG1FlZpKdlkbxzp2ccc89PLlsGaxdy8JPPuEUMZ5a5WAYN22hqKCAHsnJqBMSOGH2bF566y3GjRuHTqslsQWBcEBoI9fQgutbKQJh3V4EwsyePSmtrARg165dVIlNqXaj6LGzZQvs3AkEM4M+//xz8goKqKivh9DvdnsFQnMTgbBFB6ECxk00DdXV6LXaRpsGJqORVWvXYjEa2ZyXR98ePfhz61ZwOHj3m2948IMPWL1pE1vy8jhn2jS27dqlyO7NHYUSBcIjZVkeB5wIzJUkaWr0SVmW58myfJgsy4eNHz8+NhF2AF999RWnnHJKowW5u66Oyvp6erS3SQmgHTMGVSvvU6vVimhSkpeXR05GBpq97cgPGwYhIad3Tg5btm3j1NNO47u//uKnBQu4/Mwz2bR9u2Im2F3JwTJ2wjz6/PP833/+E3wRCFBcUUF2O923Pfv3p7iqivzdu8lpIsyrQwKhrACho6qsjLS9dTVLS4OkJIjaNTz//PO56qqr6DdoENNOOIEROTmHfE2O1jjYxk5T/LW1bC8pYUjIYdoWxkwNPprHHta8EoiSahCWlZWRmZbW7PjoI49k3Y4dyIEAZWVlPPrCC2zMzQUF3A+6EwfD2NmxbRs9kpMZP2kS27ZtA4+HbZs2cd2TT7arg3GY8ZMmkdXKAk+t0SjHQeh0tioQ6lJSOOfGG0GrpWdWFuuXLWNXeTkvvP021kCAjb//TkF5OQW7dnVx1MrgYBg3baFgxw56Z2SAJGE2mxk2bBh6vZ6xY8aQ1KTcksFgYEhODqmpqRii5jJh9Ho9bre7q0Lfb+rq60mOi2tRIMzo2TPiFrv/vvt45eWXuzo8xaPosWO3Q0ggXrViBWeccQartm7F7nJhD/1eVFdXk9oOncBiNmOPKu1gcziwRJVU0huNinMQVraw5jGaTKzbsYNjxowBYNbUqeRXVGArLGR7YSF/bt9OcU0NPVNTGTN5Mj1TUth1CD9/FCcQyrJcHPpvBfA5MDG2EXU8gUCAiy66iLKyMt5/912oqQGgMC+P7PR0NE2KpR8oSnEQ7tixg4FZWY1EjGZotc3q3cyePZvXFy7kg8WLufKqq8hKSWHLli2dHK0gVgQCAbZv3crb33zDltzc4K6aw0FxTQ3Z7azfmZKWhsPjYUtNTTPnqqRSoVapCAQCHRl+p1BZVkbq3oR1SYIjj4QWuk1eeeWVzD7jDCaNGsWy337rvCAFMWfz5s381sK/cf7SpaQmJzdKO9kXow8/nKuvuor09PRm55RUg7C8vLzFVM/0nj0x6vUUbN7MXbffzj9mzsThdlMXrg+1aVOjEgeCg5f1y5czasAABo8Zw7adO8Fq5fVffiEhIYHzzjuv3Z83Y/ZsJkyd2uI5JTkIK8vLSQ5ldDRDkiDkrhw1fDivfPABR44bx5lnnsmJd9zBui1b0Go0PP344yxfvrwLoxZ0NTWhNU5LFOzcSe8WxI7/PPUUx590UqNj0444gqvPOYesrKwWuxgbDQacLteBB9zJ1Fut9M/MbFEgTMvOpqq+nkAgQPH27WxYvrzZptTW9ev589dfWbduXaSeoeDg4Ln33uOTBQvA4+HHUD398tpaUhISqCguBrudKquVlBbmLK1hMZuxRTUpsTscmJukGCvNQVhZVtasvEfv4cMZPWwYd917LwD9+vRhaP/+bFq6lO2lpVTV1TGob1/+fOstZpxxBgN69GDn5s2xCL9boCiBUJIksyRJceGfgeOAg661Zm5uLgl6Pf/6+9/514MP8sXTT4PXy+7cXPq0s4ZaW1CKQJifm0ufjAxoR8FvgJNOOomcAQOYMnUqfYYOZdaECXz66aedFKUgZpSWgtvNRx98wOChQymuqWF3aSmsWkVgyxaKqqro1U6BUJIk+vTtyy9//UWfpu5DSUKjkHpQleXlpLWQctMWLrjgAoYOHcrkiRP5YeFCnnnmGTZt2tTBEQq6A88/8QQP33NP44MuF0t//50x7UgvBjCZTLzUirtBSTUIy8rLyWglTXT04MH88sMPfPLpp9xz3XUMyslh+9q1wUYLeXkgakUdEqxevpwxI0YweOxYthUUEKir48NffuG5557jggsuaPfnHXnkkbz2+ustntNoNIopbZG7YwcD2jBnvWjOHPQ6HVOmTOH555/HFQjw6dKlnD1zJs+88gr/+te/uiBaQSyQZZlRI0eyIdQYIEJBAZSXU7B7N71b+B068sgjm23cDDnqKE66/HJ69OjRYg1Cg8GASwkOQquV/j17tigQ6oxGLAYDtZWVlJSXs37TJvjtN4hKEf3wtdd48T//4eabb+YL0ZTx4EGW+W75ch7/8kuoqmLhwoWcesQRpCcm0i87m4qSEqiro7i2lqx2ONctFktjgdDpxNzEQai0JiVVFRXN1jx/v+wyVq1fz4RjjiHRbKZ3Tg6jRoxg3erVbCsuZsyYMYwcP56eJ5+MKjGRgX36CIFQQWQASyRJWgesABbIsvx9jGPqcP764w/GDxzIzBkzePTvf+fSZ5/FuXUru3fupG/fvh3+fWqVCp8CdgfKCgvJaseuSBhJkvj444955513wGzmgiOP5L333lNEaqig7VT9/jvz7riD5x9/nKevvZZv/vtfdldUIFdVUbhxI0kJCe1yQIU577zzKCkpaVaDEAgKhAoYO1WVlaS2o6tZS0yaOpXfli/nh6++4qTjjyfgcIACJtuCtiHLMj/8+CN/rlqFZ+3aYEdRgOpq/vv771zw97932HcpyUFYVllJZivpOmNGjOCN999n7IABJAwZwqD+/dm+cSPs2sVP27dTn5/Pff/8J/mHcJrKocBPixYx/bjjyO7Viwankz++/x6TycTwtnb9bgdKSjHeuW0bA9owZzVkZ/PNwoXM/b//Q6VS8Y85c3B7vTz98MP89u9/s3jRIsVsKAjaR97OnRSXlLBu6dLGJ0pKIC+PgoICere1drRWC2YzkydPbnGtZDQacTqdHRB151Jvs9G/T59Wu5+nJyVRUVxMSWUluUVFTL76aravWBE5X1hYSF5BAblbtlAosqUODqxW2L6dHaWlbNq9mz9++IHVW7fy1Msvc/X555OekkL5ypVYly4lv6yMoUOHtvmjLWYz1iiB0OZ0Nkox1hkMeBTyzAlTWVFBamumCK2WIb16MXjIEEaNH8+v27aBSsUll1zCjKhmrgP692fHtm1dFHH3Q1ECoSzLebIsjw79GS7L8kG5rfjXsmWMHzUKacQI/vavfzFu7FgWfPxx0EE4ZEiHf59Br1fE7kBZcTEZ+1HPB4JuloSEBFCpmDB2LAatlm+++aaDIxTEDLebmXfeyWd//YWtoYGrH36Y6WefjV6n46sVK/hz82YG7625zV6YM2cOFoulRYFQKeJ6ZWUlae0oWtwSvUeMYMWTT/LtvfeSpNfz0i23UPjrrx0UoSDW7FizBo/Hw6DsbM668kpWhrpWl27fzqqtWznttNM67LuUVIMwr6iI3q3ULh09Zgx/rF7N+H79IC2NQSNGsG39euy5uZzxwAPc/OijPP/220w+/HDR4OdgIxCA3bupzM9nZ2Ehk44/HpVKxbhhw3j+gw+Y3EJzno5ASSnGO3NzGdDG5+7IkSNJC9X6PO+887jppptIHzOGaZdcQo+kJNaKjsYHJUt++AGATU3+fd/4+GO+WLAgKBC20xhx3333cdxxxzU7bjAacXXzFGNZlmmw2zl26lSOHDu2xWsykpPJ27EDt9fLkJ49Wb59O98uWBA5X1hSwvaCAgrKyigsKOiq0AWdSXk53s2bKays5KpLL+Xahx9mwrBh9B81ivtffJH01FQqqqtZ7fUyauTIFhtctUZaaiqVUc1umjoIdaEmJUoy1exrzbPk7bcZMWECI8eN45tlyxg4cCDXX389l156aeSaAYMGsTM3N/jC54s0gTlUUJRAeCjw7gsv8OYHH3DUkUcGD6jVXHDxxby/eDF/5uYyKlRcsyMxGY04HI4O/9yOpryigsz9FAijkXr35t9XXMEdd9yhiPpxgn3jqalhS1ERX/74I+vz8tAnJADQNzubsx55hNvmz2fwoEH79dlZWVmUlJS06D7UqNX4FCCul+4lTbLNGI1MuOgipGnTuOraa7l9/nzu+s9/RFOGgwBvRQVXXnYZl194IbNmzmTptm18Hlpw/Pbrr0ybMqXFmk77i1K6GBcVFeH1+egzcGCL58dMDJZAPmziRFCpmDRtGj+uXcvnGzciSRJv/vQTl/z97xw5ZAgvPftsV4Yu6Gyqq2HbNv770ENMHTsWbSilccbRR/PJ0qVMmNg55bHVarViUox37trFgFbGzt5ITEzkySefDNaTzsxk5tixfPXpp9x03nmUbNsG9fUEmqakChTJH4sWcfTo0WyMLlvi9fLhr7/yzzffZPWOHQzvILHdaDJ1+xqENpsNg07H0WeeyVU339ziNekpKaxds4Yeyck89n//x3/uu4+fly4NusyAwrIyqhsaCAQCFBQWdmX4gnawbdu2NrvBf/35Z8545BGy09O54NJLWZuXx8wZMyLnMzIyWLhtG//9+WcmHH54u+LIyMqiPNT4BJo7CNVaLSpJUszGFEBlVdVeBUL14YeD2cyUKVO45557eOihh5pdM2jECLbm5QFQ8sMPbHzrrUap/Ac7QiCMIc0cBbLMf55+mrdvv52ps2ZFDp955pn8vGYNK7dt49hjj+3wOEwGgyJs92UVFWTsRwfnZvTpw6yhQ9FrNHz33XfBQvJ1dcEJv0iZVCS7tmyhZ0ZGMCVDkiLH+/bti9FopLCykkHt6MDalLioh2U0Skkx3lVYSP/9dFA2IjUVJImrbryRbdu3s2DlSrzl5UGRUEGTB0EUmzbx4r33orJYuOc//+Ghl17iv/Pns3zdOli2jCVr1nBU1ES0I1Cp1fgVsDmz7I8/mDRoEFIr43/g6NGYDQYmhP7/HHfccZRardz64ou89NJLqNVqTpw9mwduuYUnnnxSuNaVSCCAo6oq2P3U749siDhKSznv2Wd59ocfeOLFFyOXzzjpJGRZZuKUKZ0SjkarVcxCbWdBAQPakerWIpLEFeefzxNPP83TH37I/157jRfuuovRp5yC7PGITV6l4XRCyJVjr6/ni4ULueWmm9i0YweE02GtVjaEnIMPP/oovfdzc7cpBqOx29cgrKupIdFshrg4aCVFMj01lTVr1tAjNZUTr7qKC6++mt+3bMH966/IDQ0UVlbSIyWF9IQECsJNswTdjtknnsi3n3zSpms/+e47vlm5koEDBzJ2/HgmjRnDyeeeGznfZ9Qolm3bxjfffMORYYNRG8no0YOysEBotWJ3uTCHTBYAqFTotVrc3Vxcj6aqpoa0FhrkNcVoNHLbbbdx/PHHNzvXf+RIrHY7pRs3cv5ddzHphhv439tvd0K03RMhEMaA/731Fh/+979kpqezYfXqyPGa7dvJKyvjhFtugajBmZCQwMyZMznqqKMwt9Bl9EAxmUzKcBBWVZHZEU1a1GqkoUO5adYsHr7nHgq//ppdX31F2fff4wnbiQXKIS+PbcuXt5hCPOvss3l13jyMRiODhw3r8K9WikCYV1hIvw4uT5Cdnc2Avn355b33ePeOO5g4bBhrFy/u0O8QdDJWK0Xr1vGvTz/l+XnzUKvVSJLExClTWLVjB36NhsW7d3PkUUd16NeqFZIm+edvvzFpzJigk6kF1BoNW1asYMBhhwVfq9Xce++93HzzzVxwwQV8/vnnHH300Qw99lj+d+ed/PPqq4N/76IiUNBk+1CkvqqKWVOm8M0bb5DeqxcjBw9m6pgxvH7XXbBmDT988w0FlZVs2LiRIaNHR9436cgjmXrUUYxqJT3wQFGKg3Dnzp1o1GpS29kYrCWGz5zJ6RMncukpp/D0O+/w748/ptpu5++nncY/L76YvLw8RcxhBRCoqqL0r78AeOM//+Go8eM54bzz8Ph8HHv++ch2O5V5ebg8HpYsWcKVV17ZYd9tNJtxdnOBsDg/n8x9NJTLSE9nzaZN9Ail5Kenp3PU1Km8vGwZdYsWoVarGT1wIEePGUNheTk+t5vPn3mmC6IXtBW73c7W3btZ9vvvrV4TCAS4fe5cPC4Xi9asYeLEiQweORJJkli6ejUjx4+PXHvFlVeSl5dHUVERZ599drtiycjOpjxUc1peuxar290oxRhAr9XiUdCcpaS8vE0C4d5QmUxMGTmS/7vxRqqcTm6/5hpWhO5dhwKaWAdwqBEIBLjxttuorK8nJS6Oz999l5HjxoHXy6KPPuKIiRNbrB3w6KOPdprLz2gy4aio6JTP7igCgQAVtbWkd1QX5169OPeyy/hgyRIGXHwx/Xv1wu10Mmf2bH7Zvp2XX3mFfv36dcx3CTqP3bspXb2aVVu2MKgFAeySSy4BwOVyccQRR3T412vUavzdXCBsaGjA4XKR3kodtQPh2ptu4urbb8cdCHDTnDnMOuMMPl+wgE8//ZSjjz6amTNnotGIx0yssVVX8/K//sWRs2djsVoxms0MmDYNOT+fC595hhtuuIFhUQJ6amoq6ZmZHHnttTRYrYztYLFDpZAU4/Xr1nFTVE2alug1cmSj15dffnnk55NPPjn4g1bLtKuvJvP11/n4jTf47bPPmDt3LqOmTKHO5+Pbjz7i/H/+M/K+rVu3MmjQIFStCJOCzmf5L7/w7dKlLF23jlf+/W/8LhdVTid3vvgiao2GlatWMfuMM4K1jaPQ6/Us2sui70DRaLWKaFLy6ssvc/GMGUgm04F/WGIi77/xBnaLhezBg/noo4/4+v33eendd0lNSODnFSs477zzuO/++w/8uwSdyk8LF3LPY4+x/Kyz+PLbb7nxlltQa7XkFRcztH9/1i1aRO3OnYwYNgwpKhukI1CCg3Dntm0M3Mc6Jz0jg7ziYs6cPj1y7LHHHmP69On0SUujV2YmQwcMoGdGBt+tWMEDt93Gw889R+Xpp5PaCfNAQdvZvn07tbW1BJxOVJLEspUrW732ryVLeOyllxgzaBCF1dUUrF8fqQPYdGyE5wpqtbrdMWX06kV5TQ2y08nyVavI7tmT+Pj4RtfotFrcCsg0BLDW17NswwY+imo4sr9MmTyZ2598km+/+gpXbS1vzZvXAREqA7Fy62KW/fILiWYzf7z8MnlWKzc98gj3PvUU/vXrefrLLzn/iitafN+AAQM6LSYlOAhra2uxGAyR2nIHjCSh6d+fT374gaKiIq666irycnN5bP58am02Hn/4YW6+4w5+/vlnrrrqqo75TsGBIcv89+67mXXppeRu3MjEyZOpWbWKI265hd35+bz88sutvvXSfSzy9xeNWt3tF2t527bRLzMTqQNryIW54MIL+fKrr7j88ss54YQTWPj99xw3cyYnHXEEt37yCeeffz53tlDbQ9B1bFi6lIfuu4+ysjLe/OQTiqurGdWnD0vefZcF33xDrdPJHXfc0ex9n37+OYWFhcyYMaNdBa/bglqjUUSKcWl5Odkd9ew1mXj09ts5be5cPD4fFosF07vvoouP597XXydnyBCmzJyJLMtMnzaN9959lxkzZ3bMdwvazfIlSxjepw8VtbWcffXV6EP3z2PPPJO//e1v7MrLY9m//93lcRmNxm7vgpJlmf/On8+SefNadd+2mwEDMAOlZWUYjUbSkpNJSEvjm88/p6yigrdef52/nXceOTk5HVovVdA23nj9dU47/XRSm9T9WrduHdnZ2ZHjW7ZsYXNhIb6yMlZu3crk0D1Op9Nx6gkn8PX//odBo2HUuHEdHqPRbO72NQh3btmyz87fM449loe3beOaKHfl8OHDue+++/jbzTdz7LHH8shjj6HS6Xj7s8949b//ZUivXqz6/XdOEAJhzHA4HJw6axY9s7I48+STOXXyZH5as4byqBrhHo+Hy845h96ZmWi0WhLMZubecw+nHntss82ojsKUmIhWo6EhN5d3/vyTiy66qJkAqVeQQPjthx8yZfRokjqgJNmpF1xAfnk5J5x8MlvWrWPr7t2ceswxvPLWW5RUVPDll19G6heWlZURHx+PqSM2xboBQiDsQlxWK/fffTfnnXIKOWedRU+fD9s99/DNU0+xfccOtAkJzJkzp8vjMimgcG9ZSQkZiYmg03Xo5xoMBgYMGMDnn39OIBCgd48e3H7uubz+xRf8sXIlRUVFXH755cIF1Q1Y9/PPXProoyQ89xwOt5vR/fuzpaiIuddei9frZUYH10lrC0pIMc7bsoV+HeW8bYJKpeKTqBoqt82dyz2PPso7Tz3FsjVruPKee/YIhIFAxy0WBW2iuriYaccfz1kzZ/Lmxx9jVqnwm0wMyMnhzU8/5fFPPuHf//53i061UaNGMWrUqE6JS61WK6J2WFlVFVkduKCadvHFbDrmGLZt3coJJ56I1+dDpVJx+ezZ3HPnnfwycyZbN2+mrKKCVYsWCYGwC/nt11+pr6vjtNmz2b55M7//+ScP3HMP02fNioiDAKNHj+ann37i4Ycf7rTxsTfi4uKwdvMN3e1bt2JUqxnYQifZA8UYagYzbuJExk2cyMDERBJUKp75+GNOOO447A4Ha9eto0dH1KsW7BNZltm8cSNXzJlD+c6d3Pnoo8geD1Jorn7FhRcy+9RTueNf/wJge24uNqeTH99/n6y0NFKiBMXTzz+fKy67DEmt5rU33+zwWA0mE65u3lRux44dHL+Pkh6DJk3irv79ocnG3dy5cznllFPQ6/XoQoLTgqeeIjUzk/veeosVy5ZxwkUX4bfZkFQqVCYTsix3uFNT0DKfvfce6Xo9y1etIi0hgRlHHUVqXBxDBg/mo3feYf5rr1FVWYlOpWJVaSk/rVzJc3ffzb3PPMMjndnkTKUiIzGR3xYs4OOff+av//yn2SU6jQZPN9+YCvP9t99y6kkndchnDR07lpfeeQeA/kOHkldayo7iYs766itu//e/sdts1NbU8P1331EVquP4x+LFDO+EhrJdjVA9ugK/H1QqbrrkEsxGI7c9+igQdFG8+tprnHfhhfgliSVLlsQkpchkseDo5jsDZYWFZKakNGpA0ZGEm1B8+NZbHDF5MqccfjhPLVyI7HCwatUqJk2a1CnfK2gbpevXc+Ott/KvW28lKz6eky69lBWrV3PEEUeQlJQUs7gUIRBu306/nJwu+a4Z55zD9EmTkAYPZvKAAdRffz13XnUVi5cuZVdJCQ/cey//uO66LonlUESWZVasWMEvn37KosWLqbdaOf3YY5n32WeRazTAC6++yiOPPsqcOXOYPXt2l8ep0+txe73denHi8Xiot9tJ6WChIbtnT7J69KBHVhb33nsvP333Hc+9/jo9evdm7jXXsH7NGpIsFlatWtWh3yvYO0899BDllZUM79OHCUceid3lYv4xx5DcQvf37OzsvTrWOxNLXBy2bi4Q/vnzz0weMQIslk7/rkvuugskidlTp4LPxx2vvMKZJ5/M3846i+vvvJOPP/qIYcOHM+IAmpQJWuemSy/l+XfeYfZRRzHv7bcp3b2bbxctYs26ddgcDlZu3EiSxcJFq1bx588/s333bjRqNW9+/jmTJ09u9FlTjz6aiVOmsHHjRo7q4Lq3oIwahDt37WJuK5lkjQjVH2xK7969G73uefrpAEzYuZMXn3iCKxcs4JpHH6XB6eSKW27hhptu4osvvqB3796o1WqSk5Obpanm5+fzxBNPcMMNN9C/f/9u+8zu7ixbtIjTTjgB99df8/XPP/PUPfdw7U03IV16KaeddRYnHH44R4wZw13PPINao2Hx/Pkcef75XHHXXcEGjJ1IRnIylzz0EI899liz3yEAvU6nGAfh5h07uLwDa5eG0ev19M3KoqSqiudefZVhWVlMHjSIeR98wIKHHmLYEUfwyAsv8Ppzz3Hrww9HNqk+//xzUlJSSE9PJyMjg6SkJH764QfefvNN3v3oow6Ps6MQAmFnEQhQs20byb17Y/v+e1bW1vL1kiVs2LoVbVSjkRknnsgX33zDzp07GdLBTQTaisls7vYpxls3bWJQCzetjubEc84B4IjjjuOIAQO4df58br7uOmaMH891N95IWgd1UxO0AVkGlwt7bS3TZ81i1qxZ3HD//ZH0oVlRnb5jhU6rxdXNH5p5ubkM68QSBY0wGpEGDwZApdXy/quv8tnnn3PrrbfSIyODC/7xD/786y8McXGMGTOmUb02wf6zZcsWMjMzee+113jiqac4Y8oUrrziClatXs1l117b7PqTTzmFk085JQaRBtHq9Wg1GpxOZ7dNxygvLiY9MRFVJ0zMVSoV23bsQK/Xc1loDJwyeTKf/+9/6DQabjj7bN7+4YcO/15Byzjsdn5bvhyVJHHW2WfzwG23ccn115OYmBjr0JoRl5jY7R2Ef/7+O5O7alM1vKkeEpTuHjCArMcf58WXX6aspIRnXnuNY488kvn/+x/J+2j+IGg7Cxcs4JOPPuLPpUv57MknOfHii3nw4YdxVlczcexY7vrnPxk0eDAnTprE72vXMvbYY/HJMg6nk2mTJ/PZH3/w6S23NPpMSZJ48803aWho6BQRymixdH+BsKCAAU3q2nYER59wAq+89RaDzz+fPj17MmX4cF7617+YPW4cJ514Il6vFxUwZeJEvvnpJx685RY+XbCAYSGXdN7WrUz56CMOmzCBL778ssPLjhwK/LlqFec/8QQ6AKeTrNGjQa/n+vvvZ83u3Xz444+N3OpTL7sMgM6VBoNkpKZSY7Vy+dVXt3hep9UqwkEoyzJb8vMbNQ7rSOZeey3OmhrufOIJHrz8cm69+mquOOUUep97LqjVXDRnDkOPOIIX332X119/naVLl/LlF19w+IQJLFuxAo/Hwx9Ll3L3//0ff23cyP07djBg4MBOifWAkWX5oP1z3333yV1JXWmpvPqHH+SA3y+/fdddsl6rlZ+74QZZp9XKqfHx8teffNKl8bSVJV99JR8xalSsw9grV114ofzcLbd03Rf6/bJcVSXv/PFH+YnLLpPPP/54+eRJk+RAcXHwnCzLDoej6+LZT4D7ZQWMnWgCHo9cUlwsv/XII/Kg7Gw53mSSLz333JjFszemjRkj//r117EOY68cP3myvGD+/FiHIcuyLJetXi0/8Pe/yw9dc408pG9f+cVnn5XfefNN2e12xzq0RuzvuJFjMHa+/eorOTEuTo6zWOR4k0ne8ssvsuz1dmkM+0N6YqJcWlwc6zBaxueTl//0kzx+0KAu+8pdP/4or3/tNdm1eLHsKymRs5KT5eOmTpV3bNvWZTF0BEoaO7Isyz98/72s02rlaWPGyJeceKJ8zowZsj/0jO+OuOvqZI1aLQcCgViH0ioj+/WTV/z0U0xj2L5ypXzG1Kny4//8p5yemCirVCr5+++/j2lM+6I7z9eW//GHXF5aKk8cNkwu3LlTPvfEE2WdVitbjEbZY7c3ura6okLulZ4uJ1ks8orffpMnDRsmP3PvvfIjDz8sazQa+fHHH5f79esn+3y+To87Go/dLqtVqi79zvaQt3WrnJGUJAc68f5js9nkurq64ItAQPYVFsovXHWVXP/DD7J72za5b2amfNFpp8lDevWSl82fL9985pmyQa+Xy778Uvb+/rt8+hFHyIP69ZN/+/VXWZZlubq6utNibQ/ddewUFBTIr8+bJw8bNEg26vWyo65OluvrZbmhoVO/t708e8st8mfPPdfq+UnDhslLf/yxCyPaP4rz8+W0hARZ7sTn47aVK2VA/u3992XZ55PlaC0gEJC/ee45+brTT5cBecTQofKRI0fKKpVKHtu/v/yfK66Qe2RmyqP795dvOuMM+f/++c9Oi7OttDZ2hIOwI/D7oaCA266/nre++45R/fvjdLu58qKLuPmFFzhr1ixmnHQSJ595ZqwjbRGjydTtU4w3btrEOSec0HVfqFJBSgr9Z8zglqOOwi1JTBg9mgETJjB6wACOP/dc/vXvf7Nm3Tp0Ol0kRVmw/9hsNl559lmefvppPF4vPVJSePuddxg0YgTJTQpfdxcSLBbqampiHcZeySsspF+M3MlNyRg7lnufeQbq6zljxgzGnX8+ciBAcW4u//fww7EOTxHY7XZqamro1asXWzdu5OKLLuKbp59mbL9+lJhMDDj88FiH2CYSzGYaamrI7Ia1wt556ineeO89MrvwvtNn+vSgazrkzNjyyy/Mnz+fw8aN4/TjjuP5+fN5+umn0Wg0XHPNNQDd0uGmJGRZ5oE77+TZf/6T0+fOJcViQWMyIXXjWqk6kwlJkvB4PJ2edrY/NFRXk1dSwugpU2Iax8DDDuPTRYsAOHnaNP5csoSbbriB226/naFDh5KVlUWvXr1iGqNSkGWZc888k8y0NFZs3sy9t93G97//zmEDBmAwGNA2cYEnp6Xx2YcfUpCXx4Rp01i0ciU6k4nKykrKKiu5/PLLOf744/er4+qBoA25s7xeb/dzwJWW8u0HH3DilCmdev8xR2WwIUmoe/Zk7sMPQ3IySBJvP/ccn33+OV99+SUDx47l8COP5LbTTyf9mGPAbOazBQv4dt48zj7jDO695x5uveMOKioqiGvS9VYAgUCASYcdxrDsbK479VR2lZZi7KRGIwfKdffe26ymZTR6nU4RDsIta9cyNCen08qRAQwYPZpZhx3G4ccfD2o1hOriAiBJzPrnP5k0bRpDs7K4fNYsvCoV066/ntOnT+fau+7CarNx/UUXUSTLnHzZZTzy7LP8/vvvjB07tln36FgiBMIDZNPnn5Obm8vVjz2Gx+9n+Z9/UrV9O9OOPx67LPPGe+9xx913M/Kww2IdaquYurntXpZlNu3cyYgJE7r+y9VqUKvRA6vWr2dXXh4P33gjV82dy7DevemTk8Pgfv1YvHx5pHi2oB3IMvh8bNywgdNOOYXD+vfn2/ffZ/T06cGmFt1wARRNYkIC9bW1sQ6jVfx+PwXl5fQZOjTWoewhORmSkxmWk8MvTz9NXFYWMy69lCOPPRaD0Uh+Xh7HnHRSp3VsUzKbN2xg+tFH4/F4OHHGDH5ZvJgnbr6ZKZddBpJEFyWSdwjxZjP11dWxDqNF/li+nEXr1nF5V9ZnbNIIK2H0aK576ikuufVWbr7iCtLT0hgzZAiD+vUjMzMTWZZ58IEHCHi93H733aIu1H7w1osvUl9dzRX3349aKZt8Gg1xRiPWhgb0rdQhiyUrfv2VsYMGoetGnYSHnnkmQyZMoN7t5tv33+fp/HzKq6uZPm0aZ599NrPPPTfWIXZbHrv9dtZu2ECt1crusjJuvvBCvl20iKtPPZUTZ86kqqqqxfcdNn06h02fDgRFbYC0tDSeeeYZgNjUjlapMOh0uJzObicQnnPeeSzZsIFnQ81cupSUlMiPU88+m6lnn73nXN++pKekQEhYlBITmXXLLTxpt3PRTTdhNhj4bcECTjnvvK6Outuz7PffSTaZWLh4ceT/X7dlH88/nVaLu5s3MwXYunEjQ/v379TvUGm1fLNoEeylPE7KyJFc9fjjoFaj8fv56F//InPECPS9e3P/yy9DfDyJbjdmrZYfvv2Ws885h6svu4zHn3++U2NvD0Ig3E8CFRVUFRdzxIUXotFqeeuNNxgxdiz9+vWDkBioA4rLyrr9QtcUF4ejmw58l9XKqUcfjUalIq2TB/2+0Ol0DB4yhBfffptJTz/NaSedxJrff+ezxYsZ1r8//7z4Yq69775uNTHurjjtdp65/34+++YbdhYWolGrefKOO7jo1luDoqxCSIiPp66bCoSy281f33xDakIChu64+FWpOCLkhHqzrIxTTj2VlLg4eqen89ADD/DYs89SW1rK7PPPR9fB3cuVhCzLfPbZZyTExfHA7bdzzz/+wZlnn803X37JjVdeyYQO6tbW1cSbzTTU1cU6jBbJKygAILOFBhVdTXxWFq998glPrFmD2WhEq1Lx8nXXsWL9eq57+mkAfl64kImHH85lV1zBAFEnt1WsVisul4sLTz2V7B49+OaXX1j0xRfKEQcBJIk4oxFbXR2p3U0g9HpZtmgRk7vhhrjUqxc33n57MOPH72feq6/y7eLFPPavfwmBsAU8Hg9PPfQQz732Gv5AgItOPhmNz8dNd93Ff+bPD27uejzB/58KwqjT4bTZup3jbfH69Rw9cSLHR4tz3QFJgqZrWJWKC//v/zAGAmyvquKrzz/nmNNO67b1hLuSmpoali9bRtGuXfz3zTc5Y+bM7i8OtgG9TqcIgXDnjh0M6AqtYF+/65LUqElX39NP37MJHBpPksHAlWedxdnnnMPhAwfyxn//y6333ENcfDw7d+6MeWMtIRC2B7cbdDrKVq/mpL/9jaLKSi6cPZsX3nmn1d377i4OQqiLcTcd+Mt/+onS2lq+euGFbiMcxWdkMDfUibrnUUdxitPJikWLuOPee/nqu+9IMJt56YUXSMnJwef1YukGi8zuwp/ffUdWTg7nnnMO2ampPP7QQww78kiMRiPxChgrTUlMSKC+m4oc3330ESdfcglHhopMd2dOufpqas49FwIB5IQEHp47l8f/7/+otdn47OOPee6tt8g4BMfRzp07efCOO1ixfDkJRiMTRo7k6oceQq3Xc0UsHNUdSHxcHA3dVFzPKyriuEmTGNxdnLcGA4lRHT9NwNFTp7L+vPOoranhpw8/ZNXmzUyaOJGxo0fz+NNPM3bcuNjF2005/5RT+H7JEo4ZNw6v08l3H3zA0GnTYh1Wu7EYjVi74XNn6Qcf8NL77zN/3rxYh9IcSYKoRndzHnmEy2w2+g4cyNC+fXnhuec4JoaNm7oDfr+fQCDAxx9/zPY1a/j5++/5+auvcNXWkp6TQ/aIEY1T9xSYNWPQ6XDZ7bEOoxGuhgZqbTbe+/ZbVN24vEE0ksnEWQ8+yOYlSzhq1ix+GDCAjz79lEGDBpGcnHzIONodDgf33nEHvXv3Zs7cuVx67rls2ryZAVlZnDRhAhe10CROicRbLN12rRNNbl4eU5t0R+8WtOJYvv6++8g2mRg3fToP/ec/vPXCC2zJy+OTL77gwbvvJj01lQsuvxybzYYlSnB0Op14PJ5O1ZiEQLg36utBr8eWn8/333zDzY89xtj+/Vm6dSvX/fOf9MrO5oRTT1X8jdAUH4+jm6YYL/r5Z06YPp1JF14Y61BaRpLAZGLiiSeyYPp0nn74YWpra5kwcyYGrZa0xER+X7wYXahWlKQ5tIZcfVUVpoQENBoNj911F0+88AINDgcXzZrF6599htRNRN/9JSEhgZLy8liH0SLr165lYM+ezAh1d+z2hDpMSsA98+Zxj9uN0+Hgur//nYH9+mExmTj/jDO48bbbkCSJ5x55hCtuvJH+w4bFNu5OwOPx8Ml//8tN//d/zDn5ZJ779VcS+/Xr1LoqXU28xdItHYQ+l4vCyko27drVLWu8RVCpIC2NpLQ0zr7vPs4G7ty4kU/eeYej/5+9+45vqvr/OP66Sbr3LqWbvfdGlggIOBAXihtx740LJ+49wR9fRVRUFGXJ3nuXvdrS0r3btGmadX9/3KQDWlbTpKXn+XjwgLZpcqI9vfe+7+d8zpAhaDQaxo0cyU133smo0aMb93tpYDvWruXFF14gPTOTw2vW0KJduyZ9487Hy6tRBoSf//QTLz33HFc10n7bNWg0aPz9WTJzJgcOHeL2u+5i286dRMfFoVKp+PH77/ELCGDCzTc7e6QOodPp6NWpEwXFxbioVOQUF3NkyxZaNfEbUWfycHOjvJEFhKcTE2kZEtJkwsFKkkTHwYPJP3GCHz75hCmTJpFZUECF0cgvP/2ERq3m688/588lS/BuSlXaF2jO998z/c036dO6Nce2beOlV16he3w8hzdtwtXPr/Kc9nIQFhxMdlaWs4dxXkmnT9OqXTtnD+PC+flx42uvgUbD1OJiht16K51iYrhn5Eg++fhjDCYTx44f55Ovv2bEwIEYDAaefeEF/vr9d06lpPDfunUNNrTmlVZcIFNhIRX5+Tz+6KOoVSrmb9qEt5cX386cia68nPe6dKG9k0s/7cndywu9wYAsy40u7Fy3dStPP/OMs4dxQdzd3XnJutHC5PvvJz83l/defx3PsDC6xMZyLC2NIV27IgOvPPUUQyZNuqwu+G0yEhP584cfSM3M5JvffkOSJPy9vWkZHMz+rVtJy8mh+4ABTT4cBPDz9+fwsWPOHkatDh4+zEvPPMPdTzzh7KFcGjc3PNzcmLVoETMLCkg8dIgPP/uM7j17YjCZGN6rF0OGDmX2998zasKERve761KUFRfz1bvvMn/xYjzUan757DOunDz5svw94efrS0lxsbOHcZa0EycIDQhokoGaX+fO3DdjBvc+8gjpp0/z959/8sHLL3Pn5MlcPWIE0995h7adOjl7mA1CNptJPXGCrz79FF8fH7Jyc1m0fDmTb76ZOb/9xrSHHuK6W26h5WXw/r09PSktKXH2MM6ScPw4r3z4obOHcVG6TphA1wkTyC0qonOXLrhoNAzr358DBw9iMJnQl5Yy6d57nT3MBvfKE0/QLT6eZ558ku6DBnH65EniL7NwEMDdzQ29TufsYdSQcvIkMS1aOHsYl0aSICyMKe++y5RXXgGLhR1LlzL8rrsI9PGhe6tWPHrnnfy4YIGzR2oXsiyTmZnJ6RMnePr55/nnhx8YdMMNYDZzescOotq3R2qkmyrWR1hYGNmNtBjCRpZlkjMyiGssKz8ulLWFUv9rrmHOtGlce+eduIaE8G5eHocTEvhh1iz+mDGDQydPEhwYyG233YbBZMJVo+HQ3r106tGjQYYlAkIri8HAqt9+4+DBg+w/cICFmzcTEhhIu1at+OmHH7jmMr6LqHJxwc3FBX15OR6NqIdEhU7HjsOHGTx6tLOHctG6du8OwJARI9Dr9axbupS2nTqxc9MmjGYztzz2GHcsWcLQgQNp1akTBVlZ6CoqKC4qok3nzpRqtQy87romEwwYDAYqKipQmc0MGDiQ4f37465SkXjoEL6enuRlZRHVpQtqF5fL4gLNxj8ggGKt1tnDqNWhkyd58nJYZihJSEFBtB4yhO+HDOF7Wa6cFyvmzOHRxx/H+OijDOnVi2smTuTaJtSzMDM9nYRdu+jWuzce7u6MGzGCUH9/nrz9dm577jmkRtZI3Z58fX0pboQBYdLhw8RHRjp7GJdOpUKKjiYyOprHBw3i8dJSMg4cYM4vvzB0yBAmjBpF127dmHjvvQSHhFwWwTrA12+8wauffMKoPn04nZNDh9hY5n35Jf/3ww98++67XHP//c4eot34eHmhbWRzR1dQQEpODu27dHH2UC7JEzNmMPrKK9HpdOzas4f/zZpF0qFDjLn9dhJ27eLHefN47J57mPbRR0iSxObVq1m3dClXX3stPZvAMvXC/HxcXFzwPqP33tyvvuKnn3/m0MmT7N+xg2Br/674vn2dMcwG1xgrCE8lJhLTsqWzh1E/anXlRhd9J00ipX17Alq2RK9W06ZtW24aNYr07Gxef/NNRl17LSv/+Ye2XbqwZtky7m3Ey3BlWebfefMwGY3Etm7Ndddfj8lkwt/Tk+mPP85gW89ItZrowYOdO9gGFNaiBYeOH3f2MM4pKy0NL3d3fBpbb94LJLm4cMv06ZUfe/v40Dcujr7XXw+Ardt4UEgIO3bswNPPj3dffZW7p0zhiaeeYvHSpcTbMRxt1gGhrNeTsGYN33//PYu3bCE0OJjQkBASU1OZ9/vvtGzZkk7dujl7mA7h6eaGTqttVAHhzjVraBcdjX+1HbaaGrVajZeXF+OsB5E21uWQV44bxwdvvsmXv//OkRMnCAkMxNvTE5PZzKETJ/B0d+ea2bMZPXw4o2+9Fc/QUOUALMvK7r5Orr4rLSrir9mzOXrkCKnp6WzYtYuSsjJaBgdzRZ8+/PjvvzUe7x0R4aSRNiy/wECKGtmFGoC5vJxjaWl0aKA7S05VLdAYdeedHLvtNg5v28am9ev5/OOPmTx1KgDjBg0iMCAAjasro0aNYvzkybg0guBw/ZIl7N+1i//9+iuns7PpGhfHnhMnsMgy91x3HZ/MmYOqGbQi8PX1Jb+gwNnDOMsfv/5K/4EDnT0M+/H2JmLAAF4cMICR117Llg0bWLdqFc+9+Sae7u7069qV48nJyMATDz3E1GefRdNIf/72bt7MfwsWoDcaMRmN9OjVi//98ANjR43izS++YNPq1XTq00c5TqpUIEkMbGxN/+3Ax9u70QWEh3bupF1MTKPbHfZCSS4udLj6agB6WZdI94yJYe5HH/H3kiX8NnMmL73+Oj/98Qce7u5k5+czaexYrpk4kfCQEL75+mv6jRjhzLdQp4yUFIYMHEj7+HhSs7OJiYhg6759DO/dm3W7dvHcgw/yyuuvV4aDlzN3NzfKG1sFYUoKMdX6Y14Ogq3nnl7A+y+9xIKFC3lu6lTuvPturrnySmb//TfeHh4YTSZ69upF98bYNw545r77WLN+PX5eXmw6dIg5b73F7Q8+eFktH74QYS1akJ2b6+xh1Or4ypUUmUwY1WriIyKaTGHNpbr2kUe49qGH0Obn06ZdO9Zu3crArl15a9o0/u+vv5AkCYvFgrqeOUHjPAt0kFeefppf//2Xe+64g5Xvvkv7Tp2QZRmj0dhkKlDsxdPNDV1JCUGNqC/P+tWrGdpIDxr1FRkdzRc//FDr1ywWC7m5ucz89FO+/OMPnn7/fWRZJjYsjNDAQA4kJjKif3/uvP9++o0a5eCRKyaMGoWnhwd9evZkzKhRvPDSS7SIj+fwgQP0aSo97+zAPzCQ4tJSZw+jBktFBU9MmkT7+Hi8qjW1vVxJGg2dBg+m0+DBPDBtGobyckqLivj7l1+wmM3oSkr45PPPueuxx+jfpQsB/v68/eGHtHFCpYssy7zx6qu4urjw0VtvERcXR1yfPhgqKsjLzKRFTMxlU9F1Pr6+viSnpDh7GDXsWb6cBRs3cuTkSWcPpUH0HjWK3qNG8TiATsfJQ4c4sGcPsW3aYNTpeOHVV3nlnXfw9vDg5ccfZ/Jjj+HVSDaPOn3iBOOvu47ubdvi6+1NeFgYs777jtFXXsmq9ev57K236NSvn7OH6RDe3t6UNrLK9Z2bN9OtqS3tugCj7ruPUffdB8Dqq64iYcMGTEYj0e3aEdupEx8ZDMz/7juunTiRIb17k5iSQvv4eLp07Iik0dCqVSsm3HsvGicGp3s3bODO66/nx7//pm+nTvTq1o03XnqJg4cP89QLLzDwqqucNjZH83BzQ19e7uxh1HDk2DHGjx/v7GE0mDuefZY7nnkGJIm2vXpx66RJrJg7l4zTpzGYzQwaPpw20dHcMGYM9z/9NNtWrGDsHXfg6u5ORkoKBTk5dHFSRevTzzzD+198gcbVlSOrV9Nx9Gjl5lMzE9ayJdn5+c4exlmyExPpff31SMDjd99Nu7g4Zw/JMVQqfEJCWPLrr0TExuLm68vQQYPoEBtLx7ZtOXziBIP79+fOu+9mqPXG18Vq1gHhyx99xNtff13jgkySpGYXDgJ4uruja0RBR0F6On8vWcJrL7/s7KE4nEqlIiwsjFffe49XgQ1r1hASHs7Jw4c5cegQz7z5JlvXrsXixIPUkg0bcHV3P+vzQ5v6MomL5BcYSFEju1Cb8eyz7EtJYfXmzc4eiuNJEq6engR6ejLluecqP/3ku++Sn57OluXLKdNqCXTS3V9JklizZ89Zn3d1cyMiNtbxA3IiP39/ShpRHzV9WRl3PPAAH7/zjtN+PhzK05PWffrQulqfsbXjx5N27BinT5/m3Tfe4KX33yciJAQPNzfaxMTg7+/PV7/8gsoJFey+AQF88e67TLRWCFfXNLoU24+PtzfaRjR3CrOzeeebb5j322/OHkqD8vLzY+AZOx2rXV255fHHGTRmDMvmz6djjx4sX7SInLw8XNRqFi9bxvPTp9Ona1f+WL7cKeMed8cdjLvjDu5/+mkCwsJwt9447NkE2/fUl3sjWWJ8YNs2vv30U8aMHs3mhARm/f67s4fUsKzX2Z369+dAUlKNKq+b776bIwkJfPPNN8S1bUt0aCiPTptGUWkp7q6uvPPcc04LCCOrtUTqeIlBy+UgLDKS7Ea44mPR779z9RVX4OnmxtvffMOWxYudPSSH6jVmTOW/DyQmMnvGDFavXctbTz5JVmYmbeLjL/m5m3VA6NmIltM6m4ebW6MICGVZxlhRwVXDh9O3Z0+uvvVWZw/J6YZYl6106NgRbrwRgH5DhjhzSLWGg82Rf3Bwo6kgtJjNTH/iCebMn8+mLVsICAhw9pAalaCWLbmmGTSbbyp8/f0paQRzR5ZlJg4diq6igk5t2yrLh5qxyHbtiGzXjkUjR5KWmEheRgZ6nY4TR4+iLSpySjgI4BccXGs42Bz5+PpS2gjmjsVsZuUff/DLL79ww+jRXNGMKtHOFNm2LVOmTQNgYLUgQbZY2Lt2LYGNoC9Wi2awhPh8PDw8GkUF4W//+x/LNm5k3fbtzPrySwIawc+Hw5yxSsI3IoJ+ERH0u/pqZuv1SBoN2/77j7j27TEZDERdRn3Lm6rQqCjyiosxm0yoG1ELkr8XLeLOu+6iU48euHt4MGDcOGcPyXlUKu59+WXutVNhVeP5v3yBJEkaA3wOqIEfZFl+z8lDuix4eng0irtqLz3+OF//3//Rr1Onyh1wBaGx8gsOpqisrFHsAP7IpEkkHDnC9m3bCGtm1WhC0+PbSDb42b5qFXuOH6dP1658+/PPTp/HjUlkq1ZEWkOF/s2w2qix8vb2bhQ7Sv7yzTc89tJLBPv5se/wYWcPp1GSVCp6Xnmls4chWDWWCsIV69fz03ffccW11zp7KI2K2lp8cGalruBcLm5u+Hl5kZ+ZSWhUlFPHcmDbNnasX8/+hAROpqUx7tZb8fH359t585w6rstNk1pIL0mSGvgauBroCEySJKmjc0d1efD19ua/v/6i3Nb4Wq+H8y1hqagAgwEKC8FovPAXk+UaH1osFg7u3cu0J57ghzlzWPLjj/z2zz/iQk1o9Nw8PFCrVKQfPVr1SVk+62e8ThZLvcdwZP9+3pk2jf82bGD5hg0iHBSaBN+AADLz8tBX32yhrAzM5gt7gnrMHVmWKSsrY8Evv/DyK69w/y238OeKFY2qB68g1CUoPJx9Bw5gtG22YDZDcjKYTOf+RotFeYzBcOEvZrEof6xVV0ajkQN79/L9p5/y3PTpLP3lF06kpeHdSHpVCsK5dOjalbm//UZxRgYWgwGKiuDAgXMfT2zndCZT1eMuZA6Zzcrjrce0A3v2sH/3bl575hlOnD5N/2ZccSs0PRHBwexYtQpdSUnVnDjfeZjRqPz85+crf8vyhR9/armOKtNquXbCBBb99x+SiwsbtmzBx9//4t+McF5NrYKwL3BSluUkAEmS5gHXAeLWZT199v33PP3II4SEh+Pv5YWbiwtqtRqVWo1KklCpVKhkGZVGgyRJSnhnNleGeJIkgUqFZNs90PY5298WCxZZxmI2Y7H9W5Yxms0kZ2YS6OPDXePG8fdXXzHk5pud9t9BEC7Wa08/TYdevfD38sLV1RVXtRoXjQZXV1dcNBpU1eZD5Zwwm5VlFhYLaDTItoOt9euVh0Xr52WU5VwWQLb+bTAaSc/Lw1WjYfyAAfz5f/+Hj1hWLDQRHfv0oUfnzgSEhuLj4YGHmxuebm64urig0Whw0WhQW+eObT7IsgxmM7J17shqtfI3gCQpX7c9XpaRLRbl87a/zWZQqcjMz6eotJRurVpx9cCBPPxMc+tiJzRlN913H7//8QcBQUH4e3nh4+mJyWLBaDLh4e6Oq0ZD5e1VlUr5t+1iTpKUj61zB9txSZLAVglvuzCTZUwmE0azGYP179yiIqJCQujZrh2/fPCBUukjbuYKTcQz06dz/PhxwuPiMJhMRAYFoTeZ8HJ3x2Q2Y5Fl/Dw8lE1lrHNDslgq55MkSUjVrnUqP672Gjq9HgmlAsdksWAym9FVVCjXPhYLN115Jf975x1cPDwc/x9AEC7Rp599xvW33ELF1Kn4eHjgaj1H07i4KH+r1aglqfJjtVqNqnqAaDtOyLJy/LFe21SyHo8q/5ZlZEmiwmgkLTcXtUqF3mDgttGjmfnPPw58581TUwsIWwKnq32cBtTYtk6SpKnAVOCy3hXK3tp37crSjRspKSykpKQEfXm5cjArLa0M9cyurshlZVisd8Nkaym4rFaDXq9cfLm4QEWFcgFnu0ADZFdX1IDKzQ2V9eCqUqlQq1TEt2uHh4+POMl0MjF3Ls1LM2bwwDPPUFpQgFGnwyBJGHQ6jGVlGAwGZOs8qAw5LBZkNzclJPTwUKqmrCeZ1Q+QEihhu4sLmM2ord+jcnNDZbGgcXMjIiaGkBYtRLWtk4m5c/FcXV35c/lySrVadEVF6IqL0ckyRq0Wo06HyWzGZLEo88JorArXvbzAYECyzh3J1VW5WWV9XskabkgaTeUFHBoNktEI7u5IBgMBYWHEtm6tnKQKTiXmzsVzc3Nj4dq1yvlaYSHa4mJcfHzQ6PXoy8qo0OuV44bFgmxb3aFSgaen8rFKBToduLsjV1QoH5vNyjFKpVKOQWo1siShcXfHxWLBxcsLF1kmtGVLvP39xfmak4l5c2nUajU//P4735vNWMxmTicn4+njgy43FxWgVqkoNhoxl5UhG43IgKzRKDdyAdlkQjYYwNsbWaut/BwqVeU5nqeXF2g0mFUqNLKMxs0NjUpFfMeOaBpR/7bmSsydS3PlNdeQX1SEi4sLeTk5GI1GzOXlmEpLMZvNmM1mTCqV8jmTCbPJhMXdXTknc3NTjjkqFbi6Vl73IMtV4bq7u1Kl6+JSVTBhNOLq6kpkTAyyJOHq7o5vc9hErhG47H5TybI8E5gJMH369Atc5yfY+AYE4CuqkJolMXcuXWBwMIHBwc4ehuAkYu5cOm8fH7x9fMDJfW0E5xBz59JIkoRfYCB+4mKpWRLzpn7UajVqtZr4du2UT0REVH5NHIkub2LuXDpXV1cAQkQ7lstek+pBCKRT83d3pPVzgiAIgiAIgiAIgiAIgiBcgqYWEO4E2kiSFCdJkitwK7DQyWMSBEEQBEEQBEEQBEEQhCarSS0xlmXZJEnSo8ByQA3MlmX5kJOHJQiCIAiCIAiCIAiCIAhNVpMKCAFkWV4KLHX2OARBEARBEARBEARBEAThciDJ8uXbn1OSpB9QdjoWhOYqUpblKRf7TWLuCM3cJc0bEHNHaPbE3BGESyPO1wTh0oi5IwiXpta5c1kHhIIgCIIgCIIgCIIgCIIgnFtT26REEARBEARBEARBEARBEAQ7EgGhIAiCIAiCIAiCIAiCIDRjIiAUBEEQBEEQBEEQBEEQhGZMBISCIAiCIAiCIAiCIAiC0IyJgFAQBEEQBEEQBEEQBEEQmjEREAqCIAiCIAiCIAiCIAhCMyYCQkEQBEEQBEEQBEEQBEFoxkRAKAiCIAiCIAiCIAiCIAjNmAgIBUEQBEEQBEEQBEEQBKEZEwGhIAiCIAiCIAiCIAiCIDRjIiAUBEEQBEEQBEEQBEEQhGZMBISCIAiCIAiCIAiCIAiC0IyJgFAQBEEQBEEQBEEQBEEQmjEREAqCIAiCIAiCIAiCIAhCMyYCQkEQBEEQBEEQBEEQBEFoxjTOHkBDmjJlihwZGensYQiC07zxxhv/J8vylIv9PjF3hObsUucNiLkjNG9i7gjCpRHna4JwacTcEYRLU9fcaXIBoSRJ/sAPQGdABu6VZXlrbY+NjIxk+vTpjhucIDQyb7zxRtqlfJ+YO0JzdqnzBsTcEZo3MXcE4dKI8zVBuDRi7gjCpalr7jTFJcafA8tkWW4PdAOOOHk8QgMym8306NGD8ePHA7B69Wp69uxJ9+7dGTx4MCdPnnTyCAVBEARBEARBEARBEJq2JhUQSpLkBwwB/g9AlmWDLMtFTh2U0KA+//xzOnToUPnxQw89xC+//MK+ffu47bbbePvtt504OkEQBEEQBEEQBEEQhKavSQWEQByQC/xPkqS9kiT9IEmSV/UHSJI0VZKkXZIk7dq9e7dzRinYRVpaGkuWLGHKlKql8ZIkUVJSAkBxcTERERHOGt5lR8wdQbg0Yu4IwqURc0cQLp6YN4JwacTcEYTza2o9CDVAT+AxWZa3S5L0OfAi8KrtAbIszwRmAkyfPl12yigFu3jyySf54IMP0Gq1lZ/74YcfGDt2LB4eHvj6+rJt27Zav3fmzJnMnDkTAHd3d0aOHOmQMTtTWloaP/zwwyV/v5g7AjT+uZN2XI9PoAa/YPscvuo7b0DMnSZFlkGvBw8Puz91Y5879ibmjmAPjX3eFGYb2btay6AJ/rh52KeuQpyvCfbQ2OdOQxBzR7AHMXfOrakFhGlAmizL260fz0cJCIXLzOLFiwkNDaVXr16sW7eu8vOffvopS5cupV+/fnz44Yc8/fTTtf6wT506lalTpwIwffr0ZtGEtjm8R6HhNea5Y7HIPNr3KBXA9F0dzvv4C9GY3p/gAAUFcOIE9O9v96duzHOnIVzu709wjMY+bz57IAWjr47rh0bQb6yfXZ6zsb1HoWlq7HOnITSH9yg0PDF3zq1JBYSyLGdJknRakqR2siwfA64EDjt7XIL9bd68mYULF7J06VL0ej0lJSWMGzeOo0eP0q9fPwBuueUWxowZ4+SRCoLgKAVZRmcPQWjqLBbljyAIwgUw6JXfF6eP6u0WEAqCIAhCY9XUehACPAb8IknSfqA78K5zhyM0hBkzZpCWlsapU6eYN28eI0aM4N9//6W4uJjjx48DsHLlyhobmAiCcHnLSTUAoGqKRy6hcZBl5Y8gCMIFsB130o7pnTwSQRAEQWh4TaqCEECW5X1Ab2ePQ3A8jUbDrFmzmDhxIiqVioCAAGbPnu3sYQmC4CC51gs1Lz+1k0ciNFkiIBQE4SJU6JQKwqIcEwBmk4ykApVKcuawBEEQBKFBNLmAUGh+hg0bxrBhwwCYMGECEyZMcO6ABEFwioIs5QJN7SIuzIRLJAJCQRAukMkoYzYr/y7MNiLLMo/1P0qnQd488nmUcwcnCIIgCA1ALNQSBEEQmoTiPCUg1JWYnTwSoclqpgGhLMskrNdW9lMTBOH8bPMlKMIFo0GmtEg59hzaXOrMYQmCIAhCgxEBoSAIQlNSVNQsAw6AknwlIDToZYwGEXQIl6gZzp9NC4r4/pk0lv+Y7+yhCEKTUVGuHGfCYlwByEyqqPyaxdL8fo8IgiAIlz8REArOdfw4GMXOpIJwwfbvh5ISZ4/CKbTWgBCgXCsCQuESNMNwEGDvKi0ABzeKyidBuFBGvfL7IjzODYBTB6s2KslOMThlTIIgCELzsnJOPgu/yXHY64mAUHCu9HQoL3f2KASh6ZBlsFigrMzZI3G4knwT3v7KBiWF2eLGgnAJmukS49IiJVw/fUyPTiuW6AvChbBVEEZ3cEeS4PDWqoA9eX85S2bmknpU7G4sCLVZ9G0uP7+R4exhCEKTYtBb2LOqhLwMA8kHlIxkwRc5LJvtuBUgIiAUnKuZXqwJwiWTZTCZYMMGZ4/Eocwmpf9T16E+ACTuEzcWhEvQTI85pUVm/EOVfelSDotAQxAuhK0HoXeAmrBYV47v0lV+beuiIpbMzOODO5OdNTxBaLRyUg389395bF1UXHlTKue0gR3/FTt5ZILQuP3zZQ4/vJjOa9cm8uE9p2p8raTAVPs32ZkICAXnaqYXa6J/mlAvFovy50w63WU7n8pKzMgyRLVzIyjChZN7def/JkE4UzM95pQVm+k00BuAlEMiXBeEC2GrIHTzUBHZ1r3y812HeFfepKrtUCwIzV1JtZYwy/+XT/KBcj665xQ/vpqB2dT8jsGCcKGKc2uGgNX73aYdc8wNXhEQCs7VDC/WUg6X88TAYxzaInpBCZfAtsS4tnmzezcUFjp+TA5gq+Rw9VAR3d6djMQKls3O45e3M0VYKFy4Zna8ATBUWDBWyAS1dMEnQE1BlhGD3sIXD6eStF/MHUGoi6Fc+X3h5qHCP0SpwFWp4M43Imo8Tm6Gv1cE4VzKiqtaWayck8+H95yq3AVcW+iYKihBaIrcvWvGc9XnUm6aY9oriYBQcK5mGBDa+tX89Um2k0ciNEnnCgj1emX58WWo+oVaSLQrOakGFn6Ty+Z/ivjjwywnj05oUprZMcd2cunlp8Y3SENJnonju3Qc3VHGz29kOnl0QpNmNMLp084eRYOxVRC6uqvwCVQCQhc3FZ4+6hqPy88UPXEFoTpbGFibknzRB1cQ6uLiWjOeWzevqvCjMEsEhEJz0cwu1vSlyglnYfblGeQIDmBb01R97sgyVFSA2XEnXkaDhSUzc8lLb/jdHCsv1DxUhEa7Vn4+PNaVtOMV4gJNuDDN8KZUmfVCzdtfjU+QhtSjer55Ugl1dFozSft1nNgjKgmFS5CfDydOOOzlMpMqyDntuN2DqyrXJbysG2RJ0tmPs+0SLgg1GI1gaJ67Xds2xnL3PDtqqL78WBCEmvS6mn0r/vu/vMp/F4iAULisFRcruxfXVQl1GSvOUw6MFeUWysvEXTThItkqCG3/tjEalY8bKCDMTTOw4IscTuzRsX+DFkOFhX++yGHJzDzeviWpQV6zOkO1XlChUVUB4fBJgQCkHhF91YQL0BwDwjMqCItyqi7OtAVmPro3hU+nppB8UMwh4SJptcqNqQaSm2ao0ZT97VuSmD4hkdSjev79OofnRhwnL6PhAhijXvld4equwifAWjV4RkAY2daNrQuLavSJEgRAqa49edLZo3CKsmIzLq4SHj5nRw2ZiRUc3loq5ozQJFQvQPj13UxmPpfWoK+nK1HO2SLbup31tYJMIyZjw88bERAKzpGSAllZzfJizRYQgnKQrCi3UFJgYvfKEspKRGAoXIDauqLrrY1r7RgQ5qYZKnsrLfwml5Vz8vl0agrfPZ3GDy+ks9Za9m7Qy5UVfg2laqmXRMtqB80O/b2QJFj4dS6r5uazd02J6Eko1M2Jx5yiXONZd4YdwbaDpKePGr9g6zJJV4l3lrau8bij28scPjahiSspUdpa2PnG1JHtZfz+QRavX5/IzGeVi7HCHGPl1H1vcjLL/5dPWYmZTX8X2fW1q6u+SYl3QFUPQoCodsqmJVdODiLrlKFy0xJBqFTXhnIOZjbJPNz7CMv+l3f+B9tJWZEZL381sZ09zvragi9y+Oqx0zza9yg/v5nBhvmFbP6nyGFjE4QLdXRHGa9ec5K9a0rYs7qETX8XsW+tluO7G+58SVdipl0fT579X2yNz3t4q0jaX87jA442eIGRpkGfXRDq0gyDQRttvgkvXzVlJUrlRnxXD5L2KyeWEa3ciOnkzi3Ph+PqLvJ7oRZ1VRDaqjjseKG2Y2kxy2bn4+mrrryjZXNwU81NdjKTKijOMxEa7UqLuLPvetWXwVbJ4aHCw6uq/1NgCxdkGbJTDPz9WU7l57/Z1cHuYxAuA0467lgsMtOuVipJRt0dxPWPhjrstcutbS3cvVX4BilzJ6ilCwGhLrh7qipDy+QDIuAQLpLWurS2ogI8Pe3ylIYKC18+klr5cdL+cv75Kof965XX6nu1Lzv+K6n8+tFtZfCoXV76LPoyCy6uEmqNVFlBKFnXGD81K4ayYjOevipUKtj8TxEr5+RTkGnk2f/F1rq0UmhmGsG1Tm6agfcmJwPKjVRdiZnwODc2/V3Eg59E4hvYMFFAWbEZb381d7weQb9xZaz4Mb/yWqe6rQuL2bqwGIBB1/uTk2rg4KZSht4SgFpdy3p+QXCgtONK8cWhzWVs+beo8vMrf8qnbS+vBnnN8lILfsGuuLqpGHCtX+X8iGjlRmKCMoeeGXqcR76IQq2RaBHvVnnz115EQCg4j+3A2cyCwpJ8E217e2I0yBzcVFrjgJmRWEFGYgVxXTwYPCHAiaMUGq3zBYR23qRk0be55/y6LWD44K5TgFISP+3XeLuOAWouMQblIvHQ5jJUKolhtwSw6e+iGmX3ZrMsTi6FsznomCPLMmazTMqhcuK6eHCq2vLdFT/m07K1G6VFZoJbutDlCp8GHYut762Ht5puw31I3FdO16HeAFw5OZAlM/No18eT47vKKCkw4e2vRqUSc0c4D4sFysrA19duAaG20MQzQ46d9fkVP+ZX/nvMfcHs+K+EjgO8CI50Zeu/Rfz9WTZ9x/oR2da93mOoTqc142HdkMRWQdhpsDJ33D1VlSFgXFcPdiwtrvy+tGN6WvewT2AqNHFOvMaRZZlZz6dV3iQCWPVzAUERLuRnGJn5bBpP/xBDRbkFd09VZfhtD8V5JnwCNbh7qug6xIeodu5kpxiIaO1G2nE9Xz169uZGpw6VV55LtmjlRod+DRPACML5mE1yjRUf1cNBFzepMqhrCLqSquPO5FdbAEqQHtnOvcbrfv24ModiO7nz/E9xdh2DCAgF57BdpDXDHoRlJUrZ/YQnQnlm6PFaH/PrO1m0bONOXC2l+YLgqApCV4+aFRDt+3rxwMeRPHWFcgH39A8xRHdw5/f3sti6SLk4SjteofyM+6rPer76qDgjILzrzYjKk9mbnwtn4tNhvHVTEjmpSj+qx/odpcsV3gy41p+jO8q45sEQvPzsOyahiTGbHRYQrvwpn9wlRwEIauFy1iY6/3slo/Lfr/4Zz9+fZjN4YgDdhto/LCwvVX4neHir8PJVM/XDyMqvjb0/mD5j/AB488ZEvn7sNKeP6Zn2WxyRbewbtgiXGYMBXFyUYNBOfQgT1mlp66L8+8W5cRzYoGXJzDy6XOHNgY2lxHf1IDzWjce/iSa2swcJa7Vs+LOQVXML2L60mPdXtLXLOGx0JUqFICiB4PS/WxEQfval03WPhvLtk6cZPimQpbPyyEisILazBxoXEbQ3aw4sgsjPMPDd06dp28eLAxu0tGzjzppfC+p4rBEvXzVJ+8s5tlPHl4+kcuPTYYy4LdBu4ynOMxHRumo1SUCYCwFhyuTu2N+b1+bHoy1Q5tfi7/JIWKetDAdBWZUiAkLBWWZPS2fvmto3n4ru4E7ivnKyUyoIi7H/iqnqxx1Jkrj1xXDa9vYirosH6/8oPOvx6Sfs3wdY1L8LzlH9oOnAgNBklFk6K7dGr79DW0r57unTpDhokwN9qQUPbzUeXurKu88jbgvkqjuDajxuz8qS2r5daO7qqiDU68Hd3a4BYZ8xvjw1M4ZPNrSj82Bvht0agJuHCv9Q5QIpur07rm4q7ng9gie/j+butyIAOL7L/r05bAGhi7tywXXmnW61WmL63634dGO7yh5RBzaWMvO5NDb8Wci2xUV2H5PQhMgyrFrlsAs2o6HqNWzh4OAb/HlqZgzPzo6pEVa/dVMSh7aUseHPs0/86mPP6hL+/iyb8lKlOqS2qkBJkgiNdiU02pVuw3w4fUxZTrP421wsFpm/P89my8Iiu45LaKIyMpRNF2wsFlCrwdXVbgGhNr/q+BXVzo3eo33x8lVz/WOhvDAnlgc/UcLt9n29cPdUEdW+KsTWFti/J1O51oKnT9VcDY12xcX17Eun1t09+WB1W8beH4yLq8S897J4eZzjdncWGjEHXeNsW1zM/g2lzP84m2M7dTXCwfs/aMmAa/2Y8HgovUf70mOED7dbK5N2LVNu7h7YaL+duC0WmZI80zmXPYbHutGmpyctW7sz4Fq/s76eldxwmx8JwvnUFQ4C9LjSF4A3JlYVJdiLQW/BaJBrnCO6uKroN9aP4JYuBEW4VH7eds2lUksYDRa0habKJdH1JSoIBedxwhLjQ5tLWfx9Hsd26uhztS8Dr/Nnza8FHNlWRkC4CzEdPMg5bSC4pUuDLLEyGpSJb9vVy826PLNtb0+CW7qyco6yjKZdH0+ObCvjk/tT6HKFN1fdGUTK4XJColxrnKwKzdC5lhh7edk1IPQPcaFNT2WZ1MOfRVV+/rkfYzl9VF+jT2bbXl6YTTJ/fpTNnlVaeozwrffrWywyXz6SSmxnDyQJJEnZXOFc3DxU3PdeS2Y9n175sW+Qmr8+zSE8zo1OA73rPS6hCZJlpeLJ9u8G1m+cH2+93RpPXzXJB8rx9lfTIt4NtUb5+X1rUWu2LSrijw+zCY12xd1LRfL+crsujf/hhfTKf19I9ex1j4VSnGciL93I/g2lvH/HqcrAsEN/LwJCXc7zDMJlrbS05vHFYlF27HB3r9okq55su5q27uGJJEmExbjx4Zq6qwJbxLvW+NhQYcHVrX61DyajTEZiBdHt3dGVmPG9wN5OtnkbGu1K+skKtAVmti8tpt/Ys8MPoZloBG2URt8TRI8RvmedkxkNFlQqOLhZ6SftF2K/3+/aAjMWy4U/Z/VKQ4CgCBe2/FNEh/5edjmXFJqwU6cgOrpqdygH2LO67gKdp2fFYDZVzenpNyTy2vx4wmPtU0loK2Cq7ZxNpZJ4a2FrHu59BIC+V/shy/DTaxlMG3Oy8ns/Xt+2Rq/2SyECQsE5qoeDDXzwLCs289KYE3h4q/C0Lns8sUfHiT06fn0nq/Jx+9dr2bOqBG2BmaunBHPNgyF2H0u5Vgl2bCGfm7WC0CdQQ3Bk1YG04wBvFnyhbLhwcq+OgHAXZk9Lp9swHx74KBKhmasrIPT0tPtukrUJCHWpNSxQayR6XeXL1kVFlJeZ632AStynLH85tlNHp4FeuHlcWI+cmI7K0vw+Y3y59cVwTuzR8d3Tafz9WQ4BYS60iHe1a68doQmwzRUHtbUIiqhaTtW+79nLpNw9VQy7JZBBE/xxcVWxc3kx/3s5g7TjemI62L+1RFnx+X8vhEa58uzsWGRZ5uP7UkjaX05AmIbCbBOfTU3hkS+iCY12Pe/zCJepM8/XzGalgtDNTdnN2A68/NR8ub39BYfkkiRx49NhzP8kG4CcVEO9l8b/9395/PdDHtN+jaOsxEx4/MVd+I17IISZzyk7L//0WkbluAbf4C9C9ubGSQHhmHuD6DnS95w9OV1cVYREuZKdotw48/C2XwBTlKtUzfuHXFjMEBzhyoer2yLLMsd36TBWyPz0egaznk9n2C06xt4fjLe/BqPB+TtCCw527BiEhys3ohxg2ew8Fn5Ts/d69Y1CvPzVZ2WVaccr7BcQFtUdEFbXtpdSvNF5sDcqFTVWRiYfKKdj//oVQ4iAUHCe2kKOBrDu9wL6RJgozjv34wqzqzZ32L28pGECQlsvKGsFYf/xfiz8Jhf/UE3lbkWdBnoTHudWGRCC0gsB4OBGLYYKZVc9EXA0U+daYhwcDEVFThmWTd+xvmyYX0jCWi39x/vX67mObKvaKfnYTl3lvDmfwHAX3lnSGr8QDSqVRNchPtzxegt+fiOTt29JApQKrzunt6jX+IQmpHpA6AAX+vvZtlyxdXflZC8podwuAaFBf+nvU5Ik7n47glMH9XQb5s33z6RxeGsZ029IJLaTOyNuDxI7tDZHtQSEFuBkWhpt3exzcTT05ovfuXTEbYG07e3Ju7clk5NS/4AwL00JTJIPllOuNV90cNJ9uA8frm7L8v/lsWpuQWVIuHlBEdc8FMKg6/3rNT6hiXFgQDjm3iACwlwYNMH/glZBXTExoDJcr9DZ59iYtF/HZw8ou5Db2tFcCFsg0nOkL7Is4+6tYteyEtb9Xsiu5SXEdHQn97QRutllmEJT4YCQ/eiOMtb8VkCPK33OCgcB7ngtojIg9AlQ42KtUvfyVVNWYq48ZtSXXmep3BjS27/uufPltvZI1sOSl6+a3qN92fFf1U26rx49zZCbAji6vYycVANX3h7I+IvMNERAKDhH9ZCjOotF+aOx34+mVO0gGdTChRG3B+IbpMEvRENotCuJ+3Sc3KNj7byq/k9lxWZkWbZbCLdvrRaLWSYgXLl77OGtHAhH3xPEwOv88Q1S3u8dryn9BGRZ5oYnQzm2o4xDW5R+brbdYp8cpGwQ0b6fF5NeCsfNU4VvoJjKzUodFYS/Ll/O6DZtCKr9uxwirosHbh4qTh+roP/4+j1XWbEZLz81ZqOym9jFVCTaqrdselzpy89vZFZ+vH1JsV3vmAtNRCPdGCsgzIXAcBeO79Ix/Nb6N4qvbx+a4AhXgiOUasEp70eSlVzBnOkZpBzWV96wCq3n/BaamDPnjsXC+v37eeDTTzn+8892eYlLPecKiVJ+Vm3VUPXh7a8cZ04dKKe81FK58uRiePmpueHJMAZc58/SmXm0iHdl7xotv7ydSVwXsflcs+HACsIeV/py7cOhF/U9w24NIDjShb8+yUZfVr+AUK+z8MMLys0km7BLrKqSJIluQ33oNtSH4ZMC+OjeFA5tKWPQBH9OGM///cJlxAFzKHGfjvk52cz/OLvOx3Qf7sO+tVo8fdWoVBKv/xVPYAsXXr8+kV3LSxh1d1C9W5PNnpbOwU1KYYRtk5La2FrV2NwxPYLBEwNIO6bnwMZSjmyr2dN69S8FJB8oh+gLH4tIFQTnqKsKKiVFWarSzX63iEbfE8TLL7UjL81Qa7l9jxG+xHbywD/UhdIiMyvn5FNWYibteAVR7epf0myxyJXLTe55RwkAPX2qdieyhYPVSZLEyMlBDLjGn5Vz8hl9TxB6nYWXx56sfMzR7WW8fn0iAAOuET1umo3a5o7FAmYz73z1FaEPPsjI0aOdNjxJkghs4UJBZv3P4ip0Fjy8VfQc6cvGvwoZdc+lR5/unipe+DkW/xAXctMMfPP4adb9XihCjubCwUuML0W34T5snF9ol13Aj2yz30ZB7p4qYjt58NqfrbBYZFb/UkBBppEjOru9hNAUnHmhZrFwICmJk8nJlJeW4szYy7Z5lj0axpeVKMfXPau0yDL1most4ty4b0ZLAAZPDODlq08w47Ykgq6u9zCFpsCBAWFEq4sP42wrLFb+lF/vgDAzsaJGOAjYpdI8vqsnd78VQV66kbFTgpk+vd5PKTQlDjhni+nkgaq46tLq/ZVtKNdamH5DYuUNnXveiaAk31wZAtp2Lw6LceXYTh07/yuh37hLvxZf+E1OZTgIF9Y32katlmjd3ZPW3T25YmIAqUf0/PR6BjmpBnpd5cvulSUMvN6fFXsufDwiIBSco66A0GCw+xJJtVrC3VN1zl4cAWEulbsIXzk5kBdHnWDG7cm8ODeO6Pb1CwltTd5BWWICVRWE5+Plp+zeZ/ue91a04d+vcug00Bv/UA3rfi+ktNCsHNg96zVMoamobe5UVICrK2mZmWhLS+v+XgcJinAhP6P+F2r6MgvuXiqufyyU6x4NqXdFr23ppl+whhnL27BvrZb/dtR7mEJTcL4lxiaTXSvXL0Xfq31Z+1sBBzeV1ntjg5N7dcR0dGfYrYGU5Jlo1d0+8Y1KJXHVHcqxUlyoNTO1LDE+dOoUsixzNCmJHs4bGaD8XtcWmM7/wPMoLVSeo6Jc+V3Rto99Tq58AzXc/Hw4R7aVkWGXZxQavUawScmFcPdSUVpUv/7VOm3D9b/ue7UogmjWGngOdR7kzeuvt2f1LwXEdPTAJ0CDTwC8+kd85TJ5F1cVQS3ODrzvejOCaVefJGGd9pIDQrNJZs2vBcR39SBpfzlwcQFhdWqNRFwXD16bH28tslBzx/QWuLqpLiogFOurBOexbaZQfeIbjaDVOqxPVG18AzXc8nwYao3Ewq9zzv8N55FTbcnL8V1KycWF9lKrbWx3vBZBz5G+xHf15N53WvL4N9Hc917Leo9TaEJqCQhLzGZKSkoaTUBojwpCvc5SuZGPvXtuunmoxO6SzcmZAWH1445OBytWOGSDn3OJau+Ol6+a4zvrX/2nLTATGO5Cv7F+XHVnEPFdxR0koZ5qqSA8lJREixYtOJSY6LxxWfkGaSjJt0NAWGQmopUbEa3cGDTBv949Dau7YmIAUz8UG801K00hIPRW17uCsLy05vc/97/Yej2fIAAOW/VhW7nXpmfVuVKLeLfzFvT4h7gw5KYADm8tpbzs0s4hU4/qMehlRtwWyEOfRtJ3rF9lf+pLpVJJlWN3dbv45xIBoeAcdVUQmkzKx1qtc8ZlNfTmQAZc60fygXIslvr9YrIdNLsOqdpRyCfAvpUq9e17IDQhtc0dvZ70YqWBrrbMfksLL1VQhAs6rYXCnPqFhBU6i9gMQbCv2o47iYlKOFhe7pwxWalUEm17e3JiT/3X7upKzHj41G+ZsiDUcEZAKJtMHEpK4saJEzl06pTTgxCfQA3agvqH/GVFZqLau/PK7/Hc/rLYyEqohyZUQVj/gLBq7nn5qkWvTaH+bHOnkc+h/uP9MOhldiwtOf+Da5F6RFlpGNfFgy5X+HD3mxH2HN4lEVdegnPUFRAajcoyL2vY4UyxnT0oL7XUu+m17aB5nXWp8Nj7g89qMCoIF+XMCtuKCtIKlYa0JY2ggrDrUB8Ati+u3zxWlhiLkEOwg3NVEGZng6enUknoZFEd3MlLN9Z7uZZtgx9BsJszwo6CggIkSeKKIUM4lpGh3OC1MTp+JwGfQDXaAlO9b+qWFom5I9hJEwkIPewREGqV7w8I03DbK+H2GJbQ3DWRgDCmozsxHd1Z+1sBZvPFj7U414hKBX4hjafznwgIBeepKyD09QV9/XZgtAfb3a/kA/WrKinXWlBrJMJjXflobVvG3h9sj+EJzVUdPQjTCgqAxlFBGBrlSlQ7d07urV/gUlFtibEg1Mu5AkKDAQICnF5BCFRujFWfXYgNFRaMBvmcu+AJwkU7Y6mXrrQUb09PwsPDySkurhkKrl2rzCsH8gnUYLEo1bOXSq+zUFFuwS+48VyoCU1cIw83AIIjXakot5B69NKPOzqtGbVG4u3FrekxwteOoxOapUOHIC9P+Xcjn0OSJDHm3mByUg0s+7+8i/7+4lwTvsGaRrUaUJw9Cs5xrgpCT0+Hn1jWJizGFS9fNUkJ9QwIS814+qiQJAlPH3Wj+gUgNEG1zR2TibTcXHx8fNDqdI3iYNoi3pXsUxc/j/U6C0e2KyGnvkwEhIKdnXlH2hZ6eHs3joDQuilW2rGKS36OsmIlIPGs507IglDDGdVQer0edzc3QkJCyC0pqQoILRZl46yKS/8ZvhS+QUqoV59lxiV5ShWkrwgIBXtoIhWEfcb44uouseaXfA5tKb2kKqhybdW1jiDUm05XVSzkxH0JLlS3YT70GePLstn5HN5aStpxPRaLTNJ+Het+L8BklNm3VsuCL3LY+Hdhje8tyjXh34iqB0HsYiw4y7l6EHp5QSNYJqlSScR28eDkXh2yLF/yQa+81HLBuxYLwgU5c+5YLKRlZ9O+fXu0er3ST83JO7KGxrix478SFn2Xy+Ab/PEN1KDWSBgNFjQuEpIkkZdhoCTPxNp5hcR18WDEpEBmPpvG0R1lvLmwFRXlogehYCd17WJsMICLi3JjKqf+m1LVl2+gBr9gzQVVcuxcXkx+upEx99asSrct9RLLJAW7OjMgLC/H3d1dCQiLiqoCQtsNXgff6A2JdAEgM6mCFvFul/QcxdaAUFQQCnbRRAJCD2817fp4seO/Enb8V8KEx0O56s6gi3oOndYi+t4K9uXkjeMu1vWPhbJzWQlfPXYagFbdPEg9osdokDFWyCz4ouoc8+i2Mu56KwKNi0ROqoHItvbbDMsexBFQcJ5zVRBal0s6W+fB3vz+fhZZyYY6Tzgryi0kJujo2N+71q+Xl1pw9xYhh2AHdS2TNJvJKSigTZs2aFNSGkVAGB7nCsB/P+Tx3w9Kyf0rv8fz9i1JXD0lmFbdPFjweQ7pJ5Uqk90rSkjeX87RHUr14Ns3JyHLSvNsQai3uuaO0QiuruDh0SgqCAEi27mTeric1KN6otopxx1jhYyre8258L+XMwCI7uhOx/7emIwyq3/JJyBMCUq8RAWhYE+1BYRubgQEBKDV6TDqdLgAVFSw6fBhBvXqhSNriVq2ccfFVeLUoXJ6jry0JY4iIBTsrraAsLQUDh6E6GiIcP6GBAADr/fnwEalOGPBFzn4hWjoM8a31uIIQ4UFF1eJHf+VkHvaQN+r/di9ogRvf3HMEeykeiFR9Ru7thtRLi6OH9N52M69AFQqSEwoR+OizJ/1f9SsGty7RgtkkJtmID/DSNtenjQm4ggoOMe5KggbyRJjgB4jfPjjgyxmPZ+Gq4eKjgO8GHCtPz++mkHrHp5MeDyU+R9ns/mfIp6dHUN817MneLnWjIcICAV7qCvksFgoKCqiQ8+e7Dp2TJk/bpdWQWEvnQd7c+PTYWSnVLDxryIA3r4lCaAyMDzT7pVVO4AZ9Mp7E0uMBbs6c+7YKgg9PBrFJiUAcZ3dObS5lPcmJ+Plq6bM2lPtpmfD6HWVL9kpBuZMz6h8/OEtZbTp6cn/vZjO/g2llSekYomxYFd1BIQqlYpAPz/ysrNpER0NBgNjpk9n/8CBxDsw/NC4SES2c+fk3nLmf5JNz5E+tZ6TAUyfkEi34T5MeDy0xufFEmPBruqqICwuhtxc5eZUIwkIuw314bPN7Ug9rOeT+1P48dUMdv5XzN1vt+TPj7Jp3cODbsN8WDorj/V/FNJpkDeHNiuBYkm+Mm/iu4qdiwU7qSsnSEwESYJ27ZwzrvN4elYMZcVmti8pZt9aLXdMb0FWsqHW6x4lJFRWe3Qb7uPooZ6TOAIKzlF94tuYTErk7u7eaAJC3yANYbGuZCUr40k9omfZ7HxA2bykx5U+7LNO8KWz8nj48yhUKglDhYVdy0s4fVRPxskKOvT3ctp7EC5DtQWExcXExMSwVq9Xej/5OPdg4+qmYsRtgQBEtHLj9w+ya3y911W+nNyr4/ZXWqB2kVCp4POHUuk6xJu2fbyY/7Hy+NjO4oRTsIPzVRDajjuyrJx8OtGI24MozDGxeUFRZTgI8OdH2fz5UdU8kiRw81BxbEcZmxe4sH+DcrFmMirvzVbFKwj1YrFAWdlZ52x6vR53d2VZVEhgILk5ObQAjGVllOn1JCYmEn/FFQ4daqtuHqyaW8Cpg+XkpBp4+DNPykrM5KQaiLMeS7JTKsg5bWDlnHwmPB5KWYmZhHVa2vf1IjO5AhdXCU8fcWNKsIO6AkKjUWmn1EiudWxc3VS06u7BXW9EcHxXGVsXFfPciOMA7FhazK/vZFU+1hYOAmz6u4iWbdyY8n6kw8csXKZkuWqJ8ZkrDRux1j2Um1IRrd3w8lfTfbgPqislQqNcKSsxYzbKqDUSXYd589q1iQC8v7JNo9ufoMkFhJIkqYFdQLosy+OdPR6hHmrZaAEXF+VirREdNK97NJR18wqY+mEka+cVsvi7XNQaCS8/NR/cdQpQfiEc3lrG5w+mknpET0V5zRNpUckh2MU5lhgXFBURExNDSXl5o9gFvLqhNwdWBoTvr2xDZlIFbXvVDM1lWeaetyPoMsQHd08VhVlGspIriG7fuPpyCE3U+XoQqlSgVlcFhk7k7qni9pdb0LaXJ/97JaPWx1x1ZxDjHwxm5U/5LP4+jz8+zKZ1D09uejaMGbcnM+TGAFxcRcgh2EFRERw5osyPamoEhEFB5Fp7eBZa/05MSuIqhw4UWnX3ZNVcpUXNwU2lrPw5nx1Likk/qRxLbnulBe9NTq58vCzL/PtVDpv+LiKuiwdpx/T0rmNZpSBckroCwkayMdaZJEmi3zg/+o3zIyTKlYXf5AJKBXvaMT1bFxVXPtbFTSKilRsph/X0uNK3snpdEOrtXCsNm8CmJSGRrtz+covKj/uN8zvrMS/Pi8NkkBtdOAhNMCAEngCOAGIP9aastolvNCp90zSaqq+rnH+B022oD92GKtVYnQZ5sfi7XHwC1Yy8I6iyyumxr6N488YkTuxRlqipVPDgp1HIFpkNfxYy8o5Ap41fuAzVscQ4JiZG2cW4kQWEADc/F4Z3gAafAA0+vc4+9EiSRJ8xVQfQiU+FOXJ4wuXuHBWExRUV3Dx6NAuefBLPigqnB4Q2vUb5ggR5aUZ6XeXL9BuUu83dhvlw9ZRgXFxVjLo7GI2riu1Lipn8agtCo135eF1bXNycf+wULhMWS63nY3q9HjdrK4uQ4GBys5XzocJ8ZZVF4qlTDh0mQLu+XnQc4EVpkZnUI3oWfF7VFD71qL5GOAiwdl4h26yBR/IBJawZerM4XxPsxFZBqNcrxxXbHDIYlArCoiKnDu98xtwbTIcBXmQlG+g3Vjk/a9vHi13Lipn8WgQubhIV5RaO79TR52pxWS7YWW0VhCaT8ucy0LJ14y2AaFIBoSRJkcA44B3gaScPR6iPugJCW9NRFxflAOreuCZPVDt3Bt/gz4Br/AmPdyX1cDlDblIqNR78JJKcVANqjUTLNu4EtVDeS5crGldfAaEJqyPkKC8vR5ZlwsLClICwosJJA6zbsFvERZfQCNTSg/DLefNYsWIF28aPZ0Tv3k5fnm+jUkn0GV0Vmr8wJxaTUaZVt6q+ahoXiVF3BTHqrqodJz28RcW6YEe287UzKqFqVBCGhlYFhHlKr6XE1FTHjhOl+vbRL6PJSTXw16fZlZsuVOfiKjHgOn9O7NZV3uSd/FoL5r6ZiW+QpnJjIEGoN1tAeOCAsiFJmPXGp9EIAQGNpq3FucR08CCmQ1Wrl35j/SrDQgBPH3Wt1VGCUC91VRAajY1+mfHloEkFhMBnwPNAnWfvkiRNBaYCjB8vViA3amfeGTCZqnZetS0zdndX7rzt3g2DBjlnnNWoVBK3TasqGb77rZaV/27Z2r1R3w04HzF3moAzl6pYPy4oKiIwIAAfHx+0ZWXI5eUO3T2yuRNzpwmoq4LQYOCnf/5hzJgxrNu/nxETJjhnfBcgpuPl149TzJ0m4jwBYWhEBLnWisHCggJCAgNJTEtz9CgrhUa78tCnURzbVUZZsZmWrd3QaS1888RpHvg4ktbdPSnOMzH3rUy6XOHNwGv9aRHvhpuHqkksLxbzpgmx9VIzV/WTxWhUNpLTaBpFW4vmRMydJuJcS4wNBkhPV6pw/f2dMrzLXZNZgyJJ0nggR5bl3ed6nCzLM2VZ7i3Lcu9evXo5aHTCRatt4tt6EELNPoR6PZSU1Ph2s9lMjx49xC93OxJzp+myBYQuLi5oNBr0Z8wXoWGJudMEnGOJcX5REXfccQfr9u5tlNW3lzMxd5oAWxXUmbsYVwsII6KjScvOBrOZwoICenfrRmJ6OhYn94pq19uLnlf6EhbjRlxnDz5c3ZbW3ZUKXL9gDY98HsWQGwMAiOvsQUSrplE9KOZNE1HH3KnsfevmJo45DibmThNR1yYltoDw2DHYvLnG/Fm1dCmL5s938EAvT00mIAQGAddKknQKmAeMkCRprnOHJFyy6pO9ekBoa4Lt6lo16c3ms3oOfP7553To0MFBgxWERqKOCsLC4mICA5SLHB9vb0oKlCbtVFQ0+h43guAQdWxSIhsMlGi1jBo1it2HD2PR6ZwwOEFoxGw3dM+oItRXVODuoVS1tmnblhPZ2aDTUVhURGxcHAHe3qQkJTlr1ILgfLZg0DZ/bGwtldzcGtWmjILQaJxvk5LycqWCsFrP9fWLFzP/p58cPNDLU5MJCGVZfkmW5UhZlmOBW4E1sixPdvKwBHuothNr5RJjb28oK1P+bQsGrb8E0tLSWLJkCVOmTHHwQAWhkbEtMS4pITBQ6fEXFRXFKVvvp6wsOHTIWaMThMbHdryx9e8sK8PFxYXg4GAC/PxITUlx4uAEoRGqXgFVLeTQV1RUVhC2adOGE+npUFZGYUkJAUFBdGnThv2bNgGQkZFBTk5OrU8vCJe12ioIjUYWrVzJgjMqoARBqMZ2vDkzXHd1VVqQnRGwl5WVcULclLKLJhMQCpeZ81UQenuDVlv1eag8iD755JN88MEHqBrBDseC4FB19SAsLq4MCLt1707CqVNVjXyLimr2vhGE5qiOuVOi1eJr3ZSkfZs2HD1xwtEjE4TGra5NSioqcPdUlutGRERQWl5OSXZ2ZUDYtWtXDuzcCcDHH3/MF1984fChC4JT1VFBuP/YMSZOmsTPy5eLgFAQalN9ibGNxcKc1av5bfNmZC8v7n//fYrz8yu/XFpayom0NFGVawdNbZMSAGRZXgesc/IwhPqoLSA8s4IwMbHq8wB6PYsXLyY0NJRevXqxbt26Op9+5syZzJw5E6DyDvflLs2JDcEFBzlXQBik7GLarVs3EtatUypubaX4hYUQHHxBL9Hc5o6YN83EuQJCX18AOnTowJHERMZc4kuIuSNcturYpCTQ+nMuSRKtY2I4cfIkhSUldAwKokVoKAt//BFkmYKCAox17DzZ3OYNiLnTbNTWg9Bs5nBqKrGxscpGPhUVkJen7Gqsvrgd6MXcES5b1SvWbX+bTOxNTkZ2daVNp0788O+/PPrAA3Rr3x6AMp2OvJISCpOTCWjX7pxPL+bOuTXJgFC4DNRVQWjbycu2xFiWaywx3rx5MwsXLmTp0qXo9XpKSkqYPHkyc+fWbEc5depUpk6d6oA3IghOZOtBqNXiHx0NKAHhn7NnKwGhrc9Nfv4FB4Ri7gjNQh0B4b6VKy/5KcXcES5LdW1SYjBUVhACtImLUwLC0lICAgPp3LkzTzz1FNtWrKAwPZ28wsJan17MG+GyVVsFodFIicFAjx49WLJ4MXJFBdLBg9ChA4SFsWP9ej6YMYP5y5ad9+nF3BEcTpahtBSsKy8a9HVq2cy0tKKCsvJyfvrvPwAK8vKU8SQnU2btIX3i8GH6nicgFHPn3MQaTaHxqF5BqNEowUZ5ufJ5lQr0embMmEFaWhqnTp1i3rx5jBgx4qxwUBAuW3VUQZVXVODl7Q0oAeH+xERkW0AYFqbcnRaE5uwCKgjbdezIMVv/zuJiEBuWCM5kNp+1qY5T1LbJAjU3KQGIiYwk9fRpCnU6AgICaNu2LTOefZZXpk+nMD+fU6K/p9AcnRmuG40Ul5cTFRWFu5sbOZmZyg3d0lIATh05QqJtBZUgOIPJBBkZtX+ttBR27274MdS2i7E1IMwxGNiUkEBkeDgF+flKS7L8fMp0OgJ8fDhx8mTDj+8yJwJCwTnO0YNQtn3s46P8IjKZlIrCigpITYX0dMePVxAag9pCDouFcoOh8kItMDAQjUZDQVaWMndCQ5WwQ/QhFJqzM+eOVUlpKb5+fgCEt2xJbnGxEoQkJYHoRyg40/HjcPIkJCRU9WR2htoqCC2WsyoI/QMCKC4qUioIAwIA6DdkCNk5ORSVlJCRl4dB9IYSmpPaKggNBkoqKvDz86NVXByJSUlc9/rrHNi7F1AqogpKSpw4aKHZKy5Wjju13aBy1I2ruioI9Xpy8vI4ffo03Tt1oqCgQAnYDQbKystpFxdHRmZmw4/vMicCQsE56uhB+H9//MGDDz6ofGzbqMQWEOp0SiWUdZnKsGHDWLx4sYMHLjQ7SUnQWE/WrAdQvdFYo4dGy7Aw0lJTMev1GNRq8PVFrtbIVxCaPevcKS4vx88aEAYGBlJQWqpU3ur1kJXFr++9R1lBgZMHKzRL5eXKhVpGBuTmOm8c1ftE2y7YzGb0JlON445/QABFxcU1AsLQqCiy8/MpLCnBRa3m9LFjjh69IDjXmeG6wUCxXo+vry/x8fEkJSWxLzmZQ4cPA1CYn0+BM28ICEJFhXLtXVtbiFqqyRtMLQGhtryclJQUSktLad+mDflnBITxMTHkiFVT9ebQgFCSpA6SJL3myNcUmoBqEz81I4PffvsNnU6nhIK2CkJ/f+XfZWVixy/BcWQZjh6FLVuU0MDZ6qggPDMgjGzZkvS0NP5YuZJWvXvT6d57+fGTTxrHcjVBcIbqc0elquxvW1JeXrnEOCAggAKtFtlgAL0eiyzz0FtvsW7BAicNWmh29HqlctD277w85RyoqMh5Y6oeENqYTGcdd/wDAynSainUaisDwuDISAq0WvKLi+ncqhWnDh505MgFwblqqyCsqKDEemMqvlUrkjIzySoqIsW6BL8gP5/S8nIMZWVOHLjQbK1bp9ycAsjJOfvrtWxY1SBqW2JsNFJq3X+gZcuWBIeEUFBYqOQCsqwEhPHxZDvzhtplwtEVhB2B1x38mgIoW343pnCgjgrC/KIitFqtUhloW2JsNoNtGUtJiQgIhYZz5l3bsjJwd1cC6sZYgXeOgDAtPZ2UjAziYmPpPWAAy3buFL0Ihear+jFHkipPPkuslRyg7GTnotFQVlQEFRUkRUVRotOxe8sW54xZaH6SkuDYMeVcx7YTvZcXNIYq1uoXhrUEhH4BAeSVlFBuMOBjbWCvcXHB39ubCqOR3j16cNC6jFIQmoVqS/JrVBCWleHr60tMfDz7T53CYDSSkpUFRiOF1psBhdnZzhmz0HzJsnIdVFys7Kpd240pRwaEtS0xtoaXUVFRBAYHU1BUpBwrgTK9nrhWrchpDMfLJs4uAaEkSdEX8gcIscfrCZdg377G2bvPVskBYDJRUFREu3btSEhIqLnEWKNRAkOLpfIXAXv3No6qLuHyUFICW7dWfVxQAJmZ4Our7ADcGMK12qqgzOazlxhHRZGemUluYSHXXHMNb7zxBuv27VM2LhGE5qiOuVO9ghAg0NdXuTAzm9lz6BAeHh7sPnxYuckmCA3JbFb6LMfGQnKycq7j5QUREcp5kLN+BqvPHdsFW20VhEFBnMrJwd/bG0mSKj8fGhiIv7c3Q6+8kvXbtjlq1IJQdx81RzuzB6FOh5+fH9HR0eyw9rpNLS6G7Gwl8AAKREAoOJrJpPxdVKQEhLVt1OaoJcZ1BIRanQ5vb2+io6MJDAmhoLiYYydO8PisWZTq9cS3bi0CQjuwVwXhKSD5Av58Y6fXEy6GLCvVT42p8s422WsJCLt27UpaWhq4uSmf1+lArVYCQ0/PqpPkjAznNu4WLi9arTJHbCXtCQnK8mJbQGgrWc/Ornt3r4ZWW8hhrSD0qLabZGRcHGnZ2eQWFRESGkpsbCyeHh4ctfa4EYQGcfp04wjSz0dlPfWpLSD086MgI4MjWVnMnz+fW265hd2JiUo1MSjHH9vyG0Gwp5IS5RwnLg6yspRK18hI5fjj4VF1c9TRaqsWqSMgTMnJIcDa09MmNCiIAF9fho4ezYaEBCyN6VxUaNpkGfbvr/1rer3zNzY8TwVhdHQ0p3Nz8ff1JSUvD9LTKSwuBqBALJMUHKGkpKpS0Hb9U1YGfn7KHDrz97/Z7JgKwuqqVxDqdLRq1UqpIAwJIb+4mJ0HDrB83z50FRXEtWlDdm29E4WLYq+AUAssBK49z5/37fR6wsUoLlbuCthOyhw9sWtTPSC0MZspKCqiW7duSkAISihYVqZUEHp7K0s9zWbll1b1akJBqC9b2KzTKXPGYgEPD65+8EGWbt6sfFxUBImJykmnM9S2TNK2m2T1CsLISNKKisjVagkJDQWgZ+fOHBABodAQSkuV0Dwrq/EGhHUtMT4zIPT3pyAzkzd/+401a9Zw9913U24wkH7yJMePH+fNJ59EPnrUCW9AuNzJRUUsP3RIqRqUZaW9Rdu2SkDo6uq8m7y1tYQxmc467vgFBFBhNBJQbT4BhIaEEODrS8uoKAJ8fTlQvVJfEOpDp4OUFOVa4MzlkGVlSnHBqVPOGJmitupbg4GS0lL8/PyIiooCoF/37qRkZCAXFlKo1RIREkJBXp4ydlENJTSkjIyqaxpbBSEoxx83t7NviFav7GtItcwd2WisDAijo6MJCg2loLSUpPR0UrKzcVGrCY+MJLe4GLl6z1zhomns9Dw7AD9Zlpec60GSJLmf6+tCA8nPrzq51Olgwwbo0kWphOrZ0zljqq2C0Gwmv6CArl27MmfOHOVzPj7KLkpqNURHQ4sWyse2EwFRySHU1+HDys+WVqsEB+Xlys9YRATmuDjWbtnCjoQE0tevx/3oUSU8tAUM1ZZROZxarfxdyxLjyMhI0vLz8VCrCQlROjvExcRwKiVFCdht3ysI9pCVpVTYmkyN92errk1KdLqzA8LsbBIzM1m4cCEDBw5kSO/ebNiwgSI3N17/9lt8XVx4skcPJ7wJ4XKWePAgYx5+mCUxMYwNDKz8mX3//feZ1Lkz0Y0hIKy+i/GZFYT+/gAEWP+2CW3RgmJrBe64IUP4d+FCug0b1oADFpoNW2V3SooSdHTsqJyXhYYqXwsPV1rFWCw1CxIcpbZwvaKCYq0WX19ffHx8CPD3p33nzmzZu5dCa+DRKjpaqSBMT1duwAUGOn7sQvNQfVWENVTLLylh7v/9H09ccYWSG9j2AQDH9iA849/lWi2uLi58/PHHBAYGUlJSQoFeT1JBARUGA4G+vri5u+Pp5kZRbi4B4eENP87LlL1+W24A2lzA43KtjxUcqahIOVgaDMrueJKk9O/LyHB+D79qyySRZQqsAWFaWhqyLCtVg6BUELq4KL+k3NyUkAaUE4DG2FtRaDKuue8+ti1eTEF6Oo/Mnl1ZQXg4J4f9R44QFBREQEAAKdUrOlxdlb6epaWOHWwdS4zLKypqXKjFx8eTnJFBVmFhZUAYGxvLqdOnYeXKxtVuQGj6tFrld3F5ec2eNRZL46l+qKuCUKer3FABrDsZ5+aSmJ5Oq1atABg6eDDrt24l6cAB+nXsyOrt2x09euFyYK3OsBQWkrR0qfI5s7kydEvYt4+IFi14/PHHKXV1ZU9yMvv27ePtt9/mz3XrGkcPQptaKgi9vb1RqVRnLzGOjSUgIgKAiePG8deSc9YSCMKFswWE+fnK+diJE5CWpvTwzMlRigw8PR1/rlab6hWEWi1+1nkSHRNDi+hounXrxu68PArLy2kdF0dhYSHlubmsW7OGioqKquseQbAjs16PyTY/rMeoPYmJPD1tGqeKis7uQ+ioJca1BISlWi3eXl7Exsbi6+tLYGAg+YWFJFrH72VttRQWEEC2yAbqxS4BoSzLb8myHHUBj9sgy/Jwe7ymcIakpJpBmdEIJ08q/y4pgZAQJRTIzIR+/ZSPnbnxwpkVhGYzZklCq9USGRmJRqOhqKioZkBoYwsIXVyUkHPfvsaxbFpo3HbvrlqSbv2ZQ5ZJOHmSJYsWsSUhgW//+YeyvDwsRUVMuP9+nnvuOeLj44mLiyM5PR26d4dWrZS7uWlpzg0/zrHE2Nvbm7DQUNLz8qoCwvh4klNTld8NjXUZqNA0abXK3LJVqdvk5cGOHWc3mna2aj0Iy/R6vG3HGSAwIICkrCwMZjOh1uX5Q4cNY93OnSSfPMm4sWM5YptHgnChLBZYswaKivjpk0+49uGHlcBvwwY4dAjS0kg4fpx7772XoUOH8uKsWXy2eDE33HAD5eXlLNuyRZlf5eWOXzJpm7e2Yw4oAeEZN6ZUKhW+np4EBATU+PYuXbrQpUsXAAZecQWZubmkpKQ4ZOjCZa6sDCSJEwcP8vbvv1etMDp2TLne8fJSQkJn9SuvJeQwlJVhMpkq505sbCwREREMGjSIdevXU15eTkxsLAXp6dz24YeMfuYZFs+fz2sPPECWde6bTCZM1ZeDCsIl+vTnn3l79mzlOFRYCG5uZBYWYrFY+O6ff84OCG3nc47c/KdaQOhT7XzN09OT6Ohotm/fTnhISGVAGBoYSHZGBteOGoVBbDB3SZxQby00iIwM5W4ZwMGDSjh45IgSYpSXK4FGaamy/MvfXwkJQ0OrNl5wtDMDQpOJIr0eX19f1Gq1skQyLa0qILRe0O3YsYM9qanK3UJ/f+UizWKpfaclQbCpqGDtsmWYDh1SPj5+HPbvx6LTkVlYyKp9+9hTVoYsyxxMSGDRxo2knj7N2rVriYuLUwLC5OSq5+vcGVq3dvwSd1muCjeqb1JyRkAI0LlrV9zd3fHy8gIgrnVrTmVnM3/LFgZcfTWyLLNo0SLmzp3r2PcgXF5kGVmrxShJykYKRqMSvhcVKX+MRuX3NcD27Y1rgx9rQGibIwCBUVHszM2lVatWlTuxdu3fn+yCArbv38/Ia64hLT+fchGyCxfBnJXF4Cef5PDq1Xw4Zw6pOTnKskhvb+Xm7uHDJBQU0K17d1588UUWLllCwqFDJCcnM3nyZLYlJFBWVKSc09l6NDtKHZvKlVdU4GbbTM7K37pksroJEybw8ssvA6D29aV/hw7s3LmzoUctNAfWzRS2HjnCzxs2KJvKlZcrRQUqlTK/fHycV0F45vJ8k4mS8nL8/Pwqjy9ff/01N954I4MGDWLx4sX4+/sTFBXFvhMnWHfgAI9MnMjfc+bw/l9/Mfyqq5Blmbdfeonpzz3nnPckXB6Ki2HbNk5nZpKUmakcjwoLefbHHzldWsrYsWOZu2gRljPDdUfd8K2rgrBaQAgwdepUAHr36oWXdTVIZHg4B/bsYdHKleSmpTmv+r4Js0tAKEnSNEmSWpzxORE+OorZrEx022YkyclKQNiunbK7l7e3sjTSYlEOlDY+PlXl+c5SbalXgU5HoLXPRmRkJPfeey8publkhoTQtl073nrrLb755hu++OsvMJmQbSehbm5iN2PhnPQ5OVz12ms8+9ZbSmCRmgoFBeSkpODt4cHB1FRWbNlCYGAg+/btY92xY0yZMgWLxVJ7QKjRKHemnREQ2voeVgs5agsIO3XqREhISOVJaEybNiRmZfHgd99xODGRzLQ0fv31VxYsWODY9yBcXnQ6ft20icmffkpKcTEFJpOyzGvjRmWe+ftXVdtad2msDAwdqY6AsFSnqxkQhoSwa9++yuXFAGp3dwYPGEBaXh7tOncmvmVLjh886MjRC03c0j/+YPORIzzyxhu4uLsjA8WnTys3art1gwED2HfwIN26daN169bodDqOHj3KSy+9xJQpU4iPieHY8ePKjWBnVQ5JUuU5m7miAn1FBZ7Ve1MB/sHBBLRsWfdzeHnROy6OXbt2AWCsqGDxggWcOHGiIUcuXI5yc6G4mCSdjuTsbJKzsthYWMjW06chLAyuuIJ5S5di9vRsHNcItrYWBkONvrctW7bEw8ODgQMHcvjwYQYOHMhVo0ezJzmZiVddxcBRo5i/Zg3XDBqEoaKCA3v3svjffzl1/LgT34zQpO3bp1y/lJaSX1REZmGhspKvtJSPf/uNLRkZjBgxgqCgIDZu21bzex1VQVjLjSltLQHhPffcw5NPPknLmBi8rRlCbFwc69euBSA1IYF/P/ywYcd6GbJXiPcWULnEWJIkNWCUJMlJO2A0M4WFSthn233V21upbmrTRvkTEqJMMBeXqoo8UKo9nLXJh23iq9WVFYT5paWVAeH06dPx9vZm5apVJFssaLVavv32Ww4cOMCW3bvBxYXuEydySqOBli0bR38RodE6vGcPMRERzF65ksIdO9iRlMT8tWtJP3aM2JYtuf/++9m8eTOTJ09mb1ERp4qLGTp0KKGhoVVLjK0BoV6vZ+XKleTrdM7ZRftCKwg7d65cXgzg7euLn6cnzz38MP26dGHP/PlsXLOGIyLoEOpDq2X9kSNsOXiQJ7/6ik8WL1aqBCMjleNL165Km4vt25XK25wc2LLFuRtMVbsxdWYF4eDBgwkLC6PnGRt4DRs9Gl9fXwICAujQujVHDhxw9KiFJmz2X39x1y23sO7AAabedx9RoaH8u3Qpz33wAcNvu40/ly1Dq9VWVq7279+fVq1a8e677zJ48GBaxceTlJSknO85OiCsvsTYOnfKtFq8PD1RnbHxg19gIAHBwXU/l0ZDnw4d2LVtG6WlpYy64gruvvtuvv7kkwZ8A8JlKSGBlVlZtL76ahJOncJoNPLSRx/x/erV0LIlGaWlTJo0ia//+EO5OeWM87UzKwjNZgp1usoNfaoLCgoiLy+PhQsX0q5dO44cPsxns2bRfcgQDCYT/YYMYWzv3vz0ySfsOXmSzOzsGt+/efNmvvrqqwZ+Q0KTZzTC6dPKfNDryS8qIsO68WeJdWXE5s2badGiBRNvuIHF69fX7Dvo6ApCW04AlJaW4l290All3nzwwQeEh4dXnsvFtm7N+n37APhv+XJue+stSktKwGJh+9q15NtWXAp1stcuxrVt5enE7T2bmfx8JQQEpTrD3x86dFA+blNt7xhX15oVhM4OCFUq5WTTetAsKCurDAgHDBjATTfdxNatWwkPD6dnz57s2LGD/fv34+7uTm7LlhxLTGSHVkusr6/oqSbUzWQiYc8e+vfqRU5uLpuWLeOD5cs5lZLCl+7utAwP56233sLNzY0xY8bwwgsvUFFRQVxcHA899BD9+/enqKiIpKQkZFnmlVde4ZtvvuGlp5/m1SuvdOx7qaWCUDabMRiNZy31GjVqVGX1oM2f77xD/9tuo6CsjH/WrqXCaCT51CmM+fm4+Ps33h1oBefQamseM0DpU+Pvr9yYsT5m25EjpGVnU1BSQo7BACNGKEs6LBbw84PBg5UTTFdXJdw4dUrpp2btF+MQdexiXHZGBWHHjh05duzYWd8+ZswYVq9erYQ3ffuyeuNGbn36aUeMXGjCytLSsJjNHE5O5s/PPkOr1XL7/fez8O+/mT53Lp179SI8PJznn3+efv36VQZuAwYMqFFlFB8fT1JKCowbp4SEjlRLQKgtKanRC8omKiqKqKhztyTvNX48u2bM4KNp0/DXaPj6lVf4Y8WKhhi5cLkyGJANBp79+GO8vb1ZtmcPrq6ubN68meLOnSEwkN2LFtGxY0fenDGDCX/8QdTJk8pNKkc6c5mk2Ux+aSlBQUG1Ptyv2gY/PtbHxPv74+3tTf8RI+gWGsrVTzxBp/btyax23ZOfn8+VV15JsL8/jz76aMO8F+HyYFtuW14Oskx+SQmZhYW8+c8/9LX+7i4uLqZFixaYTCZWbtqk3NRt21apzHViBWFpaelZFYQ2NQLCDh3ItW7qc+joUXQVFVw5aBDjBg7knzVrePSBB7j32WcbdvxNnFgG3FRVD8RycpSAMCREuStQx+TBx0e5WLNRq5U/zlqbL0k1lnrZ+nLY9O/fn23btpGfn09QUBB9+/YlKiqKQYMGsTwhgYqKCvbs2aO8L7G7l1CNxWKhvLycHRs2cOSnn0hISKBbz54MHT6c9xYsIC03l9CwMGYvWkTLli3x8vJixowZdO/enQMHDpCcnExsbCzTp0+nXbt2tG/fnqKiIgYPHszvv//OM888Q8KRI84J2M+oIKzQ6XBzdT0rDAwNDeX222+v8blhjz6Ke2AgPQcM4Oflyxl7zTW0DA4m8c8/ISur8ew4KzQOmzYp1X9arfLzYTJRfPgwJtvSpvJytFlZJKWlMXToUIJDQkhISOCXX35BZzJxzNubSZMmKccZV1fle1q3Vn5nO3on7Vp2MTZVVGAym8+qvq1Np06dWGrdeXby7bczf9UqtI1h2ZrQqE17+mleeeEFUnJyaN2hA38tWYJ/QABREREkZ2fzwosv8uKLL3Lq1CkGDBhQ+X2PP/44n1Srqou3tohIqKjgbkcvl6p+oWadO7bdJM/0888/M3r06HM+XWi3bowZOZI3vvySV15/nbgOHUh1Vm9SoWmQ5aqVQiYTFBayLjERs9nMXXfdRXlFBYMHD8bLy4vExER0Oh27du3i+uuv59FHH+WFb75ReuI6k7UYIr+0lOBzVdmeQaVSsWDBAgYMHsxVjzxCeno6K5YtI8vaqiMjI4Pjx47RMTqanPx8sSmDcG62Ddas1y/5paUUFhfz9cKFbDx8uPJhLVq0IDg4mLyyMmXu2H6uHF1BaMsJZBltWRne1W6cVTdhwgSef/55QKkgtDmUmEir8HDy8/P58d9/SUhK4qRoaXFe9qogBKjtJ6WRbFl4mTEYYOtWGDJEqcAoLVU2IfHygsTEsys+bPr0OftztipC28Wbo9gqCKs3i6+oqFHJ0aVLF1JSUkhOTiYoKIj4+Hg0Gg1xcXFs3boVgLVr1zK7dWvujYhQfulJknIxKokC1mbJZII9e5h38iQ//fQTUnExpSYTBpWKtx56CC8vL9768EOWLVvG6dOnueuuu3hj7NjKb/fz8yM8PJycnJzKalbb50+ePMm4cePw8/Nj0qRJXHfddcrPmdGoLN93hFo2KdGXleF+RvXg+YwaNYr33nuPhx9+mBvHj+e1P/7gw3btkJOSiL31VuV3idC8VVTw2k8/8UJoKNn797Ph4EHunjyZCe+/T9foaD4LD0d36BBrDh6kT8+eDBo8mHbt2nHy5Ekefvhhjh8/jre3NwsWLMBkMqGx7kRfVFSEv5ub4wPC6qxzqKy0FC9Pz7PC9fNp0bo1V3TqxN9//81tt93G5598wjPPP3/RzyNcPmbPnk2XLl3o06cPp06cIFatRg4N5e+1a3FzcSHQ17dGv76oyEg0ajU9e/fG3d2dFi1aMHDgwMqv+/j44FPtXC6+dWsWGAzsOn6cZbt3KxdpKgfd46+tgrC0tMb4LtYXM2fStW9feo0eTdbhw6RmZtppsEKTJ8vKzUpXV04tW0bM6NFIej3FmzbhN2EC63/6Ca/SUr777z8eeughvL29UalUDBs2DLPZTElJCTt37mTHjh3cf//9dOnShdGjRlX2XH/rrbe44YYb6NSpk2Pei0qlzFfrtU5eSUmdFYR1GTlyZOW/w8PDkWWZMr0evVZL7969uXnCBNpERJBfVERaairx1QISQajOotdzzZtvsujjj1EB+SUl+Pj4kJOXR3K1Zevh4eGUlJSQZ7sRamtt4aiA0MY2f8xmtBUVNSrrqwsLCyMsLAyAmJgYAFpFR3MyLY237riD56+/npC770aWZU4mJTlm7E2YPQPCGZIk2cpPbGfJH0iSdOZaCFmW5Vvs+LrNg9FYddC07dh74oSS6kdEKBPIwwN69ICLOfDYAsLqlYWOckYFYaleX6N02MXFhZiYGPbs2UOfPn249957yc3NZeXKlSxYsIDo6GhlV+M9e7ht+XLci4qU3WkjIiAuzvHvR3C+4mLSDh1i1fLlrFm9GndXV4JCQ4mLi2Po0KG4ubmRmJhIZGQkJpOJGTNmEG09kNh0796d48ePn3WxL0kSc+bMIS0tjbZt25KRkUEp4F1e7tiA8IwlxuWXEBAGBATw1FNPAXDH/ffz2ksvsWTJEp758ksOduqEOjyc1157jZEjR3LnnXfa+10ITUBBWhpv/f47nWJiePm33yjQaknWajmYkkJCUhID5s3j7vffp1VYGE+9/DK33HYbFosFV1dXsrOz6WVdOllRUcGxY8fo1KkTCxYs4Pbbb2fVDz8wsLYbVg2pliXGZcXFeJ2xycIFcXHh5iFD+HP+fOIDAnjuxRcZNmgQvQcPtt94hSZl5pdfMvSKK/D396dt27Z8dO+9DBk3DpMsk5aZyaCuXWs8Pioqim6tW+NhXWa/ceNG4s5x3tKqVSuSUlJITEwku6iIgtxcAq0XQw2utoBQq61XQBgaGsq0adOUf0dFUVxaSnl5eeV/D6GZysyEzEyKtFpcfXwY9eST/PT116h1Om556imSRozgg5kz8fH0ZNnu3Xz1888UFBTQvn17brzxRnr16sWBAwcYP348ISEhDBo0CH9/f9IzMjAbDKiNRv7++286derk+IDQGnLkabUEnWsjnwsgSRJhgYGs+u8/MjMz+fvff7l9+HAyCwpIOX5cBIRCnQpzc1m6axe7EhL4cdkySvV6evbsyc6dO0nKyqJ1bCxpWVn4+fkpFYRaLXh6nh0QNuQS4zpawpTo9fi2aFH391nZbrr16NyZxNRUwqOiUPv5MWLkSApzc0lMTW24sV8m7BUQbgDUQEi1z623Pn9Ird8hXDijUQkD8/KUakGzGcLDlQNpt24QHV312Is96Hh4KDuBBQU5PuSwVRBCVUB4xp2BqKgoEhISGDNmDC1btqRly5YcOXKEhIQERowYwbfffsu0adPYl5ND/6NHlcBUVD81W3u3bqXPlCn4e3sT27IlfsHBLFq8mICAgMplhJGRkQBoNBrWr19PQEBAjefo3r17nUs0goODK5eGdOjQgf0pKQzs168B39EZaqsg1OkuOiCs7uabbybl0CGWLF2K3mBg3Zo1/LZmDS7AzK+/FgFhM/P222/j5uZGJ2v48Ozs2fTq3Zs33nuPqVOn8uhjjxEbG8uku+9m4vXXs2LFCm6eNKnGzZ2YmBjefPNNnnjiCUaNGsXq1aspKipi6tSp9OnThz3HjzOwWzfHvrHqF2q2jRaKimpUrV+M8cOH8/A33xCtUuHj6cmfP/5YIyCUZVlUFF7uZBkMBkxqNQmHD+Om0WApLub2ESOY/ttvPGMyMWnCBObOn0989fM0YNzEicTHxlZ+XH3X7NrExMSQmZnJAevmOEcOHWKQEwPCc/WCulgqDw9aBgWRlpbG77//zmOPPVaj3YzQfMhFRZgLC3nxiy+ICg0lKSuLY4cOkZeayqmcHI6uXs3GgwfRVVTQtWtXQkJCCLG2ttBoNHTo0IGxY8fywgsv1HjeoKAgMvR6onQ6UlNTKbNWEzrEGcUQ+SUltLPD8S88KIi5c+fi4ebG6fR04uPiSM/LIyUx0Q6DFi5XOdZq7c0JCfywYgUBPj5ERkayf/9+krKzGTl8ON88/DCSJBEUFER+cTHP/vYb1w0dyhVt2jimgrD6c1fbzLREryegjgrCM/3yyy+c3L2b+UuXEt6uHbRuzaeffkp5SQl9+vYV52jnYZf1CbIsD5NlefiF/rHHa16Wysth1y4lsLPJz4eNG5VwsLxcqY4LClI2IenZszIcXLVqFdu3b6/1ab/++ms6d+7MPuuOPjUEBiobm6SnN8AbqkMtVVC2JcZnnnBGRkaSmppaoxw/MjISnU5HaGgoY8eOpW/fvuxMTVWWVnfurPTLAuU9mUxwxk5fwmWoqAjWrGH+H38QFhiIWpJ49913ufuee2jRokWdPcZCQ0NxOSMYv+2223jiiSfO+5L9+/dn86FDVf08HKX63AFlifEF9FA7l3YdO7Jq3z5UKhVvfPEFeXl5/PbEE+w7cAC9M3b+ExyurLQUjEbmz5vHe2+/zeply7iiVy/S8vK4+/776datG9u3b+e1117jzjvv5PDhw/w+fz5pmZm1VhI99NBDHDhwgCFDhvDkk08yZswYbrvtNm655Rb2nzjhnB6EKhVbjhzhmqeeqqogvMSQw79zZyaNHctXCxfy3jvv8POCBeRZjzWyLCu9cpcvt+c7EBoLsxmAI2vW8Ogtt7B3yxYCvb3ZfegQP/37L9O/+YY2MTF8t3AhVwwbRtc2bWqEgQBh8fEMu+mmC35JV1dXevTowbJly2gfHc2Rar2iHMaOS4xr0GiIDg3lUEICr7/+Otu2bbPP8wqNnmww8PDkyWxevx6AX+fP5/Y33uBESgqLNmzAbLFw/OBBth4+TIC/P+99+y3xUVHExsbWWHZra2FRl9jYWE4VFVGak0NBQQE6nc4xxyDbjSm1uqqCsLj4opcY16ZFWBj/Ll/OfVddBSgbGcVERnJKLJ8UziE7KwuAY+npGE0mggICuOaaa7jt1lvJLS7G38+Pq6w/U76+vuj1ehatWcOqzZuVJ3DGEmNZBqORkvLyOpcYn2n48OGER0QAEN62LVg30GrbqRNqlYq8tLTKx1osFky2CkkBcMImJZIk1a+u+nKWmKjsTmedvIASfJSVKaHX0KHQtSsMHKhsRFKtWnD27Nl8/fXXtT7t2rVrKSwsZPXq1Wd/sWVLiI11fMgBNRpe2yoIz6zmsO2GV70fnK0CLMS6c3OfPn3YefAgdO9OisVCUWYmpKbCnj1w8iTs3++gNyQ4kslkQqvV8vEbb/DSAw/w29atzF22jN9nzWLF7NncdPvtl7SbW6tWrWqceNZl5MiRrNq1q6rs3hGsJ5snMjKUBq+2CsL6BoRdumAwmbhu2DDScnL47tFH8Q8Opn3LlqxZuRJLQ+9WJjjVjh07aBEezvLPPiP51ClGDxjAp/Pm8eQDD3DXXXcxdvz4s76nffv2qFSqOkMCSZJo3749o0aN4q677iI/P59PPvmELl26sP/YMef0IFSpOJSaSmJ6OpmZmSSmpV1yQEhUFN/Om8dff/3FA489xh3jxvHIffcBsHr1avbs3s3Hjt5MQnCI9D/+4PiePfzf//0ff2/cyNXXXccVPXoQExzMvddcQ+t27biiXz+yi4oY2r2iMwABAABJREFUNHIk902ZwpgJE+r9usOGDcNkMnHNoEEcPnLEDu/kAtW2SUlZGT52rPKLb9mS77//HovFQsLOnXZ7XqFxW/XVVyxesYLrrruOw/PnM3/5chKSkkjOymK7tdXL8dRUthw7xjPPPsvctWu5c+pUXn311Yta3RAbG0tKQQGnT54EUCoI166tDPsbTPWA0FZBWFx8UZuU1KX74MHcO2kSr7/8MgBxrVsTExNDSkpKvZ9buEyVlJBjzRiOWwuDglq04J577uH6G24AwM/fv/LhtirC48nJ7Dp4kEcffRS9rc2ZM5YYX0RACBBkrbIPt+YGNh3i4jiwcyeFhYV89NFHzPzuOx575BG7DP1yYc8ehOckSVJn4DngVuDS18NdzrRapSIwKwu2bVOCu5ISZe2/u7vypw7Hjx8nIyMDWZbZsGEDgwcPRq1WA3DkyBFGjRpFal1r7l1cHHuxZqsgtG0oYgsIy8vPqiC0BYTV77ZFWO8IhIaGAtC3b9/K3f4ee+ophrZowTO+vkp15KlTmPR6NAaD4zdiERrUt99+y+rVq8k4dgyNtzfHDAZenDyZQePGITlgufywYcO44/bb0ZeWUr947uLoDAY6P/ooHz77LI/ffDP68vJ6B4TxbdqgUau5/rrr+OCJJ2jdtStERzOqf3/GX3cd33z+OQ8+9pid3oHQqBQUsGbRItq1bMkNr73G8K5d+WXJEl6cO5dO117LDfffX6+n79OnD//73/8qP+7SpQsHjx7FUl7u2DuU1gu1lNxccgoK+HTWLFZu2EBwPfrVqlQqbrCeVL/yxhtEde1Keno6L7/0Ep9PmcKrv/1GcnLyOXvLCU2LobSULlOngiRhsVhY9+OP/PPPP3QeNYr3LRYievUCYMiIEfy3Zg2hYWFMmjrVLq89dOhQvvrqK/p16sTPtmoORzhzibHFglanw9uOAeE9117LFQ89RGxoKPt27SI/P98uVVZCI6LTKdc2I0ZAejrPv/wy3/35J99/8w2Fu3Zxx0svcTIjgwqTCdn6M9enSxdW7NtHSFgYL774IlOnTq0sDrgYsbGxnMrJIcQanunKypTCCLNZuQ5pSLYlxnauIHzjrbeUf+Tm8vn99xPTujX9y8t5/YsvSEpKws/PT8whQWEtAnr9sccoKS4G4HhGBt7e3pU/I7a/qweEoLRWKigoYPm2bZg3b+axQYNo5+3t2IDQYoGKiosPCCMikCSJEGteYDN0wADWrlxJoSzz2muvcfPVV3P42DF7jf6yYLfzc0mSbpMkaZkkSYckSVosSdIg6+e7SJK0BEgAxgFv2+s1LzslJRAVpfQZ1OkgIUFZbtyjh/Knmn379lUeQGVZ5vjx45jNZjZt2sSIESPYtGkTx44dY86cOSQlJXHllVdy+vRpAAwGA0eOHOERW1ru4uLYCsLqAWH1CsJaAkJbtWD1g5ybm1tl3xGATp06cfr0aYqLi9myZQuJWq3y3ys6mi379zPoxReV/7bC5cNi4Y85c1i3Zg0Hk5NZsWYNfy9YwEOffOKQcBDA39+fzm3asHXHDoe8HgCyzJaDB4kLC2Pa559jMhqVgLCejd1dXFzo1LYt3QcNovW11yo3J1Qq3nnrLRa98w4/zppln/ELjYvJxN8ffcTqxYt58fnn2b98OZ+89hqSWk3Xu+5CfUZ/Tnvw9/cnKDCQZEc3ibYFhDk55BcVcSo1lePp6Zfcg/BMPjExXNu3LwMHDiTU05P7H3qIq3v14r9//rHL8wuNw7Z162gVEcHC115j8IABdLv2WqZ//z033nknMW3a4BIeDsB1t93G4lWr7PraQ4cOZfbs2URFRHC62vKoBifLLNq5kwc++kg5ZzMa0VZU2G+JMTC4Xz9uHDmSl268kX+WLycyMpJ0R7a+ERrcid27uX7aNOTyctbOn8+8pUs5uGQJk+66iwc//5ybJk5k0ogRRIWFER4URGxEBFePGoVWp+PxJ55ArVZfUjgISkCYmJZGqnXe6Gw9CBt6dUQDVhBW8vXl8WuuQe3jQ+f+/ekSE0OHDh0YM2qUaBEjKI4fR96/n2/++Yc/Nm4EID0/n7snT+Yma6uLyoDwjFA5ODiYPt2742vd0C0tO7vq2r2h1NaKTK+/6IAwIjKSMWPGnNWC4MqrrmL1pk1s376d8vJyFq5axaHERLFaqhq7BISSJN0HzAWigANAILBKkqRHgV1AX+AFIEaW5bfs8ZqXhbw8ZRlscTHs3KlMAE9PpfKtfXto1Yo9hw8zfOJEjNVCj/3799OjRw/WrFnDM888Q3Z2Nm5ubkyaNImHHnoIWZb59ttvGTVqFPfffz8RERG0adOG06dPk5ubS4sWLVi1ahXz5s1TQkaNpv4B4c6dF/4c1e9GV+9BWF5e5xLjM++CRUZGVlYQajQaunXrxq+//kp+fj4n8/IgLAyCgli4axd7ExMxHjmiLDsWPQaaPouFjKVLOXjsGMG+vsRHRuJtxwuVizFy0CBWWg+2DiHLrN27lxsHDSIkMJBTGRno7bTz4/a9e+lqrX6xkWJjGf3YY5xKS+OEI5e0CQ6RuHcvE2fMYNW+fQweP55WQ4bQdty4Bn/drl27Kn0IHdXDBipPOFNycgDYe/QoulraWlwylYppU6bw6mOP8eczz6Bq3ZqrR45k4YIFHBFzp+krLYXyclavWsXIQYMY/PTTLF65EsnVtWpjtP79wRpgaDQa2rZta9chuLm5MXHiRKIiIkizNpp3CFkmJS+Pjfv3VwaEpXYOCAkO5s8nnuCu8ePx9fBgZLduvPPmmwCsmDOHA+vW2e+1BKd488MP+Xf7dg6vX88nv/3GmzNmED1kCKBUY7/47rt89+uvtG/ThrioKIaPHs3QsWN5+umnuffee+v12iNGjGDJmjWcSE7G38+PMmsVlUMDQlsFYWGhfSv73NwgLk7ZdNLHh8+eeYZl06cT5+vLI5MnI5eX2++1BOcwGpVCl7KyqhV/6elo8/NrPq6kBGrZZHHO778z47PPyCspIaOggGDrzd+RI0dyxx13AFWtvHyrtfQCJSBs07o1Xz3xBOPHjyctJ6cq8G4o1TdktL2WXo/2IgNCHx8fli5detbnB44axYHERJYuWkSovz+FJSV4urqSLPp3VrJXBeFjwBxZljvJsnyrLMsDgZeAz4EdQBtZlj+SZdmB20Y1cgUFSjhYUACHDkFWFp8vW8bEiRMpbt9e2YwkLo75J0+yadMmrr/+embMmIFWq+W9997D09OT5557jk8++YS//vqLtm3b8sgjj3D48GEeeOABfv/9dx5++GGuueYaOnToQHR0NKmpqcyfP5+CgoLKv9PT05UKQltwZjZfWs++goKLC99s1YPVAsJSna7WJcahoaFnXcTNnDmToUOHVn7cp08fPv30U/r06UNiYiKnT5+mddeuyjIcSeL44cNw+DCsWQMpKXDwoAgLmxCzwUDmiROcmDeP2666ildnzWLS7bdz5dix9Km2c6ijjbziClZt2eLQ19y0fz9DO3WiXVwcx06dsksFISgXn7XReHsz/oor+POnnxg/Zgz7V64EqKxgFpquX3/5hftuuIG5c+cS5qgdUYEuXbuyPyXF8b+DrUuMfb29OWmtTrLXTqwA7Xv3Zkr37riHhoK7O6OuvZZ127czaMAAnn7qKbu9juB40x59lO/ffJNFK1YwcujQqouX6hp6qaJVWGgoRSUlVDiqNYwsU6zTcez0acoNBqWCUK+3b0DYogVIEm6tWpE+ezb/e+EF/vjjDxYuXMhNDz/M7VOmiCbyTUhBQQGJJ04oH+TlcWznTpZt3MjkUaP4v5kz2Xj4MDfeckvNeaRSgY8PHXr1Ir5zZ2bPns3w4cP5+OOP6/2z1rp1a3p07cpXf/3FuL590RUUKF9wRMVQtXZKutJSzGaz/W5M2XTuXPnfsv3YsQyfMoXZ77xDwsGDdOzUicNnbGpkNBpFtVQjNfvbb9lqLTyoKCvjq2nTOLhmDXM+/JCMDRvQ7tuHXFLCOy+9RGR0dM3fi7t3K8v4zeYaAd6ideuYPm8eXtZrhfbWm1fB1c77bAHhmTvIh4aG0qZNG24bPpzOnTuTnpOjFBY19M+P7XdD9QrCsrKLCgjr4uHrywu3386ho0e598oriQ0NpW+7dhzcswdztb6kOTk5lJeXk5GRUe/XbGrs1YOwFfDMGZ/7EfgEeEeW5SI7vU7TZzTCsWOQlgY9e3J8927a5udDhw78/c47FJSV8fa77/LMM89w/fXXk52dzdy5c8nJyWHNmjUMHz6cjIwMXn31VV566SW6devGK6+8wvXXX0+bNm34/PPPueWWW7jrrrvo168fhw4dIjMzk7CwMIqKivi///s/IiMj2bhxIy4uLuzfv5/IgQOrqv8qKiAjQ9kM5WLI8oX/sjizghDqDAi9vb1JTU09ayvy3r171/h41KhR7Nq1i88++4xBgwYxdepUunTpwpEjR+jTuzcHPT3p1KmTsgnMkSPK+/XyUu66CY3e7ePHs3LbNkr1eoYNHMi6Zcs4efIkJpMJozM22LHq37cvhxITKS0ttWvQUCdZJreoiIigINq3asXRU6doodHgbi39byhXXXUV9z3/PJFBQVx1883cceedlJaX893MmQ36uoL9mEwmUlJSaGWtys7Yto3vf/mFv3/6ib5jxzp0LF27duXPjRuV38MOagmALGOyWMgsLGRgjx6st26EYNcLtfh45f1Ye/gEtm5N3u+/Y1Gr6f7QQ+zcvJmA0FBm//ijfZeYCQ3mZEICv82Zw+yFCynR6YiPiGD4iBFOHZPK1ZUWISGkpaXRqlWrhn9BWaa4vByLxcKhU6fo3aEDWr3evsc8jQY6dIDwcNQeHgQHB/Pw2LFMuvVWZj/1FHNXraJzp0589vnnjBkzxn6vKzSIaY8+Snp6Oq/PmMGc999nd2IiT44fz4BrrmHUpEk88fjjdf783HPPPcomInb20fvvU7h+Padzc1ly8KDySQdWEN749tvcc+utRFn7ojUYa4DiHRLCzv37+fGllxgzahRbt2+v3DCsqKiIV155hfusG2sJTmSxQGZm5caj8+fOpWunTnTp0YOj27bx2IwZ3LJrFweOHePvNWuICwtDq1Kx5/BhfDw9OXHwIB06doTycj754w9cvb152M+PHWvW0P/aayEykgPJySBJTLzuOubMm0f7du3YtH07wdWW7Gs0Gnx9fc8KCF977TXcJQn27aNly5ZK2OzICkKVinK9noRDhygpLbVLQAjw0jPP0DcqiqDQUCwaDe5eXvz1558898orfPjhh0SEhzN8xAhunzyZU6dOsXz58srvLS4uZvbs2Tx1Gd/4tVdA6AVoz/ic7eMcO71G02Vd2lRaXAzbtuEdFsZTS5dy6vvv+eeff/hoyhSua9eO3QcPsnnzZkaOHMmhQ4dwcXFBp9MxceJENBoNDzzwAGPGjOG5556jQ4cOTJs2jcWLF7Njxw769u0LwGPWjQRsy287d+5M586dAWVzj9LSUh5//HGef/55xowZQ0JCAmOHDq0KCE2mSztgWiwX/suieg/CM5YY13bCUFdlU3Vjx45lrPUit0WLFhw/fpwjR47g4uLCG2+8wQ9z5hDUogUd2rUj5PRpXGNjITkZQkOVv319lQ1ihEbnVGIiK7dtIzU1FQ9fXyRJIikpqXL5uTO5eXnRIjiYzMxM2kREKG0DYmIu/AksFuVmwUX87JVXVODp6kr7Vq3YvXMnAfHx9d6k5HyuvO46dI8/zrsvvMDG7dv5ftYs3Fxd+frbbys3QxIaMYuFVx5+mK/mzuXZCROocHFh6YYNPHrnnfS96iqHD6dr1668duqUw3vfns7LI9TPj8jwcPy9vDBLkn0DQrVa6d9p4+KC9/XXQ0UFP773HstXr8ZQUUH79u357rvvWLlyJV988cUFHeMEx1qxYgXHDx3ijTfeYOKgQaz68kt+2riRq9q0QXXGEiyH02iICg93XEAIFOt0qFQqEpKS6G0wUGrvCkJQAnZQWu0Ar773Ho9eey2hnTtz8623snL1am6dNImE/fsbxfFfqF1OTg7zFi7EVaPh4QceYGjv3gxq25bHHn0U35gYDNdfj+ocmwZ26NChQcbVpWdPyMpiQUFBVQ/Chl4JYQs6VCq2HjmC36pVxDnwZ1dydeWeZ5/lt/XruXLECAZ27MitI0aw/sgRFi1aJALCRkAuKeHAwoV0feghAE5nZWE2m2nXujWTr7sOgN9XrkRt7aGsUavxdHMj8dgx7rjpJnYvWcLqWbMICA3lnXnzCA4KYv/+/cxasYJNkkTP4cNJycnh/fffZ8CAASxavpxO1lzgzKXuISEhBJzRezosLEypSDQaiYyMZIWjlhhbcwILcNOrr7Lj6NGL7kF4LuqWLRnVowf070+voUMpLCqi3bhx+Hp48M706Uy4+mp05eXMnDkTb09PsrKyCAgIIDMzk3fffZdZs2Zx9913ExAQwLZt2wgMDLR7WxFnsucuxgMlSap+S1wFyMAgSZLCqz9QluWzF4RfZsw5Oah8fZEANm5k7smTPPDUU6jUaibedBMrV67kscce49lnn+WN117j9Z496dy5M926dWPSpEmo1WreffddVCpVZXNNV1dX1qxZAyjL+1asWEFkZGTlRh7nM3r0aG666SZUKhUuLi7ccMMNytr86j0IzeZLCwgvpoIQQJJYuWsX3QYNItS2xLiszC4XawMGDGDChAm4Wk9ArrjiCn7++WfeeOMNiouLGTl8OJu3beOde+9lpE6nbAxz7Jjyd0Pe1RMuiMViYe7cudx+++2UlZXx0P33c8/48XhV21nLURdF5+XiQlhAAFlZWbTx9FTuAl5MQKjXKz97FxoQyjI6vR5Pd3fat27NNz/+SEp6OiOsTYYbSnBUFH999RXj7r6bsbfeytSbbuLmxx/nn/nzGX/99SLgaOT2rFzJT3/9xaOTJ/Pb2rXE+vgwsk8fXrBtOuBgbdu2JaeoiJyMDELtuBPq+ew9eZLu8fGEBgYSGRyMUaOx/1Kv2ri5MWTyZIbcfjts3sy4vn0ZeeutANxyyy2McHJFmlDT7t27uXHiRLzc3Nj4yy+0HzECzGY+vP12Zw9NodEQFRZWufFcg7MuMe7WujX7ExPBZEJbXm7/gPAMLlFRhN5yCwCSLDPK1ZXR//zD8n//ZcqjjzboawuX5siRI/Tt04e7R45kwebNHE1MZNPmzbhWu6g/VzjYoFQq0GjwcnNDp9Mpn6u2nLAhbDp0iLs+/5w3HnyQ3JISVmzdylhH35Rr0YIHb7iByW++yfpPPyVMpeL6/v1pdf31GAwGXFxc2Lt3Lz179nTsuAQA9u/Zw5UvvMCcyEi279pFalYWR06dwmyx8L/ff6djdDQFWi1dWrcmq6AAi6srt02ahEdwMN07deLpDz8kMjSUwykpdOvenbfeeovRo0dzxeDBvPzHH3zi4kLr2NjKarft27eTm5uLJElnhYHLli2r/frKGghGBgeTnpfX8EuMq600XLdrF6eysiivqMBisdjvesPHB4YMAT8/CAkhoKKC1e++S4vwcIY//TSffPstL996K3lFRew6eZIOHTrQvXt3EhIS6NixIz26dmX+/Pm0bduWzz77jODgYGZdRhs62jMg/KSOz39+xscycNmWnJjNZn744QemPf88ndq04Zcvv0SflMQz06axef58WvTpw8CBA3nyySd57rnnAFixejVbt26l2No094svvjjv60iSxMiRIy9qbN999x0ApaWlvPrqqwwcOJDp06fX3MXYZFImZvUdhC7EJVQQPvnFF9ycmsrro0dXBoT2WLLy66+/1ijfv/LKKzl8+DChoaFoNBo++/JLRowYwaRXXmHTihXkl5VRkpDAmC5dIDz8HM8sOMK2pUu56667OLJjB6W5ufirVLz7ViPd20ijIdzfn6ysLCVgvtgD5sUG69aA0MPdnf5du9K/fXsMJhPPPvvsxb3uJZjw8MPKPzw96XDttUxctIh7p0yh5zffMPrqq5kyZYpYNtkImc1mXnr9dV55+mkeefll3jGbUe/cqZwcOemGiEajYVjPnqxcs4bbO3QAnU4Zix16adZJltlx5Ah927XDLTiYloGBGL28HBMQ2kgSDBjAsMhI9ri7M2/1alYsXSoCQifTFxYilZbiFhWFMTmZl55+mrcnT+bxO++Evn0d1lvwgqnVRIWGOjYgLCtjSI8e7Dt2TOlBWEtLmAYlSdCuHaOHDWPJf/+JgLCRkGWZHTt20Lp1a4KCglixaBGTrriCL2fNInPyZCwWS41w0OlcXfF0c6PMFhA2cAXhuv37ScnKYsuBAxhNJtJycoirXmXuIBPuvptD4eGEjRgBqakE5+bSPjKSdevW4e3tzaBBg5g1axZTpkxx+Niau5PHj5Ov1fLn3Lls3LEDiyzj6+WFRq0mt6iIL198kS5BQaxMTSUvL48HX3mlcpVg9+7dyfvmG9atWMHcf/4hPj6eq666ivfee48HH3yQPn368PqyZXSt1parTZs2WCwWAgMDz1oB1Lp167oHqtEQmZzMkdRUXvr+e2bMmNEg/z2AqgxCpWLWX3/xwNVX878VK0gtKrLv8vzqN6jd3Ohyxx3g6sq3eXmMfPhhnp42jYC8PN6YM4cjaWlk63R888EH3Nq1K6988gmPPPIIPXr0ID87m5KyMooLCvj0iy9o+f/snXd4U9Ubxz83aZPuvSilZZVR9t5TEEFcCIgLXPBDEfeeiBvFBYiiuBUFRBBEhgwZypS9dylddLfpSJvc3x+3t0k6oIW0SdvzeR4fSHKTe4p9c875nu/7vsXp4rUZewmEopBbMY9MncruXbtY+8YbrNmzh84jRmAym3n//vvpOHw4aLUcOHCgTEpgr169amyMXl5evPzyy8iyTFZWFonJyYRpNMpJmlrs1Gyu/KJYnWCrMNFm5ORw7Px5fvr9d14ZOhSKijDk5tpls1bel4der+e6664jMjKS1q1bc8MNN/DLL7/QZ+hQxaFpNhP+2We8/NJL3HLPPVc9BsEVYjazeP58Hhw9mkVLlpCcmcmJH39Ep6YeORuuroT5+ZGUlHRl6flVEdZRFuN5BQW46/W4engw7+GHwctLOc2rYaZ98AEvP/ggr7z3HtvXrWPhwoVs27atxLkrcCAZGWA2k+/hwU1Dh1JYUMADTzwBoCwIO3RwyO+MNcP69GH1+vXcOWUKnD2rjKc60zOKBcJnbr4ZwsIoTEzkgk5Xo81ZgJI05Pb9+pFtNnP3u+9y7YgRQiR0EMuWLWPcbbcR6O3NR3Pm8PX77+NiNjPpwQerXou5ptDpCPbxISElRamr7O1dvfEsy2Tl5tKvUye+/eMPzAUFxCUnl2xSa5Jh11/PYzNn0qhRI1xdXfnoo4+48cYba3wc9Z2MjAwWLFjA/v37+WPFCu64/XbemTGDv9et49YBAyA4mIeefRbXmqoxW1l0OsVBqHb2reYahDl5eTRr2JD9asMWoElVskzshBQeTpPbbwc3N2WejY7m7iFD+GbePGLatuXm/v154/XXMRqNPKQeBguqHZPJxKlTpwBY9uefpGdnExMVRdvOnRnYrx8PPfEE7QcPpnVgIDHqPshqjztoyBAWPPssMd268VZxqTGAZ599FoC3336bl19+uUwn3+bNm/PNN99UbbAuLoT6+vL1o49y36xZTHvlFark5UtMrLwJp1ggnDp3LjsPHmTOnXey9ehRMqq7ZmixBtH3gQdIGjNGqcdoNvNyw4ZIFy4gybLSJdrfn77du/PZqlUc2L8fZJkWEREsWbqUm6+7Do23N+OKs0RqK3ZZUciyfM4en3M5JElqBHwHhKI4EefJslzaoVjjmEwm/vjjD1544QUyU1M58Mkn+DVpQudrr+Xma6/Fv18/QkNCSgQ3j2puKFBZJEmiR48eLF26lAkREbgXFl6dQFiFJiXbjh6lX4cOnL14kUNnzxLdpAmSJFWruDBv3jzc3NxK7MlTpkzhmmuuoVGjRqSkpPDHTz/xzMsvM3LwYBIOHqRRy5ZIzpLKWk/YtnYtP2zcyPpNm3jWxYW9R48SOnKk86Z+F0+YiYmJigu3qukqVXQQFhqNSJKkLLo1GiVeHSX0eHuj7dyZNz/7DPnAAYY99xxfffUVkydPdsx4BCWc3LiRzdu389O2bQRqtfy4ZQtaa3dedTr1KsmQvn156+uvlQeFheV3hbUjZrOZXUeP0q1FCwI7deK60FDkgQORqjlNskLatKGnmxuPnDnD6NGjOXbsGMFWBcMF1UtiXBxPPf00a9etY+1bb1FQVMSz06fTqUkTPp0xA1dnnvu9vPDX6TicmgqHDysb/ur83Sl2EEZHReGm17N49WqC/P2JcoDQ0aBVK2K/+460xo3ZefIk06dPZ9iwYeTm5pZJlxNUD6tXrmTCPffQKCqK3bt3Ex4QwHfffMPzffuyads2PnnjDQDnPPTQ6fDw8sJQUwJhfj7RkZFs2rOHRsHBnL94kSaOao5oPe9LEnfccQcvXXsthw4d4o277ybCz49R777Lgw8+WL1NVASAks3Xpk0bmoWG4uriQnp2Nt7u7kSGhfHLkiWYTCbWrV9P806dKmzm5tmoEbc9+miFe6Rbb72VUaNGlfn/qdVqGTlyZNUG7OqK5OPDbf368e7y5fx34AC9LuU4LM2uXVBslKoMMvDjhg0cWrOGgJQUoqOiOKp2H68BSpq1aDRooqOheXPYsEH5s1kzht1yC4eDgxk+fTqyLLNqxgxmL17Ml199xcbt2+nUqRP7i+vlenl5lfSDqC3YZWcpSdJFlP+XlUKW5Ss9diwCnpRl+T9JkryB3ZIkrZVl+fDl3mhvkpOT8fT0ZMmSJTz44IPotFq+nz6dzsHB+DVrpqjk4eG0atmypodWJQYPHszjjz/OniFD+HzAAIvAYTJVvrukOsFWIcX43yNH6N2uHQ2zs9l5/DgNQkKqPV2ldGcmSZJKiiF7eXkx5fnnWf/nnzTp2pWLmZk0a9AA/6AgXp8xwzkXOnWBlBQS168ny9ubA5mZPDhlCnNfeYW27doBEFVNxarthosLYb6+7FQFwmp2EObl5eHh5lZS9BoAR9f/8/VF6tiRN8aO5foXX0Sr1XLjjTdy5MgRunbtWrNpaAJMJhOjn3ySUD8/runUiSeefx5tTabRVpLmzZuTbTCQnJxMyJUI3VlZkJZm2xTkEuQYDMiyTKCvb8liWnKwu0UbEsJj117L8cxMnnvuOebOnSscuDXAqb17uXb4cMb078/il1+mb/fu0KABu9q2hTZtqlZH1hEUC4TpaWnKIVE111EDyMzJwdfbm/bNmvHy559ze3Hx/BrH2xufLl3wOX6ciKgoHjxzhnbt2nH+/Hn++usv+vTp45hx1QP++OMP+vfvz5LvviMtPR2DwcDrkybRrGNHFnz9NSGjRzNh0CAinNV5C6DX4xEQQG5BgfK4mgVCQ7FA+MeWLfSLicEsy5dO46xBAlq14sPJk3lkzhz6jxqFj4cH2rfe4tUXX2Ts2LG07djR0UOskxQWFpKYmMjCefOIjY0lNjaWa7p04d+DBxnWtSsBxYc9Wq2WxcuXX/rDtNrLuvLsJvZ26QLZ2bB7Nz3bt2fbf//R65ZbKvdetXRZFXSCCykpuLq40CAsDFJSaNG8OT7FbkuHIElKzcLitaoUEUHIoEH0+vVXsvPyCLnpJrqlpfH6xIkAzHjnHf7evJlWrVrRsGFDPv/8c8eN/Qqwl/VkDlUQCK8UWZYTgITiv2dLknQEaAjUqEC4bds2brnlFrRaLa6urmxfsIBWQUFofXygoABqUaHXp59+mokTJ9K6WTMePXCAGDVlpIp10ar0Hlkm7uJFel1zDUEmE7s2bWJwx46OFxIkicXr1rFl2zY6de7Mge3bubBhA3fdeSf3P/AAIb6+3DxmDI2cffNQS4g9d47nJ01i5bZtBHl5Eezry8+PP87gsWMdPbTKI0mEBgaSuGvXlacYV+E9uapA2KuXxe3rBG4wvLzoPmECa3Q67vnwQ554/HFiWrQgOT2dlStXVltXQoEVZjPm5GRefv99PN3cWLV4MZJG47Rih6TT0allS/bs2cMwX1+oqjCWnQ0XL1ZaIMzOycHbw6OkMx5Q+UOw6sLbG5o2Zdrgwdzz2Wf06dOHoKAg7rrrLu50loYYdYzflyzhgfvv5/XnnuN/xWlYJVShm7xD0WjwDwggPTX1yg6mqkpxkxLfmBimjB7Nmq1b+Z8jfz8jI6FBA1zOnWNUz56km0zc3aMHi3/4QQiE1URhYSG3jR3Ljddcw4atW/n0uec4feoUL86ZA1ot47p0IcnXl9CAAMcfWl6K1q3xdHXFkJ+vPK7mRgs5eXl0K/5eCfHz49zXX6N1Fqe4nx/3jhzJHX37om/eHLRabh84kPnz5rF6+XL+3bePgwcP8thjj7Fu3TrhKrQT42+5hb/++QedRsNTd9zB+z/9xE1Dh5Kdk8PoG29U1m3OiJeXIkhqNPTq2JFlmzbxeGXfq8ZZFXSCA2fO0K5JkxIzxLUDBuDp6L2E9UG2qytERPDoAw+Qn5sLWi0di9O8/Tw9+XXJEjKzsoiLi6NpbVlbWGGvFONp9vicqiBJUmOgE7C9pu6ZmJjIE088wcaNG5kzbRp4eNBdqyXCzU2p6eQkqcNVxc/PjxF9+rBl82Zi1FPhqkyaVXUQopyqeUVE0DI8nF+++gqDnRqUXC2STke//v0B6HXNNdCyJd3btuX1NWvYt3Ytc+fO5Z/du/Gz6qgrqDxFycn8OGsWX69dy4HDhxk/ZAjxFy7gri7WCgqgpmuCXSVhYWEkJiRc2UbNun5nJRZfuXl5uOv14OOj1J4C5xAIAXx96TR2LP82a0ZGYSHhgYHM++knbrnxRrZu20ZgYKCjR1gnyc/P55477mDd33/j5eJCw9BQfp09G8lRaUyVxdWVzqpA2KdPtYvr2QYD3p6eFoFQkpyj+USzZoSEh/OHnx9zd+0iIyWFJx5/nMWLFzNv3jyRdmwnfvvtNz7/7DP+27mTFbNn072WC7D+YWEWB2E1C4Sm4hrR3pGR3Ni/Pzd27Ajh4dV6z8vi6grNmzPrxRdxKSri0MWL3PzYYzzxwgs0atTIsWOrY8iyzO7t24kICMCQnc2EsWN5YPp05XdP/Q7t1o1asXLT6fDw87tyB6HJVKV5Iyc/n6jwcFxdXQn280Mry84x76j0729TR+6111/ntWefpd+4cTRr2hRjsdstPj6+TjReqEkO7NtHVJMm+Fg16dn8999s/Ocfzn7zDZ7R0WSYzSxYvZpJU6Zw63XXEd67twNHXAnc3aFfP4Z6evLIO+9QUFBQua7CVelVUOw0PHDmDO2bNi0RCEMjIhjVt+9VDL56iB4ypOR7JKJVK27q0YOo8HA++e03mjRogLtez4WEBE6dOlV+h2gnxbFVyq8QSZK8gF+Bx2RZzir12iRgElD1/PpSmM1msrOz+fDDD9m+fTt79+7lnnvu4cTSpXgaDJCXB4GB0KPHVd3HGYhp04bDBw/C9dcrT1yJg7AK1uGc/Hw8PT0Vp965c/x7/DjR0dFVG3RNEBFBZIsWfOHvDwEBTHn9dcbeeitjxo3jgQceqFMnavaMnYoYc+utpOXn89i11zLip5/Qh4UpwroTiMNXSmiDBpYmJVVN9bIW1yvxu5SXn684CMFyvTMdTAQE4DF0KOqIJrVoQWxyMt3atmXH9u0E1cJTtMpQE7FTGmNeHhmxsTz8zDPIKSnsfv99Ujw86NSoEZJVsWqnxdWVztHRLPvvP2UOvZL6nVU4lMrKycHHy8t53IPWuLsjtW/PQwEB4O7OxP79mb5iBaNGjWLsqFGkJCcz7a236tR8o1ITsZNrMPDogw/yv6FDmT5zJt1vv71a7lOT+IeFkZ6eXiMOwmyDAU8PD6XBkSQp93RwkyMVfc+eIEm0S0+nU9OmtGvXjpUrV9Lb2TfaV0lNzjmT7r+f9evWMbJ3bz7+5RfLC870HVoF9J6eFJnNFJlMuFQ1dnbuhFatoDImgeK9jo+XF2FBQQSrZY6cJHbKQ1N8sLh55UqOr11LgsHAO99/z3///VdnBMKaiJ1Co5HhQ4dyy/XX897cuSxfvhxzcjKPvPwynz/9NJ7Dh4OLC34FBZz/+muk8HDCIyKqZSx2x9ubsOBgYpo3Z8OGDVx33XWXf09VHITnz0N8PAfPnmVg+/aWckqlmrs6DVbjknQ6lv78Mz8uXox22TJeHTuWhIwM/j1+nObNm7NixQquV3UWJ8d5v6UqQJIkVxRx8EdZlpeUfl2W5XnAPIBp06ZdcdqzwWBg8KBB7Nq9mzvvvJOHHnqIpsHBtHFxgZQUGDRIKQ7tpOlbVSWmY0dWr1tn2aRVp4NQljEUC4ReXl70b9OGp776io9mz67aoGuKTp1KOgV+8MYbvPrxx8yaNYvjx48zdepUGjVqVCc2bvaKHWtSY2M5EhtL3759OX7gAP8cPMi5+HjcXFxq7eKyNA2josjMyuLixYsEVzVN0jo9vxJpBbm5ubiXFgidxUFYHv7+vPHjj+Tdfz/Drr2Wxq1b897MmTRu3BiNs6ZRXAHVETvlkZKSwuT77iMpPh5DTg6HT5/mpl69+Oarr/Awm4ls1cp5G/qUxtWVmIgI3v311yuv31kVB2FOjq2D0Nk2aeHhJa6sYB8fPgoI4J0ff2TtL79wIj6ewLAwHnn0UQcP0v5UZ+ycOXMGnU7Hm089Rf8OHXjxm2+cy71zFfgHB5Oemak8qOYahJnZ2fiqLhhJUu7nLPFTPI9I/v78+vLLLNu2jTGjR/PujBlsWLWKbt26MVnETZUwmUys+vNPOrVuzb4tW/h18WLC/PwYPmKEPW/jMCQXFzz0enKNRnyqOu9YN3O8FEVFkJiIoaAATw8PwkNCCPb0VOLGWWLnEmiiomh1zz20Sk9n7bZtLFqwABcXF1q1aoWXl1etdrbXxHptyfz5hPj48NOSJfy9dStxiYnk5OWxZflyuvfrZ9n/uLkhDRpU7U3a7I5Gwy1Dh/Lbb7/ZXyA0GsFoJCE1lYjgYMuc7awCYWkaN6ZL9+70jYlhwh13QFER+enp/LRjB2+/+Sbbtm1j8uTJXDh3jm69egF2rBNpR5z/W8oKSfkXnA8ckWX5g+q4h8lkYvfu3bzwwgvEBAby744daNLSwNcX8vOVL/bmzZUaG506VccQHEJM164cPn1aCUyo3hqEFKcYF7vGnhk1iqGvvsoNN9xQ+XvWJJIEAQEA6Fu04J1bb+WRYcN46rff6Nq1K5IkMWvWLPLz8xk5ciQBxdfWd87u3Uvva64BScLLy4vk1FSmjh2LmzMLWleAq48P1/TowZ///MP44vT0SlNFcb2kSYk1zuQgLA+Nhtc/+YQPn3+evNRUunTuzC2jRvHVV185emS1BlmWWfPjj7zz8cfEhIUxoGdPUjIz2XXwIJpasNkoFzc3mgUEcOrUKeTCQqQrcd9WNcXY2xsaNlS+0535gCIqCm1EBC8WNzw78dtv3PD883z/ww/MnDmT/lX9nqlHyLKMJEmsXbWKsWPGoHdzo6GfH+u2bq0z4iCAd0AAeQUFFBYV4VrNDsLMnBxbgRCc72BKkmDwYG6KikLn6spn8+YR5uXFK6+9xug772TatGm88MILhDs6NboWsHzhQsaOH49WoyEyNJQHxo3jnffeQ+Ooju/2xsUFT72e3KKiqguElZ13srLgxAly8vLw8vDgugEDaN+4sZJ1VltwcYHgYLp06sTbzz7L8j//JCgggGZNm/LnmjVOKWo4im3r1tFj8OCSf5OPP/2Ul557jiZmM4mFhfS+8Ub+PXSI7uWJabUxg0qSuHnwYPqOH8+nn36quMsvRVUyDU0mMJtJycoi0M/P+R2E5dCqe3c2/vijUi/XaMQtJYU7XF15+5tvWL18OTNmzEDv4kLHzp3p1asX786Y4eghl6G27Sz6AHcDByRJ2lv83AuyLK+01w2ef/55Fi5cyOihQ3l37Fg0SUnKhsLPDzIyoH372uPQqAKNmjQhw2AgMy4OXxeXancQ5uTl4VncWXNgu3acWLu2dghrbm4wZAjhu3bx0wcfIIeFsXXrVkaMGEFBQQFDhgxh6dKluDrz5rMGkAsLeXDKFB6+5x4eGTeO4/v2EdSwoXN3t7tS3N0Z2bs3KzZsYHy/fpV2AwJVFtdzrVOMCwuVP2vBptfD25sXZ8+GjAye/ftvmj/wAAsXLuTmm28WXVsvQVZWFtu3b+eNadPISEzkppEjeeW113DR6ZTDnNoqDgK4u+Pj44OXpyfxFy/SsKrf/1XsAJ6Vk6PUAmrVCs6dc/5/O60WimupRQ8bxl4PD36Pi2Ps2LE8//zzTJw4EQ9nPxyoYeZ/+SVbtmwhzNeX+d9/z9I332RA06bQsycEBTl6eHZFcnPDz9OTDIOB4KqKHMnJyu9/JWPORiBUccbfPY0GoqMZfvfdDB89GiIjyR8+nGbNmuHj40NiYiKLFy929CidF7MZWZKYO2cOXz72GLf07o33DTc4/3dlVSl2EBqqeMgEVF4gNJnAZCInPx+vVq2YNnQorF1b62psA1w7bBg/Jiez88wZTicmcvLkScaOHcvs2bMJrYU/j72JPXWKXkOGcHDHDsKbN+fwgQPEJydz0113odXrSzSD6+pSbVSNhuaRkQQHB7Nt27bLN4eqioOwOHZSMzMJ9PUtaYzi1M2PSuPmpuhFKiEhuEVFcWL2bAqKith26hRxFy+yevduvvj8c1548UV8fHxYtWoV1113nVOI77XqW1+W5S1AtfyrLV26lOnTp5Ny8SL/ffABQRERilNQq1UaAri41Jl04vLQaDS0btqUIydP0rNNm2rvYqymGINirW1Sm9yYOp3yu3DsGJKLC3179uSNN96gV69evP766/Tp04d3332Xvn371k+h0GDglzfeIC4+nqdfew1XLy86d+vm6FFVH+7u9GvVitc//1z5vqiKQFhFcd0mxdjHp9IdXJ0GPz+8hwxh7uTJvD5tGgsXLGDGzJk0bdrU0SNzOgoKChjUvz8ao5F7Bg9m8uOPo7UucOyMG/SqEhxMdKNGnExIoGFVa9DK8pU1KQHndxCWxtcXt/79Gbt9OwHvvcecBQuYN28eTz/9NKGhoQwfPtzRI3QoCfv2sWbdOr798Ue27d9PsK8vx1aswL9zZ+W7uC4eQuh0+Ht6kp6TU3WB8OJF5d+kkgJhamYmgeq1apaJMx9MWbkEv5k1i6N//01EYCDNH3qIbdu2sXv3biZNmlQ/12cV8OXcuUx/9VUMBQU0DQtjzOOP4x4QUPfEQQCtlmBfX86nptKsugRCs1kRCPPy8AoNLTEXOJ3zthJ4R0Vxx/XXM87VFdloJCsnh2k//cToUaNYtWZNyV6uXnL2LL9/9x0AUx99lL+3b8fXy4vPnnwSbS1yvFUZSQKzmdtuu4358+fTp0sXRcCrSNiqyl5HFQizsgiMjlYclgMH1n5zlqcndOuGvqiIAe7u0LMnd6amctfEicz65BMGX3MNI0aM4NChQ8TExDh6tLVLILQ3Z8+exd3dnR07djBlyhS++uorBvj44BYcrIiD9YyYFi04FBtLz06dqs9BWFQEOTk2Kcb06VO5gr/ORHi4slA+dgyKinjkf/+DfftYOm8e3/75J48++iienp5s2bLl8tbrOsCKZcuY/f77jO7enVOxscz76y/++PNPXGujdb6qeHjQyN2d+LQ0TIC2GsV1myYlOh20a1e1sToDnp6MmjyZ64cMYcIbb9Cre3eGDBrEl999h3stXDzbm/NnzjD/2285c+gQjb28WPzjj0g+PrXvO7IyBAXRPDiYk/HxDKiJFGP1+8jFpValqwDKgUCfPgzZtYtrnniChbt28eOCBezat4/nXniBcePGERIS4uhR1jiG+Hj6Dh9Oek4OJpOJX959F9/AQPzreJMKdDr8vbxIz8m5sk6sVXDfpmVlEejvrzzIy6vavRyM1Lo1rZs3h8xMbu7enVtuuQWTycS6devo1rUrrVu35uZbbnH0MB3Krp9+4oXnn2f1/PmEREURHhSEVMdTsYd368bv27Yx8Nprq/bGyjrXTSZkk0mpQagKaLV1fePnBwMHosnLg6Ii/PV6PmzUiPtffpn+XbuyZtMmAmtxTcIrRpaZ+OCDLPv3X+4aMoQf/vqLb557juEjRxJSi7rVXhEaDcgyU6ZMITo6mlPXXUezoUNBnSdKU5W9jtlMXl4eRSYTXp06KcJgbXIPXoqwMOXP4GDlZ/LzY9qDD9Jz0iQ2rl6Nv5cX0158kUZNmzJz5kyHDrWWVcW0L8uWLaNFixZMnjyZn3/8kWEREbhlZtZpp+CliImJ4fD584rwUF1djBMT4ehRmxTjyp5iOxWSBE2aQN++SsrAunVQWIhm3z7uHTqUfT/8gKuLC6NHj2bTpk2OHm21kZ2dzaeffspjDz/MwPbt+f3IEdIDAtizbx89e/Z09PBqBr0et7Aw/L29ScrKqtb0/Ny8PIuDsDbTsCH6fv34+fnnObNsGXJqKoN692bLli2OHplDWf/TT9xy7bWsXriQM8eO8d2iRUhRUcqiq7afnpZHQADNg4I4mZh4ZaleVRA5sg0GfNQaWuHhtVNc9/CA/v2RBg7kthEj+H36dJa//DJ/r1pFqxYtePLBB5k9ezYbN25ErsK/TW1m8bff0rpFC1auXs2LL7/MLU88weAJExw9rOrnagTCKorrqZmZlhIwublVu5cz4OoKQUHcet11JCcns/n11zmwcyfvvfMODz/4INnZ2Y4eoWMwmdj922+MfPhhPps3j0633krDrl2RaltmwhVwS9++/LZpE3JVD6Yq61w3m8nPy0Pn4lI3TAKSpMw/Pj6g16Np356vXnyRIZ070zYmhiemTOG7OXP4c9EiR4+0WikoKGDPnj0AnNm7l9/++YePJ0/mgw8/5J5hw7jrxRcJ6dPHIgTVVSQJZBl/f3+mTp3K6199delmWVV0EKZmZBDo4+MUqbbVgpXg2fyGG/jw0UcJd3fnkyefZNHSpXw6Zw7nTp2i0IHzbb12ED46ZgyPjByJZDAo9QXz86F379qVemRHYjp0YOOqVVcuEFbmPUVFmIxGCouKcKsLQgdA69YQEqJMnElJsGcPUlAQvzz5JD+fPMno0aMZM2YMgwcPJioqiq5duzp6xFeNLMs8P2kS2/fsIT8vj6GdOvHcJ584d9pRddK6NZENGhB78SLhVVlwXomDsLaeQpdGo4HBg/EAvl+2jG9ffZVbbr6ZVx5/HL/QUMbefTf6unJqeDkKC9m5fDm3T5nC008/zZOPPork5lb348nNjejISBZt2VLtIkeWwUBoXTn8c3EpqW/TpXVrfm3enLi77uKjJUs4sGYNcz7+mNatWrF42bI61S3cmh3//MOrr7zCof37+fjDD+nZqxc9izsC1gs0Gvy9vRWBsJrdt2lZWQRal4GopWu3a0aPZmtBAS3HjeMnk4m83FyW/PMPXdq148svv2TJ778z4/3360ddXFlm8/z53Pbii3w2Zw43jx3r6BHVKO3btKHIZOLE2bO0aNu28m+sQg3CnLw8vOrKeq00Wi1Sr16807EjE/74gxsfeYQ5X3xB55YtGT5mjKNHV20sWrCAB/73P/5bsYLP58/n7ptv5vY33gAXF75etcrRw6s5ilOMAR577DGaf/ghZ86coUlFtX6tahAWFRWRl5eHJEm4urqWXedb1x+sD7i5cfe0adwNGJOScCsoYPXevbRr357oiAiuGzyY+6dOpXGTJmhq8PukXguEuLsjJScreeFBQdCgQd10aVSSmE6dyncQHjqk/NtU5PSr4smAITcXTze3unUyoHYmi4hQ3D6enjTYv5/HQ0K4ae1aFq9axddff83+/fsZNmwYU6dOJT8/n5iYGEuqdS1i259/8vPy5dx75508+/TTSlp+XRczLoWfH41ateJ8aio9rWPn4kXFSl4Rl4mdhIQEvvvuO5599llITiY3La1sF+M6gNbbm/umTAGzmSV//kmewcDsWbNY8Ntvdb4+4fKlS/nq/ffZffIkb7z+OhMfftjRQ6pRmrdqxclFi6ouclxJDcJa+F17WXx8oGdPIiSJ92+8EXJzKTx0iP4PPcQrzz7L9aNG0asOCWe71q1j565dTHv3XaZPmMCwPn0YWc/EDZWGISHEpqXViIMwSk0da9GidnbdBLQNGtBz8mTw8qLb7beD2Uz/nj1ptXgxA4YOpXFoKKbCQj6eM6fOCusA+/fu5YUnnuDgkSPMnjuXm0ePdvSQahypXz/69ejBlt27OVxUREFBAbfddtvl31gFgdCQn49XHVyvWSO5uxMzejQLsrM5HxvLlI8/Zt+OHXTo3t3RQ7MbGxYtosvAgfgEB7N66VJ6t23LwNtuQ6/TsXvXrrpZp/NyFKcYA/j6+jK8WzfWbdpEW5OJ9PR0unfvTmBgILIsM2jQICKCg/nq9tvRmc38/PPP/PbbbwQGBtK6dWsef/xx289W6w/WF4HQCl1oKKNfeYUO27bRu0sXMvPzWb1pE+vHjeNUbCzbNm2iafv2ZGZmkp+fX61Ngurhb7UV/v4V58vXQxo3bUpqbi7peXn4W2/WsrMv/e9UkQsqPl4RX62DvKiInJwcPOvqqRooPzMoKWwHDtA0JYVnunThmTFjyMzN5ZV58xhbvKFp0qQJK1eurHVi6Scffshjkyfz2LRpjh6K0xAZGUnsxYtKyoosK/9P//sPBgyo2HFhFTv//PMPubm5DBkypOTl77//nhkzZvDMM88gJSaSm56OuypG1zWaNeO+997jPldX5NxcPnzsMbp36sSTDzzAk2+/XedcHSdOnGDeZ5/x3Tff8MHjj/PEgAGX7wRXB2neti0nL1xANpmq1oGsOMU4KSkJ9+KOyBWybRvZmZmWJiV1Des51ssL15AQPv/oI5575RW+X7CA/gMH8vY77xAREeG4MV4lWWlp/PLll3z46adgNjP5hhv434cfOnpYDqVN8+Zs3L27RgTCkiYlLVtW7V7OhipuqmvaPn2Y3KMHA7p1I7hBA0Y+/DATb7+d+b/84rgxViMfv/ceb775Ji/dfju/Ll6MvjaW+LETfbt359VZs3D18CArK4sbb7zx8nWQK1vawmwmJz8fzzouEKp0GzuWbgUFZGo0XDt8OHdPmMCUqVNp0qSJo4d2VRzdvZthd9zBmGuvZeuBA6SnpbF382YyJQm9Xk9ILZ5TrworByFAz5Yt+WHxYva+/jrt2rVDkiQ2bdrEX3/9RVJSEmnJyfxz9CgD+/Rh//79HDhwgMDAQExpaWzv0IGOffpYnIQmE6nZ2fVSIATA3Z3oQYOIHjQIgP8lJNC5e3daRkRw3z33ENO0KYXu7qSmprJk5cpqG0bdPSITVBmtVkuvXr3Ysnev7eKxuKNQhVTkgkpMVFK3rSk+VasXk6YkKWlggwdDly6QkYFvWhofjx/P0V9+4cCsWZw/eZIpU6aQlJTk6NFWidseeIB7H3vM0cNwKiIjIzmbmMi4e+9l+vTpypOXiR1DTg5vLlyIbDIxZ84cXnjhBZvXFy5cSFpaGhcvXoSiorqVYlwexeUdJA8Pnpgxg52bNrHln3/o3LYtc+fMYePGjY4dnx2QZZkzx44xsG9f5NhY1sybx53PP0+/fv3qtGulInzbtcPD05PE1FTLk0YjbN9+6TcWixxPPfUUEydOLP+awkKlMVZ+Plk5OZcWEesY7YcNY+WKFRz68UcaFBbSvk0bRvTtS9zx444e2hWRnpzM8t9/p02TJhzatInX3nvP0UNyOG2KG8tVa4pxbCxp2dkE1tWDqeKO5q3vvJOgAQNYv2wZGzZtYszIkcydNcvRo7Mbm1as4PZrr+WzWbPYsXQpj8ydW6/FQVAEwrikJH744Qe6du3Kr19/XXbPYo0sV965XtdTjEvj6QkBAdz74ousnz0bl/Pn6da5Mwu//po/vvnG0aO7Yj5+910evvNOflm1isFt2jCyd2+adOxIx44dad26taOH5zisHISgCIR/b9vGnXfeyYoVK/jvv/+YN28eo0aN4o033mBg797sOnkSzGaOHDnCqVOnOHToEIeOHmX4rbcyf/78ks+STSaSMzMJqouN+a4A9wYNOPz99yz89lukoiL+3LKFHxYuZN3ff/Pzd99x9L//eHrcOE5u3crcp56y233r325EcEkGDBjAxp07ywqEl5oQK2pSYjaXXbgWFSm2+/oyaar4+0PnztCrl+L28PPDtWNH/po+HTeNhjZt2nD77bczevRocnJyHD3ay3LzbbfhK768bWjatCnzli/n9NmzfPLJJ5w+fZqWkyaRcvEiAPPnz+d///ufzXu27t7NSz/8wG8rVrB161YOHTrE1KlTObN6NUf37ycuLo6ePXty8OBBCvPzyc3Px72+1OXz86NJhw6sWL+eaRMmsGXRIsaNGsVX776LXFXHjJOQl5NDtzZtuPWmm3hk7Fje/+ILOtx6a/1Oz3d1pXl0NCfj4izPGY2QmXnp9xWLHHv37uX3339n//79ZS6Rjx9HPnMGiorIzs2tmynGlyI4GK8BA5jx8cfEbdtGl/btuXboUOZMn46pqqKSg4lq1YrfN29m0bp1ShOFetixuTQx3bpxNDYWc1FR1d5YWYHQbIZ9+2yblNRltFo8oqP5/rPP6NywIZ9+8AFd2renYXAwj44fj6mw0NEjvCKWf/89Y+6+m+69erFp40YaDxzo6CE5Be3atGHb11/Tu3dv7rvvPn748UdISan4DZepGb1v3z5yi5sK5OXmsnDr1vq319FoaHP77bzz3Xd8/swz/G/qVMY/8giL58wh9exZR4+uysz64Qfe++ILdvzzD1+sWMGPa9bU61JkJZRyELZr1Ah3NzfGjBmDr68vQUFBvPvuu/z444/ceuutdG3fvkQgPHz4MG5ubhQWFrLj8GGMRiMffPAB+fn5ZGZmMuSxx3h8/vz66yAsB2nAABp06cKGPXt4/5VXuHPoUDq1aMHEyZPp1KsXc5YupeeIEUz98EPiTpwg+cKFq76nEAgFNgwcOJC/d+2ynQAvt5g0m/l23Try8/Jsn7cWFmVZSTk2mRTbfX2bNFVcXSE6WumUHRxMWO/efDBmDLvnzmVA69a4ZmXRqFEjevXqxbJlyzh27JijRyyoJCNHjuToL7/wz4oVTJw4kWHDhnH8wgWOFzt2fvvtN/7++2+SkpJ46623OHv2LP/s3k3Pli2Z+txz5OTkMH36dFatWsVvS5fy2dy53H///bRv354JEybwzMcfk2c01m0HYTlI7u6MfvFFfty4kdXLlzNr/nyeHT+ef//4g3mffUZRVTfHDsTdy4sPXn2Vvm3a8OR774EQ2QFo3rw5JxMS2Pbvv8oBidXcUWE3XlmmwGjk5MmT3HbbbeV2i7/3+ef5+KuvLAKh2sW4vhEWhkfr1kyfM4eXX3qJRb/+yvVDhnD2xAlHj6xqSJLiXBAA4N22LUGBgZyxFtcrQxXqqAF120FYDn1uuonnP/+c/7Zu5Z3bb2fV11+z/9AhhvXoweQxY2pdh/Bu/fqxYc0aHn/tNYLreE3fqiBptfSIiQFgxIgR/Lt/P6998gk7duwo/w1WjRbK4/bbb+fnn38GYO2WLXy4bBmHY2PtPu5agbs7tz7/PKkZGXz6zju8NnMmQ4cNw5CUVPWSCA7ERadD6+pK5x496kY3anth7SCUZVy1Wv5ZvJgBAwYA0KFDB86cOUO/fv0A6NahAzuOHyc3N5f4+HgGDRpE165d8ff2ZvSIEfTq1Ytbb72Vn376CQ+9ngeGDqVjixaO+umcD1WU1mq59aGH+HLZMt6dOZOt69Yxb/ZsdmzZQpeWLenfsSM9e/dmcL9+JCUlkZWVdcW3FCstgQ3dunXj6JkzZGRksGfPHtauXWuzWTtx4gRPlbKwJiUnc8/HH7N682bbD7N2EObmwv79JQ7CeisQliYqCgICiOrYkcl9+/L1U0/x57x5dGjdmunTp9O3b1/efPNNCgoKHD1SwWXQarU0iYjAVaPhueeeIy0tDV9PT06dOsX69evZtGkT58+fZ+TIkaxatYqbbrqJtf/8w/OjR3PLiBEMGjSIJ598ktdee421u3bx/S+/MGnSJGIaNSIuLo4t+/aRW1BQ7wRCazr06cParVv56a+/GD95Mp++/z4vPfQQRbXI2dH/ttv45NdfcakPZRYqycCBA3ln8WL6DxjAlClTyExP5/n581m6dCn33HNPmeuzs7P5cskSth09StOmTencuTOHDx+2ueaXX37hlzVr+GPjRigqIsNgwKe+OQhLIUkSt0+cyNoVK+gbEUHPHj3444cfMOfmklf6gE9QK2jTqhUHT50CwGAwVO5NxQLh008/zblz5yq+rnj9lpqVRUA9rNftGh7O0Oeeo93Ikfz8xx/0vuYaNu/Ywd0jRjDh+utZu2BBrRA7who3JqZbN0cPw/nQaEr+/3l5eXFNt258s2QJo0aN4t9//y25LCkpSXGoWwmEBoOB3bt3069fPwrPnCFp926OHDnChg0bANh39Ch3DBjAb6+9VuM/ljOhcXHhtoceYv+JE8RERBAQEcFd119P0qFDjh6a4GqQJMjPt9nnd2zVqqSefvv27Wnfvj3+xfNGi8aNCfLxIaJbN6Kjo2nXrh2tW7cmJiqKPp07880337Bv717mffwxo/v25dMHH2Ts0KEO+/GcHq2WHoMH075XL+6eOJG2XbuyescO7r7tNiQgIjCQpo0bs2nZsiu+hRAIBTbo9Xp6dOjAN7/9xvDhw5kwYQIdJ01i3ZYtyLLMfffdxyeffMJ2q/pQqzdsQO/qytJ162w/zNpBWFBQUg+qXqYYX4p27RRX4eDBuHXrRs+QED674w52T5/OprfeYuuaNYwaNYpdu3Y5eqSCy6HVgtmMn58fB3ft4sHrruPHRYsYOnQonTp1okuXLpw+fZq1a9cydOhQ/tmzh16tWjHrjTf4/vvvAejduzerdu2id7duREVFcXP//nz+9NMcOnuW3SdP0rRRIwf/kI4lKDiYg0ePcvTsWf5cvpy///mH1k2acODXXzly6BBxVXXTCBzOvffey7gBA1jw/fesW7eOz+bPZ8aSJfz+++/88MMPvP/++xw+fLjkoGTp0qW8OHcu177yCh3atycmJsZGINyzZw9Tp05l5XvvsW3/fs4lJZFnNNIwPNxRP6JT4dqoES99+y3fzJzJ86++Sr/u3QkPC2PG6687emiCKtKmVSsOnT7N8uXLadasWeXcbcUC4Q8//MDOnTsrvq6oiLyCAmRZxqOuNvi5HMUb3tCwMKa/9x6LZs+mbaNG9OzenQlTp7KqFtdXq/dYCYQAnz/7LHsWLWLGjBnceOON7Nu3D4DZs2czZMgQkhISAPh7xw5CQkJYvXo1W7Zs4eO5c/l740ZiYmLYsGEDsiyz//hxru/alR71uU6dFZJWyw8//0zamTM0DA0lpndvnh4/3tHDElwpgYGg08GRI+U6a2+66SaeeOKJksdaSWLHzJnsXbmS5cuX88gjj/Diiy/yxeOPc+cNN6DVarlp2DD2HjvGgLZtlTeJVO4qM2HqVPZs3MiyX34hdccORt555xV/Vv3uYiwol4E9e/LU++/z5ptv8r///Y/lb77JAy++yB/dunHq1CnefvttPvzwwxIr/coNG3j21luZs2oVBoMBT3Uhad2gwWhU/szLq98pxpfD31/pelu8yG+dm8vS6Ghe//lnRt18MyFhYbi7uzNr1iw6duzo2LEKymK14GwQEkLTsDDeW7qUyZMn8/HHH/P666/Ts2dP9Ho977//Pg9dcw3BxSmybsWOsqioKCKDg3lowgTlcUAAk0aM4ONffiE7N5eu7ds75mdzIvyKU3MbtG7Nv/v28d2cOQx+4AE0gMls5rbhw3lj9mz8g4IcOk5B5ZAkidfuuQf69WP1unXMnDsXs9nMwoUL6dy5My+++CJZWVmKK/CXX1izZg3TJ06kfVAQbgMHEtqwoSIQmkywcSOfL1rEY489xqC2bYlu2JB3f/2VgW3bonURS54SNBquu/deho4fz3tPPcXDISG8+MknbF6/nrkffUREhw5QUIAMSPWl7mktpE1MDKsWLeLzhx8mLS2NlHPnCNbp4FJiuNlMbm4uiYmJnCp2H5ZLURHnU1II8fVFEqndAMRcfz0xw4eDiwtRbdow9dFHeUOn49Zx43BxcVHmf/FvVTsoVUct2McHPD254447OHv2LG+//TYvvfQS//77L+3ataNZmzbc3qcPf+zZg7u7Oz/88AP33nsvny5YwIAuXbj//vv56KOPOHz4MPuOH2fazTcLkcOa4GA8gXe/+Yap06czoGdP+v7wA0VGIy2aNaNd794ljeoETo6LCzRrBseOWfb5VrHUtWtXunbtarm+eE8b2aCBkjmn0rChIjQCo4YN48+VK2kcFKR8loidKqNxdyeoTRv7fJZdPkVQpxg2cCDRkZE8+uij+Pn5cffAgUSEhjJ37lzatWvHxIkTWbt2LbGxsRQVFbF282YmDRvGtb168dZbb1k+qNh6LMsyX3z7LcF33cW9b78tUowrgyQp/3l6ouvVi9cnTGD/J58wfcIE7hwwgOuvv54vvvjC0aMUlEajsZksm4SGYjKZ6NGjBy4uLrzyyiu88847JZc3jYhQ/mLl+pCA/Z98wnC1kHheHhiNdGnWjFt69Sqx8AuK0WoZ/8gjnE9I4Nz58+zfu5dCo5FBPXqwcfFiTv33H4YjRxw9SsHlKBbXr7nmGi6mptK8QQMMBgMrV67ku+++Y+PGjRw/fpyVK1eydu1aru3WjV6tWtGpQwcaNGiA0Wgk+cIFjJmZLF68mDvvvBMKC7l/2DDm/vkng9u3FwvOctBqtTz34Yfc/vzzHD54kB7t2tFpwABmPvEEY667jseKDyoEzkmbmBiWbtlCUFAQnTp14vh//4FVgfK//vqrTIkSc1ERp4ud1qdPn7Z5befOnbzzzjukpaWBycTPmzdzQ/fuInZUNBplcwyMGD2ahyZM4NN336VhSAitIiPpFRPDjKefdvAgBZWilIPQOutp4sSJHDhwgH79+rF9+3YWLlzIucOH8dTr+fKVVxg3bhxHjhzh7rvvRu/qyq9//cXdd9/NHXfcwXvTphGXlETLhg1F3FRARGQkT02cyC3jxzN/3jyGjxnDc/fcA0VFkJ3t6OEJKoOXF+TkXLY2p81r5TUzLX5tcNeu7Jg503KtiB2HIo7TBWXo3rkzhxYtQuPmpgSz2Uyv9u35asECxo8fj4+PDxMmTODTTz/lxhtvJCIsjIbBwcx47DHajh3LSy+9pHTySk9n3pIl/LB+PS4mE3+++ipjZ8wgNjmZzu3aOfrHrD3o9dCrF37p6Yw4cQLy8nhgzBgKRLqc81GcYgyAyUST0FAAunfvXvxyqSLH5U2sZjO+np6W54oFwnfGj8dNpxOTZgWoDsxwLy8+//VXvvjwQ5544QXSs7LIys1l8l138fgzzxDUuLFjByoon2JxfdCgQbi4uPDQiBF8tGYNwcHBtG/fns2bN+Pu7s77779P48aNaRIWBunpIMtIksRdd91F32uu4dYOHejapQtRUVFw8CAPXXstCcnJ3Nyzp6N/QqfHLTCQlz75hNGTJ/Ps5MmYTSaWbthA7MCBPPnMM/QdMcLRQxSUIiYmhoLCQm6++WaOHz/O7n37CDQaadWtG3l5edx0000sWbKE4OBgOnfuDMDTn3/OtuPH8fDwKOMgfOmllzh69CgAz95zD9+uX88vTz8t5p0KePytt3jslVc4eeQIGdnZHDx8mBdefJF7R4/Gv0ULXOph7cZaQ3kCYfEBb3BwMIcOHWLq1Kn89ddfSpMeV1c+mjgRIiJIKs6UatOmDY/efjtZ2dkEBwdz/z330KJ1a14ePx4X0dTikkx8+mkG9+lDy2uvJTU1ld5dupBy8834urjQoXt3Vq5bx7xffyXfaCREdK13PtzclPhR6xdXRiAsfY1VzElGI0E+Pso+SjVaCByGcBAKyqLRWH4xioO5W0wMqampxBR3/Bo/fjyLFi3ijz/+YMSAAaDVEhESQteuXfnxxx/p27cv17/4Iu999x1ffPEFexYtomt0NK/dcQdZublMGTPGMT9bbcbfH7p3h27dcHFxwbMWFMeud1gvOE0mIoODGTtyJC0q6sZl1QWsBHViVP/MzwdZJjwwkID62oW1ikiSxKQnnuC/Y8c4c/Ysu9asISU+nhbt2/PsE0+Qmprq6CEKSlMsroeEhHBm0ybuGjiQt4sLvEdHR6PX67nrrruQJIm5c+eWWXDOnj2bB++7jxm//caMt94qcYNIwBt33UVEUJAQOSpJq5gYlv39N8s3bWLz778zYtAgRt91F8MHDSKvso0wBDWCp48PXVu0YPTo0bRo0YLn33uPW59/HlmWWbNmDbm5uXz33Xd06dKFgwcPIssyCzdt4p+DB+nfv7+NQJicnMz27dt59dVX2b59OydPnqSgsJAuzZuL2LkEkpsb0Z060a1/f+6dPJlWUVFE9u9Pw6ZN+f6jj5DFWs05uYSDUGX69OnMnz9feWC1XuvRowdhYWGEhIQw+ZZbeOauuwCIbtSIXR98wGvjxinXiripEBcvL1peey0AgYGBbNu1i5DAQEy+vjzz3ntcTE8nJDSUz954w8EjFVSIlxeonXKv0kGI6nTXapXYFLHjUIRAKChLKZEDoGtxoV1VIOzQoQOyLDN79mzuuummks3d2LFjmThxIjfddBMpmZncOWwYvXv3RlNYCJLE3YMGsfPDD2ncsKFDfrQ6gYcHtGkDxe40gRNRKsXY1cWFX2bPRlNRTSKz2dZ1CLYCodmsNPex7ngrJs3KI0ng5kaTnj35fOlS9v75J4aTJzEkJjp6ZILSWMVOREgIwb6+3FF8kOTi4kJMTAz9+vUjISGBHj16WBaaVrHz+OTJnPniC9rHxCh1b0vHnYidyiNJoNUS2aMHE199ldM7d/LwzTfj7uHh6JEJrNFo2Pnxx7Ru3ZoWLVqQm5fHxYwM/vvvP3799Vd69uzJL7/8go+PDzNmzGD//v24aLWE+PkxePBg4uPjlRTk3Fx+/fxzRowYwaBBg9ixYwdbt2+nb0yMUtZCxE6lWbR8OQkXLvDnggW8P3s2nWJiePnJJx09LEFpNBplnihuPmJTN70Yf39/evfurTywOpSKiYlROhuXfl9eHl2aN0cqKipJRRdUDv+gIN769ls++vZbEuLjWbV2LSf+/JNX3n/f0UMTVISXF2RmKn+/lEBYznqttM5AQYEyz2g0QiB0AsS3l6AsGo0S8LGxEBwMQOOwMPr160ebNm0gNxcpLY377rsPk8lEm2bNIC4OZJkJEybQvXVrOkZFMbF5c0s6n9EI7u6QmysmTUHdRaOBo0eV3/XiwruXtMrLsiIQqpNncRyVvC8vTxEHXVwUJyGISfMqiOzTh9kLFigiu8C5KOdgynox+eWXX9KyZUt0alyVdyJd7NotEdfd3UE43uyCR7NmXP/oo44ehqA0VsJ6z549eXz8eHwliffee49Vq1axbNkyBg4cyFtvvcXzzz9PZGQkN3TrRuumTekzbBj//vsvN9xwA39+/TU/L1nCk6+9RuPGjTHm5fHzb78x0k4Fz+sTQcUH4J2vu47/9u9n808/kV+Z7tKCmkWjUQ5g//sPhg4t10FoQynXenDx/sjmfeo6DZR1m1ivXRGSuzuu7u40GjzY0UMRXAoPD7h4Ufl7afHPZLI0nbmUGcLaQaiuzU0mETsORig1grJoNIowceAA9OgBgGQ2s2nTJuX1uDg4f56XXnpJeXzsWInIodfr6dioESQn0yw0VNmggSIQenkpXwBarQh8Qd1EtcgnJIDagORyC07rSVOdaNXXCgqUGpRarSI4qt3ABVeO2mVd4FxotbB7N/TqVTbNHkrqp5VwOfdtUZEirufliaLXgrqLlbAeFRXFB08+SfqpUzS7/34GDBhA3759iYyMZMyYMSxYsICZM2eydto0+nbuDO3bs2jRIgYOHMi0GTM4cOoUw4YNQ5Ik7h86lHcXLuTdsWOV+4jYuSK0Hh4MfOABRw9DUB5qjcDAQKWxTzkOQhtK11HLzobTp20FQrUeGyjiiIgbQV1Gpyu/BmFcHKSlQceOyuPSZoicHMUwBLYOQj8/5XnhIHQ4QiAUVExoKJw5o/zdOvALCpRTNxWzWQlmNfCLiixCiRr4RqMyCWdmCoFQUHeJjARfXzh4EBo0UJ6zjp34eOV33/o160mzqKisyOHioiw0VYFQxI6gLlLcjIfk5Mp3xbOOHbCNHaNRiRvVsS5iR1AXUdN/1XVYURH+Hh588803REREoNVqOXfuHADXX389Z8+epXerViWxpdVqeeeddxg5YgRv3Xsver0eCgp45667eHjkSCL8/Cz3EQjqEq6uMHy4cjB77pxtPTRQ1l/nzkGzZspjNcasxcCMjJJmjoDiIHRxUd7r6lq25ppAUJfQ6ZT9ful6nvn5toaG0ge68fFK7KivybJyvbe35fPEnONQhEAoKIuHBwQEKALhyZPKc6UFwqIiy2P1ZMA6PUwVCK2tw56eluKjAkFdxMdH+e/UKeX0DGxj5+JFZdGoCoSXih2TSRHiXV2V//R65dRNTJqCukh2tvJnWpql5ubl0vNdXCyxU1pcV2PHxcVSa0ogqItoNHD+vHJAVeyCuvHGG8tcNmnSJPr06IHGYLCZl/r06UPa7t1IasOS4qLzJeIgiHlHUDdxcbE4zcF2zsnKUkwSqkCozjnWB7rqXshaIPTxUeYxFxdbM4VAUNdQRXBX17ICobVOYDaXXa+p6fiqGUKrVWJR6AROgfg/ICiLhwf06WOxDls3XgAlqK0nvdLWYWsHoVqHAJR0Y3WzJhabgrqMr6/ldOxSsVPaBVVaXFcdhDqdpaahQFAX6d8fBgxQNlalN12guM+tY6n0ifS//0J6uvJ3VSDU6ZT40euV58W8I6iLmEywf7/iyrhEmmRgYCD9+/Sx3agVI6mbNFCEETX9Up13ROwI6ioVpUmWzpYymcqKHEVFts7DvDxl/QcixVhQ91Hnh9ICYXmZhpfSCdSMD7WkktAJHI4QCAUVo9NZioyqFuDk5LIOwtIbNXXSVIVF1cnh6amcrInAF9R19HqljsblTtVKOwiLiixOp9Kx4+2tPC9iR1AX8fVV5gcXF4tT1lroOHCgbI1O681acSdWwDbFWE3PBxE7grqLXq9kfJQnrltT2nmrogodsqy4eQMDledF7AjqOnq9MmeUrmmr7nVUUaM817r6n3UdNXWtJpqUCOo6pQVCk0k5qKrMXkftSWC91wkNhfbthU7gBAiBUFAxqutCDfzsbNi+Xdm8WZ+YleeCUt+nbtR0OqVJSadOlpNpgaCuoi44SwuEeXmXP1VTJ1LV0eHqCo0aQcuWlnpTAkFdxc1N6TxcOnZycy/vXLdOWbFOMRYih6CuExOjrM3UTZm1uH76tO36TN18qYLgkSOW96kbNx8f5bFwrgvqOmpmk2qKUFEdTtaie+mSMGqmh3W9dS8vkSYpqB+oXYpV4fzMGdizR9nrlGcksp6H1NRk64wPjUZZAwqB0OGIby9BxagLQ51OCW7VvZGfrwSuGvyybNukxFogNJstGzUVEfiCuo61uK4uIg0GJRbKEzmsF5zq+9TYcbEqFSsWnIK6jl5vEcZL1+asqOi1LCuxoqaJFRVZFpwNGljcUAJBXWToUIiIUOYHa5Fc/fPQIdt0Lo3GUlS+sFBxHqqxpbrYVReUTifWa4K6j05nO+ecPGlxpFsLhKVrEKqodW4lSSmnpMaYiB1BXUZNo1f3OqdOKTFSXqZhafet+n7rFGMVETsOR+w2BRWjnqq5uCiBn5KipDqqCr8qdFwq8K1PBlRE4AvqOqXdt8ePw6ZNynOXcxCC5SRbFUpURAdwQV1HbVCizh+nTyv/ge2cI0mWgylVDFFrdlqnGDdqBKITq6Auo8aMXm85VFJjwmBQ/lQFQvVAV40dNaasxRAhEArqG9YC4cWLiqs2OVl5zXreKZ0mCcpzWq1yQKXTKQJhhw5iryOoH6ixox5OhYRY9vzqPFRexgfY6gSlBUKBQxFdjAWXxnrSTE9X0liOHVOes3YQlhf4qsBYUFBW5BAI6jLWAmFeniKuq80SKqrLYS10WE+awkEoqE+osaPTKbVssrMtc4vqcirtgrKOKVVct15wqnOO2KwJ6jJ6vSIIWqdKqgKhdW1bNXZUIR1su7gWFirN6jQa5TNF3AjqOurvuVaruAf1emXvotFc3gyhphOrAqEkKc717GzH/CwCQU2ilnIpLFTS6/38lKZyYOlOrIrrpdP2rbOlhJHIqRC7TcGlUQVCtYB1eLhSC81aIFQ3a9bpYKq7Q500hXVYUJ+wFgizs5VJMyJCcWWUdhCqKSvWtW/USVM4CAX1DWsHYVYWNGyoxIKrqyV2VBeUWkfNOqasBUJ1wakK6yJ2BHUZa3HdZIKzZyExUXmuPHHd2kGoHkYZjRYnrp+f4oYScSOo6+h0FqEvIwMiI5XnvbzKmiEu5yBUEXsdQX1A1QlAmYOCgyEsrKxOIFKMaxVCIBRcGr1eCVpZVjZuLi7KxKmeFkD5DkLrVuUFBWLSFNQv1FNkdaOm10Pz5ooDF2wb/KjiuslkW/ezIgehiB1BXaZ0/U5PT/D3V7ocX07kAEvsWC84hWtdUB9Q110uLkrK8KFDEBdnqQkF5dcgtH6/tQuqTx/FSSjmHEFdRxUItVplDxMWpjzv6VnWQWhdb119jxAIBfUVnc6yT1EbkrZqdWmdwGRSXhcCodMiBELBpVEDX5IsGzdQnrtUirFerwS4VqvUJRCBL6hPqOKgOmm6uSkx4OlZ8aSpugXVSVOtQWgtEAqhQ1DXsRYIQYmdVq0gKspWIFRd6qrTVnUJ6nSWrpJq7AgHoaA+oB7iarVKN8nQUOV338+v4vR868Y/bm6KsChqQQnqG9YOQp1OOZBq3dpS1xPKd0G5uV1aIBQI6jotWyop9WD7+2+tE5RXv1Nt5qOmHgtx3akQ316CS+Pjo5wga7WW1C+wWIfz821rDICyMVMnTbWrnqhBKKhv6PWWCa907JRX9Fo9jbYWCEUHcEF9Q40VNXb0eqULsZ+fbdxYpxiri031+vx8y8EWWMREETuCuozqINRqlZrRDRtC9+6KUFiRQFhYaIkLvV4RCK03aqKshaA+EBamlFBS9zqSpGR9lE6TLE/kEA5CQX3Gx8dysGttJFJjJz5eiQ3rvY5qoqhIJxBx43CEQCi4NM2bK5Om2rlYRXVB7d8PaWmWVC+12YL1QjU/X0yagvpHly4QEKD8vfSkWVgIOTm2XYxVt6CLixIvqvBR2kEoYkdQl9HplEMpa/et+nzpGoTWKcaqQKjTld2ogXBzCOo+qmtdPYT19FTqQbm7XzrFWI0dNzchcgjqJz4+EBRkacyjou511IwOdQ2mrs+szRAidgT1FesMDhU1do4ftxzaWu91XF1FpqETI1bMgspR3qRZVGTp0mV9MqCKHGrgl64toE6mAkFdxsurrMgBFoFw82aLa1BdbGq1yuuqg1DdyKkIgVBQ19Fo4JprLCKHOu+4uFhqC+7aZYkPNXY8PJTrdLqyi00QsSOo+3h7K05ba4EQlJi4lIPQy0t57OoqHISC+k152VKFhXD0KMTG2h5MWWdLlWeGUEvGCAT1gdI6gRo7BoPldWvjg3qYpXYKt16zWTc+ETgE8c0lqBylHYQ6ndJhMj8frr3WMmGqIoePj3JdRobyJaBu3gAaN7bUKxQI6jKquFdaXM/MtHQtVh2Eqriu1sDRaJTUMGvati3rjBII6iLqwrH0ojEpyXIwpTo5TCZLt1XVfWs9XwH07Fn2OYGgLuHpCR06wOHDltRHsAiEWVlw8KBSL8paIPT0tKzVSteCcneHZs0c8uMIBDVOedlS1maIggJboUOt25mfr7yu7n1AWb+FhNTc2AUCR6LuXVTUvY5er2RTeXjY7nUaNFDi58IF5bH1Pqlly5ofv8CGWicQSpJ0HfAxoAW+lGX5HQcPqX5Q+lQtIEBZaHp6KkFtMtkGfqNGynVBQWVP0cSJmqC+oAqE1rHj5QXnzyuLxwYNyjoIO3ZU/uzb13axCZZUMIGgrlP6NBqU3//z5xXBonFjZWGppn55eCjlMPz8lNeio23fWzqWBIK6ilZrcQ+CpS5nbKwSK2qKvtqkJDhYWZe5uSnva9jQ8l6NBpo2rfmfQSBwBKUFQr1ecUDl5Chrs4AAOH3aEkcNGigNTdRyMV5etp8n3LeC+kLpNZufnxIrAQHQubMipFs7CNVO4X36iKxCJ6RWKTWSJGmBOcBQIA7YKUnS77IsH3bsyOoBDRvabrC8vZWTAm9v5bHq5DhyxLYJiRA0BPUZtemI9alaeDicOKGI6BERSjH5rCxF+PD2tgjovr6OGbNA4Ay4uto6z0GZhw4dUgQLDw9lUXnunPJndLSyCAVo167mxysQOAulhQq1K+vZs9C/v7KW27VLEQxzc5UNXECA8nx4uMOGLRA4nIAARdiwflxQoOxlVOODVqvMQ6qoLpzpAoEyd1jv+YODlT/VuUjtVnzqlK1OIMRBp6S2/V/pDpyUZfm0LMtG4GfgJgePqX4QHV02tTE01CIaajSK40lN8RIIBEpcDBlie4rs42OpFQXg7690miwsFLEjEKj4+0O3brbPNWyobMr8/ZXHkZHQpo1yIi1iRyBQiIoqm6LVrJkiZPj6KvNR06aKW7CgQHF9CIetQKDsdawFQo1GiSd1zgHo2lWJGTHnCAQW2rUr21QxONhiJNLroVcvsdepJdQqByHQEDhv9TgO6GF9gSRJk4BJACNHjqy5kdVH2rSxFT78/JTOrWptNUGtQsRONVFeod0+fWyfDwiAfv1Ebc5aioidaqJ07Oj1Ss1b9fTZ1VVJ8QoOtj2RFtQaROxUA+XNOaGhEBhoeRwYqPwXHS2KwddCRNzUINHRSoaUip+f8l9hoaNGJLgKROzUIJ072+oEAQGKSCh0AqentjkIL4ssy/NkWe4qy3LXLl26OHo4dZvyuhFLkqgxWEsRsVODlLchU7t+C2odInZqkPJixMVF1HqqpYjYqUHKW5sJcbBWIuKmBpGk8ucdETu1EhE7NYjQCWottU0gvAA0snocUfycQCAQCAQCgUAgEAgEAoFAILgCaptAuBOIliSpiSRJOmAc8LuDxyQQCAQCgUAgEAgEAoFAIBDUWmqVx1OW5SJJkh4GVgNa4CtZlg85eFgCgUAgEAgEAoFAIBAIBAJBraVWCYQAsiyvBFY6ehwCgUAgEAgEAoFAIBAIBAJBXUCS63DXTEmSvkTpdCwQ1FciZFl+oKpvErEjqOdcUdyAiB1BvUfEjkBwZYj1mkBwZYjYEQiujHJjp04LhAKBQCAQCAQCgUAgEAgEAoHg0tS2JiUCgUAgEAgEAoFAIBAIBAKBwI4IgVAgEAgEAoFAIBAIBAKBQCCoxwiBUCAQCAQCgUAgEAgEAoFAIKjHCIFQIBAIBAKBQCAQCAQCgUAgqMcIgVAgEAgEAoFAIBAIBAKBQCCoxwiBUCAQCAQCgUAgEAgEAoFAIKjHCIFQIBAIBAKBQCAQCAQCgUAgqMcIgVAgEAgEAoFAIBAIBAKBQCCoxwiBUCAQCAQCgUAgEAgEAoFAIKjHCIFQIBAIBAKBQCAQCAQCgUAgqMcIgVAgEAgEAoFAIBAIBAKBQCCoxwiBUCAQCAQCgUAgEAgEAoFAIKjHCIFQIBAIBAKBQCAQCAQCgUAgqMfUOoFQkiQ/SZIWS5J0VJKkI5Ik9XL0mAQCgUAgEAgEAoFAIBAIBILaioujB3AFfAyskmV5tCRJOsDD0QMSCAQCgUAgEAgEAoFAIBAIaiuSLMuOHkOlkSTJF9gLNJUrMfAHHnhAjoiIqPZxCQTOymuvvTZfluUHqvo+ETuC+syVxg2I2BHUb0TsCARXhlivCQRXhogdgeDKqCh2aptA2BGYBxwGOgC7gUdlWTZYXTMJmAQwcuTILsuXL3fASAUC50CSpNdkWZ5WyWtF7AgEVC1uiq8XsSMQIGJHILhSxHpNILgyROwIBFdGRbFT22oQugCdgbmyLHcCDMBz1hfIsjxPluWusix37dKliyPGKLAjJpOJTp06MXLkSADuv/9+OnToQPv27Rk9ejQ5OTkOHmHdQcSOQHBliNgRCK4METsCQdURcSMQXBkidgSCy1PbBMI4IE6W5e3FjxejCIaCOsrHH39M69atSx5/+OGH7Nu3j/379xMZGcns2bMdODqBQCAQCAQCgUAgEAgEgtpPrRIIZVlOBM5LktSy+KlrUNKNBXWQuLg4/vjjDx54wJIa7+PjA4Asy+Tl5SFJkqOGJxAIBAKBQCAQCAQCgUBQJ6iNXYynAj8WdzA+Ddzr4PEIqonHHnuMGTNmkJ2dbfP8vffey8qVK4mJiWHmzJnlvnfevHnMmzcPADc3N4YMGVLt43U0cXFxfPnll44ehqCW46yxU1Qo8/fCNPxCXOk02BsZ0Gqv/oBAxI2TYDCATgeuro4eyRXjrLEjy7LNYdqZA3noPTSEN9Nf1eeK2BHYA2eNm+pExI7AHojYEQiuDBE7l6bWCYSyLO8Fujp6HILqZcWKFYSEhNClSxc2btxo89rXX3+NyWRi6tSp/PLLL9x7b1mNeNKkSUyaNAmAadOmMW3atBoYtWOpDz+joALS0kCjAT+/q/4oZ42dc4fzSFt9FgpAc0iHIdPEe+taXPXnOsvPV+85dgxCQqAWdxR0xtjZ+EsaC99L4oNNLXHzUJJGHup6BIBp37e+1FsvizP8fILajzPGTXkU5Jkx5pvx9r/6rZOz/oyC2oWzxs6CtxOIaOlGv1H+dv9sZ/kZBbUbZ42d6qQqP2OtSjEW1BD5+XDYsZnbW7du5ffff6dx48aMGzeO9evXc9ddd5W8rtVqGTduHL/++qsDRykQOAkXLkBioqNHUa0knjWW/D051ogh08SpfbkOHJHArsgymM2OHkWdY+vSDABS4oyXvlAgEJTBbJYpKpQBePfuMzw79ISDRyQQOD+bf81gwVt1e00qENRlhEAoKEteHqSkOHQIb7/9NnFxcZw9e5aff/6ZwYMH8/3333Py5ElASZn6/fffadWqlUPHKRA4BUYjFBY6ehTVSvK5sgLHwhlJDhiJoFqQZeU/gV3xDlDcTpkpRQCYiiz/xmaz+PcWCC7Frx8k8Uivo5jNss0hlUAguDy/zEgkPblur00FgrpIrUsxFtQATurkkGWZCRMmkJWVhSzLdOjQgblz5zp6WAKB4zEaoQ437DGZZA5szsbNU0O+Qflu0mgg7ng+xgIzOr0466r1OOm8U9vxCVSWeWkJyibNkGUqeS07zYRvkFgGCgQVseHndECJFRWzWUajqbvzrUBwNaiOW4C/F6ZTkGtm/LRwB45IIBBUFbGrEpTFyZwcAwcOZMWKFWg0GrZu3cqBAwc4ePAgP/74Y0lXY4GgXmM0Kv/VUc4dyiPueAGjnwgtea55Zw9kuXxnoaAW4mTzTnUhyzI7/szEWFAzYqinrxaABW8nkhJvxJBhETpS40XsCAQVsW+jpUHeyzecLPl7QZ44yBAIKqIg1zY+XHQSeQYTP7+bSH6uiB2B4FLITrIOFgKhoCyV2aiZzVBUVDPjqcNcOJnPZ0+er7HNoqCOUsdTjA2ZiqgR3tzSdbVFFw8AEk4XOGRMAjtjNtcLB+Gxnbl883I8v8+5WCP3s04pjjtWUBJLACkX6u53hkBwtfy7PKPk79auKNXFLhAIypJnMNk81rpI/L0wnU2L0tmwIM1BoxIInJ88g4kp3Y6yeUm6o4ciBEJBOVQm1SsuDo4erZnx1GF+/SCZ/X/ncGK3aLYguAqcQCA0m2XOHMyrls9WN2TuXhqCI1wBaNzWHUmCJOEgrDs4yclpdZJT7OBLT6qZeC0yWv5N05MKmfv4+ZLH37+WUGPjEAiqg11rsnjjttMknLH/QVG+wUxYY13Z53OEQCgQVETp+EhPLCSx+CA3L9tU3lsEAgGQnarEx6L3HF9fXQiEgrJUxkFoMin/Ca4KNf1rxWcXKTSKRafgCigqUgR9B6cYb/w5nffuOcuxnQa7f7YqELp5ahkxMRiAsCZ6fINdSI1XBA5Zlln1VQpxx/Ptfn9BDVBPHIRycWMQjbZmapgVGc0EhrviqpOIO5ZPntXmzVQks+h9xy9EBYIrZdH7icSfKuDkHvsfsuYblNgpTV6OWPsKag/JsUaSY2tufaimET/yaSTRnT3YvymHHX9mAWDMr/uHgIK6RUq8kazUmsmYNOYrsWPtWL8YZ2TjL2Wdt8d2GmzKYNgbIRAKylIZgVAUlL8iZFm2SfFy81RC8NzhfPZtzHHUsAS1GaMR3N0VobAGHFhms8wf8y6WcR4lnlVOiM8fs79Ap4oa7l4aelzvyyf/tiIgzJXAcFf2/53Ngc3ZxB0v4PdPL/LTmwl2v7+gBqjDNQgNWSZSi5uEqGJ3TfUUKjTK6Nw0+Ie5cnSHIt53HWap3VtTC1+BoDqQigMpPdH+Tth8gxk3Ly0TZzS0ef63j5M5uCUHs1nmoa5HWPtdqt3vLRDYi2mjTjFt1CmObLf/4W15qHOc3kODd4C21GtCXBfULl658RTPDTtRI/eyLl+xeUk6sUfzmfv4eRa+l1Rmrfbxg7F8/lRctY1FCISCslRW/Kujm7nqZPU3qTx9zXFS4o3EHc9n69KMktcMGWKjJrgCCgpArwcXl2qvC5qeXMjj/Y7xx7wUXrz+JKu+Sil5TeuibNRUR589yTeY0GjAVa/cw8VV+dMn0IW8HDNzH4/j1w8UJ5R/WFnHh6AWUIcPnd6580xJkwP1gEhTavVVXYWpTYUyLq4SAQ1cSU9Svh+unxTE4/Oi8PTVEnc8n5R4I7IsO01xbIGgMlgfuK79NpWMi/ade/JyTLh7aeg02LYZ3ql9eXz62HnSikXJ3z+tmXqiAkFVMZks3+mzpsSWeT0rrcims709sGR8aPAJdLF5TZ2DSpObbSJXpB8LnIzXbj1l89hcnAFiNsslf7cn1g2wFryVyI+vJ5CRrMRMTZdTEgKhoCyVdRCKzUSVkGWZP79QBJV1P6TxwQPnAEpqqqUnFbHg7QT2rFes+GazLDp+CS6P0Qg6Hbi6VmsdwqJCmYUzkigssMS99cZIdRSeP1o9DkJ3L22JW0RFtgqP48V1PGOP5PNQ1yPEnxLNS2oVdXhOUd2DeQZTSQ1C61SrzJQiXhxxsloKUxsLZFx0Eq17eJY8FxiuI7qzB09/0xiNRmLFZyl8+uh5PpxUdgMpqAXs2ePwEhPVhSHLxKL3E9m6NKNEQDh3OI99G7NZ8VlKSRMekwm7p8vnG8wlWR6lBX1XnWRJ25Rl0WhO4JQklxIVrEWNpHMFPHftCaaPPlX6bVeFmoLv5qnBP9T2wDYzpXyB8NmhJ3jmmuN2HYdAcDUYskw2otyqr1J4uPtRCo1m5j93gSf6H7P7PUs3wLp43oi2uBzN8rkXyxUlq+tg1+XylwjqHSLFuFooNMoUFheM/3thus3zbh4a1nyrpKls/jWDT3f5sObbVH6fc5H31rfA00db7mcKBDYCodEIHh52++jD/+bw/fR4ju/KrdAZuPiDJPZtzMZUXDPjwokCzGYZjcZ+OZR5OWbcvMqeZ416PITw5npWfmFxMqrjfOO20zw+L4rozvb79xBUI3VMIFQXbdauiD3rskscT9bP//xOIhkXi9i1Kot+o/ztOg7VQdhpiDd/zLtIzxv8Shy4IY10tO7pyY6VmSXX716bRZehPhV9nMAZSU1VnOS6sg01qpXjxyE6ulrz5Zd+klySafHjGwk89FEjPn3sfLnX6t3t53kwFckUFsi4eSprr/c3tsSYb+a5a5VUM/8wVy4WC4QmE7x1+xkkCW57NoxW3T0r/FyBoCbYtSaLuOP5ePnZ7h0KcpXD1oI8M6/dehqA7LTqcxCWzugwZJjIzzXz2RPnCWuiZ9yzYQAlQj8oc2f8qQIaNnez67gEgqpw8bytuL7xF2XffvZgPnvWK7X/Co1mXHX2m3dUB2H/Mf7kZpnYtTqr5LWTe3I5fzSfyNZuNrWkjfkyenf7z8HCQSgoSz13EKYmFLLvb/sX/jQWB35AqQkzK6WojFPwzME8fp+juLP+/T2j5PmUeCPvjj9D3AnRiEFQjMmkpBfrdHZ3EJ45kMe/v2dWKA5qNLD+pzRS4wvJuFiEm6eGgjxzmYn1ask3mEqcHNYEhesY+b/gksfe/raL4Q8nnRPNf2oLdezQ6dTePFLijcx//kLJcz9MT2D7H4oYl3qhkP/WZZEcaywpNH3iv1zWLyhbjPpqKDQqDsKgcB3vrW9RsiFTadPHC4AGTfUAzH/+guhsXNtwROwUFsKxY9XqWk9LLGTbikyb59b9WLbe36jHQgBLmQt7oK7J1HnHzUODT4ALT3wZRddhPhgyTSSetcxzybFGks4Z+eShWM4dybPbOASCK+GrFy6w5ptUlnyUbPO8mk58ap/9m/qoqHsdvbuGwAalBMIsE58/eZ7ju3LZtCidP+en8NNblrrRO1dlsnttNm+OO1Mt+zCBoLIc2GzbF0CtAbjuB8sc9PadZ+y6x1DF9RseDGbgbZbDYvUM7viuXNZ+l8ZTgyxu29wsk00pAXshBEJBWSqz2KzDAuHM+87y+ZNxNida9kBdcA6+I8BmIdtpiA8jJgbZXPvePWdL/r7ko2SWf3aRc0fyeOXGU5w7nM/nT1ZfYVJBLcNsVpQ61UFoR3yDyprMX/21Ke36KaKC9e+x3l3Do3MjActJm73IL04xvhxNO5R1C67+OpWE0wV8+2o8Zw/lcfaQ2Lw5JXVsTjm208ArN57iyDYDA8b606q7p43RKjWhkC+fvcA/xQdATdq5A7B4pn3TJIuMZlx1yo3LO+nueYMvLyxowoMfRZQ8V1PF7AV2whEdwA3FvyOm6qkblnC6gJn3n7VZh7Xv78WxnWWFjTa9vQiN0pFvMJOVWkR68tWLltYuKGuad/QgNEqHIdPE8V3liyxLP0ku93mBoLr5YOI5Zk+tuFREbpbye33yP9vfXXumKRbkmtG5SWg0Su1blU7XeAPYxPDyuRfZsiSj5PHXL8WXjO3g5hzWL0gThghBjZMSb+TPL1PKfW3/JotwmHjGWOYQ62qwFtebtHPn0c8i+WhLS95eHQ3Ab58ks3SW7fyy+ptUpvY4Sna6fWvQC4FQUBZ1orjUhFGLNnPmKirrGReVILPHItOawuKaU77BLjzwjtIVL6KFnvHTGjDyf8EMGFs2tUznJtGsoztbfk1nxWeWL6uM5CKbNuiCeowqEFZDk5KmHTz43/sR3PZMKKA0BQmN0nP/Ow0ZNM6/JGXew1vDSwubEhXjTq8bfdnyazrGfPttWPNyzOU6CFX6j/Gn4yDvktTJvqP8eGFBE0Iidaz8IoXXx55m+x+ZzJhwlhkTzrJ/kziZdjocIXLUAC26enDbM2E88mkks7a34rkfmnCblYtvzTep+AW74BdsK8afO5xnlw7DRcUOworQaCQiot0IbODK6CeUON+2PBOzWWkAsX9Ttmhe4uw4wkGYU7xJqgaBMDnWyOtjT5OeVISnr+VgqHOp1PfbXwjj1V+b0qCpHncvDXk5Jp4bdoIXR5y86jGo3VbdyyltoaZtJpy2rXOrHpiZRa8FgQM4/G8OJ/fkcvjfsgc8T3wRBShuo5R4IztXZdm8bs965/m5ZvQeStyoXYyH3RtIp2ts4/faCYHlvn/TYuWAeeeqTBbPTGLmfefsNjaBoDJcOF5+DfOoGDeunxTE5A8sB6qH/7HfgWq+wYyLq4SLq4QkSbTs6onOTVMmO8qaTYuUeDl3yL5CuqhBKCiLtUBYUW2Z6liQqkKHndnyWwbpUwsx5pnZ/kcmuVlmzh7KY9i9gXQc5F2m8YFKemIRQeH2q+lTYHUy4B+mhF5ML68SV8eYp0Jp2t6dr1+KL3nPlE8iSTxbwIK3EklLsAiWpiKZ+c9foONgb6I7e5RJWxbUI8xmJU5dXe0uEIY309NhoHfJ4xZdldpKOr2GTkN82PCzMjGNeSqsJJWk02Af/v09kzMH82jZ1T61mHIyimgYra/wdTVtcs4jysl5dGcPIqLdCI5wtRSSt+Lvhekc25lL9+Gi1prT4KhDp/R08PZWBHY7oooI1gc/Go1EZCs3GrXU4x2g5ctnlfTj254LIzRKV1LXJuWCkXfHn6VxW3ee+abxVY1DSTG+/LwqSRKD7wig0Ghm2eyLPNz9KK56icICGY0GPHy0BIa7cu+bDQlpVMO17gSXxhECoeogtPOck5VaxEeTLYLA8PuD0LooB7ft+nuVPD9nZyubtZubl9ZGUL/aOriqg1AVOqzx9LN8V4yf1oCmHTzIyzER3kzPgrcSObpDOHBrDWlpEBDg6FHYhTMH8ggKtzx+cn4UM+8/h6evFo/iOua5WSZeuVFZJ7Uf4IVWK7FnfTbZqSYykorwCXK56prnxjxzST1QjUYqiVXruGjb14ubp4bQ91Y/jmwzENZYT3RnDz55KJajOwxotZZGXtadXUXJGEF1IssySz5KZt2PSqmXpu3dOb3fknUkm+H6SUpZo6lzIln9VUq5e4wrxVpct6a0TnHXKw04/E8O//1lMTt8+th5pv3WzG7rMyEQCspSGQdhZV6vCjk5Sie+fv3s95nF5GaZyj1R/uKZC4RE6rh2QiC9b/Lj3fFnaBhtKYqblmhfB6E6yencJRo2d+PprxsT2dpyP41GouNgixhz27NhRHf2sDmtju7swR0vhvHarafZtzG7pHbVrO2t7DpWQS1CFdY1mmqtBzVgrO0iunlHDx76qBGbFqfTsrsltbdpByVV8sx++wiEsiyTk27CO+Dy05W6qfMJVK41We1d73qlAT9MV2rdHNlm4Mg2AxsWpBEy8qqHKLAHjhA5ZBl27oROnSA4+PLXV4HOQ324oXcoHQd5l3lNkiQ6X+NDqx4Z+AS60GGAcs1tz4Tyy4wkXrlJ6SqZEnf1C0+1SUllGXJ3IMd25HJ0h4HCApmeI33ZtiKTnAylA/PyuRe5783wCg/WBA6gDqUYb/41nW7hRXQe4s2E6eElTgqVsU+H4uFTtqO9u6eGo9stro/0pKIy9c+qgloEvrzSFp2u8ebRzyIJbOBKQANXGyEyuJGObSsyMWSZmPVQLAV5Zq65K4ALJwoYMTEIb3+x7XIajEbYsQOuu87RI7E7ka3daNbBg6e+isIvxBVN8a+xtdgR1FBHmz5e7FmfTcbFQj76XywNm+t58eemV3Xv0iKHGquqSOniKnH/20oWVVC4jn6jLILGqMdC+OnNBHre4MfP7ySWPJ+XY2LZnIsc3WaADlc1PIGgXLLSiljzTSrrf7LUgX7g3YbMmHCWjGRlM3Hbc5bsj9Y9PDm6XdlH2KsxY0GuGbdyBEIArdYy3Ua0cKPzEB8STp9l5OQgvns1gYI8M38vTCfpbAEuOoleN/qVrC2vBDFTCcqiCn9ms/IbWdE19lyQFhZaUlbsTMfB3gSmuZbbaCE51sgPryfQ+yY/zh3O59xhi0XX2rFnDwpyLQ5CsNScssZVp+GBdxqy8osUWnZTRJcGTfXc/kIYC95KpMNAb0Kj9Nz3VkO+esFS/H7m/eegsV2HK6gtyLJFIMytvsLT5dG2rxdt+3rZPOfhrTiN4k+Vb9GvKgV5Svfv0t34ysPdW7nGs/jabsN9OLrDwNNfN6ZJO3eCI3QU5Jn57PHzdTGbtXbjCAdhSorS/dXOLigAb38XBt52aWfKI3MibR67lRIjyqsBWlUKjTKu+sovXLVaiVGPh/DW7We465UG9L7Rj85DfDBkmTi1N5ctSzJITyzkwY8aXfXYBHbCUSnGOp3dY8fTV4ubh4abHwkpt2ZmRTFVusv9hRP5VykQVpxirNVKFR5+NW6rrOt++ziZ2KPKevKnNxWhI7y5nn6j/EXKvrNQR8tavLGieckhadP2yj6i0GhGq6VE/Lh2QiBD7g4oSS0+sVtZO144efXrNmOeXG5HcTXdeNDtARV2HI9o4cYz3zYBYOtvGZw/psTQkwOVpgx9bvHjhOihJagGPn3kPLFH83Hz0DDxvQiM+Wb8gl0Z/kAQC95KpPdNfjRpa7tvD4nUUVQo8/VL8dz/VsOrHkNeTvkNGQHeWdOCp69R4iAgzAU3Dw0vL1TE/KgYd75+6QIbrJrc7f87hymfNKJlN08ObM4u97D6UgiBUFAWR9QgNJuVhWZhoZIqaUfCGuuZ9k1z8nPN7P87m7++TyXueAF9R/mVFMf99LHzJdfr3CSM+XKJbbjQaCbzYhFBDa/Otqva5XUVTIwqnYf40HmIbepjv1H+dB3mUzKpdr3WB98gFz6cpKTiBIa7UrPSkMBpsG5SUg1Cx5UQ1ljHrtVZhDXWMWLi1TmzcooL73pdogaHyl2vNGDPumwaNlfSkXuO9KXzEEvcRHdWFssfbGqJLMOpvbksXH1VwxPYC0eIHInFDoVqarRQVVp28yC8mZ7mnT1ITyzk9L6rb6hTZKyagxAgItqNd9dGl7id1EOAyFZuJJwu4NTePL556QLUjcy82o26Fqvp2CkqAjc3u8fOwNsCmDatZZXfpzr9XPUSbp4aVnx2kfb9r9w9UVGTksvRqrsHzTt58M+yDAAmTA/HmGdmwduJLHgrkd8+SsbDR4tL1ysemsBeOCJuzGbYtg1atoTA8mvwXSlRbdwhHfxDXco4bF11Gu56JZxvX40nppcnN09VOn97+CgHSGo2EihZG1fjEM/PNZcrrPuHuDJtSTOCIiq3x3tsXiRZKUUc2JzDko+S6TTYmzteCOO11654aAJBuexdn0V4riJGN+vkQeselgOgwgJl717eQWtMb088fbTsXpPFsHsCiWjhVuaaqnCpbCnrerzWfwcICHNlyN2BnNpr28B0/nMX8A7QcjGukEc/sz2QvhyiSYmgLI4SCKFaHVBuHhq6D/fliS8b8/aqaK67z9I5+OAWi3sxppcXrXt6klDsgPr2lXheuenUVde+sO5OdCW4e2ptLMzRnT2Ys7MVU+dEcs/08Eu8U1CnsW5SUo0pxlfCis9TrtotkZ2ubEArk2LsE+DCgDH+JYtbSZLKjTedmwa9u4aYXl5lXhM4CEc4CI1GJW6cRCD0C3blpV+aMu7ZMJq0d8eQZbrqZj9FRvMlm5RURHmpkA2a6nnyy8bc8WIYh+xYmFtwFVhnfNQkJpPiIHSS2FEFicZt3Olzsx/xJwswm6/8+yT/EinGl0KSJMa/1qDkcffhPvS71VKHND/XXGFpb0ENYzbX/LxTWKjUvd21S/nTjrTp7cms7a0qFPd6XO/L9N+bce+bFqeTRiMR1lhPnFVThvKyrarCpdIkQyJ1lU7FdPfUEhql55o7A3j2u8bc/05DUdpCUC2oztlh9wYyfloDm9fUmn6N25bN+vMPcWXa0mZoNLBn3dU3P8xOK7qkGeKZbxtz18sNyo2DNr29GHR7AH4hytpt9BOhuOolLsYp8Zx4umruYCEQCspS2QWnPRek6j3zrt4xcTncPDT4BrkQEObK/96PKPN6s47uhDfTk3BaWWCqRUCvNuW44CoFwvKQJInWPTxLuucJ6iFqk5Jq6GJ8pQx/wCK+J527ujpq2WnKz3SpLl6COoCj3BzVkCZpD/xDFZeFWvumKpjNMhdO5rPqqxRMJqrsILwcfW/xt+niJ3Agla0ZXR33dSLXemhjxTXeY6QvXn4umM2WOoKXIjnWaHOItWtNFl+/dIG8HBMajZJRUlWCwnU8Pi+K+96yCBotuiju9deWNuP5n5pU+TMF1YAjxHWzGfR6aN0aTpyw60dLkoRWe+nf16BwXZkmJG362O4hzl5lN1RjnvmymVJVQZIkomLc7VLjTSAoj4iWbtz2TCg3TQkpczjatq8XLyxoUmFTQ08fLY3bunP436svk5addul6643buNP7Jr9yX3NxlRjzZChPfBlFj+t96XurH68ta87EGcqBQFXLBwiBUFAWdbKsYw7C8lBrYqhcd18gA8cFENHSjUKjzNmDFsEy5cLVn6qB0qREILAbag1CJ9qsNW3vwevLmwOw9JNkTEWX/64wZJl4/76zJJwu4NzhPC4WN2hQO1N6ieLudRtHOAidzAVljVpDyroza2XIM5j45uV43hx3hjXfpALQtp/9nbJXk74psCPq2qmmf4dVcd1JYqfLUG9mbW9F7xv9SmrQGjIuPbZzh/OYNuoUfy+0OLm+euECO1dlkZNhwt2rbDOUyhLd2YOu11o2lJM/bMRbfzYnOEKHh7c47HIK1NipSYHQZFLWa56eTpPxceNDIcza1opZ21rhqpds9j1VYcPPafy9MK3CTqyCOkYdqt/ZYYB3mUaM1kREu11yLoju4kHcsXyKCq98DWssMJOfa75qM0RQuI4Jr4Wj02tw89DQabAP0Z09iK+iQCh2XIKKcYRAWAMOQmuatHPnntfDCWroyqbFGVz/v2C0WokOA73x8NbYdNG62o6SxnyzkglqZzeHoJahLhDtlSrhpCnGgQ1c0Whg/6Ycdq3Oosf1vuVeV2g0c+FEAR9OPEehUeav71P5d3kmAJ/uak3yOSOuOgn/UDFd1WnUdK+avqeTOgjVw6ustKqN7YtnLnB0u5L+m59rpn1/L6Jal02NEdQRHOUgNJmc6lBKcU8pf1cbWuVkFBESWXHt6MSzypruxO7cMs1PkmONZRqfXA1uHpoK0y4FDqIyZojquKdWa9uS1EnQukg0jHYj7nhZB6HZLJMaX0hAA9cSl+KCtxPwC3Fl+P1Kxsii95NKrrdnppTACUlJgbNnoesliqkWFAtSen2NDMmRhDfTYzIp80Z4syv7ebPTlO8D9XDYnoycHIzWBb5bUvn3iAgWlKUytnt7p4OpIkcNC4SSJNF9uC9N23twz/TwkonPzUND655eNnU5SqdK7lyVyd+LKl9DpCBPRueuETU06juHDkFCgv0+z1ogdJLNmspDnyidTtMSywqXsixzbKeBBW8lMmPCWQqNyveOdfHdf5ZlcPZQPqGNK1+3RlCLudycYu/f71rgIMyuooPw+E7b2oD+YfZt+iVwMoSDsAzqHGLIvPTYslKU2JKKd0IJVjWaju/KrXKDEkEtwxEpxuoBsRPVvrXGP9SlJC6s2bEyk1dvPsUv71pME5t/zWD53IsUFcq8d+9Zm+vNJtGpu06jNhW9FOfOKSJiPaBBsSgYf+ryLj1Zlvlzfgorv7jI2UOK5pF0roAvnlEajFSmIWNVie7sUdLRvLIIS4agLI5KMXZ3t5w4OAENo/XsXgtdh/mQk27i8L8Gm+5eX78UDyhdVoeOD0TnZruYTLlgJDDclXyDUiTekFFUpvaHoB5SVGRfoUOtQehEbg6VmJ5eeHhryqRJ/v5pMrlZZjYtLiuw//VDWsnff3hdEVK7XVd+7Q9BHaIyDsKtW6FbN/Co2kLnkvfU6ZzKeavi5adFo4GsNMsm0myW2bIkg27X+ZQ0TzhzII/vX4sn8ayRd9dGl/mcACEQ1m0c4SC0rnvrhCKHxUFYdmw/vZXAkW0GXv+9eUkZi9wsZc27dFayzbUXTjjPelRQDTgixdjaQehk6zUA3yCXEge6NcmxSqwknCogK63I5poNC9I4c8Bi7ug4yJs+t/hV+1gFDsRsrlyfAnvOSwYDJCZCs2b2+0w7ERqlQ6OBCyfybUpLlIch08TyuRcBpZHjp7ta88P0BGKPKM5dZ1mzCYFQUDGXC2x7C4R6vVNt1CJaKu3KW3bzpKhQ5pd3E7l4vrBMysof81L4Y14KbXp7cs/rDckzmPhoUqyNa2rAWH9SLhQS2NA5Al/gQOztvlVrEGq1lkl7+3bo2dN+acxXgU+gi41AmHLByKqvUsu91jtAW2KzH3xHAGkJhezdkE2na4RAWKeprJPD3uK66iCsYed6ZdBoJDz9tCVNegC2rcjk53cSyUwpou8oP/xDXPnsyfMlMZNwuqBMyAdFiDmnTuMokUOdc5xQ5LiUg3DLkgwAUhMKOfmfUvM6Nd5IVmoRh7bm0OsG35ISF65X0P1bUIuobLZUUZFyAGsPVAehE6YYA/gEuZCXY8ZYYEan12AyyRzamlOyZstILmLJR8nsWJlZ8p7fPrEI6w9+GEG7fqI+bZ2nMiYhe+91cnLg/HmnFAhddRoaRrtxzqrBz/lj+Xz90gW6XefLsHsDKTLKmEwyb995xua9siyXNDEFCG1ccVmMmkQIhIKyVGbBWR0pxm5uygmBk9C2jxfPfteYyNZuJanGccfzCYnUIcsyrjoJN08NzTt5sGd9Nof+MbBsTjIHt+SU6Ty5bXkmeg8Nbfvav1i8oJZRXen5oDg6DAalPojBAF6O/33zDnApETCAkq7g5WF9Xc+RvjRoqseQaaqWmhwCJ6KyLih7O9edOMUYwCfAIq6nxBtZ8Zly6vznlyn8+WUKD89uZBMzH/0vtuTvgeGu3P58GC27e9bsoAU1i6M6sTqxyOHmqcFVJ5XETmZKEdv/yOTcIctBwJIPk0g8a6RBUz0Jpwv46/tUzGboe6t/iUD43A+i23CdpjJ7ndRUOH0aune33z21Wqd13/oGFTfHSikiqKGOr1+8YLNmS08q5Mi2st1ab3smlJVfpNCknah3Wy+ojIOwOjINnfAwV6VxWzd2rsrCZJLRaiUObc0h8YyRzb+mk5tlYt2PaeW+7+HuR23+mVx1zlHawjlGIXAuKrNZq47Ad3MDo7Hmi21fgqgYdyRJIqyJah9WhMK8HDOFRpmhEwK548UGJddvWZJRRhwcfn8QBXlmslKLCBIOQkF1CoSursopG0BWlv3ucRX4BFlq2pjNMv8sy8BVJxHRQm+Tct91mA+9brA0Mglo4IrWRRLiYH2gsiJHdcSOEwuEYU30nDmQR6HRzPznLlCQa/uzz374fLnvGz+tAS/81ISYXl4ldXUFdRRHOgidVOSQJInAhq5cPF+IqUhm5v1nWTormT3rLULHnvXZuLhKXHOX0pxELW0R3kzPfW815LWlzWjQtO4X16/XVGbeMZns71rXapX4sfc+yg6oAmHGReVnLn2gazYrB7m3PRvGlE8a0biNG9dPCmLA2ADeXdsCLz+xXqsXVGYtVh3rtcrUPnQQbfp4kW8w8/sc5SBXbYKVkVxUoTgY1kRn8xXQfbjzZEuJSBZUTE0KhLJsOVWzp53fTrjqNIRE6kq6e2UWCx6+QS42TRWsue+thsSfzKf9AC/+nJ8CQHAj57AOC6oJg0GZvPz8Kr6mOsR1Na/QxcVWIAwPt999rpDABq7sXpPFj28mkJZQSHKskfveakjXa3345/cMfpiewIiJQVw/KQhZhrHPhFFklPHwFvU66w2OdhA6YZokQMfB3uxem8WjvY8BMHFGQ7589kKZf4L+o/1t6nl2udbHaU6hBdWMox2ETho7IY10XDxvJPZIPikXCmkYrSesiZ6oGDd2r8ni3OF8GjTVlxEB9e6ay9aQEtQRKpstZe85Rz3QVePHifY7YU30aDSwZ102Tdu7o3WRaN7JnWM7c22ua9PHk6BwHW16Oz5LReAAHOEgVA+jcnPB1/fS1zqA9v296TzEm7XfpXLiv1zOHry02/HZ7xsT1dqduBP5HPnXQK8bfZ1KYHeekQicB0elGGs0ymbNaHSqCVOlZXdP/l6YztODj2PIKm5HXnza9tRXUbx/3zkAugz1odtwH9r394ZrfTCbLV+QMb1EuledJilJEeguJxBWl4NQp1OEQVdXyK44lbcmGTohkE2L0tn6WwagxEeXoUqNmt43+hHYwJXI1m5IkoQkgd5dQi+yVOoXjnQQuro6pQsKoP0AL4aOD2TX6kx63+RHp8E++AYn2bjUvQO0jJwchE+glvPHCmjX30uIg/UJ4SAsl+BGOvZvyinprvrwrMgSd1TSOSPnDucTGeNGaJQ4tK23VDZbqrrWa2qKvhPtdwLCXOl9sx8bFqQR1liHqUgmppcXx3bmEhqlI+mc4ooKChdxU6+prEBo79gBJc3YCQVCgF43+vHfX9kl4mB0Zw+6XufDuUP5/LMso+S6wHBXolorG52IaDciot0cMdxLIgRCQVkqO2mqf9qjEYK6UVMFQk/nE9L63OzH3wvTMWSZ0GggsrVbSb2Npu09GP1kKMnnjIx7LszmfRqNxG3PhGIqoqTzpKCOIsuX3zBVQ5OSzGIx0NfLCxISIDDQaQRCTx8tIZE6Yo/m0+dmP+54MaykEzgoTYAE9RxHOAjVGHTC7t8qrjoNtzwSwi2PhJQ899hnUSScLuCH6QkYskzcNCUELz8XRkwMduBIBQ6jso0W4uOhYUP73LO0g7CwUImj3FzlOb3jU3MbtbJsuPTumhJxEJSmcS6uEsMfCMLDW8vEGQ3ZtTqL1j3FXFSvcJQZQlu8D3BSgX3MU6Ec2prDgrcTASWWJkwPp3knDyQJiozOlRYtcACVTTG2d7YUKPOMk9Kmtxev/tqU4EY6DmzKoWELPUHhOvqNgtP7c0k8Y6TnSF/GT3N8dtflEAKhoCyV3ayp19hLILR2EDohES3ceOqrKPJyzLTs5olGq4h/KoNvD6jwvQPGVvyaoA5hNte8QGg288b77/PdggVs+P57YgoKwN8fkpPtF59XPUTlu6TzUB8bcVAgACwlJmqyK566USun0cLmzZsxGAxcd9119rmXHQmJ1BESqSMvx8R30xKIaOl8J8+CGqQyAmFhIezfXz0CYW4ubNsG/frB8ePg7e0UXSa7XedD804eAJiKbL9XIqLduO0Zy0Fup8E+dBos0orrHZUV1+25XlNrEILTNvlx1WnoOsyXtd+lEtjAlagYN2FuENjiCAehmp7vxI1KAEKjlAOyDgNtu3m/ssjx82JVEAKhoCxVcRBa2+WvBmuB0GCwnUSdiKbtPRw9BIEz46DOXqfOnMFkMrFx715at2rFz2vWMK5ZM6SCAqX5j4O59YlQFryZSOO2jh+LwAmRZeX7vzLiup1iJ+7cOUhNJaIcF8eaNWtITk52SoFQpedIP9r197Zp9COohzjSBeXmBp07w+HDyvM5OeDhHGskSZIICHOe1E2BE+LoGoRqzXUnZPgDQbTo6kHTDu64e4o5RlCKysRFdcw7Pj5O04CxrlPrCtVIkqSVJGmPJEkrHD2WOktlT9Ws/7xarAXCw4fh7FnIz7fPZwsENYUjUozNZs6dP0/37t1JyszEkJ/PHVOmsOf8eac5aWvZ1ZNpvzUTC01B+TjAQTjvyy+ZtXy5pZuk1edmJiWRcPasXe5TnQhxUFDiEndEzWiAsDBL1kdOTs3WQhQIrgYHOAgH3H032eq6zEkdhABuHhra9PYSazZB+TjIDEFAAGRmOl3377pIrRMIgUeBI44eRJ2mqjUI7YHaiVWnUz6zoAB27ICMDPt8vkBQEzgixViWOXvunCIQpqaSXlAAwIK//3YagVAguCSq4HC5BaUdF5wF+fkkpBd3/i21UctMTibxwgW73EcgqFZUN19lDnTtmZ5v3WhBkiAvj5/++ov1//xjn3sIBNWNAxyE/+zdS3xKivLAibuACwSXpLI1CO19MOXurtS7NRjs97mCcqlVAqEkSRHA9cCXjh5LnUZN96psirE9UBecDRpA48aKQJifr/wpENQWHOAgzDEYyMvLo23btiQlJZEWHY2vry8/rl6NQV2ICgTOjOqCkqQa6yhZWFhIQlqa8qBUmnFmZiaJFy/a5T4CQbUiy8rvr6MEQlAOdtPS+H3HDrbv22efewgE1Y0aDzU05xiNRoqKikhS5x0ndhAKBJfEken5fn7CPFQD1CqBEPgIeAYQOQzViSoQVmZStHeKsacnhIQoKStGo1JcWyCoLTigcO+5xEQiIyMJCwsjKSmJ9Px8OnTowIAePZj5+ed2u49AUG2oc86lDqbs7Fo3FhSQaC0QWs01mVlZJKalIYs0FoGz40AH4XvvvYfJZFIcHenpnE9JwSCcHYLaQg2nGKuxkZSaqjzhpF2MBYLLUsPiesk9hUBYY9QagVCSpJFAsizLuy9z3SRJknZJkrRr9+5LXiqoiKo4CO21gVLvCcppdE6O8pwQCGsMETt2oLIpxnYUHs4lJREVFUVoaKjiIExLw9/fn8cefJDf/vrLbvcRVIyInavE2kFY0YLSziKHsaDA4iB0c7OpeZuRlUVhURFpSUl2uZegYkTsXCVq/c4aFgiT0tN55plnSE5OBr0e0tKITUkhJzfXPvcQXBIRN3aghhv8qAJhcloae/fu5bXPPxcpxg5AxI4dcEQHcPUwTAiENUKtEQiBPsCNkiSdBX4GBkuS9EPpi2RZnifLcldZlrt26dKlpsdYN3BkijEoAqG6yBQCYY0hYscO1HQNQllm18mTREdHlwiE6enpBAQE0LxdO86IOmo1goidq0QVCC8376jX2gGj0UhqZiZGo7GMQJiZk4ObTkfi6dN2uZegYkTsXCVms0NSjPeeOAFAUlISuLpSlJ5OfGoqOcJBWCOIuLEDlRU57DTnlDgIU1LYunUrf23fLhyEDkDEjh1wRIqxqhP4+iqdjEVDrGql1giEsiw/L8tyhCzLjYFxwHpZlu9y8LDqJlU5kbZ3ijEoAqFKYaFNMdL8/Hy6d+9Ohw4daNOmDa+++qp97i8Q2IMaLtybkZbGrBUrePjhh/H29sZkMhEXF4e/vz8BERGYZZn02Fi73EsgqDYq4yCszIK0ChiLO68mJSVZBEJZhpwcMg0GWkRGkihiR+DsOMhBuPf4caA4fnQ6ElJTMZvNGISDUFBbUPcdlRE57LDXsU4xPnHiBGmZmUIgFNROHOEgVGsQuriAhwdkZ9vvswVlqDUCoaAGcUSKsdrFGJR6NqpYaDDAhg1KPUJAr9ezfv169u3bx969e1m1ahXbtm2zzxgEgqulhh2EWzZvpmOzZrRq1QpJkggNDeXo0aP4+/sjSRJNIiI4s3evXe4lEFQblXEQ2nnOKSyeU15//XUSs7MVgTAxEXnLFrJyc2nZrBkJwoErcBasHK42OKgG4Z6jR9Hr9SQmJnI+NZXY4qY+IsVYUGuoYTNEbnFsJKekcPLkSVIzMpQU47g4paySQFBbcKSDEBQXYWam/T5bUIZaKRDKsrxRluWRjh5HnaUyAqGKPVOMtVrLY51OcXVkZirjSEiAzEwkScLLywtycigsKKCwsBBJFRYFgpogJ6fiiakyp812nDRzsrMJ9PEpeRwWFsbBgwfx9/cHoEnjxpw5dswu9xIIKsPSpUuJj4+v2psq4yBUsaODUJIkvvjiC/45cEARYJKTyc3OxtXFhaiICC4IgVDgLGzeXL6I4IAuxnc9/jiL165lyJAhLFmyhBunTOF8VhaBAQFCIBTUHmpYXDcYDHi6u5NULBCmZWYiFxQoAmFaGn379iVVbWAiEDgzlRHOq6sGIYCXl012ocD+uDh6AAInRD1Vc5SDEBSBUKeDlBQOxcbSRpKUwqR9+2IymejSqRMn4+OZ8vDD9OjRo8zHzZs3j3nz5gHg5uZmnzE6OXFxcY4eQv0gLk5xtLZvX/Y1dTI0mZRNW3nYuei1p9Xvd7t27fjyyy8JCAgAigXCKqZJ1rfYEXFjXz7++GMefvhhbr311sq/Sf3+l6SK55TKdM2rAkajkdFDh3I8KYnE9HRFIMzMJFOjwc/Li0aNGnH88OEqfaaIHUG1YTQqdZe8vGyfr4wLys7p+ecuXOCvr75ie0ICb7/9Nu56PXFZWbRu2RKD2vinCtS3uAERO06BKq7XUL11g8FAk7Aw4hMTSUxKQpIkDBkZeLm6QmEh//33H7GxsQQGBlb6M0XsCBxCDTsIMzIyeGPWLN7/7DPlCU9PqOpBdClE7FwaIRAKyqI6CC8X+JV1GVYGa+swKOKglxfZsbF0ePRRDGvWoC8oAECr1bL388/JCAnhlqlTOXjwIG3btrX5uEmTJjFp0iT7jE0gsKagoOLmOTUsEOZkZ+Pl4VHyuFu3bnz55ZcWB2HTphyrYgq+iB3B1ZCZmUl2VWvDqPPJpeYde3cxNhr53913s/n8eRJTUxXxxc2NDF9ffL29aRQZybp166r0mSJ2BNVCUZHye5+VBeHhtq9VpkmJ9bV2IMdgwMfHh1CzmczMTHK0WuKB5s2asf0KXLcibgQOwQEOwuiGDdl46BAhISHIZjNpKSl4BQZSmJtLXl4eSXFx0KlTpT9TxI6gxjh7Fho2VMqA1XAH8NjYWD759VfenjMHV1AEwqt0EIrYuTS1MsVYUM1U1kF4uYm1KpQWCENCIDiYdIMBk8lEbHi4bQ2ewkL83NwYNGgQq1atss8YBILKkJ+viITlUVmnhr0chDk5Ng7Crl27ApQIhI0iI4lLSrLLvQSCynDFAuHlHITW19qBwsJCXHU6wsLCSExJAaCodWu2nj6Nb0gIjaKiOJ+YaJd7CQRXRWEh55KT+ajY7WCD9Xrtcu5be807eXl4enoSGhoKgMlk4uipUzRr1gxDRbUSBQJnoyrp+XZqUhLs48Px/ftZtGgRAQEBpKWmQkEBmcWpxUmiMZbAWTlzxlLmooablOTm5lJYVMTx06eVJzw8RIpxNSMEQkFZHOEgVD9PpVkzCA4ms/gL4ExsLGi1XLxwgYyMDCgsJC8zk7Vr19KqVSv7jEEgqASnT5/mUEV1/dR4uFSjErPZfjUIc3JsHIRt27ZFr9eXCIThkZHEF4sfpKUpThSBoBrJTE8nq6pphpVpUlINXYx1er0iECYlwTXXsP7gQSb973/4+vnRqEkTzicn07tTJ06IRj8CR2I0MuSVV3j8ww/JKV2HUE3Pr0yDHzsKhF5eXoSEhADg7u7OgQMHaNa8OTl5eXa5h0BQ7TiiBqFeT1BoKD169CAwKIi0jAz2njrFmVOnAEgSdW8FzkphoWUPoc47lyttYae9jtrg58CRI8oTrq5K7IoDqWqjRgRCSZIqX1BB4Hgq28W4OlOMAbRaMoq/FE6fPg1ubiT8/TeDevem/cMP023sWIYOHcrIkaJfjaDm+PaPP5j588/lv2idYlwR9q5BaCUQ6nQ6li5dSrNmzQAIb9SI+NRU5Z779kGxWCinpZG+Zo1dxiCox2Rl2TyUZZnMrCyyizuaVprKNCmxc91bY2EhOp2OBg0akJiYCG5uxMbG4uHhQYMGDQhu2JDs3Fz+3buXHUuW2Lcbn0BQBWSjkfMpKYT6+ZGUkFDqxSqk59vrYCo3F08vLxo0aICnpycdOnQgLi6O5i1aCIFQUHtwhECo05XsdQICAkjLz2faggV8v3w5QNn4FgichaIiy96mspmGdnQQAhy0rgtt1ahEvszcJssyRcIgUSXsJhBKktRbkqR3JEmaKUlSv+LnxkuSlAQkS5KUU/yaqHvo7FQm8OHyLsOqUJ5ACGQWdyz66aefeHTuXNr7+rLnm2/YP2sWB7//nldeecU+9xcIKoPZTFJqKsfi4sqPDzV2LiUQFn+OPSjtIAS47rrr0BbHTWhYGMmZmZiys5XUgOLU6DVLlnD9E0/YZQyCeorJBH//beNKzcvLo8hkIruiLt8VYe0grKkuxoWFFgdhcSpxbGwsTz31FJ9//jkanY6GxcXi958+raTXCAQ1QVoaWLlW87Ky0EgSTRo0UGqUWWPd4KcG6nfKslySYhweHs6BAwdKUo2jGjfGZDZTWF4JDjsWrBcIVN555x3yr9RF5IAUY0+93kYgTDUYSMvJ4cT58wAkiZIwghrk3LlzbNiw4fIXyrKy5rN2EFZmr2NHB6He1ZUDhw5ZnvT1hcxM4uPjadOmzSXfv3LlSu655x67jKW+YBeBUJKkW4BNwHjgJmC9JEmPA58DPwNTgG+Bh4HH7XFPQTXiDE1KiskIC0On07F582Y27dunXJedrVxbUR04gaCqyDIcPIjZZLr0Aq2ggKSsLI7HxytdJQG2bbNMkmazbQHfiu5lp7gxGAx4urtX+LpOp8Pf25uLakp0QQEUFLD8zz9JL+X+EgiqhJrqaLU5y8jIALjyGoSVSTG2o4PQ1dWV0NBQkpKSkGWZ2NhYoqKi0Ov1AESGhjKyb18OXLwIx4/b70BMILgU2dk27tzU5GQCfH0JDQggsXQKYmVix44CYUFBAVqNBtfiGGnSpAnBwcFotVqCgoLwdHPDkJXFhg0bbFwdHzz7LF++/fZV318gUDEajbz00kucudLDm5p2EKo1oyUJKHYQ5uWRnpvLifh43HQ6kqrqvhcIroI1a9Ywd+7cy1+oCoPWDsLLdQC3Y1mY3NxcOjdrVlYgzMjg1KlTHD16FKO6JyuHCxcukCDcuVXCXg7C51GEwIayLDcvfjwDmC7L8qOyLH8my/IU4E1ggp3uKbgSKuq+ak1VUozttGEyFxVx5uzZMs9nZmbSrl07JEkiSa1rlZurdDASAqHAHuTlKb9TZ86wdsUKRo8eXfG1+fkkZWaSkplJWkKC8vt/8aIlrtSOkperQWjHbpJenp6XvCY8KIhPP/uMO2bO5Nzp08hr1/LHli0iFUxwdRgMzFu1Ctnq9yiz2Dl4VU1KaqiLcWGxg9DNzQ0PDw/S09OJjY2lUaNGJdd8/vTTzHjhBfYfOqQUxS4WQAWCakVthJWWBunppF28SICfH2HBwSTGx9teqx6u1lDslBxKFbvUAYKDgwkJCUGj0eDl7k5SYiJDhgwh3mqsu/fs4cjx41d9f4FA5cyZM5hMJvbs2cMNN9xQ9Q+oYZGj9IFuQEAAaQYD6QYDZ5OTiY6MJKm4WUkJ589DVR35AkElyczMLEnfBZQyROXtX9Q9jrUZorLiup3ct+0bNyYxMZF///2Xvn378ueOHZCRwYUTJ5BlmfPFLtzySEtLK1mfCiqHvQTCVsA3suW4cD6gBTaXum4T0MRO9xSo5OZWfgLZtEkRRADS06G8jllV6WJsDzeH2czS7dvp3KULeVabzTNnzpCens7gwYP5448/uJiejrm4+QIeHsoX1eXszQJBRZw+rSy+du7EfPw4T86fz9H9+4mNjWX27Nl8/PHHlmv/+09xDOblkZieToCPD8ePHi1/0rycQKheZwdK1yAsj/DgYF7/6iv+OX6cHdu3cyo1lfSCAiEQCq6Ki7Gx/O/TT7lo5WjKLBbQsioSCC/lDrycC+pyn1FF1BRjUFxQR48eJTY2lsjIyJJrWrRoQctOnSgqKmJHfHxJDU+BoFrJy1MEwvPn4fhx0lJTCfDzIzQ4mKSEBNt6S1WpQWgnkcPL3d0m4yM4OJiwsDAAPN3c2LB2LWazmXTVDWU2ExsfT4qIH4EdOV4sOG/cuJEdO3ZU/QNqWuTIybERCIODg0nOzCQ9OxuT2Ux0kyaKEcI6Xg8fVg6hBQI7kpqayq233kpGRgYG627Ae/Yo2RKlUR2E6p+qBlBD7ttcgwFvDw9at27NuHHjuHDhAtv374eCAuKK6xKeu8QBVFpaGlkia6pK2Esg9AKs/+XVv+eWui4PcLPTPQUqsbGgdva5HPn5lpSwkyeh9Gk01HyKsSyz+J9/yM/P57fffit5etSoUaxYsYKgoCCGDx+Or68vqU2bKi+6uoJeL1yEgivn7FnFoZGby9n//uODZctYt2oVCfHx7Nq1iyNqTOXnw4ULcOwYpKSQlJ5Ov06dOHb0qCXN2HrSrGyKsR1ip1IOwtBQGgYGcv2wYSTGx7PlxAmuueYacvLzkc1m8vPzRfFeQflcImXjyMGDAJw5ebLkucy0NHw8PMhWF5zWC8+0NFi7FpN6KCXLSiql+vfLiRz27mJc3KQEYODAgaxbt464uDgbByFdu6IJC+OTTz7h/tdeKxEITSaTiBnB1WEy2caHNfn5SkxkZsLFi6QmJhIYEEBYSAgLli61dUvVcIpxjpomaSUQhoSElAiEXu7urF22DIC04tqeZGYSe/EiKenpV31/gUDl+O7dAOzcvr3izX9hIRw7xunTp1m5cqXta5WpQahSDQ7CsLAwzmdmklt8WNu8aVNSs7Mxq/NuUpIyB+eW3krD4cOH2bNnz1WPSVA/OXHiBKtXryYjJYVcNXZkmfMXLiCfOVOyp8nKylIOdkqnGKtmiBoS13NzcvBwc6Ndu3bEx8fzyCOPcOr0aYiIIK5YGDynllIqh/T0dOEgrCL27GJc3m+AqEhcE+TkQGrq5V1LhYVKMBfXISM5ufwW4ZUV/+wkEBrz81m5axdvv/02P/30U8nziYmJ7N27F19fXwBCQ0NJTE0FV1fe/PprzmZkiBbngisjK0vZnGVmQmEhh4rT21fv2EFhURG7d+7kolqvwmAAb29ISCDn1ClkoHObNhw7frysQFgZB6EdxXVDbi6elxEIGzdqxK2DBhHeqBGJ6elsOXyYQYMGoZEkjHl5TJ06lU8//fSqxyKoY8gyrF9f7uYE4MjRowCctSoNkZmWRkRgoCIQGgxKExOV2FgyZZnoXr04cOCAEnvbt1vuBTXexdi1WCAcPHgw33zzDX5+fnhYO3KLa0WNGTOGhIsXuXDqFKa8PIYMGcLs2bPtMg5B/SRl5062//wz8qlTyKUajxTm5BCflgZZWfyXlMSxw4cJCAggrEEDTpw9a1sn19p9W1MpxqUEwhtvvJGZM2cC4OXhwart29G5upJW7HwqSknhQlqaEAgFV0dKipLNUczx//6jUXAwBw4dIj8/v/waZBcuwKlTrFq1is8++8z2tco4CO2dYmw1v4SFhXHEStQIDArCXa8nRxUy0tIgKKjcOfi77/7P3nlGR1V1Yfi5yaT33kkICSQQSuihN0E6SFdQpAkKgoqABf0sgEgTRbAgIoiAKAgISJEO0juEGtJ7732+H3duMplMSNBA2n3Wygozc2dyEmbPOec97957Q+nfR0amgoSFhZGRkUFMWBgZUumUnBx6vPce54KDi95zS5cu5cMPPyzOlqoqB2FmJsaGhjRr1owePXrQunVrHjx4AD4+RGRm0tDDg5D798vsZiw7CB+fyhQI1wuCcE4QhHPAP6r7Nkr3qe5fV4k/T0YiLU10LcXHixOKpjiRny9+SZNnbq5oWbe2LhbYEhKKPwAqmmJcSTUIE+PjMdDTY+TIkfzzzz/Ex8eTkpJCfHw8OTk5WFpaAuJk+uDBAzKVStb98QcXg4KK06URU5Lv3bsn3njwoGL1FmXqJnFxYG8PKSl8tnMney5cQFdXl9z8fIwNDLgZGEisVM8iI4PYwkJo1IiYjAwcHR1p1KgRd+/fL44pzcK9ZQmEFam19hhUxEE4Z9YslixciKOLC1GJiZy6epVOnTphamREekoKhw8fFgUbGRl1MjLEz1Bti6r79wkMDsbQwIDgkJCiu1OSknC1tRUFwuRkMQ6kz+GsLOb/+isPIyO5e+uWuACVnFJPOU0SIDc/vyjFuEuXLsTExPD1119rvVYQBNq2bcv52FgWvfoq58+e5drVq5UyDpk6SGEh69et473Vq5n38ccsXLy4xMM7jxzhhS++AKWSiV9+yVd79mBta4uDyqWXrp5yKMXOU6xBqJlibGZmhq+vLwDnbt8mMyeH/h07kqhy3EaFhKBUKolPTmbXrl1FTo6yNnMyMlpJTBT3KiruhobSuUULClTrLW21b1MDA3nzu+8IDw4mQTNVtyq6GKsJhE5OTkRHR+Pq6gqAhY0NlqamJEup+JmZZJmaUqDl9woLC/v3zVlk6jzhqoPdoJAQMrOzxXVaRgbRSUmcffCgSCA8f/YsIWqOwlIOwvJ0gkra62RmZGBsZMSUKVNYv349DRo0EAVCPT3CMzLo2K4dX27YwIzp07U+Pyk2tuxDBBmtVJZA+BNwDrip9vUTcF7jvnPAhkr6mTIFBeLpWGYm1K8PUVGiI0OzUOf9++KXukCYkACOjsV1/G7fFidfqPjJwOO4oA4dKvParMxMjAwMcHZ2xsTEhF69evHpp58WpXCpOwgnT57MJ5s3ExoVRURSkrjBTE6G8+dZvXo1H3/8sfh3uHVLdEjK1H4yMx8tBoeHQ0YGV/btIy4qin379vHjzz/jPGgQBy5f5tu9e/lu/3569eyJIAi08/FBqVQSp1qI5icn49G/P7FGRkS7ueHg4ECjxo25ExRU+lStPNu9ejpYZWzWKuAg1Pf0RN/bG0cXF+5GRhIaFUXTpk0xNTLiTmAgQUFB3L55k0mTJnFLVctDRobkZH4/fZosbXXDgoIITE6me+fOBIeGip+5ubmiQOjoSFpmZnFDD9UhVGxkJBu3b2dkjx5EBAWJAqRSKbrZHydNspJEhTw1gdDc3Jz4+HiGDRtW5vVt27bl6127WLVrF9/Nm8fd69flxabM45OXB4cPc/jGDcJiYgi8f5+vfvmFMWPGiEXW8/O5FRLCnfBwghMTuXLtGjFJSdi4uFC/QQMaODqSrp6aXJH6nU84xVidbZ9+yo3ffqO+mxtJqjk0NDgYHy8v4lNSeOuttzh8+DAPHz6kUaNG/3k8MnWHwCtXWL5li7h/ycggNCaG9q1aFT1eyiFUWMjZixdZuXs3QTdukKDZxfRJdDF+RPZIZmYmxho1CHV0dIr2PhZOTqJAqNqLZSQk0HrECL7atq1UbIeFhZVw78vIPA7hDx4A8CA0lIysLLh1i8wrV0jPyuL83buQmYkyPZ3zZ84Q9uCBuMfR03t8B2El9SqQHISmpqY4OTnh5OREWloa8+bN4+7du3Tv25fk9HRuX7oE0dFihsqlS2IpKSBRNRfJLsKKUykCoVKpfPlxvirjZ9Yq8vPFhiGPS0yMGABGRuDiIgohglC68UhOjhgsUr2+nBxxgrWxAUPD4o55j9No4XEFwqysMj9IsjIyMFJt1Nq1a8fVq1c5f/580ePqDsL4+Hj2nDlDfn4+kZJAmJgIcXEE3bnD8aNHRXHQyalUQXn5tLoWkpcHp05BWQ44pRJu3SL99m3aDhqEm4cHI0aMYN+xYzg4OLD1n38Ij4tDqVQy/uWXadasGZ6urhjq6xOnisno0FCysrPZv38/S1aswNXVFW8/Px6Eh1MoOVgfRyCESksxTs/MxNTUtELXOjo5cfbuXRr7+KCrq4upkRH79u+nXZMmXL12jXXr1rFgwYL/PCaZ2kHQ9esM/+wz3vr445IPpKaSkpXFxStXGDRwIAfPnWP8hAlknztHSnIyrs7OYn3LxERxcRgTA3fv8v3OnYwcPhz/xo0JDw0tTpvKyqqYcF6JIkdBQQGFhYXoKhRF9xkaPro8ctu2bfn7xAl+/PlnuvXqxb2HD2nZsiVnpTRpGZmKkJBAnqEhJ2/cICw+npCoKCyMjdm/fz/Hjx+H7GwCIyOJio/n5xMnaN68OSB2PHVyc+PcsmVkSGs2qBIHoYmBQZkC4cBu3WjSti3WVlYkqoSO0JAQmjRuTFZuLkFBQdy5fp3z589z7949wjXSq2XqOFlZZR7u/338ON8eOMCfq1ZxdPNmIhISaNWyJQAKXV1SNbvM5+RwISiIwsJCTl28SIJmDbLHMUNUNHZOniyR2aSOpkCoq6uLnZ0dVlZW2NnZYWFhgaW5eZFA+MXmzSgFga0nT5aqtx7+8CEhwcEUqo0rMjKS2bNnV2ycMnWaMFXmR0pamuggzM0lLjgYHR0dzt28CXFxhPz+Ozn5+YRFR4t7LUPDx+9iXN41FURyEEoIgoCnpye7d+/G0NCQUWPGcOyPP4iJjRW1kPh4iItj+/ffU5ieTlJyMgpdXbkO4WNQmSnGMv+W+Hi4dq3866QaghKJiWBlJQp9xsZgYQHe3uJEon7CLKWJ5eaKE11aGuTmkqGjQ3hqqiiy5eaWFAQrEtQVnTTLcX5kZWZipKoF1adPHxo3bsylS5do2LAhUCwQOjg4YGFhwXVVGnFEXBxkZZEWGcnBCxcICgwkNDyc4JQUaNSolED44YcfygJIbSM4GKW1NSQkUJCURPPmzUmWFomxsaIzNieH0wcO0NbXl1OrV+Pt6sqO48eZOHEi+y9exNXRkcWLFzN48GCuXLmCi5MTTd3dSc3MJC8xUXQ7AbNnzyY/P59Vq1ZhYmeHjZkZoVJ6ZUFBscjxqNipRAdhXl4eBQUF6JcjbEg4OTmRX1BAsxYtALFW1IVLl+jdogUC0LF1aw789RcP1JpOyNRd1m/ZwvjnnuOXffuIU4noALfPnuWVNWsYOHAgnXv25EF0NDdjY3np/fcJCQ3F2toaAz09MuLiiBIEcbH24AHHb95kwKBBuDg7E3jnDk2few6lQlGcZlxe6n0lnkbn5eWhp1Ag6OpW+DnPPPMMx48fp2/fvjjVq0dGVhY3b97kxIkT/3k8MnWHgthYJi5dSosWLTDQ0yMwLIxTn3/O22+9JTYdSE/ndkQE+np6fLN7N7Nnz0ZXVxdra2swMMDU1JT07OziBj8VqUFYiXXU0tPTMX2Eg5BmzcDFBStra9G1kZHBvYcPaeDtjY25OYWFhdy+eZNrqjXvL7/8IseQTDGRkSCVClInN5fAkBDuRUSw9Lff+H7LFowNDanv5QVAQ2dnUtXSjwkPh5QULgYFoaOjQ3hsLImpqSUEtSKRoyLllCo670glnbSQlZ2NkcZ6zcnJCSsrK3x8fHBzc8PSzIzYsDDuHzrE9eBg5sydy53wcMLv3ePu3btER0ezevVqImJiMDE05ObNm0W/06VLl/jqq6/IlmuzyzyKqCjCw8OxV2XnZWRns//kSY5ev46ftzdxSUkEXbvGoZAQenXvTlZ2NjP+9z+O3rpV0kFYkfT8SjJDaIrrAF9//TXHjh0jIiICfX19vH19iZWMQ9nZXL93j2ELF3J6/34SU1KoZ28vC4SPQaUIhIIgvPo4X5XxM2sVWVlioxFtQRQbC+fOiY/9848oeEgkJECTJqA6YaZNGzHV2M5OrLEmkZ8vBkxGBpiYiM+ztGTzli28uXp1ca2pitZRk66p6GatHIEwOysLQ5WDcNKkSWzZsoW0tDR8fX2xs7PDxsYGgO7du/PFF19gYGCAi4sLkXFxkJ3NsRMnmLBqFQ+joujdpQsvLV/OmZs3xQ+u9HTxd4+L4+jRo3zyySeyLb82kZbG5KVLaT1rFsf27OHatWucPHlStJhfuybGj5sbRy5epEfHjrR66SV6+PlhamTEkGHDiIiLw9PLizlz5mCkmnzqubri5eqKtbk5Cfv2EaFQYGpqSmxsLG+//TYODg6gq4uPmxtnzpwRu2nn5xdv1MoTOcqrtVZBMjIyMDU2rrDIYWdnhyAING3WDBAFwgdBQTiYmeHj6sqQ5s2Z2q8fn3/00X8al0wtIC2N7ceOMe2tt+jo48Oc2bPp27s3ANPeeQc9ExMWL16Mr68v+/fv58TJk9wPD+f42bOM7NcPMyMjziQn03D4cApTUlDm5XHx/n1atWqFa716HDx1ihtBQaQbGj6eg7CSTqNzc3PRVyiKmpBUBD09PTp16gSAYGREQzc3bG1tOXfu3H8ej0zd4erZs/xz/Tp79+7FzcEBPYUCGycn/AWBKxcvUpiWxt2wMLp36UJkXBx9+/alSZMm2NnZgakp+t27owRyJTGksLB4TnlUinElxY62JiUlULkLrW1tSYyJgaNHCQwPp3HTpthZWmJrbs6de/e4evEiLb28mDt3LkuXLv3P45KpJaSklDA4BAYGioJXWhqBkZEIgsCxGzc4cvkyrvb22Lu6Mq1vX+rZ2ZGqnol19y5ERnLx7l26tW4NgEJHh1TNckoVETkeJ3akmrpayMzMLNkECzEzysrKin379uHn54elhQXb/viDEVOmcC86msaNGzPs2Wd5e84cGjVqxLJly5g5cybmJiY0cnGhTZs2/PjDD4DYMCw3N5cLFy5UbKwydZJhw4dz/cEDWjRoAIjZdd/u2MGafftwcnRk2qRJvLdxI4t+/JFZM2fiZmfH6s2bWb51a9U5CLXETteuXbG1tS26bevsTGJqKqu2buXIsWP8fOwYNlZWbNqyhaycHFxtbIpSjHft2kVmGQ34ZEQqy0G46jG+vqqkn1l7kNJv1d+s2dnifTduiGKfVAMgIkIMurw88XrVCQAg2n91dESBUN09l5srBml8vNiNtbAQLC2JiIggStUpDygd+BVJMa5I4EvXlFODUMLd3R0Ae3t77t69Ky6MEVO8xo8fj6+vL506dSIiJkasQxIWJqaJAr/u2sWLL79M//79CS4shKgo/v71VyZPnMj169fp2bMnBw8eLH/MMjWC/JQU/jhwAHsHB+YtXAjAsV9+EePF15cILy9mrF3L9wcP8kyvXqBQMHDUKPp37oyLiwtmZmY08PYu8ZovDB/OV2+/jb21NbG6ukQUFDBo0CBeeuklOnfuXHTduy+/zJsrV5JcUCDG5uM0WqiEwr0ZGRmYaBSLfxR6enrY2trSTBIITUwIDgvDwdmZT8aOZeygQcyYM4eff/utyC2WnZ3NhAkTOHr06H8aq0zNIv32bR7GxuLfpg3d27Vj/YYNnDlzhoyMDM7fvMnqNWuwt7dHR0eH3r17Y2hkxIFlyzjz/fc4OTtjZmXF93/9RXpmJmHx8YSmpqKnp4ezszMu9eqRo6rdF1NQUFogfJTIUUmn0Xl5eejr6VU4dkphaMig9u357O235RRjGe1oSTM8/dNPnL55k06dO2NqaoqbkxPujo4ILi60aNiQK1evEnz3LlYWFrRs25YWLVpgY2PDvn376Nixo/giZmaYGhuTIa3xcnKKRLmnIa5nZGRg+ogUYwlrW1uSUlJAqSQwIgJfX19sLS0Z2KYNtx884Oq1a7w1eDAerq6EaXOMydRJ/vzrL6atWFFU33nIkCF07NiR3MREAkND6dq1K8bGxkQlJuLq6IiuiQmrp03Dwty8WCAsKICMDNIiI4lNSuLZbt2wMDHB2dqahNu3ixudPAmRQ6ksu5xSdrZWgdDa2rrotqW5Odfv3uVGaCh3wsLw9vbm1dmz2fLXX5gYG/Pll18C4GZri4e9PZbGxmz+/ntQKgkODsbAwIBTp04BsHXrVu7LGSEyauSlp7P73DlMTU1p2qgRgiBgaWJCeGwsFx88wN7RkTfefptkYMCgQXTt1Qs3Gxs8nJw4ceWKmMJbkbq38Hg6QUEBnDlT5sPaHISaKIyNsTQx4fs//+TspUvsPn+eVcuXs2nvXixNTLAwNiYlKYmcnByef/552bleDpVVg1DnMb4qntNTV8jOFoNNvVPVyZMQFCQWBXVygsBAaNFCrDcYGSkWhbez075Is7EpKRDm5YkdW5OTRYEQwNKSyMhIohISiusfqguClWkdLqf+TVZWVgnbvbm5ORYWFtjZ2RWlF6vzzDPPMHjwYCKjokCpJEy12fT09MTCwoKJEyfSpUsXzoeHQ1QUly5cYP3evRgaGtK1SxcCAwOlH1z+2GWqNWcuXsTNzY1JkydzPjCQ7q1asfbPP3EbMoT3Vq1ixIgRIAgcXL+ezv37A9BtxAh+3rsXQRDw8fHB09OzxGsaOjpi06gRdq6uxFlbExERQePGjVm/fj06avHWfehQ/OrV4+SdO2LsVCTVqyJCSAVJT09/LIEQYMGCBbRr1w4QHYR5+fk4uLnRu0sXHHx8sPfzw8TAgD2//867s2bxv//9j6NHj7J06VLS0tLw9PQkPT39P41bppqTn8+lo0dp6ueHnp4ezwwYgKOVFYZ6emz84QdaenlhZm9f6mk2rq44CgLo6TFqzBh+3bYNK0tL7kREcDYhgVZ+fgC4eHgUPSc2I6P4MKyaOwhLYGDA/0aNYkLTpmSmp8vdJGVKEhUFx46VuCs1KYmeU6bw8S+/0KZtWwDcnJ1xd3YGb28c/f0x0NPj5x07aNuqFf369WPmzJkAODs7l5h7TExMSFe5oIbPn8+Zq1crlp5fWU1KKiIQ2tlxKyyMyRs2cDciAh8fH5wdHenWoQN6urq42NkxunNnzixZQphm8wiZ2k1OTnETK3Xy81m2ZQtrDx4kRlXaJSIigsSYGI4dOUJ6VhZDhw5lxIgR6Ojo4OLkJBojjI0xt7QsFghVa5R79+/j7eFBQ29vXG1ssLGwIDEysrhh45NoyFhYqPXavLw8lEoleqpyShI9e/YsWpMBWJibcy8igvyCAgyNjLCyssK/bVsWvv8+S19+mdzcXKaNHYubjQ0fvvIKp1ev5sKtW8TcukVwcDCDBw3ir7/+AuCrr75i//79FRu3TO1GqYT4eEJu3MDF1pbImBgc7e2xMDbG1MiIsNhY8gsKsHNxwdbenn3Hj7Ny5UoQBNwcHOjXti0t/fy4dv9+yX1MZWUaFhQUO3u1kJmVVa5AiCBgb2XF9ZAQYuPjiYiP59khQ+jZogXWpqZYmJiQmpTE8ePHycjIIPDmzfLHVYdRlH+JTKUTFCSe+Nrbi8JfZiZYWooCoaOjGEzZ2WLnYXd3UQjU1wdra2jcGC5eFJ+vqr1RCslJmJ0t/jsvDxo2FNMupYYGlpZERUURHR8vWvrh3zUpedQ16teqf9dA00EIUK9ePey1bEIBPv/8c5RKJZMmTSKtY0fCfv6Zli1bUr9+/aJrmjZtyvXgYEZ4eBB0/z75BQU0dXfH18iII4GBYgrD0aPQtasokLq5lf97yFQfCgogOZndZ88yYOBAuvXpgyAITJ06lbtRUQzs1YuxU6agq6vLypUrS2yu1Bk2bFhR2mARNjZgY4OdgwORMTFERETQs2fP0k92daVlQACXQ0MZIKUYVyTV63FqEBYUiKfdWmIhJSUFSzMzcQKuIJMnTy76t6mq+7G9qyv4+IC5OQgC9Z2d+enbbzl24QLuXl6sWbOG559/nl27dvHw4UP27NnDqFGjKvwzZWoY4eFciIykjWrT0rxXLx6eOcOwceP47PPPeWXwYO3Ps7AQP1ddXPjkk09o2rQpR/fv55dTp9hz6RLffPMNACY2NrjY2GBnZ0dsaqp4UGNiQlxKCmamphiWJdo9brH4R5Cbm4ueQvHvHYQqcVHIz2d0jx5s2LCBDz/8sOjhsLAwrK2ty+0wLlMLUSrFUjB5eaIQoa9PQkICG9eswdLUlOi4ONqqBEJ3b29xbQdgYUHv1q1ZtnkzH/3vf3Tq1Kn03KTC1NSUjORkCvPyOHj5Mn0CA8Vurk8pxdhWX7/c2LGysyMiIYG127bh5uaGqakp32zejGF6On3at8fezg4hNRU7IC0jQzwoLm8DKFMriLl6laBz59gfH0+nTp3o1asXAA+vX+dmWBgjevXi559/ZsqcOSgLCujepAnfbthAU19fXn31VQoLCzm2fz+uLi7i+7BnT8w3bChuUpKWRmpuLnfCw2no6Un3bt1YFB3Nqp07SYiPL274UVAgmjCeQopxliRwaMTN2LFjS9y2tLQkv6AAI0NDvFV12AHe+eQTUvfvJzgri3f79CG2a1e8unYFAwP6d+/O71u2EHz/PismTGDM559z+fJlgoKCZAehjCiYnzsHubkEXb+Op5sbgiBga2uLpYkJBvr63FE1i9K2737z+ecxy8vjkxMnCI2JKbnXqazYKUNYl9CWYqwNe2trboWEEBIbS05eHhYWFnz21lv8uHkzaXl57N+/n5DISOrZ23P76tXyx1WHqawahIKqvuB1QRDSBEG4JwjCYkEQyv/frIsEB4tdV//5B0JCuH7lChNXrizq9ojKGUdeHtjail9+fhw6dIjDV66IIqGJCTg4AJCfn0+eyo5/6dIlcnNzRSEwLEysw5afLzoHmzUTxY+2bcHQkMjISDIyMkiXUpsfp0nJ42zWyhMINRyEAPXr18fR0bHMl5TcX1euXCEsLIxPPvmEr7/+uujxpk2bcv3GDfD05GFcHMO7dqV3kyb4GhtzOzBQFEsLC8WFvOz+qFkUFIgO23/+Yff58wwcOBBra2tmvf463QYN4v3582keEMChQ4f4448/yhQHAebOnVucuqXBqFGjePfdd7l06RIuLi5ar2nRpQtX7tx5vBTjxxEI09LEQwQtJCUlYWlq+q9FDqn7sYObmyhAqmKwvpsbB06fJi45mWvXrtGlSxcGDhzI//73P+zt7PhVJfTI1C6k2iyZYWFsPXaM9u3biw8oFBg2bEgLHx8SEhOZMmGC9hdo0AA6dwZ9fXR0dBg9ejS+zZvz04EDvPPOOwwbNky8zsCAu998Q6sWLYhNSREFwpwcXnj7bbbu3ftURI7/7CAE8ZDO1pYJXbuyatUqli9fTv7Dh+xfvpxWLVvSt29f/vrrLwoLC7l8+TLnz5//z+OWqf4oHz7kjW+/ZfGePUW11F599VXe/OADvnz/fd56662iMg/TZs/mI6n+nrk5z/r5kZqZSecePR75M0xNTUlPTeXBrVukZmaKDtanlWJcXpMSFTZOTujq6LBi+XK6desmjtvcHIWpKQ7Gxgjp6eDigo6xMS42NmJnc5k6wdYdO+j51lt8/PHH/Pnnn0X3X79wgbZNmzJxzBg2b99OVGQkTlZWtGrXjt9PnaJf377o6uqip6eHt6cnLmqmAHNz86I5jLQ0urz7Lot++42GXl6Yu7oycMwYbKysSEhLEwVCab+jq1u57tsyBMLMzExxn1NO3FhaWQEwqE8fmjZtWuIxcwcHPhs1CnMjI7y8vMDFBWxtGf3882zZtYvgkBAaWloybcoUVq1aRVRUFPfk9H2ZqCiUtrYcTE3l9KVLNFBlcdjY2mJhYoKxau1vaGhYVNJLnWZNmlDf15d69esTIukTOjrlN2SEx8s0fJRAmJVVIYHQQVWT8HpICA42NgiCQKNWrfhs6lQszM3Z9OuvBLRpw8KxYwm8dYvQv/8uf2x1lMqqQTgVsb5gNPA1cAl4E7neYGmys4mKjiazbVtRrHNz48ClS6z77Teu3LsHd+5ASAjh2dlih2K12hSLFi1iwIAB/HX9OrRqhVJHh6ysLJYvX06zZs2IjIxkxIgRbN++XRQIHz4UxUZpAqxXT9zUqITFyMhIjIyMiEpPL3YMVtQ6DI+fYvwoB6GGQLh27VqGDh36yJcdMGAAO3fuJExVp8NB9XuBKBBeu3YN3N0JSkri41dfZfaIEdRv3Zro6GhCrlxhzIoV5IaFES51opWpvkRHF6WNkJiIUkeHf3JzSUxPp7WqAPXyL74ocfrl4OCAh1o64+Py3HPP8emnnxIcHFwqDVmiRYsWXLl5szjFuKICYUVrED7iVC0pKQkrM7N/LxCamKCvUGChFjcgivOpmZnUs7fH39cXo5AQBvTqxf3795k7ZAgHz54lOzWVd+bN4ytVPRyZms3+/fuxsLDghWefZeaiRTTw8mL06NElrhk1YABrpk7FpnFj7S8iva/VaOTjg56eHi+++GLxnbq6GFtZ4VCvnthxLjeX9Lg4jp45Q1RcXPkNfiqhBmGRQPhvHYQgCuq2tvi3bs2WVav48MMP+d/Chby2bBlLX3qJzk2bMnnyZN5++2369OnDwIED+X3Tpv88dpnqTeDff7PtxAm+2rGD86dOkZCQwLGjR7n7448MHzuWpUuXoq9yDVpaWhavW/T1eaZ/f/ybNaO51HiuDEzMzEhPTeXiuXPoKRRi47WnmWJcAaefpa0td+/cYdYbb/DTTz8VP2BkJGbNZGWBpyf4++NqZ0fYgwf/eWwy1ZhTp4qce8EhIXRu0YIZQ4dy5dIljh8/Tn5+PsH37uFRvz5dBw8mPCqKE4cP42xjQ2uVwDxgyJCil1u6Zg3PjRtXdNvc0lJ0EIaFEXrhAlfv3uV6SAgNGzUSP6sbNCgWCHNzITeXaGlNWZkNfsqoQZiZmSkKMeVkfEhlld57++0i130RJibi39DVVdwfqug9dCihMTFkZ2djb21Nz7Zt+f3339HX0+P+nTsVG7dM7SUjgxnLljF88mQWbNtWtJ9p6ufHwPbtMVEJb23atMHZ2bn0842NwcGBeu7uhMbHi/sdQXh0r4LH3etUkkBob2uLh7Mz96OiisRCHBygXj0szM1p17Ili2fPpnvz5py6dAnP3r1JDAsrf3x1kMpKMZ4GfKlUKmdJdwiC8CKwThCEV5VKZU4l/ZwaiVKpJDo6GicnJ7IiIuj49tvYfvEFhw4dwjw3l4sPH9KsWTMW7tmDq4UFfevX58UvvmDvwYP4qyaTvLw8zp07x9q1a5k1axanT59m+vTpnDlzBnd3d/T19Vm8eDFBQUH8/PPPBDduzLzOnVEqlQhqQbV27VrGjRuHQqEgPj4ef39/orOz8TY2Li0QVtbJQHlNSrQ4CLWdYmgyePBghg8fTkREBK6uriUe8/LyIikpiQOHDhEaHo5HgwaQl4eukxN+Hh4Mevttrj18SAM7O45cv86p0aPFVAOZ6kV+PigUvPH663Tt2pUhQ4eSEBxMj8mTSUhP5423336kQ/C/8tJLLzF69GgMNFLgJby9vYmOiyM1JQXzsuqoqdLMgMevQfiIgtf/WSA0NcXe0hJBIw3SQ9XZ7P3p08mKiIC0NHq7uKCnp0fvgAB+P3OGj+bMYeX69bTw9eWFsWMxNzdHoZArVtQogoPFQyozMxYtXMi6999n4Q8/kJCWxoPQUHQ1NjLNWrWimZOTuMGvIJ06deKnn34q0WkOgB49sL9xgw0bNnDn2DGGBwSQl5dHTELCUxE5ipqU/BcHobRJ09OjZ0oKrerXZ/nGjezasoVe/v6QlIRvQADjxo3j+++/xxX4cOFChr3wwn8ev8zTIzMsjPDLl2k4aFCpx0Jv3+bPnTuZKs1D6ekcv3mTXs88QwNzczo99xx2VlboA14dO4rlYh6BdZcuXKpA2pOpqSkZeXlcunCBnm3aFDsIyxM5KlISphwyKigQAniqyuAI6nGmpyeOx91dnBdtbXFzciJMVXMOYN++fcTGxvLSSy/95/HKVAMyMwm6dQtPX18wMCAkIoKJ06bRzdMTz2efpVfPnuzfvp2QkBDcmzRBYWnJ0G7dWP3VV3g5OtK8QwdemzyZ5v7+RS8puXAlzC0sSIuNhZAQ9sXH07t3bw4dOkRDX9+ia2ysrUlQOdYLc3LwGj+e4J49sZXWbNrEu8c9mHpEinGFHIQqU4irh0fpta1UIqphQ1EsVGFgaMid06cJPXQIwc2NFgoFmZmZdPTz45+bN8nPzy9an0VERJCVlSU6ENVITk7WWvNdppqTkQHXr3M6JISo+/cZNm9eqffx8VOn2H34MOfPn8evSRMaNGoEgIenJ59MmkSfd97B0tSUHTt2aH8P+PqCIFAvJEQUCHNyis0Q6nNKZqYoJsK/2+tAsdFCg4oKhI19fUGh4KuNG3GUDCOGhuDtzUtDhjCsVSuEq1dx8vPDWyWG7v/jD8bMmMHt27cxMDAoUa6sLlNZO2sv4A+N+7arXl+79aYWEx0dTYpU1w/4448/aNmyJcrCQpZ9/jmtmjfH0dGRLVu2gI0NF8LCWLt2LRcvX2bFunWsP3SI6IQEzp07V/QaV69excPDgzFjxtCuXTtcXFxQKBSYmppy8uRJ3nvvPTZt2oSrqyt79+7lnSVL2HjkCM8tXlxC+JozZw63bt0iNjYWKysr3NzciAYxjVldIHyUSPhvTgbg0U1KyhBgHoW/vz/+/v5YWlqWql2jUCjYuXMno0ePxtraGqP69cHDA2xs+HX2bBo0aEC3zp35eu9eLj14QJ7q/ysqKoqCSlhEy1QCDx/CmTPkpKezbs8e9vz1F5mnT7N69WpaNGtGWFgYc+fOfeLDKEscBNDV1cXTw4OHYWHaU4wLC0Hdwl7ZDsL/mGLsoBI41KnfpAlWFhZMmjeP17/4Alq2xDwvj7ObNtHE359+PXvy2bffsumTT7hx9y5+fn54eHiwevVqHsgukOpPRoZYd/X2bbh3jzN79hBy+zbj2rVj7Ycf8tXs2VipuROKcHUVG2U9BqampowZM6b0A4KAvb09ly5dYtPhwyzdvp327dsTm5hY/H7PyhLr8Eo87kYtL0+sw6uFSnEQ1qsnlu5wcICCAnq1bo2eri5dnn1WrCmcnMyIESOYMWMG48aNo5u7O7cePiRZKqQvUyPYvm0br378cck7L18mf/dupowdyyeff86ct98W709I4PidO3Tp0oXpU6aw/vXXqWdjQ5emTcUU/ErCxMSE9IICAm/fpn+3bqKD8CmlGKenp2NagY3aI2nYUPxSUc/VlQeqVMiMjAyee+453n//fY4fP84ff/xB+5Yt2blx43/7mTJVRnZUFM1ef51AlfgdEhmJu7c39u3aYWFkRH5BAef++ovg8HA8VOJFz169uBQYiLOjI4aGhqz67ruSQrMG5tbWpGZnQ7t27D16lJdeeom1a9bQIiCg6BpnJyfCU1IgN5eo0FAysrPFFNzKjJ0y1mxFDsIKpBgbGxhgqZHZAYgCoa5usQijhoGnJ95jxoCtLYbp6fj7+9PYxQV7CwtuqP7uFy5cwM/Pj+HDh6NUG+O1a9dKCa4yNYOwmzeZuXAhWzdt4vNNm8SDXxWFhYUEBgay/e+/mTZlCg0bNmTNmjV07ttXvMDAAAwMMDE2xtrcHBsbm1IHw4D4nhUE6tWrJwqEKSnFDkL1uDh1qnjN9bjllB5hJCosLCQ3Lw/DCsw7U+fPZ8l33wHgoFFP0dHJCS9bW3B2RvDy4tb33zNr4kT27NsHwLx581izZg0ffPABly5dKn/MtZzKEggNgUyN+6QWsXWu6vCUKVOYMGYMhWlp3L9/nzWrVhEdHc21/fv5ats2Pl6yhJdeeolvv/2WZ/v2JTI6Gn9/f3bs2MGSxYvZ8c8/ACVqFh09epROnTohCALr16/n+PHj/PTTT4wfP57WrVvTs2dPEhIS6Nq1K3/++Sfdunbl8z//5F50dJEAkJubS1JSEmFhYURERODs7IyjoyNR+fliDTL1Tqzw6BNnSeSojBTjrCwMNRyEFUEQBLZt28aZMlqjd+7cmT179vDqq6+KqdqOjqBQUL9rV7bv2kX/QYNITk9HX0+PbVu30r59exo1akTjxo3l9udVTX4+3LsHqan8tW0bRgYGHL5wAfdJk/ho82bmvvfeIxeLT5P6Hh4ERUQUx466+FdQUFyfEEoKhP+xLkeRg/AxmpSoY2pmhr1aCQOJNgEBfPC//yHo6Yknb7q6UK8e/kZGCJaWjJ0wgRVvvMHQmTPp0qQJ/Xv1Ysdvv3H48GHat2/PF198QURExL8ak8wTpqBALFZ9+rS4yYiNZfGCBcx+4w0UAQF0ff55Xpg+Xftz9fWLHQyVgJRa2drXl6CYGN555x1i1R2E6eliaQGJx92oRUSInyFayM3NRU8qvfFfMTKCPn0Y+cILvP/ii+gbGorCYWYmBrq6fPnllxjExWFYWEhA48a8+PzzYhkQmRrB7du3iYyLK74jN5eEe/dwmzKFfAsLLqxZw7p163jv3Xc5tm8ff1+4QPfu3bGqX58xY8eyefNmPlq0qHLeaypMTU3JyM/nfnAwndu3JykpiaycnKdTgzAjoygl7V+j3pwFGNS3L5t+/52CggLu3r2Lt7c3CxcuZMyYMUyZMoX61tacPHr0v/1MmSrj2IEDZGRnE3j9Oty5Q0hsLO4NGoCBAS8MG8Yb06Zx/vp1glNScFe5d7r06QOAk5NThX6Gtbs712NiCI+J4ejRo/Tp04eXp0wpsbfwbNmSh4mJoKdHqOrw6f79+5WXnv+IvU5FHYT1PDzo06oVgraMDDMz6NSp7M8SMzNxn5OaSu/OnfH39GT2iy/Sq1cvHj58yHvvvcdnn31GQUFBUXfjM2fO8Ofu3YSFhRXXcJSpnkRFib0F1NizZw9r9u3j9P37nL99m3i1Wq7HDh6kdcuWHLx4kU6qNP2JkyYV1/e3toZWrTAxNsamAu5RNzc3wuPi2Ltrl3YHYUFB8e1/s9dR/65Geno6xkZGCBXc6xgYGmJhZoajZvNRqdlr8+Zgbo7wzDP0GzOGv06dIiMjg4MHD3Lv3j2+/fZbxo8fL/ZzqMNUZk7YMEEQWqvd1gGUwAhBENqr3a9UKpVrKvHnVitSU1M5evQoFoaG2Lm5YWJujqFSybCOHZkyaxYtW7XCt0kT3OvXZ/z48fTu3ZvJkyejUCho1qwZDg4OvD13Lm2aN+fMmTPcvHkTHx8ffv75Z5YtWwaIwlibNm0AmDFjBmPGjMHGxgYvLy/8/Pzo168fJ0+eZNGiRVhbWBQJhHGqRW5oaCh//fUXPXv2xNramsjIyJJioDT5SMGvmXr7uCcD5QiE2dnZ2P3Lbo86OjqPtAMHBAQQoHaCCIiLU9Vjjo6O9GrdmnGvv84XX3zBa6+9xp9//sngwYP57ptv2Przz2z9/Xd09PRE14naolbmyRF34QJvrlzJP1evkpiaypqPP2bSu+/yTOfOfP7FF3ippY1UNfU9PHgoCYSaDkJNYbCSU4wb2tj8axfUMz164GluXup+KysrZs2aVfJONzdRbLG0xN3Dg1nLlwPw8/z5mBQUoGdqym+//ca1a9f44IMP+Prrr7lx48Yj3ZcyTx9leDiz167l2NWrODo64uLqyrXoaDa9/rpWV8KTxNXVFVdXV7YuX05UUBAm7u7ExMeXdN+qv/cf10Go+Xw1KsVBqI6uLg27duXtli3F2zo64kL0n3/EOmt37kDz5owdMoT9hw7x2iuvYGFhob07uky14s6DB0QmJIgN5JKTwcWFH44fp3fv3mJtvVu3eKl7d37fsIGViYl06tSpeE3SogXuT2BMpqampObmEhwdjVeTJjRq1IijFy7Qt3t37U+QRI5KqN+ZkZHx3x2EGrQbOhTr+fPZvW4dWaamNGrUiLFjx9K4cWPMjI25+tNPbFbLqJGpnmRkZDBx4kQ2btyIQqEgPy8PPX199hw8iImREXeuXiXDyYn07OyietGff/cd9+/fp3v37mRlZRXVjbb39MTHzQ3nMhrEadK1WzeGDB1K8+bNadKkCTY2NqWuqe/ry8PQUNDXJ+TaNUAlEDo7V07t20dkS1XUQWhfrx7b1RoulkLLmq0Eurrg6cknAweKt9u0ISosjBHDhpGYkMDLHh6YTJ3KZ599Rm5uLoMHD8bIyAgdHR3u3b1Lq9atH/36Mk+PwkKu//QTOo0asWzJEl4ZOJC01FR6zZqFUqnk+d69iU5IIC8/nwuBgXRs144DR4/yvGoeOHrgAJnZ2dyPiqK1SjMogSCAoSHGxsaiVlAORkZG9OvRg+ffeYcz69fjoymcq7tnK1EnSExMFMf3GOs1OwcHsQGjOo6OYlkdCV1d3Bo1wsXWlk8//BATExPOnTtHVlYWnp6ezH7rLb78SmylcfbsWQRBoG3bthUeQ02nMgXCt8u4XzMHUAnUSoFww4YNTJ8+nS4dO7Jq9GgUOjq4+vlBXh4/nD3Lm+++ywVVxy5jY2OCgoKwtbUt4YRycHDAyd6esWPH8uGCBfTv379ImOquZfGnp6dXdMI2adKkog1Hy5YtEQSB1IwMLty7h6GRUVGn4wsXLrBr1y7u3r3LwYMH2bFjR7FAmJdXHIRlnZpV4skAQFZ2NkZaJvMnTYcOHTh79ixHf/+dwuxsZgweDNevM6hrV0aMGMEL48ZhrK/PsO7dGTVxIh8sWsTIkSP59NNPn/pY6wxKJfkHDtBvxgw69OrFn6++ilt+Pib9+nEtKIgR48ZVK3EQwNPbm5O//86nixfz/uDBpVOMpe/SIrOSCvcmJSVhWa/evxY5HJs1w9HPr2IXm5iI6aUai1PLhg3FBkcPHoCLC81sbflj40YGDBlCjx49+Oqrr2gpiSYyVYtSyffff8+x69dZtGIFKSkpbNu2je07dlSotktl07BhQ27duoVZbCz1bW2JsrcnNj6+5CZL/b3/b1K9yrg2Ly/vv3cx1kShKFE4nrZt4epV8cvHBxwdeXHmTF6cMIGd333HjNde48q1a/KhUzXnTnAwKRkZrPn+e7LS05nauzdfb9/Otj/+EC/w9GT5N9+w3MaGgYMHM2fOnCc+JhMTE+4GB2NpaoqJpycLFixg5owZ9OncWUwLCg0Va99KdcYqM8W4MhyEGgi6unz4wQfMnjuX5154AZ/69RGAVq1aQXw8aU5OPAgN5fnRo2nbvj0zZszg4cOHKBQKdHR0qFevXqWOR+bfcfLPP9m6dStjn3+eXRs3khAfz6/79rHjxAkmjxvH7Xv3CDEwwM3NrUR9vQYNGuDg4MCtW7eK648LAl++/TbNyhK9NdDV1WXx4sWPNA3Uq1ePyMhI8nR1CQ0Oxs7aWhQIu3Ur30Go2j89knIchMYGBuWv1/T1H7uURymcneHWLVEQMTZm1htvcGTCBP587z30PT0ZZWDA+0uWMHHiRDZs2MAH776Lr4MDd69flwXC6kJ+PuF379JswgSe796dP06fRsjI4FpwML1ee43QkBC2HDoEwHODBnHu0iXGv/QSO7dsoVtkJE5OThw9cYLx/fvzIDb2kVl6JiYmWFew/uT2X3/lvYkT+ebQIb7o27ekg1B9zfVvyimpf1cjKSkJa3Pzx9rr2NvbFzslJczMtF47oFs3Fi5bxo8//sjLL79M27ZtWb9mDU2bNWPM8OEEdO3K//73Pzw8PGSB8HFRKpVPrktANUepVBIVFYWpqSkfffQRy5cvp5uXFx6WlmJB6thYaN6ccS1a0OXZZ/FWudeg7EYc06ZPp+/gwUx/8010dHT4559/yMrKKrcZg3o9ts6dOzN58mT27t3Le+vXY2BoKKbaAr///jvdunXD1taWBg0aEBQUVCwQhoaSb2fHzatXaV5eDcLKbFLyGIXvKwtBVVNh3MyZjBswABISxHSxs2eZPmkSZ44eZdvq1ew6dIhpM2cS0KkTGzdupF+/fnTo0OGpj7c2ExgYiDI9ncYeHuw+fx49Gxu++PprhKwssfCtnh4LqmnH3Pqensw6eZLd584xp39/9LUJhNpO1SoaO49KMTYx+dcpxsDjiYuap3EAqnpBKBRi2qpqQbBp1iw+/P571ixezPdbt/778cn8Z6KDgjBQKHj3tdfYe+4cW375hQDVQdLw4cOrdGxmZmbihsjeHltDQxKSkijMzxdFDqnkhYTk5JD+XZ649whxPTc3F31d3cpzEGpDR0d0q0dGihs2EBeoZmYMGj2aH0+cYObMmaxevbralEuQKUlhQQH3IyKwt7Tky61bCU9M5Nvdu+nYvn3xRsHQEEG1+fpTdQD8pDE1NeV4SIh4WCYIDBgwgBfGjCE1NRVLEOtAqadHVbcUYy30Hz2aVV98wZdffsk3U6fCiRPQsSM8eECD1q25GxbGw6gobl65woYNG7h9+zZ5eXkMGjSI33//vdLHI1M+P/74I0FBQXzyySeQn8/h337Dw9GRyRMnYmVsTHxqKt99+il21tYMHz6cF198kRMzZjBUY94RBIHjx48XuXQknpk+/bEPcRYtWlTmY3p6ejg6OhKWlkZISAg9AgJEgbAiDX5yKtBr8xECYWZmplhr/UnOORJGRuJhripOHdq25czOnWIpKTMz9O7fZ/uvv2Jtb4+Hhwdjvbz44MsvuXvz5pMfm4xW4uPjOXHiBK1atUKpVLL4jTeIS05GEAR+OXIEQRD4/Z9/yMzO5vY//xAYGkpnPz96tmhBh3HjsNiyhcHDh/PW7Nn81agR78+bx6WbN9n900/oaFu7q2FsaUnpYkNlYGrK+OnT6Tp6NCuWL0eQBEJpr6IpED6u+/ZRDsLH2Ou8//77tK6g2D1+3DhMjYwYP3o07777Lr6urliGhPDpuHFMnjyZTz//nEOHDtFNLRuxqnSLp4ncdvI/UFBQQMeOHbl79y6ZmZmMHDmSSRMnwrFj4OQkFi9X2eP1oYQ4+Cjmz59f4napFNkK4ODgwLfffkvbtm05cfIkeXl5dO3aVSwyGhpa9Jqenp5iYwFdXbFWWnAwJ5RKps+ezc3Vq4tPBx4+FIvUSx3o4PEdhGU1KcnOrtJAE6SNnISODk1TUrjyzTcI/v7M7tqVwV5e2LZty89//80777xDp06d+OijjygsLOT06dOkpqbSu3fvf1VLsU6TkUHa2bP0e/55UjMzcbC0JLOggE8XLxYXisbGTz318XHx9BT7MOkKApfv3aOdv792ByH8OwdhGdclJyf/pyYllYrk4LC3h6QkLJydmWpvT59Bg1AWFiLo6JCQkKA17UfmyfHNypVMf/NNGru7Y2ZkxJCAgCJxsNqgKpStB1iYm5OQlIQdaE8xVl9wlrdxLCfFWK+yHYTaMDWF3r1LlekQXF3ZMH063T75hMmTJ7NmzRr0NEt5yDw9kpMhPr7YcQckJSTQ0NsbK1NTPF1cOHntGlfOn+fs6dMMr+JO1CYmJly5epURI0YU3WdhZkaKJBBqvvcLC0UhvqIpxnFx4ryrpfRLRmYmppVYi1RCEARWLFxI09698fH3F3/+kSNgaoplQABG+vq0adSIH958k/l79/L333+TmJhIu3btKCwsLPcAXaby2b9nDxcuXuSTjz4iLzSUv65cYdWiRdw4epSp77/P2l9+YcZHH/HFu+/i07w5QdHRLFu8mDe1uGyNjY1LZ0k9gc/n+vXrExgbS3BMDEOGD2fOZ5+hFASE8hyE/1HkqLCDsLLw9BRrSEPxYZWEtTUtXV3FZlpXryIkJdGwWTP+On366YxNphSHDx7k448/ple3bgiCwMFz57gfEcFr48bx/dat9O7ViwOHDjHjpZf4evVqLB0d6dauHR9+8w0oFDzTuzcAAX5+6BQUMOf995k1bBjmLi7l1o5u17MnWVlZj7ymCEHAq3NnCgoKCI+Kwq0y9zrq39VITEwU660/Ruz0lRqxVADvgADeyc6GM2fwdnHB18oKCgoYN3cueQUFvPfWW/h7eXH/wQNGjRrF1KlTGTJkCGfPnsXHxwelUkl+fn6tW8M9UYFQEARjYCLgA8QAPymVypAn+TOfBrGxsfz4448kJCSgLCggISQEzp1DMDUVO5aamIjiYDXAycmJ8+fP0717d7777jtatWpFaGhokQPOxsaGwsJCElNSsM7JAT09HkZGEh4eXrIuYXCwmD5laVl24BcUaFf4K5JiXJ2UeC8vOH1adK+pbM3ebdtCdDQvd+zIpi1b2L9/P3l5eaSlpXHy5El0dHQ4ceIES5YsqerRV3+OH0dpa0u6vj6LPv6Ynw8dYvDQoXz08ceE7NvHkYiIEhuf6k6DBg0YNWoUFgoFp8PCaKd+YlbWpFlZDkJj4+ohEAoCuKuqbaliuVHbtiiB1194gUJdXdZt28aXq1bxzz//sG7duqoba20lNxeuX4dWraCwkOTkZN794AP++uYb5i1dyq9//IFLNRdo7W1tiU1IEAVCbQ5CKE7hL+99X56DsDJrED4KbYtGU1PM7e05/ssvPD9nDj179mTdunV4eXnx/fff06VLFxpJ7lyZJ0p0dDSK4GAuHjrEQ0GgdevWtO7Th5sXL2JvZsaKTz5h3c8/Y2RggJ+/P82rQQqemZkZmZmZJQQVS3NzklNTxZqHBQWl0/MrWgsKxEL4tralBMKCggKys7PFZgtPAN/Onbm8ciV+PXqItaIiI8V5RUeHBi4udAsIwLVNG3708ICICKz8/LCysuLmzZs0bdr0iYxJpmzOnTlDVFwcO5YvZ8HatbjUq0fvMWPo37cvODjw1gcfMHXsWIzs7dExNeXIX3/RVSVkVBWNGjVi8OTJFBQUsHjJEhQKBVFJSTiXV4PwP3ZifaoOQtCe8SHh5ATh4RAYKBo/OnWijZkZc5YtIzMzk3379hEaGsq0adNk08NTIujaNe4/eICLtTWG+vqExcay+OWXmfbRR8ycP58dO3YQGx/P3A8/xLdJE1ycnPhkxgwxe0eNnZs3o29pydL33uOVHj0q1FhuoFSvsoIIgkD79u05c/48blLZocrY66h/VyMxMfGxU4wfCwMDMV5CQ3mlXz+aOzpChw7oCAKTli1j4tCh5CYlYTF6NLt27SI3N5fU1FRmzJhBt27dOHHkCH4+Pkx5/XVcXV2rpGTPk6BS/tqCICwTBOGuxn1mwCXgC2AUMB+4KghCw8r4mVXJO2++yak//2Tf7t18OHAgwqlTCI6O4husXTux9lA1wcnJCSsrK8aMGcPdu3fx9/fH0dGxqMmJIAg0aNCAB8HBYhAbG/Pw4UNSU1NJV++Kp622gHrgK5Vw8KD2QVREIKxOk5CODrRvL/4/Sh9ITZtCly6YJiRw5tgx9u/fz7Zt29i6dStH9+3jr7VrWbduHdOnTydbavMuU5rERN748kss27Wj24AB3AkPZ+eff/LVmjVY29nh/+KLvPnOOzWquYWRkRFbtmyhU58+/HPrVtk1CEG7uJ6RIdYp00Y5TUqqjUCoBUEQ+OKrr7C3taUgO5tnW7fmlVde4ZdffiFMoxObTCWQkkJcYCB5wcFkHzrEN++9R79Wrej18stcuHMHF29vsXFGNcbO1pbYpCTxRllNSh6npo2W686fP8+kSZNEgbAqadIE07AwdmzbxqBBgwgICGD//v3MmzePNWtqZZnmaoNStRY58tdfuNerx4wPPmDskiUs/uorPvzgA0hJIfDaNdo1b07vF1/E2cGBxu7u6P6Xcg6VyPDhw7l+/TovvfRS0X2SgxDQHjuP06RETVzPzMxk8eLFRf82NjLS3mG1MtDXp+moUeJ62sAA6tcvmt9eHz2aUWPGiIKhUik6Pm/coHuHDuzevZtbt26Rm5vLgzt3GDFgQNH/sUzlEhEYiLKwkLi4OBKTkxk7ejSvLFjA5GHD2HPwIHoGBiUMEiaenuioRIpuffpUeTmF5cuXk5KURMLmzfg1a0bTpk258fBhcbzcuyc6iiX+TRfjspqUPE2B8FE4OxeXVGrUCMzN8enQgQ6+vqxZtoxP58/np+++Y+LEiUVxlJCQwMWLF6t44LWXoHv3yMnL4+SlS5y7dg0zY2PmrFqFmZsb3t7ejBo1io8//hh7Nze+e+cd2jdoQOdOnUq9joGnJ4K1NW+vWYO5VBP9CdCuXTvOnj8vvteDgiAxUXxAs5xSJTgIk5KSRAfhk5x/fX2hY0eeb92aJk2aFLuXFQqE1q0xaNkSNzs7srOz2b17N0OHDsXa2prg4GAKU1M5dvgwI4cPZ5dUmxhISUl5cuN9ClTWLN8d+FnjvtlAQ2CSUqlcJwiCHXAQUSgcV0k/96myYcMGNm3axMVz57i0fj319PTAwkI86fT2frJv3n+Jk5MTjRs3pmvXroDYOTIyMrLEJO3p6cn9oCDamJmBsTHBwcEARCQk0CgoqLh1uXoxUs0mJYWFYhFfbalf5dUgzM7GsDo5CEE8lbG1LXmfgYF4X2wsNq6u7N69mzsHD2Jz7RoUFnLiu++YsXo1X3/9NW+99VbVjLsas3XDBq7+/Td/nD3L4aNH2bBhA4sXL641J5QdOnRg3rx5JdNVKnKqlp0N0sZOE83JVkVBQQFpaWlYGBlVy88diedGjuS5kSMBuPXbb5iZmmJmZMTs2bP5/vvvMS+vI59MhTl99CidXnwRRysrlLq6FOTmcnLDhlInzNUZG2trEqVFVXkpxuWh4SD88ccfeeGFF7hz547YJbVHj0oe/WOiqlOs++ABs2fPplmzZgwdOhRzIyO2//orK1asqPLNdG2lf+fOjBoxgt927sTJyopf//6bh2fPYuvoiJufH3dOnuTWzZv4NhTPs91cXWlejRb7FhYWWGh0nbS0sCC5PIHwESn3e/fuZciQIaWef+XKFRYtWsTcuXNJT0/H1MTkyYocZdTmHvfee+JnmSCItQnT0uDOHd7o2ZOOs2bxv//9jylTpmCYl8dve/Zw6uhROlWwwYVMxchITcWzWTM8XVyITEwkwNeXVd99x9fffot+DVnHmahcsSbNmoGpKX5+ftx4+JDeUmwkJorp7VLThkpMMTbS168eAqFCIQoi6p8hurq8P28efcaNIy8vj9CNG/F/4w3OnDlDQEAACz78kKPHjnHp+vWqG3ctJig4GH2FgqycHDKzs2nVsGGJ8kru7u64q7J0hk2ZwjA/v6ISZmXyBBugtW7dms8++wy6dxd7LUjxom2vUwkpxjaPmWL82OjoiDWidXVLuy5NTcHYGC9HRxo1a8aeffvo5e3Nq59+CllZZP39NzZjxpCVk8PD69eZOXMmI0eOZPLkydy6devJjfkJU1k7Bw9A82hhGHBLqVSuA1AqlXGCICwDPqqkn/nUSElJ4YUXXuDSpUs0aNAALycn6nXtCpcvg4dH+UFahfj5+ZGdnY23tzcODg7Y29uX2nR06tSJ/QcOMGbkyCIHoYGBgSgQGhuLrhP1dC/1GoRlCSDaqCkpxo/CwUHs2BoYiF9AAH4tWoin3JaWND5zhlWff06XZ5+lf//++Pj4VPVonz7Z2eIHbEqKmJKuEq9iQ0N5bcYM7G1tWfTZZ7Rq1UrsUFiL8PT0JD8/n7CICOqV16REM3Y0JtA5c+Ywd+5cbMqIq9jYWGxsbEQLeHVYcFaAxq1bs8HAgOS8PGZs2kS/3r0ZMXIkffr1q5uxUsn88MsvvDt1KsOGDiVJVxf9nBwadutW1cN6LKytrIoFQm0pxo+z4NSIqxkzZtCmTRuio6Pp1qULvavD54+vLxw9CgoFvd3d6ffss7S3smLjkSMM69+fNp06MWfu3GrjXKsNZGVmcvTCBY6cP099R0cOrVrFgaNHqdeqFQgCiz78kK7jxmFlYcHSDz4A4JVp08ipRgKhNizMzYsdhGU1+CljDXb16lWmTZumVSC8desWKSkp5OXlFTcoqYo5RzNV38wMWrXCNymJpZ9+irONDXM++YRbt28zrX9/li9eTEdVPS+Zf0/0pUvkZmdTr0MHzh4+TAtvb5bMnYuXjg6mbdrUqGyPEjRuDIh7pDO7dxfHS1mxo37f3bvQoEHpw1kNkSM4OJirV68yePBgMjMzsasuDkIQ964a+PfpQ2NXV+wsLTFr147nVK52pVLJhk2byMrOJj4+HltN84TMf6OwkKDwcDo1bUp4bCxRiYnUkxqbaUMyJWkcEj1NvLy8ihuc5uQUd/nWJhCqm4vK4hFGosTERLzt7Z987AhCUSO5UujoMLFvXzwdHDh58iQtbW3hyhVITMSoVSua1KvHpQcPuHDpEtsPHCAvN5fbt2+LzuEamnJcWX9tBVCUVykIgjXgCxzWuC4Y0Og7Xb1RKpU8//zzuLq6cv/UKXa98w4b33hDrE3XuXO1FgcBhg0bxqJFixAEgS+//JL27duXumb06NHs3LmTzLw8MDLi4cOHtGnThoj4eDFYJfdgeSnGoH3j9ojaAlDDBEI7O/Hk2t4ezpwRPxjd3cXTE19ffNPSWLRgAaNHj66bKS4XL8KJExz85hu2ffYZ4bduMbJ3b/yaNePVF1/k1oMHjK7iAu9PCkEQ6NChAxOmTGHv2bPindre+9piR+29kpeXx8qVK7l9+zYolZy6dYs333yzxM96+PAh9evXF+Oyuiw4y8PODlxdsezUiZ9efZW2bm6c27uXzh068NMPPxASUuPL0z517l+6RJuWLTHQ1+eXvXt57bXX8H/mGXr06EGnvn2L6kHWFKytrUlUd0Fp1lF73JQVpZIvvviCo0ePkpGRQVhYGNHR0Tg+jcVmRTAwgCZNxBq/oaFs+fBD3pw4keM7dvCsnx97f/2VWTNmkJ+fX9UjrbGkRUay97PPoKCAwrQ0zuzdSzNPTw7t2MGFffvw6tuXV6dNKzqAmTJrFp+9/DK3g4Pxbd4cAON69bCq5jXupBqEQMUchHFx4oEeEB4eTkxMTPH7TG1OCgwMBMQUw4yMDEyMjKpH7ID4f+bmxoR27XjWwoJLixdzaeVKFi9eTFRYGP4tWrBy5cq6uRb7j2RlZXHx4kVmvvUW01VNRU4cPUq3du3o8tJLOI8Zg3ktONjz8/PjpnqKsTaBUDN2goO1dzXW2AcdOHCA//3vf0A1cxCWhULBtx9/zEezZoGjI739/Vm+bBmjRo1i+pAh9GjalCNllZKS+VckJycz5NlniUhMpFfHjjRyc6O+kxP1ytMWfHyeqEOwPNzc3IiKiiK3oECcR8oSCNXXa/n5cP689hd8hIaQlJSE1ZN2rku4upbpYh/ety8t3d058vnntG3ZUixz0bYt1KvH0O7dGdutGwdPnQLg161bUSqVzJ49W+zyXgOprL/2XaCb2u0Bqu/7Na6zBxL/7Q8RBMFNEIQjgiDcEgThpiAIM//ta1WEgoICli9fTlxEBF9NmIDxw4dYKRR4+/iIb9QalLoFMHLkSK1dRB0dHWnZsiVHbtzgXnQ08fHxtG3blvC4OPGC/Hzt7cvLchBqUk4NwuycHIxqisJuaChaqiXnoItLsbPL3R309Jg4aBApKSlcLauuXG0lLY07d+7wv+3bmfjNN7z33Xd06dEDWzs7zp44wcdffVXVI3zi9OjRg6PHjnHq5k3xDm2pxo9wEN68eZMjR46Qm5tLXFwcKJVcDwlhu1pdC1AJhB4exa9VEzAxAX9/sLdHp2dPlm/bxqaNGznw9dd8tXQpLVu25Nq1a1U9yhpDdkIC/QcNYmybNiTt2kXEjz/ipN6psAZiY2MjCoR37hQ3WtB03z5OinFhITt37uQPVfyEhoYSExODo4ND9YkbV1fo1QuaN0c3NhbByQnzZs2YsngxO1evJvDsWQLat+fGjRtVPdIaRXh4OElJSXR/5hmGzJ/Pzi++oHHjxvR9/nm6tmtHx379MG7cWEwf8vMrfqIg8NKbb3Lg00+pr35/NcfCwoKUtDTxRlkuKPWyMMHBYh0yxL+VUqkkOjpafExtTlIXCNPT0zGtbnVvPT3FFDdHRxT9+tHslVcwa9yYw5s2sXz0aNZ98w3NmzfHxsaGu3fvlv96MgDMmzuXDh06sP/8eY5dvszMMWNY+8svdO7cWbyghu1/ysLZ2ZmYxMSSAkV5AqFGbeiNGzfyww8/lNoHhYeHc/36deLi4khJSak+NQgfQaMBA2g8cCAIAp2eeQZHGxv2bN/O/0aOpH/Xrmz48UcAjh49WvHOtzIAREREsO6HH1ixeDHvzJ5NRkYGyxYu5P7du/g0asTLo0fzybRp1Hdxod6jmsxUA/T09HB1dSU4Pl5skJebKz7wqPVaXl5xrUJNykkxtn7SNQglVBmBWjEzAycn/F1c0KlXD/r0ETPlgHfffZd3Zs0iLSMDXR0dEpKSsDU359tvv+V8WaJoNaeyPuFXAd8LgmCB2K34deAhcEDjut7Af1nl5gNvKZXKS6omKBcFQTioVCorNck7IiKCHTt2sPrrr0VnxvTp6NnbF705yMiozB9XLWjRogWXQkN54dlnef/99zE3N+femTPig1Lgl+eC0rxGorwmJTVJIITi+gReXqUfc3FBCAri+f79GTBgANnZ2bz++us0aNCAe/fu8cwzz6BUKumkpbhsTaagoID9P/7Iyx9/TL+BAzl0+DB5eXlMf/VVln7/fY21WD8ur732GoIgcGPPHggNfXQNQsl2r1RyIyiIK5GR7Nu3j8OHReN1XFwcFBYSk5xMSEgICQkJRQJ/kUBYzReb5eLkhP/o0Vxo0YIN69bxwgsvcPnyZQB0dXXl9DBt5OejTE9n3muv4efnx8xVq0ChwDgxsUpPlCsDaxsb7iUliSlc0gl6YWFxDah/kWIcERFBnup0u9o5CCV0dERXeq9exfcJAtYdOnBw3TrWbttG9+7d6dKlCx988AHNVc42Ge0olUq6BASQlJLC0Pbt6f3MMwx9+21+WLkSg7Q0WqtqMpeF4OLCM+++W31E5ApgaWFBzIMH4o2y6ndKB1NSPKkJGQCRkZG4urqWEEACAwNxcnIiPj6enJycqksxLgt9fbFzu5mZ+PuphCujFi3o4e3NxebNOadQ8P0vv/DZokUU5uTw46ZN8tzyCFYuXcrvmzfz9VtvEZ6fz/1r17gbFMS7I0fSs3//qh5epWJjY0N8cjJkZUFmZpkOwst373Ls0iXGjRsnln5R28+cOHGCgoICJo4YUfScgoICIiIiKCgowN/fn4iICPp9+GH1ih1tqAm/hu7u3PnuO4SkJPDyYrybG4t79+bXLVt4cfx4Vq9ezYQJE6pwsDWLLxcs4Iu1a3G2taVZ/foM7tKFq/fvc2HfPuoFBCAkJuLo7MxcPT2cte0vqxmenp4ERUfT0MamYg7CMhrHlXqeBomJiVibmlZ97DRtKhqmoqPFLFL18bi746FKTe7t78++ixcZ3qED3/z1FyFqB1Opqak1pvZ6pQiESqVyvSAITsBrgCVi9+LXlEplnnSNqknJYP5DDUKlUhkFRKn+nSYIQiDgAlSaQBgREUHnDh3o0LQpC8eMYXDLlgheXiXrNZSlLtdgfH19WblyJW5ubsyfP5+dO3fy12+/iQ9qCoRKJZfv3yc8Pp6B9vZF95X4rk5FBMIalgpXJs7OEB7O9IAAvFu2pFOXLvTs2RN7e3u6dOnChAkTiI6OJjo6umb/zrm5JcSIzz/4gA0//8w3337L0GHDiu4/cuxYVYyuytDR0cHOzo6EtDS4f1+sFQIl3/taJs1jV6/yx+3b5OXlER0djYGBAbGxsaBUEqPqqHfx4kV69+4NiAJh+9atq37CrAwEAXx9GTdqFD/+/TdTRo7kyNmzTBg3jrHPPINH584INVz4qgwKw8NJSU9n/JQp7D9zBg8HB05fvlxcm0uLO7ymUSLFWH3eURcIHyPFWKkSCCMiIgDRQVgkEKanP6HfonIR/PyYHBdHv5Ej2bh3L6NHj+bAgQO4VXOHQVVy6+pVCnNz+eLjjxkxZgw5+vp06t2bfv36VfxFapiAZGFuzl3pPV1Wer76oa7aZk1dIFR//s2bN8nMzKR9+/ZERESQlZWFaXVKMZaQ1qGamJigcHenQ0wMymbN6PTWWwiCwPTXX6e1lnI7MuIe6KOPP+byrl24q2rY5iUmojAyQqgBDrjHxdTUlLz8fLLu3sUoK6tM9+1P+/axcvt2TExMmOziUuKa4OBgdNRi69rt2zzXrx+enp54q7n6TWra38/SEsHeHurVA2trDAsLWfX22wweOxY3e3s2/fSTLBBWAKVSycmjR/n51185vWIFHg4OGDdoQK/Jk/n6q69w79BBvNDGBmxsCKhfv2oHXEEaNGjAg+hosUzKo2oQqmsAGmu3AwcO0KJFC4IvX6YNFDd4BDZv3kyrVq1ITEx8einG5aFQiK51a+tSDxlaWeFkbc0LffpwPSSEcc88Q0ZuLrvOnSMsLAxDQ0O8vLw4d+4cjRo1qoLBPx6V5hFXKpWLgEWPeDyOSqw/KAiCB+APnNW4fwowBWDAgAGln6hGYWEhgiAgCAI5OTl8++23bN28mfFdu/LBtGmiU8zSUmxMUctp3LgxN27c4KWXXgIgICCAF69cIb+gAIUWgfDAxYvsunqVgQsXlnzsMR2EhYWFpGVkYB6co6QAAQAASURBVFZDFPVy0deHTp1wAsbXqweWllw9eRITQM/YmGVLl9Kte3cOHTrEwIEDq3q0JXic2Pn7228J6NkTY3d38vT1+fr779mzeTPNe/Z8GkOt1tjY2IgC4aMK9woCubm5dGjdmn+2bSMmMZGQkBAKCwtp1qwZHh4eJRyE9dzcGDNmDFlZWfzyyy8EBQUxZtiwat3B+HERfH35cckSvv3hB94dPpw5X3/NB4sXs23FCobPmlXVw3skjxM7j8vLI0dy9tIlHoSG4mxrS7/OnQm/cQMTfX2MalmxcGsbGxIlkeNRKStSPGVnixs6VWdKgOvXr9O0aVMoLCQlLY3MzEwA6tevX9JBqLq/2qOrC02b4nL1KvN69CA5Lo7mzZrx1Vdf8fwLL5CZmVnUmbMmUpmxc//+fYIfPuT8wYMM6NqVl1SfG8bweOJgDcTS0pJk9RRjddTmnYS4ODLy8qintlkLCwvDx8enSEiXxMMPPviAOXPmcOfOHVasWMHly5cZ3b9/9dioVZQmTaBJEwIcHFh8/ToJaWm88PzzNGvWDN9mzUhLS2PevHk41LB1/hOZcwoL2fPrrzzbpg3uai5bPS2b4dqCIAjYWlmREBeHq5NTmQ7ChNRU3NzcxDR8J6cS14SEhKCnp1c0Vy1Zu5YHDx4QHx/Phg0bqF+/Pr6+vuiePFmz1myCIJZTktDRof/Uqay6f59OjRvT79NPadG4MQuXLCE3L4++ffvWiKY1T3K9po2/fvuN8VOm0LdrV1qNHi2uW1xcOHXhwhP/2U8ST09PHlxU9afVttfR0Sm5XtM8uAIWLFjASy+9xCuvvMK9NWvwUHt8zZo1DBkyhNjYWBwtLKpP7KgaHJVCEFjyyisMHjmS59u1Q3BzI8DLC/PRo+nfvz/Z2dmkpqby96FDNUIgrEGzfDGCIJgCvwOzlEplqvpjSqXyO6VS2VqpVLYur0vqp59+yvz581m/fj3Dhg3j119/paOvL+/NmQMBAaKd1M2txqdtVQRfX18AWrZsCYC9vT1uTk5cDg7W6iCMT0nh3MWL/HbsmNjJ6FEuwUeIh7GxsViYmmJQk9102rCzg6tX4fRpLG/dQi8qCm7ehMOHGdKtG19++WXRqX114XFiZ/PFi/QaPpzpo0bRu1Mn/Dw8aN6jx1MaafWmSCDMzy89aRYWsnbnTs5dv86Nu3e5ePEiYRERxCQlERoaSkREBP/88w/Dhw8nJiaGv0+fJiY5mQ/feYcff/yR9evXs3DhQu7cuUN9N7eatVErDxMTPHr2ZNGPPzJ58WJOL1vG7q+/ZsJ77+Fga8vrU6dW9QjL5HFip5wXKlGjZe+uXZw8dYoNM2fy4MwZPvrsM77avBnbhg0x0tKFsKZjbW0txg5od67fukWh6t8AhIeLtdSAZcuWceHCBVq1akWhaiEaHhtbVN6gTZs2PHjwgOTkZGwsLWuWQ8zBQUw/trbms549ObFgAXNnz8bT0xM7Oztmz55dYxuZVErsxMVBaioL336bfv36seK773j5lVcqd6DVHAtLS1LS00Xhu6wUYx0dVn/zDfPmzSuxWQsPD6ddu3YlHIQpKSkcPHiQqVOnYmNjw8WLFykoKKheTUoeAx1PT+aMHMmrU6fSv1kzcmJiOLp/P8rsbJo3bcoFqaRODeG/xE1mZiZz584lNymJq7/8wraFC1EmJtKzTRs++OQTBvTrV7M+H/8jtpaWYppxfn7ZAmFKCo0bNxYFQlV6fkZGBoWFhYSEhIjiemEhYXFx7D1+nA4dOpCSkkKnTp1o2rQpCoVCdEdVF5Hj32JkxNTZs/EbMIAHq1fzWo8evDljBksXLKBnz57kaGveUs2otPWaFv767TemjhlTdLsgN5d333mHNcuWsf6PP0SXYDVvblpRfHx8uB0aKt7QJhBCyYwPqayFmk4QGxvLsWPHyM/P51ZYWInHgoOD+fvvv3F2dsZAoagR884L8+dj2qQJgo0N2NoimJribmdHYGAgURER9PH35/Bff5FYVi3GakSNqzIrCIIeoji4SalUbv8vrzW1Xz86jRyJo7MzvZ99ljdefhmTW7e0tn+v7VhZWeHo6Ij6h2X3jh05cv8+bSSlW0MgzM/PZ8yCBfhs386ksWN5tWFD9LSIgPl5eUxcsYK133yDQqlk+/bt6OrqMmTIECIjI3Gxta19ixE3N7FDpaWluCCQ2qYnJvJyWho37t3jueee4+zZszWyFs63P/zAN6tXU5CYSEBaGiPnzKl9/4f/kiKBEEpPmsCyn39mUFwcDVRpUcFhYcQmJ5OTk4OVlRXGxsbY29tz7Ngxtm3bhpOVFR3atsWnVSvy8/OZO3cuzz77LJ7u7mIXrdqG6gTad/RofE1NOdK0KTapqTw7dSrfLFjA8z16YO7rWytLPWQFBfHzihVMWrKEf86fZ/z48WxZuJDWr7wCgsCLqgOc2oqNnR2JUuxIGw1V7OTk5NBl9GiOf/01/tKcpOaCWrp0KSdPniQvL4+4uDgcCguJiIujVatWnDhxglatWrFnzx5sbW3RlZyINQkdHbHubb16NCko4G6DBly7cYMGrq688O23dO3ShZ9//pn6np5VPdKnQnRUFEeOHqWPjw/Llizh3J07XLx3j32//kq9Bg3wbtasqof4VLG0sCA5ORkuXix9GKvmILx56xZXb9woih2pVlqbNm24IDlalEoOnD5Np06dMDExwdbWFqVSiZ2dXfVMMa4IRkbQpw/u+fksb9iQwuRkiI9HJzubbtbWDBo0iH0HD9aJ+p4rFi1i2dKlJF+/zu6LF9HV0WHt9u0kJCfzYt++9H/++aoe4lPF1tqa+NRUUSDUJq7r6JCYlkbHzp0JUR1I3bl7F/8ePViyZAnm5uakpaWRnpbGF7t2MX7IEAxdXLh8+TJWqiYGgCg+1nSBEMRGDkolegkJTJ4/n8mjR1NYWMjId9/llTFjGDR2LD179sTCwqKqR/rU2bJpEzsOHmRlfDz6RkasWLgQS0tLhr78clUPrdLx8/PjxsOH4g1Nk5BSSUpGBkb5+ehr9iiQ5iMgJiaGQ4cOAXAzNJR+qmtyc3MJDw8nPj5erNlfWFgz5h3J7NSxo/h5YmGBu6MjZsbG/DRzJgZmZniMHcsBDw8uXryIt7c3V65cwc3NTWsT2aqkRgmEgqik/AAEKpXK5f/19ewbNOD6l1+iLwgIjo5w44ao7EtNKOoYBw8epLGadbZV164c/usv8U0OJdJW4lNSeGnsWAbWr89dIyMWLF1K4xkz2LB1Kxu3bCm6bunSpZjm5LDhyBG67tjB7MWLyc7Opk+fPgwZMoSIiAhRIKwJgf84GBtrF5qtrbHo3JnvDAxoMn06S5cuJSYmhr/27WPll19iZ2eHIAhiilw1RldXl9dmzBBvZGSUSPGr69jY2JAg1VHTEAgjo6K4HRyM49WrJDk7o6enx8PQUGKSktDR0cFFdbJoZ2dHVFQUAOHx8TioUkkVCgWBgYEYGBggJCfXmk6CWlGVHWil6pq4ftEi5n7+OXM/+4zpAwcye9Ys1h49ysxZs9Cv6S5vpZLc8+eZ8eGH/HTwICdCQth34gQbFyygx/jxdUZ8t7a1LU4x1nCeHzl3jvSMDCITEvDXKHqdnZ1NdHQ0u3btAsRaag5KJRHx8Xh6enL79m08PDy4efMmDx8+LLFArXGo3uvGffrQvndviI5mn5UVCzZvZtjAgTh7eGBqakrzhg2Z9/HHNfIA6lFc37uXL9euZcfhwzRwdGR6ZCQtW7fGv0cPDBwd6Tl0aFUPsUqwsLQkJTNTnHMKCkq+v9UchDdv3eLOnTtkZGRgUljIF198QceOHWnQoEFRt28KC9l74kRRWratav759ttvsUxOrrnrNR0dMX7q1RO7UAIkJjKkY0fygN69evHg4UNMa/EeIDUlheUrV7J73ToWf/89q7/9li5durDw1Vd5f9AgOtcxcRDA1spKFAgl17paA7lLDx6w8+xZElJTadKkCWdVTtNTZ8/SvHlz3nzzTZo3b05SUhL3g4JYf/gwl3//nXMpKbi4uJT8/K0tAiGInyfSgaWDAzpKJT9t306Xjh05OHUqbdu1Y/uuXbVu/nkUt2/fZt+xY5gbGdGsZUusTU0Jiorin0OHauXfwcPDg8TUVFIzMzGXGlGq1mbbdu5k5KRJvD9rFp9Ijkq1ddsPP/5IVFQUSUlJJCUlYWhgwM3Q0CKBMSwsDB0dHbKysvDy8qo5AqE6CgWYm+Pp5kYzPT0aDh8OgsDd9es5FBODf4sWdO7ShZMnT/LGG2/QpUMHuvbsKZYrqAbUtN1lR2AccF0QhCuq+95VKpV7/9WrWVlh4O0t/icGBkLr1uBYaWUSaxx+fn4lbnt5efFtUFDxHeoOwtRU3pk4kQ45OdCnD+dPnWLTsWP8/s8/bFAqiz4MV69eTVhYGABf/vQTXbp0oX///ixU1S6stQLho7CxQadHD75/7TW+PH6cBg0bMq9vXwYMGICbmxuJiYn8+uuvtGzZEsua4JKSxcESGBsbo1QqxeY70ge9atI7cvo03Vq35uKNG8TExNCzZ0+Cw8KISU7G19dX7CCJKBACGBoYUFhQgKXaSayhoaH4j/z82i0QatD++ec5NmIE0UlJTJk0CYeOHTEzMkI3IoI3Fy0SN8Y19MQ6NTCQDqNH4+Luzj/79rF6yRL+2ryZVn37VvXQnipGRkbFsSPVMiosRKlUsmXfPhQKBVEJCdy+e5dvFi3ii1deEdO6wsLQ1dWloKAAAwMDbt26RW5mJhFxcbjWq4eHhwcuLi64u7vj7u4OCQm1Y84RBHByQjc2lvlTphAeE0MTV1dsrK1ZsH49jX18GPzCC1U9ykqjICuLcTNm0LddOy78+CNu3bvz3aZNvDh+PCYmJijLaIRWF7CWDqby80sL4KrbeQUF3A8KwsfHh2sPHtDew4MFCxZw9uxZ8vLyCJXSxZRKLt++zfQPPgBEgdDZ2ZmhQ4eKB+m1IXYkVPX1Rsydyy9HjrBx3Tqmvf56FQ/qyZAXFcXqr77imdat6fvSS/RV1RwHWLpxY51aT6hTJBBKrnWlksLCQr5YsYJzO3dyMzGRhNRUMcU4JgaAsxcvMmbMGAICAoiOjiYyMpJVP/xAGy8v6jk4YN6mTel029okEGoiCJjY23P+3DnywsJoO3Qof/z2G0OHD0eZl1frG81dOXOG1h074ufuzuCuXdn/zz/06d2bPs8+i2clpzFXF3R0dPDx8OBmZCQBUtdllU5w8/Ztmnh6ci0wUGwYp1Qyf8EC/telC7EREVy9epVz586hUCjIz8+nV4cO3AoLIz4hgbDoaBISEmjbti1nzpwRBcIaHDsfTZ8uin62tqBU4t2uHd6JiYzZsoV1hw/T2Nyc9WvX8umnn7JryxbMHBxo0aIFUVFR+Pj4VNm4a9RsoFQqTwKVK8NLqUpOTqLrS6YILy8v7kv2YSiVYmxrZwcq8a+RlxffrVtHVlYWSUlJREZGkpOTQ2RkJNYWFnT09mbHmTPMnT+fUaNG8eabb5KUlFQ3BUIAAwM6Pf88nZo0ER2rWVmMfvZZFGZm/HD6NKNGjWLw4MGsXbu2qkcq85gIgoCNhQUJaWm4qokcAHsPH2ZUnz6iK9DamhEjRnBk1y5iU1IY8eyz6KoW6JJAOLpPHw6dPo2gbeNbxwRCdHTAwABHR0d2/fknkZGRpEZF0alnT7757Te6N2/O6t9+Q9fIqEa5w86eOsWbU6bQsUsXvl2/HoB13bvXrf9bFYIgYGNuTkxKCh729kX1a959910uBgby0vDhRCYksPL779n19998MXkyF27e5HxaGgEBAdy5c4d27dqxcuVK8pKTaeTkRL9evZg5c2aRCwqo2Q5CbTRvjgB89/nnouCRm4uDnx8T3ngD/06dqOfuXtUj/O8olSybMwcLa2sWrl+PoKsLurpMe+21oktqo0ujotja2pKWlUVWRgZGhoZF7++bN2+iCA5m5NSprJ0+HRcnJzp06MDlu3fxUjUg8Pb2JiMjg9DQUHJzcxFycwmNjhbFdMDf35/Zs2eLP6gmOjkqgrk5b06ezNA33+TGhQvcDwrCzs6OHgMG4KirS/eOHTEyM6uxJoIDmzYx/vXXUejosFPburKauFaqAltra+Lj40s4CKOionhr9mx0dXQwMjYmKzMTHx8fsQYhcPbSJSa89hqvvfYa6enpTJ8+nfWbN7P17bdBqcTS0pIX1A9npJqftTF21NCxssLAyoqFr7/O7HnzCDx9mm83bWLT+vV0qsWNon7bsIE3R4zgsxkzyDc35520NAyl7sS1GD8vL66GhZUSCEPCwni2fXt2nTkDqjqdC1aswFcQWPbRR9T39OTSpUv4+Phw584dBvXowXtLl9KyTx8KgPfffx8fHx/Cw8NrroNQhXWTJsXreUEAlehnWVDAm56e5Gdns37fPuzMzZnx5puERUczcfRorl+/zumrV6ts3HVvB1IWsjhYCgcHB7KyskhOT8fS1LSkQJicLAqEISEANPT0LKodFRYWxrRp08jMzMTDw4Mr27ezf9Mmdpw5Q9u2bdHR0aFZs2ZcvXqViIgIOlhb19jA/084Oop11O7cgWbNUISHg6kpE3196b55Mx2ef57CwkJ06uLfpoZjY24uCoSSKFFYSG5uLnuPHGHJ9Ok0a9MGH1NTbujpserePfILChg9ahRZqhNnQ0NDRo4cyRvDhlGQlqa9+U9dEwg1cHZ2xtnZmcjISO5dusTrs2czbexYMjMy6NqpE5Pnz6/qIT6S9PR0rl69ysjnnuO9CROY9NFHxQ/W4f/X1t7e/PPggSgQ6ulx89Yt1q5dS+DWrfz6zz/8c/o0f546RUZmJrm5uXy1ZQt/nDjBc889x6FDh1i4cCEff/wxurq6xCUm8r+2bYsE9yJq8GLzkahlATzj4cEbZ8/Sp2dPjpw8SVZWlljsuwZ0mdTGzfPnWbF5M+cuXqz1bpR/g45CgautLeGxsXg7O4NCwd27d2nVqhUmBgbkFBQw4fPPade6NX5+ftw6epR7wcF4e3sDYGJigomJCdOmTcM6JYXsnJyimkjOzs688cYb4g8qKKi1n0+dhw7laEEBh27fpru/PyEPHnDi118Jjo1l9PTpdGvalF//+APDGtbxGOCDBQv4YskSRnbqBFJ6tQwAtjY23FFvtlhYSEREBI0bN+bLsWMZumgRFiYmWFhYoFQqiUlK4l5QEC1atEBXVxcLCwsWL17Mu5Mn45uSon29VoMdUP+GfuPHc+LkSVauX8/8N95g3MSJ3AkKEuvRSVkwNXUOzsqCuDj2HTqEk40N9Tt2ZMvOnWz68Ud0OnZEX3Jx1wFeGDCAl99/n6EtWuBgZ1f0e4dGRDBi2DBW/f47OdnZRfVtD165wt1799DT1ycvLw9nZ2fy8vJo1bgxV774gjO5uazasoXPP/+cSZMmERAQQMeAADh3rip/zf+G5vpTQlcXmjRBAaxfuBBTU1N6vPwy9ezs+G7TJvQVChbNmUPnHj3o2KfPUz8ArZ2zvEylIAgCXg0a8CA6mlaSgo/YdCQtKwtLa+uiD4NGDRoAYG1lxe+//87du3dJSEhg0KBBGOrr08rLC3dnZzxVCxN/f38uXrwoOgi9vWvuRPFfMTQEqSi2qpM02dl4PniAjYkJo0aN4tNPP60RLdFlirGxsCiuQ6ijA4WFHD58GJ8GDXB2cMBZ5b5t6ObGrfv3cbCwoHevXiU2Xlu3boXLl9nwxhtaO4DXdYFQQt/YmCadOvHHvn30aNcOe2trFn/zDeHh4fxvzRqEavrZ8tasWezYsYPxvXrx6kcfFdWWq+v0bNGCv69fZ0xAAEpdXd58/33ef/99bC0tcXZ0ZM/Jk/g0aEBUYiJhkZGEx8SQmpqKh4cHBgYGRXU89XR1ScvKoqFCAcnJJZva1AEnB8CbCxeSmpREA09P9HV1qWdnxy8rVtBk4MAa9/s3btOG81euFJVhkNFARwc3OzvCkpJEgbCwkPnz5zN37lwiL15k+IQJzJ49mxWffsrlhw/Z/dNP3A8OFt0ZKtzd3dm+fTt2xsbUc3TUviGpzfOOpSV+kyZRJLMrlWKWjKMjSRkZTH7hBV4eO5Yfdu4s6o5eE7h36RLB0dE89+KLtff/7j9gY21NbEpK8R2FhURGRtKgfn16+vvj7u5OdkICAuDo4MDeixfxa9SoxGGLs7MzzoIAFy5oX6/VMYFQsLDgs3XrWKCvj66BAXv++ot506YRfP8+IwcP5qdff2XLn39iUQPF9j83bmTJqlXcCA1FX6FAVxAY1KkTbXr2FC+oQzH2TMeO9OvcmR///pt5Y8cWOwjDw/FydcXDzY17YWFcDAwE4O9r18jIyOD69evo6Ohgb2/Pxo0bsUtIQLh/n+eaN8e1RQsOHz7Mq6++irm5uejsreV/04HTp6PMymLjtWuYWFvz3nff4eHszLtLljDgn38ImTOHLVu2lOgT8aSpWStEmaeOl7c396OixOBUBX5iYiLWZmbo6OoWCYQN69cHoFfnznzzzTdMmzYNFxcXGjZsCEolrra2PPj1V4Tz5wFo3749Z86c4cGDB9SriynGj8LbG3r14v1Ro0iMjGTZ55/z8ssvs3v3bgIDAylQaxYjUz1xsbNj2+nTFBYWgkKBsrCQhQsXMnnUqKJukhQW4ujoyNxJk3C1sdF+4qjZGUyd2rxR+xeYWVlx+vBhdu/dy6lz59i0Zw8H1RomVQdysrKY+/LL2Jibs3vHDu7u3s3nP/0ki4Nq9GzZkr1nzrDxyBG2nz5NaHg4r776KiiVODk4kJSaSrsWLXB3dyckIoLw2FiMjIzwUDWFkgTCkd260drLC52kJJAan0gUFtauFOOyMDXlf998Q8r+/SScPcvMOXPoNn48v335ZVWP7LERBEEWBx+FoSFuTZoQlphYdCh19uxZxo0bx/dvvUWfZ57h+saN2Nva4uvrS2BoKPdCQoochCAKhMnJydyLjMTN3l77z8nPrztChyCIbjt9faysrFi/dSvKggIG9epFbmIix9evJzsurqpHWS4Xzp3jxREjUMjrBa10bN2av69dIz07W7xD5SB0cXYGQcDd3R0bc3MoLMTV2Zmfjx6lnSo9vwSPWq/VMYEQADMzdFUi6toNG7h6+TK6urqMmTOHs7dvs2rxYhISEqp4kBUj7NIlcrOyOPrnn6zbvJl6DRuyacsW1q5fz841a1j9yy/inriu4ebG4BEjOHD5MigUXLp5k7lz5xIeFYWbkxONfXwImDqVNWvW0MzXl/D4eACys7Np3rw5Dg4O2NvbF5dRUipp27Yt8+bNE8VBEOuL14HPLsHIiLHz5zN0wgTOr1nDjLff5uvPPmP/mTNcv36dNUuW0L5pUwpUjWNzUlIokD6zngC1/y8u859o1qwZ5y5fZlTv3kUCYXxiIraWluLiSakEpRJbKytOf/45B+Pi+HXXLrp27UpcXBytW7cucmvoFhQUTZwBAQFMnToVCwsLfF1dZYFQE4WCMSNH0s7bmwaTJtExIIDFn35KdEICBQUFbNy4UWz9LlMtWTFrFh0nT+bI9eusOXCA+t7eJCQk8OLQoeJkp6NTFAsfvvoqMzt21H7qrNHJtQSyQFgKPZWA4AAs/PBDZs+fz05PT+qbm8NTPHkrCz0DA4wtLTl7+jQ2Tk5YqlL4ZIpp/NxzTLlxg+V//MHdqCgOb98uFnhWKnFW1f9q17w5ifn5hERGEh4by6ZffqGDqt5PgwYNaNq0KVMHDiQ8LKy4q6s6dcRBCICJCQpVJ/AJjRvj37IlQwYO5GRgIJ8tWYKhtAiXqfG41a9PWEwM6OqSkp5OvKqLN6Gh4vtddTDl5uZGamYmF2/d4nnVewNEgdDJyYmEuDjqleXsqcPzjqmZGZt++41uPXti6eyMiaEhL/z1Fx+sXo21qtlJdWTM1KmMriMpj/8Gd3d3uvn788PffzNzwIAigdDZyalIIFTGxkJhIc+PHMnUmTOZMGFC6ReS5pW8PDh/Htq0KX6sLgqEarh4efH3nj1gZsaxP//E2MaGDgMG8NOOHdwJCqrW9WNToqPx6dCBBq6uXH/wACMDA8Lu38dGPrACc3O69u3L6AkTmLJiBduPH0fXwAALMzOMjIz4/OOPSRowgPXXruFuacm1wEDqu7uTlZODj48P9tJBlFp6fynq0pxjZQWWlphYWtJX5VJ/cO8euvn5LFHVKD+1fz/eLVrw9qRJ+DVuzLzPPnsiNWTryF9c5t/Sv39/Xvj+e5YqFCxatw7vhw+x19cXBUIoIRIG+PgQqKuLQqEgICCAXr16idfcvi1OjCrVG8DT0xNjY2MGDRok2lir8eRQZbRogae7O4vPnGFc9+44OTiAri5bT59m4sSJYvHxuvKhWcOws7GhU/Pm7Lt4kb8vX6ZNQQH79u1DkZcnxoFqowaijdvazOzRDsLsbMjMLFkrNT+/uJaLTCmGT5zIncuXafXMM/Rv25YP583D2dsbY5XTrCrQ0dHhwxUrquzn1wQEGxv+9/bbvNmhA7czMmgrOTWUShxVaY/tmjXjVnw8V+7cQaGrK3ZXVeHj48OlS5dQHDsG7u5inGguOuuKg1AL/m3bcvnQIaZMnYq7uzujBg7kw48/xqYK40KmcnBzc+PKzZsUAJeCgmjSuLFYw1iaR1TOQkEQ8HF15fC5cyxevbro+R4eHrRt04agq1epZ28PFy9Cs2YlNx91abOmBV1raw6dOUNafDzo6hLQujXfu7iwc+tWeg0aVNXDK5PqLMBUOYLA/yZPpsf06TzbujWNLC2JjIykS0BAkUCYducOKJWMGzmSzxctooO/f+nXkQTCnBxxvaZOLa7dWWFUglpXVfOWjIgIfFq04MqZM/gHBFTlyMrkw+nTuXb9Oh38/LA2MuLjCRM4fvGiLA6qYWpuzovdu2NpZcXt335jw7VrbNmwAQRBLGGRnEybV17h4KZNADzTvTuXbtzgvffew1LSEsrLlqpLTZQEocReb9nataQkJrLyl1/o2bYtQ8eMwczEhNikJGISErgzYADzPviARh07Vuow6vinlUx5+Pv7k5qZycodO/hh506Mjh8nNjqamcOHixdIi09VUHt7eBAQEICJiUnxi0iTZn5+kWtDEATGjh3L8GHDIClJnji1oaMD1tbMGTcOGjYUW6QnJDBSqWTVzp1s3ryZcePGVfUoZbQhCHjVr8/Pu3bRwc+PPd99J6Yq3b8vfvirb9oedXImNVMIC4O0tBJNCOr6Rq08dHR0mP/117z50Ue8/sordBo1CgM9PWbNncu4F18s2dVWpnqhUGBubExbtdq3KJXo6etzZP166ru44O7uzupt23DVUgBaoVAUx1V+fmkHYW1tUlJBrJs25bejR3l4/Tqff/YZDfz8+OaLLxg9aVJVD03mP+Dm5sZX167h/+qrBEdHM2rMGPEBaQ2mNu9M7N0bew8PmjZtWvT8F198kYF9+7Jw+nR869WDhARR7JAFwhIYGBhgoCplcC80lKObNzN6/HhOHziAZ6NGYGZWxSOUeSzMzWnaqROvDR7M6r17WdmwoeggdHQEQeCFF14gzswMCgsxNjLi3jffoKNNICosLDZDaK7n6riDUBv6Dg6M6NePlYsWMXnUKHxbtBA7vlYjRr74IntefpkVK1bQ2c8PbGwYkphY1cOqXggCX7/6KqjcgK+++ip9mjUrtdfxdHPD3NiYDm3aEJ+aShP1/2spdrQJhHUkxfhRWFhbEx4Rwb0rV3j+xRdp6OKCq60tJ65cIScvj+yCAgKDgzl98SIGpqboVsJnTd3+i8uUi46ODvPGjOGNVavY8P779J05kwPbtjFKaqwhOaFUk2GnNm34+++/S76ItDjNzS0xQS5fvly878iROuvmKBdBgC5dim87OCDo6LB22jSse/euunHJPBoPD7z8/Aj8+mueVU8fVipL1CAESj6miVIpxkxubmmRQ96olY8gYGJnxw/btkF+Pse/+46NBw+S1ru3LBBWZ3R1SwkaUux0bdcOgK5duzJ58mT6qKdxqSPFlbbNWk4O1NBuvpWGnh71W7Zkza+/MvXQIXoNH05kdDR6gGlhIePfe6/YdVSHxdSaRLt27ejVrh1dfHzY8vffdJEcBVrmnWl9+4KTU4nnW1lZYWVqytoZM8DcXHRBac47shOqJDo6dHvhBebfv0+LLl2YMmAAS7dskWOmJmFqCqamDOzcmXGfflrUpMTFyQnS0nBxcRGbKSqVUFgounKzsuDYMejatfh11FOMCwuL4w7qVu3Ox+D1999n+vjxTHn/fSgsxK9JE5Z+9RVuqsaXVU2Ttm25cPNmyTs1PjdlEN/bCgXk5mJoaEgTLy9ITS0x5zRwc+Pc0qV4DBlCX8lkJCHtdep6ivEjsLOzw+6ZZ7gfFkZacDDZ6en0fu45nB0c2PL33xjo6TG0Tx/uhYfz07p1tOvWDf3/UN9c/ovLlMvrI0bw3IABuBgbI1hbM3rQIIiIEB9USzEGEJRKsV6UOlLgFxSUFgJzc+uWdbgysLOjUZMmYtqpTPXEzg4vVVfqhu7u2gXCijgIpdjRVkdNnjQrjq4u6OrSZepUuoweXbKjrUz1Q6EoFgk1Y0dHBwoK8PbxoXe7dmKDH21I10ubNXUyM8HC4sn+DjWI5r168eeaNXz/yy8Y6Olx/OpVft23D2dLS2ZMnEgLzcW8TLXEzs6OL995B1JSGN6xI0h1itVjR33eKa/urTb3rSx0aOW1Dz/kuQkT6NyhA85z5tCjUSP8+vZFIaci1hj8fXyITU5myebNxMbG4lGvHty6JT4ozUVS/OTkiF/qqGdLQbErCmQHYRm41K/PjmPHUCqVfLtsGTt37ODL994rqrcml9GpIUgCoVqjn6I1nNqc08jVFfT0sNc8oFePnYwMUM9CrGspxuWgUCiw8vICYNWCBfi2bcu3P/2Ek4MD02bO5O2JE5nw8su0Cwjg599++/c/p7IGLFOLUSjE7oFSt6mCguLTUSn4y3NBSddrLjZzc+UOnv+GFi1kcaia00D1Ad7I07OUC0prirG22JEm2ZwcWSCsDPT0QEtKqkw1Q6EodhBKc4s076idSC959VUyU1K0v4YkrmsTObKyZBeABu3GjKGdKiU1PjqaP3/5hej4eAZOm0bfn35i5rx5NKnkGjcyTwA9vdKxo+kgVL//5k3w8SkWL9RT86Fk7EiHvLI7TitObm7s3L2budOns3LTJlpt2EBrLy9mLlmCiexYr/bo6Ovz8sCB7D9/noMHD2JmYlJsatCMHW3zijTnSDGkLgrKAuEjEQSBqbNn02fYMJo0bsy31tb4e3nxyfLlZGZm8mw1ru8pQ7FAqLleU5+HHtV0UdrrxMSI5ZTati1+TE4xLpOuqjXb/I8/Jj8/n1YdOuDv78+7ISH4tG7N2RMnaKfWiOxxkGd5mfLR1RUXnerBLU10Gg5C0tPFOmsSsbGiuKG++FQXQvLyZIHw36CnJ6dlV3MsrK3xc3enccOGJWNHQ+R4ZCqkJK4rlbJAKFN3MDICZ+fSi0uNE+lmnp609/HR/hqa7g11srLEnyGjFVtHR8a/+SbzFi7krZdfJjM3l3ETJ5Itu9arP+W5b9XXYIWFYn3b3Nzi56vPR1AyduQ5p1yatGjBnydPcvvBA5ydnTkWGEibtm0Z2b9/VQ9NpjwUCpbMns2hzz6jRYsWJecQzdh51HpNQlNclwXCcqlfvz7nzp/n4YMHuDk702/gQHLCw6t6WDLlIQmE6nOLrm7Jvc6jzBDq2VJqDU0Bed6pIAqFAn9V8yRTd3d++fJLnP9DtpT8F5cpH2Nj8UvzZABKn6qlpUFyMqjcU9y5I1qO1e3C6jVs5BRjmdqKri7Xv/oKHBxEyzyI731DQ+0OwqAgsbh5o0bFryFNmtJz1ZEnTZnaikIhNuQJDCw572gTPrQtNqXHpNjR3MjJAmGFmfX55yjz8xnbuzf9e/Vi94EDGKt3U5epXqi7b9U3a2U5CAsKSs4thYXia8gC4X/CyNiYVVu3kp+ezr41a2hSTbu0yqihaYZQF/WkeUfzQFe9zqC6oCjdlpAFwgrjp2rG98O6dSy6dg23Pn2qeEQy5WJkJO5t1GNHX7/0PKT+XR31+p3a9jryeu2x6SI1KfuXyDO9TPk0ayaKfLdvi7c1J031TZpmvafcXPE+9VM19cdlB6FMbUWKEfVTNSl2tDkIc3JK11spLCwW0OVi8TJ1DfU40SYQShu2y5fBzU3s9C6h3qlYfc6RHO1y7FQYQaFgw2+/sWH5cgzlA73qjWZ6viQCqseO+nxUWFhyblFPzYfSIoccN4+FwtSUgW+/XdXDkKkIZmblC4Tqex3pGoVCTI1MTX20g1COncfCwNkZN2fnqh6GTEVo21Y0CGlmfGhzEGoKhFevioe2Uq8Czb2OnGJcJcgpxjIVQ1UYHtCeYiwFvKb6L3VfVT8503xc3nDI1Eak97x6Zy71jZqmg1Azdo4fL13DRh3ZzSFT2ymnBmGRQJidXbJgvHpKpfRcCdk9+K/Qtbbm5U8/RUeer6s3lpZinVV1x5MUN5o1o7W5BDVdULKDUKau4OkJrq7aBUJtNQih+HZYGMTFlR07soNQprajbb2mzUGYkQHR0cXPi4kR7yurV4E871QJskAoUzG0BT4UT5rqdTnUhURpEi3LQSg3KZGpzZRVuFebg1BdIMzNhZQUUfTQ5oLKzy9+HRmZ2orm4lJTXJc2bJqnztnZJQVC9djJzpYFQpnai7k5eHgUr9keJXKou6AkyhMIZZFDpjYjrakk561mQ8aymvjk5Dw6W0oWCGVqO5r7Gs15R4qd+HhRUAfxPinTULpeWw1C+WDyqSNLsjIVQ1uxeOl+adKUCoxKj6kXvi5rwSmnGMvUZvT0tKesaDoIpZQuaWLMyhK/q2/I1ONGWzqyjExtQ/NgqqwahOqxk5oK584Vn15Lz5XIzgYDg6f3O8jIVAXSpkw9tbE8kQPEx9TdGrKDUKauURFxXfN7dnbJurcgi+sydQv1fY02cV39YEp6LDe3+DllZUvJKcZVguwglKkY6gvLsiZNKZVS3QWl/nwJOcVYpq7QpUvpwr3aahBKAqEUG5mZxa+hbdLMypIFQpnaj3rdJ6mIdXnu24wMUUCXmjKoXwfiRk6OHZnaTnkihyCUFghzc8U6UmW5oGSBUKYuoC121OcibfsZqcSF7CCUqauUZSRSn3d0dMR5Rpp71EvDyCnG1QpZIJSpOOqTpjbbvXrHSMk2LFFWZy85xVimNmNgULYLStNBKD0OxQ5C0C4QymmSMnWBsuacRwmEmZnFC1FtC86cHNlBKFP7KUvk0FyvQXEchYeLzejKih15oyZTF5BqrmvLltKWgq8+/5TlIJSd6zK1Hc2mcmXpBOrxoi4QypmG1QpZIJSpOOpFr7U1KdEMbm0OQoWi9KQpuzlkajPa6qhpdvaqiEAoPV96XI4bmdqOpkMdSovr0mZOM3YkB6F6DVCQ5xyZuoEUJ4/K+JBQd9/m5pYtEMoHujJ1gUe5bzVT8AsLtbug1Bs7KpVi6QsLi6czfhmZquBRpcjU552Cgkc7CKE4dvLyxPiRMw2fOrJAKFNx9PWLuxKrNykJDRWDXJtAKG3EpOvV67FJKZXyqZpMbUZ9oaieYgylxXV1F5S0EdPVLf5Sr3cjOwhlajva5pyyFqHSglNKz5ealOjrlxQIZQehTF3gcRyE6gIhlBTX1QXCpCSxS7KMTG1Gm7iuoyPOHZq1BAsKiktaQPFj6vNOWpq4F5LdtzK1Gc1SZNqamWruddQbMZY1JxkbP53xy5RAFghlKo6BgRjMmg7C8HBITtYuEEqBLT2mp1fS6SGLHDK1HUnkgPLT89VjQzpt1tERF5bSyZv0uOyCkqntGBqKYrg2JwdoF9ezsorFQUEoOeeA7CCUqRuU5YLKy9N+oAvFAqHkINTXL+mCSk4GK6un9ivIyFQJ6q509fXarVsQGVk6qyM7u3ivI12vHjspKbKwLlP7EQTxfa+pE2hLz1d3EJqaiv/WNidlZoKJydMZv0wJZIFQpuJo26xJ9uDc3EcLhJqTZlCQ+CWfDMjUdvT0ijutqseOUgkHD5a8T/1UTX3SlOoWSo/LDkKZuoC2Qyn1YvGCoN19a2paLBKqu9aVSlEgkR2EMrUdhaK41pN67ISFQWBg6fVaYWHJ9HxN921qqrgGlFO9ZGo70mGsegdwySGYna3dQSit19SzpQoKxOsfPgRr66c3fhmZqkJdJ9DmIJTiqaBAvJ2TUywAqrtvCwpEMT4mRtYJqghZIJSpONoCPzVV/K7ZoUsSCDUDX9qsRUeLC1VZ5JCpC0hCh6ZAmJtbUlyXJk3N2FF3ECYkiE4POXZkajuS+1Y9rUtHR3z/37hRLGRAcbF4EGNH00GoVIrCiJ5e8WZPRqa2YmQkCn6aAiGIc5FmHTXJlS7FlKaDMCYGbG2f7u8gI1MVqK/XpJhJSxO/5+WVdORqCoSaKcbBwWI2iLv7U/0VZGSqBEknqEiDH6l+p6a4bmAgxlVwsJihKDsIqwRZIJSpONrcHOpoTppZWcWBrXmqlpYmfmDIJwMydQFDw2J3ho7Gx666+FFYWCwYSi4nHZ1iF2FBAVy9Ci1ayMXiZWo/kgMwK6vkYrOgAKKiSjdTkDpFSiKguoMwPR0iIqBZs6r7fWRknhbGxqKbVn1+kYRxTZEjP18U3U1NxXlFEtclgVASOjw8quI3kZF5umgT17Ozxe/qJWGkuSU7u7TIIcVOSgo4OMiHUjJ1A23iuiDAiRMl13EgzjvaxHVJIJTKxcg6QZUgC4QyFUebg7BNG3B0FP+taQ/OyQEzM/E+9UkzK0ucZM3MZBeUTN3AwEDcgGkT1qG4cYlCIW7qJJFDEkAkB2FOjhiDUszJyNR2DA3FmFBfbELJbquSyJGTU5wGKd0vpecnJIgOKDl2ZOoCkkCoLnKoN+tRd9gWForXGhuXFNeltVxMjLhek9ZzMjK1GW3llAICoGFD8d+aNdVzcsS9jDTXQHHspKaCufnT/x1kZKoCbQ7CtLTiOUZber66kUja7+Tlia8TEAA2Nk//95CRBUKZx0BbmqSjY3HRak2BUCrcq75JUyjEQtfm5tCqlbxZk6kbSCLHowRC6UsSCBWKYheHtPhMThY3afJptExdwcCgZOxIGzClsrTTSWpAoi5ySHNPXJxcB0qm7mBkVFoglDp8Q3GnYil2MjLEjZo2B2F0NDg5Vc3vISPztNHmIDQyKjuNWHKuKxQlhffsbPHgSnZAydQVtBmJtDWV09UVRcCyyillZIjxZWNTOutK5qkg/9VlKo62kwEoLlqtPmlmZxc7oqRJU/pKShIFQjOzsgUTGZnahKGhOOGpT3S9ehVvurQJhJLIYWsLzZuLj0mxIyNTV5BiR921YWNT7LpVdzqppxirOwh1dUUHodyBVaauIK291MX1+vXB31/8t7Qek1xQmgKh+mMxMfJhrkzdQSoJU9ZeR6rfqe4gVD/UtbAQb8vrNZm6hoGBGDvS+gugQQPtacRZWWIMqWsI0ld6upxhWMXIAqFMxTEwKH0yAMWTpbpAmJEhTrLS4wqF+CEgXSMXu5apS2i6oECc/LRNjJJAqK9fspC8LBDK1EWk2JHmHF1d6NChpJAh1ayRUoz19cXFqZUVWFoWF8mWUyRl6hLGxmJ6l/qmTDqU0hTXMzLE66XYMTERN3XZ2eK/pfWcjExtx8iodIoxlN7r6OmJDsH8/OL1mo4OdO4s/js3Vz6UkqlbaJaEAWjcGOzsxH9LMSSVXTIwEOcbe/tisVChEOct2XlbpcgCoUzFMTAQT9Q0T9WkgFevvaEuELZuLU6SnTsXW41lgVCmLqHpgpJQX3BqEwi7di15fX6+HDsydQtjY/GkWV0sh5I1OjVTjM3MRJehs7O48NTREecgOTVfpi5hYiI6MdTnHSnlXjONODNTvN7AQLzG318U10F2D8rULaRsqfz8kkKHZraUnp4YN5KoLsWO+jXyek2mLiGVttDc60hNFdWNRNJeB6BdO3G+adNGdhBWExTlXyIjo4apqajsq6MtxTg+vvjkTFpkGhiILg4ovdmTkanNmJmV7CYpIcWOVJ9TR6fkpKneqTg5ufi1ZGTqCpqNriT09MQaNlIqpFIpCokGBqLLVt1pq6sr1x+UqXuYmYmdu7UdTKl3+c7IKM7waNCgWEiXnufg8HTHLSNTlejqFtcQLM9BmJBQvF5r1apkrVwdHbnBgkzdQr0Wpzqa2VIKRbGDUB1jY/G5BQXymq2KkVUamcfD1LRkoWvQ7oLKyNBe1NrTEzw8nvgwZWSqFVLjBG0CoWYzBXWBUB3pZE1Gpi4hCYTaYqegoLjOoLTg1JYKKdUtlJGpSzwqdtTdt3l5otNWekxCEMR5Ry5rIVPXsLISm/OUV289M7PYJahufDA2Bnd3ec0mU/cwMxPLvaij7iCUGpFkZmo3POTlid/lg6kqRRYIZR4PE5PSJwPaBELQHtzSZk5Gpq5hbl72Rk2KCykFX9vJmewclKmL6OmVTN2S0NcXazxJ4npBgRg/2tJS2raVXesydY/yBEIpxRjKdmvI6cUydRFr69ICoWb6sHRIJTVgUMfICPz8nvw4ZWSqG6amYsaUOpoCYVkOQoBGjcDb+8mPU+aRyDUIZR4PU9Oy0yQlcTA7W7wt24NlZIopSyBUd0DFxRXfLyMjI2Jmpj1lRXJBKRSiOGhlpf0AShYHZeoiUrrWo5zr0lwjr9dkZIqxti6OEQlBKHY/QXHsuLo+/fHJyFRXzMzKrkGoHj9Q7FxXR6EoWV5JpkqocatmQRCeBVYCusBapVL5WRUPqW5hZlZavJA2X6amYmFrU1NwcXn6Y5ORqc5YWUFqasn7pI2ao6OYAmlrq/1ETUamLuPqWtqlIcWOqWmxa1A+dZaRKUbqDqmZdi/ViJI6gOvrF9eKlpGRAQsL8PIqfb9UKsbevljokFPwZWSKsbAoNjtISLqBiYm43ykoEA+w5NipttQogVAQBF3ga+AZIBw4LwjCLqVSeatqR1aHMDeHDh1K3ic5oNQL8soih4xMSVxcSgvn0kZNct/Wq1c1Y5ORqc64uZW+T3IQenqKtwcOfLpjkpGpCbRpU/o+SVz39xc3bM88U9qhKyNTl9HRAR+f0vdLa7Z27cTb/fs/3XHJyFR3bGyKm5RKSI5AY2MxhRjA1/fpjkvmsahpK4K2wH2lUhmkVCpzgS3A4CoeU91DW7qWNmehjIzMozE0FCdMGRmZx8PISJ5zZGT+Df9n777Doyq6AA7/bnrvlRSSEHrvvRdpShcQO4hiBVQUVFBQioICNoqiCCIgKkWEDwSkSpXeeyCQBuk9m/v9cbOpm5CEhBTO+zw8JLt3d2+SPTtzz5yZsbPT2h0bm7zTKIUQ+bO2zjn9UWJHiLwM7VXg6pp3yr4otyraJ5sXcCPb9zczbsukKMpoRVEOK4py+MiRIw/05B5q7dvLxVoFJ7FTBszMoF27sj4LcZ8kdsqAuzs0alTWZyHuk8ROGfD3l2VgKjiJmzIiu3pXeBI7ZaRVK0kKViAVLUF4T6qqLlJVtZmqqs2aNm1a1qcjRIUhsSNE8UjslBHpbFZ4EjtCFJ3EjRDFI7EjxL1VtARhMJB9MSLvjNuEEEIIIYQQQgghhBDFUNEShIeA6oqi+CuKYgYMA9aX8TkJIYQQQgghhBBCCFFhVahdjFVVTVMU5VXgf4AxsERV1dNlfFpCCCGEEEIIIYQQQlRYFSpBCKCq6l/AX2V9HkIIIYQQQgghhBBCVAaKqqplfQ6lRlGU79B2OhbiYeWtquqooj5IYkc85IoVNyCxIx56EjtCFI/014QoHokdIYrHYOxU6gShEEIIIYQQQgghhBCiYBVtkxIhhBBCCCGEEEIIIUQJkgShEEIIIYQQQgghhBAPMUkQCiGEEEIIIYQQQgjxEJMEoRBCCCGEEEIIIYQQDzFJEAohhBBCCCGEEEII8RCTBKEQQgghhBBCCCGEEA8xSRAKIYQQQgghhBBCCPEQkwShEEIIIYQQQgghhBAPMUkQCiGEEEIIIYQQQgjxEJMEoRBCCCGEEEIIIYQQDzFJEAohhBBCCCGEEEII8RCTBKEQQgghhBBCCCGEEA8xSRAKIYQQQgghhBBCCPEQkwShEEIIIYQQQgghhBAPMUkQCiGEEEIIIYQQQgjxEDMp6xMoTaNGjVK9vb3L+jSEKDMfffTR96qqjirq4yR2xMOsuHEDEjvi4SaxI0TxSH9NiOKR2BGiePKLnUqdIPT29ubDDz8s69MQosx89NFHN4vzOIkd8TArbtyAxI54uEnsCFE80l8TongkdoQonvxiR6YYi3IpKSmJFi1a0LBhQ+rWrcuUKVMAuHr1Ki1btiQwMJChQ4eSkpJSxmcqhBBCCCGEEEIIUbFJglCUS+bm5mzfvp3jx49z7NgxNm/ezP79+3nnnXcYN24cly5dwtHRke+//76sT1UIIYQQQgghhBCiQpMEoSiXFEXBxsYGgNTUVFJTU1EUhe3btzN48GAAnnnmGdauXVuGZymEEEIIIYQQQghR8VXqNQhFGUlO1v7Z2d3X0+h0Opo2bcqlS5d45ZVXqFatGg4ODpiYaG9bb29vgoODDT520aJFLFq0CAALCwu6det2X+dSEdy8eZPvvvuurE9DVHCFjZ3j/8Ry80ISzXva4+Zrlnl7erqKqoKxsfJAzvd+SdyIkvKwtTsSO6IkPGxxAxI7omRI7AhRPBI7BZMEoSh5oaFw9y40anRfT2NsbMyxY8eIiopiwIABnDt3rtCPHT16NKNHjwbgww8/fCgWoX0YfkZR+gobO8s+usW/cdEM7+NB+0GOgJYc/PKVIM4fSuCbw7Uf1CnfF4kbUVIetnansv984sF42OIGJHZEyZDYEaJ4JHYKVuGmGCuKMk5RlNOKopxSFOUXRVEsyvqcRC6qqv0rIQ4ODnTu3Jl///2XqKgo0tLSAC0T7uXlVWKvI4QoPBtHbXwpKiwt87aFb97k/KGEsjolISqEq6cS+XNhOInxurI+FSEqpLCgFHauvgvAvvVRfPdusTfPFkIIIUQ2FSpBqCiKF/A60ExV1XqAMTCsbM9KlIbw8HCioqIASExMZOvWrdSuXZvOnTuzZs0aAJYuXUq/fv3K8CyFeHjp0rRBgLCgrJ3ET+6Oy/xaLcFBAiEqk52r7vLX4gjWfRWeeVtqSjrnD8eX4VkJUXHMGXWNVZ+GkpSQzvKpt/nv71hSktLL+rSEEEKICq9CJQgzmACWiqKYAFbArTI+H5FbCVQQ3r59m86dO9OgQQOaN29O9+7d6du3L7NmzeLzzz8nMDCQO3fuMHLkyBI6aSFEUSTGadVPQWcTM28zMc1adzA1WRKEQhiij41dv0by9etBpKWqrP0ynHkvBXHjfFIZn50Q5dvhLTHE3tXan9i7WRXsty4nl9UpCSGEEJVGhVqDUFXVYEVRZgNBQCKwRVXVLWV8WiK3EqgcatCgAUePHs1ze0BAAAcPHrzv5xeiXLpwAWxtwdOzrM8kh9P74jizL45B490xMtKSgElxWrVG+M1Uwm+mYONoTFqqinMVU+7cSiUpIR0zi4o4BiVE6YqP0eHobkKVauac3hfP5eMJ3DinJQYvHU3Ap6asnCJEfpZMytqcLvZuGraOxsRG6rh5IQm/upZleGZCCCFExVehrt4URXEE+gH+QBXAWlGUJ3MdM1pRlMOKohw+cuRIWZymKOE1CMWDIbFTDiQmajuAlzMrZ4SwY2UkhzbHZN6WGJ+OqZmCosDHj1/h6LZYANyrajsaP4jpXqqqkhCbdx23i/8lcHR7jIFHlA6JHZGff1bdZdHbN4mOyKp0SoxNx7uGBQPHugMQe1eXWQl15XiiwecpaWmpKj9+EEzwpbKtWJTYEUWhX9pCL+aODgtr7VImOjzN0EMqJYkbIYpHYkcUl6qqRIallvVpPBAVKkEIdAOuqqoarqpqKvA70Cb7AaqqLlJVtZmqqs2aNm1aJif50CvHCcI7t1MZ1/68TOMyQGKnHCiHsZOerpIUryX7Dv4VnXl7Unw6gU2sePP7qqSnw6bFEUBWgjA5ofQThBu+DeetzhfyJDm+GH2dxROC83lUyZPYEYakpaqs/iyUYztiOb0va33OhFgdlrbG2DoZAxAVlpq5lmfMnQeT5Dh3MJ6Dm2JYOz8s87bI0FT2rY8iJfnBreUmsSOK4uCm6Bzfx95NIzGjmv1BtDnlhcSNKI6EWN0D/XwvjyR2RHFt+/ku7/W+RNiNlHsfXEJ2rLzLy83Okp7+YK8NK1qCMAhopSiKlaIoCtAVOFvG5yRyK2cJjuyOboshOTGdvWujMm9LS9XO98qJBCKCtaAPvZ4sO0yKB68cxk7otRTiY3RY2xtz4XACSRkXYUnxOixtjAhoYEXdttbcua2NqnlV16ZHlvbFWny0js1L7gCwfcXdzNuzb44SG5kz2aKqqmyeIh6Yi/9l7egdei2rQ5kUl46VrRFWdsYYGcH1M0mZoR8f/WDanZsZg2R2LlkrzayYHsLyqbcZ2/Y8EbceXAdYiMLa/vNd3Kua8ca3voCWUE+I0WIm6SFKEApRHG91vsDnI6+X9WkIUWHo0lS2r7hLXFQav8/VBlSzr31b2n6dHQqQORD2oFSoBKGqqgeANcB/wEm0819UpiclDCunF+H6jqSFlfbWv3M7lddbn+PlZmeZ/fx1vns3GFVV+WjQFeaODir18zm9L46x7c4ZnCYpHkLlsILw7H5tZ9VB49zQpamM73Cece3PE3I1BQtrrQKqdkvrzOPdfPVTjEv35ziwUaskMTVTiArLaqyzf/33sqzEYXyMjunDr/JW5wsPzRQBUbYuHU3AyAicq5gSek1bOiA9Xc2sIDQyUrB2MObaSW1asXtVs8w2qjRFhqVyao9W0ajL3s/N9tlzWzZ8EGXoyomEHAM/elFhadRsYU3N5tbYOhpz43wS6RnXTaU9KJWerrLm81CunkxEpytf7bQoA/HxcOZMWZ9FoekrB4POJT3waiQhKqqj22NZ83koE7pdzLztQVWrZy9y0G8M+aBUqAQhgKqqU1RVraWqaj1VVZ9SVVV6seVNOUtwZBd+U0sM6BNyJ3bG5rj/9uVkEmK1wH8Q05D/XBBOSpJK8MWcb+PLxxNyVDkKURZSktLZ8ctdPPzNaNrDLvP25EQtRpw8tOqj+h1sM+/TrwdV2g3o+UPxePibUbuVNVdPJBKSkYAJvpQVS1t/usOZf7VEyLbldwi+lExiXDqXjiYYfE4hStK1U4l4VjPHt7YFIRkVhHFROlQVrGy1OLFzMsmsvq1ax4L4aF2pV7m+1/sSV05oScm4bB3Q7En9g5ti+Hv5nVI9DyHys/Ctm6z5PJTbV7I+z1NT0omP0WHnrLU7DTracmJn1tT90m5zIoJT2b7iLp89d40/5oXd+wGicktMhMjIsj6LPBZPuMmyqbfy3B52Pasq/FZGPykiOIWYB1ANlZ6ucu5gfL6JydnPX2POyGulfh5CFJWRcd7b9HmC0vbFC1nVvklSQSgqvHJYBZWcmM73k4I5skXbuEC/mPW5A/HYOBgz8Wd/npzsSWqKmlkxBVqH9EHIve7U56Ou8/PHtzm9L47jO2NlWuTDopzFTsjVZO7cTmXQOHdMzYx47GXXzPsee9mVbk85A+DkYUrXEU688KlX5s7FpT3d6+aFJHxqWmDrZEJSQjpTB1/JPGeAqeurYetkzL8btErDU3viqNbQElNzheunteT/z5/cLtVzFA+voHNJXPovAf/6llSpZk74jRRSktL55o0bAJhnVLHbOmnJDlMzBY8Ac1JTVFKTS+8zIPcmD7F3s0al74ak0uwRO4yN4ciWGH6fG5Znmr6oZFQVzp8v67PIw9hEAWDP71kJmJg72nvVPmNafPZBK3gwbY7ezlV38yQ7jmyNYfGEm9Jfe1iU07/z0e2x/Ls+mnVfh7F5SQTnD8Wjqlr1q97Ct25y80ISk/td5sP+l0s9ds7si2f+y0F8+UqQwde6ciKRy7k26PpvWwxL3guWakdRpvRrsAOYW2r9tsRYHcd3xpZ6/ygk+9I08ZIgFBVdOUtygLaZgT45CFnTEG9eSKJOGxt8alpQr60NZhYKv34WknlcRHDpTkXU/5r2b4jKsfi2Psny9es3WPjmTa6efDA7W4oyVs5i5+TuOEzNFeq01qYQ93zehbl7a/L5rpr0fN4l830KMGicO4272GUmPkpzF+P4GB2RoWl4VbfI3OhBL/RaCjYOxrhUMSOwkRVXTyRy/nA8Ny8kU7+DLf71LNm+4i7v9bnI3j+iSu0cxcPt5K5YrO2N6TXSBa/qFqgqjG13nqCzWpKhah1LAKoEmmf+b+OgvZdLcx3C8Js51xbUr6WTnq4SFZqKi5cpumwvf2ZfPKkp6fw6OyRzjV5RiaSnw6VLZX0WOaSnq5kxcPh/MZnTefUDqXbOWpxUrWOR+RivQPNSryC8eT4JIyMY8b4nOl1WFRbAms9D+X5iMEe3xxJ6XeLkoVGO+muQs6jhfz/cYf034Syfdps7t1O5cDghM8Fx51YqX76iLaOUlJDOleOlO6tCvwnX+UMJ7P1DS/rHRqbxwWOXeKPNuczjEuN1RIZq111LJgZz+H8xMptKlKnslXsu3qYAXDudyMI3b/L9u6W7GWL265uDm6JzDD4d3hLDztV5l+EoKZIgFCWvnDWYiXE69v4RhW9tCxp0tKHdQAdunE9i7kvXiQxNwzNAWzPNztmEFr3tiY3MujqKiyzdOf/6TvDpffH8+MGtzOB3rmKa47jZz1/n+D+xmSNvNy8mcWpPnIysVTblLHZAGzHT9oTSmJkbZa7hafB4q9KfYqzvbHr4m+U4tx8n32Lv2ijc/bSY9qtnyd2QVOa9FIRzFVM6DHFkwBtu2DmbEBkqlVGi9JiYKnz4RzUc3U3xqm6e474X53jjU1NLbtTKWL/TycMUa/vSTxCGXNVip/Vj9nR7yonYu2no0lQiQ9PQ6cDRwxTvGtr5mlkoLJ1yi3HtzrNjZSST+12W6fmVTTkblAJtan5qskpgYytiI3WZUyNjIjIShBkVhJY2WRdP7n5mmUtflJbbV1Jw8zXDu6YWH3duZQ0gZ18v8dwBbRbKjfNJD2wWiigD5TB2Lh7J+/ls62jM5WNakcGb31c1uE50aU+ZDLmWjJWtEdZ2xlzJqBTc8ctd7txKJTUl6zzmjwnivT6XWDzhZubaoitnhHDtdCK6NPWBLP0kHm4pyels+/lO5myL7HsE6Pto/67XCnpKuz+U/Vprz+9R7Po1q6J+yaRgVn0aSlxU6VzLSIJQlI5y1GjuXqMF1KiZXrw0x4e+L7liZWvEhcNaYFeplnXxFtDAMsdjS7N8WF+xkZ2+wxkfraN6EysGv+meed/Ct24ypf8l/loczvThV/lm7A02LowotfMTZaCcdTh9a1vwwqfeRXqMmYWCopRuOby+ksTe1QQTs6wE4cG/ogloYEmHIY4AtOxrnzkC17KPPRZWRlStY8nM/1Vnzj81eGeZX6mdo3i41W5tk1lh6+JlSs/nnXn9G196jXLJrMgFqN3Kml6jXBj8ljtWdhkJwlLcqOTGOa0K6vG3PfCpaYFOp03l+uBRrYrMuYopr37py/RNgTwztQpA5oUawNwXtcEqSXxUEuWszfln1V1WzgzBwc2EPqNdAG1N5mmPX2HhWzcBLZmuN25RVcZ/VxVzK6NSn4IVFpSCW1UznD2119evHZqSlI6REfR4xhk3XzNO740j4lYKM0Zc5edptzl/OF42hqiMylnsXDqWwFevaUtY2DoZM21DIM172hEVlsaOFXextjOmSqA57y73B8iRUC/tDRDCglJw9zOnbjsbLh1LID1dzUxaQtba1dfPaAnAo9u19eHf/tEPG0djPnv2Gq+1OpdjTTYhSsOfC8L57Ysw/pgfhk6n5tg9OPcSLenppbe0xYGN0YTfTKVx16w13v/34x1SktNzLHexb120oYfnkPu8C8OkyI8Q4l7KUYMJ2mjzS1944+KVUSnoZMLsHTXZsvQOFw7HU62RVeaxAQ2scjw2+/pMJS3mTlqOqVwAQWeTcK5iSlyUjpZ9Leky3AlUMtcOib2r48+FEZhbGuFV3Zy/l9/BraoZto4GVlEVFU85i5367W2p3sTq3gdmY2SkYGFtlKNRLWlZU81M6DrCiWunEjm5Ow7nKqa8tcQv8zg7JxOmbQjkn5WRtB/skOM5LG2MqVo754CAECXFt1bW9EdFUXjsZTcAarWwznGcsbHCoy9pa3vqK6RKs/r28rEEfGpZYG5phE/GOf79U1b1k0sVs8xNIBp3MeWrg7ZcOJyAq48Zx7bH8NsXYSx86yaeAeZQp9ROUzwo5azNiY/WcfNCMkMnuBPQ0BIjIzj4V0yOzUpsHbMuXfTt09G/Y0gpxQrC9HSViJsp1G1rjbW9MabmCuE3Uvh23A18a1uQng5V61qQlqqy69dIdq3WBqYPborh4CZteZunpnjSso89J3fHFfRSoqIoZ7Fz/mA8zauAosCU36phZWuMo7spUeFpRIWnMeJ9T61/ZqVgbW9MfLQOWydjYu/qSnUDhMQ4HVdPJtKmnwOBja04+Fc0R7bGcP1MIq0fs+epydpA1D+r7rL6s1BsnYyp3sSKtFQV/3qWjF9clV1rorh+OpF2Ax3YfLjUTlU85A5sjObvZVp/aPuKuyTFp5Oebcf6tGzVrt2fdmbrT3e4cysFr0CLPM91P/atj2L5VG2NdH3uArTl0Y7viOWH97M2Idr28x28a5pTp5UNSQnpmFsqmTOrdGkq34y9wYVD8Qx4w52ikAShKHnlbFStVV8HajazznN7j2ec6fGMc47b3HzNGL9YK8Gf2PNijh0eS5p+HUQPf7PMaV+XjiZQq6U1ujQ1cz2qLk845VhceOBYN2o0s8bGwZj5LwexdPItnD1NMW5aaqcqHpRyFjvFZWVrTEIpVkHFRKShKFqC0NhY4ZmpVZgx4ipPTvbMc6yZuVGeOBeiPNJXHJbWJiWqqnLtdCJt+zsAWntnYWWUY9qWo0fObqGRkZKZ1OzyhBO/faHt3hoZkoqdJAgrD1XVMgvlRIve9piaGeFd0yLHNC795iW56SsIVVXNsexESbkbok2FdPPVlrVw9jRlZ0YS8OTuOIyNoVojKzz8zfl3fRR/L8+7NtSyj27z6+xQkuLTcetb4qcoHrRy1l+zdzXhw9+rZU4hBnB0z/o8b9ItqxLJ2k5LEDp5mJIQrcsxjbKkndgVR2qySove9hkbyxnzw3tagsO9atYMrg5DHLl8XEsCZr9mc69qzpBss6kkQShK2q3L2gDUsR2xOW7fty6KBh1sMr9PS82K9ybdbNn60x0ibqaWaIIwLCglMzkIWWvu6mXfT8HaXkvwf/XqDZ6f7sWSScF0GOxI63722DiYcGpPHGf3x+PkYcqFw/GQc1+vAkmCUJS8ctRgFkdgY21EWh94pUW/EO+jY1xZMycUVYV/VkVmVl5lHyV/Zb4P5w/G07KvfY4PovdXBXDtdCL2LiZ8/V2pnap4kCp4/ABY2hqRWIodzuiINGwcjTE21i4ErWyNmbY+sNReT4gHwdRCez+X9AY/6ekqZ/fH41vbgpQkFVcf7QLSyEihblsbjmzN6nCamuW/8oyiKHyyMRATM4XUZJX5C0r0NEVZ0Lc35SRB6BlgzoAX3TLXF3zyA0+mP3EV0Co26rTJO9gLYGZpRHo6pKaomJmXQoIwYzqxvprDs5p5jh0ma7Wywc7JBDsnE16e58PP024zcKw734y9keN5HFxNaD/GkZ3nEBVdOeurtX7UIUdyEMCruna9YGyi5Fiz09JW+5y3tjfGwsa4VGZ8nNkfR2qSSmSIFjveNcwxMdUGdL96VYsL/SZdoLVHI6d7lfh5CHEvQWcTObU3juP/xOa57/oZbWbfnVup+Na24PlPvDAyBpuMa/SS3LwtLVVl0ds3c9yWEJMVmw6uJpzYlVWB7l3DnPOHtAG0JZO0DVN2rYlkV8bSai5eprhXNeODXwNIS1WZPqPw5yIJQlHyytmoWnHZOBpn7vBYGvSbJFRvYsUnG6tzZGsM308M5sBGbT0BV5+sdXbqtrGhbhubPM9hYqoQ2KhoU0BFOVYJ4ga0hF1pTzHWT4MUorLQVxBmXzy+JOz6NZLVn4XS41mtktbeNSt2Ogxx5NqpRHqNcsG3zr1HwR3dTe95jKhAylmb06SbHd2fyqr49q5hQfUmVujSVAa87pbv46xsteRHUlw6ZuYlv7x6dHjWurcAQ95yx9hE4fTeOBLj0jM39gGo1tCKyWuqAfD6N764eJtyem88Z/+N48U53iiKws4PS/wUxYNWzq51DFXXBja24qXPvTPX+NPTJwut7Y2xKqUBXX0SsPNwJ8wslMzBpzqtbPjmcG1uX0nGw9+soKcQ4oG4ezuVb97Q3q9V61hkroUJWkFCg442PPdxFbxrWmS2L6qqYmljRERwqsHnLI6o8FRuXU6m3UAHjE0Udq6OpE4ba1y8TfHwN2fxhJzJQw//rAQhaLNC9Js4AkQEp9LxcUeMjJQiD5zJFZYoHeWo0SwuW0eTHDsal7Sw6ymYWSiZuyI17W5HtUaWTOqlLRbvX1/WR3volLMOZ3FZ2hoTcbPkRtVyu3s7FQdXab5E5ZI1xbhkk+v6KcT6qSkObllJvupNrJi2QapvH1rZKwjLqTcW+N6zuDH7DuClMXikXxLGwU17bgdXU57/xIvPnrvG1ZOJOFcxnDjXT8/vOMSMjhmbZ4lKpBzHjV6DDrZ5bsteQWhpa1yquxif3BWbGZ/ZeQaYGzhaiAcvexjfuZWKomi32buYEB2RhpuvWZ49ChRFwdbJhLiokssT6NcCrd3KmsZd7Bg6wQPQBp0gq484dX01Lh5JoHpTq8ylLryqm/P8J178OjuUcwfjGTTOjdQUlWaPFGFecTZyhSVKXgVoMAvD1smY4IvJ9z6wGHQ6laPbY6jb1ibHejkOrqa8taQqxqZKqayjI8q5ShI7VrZGJb6mTci1ZOKjdfjXtyT0WkqezR6EqOhMzfVTjEvucyAyNJUT/2hTUu7c0ka69UkOISpCm2NkdO++kD4BUZIXa9lFhadhYWWEpXXORIc+mZ99IXnxkKgAsZMf/aY/3jUsCLmaQlIJ72KcGJ/1fBHBqTkqbIUob9yrmkFGIeCg8e7UbmVNWorKV68GER0BVaoZfv9aWGtr35YU/Y7IFtaGNx596XNvQq+n4FLFDJcqZugyNlBxrmLKe78EAPDI885c/C+BZo/YY+9S/L6e9BJFyaskVVC2TibERSbc+8AiSk9XuX05mdi7Ohp0zDuyl3uUQjxEKknsWNoak1jCI9JTB18BoFpDS1JTVDxk9Flkp9OBccXezd3YWMHYRCnRNQgP/y+G+Bgdw971YOXMEACZni+yVIAKwsLQJwhLcnMsVVXZ/2c05pZGRIWl5piar2fjYAIkG7xPVHIVuL/W/Wlnti2/S4ve9pzeG0d0RMkupxRxM+e0Sy1OhCifPKuZwzkY8LobLXvbZ95unbFZaO61PfW0BGHJtTnJ+gShleFlMjz8zPHwy7r2MTZWePUrnxzVuDWbWfPl/lr3fS4SsaLkVdAGMzdbR2PiY3To0tR8d85LT1dJyViEd9rjV5iw1A+/uoanBquqyp8LI9j0XUTmbR5+MuossqnAHc7srGyNSEpIR6dTMzcSuR8h17IqeS8fTwTyH9ETD6l//4VGjcAm71qtFYmZhUJqCSYIE+N0GBlB+0EO2DmbcPVkYonEpKgkKkmC0Moua4pxSblwOIFlH2m7SVraGFG1Tt6+3TNTq3B0W4z05R5GFThmWj/qQOtHHQBtQLckE+sA5w7G5/je0BRjIcqLKtXMGTDSm7ptc85MempKFf5edsfgZz9oCUL9zIySoF+7PfeaoQWp06p0+rwlv5KvEJUkyaHfoaigKSsL37zJ+A7nte3D0XYhzk9spC5HchBkWoqonKwzRovjSmgNz+yL7ur51Lr3hgriIZKWplURVnBmFkakJJdc+5kUn46ljTGKotCos22BGz2Ih1Al6KtBzjUIS8q/G6Izv06MS8eret5BKXsXEzoNdZIlYR5GleRax9q+5BOEe/+IonoTK+pm7DouCUKRQ1AQXLpU1meRSd8/0m+ko+fmY8YTkzzzLRKysDYu0SnGmRWERUgQlpayPwNR+VSSRtPWSWvQ8tvJOD5ax8ndcZlfg5bIiI/RsWPlXdLTc/4OQq/lTXJY2UkIimwqSey4eGkLtoeX0EYl+ukvn/wVyIDX3XjjW1+pghI5VZLYKekKwqT49HLR2RTlXAWPHXNLbXr+0W0xXD2VWCLPGR2eSrWGljh5aO2Zb20ZlBLZVJI2x9LWiNQUlZQS3BwrMjQV39oWmVMzZQq+yCE5GVJKbyPDB6Wk1yC8G5Ka+bxlrezPQIhyytZJa9Byr81x5UQC308K5tiO2Mzb9NMer51K5OvXb/Dr7FDO7MtZYp99miSAtZ2xjDqLnCpBZxPA1UfrFN5rJ+M7t1MJvpR0z+eLyYhBOycTuj/tTM3mskGJyKWSXKyZWRhlblKilsDPIwlCUaBKMsVYURR0aSrXTifx1atBJfKccVE6rB2M6f2CCwD+DQxPMxMPqUrS5ljblez6ncmJ6aQmq9g4GtP7BVdenutD1xFOJfLcopKoJLFjYW1EfLTO4Cynojp7ID5zlqF5PmsQPkhlfwai8qkkga9f9PPGuST+2xbDrctagu/r129wZEsM21fczTz27P6sZOC1jNHrmxeSSE9XMy/y9BWE7lXNmLmlOh/+Ue2B/ByiAqkksePsaYqRERzbHpvvMZFhqXzw6CU+GXY1x6YMV08mEp+roxpzJw0bB+N8y/yFqAxxA9pOxqnJ6ez5I5Kx7c4Tk6uCPTIslcvHC795VlJ8ernobIpyqpLEDcDTH3ri5mtGYlw6d24bXhcqNjKN7ycFc/1M3irD0OvJ3LyYNWAVH6XDxsGENv0c+HxXTVyqyJIwIptKEjv69TtLamO52EitzbJ1NMHa3ph67Wwws5A2SGRTSa519IOvHw68zE8f3uLqyeJXrx/+X9aSFkZGZX+tIzW/ouRVgqAHbVTNzdeMXWsiiQpLw9zSiEeec85cRPT2lWSqN7Hi4n9ZF2vGJtooNsD6b8L5c0E46ekw9B0PbpxLwq+uBROW+pfJzyMqgErSaBqbKLh4m3FiVxznDsZTq0XOir/NSyJY/0145vd710Zh42BMw862fPbcNarWsaBJdzs6D3PCxFQhOiINexdprkQBKknsmFloi16v+ETbcTjkajJ2Tlnv/enDrxIfrePrQ7XyrUCPDEtl48IIHp/gTlK8TtZ/EvmrJBWEAK36OuBb25KPh15hyaRg3v7BL/O+6Ig0tvwYwbmDCdy+kgwqjJzhlXm/Tqfy0aArAHy2vQZWtkbERemwydjFMr9dJcVDrhLETWE3+Nn9eyT2LiY06GBb4HHxGeu22zhKuyPyUQniBnK2C/v/jMbKzhj/+sWrNDe0DFlZkhZPlLxKcqEGUL2pFVFh2mhYcmJ6jqQGgKOHaY5Opr4zqZeeMSC3alYIN84lUTWfHY6FACpN3ADaOoEmCn8uCCc5UQsEfTVt7jj6dXYoP7x/i2lDtAu062eS+GNeGAc2RnP2QDwXDiXIGjaiYJWk3YmN1BGSraOob3/09BdxCQVUe/wxL4x966I4tScuY4qxXKiJfFSCmMmuSjVz2g104OrJRLYuu5O5m+qxHbHsWBmpJQeBI1tjOLo9BoCU5HS2Lr2T+Rxvd7nAvDFBpKWqWDtI7Ih8VJLYsbLVUgEJsQUnCH+ZHsKC8TdJSii40jA2Y3O63NdDQmSqJP213NPy89uz4F5uX03myomSWTu3pEiCUJS8ShL4AAPHutF5uBPjFlU1eL+tozFNu9vRso89g8e7F9ggJiWk4ycJQnEvlSR2HN1NeXyCO1dOJLLr10jOH47nvT6XOLwlJsdxwyd5ZK6Bc+dWzmlhsXfT+PKVIJIS0mk/yPGBnbuogCpJuxMZqsVAi972AESHZ3U4dbqsn2/u6Osc3xlLbGRajrUKT+2J4/D/tBjb9F0EIddSZA1Ckb9KVEGo1+wRO0BLlM9/OYjZz1/jzq2spLuDmzbYtHhCMH99F8Gyj27nGbS6cFibGSLVtyJflaTNMbQDeFqqSkRGzETcSuHsgaxllP5aHM4Xo69zZGtWX+7ifwncDUklPlrH1YxEh42jDOqKfFSS2HFw0zawenmeD/71LYm9W7R1PKMj0ni1xdnM4ojOw51yFB2VJYleIQpgaW3MkDfdAXjhUy8WTwgGtK3Pw26kZJbQP/NRFQD2/B4JaOsMhl5PyTwOtLUKGnSyedA/gqhIKkmjqdd+oCP71kaxd20UW3/SERelY8mk4DzHtO3vwNn98Xz9+o0c9+kv2gaNc6Nhp4KntYiHXCWJm1e/9OFuSBrNethxclcskaGphAWlsOHbcPzqZe2iGnwpmYVv3gTgycmetHnMgYRYHd+MzYqh4ItatVRiXMksPi8qoUqYIPSpmXO34SsnEnNUZzTuakdirI79f0bz54Lw3A/PQb9kjBB5VJL+mp2LCaZmCnt+j6JaIyvsnE1YMyeUfeui+HxXTVZ8HJJZiQvw9zJt/fX0dJWm3e1IT1f5YvR1LKyN8KpuzuVjWqzZOklyXeSjksROq0ft8aphTtXaluz5PZKQKylEhqaiS1Nx8cpaszb4UhInd8XxyHPOOZaGObUnLnOmIUCvkc7YOJSP1Fz5OIsiUBTFAfgOqAeowPOqqv5bpiclcqokgZ9b4y52OLiFEhWWhltVLfFnm2uELC5j7Y2ABpaEXk+hcTdbjIwUNn0fQYMONljKVC9RkEoYN52GObF08i0Amvaw40iuCkLQFuSt2yb/5Lm+mkqIfFWSdieggRUBDbSvHdxM+WdVJP+s0gaejm7LGTt9X3LlzwXh/Ls+irhIHRePxOd+OgBi7kiCUDw8LG2MGTXTi7CglDyVgQDpOhXnKqb3fJ7OwxwzqxGFyKMStDcAZuZGeFYz5+rJRD4ccBlzSyP0OYy7t1NJz1a53vpRe/7doG2mEBaUgqqq3M3YECgpPj0zOehcxVSud0SlZ2SkULW2NjPQztmEEzvjeK/PJQC+OVyblOR0dKkq88cEERupo3Yra6rWyZpJeO101sDVy3N9yk1yECpgghCYB2xWVXWwoihmgFVZn5DIpZI0moa8u9yf2LtpbF6irVdjlKv90ycI+7zoioe/OR2GOBJ+I4U9v0fS+wXXB326oqKpJEmO7Fr2tsfW0RhVhSqB5pkJwkeecyaw8b0/vvu+5JonES9EHpUwduq1s8lcMw3IMdLcbqADvUe5YGSkVdrqL8wAXpnvg5GxgompwuVjCTTsLNW3Ih+VsIIQoEk3O1RVJaChJTF3clauN+xkS0BDbTrYrjWROR43+E131swJBWDIWx4P9JxFBVOJ2pxHx7hmzuDQrxkNEBGckqOKtnkve1o96sDNC0n8OjuUkKsphN/Mu7nC69/4lv5Ji4qrEsWOXu5NfhJidSwYf5NLR7M2Mt2y9A63LyfjW9uCXqNcOLEzNvM+z2rmD+xcC6NCXXUpimIPdACeBVBVNQUoX9u+iEoZ+Hp2TibYOZlQvYkVR7bEZK4/oPfstCr8vewOju4mdH/aGQDvGhbM2lqjLE5XVDSVNG7qtM5bHdh+kCNOHjnjZ/gkD36Zru3eOmqWF1XrWOLsee9KDyEqY+w89rIrPrUsqNfOhsP/i87c2XjW1uqZ60a17Gufp0rKr65l5v3Vm8gYqihAJU0QAiiKQo2m1uh0Kn71LGk/yIEWve0xNtbKo4a+40699jbUamHNiV2xOLqb4lfXgjVzQun2pFMZn70o9ypRzNRto8VB9qnEAAvG38zxvY2jMd7VLXD3M+P3L0L5dtwNajSzznFMtyedcPU2Q4h8VcI8QY2mVhzdFku7gQ7s+T2KtzpfyHG/jYMxR7dpCcGQaymc2BmXY8MfR/fylZKraCtX+wPhwA+KohxVFOU7RVFyfDIpijJaUZTDiqIcPnLkSNmcpaj02g9y4N3l/tRqkbNhbNHLnkkrAnKsMVBRSOyUA5Ww0cyPofVp2g905MPfq9HtKScadbatMMlBiZ1yoBLGjrGJQrMedlhYGeWotrV1NMHISGtjHN1Meelzb6auq5Z5v5VdxenaSeyUsUoWM4YYGytM+NGP1o86ZCYHQUsg1mtrg4mpQpOudvjXs0RRFL45XJsBb7iV4Rnfm8RNOVGJ4qfjUG0juEeec873GDsnk8z/e450ISI4lX3romjcxZZ+r2qzpNJSy/fvRGKnHEhPr1SxA9B+sCOzd9SgRS/DSyKN+MAzx/f65OCEpX6MmuWV2acrLypOL1JjAjQBvlVVtTEQD7yb/QBVVRepqtpMVdVmTZs2LYtzFJXwQi03RVHwrWVx7wMrEImdcuAhiJ33VvozaJwbpmaGmx83XzMGvuFe7hrLgkjslAOVPHbcq+ZfkdGggy0uXmb0fdGFJt1sK9QAlcROGavEFYT3o7zHkMRNOVDJ2pyGHW355nBtOg3Vqmc9A8ypWke7zuk83IlX5vtg55xV5dRntCsdBjviGWDO0Hc8qNFUK5jwb2CZ98nLEYkdURqMjBSsbI3xrGaOlW3e65sGHWxoO8CBriOcaNxFW/bF1dsUv7qWNOla/ta6LV/1jPd2E7ipquqBjO/XkCtBKMqBStZoCiFKjlegBV6BlSu5LsqBSt7mKIrCS597F7irqqxzK4pMEoRCFE8ljRl7FxNm/q861g7GbP/5LtfPJKFLVQ1uJDf0HXdAa5/snE345K9AHFwrWmpBPHCVOE9gbWfMp9tqsOyj2xzYGM3QCe5YO5igKAoj3tOqCP/6LoKj22Pxb1B+l4CpUFGsqmqIoig3FEWpqarqeaArcKasz0vkUkmDXohSV4kbTSFKzUOS5GjQQTYbESWskseMEKWmEvfX9JWCNTOWUfKvb7gqMHelraNbxVgWRpSxShw7oFUTDn3Hg7ptbWjWI291YLuBDtg6GtOke/mrHNSrUAnCDK8BP2fsYHwFeK6Mz0fkVskDX4hSI3EjRNE9JAlCIUqcxI4QxfMQXOv41rJg5pbq2DrmXTNaiGJ7CGLHwsrIYHIQtDU82w9yfMBnVDQVLkGoquoxoFlZn4coQCUPeiFKzUPQaApR4iTJIYQQ4kF6SNob/cYkQpSYhyR2KjKJelE6SjH4/fz8sLW1xdjYGBMTEw4fPlxqryXEAyUJQiGKTmJGiOKR5LoQxZM9dsr5pjZClCtyrVPuSYJQlLwHEPg7duzAxcWlVF9DiAdOGkwhik6SHEIUj8SOEMUjMSNE8UiCsNyTBKEoOTod3LghQS/E/ZD4EaJoJMkhRPFI7Ahxf6SCUIiikfam3JMEoSg5SUlw6RKYmJRq8CuKQo8ePVAUhRdffJHRo0fnOWbRokUsWrQIAAsLi1I7l/Lk5s2bZX0K4n6UkwbzYYsdiZtKoJwkOSR2RIVTDtqdhy1uQGKnUpDYKRMSO5VAOagglNgpmCQIRcnRB3wpB/2ePXvw8vIiLCyM7t27U6tWLTp06JDjmNGjRxtMHApRbpWTJIfEjqhwysGFGkjsiAqoHLQ7EjeiQpLYEaJ4ykGCUGKnYEZlfQKiElFVSE8v9cD38vICwM3NjQEDBnDw4MFSey0hHphy0NkUokKS2BGieCRmhCgeaXeEKB6JmXLvvhOEiqL0VRRlm6IoZxVFWacoSgcDx7RUFEV3v68lyrnsicFSCv74+HhiY2Mzv96yZQv16tUrldcS4oGSBlOI4pELNSGKR2JHiOKRmBGieMpBBaEo2H0lCBVF6Q6sAyyAbYA3sENRlDmKIiu2PnSyVxCWktDQUNq1a0fDhg1p0aIFffr0oWfPnqX2ekI8MHKhJkTxSMwIcX8khoQoGumzCVE8kiAs9+53DcIpwE+qqj6nv0FRlOeB+UCAoijDVVVNus/XEBXFA5hiHBAQwPHjx0vluYUQQlRAcqEmRNHdvAkpKWV9FkJUTNLuCCEqqfudYlwPWJ79BlVVlwAdgVbAdkVRnO7zNURF8YA2KRGiUpLOphDFI7EjRNFduQIZS7ZI7AghhHggJFdQ7t1vgjAJsM59o6qqR4C2gCuwD/C/z9cRFYE+2Et5mrEQlZIkOYQoHokZIYpOVUGny/paCFF40mcTongkT1Du3W+C8ATQy9AdqqpeQUsSxgE/3ufriIpEJ/vRCFEk0dFw/XpZn4UQFZNcqAlRPOnpZX0GQlRM0u4IUTxSQVju3W+C8Degd37TiFVVDUObbrwLkE1LKrvsFYTZvxdCFCw2FsLCtK8lboQoGrlQE6LopIJQiOKTmBGi+CR+yrX7ShCqqrpQVdWqqqreLeCYeFVVe6iqer/JSFHeyUWaEMWT/UJNCFE0MiglRNGlp0u7I0RxyTWPEMUjMVPu3e8uxgAoitIU8ANCgCOyc/FDKnfAqyooUjgqxD1JJYcQQogHTZLrQhSPxIwQxSOxU+7dV4JQURQ3YB3QAm0KsQoEKYoyOGOjEvEwkYAXongkQShE8Vy5AgkJ2tcSO0IUXvZ1oCR2hCgaiR0hikdipty732m/M4EA4GmgDtAXbVOS7+/zeUVFZKiCUAhxbzLFWIjiiY/X/oG0OULohYRAVFTBx8jAlBD3T2JHiKKRTUrKvftNEHYBJqmq+rOqqudUVd0EPAXUVxTF5f5PT5QrJ04UvONd9mBXFAl+IUC7ALt2reBjVFWmeglRHKoKaWlZXwshtE2v7ua7PLgme7sjhCi8GzekvRGiuCR2yr37TRD6AKdz3XYabbqx130+tyhvgoMhNTX/+3MnCIUQkJgIly8XfIxUEApRPBI7QuRVmOSfVBAKUXSqCseOyaCuEMUlFYTl3v0mCBUgd89c3yMplV2LFUUxVhTlqKIof5bG84sCpKcXHNDZ7zMykuAXArS4KcyFGkjlrRBFlX0nVokdITT36q+BVBAKURz6uJKBKSGKRxKE5V5J7GL8o6Io8QZuX6YoSkL2G1RVbVECr/cGcBawK4HnEkVhqDOZnAympnkTglJBKISmsJUcIAlCIYpKqqCEyKso7U7ur4UQ+dPHiixtIUTxSMyUe/ebIFyaz+25px2XCEVRvIE+wCfA+NJ4DVEAQx3O06fBwwOqVMm6TZ8clA8AIQpfyQFaol0IoYmI0NZRq1Ej/2NkirEQeUmCUIjSoY8rGZgSonikgrDcu68Eoaqqz5XUiRTSXGACYPuAX1foG8TcHc60tLyNpKJIJZQQeoVJYEgFoRB5JSRAbGzBx0gFoRB5FWVpCyFE0el0MltKiOKQBGG5V2HKVRRF6QuEqap65B7HjVYU5bCiKIePHCnwUFEU+kDO3eHU6bLukynGFZrETikpTEMoFYQVmsROKZGNFio9iZ1SUpR2J/fXotyTuClD2QsmZL31CkdipxyQmCn3SuRqVFGUDoqirFAU5YqiKPEZ/64oivKzoijtSuI1gLbAY4qiXANWAl0URVme+yBVVRepqtpMVdVmTZs2LaGXFkVKEMoU4wpJYqeU6KcYF2aDH6kgrJAkdkpJUaugJHYqHImdUlKUKcaS5KhwJG7KUO5iCImdCkVipwzFxsKBA1JBWAHcd4JQUZQPgH+ADsAeYB4wP+PrjsBORVHev9/XUVV1oqqq3qqq+gHDgO2qqj55v88rCknf0cwd0Dpd3vv0U4yFEPkn1w0dIxdqQmQp6jpqQghNUZLr0l8TovCytzky60OIwktNhaQkSRBWAPe1BmFGdeBHwDTgI1VV03PdbwxMAT5SFGW7qqr77uf1RBnKL8mRfQMGqSAUIq/s01GMjQ0fIxWEQuQlFYRCFM+9kuu5kxwSO0IUjlQQClE8+j6dxEy5d79DHy8Bm1VVnZI7OQigqqpOVdXJwP+AMff5Wtmf9x9VVfuW1POJQijqFGMZkRZCY2iNzvyOkQs1IbIUpYJQLtSEyFKUBKHEjhCFJ8l1IYone7skcVOu3W+CsBXaeoD3shJofZ+vJcpSUaYYg3Q4hdDLbwfw7GSqlxB5yTRJIYqnKFUaMk1SiMKTDRmFKB7ZVK7CuN9egQdwtRDHXQU87/O1RFkqzBRjPZliLEQWWYNQiOKRjRaEKJ57xU72+2RAV4jCkwpCIYpHEoQVxv0mCK2A5EIclwJY3OdribJU0BRj2aREiPwVJUEoF2pCZClKBaFcqAmRpahrEAohCkcqCIUoHlWFtDSJmwrgvjYpydBGURSXexxTqwReR5QlQ9Mk9dWDskmJEPmTKcZCFI9UEApRPIaS6zodJCaCjU3O22VgSogsQUHg6QmmpobvlwpCIYpH3yZJm1PulUSC8PNCHifvhIrMUBVU7jJhqSAUIq/8Nik5dgzq1tU6oZLkECKv/CoIk5LAImNSQvbYEUJosg/e6oWFwY0b0KKFJDmEyM/Vq2BrC46Ohu+X6flCGJaSAmZm+d+fvb+WlvZgzkkUy/0mCP1L5CxE+ZdfBWH2/3MnCKXRFCL/CsLQUKhePWeCUBLrQmQxVEGYnAy7d0P37lnHgLQ5QmRnKHYK2lROCKFJT88qgDBEpucLkZeqwrZt0KtXwceAtDkVwH0lCFVVvV5SJyLKOUNVUIYqCLM3lnKxJkTR1u+USg4hshiqIExNzTnyLLEjRF6GYictzXCCUGJHiCz3Wvs2d3JdYkcI7ZpG38bklzjPPsVYHzuSLCyX7itBqChKOIWbOqyibVQSBPwBfKGqagHDM6LcKWiKcfYOp5GRTDEWIjtDFYT6nbwMVd9KZ1MIjaEqqOxJDsj6WpIcQmQpagWhxI4QmqJWEErsCJEVBzpd/glCQzMNJV9QLt3vFOOvKfzagpZANWAa4ARMus/XFg9SQVOMs1cQGhvLJiWiUtiyYQM+vr7Ubtjw/p6osMl1kM6mENlljx19hzMtzXCMSCdTiCz5bVJiaDaITJMUIktRKwiFEHmvawyRKcYVxv1OMf6wqI9RFOVV4C0kQVixFGWKsVQQikpg4Rdf4OXoyPxVq8DE8EfloUOHaNy4MSb53A8Yjh39FElDFYRCVAaxsdpC7/cj+8BU7gShfuRZKgiFyMvQJiX5TTGWCkIhshSlglBiRwiNvm0pTOzIXgXlXlkMG24HrpTB64r7YaiC0FAVlLFx1v0S+KICCw4NZfOBA3D7dp77jhw5wj///EOHDh3477//Cn6iosSOJDlEJRB96xbp27fDnTv390RFSa5L7IhKJi0tDV22iy1VVTlx4kThHlyUKcZSQShEFkMVhJcvw/WMZfdzJzmEEPmvt27oGEkQlnsPvFegquoZVVW7POjXFfepoGmSuTuaEviiErgVHk5QeDiXjx/Pc9+0adPo3LkzSUlJhBlIIOZQmNjJPT1fiAqsbceO+I8ezeVduwzen5Z9k5GCGBqRLihBKERlkZ7Ou+++y2effZZ506mjR2nZogVxcXH3fnxhEoT6mJH+mhBZDFUQJiRAUpL2tSQ5hMhLHzO5Y+f2bUhJ0b7OvkkJSOyUY9KjFoWTX5Ije9VG9iSHJDpEBZaelkZIZCRPDh3Ksl9/zXP/fwcP8u1bb9GnVSvCg4IKfjJDsWMoySEVhKISUFWVy0FBjBkzhs4vvki/Xr3Ys2dP5v3x8fFU9fDg+MGDhXky7X9DsZO7ulBiR1QSwbt28dvMmWzbtImj+/Zl3n7t/HmSkpPZ8Mcf936SwuxirJ/xIbEjhMbQjA8wPD1fkhxCZMkvdq5cgeho7Wupvq0wJEEoCie/TUpMTAzvYqz/vjAOHiy4JFmIUrDyl1/4cu5cVAPv0/CbN7G3tmbs22+z6M8/OX3oEOkZ79Hw8HBiYmN58Z13qB0YSFhISMEvVJQpxqXRYEZFZY1852f/fggLK/nXFg+diLAwrC0seGfyZH5ZvJjWXl5MfPvtzPt//+UXImNjmTl9+r2fzNB0yPyS62VRySHtlihpsbF8Pm8ez378MSfOnePM2bOZd12/cgVnW1vWrFpV8HPkN9Ur9yYlUkEoRE75raOWPUGYnp6V4JAkhxCagmIn96wpqb4t9yRBWElEREQQExNTei+Qe0F40ALexOT+KwjDwiA5uWTPV4gC6HQ63n3zTebMmMHCbFO49IKvXqWKiwv16tfnuccfp//AgdSsXp2kpCT+O3SIxv7+KE5OuLm5ERYaWvCL6TdYKEwFYVEazdRUuHix4GNu3ODkihX8V9AFZXw8M775hqPZqryEyI+qqjz+2GPUCQzkpoHq2aDz5/F1c0MxMqJtv348M3o0Z7MlOX766Se+evFF/ti06d5TjfVxkL3DWdD6nSUsPT2dXl27cu1KPssm79unjY4LUULizp1j+c6d1PT1pXlgIJeCgjLj5Nq1a3Rr1IhLly4V/CTZB2tzbyyXXwWhEJXBvTYYKczjs/+vp9PlfF4TE0lyiEopLS2NtWvXFv2BRa2+LaHYUVU1Rx9TlAzpFVQSr732GnPmzDF8Z3Q06NesiYoq3gvoO5O5O5u5E4RFrSBMT9eO069PIMQDsGHFCjzs7Vn33XdMmTWLaH35e4ZbQUF4ubsD8MnXX3Nx0yZqubiwdMkS9m7bRrN69cDICFc3N8LDwwt+MUOxk1+CMPtj7iUhAa5dK/CQY7t303XyZB554w32/u9/Bo9JvnKF6WvWsGDZsoJ/hqgoiI+/93mJSu33Vas4f+oUjXx9+f3nn/PcH3TpEr5VqmR+71G3LmlpaURERJCUlMS/hw8zZOBA3B0duXHjRsEvVpQKwlKYJvm/FSvYvH07h3bsyHunqvLU5MlsWrGCBV98UaKvKx5OCfHx1O7dm0H9+7Psiy+Y8coreDk7ZyYEr9+4QYv69Qm9V5uTvcIp98CUrEEoKrHdS5ey86efiv8E+VVBpabmTIDIhoyiktClpZGWmpr5/aZNmxg2bFjmrKlCK0z1bSlMzz964AAd27a97+cROUmCsBJISUnhr7/+4siRI3nu27F2LZ+8/jpfv/cez/Xpg5ufH2eyrWmzZMmS/BOL2ekbxHtNMTYxyZkkvBf9B0k+CUKdTsesWbMMTgPNIykJjh2DW7cK99qi0tLpdOzfv58QA9N/VVVl2scf8/a4cTRs356ezZrx+eef8+uvv3L58mXmzJlD8LVrVPH01B5gZAT16jHxhReYPn06P69ezZCBAwFw8/AgrLAJwsJOMS5s7KSlFZhYj4mJYfBbbzF/zhxeHjGCTfmsW7Vj2zbc3dz4fccOzp07Z/CY23/9xavPPsubTz1FekJC4c5PVEozpk1j+ltvMbRfP9Zu2JDn/qCrV/H19s78XrGyopa3N2dPneLQoUPU8fPDtk4d/N3cuHKvClh99e29Nikp4SnGqqqipqfzzddf41+lCmcN7BybcPcuq/fsoc/kyYwZP55b0u6Iezh+/Dip2S7Ecjt18CCuDg5889131O7Rg84vvUQ9Pz/2/PMPANdv3qRZ8+bciY7OsbtxHvrkX+7K9QdQQXjs2DHCjxyBoCBJnIgHKy2N6V9/zcj33y/8Rli5FbYKStZbF5XE7EmTeGXAAIiLY1jPnkwYN47k5OR7Fz/kVpjYyb5JSWFjJz1dm61hoD3R6XTs37GD8MhIku+1lJIoEkkQVmSqCvHx/PPPPzg4OHD06NEcd+t0Ol4ZP54fdu7kp+3bad6sGf27d2fBggWZx2z5/XfmzJyZY/Qg39cylOTIXUHo7AyNGhX+Yu0eCcLTJ0/y7rvvcv3kyXyfIiYmhg0//gg7d2rnePIkJCbe+7VFpTV79mweHziQ2jVr8vXXX2feHh8fz4iM5N6AkSPBzo5Jgwfz8ccf89RTT9G6VStmfvwxb3/6KU3btcvxnG0eeYSWNWtioqo079ULALcqVQi/e7fgk8mdSAfDSQ5zczA1zfr+XvQXe/l0hFf88AMNq1Vj2DPPULdRI86cO8fJkyfzjAqu/ftvRo8aRf/WrenUrh2vvfxyjvtT4uJo/8ILWAYGsun4cXYY2LRFPBz+27+f8IgIej7zDN0feYTDJ05wN9v7PyUlhatXruBbtWrWgxSF2n5+nDt5kl1bttChbl1wcSHA25ur+SSkMxlqdwxtUlLCU4w/nTmTZ4YMYeeJE7z16qsGp68c3ruXRtWr8+eGDXRr3JgDu3dz5swZtm3bVmLnISqPlJQU2rdty/tvvpnvMScOHqR+rVraN8bGYGbGxGee4b3JkwkJCeH6rVtUr18fOysr7ty5k/+L6ZeEyV1Vq1+DUP9PnyAswalejw8YQMtHH6V2+/ZcPnw4/4OvX8+37RIiu/j4eEYMHXrPiqa4CxfYc/YsznZ2rFyyJOd90dFcK8w0xMJWQWVfM7qEEuEXTp0i/cgRbXZIQZKTWbl8Od8tXJj/MXFxWTPHhCjAxYsXWbplC2EnT7L+n3+o5epKTS8vgi5fLtoTGYod/WZZ9zPFOC0N7twxGBeNGjViwY8/AnBLlnspUZIgrKDS09P59IMP6N2xI9MmTmTSsGEkJiQQmm09tA1//IGjhQXnz53j3+PHefmjj3h/0iSWr13LmTNnADhx5gyqTsfmFSvu9YKGpxibmuadsmJhkfX9veg/SPJJUO7fuhWA3cuX5/sUW/74g4GjRnHWyYndUVF8unkzUefPF/y6MtJQael0Or6ZP5+1U6awa9Yspk2bRkJGw/LzsmWEBQWx/a+/MMpIKtRs1Ii/f/yR44cP83ibNlzevp0je/YwZuzYnE/s6ck3b73Fyo8/RrG2BsDV05OweyUIC6ogzJ7kCAiAwMCiV9/ms37nrytX8uTgwQDUbd6co+fO0aZNG/7999/MY9KTk1n/77/0HzKExT/+yIU//mDtb79xIlvF1KJ58wjw8eGz2bN5ecwYvv3uO9J1OlJTU1FVlYkTJ7Jjxw5++OEHWrduzfDhw4s+NUGUK/Hx8Tn+hjdu3OD1119nwMCBjH/uOYytrbFyd6dL48Zs3LiRxMREEhMTGdqnD/NWrMC3Zs0cz1e3Rg0O7N/P2t9/p+sjj4CREQH+/ly5cKHgE9En1x/wFONff/6ZZb//TvtWrWjRvj1nDXSU9+3dS5smTejdpw/tmzdn744dPPHEEwwfPjznesDp6VrnVjzU9vz9N74uLvz8yy/8baDyFuDk8eM0aNAgx20tO3RgYLt2vDNhAmk6He4BAbg7OBB2+3b+L6afYpzf2rf6BKG+rSmh2Dn633/okpP5fPZsunbsyLuTJhk+MCGBO3v38r8vvuCpJ58s3AwR8dD6d/t2VqxezZyZM3llzBh0Oh3vv/8+x44dyzzm2pUrDHn2WVo0a8aEl15i0aJFTJ06lfj4eO7cuUPzJk1o2Lw5mzZtKvjFCltBWJw1CP/7L9/kX2JiIg2bNmXAuHGsmjHD4DFR4eEQEgLbtvH1rFksnD8/35eaMX48M197TZtVJYl4UYCg4GCcHRz4cuFCbCws+OOPP6jt709QxtIWu//8k9MHDtz7iQwlCA3116BoyXV9fiA2NsfNiYmJnDp1ipMXLuBgbU3w4cOQq1Aqz/nJtUmhSYKwAlJVlfffeYff1q7Fq2pVkpKTGTVsGI1r1ODPP//MXE/t9+XLGfHYYxhbWGCUUV3h26AB8158kZ7duhEbG8vV4GCe7dePg/cKfn2DmLuzaWqaM8D1QV9CU4wP7N5N7cBAdp8+zf9WrmTmu+/mSSZeOHECDxcXhjz3HAMHDmTDvn3M++YbkgwlAdPTYdcu+PtvuFdiR5RbOp2O27dvk5iYSHx8PKqqsmXLFv764w/aN2lCfW9vmjzxBPWbNaNzkyYMHDgQPz8/pk2dyvjhw7GvVi3ryerVo7ObGzVTUvhqyhTsGjemeuPGeV/UxASXnj1p/NRTmTe5ensTHhVV8AVOflVQ2S/e9BdrRR1VA4OxcycigsMnTtBz2DAAqtepw82ICOLi4tifUeF099Ytfv3mG+xtbalRsya4u2PXti3Pde3KR1OmsH//fr799lumz53L7ClTAHjqhRe4HBqKs7Mzrq6u9OzZk9WrV9OnTx9+/O47PuzViyunTvHUU0+xw9C6baJci4qKYseOHdStU4dpH34IQFxcHI8//jhp8fGsevtt3si4HVtb+jVrxuTJk3FzcyPQ35/9R46w46+/6JORmNZ7evBg1mzYQEx0NI888QQA/oGBXLnXCLU+dnJ3OPVtkT5OSjBBePv2bS5fv84n77/PmHHjqNWoEReCg9Hl6pz+e/AgbVq2BKBl69bM/e47vDw86Nm1K2+//TZHjx4lMTGR/StX0qlTJ2L37UO918CVqLTW//orj/fuzdLZs3nuhRcMThE+ee4c9Zs1y3ljrVq80K8fPy1bxmuPPoqRlRXuTk6EFrR+Z/YKwtwDU/p1CbMfU5TYuXwZ8tmYa/WyZQzt3Jn+TzzBp/PmsWnPHmIMrHsdfO4c7k8/zaApU9i2ZQtbMwaCxcNHVVWCg4MLnHq/e+tW7KysmPDee/y+ejWBgYHMmTMnayMFnY7p48fj6enJj8uX8+iIEZy9dIkpU6Zw6tQpdu/ejY+TEzOeeYbfDKybm0N+FYSGpucXdYpxVFSeJIfeP1u30tDfn7Z9+vDyvHlcz7WsxS8//YSLhwevv/oq9V9/nZPXr3Px+nXCQkNRVZXvv/+eqIxYu3LwILNXreKHvXsZMn48twtKmoiH3o2QEPr16sXaf/7B38sLPDzw9fLi2H//sXP7dp4bM4ZXXn313k9kKLmeO0GoXzamONc6uTZivXrpEn7u7rzSpw+dmzfnf5s28dXChQaLJnZu3Ej/du1I+/tvqawtJJOyPoGiUBTFB/gJcAdUYJGqqvPK9qxKj06n4++//8bb25uaNWty48YNjh07xsplyzh79CjbZszAdfBg0gDjqChmvvACfSdNYsyYMbz+0kts3L6dGbNn53xSY2OemjaNRZs38+nUqVSvUoWGzZrx+712LDJ0oabTadMis1dBZW8s73eKsaqy//hx3nr/faZPm0b8woVsOnKEccOGYd6oUeZhF86f58O33iLJ0hI7Ozsa1KpF5y5d+HT5cjZs2MDadeuYP28eXLxI2q1bvPvdd2w+fJiejRoxY9kyTPXTOkW5lp6ezsTXXqNp+/Ys/v579u3bR5UqVbC0tMTDzY2bFy9ibW7OmKee4tlnnwVbW2jQgCWvv847q1cz7PHH2fHbbzwyZEjOJ7a0hHr1tNHdjh0LPglFybE4tZWDA6YmJsRERmLv5JTfiRuuIMxdfVuCsfPfnj00rl4dy4yNVszMzKgeGIi5orBi1SpmffUVifHxVHVxYXT234eREaNHjODV+fPp168f6enpHPj0UwK6dQPA3t6e/X/9xc39+7Fq1Ijfduygm4sLfk2bYh4aiuLtTUCtWny/fj3Dhw7l5OnTuLq6ZpyujuPHjxMVFYWbmxsuLi54eHjc++cUD8S///xDv0GDqOrjw4jWrZn/5ZeE373L2rVr6dWuHV8NGYJR9epZFeImJjzWsyebLl3is7Fj2fLHH1g4OtIpY/p9dm7VqzP7+edx9/TEyMoKgIBatbiyeHHBJ6UfmMpdQWhmlpXkMDLSjsld4V6QsDBtgEg/nTOb31eu5JGmTZk0dWpmTNasWpVdmzfTecgQTp06RUREBPuOHePreVr3o1OfPvw4dizDOnYk3sKCHu+/T58+fbA2NycqMpLAwED8e/fGztKSX9eto2mLFnleNz09HVVVMc6++L2osBITEzl37hyNGzfm448/Zs2ff/LPxo0ENmmC80cfsW/3btp36sTBgwe5cOEC/Xv25NjFizRo1SrnEykKTYYMYcqWLbzRvz+YmeHu4kJocHD+L559w7jsbUx6ela7kz1BWJQkR3S0Nkib0bZkt3nzZr7JGEyycnSkcY0aHNy6lW652tw1a9Yw+JFHeOPdd7mxdSvvTZhA9+7dURSFhIQEXnrpJT7//HNcbG21izmdTmunbW0Lf56iQhjQrRu7Dh/GyMSE2XPmkJKSgru7O48++ijx8fGMHTuWXVu3MvW119ixezdL3nyT8zY2BEVGsixjY7Ut33/Pr9u3c/7iRdwy3pcbpk/n/UWLCDp/nqDgYGp7exPQpAlrv/++4BMylOTQT83PPvPDygpcXLSN2wrb7qSl5bv80eb16+nXpQsT3nmHW6dP8+38+cz87jsAUlNTefm11/hs5EhmrF7N0OHDSTcy4taJEzzSvTvPjhzJxIkTefPNNwkICMAyLY0JL7/MK++9x/RJk2jTrx8jX34Ze3t7zM3N+fbbb0lPT6d58+ZMnDiRatkGzKUdqoQydvYeNWYMffr0YcCAAZl3qTodN8LD6d2/P9/+9BNDH3kEAF8fHybNm8fH8+bRokYNgoKDOXjwIC0M9F1yvA4Ufs3owrY7BioIIyIiOHfsGLX9/Pjqjz8YO2oU8379lbikJOJTUpgwbRqKl1fGS6ps++svNh0+zHurVjHL19dg30/kVKEShEAa8Kaqqv8pimILHFEUZauqqmfK+sRK0pYtW4iKimLv3r38tXEjAM4uLty9exdfb286VavGkj//xLpmTTAx0f6ItrY09/bm1rlz3PnzT97/+Wee7N8fr8DAvC9gYcGLzz7LMxMm8NqAAdRq0ICz99qoRJ/kyD7Kp68gNJTkKIEKwuiQEK6HhfHUs88yY/p0ftu7F18fH/7cuJFB2RKE569cYdS4cbTr2jXztuf69+fE6dMMGTKEu5GRnD14kH8OH8bRwYEmTZvy/Y8/MnXsWAY/9hhTPvmEqlWrEhsbi4WFBTuWLeOH336jU8eOpJmaMvnjjwv3s4hS9dWcOWzauJHd//5Lvw4dmP7kk1xITeXqtWuYR0cz9osvMA0IgIzpvwCYmWHZrBnzjY0hLY1n33wTsm2gkMnLS7v4sLMr8nl5u7py89w57Nu0MXxAfhWE+iSH/pgSjJ2TR49SP9c0zxmzZuFmYUGbnj2Z/sYbvNOzJ0bNmoGNTc6fp1491s6YwVFFIfLAAQJat9bONYN5tWpUs7SECxd4tU4d7TPg8mXw8AA/P6r7+jKzVSvMFYXWrVszc+ZMqlatSu/evXF1dcXW1pbIyEjCw8P5+OOPeeWVVwr384pSkZSUhJGREaNGjuTr8eMZ0q0bWFnRpUULjiUl8efatTSKjIQWLcDePsdjnevVY/V774FOxwtjx2pr0Bri6cmokSPBwSHzpioBAYRERBR8coaS62lpWrJAP00SoGFD7UKtoGmX2cXFaZtZ5eokpqen89XXX/PtO+/kiMMRAwbw88qVdB4yhKlTp7J7924sTU3xrlcPAHNXV578+GNQVex37ODAnj2o4eHsXL4c1w4d8K1blz179nDnwAGGDR2KW5Uq2NjYMHz4cG7fvk3k3busWbOGJk2b8tNPP2Fubi4XaBVIeno6Op2OsLAwYmNjee+993B2dmbVqlXMnTuX7xYt4r/Fi3HLuLga2Lkzv69aRftOnZj4xhtcunqVebNmUb9GDTz0m2NlZ2XFh4sWaQOyioK7qyuhRZ1irNPlrLTNXrVelArC1FSD0yRDQ0O5HhxMiy5dMm9r3bIl+3fupNuQIfz99984OzvTqFEjflm/nskTJ9K6fXta1q7NjBUr+OrLL3nl1VeZN28eGzZsIC4ujsHNm7P4l1/wdHKie9euPPfBB5nPnZCQQFBQENHR0bRo0YI7d+7g7OyMIptGlDuqqjJnzhxGjx6NnZ0dSUlJzPv0U24FB3P89GlCd+7k8K5dvP/113jVqMH/tm7F3sGBK1eu0LdHDzzt7Xl2/HjemDkTLl6k9blzeNnZMfbIES6cOsUTb7/NsqVLM5ODAK1efJH6u3cTdOUKV65do2ZgIFWrV+d6QYl1yKpwutea0SYm2pIwBjawyldamsHljW7fvs2qtWvZnrGU0th33qFFu3YM6tKFJoMHs2fPHgKrVGHclCm8/vbbGFevrj3uwAF+W76c18aO5bPPPmPokCGs+ekn9m3ZwlsffYSxmRnTv/iCLp6ebA0O5vr160RGRjJr1iwszczYtHQpvR55hOkzZxIcHMyrr77KoEGDsLS05Jdffin8zyVKVUJCAsePH8fPz4+YmBhq5urb38vu1av5c/16lv32G1ZmZsybN4/p06fTpk0bokJCMDIyol3nzgD4+/kB4OvnR0pqKmvefZeGderw3YYNbFy3rnAJwnvFTlHX70xL0xLykZHcjYggMjqap4YO5cbNm/Tr2BFMTani5UVsYiJLFi1izqef4v7DDzz7/vucPn2aoUOH4mtnx/yPP2b6/Pl0bdKEFu7u2Ds4GG4vEhO1tjZj5uXWrVv56quvqFKlCp07d+axxx7DQj9QXolVqAShqqq3gdsZX8cqinIW8AIqXIJQp9Nx6NAh0tPTaZORWFBVlZVLl/LmhAm0bNQInaKwd9o0XB0dGfDFF7hZWbH+nXe0BEedOjkTCRkdR+Pr13Fr0oRF7dpBRqAb8sRrr9G2WjX8/PxI9PfnUnAwOp0u/4uS/NZRyz7FOHeSo7BVUIpiMMlxaM8emtSqhampKU8/9xxLlizhow8/5IuZMxk4cSKKkRHExHDh5k1q1K+f47Fzli1j13ff0fnFF5n93HNsPH6c8NBQQiMi8Pf3x8zMjDU//cSMGTN44oknCAkJwc7Ojvi4OOr5+jJ4wAD27N3LtgMHGPHUU1SrWZNz587h4uLCwoULefzxxwkMDJTOaAlaunQpiqLQrk0bboWEcOfOHfr160d8fDwnT5xg6iefsG/BAmpYW2sJvTt3aG5sDFWqaAmDunUNb1Tg4qId4+kJ+VX5QbGSgwBVfXy4fuYMdfNLEKan56y0hZKpINQ3vAbK6U+cPEnbbEl0gH79+qGmp/PBqFGMHTFCW1/CxSXv87q7w7lzNK5WTesAZ3RGMymK9vuvUkWbamZvryUQ9Z8dxsbg68tHr75K8xYtmDV3Lnv37WPV558zpE8fbT22atW4duIE7Z5+Gnd3dwZnm5KqrzJs37595tII4v7cuHEDFxcXjh8/TqtWrTJ3h09KSmL27Nk80q0bfs7ODH7zTa1CUFXpevs2XVNStEo7T888yUFAu/3cOe2ip1WrHNW1eeT6jHby8OBubGze9352+gux3CPSuSsITU2LtklJaqqWUExO1mIzw4EDBzDS6ejYv3+Ow4ePGEGDdu145+JF/rd5M5amprRp0iTna+o7jI6OsH07iqUlnUaNyvzM6dWrFzRpwt/79+NesyYNGjXit/XrCQgIwD4lheUvv8wzX32Fp6cnVlZWPPHEE8yYMQMzMzMiIiJYunQpMTExtGzZku7du0vl+wOiqiqJiYlYZVS+ZqfT6di3bx+vjB5NaEQEyampxMXF0aVzZzZu3Ej7du0YM2YMe+fOzUwOAgwfNIg2Tz6Jvbs7B0+c4MhPP9HoiSdY+Omn+Z9ItoEvdze3ghOEhjYp0U/N1yc/9Me4umq3F1ZamsE1zf7+3//oVL8+JhkV4wCtOnXi29mzab1+PX2GDGHUyJE08fUlKTGR7v36AWDk4sKSd9/lhS+/ZMkPPxAcHMye6dMZNGMGr2zdSq0aNTh+8yYnlizh73PnCA8PJzExkQMHDuDs7IyDvT1BN26QmprKoIED8Q8IYNy4cZmV62lpabzyyisMGTKE6tWrUzX7BkqiVKmqyrZt2zh16hRvv/02IbdvU6NmTa5cvMjBrVsxNTLio5dewrRRI1o3aMC2li3h7l12NGzIDRsbBjRsiE1cHEqzZlmDT4GB4OeHz7596FJSeO+dd3i+Xz/6ZquKAsDYmKq+vly9do3Lly/Tu00bfKtXJygkBFVV8++769e9LagKSp+Ah8Jf6+grEA1UEH4wcSLPd+tGnbZtAfCrW5fFixfT94UXsBw/nviUFF7u1Qs8PTHO1uZ4tmzJq3Z2WDg7M6RPH+zPnGFcx46MGzkya1DX2JhuL75It6NHtSSLPtajo2n/wgtUc3Rk3iefcCsqil9++QVLMzPOhYTQoUMHRo4cSYsWLXjjjTd45JFH8PHx4fHHH7/3zyruW/b36Jw5c5g+fTr169fn9OnTdGndmq+//RbfbH3zpKQkVq5cydWrVxk1ahQ+Pj7aey4ykhVr1rD4jz+o6ubG77//zt2YGJ4cPpzT585x49IlfNzccHBwwMfdHf+MatIatWtT19eXgU88gaIodLx7l1krVxZ80vq4MBQ72atv9e/BokwxdnQkKTaWxzp35nxwMDFxcaSkpmZWv3r5+2NsbMyQ4cMhMZH/rV/PM++9x/vvvce5c+c4q6p8uXgxSTodf2zZwiszZzJtyhSGjRrF7Nmz2bhxIwsWLMDJyYk7Gzbg4+aGdZ06/H3lCk8//TSfTprE1Zs3mTVrFnv37qVhw4YEBgby6cyZrP/zT3Q6HVu2bKFZs2bMnDmT119/nW+//ZZPC2rTy7kKlSDMTlEUP6AxcCDX7aOB0QB9+/Z98CdWCP9u3MizY8dibmpKbHw8tvb2xMXFERgQQPClS6z96itaBARo1RAtWoCREWs++ACdtTXUrKl9yBtia6s9pn37PFVBuRmZmeGfcRFkBXg4OnL1/HkC69Qx/ID8Kjmyr0uYO9ALmyC0sDC4Scn+f/+lVcZacK++9hrtO3Sgffv2TJk0CSd7e8Y88wwvPfII6YqS2RnMrn3nzhydO5cGPXvyZsYHqUO2ChfLwECmvvwyU0eM0DrfCQnahW6LFuDqymvAeyNH0rlTJ+o1asSevXtJSUmhWbNmfPHFF8TGxuLl5cXo0aN599137/2zlnNlGTtnz57lrfHjadakCZPffhsXT0/uREezedMmNm/eTFJsLB+PHk2NgQO1RZq9veH8ea0KqGFD7e9XUIKgbt1SO3dfPz+CLl7M/wB9cj37RZU+yZFfcr0wdDrtOQxVEJ49y0vPPpvndsXIiKn3mtZpYaF9zly+DM2a5Z/0URStajA/TZrQ19iYvo0aERYRgZuHh/b55OQE587h5+jIn1On0uPll7l+/ToxMTFs3riRoKAgXJyccHRxof/Agbz00ksGL87Lk/Lc7oSFheHv74+biwsJSUk0bdqUmJgYbGxscLC15aNhw5i+ejX//fUXij7JpSjQsqWWPAsJAV9fw09uZKTtWn/zZsHJQQNs7OxITk0lOS4O8/ymDubeHEuf2Mi+BmFRE+uQFYuRkaz/919atW2Lm5sbB3ftokO9eii5EudetWszpm9fWjZrRpdGjWjfqBFe+bWV/v5a4tFQIsLdnR8/+EBra3Q6RnzyiXb+169Dmzb85euLUXw86ba2vPz110ybNg1XV1fef/99unbuTLVq1Zg4cSJ//PEHc+fOxTp7tXQFVZ5jR6fT8cywYew/eJDnRo/m22+/JTk5mcWLF+Pu7k7/fv3wdHDg1d69aVevHq729ti5u2OWkEDwSy9BaioX+/enaadO2oBKhuqNGnHshx9o+8YbtKtThxq9e3N4yxZq5J5enA93T08u7N+f/wH6xLmhCsLcaxA2barFb1EqCA3113btol2upHmX3r2ZOmUKg0eMYOLQoWw7eJAt69ax/KefMM3WR23cpw8HAgJYHRJCcwcHAqtWZYGvL59/9RXr/voLVaejZtWqxIaFMX7MGFKMjenZrRtmkZFw+jRR0dGgKExeu5bQW7eoUaMGbdu2xdPTk+PHj2MMLF++HFVVGTp0KOPGjcuzGUxFU57iJikpiZiYGFxdXVm6dCn//vsvnTp04M1x43Cwtyc+KYlPn32WCZ9/joeHB1GRkZxfv17b7V4/aGtkpPXlQkLo7OICERFa/8LXVxsA0lMUMDVFadmSdwcPZvrKlXzy558Gz8vXz4+dhw9z+do1qtWujbWrK9YWFoSHh+Pm5mb4h9FPw8+d5MhdVVjcJWESEwm7epV5H33Exz/8gKIonDh0iPkffphjCn2/wYOpkpRE1O3b/LZ9O08MHmy4j+vvz6gWLSA4GNq1MzwN38kJOnWC8PCsc61ZE+zsGOnvz8jXXuPYpk0s3rePzx5/nNikJA7odEydM4cTJ04wZswY/vvvP2bOnEnfvn3LfX/sXspT7Oj9+++/fPnll6xYsQJVVXmkdWveevttfOrUYf78+QwaNIjjhw9z48cfeeeHH/jik0/o2qYN1du1Y+HChVja2LBj82b8fXwYPXo0f/31F8GnTjHxjTc4dvUq61esoJanJ9U6duTJQYOIvXmThZ99RnUvL3wy2qZhI0bQImM5oYbt2nF861aU6tVBVWlrYcHhiRNJSkrKv3pOn1wvTAVhUZa2SE0lJDqaeoMH80jr1jSrVg03R0fm/vEHARnVlNUbNqRVq1bY2NjQsUcP3vvoI95/4QUunTjBpMcfZ+66dfjXrk3Lu3eZNXs2YVFRfDxzJilmZnz91VfU8vWldu3aPNG7N3/v24e9szMvde/OL3v28MXIkQyrWxdq1uSFxx6jxiOPYAwYmZgQExtL506dcHRw4J9duxg+fDgLFiwg6u5dlq9YweTJk7G5Rz6mvKqQCUJFUWyA34CxqqrmWLVSVdVFwCKADz/88IFvjZaamsq5c+cICgpi9+7d9OzZk09nzGDSBx8QceMGm9etY8PffzPvxRcZ3Ls3UbdvsykqivT4eM4dP86U+fMx0V94xMdnjhqbuLre+49Vt66WMLC0LPJ516palXMnTuSfINQHtaEqqPtJcuinixkou9938CAjn3wSAEdHRzp16gTAhoULiYuN5aUPPmDJL7/w8ssvGxwNVBwcaODnl38Sw8hIS4DExmaVFGebAgcwdf58BnXqxPmrV5k/ciQx8fE09PLC2NSURD8/Ll+7xsCRIzl48CBjx46lQ4cOhIeHY2VlxZ07d/DN78K6HCqr2Ll86RJ9evVixogRjOrVS3sPp6ZyJTiY77Zv59s33qBn795Qo4b2vtL/TgMDwcfnnsnw0uYbGEjQuXP5H6BPcmRP5OlL5vObYlzYUTVLyzwJwpSUFM5du0ZdQxutFJafX4EVyIViaqpdfAYF4Va9usE4bKSq7Jg7lw9/+42q/v58+vzzNK9VC3MjI5Zs3cratWvZvHEjs2bPpkaNGjkSImlpaZgUpfKlFJV1u3P58mV8fHwwyzYVHODXX3/l0K5dPNqqFU/26EGvZs3YdPQo5tWq0cvbG+PERPD2ZtwXX2CSu0JQ//293gfOzvlPLS6Aoig42dkRGRqKR34JwtwVhDqd4Soo7QmLluSws+Pu8eMMHz6cEYMGsejnn/nvwAHaN22at/0yMeGDRYvosHAhnWvXxrRnz/zbuHutq9mypXb+KSlw4YKWkG/RAmxtqT50qJZcDAtjaWoqHd5/n/A7dzj2228EZFSExTz5JIMnTMDHx4dt27bR+H7ivBwo69jJj06n482xYwm+eJGOtWqx5pdf2DJvHtH29vQdOpS0tDR+fvNN+j79dFYVnn4tzIQEvGNiICwM78hIre3Kzs0NL09Ptn/2GUl37oCFBXXutf5tNn7Vq7Ns1ar8D9BXcmRfg1CfINTffz/J9eTkrKmYGY4ePcqA117LcaiDgwOH//c/Es+cIblqVWY2a4a1uTnNM/pymTw9Mb52jeHNmsGVKxAYSKe6denUpw8AiokJ//vySzzs7LBSVS12du7U2pg2bXCwtIS0NObb2EB6Op+MGcOey5cJDwlhUM+edDI2RufhQZqLC3OWLqVr167s378/x9priYmJnDx5ksaNG1eI6tyyjJvz58/z2muv0aFDB/7ZsYP9Bw5gZGRE3Tp1iI2JwdrEhKU//sjW+fNpGxCAUUZ/Z/jAgSgxMew/fx7fli3zVqVbWGjtjaen9n4tKBFlYcH4L75g/Isv5qlO1/MNCOBKUBBBoaH41asHJib4uroSdOlSwQlCE5O8yyllXxImdwVhYegTJYmJ/PXrr0xfupQm3bszaMQILt+8SbWmTfM8pPmwYaAodG/bNu9MDj0LCy2x6uycVcVuiLGx4bYpY0mDRoMH83XDhlCzJlYJCTx24QKPfvop5yMjqWVjA9bWPHr9Oj/99BMvvfQSsbGx2NjYoCgK//77L+vXr2f69OkVYlZVeWlzprz5JiNGjmTS5MlYmZmxevVqBvfvz55//uGfI0e4O2UK10ND+fT113mme3fib9/GtksX3gkIoE6rVsxduhRPFxduR0TgYGfHjlmzqFO9Oo1feokFCxaQHhbG8h07sDI3p/uAAZjqdFTz9KRLQABNRoyg1wsvMLJfP+plxM+n2ZcaMzPDWN9uKQp21apRzdOTU/v30yz357defsl1/X36/4u6A3haGhdu3qRW7dr8/OefcOAAmJnR4cknaZyxUVyLFi3YuXMnAP41a2JsZMTqzZv5d/Nm4kxMSLSzw8jIiEbNmxMRE0O/du2wMjFh1KhR7F26lPoWFlx9+mkavPQSLRs0YMqsWaz+4QfczM0ZMmaMFicJCXhevMiVRYsIT0/n1U8/pXWdOizdtg2dqmKsqixZsgSA5StWkJaWxnuTJhFQrRpvvPFGod4T5Un5uLoqAkVRTNGSgz+rqvp7WZ+PXmRkJL/++ivffPMNCQkJ+Pn44JkxX71bo0Y82qcPjfz88PLx4dyxY9h6eYGi4HDyJMNv3tSepEGDnGsiFbU6wNAUsEKqVaMG5/77j74Zu57mkd86avklCPXf34u+ExAdrVVVREeDpyf//PMPJ86fp1vPnnkeUrN3bwC2d+/ON4sXM27cOMPP7eCgVXMUtLC1omhTS/OZXmpsbU2TESNosnev9vfw8NASUvHxWF66RD1FYf/s2fx2+zZPPvkkrq6uHD9+HNuM13zs0UcZOGgQnTt3xq6YU1grutTUVObOncuZM2e4ceUKanIyzl5evPTKKxw5coQfFyxgXN++jJowQatkqFkT0tII6NaN6bVra4ny6tXzdsRMTXOOLJcR34AAtuzcmVVRm5uhHcBTU7UOp356cHFjx9oaNTSU1OvXMcuoWNq5YwcN/PywNbSW1YOmKIYrqfSaNKGutTW/OjpqcZqQoI10qyovpKbyXI8eTF28mOHDhnHn7l3Gjh3LiSNHuBUczOHjx6lXvz6TJ0+mb9++FaJjWtLCw8MZPXo0O3fuxMjIiI7t2nHz9m0szMx46oknGD9hArFxcfy+YAEDXngBYmMZ1KABnDypvV9r1IAqVTApozhysrPjbmgoHobWyoW8FYT5TZOEom20kJZGjLs7EydMoFvTpvzx11/027iR/06c4I3Row0+xNzamh6vvqp9Ht3Pe03/GWFhobX5eV7IHHx8qNKmDeeXLCE8IkLbzMfVFVQVu7AwtkycyK9XrtC/f38OHjyIe8baW7GxsRw/fhwzMzP279/Pq6++KtP0C3Ds2DGeeOIJJk+ezO+//46iKHi6uLBt1y6Cg4Op7enJn0uX4pCaSnp4OMYBAXDnDlf27iX+xg2q+PgYTqBbWWn/3N3zJNIA7T3QpAkBERHg5lbk91O1Jk24EhysvRevXdMGyrL3LwxVEKamau1lWlrONQihyLGDsbE2qJuRwNHpdBw/e5bGBpbZMAkIwNbXF9vkZJxsbGhRuzZGhqqN69SBXbu02QHZpv3rBfTvn/V71Omypk/rbzMxgR49IDkZjyNHGOzgoPXXUlK0PsWVK5CSwtQOHXA1M6NLly5UrVqVI0eO0KBBAy5cuICLoyPJaWl888039OrV66FsUwrj2/nzMUtMJOLaNV7r0YPf33iDdBsbPpw3j3deeYUUR0f2HTtG+xdeyJpaGx2N9/794OXFoCpVCr5eMfD3N8jKCpo0yfdu3+rVOXn5MvWqVsXC0VG7zdOT6xcv0qygJWFMTXMu3aJPEOoHY7O/74qynJKFBSQl8c/OnfRu0YIvvviCrr17k5KaiouhYgJ9W5HfueplbMZwX9zctH96vr4okZHUuntXWzYjLo4Phw2j1wcf4O/vzxNPPIG/vz/eVapw5MgRLE1NOX36NM888wz9+/eXNXQLsG7dOsJCQ5n2xRecOX+e3zL2Gqjh5cWwESNoHBDAgvHjefvbb5n5/PM8N3gwmJtj6+IC9vYENm3K+08+SYs2bZg5fz7P9OrFun/+oeHQoSjm5qyfNIl277yDjYUFT/bqRWpaGqYZg8cL33iD5s2bY9elC96TJvHFihVs2rDh3ietKPh6eRF85UrBCUJDFYS5l1PKfp1UyATh7bt38dRf09SvD4pCu1zFUPr3nKIozBw/nqYdOuBSrx4uwGcLFgBgYWFBo8BAOrZvz+uPP878O3dwTEmB5s2pbWrK0M2b6fPoo3Tv3p3u3bppOyfrP6tsbKBxY1wbNsQ1PZ3tbduSFBfH6L59uXzzJiaqSsdJk+jeqBFbjx1jYOvWfPnVV3h7e3P8+HHee++9HINSeklJSZibm5e79qZCJQgV7bf3PXBWVdXPy/p89Hb8/Tcjhg+nfb16vPXEE4x45hmUkyfBxISRHTvSzsMDRVW1dTRyX7RXraqV0rdpk7PRecBqN2vGgc2b8z8gPT3nCBqUTAWhfidkGxs4flxb76prVz6ePJnPxozBvoDkgq2TE++8807+z21srO1Oe7+MjKBt25y7LtnZZf4tnQ4c4AVTU574/nvWX7/OI3XrcvfcOdycnPhiwwa++PhjJk+axIzPPqNVq1Y4FbQOXiWyfNkyPps1i9CICJpUr06fVq3o4+WFracn/504wWOPPsqQzp0Z0bUrr86bp/29cm8g0qCBVt1Zji9yq1atStCdO9oFk6FqRkPJ9dRU7X2vX4+muLHj6cnozz/n2tmzvPL++9Rv2JC1y5bRr2vXoq0rVVYURRsUqVZNGxzQJ38AOnXCxMSEqb6+TD1/nuNRUXy/aRPNnZ1p2KUL9d99lwNXrjBx3Di+nD+fxd99h6enZ54qusrq4L59DBowgCe6dGHld98Roijs3buXqkOHEnr2LG9Nm8bbAwbQ8ZFHaDNokPZ7tbfP6uxYWhar8q8kOTs4cDcsLP8DcsdO9gShoSRHESoIZy9ezLmoKJYtW8atdesY9Mwz3ImJoV5BF2NmZjk27ClVvr4Yp6biUb16zgtAT08ICWEIcOqxx+jRowfTpk3j3NmzzJ4zh6peXtyJisLa2Jiga9eY/Xm56SqVG6qqMnPyZH5avZpGVasyZ8YMnunShVQnJxIuX2bJ2LH42Nri0bq1lnwLD8fYw0MbcDx5EvvISOxTUu5dXZtr1/scTEy0BGIxLgq8fXwIjYoi+do1zENC8m6wlX0NwtwJwvT0rNuKOiilqloMOjlx9I8/iLa3p2OfPly6cAEXOzsc/f3z/1mNjWng50f75s0NH2NvnzlgYVD2PoD+d5r7d2dsrCWN2rXLGlzQ059bVBSvAY1q1+ZufDwdFi3i319/pcHrr+NtZ8fmoCDeGjuWd999l4ULF9K6det7/loqo19++YWVK1eyYsUKLCwsOHfuHH5+fkRHRfHzihUc+PlnAkxNtd9x3bqQksLcNWsy11yumn3qprGxNs3V0VGroH5AF8Kuvr4MbdeOj6dNy7ytqrc3QVeu5P+g/NYgzK+/Vlj6JKOFBf8cPsyaH36g46BBnPnvP6pVqYJSHvtrjo7aPwAnJ5p27synERGMGDGCt59+mkb16hF78SLPNGpEl4YNWXX4MDMmT2bD+vX88OOP5S7h8SCoqkpycnK+03Dv3LnDC889R1RsLFWcnPjtr7/o16oV9jY2tKpfnzXbtrFtyxaws2Nws2bYNWyYtwIdmLxkCSgKPZ94gqT0dEZfuYKS8beqNmAA34WHM/C99zi4cycO2dqprkOHaoONwIihQ/l47lzaZGxQci+eHh7cvnEj/wPyqyDMvSGjftC3CFOMb9+5k5UgLMQU9xETJ+Z737effUZAgwZQpQqOp05pifuM6/Jlv/6a9b5VFMMDGfrBN39/LNLSqOrvT9Xbt0FV6bduHZOefZY7s2cz4+WX6dy0KY9368a3hw/TsmVLFi9eTKNGjdiwYQOvvfYaOp2Odu3aMWLEiPyLncpIOfxEKlBb4CngpKIoxzJum6Sq6l8P6gROnjzJ5r/+wt7Wlq07dnD2zBkiQkNZPnUq3YYM0ZJ9R45o1Wvu7nSIjdVGuIKDDZd329lBIYOzNNVq3JilixdrGwgcPw7ZdqLj8GGIitI6WLlGBiLj4rBNTcUEuBUWxjMTJ7J52zaMwXCHMzpae42AAO17nY6ktDTm/vEHjeztmb1xI+sdHNh/6BC/fffdA+tI3FNBCaqMDq/1jRsMB1AUnEaMAFNTpjg5MVmn44f16/n0k084fuYMn3/+Oc2aNcPKyorNmzfj4eFBnz59KuSuSKmpqZw9exaA7xcs4NShQ7Rq3pyE5GQ2/v03344ejZ+3N9Vq1NA+aB0cwNOTR65e5e1XXsHE0VG7+M3v92tjU+ZTiO/F19eX62FhWnI7OjrvaK4+uZ57inF+uxjrv7+XtDT2/Pcf2/fvx0JVGTpsGM3q1uXStWvs2r79/n+wB8nUNO+GKfqqNn9/cHenYUgI8+vU0S7YHR0hNZV+R4/St00bJnz+OQ3q1qWqvz/rN2zA2tqakydPYm5uTtWqVfE2tHN1OZeWlsbw4cOJjo7Gw8OD1q1b0759e6pVq8aOHTsY/dxzzJ8wgYGjRmkLsh8/TtVx4yAsDLp1o1+7dhi5uqIYmoJaTn4fTo6O3A0PN3ynfhqXsXFWh7OgCkIoUoIw6PZtnn3uOXwDAvAdOZKT/fqx588/MSsvld5GRvlPK/PwAAcHPlQUfNzd+eqrr/C1sODvDz6gQZ06kJREZHo6zcaMISAwkFGjRj00iXO969ev8/Py5aSkpmJqZIS5qSkHDh/m6LFjNG3YkDP//cekgQMZ0aMHRjqdtnRFeDgMG6YN9ri6ZvXZXF0zL6oypzPqE273o3p1g+v53YuJiQk+Vapw7exZalpY5NxVOCwMzp7Vzs3CIiupoT/f1NTiT8/PqNzbFhXF8Ndfx9LEhPc/+ABFp6N57doF/z4Uha/ffhvXgnbgLOLunAW9Vr6JWQcHaNGC9idOaD/zrVv0fvJJrZ9haUnPa9d4pGlTVv/5J4MHDWLvvn34+fmRkJDAyy++yD87d/LRtGkMGjSowq4tVZAtW7Zw+NAhvvnySxrUrImzkxMeLi6ERETQsnlzLp4/z5vDhhHQq1fmWqqF2uDN2FhL3D5AiokJv2zcmGMWkW/VqlwvaoJQP+PjfpeEMTFh/uHD2Lq40PSRR6jl7c2vv/xCNR+fYvx0ZaBmTZ7t3ZunW7XCyNJS+2zp0SNzyY7R9eoxolcvur76Ko8//jh+fn588MEHrFq1isOHD/PYY4/RqFEjvEqi4rEcuX79Or6+viiKwoL58/lx6VL2HzmCoihcvHgRV1dXflu9mqCLF/l7926e6tyZznXr4uTiQts33uDZxx+n/9ixqFeu8PyAAdrAEWDXpUtWgjY3/fvP2hoLwL9hw6z7HBzoM2oUt3x8cPT1zdlHypYsHPX22zTr3LnQ1Z5VvL25pZ/xaKj9K2yC0MlJS7hfvlz4CsKICDxLKE6aZh+8yLWZY5GT2iYmWtuR0VdbO2cO1KjBEX9/qFmTGq6u4OvLFHNzHq1Rg0dGjyYhIQF3d3dWrFjBrVu3MFNVFnz1Fet++40Zn37KnM8/Z/Xq1WU++6NCJQhVVd0DPPCM0bVr15g1axZ//vknyYmJDO/UiVNRUbSpU4dJHTtSz98f086dtay2q6t2oWturjWaXl5aJy2/UdFyonbt2py9cQM1NBQlPl5LZpiZacEdGpoV+KB9MJiYcCsighZNm/Jev36MefRRjpw7x987d7Jhwwb663dQyi06WluQPVuCcOeRI0ycO5fG1apxOTycx996i5p+ftgbGDUpl/RBXLVq3umULVuiGBnxfL16PH/8OAdPneKdRYv45MMPiYuPp1fz5gRFR/PiqFFMevddatWrR89evcr8g6EwDh06xNAhQ1ABXWoqz3TuzIQXX+R/mzeTAuz8/ns827bVLrZyN3IBARXrw6cAPj4+RMXFEXHiBC62tnkThKqqfRbExWlf6xvK3Bv83KuCMCVFe6/pR5p1Ov49coRHH32UMc8/T3psLEOeeooJb7xBzQq+LlkeVlZZnxl6pqbQogXGqsqcRYuYHRrK7M8/p3GjRqiqSoNq1UjLWLukS4cOdO/Th/bt21Mr+zIO5VRsbCxPP/UUCeHhDG3XjmQjI3atXs2H772Hp5cXSmoqbw4YwMCxY7MuhDN2QNS//4xbtXpw1W7F5OToyN2ICG0dWEXJORigjwlLS23gDfImCIuzFlTG89wOC8sakbazw8nOjsdef71kfrAHwcICpW1bRpmbM6p9e61T3qmT9nsIC8PR1ZU/pkzh1SVLmDt3LlOmTKFatWps37qVZi1bcurUKTp16kSTAqboVUTHjh1jxYoVLPn+e4a3bYuTpydxUVHcSkgg0MaG98aNY+GmTSz/5hsatmihfY5cu5bvOmb5Kolp+feRjA4ICODKmTPaZ332BGF4uDYtytVVS4zExmq36y/o7id2MqaLTf34Y75ZuJD6Fha0feop3J2d+aQQ1Q/V2rS5r6VwSoy9vbaZnyH16qEAQz08CI+MpGnTpvTq1Yv/Dh6kqY8P344ezddLlvD2m2/y408/0TtjyZuK7tSpU3z79df8tWEDDQID2TV/Pr61a3MnNpaLBw7gYmvL57/9xuT336fra69p75mKsElSriWGqtasyd5//83/eP21Tu4ZHxYWOftr2fvohUxy6BSFdydO5MyZMyjGxrRt2pSff/uNZzI2jCz3TEygRQuMwsK0RLupac7PDhcXrAMC2PbJJ8zatInzp07hVaUKNby8GNiqFfM++YT/zp6lU/v2NGzShHcmTarwA1cpKSk0bNiQGdOn89uvvxJ09SoRd+7w3rvv4uLszEcff0xCYiKPNG1Kw8BABtavz7h338UoORkcHXlh40Y69ugBioISEIB59mvI+5nh4eSE88CBBW4eZ21tTdsirH3r6ePD4W3btIKhI0ega9ecBxS0fqc+aZiervXzXF21ZR8KmyAMD6d2ixaFPtcyo++Hd++u/e/qqn1OVq9Ok0uXuLV8Ocnp6cSEh7Px0CFa1apFQPXqNHz2WU6fPMnwoUO5fvMmP//8M4GBgTRo0ABzc/MyWW+9slyj3zdVVdm8eTN///03r7/6KtbW1lgCM+bM4duFC3l50CC2f/wxfn5+mLq5adnisDAtE5678iv7jroVpCrMxcUFRVEIu3gRdyMjiIzULsrj47MaRSMjrsfHUzUmBtXWllFffUWVKlX4++hRxgBnrl2jmr8/kyZNouGsWfh7ePDKsGGMGDmSNvpgSU3VEiX6zqpOx6Zdu3B0dOTo5cscOHCAgQMH8vjgwWX2uyhR+sbPxQW6dqVF/frsaNJE++BXFO32//7jVHAw0376iWWLFzPto494+913OXToEO7u7ty5c4cRI0aUq8TGzMmTmfPVVyx67TUGtG+v/TxubuDlxSMjR+bsNBRj05yKxMTEhHZNm/LPnj0Mbt8+Z7LvyJGs0WdTUy1ZmrELX47pX4UZkb5wQXucvspCp+PkuXN06tGDmhlrmZ24dKlCJJdLlKKAoyOKoyNvT57M688+iylg5OUFaWnEXb7MojVr2LFmDR+89x5vTZjAk08+qa3rVs6kpaXx1++/M3HyZNpWq8aXn32Gea1aEBXFy+++y7lt21i8bBkzp0zB1Nu74J2Dy0slXAGcnJy4e+eO1lFMT4fsiW392m3W1tqgEuRNEELhKjkOHdI2NdIPVKSmciskhCrlfODunszNteVJbt7U2mt97GdUIDTo359dvr5sPHOGBd9/T1BQEE39/fnlp59o06QJs2fMoP/gwTzSsycNGjTAP78pouWMqqrs2LGDf/75h8unTzN69GiatW7N5g0bGPPGG4zq04e9M2dSs0sXbcZC3bpatUtcHPj48O3IkTnbpezVFxVEtRo1mLt2LdNXr2b2a6/RUp/ojYrS/tevr3z7tva9vs+VfeOSe8WOqmqbgbRrl3XhZ2LC+fPnadWqFd729rwzaBCfrVlD76eeuvdJV6BN2/D15dVXXmFQu3asP3CAp557jh4vv4wSFUWvnj3Zt2cP/Z96imFDh+JfvTqDBw9mzZo1tG/fng0bNtC8efNys0NqdpGRkXz77bd8v3gxSfHxzJ85k307drB840ae7t6d/UuW4G5lpW3gZ2GBO+Bevz6YmrKofXvtb1iB+xi+tWpxPTjY8NqgkHW7vo3RbyhlYZG1rEUxq2+vh4fj4uKCX0YV13OjR6NLT+eJ558vuR/wQchvgxcAU1Osu3Vjqq8v6cbGXDxzhhqdO6PY2fHe4cMkpKby9bJlrFu9mkvnz1OjXj0CAwNxc3OjZcuWFWKH5MT4eGIiIrB2dmb7pk0kxsfz+uuvU9vbGxsTE76fOpUfN2/mWnIy66ZPp37r1jjXqKElq3PNelg0Y0bWTAFFKdmlgQq7lmchefr5cSs0VGtTExK09tTUNOt19LOl4uKyHqSfnh8fr31fjOT693/8wfXg4KwB3YpA/zPqB1FMTKBWLUwdHTE1M8PGwYEXnn468zPmwG+/ceLYMbq8/DK9mzXj6aefxtvDAz9fX/z8/AioVYvwGzeY9tlnOD+gpYEe+gRhaGgou3fvZsqUKagpKTSuXp16detiYmxMeno6fZo35/gvv+DduLEWCNn/MBX94iIbRVHo1KoV6//+mxd69CDl2DGi0tJw8/XNXFh375EjdBw2jNv793MsNJQrISFs3r2bpg0acP3aNc5cu8akceO4dPs2E7/6ip8WLOCHtWu5Hh7On9kThKAlIN3cUNPS+GvnTr788kvWrVtHixYt2LdvX4VoJIol90LAAF26UM/EhFU9epCenMySr7/mu7lzqeHhwZ5//8XWzIxW8+bRsEEDpk2fjm/GB0ZZ8rGx4b/Vq/Hp0CFvlVJ5mRb+AHVp25ZtR48yqE0blIQErVGIjoZbt7QD9NVRcXHahamJCaqi8MacOey9coWtEybgZGCqZFJSUtbU85SUrEYWIC2Nk2fP8tqbb2be9NAlB3Pz9sY81/RZm2rVGN+0KYSHs23tWtZfuUKSgV3THxRVVdmyZQu//fYbZ86cwc7WlpS4OCLCwrgREkLNKlWY3L8/Q8eNy0z06NdHqdW3L3OaNLn/HabLCScXF+6GhEB0NCmxscz65htemDABDx8fLbmur1LRv+/T0ohNTtY2gTJUBZVfZzMuTlvmQ58gTEvjdkhIxepw5kdRtGn3hri6Qrdu9Kldmz49emTtOH3pEiQmMnP0aN6fN49vZ87k8IULvPraazRv3hwzMzOsrKyIiIigZs2aBAYGlosdw1VVZeGCBcyeMwcLYED79rTw8ODFUaO4FhpKo4AAVk2YQOenntJ+TjMzbX1T0N5H+mUMKsGg1aDBg/krIgLF3JyPf/iBDS++qMVD9gRh9gpC/a73Rkbs3LOHKk5OVL/HeshJcXFYxMZqg+FVqkBqKjEpKcTGxmrJdSMj3pw5kydeeKH8TM0vSbVq4enqyou9emm/S/2aVFWr0qZOHbb6+rLtf/9j1dKlTJs2jeY1arDkq6+o7uXFgi+/pEGjRox59VW6detWppvUpaenM3zwYG7evMmla9fo2bAha8aP55aZGZ/Nn0+glxenNm3C1cfH8HWNvgqvpKaAlyFff3+CIiK0ODH0/s+eINRvhpOSoiXbi7I5VlwcnD6trbkImTux1sg2M6pxly58nX05p8rC0hIaNsQIqJl9Hfi2bbEC3m7UiBHnzjHu3XeJUFWO7tjBrfBwTly8SMOGDenasiVDR41i565dvPjii+Viw5P169ezauVKjFWV4KtXuX37No7Ozhw4fpwPRo7kty1b2LhwId6Wlig1atC+b19teRxj45zvkdzvlwpUwV/Fx4czN2/Svn9/fp84kWPffINXtWrUGTRIa18MbVKSkqL1u/TtUGFiR5+ENzJi165djJo5E0VRyuWgfpHp+/R6Gddszq1b07FWLWYfOsRzQ4eyc+dOvt60iZDQUG7fuEF8WBiu5ua0a9GCV8eO5dnnn8fa2hpVVUlNTSUmJoaXR4/mux9/LLG2pux7fGVo/qxZfDh9Ok1r1GDmk0/St2NHFG9vVGtroiMi0AHOfn5aJ/MhSHo8P2IEH02bRv/OnRkyaRIXb9/m8HffcTUqitrm5ny+fDlWlpZs2LiRS7GxPNG1K35+fng4OuLn74+NpSUvjx9PwzZtGPX77xw5cQI/d3cOHD3KhQsXmD17NvHBwTzVujVJQUH0f+UVjpw6hS49neHDhzNixAhAW9PtoaKfquTtjREwauJERkVEaBd3GR2VuYcO8fO6dbz03HOER0XRrUcPnn76aXr16lUmpzxiwoQyed3yqk/PnrSYO5eTwcFsb9QIM2vrrMoN0P6ONjYQG8uFa9cY//77jHn9dVZt20b/IUN4Yf581gwezJHDh/GJjMQduHX1Kk1at+b27dsEBQURf+ECdbJN1U5NTub8xYvUrVv3wf/AFU1GYqTr4MF0vXpVmx5TBhITEujWsSNx0dE83bkzIwYMIC4+HlMLC1zr18fD3h7PJk20HRQNTcUzM6s0yUEAJy8vTp06RUpkJANnzGDb0aN416ql7dp396428mxurnU4U1O5GxFB1R49uLp9Oy729vmvQZiUlLN6PzWVZcuW8fjUqZibmJCc0aF6UCOxZcrYOG/lVu3aADgCX7dpA3fvErxnD+O/+46Df/9NXEwMd+PiqOLszLkbN6hRuzbj336bnj17ltkgxNnjx3m0Xz/szMxY+corNG3TBsXDAzw8eGPuXO0CRL/G3kOwEVi3bt3oZm9PorU1NTp25LG2bfn1t98w12+mYGSkxY6REekJCXyzbBn1mzbl+pkzvDZnDk3r1WP7woXak+mT66mp2j8rKw4fPkyXLl04MHMmVZ2csKpSBdLSuBQSQrVq1TLfB0YuLnjlXj+2MsnvM8LKioYDB9KwWzee2LWLRStX8v4HH2Dk5ARGRty+cIE9f/7J7A8/5IUXXmD58uVl1l/7acECgs6eZcyAAdRxd6fZgAHg6UljU1P6vPBC1oX9Q8DNzY3YxETiQ0OxPnJEW5Yh+3IB+s1t9HFkapq55NKu06fx8POjRvaBKTA8MJWYqFVa6duotDTO37hBzUqQZL1vDg5UadmSVatWaZ9TGTNr4sPC2L9pE8t27KBBw4bU8/Nj7e+/88uqVWXWVn86dSpbtmzhxrVrvD14MFGxsZg7OJCamEhcbCzrfv6Zto88wpRPP9X6lfq/d2VIZuXi6enJtZAQboaHU/uVVzAB2jduzK+PPQY7dmg/e8b64Jn01bdFmS11/brW/2vShOnTp9O8Zk0OnT9fOQZ0C2Dk6MibkyeDhwcDatSg4aOPkpKYSFV/fyxUFVxc2LRwIYtXrmTRt9/i5+PDwaNHibh7l0BfX8IiIhjerx/PvvwyPj4+eHt739f66w9Hi5CPvu3b8+TWrTj5+2vZb3d3MDJCARwehguHXB7p3ZtPZ83Ce9gw3njsMTo3asRjEyZw7tYtXuzenb9372bOtGn8tmoV5i4uDGvaFICdM2eyx8yMAYMHU7tmTXBw4GJQEP/s20f3xo0JS0ri/fff59KlS9wNCeF/e/fSpHZt+r/yCkvWreO5YcOk8im77AuiZ7Dv3p2X27bl5dde4/KBA+y9cYOA/BavFQ9crfr1iV67lsdmzODRwYOZO3cutdPSwMYGNTaWxMRErDIShO9++CHHL1/mvU8+oVWdOsyfP59WderQqEkTrl+/TiM/P/qeP493bCyhoaEEBwfzySefEHH+PL+/9RYkJPDj6tWcWL+ewGrVKm+1bWkIDNT+lRFLS0s+eu45uvTogZGrq9Y5MjHJuztuBVma4n45ubqyZOtWFm/eTPuWLZk6ejSH//uP5zp00A7IthA38fGs+9//iEtI4NDp0/Rq1crwaHRMjLaxVrbKjFshITz96ae4tG5Nr169CImJwcPDQ9od0Ko9vLzwGjKEVa1aZU7ZJzkZTEzQhYTw8SefMGHsWDb16MH8L78sk90p/X19WfPppzRs1gzFzy/v9MCHICmYR926WJqacmrTJno/8wx/rVqFo6UlN86fZ9DAgZimpmJqYcHpo0f5cMECVCMjujdvzuz332fm11/T6fnn+X7lSgKsrVFUFYKDuXzsGMaNGjFmzBhcnZ2Zv3Ej6z74gO179/LGSy+RGh9P9fw2z3kY2dnh0bs3k9u2zbHWsmfr1gxp1IghI0fy77p1/LhiRdkN6D7+OI927oxzxsBADvpquYeEkZERPp6e3Dh9mlr6qY/ZBwz1yVJnZ23tWzu7zATh1F9+4dz8+ez77jt8HR1RVRWdToeJod9fSopWgZiUpH3G6nRcCAqiZqtWD+xnLdcUJU81lbW3N11r1qTrG2/w6fbtOFWpwsQ5c5g7ezbTZswok9Ps2bQp/ubm9Hj8cexdXLT3R1gYwaampKak4Jd7rfxKXEzk7u6Ooih8OWkSKba2tPHy4plJk+DiRa2/AFohhIWFNkshMFBLFuoHeSHnrA8wmCBMjokh7MQJLkVEcPToUfZNn06T8eNxqcwDUXr6AgB/fwIMLPnS++236TV6NM8OHszNsDB2LViAib0978+cybSVK/l54UIWzJzJ7chI7sbFsW7DBlrqq5iLSlXVSvtvypQpqiiClBQ1fd06NeT8eVW9fl3V7d6tPtGli9q7e3fV2MhIHdS3r3r3xg3VxtJSDfT3V4//+KP2uE2b1PTkZPXwwoWqGhWlqqqqeri4qLUDA9W1772nLpwwQQXUjz/+WJ03frzatHZt1dbaWt29e7fq6uCg3jxxogx/6AooOlpVL1xQ1ZiYex4KfKhK7JS+9HRVjYpS44KD1Y9eeEFtUL26mrp+vZp09Kg6omNH1dTUVP3s/fdV9fBhtUZAgDrz1VdVQJ32/POqqqrq5UWL1GXLlqmJiYlqj2bNVBdnZ7WBn58KqJs2bVLd3NxUF3t7NWrtWvXE9u2qq6ur+tqjj6o3rl8v4x+8cipu3KgSO0Vy9OhR9f/s3XV4FFf3wPHv7Gbj7gZJ0AQvDqVQKOVFqtSot9Bf3d3d3fuWutOWvvVCDQotFC3F3eJC3LMyvz8mk5XsBgtE9nyeh4dks9mdJHv2zpx77rknjh6t5vz0k2o2m9VFn3+ujujfX1XnzVPVn39W1d9+UysrK1XzsmWqmp2tTh07Vu3etat6+qRJat8ePdRTpkxR5z78sPrRRx+puXv2qOr8+apaWKiqP/ygqjab+vfff6vHHXecOnnIENXXZFKvvfxy9bYbblDjIyPV4cOHt/WP33HU1Khlv/yiXnLSSWrVfsYdiZ228c7996s9EhPVmMhINTIkRB0zaJA6atQotWTePPXtF15QL/jPf9T0nj1VP19fdfOCBWrWX3+ps6ZPV0877TS1e2qqWvbbb6q6dat6wuDB6tSpU9Xg4GD1zeeeUxVFUQH19NNPV8+eNk01+fiod9xxR1v/uB1Laamqbtq037vJ+drRceLYseoXd96pqt99p6rZ2apqtWpfyM5W5z/7rJr111/a7StWqLt371bvv/BCtaG0VE2MilJvuu46NSMtTd28YIF6/PHHq4MyMlTb5s2qajY3PU5VVZX6wA03qLZvv1XVwkL1jz/+UFd+8YU6YeRIdf78+W34k3cwNpuqbt2q2rZs2e9dJXaOjltvvVWtqqpSVVVVa8rLVT+TSW1YuFBV//pLi6eiIlXdt09V//xT+4afftLe/37/XbXZbKr6xx/aNayqqurSpdr5moM33nhDnTR6tDpj7FgVUO+87jpVXbJErTiA611vYsvOVi07djT/gtWqqsXFqrpnj/rbU0+pOf/+u9/H8hQ73jNtJPbPZEJJTSWuRw/o2hVDeDifPPkkX3/+OUH+/pxx0klExMTQIzGRnXv20FPPdCsKCjCkV6+mmYFeKSnszMzkhAEDGN84azl58mSuO/10ls6ZQ2p8PFOnTuWdO+8k6TBKYL1SaKjW1NZldzbRhhp7EwUlJnLf00+TFBrKCffdx+SrrqK6vp6N69bx5Ouvs3n7djJzcpg5fToGg4HhvXuDqtItIYELLrgAf39/fn7mGe678UbW7dlDZFgYb7/9NvHx8YQEBNDz0ks57tRTueLyy3n58stJ9rbl+KJTGTRoEL8sXEji2LH4+PhwzNChrN+2jVrgn9xcbp89m/DwcG5++WVsdXX8uXo1d151FV//8gtnnHAC0086ide/+YZnn32Wi2fNQrXZsNTW8u+OHZirqnj22WcZ1L8/izds4JYZM3j97bdZumwZSTExHX+DkqMpIICwE07gvRdfJKgj7Fzqhc467TSmDB7M8vnzOf6YYyitrGTw4MGcce+9LFm+nOG9ezNh3DgC/f3plZpKcnw8d152Gd988w37Skq4/rnnOO3KK1mzfTs//fQTI0aMYOLo0aiqSnpyMl9//TVXn3suD197LZMmTWrrH7djCQ9vWtYv2t4Vl13G/R9/zOmPP87Ov//moVmzqM/KgjVruOv11/n4u++0CsKSEn777Tce++QTrrr5Zsqrq3n2scc46bjj6HfiiQwfPpyqmhqW//uv1m8wMxOAefPm8dBLL7F440aorOTpp59m4qxZbN+7l+EdYSfW9kJRoFcvFFmW3W4888wzTecAAaGhdImJ4cl33uGnjRu57JVXKNNbfeg9Ca1W8PVl8+7dpKamsm7HDo89CC0WC0899RSLV69mwfr1zH3xRe4891yIjdX6TosmSlISRr2/siODQVtNkZLCCTfeSGL//of8HF69xFi40bgbKtDUlNo3OJjfHnmEgaecAj4+jOvbl7KGBgL0CwV9VzzVvtyrZ9eu+Pv7ExwWRo/AQF584QWOOeYYlD//xDcsjHNPOAFT9+6cfMwxXtP7RHgHJTSU7+6/ny83bqTCx4dLunfHr1cvrp01i0sfeYTUpCRi4uJ4+tFHGd23b/M+asCkUaMAOPP445n91Vc8//zzbJw/n3JVpW/v3lw5ebJXLQsSnZjD8uqQmBgmDx5MwllnERocTFxYGCtXruTECRO4bMYMosPDmTZxIj3efJNbL7yQkIwMLh44EMuIEYwaOZLH5szhx/Xr2bNnD4O+/55/N25k6W+/ceOoUaT06EG3AQM4/9RT2bpiBdnuejwKzwwG+4Yfot0JSUzk5csvh/79efmWW6gzGEidMoXpEybw3pw5XPHyy3RPSqI4OxtDY++nHqmpzJ07F5/qas6cOZNbL7yQs445hpf++INx48aRlpDAR48+SnF2Nne+/z4jIyIYd++9nvvyCdEBTD/3XH7++GOW7dzJ9NtuI7OoiOWbNzPnpZdYt2cPccuXc6efH5jNbNu6lQvHj+e9Dz7gmB49MKgqT157LSdMmsSk884j0mbjufff58uHHmq6lvn2228Z3KcPM559lrPWr2fFihXcO2sW004+mQhpDSQ6kb6pqTzx5Zf4ff89NrOZ/yxaxFlnnw1WK3M++YR3XniBp6KjueLppxk6dCjnPvIIK08+mbUbNlC2bBndVZWU0FD8/Px45ZVX6N69O0mhoezNz2f6uHEoFRUy3hwq1w1ED5JkZoRnek8fVWVYr15aHwGjkalDhpCvqvbEnt5o1CHRcfakSVhVFfz8UIxGbvi//9MuMBobYN91zjnajoJWq1TCic7FYMAnKopzZ8zQBrZFi8Bg4LqrruKpl15i6rhxYDJxy403wpIlzROEikLv5GTuuegixvbqxZyFC5l16aUYu3TBt29fTKoKe/Y49VgTolMICODLO+5gncnEgJQUDPv2wTHHMCA9nXtfeIEhvXuTkJTEtsWLUYqKmvrX+Pj48P477zB0+HDuufpqbr3nHpIuuwxff39SExNRSkogIIDLzj4bMjMZdMIJDOqETcSFFwsLg8BA8PcnKTFRuzgwGvnkySe5/513GJSSgt+UKUzt3dtpQveMM85ALSpixxdfkJKUBKWl9D3xRBKOOQalqooLTjuNbatWsbu6Gr/oaLlYEx2e4uPD7Ntu44vNmznn+uv5+9VXefi99zj5tttIiY1l6erVnHHmmdw4ciTbNm/mgpEjWZ6bS0ZyMthsGBSF/4wfD4rCtRdcwNDp05lx993cctFFBBsM/Pjjj6z88EP+Wb+eix96iIjISG497TRw7VcnRAc3dcwYThwxgpm3385b99/PLwsXYgwM5NNXXqHK3x+LqnLV9deTW1zM8i+/5IITT+SGO+5g3aZNbNm4EYvNxtM33MDZV13Fo48+yrJly/jsiSfYUVyMYjZrvQ0DAtr6x/RKkiAU+6coWslwYxJj0vDhTJoxQ2vCC1riT29Aqt9n9GitxLimRmtaWlamNZpvTBBiNmu3HUb5qxDtVv/+2qDmkPyLiolh1pQpJHbtqiXX9bhxU0GoNDTw6M03Y8vNZdXSpYQGBGiJ9KAgKCzU7uQlG1kIL+Ljg8Hfn0EjR2o7QDZWyd56+eVMmzmTJ668Enx8tA0V9H+N9+nbrx+Fn3xCSEYG7NnDjGnTyKupQbFYtPHLZNLGnJqaZs3Rhejw/P3tk0a+vk1xERwayvN33glbtngcdxSDgZT4eO2cLjqaQf7+WoyUloKvL726d+fFPn208UeIzmDUKE4ZMYLntm5lxOmn82GvXvSeMYNzR45kTX4+kZGRnPLwwwSHhvLI6adz6aWXEl5UZI+dxvgKCgpi/ptv8tmcOUy78UZsRiMvvvgi3ePj6d6tGx//+CNGPz/t/C02to1/aCFa1+UzZjRtbHbioEE8fN99fDd/PrWVlVgVhVWzZ9Pvoou4eupUDAYDb1x/Pcfffz9VNTW8cccdLN29mx9/+w1rYCBTpkyhZ8+e3HbmmTSkpGjjT339YVfCiUMjCUJxYBxOODEaobbWnqBwU0GIomhJQJNJq0QsKdGWLFutWuLEbNbuIzMDojMKDbV/PHSo9r+PDy9feSVqVJQWF54ShIqiDYpBQRhCQ+kZH68l200mLQ4rKyU5KDqvUaO0+KmuboqLKRMnctbEiUwcPFhLclitbmMnJCBAS3KEhvL4VVdRmZKijTU+Plr8FBdrE1adeKdB4cX017WvrxYjoJ2v1dVpMaAonscdVdViJzVVq1AHLXb8/bXYqaz0zp2iRecUFIQ/cPOrrwIQnZDA3LlziQkPp++AAShGI1W7djFnwQJ6pKVx26xZsHRp89hRFFISE7lz+nQGp6WRdvbZ2i7ff/8NwcHcf955VCkKJCVpsShEZxIRoY0tvr6kd+3KM489RreMDD549FHWFhSQ0bMnl82axcXp6QCEBQayYP58Cisq6F1RwbTgYJKHDGHnvn28Ons2AAEGAwEJCZCVZb9WEkedJAjFgRk6VLuwAu3NoLbWvjTYYPCcIAwM1N5AsrK0JIc+g20waCejfn5t8/MIcbToSxl9fDA4NO3db4IwMlKr2Kiq0k4s9Sqo6motpoTojPTkemho08eKycQXjz2mvfZ9fLSxxc2FGqB9LTycsMpKwpKTtcbxeuxUVkJCQhv8UEIcRdHR2vkWaPFSWWmvwjAY7MlD/cLLMXZiYmD9eu0++iSvyaRV3srElOisFIXx48c73XTptGks27qVAMdrHXfnbBYLqCqThgyBlBTtNrMZgoIYkpKiXSvJtY7ojPTNSgGld28unToVDAbitm1jp6KAry9vzp4NP/zQFDsRkZFEJCTAypWEmUw8MGMGakgIEyZM0GLLYrGvOJQWZG1GEoTiwDgGqZ4gNJm0z91VEIKWAAwL0/7V1UF+vr1voeP3CuENjEbtoquhwb5k38MS46ay+uBgLUEYGGivIGzs7SlEpxYcbN80y8dHO2k0m7UYqKtznyDUq6DS0iA7W/tYT3LoVVVStS46u+ho+8c+Plpyz/V8zZXNpsWYvhyyrMw+VplMMu4Ir3PiqFEsfeste3LdaHQ/7tTXa7Hh66udr0VG2qtvFUWb2JLkuujsevZs+jA9LY30+Hh7nOjJdZvNOXZqarj17LO1+DAa7WOOHnMSN21G6jbFwXOXINQHTZ3jEmODAYYM0Wal9e8xmeRkU3gXRbEvz9crCFXVecDU76efcOoJQsckB8igKbyLyaTFgH7yuL9lkgEBWiVUQYFzFRRIglB4F/18zbGC0F2Sw3HiKj4edu9uHjtyzia8iOLrS0JgYMuxA/YJ3aQk2LhRu48eO35+WuJd+qgJb+LjoyXG9zfu1NZqE1rV1bBypRZL+jgkeYI2JQlCcfD0HlD7GzT13k+gvQGMGQPdu2ufm0xyoSa8j4+PdiGmx46vrzYguiYI9RPK8HCtUW9lpVyoCe+lJzkcW1R4WmKsJzq6doVt27TqKb0HIUhyXXgXfUm+PubonztstOCUIATtPK2iQvsn447wVnr1rWPsWCzNq6D0Cd3u3bWYysuz943WKwulj5rwJnqCUD/fcqy+dRx3amu15cTDh2uxlpPjfH0kY06bkXcscfD0RruOJ4319e6Xe+n3Aa2fVHKy9rGvr1yoCe+jJ8z1//39tQHS3VL7gADt6126wI4d2g54BoP2vRI7wpu4Jjn0ikLX6luw3y82Vqvo0E84pYJQeCP9fM2x+ryurvmKD8cqJ4MBjjsOBg3SLt4cKzqE8BZ6QlCPCz8/e+y4Jgj1+6SkwM6dWtw19mCT8zXhdfTkup7g81RIVFtrX/GRlqZVrjvGmyQI24z0IBQHz7WPYECAFuTuqjk8XYxJkkN4I72SSY8P/WLNtfIW7INkerrWCDgoyH67DJrCm+hjjv66dzfmON5Xvy09Xask9PPTTk717xXCW7ier+ljTlBQ89hxPCczmbQEu/6xn5/0jBbexbElEmjxUVHR/FrHarWPTQkJWoKwd2/tc8exRwhvYTQ69601mdwXElks9nEnOVkbryIjtc/9/CRP0IYkQSgOnqcTTncXa/rOx66SkyXJIbyPj49zLxo/Py3R4aimxvlzo9GeHARtub6nuBKiM3KsxtA/NxqbL88H51gBbYMf0Gawu3eXKijhXfTzNXcVhK4Tup52jJReUMIbuYudwkL31zr6uGM0wrhx9tslQSi8keukbmCgdm3T0rhjMEBiov0x+veX87U2JAlCcfD0PlD60hV3s2pWq/a/p2qNmJgjf5xCtDdGo3OC0N9f69Ph2J/Gam15UBw48MgdnxDtlWtyPSBAO+E80EkpRYE+fY7c8QnRHrkuMdZ387ZaDzx2IiNh8OAjd4xCtEctJdcd+6iB1kLJnYgImdAV3sddgrC62nnM0fME+iSuK5mUalOSIBQHz7HhO2gXanV12sd68OtVULIkRQg7H5e3XD8/KC7efxWUEN7Ox8f5hFFfZux6EikXY0LYua74gOa9b/dXQWgwSFwJ76PHjGuC0NfXHjN6L09PCcK4uCN7jEK0R/r5mh4nQUFQUuJ8rVNVpf0veYJ2qcNtUqIoymRFUbYqirJDUZQ72/p4vJJrglA/2XRsel1dffSPS4j2zrUKSo8dV55m1ITwVodbQSiEN3KtgoLmCUL93E0mpoSw0/vZOm7IaDY7V9/q1zp6pa4QovmErl5B6LhaSk8QinapQyUIFUUxAq8BU4A+wLmKosiaoaPNXYLQtadNYCCEhbXN8QnRXrnrQVhX5zxo9uoFPXse/WMToj1zPeH093efIJRxRwg7g8G5fyc0TxDq448kOYSwc91UTo8jx43lTCbn8zchhPsEYVWV8/ma0WjfkES0Ox1tifFwYIeqqrsAFEWZA5wKbGrTo/I2gYEQHm7/3GjU3gwaGuzBP3y4c0WhEEJbwuU4QAYGanHiuDGJvvudEMIuNNS5OjAwUKvkcBxn/vMf50SIEALS0povz3ec0A0OhpNPbptjE6K98vdvXlWrt1TSY6dnT+jR4+gfmxDtWUCAc8uKgACt+tbRhAkyKdWOdbRpjyQgy+Hz7MbbxNEUHg79+jnfNmiQc1WhosismhCuunaFLl3sn/v7awlBWRYpRMsGDHCuDkxI0JKGjglBSQ4K0Vzfvs7nYz17Nt8wSwjhLCAAxoxxvq1/f+1/1+sdIYRdfLw27ugMBu1zx1jx9ZUEYTvW6TI4iqJcrijKKkVRVq1evbqtD8d7xMXB5MltfRTiMEjstJFeveDYY9v6KMRhkNhpAwYDjBsH6eltfSTiMEjstAGTCaZOhZiYtj4ScYgkbtpIWJhWbeuYIBQdisROG+nWDU46qa2PQhygjpYgzAEcym9Ibrytiaqqs1VVHaqq6tAhQ4Yc1YMToiOT2BHi0EjsCHFoJHaEOHgSN0IcGokdIfavoyUIVwI9FUVJUxTFF5gBfNfGxySEEEIIIYQQQgghRIfVoTYpUVXVoijKtcDPgBF4V1XVjW18WEIIIYQQQgghhBBCdFgdKkEIoKrqT8BPbX0cQgghhBBCCCGEEEJ0Boqqqm19DEeMoihvo+10LIS3SlZV9bKD/SaJHeHlDiluQGJHeD2JHSEOjZyvCXFoJHaEODRuY6dTJwiFEEIIIYQQQgghhBAt62iblAghhBBCCCGEEEIIIVqRJAiFEEIIIYQQQgghhPBikiAUQgghhBBCCCGEEMKLSYJQCCGEEEIIIYQQQggvJglCIYQQQgghhBBCCCG8mCQIhRBCCCGEEEIIIYTwYpIgFEIIIYQQQgghhBDCi0mCUAghhBBCCCGEEEIILyYJQiGEEEIIIYQQQgghvJgkCIUQQgghhBBCCCGE8GKSIBRCCCGEEEIIIYQQwotJglAIIYQQQgghhBBCCC8mCUIhhBBCCCGEEEIIIbyYJAiFEEIIIYQQQgghhPBikiAUQgghhBBCCCGEEMKLSYJQCCGEEEIIIYQQQggv5tPWB3AkXXbZZWpycnJbH4YQbeahhx56R1XVyw72+yR2hDc71LgBiR3h3SR2hDg0cr4mxKGR2BHi0HiKnU6dIExOTubBBx9s68MQos089NBD2YfyfRI7wpsdatyAxI7wbhI7QhwaOV8T4tBI7AhxaDzFjiwxFu3SzJkziY2NpV+/fk63v/LKK6Snp9O3b19uv/32Njo6IYQQQgghhBBCiM5DEoSiXbrkkkuYP3++020LFy7k22+/Ze3atWzcuJFbb721jY5OCCGEEEIIIYQQovPocAlCRVHCFUWZqyjKFkVRNiuKMqqtj0m0vrFjxxIZGel02xtvvMGdd96Jn58fALGxsW1xaEIIIYQQQgghhBCdSkfsQfgSMF9V1TMVRfEFAtv6gMTRsW3bNv7880/uuece/P39efbZZxk2bJjb+86ePZvZs2cD4O/vz8SJE4/mobaJ7Oxs3n777bY+DHGoKivBZoOwsDY9DG+LHYkb0VokdoQ4eN4WNyCxI1qHxI4Qh0Zip2UdKkGoKEoYMBa4BEBV1QagoS2PSRw9FouFkpISli1bxsqVKzn77LPZtWsXiqI0u+/ll1/O5ZdfDsCDDz7oFU1oveFn7NTy88FiafME4eHETmmhGYCIWNOROLQjQuJGtBZvG3c6+88njg5vixuQ2BGtQ2JHiEMjsdOyjrbEOA0oAt5TFGWNoihvK4oS5HgHRVEuVxRllaIoq1avXt02RymOiOTkZKZPn46iKAwfPhyDwcC+ffva+rA6DYmdNmazgaq29VEclnum7uCeqTva+jCOOokdIQ6NxI4QB0/iRohDI7HTxsxmqKho66MQ+9HREoQ+wGDgDVVVjwGqgTsd76Cq6mxVVYeqqjp0yJAhbXGM4gg57bTTWLhwIaAtN25oaCA6OrqNj6rzkNhpYx08Oag6HL/V2rF/loMlsSPEoZHYEeLgSdwIcWgkdtpYYSFs397WRyH2o6MlCLOBbFVVlzd+PhctYSg6mXPPPZdRo0axdetWkpOTeeedd5g5cya7du2iX79+zJgxgw8++MDt8mIhOiRVbZdJwnWLK1n1i322L3NLHVtWVAOwcWkVO9bUAFBZYm26T96u+qN7kEIIIYQQQoj2qxOslvIGHaoHoaqq+YqiZCmK0ltV1a3ACcCmtj4u0fo+++wzt7d//PHHR/lIhDhKbLa2PgK3/ntzNgBd0/2J7erLkxfsBuDlv9N57fosAF5flcHeTbVN37NzTQ3JPf2P/sEKIYQQQggh2h9JDnYIHa2CEOA64BNFUdYBg4DH2/ZwhBCiFbTDCkLHZcMPTt/p9LXrR21xut/c5woIj/UhJNLIhr+qjuhxWS0qVqvqdHxCCCGEEEKIdkoqCDuEDlVBCKCq6r/A0LY+DiGEOCwFBRAbC/oyeVW1f9xO/PTWPoYl2j83N7ivcty2uoaibDPn35tA/u56Fn1RSm21lYAgY6sfU0O9jedm7iVrax0Alz6WiLlexVxnY9zZkVSUWDD5KgQEt/5zCyGEEMLLNTTAmjUwYkRbH4kQHYskBzuEjlhBKIQQHd/69VBrX5bbEWbVinPNTp9f+XwyAIs+LwWgz6ggBo4PwWJWWbfoyFQR/v1tWVNyEGD3ulo+fjiPz58u4MUr93LnpO08c8mepq/v2VjLbx8Xc/fU7dw2YRuL55YekeMSQghx5JgbbGxeXu10W0OdjU8fz6Oi2HJEn7uqzEJ1hXX/dxTewWyGysq2PgohOp52uFpKNCcJQiGEaAuug2Q7HTRHnhRGVKIJwCkxZzBA1wytz+C/CysJiTQSEWei24AA4lJ8+fHNolbZzbgk38zc5wuwWrTHWvVzBYnd/XhlWTpJPf0oybMnLbet0jZMyd/TwNznCwB4+uI9/O/FQsoKtQu8OU/m8+uHxQD89bUkC8XRVVdj4/UbsyjMbGj2tZJ8MzvW1LBrXc0Ree68XfUs/6n8iDy2EEfa/14s5JVrMtm4tKppI6w1v1fy1//K+OHNoiP63G/clM1tE7bx5/9KeeiMna0ytokOrJ2erx2MFfPKuXroZkl8i6OrAxRDCEkQCiFE+9AONyk58aIoLnowkUsf1dYZb3ToLZjQzY/wGBPhsVqnivhUPwAMBoUTL45iX46ZoqzmSZCD9fXLhSz4tITNy6qxWlX2bKil77HBGH0UohJMzaoadQs+LXHaTXnUKWGcem1M02M+fNZOPn0s/7CPT7QDDQ3t8oRz+z81lBY6vz7XL65kw19VfPtqodPtr9+Yxb0n7eD5/9vLszP3HpHjeWzGLj64P5eaSiurfqnAZmt/vzMhPMnepk1QvXZ9Fo+cvYsFn5Ww6mct4b11RTW1VUcu0bF7vVbt/9nj+RTsbaC6XJIqXq0dnq85qihxrqgt32dh4ZwSp4mpn9/TJkoXflYiY4E4etrhuZpoThKEQgjRFjpABaGvvzZEhERqScAV8ypI6ObH+BkRXP6strw4tW8AAKHR9pa2ST20ZGH+7sNPEPoFaMew6e8qXr02E6sV4lJ9AYhKNFGUbX+OlD7+XPZUUtPnj5y9q+njsWdGMPasCM64KRb/QAP5uxswyAjYOaxbB0VHtoLoYJkbbLxw+V5evNw52VdXo11YGnzs/UYrSixHfGMfi1ltuqadfVs2796dwz+/yRI5r7dnT7tPdugUlx69c58rYONSbclxUbaZb19tvfeAr14o4OVrMpuWNJt8nZ+7tlIShF6tHZ6v6fZsrOXOSdtZ9UsFANnb67hr8na+fLaAV67NbLqfuV6L+5/e2seSr8uO6DH9+b9SNiw5smOc6CCkgrBDkMsj0bqqq2HLlv3fT2CzqU0Xi663L/mmzOOGEKKTcJcgbKf0BCHApEuiOOvWeGKStSTdxAsjAegxKKDpPnGpeoKwnsPlH6QNU398XsrWldrSy9gueoLQl4Y6++/NL8DA4BNCeX1VBre+m0JkvLY0+uHvupPSJ4CAICMnnB/Fbe+ncumjiTz9e6/DPj7RDlgs7S7J8cv7WnVGUbYZi9n+Gt2X3bzidd7b+47IMexcW8OCT0vI3lZHwV57LOpL8euq7UmOn97ex8tXZ8rO4N5m61atArcdmf/uPv57cxZPX7ybtX9oSey6Gtt+x5OyIvfV5Idi2fflbFlezUcP5WKzqU4xDDhVENZWW/n65UIa6trXe5A4gtppgrAou4GnL94DwN/flaGqKmsX2ieCinPNLP2uDABzvcO4lNN6seOqMLOBzx7P5/UbsvZ7X1VV5dqns2uHcSOakwShaF01NVBS0tZHcUCqK6zMf3dfU2+zo+3LZwu4eezWZs+/fnEVnzyax49vHpmLRtFOuA6S7fSEE8AvQKH7oADShwdxzAkhTl/rNiCQJ+b3ZMwZEU23+QcaCI/1oWDv4V94uls2FttVSxAm9fJzul1xGNG6DQjkoW+78/B33YlO9HW6X0I3P4ZNDiMwRHY67hTacewAvHt3DgClhWZ2/qsl5/75tYK6Ght5u7RdvwHGz7DHUGss+Xpu1l7mPl/A4+ftJnenllw58aIoUvpovUMr9mnL0KwWlR/+W8SWFdVcM2wL95+6o9kSNdFJtbPEuqqqfPd6EesWV7FnYx1v3poNwJfP5lNV5r5qb9zZEUQnmVi3qKqpaupwmfy1isGyQguFmQ3N3l5qKuy/t5/fLebXD4tZ/qP09/Qa7SxudB8+mNv08b7sBpb9UM6Ps52vJT5+OA8AS4P9RX2kroOsVpUHp+90uq2+1sY/v2lxWpJvdoqbhZ+VcsPordIXsTOTCsIOQRKEonW108C3WlR2rnXuB/XOXTl893oRi74oxWZTqSqz8O49Ofz+SfER7WWjW/6DNijuXOvckF6vKmyN/m2inXOMlXYaO6At7brl7VSuf70rvn7Nh42waB+MRqXZbZWtkGSoLrdiMGhJwXPvjmfM9HBCIrXEXpfe/s7HaXA+BqNRaZYcFJ1QO4wdH5PCoz/0ALTeaaqq8uQFu9m1Tutlpqpa5WDmFq2vWnyaL5MuiW76/no31eWHI3urliA87sxw7vgwjZAII2VFFnZvqOW6kc5V//tytM1SSvLNvH9/LjeO2cKLV+5l72b7ruuO/T1FB9bOkutlRc3HDFVV2flvrZt7axJ7+BGXor3P68n4w9VQayOhmzYBpVeuO6qusGK1qNTX2ihu3Chr4WclTVW49bU2qsosUlXYWbWzuAHYsabGKU6Kss3s2dA8bvTWKo6vzfraVh5vttVRWmh2u7v43OcKePvOHJb/WM5r12fxwQO5VJZq91s8V5ss27X2yGzUJdqBdhg7HUXuznr++b11JsH2RxKEonW1w6Av32fhupFbeG7WXu47aQdWi1bCvqWxt8zc5wuY9/Y+tq2qYdXPFXz1QiG3HL+NhXNatxJy66pqfv+kuOnzxMY+bRuXVDvdT+9tk7erXkrtOzN3FYSdSHC4karSw0+011ba6D4okAf/153jpkdw3t0JTb2oAkOMTknChDRJBnqldnjCGRFvIjLexOSZUZTmm8nf3UBliZWEbn7c/VkaADv/rSFrSx0mP4V75nQjLNqHc++OB3DbfuJw6DuQhzX2Cg2L8aGs0MKnj2nVJP2PC+a1lelc+bzWW/TtO3K496QdrPipnIY6lW2ranjpykzqamxkbqnjyQt2t+rxiTbSzuJm7cJKTH4K6cODmm57955c9mU3cOxp4by2Mp3Lnkxy+p7YLr4YfRTXhzos9bUq3RvbZmz+W+ud5nhM1eVW3rkrh5uO20plYxIkf08DW1ZUU77PwieP5nH7xO3cPnEb5fssLo8t53UdXjusINy6srrZbfrmOgCDxmurP0KjfHjywt2YHSoIy90k5g+Vzaby+Hm7eebiPU1V6rr8PfVNG6V88EBu00ST/r/ec3r1LxWsnF9OrUMbjNyd9axdJH1zO5zSUq0NjK4dxk5H8eg5u3j7jpyj0gpGEoSidbXDC7W//lfa9LHNBh89nMf/XnTeQXLdoirKCrU3sD6jtJNAvcKvtbx0ZSZfvVDYtHRMn73busJ5UNdn0PP3NDD/3WIqSizcOn4rr12fyYp55cx5Mp+l35W1+gWkOMo6wCYlhyM4wqdpVvhwVJdbCQz1vBT41vdSeGWZltg47frYw34+0QG1w7iJiNP6X0Yn+2K1wg//1TZQuOypJJJ7+jPhvEiyttaxdWU1XXr7N1Xg6j03W7uCcEvTZguGpuPb8FcVOdvrOeOmWK54LhlFURgw1rmFwGVPJnH1S124aXYKddU2bh67lScv2E1QuCzP7xTaWfVtdbmV486I4JqXu/Dg190BLVlgs0H3QQEoitLUYgJgyqxoeg8L4pw74wmP0ZLfh1u1ZzGrWC0qkfEm4lJ8WbdYSxAO/U9o031qKqz829jbzTEJA1q17qqfKxqPReWuydv5+JE8bDaVXetquHfajsM6PtEOtOPztZvfSuHJn3viY1LI3qYl3u77ohuXP5PM+BkRlBVZyNxc5/Q9O/+tabW2EgV7tARgWZGl6bpq3Nla+4yHz9zl9nvydtZjtarsy9G+d8W8Ct67N5fXrsvCYlbJ31PP23dm8949rVMhLI6izZu1JKGuHceOq+oKK79+VMz79+c69bjN21XPpr+PzKY7NZVW9m6upa7GxkcP51KS37w/aG2VfYxb/WuF02aNrUUShKJ1tdOZgaSefk2zZyt+KmfRF6Wk9bdvqlBZaqEk34xfgIFrXu7C5JlRZG6pY+8mz8taDoZjgOsf67PKmVvqWPSFvVrRcSZv45Iqtq6opqbSxsal1bx/Xy6L55by8cN5vHptJkXZDezLbaBGdtTreFwHyXYaO4cqJMLosWfUwaiptBIY6nmoMvkaMPpoiQ09+SG8TDtLcgBExGnJivjGHbfXLKik7+igpqWQx5wQgrleJWd7Pb0dKpP8A1svQWhsIYc39XL7cuZuAwIxOCzPv/L5ZM66NY5b301h8MRQ+h0bTI9jAphwXiRp/QMIiTByySOJh318oo3pMdOOYmfyzGjOvDkOo49CbBdfp6ScvuTXMTmtfz0i1sT0m+IAmiqUDpVe4efrb6DbQPt5YsaoIM64SZuEchzbHCuxAJZ+WwbAuXfHc/69CQz9TyhLvy3j+cv28uq1Wfj4tm61o2gD7TjJkdY/gNAoH069NgbQJp302HHccO7Y08KbPq6tsrG6lfp3bv9HWx5s8lUoa2zrlDHSPsa5Oy9c8GkJ375SSG2Vjf972l4hvGtdLdeP2sLDZ+4if3eDUxWv6CBc46QDXess/KyEr18qZMVP5Xz1vFZYZLWoPHL2Ll69LuuIVPK9fUcOT124h7V/VPL3d+W8el0mpYVmp6IgPfHeUG/jnbtyeP6yva1+HD77v4sQB6EdDpojpoVx53/TMBoVtv9TwwuXa4F0xk2xPDtT+7is0MKCT0uISTahKAojTw7njzmlzH2+gFveTj3sY3BM+hVmNhAQbKSyxMrx50Sw7IdyVv1SwbizIxuPxUxyLz8Su/ux6ucKvnu9qOl7T7woivpabXn0rnW1PHCa1vw3JtmEMuiwD1O0pXYYO4cjOMIHc73Wi0lfNnKwVFXVKghlMxHRknYYN3oFYWq/AC64LwGjj8KwKaFNibjuAwM54fxIfv+khAFjg5u+zz9Ie63XVR/+SbR/kNGp2fudH6c1fZySEcCAscGsW1xFcm/nzX5cqwhB60N65s1xzjd+f9iHKNpSO7xQc10qPPOxpKZqvLhU7XUaGmW/dAkIsY8tejI+f08Dyb2c+9MeDL0C0TdAoduAQP7+TltNEh7jwwnnR7FxSTWLvyx1+p5xZ0eQ3Muf9YsrmyoL+40JJiLWxOhTw0gfEdS0OcTZt8cxf9UhH55oD9rhmBMQbOT5xb2bYmjEtDC+eqEQHA7VMbl+6rUx9B4eRFCYkdeuy2yVntGqqvJnYx9Bv0ADZUUWDAYtFv7v6STeuj3HqX/tqyvS2bqyhtm3ZvPbxyX0HR3EoPEh3PhmV+prbJQVWfjs8fym+59yTSyzPzrswxRHWztfLbVnYy13/mc76cMDueSRJOY+X0D5PguR8aam+/y7oILSgtimPtKgVclGxJrcPeRBq66w8vVLhWxpXFWob96Tv7uBe6buoN8Y+3niws9K6DUssOlz1zYWrUEShKJ1tcPAj07ybVq+1XOwPaCSejY/gdSz8rFdfBkzPZxFX5RibrAddmWSY4VfUWZD0w7F0Um+HDMhhL+/L+fB6TuxmlVKC8wcPyOSIZNCWTGvguJcMwHBBs66NY6RJ4U3Pc6aBRV883Ihw6eGEZ3sy7wVh3WI4mjr9EuMtRPRVT9XULCnnuk3xu3nO5qrqbBhrlebki1CYLHA8uVw7LH229phBaF+kWYwKIw+Ndztfc64KY7Js6IJclhC79dYQdgqLSQUCAwxUFOpPVbXdOcxb9aTSVSWWKXy1puoKjT2cG2PFYQt0atrHTfEcpw8ik3xRVGgYM/hbaDT0FhB6BdgaOoVDTT1vp1+UyyPn+vcg3PKrGhCo3wYNCGEL57OJ7mXf9OFo6IojD4lnB6DAtm8rIrhU8MkQdjR5OZqu3vEaz1i2+OY03NIYFOMAASH+3DB/QkkObyGQx0qCIPCjAydpFXgBkf4UFly+Cs+qsut5Oyox9dfoarMyvIfy4nt6ovBoNB3dDAGg/O8hMGgkDEiiJvfTuGXD4o5+coYFEWh1xB7peDoU8LZsqKajJFBTpXuooNwV0HYzmJn45IqAhMtrJhXwfSb4ljwqbaqb/jUMEIijdz6bioPn7mLe1zaQ8x9toDLnkpqGhsOx6LPS5qqz8HeFka34S/7kuYl35Sx5Jsyp68v+LSE/D31nHd3QrPHttlUDvYQJUEoWlcHSHKcfn0sO9bU4Bdg4NEferD2j0q+fLYAgJmP20vb0wYE8NvHJWRvrXdajnwoHPsFbFtV09SzJmNUEHWNTXgdl8WMPSuC2C6+nHN7HAYfhcETQ50uIgGOmRDKMRPsy28kQdjBeMESY4BPHtWqJupqbMSl+HLC+VEH/Bj6cvyIeBmqRCOLBapcer90gHHHE9f3df0CrzUqCOtrbIw/N5LNy6pI7dd8DDP5GoiMl+Sg19i3DzIzYfBg7XN9zGnnsXPLOynUVjrHw92fpbF+cRW+/vbXr6+fgahEE/m7D2+JsZ6c9wu0L810TK4n9/Tn/HsTsFpUTH4KYTE+TVWNQaFGLn00qfmDArFdfYntGnlYxybaSHk5+PjYE4TtcMxx3LBNN/qUcKfPezgUSTgmNUIija1SQagXWaT2DWDb6hrKCi0Mn6Jdp/j6awl3vS+i67HPetx93Bh9tOSi6KDcFUO0Q4qiHdqPb9pX7e34p4boJF9ikn057554PnpIu57RV1+sWVBJcZ6Z6ETPGyTabCpfvVDImOnhJKT5ebxfpZtNHQeNDyE62cSSr8uwNKjN2lk4mvu8lsdQG/OvJj+F3etrOeXqGDYuqaK+9uB+73LVJVpXB0hynHhRFCdepCUpIuNNTlWFA4+3L61K6qENtoVZDa2QINQCPyrRxJoF2vKTa17uQkKaH9vCtTAMiTA2vUHEdtHebPRlx8ILtMMTzsOR7HKy+tf/ygA4/pzIA95xsiRPSxA6lvkLL+cuTjrAuHOg/FopQWgxq1jMKv5BBu7+tFtrHJro6Mxm590kO8h4031gYLPbknv6k+xmFUhcqh8Few+3glD7vfj6GzAYFO77spvTsmZw7t8mvEAHSXLsT1CokcufScbc4Dy+hET4tErPaD1BGJ/mx7bVWi9Cx8mpxO72BOGwyaHNH0B0Ph1ktdTVL3Vh9q3Z/PlVWdNtJflmopO0649RJ4ezcn4FW5ZX02NwIN0HBfL1y4XMvjWb295P9bgSo7TAwsLPSli/uJKHv+3h8fnzdtrHrcufSaay1NJUHJTUw58PHsgFtLhRDAorfnK/keqSb8owGLSEp9UKb9yUjdWiMuG8SDiINqOSIBStq50GfksiPCQf9KolPUkBUFZkpqLY2myplsWsle96SnzUVdmXrAAMOTG0qWlval/tsS56KJGsrXWkuan0EJ2Mu6VdHSxu9ici1kRUooniXOcduHavr6XHMc0v+NzRKwgjEyRBKFrQAccdT0IijZj8FIqyWmejBb9AqRAUjTwlOTpJ7IA2Cbtn/eFtLqf3INTP11qq+hBewt2Kjw4aN/qGjY6CI4zs3Xj4O6GWNm5KMvGiSPqMDiIsxsepH+j0m+IYfGIo/Y8LbpVlmaID6ABLjBUFug0M4OqXu/DSlZlOXzM4tLQwNJ5ORcab6D82mK9fLiR7Wz3r/6xi8AnuE976eOJaBQ/aJiNv3ZbN9JviKG7MNfQ/LpiBxzvHx4DjgzH6KFgtKukjghh1cjgjTwrj5aszCY/xweijUJxnpu+xwRxzQgijTg4D4LlZe9m1rpbugwI4/YZYNjxy4L8TOXMUh8dsdq7eaGdBfyACQwyMOiWMG97o6nS7yddAcLiR798oIr+xp81j5+zmyQt2N3uMJ87fzWPn7vL4HDWVVgwGOOvWOHoODuSC+xOaemmk9AnghT9703d0MJMvjab3MNmlq9NzFyftcNA8XO6WNerNdCtKLLxybSb7cj2flBZlNeAXoMWhEIDnCsJOEjsGg0JST3+yt9YBsGtdjVNfmgOlVyD6S4JQ6Dy1tegksQMQlWCiusLKf2/OYv2flS3e99mZe3h25p5mtzftYhwgCQzRqJNUEHoSEulDUbaZjUur9n/nFpQXWVAUbYJ4wNgQUjICnHqGhkb6MGBsiCQHvU07j52QSB8Cgoz0HhrE7R+kAloycMz0cM6/z97T7z+XRmPyVehxTCAmXwMTL9BW+W1cYo+buhobL16xl72btImq2sY9CPRxxdGONTVsXFrNl88UUF1mZcJ5kVz1Qpdm8REQZOSB/3Vj/LmR9D9OW26v98c95ZoY7vuyG3d/lsbVLyYz+pRwFEXRenkO1Yoxzr0rwSkOD4ScOYrDs2EDFBTYP++AF2qKonDh/YluE3N6yf33jTsJ6ztCupbn5+2qJ393g9PuXI5qq2wEBBvpPSyIm2anNNvV9VB3eRUdlBdUEAJOzbGHT9VmtKrLG2PqjSI2L6tm9S8VNNTZsNma//x7NtbRNcNfTiaFnbsEYSeLneRefmRvq0NVVZ6duZePH8lrmoU+UPpJqX+QjC3Cg04WN2BfEbJucRVv3JTNA6ftoCjbPgm1aVkVi77QGtDvWlfrtCOlrr7GuYJQCI9jTieJIX2H1JXz3C9bPFDlRRaCI4wH3EZGeIEOUH3r2MYotW8Ar6/K4NUVGZx3dwJRDiuYeg4O5KWl6U0tJ6bfGMfA40PY+a82jthsKt+8Usi21TXaDuLY9yCwmFV+fn8fVqv9Z9evbRrqbNTV2FoshohO9OWsW+IIbmxLFhrpw6sr0hl5Uji+/gaSeza/VppyWTQPfdOdxO4HXwUvo584PFar9k/XiZZ6gdZUGpov0yovsvfxcUxsZG+rc7pfaYGZZT+UUVdlJSBEwk246AB9OQ5HVKI2sA48PoQLGmfh9KT73o3agFpZYuXGMVv534uFTt9rbrCRvbXObRWi8GKdvIIQtMR6TaWNZy7Z03Sbu0RGS/Zu0saipJ6yPFI08oIKwvBY585JRdlmfvmguOnzV6/N4vOnC1y/zUlJnhlF0XZ2FQLo9LGTMSKIXkMD2bm2lsrSQ9+spKbSSlCYrPgQDjrAhG6vYQfW9sidlD7+FGY2UFtlZeOSKhZ/WQqAX4CCqqr8u8Beyf7tq0VcN2ILO/7V+nOa67X3EX1lVdBBrpba367eJl8DMcmeN1Bp8bEP6buE0HWAwD8cN77ZFZOf0mx3r9IC++eOjX0d+60V55n5/Kl8Pnwwj51rawkIlkFTNPKSJcb65j79xwbjY1IICDZQXWbBalUp2KNVdSz4VKvmWP2LvXvurnU13DB6KxazSpf05o3ohRdzFyOdLLke0zgxtWejfcJpe2PD9/3J3VnPT2/vY/s/NYREGpsmuYTwhh6EXXr7kz4iiLs/TcPkp1087VrbPLmubxznyGZTUVWVvZvqiE/zk+X5wlknX/Gh94y+48Tt7MttYOuq6oN+jNoqG/5Bcq0jHHSACkJPG4wcCP0aZcW8iqZdjkHLE/zzWyVLvilr9j1rF2pJw5rGVYl67qA9tVOS6TFx+Np54B+O8BgTGSOD2JdtRnX4ucqK7IlAx+Thvhzt9h3/1vD8ZXubbi/ONRM/Wi7URCMvWWIcneTL84t749fYyyk43EhJvoWNS6owNzj/vBFx9uFo3WJ7Pw9JcAgnXrDE2N1rfufaA0sQPjtzD3XVNqISTaT2DZDl+UKjx427KqhOxC/AwPWvaf2kn/y5J58/XcDmZVWoqsrCz0qb7lfoZhOg52btbdpJctCE5hs5CC/WyZcYg3M10v2n7ATg2YW9CAwxsmdjLXEpvvstdNBWS7WfJIdoQ/WNLbc6QILwcHQbGEBIpJHPn8pvuu2E8yNZ9EVpUx9CV3rrl5oK5zFYXz7cHsj0mDg8HWT78sMREWcid2e90xLIsgILJflm7vzPdpb/aO/ZUdy44ULO9ua9CNMGHHoJs+hkvKAKSucfaGhKUgSF+7D2j0r+e3M2AF0z7NWBFcVWtq6qxmpx/h3EdJEdjIWLTrzUC5z74QAMmRTKng21Tr1rPNE3JynONRPTRZLrArBYYMEC90vzoVPFjqOAYCMRcT7UlFtZ/Wslc5+3Ly3O2WY/R1NVrXJw9/pa6mq0XlDHnRnRFocs2isvSK73Ht68D3tRdgNWi8rTF+/hkbN38eKVe5uWQ7qj9VuX1IIA9u6FPXs6/YRuQJCRm2ankO4QP137+GMxq6x3KHYYd3YEfUZp9/nlg2JKC8zNKtnbUwWhRLE4PJ088MHew+n3T0qabisrsvDbR8VUFFv47SPt9q7p/k0VhHqDeEeOyRAhgE5/wunKdfC74rlkjjsjnL6jgyjJN/PSlZm8dn0WOQ69PANkuYpw5CWVHPpF1qWPJpI+PIiGOpXSAjM7/q3hyQt2c/XQzdRWNx9nHEmCUADa2NLQ4BVLjF0FhhqxWuHdu3Ocbs/cbB9jbhm3zaltTHyaL2nS+1Y48oLYGXJiCM8v7u20tL4oy57EKCu0sG1VDV+/XMhbt2e73fG4rtom7ZSERq8U9IJCovhUP65/Xatc7z0skK6Ny44L9tor1c+5PZ5rX+lKYIgBc73Kw2fuIneHczFRWIxUEIrOwgsCf9jksKaPI+NNhEQa2bikikVflDrdr+eQQErzzZgbbKz53d6UtEtvfy64P6Fp5kAIj0uMO1nsuIpPtScsTrwoiohYE+feleA0c71lRTUbl2q9b449LfxoH6Jo71xjp5Mm1vUqwuAIHyIbd9Erzbfw/GV7ydyiJTfydtY7tb5wJdW3AtB64P6CAAEAAElEQVRixPFiTdfJxxvA44YJjku/6mpsZG2xJwx9/eXSSLjwgokpRVHwDzRQV2MfUz+4L4eyQueKwRU/lbNmQaXT6ildbZVVKgiFxnG88ZJiiBf+7M01L3clposvQWHGxlYv/k67etdUaj9/fa2NNQ4bmLzwZ28C29HyfIlicXg6eW8B0HraPPlLT55Z0IuHvulOUg9txyJVhZtmpwBa/7TEHn7YbFqlYdZW+8lmaJSR0aeE73e3IeElLBb3A6QXJAh7DdMSgb2HBXLadTFNt+v9OByNmR7O+fcmHLVjEx2E6wlnJ7xQA5hwfiSgJfmiGhOEO9Y49yHc+W8td03ewTevau0vGuqc31ekf6dw4mVLjMG5an3KrOimjx0rCAGnczaDUc7VhAsvWGLsKqix+vb1G7Pcfl1vZwHw09v72Li0ioY6FX+pIBTgvnrQ8fZOyC/AgI9JwWBQePjb7jz0TXdufS+Vl5b2brqPu+ud5F5++AW0r5Rc+6llFB2Tl8xIh0baQ0UvAQ6JMNLjmAAuuD+B7gMDqCjWyvA3/61VP6X1D2D3+lqZjRbONm6EoMaKuU6a3PCk97BAhk8JZeKFUU6bJ/Q/LoQx0+s49ZpYXrpqL9nb6mX3YuGepyRHJzPq5HCGnBiKr78Bc4P2M37/RpHTfb5+WUsM/vJ+MSdfGUNxntnp63piUXg5x4nbTr5M0lVgqJasCIk0OlUTuv7IjhWEBjllE668YImx7oL7E9i9rpbTb4jll/eL+eWD4qavxSSbKMrWxpnyIq2ysK7Gxg//tY9NUkEoAOcYcU2ue8HmaZ6W2t/5URoPTtc2Aeqa7s/JV8fQd3Tw0Ty0AyIJQnF4vGhmQBceq4VNQnc/FEVh9CnhAPgHaYPl9n9qCAo1MvbMCHavr6W2qnNewIpDZLVq/xx5QSUHgMnXwCWPJDW7PSzah/Pu1qoFzfXa7yCph99RPTbRQXhJBSHYlzqafA2ERBipLPXcc7CsyEJxrnOCUHYwFoBzJYeXVUHp1RoBQUZGnxZO9rY6bDZtmWRYtE/ThguOFYUyOSWa8aIE4ehTwpuua6b+X3RTgvCG/3YlrX8AHz2UR2m+uannevZW52rc9lYJJdqIngh0FztefG4S29WXqAQTxXlmZtwVT2rf9tnvVqJYHB4vWGLsqvewIBK6+TXrjxYW7UOPY7SdikOj7X2jaipabiQvvIyqNk8IdvKYORgXPqD165SLNOGWF/SCcifSoRpw+NQwXvyrNzfNTuGMm2IBKCs0OyUI+46Wnreikad+nV4QOxFxWtxMujQK/0ADFz2YyElXRnPadbHMuDO+6X7l+yyEx/pw/etdmX5jbFsdrugovCC5Ds79OGO7+uLrZ2DW40mkjwiissSC1ao2W67fnjZaEG3I0xJjL8gT7E/3QVpSMCqx/a7ykCgWh8dLlhg7Sh8exH1fdHP7tRHTwtixpobaSqsMksI9vWE8eEUV1MHqNiCQa1/p2taHIdorT5uUdPLYOf++BB4/dzegLfPy9TfQc3AgweFGvnqhkNICC8W5DZh8FZ5Z0AujyXtn6IULx1hxV0HYiWMnKNTI66synG6LTvRl0sVRrFtc6XR7WaGF9OGSWBdueFEFoSeOGyiExfigqlCSZ6Yo275T6/HnRJAxUmJI4Dk34EUx48l59yQw/rxIQiLab56g/R6Z6Bi8YBfjg6EPjJWlVmKSTUy7PNppF2QhnCoIdV5woSZEq/KyC7Tknv68viqDrSur6T4osOl2veXFF0/nU1VmJaGbn/S9Fc489YLyktjxJGNkEKNODmPiRVE8ctautj4c0Z5J7ODrb5906jVEG4P+/KqU9Yurmm7vOSRQNmQUmpbGHS+MH0e+/gZSMtrn0mKdJAjF4ZHSYSeR8SZOvCiK9BFBKIrCtMtj9v9NwrvIEmMhDp0X7sTqqPcw5+qMgGAjgSEGqsq0VhbdBrTvk07RBrx4iXFLTL4GLnwgEYD/XBpFQjfpeys88OJxZ8C4YNYtqnLqaRuX4kd8qi+/fVQCQGpffzJGBjNgbEhbHaZobzyNL16eJ+goOlyCUFEUI7AKyFFV9aS2Ph6B18+quTr9eulfI1rgbvbMyy/UhDgg5eWSXHcjJNKHmkptmVdvWSIpXO1vibHg1GvkvE20wIuXGF/+TDJWS/Of84L7E3h25l4AgsKMnHyVFEQIB+4qCL0gXjqLjrgO5QZgc1sfhGgkpcNCHBx3PQjlQk2I/Vu9GqoalzN5WQ/Clhx3RgQAV72QzJATpYJDuNBjxAt7EArRKjxN6noBg0HB5Ns8XdBtQCC3f5AKgFX2YhSu9PHGMS8gY06H0aEShIqiJAPTgLfb+li8ntUKDQ1tMmi+8MIL9O3bl379+nHuuedSV1e3/28Sor1oaYmxDJpCeCax49b4cyN48uee9D8uxGkZmBCALDEW4lCZzbBvn1dXELaka4Y/U2ZFc+5d8fu/s/AuLa2WEu1eh0oQAi8CtwMey20URblcUZRViqKsWr169VE7MK+TnQ1btzZ/AzjCvQVycnJ4+eWXWbVqFRs2bMBqtTJnzpwj9nzeRGLnKJElxp2OxM5R4lh963ibl1MUhdCoDtcxBpDYOSq8eBfjzkri5igpLYUdO7SPZXl+MwaDwslXxRCT7NvWh3LAJHaOEncTujYbGAwy5nQARz1BqChK5CF+30lAoaqqLUazqqqzVVUdqqrq0CFDhhzSMYoDYLU6lw/rjsISY4vFQm1tLRaLhZqaGhITE4/o83kLiZ2jxFMVlKGjzdcIncTOUSIVhJ2OxM5R4ClBKDHTYUncHCXulkmCjDsdmMTOUeJ4vuZ4myQIO4RWmXJWFGUxcKWqqpv2c78rgUeB6EN4mmOBUxRFmQr4A6GKonysquoFh/BY4nC5GzD124+gpKQkbr31Vrp27UpAQACTJk1i0qRJze43e/ZsZs+eDYC/v/8RPab2Ijs7u60PQRwIT1VQ7WTQ9LbYkbjpQDzNSDt+3oYkdkS71NJuku5uP8q8LW5AYqfD0MecdrrqQ2JHtFvuNifRE4TtoAJXYqdlrbUmJQRYoyjKS8CDqqrWOH5RUZShwOvAYOC/h/IEqqreBdzV+HjHA7dKcrANOSYIPS1ZOQK9kEpLS/n222/ZvXs34eHhnHXWWXz88cdccIHzS+Hyyy/n8ssvb/XnF+KwtfMKQokd0W7ZbM27obeDxKBOYke0S+28glDiRrRbnioI20lyXWJHtFvuEoTtqBhCYqdlrXVFOgS4A7gC2KIoynQARVEiFEV5E1gGWIFhqqpe20rPKdqS44nmUSy7/+2330hLSyMmJgaTycT06dNZunTpEXkuIY6IlhKE7WDQFKLd8jQj7fi/EMLZ/hKEEjtCuOepGEJiRoiWOa6WaofFEKJlrfJXUlXVpqrqi0AGWjLwS0VRFgLbgNOBK1RVHaWq6ppWer4/VFU9qTUeSxyiNho0u3btyrJly6ipqUFVVX7//XcyMjKO6HMK0arc9eXQZ9WEEJ55ih39a0KI5mSTEiEOjbte6/rtILEjREvacTsl0bJWvSJVVTUXeATYC4wDIoBrVVV9pzWfR7QDnvpyHOFBc8SIEZx55pkMHjyY/v37Y7PZpERYdCwt9eWQQVMIzzzNSAshPNNjxl3DeCGEZ1JBKMShkQrCDq3V/kqKogQpivIssArYB5wM/AR8pijKZ4qiJLTWc4l2YH87ex1BDz30EFu2bGHDhg189NFH+Pn5HfHnFKLVtPNNSoRolzxtSmKzaf1uJXaEcE8qCIU4NO5awugfyzmbEJ552lROUeScrQNolQShoihnApuBmcBNwAhVVX9UVfUUtCXGI9B6E96kKIqkjjuD/c2qSeAL4Z6nHoRHYFMfIToNTwlCuVATomUt9SCU2BHCs5aWGEvsCOGZu7jRxxxJELZ7rZWs+wL4A0hXVfV1VbX/1VVV/Q7oA7wGPAG0Sh9C0cYkQSjEoZElxkIcPE/LJKV/pxAt83ReJrEjRMtautaR2BHCMymG6NBa693teFVVL1JVtdDdF1VVrVNV9W5gEFDUSs8p2tL+GvcKIdw70E1KLBaQHbqF0EgFoRCHRioIhTg0LbVTktgRwjNP1zrulhivXCn5g3amtXYxXnyA99uiqurE1nhO0cakglCIQ9NS417HuLFaoaLi6B+fEO1RSz0IpZJDCM9a6kHoKclRVSUXbEK4u87Rb5dxRwjPPFUQuouboiJoaICCgqN3fKJF8u4mDs3+yu4lQSiEe+5m1dzFjbv7CeGtWtqJVcYcITxrKXaMRvffs24dlJYe2eMSor3Tz8Ncr3WkB6EQLfNUDOGuglBVtYKIzZuP/nEKt3xa40EURbEBB/ouqaqq2irPK9qQu51Y9duNRhk0hfCkpR6E7u5XX69Vc0RFHd3jFKI9aamCUMYcIfbvYCoIPZ3jCeFNHGNGiiGEOHDurnU8LTF2TMSLdqG1EnXX03KCUEHbzXh8Kz2faGvuSof1j6UBqRB29fXg52f/3N2SfHcXavpgWVIC2dmSIBTebX89CN3ZsQNMJkhJOfLHJ0R75e5cTf/cddypqrJfvMnFmvB2nuJAEoRCtMzT0nx3OQJV1doqSTy1G62SIFRV9VV3tyuKogDnAPcAfYF5wKOt8ZyijbkrHdY/lkFTCI3NBgsWwJQp2uee4qKlCkKp5BCieYJQ11IVVH29jEVCuEuuK4r7/p3Z2dptUs0hhP08zLXiSXrfCtEyT/sTGAzO8aT/LwnCduWIvLspimJUFOVSYAvwMbAVGKKq6jRVVf8+Es8pjrKWZgakglAIjdWq7Uascxcz+v/uehDqjyGDpvB2rn3UWoodnVRBCXHw4467nmtCeKND2cV4yxaJHSE8nZO55gg8baIl2lSrJggVRfFVFOVqYCcwG1gB9FdV9UxVVde05nOJNqaXA+sf6/+76y0ghLdy1y/NUUtLjGXQFMLuUJYYS/WtEPuvXHfX2kIShELYxxBPy/MdbdumLdHfsUPGHSE8Xc+45gmkGKJdapUEoaIogYqi3ALsBp4HfgF6q6p6oaqqsiVNZ+SuB6Ee+I63OTKbj86xCdFeuEuiu9PSrJoMmsIbWa3Ou6h6qiB0l1xvaICiIklyCO+Unw9ZWfbPPVUQulsm6VhBKEkO4e0cz932t9FCcTFUV0vsCO+0YoXzdb5jDBzIEmOZ0G1XWmuTkr1AJPAH8DSQBfgritLH3Z1VVd3USs8r2oqn7cv1wHdkNmv3/esvOOGEo3ucQrQldzHiyPF2151YpYJQeLPSUti6FY49Vvu8pRgyGOzJeNA29tmzR9scSGJHeJuKCucLtUNpbSHjjvBGOTkQHg5BQdrn7uLBcULXtfrW04ZAQnR2JSXauGMyaZ976kEoxRAdQmslCPXtNccDx7dwPwVQAWMrPa84WjZsgB49wN9f+9zdDJmnJcZ//QUDBjj3YhPCGzhWPbkmAMF99a3r16xWmVUT3sf1ZPFgq6BkmaTwVq7JvUNpbSGxI7xRTo52LqYnCN1VQelx4+6czXXViBDewnXccRcDssS4w2itBOH4Vnoc0V4VFUFysvsE4f6WGFut2qyCBL7wNgezxNhTBaEMmsIbuSbGPSXJPcWObLQgvNWBTirpseN6myTXhbc6kCSHpwpCSRAKb+Z4rbK/qnVH+lgl1zrtSqskCFVVXdQajyPasZZmpF1Lhz3NqkkVlPA2B7tJiaevyaApvM3+LtT2VwWlJwll3BHe5nAqCPWJXhl3hDdyvVbxtPx+fxWEMu4Ib+KpR7QrdysN5VqnXWqtCkLR2R3IoOmu+Sho3yczA8Ib7a8Hoc510NQv0EBiR3inA11i7G5GWioIhTdz7McJB9+D0DHBLoQ3OdhiCKkgFOLA+63vb4mxjDntRmvtYmxTFMXq4V+DoijZiqJ8qijKgNZ4PtEGDqa3gCt90JQBU3gb19lkxxhwHSBdkxyOjyGDpvA2B7rE2F1/T8fkoIw7wtu4Vs56igHp3ymEs/1NTIFzBaG7YghP3ydEZ+WunZLj2OKaXHfkusJKYqddaK0KwusBT39RI5AAnAQsUxRlrKqqq1rpecXR4jpo7q9xr7tZNQl64W1aWmLsGCeus2qOFYRSdi+80YEuMW6pglBiR3gjq7X5xZnB4L7Kw2Bw3kDO8WsSO8LbuCbXW6ogdOVYdSuxI7yJuyXGjmOOzt1KQ8cKQv1zd/EljqrW6kH46v7uoyjK3cCvwCPAlNZ4XnEUtTQjfaBLjPWPPVVKCdHZeLogc622bWlWTZLrwhvtr4LwQHoQyjJJ4Y08XZS5m7DyVEEoyXXhjVqqIDzQYgjX7xOiszuYCkJP/dYldtqVo5apUVVVBf4LjDpazylaibsZMXcXXY4nmzJoCuF+ibEeI45Jjf3NqkncCG9zsD0IZYmxEBp3seO6W7F+u7vkulRBCW/VWrsYy8SU8CauFYSOq6JaWi0FssS4nTram5SUA35H+TnF4XI34HmaVWupB6Hr9wnR2XmqIAT3FYSys5cQmgO5UNPv5ynJIbEjvJG7jRZaquZwva9cqAlv1VLlursKQkdyrSO8lbsKQv2axlFL1zoSO+3K0V7rOQ7YeZSfUxwudyeLB1p2L4EvvJm7QVOv5HC8MHOXXHeMHZmNFt6mpQrC/W3w41g9KGOO8DbuYsc1QeiukkP/mlRBCW/laWLK3Zjjrp2SJNeFN/KUIIQDO19zfAwZd9qFVqkgVBQlsIUvG4F44FTgVuD21nhOcRS5S+55Krt3bUoqgS+8WUs9n/Z3wikVhMKbuavkMBq1293FjrsKQsflkkJ4C9fYcW1t4XibVEEJoXE3qeSuT+eBrJaScUd4E0+rpdxd18gS4w6htZYYV4HHXYx1DcALqqq+3ErPKY4W194Crh+3VEGo309OOIU38rSzF8gmJUK0xFMVlOsOrbLRghDO3FVBuU5MtVRBKBdqwht5aqfk2lvdUwWhJNeFt3LXb92xB6Fuf6ulHD8Xbaq1EoQz8ZwgtABFwApVVcta6fnE0eSpglCv2pDmo0K419LOXgfauFcShMIbuUtyGI1gNu+/gtCx/6DEjvA2LS0xdjxvk57RQth52pDRaHRePuyY5JAEoRDuKwj1GHHdkNHdxiUgsdPOtEqCUFXV9w/kfoqijAduV1V1Sms8rzhK3FUQqir4+DhfxHmaaQMpuxfeyd2g6diDUOc4mEqCUAj7616PDcdKQdcKQqOx+YWaVBAKb+WYzAD3CUJ3Kz70+8qFmvBGnvqt67HjuhKkpeX5cq0jvElLm5QcTDslx89Fm2q1XYwVRQkHJgNdgF3Ad6qqmhu/dhZwBzAY2NZazymOEneB7xj8Onc7e+nfY7E4fy6EN9jfLsbuBk2du0HTXcWHEJ2R47ijx4qeXJdNSoTwbH9LjPXb3FVB2WySIBTeydMSYx8f+8f6190tz5fYEd7KtZDIMUY8FUM43tbS56JNtNYmJf2BX4A4h5v/URTlDOBTYBSwETgf+Lw1nlMcRS01H3W83V3ZvZQOC2/mesLpWgXV0gmn6wy0qsLevRAVBSEhR/7YhWhLLW3w4xo77jYpAdkBXHgfd5WznnoQeqogdLdqRIjOzt11ih4nB7taSq51hDc50ArCltop6Ww2KCzUEvORkUfn+EUzhv3f5YA8DlSgJQIDgQygBFgJ9AMuUlW1v6qqn6mqeshnHIqidFEUZaGiKJsURdmoKMoNrXHwYj/cbbTgKfA9lQ5L2b3wRvurINQdSHWgqkJeHlRUtP5xCtHeuGt63VIFoetFnf4YcqEmvImnTeXc7WLsugpEv93xY4sFNm48cscrRHvhaYmx0di8rYVr7MgySeHNPG3IqFcQ7m/Fh+vneXlQXHxkj1m0qLUShEOB+1RVXa6qap2qqluBq4Bo4BZVVT9upeexND5eH2AkcI2iKH1a6bGFJ64zA66lwweyTNLxMWTZl/AWrv3R9pfk2F8FodlsjyUhOrOWNvg5kF2M9a/JWCO8SUubykHzHoTu7uv4cW0tZGcf2WMWoj3wlFw3Gg+8j5oUQwhvtL8KQp3j7Z4qCFUVGhrkWqeNtVaCMA7Y43Kb/vnaVnoOVFXNU1X1n8aPK4HNQFJrPb7woKXty91dqLU0aKoq7N4N26QVpfACVqtzgrCljRY8bVKik0FTeBM9Jhxj50ArCN19/McfWvwI0Zl52lTONXYOtIJQJqWEt2gpue54ftbS+Zqn8zchOrOWEoT7a6fk7lpHxp0211oJQgBP74aWVnyOJoqipALHAMtdbr9cUZRViqKsWr169ZF4au/jaZmkp94C7r7X8f/aWrlQa4ckdo4Am82+2zd4Xp7fUvWt4+cyaLZLEjtHgNWqxY6nCsIDWWKsfx2gqkqLH9GuSOy0MtdJKXAfO+6qoPT7On4sk1LtksTNEdDSEmPHZHpLGzJ6+ly0GxI7R8DBXOscyCYlMu60udZMEP6sKEqh/g/Ia7z9d8fbG792WBRFCQa+Am5UVdWpIZeqqrNVVR2qqurQIUOGHO5TCXDfLN7TzICnCkKdJDnaLYmdI0AfNPeXXD+QsnurVesHJbHT7kjsHAF6osNdD0I92eGuF5R+X53NpsWN3k9NtCsSO63MdcwB98vz3VVBuX5ss9mT6jLutCsSN0eA1arFhLudWB3Pz9ydr7lrCVNWBtu3H9UfQeyfxM4R4Dqh29IuxvvLE9hskiBsB1plF2PgoVZ6nP1SFMWElhz8RFXV/x2t5/Vq7i7UPCU5PO3spdNnBvQLPSE6M3dVUI6DpuuJqCPX2NGrbiXJITo7fbLJ0/J81ws12H8VFMgJp+j89DHHcZWG47mZ4zmbaxUUNK++dUwQynmb6MzcTei666PWUt9bx/uUl0NJyZE9ZiHaA3f91g+nglAKidpcqyQIVVU9KglCRVEU4B1gs6qqzx+N5xR4HjRdTy7dVRC6zqrpMwO+vkfn2IVoS4eyxNhTcr2+XvtfBk3R2Vmt9nhwjB2fxlMWPU7cjTn6fR0/luS68Bb6hZpros+1j5q7Kij9a44fSwWh8BausaPHjWtBxIGs+JBlksKb7K8Y4mB6EJrN2v0kdtpUay4xPhqOBS4EJiiK8m/jv6ltfVCdnh747sruPQW+TgZN4c3cbVLiqez+QCsIJXZEZ6fHjbteg+BcqX4gS4wldoS3cL1QA+dEumOS8EB2MZYEofAWLS2TdExquNsBXK51hDdzVwzhOua43u6uqtBgkGKIdqK1lhgfFaqq/gUo+72jaF3uSoc9bVJyoIOmVBAKb3CgPQg9zaoZjfa+ODJoCm+hjzmedjHWE4eeKgg9LZOUCkLR2blLELpbYry//p16jEmCUHgLTxtjuSbW3bW2cHetU18vY47wDgdaQeiuGEI/j9MnhuVap13oaBWEoi3sL/B1rstY9Nsc6U2vZdAU3sBiAZPpwPtyeOrpaTRqiXWTyXnQlF1ZRWdksdgThK4z0uC+B6EjPYb0iS2pIBTewnFSyl2iQ/98f0uM9dhxTRDW1sq4Izqn/fUgdLeL8f7aKcmYI7yB60pDT9c6noohHFeH1NeDn5/EThuTBKHYP9fSYdeye9cKwpYGTZkZEN5Cr9JoacmK431dm147VuTqg6a/vz12Kivhr7+O/M8hxNHmaYnxgVYQulZBSQ9C4S0ck+stVUK522jB8b6OCUK9ugNgyxbIzDw6P4sQR5Ona50DmZhyHH8cxx0Zc4Q3sFqdiyH2t4uxI8dzO72C0PFaR7QJSRCK/Wup7N5dD0L9Po7/g/Y1fQdjCXzRga1bt449e/Y431hXB9nZ9s/1JIdjFZRjQsNd2b27JcZgHzQDAuyxU1Oj/XOt0hWio3OMHXctLBzHGXe7GOtjkWMFoa+vjDui8ykvd35d6+dr7pLrB7LRgn5fg8FeBeU47tTVQXX10f0ZhTgaXHtGe1pi7G5iyrWPmvQgFN7EdbWUa9/bA73WMRiajzmiTUiCUDhT1eazw56WGOu7ezl+r7slxvrnRqN2cimBL9rQvn37WLRoUbPbLRYLS5Yscbrtt99+o6yszOm24uJipkyZwsSJEyksLLR/IT8f/v3XfvHk7kLNXbN4/XZ3M9KOFYQNDc6zanV12vfpVblCdBZ67LguMXatIHQ35uj31ccn/ULNcdwpKNAqoYTo6NaudT5nc7c837GnJ+w/yeE4gWU2O487tbXaxJQQHV1dnfPnepLD02op1w1+PPXI1XdCbmjQ/tfvs3y5JNdF56RXELobcw6kGMKxnZLZLBO67YAkCIWz6mrthNMx6bC/QXN/S4xbqoIS4gDYbDby8vKwuS5Zb7R7925++OEHrrziCh69+2727dvn9PXq6mreeustdu/ezRVXXMGUKVM47bTTeOGFF5ru8+mnnzJu3Dg2bNjARRddRHp6OqeccgrXXnstAKqq8vjjj5Oamsql553HJZdcwtChQ1m2bJn2ABUV2mt7507tc09VUK6DZkt9CVsaNGtrtf9dLtaWL1/O448/zqeffspTTz3Fn3/+2fS1FStWYLFYqG+M76qqqmYJUEeqqlJSUkJdTQ1VVVUe7yfEYSkoAMdku7vY0W8D9xstuC4x1qug9A1+AgLsy71KS52fT4gOQHVXLV5dDbm59s8dk+v6+OJpF2N3Kz70sclxibHrxJQkOURHp6qwYIHWqkXnbpmkY+Wt67jj+niO1zoWi3abj4993Ckr0/4J0dG5XsO7xo7VeujFEBZL8x6EjnEqjgpJEApnetKhqMh+m8WiJSZammUGz0uMXaugfH2dZ7eXLJE+HV4qJyeHH374gR9//JGff/6ZTZs2UVxcTENDA+Xl5Xz99ddMnTqVoKAg+vTpw5CBA3ns0UdZtGgR+fn5/PTTT9xyyy0MHz6cFx5/nG7+/mT98w8pKSkcN3o0i376ie+/+YYB/fox5513GDx4MLm7dvHIBRewLyuLN157DVVVycnJ4eknnqBfSgojRozA38eHT157jd1//MGK5ct57733eOutt5gzZw5bPvqIR8eP597zz+fVV1/l5JNPZsaMGZhLS6FrV/sJoKdKDteye9d+Np6WGDsMmmazmYdeeom6hgZ7zAJ33HEHZ511FgUFBcydO5ec7ds5+8wzqa6upqKigrFjx/Liiy8SFhbGjBkzGDZsGKNHjeKm66/ngw8+oLJxEP74448ZN24c/znhBFJTUggNCyMyMpJZs2ZxxRVXMH/+fHbu3MmKFSt49NFHmThxIi+88AJz585l3rx55OTkOF3MNug94ESHVlVVtd9E8QsvvMDGjRs9fn3t2rWsXLmS2bNnk5mZyXfffQc5Oc4VfXrsuI4vPj7ax/qY4ji+uEsQ6nHmWkFYU6OdcDpMOGRlZfHYY49hbtyAoby8vCmRLsSRVltby4QJE8jMzCQ3N5d58+YBkJ+fT11dHevWrSMlJYVNmzZRWlqqfVNdnfb6rqqyTxS5xo4+5riOO45jkc713M5q1W7z9dUeV9+cRK9eb7R48WLGjBmjxTLw2KOPkpeXd8R/Z8K7eHo/bmhowGKxkJOT4/br27Zt48EHH6ShoYFa/Xypvl57fTu2hXFdJuk4UeW6wY+76lvXntG+vtqYZbXaKwol0SE6uooKmD/fXgyhjyeOy/MPp52S2azFof44DQ2waJEUFh1lPm19AOLIUFWViooKwsLCDu4ba2u1YC4shORk7TZ3pcOeZgbcnXC6Br7RaO9D2NAAJSXaCW54uNOhrFmzhl69ehEUFOTxcOvr6/npp584/fTTD+7nFEfc7t27Wb16NZs2bSI8PJwTTjiBffv2UVRUxN69e/nmm2/YuGEDI/r1QwkMxGKzsXvPHooLCqisrcVkMjH2mGM449xz+fKKKwgMCmL+X3/x++bN3PHZZ+zMz6d3SgonDh7M0ueeo2dGhpZAUxReLSzk4x9/5JprriHAz4+XZ81i2kknacuAATIyuLm0lF7TpnH+uefy86+/csmECVx72ml8smAB95x9NkpAAJjNfP/MM4y/+moqystZ/sMPJBmNMGQIrFjBKYMGsevNNzn7tdd44u23uX/2bNi+XYsFT1VQ7prF6x972rhEn1VrvFBbsmQJD/73v2zZto3ef/zB+99/T58+fdi4fj3rZs8mPCFB+1kDAyncsYMnHn2UjH79sFmt3HvvvVw4bhzDunThtCFD2L5hA2U5Oby3dClXXXUVUVFR1FRXM3PKFLBYmDdvHkpiIkUrVvDSggVEBAbywC23kF1URFxEBEMzMrhy0iS+X7WKJb/9RlltLWvXr0dRFPr378/OnTvJzs7Gx8eHiIgIevTogaqqnHLKKfTt25eRI0cSFRUFQGFhIf/++y9VVVWMHDmSxMTEo/BK9W5lZWWsWrqU1F69SEhIcPt+u2zZMt595x3+WLgQf39/7n/wQaZNm0ZAQEDTfWw2G0uWLOG2225j4e+/M/d//8PX17fp62vXrmXp0qXce++9REREYLFYeOihhwgKCOCUd9/VLpzKyyEszHP/Tj1B6K6C0JFjgtBg0MYdPz+oraWkpIS/fv6Z/jExsGEDaQMGUFVVxejRo4kMC+P7779n8ODBfDV3LhddcAHPPP+800NbrVZee+01AIZ060bGsGEUlZXRu3fvVvhriM5GVVXeffddNm3axMyZM5kzZw433XQTkZGRTfdZtWoVX375JX8vXcrDDz1EYUEBK1etIic3l/POO4/u3bvz14IFDOrfnwEDBpCUlMTy5csJqK0lLCREO3fKzIT09OYVhI6rOOwH5T657pj4cHO+9uJzz3H3gw+SEBnJq6+9xpTG864P3n2XrtHRXHnllfTt25f77r+fEJOJ6++4w+Pv47zzznN6/xDep6CgAH9//xavUyorK3njjTeIjozk5ltu4avPP+eEyZMBbVwym81MmzaNadOm8euvv/LUU0/Rq1cvjjvuuKbHePrpp/nwww/JysqiurqaOXPmaAl1Pz8tQdi7tz0h3tK1jrtztpZ6RusrPqxW+0SuS4Jw27ZtlJeXc/fdd/PYY48xfPjwpq9ZGxMiRof4ra6udjtG//3334wYMQKDu42HhGhN5eXamLNjB3TrZr+u0fvWgv18raWVUTrXax39/E9fql9drd2nqko7PxRHhSQIj6Lq6mrq6uqaLobdfT0/P5+uXbvS0NBAZmYmvXv3xmAwYLPZ2LF9OzExMWzdto3vv/+e9PR0fv31V8477zwmNw6YDQ0NrFu3jttvv53169fzySefsH37dq655hoAKioqOOWUU4iPjwege/fujBo1ipNOOkk7iLo6SEiAffvsmf6WZtVcZwbczao5Dpp1ddqbRuMJZ/2+ffxv0SLO7t8fo0OCsK6ujgkTJtC9e3e+//57EhIS3P7OPv/8cy6++GLmzJnDyJEjSUlJafpabm4uiYmJ1NfX4+fnR93u3fiEhOATHX2wfzrhYtmyZYSFhZGUlERWVhapqamsWbOGffv2kZeTw6fvvsu2rCxGDhxIv2HD+PPPP3n99deJCQ4mNjKS+NRU7jj3XE5MTcUvPh6Ki7UH9vXVTqTCwlAbGlD0Wd6+faGhgSmDBzNl1y6Ij9dmr6xWbYDy99duA2howOTry6WPP86l06drr90RI5q+Rk0NhIejpKTw5iOP8M+yZTz90kskjxgBMTHc27s3DBrUtDSk98KF7Pnf/8hZv560ujoYPhyCgqBXL1i3jpDQUP578cUMuuYabn/rLfwDA7WTQE89CN0tMXbdJQ+a91yzWptOOOfPn8+NZ5xBncHA2o0b+e6773j9pZe4/thjCe/fXxtQjzsOgoJ48b77GHb++VhVlQfOPZd7P/qIK6+5hiHHHqsttbz4YggJgexsbNu2sTc/H/z9SRs0CPr0aToRjouP5/EBA8DPj9tuvlk7AQ4L036mwkLO7NcPgoOhthY1MpKcLVtYv28fqeHh9I6Kos5mY4vBwI78fKoMBjZs2sQPP/zA2rVrCQ0NpaamBtVmY1BGBv4hIVxxxRVMmDCB888/n+joaLp27Up2djYGg4GUlBTi4uKO+Ou8vTObzVitVvz8/FAckmQN9fU0NDSwctUq3n77bebPn88l553HpJNOIjAggH3FxeRkZvLvunV8NXcufZKSyK2ooLqmhpNPP52EgACsvr5Eh4bSc8AALrvsMq6eOpVHZ8wgq7CQx++9l7//+IPnXn2V+vp6br/tNl5/4w3CQkP56OabueezzwgODmbOnDlMnz6dbdu2MWnSJAampvL+E09w8mWXsWHtWh65+WbmrVhBaX4+Eb17a0uNXROE7pYY6yei7majoXnVlJ4gtFr5+uuvuf6hh+jfoweJXbrw6ddf885//8vw1FQ+ve02fszOJnvLFl649FKue/NNzjnvPHr37s2t11zDn4sXU1lfT+/4eCIiI/k4K4u1e/bg6+PDnXfeSXBoKFFRUSz99ltWbtlCTFwcWzdvJi4piVFjx3LeeedhNBpJSEhgxbJl/DhvHsXFxfTr148ePXrQv39/+vXrJxd5bay0tJS8vDyWLFlCenq6U7KhtraWvxcvprymhvT0dFRV5aevv0YFzrvkEpKSkgAtsfH1//7HT999x4YNG5g2ejQDBw6kZ1ISP3z1FU+98AIhoaEAnHLSScRERPC/O+/k7i+/JNDXl3A/P37//XdWrlzJihUrOH34cD689Va2PfUUd9x7L6mpqYwcNIgFb7yBITVVW4nRu7f7ynVPFYSuyXXX/tL6pFTj+dpfS5bwys03kxwby4X/938szsigZ8+efP/996x45hlur6lh8qRJJERE8OP33zclCJf+/jt5WVmcccklvPbqq1x3/fVs27SJHdu301Bby9wffsDPz4/6+noevvlmTGFhVFVXc+p//oNfdDRpaWnExMQ0HabVamXp0qXU1NQwceJEpwSKOHJsNht//PEHCQkJZGRkNPt6cVERyxpXFcTFxYGq8vrrr5PYGBP19fV8/ckn7M7PB1Xlvfffp6y4mNtuuIExJ57IkIwMyiorydm2jV+WLMEGfPLFF5gsFv7dto1nrrySGTNm8P5HHzFi1ChOnDiR6poaJg8bxo/ff8/0sWO56aabGDpkCAsWLuTjjz/Gajbz1ZdfMqBPH9577z1CQ0PZuHEjfcPCIDpaO4/Jy4OkJPfXOq4JQk+9b11bwuj91kF7XIuFsoYG7nn0UZ765BOCg4MBmDVzJstXrKBrbCwP33MPDz7xBEOHDgXgjttuw2AwkJSURFrXrpTX1HDVVVexetEieg8Z0vTU33z9NadPn865kyZx9e23kxAXR3FhIcMnTGj2N8rLy6OgoIA+ffpgMpmw2WwSP17OYrFgs9mcJnQdqarqdH5JZSXExmqv65IS7bzfcVIKml/r2B9s/+2U9P/1QiK9pUVFhVOCsKamhoCAAOdja/TYgw8y7eSTGeQQJzpbbS0Gf//mE8vCiSQID4W+7MLHB5vNxj+rVlG5Zw/W8HBsQGBgILE+Pixdt44Vq1fz799/szUnB7PZjAL0TEmhW7duBAYGYqmpYfWWLVjq6iirqiLYz4+i8nJsqkp0dDSq1UpydDQbduwgMiiIfRUVxMfGMmXsWF7+6SfOmjqV6667jsLCQk4//XQWLVyIYrFwy403EhQQwMknn0xYWBgRERH07t2bOXPmEBISwpTjjsNiNrO3oIArrriCoUOH8tBDDzFIUSAqSlsmqQeju92J9FJidxswQMt91BwCf9o557Bq7VrKAgO56uGHAW1p18cff8yQIUM4vndv+mdkMG3cON7/5hsURaGhoQGTyUR9fT0vvfACM089lcsvuww/o5G3Hn+c5BEjWL58Oddeey1DjzmGf9aupUt8PMWlpfj7+tJgs5GekUH//v0ZOHAgmXv2EGIyERIRQXFlJXu3bAE/P3qlp9OvXz8iIiIICwvDarXSvXt3SkpKCA8PZ+3atYQ1Lr3s0qWLVw2yn7/zDj8sXEhmVhYJCQnk5eUxoE8fEoODiQwM5OZTT+WkgQMxBQRoFxj/+Y/9myMitGqH5GRtkElM1CpWbTbYtQv694fsbBQ9CVdX51xdqldeJCbaZ3wd+fpqF0oAw4Y5D06+vtq/RhNmzmTCGWdog5s+K9t4cgZotw8bhu8//5B2yinawKgn+OPjtX6dGRmkDB7MoDffZP78+ZyWmopaWori739oS4x1jieh+s/QmHD86aef+O/FFzP6ggu0jVEMBv47Y4b2c+uVv43ix43j79deIzsnhxEXXMCEqVMZPG2admyOkxU9e2JISSFNT2q6CgzUEobuxMZqJwkpKVBVhVJaSnK/fiTn5EBoKMTEEFhWxuA9exgcFaX9Tfv1g1mzsNps7CooIDQoCENpKTEBAVBfz26bjV9XrODZJ5+kvLqavXv30iMtDZvZzM7MTOobGoiMjGTIkCEMGDCAPhkZDB8xQks0qipxcXHs2rWLvn37Eu5SndzeFRUVsWjRIqqqqqipqSEkJITExESMRiNLlixh3759bNq4kdWrVlFZXY3NZiMkJIQQf39CIiPJy86muraWnj168H8nnsjdjz3G+7/9xtN3301t4+N1j4ujT3w8j33wAXEDBsDOnWzMzOSvrVvJzcvD32Bg4/r1PPXii3zy2GOceOaZ2vt3aCgXbd1Kv0mT+Pq778grKuLYvn3J++QTwgMD8enWjTPHjmWd2cxJV1/NAw88QPG+fTx5/fVcOnGidrI3bx79oqL4/MUXGXfhhazatYsTx44FfWdwT8l1dxWE7l6rjsl1RXFKrq9o7BmaWVTE5r176dWrF1lZWfz5ySf4DR3K9MBAOOMMiIxkU2YmZ59yChW1tUwePJiPn3gCf4uFPtOnw/r1kJyM1Wbj38WLueeVV0iKiKCitpZRGRmcd9NN5GVnk3HrrZRu2sTnf/7JxWefjUFVyS4qon9KCqeNGUPcoEFs3LuX7xct4uHsbPYVF5OYmEhAQAD9+/cnNiaG1atXU1pWRnx8PIMGDSItLY1u3boxcOBAQkNDqa2tZdu2bZSVlVFdXU2/fv1IS0s7si/Sdiw/P5+ysjJ69OjRdM7g5+dHVVUVq1evprCwkE2bNtGrVy82bdrE7bffjtVqxWazsXfPHv4zeTIhwcEcO2AAjz/8MH179WLcf/6DyWrlyRdeoFtUFJFRUWzNysLHYGBASgoGg4GnnnuObnFx+IaGUl1eTpS/PxOGDOHtd94hODmZC046iYwRI3j5ySe59brryCspIdDPj3duvJFThg2DtDSmjBoFqsr9H37I9dddx9BevXj6ssvoM3QoSkAAvSsree2uu9g6YgR3f/ABs7//nivvv1+LBbO5eex4qoJyd6HmmDTUqzf08zWzmbUbNvDQJZfQd/RoniksZNKkSVx99dWkxsSQetppvJOSwtWPP86FU6dy5v33M3v2bPr378+ZM2ZQX1+PEhTEQ/fdx9z77+esRx7hvvPOY0NJCZNOOIHTzjiD/335JWEWC/1SUjAZDNx0441gMrEjK4uoiAh6d+tGj7Q0vv35ZyLCw/Hx82Pq1KkMHzqUyOhoJk6cyKxZs9i6dSsrVqxgzZo1BAQEEBUVxe7du6murqZ///4MGjSIuLg40tPTO9zY0Nps9fWoVitr/v2XnVlZlJSU0NDQwJgxY7QxJSSEBQsW8Pzzz5OXl0dUcDDFZWVExsZSVFTEMQMHsnHTJqKjo9m+dSu+fn48eeWVWOvqyM3Pp29GBrHR0QT7+lJntRLl60v/tDR25OUxtnt3bn74YZ587z0++/RT9uTm4uvrq8WryURkaCjTx43j4euvZ0NWFv1POYU+vXpx1223kVVYyKnDh3Px8ccz4cQTqcrNJSQ8nJdnzSJ15kx+/vlnbrrpJgb27s0X993H1l27CAoL47Rp0xg9ejQfP/00J48bp51H7typJQg99SB0TXC4633rWKmrV62HhGhft1pZ8fffXH7XXezJzWXY559zyaxZrFy5ksydO8n95BP8TCbSL7uM/0ycyF133cWNt9zCRx98gM1mw2Kx0DUmhtzSUv5v0iSOO+EExowYwaAhQ1i3eTMLfv+dXx57jIU7dzLz4ospLCsjMiSE4upqenfrhq+fH9ERESz4809MRiMBQUHkFxWRnp7Ojh07mDhxImFhYUybNg2AjRs3EhQURHZ2NhEREaxcuZLTTjsNRVEYOHAgAwYMYO3atcTFxVFUVERdXR11dXVMnjzZbbJGHH0///gjfkFBVJeXM+3UU5tuX7BgAYMHDyY8PJz533/PBRdfTHBwMDW1tVz2f//HlVdeSXRkJAFBQXw1ezb3P/UUGcccw9y5c+1/28pK7Vw/Pl67dtOvdRzP11yT6+7GGE/FEOCUJ1i0cCFjwsP58vPPee7zz5k3bx4+Pj4MHDCAkNBQXn/9dczFxezatYv/u+UWamtqePypp1i5YAFffvcdq3/7jQFTplBSWkpcVBRjhwxh5nnn8X/33gtoE+0mPcEpmnh9gtBqtVJVVUV1dTXV1dVUVlZStGMHCmDetw9baCibt22juK6Obdu3ExIUROmePVRWVdHg58eu3buJCAoiLjQUo9GIwd+f6vp68nNzGdSjB+OGD+esiy+mT5cu+Pv4EBgQwPLt28nJz6fWZkM1GLhj0iR8oqIIMJlIHTyYslWrCPLzwycqiu1bt5JfVUW/xEQiTzqJ6upqAtavxxAaqpX5GgzcMHUq+eXlTL/tNi4ZO5YH7rkHdu5kuK8vqcHBnDtuHOfcdhs1dXWEhITw03//Sx89IVJZyTUzZ/Ll/PlMnjyZ1bNnk5SQoF3wFxVpF/c2m3PZvR74rs1G3c1IO17UuZxwlhQVsWLtWha/9hqTbrqJ7mPGsGHDBl5++WVqa2p4/e67OWP4cM6/+GLOPP98xh13HJu2bKGqqorjR4xg1+7dDEpL460bbuCt//s//mho4I477qC+cTD87bHHWLtrF7889xzFRUVEHnccZStW4FdXx9a9e1lbVMSqX3+lV0QENapK0ebNRERGMj45GdXHh62Zmby9YAHlNTUUlZZiVFV27NlDRGgopRUVDB0yhJq6Ovbt24fBYOC0U08luUsXDKrK1n//Zevu3TRYrRwzYAAjR49m2KhRpKam4u/vT3FxMUajkYiIiKP2Wm9NL9x+Oy+ccQaqnx+KzYalvh4fPTEXHq69trKytJOu8nLtNdCYXMDXF1JTtY91sbHa/3oVoMPyK/z93R+E6y7a7vj57f+H2V/Jeng46DOxjlVrRiMce6x2EmgwMOOii3j55ZeZ9sornHvFFUwcP54hiYm88/LL3HzWWfQaOLB5Xw5ovsTYddB0jCmjkT82bqSmqooR6ena8Qwfrs3iDR2qJV9d+fiQfMYZJDdeEI6aMcPzz+phBnG/FAX0hERoqP1v26OH/T4REfbjKy7WqjlLSzH6+dEzPl6bkPD1hXHjwGol7Y8/uHzKFC4fNUr7XQUHa4nFiAjMJSVYqqooNBpZtWwZG/Pzeeenn7i7sJDQgADw8SErN5duiYnsyMnh2CFDmHrGGQwcOJDU1FQSEhIwuSaWj5LvvvuOiIgI6uvrCQsLY8eOHaxaupTi/HysNhulRUUsXb+eYwcMICIpiUCjkcqaGnJ27qSqvp7jR48myt+fG8aNI+OKK0gLCqKhro5KRaGyrIxKo5FIg4HEhAStArdPH4iP55mZM7XXSWws7N6tJXwjI+09YdPS6FtcTN/iYu3vtnOndt+8PC1R7zABEjNsGFvXrKFo1Sq6mkz4DRigxXTj69QUFcWQ7GyyP/mEFbt3Q0UFozIyYOBALZ6LimDFCpgwgWEDB7J81y5OjIyENWuaLqj0dgFO4467XYxdY0f/X5/E0r/u5wcWCytXrODt228n8dhjmf3UU3RJT+fuk04idPJk7XEdlrU/+vzzPGg2891XX3HKeefh4xj/I0cCYASGnH0284cOhS5dtBPmgAD7exnA5Mkcd9llWvVyUJB9oqKuTnufLCyEmBjYu5e8ffsoKi2lSlVZu2cPuXv3csPYsUTHxrK3spJNVVUsXryYt99+m/Xr12MwGPDx8aFLXBzhYWEEBAWxet06UlNTiYiI4KSTTiIiIqIpidytWzfS09Ox2WwdaolnQUEBH374oXYx05jkrq6uJi8ri61r12oTrhYLW7Kzyc3NJSIigtzcXMxmM/6+viQnJ7MnM5Ne3brRLS6OlLAw3po/n9jwcLokJ2NQFBSjEYPNxts33sjpI0dCYiLlZWX8+MsvLFy4kAabjR8eeIChp56qvR5rauyvSbMZS2kpv27ahJKbS46icOmdd2JwGIP6p6YCcPNzz3Hzzp28+PLLZBUVccqtt2pxoShNlRm3+vuz+cEHOWHYMIYNHqyNrSEhsGoVSUVFJF13He+OHs24M8/kpJkzSfbz095TXSsI9WQf7L+Pmn4fx/HJaAQfH6oqK8nJz9eW0ickcPHUqZQCt993H8tffx0iIwmZMIGP0tIgNpbvrVZe/vBDnti1i8cvvpicigruvOkmZt93H6ffcgvZV1xBYmIidZs3M/f99/l78WIuGDmSmRddhGngQFAUnqithb17sVmtbN25k+27d7N+61b+99JLDElLg337aDCb+fWff6hWVb6YN4/bbruN9J49GZ2ezsD4eCotFuqyszn+mGMIiIxk7aZNvPXWWxQWFLBl61YCAwOpq62lvnHiOT09nZCQEAIDA4mPj6d79+4YDAZCQkLo0aMHEydORFEUioqK8PPzw2KxUFBQQHp6ertNjqxcuZI1a9ZQW1uL1WqltraW7Kws1ixdyqqNG/ExGumWmEi/Xr2IjIzEFhDAm2+8QWlpKbU1NUwYOpQ7pkxhYGoqPfv3p7aqin/WriUtPp5lW7eSNGUKhfX1jMzIINZgQOnXT5t8DA7mtmXLyM3Lo8JspnjLFv5zxRUYIiLsrzt/fz4cORKMRnK2baNo0ybUXr2oN5sZMXQois0GiYn0b6wEmnrVVUwZPJgPv/2W404+mW5DhkBoKGE2G5SXE5qby8xJkzj55JN57I47uG3cOBg8mBM2b+ai+npCGxoI/7//451PPuHkKVO0cxV9+a+7DRk9JdfdFUO49oxuvOYx19Zy5uWXc/8VVxDl58d1997Lq2+8wb6CAh656CKi+/aF+Hhyliwhe9cuxsycyYrFi0mNicFiszG0Xz/enDNHm0BPTOSazZtZ/fvv/PPvv5yQlMS7f/1F6IABnAg8lJlJbXExwTExlO/axcadO6mvqGB7Tg4vPvII+PvTtbAQq8nEgj//pHtSEos3b6aqvp6577yDr68vXYKCKLNaSU5OpmjvXs6cPJlFS5bg6+vLe++8w4ZNm0iMj6e6tpakiAhMAQHUNjQwffp0evfsSXllJT169CAxMZE9u3bRLyODISNHEhsbS1VVFfHx8SQlJZGZmdmUrALabfy0NlVVWblyJd26dSO6cUWbtaEBjEbq6+v54IMPGDJkSNNy89LSUiIiIqisrOSGyy+nMC+PyVOnUp+XR2BqKuXZ2QQDoVFRDO7bl3krVvDqm28SGxVFSVkZP372Gb1TUwmLiuLKe+/lonPO4dhjj+Xt2bO55ZRTmDBoEBEhIby2aBH9+/Wjob6eSy66iN9++olXrrqKOz//nISEBO66/XamnHQSPSsqUEJDtTEwP99zSxiHa52XvvmGi0aMIMJdQUQLFYQffvQRF195JRdOncrPS5cyefhwenXvTmhwMCcPGcL4QYM4/8wziQwJoayqioaSEiIjIujfrRsrtmwhPSODgrIyQgIDMZlMnDVhAg02G/c9/zy9jz2WVcuX8/Djj/PgeedRANrkeEAApqAgSvLyCA4J4cV332Xpd9/x3Y8/cvqFFzL+P/8hJyeH+fPnM3PmzE77ulXc7orWSTz44IPqgw8+6PHrTzz4II8+/TQ+RiOBQUEE+fkR7OdHbGgoVqsVo9GIUVHomZBAbGwsvZKTqSkvJyo2luDUVHxzc0lOSyMlIwN69tSq7oqLtRP+mBht4Ckq0pYr1tZqA4/J1HwJR0GBlkjRlZdrJ4uZmVoJfHCwVgbfWJLuxGrVLvasVtSsLJSuXbWLOVXVjqegAEJCmHzaaRw7ejT3TZ6sXZiMHGlP4uzeDarK7a+8Qta+fYw99VSuOuccrXH8oEHw229c/+23XHfccfQ8/3xtkKqttW+asGMHDSeeyLVnnMG5V1/N+NRU7fbu3bX7FhRoS5bj47U3k759IS+Pr3fu5M3XX2f+nDm89txz3PbOO1x00UVMOu44pkdHa8mEPn0gOJi/3nmHV774gqduv52Q6mpe/f57Bh97LCeffLJ2v9pa7efZtk37eWw2OOYY7WTbXRKpqko7LqtVmwlpvHhk82bt8/p67aLNatXuW1UFoaHUR0XhZ7ViaWjAJztbe56wMP5dt45f162joKICs9lM75QUuicnY6yrY3NmJgs3bGBDbi57s7MxGgz4+vhgU1VMPj7UNjTQ0NBAbGQkPbp2ZdDw4SR16UJat26U5uayevVquqWnYzab6dqtGyNGjCA9Pf2AYkBRlIdUVfUcBB7sL3YA7XduNmu/Ay+fhQdtef/06dPxMZtZsGQJBqMRPx8fzj3tNOb+8AN7c3Mx/v239pretIks4P133+Wul17CZ9kyOPFEWLpUi9/oaFi+XHtd5uVp7yd5edSNGsXxxx3HlRdfzCUTJzpXO3Z0Npt9t1nQ4lGfXCgq0uIxI8Mez1VV8OefWt8t/f1x3z4t4ZuXp70fBAVRtXs33y9cyB979rBu506yc3P5+uuvm5bxuHOocQMtx47VauWsadPIKy7G12SisrqabgkJDE5IIDEtDYPNRpDJxNjUVGJ69dKSpiEh2lLwXr209+7ycu02vfdYXZ0Whzt3ar+LvDzt9uBgbazxOcLzgJ4qYEF7/8/N1V6/Npv299M1/n2X/vgjZ86cyYrVq0nevh2GDKF60yY+XbiQWZMnY4iK0jb/mTcPJk7UmmOnp2vvP7Gx2jg5fDj88ANMnaq9Pn791b5UuapKe32MH0/t4sVETZ9O8e+/EzB6NLY//sBQVwejRztPWLSlhgbtb6a3KQgO1qoua2u1/qbV1fYEY00NRTU11JtMWoIoMBAsFuoqK1m6bh15dXX8mZNDRXk5hoYGKsrK2JGTw7bMTAICAujRvTsB/v4cc8wxYDA0rTaw1NdjMhoJCgkhLi6OjH79iIuLc+qb58mRip2sbdt44eGHqbJYCA4OprKsjKCgIGKNRtL79KGyqorq0lJG9O9Pl4AAYuPiqCwtJSAwkEIfHzavX8+EjAyUiAjtdRMRAZs2gapSlJ2NX2QkalUVpthYAocOtZ+vgfb6qas78P5HJSXOk1yeVFdrf2N3r73cXK0/2qBBzhM3ej+zxvPBu+66i4KCAkbFxLCrvp7pffrQ/cQTsfz7L7EnnKB9z5o1MGAA/Psv5qAgzOHhBDZewJkjI7n/hht4/OOPUWprtTHIz087psxM7edISWHZ779zzZNPsvq337Tz1dpaWLWKYpuNqJEjm0/G6ecGFRXa69lk0mLR3TlZZaV2/6IiLdYPNHHtuFlKXR0UFlKXmYm/r6/WeiQuTjsfBO1cV0+M+vhAXR3V9fVU+PjgbzbjZzRSazSyrbiYmoICqg0G8urq2JGdDVVVVNXW8te6dWQXFxPg709dbS119fX4+PgQHhaGTVXp2rUrE8ePJzYxEX9fX/wCAujTpw/79u1jwoQJ+52UOlLnayvmzeOlV1/FHwgKDydIUUiOiqJX9+6MOe001IYGAktK7JMyhYVgNNJQW0tt9+6EgfZ6Mxrt7+X6+bHZrLUl2rVLO+c3Gj1PNBYW2ieD3amr08av/VU/q6r2vJ6eZ8EC/t28mT4DB+Lbtav2msrP114LxxxD+bZtdB01iuuvvJJ7H34Yv0WLtLFj/nwYO1bbEGHKFO31X1qqxUJ5ufbxsGGwbp02HldXa7cPGKAdd2am9jMmJmrx27UrmM3MWbmSN959l0Wff44ZeO6558jo2xdbYSGnX3+983uFqrLtjz/44OOPmXHBBSjBwcQlJxPjrs1SXZ32uzjYiR6zWXuPCgnRvnffPu3vXlGhjctJSdrPVl+vfb53r/ZcJhNYLJRXVRFkMuHj46P9PW021NJSqmtr+XfnTkLj4thdVUVRbi5d/fzYlJXF6sxMSmtrCQRySkrIKiykS0ICW/bsISYmhtLSUvr36sWQ4cPJ3bED/8bk6Nlnn0337t3p1atXiz/SEb3WaUnj+6i+2WFBQQE9e/Zk+dKlLPnzT8aMH09ISAjzfvyRzevWMWzMGP73+efkFxRQVVVFUFAQvXr0YMGffxIUHExtbS3dExOpqKnh0gsuYNEff7Bs3Tq6de3KvuJizhgzhvHjxvHrr78SHBpKXU0NYSEhFJSWsq+sjL+3bCE0MJC/5s0j2WikMDiYpx5+mNKyMub/+SenjhnDZ7//Tr+UFCprali5ejW+ehHG9u2oe/fy/T//MOPJJ1n13nv0GTeOymXL2Ozry+mXXEJVXR1nHnssb/30E4aSEq0PYXo65rVrqUlJ4aXHHuPO11/Hd8MG7f23sJByRSFyyBAee+wx7ujTh6rx4wnZtUsbg7t21VZj2GyQmckveXl8PGcOH375Jdb160k65xw+vP12/vv778ycMIGTTjyRnB072FdfT//RozHobQIiI1m7YgU333gjS9es4fmHHuKsWbPYuXEjPTMyWDpvHnO+/ppV69axaMkSvn3jDWZ/9hkqcMfFFzN/40aSQkPp0a0bdVVVNFRWEpGczLc//shPy5bRKzmZqWPH8tbXX9Onf3/+XbOG2LAwuvfpA6rKnr17GTtuHNOmTqVo3z4WL17M1ddcw7Jly/jmm2/o2bMnjz76KLGxseTn5xMfH9/Ut9tkMjFixIg2SzR6ih2vThAWrFpFYE0NIWFhWrIoNla7qOze3T4rtHmz9mavJ/iio51nitwxm7UToKO95NTTcivAsm4dRqsVJT3deTBRVe0iOyCAopAQBh17LHXA9598wujgYBg6lK2ffkr6rFn837RpzP7hB+0iRT8x8PWF7dv5ur6eu2++mcKqKlbPmUNqWpq9CqW4mG3//EOvYcNYuWgRH6xdyxMXXcRdn35KF+CO116DpUup7tGDoMpK7USub1+tGkOXk6MNyqmp2kl6S5sXVFdrf6tDrYY6GDabdlKr75BpsWgnET17agNuaan2Oigvh5wcrKWlVAcFEdq9O/U1NVSUlBCYnY1vcjL5lZVs2bGDDf/8Q2ZREdlFRYSGhNA/PZ09u3cT6OdHVmUlv61aRVhkJPfddRfnX3xxi4fXZoOml6qqqmLoMcdwXJ8+jB8/nlHp6aT178/A8eN57d13GWOzwcCBZP3+O8OvvBJ/o5H7H3iAbjU1XPf++5w6ZAiPPPOM9j6zbJl2wZ+by8qiIop27OCLXbuozsxkzgMPYOzZ0zlGvFEL73lOSkq096LSUu1kPCPDvqTcjSOV5MBmg40btckbfZOm+Hjt/UzvjWqxaBNNXtRj8dFHH+XPP//khwcfZNz11xNkNLJ03TrOPvFE3n7hBYzdusGPP2oXbT/9pP39qqu1i9XsbO2iTf+6wQC//KL9nS0Wlq9aRbfoaKJPPZXzpk7FbDIx9403tHG+rEwbK1p4LbQrNpt9V7/6eu39Qb/YCw62X2hWVtonALdt0xIiSUla4ri2Ftvu3RQXFrI9J4fymhp25eSgmkyUVVZSWlODyWajwWKhuq6OrJISdhUVkVtURM+ePVmyZAn+nqq6OYKxY7FoF+BVVdrP7+en/Q6Sk+3VmvoFe1ycdr+AgKaetoD7ZHZtrZY8SEvTvsfPr3nbinasuLiYbt26MSIjgxGjRvHuRx9Ra7NhsNn48qOPGD9mDGzciNq3L3UrVvD0N9+w8N9/WfjOOyi+vizato3jp00jMzOTLpGR2sSUry+EhZGzejVJ6enUNvYcTIuO5pm339YSCx1Vba32+gkNtb+m4uK0WCos1GIpJUWLoeJi7TUWFwcmE5bSUgpWraK2oYHQhATCo6O1pF9JCTuzsrTJp2XLqKyr0/rA+vqyZvt2QsPCiIyO5tdff23x0I7Y+Zpeqeznp73W4+O1IoSYmM65rC4nR/v5PPR7R1WZ98YbPPrxxwwcNAj27uX8O+7g2MpK1ickkLJrF6Fnnqm1vKis1F7v5eX2lRrr1mljUFWVdk4xcKCWEMzN1RKFycnauNStGzQ0MP3uu5k+ahQXXHaZ9jtfvlz7vQ8demCrXNqaqmpxoLeIMpu1329xsZYc1pOLDQ3ae21Rkfa7CgjQEkD19drvaccOLSkUEqJ9T309ezZsICsri6QuXdiak8Nff/9NakICFoOBiro6flm/ntNPPplrb7+9xUM8UrHz9ttvYzabOfXUU1mzZg1z584lJzubwd26oRiN/Pjzz0THxbF5506sVisJCQns2LWLpKgozhw1ih8bV0VMHDSIjKQkNuTlMSg5mYvOPpvtJSXU5eSwfN06Tp80CWtpKVEpKRjT0pj77bdsWrGCgYMHM2XCBLYtWULkqFEk9+2rjU81NdrvV59ELimBujq+W7GCjL596elm07TqwkL8Q0OpKS8nJCamqfDFSWUlVFZSvWQJQccfr71eS0thyRJs4eHU+vgw9dZbyejXj/8791wKly8ncdgwzrjwQvZVVuJrMHDV9dfz0EknQXIyL7/4IpklJcz97jssJhNnDB7Mx0uWMHXUKHr06sUDzz+vxZOiwJ49XPXJJ7z/9dd8+s47rP/jD75ZsYJ/nn4aTjjBPi63NCmN1ivVXR/n+vp6zGZzU//PA7l+MFdVkblmDd1HjQJg7VtvUVBSwohx47ApCl/OmUNibCxdEhL4cN48tmdn42cy0SMxkc///ptRo0dz/uTJ/L58OZ9++y2pKSms/ucfevXsSWZmJimJidTU15PYpQtbNm0io08f+vXvT2BgIJm7dpGWmsrDTzzBn3/+yaJffyWtVy9SUlMpLy+nd+/e9O3bF9D6SPo0FgPk5eVRXl5+2IVEXr3EOO5Aqm/69Tv4B26rk8sWXug+Awa4/4KiaBs4+PgQYzSSU1DAG2+9xS0PPMAp/fpR+8MP7F69mgvPPZcvv/+ex4qKiLZYWLFhAy+99x7h4eE8duqpvPvRR9x6xhlkBQZyz0sv8cmLL2qPb7Px98aNjL7qKn54+WUWLF7Mz2vX8sOvv1Lb0MDSZ5/VBpq0NILWr9dOxBzfCHRJSVoC90B+t0fzYs9gaH4h79BMu2lJZWgodOmCEdDfjv2AGNBeYwEBdDEY6DJxIifq36sPxvrftb4eCgqo27WLnZs2ETtw4BH6ocShCg4OZtGPP+K3fTvhvXppFyCKwhkTJjBnzhy+3bePXbW1bN+6lRuvvJLjYmKYeued+KgqDz/xBA/dey833XUXb771FlOTkvCLiKCnjw8zH36Y3dnZRMXGsumttzA2NLQ8E+8tDnQzh8hI7V9lpXZS1Jbv0f37ax+7blCj8/HxquQgwB133MGXX37JOffdR2VlJX7BwWxcuJBLr7uO2x9/nOfeeqvpd7Vs61Z6JyXx159/cvIZZ7S4wU9VVRUn33cf/zdlChn19WzPzuavN9+0J4w6WuWzvvLAx0dLaIB2gem6ukBP4KSkaP9cHyY5mZiKCmL08cls1hIniqL9Hx1t331zzx6oqcGal8e/2dladVZb8PHRLrhb0lg1DNj/xo7H6+6iIiDAXrHkbpVGOxcVFcX27duJyc1FiYzk5uOOY1/Pniz88EPefO898vPyKNq8mafOPhuj1UpoSAi5+/bx48KFnDR5MvMak1a///47XWNimBAYCIrC/KVLOenqq3npllu4+7//5fiBA3n0vPM6TjLdk4AA+yS5yaRNFOji4+3J5qAg5zYBgE9sLEnR0VoixKWiq4eq0sNq5XhF0b5uNGoXvo0bo5WYzUfyp2qZv782aQ1awqazc1yR5Y6iMOXqq+kydiwTJ07kkuOP58yzz+aja6/llLPO4twxY3jnzDOb2il9+vvv/Prbb7zzf//H1TfeyJTu3TklI4NfFy/Gt66O4wcO9LhMUjUa+XPFCl4+7zztHMRkgjFjjuzP39oURXsNuU4MORZqOCaaEhOdv2Yyae+tSUnNzndSMzJIbfy4GzDlggu09+6sLLBauSMvzzlGj7JuBgOz587ltltu4Zi+fTlz2DCOHzyYbQUFWGprufP88zHU1TH4qqvo1b071NRgM5shIADDscfy+Pr1WqI6MFD7PzNTG5MSE+kNYLEwMCtLK35x2Njm7GuvdTqOAf36Of/u9PFfH+caJ5hPcelB7iio8ZohRP87uqtcDwmBwECCBg60X8tGRGg9ymNjCYqI4KtvvuGuu+5i5nXXEWS18s/993PSscfywMMPE1lQwNBrriG8vJydFRV88cMPFBUV8fhFFxEwcCBzZs/mjjvu4I/vv2f+Bx9gjIzkzH79UHx86Gmz8evffxMZHMw5M2diNBi4b+ZM7ffmeM6+n0kNT5u8+fn54eeYkD+A6wdTcDDdHTYpG3jRRdq5iJ8fqCqXH3NM09/iuRkztNsbe5A+sX5902T/1LQ0Lh44kOzSUsbeeSfrdu6k94ABRMXHY87N5ZNffmHAuedSUFPDzrIyampq6J6UxJzffiPwpZcY1r8/43v2ZOE//5DZuIx6xYYNhIaGUlhYSGV1NTdffz21tbV8MmeO1jaiZ0+efvppxhzi+41XJwhFI8eA8fHhsssuo6iggC1//UUQaL1gHn4Y//Jy3njjDZSiIt75+muuPOsssgoL6XrppaT16MHZs2ahjB1Lr4wMVq1bx9AePbj0zjv56Y8/uPSEE7j2yScJMZl4/+WXKd65k67x8XTXT8y7dtWCrKUL4w40q39QPJ1wu/68fn7QtSv+XbvSd+zYA0+OiKMqrksXbUmGQ1+OS6ZNY8w116DW1XHxJZcw5eSTmXXOOSgrV7Jh2TLUtWvpcs45zHnrLVIGD+b48eN57PffATj3+OOJCAnh7muuIX7KFILGjLHvBC4Ojr40tz1w7dfqxUwmE1999RVjRo3i9fvuY/qIEZCWxpfPP0/vk0/murvu4p+//ybIZGLyrbeS1qULmbm5fBsWRv/oaB674grqd+7k+aFDiYyNbUoQvvT55yRFR/PpwoXU//EH/7vzTvwdK8q8ldHo3K/UcTmtHh8Gg3Zb4yy00WZjSE2NjDvtUGxsbFO1W0RgIBHp6YQcdxw3v/EGvy9ezJi+ffly7lwee/RRlv/9N+89+yy3P/EEad278+U333DC4MHcfPPNmM1mMv+fvfMOb6p64/jnJt3p3ruFQpmy95A9RMQB4vanoogbUURFERcqKIo4EScqCgoqQ1EEEdl7r0Khe6dtutvk/v64TZq26Q50nc/z8LS9ubm5LXlzzvme7/u+33+Pm6cnD778MrcMHcrj77zDy3Pm8MKQIcpcpbX//1flSjMv52AUIM1MCJ4tOFurudK1a1cSExOR/v2XMzodN7z+Ot988w0zH3oIT09PBnXvzvUjRrDx0CHWrVtHcnQ0Sfn5rPvtNz746y9iYmJIz8jg0m23oTHW+1SpSMnM5OOVK5n38sucvHgRVwcHgtu1q7qedmuhNp8dxkwK42aQUdhuJEbeeScjb7hBcRdnZSlipbGZp3EjIDZW2TAoXUuriorKavf36lX+gqV1aE3Y2JRtUFWXKn4lP3fVaiWF3hwzR6K3tzefffaZ8jv+/jv7JYlwe3u8u3YFvZ6ffvqJhc8+S+9Bg9izZw/ffP01U4ODibjnHmZ27AgTJvDMNdcw7pFHWLRoERvCwth38iRt/fzILS5m7pQp7NXpmH399YRLUuW/WWNivl6XpDKhFspvMKrVSmaLGd0GDKBbXh64ujIoNdW0yWTbtSv3DBumCMexsYpQXFIC7doxIzkZMjORCgqU/wNJUjJf8vIoyc3l2OHD+IaEoFepeOu99wjw8mLfkiX4+/mx8/hxwhpQOkcIhIJK2NraMm/+fCVlq08fU4evJ2+6ib5PPIGbRsO+X38l0NUVbG2ZNWoUwbfcgv327eDqypuzZnHdjBk8cfYsf2zfzrr336ePszPXv/suf+/aRd8+fbALC1M+cI0LNbW61blmGkRrn6Q3ZYzF4o2dUyWJUD8/9u3bR9bGjUTeeqsyEcjNBSA4MFApcQB8M28eJYGBtOvfH3n7dr7bvZunFixg7/ffE6bXK+5agaAF0q5dOy7t3Il9bq6p+YO3pycz77mHkWPGkJyQQOFbb/HAAw9QmJHBdf378+TLL5Odnc11N95ITkEB7y5Zwquvvw6yjDYnh/d++IEdS5cy/plnmDZtGgPuvFNJc2qpm02XE2OjIEHTxNj0pnRjyt/bm+5dunDj+PE8PXEi9O3LWwsXsuPHH5k0ejSfffMNY/73Px5+8EE6q9VMnj+f/n36MPmllxjUvTuuLi58NGMGF7RaHpo1y1SXTlBPxGZQk0SSJLCz48P588m88UY633wz42SZnCFD+G/VKh559VVyCwrYuHEjR9au5c6ZMyk+fJjXfv+dle+9x/0zZ7JixQpmTJgAkoRBlrlx5kwOnzzJvqQk/vzvP+64+mpRDqa5YmdXloFi6TGoLGA1lsv+SlOa0dAnMlLJzCnN4hg8eDC/vvmmkobv6clL8+YppWEqZMx8v2QJhX5+dIyM5L3nn6evqyuuV11Fx8JCHhk7FsnGpqxhXUvA3IVr7kA3OnShUsaHZKyhak6p6GcD9Cxt3gTwYZ8+ZVkxBgOj+vate31SM4RAKKgae3tloVba/rtTSAgxMTE4nj2LY2CgqWhxRECA8oFYms9/94030iEigslPPslDt9xCvx494MIFFs6ezervv8fO0VGxn8fHN53C8AKBtTDWHzUWki/tuOrn54dfYGD5jpIVOrGGBwaaJhsScOcNN3DzVVdhb4wXgaAFY+/mpjihzDqxvvDQQ3QfPJj2WVmsjo/nkUcewSs/H1JTmTJ1qrKb2r07UV98wYBnniG8bVu+f/99VBoNU0aNIjI0lH3Ll+Nh7FJc2hlQIGhR2NkpsWDWnfiPH37A2c5OqTuH4pjqWupkW/f++0ienuDlReqmTUybNo2Fc+ey6oMPePnrr3ly2jTcNBp2rVypuHqMzh6BoKVhZ0egszOBpamsLhoNLv7+3Dx2LGdiYli1eTNjx45lrIODknbu48OyDz6AzEzmTpvGtbNmcXrvXqWJpY0NxSUlrHjySR7+/HP2rF5NqF6v1CUUCFoapc3SjDqBqWlUaXo+YHGtgyTh5e4OgYEcWbmSsA4dUJ07pzhGT5xQNnHV6svfZK8lYV4yR6UqnyVSD8RfXlA15oGvUoHBoHQxtLNTAte4o2IMeKPFXpLo360bp0+fxuHCBZMg0ikyknm33lqW3jRyZMvZGRAIzLG3VxyC/v7la6NZKoprjBso7zIoTZO0N9a0EO4NQUvHwaGsM2OpQCgBkyZMgF27mDdtmnJeXFylGo7tAgN547XXmD17Nk9OmECukxOv3n8/FBbi5eUlXNeClo29vbIpZVxQSRIuGo1Su7jie1+WkcwWaz7u7ixfvhy0WmbccAO3TZyIJjgYTp8W446g5WO+1oEyocNgYM6MGdw9c2bZcXOhQ5Lo27kze/fu5Zv338fF1pa3vviCVUuXckNQENc++CD2TSk9UiCwNnZ2yrjj6moyQwDl1zrGdY0l0RBoExioCILmZXfEfK3REQKhoGqMIoeDQ3mRw1hbzYhxMDW3D8uy0ilIrS5XsLfc19Zej0PQcrG3V+qV1GZXraSk/MTUvNFCqRMESRILNUHLxygQGmtsGieclsYcqCSuP3DffUybPh3Vhg3QuXNZx1sRO4KWjlHkML7XjbFjMFSOHXNx3cKY4+biYiqPIRwcghaPnZ3SDbZi7Oj12Do4EGpM8TPGivl4JMuEh4cz79FHIS+Ph0eMwL9fPzhxAnvz+mQCQUvEOO54eFQ2Q1Scd+XkWK65b9wQNjZhK90cFjQuQqIVVI1RIDQ6CM0FworqvlFIBIsuKKDsq5hwClo6zs5laZLG2KnYtdY4mOp0lhtnmA+aQiAUtAbs7JTxpaiovLhe0XlrSVwHkGVURkHdXFwXY46gpWNnp7gFK7qgKs7XjPOzkpJyG7qmrxVjR4w7gpaO0QVVcaPWUsaHsb60vX3ltY4kEeDtrdROAzHuCFo+5s51czOEpTmbTldWx7jixpSdHQwaJMacJoQQCAVVY6xBaFTzzQO/4o50Wlr5GjWWbMYVhUKBoKXSrp3y1bjIqmqyCYrT0FiLs+Kg6e4OPXsqzxOTTUFrwMWlzKFRlYMQlOPZ2WXiesX0L09PpfGVSM8XtAYcHZX3e21EDllW6hW6uVkUOXB0LMscEeOOoKVjFDnM60NXJa4b52vmczsol3YszBCCVoO9fWUzBFiOHfP5mjnG2DHGlZivNQmEQCioGheXslbtFVOMKwZ+WprS9t34c8VBs2NHIRAKWg9OTjB4sCJSmLugLKV6ZWeXdfM2x+g2dHERg6ag9dCzp/LVPP2xKgdhVlZZ7FRMO3ZzUwQTIXIIWgOSBH36QK9eys/VpRjn5ipfnZwsz9c6dQJfXzHuCFoHRnNDQYHytarYgfIbulC5JExkZFkapYgdQUunXTulI6+zc/UOQiifLVUxHdm8DruImyaBmDULqiY4WHEwGa30lupvGElPh+7dle8r7kirVEpnouTkskWfQNDSMTbxMXdBVRww9XqlLoelQbNCxy8xaApaBS4ucN11yvfVOTmKi5WUSvOaNhXT+I3nitgRtAZUqrJ4qG5jKj29bHwCyws147lCXBe0dOzsoG3b8vOtqkpbZGUpG0/Gn40Y52shIcqcDkTsCFo+trbKxhQoc7KqahBWdBCar3WMPxu/irhpEoj/BUH1GOsFQHk3h6WC8eY1CIXIIRAoVOeCKilRXBwWOnuJ2BG0eqpzcmRnl6WkQNlX87gxHhcTTkFrw3xjys6u7LgkKQKGeeMFIxVjR6TnC1oLXbqUfV+VGUKSlFRk83VRdV1bRewIWhNVdTE2/izLiuGoIuYbU2LMaTKIFGNB7anKzaHXK18tBXVF67BYqAlaG9UJhGB5wAQhEAoE1cVOcXF592DFGoTmx0XsCFobNdW+NY47VW1KGR8TsSNobZhvTFU3Z6tKXBcCoaA1UlPdW0slloyPibVOk0OoNYLao1IpYmDFXbWSkvLnVRX4YmdA0BqxtVU631lyQUFld4eldC8RO4LWiPmmlCXXekVx3Tg5NV+4qdViY0rQ+jCfr1kSOczHnYp11MzPFbEjaG3UJK4bY6fiWsd4rnG+JsopCVoTxhJihYWW48bW1vLzzHUCb28lq0rQ6IiRX1B7nJwUe31FoaOq3QLjV3PbvRA5BK0Ne3tlkVZx0LS0UDPHfNAMCipz6goErQUHh7Ixp7qFGlSdYty5c9UTU4GgpeLkpDQjqWljqroU4969xWJN0PqwsVEc6hU3pozrGvPxxJIZwsbGcuM5gaClo9EozUhqM1+rKnbMmwAJGg0hEApqj0aj1K6x1GyhNoHv5qZ0MxYIWht1HTRLSsrHTlVpyAJBS8bZWRE5qnJBmceF+bhjfq6xNq5A0JrQaCAlRVlw1ZQmaUzNN3Y3NuLhceXuVyBoKjg7K/O1ihtTxcXlz6tKXLexgcGDL/99CgRNDeNax9KmlLmwbswOycys2nEoaFTE/4ig9piLHBWt8+ZpKMbHjOkt5vU4fH2vzL0KBE0JS4NmVQ5CgwG2blVEQjFoClozDg7KoqyoqHaxI8uQlydSuwSCmsT1iinGcXFw5ozyHIGgNePqqszXKjoIKwqEoMROUZHidBfjjqC1o9EoDeRqm2J84IASO4Imh1h9CmqPs3PVgW/JBbV3L6SlCZFDILDkILS0UJMkxfXh6AhDhoiUfEHrRpKUcScrq/pGC8ZzMzOVcUekRQpaO46OUFBQeaOpYlM54ziUlARhYRAZeWXvUyBoari4WHYQFhWVP88YO0eOKPEj5muC1k512VIVHYSZmcr3wcFiztYEEcqNoPYYdwZqsg4DZGQoTo6hQ8HT88rcn0DQVKltijFAcjKEhor0LoEAyjamampSIklw7hx06gT9+1/ZexQImhoqlSIS5uRU74IyxlJamhI7IstD0NpxcVHGnIruW0sNGXU60Gph7FilVrRA0Jqp7VrH0VEZcwICoGdPUSe6CdLsahBKkjQeWAKogeWyLL/ZyLfUenB2Vnaka9oZcHZWUsPatgV39yt2ewJBk8XNTZlcmi/OLDkIAwMVQT04+Mren0DQVHF1hfj48ulblmInKEhJjwwLu7L3JxA0VVxcFGdTdXXUVCrFra7VikWaQADKxpOxUUl19dbd3CAiQvkq6kQLBMp8zWBQsj4qYj6+BAWJuGniNCuBUJIkNfAhMAaIA/ZJkvSbLMsnG/fOWgm2topwERdn+TEjnp4wcOCVuy+BoKnj5qa4Ai3VqDGfcPr4XLl7EgiaA23bwunT5ceYimmSAO3aXdn7EgiaOt26KWmRjo5lxyqmSYKyqBOdIwWCMvr2hV27Kh83H4dsbBSBUCAQKNjaKuv/mgRCUMxEgiZLsxIIgX5AlCzLFwAkSfoBuB4QAuGVokcPy52Ixc6zQFA93buX/7mqRgsCgaAMlQomTix/zJLIIRAIymNvX7mbqqVGCwKBoDxubjB+fOXjYq0jEFSPt7fyryIidpoVzU0gDAJizX6OA0SxoSuJJJXfjQbw8gJ//8a5H4GguSMGTYGgbjg7K+4NgUBQNwIDlTpRAoGgbnh7KzXTBAJB3bCzE3XVmxktrkmJJEnTJUnaL0nS/gMHDjT27bQOBg1SdtsEzRoRO43AdddZTjsWNCtE7Fxh3Nzgmmsa+y4EVkDEzhUmIgJ69WrsuxA0EBE3jcDAgRAe3th3IWggInYagXHjRKfiZkZzEwjjgRCzn4NLj5mQZXmZLMt9ZFnu07t37yt6cwJBc0bEjkBQP0TsCAT1Q8SOQFB3RNwIBPVDxI5AUDPNTSDcB7SXJKmNJEl2wK3Ab418TwKBQCAQCAQCgUAgEAgEAkGzpVkV8ZFluUSSpEeBTYAa+EKW5RONfFsCgUAgEAgEAoFAIBAIBAJBs6VZCYQAsixvBDY29n0IBAKBQCAQCAQCgUAgEAgELQFJluXGvofLhiRJy1E6HQsErZVgWZbvr+uTROwIWjn1ihsQsSNo9YjYEQjqh5ivCQT1Q8SOQFA/LMZOixYIBQKBQCAQCAQCgUAgEAgEAkH1NLcmJQKBQCAQCAQCgUAgEAgEAoHAigiBUCAQCAQCgUAgEAgEAoFAIGjFCIFQIBAIBAKBQCAQCAQCgUAgaMUIgVAgEAgEAoFAIBAIBAKBQCBoxQiBUCAQCAQCgUAgEAgEAoFAIGjFCIFQIBAIBAKBQCAQCAQCgUAgaMUIgVAgEAgEAoFAIBAIBAKBQCBoxQiBUCAQCAQCgUAgEAgEAoFAIGjFCIFQIBAIBAKBQCAQCAQCgUAgaMUIgVAgEAgEAoFAIBAIBAKBQCBoxQiBUCAQCAQCgUAgEAgEAoFAIGjFCIFQIBAIBAKBQCAQCAQCgUAgaMUIgVAgEAgEAoFAIBAIBAKBQCBoxQiBUCAQCAQCgUAgEAgEAoFAIGjFCIFQIBAIBAKBQCAQCAQCgUAgaMUIgVAgEAgEAoFAIBAIBAKBQCBoxdg09g1cTu6//345ODi4sW9DIGg0Xn755c9lWb6/rs8TsSNozdQ3bkDEjqB1I2JHIKgfYr4mENQPETsCQf2oKnZatEAYHBzM/PnzG/s2BIJG4+WXX46rz/NE7AhaM/WNGxCxI2jdiNgRCOqHmK8JBPVDxI5AUD+qih2RYixoksTGxjJixAg6d+5Mly5dWLJkCQC33HILPXr0oEePHoSHh9OjR4/GvVGBQCAQCAQCgUAgEAgEgmZOi3YQCpovNjY2vPPOO/Tq1QudTkfv3r0ZM2YMP/74o+mcp556Cjc3t0a8S4FAIBAIBAKBQCAQCASC5o8QCAVNkoCAAAICAgBwcXGhU6dOxMfH07lzZwBkWWbVqlVs2bKlMW9TIBAIBAKBQCAQCAQCgaDZIwRCQZPn4sWLHDp0iP79+5uObd++HT8/P9q3b2/xOcuWLWPZsmUAODg4MHr06Ctyr41JXFwcy5cvb+zbEDRzGho7//6kRZdRwrXTfax+byXFMvk5elw8rDd0ibgRWIvWNu6I2GkmyDJER0Pbto19JxZpbXEDInYE1kHEjkBQP0TsVI8QCAVNmpycHCZPnsx7772Hq6ur6fjKlSu57bbbqnze9OnTmT59OgDz589vFUVoW8PvKLj8NDR2Hl5/CgLhxRc7olZLVr23JQ9d4sy+PBbu7ojaxjrXFnEjsBatbdxp6b9fi6GkBM6cabICYWuLGxCxI7AOInYEgvohYqd6hEAoaLIUFxczefJk7rjjDm666SbT8ZKSEtasWcOBAwca8e4EAkF15GbpcfW07hBzZl8eADmZety8xfAlEAgENSLLjX0HAoFAIBAImgmii7GgSSLLMtOmTaNTp07MmjWr3GObN2+mY8eOBAcHN9LdCQSCmshOL7Hq9QyGskWuLsO61xYIWjo/Lkziq3kJjX0bgsZAloVIKBAIBAKBoFYIgVDQJNmxYwcrVqxgy5Yt9OjRgx49erBx40YAfvjhh2rTiwUCQeOjy9Bb9Xo5mXqL3wsEgprZtkrL3o1ZjX0bgsZACIQCgUAgEAhqicjREjRJhgwZglzFhParr766sjcjEAiqRJZltq7U0mOkCxo3tem4tR2EBTkG0/fCQSgQCAS1RIiDAoHAArIsc+5gHu17OSFJ1q0ZLRAImi/CQSgQCASCepN0sYifFiez4uWEcsKdtUW8gjxzgVA4CAUtj+9eS+TVqRfISCq26nX1eiEQtXpauEh4clcOudliXBBYGVlWmvy0UA5u1vHegzHsWifc5QKBoAwhEAoEAoGg3sScKgCguFBGm1w2kbZ2GnChmUCYk9lyJ+yC1oksy+z4JZPEC4XEnS2w6rXN3bxFhYZqzhS0SFq4OFiYb+CDx2L58LGYxr4VQUsjIwMOHmzsu7hsZKYom1Gxp6075hjRJhdXmQ0mEAiaLkIgFAgEgivJ6dOQktLYd2E1Lp3IB8DeSUVafJHpeG6WdQXCgtyy68WeKbTqtQWCxiYjqUzEy9dZV8TLTCm7dp5wWbU+WngNQuNYc/HE5RE5BK2YkhIwtNxNFWNacVGB9X/H6GP5zL02SrgTBYJmiBAIBQKB4EqSlweFLUfgSolRRMGs1BLS4otRqcA31I48awuEpQ7Cblc7c3q3SCcTtBxkWSbxfNlnQp7Ouu9t83R/a4uPgmZACxYHwfqbUQKBiRYuruu0ytiQexkav+1alwnAqd25Vr+2QNCS0etlog7nNeo9CIFQIBAIriQtbMKZnqCkqCScL+S/NVo8/Gxx9bKxeopxQa4ibPQa44peX5baLBA0V2RZJupQHk8NO8uvH5a5ivOtLBAaYwesLz4KmgHG8aYFjTvmmAuEBoP1f8d1n6Ryao8QOVotLTRuoKz8hHEeZ03izymbXuabXwJBS0GnLeHwVt1lufbqRcksvv8SSRcbL3aEQCgQVEFGUjHFRcJtIbAyLWiyKcsyGYnFuHnbAErzEN8wOzRu6suQYqzEYttujoAy6Yw6nGdyFuZklnDpVL5VX1MguJzs/T2bxQ9coiDPQPy5QpxcVDg4qcizssuvnEAonLetlxY09phjPtZYe2OqpFjm9+VpLH1E1DdslbTQmDFibPiWbeWmclAmPiZFF6Ivadl/R0HrY/mceJbNjrssNdF3/ZYJKJlZjYUQCAUCCxzZpuOFiVF8OTfhslz/g8diePu+i5fl2oImTgtyEOoy9BQXyYy/z4snPgnFK9CWGx7zvSwCobFJiWeALS6eas4fyWPx/Zf44NEYDAaZZ0af4627LoqC2IJmQ9Sh8ikkahsJRxfVZXUQ6rRCIGx1tPDPRPOxxtqpkhmJ1ndWCZoRLWi+ZonstLIUY2u6b2VZJju9BI2bGoMB0kUcCVoYqXFKeaVMK4t4sixTXCRflmvXBSEQCgQWSLmkBP65A9avAbB9jZaTu3K5cFS4nVotLWTCmXhBsb97B9vRoY+GV36NIKSDg0kgtKZYV5BrwN5RhUolEdrJkUN/K9b+C0fzywkt5mKIQNCUqVinU6fV4+SivgwOQuV11OqymqGCVkQLTzE2d8Va2wllXAQKWiktUCDU62X++DKNcwfziDtbgIOTCoPBuu7ywnyZ4kKZiO5KxkdqrIgjQfNGlmU+fz7eVFvTxlZp8GPeBM4amM//hINQIGhk9CUyyZcKTTvRRmt8brbe6k6olQuSTN8Lt1MrpAVMOOPOFnBqTy7H/8vBxlYioocTUNYRT+OmpqRYpjDfer9nYZ4BeydlyOo73rXcY39+lW763tqDtUBwOYg5XcCRf8rXrxk62R1HF7XVHYT5OQY0rmp8w+zZsSbTVJhe0Epo5uONJY7/l8N7D14iJbbINF+DspRJa5FmJhCKxlitkBYYO2f25vLbh6m8O/0SBgMMvtEdsK673BiTxrmh2JgSNHcSLxRx4M9sVryciCzL2Nor6xFtsnXdsdqksutlpjSe89am0V65HkiS5AD8C9ij3PtPsiy/1Lh3JWgJrPs4lT+/VkSGGYuDyw2U8VEFRPbWXJbXzdMpCzdBK6IZTzjjowrQZeh5/2GlHpOzu5r2vZ1wcCq/16RxU97T+Tp9pcfqS0GuAQeNcq3uw13KPXZyVy4efjZok0vQJhcT0NbeKq8pEFiLjKRi3HxsUKsVEf34dh0GA1z3kA+OzioGXOeOrb3EZ7PjyEiy7qTQGDu29hK52Xp+eCOJQTe4A9BlkLNVX0vQhGnGY09F/vgijQtH85l/43kAHJxUFOQZyLGy+B13tqxIfFJ0IRHdnax6fUETp5lv6BbmG7Cxk0zjDsDRbTkAqFTQdagzXQY78/d3GeRo9dDGOq+rK3XyBrW3R+OmJu6saConaN4c/bdsQzfmVAGq0qW7tU0J5oKjcBDWnkJgpCzL3YEewHhJkgY07i21UhIT4ezZxr4Lq3HuYFmK4rqPUslOKzEJG9bckS4qVKzDwZGKgLHgtgvs2ZhltesLmgHNcMK5b1MW7824xOu3RpvEQVAKwlsSGJxcldixpvs2N0tvEh7tHSsPXddO9wFg17qyeLocHS0FgrqiTSnmhYlRvPfgJZNrPDO1BBcPNddM82b4LZ44OKlQqyUcXdTkZVu/SYmDRsWYu70AOLRFx4ePx/Lh47HCxd7S0Fv4zG1hKca52XounSwvOIR3dUSlsr6D8NzBPNN8zVwsFLQSmuF8zYjBIPPk0DOseDnRdCw3W8/ejVn0u8aVt/6KZPqiYJw9lHmVNZ3lOaUmC2cPGyK6O3L+sCipJGjeGBvJ2dpJrH0/xeSStXb6vDa51H3b3ZFDW3Sc3pvLB4/FsPCeixzcnH3FGv40K4FQVsgp/dG29F/z/ORu7hQUKP9aCHYOZbtr8VGFnN6bi1+4HQB5Vkz3Mtb4MDqctMklfD0vgSPbdGKh1lpoZv/PBoPMbx+kcnZ/mYjesZ/G1Lm4y+DK7lqjkGfNlKy8bD1OZm7bF35sy4zFwXS72pmrb/ag3wQ3AA78mc3DfU7x6dNxvHzTeT57Jo6PZsaK9DDBFSUzpZgNy1KRZZm9pZtA5w/ns3djdunjJbj5VE7icHJVW73TcEGuHgeNit5jXJnzTTgd+2lMokdGkkg3bjFkZ8OePZWPN7MxpyaMi6Q7XwwwHXP1tkHjrrZqDcLcbD0pMUX0He+GxlXNn1+lmTZ5BYKmTvQxRZTbuzELWZb5cWESvy9PoyDPwLCpnmjc1KhUEq6eyjhkTXE9P0e5lpOLiogeTqTEFHH+SB6XTgmhUND8OL03lwN/ZtPmKkdG3enJ2f15png5uz/Pquv3jMRi1DYSfcYra5r3H47h5K5cLh7PZ/mz8Wz5PsNqr1UdzUogBJAkSS1J0mEgBfhLluU9FR6fLknSfkmS9h84cKBR7rFV0Ix31S4czeO/tdpyx9LiFUvvQ+8GmxZOfuHKV2su1oyOqsB25VMgP30qjrOXoSFKXRCxY2X27YOcnMrHm1HsbP9Zy5t3RpOeWExoRwfT8YAIe+avjWDeT23xC6uczmsU8qwaO9llDkKAwAh7ul3twozFIdw6xx8bW4n7FgSZHj/yj47UuGIObdFx/L8cfl6cbLV7qYiIHUFFdvySyYZlaTw3PopfP0gloocjgRH2fP1SAn98kUZmSjEefraVnqdxVVIlrblLbJ6eH9bZkcc/CuWOFxRx5dyBXC6eyK80wc3OKGH7Gi3ay1wDR8SOFSkpUf5VpIU5CI/+o8M31I6Bk9y4fa6/clCWcfWyQZduPYHQmCbp7mdDRE9HtMklzBx8huM7LIzrVxgRN1eIZjRfq8ix7WXv09TYYrat0prEhaD2ZfM204ZupvViJz9HEdIdNCqCOyhzx3emXeKtuy42uhlCxI6grhgzp2ztVVxzv7fpuF+YHdnpJSRdtJ6LMDW2CJ9gW3qOdLH4eMzpK2POanYCoSzLelmWewDBQD9JkrpWeHyZLMt9ZFnu07t370a5x1ZBMx40P54Zx/evJ5mCrLjIgDapmGvu9+aqoS5MetiXsM4O9Bzlgo2tZNV0L+O1wjo7ct+CIBZtieTpL8KAss7JjYWIHSuTnw9FFv5Pm1HsZKeXmNKqHvsolPlrIojs48Touzyxd1ThH2651p9pwmnNFOPM8gKhJfqMdaXHCBe6Xe3MjY/7MuI25T57jnRh9/os9mzMoqjQwMUT+Sx/No7Pnomzyr2J2BFUhTEN5cG3g2nXU6lf9ttHqcSdLazSQQjWda7n5xhw0JSPneBIB7wCbflmfiIL/3eRd6Zd4seFSSy4/QLbVmWw7qNUVi5IYsX8xMu6oBOxY0VkGQwW5ivNZLwxUlWq48o3Etm4PI0TO3Pp2E+DJEmEd1W6pHYb5oK7jw1ZadYTOYwbXBpXNQ++HWw6/umsWKu9Rn0RcXOFaEbzNSOZqcUsvOci234sM0KYlzLy8LPBzqFs+a+2kXB0VpGTab0xpyBX+RxydFYTGFF+nvjvai0pMUWXffOpKkTsCGpDTmYJ8VEF5UoVDbjODVu7stgZOsUDUNKPrUVKTBG+oXa4etlw/1tlpofpi5Qx6NKJfLTJxXwyK9aqjvmKNKsmJebIspwpSdJWYDxwvLHvp9XRzAZMc4wpIv+t0XL78wEkXyzCYMA0iHUd4kzXIUpdNUcXlckqbw2Mgomzu5qO/ZTUzPCujqhtJNITitm+Rst/P2cS2M6e257zx9ZeQqfVE30sn8g+TjhqREOTZkNVizXjY82A/hPd0ESrsXNQoXFVo3FVM/OTsBqfpzE5CK0jrutLZAryDDUKhFA2iBq54TEfcjL1HPlHx9fzEvh6nnJcpVL+e3wnWuUWBYJyePjZ8ugHIexZn0VoJwec3W0YNtWDXb9lUlykxL9/m6rdt/k6Ay4e1rmXfJ3eVBfUiNpG4q6XAti6UsuRf3RcOJrPhaNK+tfBzTrTuHd6by7xUYUEt3eodF1BE6MmMaMZjDtJFwuZM+YcTy4Lo30vJzKSislOLyEvW8/2nzNN50X2UcT24PYOvLMtEkeNmhM7c4iPst5CzThf07ipkSSJsM4OXDpZgF6vLB6d3ZvtEkpQW5pBzJiTm61n3x/ZXDyufJb3m+DGvt+z+H15mukcS7+Si4eNVbsY5+fosbWXUNtIuHqVn7f9uDAZSCa0owPPfmulrigCQQORZRlJUsqNrf80lY2fKTEz+6twAO6cF0C3q8u7+vpf68aad5NJiCqAsa4NvgeDQSYlpohOAxR9wDfEzvRYjxEu3DLHnx/fSmLutVEABK3Sct0Mpf564oVCcjL1tO9lnUZazWp0kyTJByguFQcdgTHAW418W62TZrSrZnRi5GbpWbMkmeJC5b6P/KNj4oM+LLg9GqDSLhcoQsflSDE2r6WmUkl4BdiSGltk6qQce6YA7yBb2nZ3YukjirU5tKMDT30RVm73QtDEaeZuDu9AO559rx1yHZt92NpL2NpJVnMQ5pqcHHV/79vaqfDwVfH6xvasXJCIxk1Nh34aOvbTcO5gHut2WOUWBVeCpCTw9VXU3SZOrzGudB7gTOcBZU18Atras2RnR9Lii0ACT38LKcZWdt8aDHK5Bj/mRPbWENlbw/aftax8I4mhk93JTCkxpaYNvM6NXeuyOLotp5JA2NhpYgIL1OQgbAb/Z9HH8omQlJSuwTe68+9qbaVzeo12oYdZ+pVx49TN24bstBIMBhmVSqr0vLpi3OAyztce+zCUEztz+HJuAgtuj6bTAA12Dir6TXCjTamTUdDCaEZrndS4ImaPLGseOWyqB6Pu9CQzuZizB/II6eBAQIQ9kb0rCwgadzW5VnQQ5ucYcHRW4kaSJG55xg9XbxucXNXEnCxg7fspxJwu4N3pl8hILObJz8JITyhm97pMIno4MXCSm0msEQguN9t/1vJOzCWe/iIcbUoxf3xeJqiveU8pTxTepewz/pH3Q4g7W4DGVY1vmB0J562zMZWdXkJxkYx3sCIMGr+OvUdpMNdjhAs/vpVkOv/UrhyTQPjq1AsAfLC3o2n8k2WZkmKZ+HOFdf4Ya1YCIRAAfC1JkholPXqVLMvrG/meWifNaNA8vFWHwSCzbVUGu35TbPYTH/Rm/adp/G72IeAbalfpuUrBeOulGBtTZ5zdyy/WPANtObRFaaHerqcTtvYSG5allTsn5nQBb9wezZwVbUxdXIsKDOz4JZM+4xq+cyGwMlXFSDOKHcDUzbsuSJKEk6vaaiKHKdWrAY4NN28bZiwOKXes9xhXIRA2J06eBGdn5V8Tp7q48Q6qPNYYcXKxboOf/BwDBgPVup36XuNGwvlCrrnfu5zTpP9EN9ITi/nr63TadlNqKH42J47zh/OVRludrXKLAmtS1ZjTTMhILCYiUHGN/7taS9tujiZnK0Cfca7cOS/AogDo5mOLwaB0UHX1avjypuKGrpOLmp4jXfmSBDJTSkzzyZ2/ZvLcd2349pVEIno4Yecg0aabY7nNAUEzpRnN1/ZuzKJvoPL90Mnu3PKMUp9zyGQPzh7Io8dIF66Z5m3xuc7uarTJ1kv5Na97CzBsqqfp+w59NKhtJH5anMy5g0rt9ZULEkmLLyb5UhG71mVRkGdg5G2ela4rEFwOstNLuHA0ny/mxnP+cB4GA8xaHsbKBUmcP5yPg0aFf5uyeVuXQc50GaR8vnsF2pGZbJ1UX2P3bxdPZcxxcFLx9tZIUyy5edswZ0U4mSkl7N2YxaG/dbx+2wWune5jukb0sXwiuiubAH9+nc76T9KQDbIyZ+tY+3tpVgKhLMtHgZ6NfR8Cms2ACaBNKublm86TGleMJMFz37fBw9eWjZ+lsW2Vsjv9v1cCUdtUnnA6uqhNhaqtQXZaCQ4aVbn6H6AM5qf35AIw+UlffELsWP12Mns2KBPQd7d3YMcvmfz0TjInd+XQc6QrBoPMqkXJ7Pw1U6k10t1qtymwBtW5OZpR/NQXjZv1BEJjbZyKaZKCVkYriB2j089aznVTmqR71en5Dk4q02JSpVbGwWvu9yaytwYnVzWL7rnIh4/F4OSqNqWhJV4oxFcIhE2LFuAgDO3kwIMvBvPp00p92NvnBnDgr2zS44uY9IivRdetETdvZUmTlVZiNYFQpQJH57Jxx8ZWYsbiYJxKS25EH8/nxzeTeGWK4t4wipkefja8tr6dcEE1d5pBzFiiQ1+N6fveY1xw0ITQqb+myvOdPdTEnrFe84OCHH25uKmIT0j5OD6xU1n/hHRwoDDfwNaVGYy41cMUPwaDzP5N2SZRRiC4HOzflA3A/14OpF0PJ657yIdls+PoPFBTpSvdQaMiNdY6RiLjWsd8Q9e4aWwkrJMjYZ0Ug9Chv3XEnytk2ew4HDQqCnIN7NmQRUR3JxLOF7JxWRr6EpmO/TXc9pw/Hy6v/b2I1ZagegoKmn2jhbAujqTGFWNjq9RcCm7vgMZNzaAb3AHFTdh/gpvF57p4qMlKtaJAmF5imsSa03OkK95ByoDpF26Pk4uaqc/4mR63d1QxbKoHzu5qdv6SiSzLfPlCAjt/zaRtN0dG3SV22pocLaRgfH1x9lCTY6WueMZGD9ZY9AmaMc1o3KkvRhHcaun5ZnXUaoN/uLJLbqxjE9zegVnLw9DrQafV8+jSED7a34mFm9tb5f4EVqQ613oz4aqhLnQf7sJ1D/ngG2pHQFs7rpvhwz2vBlUrDoL10/Nzs/U4uqgrLQy7Xe1Cux5OBLS1Z9Akd8b8z6vc4+Pu9UKbXMKvH6aajiVdLESvl8nN1ov0/OZEMxpzeo915fnv29Bvghtdh5YJaZIk0XWws0UThBFndxtyM6333jRPMbZEl8HOjLhVKbLr5FImRdz1UgDX3O9NekIxjw84zV8r0tn6QwZPDDzNVy8m8NW8BKvcn+AKYDBUXYe9CdJjRFnZin4TXE3H3tzUnjvnBVb5PEdn6/UqMGUZetQ8XwvrpKQ82zsqNeIf+zCUgZPc+G9NJj8uTOLHt5Jw0Kh4fWM7Hv8wFJ/gqjNXLCFWW4LqiYoCJydo27b88WY0aHYd7MzD90XgFWSLWl02QN72nD/j7vXGK6DqSWdwpAO712eRlWZZ2Ksr1e1sP/1lOLGnC0ypaY4aNXe+GIBf6YJNrZYY8z8v1i5JYekjsZzem8s193ub6g/8fbTBtyewJq3cQejsrrZaZy9dqUBojRgUNGNaQew4uapRqcpSTRqKsa5UbQXCwTe6E9bFkdCOZTUHwzo58tTnYeRm6ek8UFl4igYNTZDqGmMZH28mXDPNu8p0yKowiuvW6gCeoy2pVdyMv88b/zb2JoeWrYNE/LlC/vwqnbS4Ijr01bDyjbK6UW27OUKoVW5RIDDhH25PcKQD97xStZhRFc7uaoqLZArzZRycGu56zc8xVDtfU6kkJkz3YesPWoZN9SS8qwMGvbLmCmxnjy6jhDXvpbB2SQqgbA4X5RuIOZWP46AG357gSnDhgjLmtG/6m4ltuztxx4sBHN6qq1T/siZjgoOTytS1u6HkmhyENY87vqF2zFkRTnCkA5KkxJRfmB02tpIpO3LCA954+Fa/sVYVYoYnqJ4WInJYqi8oSVK14iBAWBdlkXTpZH6l7kX1ITuthNDOlotZu3raVLLPD7revdzPI2715NelKZzem4uDRsXoO4VzsMnSAtwcDcHZ3YacTKW+TFGhARtbqd6F47PSSlCpai9yCFoorSB2VCoJjZvatJPcUOrqIFSppHLioBFjTRtBE6amMaeFx4+xVqC10vNTYorwCa55cWVjK9GnQgfL+98MYvmz8RzcrOPgZp3puIunmqToQpyFQNg8aGZrnfpidCzlaEtwcKqb06giBoNMZkoxET2qb9yjcVWz6O9IHF1U5eaGKpXE6Du9yMvW8+dX6Tz8fggd+mow6JWGC28tatDtCa4UJdbLvrvcdOqvQeOq5s1N7astx2IJB42K4kIZvV4uZ0KqDzqtHkmq/XzN6CI04uSi5rbnAug80Jmz+3MZfotHve9FCISC6mnlaZIhHRxQq+HCESsJhOn6BrmgbGwlHlkaSuKFQnqOcqnWwi9oZFqIuF5fnD2UDuD6EpmZg88wbKqHqc5ZbcjN1lNSJOPmbWNy3lqjM6WgGdNqYsfGag7CjCSl8LxLLVJWBM2cVj5fM9Zq+v71JFw8bNj7exb3vhaEjW3txg3zBZ7BIJN8qYjIPlXXbasOOwcVD78Xwq51max4ORGfYFtmvBuCd5AtxYUyC9+p12UFV5pWMua4eCrrEl2GHu+ghl0r9nQB+TkGU5mK6qhOCJk4w4fx07yxs1ecwWq1hG3DtEvBlUSWoZnVYK1PGSMHjfIeLsg1oHFt2DwrN1NfmkXSsL9b9+FKqY6GIGoQCqqnlYscdg4qwro4EnUor8HXSksoojDfgE9Iw0a4Tv01jLzNs962YYH1OPXzzxhyc6s+oRXHjrO7GlmGmFNK4Wuj5b0iJcWyScQw5537LvLc+HPkZunJTivBVaQXC1pJ7Lh4Wq851uEtOsK7OIjNpNZAK3cQ2jmULao+fTqOQ3/rSI21UEPbAmcP5PJY/9NcOqU0GclIKqa4UDaVeKkvA69zZ+Hm9jzzdRsC2thja6eqVHRe0IRp4TFjxLiBZHSu60tktCn162p8dr+yXurQr37iuhGVSjKJg4ImzLlzkJFR+Xgrma85lDbjsUaasU5bUqv04iuBiDxB9bRygRCgXS8nLp3I5/TeXFJiyk829XqZOWPP8teK9Bqvc2avMmhG9hGpWi2Fm558koM7dlh+sLrFWiuIHaNN/+yBMgHVYKj8e69alMQLE6PIzy1zTGWmFpN0UYm1eddHkZFknRqggmZOK4kdF08bMlNK6lUw/uDf2aa04sQLhcSeKaDPOMtNuAQtlGZUGN6aWOoYnF1Lof3IViUN+MR/OQAkl44/AW3sG3xfzu42ojxGc6WVjDnOHsr8KiOxGL1e5vsFicydEEVRYeXPkiP/6CjMLzt+8UQ+P7+bjL5E+TvFnyvAw88GV08xZ2sVZGVBngUTTSuJHWPfgILchmd96DL0TaYZoxAIBdXTkJSV7GwotE6TgsZkyE3uSCqJ9x+OYf5N5/nutUTTY5dO5KPL0LN2SQo/Lkwq91jUoTzWf5pK7JkCdq3L5LvXEnH3tTF1iRQ0f5K1WlISquiqdoXTvZYtW8ahQ4cuy7XrQ1A7B1Qq+PWDsk6OX86t/Lfa/0c2AInnyz4rjpcu0tr3ciI/x0DihUICIhq+UBM0E6p6H7eCyaaR9MRifngziYObs00Lr5qIO1vA8jnxfP+6Mg7tWpeJWg19r3Gt4ZmCFkFVTsFW4iC0RHZa7QTCwnzlb6NNLsFgkEkoHY/EuNNKSEmBmJjKx1uJyGF0EK5alMzz15xj129ZQOX4SbxQyKdPx5VruvPrByn8/V0GW1YqLrK4s4UEta9cx1bQQmnlRiIHjSKlFeYZKMgzkJ5YTFpC7ZzrFclOL8HFs2lsJgmBUFA9DQn8CxcgOfny3NcVxDvQjjZXlRUC3fFLpsnZcWZf2a7JtlXaco/9sjSFjZ+l8cYd0ax4WVmwjbjV0+Iut6D5UVxQgDYnh9SkJMsnVNdR8jIMmp9++im7d++2+nXrS2CEPf97JbDcbtiBv7JN38uyzLmDeWW7zlFlAuGp3bm4+9hwxwsBpmMhHcSEs1UgyxAX16onnB1LU7O2/5zJ8mfjeWzAafb/mV3DsxRnByiLOFmWObRZR8cBzrh4NI0dacFlxhgbFWOnFQuEX76QYDFV8r0HL/HL0hTTz4kXlPFn/6ZsHu13mrVLUnDztmlwTSlBM0Gng8zMysdbSczYOZTJAboM82yO8gJhVqlgePG4koovyzKJFxQx5NSuXJbNjiPhfCFB7YWw3qpoxfM1o0CYpzPw9PAzvHhdFPMmna/Vc4sKDRzeqmPt+ynIsowuo0Q4CAXNhIYIhAZDi/lwuO15/3J1AfKylb9J3JkC1DblBb8dv2QCYNCX/e4d+2l44pNQRomuwy2G9FJhMDUlxfIJV7CLcVFREceOHSMtLc3q124Ifce78eam9rz5Z3ugfEHq/ZuyeXf6JYqLSgXCs8oCbd8fWRz6W0ePkS74hNji5KIMU2GdhUDYKqhK5DA+1kLGlOoYfIM7Mz8NxdO/rM7sD28kVvMMhXMHlA2rpItFPNL3NOmJxfQa3fDmWoJmQk0OwlaAcZwI7VQ2Xhz7N6fcOcVFBs4eyOPPr9MxGGT0JTJxZwsI6+xQLnVSuAdbEa3cBQUw+i5lfWLe+TSrgkBorBedn6OIiOkJxWSnK+ec3pvL4a06+k1wY9jU+ndPFTQzWnlzLGN95z0bsqr0hBQVGiqVKAP48LFYls2O469v0jm7P4/8HEOTEQibxl0Imi4NCfwWNLD6h9uzcHMk6z5O5ffP09izIYv/1mpJii6i2zBnQjo4kHC+kEN/6/j+9SQi+2jITC1h4CQ3pszyw95JJTqwtjBSE5UFe2pqquUTruCE88SJExQXFzc5gdCIq6cN1z3kw7qPUykqNJCvM/Dzu4qw2m+CG2f353JiZw56vczqt5Np282RG5/wRZIkXtvQntTYIryDRGp+i6OoCOwq/L8KgRCAyN4aXlvfjplDTlNUIKNSl40fBoPS2MfWTsWXc+PpM96VnqNciTldQPfhLgRG2LPrt0xcvW3oO17UH2w1VBc75o+3YJ75OhxZVhocPNznFFC5ePzO0k1cgEf7nWbSIz4UF8qMvssLv3A7Vi1MJupQHkMnu1/BOxc0KtVlfLQSbnzcl1F3eOHsoSY/x8CeDVnlBMLiIgO/L1fmmAU5BmRZJv6csrHba7QLBzcrDvbbn/cv50gUtHAMhlY9X/MNsyM40p4DFbI8iosM2NopcfDtK4ns36Q8/sqvEab1zLmDZVmIf3yhxJZLE6ndKSJYUD2yDHoLhTdrE/gtcMDtMlhJ/fppcTJJ0cpugG+IHddO9+HGJ3xN5/26NIXMlBLcfWxxdG54y3JB08OYWpyaXkWDmisoEB48eBAHB4cmKxACeAYobqj9m7KZf+N5stNLeO67NtzzSiDX3O9NekIxv36QQk6mniE3uZsGVgcnlUgvbolkZsLOnZWP15QmeRl4+OGH0Wotd9lubJ7/vi3drnYmJ1NPZqri3tizIYt5k87z3PhznD2Qx/evJ7H4/ksU5BroOsSZ6x7y4eVfI3jq8zBsbMXY02oQDkIkSTLNt2Z/FQ5AWnyZcyM7vYQfF5YvffPbh8omX9tujgS3d+CxD0OY9VkYPUeK2p2thoasdVoIkiTh5m2DWi1x9/wAbO0k9v6exYmdigN3+0+ZpCcoY1BxkcwvS1OJP1eAJMH4+7wB6NhfI8TB1kgrFgjVaon/vRIIwICJbiYXe5ZZ/U6jOGj+fUmxjCSBV6At3Ye7mEqWuXo1jbIWIooF1dOQTqwt8MPBzacs5avfBMWZ0ba70pXYO9COGYuD6Xa1M4e2KDtpTSXQBdYnNTkZR3v7qgVCuGKD5tmzZ+nXr1+TFgh9gpXY+faVRFMaV3CkksI1YKIbQe3s2bxCKXId2E4Igi2etDQosdBAoBHqqH377bdcunTJ6te1Br6hdoy60wvA5NaIOVVQ6TxjDTVjvVxbO5VJZBe0EkQNwnK06epIWGcHM1HDwMUT+RbP9Q21w8NPGaNs7VS06+l0xe5T0AQQKcblkCQJzwBbYk4V8OHjsUQfy2fL98r8zDdUcT/99U066z9Nwy/MjuBIB97eGskDbwU15m0LGgMROwS1c+CVXyO49Tl/rnvYByhLz9dpy89zjU0Y0+KLkGWY+KAPY+5W0vs1rupyPQ8ak6bhYxQ0XRpSR60Ffji4+9jg4qEmvKsj/3s5gCmzfMvVVet2tQshHRyIPhaNTqvHJ1SkRbZUUlNS6NimDalZWcrOs7qCGNyQ2CkuBlvbms8rRafTERERwZEjR2r9nCtNWJfKg56xYY+tnYouQ5xNjUr824i4afGkp1ft2IArJnLk5OSg0+nIysqy6nWtSWA7RUj/8PFY07HQjg48sjSE/BwD829UCmK7etkQ0FbETqulIV2MtVo4fx769Lk899ZIeAfbcfFYPqvfSWZraZdVgHteDaSkWObbV5RSIV0GOzfWLQqaAkLkqERAW3uSLynu20X3XgSUFH43HxvmTogynTfxIUUQcXIRhohWSSuvQWjEmDbsXmokWjY7jnteDcLeUVnn/O+VQBLPF7J5RTq52XpTun5gO3tCOjjw4qq2ODircHZvGtKc2F4WVE9DU4xb2AeE2kZiwe/teejdECRJwtndplJXYg8/W976K5JXfougU39NI92p4HKTmpJC53btSM3OrtoJVd8J586dkJtb63vJyckhPDy8STsI1WqJqbP9GDBRcd5W7A4Z0EYRQRw0KuzsxdDUopFlyMioflJZH4HQ2AG5DiSW1hLNtNTBsomgcVUT0LZ8w4TM1BJcPGzwDbHj6puVgvCdB2oqjUeCVkh90vOLi5WaoC2M0I4OpCcWlxMHAfpd48agSe6mn43lYwQtmEuX4Nw5y49Vt9apCb3e8nObObb25ccS7yBbwrs44u5TXsDoKsT11o0Q18vhHaQIhLoMPV/PSyA1TnGwh3V2oPsIFwwGpUzM4S06eo91NZVQCmhrj4dv7Y0hlxuxChPUjEgxLkfFrsVV4R1oJxZrLYTk5GQGDRpU7lhKWhqdIyPLHITmNLRYfB0nnM1BIAQYfosnd88P5MG3g031oYz4hCgDY7erxWSzxZOfrzhuqxMIq4qp6igpgaNH63QrzUEgBHj6yzBufLyszu21D3qbvnfQKFO57iNEx+JWTU0ienUxZDC0yPlaSMfK5SoWbYk0fW9Ml2zfS6QUt3QO7tvHjn/+sfxgQ0SO6GjFfdvCmDjDh479Ndz5YgAA+Trl7yNJEh/s7Wg6T9QcbEXIMly4UPmYpdhpoWNKTdg5qJi+KBiAglw9yRcLTbUGw7s40L6XEz+9k0xxkUzXIU13vSOiWlA9okmJQMC+ffvYvXs3xcXFpmOpaWlEtG1LYXExBXl55Z9QUyfWmqiqK1gV5OTk4Ofnh16vJ6/ivTRBug93MS3MjLS5ypE75wVw63MBjXRXgitFfloavR9/nKeXL686HbK+DsI6TkiNAmFTTjEGcNSo8S6t49lpgIahN3mYHht/nzf3vhYoxPVWgKzVYti2rYoHG9Dgp4Vu6IZ3dSSgrT0PvxdiOmbuXn/ik1Bmfxku6nW2An7+4w++WrvW8oMNSZNsoQ5Cn2A7Hv8wlL7XuOIbasfUOX6mx1Qqidvn+vPAQlFzsFVRUgKnTpU/Vt1avwWOKbWhxwgXHnwnmKICmT++SMczwBZbOxWSJNG/NIsKaNINGMWIKKiehuyqXabdg8zMTKZMmULHjh3p1KkTu3btsvprCATmHDx4EFmWSSrtXAwQn5xMUHAwni4uZFR07lUnZtTXfVtQoKSBWSAnLQ2XxES8vb1Jr65pShNGkiQGTXLHwUkMSy2dM8ePcz4+nh//+69qMaPicePPNbmg6rgp1VwchACdBjjTsZ+GKbP8yh13cFLRd7ybcKy3Avbs2MH4mTMhObnygw2pQdhCN3QdnFS8uKotXYc4879XAk3ODiMevrZNpii84PKSmp5OdEJC1aYHYYawiK2divlrIug7zq3c8SE3eohO360Nvb7y2l44CC3SdYgzviF2qNWYXLhAudJjfuFNt2Z006iEKGi6NGTgu0w70k888QTjx4/np59+oqioqFk4pgTNm4MHDwKQkJBASIjiRLiUmEhYhw54urqSkZ5OoKUn1sfJYTyv4rnnz4OjI7RtW+n0nJwcnLOz8XZ3JzU11XSPAkFT5NSJE/Tv0YOdBw5UbvDTkPR8cyGklmJZYmIinu7uZGq1tTq/MXFwUvH4R6GNfRuCRiQuNpbjMTGQkAB+5YXiBrlvW8Firv8Et5pPErRYUtPTuZicDDk54FbhvdAQB2E9Y0eWZbGpI2heGEV0g6Fs3iaalFhErZZ46oswVGqpnGvdw8+WRz8IIaidA2p1041/YdUQVE9DdgYug0CYlZXFv//+y7Rp0wCws7PD3d3dqq8hEFTk4MGDXBUZSXx8PACFOTmkZ2UR2K4dHq6uaFNTlQLYRhraidVS3BkMVaax5OTl4ezqSpeICPbu3VvbX0sgaBROnj5Nv969ySsspKRiY4SaHITVUY9Ox4mJiXQOCiKridfvFAgAtBkZJGZkkJuTA3l55V3lDen03cJdUAJBSkYGMWlp6C25xatyEJo/Xt1jdYydtWvXcuutt9bpOQJBo2N8n5u/30WKcZW4eNhUasgI0HmAM27eTduj17TvTtD4NCTF+DIIhNHR0fj4+HDvvfdy5MgRevfuzZIlS9BoynegW7ZsGcuWLQPAwaHp5vhbk7g6du8U1I68vDxSUlK4duRIEhISAIg7fZpAHx/UNjZ4urmRER8Prq4QFqY8qSGpXsbHLT23igmsLjcXFx8fJo8YwUc//cSMGTPq8iuWo7XFjoibK8+pqCim/O9/uDo5ka3V4ulsVjuvoTUIjc9V1W7/MzE+no5BQWRYIcVYxI7gcpORoXTjvRATw1XOzuDpWXncuUL1O61Fa4sbELHTGKRmZlJcUkJiXBzBxpgxUpMLqjpXej1i56OPPmL//v0NdhGK2BFcEfLzwd6+bA2i14NtacfdZppiLGKneoRAKKieJpZiXFJSwsGDB1m6dCn9+/fniSee4M033+TVV18td9706dOZPn26VV9b0DqJjY0lxM+PYC8v4mNiALh0+jRhQUpxZk83N6UGobl411AHoaVaatXEYk5eHs6+vox3c+Pel15Cq9Xi4eFh8dyaELEjuGzk5oKDAyejo+nUrRtuGg1ZmZl4mqfENyTFuDZ1CiuQlZFBWJcuRJfGdkMQsSO43GhLheyomBiu6tCh6rpp1f1siUZczIm4EVwJUjMz6dCmDRdjYggePLj8gw1NMa7DOikxMZGDBw/i5OREVFQU7du3r/VzKyJiR3BFOHYMAgOVMkdQeb1TXyNRIyJip3qslmIsSZKbJEnjJUm6TpIk19JjXSVJWiVJ0jFJkjZKknSdtV5PcIVoqIPQyikrwcHBBAcH079/fwCmTJliqg8nEFwOYmNjCfH2JsjLi4RLlyA+nktHjxLWpg0AHm5uaLVapbuXkYZ0kzSeZ2mRZyGeSkpKKCopwcHLCydZpnPnzhw/fry2v55AcEU4vmMHF374AcP581xISqJ95864u7iQmZqqNOAx0pAmJfVIsdTpdIR4ezeLJiUCgTYzE42jI1GxsZW7pzbUQShSjAUtlOL8fHIKCujZubPlzSDjnKu+DX7qMOZERUXRqVMnBg4cKErCCJoHRUWg09UtxfgyiYMLFizgww8/vCzXFpRhFYFQkqRuwClgI/ArECVJ0jBgOxAJnACCgV8kSbrBGq8puEI0sRqE/v7+hISEcObMGQD+/vtvOnfubNXXEAjMibl4kVAPD4JCQ/l140ZmPvool/LyCOvYEQBPd3cytNraLdQqPl4VlmKnihqEubm5ODs6Irm6QkEBnTp14tSpU7X99QSCy0pxcTEpKSm8+/bbzFy+nKRTp3BzdsbJyQk3Z2eyoqKg9PMcKHvfV3yv12YsqSnuLKDT6Qj29SUzK6vWzxEIGgttVha9unThfFxc1QJhVbFi5RRjrVZLnz596vQcgaAxSEtMxNPFhUB/f5JSUuD4cUX0MFLTuGPF2ElJScHXx4e+ffuyf//+Wj9PIGgsNmzfTtz58+VTjI1cCQdhbi5s3w7AmjVrOHnypHWuK6gSazkI3wDigC5AGPA3sAZYK8tyD1mWb5VluRvwPfCMlV5TcCVoiBPqMtmLly5dyh133EG3bt04fPgwzz//vNVfQyAwEnvhAiGBgQzs14+37r2XFf/8wzcbNzJw4EDATCC0VCOwPqlexripZYpxTk4Ozg4O4OwMRUV06tixTCBMT1e6HwsEjcTq1au5++67uRgXx/o9e9i0dSvhgUrPb3dnZzIzMso3WrC0Qw21W6jVI8VYl5tLSJs2ZOl0tX6OQNBYZGRm0rljR5LS0+vuIKyOeqQYnzt3jgMHDpCdnV2n5wkEV5qUhAR83N3x9vQkXauFpCSlyY+RhsZOHTalUmNj8SkupkNkJFFRUbV+nkDQWLy3ejV///df+S7GRq6EQJiZCbm5pKenc/DgQRITE61zXUGVWEsg7AsskGX5lCzLsSgioAeKIGjOd0BHK72mwEqc372bHz/7jNzc3MoPVufIqE0dtXp8OOTm5lJgnnJWgR49erB//36OHj3KL7/8Uu9aawJBbYi5cIHQsDCcPTx4cMwYpt91Fz179mT8+PEAeLi7ozW6j4xpxtZotFDLJiUmgdDWFuzs6BQRYRII9/77L59+/HG589PT03nllVeq/6UFAitx9OhRTp06xcWEBEYNHsxHGzYQXlpz0M3FRUntNU/PN2LN2Kni2rIsk5OfT3BYGJk6HXITrpcjEABos7OJbN+etKys2jsIL1OKcXR0dLmvAkFTJTU5GR9PT7y8vEjLyFDGnLqUhbGmgzAxEV+NhrY+Ply4cKHWzxMIGov0rCwSEhPLYsZaDkKttvwGcVXodFBczD9bt+Lp6UlSUlLtb15QL6wlEHoDCWY/G7/PqHCeFnCr74tIkhQiSdJWSZJOSpJ0QpKkJ+p7LUEZL7/6KrfPmMG61asrP1jV4HgZU4yfeuopUV9A0HgkJcGePab3bmxMDCFt20Jpl6vXX3uNVatWmTrPeXp4kGF0HxkHTVlWuqg2tBNrxeOWHIQ6HS6OjqBWg0ZDJ39/k0C4Zft2FnzzDbLZ8+bPn89rr70mxBDBFeH48ePExMQQl5rKzVOmsD8qivDwcADcXV3JMgodRqwhrlcndCQmwoYNAOTn52NnY4OTlxcuTk6kpaXV4TcTCK48Wp2OyI4dqxcIr1DGx8WLFwEhEAqaPvExMfh7e+Pt7U16ZqYSN5YEwvqWtqiLgzAlBR83N9o4OhIdHS3mYoKmTUkJ6TodCdnZilAH1nMQnj2rZDrVRKlL/ezp0wwfPlw4CK8AVmtScoUoAZ6SZbkzMAB4RJIkUYCugaSkpdGnfXuOHTqktDI35woOmka2bdsm2tgLGg+tFlJTISYGioq4cOmSUm/QwQEkCZWzM2q12nS6p6dnmUBo7iBUq+u/ULN0rqV4SklBd/AgzsbOYh07Ep6djU6nIzY2lkuxscSkpnJkzx4AMjIy+Oabb7C3tye9NoOyQNBAjh89iotGg5eLC1ePGAFAeEQEoAiEmVlZjZNirNej0+kU962TE5FmtW0FgiZHQgLIMlqdjvadO5OWna2MNxUFQkmqfwfwOgoV0dHRODg4CIFQ0OTZ/McfDBsxAm8fH9K0WuX9XhsHYcXHq3qsjjUIfQICcMnNRaPRkJycXOvnCgSXm0riW3GxIhBqtWUaQW0dhDVR23FHpwOVioSzZ+nt50dSUpIQ1i8z1hQIH5MkaaEkSQuBN0uPPWE8Vnr80Ya8gCzLibIsHyz9XofSGCWoQXctIFWrZWSfPhw/dQq2bq39oGllB+GBAweYOnUqp0+fJiUlpdbPEwisSm4uBAVBQgLx69eTmZdHh759wd4enJwUZ6AZHh4eaHNylB8qCoT1TfWydI6lJiVnzpCTkICzk5Pys6cnajc3brrmGn788UdiEhJoHxjIhl9/BWDDhg2MHDmS8PBwEhISEAguGwYD2bt3k5qWxoju3Qn39SWyc2fc3NwI69QJUFKMs/Ly6pZiXB21ia9S5y+ZmeiM7ltHRzqEhHD27Nla/GICwRVGluHQIfQZGWTn59MmMpLMnBz0hYWVF2oqlXVTjI8dq/Ip0dHRDBkyRAiEgibJXXfdxeY//0R/4gR/7NnDNbfeipe3N+kVS8JAw5zrtalBmJwMf/8NubmkpqXh27Yt5OTQtk0bkWYsaDKkpqYSEhLC6dOnTccKc3LILSggIT29rLGPtRyEtTESGQxQUACursTHxNDe1RVbtVrJPhFcNqwlEMYAQ4CbS/9NAS4BV5sdu7n0HAv95euOJEnhQE9gT4Xj0yVJ2i9J0v4DBw5Y46VaPKmZmYwcMoRjp05Vtt1D1UKHlQXCY8eOsXr1ahwdHcWOWiMgYqeUnBwICwOtls27djFyzBhUNjbg6Qndu1c63dPLiwyjQHglU4wzMqCoiJzc3DKBEMDTk9vHjmXlypVcSkxk8qhRHD58GIBffvmFG0aPJjAwUAiEVkTETmXk3FwenzuXIV270ikggHB/f1Q2Nrz55pv0HzAAAHc3NzJzcsqPOQ1xEFYVO5YWglotOq0WFycnsLcnMihIOAgbARE7VfPXX3+xfv16pZmCwUBWfDyuTk7Y2dnh7uxMRnZ25U0jazrXZRkuXiz/Gjk5pkXixYsXGTF8uBAIGwERN9Vz8eJF1q9fz60338w7H3+Mf3AwYeHhioMwM1M5ydy5XlO2VEMdhBcvKiJHdjYp6en4BASAhwdtg4KEQHiFEbFTNRs3bkSv1/Pjjz8qbsHUVDJSUpAkiYS0NOU9DNarQWjJQRgfD+fOlf1cWAh2dmBvT3xSEkFhYQR4e4s6hJcZqwiEsiyHy7Lcprb/Gvp6kiQ5Az8DM2VZLtc+TZblZbIs95FluU/v3r0b+lItiuXLl5OYmMjWrVuVmmSFhcjFxaRmZdG/Tx+S09LQ5eVVHjTV6sqD5mWoQahNTmbEoEG8/vrrQiBsBETsQEZ6OlNffJEX33kHPDzYfOYMo0ePVh5UqcDLq9Jz3D08yM7LwwDlHYSWBEIj9UmTlOXycRgTA+Hh5Oj1aMwFQg8Prm7blvPnz3MuLo4JN9zAidOnyc7OZvPmzUx0dyfA11cIhFZExE5lYqOi2HjgAGtmz+aWoUO5b8IEAGbMmIFXaRyZHITWqkFYVezs3at0wTM/JzMTXVaWIhDa2tIhIIDffvuN2bNn1/E3FTQEETtV89Zbb3HzzTezc+tWdp0+TWp0NJ6urgB4u7oqacbWdBBWPMeSaHL4MJw7R2FhIbGxsYxycRGdWBsBETfVs2nTJiaOGcOr06bx7Ecf8d6SJQB4enujzcnBUDEj43LX75RlcHZWHIQZGfgGBICXF+39/cu5tQSXHxE7VbNu3ToevP9+fvjhB+QTJyAqivTUVNoEBpKUno5clUBo6f1fXwdhYWH5DuNFRYpAaGtLfFoagW3bEuDpKeoQXmasIhBKklTZVlP1uW818LVsUcTB72RZXtOQa7U2Fi5cyCeffMLIkSPZuWED7NhBbkYGKpUKVw8PwgMCuJiSYlkgrEctQYsfDlotVDGZ1CYmcnWHDtx2221CIBRcXlJTwcwtpNVqAfjs449J0Gr56ptvkLt1Y/O+fWUCYRWo7exwdnQkq2I9KJVKSWe0tOCqjto4CA0GpdlCSAiZxcW4ly4aAfD0xCYnhxEDBuBga0u/8eOJTkzkhzfeYHiPHni5uhLo4SEEQoH1MXt/J8bGEu7ri0ajoWevXoweNKjS6e7u7mTm5lpO0bJmF+OSkrJxzWAAFxfIzVUchBqNSSA8ffo0H3zwARkZGZCZyV+//qp0WRYIrjA5OTns2bOHZ599lneWLuXq557j902bCAsMBMDbzY1USw1+6usgtCSuG48ZN75ycyErC5KTOXToEB3atKFHcDDR0dEUFhbW8zcVCKxAeroiXpeyYcMGxvXty4P33cf+/fsZNWoUALYODmgcHJSNqYprnfpmfdQmxbh03DHodKRnZ+Pl7w/u7vSLiGDPnj3VP1cguELs+O8/nhs4kPycHI7t3g35+aSnpBDo64uzk5PS4KfiGGNcm1S1MVUdVc39zMe1wkKwt0evVpOSlUVA+/YEeHiU71WQlwdHjihjVVJSeYFRUC+slWL8tyRJvao7QZIklSRJXwFP1/dFJKVt6OfAKVmWF9f3Oq0RvV7PxYsX+fjjj3FwcOC9Dz9UdrLOncPH3R1sbPDQaJTFWsV0rKomnPXZGcjNVURCC2i1WjwcHPD28ECr1aLX6zl69KgoRCqwPklJpO/fT8KJE5SUlNC+fXvOnTvHim+/ZcGjSqnUNX/8gZOTE23btq3+WioVns7OZBQVlXcQSlLlgvENETnMHYR6vXJtOzu0+fl4uLmVnWdrCwMHMiYigjBfX+ydnGgTHs7LX3zBXYMGgYcHgW5ulQVCS3XgBIJqiI2NpVevXmU1Y0+cgH/+gfx8EuPiCAgMhNBQ0GiUHeAKuLu5KQs1SSp7/9WUYlwd1Ynr5un/RoEwK0sRCNVquoSFsfn33xk7dizz58/n1smTGXvDDfy0YkXt/hgCgRXZsmUL/Xr25I5rr2XNn39Sotezed8+2oSFAeDj7l57B6H541VhKXYqOgiTkyEkBPR6dm7dyqDOnbG3s6NtWJhIzxdcURYvXsy+ffvKDpw/D6Vd6C9evMjOnTu5vkcPVL6+9Opltjy1scHLxaWsyY8RWQYbG+s5CI1ChRGDAZydyUxOxtnBAVtnZ3B1pX9ICHv37kVfMUtLILiSnDiB7vRpsrOyCAkMZOqAAfx45AgUFJCeloaXhwdB/v5cSklR1hgVx4mKZgjj8fpkGlbMlip1EKbodHhoNNj5+dEnIoLdu3ebTsmOjuazL74g8bvv4OBBJU1Z0CCsJRAeRREJB1h6UJIkB+BX4E7g4Qa8zmDgLmCkJEmHS/9NaMD1Wg3x8fGUlJSQmprK7Nmz2XXwIP9ERZF6/Dg+Hh5gY4NbVbtq9RUILe0oWGq0UIo2MxMPBwdszp7F3cWFnTt30qNHD/744496/MYCQTVkZTHjm29o27Mnc599lvT0dL799luytVqGXHcdY8aM4dlnn63RPQiAWo2niwsZBQWVBcKKO9L1rQVl/NlcPClttqDNz8fD3b38ue7u3HLDDbx8zz0AdOvZk179+nHT3LkQEUGgszO7du0qiy1ZVoSdHTvq5xYWtB7MUoLXrl1LdHQ09913n7KRY+zmHRtLYkICAUFB0LWr0tzHgkDoZqxBaG9f+w7g9UkxNh93DAZlgmtjQ056uiIQAipPT0b16sWtU6eyft06ru7cmbmPPsqhLVvqVCpDIGgwBgNrV61iUs+etEtPp2e7dnSKiOCf48dpU7ph5e3uTlpurmWB0Fru24oCYXGxEqsBAYpA2KYN+PvTtV07Tpw40YBfWCCoPQaDgYULF7Jp0yblQEGBUpO5tGnPxx9/zL133YWLXl+5LIxajberK+k6nWUzRH1rEFaMuezsclkqRgdhdmoqbqV1b3FwwMfdHV8fH5FmLGhc4uM5t2sX7QICUPXowd3DhvHJmjUs37SJjJQUvDw86Ne9O7tOn1bmT+YbrtAwnaAmB2GpQBifkUGQtzc4OjK8Sxe2bt3Kq6++Sm5urlIe5ssvuef779lZUMAFsWHVYKwlEE5AaRbypyRJQ80fkCTJHfgbGA1MlWX50/q+iCzL/8myLMmy3E2W5R6l/zY25MZbCxcuXKBv377Y29szbuxYPnjwQZ74/HNSEhPx8fICW1vcNRplsVZbgbA6auOCqnCuNjMTD40GUlPx8/Rk1qxZ9OzZk0WLFgkXoaB+VFEXIyUmhr/27OGzF19k4Tvv4OjoyAcffMCYnj1RBQby4osvMmLECO6+++6aX0OtxkOjQZufX37QNAqElhZctRE5qhs0DQZTN2VtcTEe7dtXuoxXp07cWCpwfvTRR6xevRqVszO4uRFZas+fNWsWny1YwNr33iMrP5+e99zD2cOHOXjwIPfddx/p6enlL1pUBLGxNf9NBC0OXXIyMb/+Clu2QGlR9V9//ZVly5YRExPDqlWrFPGwbVtITiYxMVFxEAIEBytOwgq4u7uTmZenTDitIRBW5yA0dyiqVKDRoEtLw8XFRTnu4QFnznCbnx8X1qzh4YceYszkyRw6d05JXxMILif795scR4VRUfz6669M6dcPOnRg15YtPDxjBrkFBbQp/az3dncnNSfHcoqxpXmXsXt3VVgad4zfVxx3QkM5euIEPfv3By8vuoaFcayabscCgTU5cOAAycnJZe+5rCwybWz4+/RpyM3lwIEDjOraFQIDlXgwR6VSHIT5+XVzENY0Z6tprSPL4OSELjtb2ZQqnb/h6krfbt3Yv3+/8rMo/SK40mRlkZqZybEjR4hs0wb8/Oh6yy388MMPfLF5M+lJSXh5ezN88GD+OX68vIOwqmwp42PWchDa2xOTkkKIvz9IEj06diQ+Pp558+Zx/Phxduzdy5MzZrBz504eeP55Pl+1quF/l1aOtZqUFACTgK3A75IkjQKQJCkI+A/oAlwjagY2HhcuXKBjx478+uuv9AcmTZhATn4+f8TG4hMYCDY2ikBorRTjqgbVijsDp07B779DcTHarCw8Sov4+nl4kJSUxB9//IFOp2P06NG8/vrrDfobCFoZUVHK+6siOTms2r2b6667jtvvvpt2ISHMmDGDjIwMRg4cCJJEmzZtWLZsGYMHD675dYwOQvMJZ00OwvqmGJsv4owCYXY2HgEBla8TFAQ9ewLg6emJg4ODctzRka5BQSQnJpKWlsact97iwZdfZvaKFSRkZPDUnDl88skn7N69m8mTJ5cX5w8dgqNHq3QBC1ouz82cyV2vvgr9+kFSEl988QUnT55kwoQJvPHUU3zw5puKkyMoCHJzSUhIICA4WHmys7MiwFXAzd2drNxcZLW6skBYXyeHpXPMxx1jbDo7o0tPx7nUQYiHh9LIxMZGEQTbt6dHz54cjY5GL7rlCS43xhpqZ87wzx9/0LltW4L69YPwcOxDQmjfqRMA4ZGRAPh5e5NSVYqxpfmapRSwiueYfzX/vqJA6OpKRl4e3r17g7MzAyMjefvtt3n11VeV5g8CwWVkw4YNjBs3rkwgzM5mw8GDPLRkCeTkcP78edqp1VCajl8Rbzc30i0JhJbGndpgaT1Uca1jMIBaTXa7drj6+5cdd3Ojc2gop06dIubECYaPG0dxZmb59GSB4HKSlMRjX37Jw59+qowzkgQ+Plx11VWcS0ggPSUFr8BAhg0ezL8nTiDb2JR/b0uSdUuRVRQIS7sYn09KIqJDBwDUGg3znn6ant26EX3hAjsOH2bCDTfQvXt3Tp09y26xYdVgrOUgRJblImAysBFYJ0nSo8AuwBMYJsvyP9Z6LYFl1q9fz/nz58sfLCgArZbz588TERHBuKFDscnPRzVgAPfeey/Lv/8e39BQsLXFzcnJeinGtXUQJiUpE06dDm1WFu6li7W7x4xh9erV+Pj4sHnzZoYOHao4VASC6pBlxeGWmAiXLkFqKjqdjlWrVrFlyxbCw8NZ8e67/HP6NOPGjUPt48PRDz9kzjXXADBi6NAaXsACajUezs5kmIvrxve9JYGwuhpR5s+tqp6HcUA1CoRaLR4WxBckSUnttHTc0RFVQQFjx44lIiCAV594gsScHP5ZsYIL0dF8/fXXrFu3jqysLFavXk3pCyk1RF1dq6wjKmiZZKSn89369Rw+d475H37Ihq1bmfXEE2z76Sc02dmM8Pfn0OnTPL58Od//+CN06UJiWhoBFlyD5tg5OGBrY0NeSUn52KlvLajadAA3xo6LC7rMzDIHoaenIm4OHar8s7XFzc2NwIAA9m/fDkCx+dhoJCHB9Hp6vZ5///23svNWIKgOg0F5/w8dCsnJbN+xgxHXXgtmzvB2HTsCmByEfl5eJGVmKp/nxtR+qL+D0NK4U7FJSen4JcsymTodHkFB4ObGmA4d+GfrVtasWcPkG29k9Y8/1vUvIBBUj16vpBEDO3bs4MEbbihrjpOdzdGLFzkXG0vChQskxMcT1rkzmNdmNsPLzY20vLzamyFqI67XJHKUjjs6WcbFvKlcYCAd3dw4ffo0jz3xBP+eOMHh9euVOmqWxhuBwJoYDHDpEoeiosgrKCDSrF6nn58fBUVF/Hf6NB2uuoqQ8HDsbW25mJpafj5VlYOwNptFdUgxPh8bS9tS0wMODjx1992MbteOwzt3Ep2cTI++fbntttuYNWsW+8+eRW/suCyoF1YTCAFkWS4BbgV+ApYABcAgWZaPWPN1BJZ5+eWXmTVrVvmDMTGwZw/nz55Vmi1kZysLfElizpw5rFu3jieffLLMQVhYaN0U45oC32BQ7icnR5lw+viAoyP/Gz6cAQMGwLFjuGk0PP7441y6dKlufxBB6yMzE06fhtOnyS0oYPbSpYSGhPD+u+8yatQopo0Zw+OLFvH3vn1cffXV4OSEo4MDfgUF7F26lMA2ber+mjY2eLq5odXpLKcYWyrmWx3VpUmC8hpGoZFqBMLqcHKCvDyeeeYZ3ps+nQefe451GzfSqUcPVr7yCjNmzCAiIoL58+fz3nvvKbUJDx1SCtR7e0NGBjqdjp07d5Kfn1+31xY0TYqLTQuwirz50ktMGTaMCRMm8O5773Hja69xzbBhRJaUwPnzONnZ0TsykqW//MKHH34IISEklpQQEBJS/Wuq1bhpNGRVrN9p7RRjowBjPEelAl9fdPn5ZQKhrS306qW8tr296amPP/EEzy9bxlOPPYa/vz/bS8VCgILERJa9/jolWVkAfPbZZ1x//fVMnTpVlMUQ1J6CAi5qtRgcHaF3b3ZeusTgq68ud0pYWBiTJk3Cv9R95O/rS1JGhvJ+3b5d2bypykEINW9MWRLXq3AQ6nQ6HBwcsLW1VWqL2trSJyyMc+fOsX7DBr5ZskSIGwKrUnL2LHu/+QZ9SQl79+5lqI8P7fz9Ob5vH2RncywqCnt7e35ctYpgb29su3Sp8lre7u6k5+TUPsW4Npu6lhyE5tcvjZ3s7GxczQVCd3c6hYeza+dO/t2zh3vGjmXHv/8qj4lyLoLLxenTynv2/Hly7e2JjY/n888/Z1ypWQJAkiTah4Wx69Qp+g8YALa2dAoO5lRsbOUU46rGHSumGBuNTgD4+sKpU7Tx9WXlTz/Rp1MnbG1teeSRR3j77bfx9fDg1MGDpZeTyxrpCWqNVQRCSZL2SZK0V5KkvcBuoDMglf5bZXzM7ByBldHr9Zw8eZLdu3eXLxadlQWOjpw6coSOHTsqAmHprpqtrS1jxowh0CzFOKuwsGYXlPExazgIDQblfnQ6tMYdaR8fZXKZlQUXL0JaGh4eHpSUlJBVuhATtEJkWWmkkZEBlj7sk5IUN09QEAwZwvMbNnAmKYnjS5bw32uvcem773hx7lxuveMO3N3dCTW6m3r3hogI+oaFKd1W64qNDZ49e5KRna0MZMZ7rUogrM9Czfxn445bQwVCrZZugYEM7tChTBBxcaGbvz9LliwB4NprryU2NpZH33yThKws9AEBZNnZsXb1anr27Mn//vc/brrpJtGBryWQkAB795Zb1OTn53P82DE+X7GCV159lbfeeosjR45w3XXX8djzz8OwYTBkCAwbxvVjxvDw1KmcOXOGBx54gHNRUQRYSn03x9YWd1dXpQ6hNQXC2jgInZ3JLizEtWKDnwpMf/BBfIOCSDl3jjfmzGHWrFnMmTOHdu3a0aFXLx786CMunDwJwL69e3n1lVeIj49ny5Yt1f/uglZBfn4+eXl5yoIsO9vySQUFjHzmGTZu3MgLb7zBvhMnlA1SM2xsbPj1119RlX7u+/v4lAmEen35jaOqHIR1FdctCYSSRGZmZvkxx90du2PH6NO+PUO7dGHbkSNkXrxYw19GIKglej1/rF7NkNmz+f2nn/Dz9cXbxYWRgwbxx88/Q34+R0+eZMrkyXzz++9EBAdXO5fzcncnrWKTktIUYIulLWpTv7OqjI8KjeWys7PLNqVKaderF2np6Yzt2ZMRo0bxzd9/M3/jRrRnz7Jz504SExNr+gsJBLUmPy2Nzz/8EPnSJYiO5rgk0alTJ+67775Kc7b2HToQGhSkHLexoVNICKdjYiqnGFvSCSzFRUUsOQgrOnJLU4wvXLhQJhAGB4ONDW3atCEmKYnBffuWu8Swvn35u7SJ0YYNG+jZs6cof1FHrOUgPFHh3zHga5T6gxUfE63OLgPnzp3D39+fqVOnsn79+rIHsrPRX3UV52Ji6OjtrYhu5rtXRmxscNNoyMzPL9v5rW5noKbFWm1rEJY6CPNTU5FlGceuXaFLF0WwuHRJ2dFLS0OSJEJDQ4kt3VGLiYkhMzOTadOmERUVVcUtCAdHi0KrhZwc2L0b9uzBEBvLgQMHuOWWW5h+//189MorZB49ykmtljvuuYcVP/zAR+++S9Do0TBqFKG33w7h4bw4b55JAAOU1EI/P+X7+giEgIe3t5JibC5gG2OnYo2o+joIzSeapSKHLMuVF2u1wckJzp1TCuM7OJTdk7Oz4qQ6cQIuXcLm1Cl++PhjsktKGPPCCzj7+ODftSsfr13L63PmcPLkSQoLC5k7d27dXl/Q9NBqlffe9u2waRPpJ08SEBDAgAEDeO/RRwno2ZPQ0FDCw8P5+eefGTRoUNlzVSqenD+fDz77jPfff59OnTrx0ksv1SwQennh7u9PVsX6ndbuYlxRICx9v2eUlOBVwz3a2dmx8pdfWPH669zTtStnzpxhxYoVvLFgAetffJFhvXsTV9q05eiBA/R2ceH+++/n559/Ln8hrVYZ0wStilfmzWP6ffcpjX2Sky2ekxIXR3RiIvPmzWP58uWMHDkS9xqEa39fX5LT08uaMBgXY1XFjqqG6X51KcYVxPVKm1Kl9/rw+PG8MG0aA3v04N8tW5R6tcJJKGgo6elsPXUKH09P7njgAYb06gVeXky6/nrW/f47Gfb2ZGdn89zzz3MmPp6Iq66q9nLeHh6kV6zfCdXHTl1TjCvGTul1dDpdeQchYO/nR1t/fyZefTUjx43Dyd6eC5mZ9LrnHkaPHs2KFSuq/X0EAovk5oLRjVrKM888Q4/+/Xngww85+Ouv0KYNh0+donv37hYv0b5LF/ob53q2tnQMCuJUdHTNDsLazNeMj9ekExQXUwzExcURHh6uHFOpYNgw2pTeW8V68ROvvZZ1f/xBUlISa9euJSEhgV27dlm8hSqFwyoyaloLNta4iCzL91jjOoI6kpqqpPsZDBw5coTu3bszfvx43n77bebMmQPFxRTm5HD8wgW8vb1xPnNGCURL1ntJKnNypKQojUPGjWt4mmRVCzXjNUodhNqkJDycnZHs7RVR0MEB4uIgMlJJk5ZlQkNDiYmJoWvXrtx4440kJyfj5eXFiBEjiCpNLwD45ZdfCA0N5frrr2flypUMGTKkAX9kQZOguFh5H0REKO8dHx/mPv443+/cyR133IEhK4s/jh7lwz//xNbZmeDgYO655x6CK+wqAQQGBjJp0qTyB93dFSerpZp9tcDT0xNtdrbyns7Pb5iDsLrYMe5wly7UcnJysLe3V1K96oKTU1ksmu9mSxIMHAhbt5oG6MHe3vRftIjnv/mGadOm0b59e1QJCYq718aGVatW0b9/f8JDQ3lwyBCk4mIlbivskguaOFqtqakNOTm89fLLTO7Xj4euv54+EybU+HTJ2RmA22+/vU4v6+bmppS2MNa1rG9ZC/NzqittYSaWZBQU4GGccFaHjQ306oXdxo0MGjiQk6dOMeWaa5D27iUkJITYc+co2rOHk2fPcpWnJ659+zJx4kRkWUYyjpVxcZCWVmXhfEHLZMeWLew/cYLsyZNx1WohMZE/jx5l6QcfcO211+JcUsKubdvwdHPj0KFDLFy4kNmzZ9d4XXd3d/ILC8kvKcERyguEhYXlT66Lg9BSinGFDuCVBMLAQHB0ZKqvLzg78190NDu3bWNSUJBSB7tfvxp/H4GgIkVFRbw1ezaPTJrEliNH+GH5cmzj4riqUyfw82Nohw6cnTaNL7ZtY9CgQXTp0oW1a9fiXDoWVYWXhwdpmZlKrBQXK5uiNaUYV0dVaZKgjDvGrq+WUowBPDz48vHH6T1mDI6dOvHf2rXQoQPfz5/P2u3bOXn4sOnUixcvlokkAkEV7NiyhYFt2qDKyjK9x7VaLZ9++ilr332X3zdvZs2OHfSePp39+/fTy6z2oDkPPPAAOTk5yg+lDsLvDxyoXE6p4pytLgJhLep3xsbH4+/vj52dXdlxW1vCOnXC2dGRgcOHl7vEmOuv545HHyUwMBBbW1vuvvtufv75ZwZ36qTMw8LDwc6Offv2MX36dA4ePFg2VwNl3NqxA0aNqve6sLlj1RqEgsvDBx98QFBQEJ9//nnZwexs2L2b819/zZKnn+bPP/+kR48eDB8+nP3796PT6SAri/f++INhw4fTqUsX6N9fSQer4s3u7upKVm6u8kNJSe3qqNXHQVjxq0aDNjMTD41GGUgBvLyUAt0REcr9bttGqLMzMTExyLLMmTNnGD16NBs3bqRz5878aFYU+8UXX+Tqq68mICCAqVOnEhcXV9OfWNCU2LGjLFXXyMGDoNOxLzWVjRcvsvzXX/ls40Z2f/YZC268kTdvv51fv/2WV956i+uuu47ffvuNxYsX1/411Wq4+uqahe8q8PT0JCMjQxEZs7Kqd9/WZ6Fm/Nk4CJe6oOqVXgzKfYaEKOn8jo7lH7OxgQ4dlHN69wYbG2xCQli4cCEdOnRQUtyCgpT7iYvD29ubTZs28e7ixXSaOJG90dGKy3PfPrIzM0Xtj6ZEWhocOWJ6b7366qs89dhjJP3wAz9u2sTds2dzTqfjpS++4MdNm3hlxgz6tGun/H9fJtzd3clydFTiJi2tbKFW0d1RlTvQnOpix1zkMDoIMzLw9PSs3Y1KEmg0TBo7lptvvhkpKwvc3QkODuavbdvwHD6cAE9PnCWJzuHhlJSUcLI09RhQNvRycyuLN4IWS3FxMQdPnuTqbt3o+sQT7N+xg1kPPcQD993H+PHjefHFF3lj8WI++ukn7rv5Zry8vLjppptqdW3JxgY/Ly+Sja5148arJXEdah53Ks7NzL+vIK5rtdryDkcnJ+Uzols3aNeOQYMHs+PIERb/8w8dJ09m386dfPzxx+Ubzen1tXfU1iZVTdC8MRiUTt5mPHT33bzy4Yf8+NNPXIiLY8C4cQzo1AlNYSEEBmLn7MxdU6fy/JtvMnnyZADGjRtXyU1UEe8OHUgvLb+EsYZyTSnGNcVOVQ7CCuOOTqerlGKMvT1D+vbFMTBQWQOVdmq9fdo0npw0iZMnTvDoo4+yd+9eIiIi2PbLL6anvvnmm2zYsIFvv/2WEvOUaSOWjgmaFenp6Xz//fflD8oybN6sONMrUFJczKjx49ljzCbMzgZZZs2aNYwaNYqRHTpw8913s/rAAWQHB3bv3l2ppIWRsLAwuhhNRZJEh9BQTp8/b9lBWJ9SQ5Y+243HzMaklPR0/IyZXmY4eHoSt2ED7sHB5Y5rfHz4/vnnObZnD2+99hr3jxvHzp074fx5ZdwpHXu++eYbDh8+XLnBq3HD2sz1n5WVZblRXQvFKg5CSZKKgMGyLO8r/VkCXgY+kmVZ9GpvADExMcyfP59ly5bx/PPPU1BQwCOPPKJYXwMD+WbbNhYsXUp4mzbs2rULjUbDgAED2LJlC9eHhLDt1CkMBgOdOnUypYFUhVunTmQadwqMA2J11uHqBs3qauGA8kFinMiqVKQWF+Pt6lqWLtOpU9lz+vWD5GRCNRq2/fkn/Tt2xNnJia8+/RTs7XnmmWeYMmUK//zzD7NnzyYqKopx48axaNEifv75Z4YPH87SpUu5xqz4qqBpknDuHL6pqdhkZEBpMfajBw/y9Vtvkenmxu+bNtG9e3c0Gg2/LF5MQGGhIiZqNEghIUwOCzNNFK8kHh4eZQJhZqYSa9U5CKvD0kLN+Fxb2zIHoVpdeaFWWzQa6NFDqTtnaQIZGqoIiJKkiIgVkSS46iqlZp2/P+3ateP0unWs/esvJj78MFdddRVtNBp+37OHnIICHnroIRYsWGCqnyW4wsTEKCnlNjZKvERHc16Wef/997lr1CjC7r4bfx8fJkyaxPDhw+nXrx87P/yQoMGDlWLQ9RTOa4ObmxuZ2dmKsz0vr2qBsCF11CzVIESp31lrgRBAo+Hh225TrnX2LISFEdKmDR98/jmRQUHcO2oU+Pgg7djBA7feyuuvvcb3TzyhiCfFxeDjw/fLl+MaFsaWLVu4//776dy5c+1fX9B0iYpS/o+LiymMiODpZ5/l4P79hPv58cu2bcx66ik+2byZX3btIurzz3Fv357lwcEkXrrE3aNGMemaa3h5yRKcautWCA7GPzCQpOxswjWasvd8VbFTnxRjSzUILTkIjZRu8PYfOpSdp0+Tp1Yz8447uO6GG9C4uJCdnU1kZCQ9evSgKD6ebV9/zZjhw5Vxs4oUN0ApeaHRQNu21f8OguZHdrbyvjp9GtLS0A0ezN6DB+nQoQM/rV/P49On8/bGjQwZOhRbOztl3pGfb3o/PzJnDp+tXMkNN9xQ65f06tKFtIwM5T2Vm1tWdsnGxvIGTl02dY1jpbm4bhyDSh2E7c26k5vo169yiZuQEDoNHsyhp59m39Gj2Nra4uftzb0zZnB2+HBUrq4sWrSIvLw8HB0dOX36NK+99lrZ80tKYMsW6NNHKaUjaB7IsmKU6NwZPD1ZsWIFc+fO5cYbb8TRuKFvbEp1/rySSejqym+ff86CJUu47957KSwuZs3WrWhuuIFuJ08y/+OPeW/9elYtWQKFhfQdNQpHZ2d+/vlnLly4QLdu3Wp1az5jxpCRmYlcUoJkvNf6phhXlfFhPu6UZhqmZ2Tg5eVV+RqShNuIERYvf/2kSeDuTpcpU8jas4fjx4+zafNmNO3aMSQlhcLQUFavXs3gwYP566+/aNeuXdmTtVpwc2Pv5s28sXEjTz/9NHfddRd333038+fPr+5P1GKw1mrNBqUhifl15wKBVrp+y+LCBVNuu16v599//0VrVKuNnDxJ5i+/MGHkSObeey833XQTv/zyCy+99JJSsDYjA3x9+WvnTn569VV2b9iAt7c3AOPHj+ePDRvQJyay8/Bh/vrrL5544okab8v9qquUhVrnzkoNwJochNUtGo27cdUFvll79OSCAvw8PCxfU6WCgABG9evHqSNHuOWee+gQEGD6G44aNYpjx47hkJ/P3VOnEuDnxy+rV9O+fXvmzJnDK6+8Urm7sxmff/459913n+XdN8EV47lp02jbtSujX3yRld9+S3Z2Nk899RSjx47F1cuLNhERbN++nd9//52ffvqJIddeq4hYgwcrYlcjik+enp5KDDs6KhNM4/u8qthpbAehkcBARQy0RE2ikLs7BAQo7s60NKTUVG66/Xb27dvHI488gtrXl1duv53z//7Lzm3bGDt2LC+88AIbNmyguLiYs2fPipi7EmRnK4uv4GBOXbrE4p07ufOhhxgxYgT3TJnC4hkzyMzM5GJcHB9//DHx8fGsXbuWoJtuUmpzXkZxEBQHYWZmZtkOtDVcUNWJHKULNYPBQFZWVt0EdmdnZWIeF6e43AMDCWnblpz8fO6aMoXHpk6Fjh3B15dZN9zArh076H333eQePw5duvDm2rXMW7CAadOmcfz4cUaOHGmqqytoxuTkKAu1khJIS+PHZcs4uH071w8cyLSbbsLB0ZEJEybw9Z9/MnL0aNyvuw4SErhzyhRuHTqUr9euZej119deHARwdsY/KIgkd3dlE6emGoS1GXcqjle1rUFYARdfXzbOn89/W7Yw4+GHufTDD5w8fJjXX3+dxx9/HFmW+XX1aia8+iqr9u3j2unTad++PV999ZXlC2ZnKxtvguZFXp4i5sXGws6d5epR5pw8Se6pU0od5H37wM2N41otXbp04ZmZM+napQu92rXj2ilTuHDpEiNHjlSe6OWlNCgoJTIykuTkZHwsbWRWgZeXFxkZGchOTsrnOdRcv7O+6fnmzYPAsoMQlLHFwljr4eeHd+kY9dtvvzFlxAhCAwJYuWABh1evxsfJieh//uHkyZMsXbq0/FoyOlrZEKy4vhQ0DTIyLLvuLl5U/s/S0yE7m7Vff42NjQ3Lli0jOjpaOScrSxF9IyLg9GkSExOZ9tRTBHt6svCddwjw8uLttWsZ/vjjZMTF8f7vv3Ps448ZGx4OgwYh2doyc+ZMHnroIXr06FE+dbcabPz8sLOzI89Ys9o8xbhifwGou2vd/DlmsVOlQFgdXl7K3zAtDbeAALxdXZn2zjtcN3060WfP8sKzzzJ48GBmzJjB77//Xv65Wi1ERjLnk08oTktj+vTpjBw5kuXLlyvrluxsRcRtwWuYy7mivryriuaKwaA4D86cgYwMXn/tNe644w569OihfLDn5sLFi+gTEpjy7ruM6N+fmSNGQEkJHTt25NFHH+WW8eM5u38/0+fN49ixY4ybMAEvs8XP+PHj2bB+PRvPnycgIIDBgwfTpk2bGm/N3d1d6RIcElI22azJBVVXB6H5hNPMyZGcl4dfDcE/8OqrWf3MM5y7dIlIf/+ygR0I9vbmmTFj2H/8OJ39/SE+HlDatN96661otVoumFuxS+8rLi6OZ599ljNnzjBt2jTR5aiR+HjpUn7bvJm4L77g1okTWfr11/j7+5N64QLHV6/mpeee44UXXijrYAWKw7BHj0a7Z3NMKcbGWKmuLkdtJptVNQYyukNKYyc9Pb3ug6Y16dJFSS87e1a5Lw8PwsLCuOmmm/h0+XKmvfAC3gUF/PXii9w2dix2dnY8/vjj+Pj4MHjQIO6fNg3RTOgyc/YstGuHHBnJA8uW8c2aNRTl5bFw/nxmjxgBV12Fo5NT+forUObmvsyYxh1jrFQ17hjHi9rETk0uKEkiKysLZ2dnbGzqkEih0YBOp4gVnTqBoyPBpTUFew8frixc3d2hfXuc8/I4t2YNgSEhfB8Xx6qdO1m2ahXbv/qK2E2b2LRpE4888ggPPvggssGgLOQKC5VFgxiHmg4XLyoCe2KiIgybUVJUpLwfjh5FjoiAq64iRq1myUcf8ezkyTxz4408+cYbgFJEXa/XM2rUKOVzvE0bZg0fzuKHH1bqtdYj3nx9fUkxNiqpjUBYHcZN3frUIKyIJDFu1iwcvb0hOBh7Ly/sz51j2rRpZGZm8ssvv/DdmjU4azTcPXcufTt04K5bbqmcRmektGSOoBlhMCgZBjt3wqlTFOflQWYm3333HQEBAYT078+Mxx5T3lujR5Pk4cHE555jwW23sX/pUm4fPJhpt99Onz59kCSpTCC0gEXBrRrs7e2xt7cn22AoW0c0pIux8ZyqxHWztY7FGoTV3ywTBw3i9ttv58KFC7T38OC5V17h7d9/Z/OBA4waORL/zEz83d0ZNWoUv/32mzKW5OUpaZRt2phiR5ZlpfyUoFHJPXeOtIMHyd+2DZKSiIqKItvY4b6wUJmzRUaCVkvyX39xJCqKF+++mzlz5nDttddSGBUF8fHo1GrWHjoEBQV8On8+twwbxqOPP875+HjumDSJxYsXo3F25qU//2TCtdcScsMNiqHCwQGA//3vf3z88ce8+uqrdbp/FxcXckpKlDp91ZVTgvqZIcxjR6+v/1rHy0spW5OeDn360C00lMy8PGbOnMmkN99k5bff8ukDDzCpb1+2b99OUlJpwmtGBuTm8t2ff5KUl8cvzz7LiQMHWL58OaGhoWzcsIG8HTsoLihQ/q/S09n122+cO3cOUDJTJkyYQHqFkgnNDaukGAvqQHq6sqDOyeG3JUv45KOP2Hf4MG+++SbXXnstL99yC8MiI/n61CnyS0p475tvkPbtU97k/v7Mmz2bmF276DhjBg899BCvv/46DhERipJdWAjx8XS2seHanj25+Zlnqp5wWcDBwQFJksjJy8O5JoEQapdiXFFdr+ggNAqEOTkW6wuUIzSU9nZ2dAoJITIwUBkAjcTHE96zJ+0CAugUGFhWhBRQqVRcc801rF+/nscff1zZ7d+5k6MuLky4+Wbmzp1r2h348ssvmTZtWi3/YoK6kJeXh52dXfkFuSyz4dNPmT9vHju//hrvHj2YccMNPDB4MJckibagTHZqem80Mo6OjhgMBvKLinA0b8JTXwdhxYWa8bixiHZp7CQnJ9ccN5cTlUpJ+akKLy/w8sI+O5tpajX4+DBr6FCy1GrcU1IY8corDBo0iBUrVpS39wvqTn4+HDtmagiQlZXFji1byN6zh1d++42SkhICAwM5cPAg6h07lM/B7t2V/6NGxM3NjUuXLpV3EJrvSJtvRtUmTbKiQFixFlTpORkZGXV33/r4wPHjygS7dMc9NDQUSZLoOXhwWWMejQYkCZu0NB6fNYsHHnmEwsJCfvnlFwK6dYNt26BLF5599ln69evHZwsX8kDXrki2tujValSurpxzc6N9ZGRl4VZw5dBqldThkpKy96SNDfj7s3fvXoZdfTU3DBjAcw88wMvvvMP58+eJi41lSr9+TLj33nJOJw8PD+644w4mGBv+hIcjRUUh1adERClVum+rSjG2kriu1Wrp2LFj9TdnTJdUqaBdO9i5E7VazTvvvMOdd96JoaCApR98wII33uDFmTPJT03l7SVLKi8EjSmf+fllpWkETZ+YGCWjwt6eA4mJjLjpJtqGhhKbnMyPb7+Nj50dIx56iJiSElIPHmT69Once9dd3HnttTBoEB8NHKjEmlrNxo0bq+yyWl+8vb1JLyjAzXyNUl2TkpqcUDY2VYvrZnVvLXUxrhZ7ez6bNYtNBQV8//33tA8LY8y115I3axavf/opf/zxhxJrsbFMmTKFDz74gDayTAjQJjwcQkOR9+5lx3//sXr1ajZu3MiJEydq7RgTWI+cnBzOnDjBgjlzWL9jBwZZ5uGpU/li3TpsVSqWLF3KHf36sSc1lTMXL3KTuzvf/fcfN0yezMxbbuHeKVN4+O23cerQgYX/+x+fbd9OdEwMxw4cYPPevbz05JP0Gz0aG7Waq7p14+6ZM9m5cyeffvcdP/30U6UUdpVKxZQpU+r8e7i4uKAD/HJylBivykFY22amNWUa1lcgdHJSylLY2YFGQ7d+/VC7ufHcc8+xdetWFi9YgHf37nD0KFOuv55XXnmF9559FrtTp9CGh/P4bbexdetWbGRZ2Rxs147p06fz0rx53HHuHM6urux7+21iCguZOHMmbp6e7DtwgN/XrWPr33/z8PTp/Pjzz3W75yaENQXCNpIklRawwziCt5UkqaDiibIsn6x4rNUQGwvBweyKieH+jz9mw4svEuTlxbvvvstnn37KC++/z4WMDEpKStiyZQtqtVqpAXXiBBQWopIkvnjjDeZ/9hmh5qmBXl7KBGrcOKTiYj7q0YNX1WpT2nFt6dSpE8dPnWJAbUSO6qhK5KhiVy0lM5M+ffpUf003N3BxYdE99xAZHKwIhAUFymItPR2Cg5k2aRLdOnRQBMLTp5Viv5LEtGnTuOOOO5h2331oDh5Eq9cz9rrreP+jj5g6dSoAzz//PIsWLRIC4WWgpKSEESNGEBgYyM8//4xKpUKv17PohRd4/7PP+OW774gYOdK0s6UOCqJtXJxSG6a0kU1TRpIkJc04J0cRCJWD9atBWJ371samrIGQSkVKSkrjCoS1xdUVBgyApCScbW1xzsuDoUPZtWgRH589y9VXX82BAwcI8Pcv28kH4s+eJSgyspFvvunz608/8fabb9InKIhbHnuMguJi7pg2jQ6hoZQUF/Paa68REhJC7969y5rMwGVtPlJbjOle5SaYanWZ6GFsXFXbhVpV3fQqpEnWqUGJEUfHsgY9Zvd/4MCByi6WPn3A0ZHRdnYsXryY9PR0+vfvrzzm5wfR0dhGRvLVV19x08SJvCVJXD18OL9t2IC3szPn4+O57bbb+Oqrr5R5gODKY6yFa2NDelwcy//8k7TvvqPvNdfw25o1PHXTTbh3786Y0hImH330Ef379iU4Pl4pv1CBFStWlP1ga6tsYjbAQW1y35o71+ubnm/puQZDWd1b48+1cRBWxDh+5+YyZswYXp09m3EREYTdeCM3T52KWq/H2WBg2uTJdOnShe3btyt12lJS4MIF8mxtsQHsjGl1gqbPxYv8nZHBsZgY3nrrLT5ftAh/SSLI25u2rq5w9dU8dfgwHadMwdvbm6effprHHnusbG1hb2+61Pjx461+e15eXqTl59PW2BCvurJI1a13qionU42DsE6OR3t7KCzEKMe3b98elUrFSy+9RHx8PAMHDlQc7fv3M3ngQHbu3Mm9zz9Px/BwVixdSnx0NMvff5+f9u0jOCSEoKAgPvjgA6XsUk6Oks1mbFZXhzRtQd15d948Xl26FC8XF7QnTnDi/Hn6X3stX82bRxd3dybOns3ZiRP5YcsW7JycyBo6lC+3beODTz7Bpn9/vI4d44dnnuH4nDn0GTWKRx5+GJVazfuffMKRqCgGT5mCk5MTt4wfz4DSunz9+vVj/fr1jB492mq/h7OzMzpZVt4/Dg71z5aqTYqxmUDYtWvXut+sWb3PJ158kfz8fOzs7Pjnn3/KzikoYP7kyTzw0Ud0HTSIH1as4MC//zJy5EilNqNOpzihganXXMOsmTN5/5lniLex4eGPP8bbyYn5jz/OiZMnWbx4MWcOH+bdBx/kle+/58iRIzVubmRlZZGTk0NQE5iPm2NNgdCSVW0VYP7ukEp/bvmz3aIiZWJVOrAYDAZURUWQksIJlYobb72Vr7/+mr6enpCZiY2/Pw/dfDMP9exJlK8vfn5+ZYNIWJgywTp6VPm5Y0dCQ0LKv16vXmV1AW1skBwdqZs0qNCzZ08OHT7MAGOaMVTvgqprinFVDsLaOqFUKq4dOlTZGcjMhL/+gqFDlYl8t248++KLymNRUUpqkL8/uLszZMgQBvbty9KXX+bZ665jZVQUwzp3ZuqNN5ouPX78eKZNm8axI0e4ysq7la2ZRYsW8fbbb9O9c2fSoqIY1rs3Q8eN48dVqwh2d2fv778T3Ldv+Sd16KCIYSEh5VLJmzKenp5kZGcTaNxJttTZqzYOwupEDjs7pY5PaXwlJydzVXUOvqaEm5vyzwy1pyePRkaSfPPN3HbbbfTv0IE/t2whNS+PwqIiMjIy+HLJEu5+9NFGuunGJycnB0dHR4siUVFaGgvfeYflX3zBovvu43RaGrfdcw/5+fl888wzjO3RQ+lcX1FgN0/Vb2S8vb1JS0tT3vNFRWUCen3T8ytuTFVMHSvd2a6XQAiKY7aCQ6tnz56Vzyt1hklQuTNthw6wfTuEhNC9e3eiVqxgb0kJRy5c4KlnniH24kV65Odzy+LF3HTTTfTv35/nn3+e5ORkNm7cyKBBg+hQ2ulSYAW0WqXLdOlmxKVLlwgNDeXI7t3c8OyzPPrEE3z88cdc3b8/kS4ufPbJJ+zYtYuLGzfiO2IEWTk5eHl5lTXIqqqua0U6dWpQOrm7u7tSk9o8xbgqF1R9U4zNm56YuW/rHDteXkq9OXd3po8bZxL+7Y1CUGAg7z76KL4dOvDkk0+y8LXX6JySAjY2PLRkCUfOnmX10qU8uWQJly5d4ueffyZSbB41Goc2b+btt99myrXXcuP110NoKEf276dz9+7Y5uay+Mcfef/33xk+fDjLli3juqFDTZ95dOoE9vbMXbSIJ19+GUdHxyvulPb29iY9O7u8Q70+7lvj41U51yvUIKxzirGdHRQVEaLX878JEwgrHbvvvPPOsnPc3aFtW+zPneODZ55BN24cQXfcQd+pU1Gr1fTp0oWj77+P18SJnI2JYdiwYbRp04ZrnJ3ZHhPDiM6dyU1IwO2aa5T1k6BepKam8uCDD6LT6Vi0cCE9zOYF+uJiPvv2W95/9FHCQ0Nxioykb/v2HPzoI7p17ozK3x+Xd9/lvZUr+e/vv0nIzOT2228nNDSUoUOHKu+fXr2QgKuAgwcPEhkZycWLFxkyZAgDBgww1bD91ti9GGVdm5qaWrf6tjXg4uJSJhB6eVW91qkq7dj8HPOvRsyFQyuWU6qyTmlYGEFRUWx89VVWbtrExDvuwNnZmUWLFimPu7goLvi4OJw0Gi6sW4d7aCh5Pj4EvPUWTnZ2PPXOO1x74AC9Zs7EVpJYtmABOQUFDB8+nOHDh/Pmm29anLPp9XqGDh1K9IULfPvVV1xfD0fn5cJaAqHlFjKtkdhYLly8iGdWFq5XXcUXf/3Fp59+yqVLl/jzk09wdXRk/HXXsXjxYqWr7unTihU/I8OUStmu4uJNrVYcB0OHlgVkRazUoKFXr14cOnRIESXN02qqalJiHuCHD4PxA9F8UDWfnFZVg7AuqZIajfI3yMhQdr0OHFAGUQeHMkdM375KKlhammmh9sz48dz4wgvk6PV8u2YNn86apbQwL00DsrOz45133mHi2LFsXbqUE3FxbD17lnV//42dnR2dO3fmlltuYfLkySLtqxZcunSJ2267jdzcXLb88AOR+flIXbuydsUKDkVF8d1DDzGgSxfLdQSdnMpSVxuQgnUl8fDwICMrS7nf2tTvNGIwKHFtnDRaEjmMzyudLBodVsnJyUpNq+ZK376g0zFPr+cNZ2eijx7l46eewtfeHns3N1Li4hj74osEduxYaQc0MzOTbdu2MXHixBbrsCopKWHQoEEEBgYy69FH8XBwoG/p3yEzNpaxo0fj4ezM9tWrCQkKAm9v5tx4IyWFhTgFBiqfk03cfWsSCI0TTGPc1EdctzQplWVFjDCmKpaeU+cOxkaM99YQnJwUMWrnTggJQcrLo//48fQv/b/t2rUrREfz47x5fLp1K0uXLGHz5s0cPHiQYcOGMXv2bL744gsmTZrUsPsQALD5559ZuWoVr3z2GZ8vX87811/n008/Jfrvv+nZsydRUVHMnz+fu26/HTZt4jm9ntyZM9GUClR1reFUjga8l9zc3MpSjI3v+apEDksZHRXPsZRibMFBGBUVVau61uXo0EFxYiQmKnPeipuCzs6g0zFz5kz27t3L0OHDWfDEEwT17Mmf+/Zxw7hx9Jg0ifETJvDwww8zZswYU+dJlU6nzP/MXGeCy8trb7yBu0rFs2+9xc/r1jHmttt48MEHiYiI4H8TJrD4p5/Yd/AgIUYzg16v/P906FDu/8mawkVd8PLyUjoZe3iUvectietQczmliush43FouINQpQJbW1SyzFcPPVRpk9VE27bKax45gkv37owcOZKSkhLWG8WiM2fg4EEie/Viw4YNjB8/Hn1BAbZOTkRERHD08GHOrV+PfxUdYQU1M3fuXDxsbBhx1VVMHD+em8eN491vvgFgz99/4+7iwow33ihr1iNJ9JgxQ/m+oIDxPXrw+759dOvXj84GA05OTrz11ltK1kcFunTpAiiO0vj4eAotdd8GrrrqKhYuXGjV39PFxYUcvV7J4jO+z6vSCerrIDSOY1ei3rpKpWyaJyRw29NP4zdwIMeOHSsrBwLK42FhsHkz7gBeXjg5OTFqxAi279hB56uuQnJxYfH06VzdsSNeHTvy1KOPcv3EiXzxzz8MHjyYj8wyFo18+eWXuNrZsXLWLObOncvQkSPrNy+9DFhFIJRleZs1rtPc+fenn3hw1iwydDokINDfH3s3N167/Xay/PwYfe+9YGvL66+/zu233648yd0dzp1Tdq9VKqWDcFU4OJhSMC8XPXv25Msvv4TJk8sv1moSCA0GJUe/a1dlQmnWobicQGichDZEIAwKUgbJpCRFkMzMVAb5inh5KRPRdu0gNZVe7doRHBHBsfPnmTt3LqPHjlUmq2Z1gu6++WbSduwg8vbb6dejB6M7dODnb7+lSK3m1KlTzJ07l5iYGJ588slWLxIWFBTw+uuvc+jQIdLT09Hr9fxv0iTys7Lw9/LixY8+4uGxY5n18suojx9XiuO6uXHzo49y87//Kt0+bW3LUgibOZ6enmizs5XdpuoK91ZchGVmKrXjhg1Tfq5K5JAkRSDMy1Mm2M0pxbg6XFywjYxknr+/8jcYOlT5fbduJWj8eH4uLOSWO+/kmgkTeP311wkICOCNefNY9P77eHh68ufKlXywcmWLjMfly5fjYWPDgIgI3nzhBU5cusSnn3zCDbfcwsMPPEDvPn346OuvkczqetqFh2OXm1t5Ad5EKecgrE2Dn+qw5IIyjjPGIvINdRBaizZtlM+KxESlm3jF3y0khIDEROZfcw2d1Gr+PXeOTb/8gm1JCfsmT+aa++7j8OHDBJuNXwB///03Z8+e5c4776xz8f7WRkpsLK888wxrN29mTM+e9OvfH/R6Nn34IXfOnYu9JPHTunX0K63rCShZCba2aC5ebPT6nbVOMa5LWRhLKcbGes8GA0UlJcTGxpZvGFYbNBrln7e3khVQcc7m7Ax5eTjm5LD288/56d13efKLL9Bqtfj4+PDR558TaWfH7Xfdhd/gwRQXF9OrVy+mXH89X911lyLu9OgB3t5Vd4oV1J2CgrLNyVK2rlvHP/v2cWnbNlSyzEsLFrDknXf4Y/58Emxs+Pbnn9m0dm2ZOAjKe2vMmJrfh1cIb29v0jMylPejcT1SnRmiKoxrnaqaapnVIIyJiUGlUtWp4zKgzPfs7RWBvboNv/BwZUzx9eWTTz7BwXy92L69ch+7dtFr+HD2/vkn0tmz2F99NdOnT+eG669n7H334RMWxqChQ9m9ezdjxoxh9uzZLXJuZW2io6NZ8/PPRC1bhru7O3cNGcKAJ55g2MqV3HDbbezZsYMhvXtXvY53cODRW25h7IABSGo1tmo158+fL/9/WAU2NjZ1a7bWQFxcXNAZa8KaO2jN63nWtpyS+Vfz48aNKbOSMJe1IWPbtso/YOTIkZabItnYKJqDvb0yXgF33nMPXn5+Soy0acO0GTOUMhgODkihobTPy+ON8eMJc3Vl44YN5QTC6Ohonpszh80LFtBt6lRW7dxJREQEa9asYUQTEOqt8o6SJGlLHU6XZVluxpYXlNRVg8H0ZkKvJ27dOm558EE+WrqU63v14mxUFHGnTjHsppuwPXkSXF0ZumoV+e3b09b4PFDeSKGhikNQpys3CDcG3bt359SpUxSUlODwf/bOOzyKqovD7+xueiEJqSSEUEIvUgSki8AnigIqYhcVURCxYFdU7L0rimLvXSygIAhIkd4hlEB6I430tvP9MZnsbrKhhGSTTc77PHmSzM7sTmDP3nt/93fO0QXC6t257A2aeoCXlFgEH3u7atY1bSoDX1XV0xMIK7tGVgkqtdn1W7fWhJfNmzXXVadOLF++vKoZC+XlmoOzoMAy6ObkcPdNNzH+zjvp2rUryuHDmstwwAAGDBjA8OHDOe+883jmmWdo374906dPZ/r06c3WwVQbhYWFTJo0CS+DgenXX49vfj75aWn8sn49bn5+/LpiBc9ecQVXXHaZ9m/ctatl57NVK60eXWBgk5ks1gcBAQGagzA0FFSVBd9+y2UXXUSQtQPS3q6a2azVD7X+3WisOdjqAmFOjhZDlQ7C4ODghv7TGp6oKPjnH+1v9PDQjvXvD61bM2LqVPZ3787TK1fSo0cP+vftS+LBg2xftAg/g4ERc+fy0B13MP/hh3F1drHUil27djFv3jxWPPEEvdq2hdmz2bBlCxNmzuTvNWtYsXEjh2JibMRBQNtkcqIuuK1bt+bYsWOoioJSUaFNwqD2lJXTTTG2Fhyt6ndmZGScfpOS+iYwUPuyh8kEQ4YAMDU0lKmZmZqo6O7O2QEBzJg4kQkTJjBo0CDeffddFEVhwTvv8NzTT9OvXz8ef/RRbpo+nWcqu+i2VIqLi2sussxm/l2wgLvfeoseERG8OWMGl9x2G+8+/zzR4eGc170778+cyVc7dtSsjdyvn/YeCg5u8A3bk2GvSUlxaSnudWm0UFvsuLlpYw6A2czhI0eIjIyse5MDk0kre1Adg0Fz1v73HygKl918M5c9+SQ333wzRqMRxWjkrqee0rpGJiQwZ84cbpg2jQ6Rkdzr7c2USZM4e+tWlsTGMmnOHB555BGGDBmCl5eXVqdNOG02//UX9997L52jo3nz66/Jysrir48+Yu5zz/HVM8/gXZkx9OLjj2v11UNCICWFqwYMsP9/3ITme/q4Q/fulrlWdfdtbe5Aa07kIKy21lm1ahUjR448fcEtMFAT1LduPbFAqNesB0JDQ20fMxi01O7sbEhIIMrXV5uXh4ayePFiCgoK+P2jj/ArKeHLHTsY2q8fX372GTt37uTo0aNccMEFXHHFFbbrV6GKLxct4vIhQ/AbMQJMJvzKyph/11288dprTLrySjZu3sz5J8n26Tx4MJ27dav6/VTEwcbA29ubvMJCy3xKn1/p9TzhzGoQ6vXWrYT7BnUQng7V1hiXXHKJbQkZ640zPz9trVtSwuhjx3jm/ffJzc3lzz//ZMqUKXz49ttcf+659Ln6avD25tO332bZsmVce+21JCYm2rxOdnY2m1ev5ryLLrLrKG0I6ktyzsK21qA9woAhp3Bek+Xg3r188+GHpB44wMwLL6TH9OlgNFKWlsaURx7hjnvuYXKlM7Brly50NZm0QTMqClJTCRs1qmYKhKur1kkSNGGhkfHy8qJr165sjY1lSP/+J04x1n8Gy+MlJZqyXt1BaH2tdeAbDOTm5mI0GvGq71Q4V1cYMwY2btR2wNu0wcM6sEwmTWzct08TIxQFcnOhVSu66bUCOnaE48e1JjE9exIVGsqhPXtIW76cvYmJPP7ee3ywcCGff/ll7TWhjh/XRI9m4pQDuGriRELd3Phw5kytw1O/fhAWxsUPP2wRlGNjNRHd3odZMyyGXJViXLlQe/qDD4iIjNRq7+jUlmJcVmbp0FhLqld+cTGugGtpqfZ+Mpkav4txfWEyaTGYnW05pn8eRkbS6tgxXrjsMu4eMoQV69Yx4tFHiRg0CP79l6ULFnDdgw8y8eKL+WPDhqrJd3l5Of+tWEHbrl1tGzo1IdasWYOiKAwbMsTmffHLTz9x08038/rcufQaOlRzmPn7MzgkhHXFxXy+YQNfPvII3rXFkYMmEPWBu7s7bm5u5BUV4au7oAwG+4u1U5lw1pbW7+JicXMYDCxbtowHH3yw4f6w+qRrV9vPDj8/Hrj4YrzDwvj0u++0NCSzmddee421L75I+8BADl1yCWMfeYRx//sfo0aNsnk6s9lMcnJyDfdhc+Kll15i3bp1LF68mPMHD2b65Zczac4cVFXlhwULuP2xx3hkxgxm3n03hrQ0CAvj1smTtcV49+5c3LUrFwcH2xc1FKXGQqEx0FOMh191FV8++ywJ+/Zx38MP8+/DD9tkaACnlu5lr46ai4t2rHLOFnPwYMPVv+zcWdvwPX5c+8wDFixYQJmekhccrI0VW7dCSAg+WVk8P2sW/6amcv7VV1NUVISXuztvzZzJ+7/8wuuvv46rqyuffPIJs2fPJjU1lTfeeINp06Y1zP07OeXl5cybN49du3Zx1lln8f6CBTx333189c03XHvNNSxbtoxALy+W/Pkn/axdtd26aQaH6GgtY8cJ6tgFBgaya9cuy8ZRbWud2n6unhVV3UhhbYaoPOeff/5hpG5sOB169tSeIy7OsoFaV3r0gA0btHuzimMvLy8unz0bdu5kXGWNyFsHDuSml1/m1ksvZV1MDMOHD2fjxo02zRQ2//cf/zv/fC6//HKeee45jEbj6dVYdCLKysp4+OGHSU5M5M0XXsC/cvwsLS3ls88/58MXX7RZ20y+7jrufPpp9u3bx387dvDoo4+e+AXat3eKzV0fHx/yCwst7217XYxPdVPK+rv18Wo6QZMRCOuCmxvRI0ZgLisjKioKs9lMUFAQK5YvZ/7cuVVuRKKjGZOeTv7x42RkZFQ5jfPy8ujdqxfGsjImXXwxr73/vkNuu75SjGutqqgoSiRwPzABOAa8Wh+v6UgqKiqYNWsWP333HVNHjSK4XTvOe+ghlrdvT89x43jn7bfx8vHhvvvvt1ykKFoNNX2HqkcPp1m0nXPOOazft48hp5NibO0g1H+vrXBvtV21TZs20a9fv4b5YwwGrZukniZRnU6dNIfhzp3awBsfD717Wx5XFG1wXrFCcxKWlaG0akVoSAihvXtz7rBhvP3RR/zvf/9jw4YNNrt2Jenp3DB9OtnJybz+yCN0njSp9vssLHSKSZXO8/Pm0cnPD2OXLtr/b/WdLoNB+7dtQQQEBJCdkwNmM3l5eSSlp3M0OfnkdTn0x4uKtIGillSvW958E9+oKBZMmwZmM0WlpZSUlNCqtpo0zoa/v/1SAaDFZFISoe3acdWkSZZYHjOG0Lw8fn/nHXpOmcJPX33F5Cuv5N133+X5Z56hlclEUk4O8264gdteeMGhaRgnYvv27cyYMYPUlBTKy8rwMpl44f77Of+GG9izYQO333YbP957LyOGDbOt2RQQQOepU3lixAin+rw4GYGBgRzLycG3+oTzZE1KcnO1xZ0+cbTngtI3q1xctB1us5mklBT27t3L2LFjHfMHninVHftGI759+vCQycSo4GBeXb0an+JiFr/4Iu0vvxxycugUGMjLsbHcMmMGW7ZuxbtyElpRUcGdd97J+++/z3NPPMGESy6hUzP7rN66dSuvvPIKT0ybxltTprA8IYG7n32WTUlJbN+1i5jt2/n5228ZpKcR6S7Os8+ucmc3BQHwZPj5+ZGVlcWBY8fYd/gwu/fuJS4+3rJYs27AcJopxtOmTePOKVM4q127qk6qmM3EHDrUcAKhLjxYpQfXSJ8LCNA23desAbOZGx98kBt9fHjwwQcxmUysXr2a6wYO5Krx48nLyeHnjRu59eabmTt1KqO6dWPi/PkcOHCAhx56qComBI2Z06cTd/gwN153HZvXr+fXefMYeNttTO7fn/433MA9kybxwPPP13Q9G43gZC7NKgehyWQRCE+01rHmt9+0DCZfX9tyMvYchFabUuvWrdM6NdcFRalylJ8RrVppG/plZVUivA29e2uCr9FIqKLw+znnQGYm1/ToQYSHB506dWL48OE8/vjjDBkyhFeefpo5F13Ep7/9xs8//oinhwd/LFvWLJtorVq+nCU//MDgHj2Yce21fLdyJcUpKVw7cyZdwsI4p9oazzUwkOkXXMDsW26hoKiILidb5+rzniaOj4+P5iDU52tQe83oE1Fb53CzWRtzKnWCotJSysvL699E5ECUkBB+f+wx/M85h39++ol77rmHmJgYhlh3aDcYUM45h15t27J71y5GnXsu111zDevXr2dU7968NXcunS+7jFlz59K5a1eb5zdXVFBSWorHmW4gWNFgqyVFUToBDwLXAOmVP7+nqmrRCS9sJNLT03nxuecY0q8fk4YMQbGyUb/72GPs/vdfDn30Eb4XXAAuLkT36sX5V1/NLbNm8frbb7N68eKats/wcMuEx4kYMmQIP7z3HnNPMcU4KysLf3d3FLAVCE/Rdr969WqtS1NDYTJZFHp7jw0YAKtWWcTA6imbrq6a2GsyaV/r1lVNDhSzmdnjx5NtNDJhwgRWrlyJj48PS778khtnz2ZE//78b/x4hk6bxh8//MDZ1S3mBQWsXLWK8MREOk+YYH/AboJ0GTGisW+hyeHv78/epCSoqCDm8GEAjiQlnVzkUFXi0tM5uHQpYy67zK7IUVxUxO8bN6Js28a54eFMvuQS0gsKCA4Obhn1YXS3rz18fHAZPJj3n3iCK+66i/sffhj/Vq349t57GTh6NAc3bODmN9/kn4kT+fG33xr93ysxMZGJF13EY9ddxzXDh1OYm8v6uDiufPBBKu6/n8igIOZccAEjZsywL5j6+NgsoJsDukDYwdPTdsFlb0famowMbXPFWiDUu4hXv87FRVsUmc18v3gxF198cd3TJJsCkZHg5sYQf3+GXHABHDqk1fgyGqtcJpdMncof+/Zx5ZVX8v333xMfH8+lEyfi7+nJz089xcKffuLd999n5549Nf4tjh07RmZmptMt9A4cOMC1117LY1dfzfSrr4bwcK7z82Nsv37c9txzhHl58cvvv2Oy13nayRYgVV2MgSOJiWw9eJC09HQtXf9k445ey1ZfkFYbd5YuXUr/du04KyrKIhCqKjEHDjBo8GAH/YW10KmTlr5VXFz1WRgdHQ2gNU9RVbx9fPB2dWVmjx7MvOIKbS7u4sJqNzfu/O47OnTowBtvvMEVV1wBgKqqHDt2jBV//UVBTg433nZbY/11DUZFWRklZWU8NX8+h48e5cMPP8RNVUnavp1fV69m9d9/s+Xrr/EuLOSKQYO0ua+LC37DhrHnhx9wa9u29pIITkZgYCCZmZnae76srGrM+XvLFrr16kWbNm3sZ0vpZGdrAqF1tlRtDkKzmZLyco4cOUI3qxTSRuNkGTzW2U5t2mhf3brxkIsL9154Ie+tWsXVV19N/379WLV6NQt27uTcs85i/5EjGF1cGDdmDBs3b24W2S35+fncd8cddO7cmcQjR7h06FBuv/Za2k+aRE5iIpMuuIDgVq34+q23UOxkic244w6eGTSIj+fNw9BMmih5e3uTlZFR00F4IiORvXm3vQ1d/bgurldUkHH8uPOvdVxc6DNiBKSnc/XIkWw8eJAwFxc8q8eiyUSvjh1ZvmQJ3371FdvWrePhqVO5YPRoWg0fzo0XXcQ7L77Ia4sWVV2ScPAgg885h/ySErZt306H060PXAv1LhAqitIDeBiYAiQAdwAfqqpaesILG4mSwkLuv+EGvl6xgov69eOJX37hm8hIvlqwACUkhOTkZB5/6y1W/f03vv36Vb3Jr5wxA9/sbH5fs4al8+fT3cl2z07Eeeedx+xbbyU1JQW/0lJMqorpBIE/cuRIXnjiCcabTPYFwuqDpodH1UINg4E1a9bwwAMPOPaPtMZkgoEDtQl0bQsEa6H3vPMs7h2DAfr04RFFISUhgSFDhtAuPJz/Nmzghy++YMSFFwJaPZDrb7qJrfv3V9WVUEtKmHvddfy0di0FJSVcu2wZL331Vc2aYoJTEBAQQFZODlRUsD82Fk8PD44kJp5SF+Mf1q3j9/37NYHQTprk3ytW0KdDB66bPZu7HnoIvzZtcPHzIyoqyiF/mzMwYsoUDvj5cSA5mR69euEWHQ1hYUR36cJf553HoHHj+HDRIm6aPr3R7rEwP59zhw/njgsu4MYrrgAXF1z9/Rnv4cE/Y8cS1aYNfi4uWp3bxq6P50CCgoI4lp2tOZFPNuGs7g60V/em8rrMzExMeXm0snYQqirf/fILDz3yiIP+ugbCYICwMO3f7L//NMdbdfdBx468M20a13z4IYMGDSIpIYGnrruOGTNnori48L8bb2T8uHG8/fzz3DVvXtVle3bt4torr+TQkSO89Oij3HzffU1qcp6UlMSSJUswm82MHz+etm3bUhQfz9svv8wLX37JU9OmcfMVV2jZG5X3HTZkCD8+95z273WiZnBOhLe3NwaDAbPZzNGkJLYePEhZWRnZBQUE1FYWRmfvXm0zVC+/YJVinJ2dTVpaGtv37tWus3IQHjh4kOuuv95xf2RtnEioUhRL87lqG75tRo7k26Agts2cybirr+bbb7+llbs7v/35J2agT8eOHI6LwycwkClTpzbc/TuY3Jwchvbrx6GkJC4cMIAKg4E7r7yShORkNh86RLfwcL57+mm8hw6tebG7O+7WKcXNgCoHoXWKscHA4198wU1t2ljS0GupQZhy5Ahh7drVyJZSVZWXXnqJ24YMwdPHR0u9Nps5kJBA+/btcXNWkchkgn79cMnLY7bBwJVjx3L/u+/yz1tv0SoykhETJzIiLw+Cg9mdmMhjjz3Gu+++C8D8Rx/low8/RFUULhozhtcXLMDYROvrVeee226jIDGRr9euZcuhQ/zz+ee0HjuWvt26MXTYMLp16MDXL72EoRbht+2AAexduZLO9eH+bCL4+PgQHxdXs0nJiep36nX/d+zQyglB7Q5CvfZtZcZHRm7u6Tf2aYr06gWAMTmZt66+GlX/d6h+WteuzHrxRa4fO5Y/PvuMSKt6rrfedRf9Ro3i6TfewMvLi6VLljD3ttu4/ZproKSEmy6/nB+WLauXBnz1pkQoitIfTRicCBwEpgOfq6paccILGxFzYSGXn38+BkVh5ZNP0m3yZEq9vRk4ZAjznnuOq4YP56633+bWK6+ku53/yAvvvZcL77tP+6UJTZ7PlJCQEK4dP56XFi4kMyWFroMHM6VTJ6LMZs0laTXZTE9PZ/fu3Sz56y/GX3ABRbm5JB48SLSPT+0OQg8PrTGI2Uzm8eNs376dIY394Xk6jpzqqX1BQSjDh/O2qvJTQgLGvDzeuPdeOli5BafOnMkP33/PvDvv5MV33yVm0ybuu/NOko8fZ9vixVSEhnLe2LF8++KLTK1WFys/N5cHZs9m7i230N5e4WehSdC/f3/uuOMOtg8dyv5Dhxg9cCD7Dx/m/tde4/mPP9ZOsidyqCoxSUnsPXjQck61XbWly5Zx4eDB3DRjBvv//JPNu3fjHRxMr8oBRwC8vfEeOJB+bm62i0cXF1zbt+fzxx9n1Jw5DBk6tNF28T29vPj2uefoO2KEJu5YcZa1o6kxu+s2AoGBgbyxcCHhV1xBH70ebG0OwurCh7VAqE84K2uW3XXXXYQHBPDslCmaG6a0lMS0NPbu38+YMWMc9Nc1MP7+MGqU/RIaHh64nnUW39xyC99s3Ur3wEB6X3NNlcNQAV554QVGXnopV82YQUhICIt/+IHp06cz69JLueLNN7n0hhtILy3lESsBsbHYsWMH8x99lH9Wr2b84MGY3Nx4YO5cVv7yCx++9x679+9n+RNP0HvCBE0gsp6XeXpq2QLNCIPBgK+vL4WFhew6eJCjaWl07NCB1JwcAioqqp9cU1wvKLD8bpVivL9yI3PH/v3adW5umltPUYiJiXE6V6kN4eHg4UHfnTv59aWXOFhezrGNG7n/vffoWukI25aayriZM+nUuTN9qzlNn3rqKb7//HOuvfhibnn0UadIU963dy9XXnop5/Xty/L58wk+6yzSVZXbH36YrkOH8tuGDZiOHGlRm1K6gzAxM5Nn3nmH16dOxcVgYH9iIqmVrtzaUoyLSkpof9llHMvMxBts1jqxsbHcd999JEydyhtPPaW53M1m9hw9Sndn35jQy8CEhNB63z4+uOYaSw39jh2rPl8eueoquk2fjre3N6v+/pvcjAy+f/ppXAsLmfnGG7z+xBPc/cwzjfiHnDovP/AAnqGhHDtwgAceeYSBlTUkZ955J5tXruTpZ5/FcJJGgc0t48rHx4e8/HzbFOPaSsLoP4M2L0tNtT3Har5WhZ5inJ8PZjPpOTnNoxmjTlgYDB2KUss8f9iQIYzt25f3X3kFl549bR5rd9ZZDOvRgy8WLSKyY0emXX8979x9N5Pvv5/yigqSr7yS/medxR9//nnG65z66mK8BBgH7AKuUFX1u/p43obmxcceI6uggBVr1+Li4gJGI67At999x9NPP83YRx/lf4MH80htXQCdpKZgXbjzyis5+4YbqCgvp39REa+8+iq/durEwIEDbQbN1WvX0q5dO/76+2/U8eOZ9sgjxObksGnxYptB8/jx41x99dU8Mn48rXv0IOfAAb547z3SEhK45JJLnL+orasrSp8+XKIX867mKFUMBt557z3OHjGCjGPHWLpiBXNvu42Z999fNcF8c+FCrrn8ci6aPr3Kdpy2YwfX3nQTBWVlnH3hhcybM4c7nnzS4X+ecHK6dOnCggULGHvTTSgmE6/cdx/XPvAA+w8d4uE33tDe4/ZSvcxmbVKakUFWVhYBdgTCf1av5sPKlKcBXbrwzdq1BLRpQ/9mjltqGgABAABJREFUNvE4Y05Q0qHH+efz6i23cO6557Jy5crGEQkVhb7NyJVSXwQGBvL5558T4erKg0FB/LV0KbdOmGA/TdJa+LDnIDQaoaSE8vJyfv/9d3p17QpTp1alGC9et44J48c7d3pxdU7kxoiIQPH15YpKR231IvfdR4zgjosu4pxzzmHEiBEsX7qUL559lrG33grAsnfeYcD11zNsyBBGnaQLY0Oye9Mmxv7vf8y78ko++eADfEJCoLycBX36MOmqqyguLWX3mjW0dneHtm0b7T4djZ+fH726dGHJunWM6duXEk9P0o4fp/upuG/tCYSqyv79+7ngggtY8scflFdUYHJzg6IisgoKKCkpqdkh1dkICIDhwxns7s7gnByYM0erE15eDgUF9DWZeOfgQSZNnMhflfXU4mNjufPWW9m2dy9v33ILby5ezM74eD75+mv7r1G9SUwjkrFnD7MnTuSmRx5BKSwEf39CXVz47tdfLSdVpmi3FHQH4ff//MMH33yD8fhxHhs8mGPHj5Oqixi1pBgnZWZSUlrK/v37GdC5s039wlWrVjFu3Dg++PlnXn3xRYzFxZpAeOQIPXr0aIS/tAFwc9NqFbq42G50Vv5btR42jNUvvMDrf//N05dfzohrr8U9IgKysljUrRvDJ03CYDIxcdo02luV80rato3Ajh1xq9z08GwCdZa9KueJQX36sOjee6vSs6+45hquuOaaxry1RsPHx4e8vDztPV9ero0RRiPrd+xg8KBBWraBvTJjZrOlKaPe/Kp692PQjru6VmUapmdnNw8HoY6inNAE0KtfP/6aP9+2I7LVtffOnMnlc+diNpv57umnGX7jjWA04mI08sY779DvhRcYc+65rF67lo5nkG5cXw7C/1V+jwDeVhTl7ROdrKpqk5CCr5g9m+vuvhuXapPrzp0788knnzTSXTUN2rdtS+/OnUlOTWXl6tWYzWaSkpK0B60Cf9WaNcyaNYtXXn6ZRcuWsWHXLo5XpqJYF+49fPgwf//9NxvXrsXTxwdTeTmTJkzgQHw8H7/0UuP+sfVFUJC2m6YodhsIBHXuzLrFi1m0YAGLv/+egXqB9EqGjxzJOf368ej99/PMyy+zZ9MmJl5zDVdecQVPvfwyR/ftY9jIkYydOJHuzcwJ0Vy47LLL6JOXR3GrVvTs2pXZzzxDUVERiYmJ2u6xvVQvVSUmOZmIkBD27dvH0IAAG6dURkYG8YmJ9O3cGYCze/bkng8+oE1eHtOaYZ2kBqN1a64ZO5bo4cObXVMGZ2fmzJlcMGYMU6ZO5Zf16ykuK6N9QAArNm/mBb1jWy3p+Ta7z1Yix/r16wkICGDLjh3aBNbVFQoLWbx+PdPvucdxf1xTwNdX+7KHycQjd9/N0MGDOZqQwKW33srYq66qerjN//7HgnvvZcb06ezct6+qRIaj6dG7N/tXrSIgIkIbX41GMJm4edgwDKGhXDRuHK3rqfaOM+Hn58eooUNZ899/XDlyJEsTEvhu1SpKIiI4/7LK/oG1jDt3Pf88s555huhOnWwWc/v376d///7EHzjAzytW0DoggA8+/5zZ48bRpUuXJpVuXmeMRhg0SGs+p9cwNZm0Jg7AlJkzySgoYOjQoZSXlWFUFO6cOpUPn3oKvzZtGHHVVfQaOpQlX3/N+Mo6hgBHYmL49MMP+XvJEkIjI3nn448JbOS6fSMuu4wRU6ZovziB49EReHh44OXlxcKffuK9xx/n+QULKH34YYCTOggTMzMB2LdvHwOio21iZ/Xq1UyePJkDu3YRm5REpKpyxz338PWvv/LBRx859G9sUAwGTVS3R2AgXc89lwURETB4sGXsCQig68iRfPvEE3y5dClPvfUWn3z2GVFRUTzz4IP8/Ndf9OjUiSvGj+exd9/l0KFDTaeOobu7VuO3OXz2nSHe3t7k5+dXNfi55MEHmTx1KjPvvptdo0dr9WDtxY6+aVVcrAmEtaQYZ2RnczwlhY6VDX4ycnObl4PwZAQEaDXXa2k4MmzyZJ7Zvp3cggJNHLSufRkSwrTHHiM1O5v7772X73/8sc63UV/bW/Mrv94E3j6FryZBu3btCKuW5iVUYjDw1KxZvHz77fTo3h2T0VhVDNt6srlj927OPvtsrr7sMua8/z6zLroIVVXJys62GTRTUlIYOXIkad99R/euXWkTEMCLDzzA5q+/pmc1C61TExx8wgLAbQYOZN5HH9UQB3Vefv111qxbh1dQEJOvu45H7riD5994AxcXF6J79+a+W29l7pw5qNWLugpNhujISHp16IBiMLB9xQqG9+lDYmKi9qCVg7CoqIirr76aFf/+S0FxMecNGMC+ffssg2bl//Frr73GqGHDqjo5to+KwgBs3bWrecWOI+jdm0EGAy7FxY19J4IV0dHRjBk7loGdO/PSHXcwbuRI7n35ZRZ+8w1mffJYS3p+jRTjyvhatWoVkyZNIjI8nDU7d4KrK7lZWazbu5dxztK92FH07s25I0dyw+23c9Gtt9qKiUYjE2+4gS5hYSxYsKDRblFxcyOgVy8txc3NTVucoHW5vWXmTNq0QHEQtJIww4cMYXCPHkweMoTQ0FDe/eUXvrFeGNTiXP/677/5/LPPajRa2LlzJz169OCle+7hjqefZtbjj/PtsmX8FxND58qNqmaBokBoqO0CSyciglk33kj6l19yaNEi1i1YwLy33sJv4ECIiMC7Y0cWvvwyt951FwkJCQD8+NlnnD1wINmHD/PwrFl4lZVxX1PYjBBRwy7PP/88B+PjufS88/jigQdYuHAhbYOCbB2EUMMFVSUQ7t1ria3K2Fm7di3Dhw+ne1QUe2JieGXxYo7ExbHu44+ZPHlyI/yVjUT79jBuXM2NKUXh3DlzeP/77/nxoYeYM2sW540aRfeAAFIOH+a6ESN4+6uvGNqlC6+9+GLj3HttSBwBVg5Co5HkpCR+X7uW519/nZLS0pru2+oOQrDtVWAlEJaUlPDnn3/yxBdfcO2cOc3XQXgyvL01h+4JHp92883ccdtt9scuX19m33wz/6xcyZEjR+p8G/XiIFRVdX59PI/QhDAYGNKzJ5SW4tOvH9+99lpNgVBROHDoEJ07d+amq6/m1QULuHLkSL7dupUNW7Zwdtu2BFVOSpOTk6u6gn26aBGl//6rddFrIikYTYXwzp3ZsHkzpbm5uNlJl7z90Uf5qk8fHn3gAR57+ukq0UhoQlTa7lEUoqKiiAgKsgiEUDXJWLRoEbt27eLyJUvoHhVFn44d2bJlC9MHD65azH3//fd89913rP7tN6iMP8XNjY0vvcTv2dn4+fk1wh/oxLRuraX/i4ui6WEwsHT+fJSQENLNZn784w8UReHAgQN07dq1VpEDs1mLN717caW4vmPHDiZPnkw7Pz+umDcPj2efpVe7dlw2bBi+Eje2nMgNAhAYyDM33siYBx/klltuaRKpX4LG999/j1dpKWNDQiAvj5DgYFRVJa5StALsiusFBQWkZmfz/fffM//RR6sKzavl5WzatIlFixbRJjubBU88wart29m9cSPPfvMNjz39dCP8lY1E794Y/P0JDA4m0E5zibFXXsmcVavo0aMHgQEBFOblsezLL+lb2ZzunL596X7RRfy7Zg3Dhg939N0LJ2H69OmMbNsWXzc3+nfpwqRJk2hdUsK/hw5pJ9hLMVZVEjMzaR8aahEIK7OligoLSUxMpEuXLvRo3569Bw+yaudOZl1yCd07darZRKol4+nJiOuu49DgweDvj9KmDfj7M/uVV5j99tvErVlDv4su4v5HHpF5bhMjLCyMw4cPk1VQwJfLlnFOr16s3bED4MTp+dUFwmpmiLVr1zJhwgT8vbwoUxS279/PWW3bkpGdTdeW5CA8Fbp2PeHD3medxaNXXEFKXBzt27ev00s4lTqjKMqHiqKkK4qyu7HvpdljNGq1nhSFESNH0jsqqkogTEtLY/nmzeQWFJBfUECbNm3o3rkzcd98Q2Tr1nTq1Ikrb7+d2194oWpSmpycrLk1zWZaBwYSFhEhAmEtKN7edsVBAFcPD35cuJB1y5fz4TvvOPjOhFPCKnYwGIgIDLR1ECoKaTk5PPvss3zwwQekrF3L0pdfZuyAASxduhS1ctAsKSnh7rvvZtGiRYQGB1tixc2NEH9/brz66sb7G50ZX1/ZCW6KGI0oqgqqyjkDBuDq6spFo0bx33//aY/XJhACsQcOUFJSYtPIZOfOnfTp04fZN9xA6p9/sujNN8nJy+OF66+X///TRVHoNWkSK599Fk9n7cLZTPH29kbRxxxVJTQ0lEA/P+KtN6WgRuzEJiXRJSKCzMxMdmzfzks//ACKwtGEBNzc3Ko2dC8eO5aXn3+eC/v3J8DXl+mN2AW+UWjbVnOs2sNgYO7DD3N04UKWPfooe95/n77jx1c97DtwIK/cdhszZ8ygrHohfqHRURSFzp06VW3ofvvttzx7882kpqdrJ9SSJpmYlcXYfv3Yq2d8VJ4Tc+gQnTp1wmQy0SMqip1797Jhzx6GdOokax17BAejDBuG0qOHpUGOuzsoCu2GDeOigQO5ZOJEPvjgg8a9T8GGdu3accUVV3D/++/z2c8/8+TMmQQHB9Pa17dWB2FRUREd+vblcEoKn3/zjeUcXSCsbPBjNpsJ8PFhzuzZfLJ0act0ENYHHh7MufNOzehVR5ztE+tj4PzGvokWge6Cqvw5zM+P5ORkVFVl2pw5THrgAdbs3Ennjh2rCpK2DQ8Hs5mOHTtSUlLC0nXryKnsQpSSklI14cRg0AYBEQjrROTIkSz/4QdukmYHTROjsWrCidFI26CgqhQkfdCc/d573DBtGgMHDsTFaMQ/IIAekZGUlZVx8OhRMBjYl5CAj48Pw4cPt92N0xcrEjtCc6Ny3Bk8YAC/fvAB5w4YUFMgBMxmMwcOHECtqCA1O5uBw4fzwQcf8NPq1ZgVhYLCQhISErR0SLMZxWDgvLFjWfPqqwT6+krs1IXAQLpPnSoumKaIXrNWVZk0aRK/v/YaiSkptabnr127lh/++YdObdowuH9/nnvhBZ777jtQFDbv3s0AvcaxLn64uTFr/HjWvflm82ruUx9ERhIweDAdr7+e1hdeaPvZoihMufFG2vj68tprrzXaLQonwGTSUhkVBRcXFwIDAigqKqKoqMiuC+rtd95h/b59jDrrLOLi4ykvK6tKMd4bE1PVqXhgly78vmwZbUJCCHJ1lTHndDEYmD93LuO6dGGguMeaHM888wxLNm4kJy+Pkf3788GCBdw4bpxFILQuWwGsWbOGI3FxTH/rLWbOm0dpaandXgVz587lm3vvZfIll/Drpk2oJSWkZ2a2rBqE9UWXLnAG9W+dKj9RVdXViqJENfZ9tAj0HWmjERSFsIAAUlJSWLp0KYnJydw4YQIzX3nFkjZhVWy0X9++XD1pEsezs/lyxQpmDRlCcnIy48aNs3wgeHhAeroMmnVBUVCiopBlWhOlsouqtYPw5717ATgSF4fR3Z3/DhzgBb0RkqqCyYRSubh78PXX+eq999iXlER3vdOuCIRCS6By3DG5uDBu1Chcior4/tNPtcesHIRr165l5MiRjBk4EDezmXYRESxYsIA9e/YQO3Ys6YcP07VrV1z0TnmKotVqKS6WuDkTfHwa+w4Ee1g1tfIPCGBgr174enuTnp6udRyu1qTkrbfe4ptvvuH2SZMI6tyZ+S+/THl5OUWlpazfsYOBAwdqJ+ox5+KCyWTCX/7/a6IoEBWl/WxHPFfCw3l7zhxe//NPx96XcGpYb+gCitFIaHAwaWlpBLm742Xlgjp27Bh33XsvZWVldGrblpDgYGKPHiWssBAfDw/2HDhQJRB2a9eOHz/7jOT4ePsNtoST0m7oUB6IjARpKtfk8PX15dNHHyUlJweD0chFEyeS9s8/rNcFQrBxEP7111+EBAXxz65duLm6sn79eka2b29TvzM2NpZJEyfSx8cH9ayzKC0vZ8+ePaRkZIiDsBFodp9YiqLMUBRls6Iom7ds2dLYt+O8GAyWNEkgrHVrUlJSeP3117nn1lt59vbbCfD1pXuXLtr5+gBoNHLZ5Mkseu45br/qKl799ltuvuMOdu7caXEQKormIKyokEGzCSGxU0/o4jqAwUDbwECOHj0KwLSHHuKxN98kIzeXyLZttXPMZm0Xu6KCl156iYS0NH5bsYK9GRl0DwiwnCMCYZNFYqeeqOa+7dG+PXv27NGaMlkJhDExMVx77bWUlZeTcOwYv330EXsrRfijycls2LOHs88+W3tOK5GDiooaHfOExkVipx7QC73r8yujkXbh4cTFxWmPV3MQ7tq1C0VR6BARwdm9e1NemS2SnJHB3xs2cN5552nXWbtAxAVVNwwGOl1yCW9WdsitLyRu6gkrByGgbeqGhbF161aCO3ZkX1JS1WNffvklEydM4KrRo+kcGUl0hw7cN38+t7zwAri5sWX79iqBELOZseedx/U33aT9LmUtTh8fH+jeXfvsqUckduqH0YMGcbXe2dlgINTPr6aDEEBV+fvvv5l/zz14urlx26WXsrQyfbi6g7Bjhw5gMKAoCnMuvZThN99M+7ZtadeuXeP9oS2UZjfaq6q6UFXVAaqqDujfv39j347zUm1XLTgggGPHjrF//36mXnABXj4+rHv3XebOmqWdbx3olRPV4QMGEBEUxPpNmzh69KgmEOpdJt3dtetkwtlkkNipJ6yalGAw0L1tW44dO8bXX3/Nhp07+XnZMjqEhmLU3/uVDkLMZtzd3RnVvz8xR46wNyfHIhCKg7BJI7FTT1SLneBWrTCZTFr9W31DSVU5ePAgXbp0YfELL/D3Sy8RFhDAXXfdxbCePTmalMQ/W7Yw6pxztOe0FjmkuUaTQ2KnHtA3dK0aJkSGhREfH297DlBaWsrhw4d5Y+ZMxp5zDmf36oWPjw/9oqPZmpBAXHIyA/TNK2vnk5ubjDl1xcNDS/eqRyRu6olqax2MRnp168ann35KWVkZ9yxYAIrCrn37eOqpp7jvjjv44uGHadWqFZ3at+fXv/5if1wci7du5WBsLOefX1kFS48dT8+qBkBC00Bip56wjh1FIcTf324NQtVsJiYmhqsnT2bfokVMHjqUP//8s0YH8NjYWDpERVXF4txrruGdO+7gp4ULMUppE4cjn1iCfao5CE0uLtx3992sXLkSd5MJjEa8PDzw9PDQzrdyEOoTVcVgYOV77/HPd98xZMgQQkJCLB8a+nUyaArNjWpNSkzAxIkTuf7663nuttvIyc0lWnfTgo2DEKBzZCQHYmPZFxNDt4gI7RwRCIWWgHXsVLqiunfvzsqVK3ly0SKSsrJ4/ssv2b9/P506dcLH05PWgYFQXs7LL7/MqF69OJKayuo9exipp6Tom1IgKbJC88QqxVj/vUPbthw8eBCAuNRUbnv8cfbHxjJ9+nTatWvHbRMm0L1zZwJ8fEiKiSE6PJzPvv6akYMGYcrP156n+rgjY47Q3DAaazgI+3Trxu+//84Nl1/O+t27SUpL462PP+buu+/m7H79qgS/6KgozGYzBxMSWPjVVzx9/fX46DFSfWNKYkdobphMNuJ6aOvWpKamoqoqucePV73/09PS8PLywtvTk8iICAZ16cKRI0dIS0+vWid98eWXuLq60jogoCpWFDc3rhwyhMAzqKMn1B35xBLsU31XzWDgmfnztXbZ5eXaB4NVykrVYKinuuiLMl9fAk0m1q5di6t+jQiEQnPGOnYqF27XX389o0eP5o5LL6VT+/Z01oU/0GLFxaVqgdc5IoKN27drTRbCwrRzrJ0cerqFxI7Q3LB2EFaKhT169OD6669n4a+/8tVvv/HAe++xZMkSoqOjLbFTmSIZFRLCL7/9RuvWrWmjd0W0FjlEIBSaI9U2dDEaOatrV3bs2EFifDznz5vHgi++4KslS/jss8/o2bOnxbleUYGPpycRwcH88ccfjBk61FIio3ppC0mTFJob+tzLKnb6dOtGeXk5A3v3ZuKoUXy3fDk79+9n6NChlrWN0Uh0+/a0j4zEzcWFf/75h8Fnnw0FBdrzWM/ZvL1lviY0P6rpBCGtW5Obm8tjjz3GeVOnVq33j8TFadpB5XzNRVEYPXo0f61ZA4pCaXk5N992G8uXL0cBSyx6eUkpskbEqf7VFUX5ClgPdFEUJVFRlJsa+56aLfqOtJVAWLVDXVFhEQh17DgIURRo1Qpycy3niAtKaO7oVngrgXD48OEsWbIEg9nMOQMH0isqyiKuq6pN3cLOkZHsPXCAUaNG4e7ubltbSn9+FxfpJio0P0wmmzRJKiq49dZb+fPPP5l54YX8tHQpgFYkvlMni0BYGTtRgYHs3LWLSyZMsIxX1gs1X9/G+KsEoWGpPo8yGOgTHc22bdsYf8EFTBs7loFnncUPK1YwZ84cHnzwQdvYqaggPCSEiooKxgwfbj92xEEoNEf0ckdWa51e0dEA9ImO5ooLL+TLP/5g94ED9OrVy8YMMXboUL595x06tW2Lm5sb7cLD7Yvr7dpB69YO/sMEoYGpJhC6urlx24wZPPnkk+yKiSG3sBCAo3FxREVF2WzoXnLJJbz/zTeoisKBo0dpGxBAz+7dbePGy8vyOoLDcarRXlXVK1VVDVNV1UVV1QhVVRc19j01W1xctO/2BMLaHIR6nQ29ELw9gVCfYBoMMuEUmie6O1YXOcASJ+XlfPjee1w9erTlfOvYMZsJ8ffHx9ubiy++uGZtKZ1RoywxKgjNBevFWqUbvXfv3px33nl0DQtj/ebNjBswgNGjR+Pj42PrIDSbaVeZVnz5JZfYX6iFhcGgQY3whwlCA6OL6wAGA13atSM+Pp6SkhLuu+oqukVHs+fwYSZNmkT//v1tnesVFYQHBxMWFkbXzp3tx440KRGaI3pGhr6+MRjw8fTk1VdfpXfHjoweNowjycm09vPDz8/Pso4xGPBwc2NAr15Et2tH//79USprSVc9nx4vwcHaWkgQmhN26nc+dPfdvPPOOwzp25d1e/ZoDsKjR20chFRUMHXqVNIzM1nx33/sjo2lZ2Sklupvvdbx9ta+y7jTKMi/umAfvZi7tUCoTxrLyy2Kvr0U48odaYxGTSwxm6GkxHayCdpjEvhCc8NaIIQasWNyc8NgNNo6CK3ct4rZzIN33snkyZNtU/atY0cXUgShOaHvGFs5CAGoqKBbZCSqqjK6b1/+/vtv7biqagu8yjEnqk0b5s2bR99+/ey7oAwGbbEmCM0NPXYAjEZcDAZ69uzJNVOmoLi60r3SFdXd2qVhNV8bOWAAb7zxBoo+5oDtYs3TUzalhOZLcbH2vfL9f+edd+KqKLh4eHDpmDH07tpVe9wqxZiKCigv56xu3RgxYoRNJojNuCMIzRF9rLASCFv5+DBz5kyG9+3L6q1baxUITUYjN0+Zwk/Ll7PbYKBnx4625clABMJGxtTYNyA0UXSRQ8d60mjtINSx2lXTd6QxVb693NygtLTmDnSbNpYPAEFoLlTvlKpPGvWCvidy35rNUF7Og3Pngp+fTVdwqf8kNHusN6asx5yyMjpFRmIymeiqd1gFy4QzL0+bdLq68sQTT0BhoSzUhJaFl5clW6Mydl577TW6BwdDZibdoqPx9/UlODi4pshRUUFIcDCXnX8+JCfbF9fDwiAkpHH+NkFoaEpKtO/WIl9ZGbi48MD06aTrtQV1QUTfwKqoYO6NN0K3brBrl333rSA0R/RSYXbMEBcPG8ZlDzzAbeedx8o1a5h67bU11jqjBgzgo59+olP37lzdp48ldqxd6+JcbzTkX12wj+4QLCuz/G7l5jhhkxJrB6H1tdUHzI4dRSAUmh/VHYS60KGqlo7F1ujin3Xs6OdYpxjLICk0d6wFwmrOWxd3d8aPG0e/Tp0s51ero2YTN9apXrJQE5o71RyEVFQwbNgwAnx8wMWFoWefzfwZM1AUxa5AWGO+Braxo49RgtDcsM7IqJ4t5eJCVHg4A/v00Y5Zx051M0R1963M2YTmjN70rfpaBxgQHU1ocDA9briB66+8ktGjR1tiorI51lldupCYmsqaNWsY2L27/bVOu3Y1TReCQxAHoXBiioq079aTRt0FBbWmSdqdcMqAKbQE9DQsXVzXJ5zWqfkncRDWmHCKg1BoCZzAQYiLC4u/+w7WrbOcbzZrO8zl5TXHHFmoCS0J60WUHReUv78/t0+dqh070YautbgusSO0BNzdLSnGBoM2noAWO9XNEPbWOnodw+o12AWhOVPd4GMtrpeV8dozz5C1YwcXzJihxYPZrMVNZe1Co6Jw5UUXMWjsWK3Bjx431rGjp/YLDkcEQuHE1CYQGo22QVx9V81aDKmtjpogNGesY6e68Aen5r6trUmJIDRHdCeHPp5Uc3LYLNRA+1l3EFYfcyTVS2hJWHforrZQq7Wsxek4CAWhueLpCTk52s/VyynpG772NnTLyuyPOyKsCy0B/T2up99bx05pKYOHDNFiyzp2XFxsxp0Fzz4LkZGwfr12rLpAKDQaIhAKtaMoWu1AqFEwvtZdNau6HDJoCi0aXezQ3/+KYhEIa6vfWV3o0N0cslATWgKKotU6c3e3pBmbzbYihzXWXYzFBSW0ZPz8YMwY7Wc7DkIbTjRfqx47Mu4IzZ1evaBDB+1ne7FTWzml4uKapS1KS2W+JrQs8vO179Y6gd53wF7sVKYY2wjw+rVGo8zXmggiEAq1c9ZZ9msQ2ksxtrcjbZ0mKbZ7oSUxYYL9LsbWAqG9CWdZmUUYAUkxFloeAwZYfrYWzas7OaB2gdD6Whl3hJaCXv+2enq+t/fJHYR6wXnr8cq6o6QgNFf0ZghgK5Db25g6mRlCn69J3AgtgQ4dbM0Q1crC1JppWF5uv5ySzNeaDCIQCrUTEWH5ubYUY2sHoXXdKHEQCi0Z6wHOetJoL8XYesJZWmp7jvUktHpzE0Fo7uixYy9NEmxTjKvHiCzWhJZK9RTj2tIka0sxFgeh0FKxLutiPabYi53q5ZSs52sy5ggtgR49LD/r40lZma0TsLb6ndYCoZRTanLIilM4Nap3Yq2tBqFe4NdeHTXZjRZaIrU5CHWsJ5ylpbYuqNrqFwpCS6C6g7C6QKh3BVdV24Wa9bWyIy20NGpLk9Sxdq1X39CtXk5GOhcLLYnqwl/1mminIq7bS+sXhOaOPnZYv/9P5FzXN35BNnSbIPK/IJwa1QdDfcCsrdGCvcK91h8GgtBSsCfy2Usxrs1BqF8rE06hpWHPQahj3fHOaISSEvviurhvhZaGvUYLp9OkxF6TBkFoCegih15DDexnS9nL7rDnjBKEloI+dtQWOyeqQSilyJocIhAKp4a9Bgr2mpScqCueTDaFloj+/rceNMF+7JxIIJQJp9DSsLcjXT1uQIuN6gKhdYMTGXeEloS1g7C0tPYmJfY2dK0bY+nZIoLQUrAncsCppxjLfE1oqdQmruucrAahZBo2KeR/QTg1dMXferfMnpvDOq1LHISCYImJkhJLIfja0r3suaBkR1poqejv/5ISS2fj6gs1/bzq6fnW4qLEjtCSsBYI9XHndByEsqErtFROVeSorQO4xI7QUtFFc+u1Dtgfd2orRVZWZivMC42GCITCqWEtctRmuz+VOmoyaAotDXuDpr3YOVmKsYgcQktDj53iYvsih75w0x2E1ZuUVHcfCkJLwNoFaK8DuPWGbnUXlB5T1cciQWgJWJe1qC3jo3r9zup11GS+JrRErDd0T7TWMZksOoE+3kj9ziaHCITCqaEHfnExeHhYjturQVjdsSEOQqElYy2uu7tbjtvbVRMXlCBYqO6+tXZyWKcYn6gGoWxMCS0RoxGKijSRo7ZGC/p5etdJHYNBiyeJG6GlcbIahKfivpX5mtASqS1byl7sFBfXNEPocSfjTpNABELh1NAHvqIii8hhb7EmaZKCYEttu2o6+o60yVRz0JQdaaElY12rRl+s2Usxrq0GYfU0FkFoKRgMUFh44oUa2I8dfR4nCzWhpVFbowWd6hu61uK7pBgLLRnrbCl7OkH1ckq1GYkkxbhJIAKhcGrYcxDaS/fy9NRERHsCoViHhZbIyVKM9Qmnr2/NjquSYiy0ZHSRw9oFZS/F2NPTvgtKT5OUrnhCS0N3EFrXgtKxdt/6+NQU0XUHoYw5QkvjVByEiqIJINXjRjZ0hZaMHjt6SRg4sYPQWg+QFOMmhwiEwqmhB6+1gxBq1hbw8LB8AOhY76rJoCm0NHQn04m64ikK+Ptrv0vsCIJGdZGjthTjgADL+dbXVp+ECkJLwWg8NQehvXFHHIRCS8XaQWjv/a+PO4qiiev20iQlxVhoiZxqkxJ3d21skhTjJo0IhMKpYe0gtLYOVxc5FAW8vMRBKAg61WtBgX2hw2SqOeHUY8dslgmn0PKoLnLoqKqtyFGbQCguKKGlYi927Llv/fws5+tIDUKhpXKqNQhBy/oQB6EgaJxqDUJ9U0qayjVpRCAUTg098IuKbFOMdawHTS8v+4EvdTmEloi9hZo+aFov1ABCQmybANnrbCwILQU9xdjatW4dO/qY4+GhnVN9R1pEDqGlYs9BqFPdQWg0Wn7XrxVxXWiJ6N29i4tPnPEB2oZubRkfMu4ILQ294VVFheX9fzpmCHvdw4VGQwRC4dTQi8WXltrfkbZO9/L2tr+rJrZ7oSViMNSsBeXiosWSddwAdOsGbdpYfrfX2VgQWgq6+9aeQKg399EZMkRzdOiIQCi0ZE7UpMR63HFzg9Gja14r6flCS6W6QFibgzAgQKt/a32dqsqmrtAy0dc61bOlaosd6/HFOj1fxp0mgXyCCaeGLlR4eVkCvLaC8XrKivW1sqsmtFR0gdxavHB31wbS6g7C6ojIIbRk9CY9rVpZjrm6amOR9WQTtLHJGr0GYfXjgtAS0F2A1gKGvYUa2Arw+rWFhTLuCC0TXeizV07JWlz384MBA2yv1ddKIhAKLQ3dSKSnEOvY0wk6ddJ+t75W6q03KeR/QTg1dAeTdeCbTFowg+2gGRamfelIowWhJaPHjrXI4e6uiRfVHYTV0QVCa3FREFoKeuxYbzp5elrE9VOJHRE5hJaInjasjzvW87VT2ZgSkUNoqRiNWiaUjp7xATLuCEJt2FvrmEyaKxBqloWpfm1xsXb+icYmwWFIirFwauhdu/Ri8KA5MwoKtJ9PNOHUd7L15xCEloR1QWsdDw9tMDzZZFPfVZOFmtASMRq1FEhrh5OHx6m5byV2hJaM0agt1PRFm6enZcw52bijKNo5UgtKaIkYDLabUl5emqMWTj7u6OfIuCO0NPQxxVogrB47tY07+qaUjDlNBhEIhVPHaLR1EOoC4cncgfoEtfqOgSC0BHQnh/WOtO4gzM21TQGzdy2Ig1BomZhMNdNVPDy0CefJmii4uGjiYmhow96jIDRFrLtFgiZq6LFzsvqCpaXaGGW9ISwILQWjsaZAWFCgZXxUryddnbIyCAoSgVBoedhzEHp5QX6+Nu6YTLULhPq1MuY0GeQTTDh1+vXTOg/p6IPmkSMQGFi78q9/IJx1VoPfoiA0OTw9teYj1rvOukAYFweRkbVf6+sLI0eKQCi0TMLCIDjY9piHBxw/DklJ0KNH7ddGREB4uDT4EVomkZE1RUAvLy120tK0Mak2Bg+WjA+h5dK1q7am0TGZtK+kJEsH1toYM6ZmTU9BaAkYDNq4Y2168PbWdILERK0BY21jiouLdm3Pno65V+GkOJ2DUFGU8xVFiVEU5ZCiKA809v20KEJCbIPbxUX7QDh0CLp0OfG1F11U0wkiCC0BoxE6dLA95u6u7ahlZ9t2La6Ooog4KLRcDIaaIoeHByQna48FBZ34WhEHhZaKv7+tax203w8d0txRJxIxjEYRB4WWS0hIzbHDywtiYk68oQva+CSxI7REFAX69LF9/+vllOLjoW3b2q81GLRrZc7WZHAqgVBRFCPwNjAe6A5cqShK98a9qxaOl5dmJz7RjpogCLaYTNpA2L69DIiCcDp4eGhpXJ06NfadCIJz4eWllbXo3Lmx70QQnAtPT62G2skEQkEQLBgM2pzN29s2bV9o8jhbivFA4JCqqrEAiqJ8DUwE9jbqXbVkQkLEGSgIdaFLlxPvqAmCUBMvLy1uwsIa+04EwbkICoLu3aXOkyCcLlKyQhDqRtu2NUvFCE0ep3IQAuFAgtXviZXHqlAUZYaiKJsVRdm8ZcsWh95ciyQ62rZWh+C0SOw4mA4dTlwoXnAaJHYciNGo1bOVNK5mgcSOA/Hygo4dG/suhHpA4sbBBAWJyNFMkNhxMNHRto1LBKfA2QTCk6Kq6kJVVQeoqjqgf//+jX07guA0SOwIQt2Q2BGEuiGxIwinj8SNINQNiR1BODnOJhAmAdY5eRGVxwRBEARBEARBEARBEARBqAPOJhBuAqIVRWmvKIorcAWwuJHvSRAEQRAEQRAEQRAEQRCcFqdqUqKqarmiKLOBPwEj8KGqqnsa+bYEQRAEQRAEQRAEQRAEwWlRVFVt7HtoMBRF+QCtkYkgtFQiVFWdfroXSewILZw6xQ1I7AgtHokdQagbMl8ThLohsSMIdcNu7DRrgVAQBEEQBEEQBEEQBEEQhBPjbDUIBUEQBEEQBEEQBEEQBEGoR0QgFARBEARBEARBEARBEIQWjAiEgiAIgiAIgiAIgiAIgtCCEYFQEARBEARBEARBEARBEFowIhAKgiAIgiAIgiAIgiAIQgtGBEJBEARBEARBEARBEARBaMGIQCgIgiAIgiAIgiAIgiAILRgRCAVBEARBEARBEARBEAShBSMCoSAIgiAIgiAIgiAIgiC0YEQgFARBEARBEARBEARBEIQWjAiEgiAIgiAIgiAIgiAIgtCCEYFQEARBEARBEARBEARBEFowIhAKgiAIgiAIgiAIgiAIQgtGBEJBEARBEARBEARBEARBaMGIQCgIgiAIgiAIgiAIgiAILRgRCAVBEARBEARBEARBEAShBWNq7BtoSKZPn65GREQ09m0IQqMxf/78RaqqTj/d6yR2hJZMXeMGJHaElo3EjiDUDZmvCULdkNgRhLpRW+w4nUCoKMpRIA+oAMpVVR1Q27kRERE8/vjjDrozQWh6zJ8/P7Eu10nsCC2ZusYNSOwILRuJHUGoGzJfE4S6IbEjCHWjtthx1hTjc1VVPetE4qDg3BQXFzNw4ED69OlDjx49eOyxxwA4cuQIgwYNolOnTkydOpXS0tJGvlNBEARBEARBEARBEATnxlkFQqGZ4+bmxooVK9ixYwfbt29n6dKlbNiwgfvvv5+77rqLQ4cO4e/vz6JFixr7VgVBEARBEARBEARBEJwaZxQIVeAvRVG2KIoyo/qDiqLMUBRls6Iom7ds2dIItyfUB4qi4O3tDUBZWRllZWUoisKKFSu47LLLALj++uv5+eefG/EumxcSO4JQNyR2BKFuSOwIwukjcSMIdUNiRxBOjtPVIASGqaqapChKMLBMUZT9qqqu1h9UVXUhsBDg8ccfVxvrJoUzp6Kigv79+3Po0CFuu+02OnbsiJ+fHyaT9raNiIggKSnJ7rULFy5k4cKFALi7uzNmzBiH3XdjkZiYyAcffFDn6yV2BGh5sXOmcQMSO4KGxM7pI7EjnEnc5OeUoygKXq2MDXV7DYLM11ogBw9Cu3bg6lpvT9nSxhyQ2BHqhzOJndISM+UlKp6+zXfccTqBUFXVpMrv6Yqi/AQMBFaf+CrBGTEajWzfvp2cnBwmT57M/v37T/naGTNmMGOGZjB9/PHHW0QR2pbwNwoNT0uLneb+9wmOQ2JHEE6fusZN8uESnpoaS2Q3dx54tX0D3mH9I7HTAklKguDgehUI6xo7cfuKOLy9iNFXBtTbvTgKiR2hPjiT+dpD4w+Sn1HOC5u7NdDdNQyn8zc6VYqxoiheiqL46D8D44DdjXtXQkPj5+fHueeey/r168nJyaG8vBzQlPDw8PBGvjtBEARBEATBkRzcUgBA0sGSRr4TQTgFVBXM5sa+CwA+ejiZ719OI/Fgcb0/d+6xcv77PRdVFXOe0DzJydB0iOb8HncqgRAIAf5VFGUHsBH4XVXVpY18T0IDkJGRQU5ODgBFRUUsW7aMbt26ce655/L9998D8MknnzBx4sRGvEtBEARBEATB0WSmlAHg4+9caV5CC8Vs1kTCJoDRpACwddnxen/un99M55PHktn9b369P7cgOJriQjOlJRZh32y2xHBhXsMI/mazSty+ogZ57lPFqQRCVVVjVVXtU/nVQ1XVpxv7noSGISUlhXPPPZfevXtz9tlnM3bsWCZMmMDzzz/PK6+8QqdOncjMzOSmm25q7FsVBEEQhDrxxqx45l96uLFvQxCcjsxkTSAsOF7RIM9fWmxusOcWWiBNyEGYn6M5oLJSy+v9ufOytOf84qlUjuyqKXIU5Vew9uecZu2+EpoPd4+I4ZkrjgBQXqby7485VY/lZ9d//AD88lYGz197lD8+OEbKkRJ+fiudtT/nUFGukp1eRnlZw8eO09UgFFoGvXv3Ztu2bTWOd+jQgY0bNzbCHQmCcCIqylUUAxgMSmPfiiA4Dfs3FjT2LQiCU5JV6SAsK1EpLTHj6lZ/ngdVVXlkwiEMRoXn/oyut+cVWjCq2iQchEUFFeRlacJ3TloZBccrKC024x/sUi/PnxJbQlgHN9LjSvj8yRTmfdvB5vHvXkpjw2+5hHVwpUNvz3p5TUFoSNITSgH4dUEGyz7NrDo+/9JYXlrZGU+f+nWx//d7LgC/vZvBb+9mVB0vKzHz7YtpDBzvy7QnG7bEmlM5CAWhoSkuNPPqjDiO7mlca68gOBu3D97Px/OSG/s2BMEpWbLoWIM8b1ZqGSVFNV0rqqryzzdZrFucw6wB+8hroJ1wQWgodIEQYP9/BXbf53Xl0LYi8nMqOJ5Zzvv3JdqklQlCnWjEFOOYzQVsX5nH8axy4vZodQfdvQzkpJfz2MRDPHzBoXp5ndISM9lp5Zx9vi//uzGQtKMlFOVX2LgFs9O0uC083jTclIJQG2WllvdoWlwJMZtqbuhu+DW33l/3eKb9+di3L6YBsHHJcVZ8ldWgTkIRCAWhkm9eSOXt2+M5uLWwKggFQTg5ehrW5j+PU16mUpQvaVmCcDKKCy2Tz18XZPDu3ATmXXSI1d9n18vzFxVUMP+Sw9w1PIa358STnVZWNfFMOljCty+m8fkTKQCkHimtl9cUBEdQUa6Sl11BWAc3AN69O5H37kmst+c/uLWw6udtK/JY8WUWoI11ZrNK4oH6b+4gNHMcmGL842tpPHNVLACHdxTy+q3xLLw3kffvTWT/fwUYjTDgf75kp5VV1VGL21t0xhtFJZVjmoe3gbZd3TGbYe6oA/z0RjqFeRU8PvkwMZu02MrJKDvRUwlCo5N7zBIP8y+NJX5fzc/9bX8f57f3Muptk9ValNSJ7ObOI9/YOnG/fzmNV26Oo6igYdZbIhAKApqbYtW32RzeoTkHlcosydJiM/MvPUzMZkkDE4TqZKeVcSy5lHtHH6g69vaceOaOOoCqqpQW189kuOB4BZ89kczxLMsAXFpstvldEJwNawcUwM5V+WSmlPH1c6n1Uvssbk8xZaUq7p4G9qwr4OELD/HA/w6yc3Ue21fm2Zwr9aAEZ0L/7A+Ncq06tv+/Al6dEVcvC7XYHYU2v//4WjqHdxRy7+gD3DUshmeuOmIjIgrCSXFQivG2FcdZ/nkWiQdKMJtVG5EjLa6UPWvz6dDHk9AoN8pKLffz/HVHeWt2whm9tu7idXE3ENnVver48s+yiNlUUJWqCZCdJvM3oWmTm2H/PTr2utacda4Pbbu4c3hHEX+8f4xfF2TYPfd0OZ5Zc+7n7WcktL1rjeNHdxfx7w85HEsurdV1WFdEIBQEalrdUw6XUFps5rd3M0iLK7WpASAIgsbDFx7i0YttGyzou8N/f5HFncNiiN9/5k6LJR8cY/3iXB4Yd5DEg9rzvXTjUR4Yd/CMn1sQGou0uJJaH9u+Iq/Wx06V2J1FKAo89Xsn+o/zrTr+7t2J/PG+bUpzUb6kewnOg15DLTzazeb4wa2F7Fl75t1TM5PL6DHUGzcPA1PuCQGochHqokpKbO3xKwg1cFCK8fv3JVX9nJlcRk66RTjIz6kg6VAJfc71sSs4pB49s/d0WbH297l5GPALtm1zYH1fANmp4iAUmg5ms0phnq04Zx07Otc+FsbkOcHMeDGCC28JrDqudwY/U+IruxePviqAkZf7A1q35NrquxfmVfDoxYd54H8H7boP64oIhIIAZCRqu1q3vhLBXQvbUVxo5s5hMSz/XJsQRnR2P9HlgtDiqF6Tyd3TUOW8Bc1xAZB65MwXUYesnBofP5LM45MPk3hAe96PH0222TnbtDRXGj8ITZ6KCpXNS4/X+vimpWde1+ZYYimtgkx4+hiZODvohOcW5VWw4qssXp0RR2mJiIVCLWRmQoFjP18rKlR+eDWNY8kW95H+md91kBcjL/en73k+PPlrJwwGrX7gmVKUb8Yv2MSra7pw7hUBRPfzZNvftqJ9Wpyk5QungQNSjKtnbaTFldoV4roN9iKyW811jZ6yX1d0B6GbhwFFqSlo+IdYRMMty46TerSE719JY9uK2sdCQXAEv72bwT3nHuDduxOqavvZSykOCLU082nfy6PqZ2+/M2tUUphXoZUBqBTSB17QihGXaQKhvY2FrgO98AsysXWZJXa2LtfGqILcCg5uLWTn6rpvNEsXY6FFs+KrLHauyqOwMp0rMNyVsA6unHtlACu/yqo6z8NbtHRB0Nm1Jq+GA+nuD9qx8N5EjiXZTkY9fc+8u1eW1QQ3+bCt4Ljxj1z2/JvP3R+0I7S9Kx89ojVKeWdztzN+XaGZUlKiffn6nvzceqa8TBM7YjYVkHqklPBoNzr19WTVt1rdwdveaMuBzYUs/ywTs1k9o67gRfnmqu56gW1ceejL9jxz1RG7565fnFuVMvnK9DjmLmqHi6uMe0I1EhOhVSvw8nLYSx7aWsjfX2Sx5a/j3PtJFOt+zuH3hdr44xtoYup9oVXndh7gRdLBM3etFxdU4OFlef8P+J9vjZTixBjtdV64/gjB7dyY9kSbM35doRnTwCnGqqrWEK33rM1n1bfZePka6Tncu6o7anCkK0ajZWwZMtGPdb/k0DrszDoZ6wKhq4f23He8G0nsDs3JvvidDLoN9qZtV3dat3Hh3bsSeOIyrU7iii9h6OQCrn447IxeXxDqStJBbW2xc3U+944+QPveHuz/r+ZmmL+VQOjjb5HRykrOLLb/+SbbpvSLb2sjvq1NjL8pkLPH15yrhkS5UpRfQdzeYtw9DRiMWh349r08eG9uYpXD/e1NXe2K9SdDZn9Ci8O6dtn3L6dxYHNhlRspMMIFRVGYMjeEBz5vz/2fRgFnHviC0JxYcFcicXttF2G+gSZuejactl3cieph2ZmuOMMuW2WlZvJzKphwaxA9htRclLbv5YHBqIka+mQTIHan1IcSaiEtDQ4fPvl5DUD8viJWfZtNTlo5PYZ4cdndIVx+b0jV4yFRrnj7G1FVKC0+s9gpLqiw2dxqHW6Z2N78QrjNuQe3FhLeyY3AcBfi9xXz6zsZVJTLuCdUw2x2WKOFf3/KJj2+lMzKWp05GeU8fMGhKnEQwDfA1ufg6WuoapRQVyoqVEqLVdy9LZtb/cZaFmiTbg9m6CQ/juwqorjQzNE9xWz8o/47WQrNCD1mGlAg/OP9Y/z0errNMX3jqccwb5syE7o4+OAX7Xn8x45cMy+M8E5uZ/yZX2rlIAToMsCL8TcFMmhCK7oO9OLCWwIZOcWfnkO96Xuereix9qccDm2TeZtghYPGGtBqqvcY6k1EZzdcPZQqcbBDbw+b86xdsABXPqRtUBWf4biTX612ro+/CYNB4aKZQYRGac7eqx+xCOhtu7ijVG4gD5nkx8ALWrFnbT6PTz5sU/7i5zcz6lRjWhyEQovjlelxxO8v5pXVXWo85upmWUzpBXa9WhltCvkKQksjPb6UlCMl9BnpA4DRCBXV6uh6+xnxDTDx4BftidlUwOsz4wHOOF1RL7DtF2QiqK0rYNnRe2ZJJ3xbm0iIKWbx2xns22B57PWZ8dzycgTdBnnVafdMaMY4qBZUdf77I5cdlTvED3zenuBISw2op//oRPKhEgLbuOLuqY1DJYXmqp/rQlG+GR9/i8jh4WVk9FUBBEW40He0L536ZtssyIZd6s/gCa24a7hWXsM/1IVzrwio8+sLzRAHNVooLjTz5dOpJzznmkfDcHW3jQ83T8MZL9SKC7Tr3a0chF6+RqL7eZKwv5hx17dm/8YC1v6cwxqrjuP7/isg5XAJo6+SmBGqocdMAwsetZVXuX5+GObKOZt1GnHbLpbNXFcPA6VnaIYoKdKurx6X/sEuzHkn0ubYNY+GMfb61vzwSlqVO/e39zK48912FORW8OINR7loZhD9xzre6S80ETZsgJ49HZLtkZ1aTofentz6cgSKAY4llhG3twiji0LsziTCOrhx+b0hNTIrhl/iz18fZ1aNG3Ul9YjF/Tvr9bZ2axoOneTH6u+ySYgpJrS9K65u2jmjpvqTl1XBP99YxqO+o33YtiKPZZ9mkna0hBueDq/xfCdCHIRCiyI7rayqacI9o2IAmGBVZNQeLm4KZfVQk2nvhny+ezmNjMRSfl+YQVF+w7QmF4T65pWb43hvbiJZqWXEbCqoIQ4CNqmQnQd4YqgcXc7UfXu8UiBsFWRi0IRWNrvgfkEuGAwK7bp5cPtbkVXF5Od924GyEpW3Zicw/9JYtq04Tk5GGVuXS50bAYfUgqqO2azyyaPJVSkkrdvYpnL5B7vQY4g3oC3UwJKuVVeKC8w2LiiAy+4OYeTlmoBx4zNtuO2NtlWP9RjqVeX8AMhIkBprQjUc5CDMTK79vefpY6Bdd3eGXOxX4zF3T0OVi6mu6HMza4EQYM6CSJ5fHg1oY1znAZ789IbFsfXmbfF8byV2CEIVukDoAHFdFxbGTWtddUxRFIwmhcd/6sid70Xavc7FVaH8DM0Qeg1EF/eTb8q6eWidjm99JYJJtwcz6fZgDmwu5OieIvZvLCA9vpRFDyaReODMSwYITkpZGZQ3fLfr1KMlFByvwC/EhNGkYDAoBEe6cvb5rapctT2HedPlbPulNdw9DRQXnP6a/nhWOXcM2c/BrYUkHdLe576tTfQc6l3rNZfNDSGyqzttu7pz3XxtDhcY7kpUT3eufSyMAf/T1khnjfapumbn6ny2/X166x9xEAothvT4Up692lJ/SZ/jDrvUn9/eO0a3wfYD39XNUC8pxn8sPEbszqKq2oa/LzzGPR+2o0NvT357L4Ps1DIm3BpEqyBLWOYeK8fdy4CH15nXcROEulBcaK4qCP/IhEM2j/kEGBl+qX+NTpKKovDM0mgeGHfwjGNH7yLWKshERLQ7Nz0TzuAJrTAYa05Az71C6/plMChcOCOQ3xceIz2+1KZ7Xpezs5n+XARerSSmWiyN4CDUy1jonKjjnWvl4qo+hI4T1c/1C3LBL8giVOr1p+Z924Fnrz7C/v8KWPFlFude6S8uXEHDQeJ6ZrKl7uxNz4az6EHtM7z/OF+un98Gk4v996Obp5ZirKpqnd+zxZUdvavHjtGoVKVmGgwKAy9oxYHNmhhoclFw8zRQkFvB67fG8czSaHwDTMTuLGTT0uP4h7gw7vrWCC0UPWYaOHYCw1343w2BdB/iRatAE399nMlAq/plwW1rdi7WcXFTyM85M+NCWbFtivGp4OFtZNz1rSkuNLPs00xeuP5o1WOePgaeueoIc96JpOtAx9U9FZoIDhpvtvx1nLPb2I+Pfuf5kptRXtVR2B7uXnUrbbFnbT5lpSpLFh0jL6uCyXcEM+aaEzvQo/t58sDn7QEICDVUNU1RFIVzLvJj4PhWDJnoR6e+nvzyVkZVDffYnafXvEsEQqFFkJ1exqsz4mo4MuYuaodvgImHv25P6zb2B05TPeyqqapa1SkZtA+h9IRSPnggiYKciqoU5vW/5tJjiBdZqeUUF1RQkFuBi6uBez+OsklHEwRHYDarvHTD0arf23V3x9vfxJ61+fgFmXjg8/b4trY/jLhUWt/LS89scNfFSevX0Z1W9tCdjBfcHMjY61qz4sssFr+TUfV4zKZC3pwdz+1vR+JVDw1UBCfEQS6oPevyWfZpJkd2FlV9xvcY6m3T+c4ebp715CDMN+PhffL3+KALWwFUCSphHdw4e7wv6xfn8v0raWxamku7Hh5ccX/oiZ5GaAk0sLiuqiqHdxSyd70lVbLzAE/Co91IOljCtCfb2DRXqI6rh4GKCq0ZkItrHQXCAl0gPHHsRHS2pGfO/bAdkV3d2bT0OB/PS+bjR5LxDzGx/ldLbcJ+Y30IrGWeKTRzHOAgPPfKAJ54uZPNsdf+7YKxFjG9OiZXA+WlZ+bW0lOMT0cg1HH3NDDlnhB+eSudwHBXOvX1xGiC3947xhuz4rnhqTacfX6rM7o/wclw4Gbu8Ev9bFx3OkaTwphrTry54+5lqJO4nlVZX1ffDG7T0e2MN2ONJqVKTH/qt078+1M2P76arjXVan/qzyMCodAi+POjTHKPleMTYOSci/3o0NuDZZ9k0q67tlAL7+Re67X1kWKck1FOXlYFAy9ohblC5drHwnhkwqEqd1TvEd6gwM5V+exZZ1tDpLS4gk8eS+bej6LO6B4E4XTZszafkNwSLrs7hFFXaM687LQyHr7wEBNvD65VHASq6nScqYMwL7sCRdFqHJ4OiqLg6q7Q9zwfFr+TQae+nlwzL4z9Gwv4+rlUHrnwEPN/7nhG9yY4KQ7Ylc5KLePtOQk1jt/4TJuTOsLd6iHFuLxMpaxUPaGDUOf6+TW7r55zsR/rF2viRtzeYuL2FhMU4UL3c7xx9zbgH3xm3S4FJ6WBYychpoSXb4qr+v3NDV0xmhTmvBNJRkLpCcVBwKZ+56l04c5OKyMrtYyOfTyrjukpxieLnbAOFrHPP0RrcNf3PB8+m6/YrQX36MWHufKhUIZfUrsTRWimOEAg1DvWW1O9FuCJMLkqZ1xvvaTIjKJYNohPl4HjWzFwvEUErKhQierpwZJFx/j0seQapTmEZo6DHITdz/Fmyj2hNqWSTgd3L6ON6/1UST2qGYcSYrT0Yuv6oPXFsMn+9D3PFw9vA088cerXiUAoNHvW/ZJDplsOXQd62RTJ7T2i5k6BPepj0Eyr/BA456JWVTUMWrdxJS+riCd+6UhguDbRfP++RLatyCO0vSthHdzoMcSb7LQyfl94jO9fSWPi7CAqyrXOsJIiKTQ08fuKaRdtsEkx9A9x4Y31XWtN8dIxmsBgqAeBMKscbz9jnQfukHZuvL2pa9X9B0a4cDyznD/eP8Zrt8ZBjzO6PcEZccCu9K7VeXTzhAtnBLLlr+OkHi3loS/bn1K5iCqB8BRTVlKOlPDzG+lMfz68ShTR6+FUr6N2qnQ6y5Pr57ehuKCCI7u1Lq0/vJrOD6+mY3JReH1dzSZfQguggd23+/8r4KxKs8Ztb1gKtfv4m/DxP/mSRa/fWVxoxtvv5K/36MWHqKiAdzZ3qzpmr0mJPVxcDTz+Y0eOJZVWdVN2cTUQ2t6VpIOWkgKvr+vCHUO0mtdfPZPKup9zoMPJ701oRjgoxfhMcK0HM0RpkRlXd0O9laQwGhW6n+NNVE8Pnr36CO/dk4jHkHp5asEZcFBTrPa9PE66pjkRbp6GOjUpyUnTRMWyEpXWbVxqdEiuL+qSLSUCodDsyU4ro6KNetK0rtpwcTu1otcFxyu4d/QBrpkXxpCJfjaP6enFQVb1DaY/F07y4ZIqcRDAv7KWwKV3hVSlUR7dU8TvC4+x4sssVnyp1S90cVW46pEw3DwM9BnlLfWhWiqFheDpefLz6kinvp7c+FB4jffXqQykiqJUiutnNuHMz67A+xQWhie7Fx2DQWHCLUEUF5hZ8WUWwSIQtjwcsCudn1PBre9G0HuED6OvDqC4wHzKrjtd5CgtPrWJ8TfPp3JgcyGxO4qqNqBONU3yROipxyOmqHQd6MnvC4+RmVxGeZnKoxMPY+xX56cWnJUGXrCVlZjpP86Xqx4KrdN7Vxf1CnIqCKxpjK2BvYZbhccrHYR2HFnVCY50rVH+5dZX2vLdi6nsXJ1PjyFeuLgamP1mW1Z+lcWedQWkxZXiKwJhy8KBTUrqSn2UUyopMuPqUf/rEU8fI7Neb8uOf/LYEHfy84VmQiM0lKsL/iEmjmeWU1RQUesmcFZqGT+9ns61j4VVOXut05J7DGlaa3mn62KsKIpRUZRtiqL81tj3IjgPo68KYNilfnW61tVNqerMdSKyKwuBLv8ss8ZjxxLLMLko+AVbhI6AUJcanYoumhnEdY+H0f0cSzHeqB4e3LWwnU2h4bJSrSPmwnsTee+eROmI3BLJz4eNGxv0Jbqc7XXCblonw+U0Gvxkp5cxa8A+dq/Ntzmel6WVBqhvJs4O4u7329X78wpOgANqEIa0c61yqXt4GU8rJVd3EJ5qkxLdXVtRYYm1glxd5DjzaZ6iKAye4MfdH7Rj/PRALp4VhLmi6S50hQakgWNnxBR/rn0srM7Cth47z1939ITnHdlVRHaaJSWsvMzyfs5OL8doUk67rIVO6zAXbn2lLXcsiGT68xGAlsJ22xuRPPRle+Z9J+pgi8MJBMLTma/VRm5G+QlLz5wJYe3dOP+GwAZ5bqGJ0ggN5epCx7M8MZth97+265c/PjjG6u+zAfjhlTS2LDvOncNiqkxD1gJh9WaPjY3TCYTAHcC+xr4JwXnoN9aXy+4OqXPNpBPtqpnNKqWVlnzdsZF6tJT//si1Oe9YYimt27icNE3SzcPA4Al+NXYRovt5cu1jbXjoq/Y891c0z/0VXbVrvXNVPj+8ml6nv01wYioq7NsfmhBa/c5TG9zj9mg1OP79IdvmeF52BT4B9T/hdHE10Klvw7kvhSaMA9JWep1iCQt76E1Kik8xxdhYGR5mq4+D7DStvq1/SP3VbPIPduGiW4M4/8ZAHv1e6ne2SBpYIPTxN+HqVveliR47gI17vaJCZcVXWezdoC3gXrzhKA9feKjqcb3e52/vZfDXx5m0CjTVuayFTpezvWo0a4jo7C71O1siTpBibK+ckqqqrFucc0omCYC0uNITdkoWhNPCSRyEHXp74Oqu8OmjyRRVlndRVZXf3s3g6+dSa5z/27sZmM0qRXmWSVtgRNOKG6cSCBVFiQAuBD5o7HsRnIew9memyptcDZQWq5jN2sB5LKmUZZ9moqoqnz+Rwp1DY1BV1WYn4JNHk22eIy2u9Iy7EBtNChHR7vgGmPANMPH4jx259ZUIwju5kZFQevInEJoXTjBwuroZTrmmjbUmHruzkFkD9pF4sLiqBqEg1BsOcBDWpYujjqu7Fgyn6iBUDDXPz0nX3FHWrvX65Ez+PsGJcVBNqLpi3RwhL8syJ1v7Uw7fv5zGD6/Y30zVN3j/eP8YQNV8TxDqBWdwELoqVJSrNu/9A1sK+fyJFH54Ne2E16bElrB/YwHp8aUEneFaRxCqaOLjjY6ru4GLZwVTUaFlDAJkpdbeEVwxKBTlmW2moUERTWvjyNlmeK8B9wG1zpoVRZmhKMpmRVE2b9myxWE3JjRfXN0VslLLeOH6o3w8L4lHJx7mpzfSyUwpY8NvmlMwL7uiKqULtIFWp6JCJS2utEG6E/Ue4cP9n7XnzvciT37ySZDYcTIcIHKcKbW5bxMPFJOfYzt46jvXqgpbluUBsO3vPIryzQSENq2BszoSO05GE5906s6l1d9no1rdZ+KBYv77I9fGGVVabK4SA4vyLcez08oxudQ9TdJRSOw4GU183LFu3nY8s5zkwyXE7izk3x81Z7rBYF/8Kyk0s2XZ8arfC3Katjtf4sbJ0D/Hm3DsuFQ6d/V1DVA1f8tItO3Q+unjySxZdIz0eM2c8OTlsbwxKx6gyTsIJXaciCY+3ljTsa/W50DvZhy/r6jqMbXafDPpQDGZKbYxVZ/ZHvWB0wiEiqJMANJVVT1hNKuqulBV1QGqqg7o37+/g+5OOCM2bWrSHwB6J8n4fcVsXGKZQB6zGjD/+y3XRvDwDdRcG6UlZjISSqkoVwlt3zCDpslFqZfCphI7TkYTFzlAc3OUFpttBseyUjPPXHWEt+ckAJAeX8rSj45VWe1VFSrKtfNzM7SYCghrWgNndSR2nAwnmHS6eRjIy6ogIUbrhhqzqYBXZ8TxyaPJbPtbE9C3r8zjzmExJB7QzikuqODQtkK+eSGV7LQy/IJNTarotT0kdpyMJu5cD2zjyi0vaXX/Xrj+KE9NjeWlG+OqYqQgt6IqnRggsps7oMXOrwsyaF051vg38U0piRsnQ4+ZJjxnM1UaGz5/IqXqmF5r1nqj93hmORt+y+XXBRk8fslhm9q3AD2H1b1utSOQ2HEinGCdoxPYRlvj//ZuBhuX5BKzqbDqseodjpMOlfDGTK3bzqTbg7nq4VCMpqY1V3OmLsZDgYsVRbkAcAd8FUX5XFXVaxr5voQzJT0dysvBtWnuOuk7ZKCl+erihb5bBvDTG7ZpKy6uSlVXY303rSEchEILxmxu8jUIDUaFPesKePbqIzz4RXvysivYt6EAgLi9Ws3BN2fHk5lcxphrA6qu02Ms9Yi2qGvqAqHgZDjBpHPuh+145sojJB4o5u/PM9m01HpzShuTFt6baHNNfk4Fr9ysTTp9AoyERsmYI9QzTiCuR3Z3t3vc289IQW6FzWItqK0r8fuKObStiPT4UqbeH4pfsImIzvafQxDqhBOkGFvXQ9u24jh9R/tSmKfFil4qJjutzKZ2J2CTPTX1/tAGa1IitECa+IaUNZ6+mucu+XAJH8+zLTOWk1Feta7R0WOry0BP2nXzcMxNngZO4yBUVfVBVVUjVFWNAq4AVog42Exo4h8AelDPeq0tD33Z/qTnD7vEj4LcCnat1lwe6QmldOjtUbVTLQj1ghOIHEd2aRb7xAMl5GVX8PTU2Kr6nHptND2Va/lnWYA2ES2snHDG7tSub91GBEKhHnECkUOvnfv5Eyk24iBYUliqc3i7JaUlL6uCNp1EIBTqGScYd3xraWrVrrs7ZaUq+dkWQaPHEC8Afn4zHZ8AI33P86HPSJ8qJ6Eg1AtO0KTEuo76+/clkR5fWjVf0x2EOek166pZC4Q+AU27pIXgRDiB69YaRVFoV21zKjRKMwhlpZSRElti97raxqvGxmkEQqGZok82m/CgOf25CKbcE0LPYd4ER7ri4qrg6WMJnQ69Lcp/14FeVbvU//6YU3X86kfCmnyql+BkmM1NfrF2zbwwonpoA+YrN8WRZ7UwKzxeQUWFWqMeVOFxM9lWk1APbwM+/jLpFOqRJh43wAnTTWoTCA9tK7T5vW0X2ZQS6hknENeNJoWbXwivcTyy0qXx18eZAFzzaBgdz7J0sj/7/FZNdrEmODlO4CAcPz3QpsnPm7MtWVJlJdp9V3dBgW29zq4DvRrwDoUWhRPU7azOvR9H8fq6Lky9L4TQ9q5MvT8UgEUPJNWo46nj3UTXN04pEKqq+o+qqhMa+z6EesAJPgCCI1059wot/dFoUnh9XVdeWtmlygFlvWNw2xtt8fYzYTZr7qep94fy4orOkl4s1D9OEDtDJvpx03NaPaj0ap22S4tV3r0rAbXa7SfEFBO/1+KEuvmFCBHXhfrFCUQOgNlvtWX2W225cEagzfGMxFKKC+3fv5evkf5jfQHEtS7UH+npsGdPk9/Q1ek72pe3Nna1ORbeWZuH6c1IAkJdcPO0LIMkNVJoMJxgvuYbYOKqh8KqfrfeiNJdgkX5NcvaHEvS5nYPfN4eT5+mKXYIToi1qJ6f37j3cooYDAourgZGXh7Ao991pFNfTwwGap2vAbi4Nk0prmneldBycALbfW1MuCUInwAjPYf7VB0zmpQq4dAnwMiwyX54+cqAKTQAThI7/iGmGl2IB/xPEzD2rCuo6l5sjdkMQRHaNbIjLdQ7TiJydB/sTffB3lxwcyDnXNQKgOh+nmSnlbPm+2y714R2cGXaU22458N2UkdNqD9KSqCoyOJcdwIMBoUnfunIPR+244lfOtp0OAZw8zTg7mUtEMpcTWggnMBBCNRoOKJTcFxr7nN4R1GNxz59XGtq4u0n8SPUI3qslJfDqlWNey91xDoTxLe1iZGX+zfi3Zwesl0mNC5OsKtWG0Mm+jFkol+N9uVnjfZh1mttad3Gpcl1JRKaEU4SOwaDwvyfO7L4nQyWfZrJhTMCueDmQNp2deen122b+0y+I7jq2MPfdMDctHuwCM6KE4kcoNW2ueTOENy9DFw0K5gnpxyu0RhLJyjCFaNRoUNvT7uPC0KdUFWtKZaTiOs6geGuBIZrdaD8gl2YcEsgv713DAB3TwOubuIgFByA2QxGY5Mfd3oO86Z1GxdmvBhBabGZl2+Kq3rsr08yq9Lz7VFdgBeEM0IfZ8rKLHM2J8wm0v+Mez5qR2AbV867JoDD24uq6ns2VcRBKDQuTuKCOhHV0x8NBoWew7wlrVhoWJyogK/RpDD8Uj9ah7kw6MJWKIpSlQZpjX7My9eIq5sBd08ZooR6RFXh2DGnEdet8WplZMo9obh7Guh+jjeA3d1oD0nxEhoCs9kpBUJrTC4KF9wcVPW7q4ft+CICodAgJCdr444TCIS+ASaeXNyJtl3c6djHdpNp29/Ha7lKq3fr6u584o3QhNFjpaLSKeCk487U+0M556JWBLbRNqoC27hy9vk11z9NDRkNhcYjLQ3cKkU0Jw18nWseDZPJpeBYqg+eTZzAcFee/LVT1e8BoS68s7kbW/8+zqePJXPOxX74h5gYPz2QPqN8TvBMglBHCgth+3bLuNPEF2u1cfFtQbTv7cE5F7Vi6n2hfPdyGplJpexcnS+LNKFhUFUt1Uv/uRmgb0B1HuDJgc2F+AbKHE5oAI4dg6wsTSB0srXOvR9FkZ6gdTPOTLLfZAHgtjfbSq1ooX6xTjEGiwvXyRg5xR+w3cw1GBTGTWtNeHTTNRLJaCg0HkeOQHCw9rOTDZrVGXKxX2PfgtDScCIH4Ynod54v/c6z7KZddGvQCc4WhDNAd0FZO9edcMLp42+yGXOmzA2hpMjM4rfTGXNt68a7MaH5YjbbLtScmN4jvdm5Kh9XD03QmPFiBAe3FEoHY6FhUFUtTdIJHITVad/Lg4gubnw+H5t60SHtXEmLszSe82minVgFJ0YfZ/Rxp6ICXFxqP9/JmDQ7uLFv4YTIaCg0HtY70k4+4RQEh+OEaZKC0Kjo6ZFOUjD+dHDzMDDlntDGvg2hudKM5ms3PhNOdmpZVfdITx+juNaFhsNs1gRCV1enjB0XVwODL/Zj7U85VcemPdmGr59LpfdIH7LTysQ9KNQ/9hyEgsMQgVBoPPSi1yCBLwinSzOo3ykIDsWeg1AQhJOjC4QGg9ML665uBkLaNd3ULqGZoa91nDDFWGfK3BAiu7qTnVaGb2sT7bp7cP+n7Rv7toTmTHUHoZPGjrMiAqHQeDSjlBVBcDjiIBSE00N3D8rGlCCcHrq47uIicSMIp4MeLwaD09SMro6ru4Hhl9ZsiiUIDYY4CBsVaREpNB7NKGVFEByOuKAE4fSwnnA2AyeUIDgMPVac2AUlCI2CxI4gnD7VBUInFdedFREIhcZD35HWfxYE4dQRB6EgnB7WKSuyWBOEU0ePFSdstCAIjYrEjiCcPtVNEDJfcygiEAqNhzgIBaFuxMfLoCkIp4v14kwWa4Jw6uixoiia+1bGHUE4NcRBKDQnSkthz56Gf53q8zOJHYciNQiFxsO6BuFpWIejoqLw8fHBaDRiMpnYvHlzA92gIDRR9u6F4GDtZxk0BeHUsI4VWawJwqmjL9YMBk0kFHFdEE4N6xqEEjeCs1NUBGlp0KNHw75O9ViRFGOHIgKh0HhYF4s/zUFz5cqVBAYGNsBNCYITYC2uy4RTcFZKSiAnB0JCHPN61R2EIhAKwqmhx4q1g9BobNx7EoQz4cABiI7W3tMNiaQYC82JigrHvI9V1datLvM1hyIpxoItqgpJSY57LUkxFoTTRzqAC82B7GyIjXXc61nHiskkizVBOFWqOwhl3BGcnYMHLfOohkRSjIXmREWFY97H1TehJHYcijgIBQsVFVBWBrt3Q3h4w7+e2WwZOE/DOqwoCuPGjUNRFG655RZmzJhR45yFCxeycOFCANzd3evldps6iYmJjX0LLZuiIk3waNOm4V9LVbVYbYCFWkuLHYmbRqSszDELNB1rQbAB6qhJ7AjNluoOwnoU11ta3IDETqOjqrZrkIakAR2EEjtCg1Jaqr1/rd9bjoobVdVipqzM8rr1iMTOiRGBULCwZo1WU8BRKn0dU4z//fdfwsPDSU9PZ+zYsXTt2pURI0bYnDNjxgy7wqEgNBg5OZCQ0PACoR6fFRUNsiMtsSM4jLIyx9aVsa4F1QD1oCR2hAbBbIb//oMBA8DFRTuWlqZ9d3R6vh479TjuSNwIDqeO5Y3qhPW4I/M1wZk4ckQTCXv1shxrLAdhPc8VJXZOjFOlGCuK4q4oykZFUXYoirJHUZT5jX1PzYriYs0F5ai0K7PZEvCnEfjhle7G4OBgJk+ezMaNGxvi7gTh9HDkrhpo4oqkrAjOTHm5YwVC606skiYpOAvJyXDsmDY/0zl2DDIzHXcP1g5CiR3B2XGkQGidYixlLQRnoqBAqxVtjaMdhNavKzgMpxIIgRJgtKqqfYCzgPMVRRncuLfUTNBTFktLHTeAWe9In2LgFxQUkJeXV/XzX3/9Rc+ePRvqDgXh1HFU4V49VsrLtTpqMmgKzoruIDx6VHPfNjTVHYQSO4IzEB+vvV/1VCvQFm3l5bBjBxw/3vD30IAOQkFoEFQV/vpLK5tkza5dkJWl/ewoJxSIQCg4H/n5mi6gs3y5NvY4WiCUMcfhOFWKsaqqKpBf+atL5Zd82tYHeh0ovd6AI9A/YE5D5EhLS2Py5MkAlJeXc9VVV3H++ec31B0KwqnjSNu9/l0chIIzozsI8/MtqZMNibWDsAFSjAWhQSgsBG9vW4FQX7QVFGjZH76+DXsP1QVCiR2hqVNSos2P4uJANxLojRjd3Cy/NzR6N1bdfauqDd85WRDqg4ICS/3BoiLtq7jY8SnGLi6OzTYRnEsgBFAUxQhsAToBb6uq+l+1x2cAMwAmTJjg+Bt0VvTJpv7dEQOY9a7aKX7YdOjQgR07djTgTbVcJHbOEEenGIM4CJsIEjt1RHcQ6m7YhsbaQShpkk0CiZ2ToKqa0BEQYOvkKC3V5k7l5Y5brOkCh7g5Gh2Jm1NAF9bz87WxxsVFEzz0bClwXNaHq6vtuGOdOik4FImdU0QX2PVY0Z3qjso01IV1sL/WSUqCoCAttoR6x9lSjFFVtUJV1bOACGCgoig9qz2+UFXVAaqqDujfv3+j3KNTou9M698bOvhF5GhySOycIY52EII4CJsIEjt1pLxcGwsc5VwXB2GTQ2LnJJSWgslEbEaGrUCopxjrMdTQqKo2V9NFDomdRkXi5hQoKgIPD80Bpdfv1FOL9bpqjhp3TCZbB6HQaEjsnCIFBRS5u5OXm6u9ZyvLe1WNQw0dO9Ypxi4uNV/v0CFISWnYe2jB1ItAqChKn9M49/n6eE1VVXOAlYDkl9YH9hyEDYl1oItAKDg7ubmOcxBWFwhlsik4K/qGlKNq2pjNlhRJcRAKzkBxMS/+/DMdzz+fuKNHLcdLSzVxsKzMcRtTusghDkLBGSgq4lhxsSYQFhdrx3JztXlTYzkIXV1tmw0JQlMlP59XfvqJeV99pcWLtYMQHKMTnEggLC52bKOuFkZ9OQj/VhSl34lOUBTFoCjKx8A9dX0RRVGCFEXxq/zZAxgL7K/r8wlW6As1R+4M6IgLSnBmiopg7VrHNSmR2BGcjaQkOHy4ZoxYC4SOdnKIyCE4A8XFvP/773h7eZGelqYd09OK9RR9RzsI9dRmQWjCFGZnEz56NLsSEiwCYXGxlnbsSIFQVSEqClq1gjZttPFQEJoqqgrp6VBQwNaDB0nKyrIIhO7ujecgtK5BqKc+6wJhQQEcOdKw99PCqC+BcCeaSGi3o7CiKO7AL8A1wKwzeJ0wYKWiKDuBTcAyVVV/O4Pna7kUF9tacx3tIJQUY8EZycyEvXttj+XnawNXY6QYS+wIzkBmJmRnw7ZtkJpqOV5erk0AHekglDRJwYmoKCggPi2Nfr16kakvhkpLtfdvY6VJWjuyBKGJcuDAAUpLS3npiy9sBUIvL8fGjtkM4eEs/OwzUkwmSEy0PJaWpokxgtBUSEmBjRshN5cdBw6QmpOjrXOKisDf37E6gS4QVl/rlJRo45CqarVG9+2D/ftlTleP1JdAeAHwH/CXoijDrR+odPz9DYwBLldV9b26voiqqjtVVe2rqmpvVVV7qqr6xJncdItmxw5boaOsTJv4NUaKsbigBGchOxvi423jo6BA++6owr0SO4KzkZenLcwKCizxAtq44+6uvYfFQSgINUiKiyPQ35/wNm3ItK6f5ulpcfE5Wlz38JA0SaHJsz8mhqGDB7N45UrL+7WkRBMIHbnWURTMZjP33HMP9z35pLaZrAuWKSmwc6e2iSbjkdAUiI0Fg4G8+HgOHz1Kana29j7199ecfPq405g1CIuLtXHI319z5GZmap3J9TRo4YypF4FQVdVi4GK0moBLFEU5D0BRlHDgX6AHMF5V1R/r4/WEM+DQoSrrMCUlts1J9Fbm4NgUY3FBCc5AYaH2VVZmKXQNjhcIJXYEJ2P3rl2s/u8/LX70hZH+vtU70DlK5HBxsdQhtE5ZKS6GlStrv7a8XFLDBIcTe/gwHdq1o3VgoEUgLC3VBEIdR4vrnp4iEApNm/h4YuLjGXnuuZSUlZGnu291cV0fbxzhglIUDh8+jLe3N6tXr+anbdssQkZxsTYWbdhgO68UBAewec0aFr70kuWA2azV6YyMZNfRo3Tu3JnUzEzN6dq6tW33bQeJ6yiKNvZUztdefvll3nv/fU0Q9PPTBM2QEAgM1ITCo0e179u3a/FeUgIHDzbsvTZD6q2LsaqqpcClwB/Ar4qizAbWAwHASFVV/6mv1xLqiNmsWXC3boW2bcHX17YrkbVAKE1KBMFCURH8849ms/fzs00Jyc/XvjuqE+upOAjLyzXLvSA0NsXFfLN6NR/+8Yf2vtSFhbIy7bNfn3A6WuRo1UpzBOvk5mqxrG+aVSc3F2JiGv4eBcGK2CNH6NC+vSYQ6u/X/HzNPWGonMI7SlwPCICAAPbGxVGSk9PwrykIdeXgQfbn5dG1WzciIyJIOHpU+2w3Gi2bUuCYtY7BwNatWxk8eDA//PAD0597jtvnztUeLy6GAQMgLMyS9lzJN998w7ffftuw9ye0aFYtWcIfv/5qOVBcTLGi8OI337AjMZHhw4dTbjaTFxys1c80mSznOkJc18vBWDkIv/ziC9Zv2MAr335LXH4+mceOMWrmTN745Reeev55iIuD7GxWL19OeU4OHDumiYbCaVFvAiGAqqrlwBXA98DrQDEwRFXVHfX5OkIdycvTgrusTAt0Hx/LLlZJiTbh1HFE4OsfNCIQCk2d4mJt9yorC9q313bTdPLzLV3xHO0gdHW1pMroHDyoCR+HDtUudgiCo8jLIzYzk+ScHG2ipwuEhYXapE8XCB0VO97eWlpKYCDk5FhchPpYqAv+1SktlXgSHE5sXBwdOnakdXAwx7KzITlZW+xERlrmUI4S10NCSKqo4Jzx4/lx+XLteEWFdk8yhxMaE1W1uNMLCsBsZt/hw3Tt2pW27dqRkJwMeXmorq62IocjsqUqBcJ+/foxYMAADv77Lx9+/z0lJSWWVEk3txp1PVf89BN//yiJd0LDkZCQQK5uFCorg6Ii1h48yH1PPcXKI0fo06cPISEhDL3hBhZ9/bWtg7ChY0d3EBoMVQJhamoqW7dt49D+/bz6+ecsXrWKg6mprNuyhaffeovXvv6ag/v3Q0kJ1zz/PLt27NDmdsXFMkadJvUiECqKsklRlI2KomwENgDdAaXy61v9MatzhMbg+HEIDobzzuN4RQV/bt+uKe3p6ZqgEBhoOdcRga9/0EgdNaGpU7mz+9/+/Tz/2Wfa74WFmlCoKJrY3hg1CH19awoaBw5YBEw9rUYQGoucHI6kp5OSnc0xs5nSvDwtTnbsgOhoxzoIzWYtZrp31xaJrVpZYiQvT7uXWgTC7PR03v7554a/R0Gw4khSEu2jo2kdFESmokBCgkXk1oUOR4nrBgNPPfUUrq6u7ImNZcO6dahpaVoq1z//aBtoubkipAuOJz0dtmzRfs7IINvNjcOHD9OzZ08iIyOJz8+HtDRue+stFn35peU6B6VJ7tu3jx49egAQEBVFdJs27Ni61VL2wt1dEzZXrKiabyYmJHBUOrMKDUhCUhI5ukC4ZQscPcqKHZqn6+c//6RPnz6Ehoaya9cunnnmGcqt48WRDsJKI9GKFSsY1L07O+PiSExNZc26dSSGhzNhwgTi4+N5aNo0Ln7qKY4eOkRqTg5Z6em2m7/WZWWEE1JfDsI91b52AZ+g1R+s/tieenpN4XQ5fhzV1xc8Pfnhhx+45bHHoF07rVtR69aN5yC0LnoqCE2R0lIwGvlz1y4WvPcehIZqcbNtG/Turb2HVdVxIoeiaD+7u2s/6zvPeppzeromdkhNG6GxyckhNimJ5Kwsrn/pJX5ctQrS08kuLNRKXTjaQagoxMTE8MYbb3DMaLSUC9A30PLztUZEBw+CqjJ48GAOHTrEf5s28frixTJWCY6jtJSkzEzCIyMJDAwks7iY+LAwXlm7Vnvc0eK6orB8+XJmzpzJyl27OGfoUJb99psm9EdHa43vNm+GPTLNF+qf8vJySydv/VhZGbdMmcKWdeu49dlntYNZWSzbvZvhw4fj4eFBZGQkCbm5kJbGss2b2WHdoNFBIkdeXh5+fn7aMW9vBnXtyoaVKzmak0NycrI2lzt2DDU/n/h16wBITEkhLjlZOrMKDUZiaiq5lZuin/3yC4l797KiMh2+rKyM3r17Exoaytlnn01AQACrNm+2XHw6405CwumPU5XzNcLDNX2iooLY2FhG9exJuarSpk0b/v33X5LS0ggPD8fNzY27H3mEIQMG8N4331BWXk7WsWPs2bGDkY88om1K799/evfQgqmvJiXTVFW94VS/6uM1hTqQk8OASy9l06ZNrFixgri4OFLc3KBbN+jQ4eTFR1VVS1uszvr1p985qHp3IkWRhZfQ9NDfk8XF0KYNO9PSiIuL46iPD/TqhTpypOa8daTIoXeTBIt7Ud8B1NM3Cwq0QTUxUUujFIRGoiAlhdy8PPIKCti6fz9HMjPJ3bGDtlOmsG3bNp7/5BPtREeJHAYDH374Ie+++y6dxo0j+vzzWbd6NRQWUuTnp4mDBw5ASgrmuDi2bd1K7N69HI2LI/tENQoFoT5RVSgoICUnhzZt2tC6dWsyMzNZvHgxDzzwgCaUONhBmJyaSnZ2NlOmTGHd3r34eXvz6KuvEpOZqY03BQWogJqaCoWFpKSkNPx9CS2GJUuWMHXqVJtjSYcOsfD77/n4s89YtmWLFguFhSxdu5bx48cD0LZtW+Kzs0lNSOBQQgIHDh8GYNvhww5Lk8zPz8fLy6vq8OCzz2bD6tU8+dVXPP3001UOws0pKbQfM4ZP3n6bxGPHiM/IQF23TuZxQoOQkJZGTqVA+Mq33/L3li1s3bePu+66i3bt2uHn50dYWBhjxozhggsuYNnatWTl5fH0d9+d3rizb5+WdXU66GaIXr20+CgtJTkhgYjAQDp27MgFF1yAoij8+++/hIeHa9cEBdF74EA2VW4CZKamsjkmhtU7d7Lq33/ZL5tXp0y91iA8GYqijFAUZYUjX1OopKyM/LQ0tu7cybx581ixYgVdunThv//+g44dq0SOopISnvz669obHxw4UPN4QUHNOmgnQ08x1jsUubnVKNArCI3Oxo2QkcHxzEwuvuceth44QK9evfhn9Wpo3Zqzhw9n+/btjndBubgAUFBUxK6UFFuBUHcXtmkDXbrIjpnQeBQVcTQlhaioKELDwkjNyCChqIjYAwcoKCzkjjvu4NG33yZLTztuaCp3pNesWcObb75JfHw8d0ycyP1z57IrK4suo0dDdDRFZ59Nvr8/SQcPUlpWRuqRIxyJjyc7Px/1dMc6QagL8fGwcSPJmZmEhYVVCYTr16/H3d2dL774gm//+Yf9SUkOq0G4as0ahg8fTpcuXTCZTDw6dy4jundn9KWXYgaIjOTK117jt717SY6JoUOHDlqdNUE4Q2JjY0lMTGT37t02x49Uzm++WLaM+IwMyvPzKcvL49e//mLChAkAREZGEpeWxr8mE106d+bAoUPkFRYyYO5cjp+uueF0qdyUys/Px9vbu+rw4OHD+W/HDrYfOsSyZcu0NRCwNi6OQb178/Bjj1FQUoK7uzsZubkyjxPqndKSEjJyczleWIhaUkJsairrY2IIat2aSZMm8c033wDw+OOPc//99zNmzBiW//sv/x04wAd//XXq446qahrB6ab36inGoMVHeTkp8fG0CQ8nOjqa3r17M3DgQJYsWWIRCIHIqCg2VXYtzkpNZW9aGkajkbGPPsqL+oa0cFIcKhACQcBIB7+mAJCWxqGiIqKjozGZTLRr144rrriCDRs2WM4xGtkQE8PLv/xif7FmNtc8rhcGrkvg6+KgwSACodAkefC119i7fTtHjhzh17//Jjk5mRkzZvDPP/9QXFzM9u3b+euvv8BkIikzk1/Wr3dMTZtK58iLr7zCZffdZ9ldLirS6qoBR9LTWbB4sVZfVFW11Emp9Sk4kiNHOFhURIcOHQgLCwMg/vhxjlZO5tasWUNYUBBf/fuvwxyERSUl7Nixg8GDB+PbqhUzb72VzLQ0bnzhBRISEjgeGMgLr73GXc8/z+FduwBITUzkSGIiFWYzBdadj1NSxFEoNAx5eeTl5GA2m/H19aV169ZkZGSwdu1a3nrrLZ566imufOwx3l++vPYxJyamZnZHUpL9TJCTYTYTc/AgvXv3xtXVlfHjxzPx2mt54aOPCAgIYP369dCtG7uPHGHdvn1sXruW4uJi9urp0DoSL8JpkpiYSI8ePUg5dIi0tDSyrEqnHKkUArLz86kwm0k8eJC/N22iY8eOtG/fHoBevXqxY8cOVq9fz7XXXUdySgrrY2Iwm80U6VkXDUWlyFFQUGAjEHYZOZLMggJ2x8aSlZXFzLlzScnKYt2uXdxyxx0Umc2ER0TQvmNHjgYEaEaMrCxtvSV1CYV6IOXoUUL9/XF3dSVu716OFxayes8eoiIjcXV1ZdCgQQCEhITQqlUrBg0axIHYWNbu28fxwsJTX+uUl2vn1lUn0PHwIDkxkTaRkbz++uvccMMNDBgwgIKCAiIiIqpOi+zQQbs/ICs3l31JSdx24410i4oiodLVXlFRwdVXX01cXNzp3VMLwtECodAYFBXBwYMczM+nZ8+e/Pbbb6xbt44JEybwxRdfVO3w/r5sGct37KCopKT2FOPqi7iysrrVXtOtw0ajCIRC06SwkK9WrWLzli0kJycTFBjIyJEjGTNmDP/88w8xMTFUVFSwcuVKMJn4c+tWFixZ4rD6nSVlZbz3/vukZGRw+Z13suDNN7XJY1AQGI088+qrzH/6aVQ3N22RuGkTpKZqQuHatVqNNUFoKEpKID5ec0QMGkSbNm3o3LkzCQkJHD16lI4dO+Lu7s7r8+bx8d9/O8xBuHXXLrp3716V7mXs1o03585l865d+Pv7c/jwYbZt28biv/7iYGwsAKnJyRxNTgYg+9gxrW5hVpZWg/To0Ya/b6H5cuiQ1gW4OgUFpCgKYaGhKIqCt7c3kyZNIjc3l2uuuYadO3fy/UsvsXLnztrnX9nZNdO6cnI0seF0UVVycnPx9/cHYPHixXTo2BF8fbnsssv4/vvvUVWVo0ePsv3gQTatXw/A9qVLbZ9n40apjSucFjt37qS4uJidlRs2+/btq3rsaGwsZ3XpgtFgYGCPHsTu3s2P//3H5ZdfXnVOSEgIvr6+fPXVV4waNYqoqCi+XrMGoOEFwlpSjA0mEwMHD6Z9+/bccsst/LF0Kb9u3sz6bdsYOmwYQ4cNIzw8nHbt2rF7716txueBA5CRUTeBX2iRZGRkoFafW1WmFB/evZvIsDBaeXmxtdIstC8hgaiOHe0+l6urK4MGDOCTFSvILShAPVXBT1/bn65AaFVvXVVVjubmkpyaSlj79rRt2xZvb28GDBgAYOsgrNwY8Pb0JDMvj71HjjDz7rv58uefScjIgPJyli9fzo8//si0adNq/vsIgAiELYODByEkhIOZmURHR1cdHjBgAD179mT27NmsXr2ai6dM4cWffqK0vJwKe7u8ehBZB5PeHKGu1mG9Q5Grq22a8oYNWmc8cTwJjURBaipx6ekkxMeTnJrKhPPPZ+nSpXTp0oXi4mJ+//13zjvvPNauXUuZ2UxsWhpFjuhkXOkg3Hn0KCEhIUyePJnv1qxh3YoVUFTErU8/TVx0NN//8AOlpaUsi4lh7eLF2qTg+HGtiHxwMBw6RNzOnexbtaph71doeaSkaMJZaCir1q5l5MiRREREcP755xMfH8+RI0e46aab+Oyzz5gwZgwpWVnsrm3Rk5NTf4KC2Ux6ZqbNZBKTifPuuIN9+/YxcuRIDh8+zK5duygrL+fL1avp3LYtqampHElJIdDPj+yjR2HzZrL+/ptLnnmGPatWSRF5oc5sXLmSA5VdI20oKCDF15c2bdsCoCgKn332GZs3b8ZgMBAaGsqFM2ZwKCWFLGtXqzX2Nm+LiurWydFsJvf4cVpVOtStueqqq/jqq69ITk6mpKSEbfv2sXn/fob378/2/ftt76GgoNZO4YJgj12V3Yk37diBn5cXe3fu1B44cIAjsbFcfcUVzJ49m26dOhG7fz9bY2M555xzbJ5j0KBB5OXlMWDAAAYPHsxXq1cDDhAIK9c61VOM9Xvq06cPTz/9NPPmzePtFSswmkx07NiRkSNHEhkZyZw5c7j//vvZe/w45OaSuGMHb3z33emXdRJaHPv37yc4OJjo6Gh+/vln7WBGhtZxPjmZZb/+yrkjRuDn48O2bdvoEhUFQFSlwGaPoYMHk3jsGBVmM0WnWlNQf6/W0UH4xRdfsGnTJvpNm0ZaTg6hHTpUnTJgwAAURbGZ0wUGBeHh5kb3Tp1IzsoiKS2Njh070rZtWxKOHUMtKuLDDz/khSef5OihQ+zYsUMbo6rpHonVyhm0NEQgbMaYzWZSExNRs7Iwh4Zy4MABG4EQ4P3338fd3Z1zzz2Xq6+8EleTCYPBQIku/Nk+ofbdnkBYVwdh9RTjigrIzNQmkQUFEBenLQ5PtOMtA6VQj+Smp3Nkxw72Vy7aEpKTSU5Lo01EBIqioCgKo0aN4u2332bkyJF069aNf7ZsITY1laKyMscUvXZxITEzk6ioKG677TZuu+469h84QHZaGu99/jmfffMNPXr0YNiwYVxy771ccOutXPPqq+QcOKBdHx0NrVrx5vz53H3PPVoBYUGoB9SkJI6vXcuSr77if3ffzd69exk0aBCPPfYYTz75JCUlJezatYtu3bpx2WWXYQwP57KLL+a3SsdRDdLStK96uTmVnOPHLd0krejatSsdO3Zkx44dpKSk8NRTT/HPrl0MHTSImLg4CoqK6NK+PdmHDpHfpg1jH36Yw5mZPPvNN6jiiBLqgqrywief8NXixbbHzWYOHjrEVz/9VJWaD2A0Gulo5e5w9fKib48ebLXuylrteWqI13URCCufIycnx65AGB0dTY8ePXj11Vfp1asXZeXlrN23j5unTWPb0aOW5llmM9+vWEGWPcekIID23qy2/ti9YweKopCcns6oXr3YtW0bAEX79nEkKYkBgwfz2uuv0yEqikOHDrHv6FF69uxp8xyDBg1i4MCBuLm58frrr/O//v0JCQig2N5apz4xmyktL0dVVVxdXW0emjNnDs9Wdl4eMWIEO/fuZdq0aSiKwqxZs3j11VcZNWoUs2fP5u0FCyAoiIWff84T33zD0T17iImJadh7F5yagzExjB85kndeeYVZs2ZRXFzMoaVLOWwywa5d/LF6NRdMnUorb2+27t7NuZUpxVGVQqE9hg4dCoCLyXTq9TvrKhCWlVFQWsp1113HV199Rfbx47Ty9MQtMLDqlNatW7Nr1y4bd66iKEQGB9OjSxfWxcQQHR2Ni4sLvr6+GI1Gdm7ezPJly7i2fXuuHjaMzxct0tZAq1dDSQl3zJzJEw88QFSfPhQ0dI3SJoypPp5EUZRZp3jqWfXxesKp8cOiRdz98MOM6dOHbmPGEBMTw4033mhzTkREBG+++SY33HADXbt0YcHEibS96SaKCgvxrP6E+kSzsuguUHfrsO4gtE4xPnBAS93y8NAap/j5aamQ7u6asj9unOV1dY4c0Yr3jhtn24VZEOrIl++9x9Jly5gydiy+3t7EJyaitGlDL6sd6aeffprx48czaNAg3Nzc+HHZMmLT0iisLT0ftBipj/dopYMw4dgxIiIiGDhwINHBwUR2787mrVsB+Omnn+jbty9RUVFs376dnd9+y50vvsj1Tz/Nj+++ixEgOJitsbGs2b2bzD17aB0eDr6+Z35/Qotm6rXX8vuGDUSGh3PzrbcyauxY3N3dcXd3B7SC8Rs2bODVV1/VLvDwIDQykpzairCrav059MxmcvPy7IocAB07dmTBggV069aNWbNm0cPXF7ewMM4ZM4Yxgwbh4eVFTl4eD73zDj0GDOCNt96ic3Q0QZ06kZyWVmMBKAgnpKCAHbGx+LduzZsvv8yV111H0qZNPPfSS4T6+/Pejz9y5513nvApWvv7k6s3qaqOPQdhYWFVk6tTpnK+lpuba1dcB7jlllu47rrruPDCC5kzZw7dS0vpeskl3Hb//eSlpeHToQMUF/PYl19S4OHB9YMHn949CC2Csvh48mNiMAwciKu7Ox6enuzas4fBXbqwfv9+rhkzhvs++YTXjx9n1AMPsHH/fjr26AFA76FDuXPOHIIDA/Hx8bF53htuuIHzzz8fgFatWvHzU09xzpw5DkkxLigpwcvLC8W6nhoQFBREUFAQoInso0eP5oYbbgDAw8MDDw8PAG688UZ69+7N4T172LFzJ4UlJVxz001kFRayc/duTKZ6WcoLzYy4/fuJ8vZmnLs7fTp0IDQ0FF9XVwoqKrh8wgSS8/IYNGQIrXx92bRnD5MvvhjfJUtOKBAOPucc/tevH7EZGeTm5hJ6KjdSV50gL49tBQWYzWa+/vprPD08aBMYWGP86lEZ/9b0iY5m0IABfPTdd/Tv37/qeNvQUO5+5BFuHDsWv3PO4Wp3d86bNo1b+/Zl//HjjA8O5qNPP6WguBiTwcDB3bs5a8iQ07vvZkJ9faq8VU/PI9QXZWVs2rDh/+ydZ3RUVReGnzuTSSa9VxIIhFBCDR3pvYgIKNKkI1YQFEUREEERRFHEQrNSVETxU5AioEgvAaR3Aqmk9zoz9/txM8kkJJCEQNp51mJBptw5E2bPOec9796bsJgYvt21i8etrTl37twdp2pGWrRoofzDxQVLrbbwSfNBOAjNzBTRxMJCOWXIylLSIJs0UdKOW7VSOiwfPaq4SBwc8roeGwyK6m9rq9RWM00bEwhKya2bNzl/7RoXatakR9euXDlzBo29PX28vHIf4+fnx6VLl5AkCT8/Px5ZsgRdZiYONjaFixlxcUrdmDZt8m4LDQVPz5KLhrIM5uaE6nR456QCOPr4YG1hwf+OHUOr1XLixAlGjBjB008/Tc+ePXFo145VGzYwoH17AkeNwsPbm2ZNmnDq5k06derEBzt2sKhhQ6RCJlqB4J5kZysuITs7jp0/z6mjR/EvYq5Zvnw5K1asyOdmt7WzI7SojVphLqjSYjAU6SAEZZN25swZ3nvvPQC6PP00sbGxAPQfNoxT+/cTGhfHuk2bOH36NA5OTtw6c4b2Xbpw7NgxGjVqVOS1BQKAdevWMXLkSFQ6HcknT3I1IoK6kZH88d572ANr169n18mT1HBz48knn8x1bBSFrY0NyUVlWBSMHb2+dN0kc9ZrRTkIAQYNGoS9vT21a9dm3Lhxube3b96cnWvX8sSTT4KnJ+FxcRwICmJsyUYgqMLodDpF5MrM5OfNm/l+/XrcPTzw8/PjtcWLuRIczMwnnuDQxYv0ffRRXlu9muNr13Lmxg1u3bqFT04aft/HHiPp2WcLjRlHR8fc+pkANGuGpbV10Q5CUyPE/SDLpGRk3JFeXBBJkti9e3eh9/n4+PD555+jS08nYPdubsXH88v27TT382PV55/zwssv539CUpIS5yZOK0H1I/jaNWr5+UHXrnwVHU2CVktAw4Zc0WgYPnw4//vf/1Cr1TjY2RGdmEjb7t0ZdesWTZs2LfKatk5ObN+8mdb9+hXuIDQYICQEatXKu600DsKsLNDpOH7pElqtlsjISGbNmEFWznrsXvz088+EJyfz3MyZ+QXCmjXZvW8f37/yCnh50dDODi8nJ3rNnUtKRgb9tmzB2daWU5s28frixfz7zz9cCglh2LBhxR97FaFMUoxlWVaV5E9ZvKbgToYOHcqWLVuUdNydOwk6eZLFr73Gkrlz2b17N3Z2djg5Od39It26YWlhUbhAWFQNQjOz0jsI27cHGxtF8FOpwMdHmdSMTgx3d0VA8fZWBJZDhxTH4IEDypeQpSXUqaP8WyC4H5KSQK8nJCyM65GRHPjvPx4bNIiQ2FjC4+PxMhEIgdzTYD8/P5oFBBCXnFx0g5/s7Du7N548WbpGITkL19D09LzOXWo1DWrV4se9exk0aBCgpEx6eHjQLsepYW5hwfaff2bpxx/zyiuvsOHHH7G0tGTDhg1sP3yYoc8/T2ppitcLqj364GDS/vqL9Js3iYyPp3aDBkU+tlevXvzyyy/5UkJsbGxIKaqeTWEuqMzMUjdauJuDsFu3bpw7d45Zs2bl3ubk5ISFhQX9+/fHwdGRNbt20aFDh9zY03p60qVRI8aPG8fAgQNLPiZBtSEtLY3Ro0dz5coVvv3oIyYvWICtjQ3ng4O5HR/PpytXciUykhEjRhCblMS6det48skn73pNWxsbkouq6VfQfWtc15VyvZaYmFhk7JibmzN37ly6d++e7/bH+/Th261bMYSFkRYXR0JqKgf++w8yMpBjYkhMTCzZWARViuzsbOrXr8/P69bRpEEDzl24wIELF9h7/Dh/7d3LkSNHaFq7Ng0CArDWarFu1owhI0awcP16atWokSsOAlhYWDBu3LjcxgV3xdkZbVF7HVDqtJVFx+0cB+G9BMJ7MXLkSMZMnMjSDRto27UrzZs25ZvFi3lnwQISEhLyPTZoyxZ++fzz+3o9QeXn5s2bSj1Bc3O8GjUiwNERfH3x9/cnKCiIR3Kccfb29thZWxPQsiVffPEFbm5uRV9UkqBmTeysrUkqzLme0xQ1H1lZyv6+JPNOcrJy4HzsWO4cOGr8eJZ8/XXxnu/khJO7O2BigAJq1avHqD59qGF0BdrY8PTzz5NpMLBp0ybW7t5Ny8aNqdOvHw38/Xlv6VJmTJ9Oxs2bpFez7uFlItZJkjRSkiSnArfVlCTJrMBtXpIkzUJQ5gQFBbFp0yZ++v57CAkhLjmZE5cvM3bKFF6ePZusrCyaNGlSrGvdUyA03axlZSkiXUkdhMb25UarsJ2d4hps0gSaN7/z8d7e4OwM9vZKx7/4eLh1S3EPengohewfdC0RQZVi0aJFfPrpp8oPFy4o4vPevYSEh2MwGNh3/jyDhgwh22Dg8rVrdwiEprzx8svU9vBQmpQUFgtFpUneulXygeds1kJDQ/MEQuDlp5/muaefZsaMGQA0bNjwjqeq/Pzo2acPffv25dVXX6Vdu3Z4eHhw+OhRUtLT+eqzz9i0aRMG43vIzgadTumALDZygiL44aef8JkwgVUffEAdL68SpzzZ2NqSXJRAaDDkj6nsbNi1C3IK15cIg4GEu6RJqtVqAgIC8t0mSRJnzpyhfv36OHp6curq1fxCoCTRqUMHrly9yokTJ9CXpgGEoFpwPacr9odLljB/+XKux8by6IAB3IqKwlqrJejKFSaOHMmwYcPo0qULFhYW97ymrY1N8WMnPV05jC1lsfi7pRgDvPjii/Tv3z/fbWPGjCE+M5OFGzYQdvEiNT09CYmOJvTnn3nzhRdoGRgoYqYas2PHDqVp1fPPczY4mH8OHyYlPZ3E9HROXbnCn7/+SucmTahZvz4eDg5gYcETTz3Fb4cO0Sww8I7rffDBB8yePbtYr22p1ZJhTH8siLEm+v0iy6Skp+c7ELtfxo4fz6o1a2jepQv9WrfOK9eh10N8PMu//ZbhCxawY+PGMntNQSVDryc4JATfunWVn5s0ga5dweqO4mE4eHrSrk0b1CXIZrKzsSn8cKewjI/MTEUnKEY86XQ6YmJiFIHQ1pYTJ04wYcIEbG1tqWt8L8VEq9XSp08fmptoCvPmzePjdeuU7K0cnn/+ef799186d+lCLU9PWuYcMDSoV4+o2FhCIyJ4avRoXn/99XzX1+v1BAcHl2hMlYmycvOtBXL/5yRJUgM3gII+VR9gQWlfRJIkH0mS/pYk6bwkSeckSXr53s+qHqxauZIXBgxg+19/EX7qFG5PP42DrS1u3t5oNBoaNmx4V9uwKZZabeG2+8JSjHPqoZUqZUWlwmAw0K1bN7bu3Ak1a7Jp82bWfP/9nY+XJAgIgJYtFcehVqsIFjY2isPQw0NpaCIQFJMN69axasUK0OnIvHyZ43Z2nA8OJuT2bZrVq0eLBg1wdHRk4MCBNG/eHA+PoqttdO/alf9Wry7aQVhwowaKOJ6ZWbr0/ByB0PT0fPD06bz76ac0bdqUHj163LWOCMD06dNZv349oNS7eW3KFF6dPZsxY8bQtlUr5r/zDpw+DYcPs+WPP4g7d65k4xRUG46fPEnjRo2YsXJlbie8kmBjY0NKcVOM9XrlNp2u5AOV5SI7sd4NYzq0Y07KVpcuXfLd3+uxx/hs2jTcXFy4cuWKclglRA9BAa5dvgzAd99/z7iePdl38CBfffUVlhYW9GzThk5NmzL++ecZOHCgkg1SDGxtbYvvINTplLVTKdZrMtzVQVgUNr6+zP/4Y7afOkX4+fPUrFGD6ZMn02nOHDYeOYK1SsXvxg6bgmrHV199xRtvvEFySgo+Li4cPHuWwKZN6di5M+2aNOGzVavo3K0brdu14+e33gKVirZt2+Ll5UUzk9RBI2q1GlUxU4Pv6iAsbM1WGgyGYqUYlwQ3Nzdat24NTk7MGTqUz5cvJ+72bdi3D/ngQXadPMnKxYsZ/eyzXC6qgZGgShBbMOX2xg3kuDjmjB7NlVu3qFWvnnK7sd5/IbTu2JERY8aU6HXtbWwKdxAWFjdGI1Ex5p2NGzcqvRJSU8kwMyM4OJgOHToQEhJSqhrP27dvx8pEFPXw8LjjkMvCwoK6desiSRLLvviCoZMmAdCgYUMsNBq6NWnCH/v2sWXfPk4dO0ZaWhqyLLNhwwbat29fZQ+4yqoGoVTM2+4XHfCqLMsnJEmyBYIkSfpLluVq+w0YExPD1atX+WvnTv6YM4d9587xzq+/MmDAAL4xseL26dOHzp07F+uaRU6ahTkIZVkRCEvpILxy5Qpnz57l6aef5ptvvuGll17CYDDw119/MXPmzHzW4L/++ouwsDDGDRmifOEcOqQIhKA0NQkKUtLOAgMVQVEgKILw8HBCQ0KwsrDgg/nz+eizz/Dw8eHmjRukZ2QwevRo5JzP9A8//HDvC1pYYG1vT5ZOh0Gnu/PkpTAHodFFq9PlpdQXB4MBgywTFhZGDdO6mzmnfxqNhl27dt3zMpIk5TaOAOg+bBiv//knL7z4Inv/+IMpS5cy0NmZbIOBoYsX88TBg7zw2mu0DwxEulsKgqD6YDDArVucvnqVNxYuZPL48TQwqS1YXGxsbYsWCAvGjrF+bWkWZfeoQXgvHB0dcXd3z1c/EcCubl1enDiRPceOcTIoCI/QUGzc3DBr2lQ5BCiGE0xQ9bl24QJ1PT25GhFB7x49MLewwBzwdnWlvr8/i9esyX1scV24tra2hN/NQVgwdjSakmdbyDJpmZloNBo0JW1wgpLiderqVW5lZlLDx4fZS5bg6OvLmDFjOPTtt4wcN46QsDCmTp2qOIQ1GqVu74ULnLa1JTk+ng4FRHlB5ebGoUMEnT7N/v37WTtnDqMDAvj6t9/48Jdf+GjJEmQzM1o0acLf69bRa8QI1GZmBOY4BlUqFe+//37xUonvwl0dhGXVHKuYNQhLhUqFX5MmDO/QgWdHjKBx3bokajSozM0Z/8or3L51iwXTprHgs8+o5e9/R5MUQeXm9OnTtGnThv/++4+Y27fp0LEjCUFBXImK4ostW/BycsKtGAe2TzzxRIlf287WtvgCocFQbOf60aNHiY6OBp2OC6Gh+Pn5YW5u/tAawD2eU6IJILBNG/6cO5fDISGkZmcTGhtL2w4dGDNqFNt37cLOzo64uDj2799/x6FxVaBStT6SZTkCiMj5d7IkSReAGkC1FAhlWWbS00+z699/sbWxIaB1a8Y//zyvvP46n332GY4m9QYXL15c7OsWmWJclINQoyl10esjR47Qo0cPLCwsGD58OKtXr8bT05PFixfz+++/ExgYyNWrV8nMzGTUqFHo9XoSExO5cP48b3fsiKetLTExMRw+fJgB/fvzwYsvYu7iwsvz54vJUFAop06d4plJk+jdvDkD27Xj5/37+emTT+g6Zgxd2rfn/MWLvLNkCXJJFofW1qg6dcJCoyEjPR2rgi6LoibN0sSOLBObmIi1tXU+ge9+kSwseG/BArh4kZGvv87CX36h5+zZJKWl0aNbN87cuMFTTz/NyC5d+GDjxrIp4C2olFy8eJGQkBA6BASw8r33OH3zJoEtW7Jk7lxql7VAWJTIURqBsJQOQiONGzfm2WefvXNuMTODxo0JbNSI43v38sXBg/Rp2ZLZc+cS/M8/+I4ZU/LOsYIqx7UrV3iqY0c27t9Pa5MDW293d/zr1y/VNW1tbYtuUlKwfud9ZHwkpqeXWlh3cHDA3d2dv2/cwMvXF41Gk9ud+dFJk9iQlsbS//2PqVOnEvLzz3j378+xP/9k6oIFqMzNCbt9m0uHDqH18yvV6wsqGLLM9Bkz2HbsGG8+/TQ2aWk0tLenWYsWeOzZQ7fevXMfOnj69LznmTQfGVNCx1NhWFpakl6YWG4UB8vIQZiakVGmKcb5aN2ahcuXM3TIENLMzdHa2fF2zv7n+XfewdfHh/UNGvD2228zd+5csS+qRKxcuZKBAwfiaZIOa8ratWuRZZnOHTuSkpLCuf37eXrePE5fv8608eOZ//bbJTMflAA7a2sSi2pSUnDvVIJ5JygoiLi4ONDrOXPlSrFLoz0I1Pb2dO/Zk6aWlgzu04cfjh8nKzqaD776iu5Nm3Lq5k1ef+IJpk2bxieffMLyjz9mzLhxDDQRGSszlUogNEWSJF8gEDhSzkMpF9LT0+nbty9J4eE8kVNsU6pdm7ETJ/L2ggX06tWr1NcusotxYU1KjA7CUha9PnLkCG3atKFTp05cuXKFESNGYGZmRnJyMqtXr2bGjBl8/vnnqFQq1qxZw8GDB/npp58A2B4QwPUlSzh95gw7d+5k3759LP7pJ7wdHDgbFsbK1auVmgoJCUrtnSK+ZAVVnFu3ICoKAgLIkCRGDB3Ks489xoQBA7Bzd2dkx46Q08jjiREjSP7mG4BSLaSM4vodVT6KchBqNCVPlTQYuB0bW+Si4b7w81NqlLi40Ofxx9m1axdTnniCfv360aZNG+Lj42nWoAFt166l+8CBeV0BjW5IQZVmy5YtBAcHM3/+fDQaDX07duTrjRtxdXXF3d2d4S+9VKrr2trbk1KUq6kwB6FGA0W5pu6GwUBCQkKphY4WLVrkc7UX5MnRo2nfrx/ZOh2hiYnU8/dn2Jw53AwMpOZdnieouly7coXMzEwMwMn//mPW8OG8N3o0uLrmPmbBhx9Sr0Dty+Jy1xqEBWPHOOeUZL2m18P58ySkppZaWAdo2bIlW7duvaOOE3Z2tG/enGOLF5ORlESzZ59l57ffsu7nnzkXGoqTjQ2NAgL47ttvmSwOfis/ej23du3i3zNnCPvuOxwdHOCRRyAyku4+Prx07dpDG4rWwqLwckqF7XVKS04NwgfiIMzBzteXHX/8AV5e+dZhDg4O/LtrF9bnzjH0ww85c/gwP/3wA6qcOBaxVHFJSEhgypQpLF++nFOnTuVzlL81cSJ2Xl58//33LF26lPfeeYdpjz3GkyNHcis2lmeffJJnpkyBB5jpY2drS3RRNQiLMkMUdQicg16v5+TJk0pKsF7P6YsXy1UgxMIC2rXDBXBp1oz5Q4YA0KlnT7rXqYN06xayWo1+2zYWLVjAoSNHUGVmCoGwEAr7Ji2Db9c7kSTJBvgFmCbLclKB+yYDkwEGDBjwIF6+QnD48GHSEhM5tmIFNGlCZlIS2NriBISFhWFra1vqa1tqtYWfqt2tBqGxjXlxyXEQHjt2jBEjRtC6dWsOHjyYe3fbtm0ZOXIk//33H2FhYcTHx1O3bl1GjBiBJEl8+OGHfP/bb5w8eZK6desyaNAgOnXqxPz583m+QwfajxnDnjVr6NW7N+TU/cHNLTcNU3AnVSl29Ho9ar0eQ3w8kfv2ofLywvH4cX48fpyaDg5MmzJFqVtpaQn16uW6e8aNG3dfE5KlhQXphbk5Ck6axo1bKd23kTExuOd06CpTVCrISVt+c9YsXnjxRfxMXBuOjo58OmsWL772Glbz5nHi9Gls9HpeGDWKoSNH0n3UqLIfUyWgKsVOUYTdvMmE8ePRWloytkcPfJ2deenzz3l53Di8Gja8r82Gja0tyenphQvNBRebsqx8j0tSbj3OYnOfDsJ70aBDB0Z264aLhwfb//uP5z/+mHbNmvHjDz/wuMFA/bp1ke3txcbMhKoeO2+/8gpHT58mIT0da42GJh06KGsmE8GgQ9eupb6+rZ1d0Q7CgvOOwVBk7BgMBuLj43F2ds5/jfR0iIwkMSXlvuLm2Wef5caNG7lpoqY4NWuGj4sLKz79lPiUFPZt384vhw6xc+dOrM3MCL56lUXvv89nP//M559/TucePZQ06TJ00Fc2Kmvc/Pn114yYPp2Zr7+OS61aSqNBlQq8vPBydeWtefMe2liKdBAaY6asahA+YIEQyF23FaRp27ZQrx6HH3mEfmPHMue554jNzsbWy4sPly9/sGOqoFT02Dl+/DgHDhyge/fuhIaGsmHDBn766Scmjh2Lf+3afP7TT9T08mLBrFlMfvRRJvr5YVajBtuHDWPY8OEs+eyzBz5Ge1tbroWF3XlHYWYIo0BY1DyVw9WrV3FwcOD27dvIOh2HT55kzuDBZTjqsuHRYcOU95KRAV5eTEtJocZXX+Fsa8uOf/9l/NixfPbFFyQlJXHixAkeffTR8h5yqShLgXCHJEkFrTC7C9x2368nSZIGRRxcL8vyrwXvl2V5FbAKYN68eQ9EoCwXciaqs7/8Qq02bfj333/p3rgxZg0agJsbZiYnBfcjDkLOqVpxaxCWNmVFlpEliQsXLtCoUaM77vby8sLZ2Znp06fj7Oycu2g1bqxatmzJa6+9xqRJk1i9ejURERE888wzdO/eHXQ6hvXowdb9++nVoIHiDrt0CUJDFdeJpyeU0kFSlakqsfPHH3/w0ksv8fro0bz35ZfoJQnUalKSknCwsmLFsmVQu3beE0xS/+zs7OjWrVupX/uu6fkFnRwqlRI7JXUQyjK3Y2Pv2jSlLHBxccElpymDKYOGD2dQgwZMXLqUWa+8wojGjfnjxAk2HTjAHl9fZQNczagqsVMUcnY2z40cyfP9+/PGE09g4eBAnEbD/HXrmPXGG7iVMj3SiI2NjeIgLEwgLMxBqFIpQkch9TsvXbpE/SLGI+eUqHhQAiGSxGdffAFqNW+7u6NSqfjj++8ZOHYsCz7/nOcHD+a2LBPYogXTcrqNV3eqbOxkZ5MdE8O2vXup7+3N4J49WTxsmNJNsgw/f7Z2dsV3EBpjR6VS1mwmAuH27dtZtmwZO3bsyH+NnPkpIS6u1M5bgB49enDs2LHC7/T2pl/btsx9/338fHz4+H//w93Tk/Y5c0m9Ro0YPXkyZmo1786ezc66dck6dw7zAh2TqxOVJW6effZZOnfuTLdu3Rg3diwnjx3jj02b6Ny3r9Jo0LQ+q0YDJo3XHjRarZbkgk0eoHAzRGnR6R5sinFxcHTE3NGRH/74g0fatyczLY3M9HQG9utH52oYQxU5dsLCwujXuzcx8fF89MYbXK9Vi1dffZVa3t68P2cO1yMiqOPpyYlPP1XWSrduoQVo3Ji/9u9/aLX67GxtSSyqBqHxb+P8UkydICwsDH9/f+Lj4wkODeXMhQsVt7aftTV06gSyjJubG/5eXrSoX59WrVuzbtMmNv7wAxcuX2blypXcunXrwa05HyBlJRC+U0bXuSuSog59BVyQZXnpw3jNCoEsw+XLyBERPD51Kh1atSI0MZFX+/R5IGmzRToI75ZibCIaZmRkkJqaeudJtCk5LihLS8u8NMUC/P777wQUkXZjPIXumnPy7unpmZdyaWZG/2efpXv37vy0axfvv/8+4wYPRj54kIPnzuFSqxb1n3xSpERWQbKysnj99depU6cO761YwR+//ELLnM/I5fPn+fDdd+k/YsQDe/0iBcLCakHlCJelSTF+YA7C4uDuDj16sLhGDRp06MCRQ4d49fXX0aal8eKLL/LPiRPF7iIoqHhcv36dk8eO0X/AACxzNjXrly7lZlQUv/z9N+YREeDggIuFBWE//IBZGdQFs7S0JDM7G312NuqCDT0KcxAaBcICC86EhARatGhBahEn1WkZGQ+84LXk7a38nfNznyee4MfwcHqMGEG9xo3RyzI7d+6kf7du+Lu5ERQSwqkjR5hkWmtLULmRZW5s2cJrH36Ir4cH//7zD+qEBEUYLOONgq2tbdECYWH1O01jx+Rw7PLly0rtp4IYBcL7dBDeFUli4aefok9Pp+egQQyYMIF3TOpma21sWDhxIm1btuTpOXPo9thjXL99mysXL2JexPpRUP5cvXqVH374ga1bt+Lm5kaPNm14e+hQOvTtqzygnDfNllot0YU1KSnKQRgWBnZ2iuuxOOh0EBxMikr14B2ExcDNzY1/9u4lPj6ekFOneOrpp5k+ZQrTZs3i/Pnzhbp7BWXP+wsXYmNjw5SpU5FlmTVffknKzZsMfv55hg0ezPTBg3F3deXRLl3Yt38/n8fEsO2DD2jZtSst+/alT7duismlTh1wcoKkJJAknEz6DjxonB0diYyOvvOOwsT1YmZL3b59Gzc3N5ycnNiwYwc9unQp0zrrDwRJAltbhnTuTP369Rk9Zw5+rq7M/fhjohISaNiwIatWreK1114r75GWmDIRCGVZfigCIdABGA2ckSTpVM5ts2RZ/vMhvf5DJSgoCB8bG3auXYuLnR1Bly4hq9UcP32a1OxsOnz88QNpFHDPFOPCHIQmt61fv56///6bdevWFf0isszlW7eoZ2zBXgh3q/Xk4ODAsGHD6NmzZ6H3N2/enLFjx9K3b1/GjBnD//73P+JjYjh7/jySXs+yo0d56q23uBweXqQIKag8yFevInl68vyUKQTUqsXP69aRun8/tianT/UCAli1YcMDHYelhQXphW3W7uYgLEWK8e3oaDxq1ry/wd4PZma4NG7MmlWrCA4L49nnnsNco+HHn37itZdf5u333sPOzq78xicoFdevX6dTp044a7Uc27OHRZ98QtjJk7y6aBHbtm1ThLVatXIfb9anT5nMQZIkYW1hQWpKCnYFBcKiXFCFxE5SUhJpaWlkZmZiUUjn4AcqchSBubU1w954A4ApQ4agy8ggyWCgc69euDs5ER0fj0qScLWx4fFnnnmoYxOULQaDgaFdu/Lp0qU8t3Ahvo0a8drs2UqGxwOqB2VrZ6ek5xfG3Q6mCsTOtWvXSCqs6HyOQBgeF/dg6t7mYO7mxsfLlyO7ufFmTj1qU1584QVwd2dPjx58tHgx2fv3M+bJJ+nfsydj3nxTKSsiSshUKL748ENeHDoUtZMT3hYWTO7ZE1XduuU9rFy0JTFDAFy9qsx/xRUIo6LAzo6k7Gw87jOzq6zw9vbG29ubJk2acNzfnyefeYbFy5eTmppKaGgoria1UQUPhsO7dqE2M2PK1Kn8/PPPLP3oI5p6eTF/5UrG9uzJm4sXI+Vk73QzM+Px/ftp6eODVKsWW3bvxtbOThGqjTxEYdBI28BAgs6dIyMjI7+IZ5ppaPw+Nmksd+PGDZYtW8Ynn3xyxzWjoqJwc3PD0dGR3w8cYEwlWg+9++abud8L/YYN459DhwhJTmbW2LE8+uqrTJ06tdA1aUWmUjUpkWV5P3kH81WWP//8k4iICCZNmoSDnR0OWi1Zej0GjYaJEybwbrduyuKuceMH8volalJSiHU4IiKC4ODgu7+ILHM5OPiuAuG9+PHHH4u8T6VSsWzZMgAWLFjA119/TYsWLRjx9NN0bN2a7r1788uxY+w8doyw48ex1OvRPKDfp+DBEn3mDN0fe4whffqwbcsWrnzzDaqTJ7Ft3Pihu0RL5SAsRCB84403eO211wp34coyt2NiCGjdugxHXjoGDRuW7+cfli/nhbffpn3bthw+evS+yx0IHh4xMTH06dOHOVOm0MfTk1ZTptDVx4d3Nm5k6pQptMhp5JOPMjygsrG0JDkxEbuCn/nCCl4X4b5NSUkBIDExEbeCgoxOR3RycqFp8w+Ld155BTkzE6lNGz7KymLLH3/Qq107Tp85wxMjR9L2scceeOkAQdkSERFBREQELVq0YNtvv/Hrvn3wyivciIpiy8GDaB5w92pbW1sSUlLo2LEje/bsyXPHGoX1uzkITbh+/XrhAqFeD46OhMgyPg86/bNmTSRg4cKFd97XoAEAXo6OfLR8OXt//pl333+fmUuW8NTUqbRr355Pli2j632UCBGUHXq9nh82beKf1aupb26uuAV1OqX2cwXB0tKSjOI6CHU6SE4uWb11nQ60Wq5du0Zfo2uyAuHdqhUHFi3i5JUrfLZtG1989hkNfXzISkxk/bZtTH/9dXqbdJQWlA2Xb9wgLSODf//9l+nTp7PulVfo1qlTXo0+kzWKS+PG/Pbqq0ppCpUKr5wMhfLG3s6OJv7+HNy/n+4NG0JICDRtmi92zp8/D0CAiUB46dIlduzYwdWrV0lPT89X8z0qKgp3d3ccHR05dPAg791n6ZqHikmpNDNPTz6aN09xdqam0qR+fZYvX05gYCA3btxg0KBB/P7770yYMKH8xlsMKpVAWB3IyspizJgxJCUlsW7dOjRXrvDk0KFK98UaNbC0tFQWd2ZmD8Q9CMqkmZqz0cpHMbsYR0dHExISQlpaGubm5vm6L+ViMNy3QFhcnnvuOZ599tl8heE3/fwzk8aNo2W9ejTt2pU6np4M7NWLiPh4ps+ejYev7wMfl+D+SExM5PTRo7zy4ov41qvH/FWrmD1yJNbduyvNR8qBIsX1EtYgXLt2LY8//jjt27cv9FqRUVEVUkjwatOGzUuW8Py8ebRu0YKGDRrw7OTJ9H3ssfIemuAeLFq0iB49evBcp05Qpw5zRo7k3c2b6T90KG++9dYDf31bK6tcgS8fhXViLcJBaHx+QkLCnQJhWho3ExOpZeKAfOj4+SHlvBdzc3OGPPEEAB1q1GBinz5Me+klfty0qfzGJygR2ZmZDH78cc6eP8/s2bP5ce1anuzQgU379vHZu+8+cHEQlA7gyWlpHDhwgIhr16iVna1s1O5WMzpHIBw3bhyzZs2iXr16RToI5exsToWFEZKUxCMPsT7cvejy5JN0GTiQnh07Mmb4cE6fPct3K1YIgbCCsO2336jh5ET9xx+HmBhwdMyX0l4R0Gq1pBcmEBa210lIUH7Ozi7+C+h0YGbGxYsXi6yLW66YmaHu1YtWffsyo0YNBr3yCrYWFmQDTlZWTHvxRf47f/6hfI9VF3Q6HTfCwjDXaBj02GN8+9ZbdGvXDlq0UES2ggekbm7Qpo1S2qcioVLRs107Fs6dS93p06np4qII6CYpxh999BF2dnZ83Lt37notLi6OuLg41q5dS1xcHMtNGuVERUXRunVrnJycyNbp8K+IMVMcJAmMmYnh4XwxeTKdZ8zA1tYWrVbL+++/z/Xr12nTpg2NK7AxSRSKKmeic3L45ZyJaMeOHQQEBBAdHc2owYN5ql07VAEBqJo0wcnJSREIzc0fmDgISpOSXNt9eDjcvq38u5BTNYNOh1wgxTg6OpqwsDAmTZrE+vXr73yByEgID+fSjRv4+/s/qLeRj4JdIzt168almzd5/+OP6dixI+nm5nz9xx9kZGTQpk0b9u7ezb59+zh27BhHDx9+KGMUFI8rV67w1htvEBgYyAsvvEDnjh35fccOXnnuOZ5bsKDcxEFQHIRpRaUYl6AGoXESLZQcB2G51SC8G5aWSK1b8+W337JqyhQG1K/P2DFj+Oi113hx/Hhefvllbhu/TwQVA4OBk9u3883XX/NWr17KKba7O9M+/JD9x44xZ86ch1JT0sbSkpTkZGUTduRInvhXWB21ezgIExIS7nyB1FRuJSRQszxT821sikxPmzV1Kv/u28eLL77In39WyaopVY7vly7FKjOToxs2cP7cOZ7u14/vli6lf6tWjJ406aGMQWNujkXOBj7i2jWlTpqpYz0ndr777jteW7Ikz0FoMHD06FFOnz6NwWAgODiY9PR09AVE932HDtFp/Hhu3rz54B2EJUGSwMKCzz/+mFMnTvDu00/z27ZtrF69Onc9LXi4RISH079/f7p17syESZN479VXlc+bm1uFEwdBOdDNdRCGhsLly8q/c2InNSWF1atXK7fFxysNVQoTFItCpyNdpyMyMpLapo3xKhLm5qBW03jgQK5u28bJv//mzJUr7P3nH+q6uPD8c8+JeCojZFnmwvnzeDo50apuXQa3bcvAHj0gMFAR0GrXzp86DEr8VMS1viTx6ujRONnY8MXffysHAFlZ+XSCI0eOEBYWli/FOD4+nvj4eGLCw4mLjMy7nl7P7dOncXNxwdHREQuNBp+qYNTx8qKOtzfntmzh9MaN/PL++9hYWTFt4kQ+eP/98h7dXREOwnLk5s2b+Pn50bN7dyJCQ/nnwAHeffttnunWDfv4eKXDl6/vw0+TNLXdX7+unAp07gyyzMlr1wg0SW2c8NFHPDpqFENN3EzR0dHo9Xr+/PPPwtXxhAQwM+PctWuFdjB+mLTv2pX2XbsSHx+PWq3Gzs6Ojh98wPARI3BzdSU+NpbMrCzOnD6NWwWxdldHju3ezZhJk+jWuTMbt2xhYteurJoyhZ4NG0L37iBJfPTll+U9zOKl50tSfhdUgRPp9PR0MjIyiDXprrdhwwbCwsKUQrd6PZE5VvyKiuTqSuepU+kMtBs1ihcmT6Z348bEZmTwSPv2HDl6tFxTPQV57F23jienTOGT55/Hp2ZNcHVVPpsPuah6rkCYkaHUboqIAG/ve3diNcE0xfgOUlO5FRdHzQp6Km3dsCE/vf46e0JCePHZZ5k2fTqdu3aledOm7Nm7l6ioqDvqsgkePFmpqZgX0oHUkJTEF19/zXsLFxLg5cX306Ypa5t69dj6xx8Pb1OnUmGb0+QnPCREiZm0tLzu3rJMUFAQU6dOxcHamiVvv53rIExOTiY4OJi5c+fi5OREamoqycnJOOh0YGUFVlb8tmMHqenpnDx5smIJhDnU79iRixs2oHJzo26NGry3eDFxt2/z+ltv3XEwLHhwyAYDw/v2pU2zZvTv2hWf+fOpW1E7kOZgaWmZZ4ZISVFEQMgVOS5eucLcuXN55plnlHWajU2JHYRXQkKoU6dO4ZlUFQkLi9yyVRKAkxMbFi/mkYkT+e677xg3blx5jq5Sc/HiRcaOHctz48fz7NSpdG/WjI8nT8bD2xtatizv4ZUOlQp7GxsGdejA/06cUOabzMxcvSIpMZHz588rNZ9NmpTExcWRnZ3NzatX0Zse8Op0RCUk4K5W4+jggJ+HB6qKHjPFJTAQ+0OHwM6OOl5e/Pf++ySam+M/bBjnz50joJx1kKKoIr/9ysehQ4f4a9s2ejZrRl0XF6TYWHx9fHiySxcmTJsGly4pG6COHR/62CwtLbl44wb/++UXHreyAjc3ws+fx5CZSetXXyV10CCMpTavR0Rw4swZhpqkc0VHR6PVaklMTCQmJubOF8jOJsXVlYjbt6lbQQoWm3ZSHjp5MkMbNlRqplhZMfOLLxgzdChb9u2r+JN8FeS1F15gw8aNvPHii6xZt44fZsyg18iRinsoOztvI1QBuGuKMeQJhEYXlJkZFHh8fM4i1dRBGBQUREREBCQmkpaQQEJiYoVMMS6MRoGB7D12THEjnziBKj6e6dOns3bt2vIeWrUjKSkpX/OYs//8w8hXXuH7pUvpV6OGkuZSToX+cwXCtDRl7jOKHUV1MZakkjsIo6Jo0avXA3oH94mDA51Gj6ZTZCQjO3ak2/PP8+H771PDxYVbOQdYTz31lGjE8BA5tmsX/Z94gn179lC/RQuCgoL47rvv0CQmsurnn2ncoAG9n3hC+Yzu2aOsGRwcHu4gJQkXOzsaNG1KRFiYEjMJCXl1rAwG1qxZwwsvvMCyTz7JV4MwOTmZf/75h2PHjvHrr78yfPhwkpKScIiJAScnkpyc+PWvv2hcrx4Xrl17oE1K7gdVx46gVvPUhAm07dqVvpMnc2LfPkaMGMHV8HCGDB9OnTp1ynuYVQpZlvnxxx85eeQIDRo0wNLMjNjkZBbPmoXK17dcMzmKi9bSkgxjTcGsLMUMAblzTnx8PLdv30an02FmMIBWm1cnDqUpUVxc3B2HnQkJCTz66KO8NXIko2fPpkslTXu36diRdTNn0mPGDC5cuEBwcDDjxo2jbt26nDhxgo4dO1KjRo3yHmaFJjk5mdHDh3P12jVenTmTAS1b0qdzZxoGBJRLc5EyI2cfU8fNjeshIco+LCsr1yl87PhxPD09FQch5M45xv3NleBgHEwPoQ0GohITccvMxMnBAf+q9LmytoZu3fLWrb6+2NvZMWP4cObOnMmmLVuIjY1lQP/+bPjxxwrjNhYpxg8ZWZaZP38+vXr14u0FC5g+cCCfjRzJulWr2PXjj3y1ZQsqHx/o2VNxRpWD+GGp1fLPsWNMmTqV306fZtH69dTt3Jmvfv4ZvcHA1evXcx8bGRfH+UuX7kgxbtq0ae6/70Cn4/yNGzRo0KBiCm4ODtCpk+KabNWK91asQCXLdAoM5PXx41n03ntFp38K7h9jekd8PAvefJP/bdnCuf37mfLOO/x3/jy9BgwAHx/w88stXF5RuGuTEgCDgV27dnH9+vUii8UbP1umDsKQkBAiIyPh6lWCLS3x9fV9KGmfZYqXF/Trx+yxY9m6ZQu///577mLBYDCQXZKTeUGJ+f777/H09OSHH34gKCiITxYupPugQXzw/vv0mzgRunQpN3EQwMbKineXLOHW5ctKSppR5MtxEH7++efMmjWLrIyMQhv8HDhwIPfzVKhAmJbGrcjI8k0xvhdubtC0Kf5PPEHo0aPcDA5m6rPP8uYTT+BlZ8e333wj0r0eIru3b8fZ3p4WHTrQr18/HhswADdra8wyMzkZFMThkyeV72EzM2jdGpo1e/iDVKk4/NFH9O7dm/DwcLC1JfjCBS5evMiWY8eIiI7mp59+4vnnn0eWZdIyM0GtRtbpSE5OZvfu3bRu3ZoOHTpgZ2dHck4dKTkzk06dOtG/QwfGDR1KjRo1Kq44bRyXvz+1evfmv0OHCI+JYeLLL/Pfnj20b9OGMaNGMXjwYFJNBB5B6Vm6aBHvvfMOdqmpbN+4kVGTJzNz8mRUDRtWCnEQFDPEf1eu0LlzZ0XcyMxU/s75jk1ITESWZaKiopS5RqvN16Rk//79PPnkk+zbt489e/bk3h4aGsrBgwfZuncvNWvUYPDgwQ/9vZUJKhVNu3Vj+YwZ7Ni+ndTERMaOHk2/Pn149plnaNO6NSEhIeU9ygpHamoq27dvJzIykkf79iXQy4tPZswgPSOD75Yt49mxY5U9jJdXeQ+19KhUIMvUcXbm+s2bigPVJHaOHDvG4MGDiYiIwGA0RqhUxOXsa66HhhKXmMiOHTuUskNGgdDKis6PPMKwrl3L8c09ANTqvGzQnEP6l155hUNHjtC5c2caN2rElQsX2PDVV+U4yPxUQHWmavPqq69y4K+/uPLXXxzbt48ePXtCVBTOgYE4VxCrsWXO5B4SHs6od94hMCCAWp6e/LR1KwAXr16lUffuAETGxyNfvIg+Oxu1LCOjdMN8rFMnwsLC8gTClJS8lLXsbM5cuVKhi3OaugDMzM35Y88efl2/nutnznDp8GEaL1/Ojxs3olarOXfuHEOHDs3nQhSUnPT0dPQ6HV+9/jpbL1zgwJEjdG7UiD3vvYeDsValhYXSzauCYmWasmKKiYPwrbfeYvjjjzO9U6fcJiU3b97k2rVrdO/ePVcgNBWhQ0JClALymZlcj46uMCdMJUatxqF5c6YNGcLbs2cTHRdH08aN2btvH7bW1vzz7780qGCib1Xg8qVLzHrzTZYvWsSMV18lKzub/i1asPHrr+k6ZIjyoHLe1L01ejQvrV7N33v3MnbIEIiNhawsYuLjMRgMfPXVV1y4cIHHWrWifd26ubGzc+dOXFxcGD9+PM1yBBpTgfD777+nXbt21EtN5VZ4eMUWCI1IEtSujQoYOW0aAM0+/5znlyxhww8/sHLlygrjvq/K/HvwIO/Pm8djNWrQc8ECHm/ZkjkdOsDEiVCwfnJ5zf+ShL2lJV5eXmzaupUVe/dy4dw5Uq2sOH3wIEPi4pAkiZo1a+Li4EB0XBy1tFrSU1MxGAxkZGTQNCfFydbWVpln1GpO/vcfqampfD5zJkG3b7P/3LnyeX8lRZIw9/Lif3/9RVxcHHUtLYk/e5aXP/+c0Fu3mD59Ot7e3sydO7e8R1ppuXr1Ku8vXsyhDz7A39cXrK25efkyNStgp967odVqSUpN5eLFi4o4qFLla7QQnzOPhIeH46VW3yEQRkZGEhMTw9atW0lNTaV7zr7IuO/5dc8eli5YwIjRox/uGytLvL0Z3qIFw1u1wpCZicFg4PH33mPBE09wLSmJiWPGsGPPHpHOb8LLL7/Mob//Jjg8nA6NG7Ni+XKyPT3xbdYM2zZtynt4ZUOOg9DVyorMzEz+u3KFRhYWxCQn4wEcOX6cUePG8eOPPxKTkoIb5BMIdTodcUlJzJ8/n/HjxzNiwAAMsoyNuTmd2rd/6KXVygOrunVZMW0aNzMy6DxyJPHZ2by4fDm9H3+c1atXs2rVqnIdnxAIHzDR0dHYmJnx7Y8/snXrVs7+9x+nPv8ch+xsBnbpktcWvAK5gbRaLQCDunZFY2PDxlWrWPHppzy/aBFWFhb8feAAtr6+dOzYkczsbEJDQ/GeMIFTZ8+SmJKCuUbDS0OG0LZPHz7//HPlonv3csPfn+82bGBer14cCAqiaWBgOb7LkqG2smLoM88op4jnz/Pnb78xauRIsnU62rRuzapVq/j999+Ji4ujUaNGYrIsAfLt2/y1YwcTZs7kdkwMfVq0YHKPHvz22mtY1aypLNwqqnOhAJZaLckpKbwzYwZPDhtGI2O9zpwFZ1JCAkFBQbQPDMzXaOHPP/9k+/btdO/ePdcFZeogDA0NVZyJ2dlcz6lpU2nx8mLuhAnMHTKEE9HR3Lx2jbXPPcfv//7L4wMH8t/p07nfQYL75/KZM3To1Il3xoxhQrNmdPngA1Kzsmhat67ikq4gNPX3p1/Pnpw9fx6efhqdRkPoxYss/+EHDAYDoaGhtG/fnms3b9K+Xr1cB+F3331HvXr1iIyMxM7ODrVardQgDA/n2MGDjB8/njlz5jCzaVNi4+LwqqSn9p2HD+e/unVZduoU7dq1488//8TX15f//e9/uLq68vjjj4t55z44ePAg2dnZXNi5k+4DB/Llt9+y/9QpvuvbF7P0dP56/XVU7u7g6an8qShIEkgSXh4ebD9yhH1nz1Lf0xNrNzeuRkTw54ED+Pn5AeDq4EBMQgK1vL1JTkpCo9GQnZ1N05zPjZ2dnSIQ2tvzy44dPPHEE0h6Pa1atGDz5s3l+S5LjJOTE045KXyOjo58n53NvrNn6fn221hZWjJw4ECsra2pU6dOxXVGVjCyMjP5YNo0Vvz8M4smTsS/Tx9lTezrS60GDSpdyqSllRWg7NOyUlMxd3BQBMKc240CYUREhOL2srAAvR6DTkf/AQMYOHAgiYmJJCQk5FuvGUsrRcbGEtCw4UN9T2WOhYWyToiNRWVtjUqvZ+t770GNGujS0vhj5EgWzp3LC6+8IkwSQFhYGL9s2sSV1av5LzqaJs2bo6pTBwtJosugQeU9vLJDpYKsLCQzM+rUqUPL3r15onNnNu3dS+jXX3MkKIjlK1bg5elJWFxcrkAYHx+Ps7MzsbGxJCQnc/PmTS5cuEBU69a4OTggGQzKd0p1+E6WJB574QW4cQO8vDAA0ief8NRTTxESEsIHH3yAw8MuWWKCEAgfEFlZWfTq1Yvjx4+jz86md69ejB4wgHbDhuHQsWOFnkgtLS3RmJnxw5tvom7ZEiSJpjmui96BgXz+/ff8e/Ik//vtNzydnLBzd+fMmTP8tXMnYyZMwNfbm8Z16mDj4sK8efNyO7h+tXo17y9dShtLS7b+9RcfLFtWvm+0NKjV0KQJ/a2suNGlCzqDAQtJYtZPP+Hv74+1tTWBgYGsXr26cjhVyhudjtdfeok/Dh/my8mT6dakCdYNGiDVrq04mlSqO2uQVWAstVreXLYMK60Wy8xMGqnV0LQpoZGR7PzrLzwNBiRJIvjWrbwmJXo9t2/fVtJYUJyDHh4euQ5CXWYmt2/fRpZlsjMyuH7rVuUWCCUJ2rQBg4EWhw/Tok8f8PRkfMOGbD15ksd696Znjx4MGz0a38r8PsuB+Ph49u/fz6NdurBx0yZ+27KFgwcO8PbUqbwwfz4AfrIMV6+Wn+OpKCSJxg0a8PmOHUQkJfHT1q188b//4WltjaW5OQkJCbRr105JZzGp3xkSEoKZmRnJycncuHEDT09PxUGYkcEX335Ljx49OHjgAKft7AgICKi8YoCzM2Y+PrxqMFB7xgx69eqFJEk82qULpy9d4sCBAyxZsqS8R1mpCAsN5eMlS9BlZbH+xx8xAAlJSfitW4eZLLNk7lxccwRljZubslmuQDVvc5EkvHKaopiZmXHi6lWsw8NJTUvj4JkzPPXUUwC42NsTEx8PNWuSnJSEt7c3UVFRNPHxAVnOFQgPXL/O6s2b2b13ryKYVMRSMCXBygpat6ZT48bcrlWLT7ZsoVWrVrg6O/NIu3a88847NGrWTAjshXD75EnOXbrE/w4cYNfOndRxceGnTZvo0KlT/k28s3P5DbKUGA8i1Wo1769dS7alJe9On644BVG+CyBHIPTwUNZs5uYkxcSwY8cOAurWJSEujoSEBKUETA7R0dG5JWDqVdCmWCVCqwXTmnCPPAIWFpipVKz9/HPGTp3Kn9u3c+DYsfIbYzmyetUq3l+wgDr+/pw4eZLXBw/G5ZFH6FGV6ugVRKXKbYbl7+9P7Zo1+WXbNjxdXfn0jz+QAB8fH2p4eREWF0dgznPi4uOpU6cOqSkpmJuZERYWpgiEt2/j5uioiIN6feWfc4qLo2PuWlyl0/Hp5Mk8vXQpHRs35n+//YaziwuBgYHlUuuzmvwPPDyOHTvGL7/8wrVr17DWaknesoXEpCQcLS0VwaNOnQotDgJ4uLnRrWVLtNnZSqptZiZNcj6cQ9q3Z/fZs1y5coUb16/j4ejIpj//ZNLgwRw5cgRZlnF1dFSsx66uitVer1cKGm/aROPGjRk0ezbfrV5dubuY1q6NWUYGZtnZYGnJ+2PG8N7AgejT0/ng3395pF07Tp0+Xbnf4wMgIyICTUYGIbLMZ598Qnp0NL/+8w9nTp5Ufld79yp1uEy7RlYgd+29MBa8XjR5MsfPnIGwMKhdm71BQcz74QeGq9X079+fm9ev54kcOh2RkZFERUXx7rvv8scff+Dv76+cSCcnE7FjR+7nKCo6mus3b9KlZ8/yfJv3T04hY0y7HNatyzeffsqmn37ieFAQbZYto17NmnyxciVN27Ytn3FWEuLi4rC1teW1qVNZu3Ej7g4OuDs48NJjjzF9/HjaDBiQ92BJujM9siKgUtGkQQP2/fcfNRo3xtrSkmydjpsGAxYaDV5eXvj7+/PP77/n1e/U6QgJCcntWhwXF0e9evUUgVCv5/CZM3z29dcMGTKEI/Xq0bKClPEoNQEB0KABQ7y8aFqzJm4WFtjVqkVccDDNpkwh4uZNFixeXHlLEDwEoqKisLW1RZ+WRofWrRnYujU1vL3Zt2YNxw4dws7BgdELF/LFzJk8/cYbeU+0tS2/Qd8LlYrajo480bkzfu3asW3zZq6GhCiNf9LT8zkIo+PiQKUiJSUFW1tbflq7loDYWDAYcmsQfvzTTyyYMIEmTZrAv/9Wjc2auzvIMg7W1rzZtCnP9umDk4cHwxYsoEvnzny4YAHjX365vEdZobh49Ci9BwzA08mJRwIC+GzMGDqPG4e6iggf9vb21K1RA3sPDzYfOIDG1pZ3MzKQ9Xo6zZxJzbp1cXZ2Vmp7NmuWKxDG56QQnz9/npS0NGJiYvIEwjNniI6MpEWLFsSHh2NZkb83SotJOZJ63brx72+/4daiBVs2b+bQwYOMGj+egICAchzgw+HZsWNJiY9nz6FD/DJnDlGxsTzy3HO4deny8LrYlxcODpCRAY6OrFq1CjsrK659/z2nk5MZ/vrrTH/2WSRJwqdGDUJyHLUGSSI+Pp5mzZsTcesWZioVaZmZikAYHY2bk5NSwzA7u3o4CAtiZka3YcO42bAha/bsYeKkSdTx9KRFy5b8+NtvD384D/0VqyiyLHPy5EkGDx7MyJEjaeHry8SWLVF5e+Po5gb//Qft21eKhVY9f392LFqkdIjMqbthq1bz1fz5jGjShN7Dh9Nn8mR27dqFh6Mj3t7eeLu6EnTyJAMGDGDqkCGg12NlZaUUxU5J4e/jx9Gam7N69Wq2LV/OiFGjyvtt3h8qFRRoTa4KC0Ol1fKWhwfxISH07NyZebNn07xRI2JTUwls167yNZYoIxITE/nxhx94a+ZMHG1tiUlM5JnevfFzdubv3btx8fZWHtitW8V0aBSTmZMm8cozz3Bu716+++cfspydWfHppySGhxMSE8PGX3/l408+YcL48fmalBgFwkOHDnH06FHGjx/P33//DdnZhIaE4OPjQ1ZWFkPfe49rcXF89sUX5f1Wyx61GtsmTRjfpAnjMzJ4+fvv2XXmDH0HDODZF1/E3sGB9oGB1PT2xjNnw1vdMdy+zeGzZ+k7aBAZmZl4OTlxac0aYtPTadGpE5KtrdLVtDIgSdRyc0OtUvHOO+9glZ7OoePH+ffoUaITE2nq7U2dOnX4OiQkV1w3ZGcTFhZGaGho7mW8vb1JTEwkPj6e0OhounTpgqe7O6u3beOlmTPL8Q2WESoVNG5MXTMz5cDR0hInDw/+XrmS5d98w7iRIxk6ciQJISHMfO89NEYxvpojyzIvPfMM6zduRKNS0a5+fdq3aMGnixYpKT69e9MgpzHc6Ro1qJlTT6xSIEnYxcezac0azmVl0djNjQ+//JLaLi7878iRXIEw10GoUpGcnIytrS2tAwNh924wGHJrEIbHxNDY+L2h01WKdWuxkCRwdcXCxQXPBg1ApeI3c3OOXL7M4LfeonnnzgRWotI3D5JLly7RrX9/Fs2cydhRo5TPgJ1drruuKmBrZ8eV9esZ9NFH/C8oCDMzM9ITE7mZlMSBCxe4nZZGQECA4iA0loLSaHIFwguXLwNw69atPIHw9m2iIyIYNmwYXbTaqhM7RaFSofH3p2+rVgweOpRJffrQu0cPZs6aRf369endu3d5j7DskWX++f57tv75J26uriydOpVHJkxQxK34+KovDoKyT3N2Bo0G5xz3cANvb2o5OrLi+eeZNGcOAHXr1OHa6dMkJSXRaORIouLi8PPz43xQEGpJwtXbmzNnznDj5k3cnZyUPVFWVvUUCAF8fTFzd+eZli0ZNXAgkpcXddq145mJE5k3fz5Z6enMeeMNuvbuzaTJkx/oUKr4N9eDRafTsWzZMn7+8Ucy4+NJTEvj1VGjmP7ss0oKV4cOeY05KlCtp3siSUpaiaur8nOOkDGhf3+IjMTd2ZkWLVqwbft22uSk33i6uLDhn394+eWX6dW+PcTGIkkSrq6uRIaH8/q337Jo5kzatG5Nm2HDquakaTxVbd+exZ9/zh/ffMP0V18lJT0dWysr2nbsyOy33yYgIKDqp7JERyuTh0pFUFAQI4cOpaG3N78vWoS+QQPqp6fj1rmzkrJluoGtxOIgKOn5lmo1AZ6eXLh6leW//86MxYvp1749ALejoujXrx/Z2dl8s2ULoxo2xDwnxTglJUUplg3UrVuXTZs2gV7PX8eP0yhnkbp7925uR0VV/VovWi31hgyh3rhxNGnYkP8dPMj5uDhWLF1KZnY2MyZPZsATT+DTuHG1E93T0tLYuXMnISdOsHTVKlLS0/n+5Zfp16YNmkaNUPn64qvXV75YUqmQUlI4vGIF9UeNQh0dzcpPPkGdns7u06fxzhEIrxnT89VqoqKj0ev1GEzKEHh7e3P8+HGOnjpFSz8/zGSZd159leHPPUerVq3K8Q2WIWp1/gMqb2/qenvz0SOPMHnUKE7v2cO169c5f/kya3/5pfKmVZcBt0NC+PWzz7h08yYHjh0jZM8erqemMnX2bD5culQRWR0dlXko53vVt3fvyrXJU6ng9m1o3JhGWi2NbGz4/Y8/CPD05MSNG3kOQnt7Ll69Su8JE7DVarG1tc0r4aHX56YYh8fE4GV0PlUlgdCIsf4vQOfOtO3cmcW3btGne3ecnZxwtLKicZs21PX353VTF2lVJTMTgKvBwYwYNoyImBjM1GrmjRzJ2GnT8q/RqhI5nVg93dwAcHN15eTZs5zL6XR99eZNevTtqxxAGQVCtTq3CdatsDAAbt68SVZWFqmpqVgbDERHRfFIt260dnSserFTBFPHjaN3ixaMnzuXgcuX89vhw3yydCkvvvQSr7z6ankPr9TExcWh1WrR6XRMmTKFOXPmcGL7dl6cPZvvv/+efgMH5n9CVXSMFkXNmpCenvezuTmWwGSTZkV1a9fmwLZtbNu2jbCYGLRaLV5eXrjY2yPJMs516pCens6/Bw/i7+wsBEIAS0vUNWpgm6MpbFuxgu9+/ZXeXbsiGwz0adaM12bM4MmnnsLc3Jzf1q/n8eHDsS7jz171+OYqYw7+/DPfr13LvosX8bCx4d2nnkLv6UnPHj1QJyTAxYvKl4RRHKxsGDfcph82rVb5IsiZULt06cI333zDwJxOxB7OzqSnpysF4PX63EWni4sL7y5ahKu9PQPats0rPlrFBTK1uzuD3niDPu3aIWs0SDY2zFywgMcHDCAmLo62TZqw+LPPaN68eXkPtWy5fh19Vhabvv6aoOhoLt26xfFz53h/9GjGPP20suny8CjvUT44VCrIyMDe0REnJycWfPkldX182HXsGJ0CAtC4uKDVaqnl7c2Ed9+lZseO9JBlIiMjUavVXL9+HUtLS3x9fUlPTyc4OJhP//iD40eOMG/hQvq2alX1xUEjOWnVnSZPplOfPsomxtqar1at4p8jR3jzww/J0un4aPZsXsw5razSyDIYDEydNImTx47RzM+Pb77/nq6tWimbkKNHwcdH+X6tjIurnIOpgMaNlfFbWDCpb19Gt2pF11mz8Pb2pkaNGqSkpXE9NJQ6jRsTGhlJw4YNOXfuHJ5OTkTExVGzZk22b9/OqQsXaOnnB+npDOvfH+cvv6zy7iAzFxe+3rABrl0j3cODRwcO5NF+/Qhs2ZJmzZpRu2ZNGjdujLWdXXkPteyJjibx7Fn0zZrh5OREVFQU586dY9SwYbQOCMDC1pY9v/+ObaNGNAP27tuX99yCTUcqWwqlJCkHckZ3l5kZUwYOxNXKise6daN5zgGVi50dc5YupVaNGqSlpdG1Z888gTAnxTgyMpKIuDg8HRyU9VpVFAhNyVnvjn73XQY9/jhn//2XzUFBXLt4kb937ODfvXuZMXMmXbt2Ld9xPiBkg4FNixez5dAhthw8yLuTJtHZ35/Dly4x4aWXqq44CErcyDJebm7YWFry1JNPsnDtWgxWVjja2BCfkkLTpk05dOhQnkCoUhGfUx/aSFZWFpaWlty+fZs6ej3R0dG4urhUK6Gj/eOP075XL7C3p9/48fSLiiLk2jVaT56MWc7aZeILL2BdyfbFb77wAo5ubsQmJHDxxAlatWiBmUrF7s2badatW3kPr3wpOE+ameV1+c6ZV+r6+nI1PJzNmzcza/RoToSHU69ePZr7+xMSGYmPjw+yLPP37t10GDNGiZfMzKo955SQlk89RYvGjflpyxay4uIY/fbb3B4wgAb+/sQlJOBobU18ZGSZ74PE/8DdCA3lQmgo58+exbN+fY6dOMHJkyfZ/vvvzHzxRUZ36UK7nj1Ru7kpoockKelcen3lPkUwinemmwiNJq+brMHAmDFjaFi7Nt7h4QB45Gzma9SokduUBGDYsGEs++QTfn/tNaSMDKW2QDUKfMsOHXIXFZ8uXsynMTHEhIWx+dAhenfvzgcLFtBnyBBcHRwwM6nrUZmQZRmALxcsYOOvv3IpJIS6tWvTv1kzHu/alR/mz8fK17fybbpKgyQpdTksLFi6dCmNa9VizUcfsXTjRhaOGYN1zkbt7enT+XXzZvYdOEDjnA6s9erV41ZwMBOHDaNhw4bUr1+fj1eupHvTpvi6uzN88GDsck6sqxVmZmCSUjzx3XeZKEnEXLtG3IkT9HjhBaxsbKjp5UW3oUOrpKPwwN69LJs7l/CYGCITEjixYwd2vr75D6Eqe11KlQoSE/NSotVq1AYDVlotdTw88K5RA5VKxZuTJjH9/ff5348/EhIZSZ06dbh9+zaNfXyIiIujadOmBAcHc+HaNR7x8VEOtrKy6NmpU6WqZ1pqnJ3B2RlLYMuqVaz/9VcikpP5ctEiEpKSCI2J4dvPPuOxMWPKe6Rlg8EAiYns+uknhr/1Fmbm5rQNDOTfI0dwsLJixXPPMfCNN3I7k1ZJVCrlcMCIWk3nhg2V+cjSMnfN5eXkRE0fHxbOmMGol1/O7yDMEQgPHDiAtVaL1sJCWfNVl46SgG2rVrRv3Jj2ObWBk69cYeM33zBxxAjq1K3L3FdfpeOAAUhVZQ2bkMDrkyez8+RJnh84kAVTplCzf3/IzqYRVG1xEJT4MBjwtLOjvq8vCxcv5p0xYzgeHs7EXr34cPNmmjdvzs2bN5U4UasVgTDHQQjg5uBAVEIC9XLWccf37iU4JARXJyeIiqryZohcHByUP6Cs9WvUwCcwkE+Cg5n78cfUdHfn0qlTfL5hQ3mOssS8M2cODdu1w8/Tk10//4w+IoKU27epZVo/W6Cg0eQzEgH41arF9fBwwnfsYNnatbi3awcuLvSwtmbmp59Sv2FDrK2t2bhxI+7CQVg4KhVS48YMzzFkAXz41ltcP3OG9n368E9QENPnzeOZmTMxL8PMoSoyy5UxycmkxcUx7bnn+OPIEVr7+3MzLo72gYG0dHZm8apVuD/5ZJ7YVXACaNasfMZdVhQmEOYUhEetBllGkiTatGoFhw4BhQiEej0AM2fOZObkyXD8uPLFkZ1d9Rcdppi+Vz8/8PPDBXhmwAA69urFkEmTmPbmm9iYm/NIq1a8vWQJ7jldOOvWrVtuw74XZ//9l5iMDLZv3conK1ZgodFQv0YN3l2yhHpNmlDL17fqp1EXhiQpn3MLC4YOHQo6Hc19fFCpVLStVw9Nzhf8U48+ilVmJmPeeYdFKSloLCyoXbs2cno6y2bPBj8/HnnkEb5av573Ro6EzEz6du0K58+X7/urCOR8rlz8/HDx82NlQgLPv/UWjnZ2hD77LFYWFvjXrk271q154913sbW3L+cBlwC9HvnGDU4mJHA7MpKjQUGcOH6c40ePMmfiRGq3bEmXWrXQmiwUqgxqNaSm5jUoMjNT5guVivfGjMHh8ccBeGX0aHz69OHcpUv8vGcPtRo0IDg4mMY1a/LXyZO4ubnh7u7OzkOHGPfII0qnvaysypdyXQZYtWzJM66uoNMxd/58cHDgyN9/M3TECNZu3szihQup3bBheQ+zVNyOiMDW1pYPX3qJjIwMvt65k03r1mEeFcX1sDC+f/NN7AMCwN6+StVNK5SmTfN3kTWu17TaPAEQGNC2LR2efprgc+cA7hAIGzZsyOuvv46Pk1Ne1kg1yPjIxSioAmg02DZtysR33mHMzJl8+9FHTJ42Dds33+STxYvx9PCgdkCAIjxXooOHt956i8GDB9OqVSs2r1zJzwcPEnTyJM6Ojnmb8uqyRlepIDubzm5uqF54AUtLSxY9+yx4eLBl40Y+3LyZOnXqoNPpSExMxF6lYvLCheg0Guzt7UlMTKSOhweJ6en4+voSER7OM8uXk5SWpjRrLOA0rI4Mf/VVhr/4IjevXKFlp04szczEwsICAL1eX+HLX3g0asTxXbuoUb8+WgcHCAjAOSmpUsX8Q8PoIMwxEgFYW1nhZGdHi3btcHdxyTffLJ48Gbp25aeffgLAzSgQpqfnlvsQFI5Xly54dewIWi3d6tfHd+VK/H19mTR+PJdu3GDEqFFcv36dUaNG4VTKxrjVXiDMzs4mIiwMJxcXbGxs+GPtWtZ88QWnrl+na5s2XD58GFsfH6UbaUqKcjJiXCxV9UnU1J1i3KyZm+eeDGAw5P4ujDU8vLy8ICYm36IUvV6pNZeaqixaq/rvrTiYm9Owd2/OnT6NdPMml1NS2Przz3Tt1In0rCxsraxoFxjI+AkT6NSzJ47GepAVhE8/+4yL58/TsE4dLv/8M9Y1aiiFan19y3to5UtOinGuuG5mRis/P/xr1EBjYZEXO7LMI4GBJCcn08jHh1S1Gjc3N3RxcbnieocOHVi9ejWPNGyoXFOtrlbu2+LSf/x4bj72GLi5EX7wIBkGA+eOH+eHbdvo2aED8+fNw8/NjboVvQ7s9evcunKFybNmcSUigjru7tT09mZYQAArJ0/Go3v3/N29qxoNGiiihLHzu3HOUavx8/LKXTBaaDQ89eijdHnsMTo2bMjHs2dz7do1muV899jY2NCgQQN27NhBPT8/5RpZWVX7d1cUajUU6GjctkcPLv79N0vff5+OnTpx8OhREpKTMTMzIyIkhK49e2JWQb9nDAYDU0aN4tvffkMlScrBS6NGdOjXjx9Gj6bro48C8Eg5j/OhU3B9YNx05xzoGlFJEs6urhhq1gRyBMKc+Qa9npYtW5KamoqXn58Si2lpYs6xs0NjZ8czS5YwafFi3hozhmemTCEqIYHk9HQWvfIK0xYtKu9RFouL587x2bJlfLt6NW9Mm8aCDz5gy/btOFew9eVDQ5IgO5t6TZpQr3Vr5bacz71vTg1SR0dHatWqxc3ISJqqVPyxbx8ac3Pq16/P0aNHqe3uzo34eHx8fDh65AgA/Tp0UByEN2+W1zurOOSI7rWaNKGJry/Txo3jekgIBp2Ok5cu8d+ZM+gNBrKzs3NrpVY0/Nq2zftBo8l/GCPIw8xM2fsX0Aka16nDM888k084NM00bJhzSOnm4qJcIyND6AT3QqPJ/R2p1Gp+//tv1i1ZwuI1a7DUaBjz55/07tCBBjVr0ivncL2kVOuZ//j+/QwbOZL01FTlBKhWLZLj4nh39mxmt25N6zZt8k4JKrCbq8wxLhhNT3aMga9S5Q/wnN+Ph6srllotHh4eiq3eVCA0fmEkJVW7FON7ocqx5dcH6nfowPR33kEXFoZsYcEbb7zBRwsX8vKrrzJ1+nQmv/AC1hVkk7tq40ZlAqguzoLiIkmKGGHiWGlYuzYnv/jijsnRycGB8PBwrq9fzxdBQbi5uWEWF5f7mI4dO2JtZaUIHxkZSgyJSfNOzMxy61p6dewIQJ3OnXl06lRWvPUWC95+myvh4ajNzenXpw/+AQFMmDABt5xDjfIiNTWV6OhofH19yQwJYeIzz7DlyBFee/ll/hgzBo2Pj/Kd6eioHLBUdbRaRSQ0YhQ3jOkqJgvOicOGce7GDTa88QZW7u6sWbUKl6NHeX7lSmxsbGjYsCH7//0XT09PZc7JzKyWDsKisAoIYPaqVdjPm0fTJk2w1mox02iwUKlwd3fnyT59ePm995AqkLvjQlAQ/QYMwM/Dg+A//8TZ3Z0D27fTeuhQtKbptYK8NZbRSWgkZ83m4uKC1sICGxubfHOStbU1TZo0wcvRUYmXjAyxXjNBUqlY+OmnLExLQydJ3IqMpN+gQfyyZw/2Wi1jxo6lZZMm+LVpU95DzUdGRgY7t27lw3ff5eWRI2nTpAmff/stP6xcSZt27cp7eOWHcf1q+v1hYQFpadSuUYPm9etjYWGRKxDWTk0lMiYGgG49e3Lh3DlcbG1xdHSkefPmvPfuuwT4+PDnhx8q85aInTwkiW8XLuTDb79lWN++WDs6cuLAAQb07k10zu/00IED+Pj7l/NABaXG+Hk36gUABgNbly7FrEMHCAoqVCCsV68eKpUKN1fXPAehWK+VDI2Gp2fN4ulZs4g5f5744GD8/f3vq9lapfr2kiTpa2AAECXL8n3nWF04coTF06bx5JgxpB86xKngYFq2b495Vel0WFrc3KBga/rCTqRNBCIra2tuHDiARqPJF/hAvtod4mTg7kj29mhyUiKXbtgAKhV/r1vHiq+/5r333sPL05OEpCQGP/EEHyxZkmvVL5/BCnHwDmxtFfegqT3ezAxLYwyYxo5KhaurK64BAbR97jn+PXqUDE/PXIHez8+PKzt3Yp6aqsSNlZWInRKgMjPjhfff54VZs0iPiiL85Ene/eorrsTHkzVokPI995AxGAzs3bGDbVu3svmPP4iOj8fLzY3s9HSaBgYSHBqKg7GOD1Ttumn3wpjaaPyeMREIA5s25e8dO2D/fgC83N1Bo2HvDz/g6upKgwYNqOfjg6TVKgJJNU0xLhJJAisrpixaxNNTpmBuY4N1RAQ6Jyd+/O47Plu7ljMXL7J8wwasKshnsE79+vxv/Xqatm6NlFPjuVODBiLVqzBM12vGovGQKxBKajU13d3vSDEGaNe2LY6xsUIgLAonJ3BywgyoU6MGpw8dYtuXXxKj1/P9qlW8eOkSwx59lBnz5uFbQcSO9Z9+yvdr1/LUY48xYdYsrKytGTBkSPWoC303jHFiuhYwN4eEBKxtbDiZk/poFAivBQfnPqxFixZE3biBg7U1Dg4OBAYGciM4mE7duikHUtWtnFIxqNW/P8tzXN4ATwwdSqsvvsDO25v/Ll5kQN++bNu9G6/qnolUWTE9mDKZV8yMa6+CBqOcNZ1Wq+Wfdetwd3VVxEGRaXhfuAQE4BIQcN/XqWwz/7fAZ8D3ZXGx0Sat1y0fe4z2ZXHRqkJB4ck08E1TjI2Lc5UKd+PGtqBAaCxybbQOiwVn8cj5guw2dizdRo4kJCiI6CtXsNNoeO2LL1i3Zg0TX3yxnAcpyIerKxQsXqxW58WA6eRoFD5ybu/cubMST0YHL+Dp4qLEm3HBKWKnZKhUYG+Ppb09fv7+fNOli/L7LadO2nq9nvlvvkm3Fi34Yv58OrZuzbVLl0jVaAjs3btMCwxXCYwHS6ZzitFVWDCegNY5tRn79u2LTXi4Mo9lZYnNWlGoVDga3TOOjpgBT8+cyWPPPMOLw4fj5eZG99at6T1oEPaurtRv0IDmzZuXSyMgCxsbmnXvfsf4BYVQRIpx7ryjUlHTzS2/QJgz78ybOxfVP/8oc016uphz7oGFtzeD5swBrZZJej2hN27wxcKFPNKuHU8MGMCcJUvK3a0+ccYMJr72Wv5D3eouDoLy2X7ssTtvy8pSDudyYqNWrVrcOHoU92vXcHVyIjoujnr16vHyypV8tGQJDvb2NGrUCI1GQz1v77w5R8ROfgqYCszc3Bg6bx4AvWWZpJQUGjVpwooFC2jarRurv/ySD7/4oko2nquSmDoICylFdodAaLLX6dSypfKc6lYHtQJTqb69ZFn+V5Ik3/IeR7WksJMB40bNeLtJLZs7BEKj3T6ngYOghGg0+LRrh0+7diDL/NKuXfV2F1Umipo0TcT1fJs0k0kTvT6vWLw4Vbt/7sNuXxZoNBr+PnUq322Ny+Ckr8pibAJm0hXPVOTIjZUCQmGtWrWo1bOn4gZJSxMCYQmxd3Ji3Z9/EnXrFjvWr2fX1q2kZWQQHBvLoZMnUQkhu2JjnHNM5xbIm3dUKmaPGUO9nj2V2DDeB4qLw9Ex70BXrNfujbGkiFqNd926LPz6awbu3cu3S5bQvUMH+g0cyICBA+lSXp1PhcBSfIxZUCbieu/evRmwZAnm9erRp2NH1v3+u+L0NxhwsrXF0cEBCwsLGgcEUK927TyBUHxPFhtJknh3xQqeevxxeo0ahTR/Phq1muCwMIYPH85To0aV9xAF9+JeOkHBNVvBwyu1Ok8gFLFT7lS5WUOSpMmSJB2XJOl4UFBQeQ+n6lDYiXQhLqjc2wvUjMoNfOEgvH8kCZWvL6oyPpUWsfOAKGrSNI0d00mz4IZOq1UWnFlZQuSooIjYeUAYHYSSVCwHYaG1b3U6IRCWBjMz3OrUYfScOXy3cyc/b93Ksb/+yksXKiNE7DwAinIQmsROl6ZNlRqdBRyEuSKiRpPXHEtQYtp16cKXmzYxZ+JE7KKi+N/q1WV6fRE3DwjjPGEyv7QIDOTRVq1Y8uGHdGvfHq2FBY6OjqDXM7RDBxa/+y4Aa7/8kgEdOyrxIxr8lIqm/fpx/tQpVrz2Gsc2b6aLpydeZSxwi9h5QBQj07CwGoT5HicchBWGKicQyrK8SpblVrIst2rZsmV5D6fqUNiJ9N1OBkD5OSxMmShNHYQi8CskInYeEMWZNE3dt4U5CI2NFoSbo0IiYucBYWaWJxAWjB2jwG6aqlIwdiwslLgBIXTcL7a24OVV5pcVsfMAMDpsC9a9Nb2vCPdt7oGumZkSO0LkKDWSVsuwN95gztdfs3TFijK9toibB0QRGR8rX3qJa9euMXrIEJbNnEnt2rXBYMDG0hKfnHTtRvXrY2llpcw7qalir1NKnGvWZNCbb+LRoQMvf/EFHUeMKNPri9h5QBTRpCRfpuG9BELjNUTslDtVTiAUPCDuJXIU5ea4cQPi4vLXIBSBL6hOFMdBWNSkaRQ5dDqR7iWofpimGN+lfuddHYRizhFUR4xZG/eqBVWYUGjcqMmyEAjLAo0GbGzKexSC4lCIgxCDAZWZGbVq1UKj1TJ5yBClQWDBecdYa9rcHFJSxLxzv0iS+P6pTBSlExQ275jeD3mlyIxagSiLUO6I/wFB8bhXinFRJ9JZWcoGzbjg1OnEF76gelFU7BTHQWgwKPGi0Sgn0kIgFFQnTFOMC3QAB/JipzCB0Ciu6/VioyaofpiZ3Xn4VFSqF+Sfg4wpxsbrCATVBVOB8F7rtaLS84WDUFAdKaKLcZHzjrF0TFJS/hRjETcVgkolEEqS9ANwCKgvSVKoJEkTy3tM1YZ7pRgX1qHVYMhzPhlPBUAEv6B6UZwGP4V19srIyL9ZE0WvBdWNuzUpgbzYMRU3ZFlxrRsdhCDmHEH1o6C4freMD9ManwVTvYRAKKhOFOEgvGe2FOR3EIqDKUF1oyQNGY3mB4MBgoIgPl4IhBWMSjXzy7JctoUIBMWnONZh01M0Y+BnZ+eJIWLBKaiOFDd2jJs4Yxzt2aOkJRkXnKJ+p6C6YUw1McYGFO7mML0vMRFOnVL+rdHkn3sEguqCUVw3CoQFnbeFbdTCw+H6dbFeE1RfilqvGTNBCnPfFpybjJkeYr0mqE4UJq4XlfGhUuXFUlaWYiYyHmgJI0SFQMz8guJxL+uw6e3Gk7Ps7LzbhINQUF0xbbRwNwehaVxlZytxlJ6e5yAU6cWC6kZhDsKiYscYX5mZyh9TJ5RYcAqqGwU7gN+trIVGo/ydmqqkezk4CIFQUD0xTa2/26EUKH8b48tYb910vhF7HUF1wiiiF4ydguK6cb1m/NnUSGRnVz5jF9xBpUoxFpQjhdVRM220UNBBqNHkdY803i8EQkF1pDhpkqa1B/V65UQNlL+NDkIhEAqqGwVFDig8doxzTsHTaFDiT4gcguqGWp1/3jGNG6Oz0LhZM27oMjPvrEEoun8LqhMlMUOYxk5MDMTGKvcLB6GgOmL8vN+rJIypgzArK++xKhVYWoKX18Mfu+AOxKpZUDxK2sXY2LHYiLGjnvHfAkF14V4OQtNTNWMcGQVCyBMIhQtKUN0omCYJhbs5TF1QxoMp09NssVETVDcKzjumcQP55x2juK7TKfeZrteEuC6oThg755rudYyiORRuhjB1rhvXayDmHUH1Qq2GwMDi7XWMAmFBI5GgwiD+NwTFoygHYWG1BSRJeVxmZv77jRs142mCQFAdKOjkgKIdhEaRw1QgFCnGguqK0UF4r+ZYpk4OY+yYOgjFRk1Q3Sg47+h0+eOgoEBoOu8YY860NIxAUF3QaIrfidXM7M7YsbDI2wcJBNUJb+87Y8Q0drKzFaetEAgrPGLmFxQP42RXWJtyyO98Mg18S0ulro1RWBSLTUF1ozAH4d1cUBkZ+SdNtRrc3JSJVSCoTpib580td2vwYxQ5srKEg1AgALC3B2vrPPdtVlb+OFCrlc6ROp0iaJi6b0V6vqA6U9BBeLd66+bmeQ5C4/1aLVhZPfxxCwQVgbtlfMTGKrU6LS3vFAiFoF6hEHKtoPg0aaIsJItyECYnwz//5AmJmZnKAtV4v9ioCaojxWm0YBQ5jLGTkZE/thwcwNW1XIYvEJQbnp7QqNG9m5To9fkdhEZRHsDJCWxty2f8AkF5Ubu2crBkPJjKzr7TQRgUpNROM02TNB7wghJ7QugQVDeaNFHWXPdqUmI8mMrOzkvPV6kU0bBbt4c+bIGgQmBqJMrOzjtkMq2Ha5xnMjLyUvKFg7BCIf43BMXHxydP+EtLKzxNMjv7Tgeh0X1oZ6csWgWC6oRWC46ORTdaMMZKUlJeWld6OtjYKPeLUzVBdcWYXn+3JiUhIRAdnT9N0sYmb7Hp76+IhAJBdcS4KcvOzl/H1ih0GDM7jOs309ipUUNs2gTVD2dnJSaKchDq9XDtWl6KcXp6/vtBlFISVF9Mm49kZRUuAJrqBKZGIkGFQfxvCEqGra3i6jh9+s5J04hR5MjMVL4YNJq8U7WaNctn3AJBeWFhAS1b3lmD0LSuZ1gYnDmTVxw+PT3P9SQmTUF1xzSVWJbzYkelUsTB2Nj8nVhtbYWwLhCAEiNpaXemGJvOKxqNMudoNMqBlphzBNUdSVLmkgsXCq+jdv58nivX9EBXxI6guuPkpKy/rl9XYshYP72gTmCMMRE7FRLxvyEoGZIEtWopE2JhJwPGVErjyYBGAx4eyqJTIKjOaLVKzSdZVmz1xknTGDtG0dBUIBQih0CgnDCfPQvh4cqcY+ogNGKaYmxrKxabAgFAw4Zw8qQy5xSsQWjEKHJYWCjxJWJHUN0xxsDNm/kPdAvGhtFBqNXm1cwVCKozKhXUrauUryhMJzDOMcYUY+EgrJCI/w1BydFqlaDOyMgT/oyB7eKSF/jGpgvNmonagwKBu7syMd66lT92Cp6qqVTKgtPOLn9KmEBQXfH3V7rjxcTkP2wq6IIyNvKxshIbNYEAlDqE5uaQmHhnirExloziulariIQidgTVHWNzOL1eWY+ZihzGAypj+STjga+FhRA5BAJQyoulp+cvbWFaF9rUSGRhodRYFw2xKhTim0xQcjQaxQWVkqJ8CYAySXp55QmEWVnK7R4e5TdOgaCi4eWlpEQanYKg/C1JSlwZ/63XK10oRaFrgUDB0lJx4JoKhGp1Xn1B4yGUvb2ysWvU6OGPUSCoiFhZKU3kCqYYu7go/zau4xo3hjp1RCkYgUCSoG1bRbyIj8+LEcgTN0xdUBYWinPdmBkiEFRntFpFIzDWkAbl305OeXVuTY1E7doJcb2CIf43BKVDq1Xq2hgnTUlS6qxZW+fVG3RxEQ4ogcAUKyulXprpIlKtViZMY1pkUpIST1qtcHIIBEYsLZUFp2nsODgoJS9MU7ucnfNiSiAQKPOOLN8pENrZKXFlbQ2PPabEjFYrSsIIBEaMpWEKCoRGM4Rx3nF0VPZAjo7lM06BoCJhTCM21QA0GujQQYkpSYLUVOV2Z+fyGaPgrgg/p6B0GNOMC6YOu7gotxlPpgUCQR7W1oq71t4+7zYHB2jVCq5eVRabKpWSUikQCPIwbtBMxQtXV+Xv4OC802exQRMI8mOMHdP1mr29Mvd07y6cGwJBURQmEKrVytyTmKik5oPY8wgEBbG0LNxRa1yjpacLI1EFRgiEgtJR1CmzEAcFgqKxtFQ2Y6aTpkqlODfs7ZXFZr9+YsMmEBTEykr5u7B5x99fiZ86dcT8IxAUxBg7phux+vXLZywCQWXCON8YYwiUA10rK6VcjLGckqifJhDkx9Ky8LhwcFD+CCo04htNUDq02vwnagKB4N5IkhI3hYkctWs//PEIBJUFY8wUdiLt7q78LeoOCgR3YhQ3RLM4gaBkaLVK3JgKHUYHVNu2Sg01sXYTCO7EaIgQVEqEQCgoHVptXsdIgUBQfKytRY0ngaCkGLuuitgRCEqGtbWyWTMWixcIBMXjXmYI04ZzAoEgD9NSSoJKR6UTCCVJ6gssA9TAGlmWF5XzkKonNWvm1d4QCATFx99fuG8FgtLQsqXSWEEgEBQfjUapNSgQCEqGoyP4+JT3KASCyoevb3mPQHAfVCqBUJIkNfA50AsIBY5JkvS7LMvny3dk1RBRb0MgKB1OTuU9AoGgciJiRyAoHSLVSyAoOdbWSm1bgUAgqEZUthVDG+CqLMvXZVnOAn4EHi/nMQkEAoFAIBAIBAKBQCAQCASVlsomENYAQkx+Ds25LRdJkiZLknRckqTjQUFBD3VwAkFlRsSOQFA6ROwIBKVDxI5AUHJE3AgEpUPEjkBwbyqbQHhPZFleJctyK1mWW7Vs2bK8hyMQVBpE7AgEpUPEjkBQOkTsCAQlR8SNQFA6ROwIBPemsgmEYYBptVjvnNsEAoFAIBAIBAKBQCAQCAQCQSmQZFku7zEUG0mSzIDLQA8UYfAYMFKW5XNFPH4NShqyQFBd8ZZleVJJnyRiR1DNKVXcgIgdQbVHxI5AUDrEek0gKB0idgSC0lFo7FQqgRBAkqT+wCeAGvhaluX3yndEAoFAIBAIBAKBQCAQCAQCQeWl0gmEAoFAIBAIBAKBQCAQCAQCgaDsqGw1CAUCgUAgEAgEAoFAIBAIBAJBGSIEQoFAIBAIBAKBQCAQCAQCgaAaIwRCgUAgEAgEAoFAIBAIBAKBoBojBEKBQCAQCAQCgUAgEAgEAoGgGiMEQoFAIBAIBAKBQCAQCAQCgaAaIwRCgUAgEAgEAoFAIBAIBAKBoBojBEKBQCAQCAQCgUAgEAgEAoGgGiMEQoFAIBAIBAKBQCAQCAQCgaAaIwRCgUAgEAgEAoFAIBAIBAKBoBojBEKBQCAQCAQCgUAgEAgEAoGgGiMEQoFAIBAIBAKBQCAQCAQCgaAaIwRCgUAgEAgEAoFAIBAIBAKBoBojBEKBQCAQCAQCgUAgEAgEAoGgGiMEQoFAIBAIBAKBQCAQCAQCgaAaIwRCgUAgEAgEAoFAIBAIBAKBoBpjVt4DeJBMmjRJ9vb2Lu9hCATlxjvvvPOVLMuTSvo8ETuC6kxp4wZE7AiqNyJ2BILSIdZrAkHpELEjEJSOomKnSguE3t7ezJs3r7yHIRCUG++8805oaZ4nYkdQnSlt3ICIHUH1RsSOQFA6xHpNICgdInYEgtJRVOyIFGNBhSQjI4M2bdrQrFkzGjVqxNtvvw3AuHHjqF27Ns2bN6d58+acOnWqfAcqEAgEAoFAIBAIBAKBQFDJqdIOQkHlxcLCgj179mBjY0N2djYdO3akX79+ACxZsoQnn3yynEcoEAgEAoFAIBAIBAKBQFA1EA5CQYVEkiRsbGwAyM7OJjs7G0mSynlUAoFAIBAIBAKBQCAQCARVD+EgFFRY9Ho9LVu25OrVq7z44ou0bduWL7/8krfeeov58+fTo0cPFi1ahIWFxR3PXbVqFatWrQJAq9XSs2fPhz38h05oaChr1qwp72EIKjnVLXZE3AjKChE7gkrDjRvg5AT29nm3xcVBcjLUqvVQh1Ld4gZE7AjKBhE7AkHpqKixo8uW2fFNDLZOZnR+0rFMr12S2BECoaDColarOXXqFAkJCQwePJizZ8/y/vvv4+HhQVZWFpMnT2bx4sXMnTv3judOnjyZyZMnAzBv3rxqUYS2OrxHwYOnusVOVX9/goeHiB1BpSE2FjSa/AJhcjLExz90gbC6xQ2I2BGUDSJ2BILSUVFj5/yhFOJ2hGDrqGbevHpleu2SvEeRYiyo8Dg4ONCtWze2b9+Op6cnkiRhYWHB+PHjOXr0aHkPTyAQCAQCgaDyIMtgMNx5myyXz3gEAoFAIKjm3DiTDoBH7TuzIx8mQiAUVEiio6NJSEgAID09nb/++osGDRoQEREBgCzL/PbbbzRu3LgcRykQCAQCgUBQgUhLU9yA90Kvz/+zEAgFAoFAILgrN86kc3hLwgO5dmqiMi/rsst3LhYpxoIKSUREBGPHjkWv12MwGHjqqacYMGAA3bt3Jzo6GlmWad68OStWrCjvoQoEAoFAIBBUDMLDISsLAgKKfoxwEAoEAoFAUGz0epngs+l8NPEmAG0ftS/zBqqZ6cq8nJakCIXh1zJx8dZgbvFwPX1CIBRUSJo2bcrJkyfvuH3Pnj3lMBqBQCAQCASCSkBxhL6iBMKCtwkEAoFAIGDH1zFsWRmT+3NCtA5HN02ZvkZGqjIH376Zxax+V0iI1tFhsAOj3vIs09e5FyLFWCAQCAQCgUAgqCoUxwlYMMXYYBAOQoFAIBAICmAwyNy6kJHvtqhbWWX+OplpeYd0CdE6AK7/l17mr3MvhEAoEAgEAoFAIBBUBUrrIDTeLhAIBAKBIJfNy6I4/W9Kvtuibpa9QGh0EJoScT2TqyfTyvy17kalEwglSXKQJGmTJEkXJUm6IElS+/IeU7VApJ0IBAKBQCAQVGyKW0uw4LpOOAgFAoFAIMhHTFgWu9fHFXJ7dpm/lqmD0NE9rxLg0mduEhmcicHwcOboSicQAsuA7bIsNwCaARfKeTzVg0OHICXl3o8TCAQCgUAgEJQPxXUQii7GAoFAIBDclbmPXyv09riIshcIM9IM1GtlRbfhjszb7IdbTfPc++Y/eZ1DvyeW+WsWRqUSCCVJsgc6A18ByLKcJctyQrkOqrqQnQ06XXmPQiAQCAQCgUBwN0SKsUAgEAgEZY53PQucPTXEhj+YGoQevhYMneGBxlzFiFke1ArQ5t7/IF6zMCqVQAjUBqKBbyRJOilJ0hpJkqzLe1DVBrFwFAgEAoFAIKi4FNcJKJqUCAQCgUBQImZtqEODttbEPgAHYWaaAQurPHmufitrZn5fO/dnlVoq89csjMomEJoBLYAvZVkOBFKBN0wfIEnSZEmSjkuSdDwoKKg8xlg1EaknVR4ROwJB6RCxIxCUDhE7D4DSNikpylVYwclIMxAfVfYbtYqMiBuBoHSI2BGUhPSU/AdpM7/3BcC5hobkOD1RIWXn6NPrZLKz5HwCoZFZPygiYXLcw8nmrGwCYSgQKsvykZyfN6EIhrnIsrxKluVWsiy3atmy5UMfYJWlmomD+36NJ/Ryxr0fWIUQsSMQlA4ROwJB6RCx8wAo7nqtMIGwEq71Vs0I5a3+V9Fll/3YD/yWwIpXQ5Ar2O9FxI1AUDpE7AhKgtEl2KCNNYt2+lMrwBKAto/aY66V2FNI85LSkpHToERbiEDo7a/Fo7Y5KfH6O+57EFQqgVCW5UggRJKk+jk39QDOl+OQqg+V9GS5NMSEZ/HDwkiWv3SrvIciEAgEAoFAUDKqUZOSi0dTATh3oGwb6aUm6ln/bgSn96Y8kFQygaC8ib+dTXJ82TuSwq5m8ErnS3z9VliZX1sgeJiEXlLMQk++6o6dU15XYUc3Da4+5iREl93cYHQraq0Ll+dsHc24fDyN/Zvj2bgkkrTkBycWViqBMIcpwHpJkk4DzYGF5TucakQlXDiWlPiobH79OAqA5Dg9KQmiMYtAIBAIBIJKwv2kGFeydZ7BIKM2U2oybVp6G4Oh7MYfdSsvdWzuwGu57g6BoKowb/A1Zva6Uubu2/CrmWSkGTi+I6nCuW8FguIgyzKrXgvlh4WRWNup8ahtfsdjrO3UpCaWnUiXkaLMMZa2hctzyfE6UpP0bHgvkn9+imf7VzFl9toFqXQCoSzLp3KswU1lWR4ky3J8eY+pWlANvuANBpnPXgrh1N/JWNupAVj/bmQ5j0ogEAgEgtJz80I627+JQa+r+vO4gGolEMaEZqPXyfjU1xIbnk1yXNlt1gq6Ble+GsL2r2P4759ksjKEWCiofGSkGcjOUj67sRHZZGcp8f7fP8ll+jppSXlxGH9bGC0ElY+kWD2n/k4mO0umXisrVKo7m4NY26tJSyq7uSDdKBDaqAu9v37r/H15zx1MLbPXLkilEwgF5UQlXDiWlAuHU4m4nsnTcz15f4c/8GCKgaYl68XiUlBlEZ9tgaDknD+cwondSQ+k2cLi0cH8/nk010+nl/m1BRWQ0nYxrmTrPFmWObtfSSsO7GkLUKZujpgwxUH4yOMOAFw6lsbvX0SzckYo0zpeIuJ6Zpm9lkDwoJFlmVc6X2LNTCXt9/LxPHHh1N9lKxCaxuGutbEc35nEjTNi/hFUDgwGxT0IULOhlmFveBT6OCv7snUQGlOMLW0Kl+eefMWdD/+ux4SFNajdxJKI65ksf+nWA4ktIRAKikclWziWhguHU9GYS7TqY4eZRqJ5N1syUstW7IgKyWJGt8usnR9RptcVCCoCezfGMa3jJc4fKttaUAC/fRbF1PYXuXWxejUPElR9UpP0fPZSCGtmhvH1mw+uZlNijHByVBuqcBfjxBgdkcGZLBxxg01Lb+PlZ0Htxkrh+LIsCxMXkY2Ng5rhJpvDBm3zHBzh14RAKKg8BJ9V1k5n9qVgMMhcCUpDa6WiRU9bbp4rW4EhNcmAuVaiRU9b/vkpnq9nhfHJszfL9DUEgrIkNUnP3z/G8eGEYF5qczFXdJv6Rc18tQdNsbZTk5akL7M0+ns5CNVmEla2alr1tqNNf3tA0S6WjA8mOjSLKyfSyEwvmzm88HcsEBRGFRUIf112m1N7ktFYqPBrboW5haKbW9qo7mhvfr+c3J0EQNBO5e8OgxzYtymetGQ9Y+d74eCqKdPXEwgeJn//qFR8OLk7mYD2NmV67Run09Flyxz6PQHveu4AhVr+BYLKxtl9iqDu6q3h2n/pRIVk4eZzZ72b0mCaVpwUoyP0SgZaaxUuXmVzfUEFpAo7CDd/GsVf38fmu63/ZBes7ZUNVVm6OeJv63B012CmyZtnhs/0YN6QawDosir270ogMMXoElSpYPOyKA5vScSzjgUObhrOl3GqYmqiHltHMyYt8ubotkS+nRNOdpbM7vWx9BjlXKavJRDcLwnR2Xw0IZjI4Kx8t3cc4oCVbeFiHSgpxrpsmcx0Ga3V/e9HMoxNSopwEJriUiO/XvDh+GCS4/X0GOXEE9Pd73ssQiAUFI/i1rSRKteGXZcts2ttXovyDoMdcv+ttVHnqvllRVJs3uI1aGdSrlAIcOKvZLqPdCrT1xMIHjRRIVkkxeqoFaAlJlSZXI1dJcsSo5t378Z4bpxJ59aFDPpOcKb7SCdsHMRUJqh8xIRn8dmLIUSFZOHsqWHC+zVYPDqY0MsZZSYQmna5Cz6XzqaltwFY/Jc/to4ibqokxRH5KmkNwoLi4Nh3vGjRwy43NT8loewEwuRYHXbOyuawaRcbzDQSTp55m7KkWOHIFVQejF2+DQbYvV7Z9/jUt8DGUU1GmoGsTEOuQeJ+SU/WY5VTy71NP3vcfc1ZPDqYXz6OwmCALkMdMdeKJEZBxeDA5gRae2Vh72LGm+trs3Z+BLosAyNned71ecaDqbQkPVqr0n2eZVlmRrfLdBvhlHsYVVSKsSnGucg6J805OV6Z+87sS6H7SCdUagl7l9Kv8UR0CorHvRaOsgxbtlT4xWVBCtbdaN7NNvffljYqMlINZdoVLylWh1tNc55b6p0rRmqtVHj4mj+QtEyB4EGzZGwwSyfd5PCWRAwGqN3EktjwbJLKuH5ncnze9W5dUFJltn8dy5o3HlxKpkDwIDm2LYmoEEVUn/JFTZw8lAVfYnTZxY5psfjjO/IOpGb2usJr3S8/EDFfUAGogk1K0lPzi38qFTTvrqzZbBzK3kGYFKfDLmeD9dxHPkxa5I2ZRsKzjoVyvxAIBZWAXz6+zQutLhB+LZPAnHjRWqsY8aYHQ1/zyD0oSokvu9hJTdTniicAtRpaMvWLmoDiXty6Sum+uv2bGF5odUE00BJUCAZNdcPO2YwXPvHm5S9r3fPxxs/4/fQrSE8xkJ5i4M/VMaQl69GYS2jM7y3Peda2YOoXNVm4rS7dRzrRa4wzI2Z5EHUri7cevcqCodfuaz4UAqGgeBRHIATQVa4F078/x+PspWHY6+5MNdmgAbm24rKsQ5gUo8PO2YymnW0Z8aYH7QfaM3FRDeo0syLkUgaXjqeyaPQNUpPKNrVZIChrdNkyR7Ym5n5Wf1gYidZKRdfhjgDcOl92tQJlWSY1QY9vI+0d910+nlZm9T8EgoeBwSCTlWng4pFUtFYqZm2ojZuPOTYOatRmEglRZSkQ5p+/+ozPS+9KTdKzbU1Mmb2WoIJQXKHPYMj/uAouEEbdykv/mrSoBnM3+WFhqWxjNOYqzLUSqffhIEyK1TGj2yVO7E7CYJBJjtNjW0jtqTkb6wCKC+vknqQ77hcIKhLGTCU7ZzNGz/Piva11+eifenR6whFrOzW2TvcvchQkLUmPpW1+iaF+aytGv+2Jg5sZV4KUg6nfP48GIDa87JtzCR4yx49XOg3AlAkLa9Cmnx0AUjGzId1qKZkekTey7vHIojE9aIoNyy5WerGRBm2s0ZirePIVdwZPdaPDIAfaPqrUJkxLNjDnsatEXM/kzL7kEh8GC4FQUHyKIxBmlT5IHjZXTqRx9WQaPUY50eUpJxq0yd8+3BikZZlmnByXl7KiUkmMnutFo0dscPHWkBynZ9lzt7h1IYOrJ9LK7DUFggfBrrWxfPd2OACDp7rRtLMNs36sTZNOtqhUcP102X2GM9IMZGfJBPaw47VvfLF1VONROy8Fc8sKIXIIKge6bJkvp4XwapfLXDmRRscnHPCupwjfkiTh4GpWps1EjAJ+QHtrAnvYMvAF13z3J8fpcgX2+Khs9PqKKxAJiklxS8JAfhdhBRcIw68qTUHe+rE2LXra4VYzfxq+jYPZfaUYn9iVRFqygTUzwwi9nIleJ2PnfPcUrdWvh3Fka2KpX1MgeJBc+y+NhGgdg6e6MXdTHbRWKhzdNfkEEKODMLkMHYRJsbo7xHVJkmj/mAOt+9oTfC4jn7h++6Zo+FPpiYuD7Mop9HYc4kir3nbFFgaNuNU0R2MuEXq59IYIU4Hwxtn0IhuUFAeVSmLsO158drQB3UY4kZFmYMFT1/lyeih/fBldsmuVehSC6kVxHYSZFftLXpcts2VlNEe2JrJtTQy2Tmoeedyh0McaawCkJ5ftpFnYgtOlRv6F7rX/0tj5XWy+E3OBoKJw42w6v3+hTDa2Tmp6jXHmuaU+uHiZo7VS4dvYkguHyy510Zj6YuukpnYTSxb/VY8319dmye56+DbS5qvlKRBUZP75KY5zB1NzU6pqN7HMd7+9qxkJUWW3yDammDz1mgfPLPZGkiSmrazJrA21efIVdyKDs3h32A2Obkvkrf5Xcx0dgkpMSYS+ggJhBe1inJluYM/6ONxqmuem+BbE1klNSnzh4rpeLxdZLubotkSWPnOTjUtu59628YNIgCIFwmc+qMHAF1xxdDfju7fDOfh7AuvfjeDaf2lkZ1XM36GgjDhwoFKYIQwGmY8mKp2DfRpoi2y2YHQQllXKfHqqnrRkA86ehTddbNbVBo25xOrX88rDiL1OFeBe80d2Nuzff+ftsbHlPu+UtlafWi3h6WfBzfvImDI9EE6I0t3RfKQ0qFQST77ixuCX3XBwU95b/2dcSnaN+x6FoHpQXIGwgk+a5w6k8OfqGL57O5yLR1PpNca5yEK5RhW/rByEWRkG0pINhS44Xb2VLwSNhYRvIy271sbx2/Iolj1/k5jwLPZujBNpx1UVnQ6iosp7FCVi+1eKY69WgJZZG+rccX/DdtbcupBBRlrZxI4x9cXGpKmCxlyFtb2aVn3siQrJ4o8V0WRlio2ZoGJzZEsidQOtaP+YkgZSp6lVvvvtXc3KtAah8YDLNN2rXktrvOtp6TrckWGvu5OdaeDbOYob+ORuIbZXCYrrIDTtZFwBHYQndiex/r0IflwUScT1TIZMcyuye72dc9Hu2yltL7JufsQdt0eFZPHtnHCunlQc7+0G2NO0sw3XT6cD4OpT+GYtsLsdfSe4MOdnP7RWKtbNj+DAbwl8NPEmv3xcueZzQQlJS6vQTim9Xua/f5IJ+kv5LnfzMaduoFWRj7cu4/qd8ZFKDDp6FB47dZpa8f4O/3y3XQkSWVOVnnvNHZmZEB9/5+2nT0NK5a3B36yrLVdPphFxo3QGqeTY/HHnXqtsGtRJkkSv0c68u6Uu8371o9EjNiV6vhAIBcWjuIvGggJhSAhklF0tstKSnWVg+Uu3WDkjNN/tXZ5yLPI5xg5caWUkzMVGKAsKZ687J03XnI6VA55zpUUvu9zbUxP1zB14jZ8+uM3mZWLRWemQZbh8+e6PSUqCS5ceznjKiNs3s7C2U/PCMp9CT96cPDXIMkW6OYrDqb+T+XN1NHqdTHSoEjuFnawF9lSKbm9bE8P+XxP448voOzpdCgTlSWa6gaPbEokJzyLieib+La0Y+ZYns3+qc0f82Dioy2SjdjkolUvHU0mM0aFWk69gvBGVSqLLU07M3pgn8seEZed2hBVUUqpIinFijI41M8M4sDmBI1sTad3PnqadbYt8vJ2zGUkmmy1j6rwxbf7wljtTgf8r0KjO3dec+jnlZjoMcqBWQ8s7nmOK1krFC8t86D3WOdcNfODXQjbBgsrB9evKmuxuyHJ+Yb0CcWRrIstfVPY637ylHPq8vLJmbnfUwtBaqVCr718g1GXL3DibTlzOXsfJo2hXlpWtmpoNlNIaA55z5fS/KURcr9gZaIJ7cC8HoTpnDVIwdiqwc704tOmvHPaWtDTYnh+UGrZXTqTlNCZRYrRg6Yz7RaWSSnXN0vc/FlQvSusgDAkBrVb5U47sXheXm/LYoI01F4+mYudsdtdOQcXpimcwyEWeZhck7i4CoZWtmmUH62OmkchINRBxPZNHJ7sSfjWTNW+EkpUhc+j3BAJ72Jb4FEBQjhgMikBYr17Rj6nAi01QNldHtibSvJstVrZqLh1PJepWFgNfdM2tXVMQoxiRmqjHpUbJXu/AbwnUa2XFr5/cJiYsG2t7NYkxOlSqO1PxARzdNExbWZNPnr3Fpo/yUsQyUg30GV+0Q1hQwUlJUTZrTZsW/ZjMTCXGLO++iS8vgv5KYue3sSREZeer7+TbSIvaTMLL785USWt7NWlJ+hLNLQW5dDyVZc/dAqBNPzvsXTV3vZa5hYr5//Pjl4+j+O+fZNbMDGPG17WQJAlZlktcl0dQzhRXIFSr7xQIjX+X4/95YoyOd4ddJzVRj0oFDdvbkBSj49HJd0+RsnM2IyVeh8Egc/l4GqtmhPLIIAd6j3Mu8jkhlzKwslVRw1/LlRNpuHqb06yrLTUbaqnTtHjfK3UDragbaEVGmoH/fRbF3o3xHPkzkbb97TmxKwl3X3Nq1C3fNbCgmMTGKvsVO7uiH1NB12yRNzL5bkt4vtv6P+OCo9vdUxYlScLK/v4Ppj6feotLx9Ko31pxKxblIDTyyppaZGfJZKUb2LIimguHU/GsY0Fqkp49G+LoNcYZrZVYv1UaSqITmK7ZDIZKLRA6upuhVkP87eIfrMqynG+/0m+SCxpzid+/iManQcWYK4RAKCg+pREIK8DJwH97k4lKjqZBG2umflETWZbZuiqGlr3usgAgTyAsquh1XGQ2swdcZfy7XrTua1/oY2RZRq8DM42Ud6pWRF0Oo1hpaaNm9Fwv5bEeGpb+W5+Y0GwWDL3G51NDaNDWmm7DHWnSqeiTdEEFobgbtcJiZP9+eOQRUJXfAinkUgZT2l4EYPMnUWhtVMSGZ+NR27zI2p2QJxCWtGB8YoyO9e9G4OZjnvtrO7Y9CQc3M1xqmBd5Cq6kTFoQejnvBHrbVzEc+j2BbiOciLqVRd1AK7zrW+DqY465hVh0VngyMiA5+e6PCQ2F9HRo3PjhjKkExIRn8dWbSo0ltZlEnaaWBJ9Np2kXW+q1si7yedb2agwGReAuWDPqclAqumyZgHZFHxLdOJueKw6C0sGyVqN7Cx0uNcyZsNCLXz9RBI5vZocTdiWTlAQdkxZ549+i6BQ1QSVFrb4zxdj4dzkJhBHXM/nr+9hcsWLoax50GVp0pocpds5K7KQk6Dm2LZGMNANHtiTSfmDe+iziRiaetfOE+YQoHTX8tby8oiZn96fQpJMNkiTh16zkn3etlYoh09wIPpvOd3PD2bYmJre2WttH7akVoKXrMKcSX1fwELmfNVs5E/RXEq29wKe+Frda5oRcyGDAs673fiJKg5+0QgRCXbbM5mW36TPe5a4NeyKuZ3LpmOKgunQsDWcvzT3ruplrVZhrwdpOjXstcw7+LwGP2uZseC+SuMhsTv+TzNj5Xrj7mt/VzCGoIJRWIKyg8VRcVCoJB3cN//wYz85vY1n8Vz2s7dWkJOgI+iuZzk865JmKY8AAAQAASURBVDtkNRiURnWm9J/kgtpMonU/+yJrdz5shEAouDemi8Z7PaagQFgBTgZCL2XgH6Cm70TlFFmSpGJNmuZaFRoLiZSEwtMkQy4pqdNH/0wsUiDctiaGLStj+ORAfWLCsjHTSPfsilcQoz34jfW1CdqZxOE/Eln5aij9J7vScbBDia8neIgUZ8NV1Gl0QoJyezkKhKf3JtNa0apJTdKTmqTHztmM5z/2wc6p6M+dUSAsLD1flmUMekU0KYix83FUSFZuNoKxFlRg97sL4sNmenDpaCqdnnRk3uBrpKcYSIjWsflTJTX/wG8JgGLfH7fAC99GlmRlVN5FSZWnOIvGCurkAKXWYN9WGp79yBtrOzWO7ppiuQJN3bcFBcJPnlWEvy+ONyz0udlZBo7+mT+NUq8HR/fiLTg15iqGznAn9FIGx3fkpdl9PPkmvo20jHzLE6+6FqV2NgoeEmXhICwnTuxKQn0+DoB5v/qVKDXKNmctFBOaxflDSsZIapKe//7Jq2+1YOj1fPETfzsbv2aWqFTSXdOXi4vGXEXbR+25eT4jX+OFI1sTObI1kcadbAi7LFIpKyzFTbOvgPOOmUZxpc9c61vi72hF0LjzPZ07mMLfP8aTFKdn4sLC00HiIrP5bMottNYqMlKV75PuI51KNIbBL7vx1RthfDYlTzgJu5rJwpE3MNdKvP2LX7HnMUE5UpzYqYBGovvFyUNDbLhiAnqtx2XmbqrDiumhRIVkUa+lVb7GWrcuZHDuoDI/PfNBDXwbWebuhyqKOAhCIBQUh5IsGitg4LcdYM8Hq+6S4nkXbBzUpJpMmlG3snD10SBJUm7xd7O7nGzt/VmpRTOtw6Xc7nul3VzVqKulRl0tvce58NUboWxZEc2e9bEE9rQr85oFgjKmNAJhBYidgPY2zHi7FnWaWpEUp0OvkzHXqrC2K7wbnhFTkaMgv34Sxe71cXx2tEG+WEhP1bPh3cjcn/V6aNnbLrdD8eBpbnd9Tb9mVrmujzc31CYuIpvMNENu4fqgnUk4e2k4/Ecinz5/i5GzPfn7hzioVbzfheAhU1yRQ1d2DT3KEs86FszaUDu32RVQrO/+u4nrRREflc1PiyM5vVcRQuo0tWToDHf+3RTPod8T0VgUf85RqSQmf+jN5mVRHN6SSPeRTlw/nU7w2XQWjrxBl6ccGfa6R7GvJygHiitySFL+x1UAgdDe1QxbJzWjZnuWeF1TN9AKjYXEhxOUzq0TFtbg61lhbFlReGdug0EmMVqHwz1SMEtK6372BJ9NZ8Dzrlhaq7G2VxMTlsXcx68xd+A1ANwGlOlLCsqK0joIU1NBowHz8luL9xnvwux5dzaNKw7W9mqiQ5T9W/C5dGLDs2nZyw5DTv3O7CIawP3y8W12r4/DXCsx42tfIoOzuHw89a713QujaWdbFm7zZ+/P8dRpaolfc0uyMmS2rYlhz4Y4dq+P48lX3Ev13gQPidKWIqsARqL7pWA6fdDOJKJy4ik2IjtXIIy/nc0HY4NzH1evlfU991PlhRAIBcWntIFfzkWvXbxKP2HbOJiRkqAn9EoGp/9JZsvKGIa/4cEjjzuw81ulEYL6LlFkZasmOU7Z6EXdyqLXmKJr4RQXrZWKFz+tSfi1TP5cHU3QjiTlyyngvi8tKGuK674tKBAaJ8tynjRrN7HM7bJ6N8dgQYwNflIT9aQm6vl2ThhNu9rSaYgju9cr7pDIG1n5arBFXMskNUlP8262nMopHN+ipy1BO5Nw9daUKI5dvMzveLzRHdKksy2fTL7J17PClLqGQiCsmBR3o1YBnRwATTrb5BMHi4tpev7102l8OT2UjkMceOz5PNe7LlvOl27/++fRueIggIWViloBlgyeqiEuIpuWve9eTqMgto5mjJnnxdNzPZEk5Wvo3IEUVrwSyt6N8Zw/mEKTMnBbCR4QxY2dgodWFUAg7DDIgXfeKd2Brp2TGR0HO/D3j/E4e2lo2cuWqFuudwiEBoNMaqKew1sS0WXLOLqX7VbI2k7NuAX53VYuNczp9IQD+35J4NHJLhwLL+LJgvKltPPO1avg4AC1KueCwspOlXsoZRQwWvS0JStd+V2EXckkI82QrybgrYsZueu5mg0t8a6nxbuellYlnG+MWNur6T8pr86oxhyefMWdjFQDezbEcf10upJQU7NUlxc8aEorEFYAM8T94lE7/35jxzd5jRKN5cUgL/OwYTtrhs5wr7DiIAiBUFAc7ifFuJIHvrWDmjP7Ujiz7//s3Xd4FFUXwOHf7G56b6SQBgFC770JSFOKBRABP8SGoFixYMeCIiqiWBERFFBRUUEUURFBikoTkN5CeiO9l/n+mGyy2WwqgbTzPg9PsrOzu7PL3sydc889t+Ti65vFsXyxsCTTKTOt9PuLPpvDn98m02OEE7FhpT+P7sNr76LKL8SGOxf6F9+eP7/WnlrUFtOVIvUVnAiMgXTjBZulFSYbEL1ewc5RR3pyAV+9Ect/uzL4b1cGJ/8pWeXrpclneeH7kOKFR5JitUywsbO8aN7ahtiwXNr2dmDux0HFq3zXhhYd7XhpUysSIvNw9jCw9MNae2pRmxp4BmFNayaZZhBufC+ejJQCfv4kke7DSy66UhPzcTcZsS7IL/mcugxxYsR0rc6Zo6uBB96v+QWrMeNRr9cC7G/uCOXDRyKIPJnN9q+ScB9V46cWl1tV2o5OZzmDsA7PO5e6IM6waR7s3ZLKzfN8UBSFa+/0LBMg/P3zi3zzZlzx7eCOV2aRo5se9WHIZHd8W9rwz/wr8pKiuqpa2sJ8nwaeBeXkZiA5Pr941hNomU9pSdr5NTEqj0+eimT2mwGAVipm5dOROHsYCOlqV+VahzUx5Ukf3LwNHNuTgaOrnvTKHyKutAZeiuxSteqmJVLYO+lw87Yi8nQOwR3tCD+eXSpAmBCh/X7bS344utbvEFz9PjpRP1Sn4Zvv08ADhKYjy/3GudB3nCufPB1JclzJRWlaYj5hR7P4aXkCkx/34a3ZF0hNzNemL6KtrDd9vi/Ongb8W9eP1YnEFVKdtmMaRKwnGYSXwt1Xm86bk1XIwBtdObw9nX2/pJba52JMXnGAMDlOO3G6NjMwZmZJZ7MmxeIr4+RmKHcFZlGPNNBaUJfCyd2AosCh7elcOJ5N+34OHN2dwSvTzhXv8+NHCdzyjG/xbeP5aPTtHoy/p+Kp+JfCxk7H/e9q6RuqqvL885ftpcSlaMBTjC+Vh68Vr24pnYE45Ukfflt9kdSEfLIzC0vV1xx/jxdB7a9MgFBvUErVohL1UE1rEDbwa53+17my89tkvny1JPnBOB3eyFgPGiDmfC4x53OZ/HjVFxGqKb1eYcxMr+J+oSRD1ENVGVwy7pOXV3Z7A247AEHttWv7gTe6YW2no7AwlcmPe/PxvEh+/SyRq29xx95Zz/avkrCx0xUPBNdnsiyQqFxVO43mo9HQ4EcGbnzQm1mL/XljWxv+95wfrbvbM291i+L7+4134WJMHm/fc4FD29N5asxpUhPzad3dns6DtZUmR97qQft+jhIcbIqqEyC0tJpkA247U5/0xbO5FZ2vcuS6Oc14/ruQUllPUHqV46TYfGzsdNg5ymlJ0OCnGNeUvZOeblc7FdfevPFBb5zcSncmd32fTGGh9tmoqkrMuRz6X+d6WYOD5i4100tcRlVtO+Z9NuP5pgEHCC0ZdKMb89eHFNexDTuaXXyf1G8WZdSkBmEDv9ZpFmjNzNf9Celix5NrW5SaSmzk6FpyHjr5j7bIQseBjlfsGEUDZymD0HiuasBtB7QZI0v+DGX8vV5ce6cnz6xrSVA7O7oPd6awEI7/lcGejSnEheeSk1XYIPpPkkIhqq6yIIf5aLRxewPubDo468usbOfsbqD3tS606GiLm48VuzeUXjXSs7kVc94JwMpaR0ZKAXZOEvBosmoaIGwEGYQtOtnx1BelC2ZPedKHr16PLV7dMSU+n43vx2Njr2Pr2os4uOgbxIlTXAFVnepVT6cYX4oJD3kTeSqHrsOc8AuxYeGW1qgq5GQVMv/6M6QlFZCeVICjm57Y87mkJxcQ1EEGoESR6mQQlvf4Rsg0wDHxYW/CT2TTYYAEOISJmg5MNfBrHYDW3e2Z+3EwAK/83BorG4U5vY8X32/roCMvt5CCfIg4lYODsx53HwkjCKqX1GAeIKzq4+o5a9uy1/pj7vbkl08TibuQW7zK8W0L/K70odWItGxRuaoGORphBmF5ZrygNXBVVbnqJjdiz+cy9Wkf1ELw8i8ZkW4IacTiMmrCGYSWdOjvSIf1joSfyOaVaec4uDWNU/tL6hL2G+9Sh0cn6pWaXqjl5MCpU9Cx4+U7tsvMzduKZ79uWRwsVxQFRQE7Bz23POvL+w9FEHY0i2/fjiPmnNbZDu3pUJeHLBqa8moQWhrobSRMaz617ePAsKnudXg0ol6q6kILlhaWa0T9NRs7Ldgx5QkfzhzM5O+fUslKL+TjeZGc2p+Ji5eB5q1tZEBXaKpzrWM6xbiRXusYWVnrcPe1IuZ8LuePZNH5Kkd6jWoY1zkSIBSVq0pnsaIMwkba8EG7cJv8mE9dH4aor6pT06mRZRBWJCDUFhdPQ3FwsNvVTnS+yok+1zaME6e4Amq6SEl2NsTFWd6/ASnvwsutaJr+T8sTiDmXi5O7nhad7PAKsLK4v2iCqrOKsfkUY0sDvY1E89Yl9f+kvQiLqhogNO+bNdJrnUET3Bg0wQ0HFz2/f5FUPPsjKz2X9v0k+1YUqWkiUSMta2HKJ9i6uGTMmJmelexdf0iAUFSuOjUIG2HavRA1JhmE5XL2MJCSkE/nqxy561X/yh8gmpaqnjvMVwBvZJkc5ox1PM//l01gO1vmfdaikkeIJqemi5SA1o9rpO3H3klPSFc7Ik/l1HiVcdHIVee8Y367EV/rOLmXhAu6D3ci6nQOV98iGbiiSFWvdcwHPsvLyG1EQns78N8urWZnj5HOdXw0VScBQlF11W340Ogv1oSoUHVW9jLdp5FnEAJ0H+EkNaBE+aqaBQVa59JgKNnWiNuN6SI+va+RjFthQXUWKTFVWAh6faMOdDz4QRAFBY33/YlLVNn5o7yARiM/7wR1KFnp+45XmgOyUJUwIRmE5Rp4oxsn/s5gyM3uDWpgSgKEonKXUoPwMpw0k5OTufPOOzly5AiKorBixQr69etXq68hRK2QDMJyjZjugX8bW9r2kdppwoKqLlIC2jRjY4CwkQ9KKYrC7S83Z/2bsfS+tuGMRosrqKZTjMvrxzUieoOC3iCBDVEOqUFoUbs+DoyZ6YlvS6k7KCyo6kxDS+ccaNRtx9Zex71vB9b1YVSbBAhF5aqTOnwFAoQPPPAAo0eP5uuvvyY3N5fMzMzKHyREXZAahOXS6RQ69JfsQVGO6mYQmm5rxNNVAHqOdKZnA5qqIurApUwxbsQBQiEqJDUIyzVmplddH4Kory41g7CRt52GSAKEonLVqUF4mVOHU1JS2L59OytXrgTA2toaa2vrih8kRF251AxCuVATTVVNA4SNPJNDiEpVNcjRxBYpEaJKqjuga3yMnHdEU1XTAGETyCBsqCRAKKquKg3f0v612PDPnTuHl5cXt912G//++y89evTgrbfewsGh9DTFZcuWsWzZMgBsbW1r7fXrs4iIiLo+BGGupgFCY5upg0yoptZ2pN3UU9UJEJquZGw6Im1+TrrMpO2IeqEqbUdRLBeMr4MAYVNrNyBtp96qaQZhHQ1MSdsR9UJNFympwwxCaTsVa3ABQkVR9MBeIFJV1bF1fTxNQk2nGF+GAGF+fj779+9n6dKl9OnThwceeICFCxfy4osvltpv5syZzJw5s9ZeV4hLUt0OZx1mEErbEfWCaRuorOZRedPzr3CAUNqOaBCMbaq8GoRX+GJN2o2oN2pag7AqQfnLQNqOqFcqW+BHp6s39dal7VSs4SynUuIB4FhdH0STUo9Sh/39/fH396dPnz4ATJw4kf3799fa8wtRq6rSBupZBqEQ9UJ1sm9NMwhlyopo6i6lYHwjX8VYiApVNlW4nmUQClEv1PScIzUI660GFSBUFMUfGAMsr+tjaVKq0/BN97sMNQh9fHwICAjgxIkTAPz222+0b9++1p5fiFpV00VKpAahaOqqk7nexBb4EaJClbUd06xc830tLVwiRFNR1UxAqUEoRIl6lEgkakdDm2K8BHgMcKrj42iaqnKhZuxcmnYya7nhL126lGnTppGbm0vLli355JNPavX5hag1l1qDUE6aoqkrLNSymixRVTAYJINQCFNVCRBC6an7xn6bLFIimrIGVoNQiHqhpgFCudaptxpMBqGiKGOBOFVV91Wy30xFUfYqirJ3374KdxVVVZ0gh2mH8zI1/K5du7J3714OHTrEd999h5ubW60+f1MlbecyqGrbMRjqTV0OUX3Sdi6DqrYdvb50O5EOZ4MibecyqqjtWKpBaGmbqJek3VwmVQkQmtdRM26Xc06DIG3nMriUtQrqoO6tqFyDCRACA4DxiqKcB74AhimKstp8J1VVl6mq2lNV1Z49evS40sfYOFV3dSLz/aWz2SBI27kMahoglCBHgyJt5zKoSokKSx1OaTsNirSdy6CqU4zNZ3pIgLDBkHZzmVQlQGg+KAXabWk3DYK0ncugqtf7lqYYW2pPos5d0QChoig9FEVZUZPHqqr6hKqq/qqqBgM3A1tVVb2lVg9QWFadGoSmnUvJghJNXU2zoKTtCKGpbodTFvgRTV1lbcZSDShLQUOAM2dq//iEqK+qGiCUDEIhSlRnirGpwkItQULaTr1zpTMIg4Fbr/BriktVlVGx8kakTX8K0dRUJdBXXhaUpN2LpkyKXgtxaaozxdi0D2eavXv0qLQl0XRUZZESSxlPEiAUTdmlTDGWDMJ6qVYWKVEUZXAVd+1QG6+nquo2YFttPJeohurWIJTaAqKpk7R7IWpGil4LUTOWLsQs3V/ZIiWmC2iZZ34I0VhVNqBrbAum7UwChKIpu5T+msEAeXmX9/hEtdXWKsbbABVQKtmPov1EQ1KdkQHzDEK9XupyiKZL0u6FqJmajkhLgFA0dVUJEEL5GYSWAoRWVpf3mIWoD6oyxRhKAh2W2osQTU1NZ0sZkyGysy/v8Ylqq60A4UVgM/BKJfuNBF6vpdcUV0p1ahCa7y9ZUEJULUBoXoNQ2o5oyqra4ZQpxkKUZqldmKtoirGlAKEQTUFVAoTm1zoyKCWauprOlpJEonqrtgKEe4AWqqr+V9FOiqK0raXXE1dSTTMIjUGO/PzLf4xC1EeXmnYvHU7RVFWnw2lKLtaEqFoGoenvEiAUTd2lXOuY1u4UoqmqSc1oudapl2qrqMiPVXyu88CntfSa4kqqbDTaUg1C48iANHzRVFWnw2m+TdqOaMqqE1w3bSeyirFo6qqTQWj6mPIChHIeEk1Bdfpr5rU6pb8mmrKaXutYihMkJkJkpMWnSEhI4NSpU5d4sKIqaiVAqKrqe6qq9qvCfvtUVb2tNl5TXEGV1bMx3ce8c2kwSOqwaLqqeoFlKcgho2qiKatp9q2loKEQTUl1FimxlEFouooxSLBdNA01LadkOk1SrndEU1TdAKF5nMC0v5aSArGxFp9i0aJFPP/887VwwKIysiyZqFxVRqOh/CnGpg0/Kwvi4y/fsQpRn1xKkENGpEVTdikj0hJcF01dRasOV7RISXmrGAvR2FX1nANl24lOV7XrJCEao5pOzy8sLL0quPFnbq6Fh6t8/fXXxEsM4YqolQChorlHUZTDiqKkKYpySlGUVxVFsa+N5xd1rKYZhJZShy9ehLCwy3u8QtQX1QkQmpIgh2jqpH6nEDVT1SnGxn2NPy314UAChKJpqcpKrObtxBhcl/OOaIqqk31rPjBlDK6b1o/OySnz0MOHDxMREUFcXFwtHrgoT21lEM4C3gFigHeB/cDDwNJaen5R16pag9D0d2PDNz+RSmdTNBU1raNmKYPw5EkID788xylEfVOV6fnltR0JEIqmrLanGEtbEk3BpUyTNG87QjQl1c0gtLTN9LxjIUB45swZOnfuLAHCK6S2AoSzgbdVVR2hquo8VVUnA3cAtyqKYlNLryHqSlVGo6vT4ZQAoWgqqnPSrCwLKidHm6IvRFNwqRmEcp4RTZGl6cMV7WO6TVYxFk1Zdfprpucd4zRJySAUjcwvv/zCmjVrKt/xUqYYWyptkZtb5rmSk5Np3bo18fHxqDKV/7KrrQBhK+A7s23ri56/ZS29hqgrluo8WWI+MmAp7V4ChKIpqWrafVVqEErbEU1JddqO+ePM286pU5CdXbvHJ0R9k5sL58+XvQizpKKpXhIgFE3RpUyTNA9yCNGARUVFAbBly5aqBwgrC5CXV/vWfKahqmr/8vJKPTw5ORkfHx9sbW1JSUkp3n7x4sUqvy9RdbUVILQFMs22GVNd7GrpNURduZQMQvMOZ2GhjLCJpuNSsqAsBQjz8y/PcQpR39Rm9m1MDKSnX57jFKK+SE/XajxXFiCsbMaHBAhFU1TVc47575JBKBqRqKgo2rVrR2FhIWFhYRw4cKDyB1UnTmD8HUoSiUxnGhp/mk0zTk5OxtXVlWbNmrFz507uvfdeDhw4gL+/P+nSv6t1hlp8rgmKovQ0ua0DVGCSoih9Tbarqqq+X4uvK64Ena7iTmJFDV8yCEVTVdMAoTELyjzwIaPToqmp6RRj0ws1qX0rmgLTAdjKMgiNqlKDUNqOaApqOsXYUtvJzoZz56Bdu8t7zELUsvPnz5Oamkp4eDhhYWHExMQQExODj49P+Q+qaoDQUrZgeZnrOTng5FT88KS4OELatsXLy4tp06bh5ubGP//8Q1ZWFidPnqR79+6X+M6FqdoMED5azvbHzW6rgAQIG4qICIiP1xpvRdlLlaUOS4BQNEVVTbsvL4PQPGgobUc0FdXpcFoKEJpOT5HsW9EUGM8R1ckgNN9mPuPD9KcQjVltzpbKzNQy1yVAKBqQ1atXk1/UVzp69ChhYWG0bduWAwcOcM0111T84KpMsS+vBqGlzHXTDMLUVJJPncK1b1+aNWvG33//zccff8zEiRNRFIXjx49LgLCW1coUY1VVddX4p6+N1xRXSFISpKRUbTS6KnU5JJNDNCWWMgEt7SM1CIUorSrBdbDcdswXKZHzjmgKjN/zqgQIoeqrGEvbEU2BsQ1Uds6paKEF07Yjg1KigXnggQf46quvANi3bx9JSUmMHj2af//9t/wHnT4NiYlVC65D2YEp8+n5xvaUm1uyX04OyWlpuNnb06xZM3r16sW4ceNwdXVlzJAhrFy5knHjxiGLl9Se2swgFI2RscNpY1P9UTVLtQUkyCGaEks10iyxVEfN0hRj6XCKpuJSMwilfqdoaoz9Nb2+ZouUWJo6CdJnE01HdYIcljIITYMcZossCFFf7dmzB2tray5evMjvv/9O27Zt2bx5M/7+/nTo0IE///yz/AcnJ0NWVs2yb8vLIDQYSvfZsrNJTk/HVa+nRXAwAQEBWFtbc+zAAba+8w7T3ngDRVH47bffCA0NxcnJCVdX10v9WJq0yxogVBTFHrgDaAvEAqtUVQ27nK8papmxpk11Gr7pNku1BYy11KqyMrIQDV1NghzmtTqMP+VCTTQV1ZnuVVn2rWQQiqbA2L8yBggr2q+iYvESIBRNkfGcU5V661XJICwoKLl+EqIee/XVV8nM1NaazcrKYty4cSxevJjBgwfTtm1bPv744/IfbOxfXcr0fEsBQtM+XE4OyZmZuMbG8sS4cdCpEwA+Tk608/dHp9Px0ksvsWTJEnJychg7diwPPPDApXwkTV6tBAgVRXkDGKeqahuTbU7AP0BrIAlwAR5WFKW3qqona+N1xRVgPMlVdVTN9PeKpqwYO7FCNGbVmWJsyjz71vgckgUlmopLySC0VL9T2o5o7EwXKIHqTzG2VDMaJEAoGr+EhKrXW69oBXDzup35+WBtfXmPXYhLdPz4cU6ePImPmxsxSUncMHgwI0eOBCA0NJTjx4+jqiqKpYEnY4DQ2rpqs6Wg8uC6+SBvTg7J2dm4enmhpKVpj4mKgowMOrdowab33mPgtGm8+uqrpKen07Vr10v7QETt1CAEhgKrzbY9ArQB7lJV1RPwA84Dz9TSa4orwbSmTU2Lj5qn3YN0OEXTUJ0gh/mUSEvZt9JuRFNw4sSlBwilfqdoakwDhFWdYmxU0RRjWaRENHYpKXDxYs0XWihvJVYZmBL1XF5eHmfPnqWwsJBxvXoBEBAfz/ABAxg+fDienp7odDri4uIsP0F1Mgih8gxCS2VijFOMBw+G9HRt23//wenT6L29Gd22LY46HWPHjsXa2prIyMhL+EQE1F6AMBjYZ7ZtAnBUVdUVAKqqxgNvAANq6TXFlVCTKcaVpQ4bn1eIxuy//7SV7KqyirGlGoSWOqHS2RSNXWEhnDxZct6pyQrgBoPU7xRNj+l3vrqLlJR3zjFf8EeIxsi0fmdNghzlZRBKHUJRz509e5YALy8Cvbzo27Ejb9x+O77u7hAdDYCiKLRt25bDhw9bfgLjAKz5wKyl/aq6VoHZc+VnZJCZlYWjp6e2PTUVsrO1fl1AADg5we7dLF68mI8++oioqKja+GiatNoKEBqAbOMNRVHcgXbAVrP9zgM+tfSa4kowHZGubg3C8lKHQTqcovFLSICMjKpnQVnaJgv8iKbG9BxRlYu1qtQglLYjGoG8vDx++umn8neoagahpRqE5Z1zLAUIs7JkkFc0LlWdLVVekMN8kRLjfRIgFPXc8WPHCPXz49EbbuCq4cN5+Prr0bdurU3hLTJjxgwefPDB4jqFpVxK2ylvtpRZHy4lMRFnZ2d0Oh3Y20N4OPj4aNOaXV2he3cAmtna0rNnT8kgrAW1FSA8CQwxuT226OfPZvs1Ay7W0muKWvDrr7+yefPm8ncwNtiapg5L0WvRVFU17R7KZkqZBtdNO6GqKhdmonEzrd1U1bYjNQhFE/Dnn38yZswYTp06ZXkH036Yeb/L/DFVXcVYry/bX9u3j5+//JJWrVqRlJRUO29OiLpkGuSoSnC9smsd0/OYEPXYkQMHaBsYyJxbbyWkZ08IDoY2bbSpvCdOwLlz3HnnndjqdOz/+++yT2C8NqnpYqblrWJscq2TnJRUsiqxgwNERICHBwwbpt0G8PWF6Gia29oSGRmJWlnfUVSotgKE7wDzFEV5W1GUp4DXgHPAFrP9RgJHauk1xSVSVZUHH3yQV199tfydjA20qnU5tCcu+Wk+qiY1CEVTYcxautRVjM1HpKXDKRoz03NETduOrGIsGqG//voLBwcHFi1aZHmH8jIIc3O1aftGFc34sJRBaN6WUlKY+cgj5Ofns3Pnztp7g0JcJqqqkldRNl9NAoSm7aSKGYSZmZkEBQUxaNCgS3xHQtSOLVu2cPXAgdC3L3h5aSsEGwxaht7JkxATg6IoBDg5ERsWVvYJLiVOYOm8Y15HurCQxORkPDw8tNtubtq/wECwsip5bi8vSEjAMToaK72e8PBw9uzZw8WLF/nss89q9uE0YbUSIFRVdSXwLHAj8ARwArhBVdXiv4yKongB1wHf18ZrikuTk5PDe++9R0FBAfv27St/FNi04VekstoCkkEomprqFO4tbxVjS0Wvpe2IxuxSAoSqWhIgNG83ElgXDdyePXtYtGgRv//+O59++mnZHUwDeebBP2OWh6mq1Iy2sip9zklPJy09nYSkJG677Ta+/PJL1q5dWztvUIjL5OOPP6Zbt26kpqZa3sE4oGserLCkonJKlZx3oqOjUVWVf//9l8TExEt4R0LUXFZWFkuWLCExMZGDR44wdPjwstchQUHav5QUKCzEx9mZ2JiYsk9WXoAwK0v7Z1SdKcamA1MFBSRmZJQECENCoHdvbR9Trq5abcLkZJo3a0aHDh0YOXIk06dPZ/r06ezZs6dGn1VTVVsZhKiq+oqqqv6qqjqqqjpYVdXDZvfHq6rqo6rq+7X1mqJmdu/ejaenJ6tXr+bLL79kyJAhdOrUif3795fdubwahKmpEB9fcruitHupQSiaouoECM07pJYu1mTKimgKLjVAWM3MW5mGIhoCVVXZs2cPY8eO5eWXX+bbb7+1tJP2s7zpjqZtoqpTjM1rECYnczwqitCgIIYMGcLq1at5+OGHUVWVfDk3iXpqzZo1KIrC008/bXmHS51ibH7eKWeRktjYWJo3b07fvn3ZtWtXLbwzIapvx44dPPTQQ4waNYqru3TBLiio7E5ubtC5szbgmpiIt6srsbGxZfcrL0AYFqb9M7JUiqy8RCLT0hYFBSSkp+Pp6Vnxm7KyAhsbyMujQ3Awzz77LK+//jqbNm3inltvrXi2pCij1gKEomEoKCjgzjvvZMWKFezevZvOnTuzfPlyOnTowIkTJ8o+oLwahAkJpQqYlmr4RsbCvbKKsWiKqroCOJSfQWge6LCykgChaNxMzw3mtTlzcsruX16xePNzjYVBqWXLluHs7CwXaqLe+/PPP3F3d8ff35/mdnbEnDtXdqfyphibtwHzKfnGfcpbTdK07aSmcvTiRdoFBtK3b1/eeOMNsrOzefbZZ7G2tmbMmDF06tSJY8eOsWbNmtr7AISogn379nHw4MFS22JjYzl48CDr169n9erV5S+0AFULEBr3Mw0GVjGDMC4ujmbNmjFw4EC2b99eg3coxKX7c9s2br72WkYPHcqKRx/VVgEuj4sLREfj7epKTFxc2fvLCxCal3apanDdvExMQQGJaWklGYQVcXUFBwe+fPJJHr31Vm4NDmbVM89wf79+/HvwIKmpqeRY6keKMiRA2MTs3LkTg8HAxIkTi7c1a9aMVgEBJFbU8C3VDTC/4Kooddj0RKrTlX5sfj789x9RUVH8bakAqhD10JEjR8gyTZ83Z3rSrCggXt20e7N2l5mZSURExCW8EyGunJdeeonHH3+ct956y3L2nnmA0HSf3bu1lcGNzDMITS/UqpC1/uOPP2JlZcWxY8cu8V0JcXmkpKSQl5fHhx9+yMyZM1EUBV8bG2JMZ3AYmU8xNv/umwYrKrpQM21PxqleJjXVjkVH087fHxsbGx5++GHat2/PBx98wJo1a7j66quxtbVl5cqV3HLLLXIxJq4YVVWZdeedvPP666W2//LLLwwbNozWrVvTr18/vv/eQqUr02udigZ1LdXvtFSXsLBQW2HVPIPw6FG8mzXjhhtuYNWqVdx3333ExsYyY8YMTprWCRXiMlBVleuuvpq1q1YxvVcvXrr+elxDQyt+kLs7REVpGYSW4gTlJRJVFCcw3cfSgoymU4wLC0moaoAwIABCQ9Hl5MD+/diEhDB9+HBCOnUiOjqaGTNmMG/ePL744ovyyw0IQAKETc66deuYPHkyimkDBTxyc0kIDy/7gPI6nFUZGSgvg9A8yBETQ+LBg/Tr149JkyZRKNmFop778MMP6dy5M++/X0HFhKoW7q2sfqdph9NCBuGKFSsICAhg+fLll/COhLj8YmJieOONN3B0dGTp0qX8+uuvZXeqKECYl1f6gstSgNDSVK9yMm9jY2Pp2bMn58+fv/Q3J0Qtyc7OLg5aT5kyhYEDB7J161amT58OgLetLdEJCWUD7OYzOXJytG2WMgjLC3JYmrJvGnAvKOD4hQu08/Utfmj79u1JSEhg2LBhPPzww9x0002sWrUKgMjIyNr5UISoxF9//cWRY8c4eOBA8bbc3Fy2bdvGkCFDAOjZti0n/v237IOrk0FY3rWO+cCUtXXp805aGnH//UczJyc6derE0aNHKSgoIDg4mK+//ppPPvnkEj8BIUoUFhaydetWXnrpJaZMmUKfPn2Yc8897Ny7l5jkZPqNH6/VF2zZsuIn8vODvDy83dyIrWhgyry/VlBQtnRSVQamLGUQpqZWPsUYtIVKmjfXntPeXqtXOHgwhpAQ2gQE8P333/Px8uVMnTq1eKAgJT6eg9u2Vf7cTUyDChAqihKgKMrviqIcVRTlP0VRHqjrY2oI5s2bx6JFi8jNzeXrr7/mpokT4cKFkh3y8/GwsbFcMNe8ZsCff2q3CwosBwhNH1NeNod5gDAigv1nzhASHIy7uzvbtm3j7bff5p9//kFVVd555x3Gjx9fcTBGiCto9erVPPLII3z++eeWd1DVknZgumBCeftC1TIIzQvGAxcuXKBnz5589913NX9DQlwBq1ev5vrrr+eZZ57huaee4rXnny+7U0UBQtOBqeq0G73eYuH5uLg4evfuTZhJnZwM0wxFIa6g7OxsJk+ezMoVK7j2mmvIzs5mx44dtGvXjm+++QZ3d3cAHPLzsTYYSElJKf0EpkGO7Gw4fhxiY8ufZl/ZoJSxX2dWD+p8VBQtmzUr3q9Dhw60bt0a74wM2LuXLm3aFNequmDa1xTiMlrx8cc8csMNHDt7lvz8fMLCwvD09GT9+vXFAcLmBgORlqbnl1e/U1XLZAFWuIqxaXsy769FRRGbmoq3rS0Anp6evPfeexw+fJifP/uMtWvXsnjxYqmLK2qu6Dt59OhRNm3axOTJk9m7axe9W7dmTM+evPfBB8y6/noSEhNx7dJFW7nY2rri57S3B3d3vJs3JzYhodzXrNEU4/LOO2YZhFWeYmxkZwfe3iW33d3p0Lw5Qd7eDOrShX4dO/LLjz/y22+/MfG667h2woSKVzlvggyV71Kv5ANzVVXdryiKE7BPUZRfVFU9WtcHVl9FRETwzjvv4OfnR3BwMO3ataOVszP88w84OmqpwykpeDo789fp02WfwHyKsbFDaqyvZlTVizVj6rDpH42LF7mQkUGwnx+33HorN954I25ubixcuJAlS5bwxhtv8OKLLzJnzhymT5+Og4ND7X1AQlRBYWEhuqI6gbm5uezfv58NGzbw6aefcubMGUJCQswfUPK7+UkzNVUrpGtjo92u7knT7CIvMjKSm266iZdffpmbb76Zp556Cmtra9544w2WLVtGQUEBer2+Nj8OIaosKiqKPXv2EBMTw6JFi9i8eTMA1/brx/3331/2ARXVIDQ971Sn3RiDHPn5JZ3hggLi4uLo1asXO3bsAGDlypXccccdPPDAAyxevLg2PwYhKnX48GHWrVvHgX/+4XxYGIsXL6Zt27asXLmyZKfCQsjMxNfdnejoaFxdXUvuM+2HeXlBXFzpNmOeQWj6mPIyCC0ECC9ERhLQvLlWi7pZM8aMGYO7iwucOgVBQXTJzQXA19dXAoTiisjKyuLrr7/m0Lvvsm7HDj766CN+++03unfvTlhYGB06dADA38GBb6Ojyz6BeQah8XZiIpw5A336aLctZd+a1po2bWPm/bW4OOIKCxlkzI4q6lO2atmSkBMnGNqjB2+++SadO3emd+/eODs71+ZHJBqxHTt2kJeUBMeP07xnT7qMHEmzZs148bnnmNW2rdb3UVUMqalMmTYN26IgNUWDTpXq0wdvR0diExNRVbVkFqJpMoSl/pp5gBCqFCdY8913OCQmcv2QIdoiJamp1QsQBgZCs2Ylt62s6NauHS4ODrx7992cjY6m7b338ueuXYzq1IlMHx82fP45E4qy9EUDyyBUVTVaVdX9Rb+nAceA5nV7VPXbwoULmT17NgUFBTz00EPMnj1b6zQaO3MAycl4ODmRYCmD0DxACCUnwcpqEFZlZKDoucKSkwn09ub222/n4MGDHDx4kDvuuINp06Zx7733csstt9C3b182bNhQex+OEFUQFxeHn58f/xZNSzl48CCtWrXCzc2NUaNG8csvv5R9UEUBwtOntawOU6Ztx7QDWlkNwvPniTx2jO7du+Pp6cmXX37JunXr+N///sfy5cuZP38+t9xySy19EkJU35IlS5g0aRILFizgn3/+oXv37gC4GwwUFhaSlJRU+gEVtR3z805FU73Mz0NmbSfz4EHy8/Lo2LEjYWFhnD9/nnnz5rFmzRq++eYb4uLiyMjIYPPmzWRnZ9fmRyKERf/++y+KonDq3DnmjBnDc889x/Dhw0vvlJEBej0+bm7ExMSUvs+07bRsCf7+pad5VSX71tLKxibTvdLT0sjOycFjwAA4cACSkmjTpg23DhsGnp7Qti3eAQGMHzWKG264gXBLpWuEqCVffPEFa9asYdmyZfTp0AH/Hj3o3aYNb7/1FnZ2dny1di2nT5/WBngzMmju6kqEebuB8ssp5eVBUcAbKN12KlqkxFIyRF4esSkpePv5weHDJdsvXkQxGFg5Zw5PP/001113HV5eXixYsKDUIWZmZvLmm2/W8JMSjdUrzz3HlEmTuPnWWxn15JOMnDKF0ObNyczI4CYfHy1YNnAgDBrEk9On06Jz5+q/iMGAo6srqGrp2n3m/bWCAjh7tuS+qs40NDvvbNq6lVe/+EK7XVBAYkpK1aYYG7VoAWbJRA/OnMmSJ55A5+5OSJcuPDh+PH++9hrvv/Ya8+69l0efeoqff/6Z/v378/vvv1f9tRqpBhUgNKUoSjDQDfjLbPtMRVH2Koqyd9++fXVybPVFeHg4a9eu5dFHH+XNN9/krbfeYtLVV0NMDHTpAhcvQkQEnD6NZ1AQieYXamA5QGgckS4vddh0W2U1CAsKQK/nQnw8gUWNPzg4GBcXF5588kkm3nADt/buDdnZTJw4kU2bNhU/fZylYqmixqTtWPbuu+/i5OTEjBkz2LhxI/fffz+DBw8GYNiwYWzdurXsgyoLcpjWpTHPhDKeMKH8gvGgdVxPnCAqNpbmXl7MmjWLJ554gjfffBOdonDjsGEsWLCAb7/9VorxXmbSdiwrKChgzWef8fMHH/D7778TFBRUfJ+Snk4Lb2/OGTuTRqZtxbw8hXmGBlgOchi3GxdX0OlKMgiLxJ09SzNnZwI8PIiKimLgwIE888wzTL7mGrIyMhg6dChjx45lzJgxbNy4sTY/FmFC2k6Jg3//zeSBAwnx8+Pt2bM5tWsXzz79NOzcqfXbCgrg4EEIDMTXzY1o80wo8/6aMbBn2t+CimsQmg3o7j54ENVkYbnwmBgCmjdH8fGBjh1h715tsDk+Hnx8tMc5O/P90qV07NiRY8eOceTIkdr/sJo4aTdaOYg77riDd999l8cee4zF06dDUBAfPfkkh1et4rN338Xr4EH0xu99cjL+Hh4VBwjNgxWV9deMj7W0Eqv5FOP8fOISEmg2YIDWXhITtTIA+/drAQ2DgemDB/Puu++yZ88eVqxYUeoQf/75Zx555JGKF8cTlWoMbUdVVdauXcurr77KypUr+WfJEn5dupTtv/9OfEoKj06ZQuyqVbi3awdt2mgzBh0dYfDgkr/T1aQYDHQMCeGASY3PMtc6AP/9p/2sTg1Cs75eWEQEfx0/zvHjx7VFSlJSqpdBaIFV27bYdOkCvXuj9OrF63ffjd/w4dCuHeNuv507rr6aZx55hM4BAdx1110sXriwSa+J0CADhIqiOALfAA+qqlrqyldV1WWqqvZUVbVnjx496uYA64m5c+dy77330qyggPEBAUwcORLl77+hdWstg9DTU+ts9uyJR6tWJCYnl30S04u1ceNKTnhVHRkor46a8YRrDBDGxhJoa6ul8hexS0/n87vuwsvWFnbvpmuXLsUdze3bt+Pv78/+/ftr58MS0nbMbN26lf379/PB+++zce5chvfvzx133MG9997Lq6++CmgBwt9//73sSaS8IAdU3uG0FOQw7mfa4czMRLWxIfLiRZpbWzN37lyee+45FEXh6VmzGBEaipOTE0OGDOHNN9+U+hqXkbSd0s6cOUNWVhY7d+7Ey8mJ4UFBtHFw0IIcRmlptPT25qwxk93ItK2Y1u+sKMhhPhoNJecdSxmEhYXERUTQzNsb63//pXnz5jz88MPce++9KMeO0T80lISEBE6ePElISAg//vhj7X5Aopi0nRL/HjzIzFGjOPTWWygtWhCcnIzd/v3a9/bwYTh/XjsHtG+Pj6srMeYBQvPAn3FqsKUahOW1HZP+2v7Dh+k/dSobdu0qfuyF6GgCAwK0xzRvDj16aP22hAQwXsA5OUFaGoEeHqxZs4ZevXqxc+fOy/CJNV3SbmDP7t10a9uW7d9+y/6lS2l31VVga4utry+GlBQ4ckT73iYkaN/v06dxb92a7NzcsrVmy1vFuLCw8gChpSCHpQzCggJiYmPxbt4cOnTQMnDPn4f27SEoCHr2xO78eWZMm0bXrl3JysrijMk10ffff09hYSGHTbMPRbU19Laz4MUXue+++3j4wQf58P332TR/Pr4TJ9J56lT6DRrEr19+yeQHHsBar9eyB00ZSxvVhMHAgM6dS/8tN73W0euhZ0/tHAUVr2JsnqVudi4Ki4hgeNeu/Pbbb6j5+STWQoAQOzvt3GRtrR3riBElU6xtbXlq4UL+/vprPnjkER654QZWffghCx5/nC3vv4+alcWXn37K+q++KvWUjbleaEOrQYiiKFZowcE1qqqur+vjqa82bdrEvn37tJXkDh7UGt5ff4GbmzZSBdofDmtr8PDAw9OTBPMMQtPaApZGpCurQViVlVgLCsBg0DqdxmnPLVpAWpo2qta9u1ZLZ9s22nl7c/LkSZ577jneeecdevbsyebNm4unrAlRmx566CFOnz7N1V270nbQIF4LCWHhW2+hN065AgL8/fFxcWHXjh0MvOqqkgebnhRN6zdB5R1OS0EO434GQ8lj8/NJyc5GbzDgVFQewMbGhhMnTuAbHU2aszMhrVvj4efH7a+8gp2dHY899lhtf0xClPLWW2/x3HPPYW9vz5jRoxlr7IAfPaq1g6FDtfNOWhot/Pw4Z3IBBFiesmK63fj9ryiTA0pnEJrWIARISSEuJ4dmgYFga8vBH3/EuW1bLas+PZ0pV13F8Btv5PqbbiIzK4vBgweTlpaGk5NT8aFdvHgRJycnrIydYSEuQUREBEeOH6frggXYqyq0a6cF4LKytMDbqVNaG+rdGxQFPw8PIsyn7xqDEqbnj6JBWKBs9q3p76YzPora2vuff841gwcz8/XXSXd3Z9qsWYTHxhJgDBCCdoHl66tlQxnrWjk5weHDtElKIigoiFdffZXrr7+e2267jYceeghfkxWQhaiJU6dO8d2aNQwMCcHw9990GDVKC7KBFnzz9IS//9YCIgkJkJQE9vYowcE09/QkMjKSNm3alDyheQZhRgY4O5cd0DXdx/y8Y3rNZCGDMOHiRQBtsSFFKbmO8vcveW53d4iPR/HzY+TIkdx22208/fTTXH311Wz64QeGduvGwQMH6N27d21+nKKBOH/2LItff50AX1++njuXgZ06aecKQ0k4p/911xX90l/7W1xbDAYGdOjAil27SraZT8339i7dZ6tspqGFOEFubi5x8fE8MmYMhw4d4mL//lhbWWFvb1977wVK+opGPj7av5wcZiUk0LNdOyY+8QSf2djQ7tNP2Xn0KM08PHjx5Zf56KOPiIqK4sUXX2T37t0YDA0unFapBpVBqGhVMT8GjqmqKhXETZw7d47333+f+Ph4UlNTmT17NsuWLcPO2hqSk7VAW0GB1pEz8vbWphoDTq6u5OblkZOTU3K/aVagpRHpqowMVJZBmJ9PoaIQERVFwKRJ2h+z48e1YGanTlpwECAgAIeLF/Hx8eHVV1/l77//5umnn2bLli0AnD17lj+NKywLcQnOnj3Lm2++SWxkJFNGjODpadO0DqdOh/7YMe27aZSUxNT+/Vm7Zk3pJzG9AHNw0NqKcVqIpRXAoeIMQuN2s2LxUcnJNPf316YbF2Vn+fn6oiQk4Ny5M8Nbt6abmxuLFy/myy+/rMVPSYiyfvvtNxYtXMiRL75g3MiRLP/kE0aNHKkNTBUWaoM/e/ZodThdXWkZEMBZ8ynGph3OZs20i7vU1PIXWihvirHpBZtOVzqbIy2NuOxsvL29wcMDl8JCFNCmxrRty+RJk5jTti3+2dm0adOGG2+8kVatWjFp0qTiWqTjx4/ns88+uyyfo2hasrKyuPPOO3nghhtw6927OAiIi4t2wWJlBaGh2r+iwuuBPj6Emy8AUlhYEgyEkowm8+xbsNh2UtLSOBcWxsJ169i2bRtfb9nCRwsW8M0rr3D/k0+Sl5enzfYwKRUAQKtW0LZtyW0nJygooLWfH2f27WPy5Mls27aNyMhIHnroIQD27NnDu+++Wxsfn2iCpk6YwDsrVzJ44kStvZh/Jz08tO9/q1ZaKaXoaO16x8oKfy+vsrUxTfthISFaYkVeXkmAsJKMp3LrrZsES05GR9OmTZuSBR78/ctmeHl7a325jAyee/hhpk6dyvTp03njjTdo7unJdT16cGDPnlr5DEX9lpeXR1hYGOfOnWPFihW8/NRThLZty13XXsvB115j4Jgx2vc7ONjyE1xqxp05vZ4B7duza9eukrJF5oOypjM3yrvWMf3dQpwgIj4eXx8ferRsyaFDhzhw5AhdTc8vl5uNDYwYQc///Y/zO3Zw6ORJ2vXvzysvvcSRDz7g8QkTuGb0aG6bMYOL0dFc1asXz99zD2dOnybXtF5pA9fQQp4DgP8BhxVFOVi07UlVVZv8/J/Zs2dz+PBh0tPTOX/+PCNGjODqoUO1UWcXF21kt1+/ckcTFGtrPFxcSExMxM/PT9to7GwWFJQdka7q8uWV1eUoKCAqKQlXV1fsXF21k3xYmBbQNC1I6u8Pv/9Oh3btcHBwICQkBB8fH6ZOncr27du5+eabycnJ4fvvv2fgwIGAVtC31kccRKP3wQcfsHjxYu4aNYr377xTm5IP2vfx/Hnt+3zhgtam4uOZPGgQ/Z9+mveWLSt5EvNRNU9PLdAREFCzGoTGfUw7nPn5hMfH07x5c+3C8fRp7TVAa+chIdrF5e7dDOrRg4iICE6cOEFwcDA2lzLNQIhyLF68mEUzZuDfogVzBw/mj99+o++YMZCernW6QkO1c8LJk9C/Py38/dn400+ln8S07djaaueEqCit7ZjfX9EUY+N5x7jNNIMwL4/Y5GS8vLxKFuxKTNTaVvPmWuaIo6N2Ydm6Ne+99x5z585l48aNXHPNNQwaNIidO3fSsmVLbr/99svzYYpGS1VVdu3aha+vLy1btmTSpEm4OjnxxKRJ2nfPNMPCSK/XakkVCfT1Jdx8gSxjUMJ0QDc3lxvuu4+1M2diV8n0/GeWLGHL7t1EXrhAp5Mn8XR1pbmvL81tbAgJCuKrr75i7bZtvD1tWunXdXAoXRBer9eCMfHx6DMyoLCQDq6uLHvmGVoPHcqoUaM4evQoeXl57Nu3Dzs7OwkWiiqLjIzk7PnzHP/nH1p37142Ewi0dtCzp5Zk4OWlTS80GCA3F9+KFvhRFO0ccOxYSYAQimc7VVj71jSD0HyKcX4+J6KiCA0NrfjN+fho56M//qCFoyOzJkxAzcxkzqOP8vSUKfQZMIC3n32WrKws7Ozsqv/hiXpFVVW+/vJLLp49y8DrritebTsuLo5evXqRmppKfn4+VlZWFOTk8Nf69XQYMgTCw7UBV2PW9pVgMODj4sK4ceN49dVXtUV0jN/z3NzKE4kslVOycC4Ki40lKCiITsHBHD58mL3//kuPos/lSr5XANq0wRZY+MYb2u3cXG4OCWFI69bsPHWKNl5efPPnn3zwxRe8snw5rzz5JA/Nnw9AYWGhtjhSA9WgAoSqqv4JWOg5NU3p6els3bqVHj168Ndff/Hu4sUseustwsLCtKyMsDBttdROnbQHuLiU/2RWVni5uBSv2AqUrkljZD7F2Ni4K0odNq/nYZZBeDwykrbG0QF//9Lp9kY2NuDrS9/QUPr27w8xMTgkJnLH+PGMGDGCBQsWEBoayuTJk5k1a1bxRdwff/whqfiiWn755Rc2r15Nv2bNtICGcRTOy0sbhQ4I0DpwxmmSwcEkJSeTk5NTEngzD2J4eWlFqS0FCI37mHY4LWRBZeTmosvNLbnIy8/n8LlzWofCx0ebfhYfr02N6dNHe6yTE7i7Y0hN5fHHH2fkyJEkJCRw/vx5vLy8uHjxItnZ2SVtXogays3NZcf27Xx6110QGkobf3+O9e2L0qxZSSY4aAH3kBDQ6fD18SHGfEVv87ZjY6N9p6tag7AqGYT5+YTFxNBp0CBwdYWUFC3oHxSkPZ+zs/bvwgXYuhW6dyfEy4sHBw7kjrFj6TxsGAO7d2f7tm21/TGKJuDDDz/k5ZdfJj8/n5dffJGTJ07w36ZNWGVmWg4OWhDo58eFiIjSG42DuiYDutlZWXz3yy9ETJxIa0sL/BTJyMxkzXff4e3ri7+HB7t37+bW664rDq5fP3Ik06dP58Vp07h2zJgqHGCgdtF45ox2vtPrccjNZc/y5WyLj+e6Fi347OefefaDDygsKGDhwoWlpvALYWr16tW89dZbPP7448x/7jmu6dmT0B49Km4v3t7aT9PvlcGg1e+sKEAIJUEOk3NGcYCwvPOOcUAKSoL1JtlUJ6OiCDVej5XH1haGD9deb9s22LuX6a1a8YK7Ozdccw1dxoyh16pVvPD887yycGGln5uov/Kzszl87Bj3zZnDmO7deeaNN3h98WKuufZaJl5/PVPHjOH50aM5k5NDQmws+8PD6Tp2rPbgdu2u/AEbDJCTw5OjRjHyiSfKBgiNTNtORYuUmMz4yMzOxr6o7R27cIGgwEBcnZ3x8PDgi02bmHvbbVfynZbP2hpCQvCxt2eCjw/07UunW25hzI4d7N25k/c++wwHPz8SExJYsWIFhw4fbrCB/IYb2mziCgsLufnmm7nnnnto2bIlU667jmvt7Tl8+DCTJ0/Gzc1NG2Fo106b3lUZg6Hsqnimi4xYyiA07gNVrmljKYPweHh4SYCwIs2b89SECTx1991w6BDY2vLY9dcz85ZbePDeexnn5cW+tWtZsWJF8QqU9913HwXmU6GFKEdMTAznzp5liLMzDp07ayPJxhE6Hx8tC7d9e62O2qBBcO21KM2a0czDg1jTQId5kMPBoWSKcWWjapayoFSVxz/5hLZDhnDs9GntvoICDpw4Qbdu3bT9evXSao6MHFk6IOPrC2fO8NADD/DUU0/Ro3t3fv31V0DLPO7cuXPxtEkhKpOXl4eqqmRmZvLoo48yfPhwzv77L++//DJtAgPxMHZcHRxQ2rfXfjcfPCrqFPo0a0aM+Wr05t9/Y+ZfTaYYm2QQpmRl0ePaa0lKSoL8fM5ERBASEqKdjxwctOzb5s1LH0vPnlpQ/99/tX9paTidPs0vzz/Pl/ffT1ZGBuPHjy87XU0IM6dPnyYjI4OwsDDmP/ssGx59lIlXX83T8+bxwe23Y3X2bPH04arw9fYmITGxVFkYtbCQ7UeOcMI49VivLw7AR6WkVDjF+Pd//qFrhw5s3LiRTc89h6enJwO6di1ugw/MmMGxQ4d4YsqUqr9pZ2cte7hbNxgyBIYNI1CvZ3qPHrjY2HDP0KEcXb6cwR07snLlyqo/r2gSVFVlyJAhPPTQQzzy8MPER0Qwe9Ysru3WjSdmzqxyML0UKyt8XFxK99egdJ8Nyi7wU1ntWwvJEBeTk3nys8/4/rvvoKCAE+Z1D8ujKNp5KSgIgoJw6N2b8+vW0fX661FcXXnpnntY8fHH5JsPNIsGISkpibS0NPp06cLUiRO5c8QIPl6/nm1LljD/iSdo1aIF/QMDeXHKFKz79aNdnz4Muu46HihaHLHOFPXFWjs4EB8fT2ZmZkmAEKpfiqyo3eTm5uLbrx9vr17Nb7/9xvw1a7jjjjtAp+Oxhx/m3+PH6VZZYP1K8/WFq67SBrAdHOg1ejR3P/cc+Xl5fLNsGauXL6e5vT1vv/VWXR9pjTWoDEJR4ptvvtGCGefOkZmRgdPevei8vLh93DjmzJihrY6Vl1d6mm5FLAUIzWuhQakMwpSMDPQpKTgaVwGqyknTfKGFggKOX7hA2379Kj9GY6bHf/9B587g44O3wcDSZs20ulbu7vgkJ/PfgQNk5OTgceoUwx58kFm33MLYSZMYf8MNJbU/RNNhWkuzyPr164mKimLOnDnF284cPco111zD3WPGYOjRo2zb0elKsnBNn8/aGh8PD2JiYgg01pMxHVE2BjlMM6DMpxgbn9MkgzAlJYV7n3qKz5YuRSks5Gh4OG1CQnjlk0/49KabID+fgydO8GjXrtpjnJ0tv//mzSEyEuX0aWZOn07+wYO8unAhH3zwAceOHeO+W27htZdeYsioUdxwww2XvlKYaLR+/vln/ve//9GhQweSk5Jo6+aGl7U1bXv2JLR5c24ZNqxsTaUKeDVrRmJSEgUFBeiNtdOMHc68vNJtx1KAECqdYlxYUMCCTz7B4OjI/v/+48MPP2TeNddwOiyMVq1aafsOHmz5AF1ctHalqtrrhoZCfj6t/v0XrK35/NFH2XDiBFOmTOH3338vXrAkMz0dW3v7Bj29RNQeVVUZPXo0Hh4eREVF8eT06XQdMYIlQ4aw5IUX0AUEaAHqagQI9TY2+Hl7ExkZScuWLQFYv20bd7/xBn6envx7000oej3RRQH4qJSU0kGOkoMD4FRYGB1CQ7Wg+YkTvPnGG4wMCChugw42NrT284PIyKq/cXd36Nix5Fxqba313Q4dgn790CUk4HPqFC9Mm8a4l1/G19eXiRMnAnD80CGWvfMOr3/wgbSjJiApPp75Dz/MrQ8+SLegIL767DNSraw4deoU8XFxfPPkk+w4dIj3N23ilTvvRG/8211dej0+rq78Z74CuHn2rTHj3DSD0Mi83mA5GYTPvv46f+7dy5+RkYzs3589x47xSufOVT9Wk2CijY9P8e+thgzB392d7du3M2zYsOLt/+7bx6KXX+bTdetKzqeizhUWFvLjjz/StkULvlyzhlVr1+Lu6kpkXBw6nY5bpk0DR0faT5vGocGDyU5LwzM0tGQ14PqiKBCo1+tp2aIFp0+fpp23N6gqVmA5QGgMBJrGAcwyCA8fPoy7qytvfvop6cuX88n99zNk6FD45Rfuvftu+nh706Gyqfn1gM5g4OjJkyhZWXD+PKdPnmTAnDl06dqVkSNH8t1335GVlcWQIUO00lD1nAQI67nk5GQSExMJCQlBTUnh6x9+4OF580hJSeHLlSux2r8fF9CynEJD+ejuu7UgWkiItrBCVQNiBgO+rq6WMwjBYgbhs2vX4nn0KM+88ELF2RxmJ1LVYEAxzSAMC2NcVeo4GQzaVIGsrJKpA82bawXsPTzAzw/0euz//hv74GAoKODTt99m4ZtvMv+xx5g7dy5XDR3K4/PmVW0UTzRsRd/Dhffei5O1NX3GjSM2O5u77rqL/Lw8dIpCVlYWX331FcOGDeOLTz/l8ZtuYvb111frQg0bm+IAYTHjVPqcnLL1zyqrQVh0e8uWLazZtIkn5syhg58fJyIi2PTWWwwdO5a4uDgc09M5FxlJe2OWVkU6d4bt2yEpiWu6dePptWtZeOONtJg6lc4uLrS87TbWfP01O7Zv58YJE8jOziY8PBxHR0fatGlDj/btSczIoEXLlhJkb0L2f/klXcePR7G15ccff2TGjBl8+cYb/L1vH0Hu7tw8YQJZBgNzDh5kQLt22ve6GtMpDLa2uDk7k5CQoC0YAiUBQmMbKbpQW7tuHUNsbUumw1dxkZJFS5ey+IsvSE5LY87Uqbz99tvMGTCAqLg4gswL21uiKKUu1LC2hqKpbcN8fBhy7BjXPvccTz75JL27dCH8/Hk2fvcdXl5evLFsWekVX0WjtW3bNiIiIkiJi2PD5s1MGjGCQSNHEtimDTt27EBRFMaPHcvAgACu8vWFVq3Qma58aBIAqBIrK22a8YULxQHCtb/8wsL77uPlFSvYv38/PXx9iwOE5+Li+G33bq4uWpjOvO2cDg8n1DjYpCjcMmWKVrZCpys9Zaw6gQeDQauTZcrPT8u+UBStP+flRVc/P5beeScLX3mFL9eu5WJSEkmxsYRFRPDvqVO0btMGtaCAOQ88QKf6lkkiasUTDz3EiUOHuHbkSFzt7dFZW3Pi/HmeuuUWXpg9G2xs6D5wINf36YN+0KDqfQ/N+Hh6EnP4cOmN5gFCvZ7E+HheX7KEV268sWxw3dJ5xyQIcuzsWdZ++y37li6l8+zZPLdgAf06dqy8BmFVeHpy56hRzL77bl546SVGjRrFF198wSfvvsv5iAim3ngjQ4cMYcrtt+Ps6IgiwcI6tfK997jjvvtwcXBgZPfu3DlsGAu/+opbx4zhjQcfRGf8m6zT4RgUhGPdHm75jNcyqkqbli05efIka5YvR5eUxCs331yq7Rw/doyYAwcY0qmTdt4wtg3T2SSqynOrVxNmMDC0Xz8emzqVzw8cYIyxdEBR0L2n2SrN9Zlia6vFY9zcaOXnx7dPPcV1t9xCcHAwakYGLQIDuf+++/j2u+8YXN7AdD3RMD7xJkZVVZ577jkCAwN5/PHHUVWV9evX8/azz3I2OpqVn3yCoigMtbbWMhwcHbUAma0t2NtrHbJqZHEAoNPh5+HBcdOaNuXVICwKEB4NDyfAmFFloaZNqSnKRfev37GDrz/6iLUzZmgPy8vj6NmzVT9penmVzggzGEpqLII2Wu3ioq1A1r49gSEhvNe/P4UXLnDk11/5/p9/GDJkCC+99BIjR47E31K9Q9FgqapKYmIi58+f55Unn2Tf4cNY6/V07tKFpTNmcCYmhm++/JKeej1nT57k4337uG3gQH775x8W3303Nz79dNnpkJWxscHbvKaNMchhDBAaR6ONo2mVBQh1On766SecHR35eft2gnr2JCk9nc6dOjGqd2/Wr1/PT19+yciBA7G2tq78GG1ttZX+jhyhRb9+xK1ahcHeXgti9uzJhP79GdS+PUt++onoyEgK09PxcHTkYnIyB8+doyAvD4O1NX369+ell16ii7FDIxqlf//5h0+WL+etZct4+rbbWPnDD7i4uPDFkiUM8/Nj2MCB2ve5ZUvs9XoGtG1bMlpcHVZWeBcF18sECE1Ho/PzeXXpUtTRo5lmDIhXVoOwaGDq06++4ofFi1n05Zc8M3MmJxMSeOXjj2nu41Oc8Vdtxtfw9kZnZ8dns2cz7Lnn+OiDD1AAF3t7vOzt6dKxI/fedRdd+/VjwoQJNXstUS9lZ2djZWXFqlWr+PTTTzlx4gSDe/fGJi2NyV278tOPP/LEggV4NmvGqbNneeqOO3iqVy9tcLNt20u/4DEYCPLz48iRIwwZMoTY2Fh+3b+f5QsWEBMfz/vvv8/yBQuIjo8H4LMtW/j4xx85M2uWxZrRpyMiuPaGG7QbZoNVpWpP10awwXTA2dkZ2rVjzNSpzFq6FD9bW2YPHcrWAwfYsGABv/73H7Hh4fx7+jRP3HsvP/zxR82mloo6p6pq8QCjqqoUFBRw+PBhEhMT2bB5M0c3bCA3OpqTrq70HzqUXz/6iG5dukBmJnTqhF1BAW1dXS/5O+jt6Vm2BqGx/JHJeee/Y8dYtn592QBhRbOlCgtZt24d986ezZsvvkgLf3/69+7Nqi++YPcHH1zScRdTFGbPmoWnmxsfv/ce06dPZ1Dnzgxs25avlixh9aefsvW775j3/PPkZmfz3WefMXLSpNp5bVEtRw8f5olnn2XT8uWEJyczc+ZMlPx8urRuTbt+/dD17VvXh1g9oaEQH09oixacPHmSQ//9x3///ceugwd5a8kSuvr4kFVQwA3/+x9Bjo4M6d69dIkxk7aTlJjIwnXryM3L4/0FC2gbHMzz110HmzZp+xuzcmvrvHOleXvTf8oUNgCxFy8y7qab0FtZ8eu33zJp4kR++fVXOnbsyJw5cxgzZgxjqlLb9wqSAGE9k56ezg8//MDHH39MZmYmX3/2GdlRUUy9+Wa6BAbyz8qVWKWlaZlzKSlaYzXtLA0aZHlFryrw9fLi90OHSjaYrsxlYYrx8YgIVNMAYXknTZMMwgOnT/PL77+zMTSU5t7eGKKisLayqlomB2jvtzL+/tpiDSYLL+gCA+l86610Dg5m0LBhLPnkEx577DGuvfZabrzxRjp16kRkZCS+vr60Nq5aKxqExPh4Nm3YwGuvv05YZKSW/t6yJQMDArj34YcJbt+elmPGoGZmErNjB76FhWBtjd+UKQzs3Ru8vZmdlKRl3NbkJGRtXbbotXldjqIgx+rPPkN/6BBThg8v/RxmdTnW/PIL3333Hc/MmsWG335j6NixtPLzQ2dlxdg+fXjw6adp3bw529evr/pxurrCwIGQno4hPFyrn2EwgE7Hp/Png6srd02YoC0IERqq1S7V6/l77Vq8WrTAT1V579dfufqqq9i1axdtWremEHhv/nwGjx1L56qUCRD11ssvv8yXX3xBp44d2fbLL9w0YACfvvQS059+mk8WL+bW3r1RMjK0QLPpiqVGNTnvWFnh4+5euh6UsQyFkcGAmp/P6XPnOJeQYLmOmlFhIcfPnyctL4+599/P5GnTiI6Lo2/nznw/ciRcvMi8efO4ZvRoBtXW99XZGa8xY9jt7ExERAR7zp3D3tmZm+6+m7AjR5jz2GN8vm4d7y1ZQlCzZnz89deShduAJSYmMuXaa/njwAEURcHdzY23nn2Wnk5OtPD31wYs8/K43ceHLatWsW/7du54+WWcrK21qez29rVzIAYDs2+6iUmPPML06dO57bbbuHf8eNzc3Zk9YQJtbr4ZJSeHhAsXaBMUxPGwMOxsbLQgjYUsqDMREbQqykQstcCPMfCRm1u82Eits7LCqlUrNr/4IiE9euDUvTsTk5LA3p4ZAQHg7k6OtzetW7Zk07JlbNu/n2Vr1jB2/Hg+XLYMR8d6m3MjisTExNClSxdefPxxdu/Ywfdbt6LqdHi6u3Pm3DnWzJuHa9++oNNhnL8x8u67yz5ReaVUqsHHy6vM4lj5eXnk6PU4mFzrREZGcjE1lSxVxa6yAKFOx+tvv02ojQ2zFy3ilzfeoPt118HRo7z+wgvYAi2Ni9jVhrZtmXT//UwaOpTojAy8u3RB5+cHisKTgwbB2bNcPHiQTQcOMO+ZZxh6/fXFA2InTpzA2tqaFubZvaLGCgsLOXDgAJ06deLMmTO0adOG40ePMuLqq3njoYe49o47Su0/6vbbSy+e01CEhEB6Om0CA/njyBGOnzpFRlYWO/77j607d9L16qtZ/sMP2NnYcCIqqnR5JZNYwvqffuLlDz/k2t69OZeezuC+fUsvfAqlB6YaapkJb2/63XOP9r6K2v/wW2/l9cREBg8YADoddlZW7PrjD9569VUeeOQR3v3gA1555ZU6T8SQAGEdSUhI4PPPPy+eftQyIID9e/ey6LXXiIyL49OXX2bc9Ono9u6FoCAiV63SCr4HBGjBr7/+0hYkML/QuIRG5OvtTXRUlHYjNxcSE8nOy+OuRYu49fbbGX7zzcVTjNMzMohISMDONOPQJG24+KdZBuGJiAgSEhL435tvMnD/froGBHDTtddW/YKpKvspCnTvXna7Xg89ezJk1y6GPPwwyamprPj1V9596SVOx8XhXTRdp3PnzqxZs0ZqsdVTJ06c4Mf16/n0yy85e+YMFBYyuGNHXr/jDvq2aIGznZ221LmvrzY1sCjYoNjb4ztqlHaRU1Cg/bGujQxSGxt8XFw4ahYg3HvqFO1dXLSVuYpOkj/++CM2iYlMGTq0ZF8LdTkeWrqUT1auZHSLFnz41Ve8tmQJbfz9Qa9ndI8eJCUlMe+JJ7CqyepYjo7aQiamF3p9+pSMnpuN1vW++27tdk4OD7VujZ2VFV27d8e56LX9vb157p136NS1KzdNnsyUKVO0RZJEvRcXE8OGr75i7bp1REdFMW3wYKJTUzn544/Y+/mhennRfsQIevTuXTI1vjYvcooChKbB9dzsbD7ZuJGhrVrRBrSFFuLjyczK4lxcXNkahFDcdsLCw+kwYQI333wzvh4ePPTUU4y66ir0BkNxFu/QoUNZ+fjjZFV3SmdFnJxwHDyYtkV1GY0LEwUNHszGHTs4/803fPnzz3yxfTuvPP44w7t0IahjR7wlE7dByM/PJywsjPDwcO675x5GdezIxvXrKUxKoiAmBkc3NwgOLlnxvsjIGTMYOWMG/Pmndl9tBQcB/PzoHxbGqGHDGDlyJJlpaXx/332g1+Ph6sozzzzDp598woFDh5gyahQnw8LIyskhLS0NZ9OLMFUlJiaG8Lg4go0zTyxlENZkinF16HR0nTxZCwBZWZWU+ejZEwAbYOXKlYwYO5bxAweyf/VqXlmxgoEDBtCxUyfi4uK4//77GWtc5VPUuQsXLnD69Gn27NnD0aNH6d6mDd+sXUtgQAD7v/8eu7g4vJ2dSUhMxHPw4CsWBPDy8iIpOZn8/HwMBgMUFvLIJ5+QXlDA8kWLtJ30eiKLSi5FpaURkpenbS+ntMW2nTt55pVXyM3N5dFHH6V7mzbFbadTaKiWBZmQUHtvQqfT/qYMGYKv+X3W1tC2Le7+/twyfjxf//EHw7p14+V587BzdeXGWbPw9/fnjx07+PXXXxkyZEiDXWm1Pli3bh1H9u3jxUWL6N65MxfCw/Hy8iIxPp4lDz7IlHnzyj7IOGOiIbKxoUdoKM+/8w5x8fH88f77/LFrF/8cPEhhYSFLv/qKjxYtYtT//kdWfj52FgZ1f9y6leZ+fjw3bhxdZs5EiYqCmJjSs0BM63o2xAxCI/NZXr6+/O+FF/jfQw8R9tdfuLm702vaNC7m5jLu+uuZMGgQI4YP55dffy0OEm7ZsoUBAwagKAr2tdmPqIAECK+wjIwM5syZw/r167n22mtJSEggPDycyPBwgjw96dG6NUfffls78ezcqWXBBQWhXLyoBQdBm2Y7YkTtXqgBfl26EFUU8Pv588/5deNGDoeHczEtjfueeYa/rr0WZ72e/YcOMe+llwhp3pzwyMiSEWnTbEFLK7GiBQg7dOhAYVoau3bv5q/du9myalWtvo8KOTvD8OFgMOCamcnDw4fzcE4OnDgBubnkenvz6GuvMXr0aIYOHYqtrS1PPfEENjY2HDpyBB8fH5pVpz6duCTZ2dkkJSVx8u+/+enXX/lt+3aiYmIY0aULS6ZNo3NwMA6jRmFtXEggLk5rH3l55bcPY7Cgtlhb4+PszO9HjxZvemv5cp589VW6BAezuX9/nJ2cQFE4duwYDgUFbPr7b/r37Yubh0eZDmdqSgoZ2dmMHz8e5dQplr/4Is988AH3DBsGej2ejo7s3r2bnpdysWb+ONMTmPl9xts2NtC6NbOef54p//sfWW5uZGdmEtCyJWm7dvHn77/z2Xff8eSTT3LNyJFMGTWKwaNH88O333IhMpJeQ4YwcPBg6YjWsZMnT7Ju3TpSIiNZ+fnnDOvWjbuHDOHGnj2xCg7WpsX36AE6HQpowUHQvge13UmztcXbyalUBuGtTz/NuYgInrpwgahbb8Var+f0hQvo9XrOxcZaHI02tp39hw9TWFjI2rVr+efjj+nSty9B3t4lddSKskBuvuoqbUXy2uToqP0zZ2VF8LhxPN6nD1Pi4hj/v//x8RdfEJ+YiLO9PRNvvplzFy5w1113MXToUKytrWs+9VnUqvj4eN576y3WrV9PeloazVxcmDNuHDNvuw2leXNtJkfHjpU/UVFmVK1ydgZ/fxbedx8dxoxh9Zw5WJlMz3/wwQfp36kTfYYPp0doKJ///DOOdnZER0fjbGXFL7t3k338OH1UFX9/f1r6+mJj/Nts7LNdyQAhlAmwmht2zTWcOnWKFi1aoKgqH7VuzUfLl5NaWEhQUBBz7rqLw9OnM2/hwlKDzmlpaTg6Okrm7mVWWFjIiRMnUHNz+WrVKt5btYqWAQG08fXlzwMH+Om112g3eXJJfyM8HBwc8LSysvy38zLROznh4epKXFwcfn5+pCQlseKXX/D19CyVvRRVNHD19c6dBMbFMeWhh7T7zAKEv/zzD1MXLeLrTz/l13XruP/+++H06Uur31kbHB1R0LK13v/4Y+5fsICC/HxmXXMN67Ztw9vbG1cnJzy8vPh+w4aS+r6iQkd27uT71asZO3EiSZmZ3DtrFo7W1nw2bx6/7d3LrsWL2bxrF95dutB3/Pi6PtzaZ2ND5+BgVFUlJCiI3p064VhYyAcvvMCyZcvwcnNjcPfutPT15XR0NJ1MpxgDKApHTp7k9VdfpasxMcE8ax1KTzFuqBmE5bGzAzs7gorKeuzfvh1bKys+euUV7pw+nfUbNjBm1CheX7wYFzc3xo0bR8uAAMKjo/n5hx8YbD4L7TKQAOFlcuzYMby8vNi1axfHjx9nw4YNBHp788OWLUwYMYKIf/7BKSMDcnNJyszkv+PHGTBjhtYIzp7VRhfCw6F9e2001dW19AvUcnAQwLdHD2ISEnj91VdZtGgRQ9u3x8/Lix9ef517PvwQb29vfDw8uH74cH79918mDhtGyoEDxMXF4W0hCyouNZWbhgxh1tSptLW15fP16zkVFcUPGzdSePAgcZ6eBIBWY+RKMgaHTKPwXl6QlIR1RARv3nYbK/78k5j0dPZt3477a6/hYGODja0t6dnZ3HHnncyfP1+mtdSypKQk/tmzhyM//MCRxETORURw/MQJcnJz8XF15abhw3n17rsZHBKCYcAAbbGazMySKSeKUjIqdxnaR7lsbPB2c+PC+fOoqsqZM2d48e23Ofr117y8dClDrr2Wbj168NGECZw4dQpHGxvufvdd3unUiesnTiwTIDxz7hwhzZtrFzOKwlU9e7L9p59g//7izmbv3r1h1666Kdxra4tLx464mGxyHTSIsd27M/amm7h46BCf//EHby9bxk2zZ9O3fXt6tm/P/LlzORQWxsSJE3n//fdJS0vD1dVVgiGXWWFhIauWLWPJ0qWERUZiq9dz04gROCsKP65fT69hw7QOWHp6ySrdV4qrKz7OzkRduABo581t//zD+Z9+YtiMGWzcuJG+3btzOiKCPl27ci4qqsIA4b7Dh+ndqRNhcXF0b9uWnmPHQnY2JCWV1AEFbQDhSrYdBwdo0YLAFi04ePIkqqqSn5/PoQ0bWPP559zQqhX33nknSRkZDOjTh+82bEDNy8PWxYXCwkJZxfVyMPn+qKpKXFwcnp6eLH/9dZatXs3/pk/n7TffZHjXriyeNo2RPXqgtGqlfZequ/DM5fqu2dnRTFWJOHwYm3/+KVkcq+j70rNnT3zc3enXoQMTRo8mNiKC6Oho2vj7M/P558nOz2dEp06MHj2aLx94oPSFmbFe7uWoQXgJjAuyoCgoHTow86mntDbu4UH/SZO4bvJkNm3Zgk6v50x4OC7u7pw5e5Y2rVvz8Ny5zJgxQwKFtWTz5s18+8UXXExIYOyNN/Lmyy8Tn5JCTm4uM8aOZcOHH9LH11dbzdrPT+urmX72dbWAU/PmXN21K+PHj+ezzz7jrjvvZPJVV7Fuxw4SkpPxhOIMQiuDgSXr1tGvWzctQGhhev78Vat4/+23GTN6NGM8PbX3eupU2QBhHS20oHdzY84jjzDnkUe0DarKnE2bSL54kYCQEBYuX0770FDGDRnC0hUreHvRIpKzskhMS8PT05NHHnkEX98yeYpNzqF9+7j++uuxtbKiXcuWfPK//xGXksKq11/nhptvBmtrbrlwAdq147qrr67rw718bGxQEhMZf+21xJw7BzodoUFBJCYl8eSTT7Lzk09Q8vII9ffnRGQkncz6bCrw36lT2srE585p95lnrUOpRVHr+rxzuTkUnddmPfss+PhwU7dupOv1fPbmm/x++DAv33EHVi4uNPf2ZuKkSXyxbh12jo44Ozvz9YoV9OjRg7FTp9bqMUmAsJZlZWXxzDPPsHLFCtIzM+nQvj1tPD25Z8QIYmNjeXvdOjw9PbVG4ecHXl64JSQw8K67Sk4e7dppP93dr+ix29rbM/u66/h7zx52vPceofb2WuBMUVj22mu8t3IlwwcM4IMvvuDjBx6gR+/enHnlFS5cuIC3qvLFt9+y5vPP+X7pUnSFhUxfvJgWnTox69FHsdLp8PTxwc3BgWHDh2sjiN26aSfR+tLw3dzAzQ1dVhZ3ZmeDqqLefTepqkpGTg7NDAYu7tvHI+vX07FjR5566ikGDBhQtVVkm5q8PO1CxZyq8teGDew+cYLjBw+SnZvL+P79ic7I4KW33yY0IIBOLVvSx9+fqZ07E/D447QJDEQJDdUW2jBVXwK0ikLX/v3J/vBDRo8ezZHDh3lu9myCAgN55+67WR0by4LXX2eNpycebm4kJyURn5XFhbCwUs9hPEGePnuWEOPUZ/NRNdN6Hvn59WtlLwcHbVpL69bce8MN3GtlRV5GBgZbW20VvTNnSN67l1nLluHn60tWVhbdOnYkNCCAyIQEWnfqhIOjI0899RQuVzpQ1Qgkxsfzx8aNFDo5EX7wIGFhYVyIj+fIf//h4ezMkrvuoouvL269emkZ6W5u0KqV9mC9/soHB0ELgA8ZwvVz5zJ/wQKWL1/OjHHjsLG3Z8KAAUyePBlnZ2e6+PszbNAgFn30EYs+/5xHhgxBZymD8L//eGLmTPrfdBO68+dLZ7Obroxchxdr2uEqWFlZ0ePGG+kxaBA4O3Pd8OEkx8by5KpVeHh4kJ2bS7vgYM5GRTF98mQKDAZat27No48/XmfH3RgkJiaiz89n0YMP0qx1a95cvpw2ISH8tX8/zZo1Iyc9ndfvv5/HX3+dlx5+mFsee0wLQBUU1J9zjpGdHSQmYpObW7LN11f7B+gMBva+9x5+/v58PWYMN02dSkxMDH8nJmJjbc2jjz7KvQ8+yKJFi3CwsSk76+NKZxDWhKdn8a/Nu3Zlz4EDfP/RR2Tm5NAxMJDc2Fh6du7M7//8w0Mvv0xiXByPSBuqsrSYGKJjY4lNTeWDt9/GXa9n3rPPEp+by5zZs7m+Rw9aBwYy+557mH3jjbz+9tsoBQX1ewqlpyerH32Ux378kU6dOvHobbexYMIEzl+8yNBbb0Vvb09GcjIK0KVlS/aePEmEsQyG2YDusWPHOBsTw/XXXVdyP5QOrhvLc9SXtqMoOA8fjrNeD1ZWPNG1K7fPns1zixbh7u3N1T16MCA0lDaenpy+cIEunTrx6OOP4+bmRrt27ejfvz9//PEHISEhFBYWEhgY2GiD7j988w3xsbFs2bSJsxER9AgMxNXRkWUbN6IYA1em1zrG6/fGzMYGcnKYO2IEiWFhoNOht7Ji/9atuIeE4BoVBXl5dA4J4emPPqJV//50HTYMVJWc3Fy++eknXJyccHN1tTyd2FIGYX1pO5ebsfSNjQ23v/gitz//PBdPn8Y1KAhdUeKLrrCQ+2bOxNbamqiEBAZ27MiaL79kxYoVeLm48Oann2JvqU54NdWjq8uGJfXiRTb++CO/bd7Mv//+i6eNDbGpqWSrKp3bteP4O+9QYG+Pp68veisrrRPj71/SGEzn2dejlXSXPP64djxHjmidYkXRApUODhgMBiaPGcOegweZMmwYtv7+tPD359ChQzjY2HD/k0/i6eLCm6tWUeDqyn9hYWzYtYu358/HPiyMU82a8cOiRaUv1upjw7ez02q0FRSgGAy4QHG2VDNXVz51ceG3q67io59+4uknn2Tc2LEUJiXx+99/4+Pnx5yHHmLkyJG4ublp9U2akKM//0xYeDhbf/qJPCcnOvToQfOQEE6fPMm/Bw6QHB7O30eOcF3v3nRq25bs/Hw+2bQJT3t71r32GoM6d9bqBjo4aEHGqqzQWw84hYTw59tv81NSEq0LCuhRtMCHlZUVt02fTkZ+PnOfeYZunTsTFx3NwVOnCDt/XnuwhQzCVsaRdbOVjU1XyqtXHU5TJlNRrUxPUiEhuAYG8kXXrsQcPoyjvz9rVq9GcXbGx8ODUydOcPz4cQL8/Bjcpw/NW7bE18OD1h06oBgMpKamMvrqq7mYmEhop044WDgB5uTkYGVl1WgzrjKSkzl66hSFhYVEHzuGmpxMRGoqn61fz7FTpxjUvj22dnYEeHgQFBLCwC5daDFvHt2HDNHKQKSna1kcISF1/VaK9brqKq7p25fx48dz9NAh/lm6FAwGZowciefAgbRs2ZK1L7/MjcOHs/r773l8+XImPfkkLdzdSwU0CgsK2HvkCB++8IJWBiIsrHTbMV6oGae71IfviKIU11pzGzYMt/x8Pp8xg6SkJKwUhT2bN9OmVSs+efttstPTWfH++6z+7DO6dOxItx49mDhpEgHBwXX7HhqA3IwM1q1axepvvmHXX3+Rk5tLv06diPzjD+ZNmsTZiAh++Ptvvlm1ijZdutBz6lQmz5ql9dsUResT1Ee2tlo/LT29ZJtpKQC9nubu7lobKFpMKzo6moPHj3PT6NFMuP567nv4YYYMGaJl45terJm2HajzLKiqMtjbM+GBB8psH965Mz9260bfqVPJz8/nz23bOH76NDqDgYmTJvHAAw9gbW1dXD83MzMTg8GAdQPpg9SIqlr8Py0oKODrr7/mvTfe4NCxY7g7OeHi6MiUIUOIzMkhsFMnWvj40Ck4mNc/+ggcHZk8cCB+w4ahmARs6y1FQXF2ZuFjj3FN//4MtbNDMRiYPWECEampDLjhBl55+mm+2byZe8aNqzBAuHHjRiYNHIjB2lrrs5pOpzTPIKxP3yXTAXcHB7x79+aDr7/m/aIAp5KVpb2HrCymffUV723cSF5+Ps/u20czT09SUlNJSE3F1sqKIH9/Nvz8Mx8uXIi1jQ1PLFpU3AdTVZW8vLwG247Sz5/nxw0b6NG+PbqMDJZ/9BF2Vlb16//ySnN0hKwsWjg40KJ1a+177uamZXc7OGilnjIyePKWW4jPzGTdd99pAULg6++/55bHH2fUoEFlS5GVl0FYH+MEV4pOh3ubNqU2TZw7l4k33VRcTgQ3N5Li4vhgwQL2nzyJv58f3Tt3ZtT48YwaOZLONZylWb/P9HUgIy2NCzt3sm37dtJTU8lQFLx8fYlKTmbEtdcSFRXFuuXL+f2vv7iqUydG9+7NHXPmEGtri1thIdnR0VzTu7cWZMvN1b7YnTqV7VTVh4sTS+ztIS1NO9Hp9dpxmqzqO+W667DLz8fWzg70eu6fMYOpDz9Mt6Ag7p85k9be3tz99NOMGD6c1+66C2tra60+nKLQNjSUtjfeqD1RPUi7r5AxiGnO0xMGD+bqFi24uk0bYu+8k9Xr12PdsiWPPvggZ3ft4q233mLOrFm4ODvzyssv06l9e5zc3CjQ6SgoKCA/Lw8vT088G3AtwyfvvZd8RSEoJITmQUFsWr+enbt2kZyaSrvgYPr17Yt3YSG7Nm4kIi0NW0VhTPfuWA8YwPKvvsLNmLGkKMwt70Ua0gnYxQUXVeXmm2+GrVu1lYCNC/QoCrNmzUI9fRrfkBC+3bgRFwcHLhRNq8zLy+P4kSN0UhS2/P47G7ZsYfqIEdrzWqrpWd/bTkWsrCA0FJ+i1cjv7tRJ+5ujKNqU8fR0FsXFsWXjRhIvXCA6MpKNf/1FNpCSns4Tjz5KgKcnp6Ki8HZ3JzAggKjISDw8PMjIzORkWBjN3N3x8vCgVUAA/QYMoN/VV+PbrBnHTp8mMzMTV1dXevXqhXMtrIZYa3JzUbOyOHroEOFHj/LzgQMU5OVhcHbmwtmzONjbE3fuHDsPHaKlnx86wL9ZM3QODnja2TH/ttu4umNHbPr314IF9vZlVxpWlFpZAbLW2dvz3oMP8tQPP/D4sGEEFS1S4O7iwvSJEwEYeN994O/Pn2vX8r/77uPkyZO06NOn1HS1v//+Gy83NwKMdZTMR6SNg1L1LfPWyMamuDSCMUgx/OabAXjunXcgK4vnTp3i8H//cfjcOXZv3UrXF1+kTUgIrl5exEdH0yowkAk338zo669Hr9cXF7NWVZXk5GRcXFwabfC8MD2dj957jz1HjnDLrbfi6OjI2bNnyUpNZfHChXi7u3PnNdfwzZIlZNja4tGiBXpV1WpuRkVBmzZMnT+/ZLEmL6+6fkuVs7PT/m6mpZX8bvr/axac8HV1JSY6mr///Zcn7rwTbx8fdi1eTI8ePWD79vIv1vR6LRCZk9NwL9Q8PAgYMYLvlyxh0QcfMKZPH16//XayEhJ46/vvadWiBSgK1tbWqIWFZOXkYDAYcHZyYvRVV9Gzb1/SEhNp16MHu7ZswcbKikkzZ9KhQ4e6fmfVlpSUxOw77mBYu3b8uXs3LUJD8W7ThnNnz7J+wwZiExJoHxjI09Om0X/9ejydnLS/m0WDMi+98w6Oqqq1laLgeYBxEcOGwsEBfU4Owzp1Kq4XeOPw4drKsq1bc9tNN/HN5s30at2aVsHBhEVEkJeXhxWUChDu3r2bm9u1K11HDRpG9q0FxZmAxhJMDg50mzWLj269FYDEhASOb99O9+7dsU1MhOBgXn7xRYKDg+kWEoKiKFyIjyc5MxO9qrLvn3+ITkhg8tixBIeEcHDPHu65916GTphQR++wem6eO5eb587V/j9jY0syvJoyGxutln9ODmzZon3vTf8O6vWQl4eNrS2j+/fn/Y0bte2qyo8//8yiuXOZNHZs6QEo06x14zYPD4iObpw1CC+FTgdBQaU2uQUF8cSyZRQmJ3Nh926++e47zv75J/qiRb5qoh72kq+cBY88wpnz5+narRsufn58/fnnbN66FTdnZ64bMQJXFxfs8vI4+NdfuFpZ8eg99+Dr4sKEq65i5bp1uDo4aA3F/ItrWqOiobGz02ofOjpqQUKz9+bq5saMUaOKO4qDe/Zk3rx5/P7VV8y+/XY8FIXrBgzAtl07rUYaWE4dNr1YawAnzVLs7SE0FEJD8QbmXn219n70etoNHMiYW24BZ2c2rF3Lig8/5PnwcDKyszFYWaHX6dArCgkpKQzo1Qs7KyvORkXhaDDQoW1bVEXh6OnT+Pn60rdXL6wVBRc3N7oOGoR/cDBZWVkkJSURHR1N586d62y12JGDB7Prt984uGUL38XFMbpvX+YsXkzbESOwMQ1M5ORoNTUNBi1rqbH+kTcGMzMytH9QKkBoMBi4b/JkcHHhmo4d2X/8OI8sXw6qyie//sqsCROYMnIkv+zdS9f27enXtWvJc5iPqjWwDmeFTL8rRUV73by8mNyhg3ZBam2t/YyPB72eQoMBnasrBZmZnDtxgrCdO2nevTtJcXEYHB3p1rkzp8+fJy02lhPnz7N7507WfPopMcnJtA8OxtHWluSsLN754AM69Op1xd+uqqo899BDBLRsyT9//UVKejqpKSmkJSQQk5REXkEBAb6+jOneHTtHR/KSk+nTuTPpGRl4XHUVn0+ahKtOp/0Nsra2fI6pr9lO5bGzwzovj9eeew62bdO2WVuXnrZT1OFs7u9PO39/Thw/zqjevcnJy2Plhx/iGxPDH0lJ2gWeaYfTUgbhla4/WBucncHZGVtvb3oNHEgv4Hbg7dhY/tqwgYyYGDy9vfk3PJyPlixh2u23o9fpaNeqFRdTU4mJi8NKr8fGyorQoCAc7e2xcXCgdWAg10+ZgqefHwHBwej1eqLDw/F2c8PBtGB/PRAfG8tHb79N1z59eHfpUmJiYujZsydJkZHYAF9s3Yp/s2ZMHjiQl+bOJS0vj2A3N3Q6HQsef5zxd99dfPFbKnRuZaVlrUPtrjJ8JdjYaN/n5GTtwvXChdL/Z2Y1nHzd3TkaGcmBo0fp3qEDKAp9QkNL+mjlTTF2c9PazNmzMHBgnbzV2tJzyhTWjRlTUte7sJCVs2ezMikJNSWF+DNnMPj64mxlRWZSEvFRUXy7fTt7f/oJG3t7fvvxR3qGhpKan8+IwYMZNXQog0aPJvrcOcjL4/sffmDsqFG4OTgwZOxYOvTuXedB+cLCQtYsX86FkyfJVhT2/v03amYmW9LTGTJ2LB9+8AF5mzZxw+DBLH/mGbr5++PapYv2nbLwN8DRyansizS0vp29vZY1m5am3TYG+IqMuOoqXpg9m2u7d8eja1dmz51LdHQ0AYWFzH/jDe6aMoXmOh27du3i7euuK5nhYcwgNJ9i3BAHdI0Upfhvo0dgIANuuaXU3U+99x7z5s5FsbcnOT2doWPHMrZXL9oEBPDQrbfi4+3NN19/TezZs/Tr1Imb7riDl86c4a5HHkFRFLb8+CMx585xzejRNDOWPalvFEWCg+ZsbErVvC1W1F/DYKBb+/YcWLgQgIL8fH7+7Tde/eor/Js3Lzud2PxaJzAQTp7UXqMhJYzUIZ2rK8HXXMPcIUO0JLVLKB3UQP9a1Y6777uPj19/nZP79hG/ZQvXduvGV8uXY+3vX/aEnpurpc26u2up2RWdDOtRp7ra7O0hNVXrMMfFlX0vOl1JdmHRqpBz5sxhTufOWrQ/KQlba+uyIwPm01WMK0rm5FiuVdeQmC6IYTBof9SA8ffcw/g77tDec06O1hGxtgadjvNnzrB3xw5yVJXmzs7k2Npy/Ngx8vPyuG7IEGJiYvjljz9QDAYys7N5+sUXibp4EVtra2ysrGjh58fdd9zBrQ8+WCdvecjkyQyZPFlrF/HxWj1NS997G5umUZMDtLYTGal9n43B9eDgkhObXg+5uTg4OtIyKIiw8HDUwkLe37yZ9evX88MnnzD71lt5/oEH4OJF7TGWRtXqY02by8E4Bcbevni0zPhXV29rS6t+/WhlYSXatkXtrxdwyyOPaN/RwkIt0KjTaT+vcH1Xo7ycHGzz8/lt/XoG9uiBp5MTzh4eOAcG4tKyJR06dKjzi8krztZW+z9KSirZZmcHgweX3C5qO+j1tPH35+SJE6CqPP3RR/wVGUnihQvEZ2ay7aOPyta0MXY4DQbt98TEhnuhZsbO25shd91V/B77ADOffZbCM2fItLPjyI4dNHNywjcwELvgYKLi4jhz6hSZFy+SnZDAn//+y2MPPsjF1FTCExIoKCzE182N2ORknOzs6NmhAwZra5JSU3H38KBDx47Mf+UVrK/kIlBFclNTCT98mF9/+IHrhg2j19Sp7PvrL5yvuooLWVlEffoprh4e2qJHqanaecnTU/sO1NFA2mWnKNo51s5OC3hFRJS+33RwVqejS+vWPLRyJS5OTni6uZU+Z5ueY8wv1hRFW9ncdFGwhkqnK73on3Egz8sLxcurVIDCuejfI0WZzICWpakoYGvL87GxLHjoIbauXYu1lRXnY2NZ8OyzrFm1itz8fJ596y0ysrMZPmQIffv04dmXXrpS77KUTxYvZvmKFQzs1Qu7/Hy6BQYy77XXcC4KeEy59VYURcG9js6LdcLeXutnJScX98uLg3yAtZ0dz9x2G8TEMG7gQBYsXEhERARRJ07w2vvv893mzXz0+ONYW1sT4OFhOYPQdGCqMffXDAb0RbNC3IF/N2/WMrBN2tmDvXsXB4SGX389s++7jxWffUbLFi04ePAgHUJCeGDePIb26oW7vT16NzfsHB2xd3Ji0MCBXGus8SjqFwcHywHC3Fywtsbf35+8vDxiYmLY/OuvtG/bFn9f37IxAWOcwDyRqH9/ra021rZzuRQlXFyKxtFLriHPoCAeX7pUu5GUpEVay7s4s7auV7UCLxvjCLqfH6SklD8yYGurBUKys7XtZnU5StVYNL9QA+3zjIvTttW3wt+1yXghZWVV6n0G9+xJsFnq7yizh043vVFYqH3umZklHZD6kO1gbQ3Nm9f1UdQPDg5a9q2vr5bJodNB27Yl9xsMxQFxX19fklJS2L59O2lZWYwfP57r/f21zzInp/wLNSiZ7mU24i0sMF5AQ8nJsg4XQLG2teXJd96ps9evl4z/RzExWiAnKankYs3IYNA6nDodbYKC+OHXX8nMyOCTn37inwMHaHH6tJbZdOqU5ZVYjRd+3brBvn2lAwSNgWmgx8oKXdu2OAJ9zaah+Lm54Vd0IQdQfMllHOhxdgZHRwpVldjz59m2eTNqdjb+Pj4kREZy8syZOlt1vHnr1rz//felMnH6z5hheWCqKOOySbC311aDtZTJYTxHFA1YdWvbll7du+NgEggpMyXS+Djz804tXHA0CiafgZO3NwvXrNEGfx0dtfOyvT0jikoDFJw9S3pyMr9u3IhNHZ53br33Xm67/3505WTheHh4XOEjqgccHCAhoSTAnp9fKkBYfK2j04GVFf6enkRGRvLtF1+wYN48Tp09S/877uDVhQtLrn0sZRA6O8Phw9rfraYyWG5SlqqUor9PnYcOZcf+/ax66SX+/Osv9u7di0OzZmQkJ7Nm0SIUBwcKL14kKyeHzIQEdAkJV/DgRbVUFCDU6VCsrenXpQurVq3inZUrWffVV5aDgZaSIaDpnMfroSYdICylsY4wV5ejo/bH3cmpZFTNlLGzaW+vdUhNi2MbT5BQeWczIAD++EPLsBKV0+lK1agS9ZCDg1bLKjBQCxRaOmlmZICtLTqDgXYhIcycNYu7R4/WssYstRNLJ1J3dzhwoHilSiEaPHt7bcCoU6eSAKEpY1BcpyO0RQuOHD3KijVr6NexIy1atIAzZyyPSJt3OH184Oqrtc6rKGE20KNTFHxbtmTKPffU4UFZYF4buCHP1qgtPXtqfbHkZMufh05XEvzQ63n/jTdIOXu29IAuWL5YM90mLDOt7Wo2aKtv2RIXYEL37lf+uEwYJLBbljEoGBqqtZ3CQu26xPj3xSRrHYMBfw8Ptm7dyuY9e3h/6VIc7O25dcgQ+kyfDj//rD3GUgaht7eWdWpnV7YucBOms7bmthde4DaTmsAOrq7MfPnlOj4yUS1+fmVnZOj1xWUtsLLihdmz6TV1KnPGj6dfv37aAnLmfTNLyRCiTkmAUJRmmvVkY6ONepkypssbl3Y3XmiZ1l0sL4PQtLPp7AwtWkj2mWg87O21zFo3N8u1SY1ZUEVt59NXXmHSY49xm+mCJOZtx1KQo00brXBvfa3VIkR1GS+evL212+WNSOv1tAgMpENoKA8/+yx/GLMxyzvvGLeZdmBtbUuv4ChEQ2bMCrO1tTwNKz9f+2llBQYDIQEBJd9/0wBheRdrkqUuGiMrK20wNzBQC+Dl5JSeXWAWILxj+HD6PfYYt44ejYuLC6gqfTp0sNxfg9LXO5IIUb5GUu6jybJUl9E4UJKZCT4+dG/dmj/++IPexkGsihKJZFCq3pCWKcrn5KSdNE0ZO6COjtoJ1tj5NG3oloIcljqbHTte3uMX4kry9i658LKzqzDtHisrOoeEcOLIEfj1V+1+SxmElkbVbGxgxAg5iYrGw8NDy4y1sbE8dd5gKB6YUqyteefFF/nkq6/o17mzdn91pqwI0RjZ2cGAAZbvM9YbNLYjKH2hBmUv1szLwgjR2HTpov20tS1ZrMTI2F8rqn/WKTCQP7ZtIygxsey1jmm7kbYjmjonJ+jaVfv+F9VkHzhwIGzeXLKP9NfqPQkQivJZqrlobMwtW2pTjfPytNvmNQgrC3II0djY2ECzZtrvxin4powL8xizb41tx9IImvmItPmomrQj0ZgULSwDaIF281pZxos3Dw+IjKSttzevPvOMtqoqVJ5BKO1FNAXl1SX29NR+GgzaOciYNQjlTzGWPptoKiwtPKnXa99/J6fi1Yh7dOmi1bCF8rNsy0uIEKIpCQjQfl68WHGcQKYY11sSIBTV4+wMffpoHdHMzJKGD+UvUiIjaqKp6dat7HfdeCHm61vSdiqr1SknTdHU9OpVdpunpzZgVZR9S15eSbYhlN/hlBFp0dR1714ydd80QGheg1Au1kRT5e1ddjEE42wp4xRKY/ZtRbOlQPu9oEDajRCgDfaWFyAsL4NQ2k69IAFCUT06XUmWlGkWlGkNQijd8CWTQzQ1lr7n7u4QFKR1RI3Zt+XV4DDWZZG0eyHAOJUYSjqcldWvUZSyF3RCNDWmdZ6NWezGheYqmmJ8+rS2CJ20HdHYGQxapqApvV777huvdwyG0ued8mqmGQOE0l8TonScAMrWIDRNJMrJgfPn5ZxTT0iAUNSc6Wi0+aiaecMvKNBWLpKTpmiqPD1LpnoZ63eaTvWq6irGQjRlVlbaisZQeQahtB0hShj7bLGx0Lq1ts1S5oaXl7ZoUE5O2en+QjQFBgMMGaJlqhtvG2uulzdbynhfbKycc4SAsolE5QXXjf216Oi6O1ZRigQIRc0V1eWwOGXFvBYUwLlzEiAUAkpOmsZV8qD8tnPxonahZgwuCtGUGduOpZpp5hdrZ85oF3bGLBAhmjKDQbsAs7UtXa/QPMvWWD8qNPTKHp8Q9YmjY8nvxgBhbm75yRCg9dUOH76yxylEfaXTlQTNrazKr0FoMGjnpC5dyi6OKuqEBAjFpbGy0k6YxpT6iopbOzmVXSlMiKbIOF3l9OmSBRostR3jQicZGSUj2UI0ZcZzTlhYSX0oSyPStrZaFlRoqATXhQDtvJOcDG3blmwzZhBKxpMQ5TMYtOmPOp22KnhKSvkZ6t7e2rlHCKH12fbvh549K84gvPrquj1OUYoECMWlsbLSghyurqWLxptncowbp13UJSTUyWEKUa8YC17Hx5fUV7OUBeXoCGPHQmqqtl2Ips7KChITtQuw4GBtm6W207x56fprQjR1xmx1P7+SbcYSMDK7Q4jyGQwQEQH9+pWfBQXQo4eWsW6Qy2shAK3PZmurla4wkpW+6z35CyYujZUVhIfDoEHa7Ypqpllbl+6YCtFUKYrWgWzevPSCJJaybxUFXFzq5jiFqG+srLTzS0hIxTUIhRClWVlpMzlMs5skg1CIyllZaVMgPTy02+WVtZBrHCFKM7/2l9rqDYIECMWlsbLSGr4xgCEXakJUjZ1dyfRiKL/DKYQoYWurTfEyzQ6UDqcQlfPyAmfn0tskQChE5YwZ67KAnBDV07lz6Zq3pnECY1a7qHckQCguTatWWqDDlAQ5hKjc4MFlMwXLq98phNAYDDBwYOltElwXonJ6fekLNSNpN0JUrEWL0rflnCNE1Zgu9gNyrdNANLi/aoqijFYU5YSiKKcVRZlX18fT5Lm5aRkdRjKqJkTVmLcP6XAKUTOSuS5EzUgGoRDVJ+ccIWrGdK0COe/UWw3qr5qiKHrgXeAaoD0wRVGU9nV7VKIUa2vIzJQghxDVpSjaysYyqiZE9eh0WtuRDqcQ1WMwaH02aTdCVJ2iQH6+XOsIUV3Gc460nXqtof3P9AZOq6p6VlXVXOAL4Lo6PiZhysVFyyg8d650ZqEQomJ+fhAZCcnJZaftCyHKFxgIR49qnU0rq7o+GiEajhYt4OBBbTaIEKJq7Oy0uoRHj2oL/wghqqZZM21ANyIC3N3r+mhEORpagLA5EG5yO6JoWzFFUWYqirJXUZS9+/btu6IHJ4p07Aht2mgFfUWDIW2njjk4QKdO0LWrrFrcwEjbqWPe3tqqxv37y4h0AyNtp44FB2v9tS5d6vpIRDVIu6ljiqL11YKDtVrsosGQtlPHjG2nRw+51qnHGl1PWlXVZaqq9lRVtWePHj3q+nCaJheXsgV9Rb0nbace8PPTRtdEgyJtpx4ICdGC7KJBkbZTx3Q6LcAhq0k2KNJu6gEHB2jdWqbnNzDSduoBNze51qnnGlqAMBIIMLntX7RNCCGEEEIIIYQQQghRAw0tQPgP0FpRlBaKolgDNwMb6viYhBBCCCGEEEIIIYRosAx1fQDVoapqvqIoc4CfAT2wQlXV/+r4sIQQQgghhBBCCCGEaLAaVIAQQFXVH4Ef6/o4hBBCCCGEEEIIIYRoDBRVVev6GC4bRVGWo610LERT5a+q6p3VfZC0HdHE1ajdgLQd0eRJ2xGiZqS/JkTNSNsRomYstp1GHSAUQgghhBBCCCGEEEJUrKEtUiKEEEIIIYQQQgghhKhFEiAUQgghhBBCCCGEEKIJkwChEEIIIYQQQgghhBBNmAQIhRBCCCGEEEIIIYRowiRAKIQQQgghhBBCCCFEEyYBQiGEEEIIIYQQQgghmjAJEAohhBBCCCGEEEII0YRJgFAIIYQQQgghhBBCiCZMAoRCCCGEEEIIIYQQQjRhEiAUQgghhBBCCCGEEKIJkwChEEIIIYQQQgghhBBNmAQIhRBCCCGEEEIIIYRowiRAKIQQQgghhBBCCCFEEyYBQiGEEEIIIYQQQgghmjAJEAohhBBCCCGEEEII0YRJgFAIIYQQQgghhBBCiCbMUNcHcDndeeedqr+/f10fhhB15vnnn/9YVdU7q/s4aTuiKatpuwFpO6Jpk7YjRM1If02ImpG2I0TNlNd2GlyAUFGUh4A7ARU4DNymqmq2pX39/f2ZP3/+FTw6UVvCw8OZPn06sbGxKIrCzJkzeeCBB5g8eTInTpwAIDk5GVdXVw4ePFi3B1uPPf/88xE1eZy0HdGU1bTdgLQd0bRJ2xGiZqS/JkTNSNsRombKazsNKkCoKEpz4H6gvaqqWYqirANuBlbW6YGJWmcwGHjjjTfo3r07aWlp9OjRgxEjRvDll18W7zN37lxcXFzq8CiFEEIIIYQQQgghGr4GFSAsYgDsFEXJA+yBqDo+HnEZ+Pr64uvrC4CTkxPt2rUjMjKS9u3bA6CqKuvWrWPr1q11eZhCCCGEEEIIIYQQDV6DWqREVdVI4HXgAhANpKiqusV0H0VRZiqKsldRlL379u2ri8MUtez8+fMcOHCAPn36FG/bsWMH3t7etG7dug6PrHGRtiNEzUjbEaJmpO0IUX3SboSoGWk7QlSuQWUQKoriBlwHtACSga8URblFVdXVxn1UVV0GLAOYP3++WhfHKWpPeno6EyZMYMmSJTg7Oxdv//zzz5kyZUq5j1u2bBnLli0DwNbWluHDh1/2Y61rERERLF++vMaPl7YjoOm1nUttNyBtR2ik7VSftB3R1NoNSH9N1A5pO9UnbecyOHQIQkLAwaGuj6TK6lPbUVWVnd8m0yzImjY9Lt9nWJ2206AChMBw4JyqqvEAiqKsB/oDqyt8lGiQ8vLymDBhAtOmTePGG28s3p6fn8/69eupaORn5syZzJw5E4D58+c3iSK0TeE9isuvqbWdxv7+xJUjbUeI6mtq7Qak7YjaIW1H1AupqZCT06AChPWp7Rzdk078pnCIhvnz25W6LzE6j2N70hl4g9slv0513mNDCxBeAPoqimIPZAFXA3vr9pDE5aCqKnfccQft2rXj4YcfLnXfr7/+Stu2bZGl6YUQQgghhBBCiDqgqto/USMx53IB8GlhXea+JXeHkRiVR89RLtjaX7nKgA2tBuFfwNfAfuAw2vEvq9ODEpfFzp07+eyzz9i6dStdu3ala9eu/PjjjwB88cUXFU4vFkIIIYQQQgghxGXURAOEebmFHPw9DbWK733+jWd4YeKZMttzswq1Xyw8TWJUHgBZ6QU1Ps6aaGgZhKiq+hzwXF0fh7i8Bg4cWG6DW7ly5ZU9GCGEEEIIIYQQQpRo5AHCf/9I42J0HkNvdi+1/ctXY9n1fTL3vBVAxwGOlT5P3IVci9tzs7UAYX5e+Z/h6f2Z9BrtUo2jvjQNKoNQCCGEEEIIIYQQQojL6cO5EXz1emyZ7Yf+SAPg39/Tavzc5w5nsXlFIgA5mYXl7vfJ01GcPZRZ49epLgkQCiGEEEIIIYQQQoiqayQZhIWFKm/ceZ6NH8RTkF/2/Wz+JKHUbWNAb/+vqRzdk87rt58nL7f8IJ8lr912vvj3bLMAoXlGYXJcfrWe+1JIgFAIIYQQQgghhBBCVF0jCBDu3ZLKw4NPcOZgFj8tT+C+vsf5a1NKqX02vBtPenI+BQUqT15zirxclbZ9HMhKL+SdOeGcPZRFfHhemefe90sq828sW3vQXF6OyqZl8cW3v3o9ptT9SjlRu4IClV3fJ1c4Rbm6JEAohBBCCCGEEEIIIaquEQQI1y6IJje79Hv44cP4MkG36LO5ZKUVkByvZfOF9rIvdX9OVukswJSEfD5+IrLc+oPmNi0ryVI0f0xWuvbcO9YncXRPevH2A7+lsfrFaH42y3C8FBIgFEIIIYQQQgghhBBNxol/MsjO0IJvU57wKd6empBPclzpjMCTezNKTQX28LPGykYpvp2eVHoasHEBkup4+54LHP87gxP/ZNJliFPx9szUAs4dyeLzl2N4Z04465fEsualaJJitWM88FuaxanRNdHgVjEWQgghhBBCCCGEEHWogWcQnj6QSS8/GDLZjUET3HBw0bPr+2SO7s7g2etKTw3etCyBFp3sim/bO+lw9TIQH6EF6dKSCkrtb2nhkcJCFZ1O4cLxbIvHc/zvDI7/nQGAh59V8fbkuHxem3G++Pavqy+WelzUmRx2bUhm0I1uxduyMwv5c30SQ6eUXoG5MpJBKIQQQgghhBBCCCGqroEHCI1adNYCf92HO3PToyWZhE7ueob/z50R0z0AeOe+8OL77J31uHiVBPEykgsoLFQpLNQ+D+O0YFPGqcwLbzlX6TE5uuqLfw87mlXufnqDgqOrnu+XxhEbloOqqqiqyuYVCaxfEse+LamVvpYpCRAKIYQQQgghhBBCiKprBAHCW1/wo+dI5+Lbnv5WuHoZ6DXamae+aMmND3jTd5xLmcfZO+lx8SqZkPvt23HM6X2cFyacpbBQJTujoMxjKpp23LaPAw7OJUHBgnyVd/5ui4efFWcOlg4QmmYyPvJJEPbOejLTCnl+wlkW3xnGCxPPEnFCy1I0TkOuKgkQCiGEEEIIIYQQQojqacABwpCu9nQf7oSilNQS1OkUFvzYihkv+uHsrgUAXZuVrcxn66ijeSubMtvjwnOJOp1TXNvQVG52IQUFZT+v9/a24/53A/EOtga06cVDbnZHp1NIjCod4PPyt+Lu1/0BGDbVnaB2dqUWTDnzbxaxYbkc3a1NVY4+k1Pp52BKAoRCCCGEEEIIIYQQouoacHAQoG1vB6ysy4bEFEUpFTS0c9CXur9tHwccXfUM/58HQ292o1U3e1ybGZizNACAU/szSy1oYpSXrZIcl19mu5GbjzZlefp8v+JswgHXuwLg4mngpke9mf9tCM4eBt75uy0THmoGwISHvZm12B8PXyscnPXFjwGIC69eBqEsUiKEEEIIIYQQQgghqq4RTDGuiTtebo5Op6DTwaRHfErdZ++kI+5CLu4+VmUel5tdSHpy6anHLTuXTBee/Jg3/m1sCOlasm3a075MfUp7DfNMRyNrGx2dBzvReXDJyscDb3TFzlGPV4AVzz9f9fcmGYRCCCGEEEIIIYQg8nQ2z084Q0ZK2RpqQpTShAKEQ28uWSHYxr78MJqdk56s9EKyMwrR6aBDf4fi+3KzVVISSjIIgzvYcu/bAcW3HV0NjJrhWSr4B2UzGqsiqL0dzQKtq/04CRAKIYQQQgghGp24C7kU5GsXr+f/y+LA1uqt5ihEU7Tx/Xhiw3I5uS+jrg9FNARNJEBomilosCo/6ObgrCcrrYDsjAJs7HXo9CX75mYXkpGsBQjHzvLivvcCsXPUl/dUdUIChEIIIYQQQogG6b9d6SyYcrZMvadD29OYf+MZdm1IBmDRref56LFI5o06RWGh5Qvav35MIe5C7uU+ZCHqhe/fjePxESeJDSu9iEF6kpY5aOtQtcBFRmoBr912nrCjWaQnl62v9vkr0Xw6P+rSD1jUP00kOFgddk46Du9I5/cvkrB10KGYRNzOH8kiOV5rI6Nu8yhT27A+kAChEEKIyy4rvYDPXogiI1WmqwhRXUd2phN9tnqr0AnRVGxekUDkqRz+/jGl1PZfP7sIQHJsfqmAYGpiPjlZZYvHFxSorHo2ipennr28ByxEPfHfznTSkgo48Ftaqe1pF7UARm522XZiyYVj2Zw7nMWr08/z2PBTpR4XdjSLHd8ks+eHFFQJJjU+TWiKMcCYmZ606mZf4T72TiVBPwcXPc1b2xbf3rQsgZ8/ScTBWY9eX72pv1eKBAiFEEJcdrs3pLB7QwpbVibW9aEI0aBkZRTw3gPhvDkzrMqP+fGjeDYti7+MRyVE/WFcgfKLhTHsWJ8EaMG+8OPZAOTlFBIfXjorcPtXSWWCFRlFheNzs1Wiz5UOyKuqWjxVWYjGIj9P+04nRJZe5TStKIMwx8IqrJZkpZce/E29WJJFeGxPyTTlilZvFQ1UkwsQevHwR0EV7mPrWBJic/E0cO1dntz/XmCpfepzsFwChEIIIS47KxttlMx86knk6Wxyc6rWARWisYs+l1NmmuSRHekAqCabI09nc0/PY+VOhfzhwwQ2LUu4bMcpxJWQejGfvNzKzw8xJsG8z1+OYevaizwz9nRxlmBGSgGJ0aUDIN+/E1+cNZWbU8ip/ZnMG3Wq+P4XJ51l/2+p5OUW8sdXScy/4QwPDjjOl4tiyp2eLER9kJtTaHG2RkZqQan+1n+70ok5p51Ddn2fzJE/0/ng4XCSYvPIztD2M8+0/e6dOO7peazMcxunJBulJZbcPrk3s/j3qDNaWy0sVIk+m0P4iezifmF9DpiISsj/XSkFJpc6zp4G9HqFtr0dSu2TmVZ/r30MdX0AQjRmWekFRJ3JIaRLxanIQjRU545kkRCZS69RLhXuZxylzjI5IWakFrDg5nP0Gu3MbS81r5XjycstLM4mEaIhCTuWxav/O4+bt4GXfmhVvOrc+f+0LCifltbF++78NhmAI3+mM2yq+xU/ViEut/2/pbL88Uh6jnLm9gXa+SE+IpcVT0YyZqYX7fs7oNMpbF6RQHJ8PiFd7ThzMAuArxfHAuDhZ4XeoJCRUkDaxbIBE2OAfcndFzh/JKvM/csfjyyz7Y91SXTo70izQGuaBWptMiUhn/AT2YT2spfzj6hzxu/ze3vbFW87tD2NDx6OAODWF/zoOcqZd+8PL/W49x7Ubts5lWSfGzMIz/+Xxc+fJPLvNi2obuxrxZzPwSvAunhKslFaknb77KFMjv+dweCJbmz/OonYsFyaBeby3PVnivf1DrLmibUteHnKOdr1dWDyYz6IBqSJZRBWRa5JYN22gtWO6ysJEIomwzha1byVbZX2DfsvG+9g61J1BHKytCXLXTxLNx1VVcssIR52NIvv34nn+N8ZLN4eWuYPRGJ0HtFnc+g4wPES3pUQl192ZiHLHonAO9iaSY94o9Np3/Vjf2Ww9N4LAASE2uITbENhoconT0cR2M6WniOdcfO2ArQMDtOfSbF5PDXmNADnDpe9MMvOLKQgX8XWQcfen1NxcNbTcWDFbeWHD+P55dNEXtzYChs7HclxeXgH2dTOhyBELctILWDzxwmMudsLW3sdkae0zIqk2Hy+fiO2eLW82PPadmNGB0BWuvb714tjCelqR1B7u+L7fltTMo2/oEAtVePG0rlKiPro1D4t62jvz6mkXcznpsd8+P3zi4QdzS4OZFjZKOTl/J+9+w6PouoeOP6d3c2m95BGEnrvHZGiCDaKBRQUKyo/O6/oq9ix947lBXvBroiKggKK9F6kQygJCek92Wyb3x+TyW5CKAmbfj7Pw5Nks5mdhJzcuWfOPVel/wVBXDcrlp2rC8uTIAA3PBXLz+9mUJTvOC6BAa4+a8cqLSc2mpTjlhO/tKQjRXkOZl1+oPz131rbmcxkK6/ecpiCbAcJnX246uFozN4GYtrK2CPqntOplie7i/IcmH0Vfn4ngz8/zy5/ziePpfDJY64NQ3z8DRXGl7W/unp56gnCF68/VOF1ivIceHmrPDkxkbPGBWP2NeDjZ8AnwEBuup0ln2eTcqCUBW9nENLCxKV3R7J6QS65aTaS91oqHCvtsJXHxh8gP8tO+hEr/UYHseyrbHqOCGTHykKufTwGo0kpv/YUDYwkB49TeSVIZf7BRvpfEFRHZ1N9kiAUzcYXT6eyekEeD81rQ1zHkycJVy/I44unUwmJNPF/L8eVT77evSeJvRuKeX1FJ8w+WsJv05J8Pn8ylRufjqXHsEAAdq4pZPadrjtzuek2oltXvFh8Z3oSqYmlvPhnBwJCThyKqYmlpB+x0mN4gAyOos4d3lnCNy+lcXB7CbvXFdGygzfrFuZzwY3hFe4+b/ojn/NviGDeM6lsXJzPxsX5LJidzvOLO+IfbCxPDOaV7dy1a62rJ03mURt7NxbRsZ+r/P6Jyw+Ql1lxQvfOhi7lS1EWvJOBb4CRPucFEhRuwi/IyMK52pLKmefvIyzai+xjNkZeHcbZl4YQEmnCN6Dh7RQmmjanU2XNL3nsWVfE+TeEl9+gcq/mCI/14pxJYRV6pC37KoeLp7XAP8hI2iHtcUuhe4LQVQ313oxknvu9Q/nrff9aevnn8rPshEZ6kZ9t570ZyRz6t4TOA/2JaWtm7K0tymNi4x/5pOwvZdxtLWrpJyHEqR3YWsyOVYXEtvfm729yMBjA6YQ964vZuqyAXWuK6DzIn9AoEwajUl5JO/HeKExeCj2HB3Lfh63wDzayeUkBbXr44h9k5Ngha5UVhHmZ2uYlpcVOBo0JLk+MvLikA0f3lpKeZOXzJ1MJj/HCP9iIX5CBwDBj+bE+fTyF7f8U4mVWOGdSKH99ncML1x4C4I434+k2RG4Ai7qhqirb/ylgzn9dVa9Hdls4drC0QnKwKmOmRbB/c0l5daC70hJnlb03i/IceJm1x1f/nEfngf4ERZh4+Ks2TB+yh32bitm3SUvyT322JT5+BkKjvUjeV0pgmDbnueqhaBa8nUFRnoP8LNf13qu3aP129RYAezcUYy1xMnRCSDV+IqJO6MlBSRJWcM6kUHaXzXPMvq4CocgEM+lHrLy0pGN9ndppkQShaBZKihysXqBd+K35JY9/Vxzl4lsi8A0wYLOqhMd4sXBuJhNmRBEZb2bTn/kA2Cwqr9x0mHvmtiKipVd5H401v+QxfGIoAMu+zMZS5OTde5IZd1sLLropgoykir1unpyYiF+ggcvviWLI+BAO/ltSviPl/aP2Ed/Jh5uea1m+XGXpvGx2ryvi/16J43/3JZN+xEqfkYFMeTSmQkWjELUp9WApr//fEUpLnFz1YDR/fZ3DvGeOAbB/c3Gl51rZsaqQNb+47jw7HLD17wIcdpW/v9Eax+uJQpNXxWT36/93pHw5jKqqxyUHQUuWv3rz4Qq9df74tOpNT/TlLcu+zGbpPO3iOKKlFx36+WEwKvgFGul6ln+VXyuEp2xYpN1AAijMddDt7ABSD5Sycn5u+XMObCnhnElweKeFyAQz598QzudPppJ2qJSIOHN57zT3pKB7stA9VirvdPzwxftp2cG7vDoRYPe6InavKyIywcymP7UkyuJPtDjyCTAw+tpwVFXFblNluWRj43BAaSn4Nc62JrvXFlW48dTt7AD+75U4Hhm7n8M7LWSl2Bg8Npgx07REdkG2HW9fA0FhrulM257a937hVO2mrF+wkZxjtiorCLNTbRTnO1BVSOjiw9F9FpL3luLjZ6B9Hz/a9/Ejpq034TFaJbyiKES39qYgWxv/1v+eT+vuvtzwZCyRCWYykqzsWKVNCt++O4nwWC9GXh3G9uWFdOzvR1xHH6LbmI87DyHO1IofcslcmFwhT/PjG2nl1eYnEhpl4qzxIRw7VLGfbfehARz6twRriUpOuu24ryvMdWD2cb3Y7nVFtO7ue9yY0XmQf/mOr4GhRnavLWL32iIMBhh6WQjDLg8lM8XK7DuTuHBqOF88lYrDQYVxKz/LjqLA5j8LMPar1o9F1BVJEFbQc3ggr6/sxM/vZHD+9eHlj//349aUFBx/s6qhaXQJQkVRQoD3ge6ACkxVVXV1vZ6UaNCK8x3Mf1OrqDB5KeXJgo8fTTnuuU4nXP1QNHvXa9Ue/c8P4tmrD/LK1ENMeTSm/HmLP85i4MXBOOwqiVtdyyN/fjeDi26KwGE7/g9lcYGTz59MxeSlHPfaSXss/DongxueiuXYQWt5/5znpxws75GzeWkB+7cUc/XDMfQaoVUq5mfZycu0E9dRlrIIz9v0Rz7D2sODX7QhvpMPRi+lPNkBMOSSENr28mXD7/ls+6uAjYvzyz8364d2zLr8QIXnAxQXOHA61QrLWdypqspjlxyo8nNPXZlY/v6421rQY3gAz151sMJznlnYnoAQY/lFanqSlW9fOsaxQ1YKcx1s+D0fpxMcdpU/Ps0icmz1fiZCnK7Du0r49X9aL6eEzj7sWlNUvpujl1lh4n1RrP4pl41/5LPxDy12eg4PoH1vbTL18tTD5ROrPiMD2fpXAaqqYil2HpcIVFWV4gInz0yuGA9AheQgwJM/tePVWw7z9YvaOKNXeQD8+EY62/4q4MDWEkIiTTz8ZVv8g+WmVKORlgapqdCvacyiR10bjsGgEBxhKq9uimzlSrDd+kr8KY8RFG7CUuxkzS95eJkVbFbX9VlGsq38ppV/sJF75rQiN8NeYRl+m+6+FY7nW7Y7Zcd+fpjMCjc93xJffy1Gxt7aAlupimLQqh6zUmx8+7IWZ7vXuarmZdwRnpafZUeNhYEXB+MbYCAgxMjij7Mq/L6HRXsxYUYkc+93VRneOTsBv0BtuaNekQtw8wsteeqKRCzFTrJSjk8QFuU5sFsrznUu/08kAP3ODyq/HgyNdKUa3Mctp5PyOIuINTPrh3YAtOvth7efAR9/AwXZDhZ/nElJoZNrHo1BVeG5F2r6ExK1QioIT8jsbWDCPVEVHvMPMuIf1PCvqRpdghB4A/hdVdWJiqKYgcZ5m1TUmWVfZVMYm0vrbj5MmhnNu/9JxmCE3PTj7ybvWFnIw2P2oyjana2IlmYm3BPJ96+l89kTWqLj2sdj+OyJVGYM38OEeyJRVYht512+M5fV4iS3irttl90dyY9vppcnB/0CDdzxZgIo8P2raWxfXsgTExIr7Ep5dH8pCV18uO/D1mxcnM9Xzx/jf/cmM2hMMCazwrqFedhKVfqdHwRyU1p42MCLg3nurQ54l5XHnzUuGC+zgtGk4O1noMtgrUl82iFrhckPQIt4ryqPqaraRiX6pMyd06lycHtJhYvRoHAT3r4KGcmuxx79pm15f6de5wQSEGokMsFMcb6D0MiKrxsZb9birPz1tYuYzUsLWP1TLhkI4XnJ+yzlSw2vuC+KwWODefbqg+W/233OC2TY5aG07+1XIfE9aWY0QeGuS7P9m4vpMtif1j182by0AEuxkycuT6Sg0o6RpSXqcVW9rbr6cNHNEaz9Na98qdYtL7YkoqWZMdNaMO+ZVKLbeNO+jy9e3gb6jgrk5amHOVB20ys33c679yTR9Sx/Dm4v4cguCz1HBBIYZiS+86l7+Yp6oKrazLuR8g82csV9UZQUOBgxKax8IhUW7cXhnVrfssj46l3snDcljJU/5pKfZSe2vXf5cUBLcqz6Ka/8tX0DjKdsRTHw4mC2LS9k4n1RxHWoGAetuvryn/+1wlLspLTYydOTEvHyVnjg0zbkpNnYuaoQL28DKxNPcHAhasjHz8Bld0dy7lVh5Ss0Lr4lAkuxk3tH7AVgxvutjkv26fHUqb8/72zowhdPp7JlaQFmbwPevgasJSdIEOa6EoTjbmtBr3MCiW2nXZfd9GxLOvb348tnj5VvUAdw+T1R/PJeBrnpdqJbVx3HLeJcj4fHGLjqwZgqnycaCEkMNkmNKkGoKEowMBy4AUBVVStgPdnXCNG6my//fa01rbv5oCgKzy/SejV99MjR8uUhCZ198PYz8MenWQSFm7j64WgiWmqD1NmXhVbo6TRoTDC//i+T7GM2Fn+ShdEI3YcFlCcIs1Jt5FRKPiZ09mHUtWF4eStYip30GBZQYbOUs8aH8MXTqVjKkoMT7tEG+aTdFiLizJi8FAaNCabLWf58/cIx1v6ah9GkMPDiIAJCTOSkHT94C3GmWsSZy5ODoN3tHXDh8bsVD5sYwu61RSTtsdBzRAAX3BCBoijlvTbKnzchhH++z6Uoz1FlgjD7mK28Ivfx79vyxIRELrs7ku5DA5j7QDIHNhcT18mHKLcLy/97Oa5a35N+x7rveUH0PS+IWbOq9eVCnJatywoYFKct0xo8NhjfACP3ftCKPz/LJqKlF4PGanEU09abF/7owMePpjDwouDyBPe1j8cQGuXFvo3FDL8ilO3/aAm+3z/IIj/Lzlnjg8vbZoBWPaJv6mA0aitNb3xaa1vRc3ggt/ffBUCfkVpT7LMvDaH3yEB8/A3lm5g4ndqFfmS8mcd/aMvGPwr44qlUEreV4GVWaN3dt8LSaKmCaoAa+WTtnElhnDv5+F25J82MptvQAI7stJyyh3RlfoFGLrmzBZ89kcq421ow+66KO7fqbSoCQk6vqqPvqCBe+cv/pIlEHz9tw4bHv2+Lj78Rk5dWBdm6m1aNuHJWtb4FIU7pvGvCGX1deIXHFEUpr24FCAwzEtzCxOjrwnHYVQZcGITRVLHdy5RHYpjyiJaU8/YzsGVZAVuWHd+bsCjPgdOh/b0ZenkIgaEVUwp63/XwWNdN2yHjQxgyPoSCHDsGo/RUbxKkgrBJalQJQqANkAF8pChKL2AjMF1V1fLSFUVRpgHTAMaOlatXofWwqbxEBLSm1sEtTIy9tQVmbwNOp0rH/n7lVVE6Hz8Dkx6I5usXtN5rBoPCkwvacd85eynIdhDVyoyX2fX8rBRbebP5s8YFc+ndkXj7GlAUhXMmHX/hC1qlh+6VvzqWX3i670wJEBRm4pYX4kg9WIp/kLG80kRVVZ54oiY/HReJHVFTEbFmHvyizXGPz5jbCmupk8fGH+CCG8Np19uPf77PJSNZW+5bWV6GnYJsO2YfhahW3ry9vnN5Qu8/77Wq9e+jpiR2RGVdBvvz8ucVd68PaeHFxBlRxz03MNTEXbMTKjx21rgQADoP1Ppkenlrx9GTGd2HBlRIEBZk20k7pFWcz/ysDTars0IvqKd/aV+hkgM4bpmLwaDw7G/t8Q0woigK/c8PovNAP7YtL2TAhUF4mQ3kpNvwMiv88Wk2W0/e9/60SOx4WCOvIDyRoDBTWXKhZl8/eGww3c4OICjMxKPftK1QtaurzlL6093w6mQb0J0JiZtakJoKXl4QEVHfZ1Jr9DHhsrsjT+v5wRGu319vXwMDLw7in+9zAcjNsON0aHHgV8WSyQ59/bjjzXg69j9+oV/lZGJdktipJZIgbFIaW/dpE9AXeFdV1T5AETDT/Qmqqs5RVbW/qqr9+zWRHiyidgSGmrh8ehTmsomXwaDQbUjVOwWPuCKU/37cmgc+bV3+XH2JVURLr/I+UaDtTnxkl4XLpkdy7eOxBIaaync8PpHY9t6Mvi6ch+a1Oa0Lz5g23hWWobn3y6kpiR3haUHhJiJizby9vjOX3BFZPgF7++4kslKsJHTxITTK9XtcmOsgP9tRvsOdJ36v64LEjqisbU+/CsnBM9VpgB9h0a5KjJBKS+kLsh1kpdrKN1Oo3Cg+LNrrtJZmhrTwqlA1HBCiJWb044VGehEQYjrtCeapSOx4mKrKRK0KiqKUb2TiXoH+zML2jJmmJYTcr6kaOombWpCVBXl5p35eI3TvB62YPDO62l93zuTQCh9HtXL1O08/bKUo34mPn6sKvbJuQwIa3EZXEjseJhWETVLDitpTSwaSVVVdW/bxd2gJQyFqXZvuvhUq+tr00N4PijDRaYA/Ly11bVmuKDBsQuhxxzgRg0Hhsrsjq710RojGQE/0uVcsHdhSQkgLU4Vriv/dm8yuNYUEhjb8Br5C1KWQFl488VM718eRFZMZf36WxbGDVkKjq+79KZoJmaSdkvtN4NBILy6+JYI3VnU65Y1c0cQ14eR6u15+DJ94+nMSXcd+/lz5X63q3elQ8fJ2xc7udUUs+zK7yupB0Yw00Zhp7hrVaKiq6jEgSVGUTmUPnQfsrMdTEs1YTFkzXmuJ9sfRPfnxwGdtPFo9IkRTEBptqtCPpt/5QTgrrTQuyHYQUI/LT4RoqNyrNPRqKF3iNq13p3tFrmiGmnCSo7YoitLgqpxEPZDYqZJeGOF0gsl8fKVg/wuC6vqUREMiFYRNUmO8krwL+KJsB+NE4MZ6Ph/RTPU6J5CewwMY83+ufiUXTg3n9w+zaNnB+yRfKUTz5GU28NSC9mQkW9mwKJ/eIwP57pW0455XnH98f0IhBFw2PZJ//yms0Fi+z8hANi/VmsibfSXR0aw10R6EntZpgB9dzwqo79MQDYkkCKsUVNaHUHWq5b1wA8OMdBnkz1mXhNCh7/E9BkUzJLHTpDS6BKGqqluA/vV9HkL4+Bm49dX4Co+Nu60FY29tUWUfQyGEpkWcmYtu0hLrlSsIARx2udAQoiqjrw1n9LXaTpXnTQkjI9nKLS/GYbeprPwxh8Fjj99lXDQjMkk7LdPfbbibXol6IgnCKgWFa6ujOvTzK19iHN3amxuealmfpyUaCqkgbJIaXYJQiIZMURQayb4KQjQIQREmitwqBic9EE2XQf71eEZCNA4T7nHtiGzyUhhxZVg9no1oECTJIUTNSOxUycts4IHPWhOZYObg9pL6Ph3R0EiCsEmSBKEQQoh6c8eb8excXciyL3Mozncw4orqN9IWQgiBJDmEqCmJnRNq1UXrQ2jykgoIUYnETJMkCUIhhKgtRUXgL9VwJxMW7cXQy0IZckmIXGcIF4kdIapPehAKUTOSIDwlpWyJlPyYxHHkl6JJkW7WQghRW1atgtLS+j6LRsFgUCrs0iqasZISWLOmvs9CiMZHJmlC1IwkCIWoPlli3CRJglAIIWqLwyGDphDV5XRKFZQQNSFJDiFqRmLnlPQe66r8nITO0wnC9HQ4fNgzxxI1JglC0ejk5uYyceJEOnfuTJcuXVi9enV9n5IQVZPlXkJUn0zUhKgZiR0hak5i56Ri23sDcOHUiHo+E9FgeDpmCgshL8+zxxTVJj0IRaMzffp0LrzwQr777jusVivFxcX1fUpCVE0ma0JUnyTWhagZGXOEqBmJnVPyCzTyzoYu9X0aoiHxdAWhxGGDIAlC0ajk5eWxfPlyPv74YwDMZjNms7l+T0qIE5GBTojqk7gRomYkdoSoGadTYkeImpIEYZMiCULRqBw8eJAWLVpw4403snXrVvr168cbb7yBf6XdLufMmcOcOXMA8PHxqY9TrXPJycn1fQqiskY40DW32JG4aYAaYdyAxI5oABph7DS3uAGJnQZLYqfBk9hpYGqjgrAOVpBI7JxcrScIFUUJAdoBSaqqptf264mmzW63s2nTJt566y0GDRrE9OnTef7553nqqacqPG/atGlMmzatns5SiDKNcLImsSPqXSOMG5DYEQ1EI4sdiRvRIDTCcUdiR9S7RrrEWGLn5Dy2SYmiKJMVRflKUZTvFUWZUvbYo0AqsA5ILfuc/0kPJBqekpIG0w8qLi6OuLg4Bg0aBMDEiRPZtGlTPZ+VEFXw9KApRHMhS72EqJlGmOQQokGQ2BGi+jwdMxKHDYJHEoSKotwCzAPaAMHAR4qivAbMAB4CxgAzgfOAhz3xmqIO/fuvtu14AxAdHU18fDx79uwBYMmSJXTt2rWez0qIKkiCUIiak7gRovokuS5EzUhiQoiaa2RLjMXJeWqJ8V3A66qqzgBQFOUa4BNguqqqs8ue87uiKHbgVrSkoWgsnM4GFaxvvfUWU6ZMwWq10rZtWz766KP6PiUhjicJQiFqRh9vVBUUpX7PRYjGRJIcQtSMxI4Q1ddIlxiLk/NUgrAdMN3t458ABdhY6XkbgFYeek1RVxpYsPbu3ZsNGzbU92kIcXKSIBSiZtxjRxKEQlSPjDlCVF8Dm+sI0Sh4eq4jVfANgqd6EPoCRW4fF5e9La30PCvg5aHXFHVFBk0hqk8ShELUjMSOEDUj12tC1IzEjhDVVxsx04BWLTZXHtukBKjqN0T+0jYFks0Xovr0mJGBTojqkdgRomYkySFEzUjsCFFzUkHYpHhqiTHAorIeg+6WVHrMk68n6ooMmkJUn1RBCVEzEjNC1IxcrwlRMxI7QlRfbfUgtNnAZJI2M/XEUwm7J5FqwaZLdhQSovokQShEzUgFoRA1I0kOIWpGYkeI6lNVMBg8v4vx9u0QGwvR0Z45rqgWjyQIVVWd5YnjiAZKBk0hqk8ShELUjMSOEDUjG/wIUTMy1xGi+jw91uhxaLdrVYSiXnikB6GiKIsVRWnniWOJBkgGTSGqT5IcQtSMxI4QNSOxI0TNyFxHiJqpjQpC/Z+oF57apCQW2K4oymOKopg9dEzRUEiQClF9MlETomYkdoSoGYkdIWpGEoRCVJ9eQejpHoROJzgcnjmmqDZPJQh7AY8B96ElCkd66LiiIZBBU4jqk4maEDUjsSNEzUjsCFEzMtcRovpqowehe5JQ1AuPJAhVVXWoqvoy0AXYCvyhKMrniqL0VhSla+V/Z/JaiqIYFUXZrCjKL544d3EaZNAUovpkoiZEzUjsCFEzEjtC1IzMdYSovtqoINRXLkoFYb3x1C7GAKiqehS4UlGUmcCzwFWVnqKg7XZsPIOXmQ7sAoLO4BiiOmTQFKL6ZKImRM3od40ldoSoHokZIWpG5jpC1IzBUwtSqVg9KBWE9cajCUJFUYKB54BbgD+BlwCrB48fB4wBngFmeOq44hRk0BSi+iRBKETNSOwIUTN6zMjESojqkbmOENUnPQibJI+lfBVFuR7YA1wCXKOq6vmqqv6hqurflf+dwcu8DtwPnPDKR1GUaYqibFAUZcPGjRvP4KVEOcniNwsSOx4mSY5mQ2LHwyTJ0WxI7HiYjDvNgsRNLZAEYbMgseNhtdGD0OmUHoT1zCMJQkVR/gHeB74Buqiq+rUnjlvpNcYC6aqqnjSaVVWdo6pqf1VV+/fr18/Tp9E8yaDZLEjseJg+sMkA1+RJ7HiYJDmaDYkdIapP4qYWyFynWZDY8QBVBavV9b5UEDY5nqog9AYGqap6t6qq+R46ZmVnA+MVRTkEfAWMVBTl81p6LeFOBk0hak5iR4jqkQShEDUj/TuFqBmZ6whxetLTYcsW18fSg7DJ8dQuxgNVVd3kiWOd5DUeVFU1TlXV1sBkYKmqqtfU5muKMjJoClF9MlETomYkQShEzcjyfCFqTsYcIU6tuBjsdu192cW4SfLUEmOroigD3D5WFEV5UlGUaE8cX9Qz6QMgRPVJkkOImpHYEaJmJGaEqBm975kQ4uQsFlfyrjZ6EIJ2fKcT1qwBm80zxxanzVM1oSZAqXTch4FYDx2/AlVV/1JVdWxtHFtUQQZNIapPkhxC1IzEjhBnRmJHiOqTuBHi1ConCD1dQQiuCsKsLCgo8MyxxWnz4KLx4yinfopo8GSiJkTNSOwIUTMSO0LUjKdjJyvLM8cRoiGTMUeI02exVFxZ6OkehDq7XXudwkLPHV+cltpMEIqmQAZNIWpGYkeImpE+akLUjCeXe6kqrFolcSiaPrleE+L01WYFoft4Y7FobyVBWOdMHjxWG0VR9P9BY9nbtoqiWCo/UVXVnR58XVGbZKImRM3IBacQNePp2LFawWz2zLGEaMg8OVlzv/7zZIWIEA2NXK8JcfosFm2cAc/3IHRntWpvJUFY5zyZIJxXxWPfAO6/MUrZx8YqnisaIhk0hagZiR0hasbTsbN+PXTrBiEhnjmeEA2VJydr+o1huUEsmjq5XhPi9OjLfnW1WUEIYDRKgrAeeCpBeK6HjiMaGhk0hagZiR0haka/QPRU7Njt2j8hmjpV1SZUnq4gFKIpq80qKCGaEosFfH2hqMgVL7Wxi7GedAwOhtxczxxbnDaPJAhVVf3bE8cRDZCnJ2pCNBeeThBmZGjLJIODPXM8IRoqT8eOqkqSQzQPnqzmkApC0VzoCUK9r9qZstkgJQVatfLM8YRoKPSWLSUl2tigjzmeoo9dXl4V28NIq4s65ZEEoaIoS6vxdFVV1fM88bqiDkgVlBA14+nYSUsDHx9JEIqmz9Ox43RKkkM0D56sINRjxlNJEyEaKj1B6KlK84ICOHRIEoSi6bHZtOSd0aiNDZ6uvtWPZzJpCUIvL+19mw28vT3zGuKUPLXEOJuKvQarEgMMOY3niYbE00tMCgshPx9iYz1zPCEaKk/HjiQ5RHNRGwlCSXKI5kB6EApRfe5VUJ6oiJLrNdFUuScI9QrC2kgQGo1aHHp5af/sdkkQ1iFPLTGeeKLPKYqSADwAjAUygdc88Zqijnh6opabC8eOSYJQNH1SBSVEzXiyCko/nsSOaA48OVmTHoSiudCTgvry/DNNEKqq3JQSTVPlCkLw7CYl+himKNprmEzaP+kjXadqbTG3oijtFUX5ANgHjAceBFqpqvpcbb2mqAVSySFEzXj6rprEjmgu9NiR6lshqqc2Kgjtdm3lhxBNlXuC0BP067XSUigu9swxhWgI9ASh3rNTH3M8Rb9BbDBo/9yXGIs64/EEoaIo3RRFmQfsQtvdeDrQTlXV11VVLfH064la5uklJjJRE81FbSQIJXZEc1AbS1YkdkRzUBsVhFlZsG3bmR9PiIaqcgXhmdKv15KSIDHxzI8nRENRVQ/C2qgg1JcZuy8xFnXGYwlCRVH6KYryA7AN6AvcDHRQVfU9VVWtnnodUcdkmaQQNSMVhELUTG3FzooVnjmeEA1JUZHr/dqoILRaZXImmjb3BKEn5ij6mKP/E6KpqIsehO4VhLLEuF54JEGoKMpvwDqgLTBZVdXOqqp+oqqq/FVs7Dx9Z0CSHKK5kApCIWqmNmLHaoWcHIkh0fSsWqVtAAe1lyCU6zbRlNXGEmNV1ZIpdjtkZGjLjYVo7Gw2MJu1JN7Bg5CS4rm4gaorCCVBWOc8VUF4AaAAccDbiqKkn+yfh15T1AVpFi9EzdRG7MgkTTQHnowdVXVN1EBiSDQtTidYLK7JU20kCG02LW4OH4Z9+878uEI0NLWxxBhc1bcHDsDRo2d+XCHqm9Xq6kGYkaH12KwcO1lZWvKwJtwShJlmM6q//8l7EJaUwIYNNXstcUIe2cUYeMJDxxENjdMpVVBC1IRUEApRM7XRR809QejldebHFaIhKClr7e0+efJUksM9dhwObSJYUHDmxxWiocjMhIiI2ksQ6hWETqeWNGnb9syPLUR90PMB7kuM9RtTlXcAz8nRNrZq06b6r1N2g3jJ+vWMu+8+li5dyuDw8BNXECYmkrt/PyH9+nm2krGZ80iCUFVVSRA2VXolhyc3KZEKDtEcSIJQiJqprWWSIOOPaFr0HVLdm8XXVhWU3e5ayixEY1dQAOvXw0UXeT529GNYra5rt+zsMz+uEPVl5Uro3LligrAy/fe+tLTm85Wy67/XvvwSRVE4fPgwg6OjtUr5yiwWdq9axeiHHiJp/Hjw86vZa4rjeHwXY9HESJJDiJrR77ZJcl2IU8vMdCU7PFm5XlUFoRBNhV5BaLfXbhUUaMmO4mK5hhNNQ16eK248HTv6OKP377TbtbfuGwoJ0UA4t2xBPXSo6k8mJUF6OsUZGbz81FMUFxQcnyCs3L/zJAnCn3/+mRtuuKHq19Jjz2AgPSeHXr16cfTo0RP3INy+nRXp6SRnZlKUkqJVLQqPkAShODlP91HTE4QWy4n7CQjRVBg8+CdWj53Nm12JFCGair17tSQheLZyvXKSo7RUW+olRFOgjwV6ogOOT3LUtLm7fgz96y0W7TGpIhRNQW6u9rY2kuvuFYR2uzb++PvLRiWiQbrt0Ud5f+7cqj+ZlgaHDzP5xRd55YsveOyzz7SEnfv8pnLsWCwnvH47dOgQhw8frvq1VJXc4mJSs7JIy86md+/epKSkVJ0gLCyEnBzWJCYC8O2cOfzvmWeq822Lk5AEoTi52qggdDi0yWBysmeOKURDUVrqukPs6QpCfZOSnBztNdau9VxcClHfCgpcSbza2mgBID0d/v33zI8rRENQXAw+Ptrv94mSHEuX1ixJWHnsKi3VKkekD6FoCvLytLf62FAb1bd6ctDpdMWpEHXB6YTMTFS7nWefeoovvvgCAEvlpbo2G/9s3crKdev4/rvvKHKfw5SWalXqGRms2LmTpcuW8dXatSxYsACMRn7dtImlW7ceHzsnqSDMysoiIyOj6nNWVeYuWsQj775LelmCsLyC0D12Skpgzx6SvLxYsXIlMS1a8Oo33/DV77+7nlNQoJ2P3l5GVIskCEXV9ECsjU1K9B0l7faqewoI0VgdPAj792vv11b1rd2uJQjT010XuEI0ZhaLdhFXmwlCPUFSWqrdeZbkumgKLBYICHD1IDQYKk7U9EleTZbWV5UgDAiQ6zbRNOTna0k7fROR2kgQ6u+bTFpyvabVvEJU0741axh09tncPH48n86Zw30zZpCUlERcXBwLPvtMe1JuLvlr1rA7OZm/tm/nqquu4puPPtJ2rE9Ohs2beWLOHF769lvsTiede/Rg3ldfMX36dDAa+Wz5cr5ZudL1oieoIHz66afZu3cv/fv3Jysri0x9tUhlqsqRzEy279+P0WCgQ4cO7Nmzh6HjxmEtKdGuEYuKYPNmnA4Hfa+8kqioKMadfz7bDx/mQGqqq6p+1SpITsa2eLHrvByO8vMqLS3loYcewioJxCpJglAcLzdXq04Cz29S4l52b7PBmjWuMn8hGru8vNpJckDFBKHed+pEd+GEaEz0iiR98lRV7OTk1GxyVTn+9AtXWaYvmgKrVWvMfqIlxnrM1OQarnLs2O3aa8mESjR2eqLAPXZqK0EIWoKwchWUELVBVZn3xBN88O672AwGUouKWLN4MSN796Z///50jori9nvuoeToUdi4kY0bN9KvWzdSc3Lw8/Hhy48+gm3bIDUVcnPZlpjIh0uW0C4hAUVRGDZsGLm5uaRlZ7Pj8GF2HjmixZPeg1DvuekWAy+99BJz585l06ZNpKWlkZmZibOqMUlVScrMZMuePUSGh9OyZUs2b97MyjVr+GnpUha+8QasXUvO0aPs8vXF39+fv//+m45duwKQkp1NaXo6FBVhLSoiafVq4q+9FsvRo2Tv2MHWzz7jyN9/A/D7b7/x3HPPMevxx7FJVfxxPLKLcV1RFCUe+BSIAlRgjqqqb9TvWTVBhYWuPhm1keQAV18Oq1W7ixcS4pnjC1GfcnMhOFh7vzZiR19mXFKiDcaZmdChg2eOL0R9yc/XqitOllzftQvat4fIyOodu/JFqF79VFio9YQSojHTE4R6VWzlJIf7apDqqqpNhr+/VBCKxs9mA7PZlbTTK29rI0GoKNr45j7GCeEBWZmZ/PjFF9x0660o3t5QWMjujRuZMmsWAKuWLOGskSPBbueTu+7iw82buXTQIG545hkevOMOigwGsoARF1yA4uvLNZMn8+Kzz9Lv3nvZ8PLLKAYDR3Ny2J2UxISLLwZAURQGDBjAqsRE9iYlEeDtjWq1ouixo+cPymIgPz+f/Px8vvnmG1RVZefOnTgcDvLy8ggNDS3/XoqLi1m6aBHJmZnY7HaiwsOJjY0FICYmhuteeQXV6eTFW29l8ebN7E1L46yzzgKgTefOxMbG4msycWjLFjr060e3u+5idK9epOXmMnHiRNbv2YPD6WTckCFYnn+eHYcPM3PCBP6cP5+Nf/7JovXrK/5wCwu1inlg+7ZtqKpKz169aul/suFpVAlCwA7cq6rqJkVRAoGNiqL8oarqzvo+sSaluLjiRM3TyyTBVUFos0k/G9E0lJTU3jJJqLiLcXGxNnDJjniiKSgpgaCgihWERmPFikG9j1N1VZUgVBRt3ImKqvk5C9EQ2GxagjA3t+oEoR5DNV1ibDJp45qeKPTzk50iReNntWoJQn3Zr5fX8bFTUgKbNsHZZ1f/+O7jjre3q4JQlhiLM6GqvPff/zJqwgTaDR7MtGuuYfHy5Xz5ySdMnDKFdYsWsXn/fm6bMoXQsDAGnXOO9nUmE6YWLZjWvz8MHMid//kPYydM4JabbiLez4/HH3+czMxMWrZsyZ133kl8QgLv/v47O9LSOFq2qVu7sio9gIEDBzLv++9JSEggOy2N9LQ09mzfzqGkJOLCwxnp41MeA0ePHgXgyJEjAOzZsweAjIyMCgnC3377jVtuuQUvVcXbbCYyIoKAgABCQ0N59dVXmTVrFu/Mns20adNIz8yksLCQu+66C4Bhw4bx7LPPMu+LL0jcsYP89HT2p6RwIDWVVjEx/Lp2LfffdRcB4eE888wzRIeGEhMayj133smTZjMJ48bx36uuYkC/fky89FIUf38WzJ7NqNtuwz8ujpl3301xcTHL1q2r1f/ehqRRJQhVVU0FUsveL1AUZRfQEpAEoScVF7tK7t2THPrFZ34+HDsGHTtW/9juCUKrVftYEoSiKcjP1yZPJ0sQ5uRoMVSTiln345SUaJWKJ+rjIURjovc203dHrSp2bLaaJzncq6AcDi0ZKcl10djplU9ms6sHoScrCFXVlSA0m7Xkup+f7MQqGj/9d1qvIDSZqk4Q6u1cqst93NETkV5eNT+eaNJyc3PZuHEjDosFp9PJ4KAggrp1o8TXF4CtW7fSt29f3nr8cR59803OW7UKb29vUo8dY+/WrSz7+mseevFFYuLiGDxsGA88/jit2rat+CJ9+2q/k0YjF116KTt27KBLly7lnw4MDCx/f9CgQTz+1VeEBgWRlp1N165daee2WmnEiBG88MILjB07liyzmQdfe41FK1YwcvRoFi5axNo336R9dDQASUlJBAUFkZ+fT3BwMHl5eURGRpKZmUlHtzzCsmXLyMnJwWQ00r97dyLDwwHYtGkTrVu3ZtKkSaiqSklpKe3atePSSy9l3LhxALRo0YLrr7+eNWvWcO9nn+HldHL5+PH8sGABrz37LN99+inPv/46isHAmuXLuWLiRG4YPhw6dQKTiRsmT2beDz/w8W+/8Z/nnqNNVBQH0tIwv/8+r7/zDis2bMBsNLJ/924CQkL4/MMP+eKbb/h90SKiqrjR7HQ6cTgceHl5neFvRv1pVAlCd4qitAb6AGsrPT4NmAYwduzYuj+xpqC42LWUsXLjXr3youyOQrXpF6lOp2uZpCQIGwSJnTNksWiJh+xs7eOqkhypqdpjNUkQ6rGoqtprRUW54tRo9Mi3IGpGYucMlZZqMZGTo32sx457UqNST5vT5t5HVz9mYKBM1BoIiZ0z4J7kOFEftTNdYmwqmyZ4e2uvp78V9Uri5gxZra6k3YliRy9iqAk9ue5waK8hFYQNRkOJHWthIeayJayvvvgiL7/+OtFhYeBwYLXZ6NC6NSu3bWPIwIGs3bCBEf37Yy0qYvWSJYy//HLGnnsuX373Hd7h4Vw9cyajrrsO7+BggvU2R5W5JasURamQHKxs0JAh/LhgAVm5uUSEh/P000/Tr1+/8s+PHj2a3377DV9fX7IXLeLzDRtY+Pzz9Jo8mVmzZvH8V1/x/t13A5CcnMyoUaP44YcfOPfcc5k/fz6dOnU6bifjZcuW0b5dO2yFhbTt0oXI1q0BaF32VlEUFEXhkksuwd/fn8cff/y48x47dixhYWFkZGQwffp07p4xg+HDhnHZxIna9R+wYPFijJXmTLNefZWZzz7LL/Pn4xcQwLIff+Tbp59m0zffcO+99zJ+0CB6dOvG8BEjsJSWMqpnTwa3bMn111zDb4sXo6oqBoOB0tJSzhkxgs1bttAqIYEdO3diMjXOVFujPGtFUQKA74H/qKpaYa2DqqpzgDkAs2bNkm0Ka6K4WBskbbYTX3DWdNB0/7qSEu1utMWiDZqNNIiaComdM2SxaImHtDTt4xNVQdXkjpKe3DAYXBeYXl7a5NBqhbK7jKJ+SOycIatV6212ok1KVPXMKgj1KhG9CiowEMqWvYj6JbFzBir3UTvZEuMzTRDqryMJwgZB4qaG9ButlSsI7fYKsbN582Z6h4ej1GTM0V9Hv9YzGk+vB6HTCYcOQeXKL+FRDSF2rFYr3Tp35smHHiLTauV/773HiK5dSc/Lw9/fn9atW+NdUsLVd9/NO/Pnc/O4cSxau5at27fjGxzMgd27MYeGlie9MJmITEjw2PkNHTq0vLrPz8+Pyy677LjnjBo1SntnyBDGKQr8/Tc4HFx+wQVc9cMP5WNOUlISXbp0Yf369fz666/8/PPPtGvXrsJOxllZWSQnJ/PQfffx89dfc9mECRWWH7t76aWXtH6HVRgzZgxjxow5/hNliVjguOQggLe3N97e3ky5/noALpswAYDYu+9m7NCh2vVpx46MaNOG1q1bE3XRRdgPHaL3mDH06NaNwIAAvL29CQ4KItBqJefLLzn/scf44fvvuXLSpCrPtaFrdBkZRVG80JKDX6iq+kN9n0+T43Rq1Rz+/q4LTj0x4Z4gPJNBU6/i0EvvQUsWupU3C9HoWCxaFZSexDtRglAf0KtDv6h1/1pJEIqmQl9i7F7t5B47+njjiSooPUEoGy2IxuroUW0VR3S0K8lhscC6dRWSHMXFxfharShQHjvHjh2jsLCQ9u3bn/p1nE5Xdbr+OnoVlB6joMWnxaJ9zmiUm72i4dq+Xfu99fZ2Lf09eFAbg2JjQVEoyM9n4MCBHFi6lIQzKYYwmVxvT1VBmJ2tnUdKihbXfn41/x5Fg6WqKvfedRerVq/GarVy/fTp9O3alSduu43rL7uMvGPH8Bs+HD8/P0yZmRARwS0vv4zD4aCoqAjfoCAAzGXLb2vLkCFD2LBhA9dffz3WU90Q0pN1ZSs12rRsyaHUVFSHAwWtgrB///7079+fzZs3Ex4eTosWLSpUEO7cuZOuHTowacIEWlutTJw48YQv51+Xm8v5+MCQIeUfDrr5Zm2+ZTJh6tiROXfdxS/r1uFrNKKqKu8uXMi8117D97LLeHDvXm64804++fRTrrvuOiY1skRhoxrFFS1l/AGwS1XVV+v7fJqkkhItIMzm2qsg1HvagDY4G42uSZsQjVVpqXbR6b5kxdMVhEajFp8Wi3YcqeYQjZ1eHejnV3Hy5L45lif6qIHrhpSfn5bUkOX5ojHJyoLwcH7+/HO+WbSIj+fMwei+xLiwUBsfFIU/V6zgyvvu45NnnmFcXFx57Nx3332YTCZiY2O56KKLGDZs2Ilfryx20nJymL9hA/83cqR2LahfH3p7a887eBAOHNDGpIgI6NmzDn4YQlRDSQkUFvLgCy+wYOVKvn/+eToPGqTFjt5Ts2yus3zVKux2O6kpKST4+bnmQdVRVkH4w7JlbDhyhGdmzmTzjh30tNkqTryLi7VxaN06Fuzbx7P/+x/L5s/H93QS+KJRUFWVFStWsPbvv5n13HN0jY9nfP/+jL/3Xn5ZtIhbxo2jxSWXgNGIr93uuk4p6+EHWtVbUFlysC4oikJgYCBdu3YlPT399L7IYACHg0A/P/x8fXlj/nyi8vM5cuQIl156KQCxsbGEh4cTExPDgQMHWL9+PXFxcezcupWuYWG0Tkig9ciRtfeNnSn3PIXBwJBLL2XIgAHQvTt4efFwRgbGli0BuPiyy1jWqhWrExO58447KC4uJjQ0lNGjR9dtkrOGGlWCEDgbuBbYrijKlrLHHlJVdWH9nVITU1ysTZ6MxpMnCM+kglC/q+a+c5hUc4jGzmLRJmf6MpKy3meHUlK4rE8fNm3ahHKmGy0YDNrx9QShl5ckCEXj5HBARoZWdWs2V1xC71ZB+NZbbxHq48M10dFntsQYWLtnD4PCwrSP9UR7I7hQE4K8PFi1Clq2ZO78+azft4+nn36aqddcQwxuF/MWC3aHg2mPP06XLl3YsXevliB0OEhOTubrr79m+PDh7N69m/j4eAYOHIi3nuirrCx2Fm/Zwr3/+x/XjxiBD2jxqt8QA0hOhrAw7VouJUWLqYSEmt0ME+JMpKRov4cxMRUfP3AAUlNZsnEjwaGhzPvpJ54cNuz431FF4Y+//wa0alvatq1YSXu6ylZILVy/nk8WLaJDnz7c88gj/PDww4w87zztOaqqVf0WFXEEuHHWLFq2aMHCBQuYMGPGiY9dk4SlqFPZ2dkYjUb+/fdf1q1cyVPPPENkUBCb3n+ftm3bYoqIgBYt6DlkiJYI1H8P9eRgA3HHHXdQcrr9mvVezw4HbVq25IXvv2dIdjbbtm2jR48eAPTr148bb7yR3r17893nn/PPsmVk5uYydPBgBug3shrT73br1tCqVfk5G1u1qvC5blYr3Tp0wKeggMW//caxjAxuueUW/vOf/zBx4kRycnIYPHhw/Zz7KTSqBKGqqiuARvSb0wjpfQH1BJ77JiV69caZJAjdqzn0iZveF0qIxki/WCstPT5BaDCw9cABtmzZwp49e+hstx8XO06nk9TUVFqW3XU64WvoFYS+vpCbW3GJsTuLRXvewYM122lciLqQlsbBX38l+txz8dUTDZWqbzfv3s0999zDtVdeyTVXXXVcBeGyZcvIy8srvztdpbJxJj03l8E33EDWF18Q5uVVdYJQVSE9XYu1Fi08/z0LARQXF6OqatVVBA4H7N2rjSmdO2tVgYcOabtuJySQXVjIX//+y4pff+XcsWN59fvveeKpp7j5nHNY9ssvtAwI4L5nnyUhJoYpN9zA6l9/1Y7rdPL7okX06tWLI0eOUFBQQEZGBp06deLPP/+ssOTYZrNx7NgxQouKeGj2bJSCAoqKi1m6dSs/fvklQyIjub59e6zbt+MTG8vuxER+SE1lx86dRAAtfX2Z9p//ENK9+8l+CLKMUlSLqqpYLBZ8q2qp4nBAfj62gwdRjEZMMTHaY0uWwIgR5O3bx6Fjx9hx5Ajz5szh2aefpuuvv3Ll5ZeTlJnJtsRExl16KSrw+9Kl9OnTh2NpaVqCsIpK8/z8fB577DFef/31qk+2rILw34MHmTlzJnc/+CCFhYVs27+f8vqo5GTw9kZt145HHniAadOm0S46mi9/+KFigtBigbQ0UoAob29S//mHuAkTypf4q6p6wp5sovalp6cTHh7OypUrOXzgAHPmzCE/O5v9R44QGRaGzWpl2Wef0evii49vv1CHVYE1EVM50X4yZRWEOJ20iYtj/b//8ueff2I2m4mPjwcgOjqa//73v+Tm5rJ5xw6MJhNXXX01c+fO5frHHtO+vrH9Lp/ofM1mrbIQuMZs5pr8fHA62bd3L9PmzuWZp5/G39eXs4YO5cILL8TX25sJV1xRp5WiJ1ODZliiSSsq0i7a9MokPTHhoSXGh1JS+H3DBlfTXrNZm6jJjpKiHuTl5VFwsl20Cwuxl5Zy7733kpKSUvFzBw5AXh6WhQu1uHFfYlxSUl4FtSc5GYDff//9uNg5evQof/zxx0n7bQAVKwj1i2OTCbVyBWFaGvz5J2zcCHv2QGbm8QnEstfPzMxkz549J39dIWrK4YAjRwBtAlPZ6sWL6XrnnfQdOZIS/fMmk5YMcTjAYGDuTz9x7rnncujwYe3zlcadzz77jJ9++onCwkKcJxqTyqo/dpXF4aYDB7TX8fV13Ziy22HRIli6VIubTZu0nlAnaygvRA0cPHiQmJgYOnbsSGZmJkU5Oaj675mqsuDVV+lz6aW88frr7Nmyhb//9z92JSaiFhby1h9/0H7cOG66+WZ6Dh/OfVddxa1TpvDiiy/Sb9Ikrn7uOcY8+STnnnUWX77wAh07dmTvoUPadZbTybZt2xg/fjxHjhwhLS2N5ORkDh8+zK96ErHMtGnT6Ny5M90uv5y35s3j02XLGDVsGNOef54//viDh95/n1Z9+hA+ahQfz57N8AceIPXYMc466yz8EhL4YdMmXnvzTYrS0vhs1ixUfQlnmWM7dvDSHXfw1v33U1h5XBXNV6Xfk8o+/+QThp999vHjicVC+rZtfPH889z30kvc8dRTPH733RxYvZqNO3bAv//y7u+/M/z++2kZFcUFEyaw7dAhrrr1Vjbu2cPj8+cz85NPKM7LY/PevZSUlDBmzBhS9Q3nysaWBQsWsHfvXkaNGsWmTZt4++23sVUaI5YtW4bFYgGnE6fBwI5Dh7jnnnuIioqiW7dubD94sHwulb1/P8uTk5n4n/+wNzGR+++/n0uvvprFGzZgd9vA4fyhQ+l2zjm0bN2ayyZOpNsNN7Dqgw/I37GDgoIC2rdpw4TLLtNeF7AdOkTpnj2gqthWrID8Cnt5AtqYnH7wIGpN20UJANLS0mjfvj0jR4xgxIgR3Hr77UQajfRt1YpDK1aw5++/ST5wgF7jxzf93qxuFYRtExKILeuT2K9fPy2BvXlzeSyFhIQQGxFB/27duOeeewDoEhfn2iyoqenRA84+G4YNo8MVV7Do9dfZs3gxK997j0ndu/P13Lm89+KLnDdiBB9++GF9ny3QyCoIRUWqqvL555+TnJzMgw8+WL0vdmsw/eEHHzD/p58YN24cE9u3J7RjR+3zhw+D1YojPp6HP/iAa0JC6D5gwHEVhKqq8s4779CzZ88q+9k4nU6mTZvGu+++y/sLF/L7li1cOGiQq4LQx0er2qjk2muv5fbbb+esPn2051SDxWLBaDTiJUtcmq+yi7TKVQoOh4O9GzawZPFinnzjDUpKS3n11Ve55ZZbyDt2jC1//82I/v0pOHCAKx58kMjwcBZv3MivP/3Ezr17MWRnw/79ZCQm4u3vT/zVV3Pd+PEkHT7MQ5GRDDSZtIFQVaFlS3YfOcKoUaP4888/+U/79uWxU1RURJs2bbj77rtJSUkhOzsbh8NBi6oql5xOflyxgqjISPbm5zMoIICi7duZPmMGKz780NWEfts2aNcOx549rMnOJuLbb4lv3Rq/c85x3QX/+2/2BAQwYtw4FKOR715/HWtJCSXAl7/+yqSpU+nUpQutW7d2xc+xYxAa6lpSJpq90tJSVFXl33//pX///q5PqCo4neT//TdrV6xgL/DTwoUsmjcPJSWlPJH+82+/MePuu9m7fj3xl17KQ489xoxBg2DfPgAcqsoPf/3Fx198we3/93/asStV365Zs4aEhASmTJnCBRdcwO233378iaoqZ994I+3LLlY3JiYySlFcN6bKzmnRjh30uvhiPl2wgIk9e2L5+mta9u5NcM+e2t+QvXshJwcGD+bQoUP4+PgQ7dYjSAigwrVVUXo6mUeO0KosPgpTUvjigw+4dsQIfGNjiYuLw26z8dTdd/Pga6+RsWMHNzz1FG++/TY/fvwxb158MbFRURxIS6N79+4cOXKENWvWlO8u+eBbb4HRSGBcHH379iU8N5c5H33EI9Ono+TkQFQUew8f1v5uO51s3bqVRx55hICAALLLln4B/Prrr0yfPr38W9i6aRMLf/yRJd9+S6HDwWsffcSHn37Knp076dGvHy+99BIDW7UiW1G48a67+PLLL5k8eXL51++fMoWzhg4lrkULpj37LBkpKdxz330s+/tvvvnjD775/XcmjB1L3vbt3BMfz+SLLuK6u+9m3rx53H///XTt2rXiz1SvipfruSZFr3w7lpLCxgULKDp0iCueeAJF/382GHDa7bx0332MHTOGT959l63bt7N27dryZXn7161j4uTJ3DZpEtNfe41APz9KSkspWriQvCNHeOOnnzgwZw5/7d2LzeGg98CB+Pj5Me/dd1mwfDnvf/YZPy1fjs1iYfIjj/Dnpk3cdsMNxMTEsG3rVu08ysadxx57jEGDBrFkyRIuvfRS7HY7+/fvp0uXLuXf0xVXXMH999/P/QMGcCQzk+CAAMLCwliyZAmHDx/mhkmTGH322Xz4zDPM/eAD/rd4MROvuIJ58+aVL/WPj41l6/ff02/YMIqMRlbu2ME3332H/fBhbn3kEe6+7TYmPPQQLQIC6NGnD4PbtCEzOZnXnnoK76IiXvviC1pFRvLcjBlMuPde5jz1FAMuv5y5771Hp6goPvv+e6Li4vjl55959sEHSSkp4T/33ENwcHDd/ec3MkeOHCE+Pt5VqelwYHc6mX7ttYzp25dtiYksffllko8e5dprr9X+XjW3XqxuFYSd27dnRI8epDgc2vWhw6FVzLZqpbWjAPp17EiHDh3o0qULS7/7jtb6CpKmmCB0FxSEefBg4gFUlY4XXsiU6dNxWq189uabPPXEE3z3zTf4ms1MmDiRdh068Pm8edx888306tWrzk5TEoSN2Oeff85TTz5JcVERAwYM4Oyzz+aOO+7g9ttvp3///mSkp7N2xQrGXnABAC+/+y7h4eFcf9FFGDZvhj592L56NQ/MmMHrM2Yw74cf+O+KFbz45JNcNGAA8YWFAMyaPZtf1q7ls0suYdlff9GxUhXU66+/zocffsihQ4dYvXo1q1atYtq0aeWf3717Nx988AFTp05l8aZNbDlwgM///BPVZuOv/fvp3a8fdwwcWF7Ounb+fPZt3MiP333H6qVLmXjWWXQdPZr9SUnMeuQRDJWShdu3b2fOnDk8//zz+Pv7s3fvXoYOHUpJSQmPP/44w4YNY9CgQbX7nyHq3f4VKwhv147M/HyenTWLtMREBnTuzCVTpxIVHEx2djb7kpP56KOP2PLvv/Tv0YM/XnwR/7AwzrnzTgD++fFHvv7jDz6aMYPXfv+dlgkJfPvnn3z33ns8MGsWq3/8kbODglDbt+fcqVMJBtomJHDowAEG9e3L9ddfz86vv2ZfcjItw8N5Y/Zs1u/dy90PP8xzzz1XnjwB2Lt3LzabjY8++oiioiLeeOMNEhMT+eyzzyp8X6qqUlJYyN2zZ9O5dWvW7NyJj8lEu06dWLtxI2s2bqRlSAjxBgMXPPww4e3akZWaysGjR8HpJPnoUW447zwOHDvGoM6dOXzsGDmFhdxx/fWoBQXcMHMmsTExOJxORvXsyVOPPkpGXh7FFgttIiMJ9vMjwGSia5s2eIWGEuzvTybQvksXOnToQHxcHHFlywdE87Bt3TqGjx6N1WZDURS+++YbLjr/fL7/3/+ItVopKinhmtdeI8Dfn7SMDFrFxfHJRx9xw+TJ0KoV2WlpLNi6lTnTp/Pogw/y3fff8+Jbb/Gf225D2b8f5dAhvv31V2LCwzn33HM5euwYDsDoNu7k5uaya9cuzGYzdrud119/nVtvvRWD207fpaWlpB45wqqtW1lrMNCne3fW7tvH66+/zpbVq3lswgRmvvUWlwwaxDWvvEL7d98lJCSE1157jdLSUs7r1o1vZ87kcHEx9779NneMG0fP4mLOueoqzN7ezLrxRibcfjveUVEA2O12nE4n5gbWR0jUkNPJgd9+Y/GSJXS+6CKGdurE6u3b+XfZMnqOGIHNYqHP8OF8+v777N27F1+TCWtGBsv37mXO66/z7uzZLFqzhv/Nno1BUbj6ppsoLi1l+SefMCg6mvuGD2e31cr0p5/mnsce45fPPmPk8OFcc+21XDNxotZ3MDKStIwMpkyZwnfffVeeHATK+1U98sgj2sfZ2QyKjNRuvDocREdHU1JaSlJODnEtW7Jt2zZ69epFfHw8hYWFbN++nS5durB69WoKCwsJCAjA6XSyZ+9eesfGMmLaNHaVlPDDkiXEtWpFfOvWALz88suAdrOtQ5cunKf3VCvTvnt3hg0YwP1vvslDM2fyyief8NU//5BXXMzUCRPYsXw5Mb17A2DJy6Nnt278PmkSE0eO5LrJk2kXFoZDUfjs/fdZ/vfffPftt/Tp1o1OF15IQWEh5513Hv9u386RpCQC7XaKS0rwjYmhf//+rFm1iovGjJEYrG8OB870dG666y6CAwLoEhHB7J9/5rarr2bDxo2MGzGCFz7+mPCwMNZu2UK/Ll04lpXFV2vW0Dk6mu4dOtCuVy+++/lnfvn7b1799FNKrVaevv56brrxRm6ZNo3QoiLe/eILth06xLyff8ZgNJIQF8eYQYPYn5TEewu19vS/HDrEqo0b+fbbb8uX9V82dSoB8fGcf/75PPnkk/z59dcsXLuWZ++4gwmTJ7MtLY3FmZnlPdOzsrLYunVr+aqLxMREQJvf6AnCnJwcCvLzeemllzBMmMDbv/9Ojz59AGjVqhXh4eEcPHaMyMhILrv1VgK8vfnk00+58MILK/zoho0cya3vvguzZ3Nhjx707taNMWPGADD25psxms08+dJL/Pf//o+8w4d58aWXOJSSwuAJE7jswgv54YcfeO6FF7j64Ye5dtIk7n76adTnnuOCXr3YZrHQKSaGHxcvJi48nLsefZRAX18+/vBDxl98MRdecAEFBQX4hoby/LPP0rtPH15+5ZWql3U3A1989BFOVeWm//s/3p89mxYJCUybOpWiwkK8zWZ6tmvHd3/8QaCqajcSMzO13bCrWDXR5LlVEF5z2WVM6tqVJaWltO7UybWSKSurPEH4wo03EljWs+/cwYO1lRvNIUHoTlG0ZeZBQRiA6x94gC5t2/LW99+zc/9+7rrzTkrtdiLDwvjuyy/54fvv6TVgAAcOHKBbt27l17tOp5OSkhKPbn4iCcIGKCMjgxdffBGz2UxoaCjr1q3D29ub8847j8DAQJKTk9mxYwc/fvcdvz3xBDmFhVx11VWEhIQQFRXF2LFjuWDkSAK9vHjv888Z0qMHBlVlf1YWgQEBGHbuJLxTJ4ybNnHn22/z6uuvM2XAAKZccAHbNm9m+ldf8cgzz7D0iScwAHO++YbVb7zBstxcRo8ezRd33YWfyURfh4Os3FyeeuopNmzYwI033si9997Lxo0bufnmm8t/cVevXg3Ad999x+6kJPp07syNTz9N65gYRp17Lh99+SV7VqwgftUqjqak8M28eRRYLDz0wAPEx8Swbf163n7zTVAUDCkpzPrwQ7auX09wRAQfvPUW3y9YQGhwMD07d+byK69k3pdf8txNNzFk4ECe+Oor3n77bfz8/Bg+ZAh33X47nXr2xGizkVlYyPLly9m1axdt27Zl/PjxjWJnoebG6XTisNtZ/eOPHNq6lTynk6KMDPzCwgiMj8ean8+GTZv4cckSSsuWezwwZQoTbr2Vb775hqlTp5KSk0N4cDCd4uLo064d3y9YgDkwULurtWYNfz7yCFe++CIm4M0nnuCNL7/k5jvvZNq0aWRmZtKiRQs27dnDJx9/zOKEBDYdOUJ6ZiY7MjJ4d/Zsbr3hBlRfX+a2acNL8+fz+LPPMqZfP75ftQqACy+8kLvuuguL1YpP2d3o3bt3A1ozY4B///2XZcuWYbfbMbktRZg5cyZvvvkmXeLjWbZpE+cPH85/Zsxg6q23ctUVVzDqttuIjYrirWnTOJydzSXXXw/ALbfcgtlsJj8nh159+9K2TRuW7tpF2zZtWL5mDZ/88ANh4eE85nC4+rAVFDBrxQowm9mxZw8pRiMZBQU4AgL4d8cO7BYLxzIziTQa+Xz5ctKzsjhy9ChnDxhAi5YtWbtyJXZFQTGZ6NahA4POOotRF11UfufVaDSyadMm/Pz86NKlCyEhIXXwGyQ8wW6zMe/NN7EUFfH07Nm8N3065/bowa79+7liyhR6tm7N5sREzL6+OFWVb7//nrMHDCBl0yZyAgMZPXo0z3/7LSNHjuSrr75iwIABDBo0CKPRyFVTp3Lfo48S07o1HVq14rLu3Xl+wQJ+f/ZZvL29CQ8JIaWwkHiHA1VVmTdvHgsXLqRjx46kpqZSXFxMWFgY69evr3Az6JprrmHZkiV0aNWKfYcPc+P11/PfRx4hxW7nvHPPpft11+Hv68uvGzZwzvDh5BcWsnTpUgIDA7FYLPTs1ImJb73F1h07OGfUKG564w0ys7L4z113EeXry0tffsnOgwfJLijg6yVL8A8MxOl08u7//sfYceNQbDZKt26lQFHYsWMHseHhdBg79vgfrtWq9WSTeKg7dnv5kq/i/HwoLeXAsWNkJSdTqqocPnCAdX/8wfzlyxk/YgSzb7qJXcnJ9G7blp6dOzPnhx8odTo5fOwYl5x9NkP69aPEasXesiW3DRzIqIkTCfDxYeJFF/H4o49yLCeHJ+65h7atWzNo8mQUVSXKbifCaOTYjBm0aNkSHx8fXn3jDe38fH3L20lERUXx559/nvp7CguDUaO0im+HA0VRuGTIELpdey2vzJyJj48PkZGRJCQkYDabWb9+PZ06dSImKoqlb7/N+Ace4OjRowT5+xPs5wfFxXTp1Im9+/ZV2ePMaDQelxzU3Xb//fx4/vncdscdTLrqKrZt2cLkq67CVKkK0Cc4mO9feAFbXh69LryQm2+9lQEjRvDnP//Q97zzKLHZuPeuu/jj99/57s8/MXh7M3HiRNpHRxMXHU1RURERISEUFBWxYudO2kVH898ZM/i/W27hoksvJT8/H19fX7KzsxkwYAAmkwmfaq5IEZU4HGzeupWPP/6Ynt26MXXqVD7+/HNaxcdz/fXXc9H555Pg48P78+cTFxND/27d+H3HDiaPGsUzb7/NtOuu470FCzj/vPPIy8zkvunTOffyyylKTeW9554jw2jkv3PnojqdnN2jBz/Pn09k27bs27WLvt7eJLRqxcqVK8kuKOCKKVNot3Ej8+bP56MPPqBn79707dyZtOxsvoyP57yRI3n2gw/o2LFjeZJNN2LECF566SXuueceirZtw8ds5v6pUyE8nHRFITUrS6s0dzj4+++/GTx4MGvWrAEqJgh1+/fvp1urVvx35kwee+wx/vfee3Tr27f88wEBAXz++edMmDCB7l27su3gQYYMGXLcj3f4iBHM+/JLXnnlFe68807uu+++8s8ZyxLfiqLw8pw52rjh40No//7kZWfjU7bL6g/DhpUvxe4QFkavNm04a+LE8uTMzL/+IighgauvuYbnZ84k9+BBXp8/n+m//EJcRARWVeW2889nwdq1jL3oInr27s0ff/zBJZdeyuOPP46XlxdLly6la9euLP79d7r16EFcXFyTq6r3Lyjgxoce4uaLLuLOe+/Fy2Tiu4cfpuukSZSkp5PQuTOGgADXF8TGam+bU5JL51ZBaDSbMfr6Muacc7T5RV6e9pysLOjQAYDWYWGuXYHtdtfb5viz00VEMPDKK/msXz9SvL2xWSx8+9lnXHT++Rw6epRLLr2UmOho0jIyMJpM3H7LLSxfsoQVmzZhNJlY8c8/+AUGVugpXFNKVb2BmopZs2aps2bNqu/TqEBVVea++iqrNm9m5+7dRAYHExEfT1paGna7nZSUFI4cOcINV1xBi6goMg8epH/bthwym9m1bRu5WVnERUfTt21b+rVuzcBrr4Vt2zjsdJJmMNDfy4sNW7bw/PvvM3/tWmbffjup2dlkOJ2M7dYNfz8/Rt53H/379yckKIhLL76YO+69V8v6b94MwcHQvj1XTZjAdwsWEOjjo5W7z5kDffrw1scf8+hDDxHo68vBI0d46vnnSUlJYe7cuTz44IM8//zzKIrCxo0b6VN25+zmm2/m6NGjLFq0iFvOP5+IVq3YtmsXP3/xBYSHk5ydzYybbiLGzw9fLy/uuf12bO3b06JFC63k3umEjRs5tHs3/e64g/lvvsn4O++koKSEq887j0F9+3L7hRey8tAhli1fzrk9ejB0/HjtD5HNhqWggB05OXwwbx6LN27Eqqr4m0yk5OZy9rBh9OzZk82bN7NixQqmTp1KCx8fxkycSPKxY3Tp0oXNmzfj7+/P6NGjT7zbXwOlKMoTqqrOqu7X1VXspCcns+fvv9mTnMy8X3/F28eHOG9vYjp1oqS0lI3r17Nx504Ki4ro1qYNvfv0Ichkwj8qisKkJAry8vAODKR1QgLTZs4kJDiY7PR0osoqHcoHm1PtQGexQFKSVv5+gsqDI0eOcNFFF+Ht7U1gYCCXXHIJZrOZq6++mrCyi6777ruPjz/+mBeffJKb7riDm6+5Bu+cHN76+We6dOrE99On061rVzj3XB5//HGWLFnCunXriI6ORlEUUlJS+OOPPzjnnHMA2Lx5M+PGjeOHjz6iRW4ut733nlZdcsMNqKrK6tWreeX55/FVVb5cuJCPPvqI66677rhzP3r0KMHBwQQEBICqUnzkCH7uu21VxW2pXJU/r82bwWCgMCGBn956i9zcXIaMHo2vzYZaUMDy3bvZtX8/89esochiweZw4FBV+nTpQqnVyu7ERIYOG8YVV1zBJZdcgtFoZMOGDWSmpWG1WIhv2xZvLy8GDh5coSqsLtQ0bqBhjjtZW7YQGhqKoVUrVxPososwtbCQlF27+HvjRuw2GyoQpqps3L+fqJAQfBSFDZs3s3jTJlpGReEExo0Zw39nzdKWA7dujSM/n2def53QkBBKDQaGDBly3MRn3bp1pKSk8Mgjj/Doo48yadKkCp+fPXs24eHhYLXyzXvv8cAzzzAYYORIzurdm6nnn88148axLziY888/n//+97+MHTuWrl274ufnxxVXXMGgQYP4v7LlyFu2bOHiiy9meN++XDd2LNc99BAbN20ioVWr8mRH8l9/oQYG0uHss/n5558ZPXp0xZ9bVhbz58+nZcuWXHDBBVhLS8nNyCCqrGL2wL59dOnalSvGjeORG26g+NAhsoqL+e+cOWTk5dGzTRv+/vdfjAYDvTt14sCRIwQFBpKVn4/NZmP02WfjHRhITkoKm3bu5OKLLiKwRQvaxMfTu18/hg4bhtFoJCMjg5ycHNq3b4+xurtp1rHGEDsLP/2UN15/nQPp6aTl5mKz2TAoCqFBQbSNjMTHx4dWERG0796dW2bOJDwiwvX30GIpb7XgcDopLCyscmleSUkJVouF4NBQ7YGT/D396q238A4M5IPvvuOzzz4jVP+amsrM1GLzrLNg8WJ+2r2by++5h9mzZ3Pbbbdx991343Q6efvtt7n99ttpGxXFkl9+IbBNG1JTUzEWFrLs66+1a6h27Wq0YY/T6eSvv/5i5MiRp35yYaFWcaNPFtFunM2fP59Jkya5btyWlEBmJra8PLw6dtSW/JtMWtKjuJicFSsI6dGDVYsX8/gbb7D9yBFaRkVht9nw9/dnw44deJlMTJo8mdGjR9OpUyd69+5dHlNOp7POxxp3DeV6LTc3lw0bNmCwWqGgAN/YWKJatuTP33/n759+Yv7ff+Pr48M9l1zCtytWYDKbOZSaSlZeHs9Om0ZiUhLLt2/n82+/pXuvXhWqz053Q43ExET8/PyOTzgVFGgJ8IAAiIwEo5HZb77JXdOnk5KSUmFThRtuuIEHHniAWbNm8cILL9Bavy6sQtratRRlZ9M2Lg4CAjikqowYPJjl777LlpIS/t60icjISL744ovym8aHDh3isssu45NPPgHgyy+/5Md33uGb+fO1PtAjRpywLcuzzz7Ld999x6ZNm477nN1u5+DBg3To0IGlS5fSvn17EhISTvkzqxGbzbV032Jx/X3LzoaICGxHj/Lm88+TnJXF5KFDmfnpp/y9dSu9e/TgQGIixSUljOzZk7TCQo4cO0ZEaCiznnqK0RddVHW7nFpSm7FTcvQovuHhHP3pJ8yxsbQYMKDaba+ahd27tblWQQFERWltWfr31/6uZ2TAv/9qN0MvuEC7Dl24UNvBecAAraf7zp3axlwZGVBF4lzAjt9+4+f587l/xgx2btnCw6+9xuA+fbjjscd459lneeTddwny9+ezL74gKjqazp07a/O+kzhR7EiC0BOcTmz791PodJLv40PiwYPk79pFrsOBV3g4ibt389Hnn+O02XA6HIT6+nLX+PF0atmSrJIS0i0WosLDMXp5kWA00qZVKwLCw7WND+LjtWArLNR6N8XGujYraN9euzjKyYH167U/8gEBEBFBbmEhC7/4gqvvvVe7w9Sqlbbzqd3O6r17GXSKSXdSUhIbNmzgnSeeYNiAATx23XVak01/f9Q//mDIfffx0FNP8cCjj/LJJ58wYMAAfv75Zy655BImT55M3759uffee8nKyqJv377Me/NNfly+nGeGDkVt1w6nxYK/+7JfVdXOz8+v6gFVVcFu59KxY/nzn3/4ZO5cLujbl4D27Sv2pTlwQDtGTIz2h2jPHmjZUmvSGxQEZjP/Ll2KMyyMLnY7Xj4+WkIoK4tsi4UHPv8cNTeXH1avplv79hxKTaVf+/ZkFRez4+BBBvTvz9Czz2bfv/8yatw4/P39MZlMtG3btkK5b0NRbxecBQUUGwz8vXw5P/74IzlZWSQfOUKIycTQ3r2xhoSwZs0a1m3YQOe2bekQE8OFffrgYzCQYbeTkpyMj9lMn86d6d+mDWGjRmEoS8KVs9u1/+M63Amx8t/Lyhe6+fn5FBYWEhsdzbSLLuKexx+nS3Y2jBnD+IsvZkL37kwYPpyAceOYNGkSF154IU6nkw8//JBVq1YxZswYzjrrLB5++GEA5s6dy4rly/nktddg3z6s/fpVuXRKVVWsVmv9JbBV1bWLs7viYu1vl8WCIzmZvKQkwjp1AlWlIDOTX5Yt49u//mLR5s2oQL8uXYjy88Ps5cXhzEwysrMJbdGCURdfTKfOnbHn5mIpLSU8IgLV6SSqbVvat2tHvIcvnhtDkgMo30G7uLgYb29vjKqKajRyLDGRxAMHOJSaSmlKCrc//jiBfn50jI8nPiwMxWSioLiYTXv3kpGXR4CfH+f1749f2e9PWlERneLiKLRasTqd9B88mAH9+zPk3HPrZrfEzExtLFu+HM4/n6suuohvFi9m4rnnct6VV7Jy5crySVl0dDTh4eFMmzaNPXv28M477wDwwIwZ+Hh58cRNN2k9EWNjT7g73O7du+nUqVONvrfDhw+TkJCg9QEtLQWnE9Vm4+CxY6xbuZLRl1yiJT4BW0EBe5YtI8xoxBgQwMLly1GKirDZbIy4/HJ+/+ILSktL2ZeSwuYDB9h99Cg2ux1fHx98fXw4lpHBgP79adeuHROvuILzzjuvQnJq//79ZGZm4u3tTffu3eulB299x47VauWbr78mOyeHtKQk5n31FX3atiUqMpJVO3Zgt9mwFBXx7DPP0LddOyIVhSCDAWvbthiKivBKSND6UkZGNt5ddnNytMnYsGHaJKxdO9LT04ks6xGdk5MDQFhYGE899RRXjRzJ2EmTmDZjBi+88AKXDRzIuy+8oB2nY0eIiKjP76ZmiopQMzNRCgu1SkyHA1tuLnnp6Xzy00+s3bePHYcPczQri/69epF05AiHU1MZ3K8fjtJS2nbsyISrrmLUqFH41dHvQb1cr6kqZGZSWFLCtFtv5XB6Ov/u3UuP1q1x2O3kFhdTUlJCRn4+3Vu35sYrr+TyG24gpLQUc3w8pSUlfP7665wzbBgZPj4MHDYMg6LgtNsx1NHfn3Xr1jFmzBjS09PPfHzatQtMJkqjomjVujWB/v4YvL2Jb92a++67j549e/Lyyy8zZ84cRowYQWJiIrs2bQJfX5568klKtm/n2bff1pZLnnfeCftmlpSUkJSUVLFlQEOVn6/9nvj5QWEhloMHef/DD5l42WU4zGZaDhwImZk4nU7WLFvGDQ8/zNGsLK6YOJEgk4l/Vq+mbatWdOraFX+TiQFDhnDeuHEevdnVUJLrzdq+fVrir6AA4uK0BGHv3lrhUUqK1oMwIwPGjNFu9vz5p3bzafBgba6+d6+W18jN1W5uiVMrLNTmXSYT1pIS1v72Gwe3bOHd77+n1Olk1qOPMv7qq096iBPFjiwxPpm8PG0LeoOBIykpWIqK2LZmDdkHDpCbloZvXBwHDh1i3aZNbNu/H4Oi4GU00ik+nkBfX6JDQ7F7eeHncPDdPfcQ3LYtDrOZhB498Pbx0S5anE4tMAICtIRHu3bHDygOh3YnNzDQtYOpu9BQGD5cSyiGhoLBQAhw9dNPV9w1qWz50lmncbEXHx9PfHw8F0VFYQoP1+4m2e1gs6GYzdw6bhyPzJpFeno6fctK6EeMGMHTTz9N7969mTVrFhs2bOCbb77h3hkzONtgYOhzz8GSJdp5VN5ZVVG0cz8RRQEvL+Z+/jl2u/3EW6+3a+d632zWkppQXlYP0H38eO0dux1SU8uTrWGFhcz19YXu3XkftOSG1ar9ATt8mNRdu1izcyd/rF9Pn7g4Fn7yCTarFRuw8+BBcouKGNSvH5aSEnIzMigpLUUF4iIi6Na5M7c9/DCdKjffbqQO79nDxq1b+eaLLzD7+KDabDicThxFRTgcDpwWC39u3UqXuDiuHj2a6E6diB05kkyDgZUbN+J36BDXDhnCT+++i0/nzq4DZ2ZqvwdOp2tXYJut6omayVTnu4Kd6uIzKCioPAkx5847oXVr7eLKbqdT27bc9NprfPD77yy58EK2bt3K/fffT79+/fjll18AOP/881m3bh3p6elERESwfeNGegYFlVefnKivkqIo9Vvdqm/6UJmfX/n/nTEhgTC3u9WBwFX9+3PVww+jJidjtVjwNhi0O4gmExw5gsPbmwXvv8+2fftYtGEDqt2Ov48P+cXFqEBaTg67Dh8mKiKCc4YMISIqitCQEJKOHqUkLw+zjw+t27alXfv29D/nHKLKesU1aKqKChQUFFBSUsKG9evJzs6me3Q0xw4fJt1oJDMzk6StW9m5axc7kpLIys/H4XQS6OODxW4n0MeHttHRxEdEsC8tjUULFtC+c2f2rFtHSnEx6tGj+AUG8vbFFxMTH4+5oVVGR0Rof5/LluM/d/vtPHfXXYy76y7WP/98hSVXMTExREdH07t3b76ZOxfsdqxOJ38tXcoLd95ZHjsnSg4CdHb/G1RNrdwrcctiQAHaBgfTtlOnCs/1Cgx0jT/AjSNGVPh8xyFDtO+7bCf0jJ078fHywlpQQGhICI60NBavWMHRjAzefOYZrrvmGgL8/VFVFZvNhq+XF/ERERSUlpKalcX5I0cyZsIE+vfvT2BAAIGBgQQEBjb4KsSTKSwsJCcnh327d/Pv9u38MX8+fTt0oGO7dixbuZKvli1jcKdOdG7VCj+zmW+ffZY9hYUkJyZy0/jxqIpCp7PPJqhNmwrHrRABJ6k0ahSMRi129J63Xl5Eul1f6RWKISEhxMbG0i4mhl3vvANjx9K7d298t21zfX0Du+l52vz9USq1jPECIlSVe885R7u2KyoiY/161u7dS0KPHsSFhLBx1Sq8o6JY988/vDFrFrdPm0ZCVBTxZdf1NuDwoUMM6NmTC8aPp89ZZxHWokWFa4PE/fsJj4gguIG0DHA6neTn55OVns6yv//Gy2Ti23nz2L97N13i4zmcns7+lBSuOOccnpo6lX6dOhHcubM2FrVsCRYLB9etI6ZtW3wr9Rv29vfnpqefBsDt6rvOkoMAAwYMYMWKFZ65eVXWv9PbaGTJyy/z6V9/8fb333MsI4OePXsSGxtLeHg4RUVFjBw5kq2bNrHu229pe/HF7N29m3NjYrS/4U7nSZdK+vr6No7kIGjFFbrQUHxCQ7nTbek0AC1bYgCGXHstewcPJi8pidc//BDMZt6dNYsDiYkc2rOHAquVh++/n+tvvpm4li25+ppr6N6zJ79+8w2+RiM33HEH7bt3Z9++fTW+aSfqicGg/V11OrUxSO9JCNrj+hzB4XDlAPSN52SJcc24VQeafX0ZdvnlDLv0Uq676y4tJ3QGN/eafYLQWlLCD3PmUAgcSUzUKoRycigtLaXYYuGn1avJLy7Gx2zGx2ymb4cOhIaFEdGmDcXbt9MqIYHL//tf+o4bh0FRKM7KooW3t7bm3mjUMuJdumiDzokmYWXNmk/IaNTuZp+Mj8/xE3QPJE989DLf9eu1wC2bvFw9ahSPf/UVo0ePLp9sBAUF8dBDD+FwOLh92jSSDh0iOzubEF9fWLxY+4NgMGjVjDXk0ZJ1k0mr0NQFBbn6R1QWHk5M9+5cZjJxmcWiTeAyMrRkZ0YGeHlx9MAB1q9ZQ6C3N0FduuAdFISzqIhj6emsWrKEs886iwB/f1pFRZGZk4Pd6cRsNFJsszGid296de/OxGuuoWW3btofSFXVfmZGY50nwk5l5t13k5mezhWjR2NWFAx+fhgNBkzh4RgMBkq9vfnfoEG0aN/etQOh3hwatLtHPj7HDwT6HzP3HQsb686FI0Zof7x37gS7nbN69+bQiBHkZGfTrVs3oqKiynekioqKIigoiEGDBvHxxx/Tu3dvunbtSlFuLmOvuOLky30bk8r/l2UTVaVTJ47769iqFUbgskce4bKyHmGEhVVYHktWFg4/P9YuWsS6tWvJTk9n78GDxEZE0KpNGywlJezdvJmFP/7I2mnTaNuqFd26dWPWU0/R1gM9OmoqKyODndu24RMQQEF+Pt6lpRzcsoXQiAiWL1nCj+vWcTQ9HT8fH7rGxxMbGsrTBw+SEBNDTEAA4UFBxLVpwwWTJ9M1Npb48HCcycnktmiBT2EhAbGxrlhy+5m3bEyJD7ckR+sWLSA0lIevvZarnniiQp/BmJgY4uPj6dmlC9sSE5k2bRo//forRQUFDO7SpXElOcpuhAHg60uLfv0qfNrQrRtjRo4Eq5Vpqak4DAbS9u/HYLFg9PYmvF8/DL6+kJtLytGjLPz8c36YO5dnHnmEgqIiCoqLKbFaaR8XR6+ePenVpYvWRN9gAG9vuvboQW5uLu3btz9pQrU+7N2+nWunTOHfffsI9vcnPiKCXq1bc+UFF/Dv0aP88s8/dOrYkcMPPED4wIFaFUNwMJjN9D/14ZsWPXbsdtdErdIO4ACRkZHExsZqf1sB7HbOPfdc7eboaSQ5GiVF0a5F/P0hNJQWcXG4dwUdXXbNO/yaa7gvJ4etCxeSrygcOXSIYocDU2kp8SNGMG/hQp585BE27tuHqihcOW4cBxMT2X3gAHaHAz8fH2JiYxk0eDDXTp3KgAED6i3Z8d7zz/PQc88R7OvLoE6dsNrtTBw6lB6PPML+I0do06oV7cLCCE9IqJgMQrvZga8vbSvdzGhIFEWhU6WbMTVmNGrxYLPRrX17XujZkxW7drHn4MHywgS9pUxYWBiXnXceZ0+din9AAL4+Pjz+xBOu2Gks444nKQp07Ehwx448PnRo+bx3MLj+njgcHFi7lqR//+W1jz/m/dmzuX7cOHItFoboFahAx3btePKFF4iPjz+jm3iijrhtUoLBoP1fuycIzWbt+sZmc33snhjU3za1MaeuGQxaYdMZ5ksaVtahjj1666288MEHDOvdm/iQEBISEvAyGiEujgAfH2L9/PjpgQeIb9eOiOBglNPIxvpXvmPYv4lcmppMrqD28sJLUfho9mwCqmhIazQaeeq223Ci3aGmoED7hNXauANfr97Sq9n0ZGJZwrNlbCwty5bwVHbh5MnMev55Erdv50hmJi0SEjApCjanEy+LhUXLl7Nt61YeHzwYh9NJgI8PIf7+2BwOiiwWQgMDMRoMnDt0KAHBwXj5+nLxmDEMKutVV9e+XLTo9C+Aqqp6aw47oukX2mWxc/no0Vw+YADWQ4f4prCQCy64oHwzkqioKOLi4ujcuTNbtmyhbdu2+Pr6smTJEnrOmNE0J2rVcaJkRUQERmDIZZcx5LLLTvz1RUUU7NnDru3b2bp9O4H19LO0WiyMOecc1m3fTue4OGxOJ8F+flhsNuJatyYrPZ2zBg9m3jPP0O/ii1H01giBgRX7BHl7H/f7YGjdmka4EPDEFMXV9NpuB29vJg4bxpYHHqBnz55aVXtyMtHR0cTHxxPq48P/XXABqp8f1157LVtXrMBHv2BtahM1s7k8gR5b1Q7ikZHERkZyc69e3JyRoSVJw8O1ZSjFxexevZqtq1axdd8+3lq2TEsCOJ1sO3iQ8MBAEo8dIzQwkLDAQFrFxREeHU3XXr1IaNOGKydPrpdWGrExMbxYJk9EAAEAAElEQVT4wAMMHDAA3+BgLQZ8fKqMBaB594gqq4Iq34jFYHBN1HR2O7f+3/9pK0BSU8sfK5/kNeckB2i/U2Fh9Lrmmio/PerOOwFw5OaSumEDPy5ezKSRI+k1ahQRLVuyaflyLIcOsXzlSqZMnEiH9u1ZuHRpXX4H5W6/915uv/127drVYND+j8tipt8pvrbZ0WNHX+1gNNK/Z0+8AwPLE7x6gjA0NJTpkyYxtEMHRvzf/+FntRK0dasWO43pxlRtqVwUo/88TCbanX027c4+m3OmTtV+XmVzgkcffpgMi4W4wEDeefllHr/nHq65/HI6P/lkHZ+8qDZ9nKmqgtBm0/6PTSZXeyhfX1diUE8q2mwSNw1Es04Qznz5ZR5+/fXT39GsOSQ1TkQPan35k6py7tlnu5bupqdr2eqyAfTa885zDQ5lO8uWVxA2U4bwcNqfcw5V1S11Kbs7O9fppCA7m+zMTIpsNrzLek/lHDtGUW4uKxYtorS4GHt2Ns6iorr9Biprxv+X1eLlVb48Hx8fzEYj10yZ4qoSTUsjskUL4uLiCA4OJrZFC84fPpwZDz2ENS+PqMDA5j1R8wR/fwL79mVg374MrMfTMPv4MPPuuznrvPPwCwnRKhXM5hMvl6/cdxOaV+LDfbLm7Y3JYOD555/XPpeXB4cPc9/ttxMUHQ0lJbw8dSoMGYIaFkbpypXa1zXn5LrBoDULd2P296fnqFH0HDWKa90/Ubb7IIqC3enk6MGD5GRkcHDzZrLT09m+fj2bFy9m0uTJdfot6AIiIhgxZUq9vHajYzS6rteqShCmpcGmTdxz1VXa6pRDh7TH7XbXONPYlxjXEWNICHGjRnHXqFEVHh98wQUAnHPzzTyanExaenp9nJ7G2/vEK5hERe5znbLYmXDBBfTWr7d37CC0bDwJDQ2lvY8P7YcP1zZbyMjQniNJjtPn5VVhlYNvmzboHaXvfPll7szPr7B5kWjA9Ep1h0N7333csVq1m916BWFpqZZTyc3VPl92E5iSkvJ2aKJ+NesEof8pdnYRbvQkR1kFoXt/KAA2bIC+fbVBErQqF32A1HsNNPME4ekwGAwER0QQXKlSNa6sSmRwpV02RSPgfsfMx8d1V81o1BrMrl/PyIQEWtx0EwD927Vj3Lnn0r59exbNnau1KXCfuIlG7Tz3hsEyaTs590SHvqO9rrQUjEa6+vtrDbEPHtQet9lQFAUfg6F80xCJndOg3/FHuzBs1akTrTp1ovfQofV7XqL63JcYm0wVKzkAjh7V3los2tvSUi2J7j7ONPcKQk8xGlFatSL6DFrriDrkPuaUVRAO79+f4W3bap9PTSXs2DGgbIVU2eaPgOutJAg9Q9+hXDQO+oqPUy0xttu1McffX+s7D65rvKKiU7dUE3WiWScIRTXoS4wNBi3A9UakoL3vcGg7FOkJwpIS110hqSAUzZmeILTZtDuh+uTNaNR6Pfn40MXLiy7jx4PNxo8zZ6J07659rZ5cLy2V2BHNT6UKwgpJDotF6zGnJzlKSrS37uONLPUSzZG+PN9qrbqCsKhIq9LQY6W0VFt+qvd/0pOF+kRPiObC/XqtcnJdVaG0tMISY44dcxVLuI89zbVqXTRfeqycaJMSfbWMXkEYGqrFTlqaK2GYm1t1WypR52TkF6fHfdD08nLdKQBtYubnpwW5qrruvunJDUkQiubMPXbM5oqxU1xccVfvggKtz4171a3+VmJHNDcmk+tmlNlcsWq9tFRbsqJvsFBSoj3HfbzRlxhL7IjmRt9s4UQJwtDQijeg/P0rtMIo32lSJmuiOXHv36nPdfTYKSkBb29Cy1b4hAQFuSpt9b6dJpN206qBbSwoRK2rXEHoHjv6/EdfYmyxaEuMFQW2bNFWU+kramTMaRDkqlmcHj3r79a4t8KgGRjomszp1YPuEzWQKijRPFXa4KdC7LgnCFVV29BHUVxJDz2GSkrkglM0P0aja7JVOclhsWgJQn18KSnRPnZf5qUnOcqWzgrRbLgnKtxvSunxEhCgva/Hh7e36+auj4+rTYzEjmhO9CXGegVh5WIIX1+Cw8K48NxzCTSbKy6b1JPrRUXSPkQ0P5UrCN2XGOub67kvMfb21p6nj0mSIGxQJFsjTs+pKgh9fbXgLi3V/hAEBrqqN/TEiCQ5RHPkfvFYuYKwpESbqOnxVVhYsaJQj53Cwua1OYUQoMWFfsPJPW7AVUFYOUFos7mWSuqx05w3GBPN04kqCIuKtGpBs1mLHb2Sw2TSNv5JSXFVEEqSQzQ3lSsIK9/Q9fPD4OPDbx9+iGKxaLGi3wTWd+PVHxeiOalcQajHjn7TVt+MT68g1Huy+/tD69aumJEEYYMgCUJxeionCPU+aqANmr6+rrvOJSVawAOsWgWpqdoS5IICmaiJ5qeq2HG/4PT1dU3W9KVf7tW3fn7a43LBKZobvQrKy8vVV83p1KptrVbtRpS+EYnVqiXbbTat8bXe76akRGJHND+nkyC02VzxYTLBkSOuBCFIglA0P5UrCKua6+jXa2UJQ7y8tFjTN6IDiR3R/LjHivsSY71aEFx7GOgFEyaTNh716OHat0AShA2CJAjF6dGroKq6q6b3INQrCN0vQHNzXUkOvfxeiOak8hLjyj0I/fy0WNFjJyREe3/LFlfzeJDYEc2PvsRYv3B0v+DULy4VxdW/xmzWeuGuX+9a+qUocsEpmh/3BKH79VphoXZ9pk/U9BUgeuWUj4/2z2CQJIdoftw3xjpRv3U9uV5c7IqljRu1jRr16zS5XhPNjcFQcQdv/XrNvaLWy0ub55jN2rWZ0eia4+jtLOR6rUGQBKE4PXoV1ImSHO4VhEVFrrtq+h8KvXJQKghFc2MyaRM1fTDUJ2s2m1YJZTZrEzF9shYSosVQUpL2eT1m5IJTNDeVG77r4477BafZrC2N9PNz9cE1Gl0JRB8f2VFSND8n6kFYeYmxe4IQYMAAaN/eFTtCNCd6pXpVyXX32HEvhtBjLTJSWwECklwXzY/RqM1r9ESf+47FejyYTBVbJlVOEOqtYUS9kwShOD16grByBaGqasEeGOiqIHS/qxYaCnFxWm8okAShaH70/pv6oKeX4RcWaksiQbvgzM/XPufrq8VVeDi0aeOauMldNdHcuPcgBNe4U1BQMXZyc7W40Z/XuTP07Kl9LEkO0Rydaomxl5c2DhUXu5YYg3YtB9rHkuQQzZF75bp77OjXbO4tYfRYMhph4EAIDtaeK+OOaG4MBm3uohcGBQRo12qVKwj1DUtAiyX9Wk6/sSsaBEkQitNzomWSxcWuSo2qlhgHBUGfPrJMUjRfJpMWJ+7LJPUEod6rU09y+Plpn/fygqgo6NZNe9/bW3YAF82PnuRwjx27XUum6zedvL21CkL3BGFIiKuiUMYc0RzpN6L0xvAOh5boKCpyTci8vLRY0isI/fxc1R/6zsZCNDf6UkmTSbs2s1hcmy7qrSxstoqrpYKDteonPdEusSOaG33siIrS3gYFaeOLewVhSIh287ZdO+3jfv1cz5ebUg2KbCkrTo/RqN0Z0JdJ+vpCerp2d0C/4+zjo/0x0EuE/fy0PwbgmrhJBaFobvTJmX5nzMdHSxja7RWroHJyoEUL18f6UhVJcojmSp9s6eNIcLAWJ/n5rgtMs1nblCQhwfV8PXmoLxETornRb9CGhWkxEBgIx45VXMJlNlfc5btVK9fXy2RNNFcWixYvPj5arBQUaDeh3K/XiotdPTu9vbU4A9d4I9dsorkxGGDoUNf1mq+vNs8pLHQlAY3GiuOMe+FDUBAMHlxnpytOrtElCBVFuRB4AzAC76uq+nw9n1Lz0aIFxMS43t+yRZuw6QlCfbdVfXLWrZvra/WJmvQWEM1NQIB2l0yPk+ho2LFDi5fYWO0xX1+tOle/yBw0qGJ1oSTWRXMUHa3Fjx4XLVpoyUD3CsKoKK2aIzRUm6j16+dKFAYHu94Xojnp3NnVTw202ElMdCU5QGthoaqusaZ9e9fn2rVz3aQSojkZOtR1vWY0amPN0aMVr8msVq19EkDbtq6vVRQtASLJddEcuY8ZiqLFUWYmtG59el8vcdNgNKorZ0VRjMDbwGggGVivKMoCVVV31u+ZNRODBrneN5m0HmkHD0Lv3tpjvr5wzjmuZtjufH21iZsQzY2iuBKBoCU7Skq0O9AdO2qPxcZqiQ797rN+IQra4xERdXe+QjQUZrMrOQhakmP7du0iUq/QiI2tGF/u77vfqRaiOalcORsZCQcOuK7X4OSTNv1msBDNTeXEeFiYNtfp3Fn72GTS3tcTg5Xbv/TsWfvnKERjEBWlxY/MYRqdRpUgBAYC+1VVTQRQFOUr4BJAEoT1oUsXrbeAe+C73512pyiuEmMhmjNFgb59tUS6XgUFJ14K6d7XRojmzNdXS3DoS/GFEKcnLAwuuEDGEiGqKyFBq4TSKwYBOnSov/MRorGQOGm0GlvX+5ZAktvHyWWPlVMUZZqiKBsURdmwcePGOj25ZicwUO4KNCESO3WoRQtt+aRoEiR26lDLlrLTXRMisVOHJDnYZEjc1KGAAIiP127WikZPYkeIU2tsCcJTUlV1jqqq/VVV7d9PlrQKcdokdoSoGYkdIWpGYkeI6pO4EaJmJHaEOLXGliA8CsS7fRxX9pgQQgghhBBCCCGEEKIGGluCcD3QQVGUNoqimIHJwIJ6PichhBBCCCGEEEIIIRqtRtWQRFVVu6IodwKLACPwoaqqO+r5tIQQQgghhBBCCCGEaLQUVVXr+xxqjaIo76NtZCJEcxWnqurN1f0iiR3RzNUobkBiRzR7EjtC1IxcrwlRMxI7QtRMlbHTpBOEQgghhBBCCCGEEEKIk2tsPQiFEEIIIYQQQgghhBAeJAlCIYQQQgghhBBCCCGaMUkQCiGEEEIIIYQQQgjRjEmCUAghhBBCCCGEEEKIZkwShEIIIYQQQgghhBBCNGOSIBRCCCGEEEIIIYQQohmTBKEQQgghhBBCCCGEEM2YJAiFEEIIIYQQQgghhGjGJEEohBBCCCGEEEIIIUQzJglCIYQQQgghhBBCCCGaMUkQCiGEEEIIIYQQQgjRjEmCUAghhBBCCCGEEEKIZkwShEIIIYQQQgghhBBCNGOSIBRCCCGEEEIIIYQQohmTBKEQQgghhBBCCCGEEM2YJAiFEEIIIYQQQgghhGjGTPV9ArXp5ptvVuPi4ur7NISoN0888cQHqqreXN2vk9gRzVlN4wYkdkTzJrEjRM3I9ZoQNSOxI0TNnCh2mnSCMC4ujlmzZtX3aYgaSEpK4rrrriMtLQ1FUZg2bRrTp08H4K233uLtt9/GaDQyZswYXnzxxXo+24briSeeSK7J10nsiOaspnEDEjuieZPYEaJm5HpNiJqR2BGiZk4UO006QSgaL5PJxCuvvELfvn0pKCigX79+jB49mrS0NH766Se2bt2Kt7c36enp9X2qQgghhBBCCCGEEI2aJAhFgxQTE0NMTAwAgYGBdOnShaNHjzJ37lxmzpyJt7c3AJGRkfV5mkIIIYQQQgghhBCNnmxSIhq8Q4cOsXnzZgYNGsTevXv5559/GDRoECNGjGD9+vX1fXpCCCGEEEIIIYQQjZpUEIoGrbCwkAkTJvD6668TFBSE3W4nOzubNWvWsH79eq688koSExNRFKXC182ZM4c5c+YA4OPjw6hRo+rj9OtUcnIy77//fn2fhmjkmlvsSNwIT5HYEaL6mlvcgMROvThyBGw2aNeuvs/EY6oTO0V5Do7ut9Chr99xc6bGRGJHeIKMOycnCULRYNlsNiZMmMCUKVO4/PLLAa2h7OWXX46iKAwcOBCDwUBmZiYtWrSo8LXTpk1j2rRpAMyaNatZNKFtDt+jqH3NLXaa+vcnTmHzZmjbFoKDz/hQEjtCVF9zixuQ2KkXRUVgt9f3WXhUdWLnmcmJFKWWctf/tSMi1gyApdjJih9yGHFlKF7mxrGoUGJHeIKMOyfXOP4aiGZHVVVuuukmunTpwowZM8ofv/TSS1m2bBkAe/fuxWq1EhERUV+nKYQQojErLISSkvo+CwAO7yzh2KFSjx9XVVUykq0eP65o5goK6vsM6o3Drtb3KYjqstmaXIKwOkpLnABYipzlj336eAo/vJ7OvysK6+QcLMVO0o/IWCREQ9cgEoSKosQrirJMUZSdiqLsUBRlehXPOUdRlDxFUbaU/XusPs5V1I2VK1fy2WefsXTpUnr37k3v3r1ZuHAhU6dOJTExke7duzN58mQ++eSTRl0qL0RTU1zgqO9TEI1BTk59n4HGbtcmjvXM6VR54bpDPDkx0ePH3rGqiMcvPcD63/M8fmzRjC1f3iwTLjtXF3LX4N0k77PU96kId1lZcPDgiT9vtzfL31edwajNlQpzXNdoO1ZqicH8TM//XGxWJx88dLRCQnDOfcnMuvwATqck2IVoyBrKEmM7cK+qqpsURQkENiqK8oeqqjsrPe8fVVXH1sP5iTo2dOhQVLXqAeTzzz+v47MRQpyO9CNWZl1+gGsei2HI+JD6Ph3RUNlssHIljG0Aw3kDSRAm7a69ZENGkjZBW/xJFgMuPPOl1EJgt4PTqb01NZSphOf98WkW899K5801nTGWJVi2LNMqJ3euKiKug099np5wV1CgJQnbtKn68zab9jvbTBlNZQnCXC1B6HCo5ZWw2cc8nyA8vNPCxsX5ZCRZmfmZ9n+ye10RAAXZDoIjmu7fDSEauwZRQaiqaqqqqpvK3i8AdgEt6/eshBBCVEf2MS3RsvzbBlIdJhommw1UtWFUczgcDSJBeHinK0Ho6eqKgmzt51xS0Hwnx8LDrGVVQY66rRjPz7Lz7j1JHN5ZN20BfnwzHVWFlP2upf92qxafaYc93w5A1NyiZct47oMPTvyEZrzE2FLsLK8SLMzR3uak2crzpfq1mycV52t/G1ITj4+TnasLub3/LhK3FXv8dYUQZ65BJAjdKYrSGugDrK3i02cpirJVUZTfFEXpVrdnJoQQ4mSsFu1qM1P6nYmT0SdpDWGy1kCWnelVHaAlQTwpJ007XkG2/YSV+UJUi55Ur+ME4YZF+Wz/p5B3pidVfHxxPnkeXibpnqhP3OpKZBw7qCU8ju6TBGFDsu/gQTbv2XPiJzSQv/Wn5HRqvXE9pCjfoW1QUpawKyhbYpyd6koKur/vKXo82kqPH3P++U67ibzpz+bbx1SIhqxBJQgVRQkAvgf+o6pqfqVPbwJaqaraC3gLmH+CY0xTFGWDoigbNm7cWKvnK0RTIrEjzpReoVRcS5VKyXst2KwNrwpKYqea9ORCfVfuOZ1aJWN9nwdQlOuauGalePZ8ctO149msaq3FZk1J7DRS9VRBqG+mUJjrwFKs/S4nbivmw4eO8t2raR59LfekffJeLRmoqirHDmrfe15G/SWbJG6OV1xcTEFR0Qk/by0uprS4EVSsZWXBtm0eO9xDF+6rMKbolYSZR7XHWnX1oSjPs3HssKusmp9b/rGl2MnC9zPLP85I0l7bZK77HvISO0KcWoNJECqK4oWWHPxCVdUfKn9eVdV8VVULy95fCHgpinLc9rWqqs5RVbW/qqr9+/XrV+vnLURTIbEjqsvhUFn6ZXZ50k6/Qw2uHfM8pSjfwbNXH+SZySdpQl5PJHaqqaEkCPVqkjo+j11ri1jwTnqFaj73ZIT7LpOekJvuSmTURjP6MyGx00jpMXOiiiynEzZs8OhLqqrK4Z0lhLQwlS371ZblL/k8G4DSYs/Gjb6UGLTlmInbiln8SRaWYid+gQYKsu04HPVTkStxc7yiUyQIH/rgA5787LM6PKMastk8MiapqorTqWKzVvwdXfNzLplHrWQmWzEYICLO7PFduTcvLajQNiM/y84v72WUf6xfKy7/Joc7B+5i5fzcOtu4RGJHiFNrEAlCRduG9gNgl6qqr57gOdFlz0NRlIFo555Vd2cphBDC3frf8vjulTT++ET7U1zitoOxp5es6ImN9CPW8soRu00lQ5YzNz6nSi7UlXpKEH79wjF+/zCrwvKqojwHRmPZadk8O1EqLXESGW8GXMu+9m4s4pGx+3l56iFZdiyq71QVhBYLpKZ6NLayUm2UFDoZPE7baGfHyiLys+1s/UuLI71S1lPc4zAnzc7LUw/z02wtydGhnz9Op6u/p6h/xcXFFJ6gQtBmtfLpkiWkZmVpVeMNmYc2zlo4N5M7B+4+7nGHQ+t5m5FsIyzGC29fg8cThEqlwsATtc2wFDtxOuGLp1PZ/o/nllULIc5Mg0gQAmcD1wIjFUXZUvbvYkVRblUU5day50wE/lUUZSvwJjBZlataUUsO7SjhhesOkpvh+YljaYnzuAlgcYHD4/1zhKhNxQUOfv9QSwzqPW2K3SoIPd30Ot9tIpZTduwfXk/j8UsPMOvyAx5fIiNqj2q18tf27fVfQagnN+o4UekfrGUCv3ruWHnfzsJcB8EtvLTT8nCC0GFXCYvVjq3H5VfPHyP7mI3EbSWUFDasZceiEThVD8LSsv58HlzSebRsmW+P4YH0Gx3Eoo8yWfJ5Nk4nxHX0JiPJ5tFktx6HIS1M5WOOrmN/P6B+lxmLiopLSigoKalyp+Klf/xBdkEBWQUFdb4svto81Cvx1zmZFT7u0NeP+z5sBWg722cmW4mIM2PyUjw+5ujVgOdNCQO0BKGPvwHfAAMPfNqa6Nbm475GenqK+mK3qcy+6wgfPHSUf1cWcmS3q/rVUuzk+9fSKMp3sHZhHkl7LCc5UtPRIBKEqqquUFVVUVW1p6qqvcv+LVRV9T1VVd8re85sVVW7qaraS1XVwaqqrqrv8xZNw78rC5k/O73CY5v+zOfwTgsL3s44wVfV3D3D9vDWHUcqPPbWHUd48MJ9Hq8cEaK2LHgng/QjWhWJ/ntblO8sr4LydAVhQbbroj6nrFJk/e9aq9r0I1b2b2kEvYUEAEeOHGHkI4/gKK3nCYHdzq3vvcf67dvr9GXzMuz4+BkoyndwtGx31KJcB8EtTNppeTpBaFOJamXG7KNwdK8Fh0Ot0JPKfQmyEKflFBWEzpISPli82KMJwpQDWqzEtPXm6keiCYow8cen2k2qjv39KS1xenR5vh6HEXHm8qp1XevuvgDkSoKwwShPEFaRXNu/dy+dExLIKiys/8r1U1izcSPjHnusxpWOqqqy9e+Km39c9VA098xpRduefoREmjh2yEpGkpUWcV4YvZRaGXMA+o4OArS+upYiJ6OvC6dVV18e+64dAy4MqvA1W5cVyBxI1IusFCs7VxexcXE+70xP4vlrDpYnubf+VcCSL7KZ90wqnzyWwnNTDmItrfubqpZiJ/PfSq9QjauqKlaLk1ULcvn7m2yPnleDSBAKUZespc4K1UjvTE9i8cdZFe4K6BeZnp446X3Z9m1yXTRvXppf3qtDXyqzdmEeX71wzKOvLYQnuVfsrfwxl6Q9FkoKHES38QY8v/SqMMd1vNw0O6mJpVjd+hxKBWHjseXff1FVldyseu4SYrezdu9evv3rrzp7SadTJTfdRvdhAQD89n4mmSlW8rPshNRSgtBuU/HyVojr6MP+zSV8+1IatlKVEVeGArD21zxZZiyqx2YDk+mEyZbUpCRunj2b4szMKj9fE6mJpYTHeuHjZ8DX30ibHlqSzuSl0CJOq5Ct3G/tTOhxqL+Ou/Cyilz3ykJPL9MU1VNUXKwlCKuoTM9IS6NzQgLZBQUNIkG4a9cuXn7hBSgpOe5zR1NSSMrMrFGlY16mnU1/FvC/e5MrPB4Yaip/PzLBzJal+RQXOGnV1RdTLSQI7W7Vt0YjJJVVZAVFuM6jVVdXXEW09CJpj4W7z9rN23cfKe9rrapqrazkEsJdTtrxfxN2ry3iuSkH+amsgGjzElfSPfVA3d/c/uLpVBZ/ksXCuZks+jgTq8XJO/9J5j9D9/D5k6l8/WIa79ydROZRz7RdkgShaHaeuiKRBy/YB2g9bXTPTTnIj2+ms/D9zPLqJ0uRZ5MOxw66/qjsWqs1U557/9Hyx1ITtc9/8lgKy7/NISdNBsaGKi0tDVt9L5GsIwe3l1BSKRaKcit+/NyUg2z/p5DAMO2C0NMXnBUqCNNsfPtyGj7+Bu79QFsy457Mt5Y6+ezJFDYvzT/uOA1xF+TmZuvOnQBkZni+QrtaHA6yCwv5bf36OnvJBW9n4HRC+z7aEsV/VxTy2PgDAAybqCXsPL7E2KZi8lKI7+xD0h4Ly7/LIaSFiSGXhADwx6dZxy1HEw1USQkcOFDfZ6FVEPr4nDCJcSxZS1Ak7t1bo8OrqspnT6Yw/6308uVexw6Wlt+AAojv5ANAQIgRL29tOuPJ2NGP1WWwP10G+5c/ftNzLQkMNWL2Ucp3gs1MsTLz/H38/mGmJArrSbHFgsVqxW45fglgRkYGnVq31pYYN4AE4datW/n4gw+givjIy8sjp7Cw2i04Ni7O58EL9/HBg0fxMitc9WB0+ecCw4zl78e09cZqKfvdPssfo6k22lpob01mhbAYLw5u0xKhQeGuBGHbXlqCsENfP578qT3hMVrSfceqItb8ksfWvwu4Y8BuHrpoPwU59f9/Jpquo/ssJHTx4aKbI7jmsRgAZt+VRNIeS5WFQtnHbKQcKK2z+UROmo3Nf2rzmeXf5fDT7AyemHCAHSsLSejsw/VPxjLw4mD2bizmsUsO8N6MJJ6ZnFi+2qomJEEompXDu0rISrGhqlq5rnvCDrSJ0i/vZXB4h3aBUXlZyZnSl5OBtqzY/ULSy1spvxutL9Pc9OfxCQ7RMNxyyy38/PPP9X0atW7HqkJeuvEQsy47QLHbJiTHDpXS+9xAHv22LRdODS9/PCjciMlswFbq4QRhjp3AMCNB4Sb2bSxm97oizr0qjHa9/AgIMVZoUL/6p1xWL8hj7v1H+XWOKwm1bXkB04fsKU/Ei/qxdc8eADI9WF1UI3Y7OQUFJB47Vp7Q8BRVVdm5ppDHL91Pfra9bIMFB8u+0nZc7XqWPxNnRJX3I2zZwbs84eHJ5LrTqeJwaFVWnQe5khz3fdSamLauZMuWpa6749++ksadA3fx5u0VW2GIOmCzwdatcKLdWPPzISWlbs+pCqrVysA77qC4sOqNBVLLzjExMbFGx0/eW8rqBXks/iSL5685SFG+g2MHrcS2c/3Odjs7gMAwI31GBWH00nZF8OS4o8ehyaww/vYWAPz3o9b0Gx2EomiJj6Xzstm1togfXkunKN/Bgncy+O/IveW7tIo64HTCsWMUl1XjFeTkHPeUzMxMOrVpQ3Z+PmoDuLGbm5vL/sOHcVSRzMzPzye7BgnC/2fvvMObqt44/rkZ3W3SvXdLW/beo2yZIioOEJAtuLeggoufCIIgouDAiQNFAUVQ9pC9N6XQvfdM26T5/XGTNp2stCmQz/P0ob25uTkJOfec8z3f931TYkroONiBzkMVzFzmS6/7HZk035sW3W3xCqnsN/rfLawEHN3kSGUC5eUYtYqwXnCUygScvS0qzBiG/dcvwooBjznx8KuikPni6gBe/MqfgJbWbPkqk+0/ZFW+t6ul/LkynYwkc1E6M8ZHVViOu78FI2a40n2kknb97Ks83ibSnnFvelaIh/EXVLz70BW+fC2xtsvdMlkpZXw7L4mESyp2/pzFhhXpaLVirl092alquo1U8Or3gXQZqsC/uVXFY6d2F5B4uYQ9v2ajUWtvqm/Lrn2KGTN3Dgc25lb8/umz8XXu9Oond8bMZwM1wy6/flOcRD8215N9v+eQFlfKmb0FSGQCGo2WXb9k03O0I5bWZi2/qZGbm0v0yZMwahRI7sz/H41Gy2+LUwHRwffuQ1eY+oEPnkGW5KSp8XvACs9AS0bOdCP6RDFRx4pwcJY1SMhKUZ4GG3spFlYSLh0VQ/S7DBOrWSrdZMScEV+/IEfNuo/SCGhpjYu3nL+/yKD9AAc8gywr+v/uX7N56GWPOl/LTMNyKiqKEF9fkwuEZcXFFKlURPj6kpKQgIePj9Guff5AIcv/igfg1UFRVR57bpU/Lt4W9HvUCZ8wSz6aHod7gGWFyGHMvqMf46RygbBOokDYrr89Th6iW6NdP3uijhWRFF3CX6vS+W99TkW4zYVDhRQXarC2ldZ+cTPGJz8f4uLEKsBdutR8vKzM9MV9gOSUFA6fP090TAyt2rWr9XGA6KtXb/jaWq22ysYOwEv9RKdV276VCzffMCsW/NMMqNxMNWbfqRAI5QL+za1ZfigciaSyPGvKVVGs0OeUHjTRmcykMo7+k0fylRJC2toYrS1m6qGgAE6fpkgntuXn5OBY7ZT09HS8vLywsrQkLzsbhatr47fTgJzsbEpKS4mNjyeoWj/Py8+noLiYsuJi5A4OdVyhJhZWEia85YVUVvkd7TjIgY6Dql7DU+fC1W9I6ccdjVqLxKJa+eGbRD/uyORChTMwtL1NxbgDIJEIjH7GveJvhYsMhYuMEU+48vGsOLJSymjRw46z+wrYvyGHA3/mcvFQIfgapYlmzFTBxbuycM6Et73o9UAxx7bmkZuu5sEX3XHykKPVavl5QQoH/hTXEqd2FaDWRWgYkxPb8znwZ27F64C4qTxoojOfPpuAm58F8RdVdB6qMGi/vMZ1Nn+VyeavMlG4yHjp64AbaoNZIDRzV3B4cy6rXxfFODdfC9LiSyvyAFpYCSzZE8bl48VY20k4sSMfJ085cedVHP47t77L3jCqwnKkMoE5PwXy9gNXOPqPOKl19bHA0UMuJkh9VlxUtu5tx5m9BSyeEsvQqS4o3WX4R9TMhWPGNBQVFRFz7Jjo6FAqTduYtDTRdursfO1zb4BLR4pwTi/lyY992b8xl6P/5LF4SmzF5M/doBKdla0okjq4yJBZCKiNmAsKQF0q5lFzcJHBRfBvboWjuzggegVbcujvPJZMi604/5HXPHDykHPuvwLeGXOF3g84cvW06DDY9Us2zbvZ0qqXfa2vZaZhScnM5J7evcnIyrr2yQ1ITnY2Snt7nBQKcowsVl49XYyLV83j4+d5Etq+UjgIbW/Do3M8aNffoWKSacxwL0ORw8pGwtsbgquEeU39wIfDW3JZPSep1jDjlCulteZgM9NAqFRgbV1ZBbg6TUQgvKxz3F6OiaFVLY8np6TgYG9P9E04c2POqDi1q6Yz0SvYkoCWVrU8Q3T5gXFTSGgM+g5QRRwEuO9pN35fJuanUrrJGDnTlYwEUSDMTCwjpK3RmmKmPsrKQK2mUOcgLMitOW9PT0vD1c8PZ4WCrIwMFM2aNXYrKykuJleXZuPilSsEVXs4N09cF2Snp+Pm7s6OHTvo0KEDDtcQC5t3t60iDtZFQEtr+oxxpP84scKwzGBjSl6zuPBNoR93pHIBSxtxbtjnoeqybe1EdLHlnknOHPgzl4HjnTi7r6BCKEmJKcXWLBCaaQCcDQQ2CysJ4Z1tCe9sW+UcQRBw8pCTGlvpZM1JL8PFy0gdR0dqbNXxX+kq44EX3PEIsOTDXc0QBDFFmuHr6tdDIG5Cf/NmElm6qMTcDDX7/si5oTbcmbYXM2YMyM9WV4iD/R514qVvAqoYvpw85AiCQGh7G3yaWTF8umgxtrGXUFJUbtTk7arCcqxsJXgEWPLKtwF0HS6q/x6BFhW7bHr6PurE1IU+ZCSWsvLFBBY8FsO7D11h46cmzttlBoDCggJiUlKaRD4b0tPFHyNz5WQRYZ1saN7Njodf8cBWIa3iunXzqxycJFJxkmnvJAqExkwWD2LyeZmFBIUuybVfROVCccRMtyrnPrfKH98wK2wVUu6Z7AKIrsHcDDWPzhGdgzt/zkajMeeKamzKy8spKinB38+PDBMXKcnKzMRRoUCpUJDTQPkQOwxyYN66YBbvDmP2mkC6DldWeVwQBHre54itgxSpTrczZg6z6iKHi5cFFpZVp37NOtjiEWjBfc+48eGuZhV5PQE+mhFbUVzLTMNzJSqKXi++SFld1X+bmEAYHRtb6+Mp6el079qV6MQbD8GKOy8KPe9sDOHDnZVCzpyfAhGE2gUQvUBoTHFdP4ZJ63CHDBzvzLg3xJCz/mOdkEjEsGOJBNITKheQ5pyEDUxpKajVFKlUODk4kJ9XMzVPelYWrkFBOCuVZJo6tUViIjmFhVhaWHAxJqbyuFYLFy6Qpwvbz9ZtoL366qvs27fvmpf1DqldPK+OTC7w0MseFeKCfmwoN+JUVv+dl0jgnknOjJ/nWSNssz5GznRj/qZQmnWoKtBY25llCzMNg4XV9X232g90wM3Xgm4jxfW7sYuZAqTFlWLrIBXXMJOcmf93KB4BovNXIhEQBKGGKOkZZEnHwQ689kMgoe1teHK5b8V6yTvEktO782u8Tn2YHYRm7nj++yOHdq4w4glXhujEgvmbQzn4Zy6/L0uj20hlrc+zspVSXg6lKi2W1saxDxcXlFc4rfybWzN+njUPvOCOjb2U3g86EnOmmEtHi7B3ktKsgw2CIPDi6gDeH3uVslItSdElJEWXkJNeRqd7FIR1sqlzwmymYSkqLCSmsLBpCIQajZiHpwEYNFHsM7YKKY/N9eSz5ysdIa4+BgKhbmyVygTkFgJqIyfvVZdqkVsISHTRjobipLOnnI8PhPP+uKv4RVgR3LbS8dT3YSdsHKQc2ZJH34cdadXLngsHCzm2NZ+3749GaGvUZpq5BsXFxVjJ5bh5epKuy0VoKrKzs3FSKlEqFOQaWaz0CLTko21hyC2Finu0T7P6F3CCICCVGTc8X38tST3OEoWLjDfXBlf8HdzGhhe/8ufXxWnEnClm0eMx+DW3IjddjbpUyz2TXTi7r/bcc2Zujf2HDrH35Em+/vtvpt5zT80TyspEIUGtFqsIm4jLiYl4urlxOa72PJXJ6el0GzyYn7766obaeuVUEWl/imktnDxkCILA6z8HoSrU1DvXkVuIA5AxN6YMwyTrottIBS172eHgJKs418FFxqbPM/AItEQmF/jilQR6PeBIm0j7Go4UM7dOcX4+P23dSpFKhZuTE/nVHITlKhWZeXm4+PvjpFCQWUuOwkYlKYkcoE2bNlUdthoNREWRq2t/lk7IzMjIqDjWEDREagt92KUgCNgpZTU2xm6EZz71Q24p4BlsibWtlHnzjNZMM2YAcW7WqpfddZ07YoYrI2a4knxFzJPbEAJhamwpLXraMfHtWsJQ6kAqE5j0nnfF3x4Blry9IZj0+DKs7SXYKaW8N//622AWCM3c8ShcZTw215OOgyvt+Q5OMvqPc6JFDzs8g2q3Butt8SVF5UbLAagq1NTYAbOxFxUPJw85z670ryiMop8MewZa8tG+MPb+nsOP81Nw9pSzf0Mu+zeIE4ZmHWywd5aBcR3OZq5BYUEB6UVFaEtLMblE20AiZfNudoR3rgyHbNnTjhmLfXB0l5NytaTKjlunIQqOb8/HL9yqQUKMy0q12NhLKC4Q+4e+uIMeMXS/erCOeLz7SCXdDTYC7n/enaDWNpz9rwDTetjuPgoKCrCztsbF3Z3zBw+atC1ZOoFQoVSSY2SBsHUfu+vekTZEJjdu31GXXVvkqI2g1ja8/HUA385L4sCfuVUKbF06GidmNBhitGaa0XHy7Fm6d+jAmh07mFpeXjO/rd49WFZmUoEwKjGRwX36cLm2HIOlpSRnZzO2ZUuyCwtvqK3nDxTSyQt6jlZWzIEMCxvURUWYpDEFwuvoO4IgVIiDevo+4sTvS9P4anale3LXL9ns+iWbIZNdrjvU0sz1ce7cOV7++mtK1WrC3d3Jr5a6IjcxEVtraywsLUUHoSkFQrUaCgrIKS4mKDiY7JQUUfAXhIp5XF5BAVKptMJBmJGRQU5OToM1SR+WbNTct0bMy6bPnWumCRMTA56eYHnte3VTpE2k/Q2v85Vu4n0/O9W4jv7M5DIxx3v49TmC60NuIbmu8bM2zF5dM3c8XYcr6TZCWbHDrEciEfAKtqxzV1rv9DNmoRJVYXmF8FgXVjYSrKqdIwgCvUY78snhcN7ZGML8v0PoMkxREcpyPXlHzBiXouJiyrVaMtLSTN0Ucee5AcLOAltZV+kfEolA6972+IZZ0ekeRZVz2/a15+OD4bj5WTRIkRJ1aTkyuUC/R5ywspHQvNv17fbVhqObnH6POvHUcj8jttDM9VCQk4OdlRUurq5kNKAr4nrIzsnBUalE6eREjpHzIVYfb64XY/cdfdjYzS7WRjzhSuehCt77K4QVRyKYNN+bh1/14L2/Q43WRjOVnLxwgQfvvZe4jAwxdLIaiYmJfPnPPyYPM45OSmJwv361hxCrVKTk5BAeHk7OTVRjDWptzaOzPW/oOfoQY2MKhNcKMa6LgY85M2SKS8Xfhjk8//4ygx/npxingWYASElJISMvjwKVCncPjxoCYXpSEq6OoiirdHCoyPFnEnSif05ODv7+/uQWFVVu8GrE4oh5xcX4uLuTlZlJaWkpeXl5DeogbKjct+Z1yV1EfDwUFpq6FY2Kla24Vje2g/DiYfFzDOts2iJXZoHQjJk60It0ekefMRBzEN58RUi9WKN0lTPhLS+WHQjnvb9Cb8iGbObW0Wq1FJeUEOLjQ/xNJGE3Og0kEN4oUl0eQpmFxPgOwhItcksJga2sWbw7rEqhBTO3DwW5udjZ2ODi4mJygTArN1cMMXZxIcfUYWc6pHLBqDnLDJPF3wyO7nImvu1VkQC74yAHej/gWMM1ZcY4nLh0iWFDh5KYkUF5LYVK1mzaxPK//jJ5aotClYrwsDBSanHealUqUrKzCQ4ORlNejuoGBJk+YxyZ/uGNVxPXixxGDTG+SfctiGFoK45EsPS/MF78yp/J/6sM/TKH5xuPdu3acfLsWQAkgoCTmxv51dx26SkpuOgEQrlMhtqUfUfnFszNzRUFwuLiyrmbWk10cjK5hYUE+PqSnZ1Npq5/VcmRq1ZDXTlKbwKZQRVjY6FRa5GYh4i7B61W/LmLEAQx52xGQs2NvFsh5UoJcgvhpp1/xsIsEJoxUweGIcbGQl+kxFhUr6pnpnEoLi7GUi7H3cWlYgJnUpqIQKhHZtEQDkJthUvEzO1Lfk4OdtbWODo6kp1/Y0mTjU12Xp7oIHRxMa2rxACZXGiwKsZmmjZZWVmoSkoIiYjAwdaWtFrceX/t20dqbm6t7sLGRK3RYO/oKG6W6arHkpkJeXlkp6ZiZWGBtbU1Snt7cm/AnWunlGHveOPKQkMUKbmeHITXQm4hQRAEOgx04P1/QnnxK39sHG5+k9hMVS5dusTuI0cAsLG0xF6pJL+goIqAnp6WhquzMwBSqdT0AqHOQejn50deUVHF3K24oIDwmTNJzs4mIDiYrMREMpPEAou5hgJhcjLoRFFj0BA5CDVq44UYm7kNuAsFQhALgyRfqbmRdysU5mmwVUpNXl/ALBCaMVMH+klcUZ7GaNcsLqiZg9BME0elqnGoqKgIG0tLnJ2cTF8RD/h73z42//efqZtRgdxCoKzE+DkI5ZbmCeftTkFuLna2tsgtLFBrjHdvvRmycnNxcnRE4exMTl5e3YV+GnFBaewQY32xIPNiremTEB+Pr4sLgpUVvh4exFcrAJKbm8vRS5dIz82lvAkIhHIrK5zs7cnSC4AJCZCaSnJ8PJ5uYmV5pb09OY0wRuoFwjIjFse62RDjunBwkhHYypr5f4cY5Xp3O2q1mqKiIg6eOQOArZUVXbt1Y82ePagN3OnpaWm4uroCIJPJ0JhQICwuKkIYNIjs7Gz8/PzEEGOdQJiTlYVaoyG7oICA0FCygQzdvK6Kw72kpEEchE01B6GZ24C7UBwE8AyyIDOpjFKV8cadwlxNjRzrpsCsVJgxUwf2TmIHzcsy3mTC2A5CM43Avn01cmsUFhZia2WFs7Oz6SviAVsOHmTmxx9TauJFo56GzEFopukTExODto4Joz7EWCaXm1wgzC0oQOHgIOYgLC6uPYeOSgX//NNoIqHUyH1Ho668rpmmTeLly/i4uYFEgp+nJ3ExMVUe3717N13CwrC3tSXL0FFkAtQaDTIbG5zs7CqqrVJWBqWlpCQm4qEXCBUKo+f3rA15QzgIy7RIJJWpM4yBIAjmyA8jka9zoOcUFOCiUGBjacmoUaNwVChYt3ZtxXkZGRkVAqFUKjXpuFOsE/bUajVeXl7k6ov4ALkGc8mIiAiis7LISErCysKC3NxcsrOzGTZsmCgQ6l27RkCfK9Ccg9DMTVNefleKhB5Blmi1GNVFWJRXjm0TcJmblQozZupAH+aSn2mchaG6TEtZiRZrO9N3fDPXz4wPPyQzpWpS8aLCQmwsLHB2dSWjERY/1yKvsJCrqan8uWGDqZsC6EKMjejkgMochGaaPkOGDOHPxYtFR1E1CvLysLezQyqTmVwgLCsrQy6Xo1QqRYGweshzXBwcPy6G8DeS+C6VGX+hBmYH4e1AwrlzePv6AuDn7U18fHyVx3du2EDfli1xd3EhNcW0hS7UGg0yKyuclEqydP38jU8+4fOffyY5ORlPDw9ALArRGAKhTFcUyJg5CM0iR9Mmz8Al2KF5c2ysrBAEgS5t23LVoLp2emYmrjrBWmbiHIRqnRioVCpxcHAgr7AQrW5s0b8fOzs7+vfvz+69e0kSBIJ8fMjJzSUxMZEtW7Zw+swZvjPiplWDOAjV5k2pu4q7NMTYv7lYgCrmbM1Is5vF7CA0Y6aJI5ML2DpIycsyziI2N0MczBUu5sy9tw0aDWv37iU6KqrK4cK8PGytrXF2cWkSDsK8ggLclEqSqi0oTYXMQjDqQk2r1aIuM+cgvF1IT03lu7/+glOnajxWkJ+PnZ1dk3AQqjUaZDqBMLeoqKZAePUqWFpyNCGh0XJ8yiwkRg/1ArNAeDuQGB2NT4gYfurr7c1VwxDj0lJ27thBZNu2uLu6kpqaaqJWAlot6vJyZDIZTk5OZOlyJcYkJREdG0tySgqeXmLhNKVSSU61ohENQYXIYWyB0Nxvmhx//fUXu3fvJt9g7tW+VStsrKwAcHdzI8VAQE/PzMRVJ1jLZDI0Jhx39K+tUCiQy+VYyOUU6fLf5mZnY2VpiUKhwMXFhWbNmrHx8GFCmjUjNz+frKwsNBoNP27axOdbthjNRShtgCIlanOIcdOioQW8u1AcBHDykOHgLOPqaeM5egtzNdiYBUIRQRB8BUHYIQjCOUEQzgqC8Ewt5wiCICwTBOGyIAinBEFob4q23pGkpUETSdDe1LB3khrNQZibLi4wFa5mgfB2oayggKz8fNLT0qocL8rPx8bKShQIG2Hxcy3yi4rwd3cnu7aCKZcvQ1SU6IRqpKqxciNXMdaoxfmH3CwQNnnKy8vJyc3ln6NHycjJEd13BhgKhJq6cv41EhqNBplMhkKhICc/v4ZAuGX3bjbGxdFx5swaLuKGwug5CPVVjM1OqCZPQnIy3kFBAAzq35/f/vmHMp0wXZqfz5nYWDo1FYFQo0EqleLk4kJWTAykpZGdm0taRgYpaWl4eItVe5VKZaNUCJfqplXGrmJsFjmaHmvXrmXz5s3k5eTgrqtO3Kd7d7xcXABw9/Ag1WDOlp6dXSEQSk3tICwtRWlnx7vvvguAwt6+IrQ4Ly+Pvp07Vzw2atQotm3bRkhICDl5eWTrzjtw+jSXU1KMJhA2SBVjs/u2aXHpEkRHN9z1m5KDsKSkxryzoRAEAd8wS5IuG8dBqNVqKcrTmEOMDVADL2i12uZAV2CWIAjNq50zBAjV/UwDPm3cJt7BJCSAicNVrov09Ia9wdWCvZPMaDkIc9J0DkKzQHjboBcFMqrle6pwELq6ml4gLC8nr6iIAC8vsmoRCPds3crOf/+F1FSjJrauDzHE2HiTBX3iebODsOmTm5uLnbU1D9x/Pyu2bKnhvMtvgg5ChUJBbn4+WkMBXaXizR9+YOR99wGQ3UjFiBqqirHZCdXEUatJzMzEx88PgNaRkQS7ufGbLpdaemIiTgoFFv37iwJhbZtBjUV5udh3ZDKc3N3JSk6G6Giy8vJIy8wkOSMDTx8fAJSOjuQ0wsaUIAjIjTzumF3rTZP4+HjS0tLIy86meUAADz74IIP692fde+8B4O7pSar+fl1eTnpODi56B6GJqxhrNBoUtrY8/PDDgE4g1M0hc3NzcXd1ZeLEiQC88sorPP/88wwaPJjcwkKxnwGHzp8nOSuLAiPlIW2YEGOzQNikyM2tPceysWgqOQjLy8W88bGxtT9eXGz0dDGOHnJy041zT8lILENdpjWHGOvRarXJWq32mO73fOA84F3ttHuBb7UiBwClIAiejdzU25PYWKgn9HD177+zY8eORmxQHahUopuxLnJzQRfK0lgoXGVkJpXVmXD/RsjR3UCUZoHwtiFdNyFLrzYRK8rPx8baGmc3NzIbyZVXJxoN+SoV/rUJhFot32/YwFe//sqqjRuJr2vQNDLGr8QqXsvsIGz6ZKWm4mRvz7PPP8+qTZtqTMYK8vOxs7cXBUITTyrVajVSqRS5TiSsshFQWEhMWhqffPIJEUFBjSYQSmXGX6iBOcS4yVNaSkJmJt465x1yOa9PmcKc115DpVKRnpSEq84tVa9AmJEB5841bFt1DkKZTIaTlxdZCgWoVGTn55OWlUVydjaeegdhIwmEYPwCP+YchE2T+Ph4UlNTycvJwVmh4JdffkGwsACpuKh29/au7B+lpWTk51fJQWjKEGN1WRlSaeXiX+HgQK6uf+Tm5aFwcKh4TCaTsWjRIgYOGUJRSQkZOtdwoUqFRCLhspH6eUMUKdGozeH5pubrr75i3vPPi3/k54tr7FooLSigxHA8KSwUDTk3QlMQBwFiYsT3WVBQ++Pnz4tuSiOidJWRn62pMDLcCn98LGogXiGWt3ytW6VJCISGCIIQALQDDlZ7yBswVLkSqCkiIgjCNEEQjgiCcOTo0aMN1s7biszMekML/963jw+//rru58fHN+zOg57MzHodgqUFBRSkpjaadRggtIMNOWlqkq/c+o5DbroauYXQJHYGasPcd6qybNkyfvz1V0CsgmdIUX4+NjY2okBo6vB8jYa8oiL8fXzIqp4MvqiICwkJnIyJ4a2ffuLoiRON0iSZhbhQM4awDgYCYRMtUmLuO5VkpaTg5OBAixYtyMjLo6iaw7agsBA7B4fKapKmFAh1DkIAPz8/4lJTxR1ooDAtjbyiIp544gm83d3JbqRiRFK5YNxcUKVNWyA09x0dpaWig1DnvAMY+OijhLi6su7rr0lPScHV2RkAN1dX0uoSCFWqhneKa7VodDkInZ2dySoshKIisgoKSM3JITY9HV9dsRWlkxM5jTRGyo1cHCs/S42FlXnMaUpotVri4+JIi48nPzcXB3t78QFnZ4iIAMDD25tU/f1apSI9N7eyirGJQ4w1ajUyA4HQwd6ePF3/yMvLw8FAINQjkUpxsLEhJjq64rmdOnTg8okTRsmNK9F5FowtrpvHHNORlpbGk089xQcrVqApKeGHTZv4edOmWs/98I03eG3yZIiLQ6vR0KtLFzK3b7+xF2wqIcapqeDnV7dmkZcHycmiAGqkFDdKN3EOqa8zcCvkpqsJbmNNi+52t3ytW6VJjXyCINgBvwHParXam5pRaLXaVVqttqNWq+3YoUMH4zbwdqWoSIzJr4OMnBw2HzpUI89aBcnJjZKj8L/9+3l+yZI6H//yl1+YvGxZo+VRA2jdW5x8nP2vcjciL0vN4S033obEKBWuvhYIQtMcNM19pypbt27lx/XrATHJtSGFBQXY2tiIIcb5+UYbaG4KjYb84mL8Q0JqOghzcriQmMiZ2FiSsrIaJRcUGD9kpaykaYdJmvtOJVmpqTgplQiCgI+bGwmGhRaoFAhl+irGJpxUagwEQn9/f2IzMyscjzFRUfh7eyMIAo5KZe35PRsAuYWEUtXdE2Js7jsiRTk5FJeU4OTkVHnQ1ZX27dpx9dIl0lNTcdXlWLO2skJVV5hUIyzUtOXlaMrLkUgkYpGSnBy0Wi3ZBQUkZ2eTnJVFkC6XotLZudEEQpmFQEmxcd57RmIpFw4W0ibS9Au12rgr+01KChnbt6MqKSE1PZ283Fwc7HT/P1Ip6By2zl5e5BQUUFZWRqFOKLS1tQWaQBVjnWtdj0KhqAwxzstDoVDU+jwnBweioqMJ8/ZGLpPRs3dvLmZni+uzW6RBQoybsPv2bug7ly9fpkVwMO4KBdGHDvHV9u2s1K1nqnPlyhW2nT8PcXGkXLnC3rNn+fbff29sHGkKIcbl5ZCTIwqEtTkINRpROJRK4cABSEoyysvq04YtnhJLefmtfQZ5mWqcPOXGaNYt02QEQkEQ5Iji4A9arXZdLackAr4Gf/vojpm5Bh//+COr19X2kYpk5OTg4uDAkf/+q/2ERhI/Tpw+zd5aql7qORcVxT8nTqBupFAvECsOO7rLiDldzOHNuWxencHyJ+NYPSeJxMsqvpydSPTJomveFMrLtVw5VUxwW+tGarmZWyUlNpaYhAS8XV3JqOYeKioowMbGBoVCQaFKRVkd1v3GQKtWk19UhF+zZhVJrPVkxcdTpFIRolusVX+8oZBbihNDYwkdeuu+/rpmmi6ZOoEQwMfdnQTD9BZnzlCQm4udQiGGepk6xFgXJgk6B2FWVoVAePXSJQL9/QFwdHQU+04jOOk9Ai3ISCiluKCmUz4jsZS0OLF9Fw4V8lyvi6Qn1O1u12q1nNguijNN1c1hRiQxPh5vN7caG4h+fn7ExceTnp5e4YKSyeV1ixyN0J/0LihBEESBMCuLQsT8bhYyGSG+vhX9SunsTFYjbaL5NLPi8rGiazrXD2/O5X9jr9Y7b4s5q0KrhQ6Dajq6zJiI2FjiL18mxNubtKwscnNyKh2EBkgtLXG2tyc9JYXEq1fx1PUbMH2IcXUHoUKhIFdfxTg/H4Vu7KxOkLc3R86coUVoKL6enowYMYKvN21CbQTDhI292J7CXON9Lk3ZQXg3cPnyZUI8PGgZGMjRnTs5dOkShy9dIqeWSIjE5GROR0WRlZJC1OHDONja8tW2bXAjQnpTcBBmZYG9PRt27ODUxYs1ow3z88HODjp2pDw8nHVr1tRIH3UzOHmIgl5Omppt32exZn4yWSk37uzVarXkZaqxd2oaaciahEAoiDOiL4HzWq12cR2nbQDG66oZdwVytVrtrW+d3OloNGzYt4/DZ87UeUpGbi7+Hh6o6hpoGqnjJyQmkliP+BcVF0duYSFHGtkS7uQp5/j2fFa/nsSGT9JJuCS6Md97+CpH/8njw8mxPNn5Ah8/GceedVUFGHWZlqtnivn02XhUheUEtbFp1LabuXlSdPlemgcFkV5tUC0sKMDW1haJRIKdtTX5JsxDWJSXh4VcjltQEFm5uVUWYhfPnSM8NJR2HTrgpFQ2mkCo3wHLSDROMmBzDsLbh6z0dJx1TidfLy/iDR2EubkUyGTYubsjkYjTj3JT5oOqHmKckSHuPOfkEHP1KoGhoQA4OjmRnZQEhw41eJtC2tlQXg7RJ4tJiy8lI6myD715bzTzRotpOL54JYGS4nJiztRdyTI/S8PFw0V0GORQsQg00zRJiIvD2929xnG/gADiEhNJT0/HTVdoQV5fgZ9GEN3VpaUVIkdQUBCnT58mOiMDJwcH3Bwdaa7rNwAuLi5k5OfXTAyvVsOFC0ZtV7v+9mSllLF/Yy5pcaV8/04yW7/PJC9TTXm5lszkMuaNjmb160nEX1RVFI6rjdSYEgQBXH0tjNpGMzfHxVOn8BwyhEkLFhDh44MAJKemYl9LSC6Au5MTqfHxnDl7lhbNmlUcl+qd6yZCXVaGVFK59FYoleTm5wOQV1BQa4gxQLOAALLz8hjQqxeTpk+nT58+eHl48Otff91ymyysJNjYS4xWaAHMOQhNzeWoKEJcXGjZqhWr1q6leVgYvVq2ZOvmzVVPVKtJzMjAzc2NPUlJXD58mMG9ehGVmIj2Rop5mFocBMjORmVry/QZM9hw/HjNDd28PFLVanLKy3l6yRJe/ugjWrZsybFjx27pZT0CLZjyvjf2jlJ+X5bG3nU5fPlaImWl5UQdq0z3oSoqr3NTSn9uqUqLwsUsEBrSA3gM6CcIwgndz1BBEGYIgjBDd84m4ApwGfgcmGmitt5WlBcWcigqisQ6woe1Wi0ZeXl4u7ujqitvTSO5PBJTUkjJzkZdSzi0VqslKj6ewT17sutg1fSUycnJrFixosHaFdxWFPV6jFIy4S0v/Jtb1Xre+QOF/Dg/hXP7RWtzUb6GDyZcZeHEGM7+J96oAluZHYS3A1qtltSsLCQSCc3DwkjLzub48eMVjxcVFmKjC1mRy2SojZAH5mbJz83FwdYWRxcXsgoK0OommwDb9u2jdatWLF++nJeffJLsRqq47BEgJthNjTGOQJiXKU5cbRzMIkdTJyszEyddrjRfb2/iDQpLleTnExUTg5eXF6CrKGnkinI3gmG4l7+/P3Hp6WKS60OHuJSQQGBICKATCHNz60zybUwCW1ljaS3hmzeSmHdfNPMfucrVM8VcPFI52b1yqoiifHEjID+r7sVuSow4lna/V9mgbTZz6yQkJODjWbPunl9QEHHJyaRnZuKqr8Qql1NW15jTGAJhWVmFQBgYGMiYMWN45uOPcXRwwN3JiQgDQcbV1ZX0vLyaudJycyEqyig51PR0GORAYCtrvn87mXmjo/lvfQ7rPkrj1cFRPNn5Ah9Oiqlw4ALs/CmrituwrLSc795OYu2iFI5tzcfJQ45FE817e7fx74YN9O3UiU7h4XQICsLN0ZHLMTF1CmqeLi4kJyRw+tw5WjVvXnHc5A5CA9c6gLOLC5m6jdvc/HwUujDp6jTTRYG0at6cOXPmADDhkUf4/d9/6du3L9OmTbuldind5PUK5jdCebnohGqq+dbvBqIvXSLEz4+W7dqx8/RpnnjqKTq3aMFxncEmOTmZHt26QX4+iVlZjB8/ns3Hj3M5IYFWLVsiCALFhqkhLl+uO9WY/h5qapEwJ4cfd+4kKyuLq5mZkJ1dpa8XpaTQd8YMQkND2bVnD8f/+IPFzzzDpEmTbullBUGg/QAH7n3KDbmFQKtedlw9Xcwz3S+yZFosCZdUXD5RxPO9L7J+eU3HYlpcKQsei+Gj6eJmuoOzWSCsQKvV7tVqtYJWq22t1Wrb6n42abXaz7Ra7We6c7RarXaWVqsN1mq1rbRa7RFTt7upoVKpKK8WxnHx5EkKVCoS0tOJj42tMTDmZ2djKZejcHBAVdfip7EchCkplJeXk1qt4nLMhQs08/UlIT2d3j16kFpN7Dx69Cjvvvtug7Vr6FQX3vojmLGve9JlmIJXvg3khS/9mbLAm1e+C2Dx7jC8gisrDl06KgqtP7yTXOE21OPq0zRyC5ipn+ysLGwsLAgKCKB5+/ZEJybSuXPniiIghUVF2Ohy38ilUpMKhHk6gdDKygqZVFqRcyc+Lo6Pfv2V2a+/jouLC15eXqLIURcpKUZbrLn6WiCRQMpV8ft/5J+8ilBIjVpL8tUSdvyURXn59RUyiT5ZjFQm4BtWuzhvpumQlZaGk65ipI+3N/uOHuWXX34B4Nu//6Z169Y00wkIUonEpH1HU15exUEYm5rKuVOneOebb/hx925GjRoFiAJhfEYGv+7adWNhNzeBhZWE9gPsKczTYKuQoi2Hj2fGsXRGpRNz0aTKauSJl1U1+tCymXH8+21mhUDvEWB2QTV1EpOT8dYJ54b4hYQQl5pKWlYWrrrKwHILi7pdUI0wXzMUCAFmzZrFrmPHcFIoCAgMpH3XrhWPubq6kp6TU9ONond3GNHVLreQMO4NT/QGrXb9q4af5lRzSG39PotTuytzVZ3Yns/+Dbns+Cmb5CsluPmb+02jUk+uyt179jBk4EA+nzOHuY8+irtSyeXYWBzqyNnn7e5OYlwcpy9epGWrVhXHTZ6DsJqD0NXdnXTdxm1eYSEOdYQYN9NtVuk33wDuGTqUdbt2kZKSwr59+26pXUo3GTlpxhmLU2NKKVVp8Yswz9dMxeXLlwkJCuLe8ePZsXUrEx9/nNYREZzSpfE6tm8f/x04QGZ0NPlFRUycOJE/t24lKj2dkObNcXJwIMsw/DYhQQzRrQ1TCIQxMVA94jAnh9+3bmXcuHHEZGSgSU7G29ubNJ1mMO+jj2jbujWfffYZGzduxL59ewYGBZGQkGCUJnUfqeTDXWGMn+dVxQU4/9GrLJ4iztlO7qz6Ga5dlMK80dEkRVdqBQ5mB6EZY3PvvfeyvloS0sOHD9OrfXsSMjMZOXIk//77L0DFgiIjNRUXBwesLC1RFdcRqtRYAmFaGvbW1iRWEwhjo6K4nJiIj4sLHl5eZFVzQaWnp5OcnEyyEZL11oaFpQRXn6oTxeA2NrTv74B/hDVWNhIefs2j4rGkyyV8OTuR49ur3ggGjndusgVKzFQlJS4ODycnnn72WQYOHoy9jQ3hYWEV/Ss/P79iYiqTySgzoQsqPy8Pe52b0UmhIFs3aG74+WdGdutGcFgYAI7OzmTXNQFXqeDIETh92ihtkskFvEIs2fxVJjM7nuer2Yl89nwC65am8lTXC7zz4BXWLkrlxPZ8ZnW6UCM0HyD5agmx54opKy3n8rEifMOtmmxFSTMi5cnJZOTm4qSrxOrr58fm//5j4sSJ/Pzjj6zds4eZs2ZVnC8zsbhuGGIcEBDAlYQENh44wP9++41RgwYRqg8xdnFhw6FDPLhgAYdvcSF2Pdz7lBtT3vfmvU0hzP4xEFWRuPHX635lxTlKXWLs/Rty+fS5ygnu4S25XDhUyO/L0vjxfylYWksqkmibMT11uZcSkpJqFQgVbm5IBIGo1FRcdcK7TC6nrC6Ro5EchIYiR3h4OG7Ozjgqlfywfj0jHnqo4jFbW1sQBAqrzdvS4uKYvHw55UbOKe0ZZMmyA+GsOBLB1AU+2FZznbfrb89jcyudmmf2iAJh7LliVr9eNWl9+wHm/IONRl4eGORB12q1fPDBB7i5ubFw4UJ2HzlC74EDwcYG7OxwVyiIS06uUyD08fYmIS6O05cv06pt24rjUqnUpCHG1XMQurq7k6Z3EBYU1OkgDNPP43TpOwC8goJo6e/P9EGDuH/EiFtql8JVZjQHYew5cS1pFghNx9XYWAKDgrCzsyOyf38AWrVqxenz5wE4o3MS7vz9dzzc3IiIiMDS0pLNR48S2qoVzkolWfp7c3m5mHpFP+ZUn7OZokhjfDycOSPmGdy6FQoLKS0pYeeePUybNo2rSUlcOXOG1NRUtn38MXGxsXz55598uGQJ999/PwEBASCT4dysGTlGjKySyQVsFVLe2xTCI7M9KsYfZy857QfYkxZXyo6fKlNW7fip6tpn9LNuhLRrGpGG5tXWHUJqaipbt24lrlrFyMTERDq1bk12QQGnz57l7NmznDlzhk6dOgGQkZKCi1JZv0DYCJ1fq9WSkJ5Op7AwDh44QIbBpDE9NRVfFxf6t2+Pk4sLmdU6s/5cwxDQxiakrQ0f7Q2jTaQ9Z/YWcPSfPGzsJcxbF0zbvvY89IoH9z3tZrL2mbkxUmJj8XBx4amnniKwWTOSVq9mztChPPfss7z88stVkknLTRwmaVjJz0mhEHf9CgvZuX49fXv1qjhP6exMTl07gHFxDF+wgN/WrTNaGOWwaa5V/k6+UsLW76rmcvz7C7Hvbvu+ZuLkdx68woLxMTzT/SJXThUT0dXWKO0y03A8+cwz/LB1K146p1Or1q0Z0qEDq558kp+/+IILCQm0bNmy4nyZVIqmNqGjvFzcIW5gDAVCNzc3pFIp20+dYunChaz88ceK8xzd3CgqKcHfw4MFixbVvJBWKybINhIOTjLaD3Co2JzqOlxcBA+b5srgx52Z/3cI720KqTj/zN4CdvyURVpcKavnVIocSjcZD7/mgURi3phqCqjVany8vMjMzORgtVQpiamp+Pj61nySIODn5sb5mJjKIiX1uaAaYTO3uoNQEAR6R0bi5OeHRCKpshEqCAKujo6kp6RUucamf//lq3/+4c9ffoFE49YbNPy+z10XxNMr/Bg43pkOgxx4bK4X3UYoKwpeXTpSSOy5YhaMj6lyjT5jHCv6nZlGIDtbFB50399PP/2U77//nrVr17Ju7VpGduqEf6tWYGsLDg48PXw4cpkMpWHVbwO8vb2Jio4mLi2NMEMHoVxu0hBjtUZTpYqxq5sb6bm5aNPTycjLw9HDo9bn+QcF0blZsyoCIVIpm955h6f69mVkly631C6PAEtyM9R8/UYivy1JZdcvYvh9WlwpeVmV95q6Kh2Xl2uJPVfMiR35RJ8sxspGgrvZgdt4GNz3NRoNWTk5uFTbcAqKiCA9M5O5c+fy34EDyHTzHW8/PwRB4IcffuCnn36iffv24lpCvw4vLBSvr1aLP9u21f7ajeUg1GpFN6NWKwqFxcUQG8u+uDjCw8Np27YtiYmJnFapEASBfw8e5I8ff2R0z554+vlVuZTUzw+lvhK6EZFIBHqNduSdP0P4cFcz3vglCN9wUTBfuyiVhEuqiuKLekY/68aAcc7ILZqGNGfeVr5DWLduHVqtlpRqk7C09HR8fX3xcHYmPjWVCxcucOXKFY4ePUpaWhoZ6emiQGhlZdIQ45ycHGQSCWFBQTw9ezZpRUW888474ntISWFox4589uqr7C0tFQsxGJCeno6lpSXHjh1j6NChDdrO+rCwktCqtx1XThUx6ik3ug5XIAgC0xb6mKxNZmpn7S+/8NNPP7Fy1SpcDCdcOlLi4/HQuTWQSLBr2ZIH2rYlPjeXf48dQ52Xh0I3MZXJZHXng2oE8vPzKxyEjgoFWZmZaAsK2HXuHB+uWVNxnqOrK9l1CIRxp06x7/RpDp89i32bNgx68MFbblebSHsW7WiGla2E1JhSvng1keQrVUPuEy+Lf6fFlbJkWizTFomOj0tHa1aLbdXL+IO4GeOy89Ah/tu8mW4DBwIQ0LIlm9au5VxiIq9+9RWZ+fn46yoDQz0OwuJiMT9ZQEDlsaNHISgI6nBY3AxqtRqpLh+UIAi0iohg2/79vNGmTUURFRBDjAHGDh3K39ULlRQXi7vrJ0/CgAFGa5sh4970ZMQTrjg4y7h3VuVG06xlvhzenMehTbmsXZTKwYjKsXHoVBcGP950Jptm4NLZs6SkpbFq+XKWffYZSUlJFWJaXHIyPobfdwM+eOkl9ly5QpAuD1mTCDGWVV0+PPLoo2RmZtZ6vquTE+mpqQQaHPv3v/8YOnAgL33/PR7e3oSNGYOiDjfYrWCnlBHeWUZ456obTEv2hPHP15lsWJFeRRx85bsAok8U0++R2oUnM8bn9Nq1RLi4UF5WhkVZGVhY8Msvv7Bw4UL69OjB/rffhrZtQSIBHx8oLaV/x46cXb2a4DqEMR8/P7YtXkyotzdy60pHTr0VwBsBTTVx3c3NjfS8POL27UMql+NRSx5SAKm1NQcXLQLrqu4iT29vUKtpb2XFn7fQrsiHHUmMUnHo78ook58/SK34Xekmo1wj5oP2C7eibX977nm8cu7877eZVfKrNetoY96Yaizi4sRw2/bt+frrrwkODkZhZ4esWoVvqVJJu5AQVqxYQUZGBoMiI/n79Gk66fpQF4O+5KRUkqkTCD9dvhz3vDxGN2smCoRlZeImrn6O1NgCYWEhWFmBq2vl5lJcHDvPn6d///5YWlri5ubGv2fPMmLECLb89x8qCwsGdOxY81r29rjWkcfUGFjZVM6/ugxTkBhVwpEtefy6OJUOA8XXnTTfm46Dmp5b3TxzvEPYuXMnvXv3JjU1tcrxtPR0XN3c8PHwIMjPjyNHjvDjjz8SFhjIyZMnyUhLw8XRsX6BsBFCVhLj4/FycsLbywuJRMJFg+p26enpuHp5ga8vTm5uZFULk8zIyCAyMpIDBw40aBuvh+4jlSz4pxndRijN4cRNmJVLlxJz7hyvvvpqrY+nJCXhYVhRsnlzZM2a0btvX7KTk8VQEL1AaGoHYX5+hYNQ4eBAbnY2F06fxtbaGj+D3TJHFxeyCwpqOoLLy/l71y6GDxvGuiVLeGTaNPLrchreIDb2UiQSAc8gS175LoDH5nqybH847/0Vwtzfgug/tnIRFnWsiEtHCikpLq9I1qsnoqttncWBzDQuzz7zTEVOQUOysrKIT0ujU8+elQdlMggMJLRNG9Jzcwnx8aninqhTIKxtvCkpMWpBA9A5CA2EjtYtW6IpLyeiRYsq5znqRMl7Bw0iIckgDLG8HHbs4Oq+fQyfPZt169Yxd+5co7YRxN1oR/ea+WtbdLfjwRcr71Nx51U4e8l5/gt/hk93NYuDTYxThw8DsGHDBlJSUoiNFfMSlZSUcCEujpbt29f6vCEzZzJ/0SIsLcVcx6YOMdZoNFVEDoDRo0czderUWs93dXLit02bOKx7/0VFRfx7+DArVqxg/Pjx3PP88yz84IMGbXN1JBKBgJZVxRYLKwH/CGuzONjIDHniCcbNnUvw9OloS0spKyvj6NGjdO3aVRQAXFxA76yzsQGlEiwsCFUqkdTh/vEOCCAtO5uWBhW1QZf31pQOQoPCWCDm6EzLzeW/ffvo3r173esGC50bT15tHHByglatkNSTv/F6kFtImPiON2/8ElTr4zlp6opicXEXVGz4JJ2Ys2LUWfTJohrFF8wFGRuJ8nK4dKkiP+CShQtZ98svuCoUNcRk7Oz45623+G/XLhS2tox+8EGuxsbWet92cnQkS7fh8+fmzXy7c2elgxCq5mJu5BDjq6dPczErCxwcxMgNuRzKythz8iS9dFFTQUFBbNiwgVGjRuHo4MC6f/6hc+fONS9mYYFbA2xM1YbSVc6k98TomktHivjxfynILQQCWjTNtY159niHcOTIEUaMGFHTQZiZiZuHB/7+/owbPJgTJ07Qu0sX7mnVihPHj5OYmHhtgbARdqQLcnJwsLVlwv33s/rpp7moS6QKkJ6RgZuXFwQEYOvgwOXkZNq0aUOLFi2YO3cu6enpPDh6NHv27DGpk8vM7UFaWhqHT55k7Usv8dvPP5NfSyhjSmoqHrWEejgGBpKdm1tFIJSb2EFYUFCArY1YaVuhUJCbk8ORw4fpbJB3B0SRI7ugoKbIUlDAhsOHGTp8OD169SKyY0d+NAivNBYWlhK6jVAik4tih7u/Jfc/586MxT4VoSifv5zI/EeuVDyn42AH5v8dwpMf+5oF9ybC4V272LlxIyCmhtCLHAf37aNTaCgy25qh4HJ7eyL8/AgLDKxyvM4iJbWJHFqt0SeihkVKAFp36ICbiwvOBongAdzd3enbty8dO3YkNz+/cqwsKACNhq1bt7Lt5EkO7d/PEZ0I0ljYOkjpM8aRIVNccPaUM36eFyFtbRq1DWauj1PHj2Mpl3Pg2DEkEgn/6XKunTh8mGbe3tjWUaCgOk3CQSi9/gqlrq6uLFq9mh9++AGASePHM6RzZ/xDQpjz5pssmDaNFCMlir8RQtvbMHC8M8272XL/c27MXlO7OGLGuJw4cYLvv/8eAHVREak5Ofyydy+pOTkc2r+fOXPmEBgQgMLWFq5eBQPXeQUWFqJAYFP7vc7HoOqvISYPMVarq2xKKZVKilQqdp47RzfDzbXq1CUQtmoFbm4QHGyU9nkGWfLxgXDa9bOn42AHWvWyY9BEZ976I5j7nnGj89BKMWXViwmc/a+ADyfHVrlGnzGODHjMufqlzTQApWlpfPPvv5w6dw6VSsXZixc5eewYLvb2NfuGTIaNQkFofj4p27czdMQIXn/9dQbUEvng5OhIVmoqxMRw/tIltp8+TVlxce15CBvTQZifz8cffkiHCRM4dvUqqtJS/CdNIi0/n8PHj9O9e3cA5syZQ0pKCs2bN+eRYcOQy2RE1LYBp0uB0Zg88Ly4qRve2ZZ5fwTj4t00Q/HNIcZ3AFlZWaSnp9OrVy/W6EIKc3JyUCqVpGVl4ebpycfLlmF35gwf//or0++7j9Rz5/jf55+TnZ3N74sWceTyZVTV3IcVNMKEszg/H2srK3yCgrjvnnt44tNPKVerkchkpKWn01NXFc/DwwOtVsuJAwdQy2T07NmTwsJCIhwdCXZ35+DBg/Ssb5A1c9fz048/MrxTJ4KGD6fPN9/w608/8Xg1J2FKaiotWreu8VxHNzey8/KQCAIKnYhg6kILZWVlWOgmj0qlkty8PKITEuhYbTC0trYGQaA4PR1rn8qw97QrV9h39iw/jxgBRUVMHTqUNz7/nGnTpjVK+1v3tqd1b3ue7nYBdZmWjMQyhs9wJbyzDYGtrM3CYBPhl19+Yd++fUTHx4shWidOsC05mRGjR7Njxw5mPvkkz44cWefzW4WG4ldtoSeTya5fIGyAXWrDHIQAffr3Z/zEiTXOs7W1Zfv27ZCSgpeLC4mJiQQHB1OemckvR4+y/fRpVKWlHNi1q/5K4Q3EQy+LmxnDp7uY+0sT5uTp0wzu2pUNe/YwrEsX9u3bR0FBAd+uXk2Xaq7V+rimg7CBuWGB0MuL8vJyoqKiKCoqYsNff5G5dWuVx9ONVCDrRpDKBHNuaBPw+++/s337dsaNG0dqbCwuCgVHTp3i4WHDeHP+fLbt2cP0++4Tiw8olWIoYXUsLcXH6sDJ0xMrC4sqBUoApDJZkypSIggCLkolGw4e5Lc5c+p+olwuOsKqC4R6QkJqP34TSGUCUz+omRpp4GPOqIrKyU4Rx+yoY0V88nTVopKdhyoqxiMzxmfx4sVs3bqVlStX4uvry/49e3j2009xc3DguzZt0Gg0nDx3jl7h4WIYbnWCgyErC6sWLfC1tKxI5VUdJycnsqKiKD55kuTsbJoFBXHw5El66tNgGI4/tQmE+fliKHAdOTVvmrQ0UgoLcXJ2ZuP27agdHIhLTubp334jJCQEpe6eMGjQIE6dOkXz5s3xKS9HyM5GWke+Utc6jjcU/R51ot+jTd+lbhYI7wCOHj1Ku3bt8PLyIiUlhbS0NEJCQsjKyiItOxs3Ly9c/P3hwgX2/PsvzVUqkuztOZ6Xx6yHHqKZjw9nEhJQlZTU/gJ1iYO5uWIOgmp5Dm4GVWEhVpaW4OuLva8vSnt7dm/YQLv+/UnPysJNd5OxtLTE2tKSvIwMinTOrZycHFykUga1bs26X34xC4RmSEpK4vTp0wwePLjKca1Wy+erVrFs8mRwd+f+UaP47c8/KXZwYPLkyRUhXCnp6bjXkgtG6e5ObmEhEomkotqcvC6Ro5EoKytDrtuRViiV5ObmcuTsWUaNHVvjXKWDA9kXL6JRKrGzs+PChQu88corjBwwADs7O5BIGBgezsT4eKKiohr1fcxc6kt6QilBra3xDmmalvu7mT179rBhwwZy8vPJLyxkw6+/smz3bgRB4L777uPZSZN45r776nz+azNnYlttIiaTSmt3c9QlEBpZ/FBrNMgsKndvg4KCWLhwYd1PUCrxcXLiwoULeHp6MvOZZ/jl338pVqlwcnBg/4kT2NfhaGkMzOJg0+W3337j8OnTrHj/fTbs2cOEvn1ZuHUr0ZcuceDwYaa9/vp1X0sml5veQSi7/uWDq7c3jnZ2XI6K4uTJkzQPCsLaYHx19fYmPT29niuYuRMoLy/n1KlTnDhxglOnTpGRkUHU+fP4uLvj7e1NeGAgX65fz7fz5nF/mzbQooUYXlwbLVtWuupqQZBKubd796opL9BtSjWhEGMAN0dHEjMyag+B1CMI9ee4lTROQKCVjYTnVvlTqipn81cZHPwrl8BW1tz7pBtOnnKkUvMY1JD8+uuv5OXl8fPPP/Piiy8SHRXFiL592XXgAN+uWoW7UklqTo4YYlzbdyIgoGpu5zpwcnYm+vhxLtrbExwSQsuwMGLi4+lZW4hxbQJhdjZkZhpfICwtJTUnh0GDBnHy9Glcw8Px8vLi53Xr+P3336uc2kK36eYdHMzsceNqF0wB17ruMXc5ZoHwNkar1ZKXl8e8efMYFhmJm5UV6enp7N27l/z8fGJjY8nIzRXz9wF4eNBCo4G8PLzbtWOppyeUloKXF1bW1jeegzAxUdzNMoJAWFxcjLWVlTgIAmEhIdw7YQJSuRyZVourwU3GycGBbgMGcCEqiilTpvDzzz/jKgg889hjtJ40iSkzZrB3715WrVoFgFUdN4U7jQQThOg0Vb5buZLVq1dzYd8+MKgMefz4cYoKCojs0weAoffcw+RXXmH9jh107tyZdu3akZqaSkpmJh4+NXdQ5TY2WFtaUqpWV3yvTF2kpKysDLluoqxwdCQpPp6Tly/Tvlu3GueGNmvGJ198weePPEJKSgoff/wxpUVFLPrf/8QTbGz4cts2ytVqIiMjCTQICf3+yy/x8vTk5PnzjOzXj+B27Rrk/Ww7de1zjIm531wfp06dIi4ujpb+/qi1Wp7/6it8/fx49913efPNN5nRsWO9jo7mI0bUOFZn/s7aRI4GED7UGk1FkZLrwsoKXzc3HnnkEeRyOUGurpw/dIh3ly0j98oV1m7fTmZuLh06dBAdu3c45r5z/Sx6/32+fvFFuowaRcvlyxkQGcn4JUtQ2NlxYNEi2vXrd93XkltY1F1ooRFyEKqruaCuRd++fXFXqZj5/vscOHCAjsHBVYoNbT98mGNnz9KxY0fzfO0OZseOHQwZMgSFgwMlKhV9+vRBYWmJt25+Hx4Sglarpbe/P9ZBQVXmbjW4ju/JT1u21BARTR1irKmW9xbA1dOTtp0735DormfVqlWVax0LCzQXL3IuMxNXpRKP5s1Jiotj4x9/0G/IEHbu2EFmZib3jR5NYGAgH374Iffccw/tbmEuJ+8ECcAnX9z0JW6Yu7Hv5OXlkZaWxsmTJ1n6wgts3LFDFAijowkJCcHZyopPfviB2WPG8NaaNbjcoivO2dWVrIICzicnExERgaNCIUZH6PuO4Zqnto3bhhqHSktJzcpi+qBBzJ49G0dHR15++WXc3NwYNWpU7c9xdISIiBqH9X0nLTGR1IwM0H+vfHzIyclh6dKlyKRSpFIpvTp25Mi5c7i6uhIQEFARyny7cSN9xywQ3qakpaXRunVrwsLCiPDz49VevZAcP469vT1//inWsjpy6BB2VlZY6B0N4eGwY4e4K2djA7Gx0LMn2NqKOQjrcxAadvTSUsjLM+qCTVVUJDoIdSxevBiX6Gje+eUXVm3ciJvBbrOzUsmbL7/MfZMns3fvXgoLC1FaWSG0a8f8KVN4+OGH2bNnT6OFSJppYmRns3PbNi7GxxN76hT+BpPMP//8k1F9+yLoqlY5e3szeehQdl66RFRUFImJibz33nt1CoQAjnZ2qAwGxybhINSFnSidnNgbH4+VhQWKWnbFpk6fzoQJEwCIjY1l//79LB87Fv+WLSvOmfbcc7QIDWX6xx+zePFi7Ozs8PH05NOlS1n9+ees++03vv3hB75bv75x3qAZk6PVajl16hTBgYGEeHkx+513CHZ0xKmkhEILCzqsWIEiLKz+MKda3G0yqbR2oaORQoyr5yC8Hnx8feHwYb5YuZKuKhWeLVrw+eef8+rEiVjK5bg7OrJ27dqKirNmzADExcbSsn9/nN3dOX36NFy4gLuzM+lZWbQPDKyz2EJtyORyykzpICwtvSGBsGvXrnT19+etlSv57bffmNi+PRgkhn9y2jQWrVzJkSNHGqK5ZpoAj/TvT6G1mDKkRKUisk0btujytc4aNw6AiGbN8HRywjcyEgwKrN00tTgMpVJpk3MQ9urf/6Yjn6ZNm1Z1rXPggFjRtkcPcHQk9vJlvvzmG9asWYNUKuXRfv1Y+8cfeHp6Ul5ejsLOjnnz5t3COzLT0KSnp9OjRw9SU1MJ8PBgaFgYr378MeVnznD56lXufegh7hk+nCcHDsTW15e31qzB1fnWckA6eXoSl5fHR8uWMWXKFOIuXSI7M7P2IiW1OQgbUCBMSU+nZ8+eJCUlsX37dqZPn16/+1Yur/V+ou87Py1ZwrLPP2feCy+IrsuwMNRqNbGxsThqNKzeuJHVU6bQ5oUXyMvL4+TJkyxbtozw8HDjv78mxC0LhIIgWAFuWq027ponmzEaRw8fxlIQsLe3Z/nEiUg6doSEBDycnFi/fj1BQUHs37cPN8Pkm5aWMHAg6AenAQMqfr8hB2FWligu2toa7QZQXFQkOgh1tO3WDcrKGNe3L6s2bsTZwEHo7OLCvzoR1NraGmsrKwSlEtzcmBIZyXmVitDQUHbt2sVff/3Fiy++aJQ2mrkNKC4mc9Mm9h07xuDevdmyezfThg0jLS0NV1dX/vrrL+aPHw/6xZi1NZ8+8QSzd+/m8uXLFBYWcubMGcpKS3H19q71JRwdHCgyENNlMhllJqxirFarK0QOhZMTFxIS8KwtZw8wZswYNn73Hal5efz3339cvHiR9hERVSfS7u508/YmLy+P4cOH069fP8jJYVS/fgS3acNTPj68uWABpaWlFbkPzdzZxMXFYWVpyZAOHbCytKRT375iiondu7EF+vj7i9UUbxBpXfk7axMDjV2kRKutUcX4eujUvTs2Egn3RUbChQsVYTyB/v4EuLnh7OBAYmKiWSA0U0FpaSnp2dl4NWtWeVCppE1wMMn29khcXKoIZtei3iIltfWRS5cgNLRWkf5mqE3kuCbu7oR4eHDk1Cn6z5pVJfxN6eZGkUpFSUlJRZoPM3cO0SdP8tP27QAsWbKE7b//Tks3N4plMnbv3y9uugCRPXrw9TPPINjZGe27Wh2ZXF63+7YRqJ6DEGDu3LnGewFfX9HlpVv7ueqEQF8fH4oLChjbsycOoaGcPXeOcT16sHrbNrRarTk9RRNEq9Xy7IwZtO/enebNmxMQEICXTIZXmzY42tiwZ+NGouPjCQ4Px8nNDSeVCk3btkglElxuMWw2vHVrrB0dCQkJYcqUKSxZsID4K1euv0hJA21UlRUVkZufj7u7O23btiUgIICOHTve0jV79eyJkJsLYWEVx2QyGb///jsrXn+dP3fvxjssjB6tWxPRpQtXrlxh//79bN26lSeffPJW31KTxRgOwmHAL8ANzhbM3ArHDh3ioW7d+GDhQtEW6+wM5eUseeIJ1pw6hZ+fH3//+Seu1UO+DAcmg9+trK2v30Go/9uIN4Di4uIqDkIAevWiR0gIa1NTkelckOnp6di7ufHXgQMEuLtz4sQJOrdpI4Y5SyQIHh4sHjuWsvx8HnroIU6fPs3AgQORSqXk5eXdtrZgM9dHSmwsPuPH0yE0lMcnTeLLjz9mQkkJLVu2ZPHixVy8eJFeISGVAqGFBWg0hDg4sPPsWeJiYykuLsbd0bHO0ENHhQJ5UVHF302hSImNvoqxkxPRycn0rSW8GMRw+7WrVjH73Xf57LPPaB0ejlV1MVEmQ2Jry0OjRvHv7t38+eef2FtackWXk9DK0ZEANzcuXrhAq1oKuZi5s9Bqtfzvf/+jb8eOzB01qnIn1t5e3HSytoacnBsSOPTU6SCsbWwx9o60Vou6vPyGBcIHp03jwZYt4cyZKvmxOrRpQ99WrcgsKCA+NhZtz541F11abYMtfM00XRISEvB0cqqYxwBgb087f39craygc+e6iw/UgszCou4iJbX1ncuXITDwhl6jPm40xBgAiYSXp03DzcKCQEOhFBCsrHBRKMjIyMC7jo05M7cvWzdu5OFevYgcNIhpffvybMeOFGRkUF5WRvCFC3iHhgJgo1AwqF27yvlZAyCTy9E0QiGfurgpcf1G8PYGfVopwMbWFjtra7554gkECwsiw8MZ3LcvWktL2LSJ1f/+y8mTJ2lbrZiLGRMTHU1KeTnLVq2i64kTjBo9mokTJ1L677/g48P/HnuMKStWkJaVRXDz5mJkX0AAUjs7PJ2ccKnDJHC9eHp6snv37oq/HZ2cOJWXV7uDsBFDjNNTU3FxdkYqlbJlyxZsbW2R3GL+Te/wcB5SKmvN2dinRw/Gnj6N4O/P5y+9hKJ/fxYuXMgnn3zCsWPHmDBhApaWlnekWaJxspqaMSpxcXEcPnSI9sHBooNBnyfM2ZlBzZvz9csv0yoigiPHj9OvU6fruqaVtTXF11ukRN/xjTjIqoqLa+ZsEgQk7u48MGZMRcdNTk7mzJkzxKWkUFhaSnl5ObMeeKAyD2Lr1uDszKPt2nH69GnatGnDd999xyeffMKbb75ptPaaaZqcPX2aHm3acODECUbefz9HL11i0aJFZGZm8tprr9G/Xz8s1GrR/arH2ppQ4MK5cxw9doxu4eF41OOGclQoUBjk3ZTL5abPQagPMVYq0ZSX41lHeDQAjo608fJi7969zLj//trzxikUvP3kk2zbtIkuHTvy9IgRWOs/E4mEloGBnD52zPhvxkyTY9u2bezYsYPPXn4Zl7ZtcdEvJCQS0ZHu6ysK7TeRc08mlaK53hBjY+9Il5eLOQhvdLEmCNChg9hGgzCejh068Olzz+Hr7s5jEybw2WefVX1eYiKcOHHr7TbTtNm5E6pFY8TFxuLv6ioK6npsbHh+xAjenzTphoW7eouU1BWeb8S+c6NFSvQMfvBB2rm7gy7FRwWWlrg5OJCelmakFpoxNcXFxWRmZADwz7ZtDOnRg+mRkQhxcZCbi12bNjh06MDUGTPo0KWL+CS5HGSyqv3EyJi8ivFNuNZvmGqbUFu/+YbIwYOJDA8XnYWFhQiFhQiWlswaNozXXnuNpUuXcvnyZZKTkxu2bWYqyciozOlniFbLz19+yZ6NGwE4cOgQHQMCcI+Lw9fJCRQKHhg8mGlTpuDh5YWzi4soDEdEgKUlPSIiCDViVWsARxcXsvPzxfbK5dcOMTZ2xIeOlLQ03N3dAbC3t79lcVB3IdFhXwst2rRhyVNPgbMz7nI5VpaWtHZ05OjRo2i1WmbOnEnPnj3RaDS88cYbHD9+vOK5W7ZsYcqUKbfePhNR511KEITt13mNW5OpzVwXegt4TEwMLVq0oKioiA8eekgcTPU7rhIJtGsHCQmEazTY29ry7KxZ13X9Oh2EtTkF9dUkjXgDqB5iXIFUCu3bV/zZunVrLl26xNmzZwmQSomIjGR4u3aVAqFUCsHBdB05kvfOn2f4jBkMHjwYpVJJdHQ0ubm5KG7C6WLm9uDC+fNEBAcjWFlhDTw+cCAfffQR744fz+yvv2beq6+KQobhoBISQkhuLoePHaNXu3Z0DQjgVFJSna/h6OiIYPB8kzsI1erKIiW677Znffl7FAoG9unDxyUlTOjVq/bQUKUS68JCrLOy+OPll7GttqPfqlkzli1fjoOLC8OHDzfaezFjQrRaKCkRF2fZ2eI9VS5nxYoVPPfccziA6EwwLEolCFBLte/rRVZX/s5GEDnQasUchDezWLOygr59qy7EdCLpK+PG0WnoUOa89x7Tpk2rFCCTkyEtTdzEakgHiRnTkZcH+flQXFxRRCExMZFd27fj7+5e9fsiCNjdZBiY3MJCzEFYmyO1rgI/RlysaTSaG3cQgui4tbSsKRBKJLgqlaQlJYlzWB0Zly5x4dAheupy1Jm5PcjMzGRAr16kpKTw2cKF7DhyhM8//FDcJAFx/qUzNsyfP7/yiTY2VTZdGoKmWMW4oeny4IOV9ySNBgoLRceZqytPjB7NhjfeYMeOHbz++ut07NiRHTt2NGr77laO//YbIZGR2BuEtgIUpaUx5aOPCPT0xMPRkZTsbNo7O0NKijj/EgTo04eX+vblperh6TIZP73ySpVwWWPg6OxMdkGBGFpsbX3tEOOGcBCWl5OakYG7sSsj14eTEzRrJvad8nK4eJHWug2M9sHBfP/99zg7O/PVV1+xYsUKHBwcKor+fPzxx/z7778sXrwYBweHKmaO24H6Zsa9gYvAuWtc4+4oOWZiFixYwI4dO8jLy+OVV16hlZMToV27QlBQ1cWGuzu4udGqrIyrq1bhZFB8oD6sbGzqFggN/9X/buQQY5VKdUNVH1u0aAGFhcR995044FUTMAQvL2aPGAEREfj5+XHu3DkiIyP54osvCA8Pp3+/fljdBVUm7zYuXLpEuMFO0KKnnmLRnDlo4uJYs38/Qzt3FkV1Q/z88MjP55O5c3msc2f2XLqE9bm6b3uOnp5oDezkchPntFGr1RUih14g9KhPtBEEnPr140mNRlzM1uYgdHODffugvBx7jaZG+OgjI0aAQsG0adMYPnw4CxYswNEw36mZ24LY2Fi8vb2RyWSknTrF399/z6GYGFq5uWEdEIB9cDB79+7lm2++gb17aw/9srSEgICbev16Q4z1/+oFEP14s22bKM7d6s7xTeYgrKC6MOPsDFIpbleu8HCLFnzo6MjWrVsJDQ3lwrlzaI4c4XhcHM4nTzLrtdcA0WVzN1Q7vlsoT01lzY4djGnTBgvd/XDhwoUsXbqU2WPH1nyCnV1VJ8Z1UiFyXI9AqBcGm4CDEEGArl1rvY+4Ojry1LPPstbXl9a61BVff/EFry1ezBZbW/r16XNTeU7NND6//fILQU5OvPnMMzwyaxYzRo3Cyc9PFAhdXKqKC4bY2Ynh9g2IyasY30x4vjGwthYLVF65AlevivNgT08UERH8Z2EBYWEUy2R4Nm9Oamoq2dnZuLm50atXL0pKSjh16lRFKhszN0d6ejr//fcf9957L2UlJQx86SUCfH3ZdegQtra2xMTE8P7775OTmEiZWs3pK1eY8+ijFJSW4lhWJm6s6P8P6pv/WFjUWqDnVnB0dCS7sFB0x1tZXTvEuCFyEJaVkaLLP9hoWFlVptWxs4MrV/Dv0oVH+/RhUNu2PPn553z7/PM89sorZGVnc+rUKfLz85k0aRL79u2ja9eu/PTTT0yaNIlWrVrx/fff33LOxMaivhH+LHBBq9U+VN8FBEF4APj5VhohCMJXwHAgTavV1lC0BEGIBNYDV3WH1mm12rdv5TVvF86cOcOxY8f44osvaN26NT169GDOnDlIz5wRbxS13QR0IVDO/v7XbdW3srFBVVuhhfp2BozR+QsKwNaW4uJinG7U2WdrKwoZaWk1Pwe5XHwsKYnHH3+cjRs38t577zFx4kRSkpOZPnAgLy5ejL2b262/BzMmJTs7m8LCQsaOHUtsVBSfvf9+5YNWVpCYiNTfn9O//SbumtayyBDs7Jg5YgSkpjJ05kyG1rNoc/H2riIQyqTSJhViDGL+kHqRSMDHRyw4VNtk1c4OWrYUJwHR0TUcH6Hh4bwZHs6k557jiSeeYOXKlbz66qvGeDtmGhL9fVsiobS0lA4dOjBq1Cg+++wznp09m3Pnz9M6JITX/vkHawsLUrOy+Parr7BPShK/M0be/byhIiX6cae4WPz9FgXCct0i0SghKiCOQW5uYk7gCxcY3asXy5cvZ/fOnXQOCSE1L4+MggIsZTLuf/xxFi1axPLlyzl+/DgRERFoNBrKy8tvqx1mM1V5+rXX+OS33wjs0YMefn6oVCpOnDhB+9at6VTbZq29fY1w5OtBrg8xrq0fVO87tc3hbhHDTakbprp7UMcbkycz75df+PXXX9mzZw8zZsxg/5EjRLZuzWeLF/PrV18xbsYMug8bdgstN2NsNn/+OW98/DHvL1lCXFwcI0aMYO/27dzTqxf3TZ/OSWtrPFq2rExBERFhUge1qUOMTeEgrIKfnzi/S00VxytrazFlxqlTWJeWMrRPH2bNmsVff/1FYGAgQUFBSCQSpk2bhiAIzJgxgx49epiu/bcxm/74gyeefpqY2FgO7dxJmI8PXi4uLFmyhNdff5033ngDC7Wa4owM5j//PC8sWEDH7t0Z1bKlaP6xtKw9JLk6VlY3lfKlPhwdHUUHoUolmgoMx63GchCWlHApJYVQU1UP9vCAgAAkCgU/vPACuXZ2ONjYMGTgQJw++4xQPz9OHjvGDz/8QGZmJuvXrqUgN5cpTz3FihUruHjxIqdPn74jBMIDwD3XcQ0tcKtZt78GlgPf1nPOHq1We1fEsqnVaoYMGYKtrS3//fcftjY2WFha8ttvv1UmPS8urj+0Sy6/odAvK2vr2gXC+hZqxhAJjxyB9u1RqVRY1RZifC3atKl7B97HB6KimD59OuPHj8fGxoYTJ04Q/8cftBo3jkXr1nHs+HHy8/Np2bIlEonkjkw0ekdi4JyYNGkS69evp1OnTsQmJxMeEVF5nrW1eFP38oKYGFGQri3XhI2NaN93dr7m7tuMGTOqCIJyudzkRUr0ooKVlRVyuRyP67Hgh4aK95G60KcusLWt6fhwcoKjR/Fp25bZs2czZswYfv/9d5599ll8fX3p2LHjzfVnMw3KuR07OL5nD2PnzmXz5s0EBQURHx+Ph4cHDpaWnN65E1tfX3bv2kVAZiayPn3wTEoSq9Y3QOL4Oh2Ehq6n6g5CI21OqcvKkBpLHDREF2o8LCKC2R99xPOjR/PhmjWo1WpKcnLo0rMn4eHhPPLIIzz55JO8+eab/PTTT7z99tscOHCAzZs3mytK3oZotVp+2bqV4b17c/T4cbIsLJg9ezYJCQlc3LEDt9rmUgEB17fgq4ZMJqsMMa7ZkNodhGq1GOZ+CykB9KjLyozugmoeHs6Uhx7ioVmzyMnJISAggP0nT/LNqlVMfOopCvLzCWrZ0iwQNgHKyso4d+4ccrmcx15+mf5t2/LZZ5+xYf163nnnHUqKinht/HgAQkeNEudhUqk4hjg43Lr7+xYwdZESjUaDzJSbQDKZKAwamiOcnCAyEnJymJ+ezoqTJ/nmm2+YMmUKH02fjn2rVkx/9lkefPBBJkyYwIULFxo+j+IdyJWzZ0Gr5f3//Y+oM2d4bORIBoaF0emFFzh58iRbt24l+qefcGrdGq2LC8u//pqWHTqIrusboWtXo2/mVgiEJSWiAFlQUPlgfQJhfr44J7qVvKIFBeK9o7SU8/HxjLvvvpu/1q1gmNcxJASFuzv3SaXQsiUrZ87EydaWbi+9xNKlS1n23HP0VqtBqeRKdDTDhg+nfatWRF24YJq23wT19fAPgL+u4xqbgMBbaYRWq90tCELArVzjdmf9+vVcuXKF1NRULl26REFBAfeOHMnns2ZRmp1NUmBg1UVDcbFRdwjqFAgbOsRYl8+wWKXC+mbt63UNVK6ucOkSQnQ0NoGBontMq8XXyoq08+d56bnn6NixI+W6PFQjR45k6dKlKJVK47lKzBifggI4fJiizp25GhPDtm3bOHv2LM2ysti4ZQv+hgJgSIg4MdVoIDNTHKgMC5TosbUVv4u+vtd8+eqhtDKZzLQOQoMchIIgoFQqr+0ghOsPQ6gtT5arq7jrfPYsXbt3p2vXrnTu3JmVK1dSWFhIZmYmGzZsoOV1pjgw0zi88MYbHD5zhv5TpjB//nymTJnC1ClTiDl8GM/0dKx8fMDKit6DB8OWLRAVJU7s+vdvkITTMpms9nCv6uOOfqwxYrikurS0YUK9vL3Bx4dWEgn9u3bliUcfBUtLZJaWyKyt+WzmTKx796ZDly7k5+czdOhQ+vXrR0JCAiUlJaxatYrp06cbv11mjIpWqyU6Oprg4GA0UVEkXL6MVCplxODB/HfwIHsuXuTMmTN4enriVpdT8CYXTVKplPLycso1GiTVF4LVnRv6PpOfL/ZnYwiEt+IgrAtHR3oFBFBcXCxGysyejaa8nAH33UfpjBnkFRRw9sIFXnrpJR5//HGaN29u3Nc3c92sXr2aWbNm4e3tzYKJE2kZFESXp5/mwR49CO3QgVXffku4Lky8imO0b1/TNNiAegv8NAImdxDWh1JJgKcnH3TsCM7ODDx6FMfLl0Ei4dSJEyCRsGPHDl544QU++OADLBuwmMydyNWrV5n38MMs+u47HGxsWLtoEVZlZVzYtIm/Nm9mxNChOJWXg5sbglTKha+/xqJVqxt/oQYQoG1tbSlTqyktKMCieohxfRtV0dGiAF1fXvRr8d9/0KcPlJVxPjaWCEMTiKnQt6FXLxAE+nXuDBoN3Zs3p13nzvQPC4MuXeDgQaxyctj2zz/8PGcOa8+cMW27b4A6R3itVhsNRF/rAlqtthiINWaj6qCbIAgngSTgRa1We7a2kwRBmAZMA5p+8vyzZyE4mM+/+465c+dia2uLpaUlvr6+LBo/nh7NmokhKKWleDs5wfnzEB4uioMqlXEFQlvbGwsxNlaYsW7Rp1KpjJ8TUCIRC5zs3VsZKunsDK6uWHh789r992MfHMzzr7xCWloagwcPxtPTk4EDB7Jw4cKmcRNqRG6HvrP9t9944733KMzJoWWnTqxZu5aBAwcS4ecH0dGM6tSpIjk8UCkG6r+r4eE1czaB2Jfc3W9q8SSXyerOQXjqlJj3pQEnhGq1usqO9K+//kpoHRW5jIqrK2i1CP/8w9rly8HdnZdeeAHi4/l8zRrGjBnDiRMn7gpX7u3Qd3bv2sXZqChCfXxo0aoVEyZOZNKkSQhxcQSmpYmTSsN7cIsW4jF9vpcG+A7XWeCnuhCo/9eIAmGD5YLSbSAI3t5sff31qo5liYSe3buLmxVJSdh7ebF7924GDRqEAGxbs4bIhx4iKCiIgQMHGr9tTZDboe9UR6PRcOjQIbp3787jjz+Ou1bL2s2b6di8OR3at2f+8uVkFxby5NixXM7MFOdrRlxMC4Ig9p3SUnGxZkj1eZn+97ochzeCTlhRl5UZX+Tw9cX60iXWrVlDZxsb5nz/PYPGjEGQSOjatSsp8fFs2r2bkh07+Prrr7l69SpJSUk0a9bMuO24TTBFv1GpVMyaOpWt27fz5owZaIBJw4ahdnHB3taWe9q3Z8KUKUzq0QOhARznxsDUIcY3XeCnsfDzE8OP4+NxlEjE8Ss7G3btAqmUNUuWMPmVV3jggQdYvnw5/v7+pm7xDWOqMedKbCxT+vbl57AwbO3ssHJ3B5UKt7NnebxDB7GwiEHaH4sBA+o2wDQygiCgdHAgu6AA9+qhzrXNywwLmRpj3NFoKC0sJDYlpXHWN9eLfj3p5QU2NmxbsEA8FhoqrkWbNYPTp8HSkmYeHlz64w+TNvdGqK+KsRdiTkDTZd+v5Bjgr9VqCwRBGAr8AdT6DdFqtauAVQDz5s0zcgC8EVGpOLJlC6t272bb6dPs2rVLzB+WkICrk5O409uunbjY2LFDDPEqLxfLoufmios3I9446sxBWFvHN3RzGMNBqNGIDsKGSNZuayt+TrGxYjjyxYuiSCSR4NG8Oe+GhUFeHk7h4ezatQtra2s+/fRT+vTpw5w5cxg2bBiBgYFNd8fPiDT1vvPP+vWMnzKFT2bMYPuFC3y/aRPr168Xw2lTU0X3n4ND7QsxQYCePevMf4Qg3HRybJlcXreDMClJHCCqf3+KikQxxgihhGVqdZW8Zb17977la143HTuKfSs9XSxkcvAgWFoypWNHNu7cyb333st3332Hi86FWFpaSlZW1vWFQN9GmLrvFBYWYlubM1ZH4tmzTB8/niWzZmHj68v58+d5fuFCceKSmAjduomhI4b4+DRwq3XFFq4VYmz4t35SaqQQ4wZdqHl5waVLNQr8EBAgLgIuXwaZDMHNjW+//Zarf/xBmEbD9/PmMeHxxzlz5kxFTtHqlJSU3DHuDVP3nRuluLiYiIgIevToweOPP86vv/6Kr7MzMWlpPPbgg7Rs1QpLmYylzz/P6BYtyPDwEPNSduli1HboxfUa2y/Vnb76v40xX4uJAbW6YRyEcjn4+jI0PR3y81k5bpw4VwPefvttynNz6di3L1PGjGHH0aPMnj2b//77jyNHjhi3HbcJjd1vtFot8954g5jTp5k+ZgyvzZqFEB0NCgUyZ2dWzJjB8B49kObmEqxQ1B6p0QQwdYixWqNp2uuJkBDxR6USU0B5eopzgYQEKC/HIymJP+bO5elvv6VTp06Eh4fzwQcf0Lx5cxzqml83MUw15lxJSCCof398mjWrXKcUFIj3ZkdHcb7QoUPlE5qIOKjH0cmJ7MJC3OXyquNMQ0ca6vSGqMuX8ffyapqmA31eRGdnMZWHfo3j6SmaKXbuJDQ0lMuxsaL7v7ycuLg4goKCyMvLw9bWtsndF+qLo4wH2uv/EES+FQSh0bcLtFptnlarLdD9vgmQC4JQS9zb7cPfa9cy7N13cbG05NiBA4SGhuLq6oprdrboOnJ3F225giC6CAsLK8Q0Bg2CwFuK6q6BhbU1ZWo1miNHau/4hhgzB6HuGsUN4SDU4+Iifm5eXmKIgz63mpeX2JEvXACtloCAANzd3Zk3bx67d+/mny1b6N+3L927dyc/P79h2mbmujhz5gzjHn+ctcuWcf/UqSz+6CMOrlrFiBEj6BQUJLpDvbzEflGX6KZQGEWQq06dLiiouSgrKBB/TpwQRQIjYBhi3OjY24uTx7Q0OHBA/D/o2hWhUyfWPvkkro6OzJkzh1mzZpGXl8fs2bMZNWoUc+fO5YEHHiA7O9s07b6DKCkpISAggL3btpG8cWOVxyZNmsTmTZto16MHDz/4IKNnz2bIqFE8P2IExMeL38H+/cV7pP6+2IjIZLLrK1LSAA5CdUNXk7S3F90Y1YsieXuLBYAkEjh2DNLS8AS6N28OPXvSPzCQPt26VYSAlxps3GVmZrJ582ZcXV25cBvlsrmT2LBhA3FxcaxZs4b7e/YkwteX2JQU/vj6aybMmoWlgwMXv/iCiV264ODkRFBBgbjZW4fYe7PIZTLUdUV9NJSDUK2uFAgbou8EBorjo59f5ZwNaNeuHR26d8ddqWRwYCCdW7fmyy+/5Pjx4+Tm5lY8fceOHZSbUPy5U9Go1WKxvw0b+P7dd5m9ZAlCSAh06iRueCgUjOvTB6U+wkmjqRrJ0YQwdYixRqO5PfL3WVmJm+q2tuLvISHiZnevXsjz8/n0rbdIiorikbZteeThh4mMjOSfjRs5uHu3qVtucnbs2MHmzZurHCsuKiIrLw+vsLCqJgY7O1EUbN5cnIcZIQVEQ+Hq7k6aRCJ+H2obYxrKQajTPuLi4wlshI3rW8LWVuwrhmnKZDLo3Ru7jh0J9fFh4cKFfLNyJRHh4fz++++MGTOGd99913RtroP67lLVV9ISYBzwEY0TUlzZEEHwAFK1Wq1WEITOurZkNmYbjMH+/fuZP38+Eo2G//bvZ/2qVXT39Kz8IhUWigOrq2vVxZq9PeTkiDHvVlZGL18OIEilWFlYUBITg01oaKXr4VpVjI2Ug1BVUoJ1Q+04urqK7pjqeQVdXWHAANH1lJYm3pylUjh+nHCtlr/efhttSgrTvv6aCRMmVC0SY6ZROH36NBcvXuS9t97i/UmT6PXwwyCXY6nREO7oKO5yHj8OrVqJ/58mQG5hQVldIcbVF2UJCaJIqQ/TNwJlZWWmEwhBvFeUloK/f2U4pVKJZcuW/O/hhwl68EHs7e2JiYnh4MGDlJWVER0dTbt27Zg3bx5Lly41XdvvADb//Tf5ubk8NHYsKenpLJw/n+dfeYUdO3bw7bffsu633+jfrh1zFy0Sn6BUiovwixfFiakJE6ZL6ypSUldosTEFwoYqUmJImza1H9c7mhMTxdQharV4rlwObdrwbK9edH7mGezs7OjQoQNPPPEEmzdv5rHHHsPFxYXw8HDWr19PuKmq+d2FlBYXs2rVKpZ/+ilvvPoqHy5bRi+lkkGtW+NoZcXwxx7TnVgqzjdCQsT7f0JC7XlcbxGZVEpZXVEfteUgNMZCTbfgazCRw8ZGdPK7uIifn+G4ZmHB6ueeI7JtW+LVan4sKiI0NJS9e/cydOhQUlJSGDBgAKdPnzbnJzQiKf/+y8wFC8jTaDiyejXW+pQTULn5odWK82sHB9Epm5JimsZeB6YOMVaXlSG/nd3fUqmYuunECWQSCU/cdx/TR48mbNIkRj/0ECMiI/mxMaNYmhgJCQncf//9tG7dmnvuqazzevLwYYI8PZHUNlfXbYTccDGSRsbLy4tkpVKcpxiuX+oqZmrkVGQFeXnYNdHUBdfEwgLs7Nj0zjt0fO45lLa2TBs0iCdnzSI3L48TJ07wyiuvNKnijk2iEoMgCD8C+4EwQRASBEGYLAjCDEEQZuhOeQA4o8tBuAx4WKs1dv1sI5OeDiUlxMXFcfz4ccLCwhg9ejSj2rblntBQ/v3wQ7oPGSKKf/rk0f/9J1aW6tq1qutAqRQnTD4+DTLR1GMll4thxgY7svWGGBur42s0FJeU3HyRkmvh5SWGQtaGpaVoBT50SLR3FxSIYdwKBajVCP37s3zCBKKjonjyyScpMKjcpFarSUlJMe9YG4m1a9fy/PPPs+eHHwhr1oynnnqKBx98kEmTJmENPP7cc5VihlQq9pWDB8VJqQlDVq/pgmoocV2H2tRV8QRBrIBXPWdnQADeDg7s/OknTixahIONDet/+on+3bvj5uLCdytX8t1335GQkGCSZt8JaLVavv7iC94ZO5b2AQHsmj+ft+fP58CBAzz22GN8+eWXlGs0TB47tvJJ+oWcXF6RL89UyKTS2ouU1JWD0IghxpqGCJO8Uby8xNQIXl6VY7u7O5369eO7Dz5gy5YtvPXWWyQlJfHyyy+zevVqzp8/zztz5rBhwwbTtv0O4NKlS9c+SatFU1jIyP792fjNN7z9/PO82a0bybt24eDry9OPPsrSZ5+tPF8uFzd4Q0JEN0hwcINUba0z921dVYyN4SDULfrUZWUN13fc3MTPq5YN4yF9+2LdqRPdfHwIDAxkwoQJTJs2DWdnZ4YMGUJ5eTnnz59vmHbdRahUKr788kvWf/stEaNHE+LoyMYXXsC6sLAyJ60hgiDew5RKcWxpwnkhK0KMTbSEVN8uDsL6cHERizM4O0PPnkj8/Xn5vvsY2L49W/fvv6vXRGvWrGHYsGEcPXqUtWvXsnfvXgAWffABU0ePNnHrbg0vLy+SkpLE+3NjhRgbjF+FBQX1ptJp8tjY4G1vz8vTp3MlLo73Zswg0M2NIX370qJZMzZt2mTqFlahSdyltFrtI9d4fDmwvJGac8usX7+eoiNH6BAZSYt77sHa2poPPviAiZ07Y6XRiK4N/YTRwQGuXBF/b9dOFAyr4+HRKAKIlYUFKrW6qkDYWEVKSkqwaiiBEOqfoIeEiAvl06fFyb2HhzipDw4GwDI8nK3LlvHi11/Ttm1bWrduTdu2bfnss88oLi5myJAhrFmzpuHafheQkZHB9OnTeWj0aHqPG8dr06dj4ezMyBEjeGLmTKR79iA4O1d9UkCAGCZp4mIycrn8xgRCvYPQSBPU6jkITUJtu16CACEhdDt5Elxd+fHdd6GkhLKBA0kpLsb94kWmjB/P66+/ztChQxkzZkzjt/s25+M33yTq3Dm++fBDXho9Glxdidy8mcjISD776CMm9OjBoFWr8BgxouoTfXyMWjThZrlucd2YDsK8PLhyBbWu0INJEQQICqp53MuLcR06gKsrMx97jO7duyOXShnasyecOkWkRsPkmBj+/fffu6aYibE5f/48zZs3Jz4+Hh8fHzQaDWq1mri4OFxdXVEqlSQnJxN/8CCPzJxJgJsbW779Fll+PmRlYZ+eDsHBuNrb42qwcYggiA4bEBfQ1cctIyGTSimrnjcUao4thn3ISKFeJhM5evYEQaBrq1Yc3bkTa0dHhg4dikVBAQ9MmkSvFi3YvW0bBw4cYOHChRVPe+211xg7diwtW7YkPj6en3/+mRdffLHx23+bsG3bNqZMmYKTQsGaJUsY0qqV6IBp167uJ9X3WBNCKpWKDkKttkFSzlwLTVPPQXi9WFpWOuQDA5k6ZAhTBwwgfNo09u3dS6+71EW4/e+/md6rFwnNmzN98mQcFArCW7TgwqlTfKuP4rhNuSGB0FAjuBXB2GDuV1hYiF1tGsntgiBARARPaDS0nj8fh/bt+WLmTKQyGX8dOsTm9esZ3YRE5GuN8N0Ncv1JAC3QQxfyWwVdbsC7ni8WL+b9pUtxt7Hh5Icf8uzMmbSxtGTsI48g7N8v5sAzFKv0+QW7dm1Qd+D1YGVhgcrB4doOQmOHGGu1ooPQVDsDEokYnqrRiGJt9YmOtzeuV67wzddfs3PXLq5cucKuXbvYtGkTYWFhtGrVihkzZjBv3rw7rvhCQ6PVaiksLOSjjz7igZ49+fTRRxnTpg29g4ORtmolhmfpqwtXF6GcnGrm9zIBMpmM4qKimg/U5nbSC4RGdBCaNAfhtdAXaggKEl3SGg2R+rBILy9euv9+QkeM4Pfff8fZ2Zn27dtz4MAB1q5dS05ODoMGDWLy5MmmF0CbIBdPn+btZcs4vGIFDh06wMmTEBzMy6NG0bl5cyZGRMDly3iOGiWG7hkSEGCKJtfguouUXE8OwuLiqlWY60KlgsJC1JaWphcI68LRUXTjlJXxRr9+tOjYEfu0NCR794KjI5ZBQXy7YAGPTZzI8ePHcXNzM3WLbyu0Wi1L33wTQRA4uGcPPo88wswnngBBYP++fWRmZ/PWW28xY8YM2jdrxoxhw3huwgRkvr6wd6+4kVhWJv4/2dnVLPDTCMiuFZ6vx5gOQt1ir0GKlFwPekHHyQnHmBi4coV2uhzT51au5NvNm5nx8ceUlJTQo0cP/vnnH1588UUWL15MdnY2/fr1Izo6mjlz5jBy5Mi7tgLytdi0YQNDO3cGiYR7Ro4Uv+dN9V55g8j0IcamchA2Bed6Q+DhAUVFzBk3jnvvvZftO3bQtm1bU7eqUZg5cyZz5szB1dWVfYcOseatt1D6+WFtYUGYUsm8detYOXYsNn5+pm7qLeHl5cXJkycrTQ569OsaQ4whDuqvo3uNwsJCbG/XEGM9QUFY5ebSXxDAyYnwDh1Aq+UeLy8WP/44Wq2Wq1evsnXrVqZNm2bSpl7rLrW4lmO1JYzSAnfG6HGTaGJiuLB3L2+9/z6/v/oq7Vu04Je9exk+YgR26eliKGRAQE3XhlIpOqBMlD/NEBsrKwptbUXBUk9tg6gxqxjrQoxVJSVYmdo6rK82W/3/wsZGXHieP09k9+5ERkYyadIkcaF58SKbV69m3ooVvPTii3z3/femaXsTp7y8nHfffRcHBweefvppJDqRfO6rr/LNd99RoFJxdMkSKC6m7333iSH6+oEoMdHoCd6NiVwuJ/96RA79MYnk7hEIJRJxU0QQxN/z88UKzoWFEBiI64EDpMXHs27tWiZOnIhWqyUkMJB+3brRLDKSlatXs2HDBtavX28WCas5Hl55/nlemzyZQH34sG6jo3urVnQPCRFFCze3muJgE6JOgfBmchAePgxt29ZdqVyP3gWlVjd8DsKbxcCFJpSWcn9IiFj1uEsXcVMxM5N+RUVMHD+eKVOmVIQbFxQU8OGHHxITE8Obb75JoJGLmd0JaLVaBkVGkhgby4xRo1j52Wd8vGwZJ8+cQSMIWEqlvP7MM0ydOpUeERGcjIlhxq5dyOztRReVRCIW04iKEsclmcwkBRnkMlntDsK6Qozrm6/Fx4uh7tdCH2JsapHD0VHcPLSwENvevz+ChQURGRmoVCpsbGyYOnUqgYGBdOvWjRBfX1atWsXKlStRKBR0796dhx56iI8++oiAgAA8PT2bZmVME5Cfn8+ff/3FX6++Skt/f/GzvoPGXplMZtIQ49umSMmNEhwMZWU89sgjnElPZ926dXeUQHjlyhUCAwNr5KEvLy9n9erVhIWF0bx5c8L9/XEKD6ev3kF54gRLn3tOHCdMvca9RSochIJQKRLq1zP6f/UY20Go0VBQUICtCYrpGR13dzFPq5WVmD9fEAgrLsZKKuWff/5h8eLFHDt2jKlTp6LVaivWyyDOX3JycnBshPRA9d2lzDPL60Wl4rWXXuLzLVvo06oVHfv0AYWChzUayM6G1q3h3LmKkNUqyGRiiGsTwEGhIF8iEROm62nIIiUGE9fi0lLTOQj1+PvX/Vjr1nD1qpgnslkzOHtWFA3lckLKy/l07FhCH3+c8+fPE2HikNemxvnz53niiScoKSlBWlTEH998Q5sePUhMSWHPjh081LMnEkEgoFMnUZx1chInpfpKeJcuiVU/mygymaz2IiWNlYNQrTZtDsJroZ9Q6XdP4+NFt5duwW2xbx8PeXriu3w58tJSOltbi0K9vT0PvPIK9y5cyNSpU3n66adprw/du0soLy/nk08+4e9Nm0i4eJE3Zs3Co3NnfvjhBw4eP86PX3xRebK+upunp5hHtUWLJltJUk+dRUpuJgehRnN9E1F9oQVTixzXS2Ag7NsnCp96x7SLC5w8ydwePWj1228sWbKE3r178/jjj9O6dWtCQ0Lo3q0b/+3ff9eLhDExMQToHLOvv/46+/fvJzk2ltP797N982YGTZrEsw8/zMv33cfrq1fTytubp4cNw8HOjoe7dOGKVIq94aZhUJB4L/PxEedvJqJOB2FdIcZ19Q+NRtyMuxGBsCFzEF4Prq7iIiswUBxLdOJeRNu2ODs4MPWee1iyfj1/L1hA/+ee4/EuXVBMmYKFpydPPP00m1es4MsdO3j66afJzMxk1qxZvPbaaxUFtO7W4j/l5eX06tWLe7p3p0XLluJ3pynPLW4CfYixtry8RiXOxkCt0SC9HcadG8XaWvxp1oyhYWE8+/33dOrUieHDh98RxR27d+3K5s2b8fX35/DhwxUFSBITE1GpVPzx++/s27ePiX37Vt2kvINE0gqBEKoKgrUJhMbKQVjNQeh5uzsIQRy7WrUSf9eNXYJczkczZzJ8+HC6deuGhYUFV69e5e2336ZXr15MnjwZgF07d/L8c89x7MSJBm9mfXepPsBfWq32tqsW3JCUlZVRcvUqdhJJhbC3+++/+WnPHi5fuoSzra0YciKeLH4RnJzERVsTHxQcPDzIKympffe5IUQOg46vKi01vYOwPpRKMfQ4KgqOHhXFxMJC6NQJtm7FXiZj0qBBfPnllywyyDORmppKXlISoa1aNfn/f6Oycyd07MiRCxcYPnw4r8+Zw/R77kESH8/XO3aQLwh06diRZQ8+iNeQIeIk1NKyMvze1lYsLFNSIoapNoFQ4rqQy+W1L9TqCjE2zOFpBMo0mqbrIKwNw4Vonz4ACDk59Dh0SPx/Dg0VBZADB5AFBPDNtGm8vnUrgwYNqrDcOzk6En35MgMHDyYkJITWrVub4p00OLNnz2bXzp28NmMGFpmZvLJiBfHvvsusadP4bOZMrPWioCHBwWKRhGs56ZoA1wwxrv53fQ7C692pNhQ5boewOYVCdA4avjeZDAYPxuLECb6cO5f/rVnDwgULeHj4cD4cMwbBwQFlUhIPjxnDgUOH7ogF2s0wb9483nrrLY4fP05KSgo//PAD9/Tvz4v9+iH18KBbZCQvP/II8x99FGm3bkhLS/F0dkbIzOTxAQNAqaRF9XD8JrIBKJfJUJeWipuVwcGVmwE36iC8kbFIH2Js6jxqtrZiteNq2Ht5kbR6NbEZGTjY2eGsUrF35UqsU1KQ2tqCSsWQdeuwi4vjqX79+HjZMhwdHVm6dCnPPPMM06dP5+eff2b16tWMNSzsdJfw999/I9Nq+WzmTIQWLZq0+/xmkUgkCIJAuUZjkrC3O9ZBqMfKiu79+hE3fz7jxo3j008/5dFHHwUgOTkZGxsbFAqFiRt5YxQXF5Oans6h3bt5d+9eNm/ezLENG7CxteVSXh6dmzXjwtmzHDt+nM+//rrJb8zeLDUEwvJycTNfLxAaoh9XbnWtY1ikpLgYu9vsu1MrUmnlhr4BQ++9l/1hYbTr148xY8fyxx9/8MMPP5CVlERUVBRPPPEE2/78kyuXLzdKM+u7S60GugFmgVDH1atXeeCBB7gaFcXzo0cT3Lkz/m3bMuuVV1j06qs4V88/ZziRvA0GBAcHB/Ly86tOChqyOpHuuVqNpmk4CK+H0FBRsDJsa0AA2NoycdAguj33HGq1mg4dOvDdd99x/vx5SvLzeWriRN746CNTtbpBycvL4+DBgwwcOJC0Awewt7dn6rx55EgkHDh1iq+++oqRwcGQlATt2jG5eXOxIExJibjjWJ+QYWnZJIop1IdMLqesvkIL1ZP5GtlBWKZWI2/in9E1USrFjZXsbNEdIpWKCekBF5WKzzp2xM3Tk1/XrKFFYCCWWi3tAgJYsXQpx06eZMeuXbTRJ8y+Q0hOTmbVqlWc++ILPCwtoW9f7hk0iPKUFCQqlVj8oLZFuoVFxa5kU0cmk9VexfhmQoyvt0/pQ4y12ttDIIS6C12EhdEzPZ2/vvgCLl4UN1q8vCAri6eeeIJvHn2UP/74g/vuu69x29sE+OGHH/jhu+9448UXefnppykqLWXB5MmMaddOnJwLAnY+Pix48knRzezkxOCOHcXPuqRETG3RhN2XMqmUspQUseiOs3NlIbvqIrmhg7CufqP/91pCclMJMa4LW1ss2rQhtKiI1zw9oUMH7A4eFDdMUlPB3h7HvDzo1g1JXBxfPvccnn36MOfVV+nXty9lajV7165l2OOPM2zYMJRNOLWJ0dBqITubN+fPZ8VXX/HR1KkIRUXimHyHuQf1yKRSNGq1SQRCtVp9ZxQpqQe5jw+xa9dyvrSU4RMmEBERgaenJ5GRkRRkZTHhkUd4Y8ECrK8nZ3ATIC46GoDlK1dSWFpK3x49aDNkCO6Ojjz26KO0Dg1l97PPkp+ejqIJRzvdKvb29mi1WvLy8nAwLFRiGGqs52aNRFlZYvoI/aa/wfhVWFSE7e1cpORauLvTMTERDh2iq6cnr7/+OqO7duWPnTv5c+tWoqKiSI+NJbewkPz8fOwb+LOob4S/O7ec62Djxo1MmTyZOWPGEDlzJl/t38+p337j+OLFPNa3Lw8+/LCpm3jLVAiEtraVk8XaHBlG3hlQq1RIBKFph0kaUl3I1CW6bhYZyTdz5vDt3r18tmIFcx99lIlt2tCvWze6zJxJWPfud2Sl1s8++YRXZs+me9euHD9+HKWtLSFeXjzzwAN8OnUqvt27w5EjEBkp7qzpBYHu3W8Ll9O1uG4XFIjvXSYzTvJeHU2iirEx8PcX7znVJ8/+/nDwIG+NHMkbnTsjd3ERF8QSCS9ERfH5n38yeMAA+nTtysvz5tGhQwfTtP9WKC0VvxcSCVqttiKR/uNjxuDh4iKGNXp4gK0tkvh40T1zm4iA9WHUIiXXOx4ZuKCapMhxI1hbi07rgwfFCXqfPmIfCgxEolLx/rhxzHjhBSQSCS1btiQ4OJj09HRmPvEE940eTc+ePfG7zROnA+J91eC+UVpaygsvvMCmOXNoERbGvfv2kRATw+iePUW3nV74kcvFcUiPk5M4JgUFiakQmvD4JJfLUScn10xdU13wu5aD0LBvVb/37t8v5sLUb0Dpckar1WpkTXFTShDEzyMlRSy25+Ymbji5uYn/p/b2ovCrG0P6FBVBYiKLx42j23PP8dcHH9DGyorhHTqwbMkS3nzrLVO/owZDpVJhlZ0N0dHsOnGCr775hl3/+x8tJkyozLV5hyKVSFCXlWGKEfSOdxACuLhgV1JCJ4WCuY88QmTv3qg1Gvp27sxbTz/NS198wdIlS3h19mxTt/S6iI2KwsnentMXLrBkyhT6du7MkL59STpzhvlLl/L+iy9i2b8/lgUF4j3mDkUQBAICArh69SptDAXCmwkxzs0V05lV3/wsKYGCgsq/9a9RWkphaentX6SkPpydxbEqOJiJgkDLgAAG3Hcfne69lwfuv591f/zB6StX8HZ2Jv7qVZo3cOTUHX6Xun5KS0tZtWoVzZs3p1+/flUeO3/+PJMmTWLDggV069YNfH35aOJE+PffCjcCjZAwsqFRKBTk5uaKDgRD6zDUntPmVgVC3XOLCwqwugMWu/j6MrxdO+5p0YKYp54ipHdv8QYok7EhLY0BM2cSGBhIp06daj5XvwNzG5Gens5LL73E7u3b+eHFF7FycWHwxx+zeMUKxowdS1hRkRhaf/Ro1RAoqRQGDLi2W+E2QW5hUXsOQr0QWt1BaOQQY7VGg+xO6D/e3uL3pTqOjtCrF0JKiigO6kP+SkvhyhWmTJ9Oj+bN2XHhAvcMHMj8BQvIyMhg+vTpODXh0PRLly7xxRdfcOHCBU4dOsSrjz3GjPnzWbd+PY+OHUv7Nm34evx4CA8XJw0gLmz79btjQliuWyA0rMRqeNyQG3EQ6kKMm2yRkhvB0VGcWFpaVr2nWlkxKDKS/ufPM2PGDDw8PPjss89Y8v77lCQn89M33/Dc008z5uGHGTpkCEOGDTPde7gJDh06RHBwME7W1pz/7juaP/4427Zt48nnn8fe3p7mQUG0794dOnTgz379yMrMRObqWv84GxEhioZyuSgoNWFkUqlYpEQmq9pf9N+B2gTC6udUP16dggJxwaYXA3V9R6PRYNGUN6U8PCqLzemq8VagHz8EAbp2BUHANzeX+FWrEEJCIDiYZzIyuPeVV3h97twqCeLvBLKzs9m0aRNTpkxhzqOPkpCSwh/79/P1m2/SYsCAO2ZsqQ+ZVCqG518LjUYUlI24iXLH5iA0RCoVN6tsbZnZujVPPPIIOQUFyK2tsWvRgk99fOg5cSIPP/IIAU3YpQ3w008/sfeff7infXsSMjKYOGECyp49aQMUx8ayZudOWvboId5P7mBxUE9wcDBXrlyhjUJRv0B4rXRKaWniHL66QFheXrUOgv41VCoKVCpsb4dIw5tFECpyVrp27MgQX1+IiOCvpUtxt7bmpQcf5MiOHcz96SeiL1zg2KlTtGvXjhYtWjRIc651l5oiCMI913EdrVarfccYDTIVzz//PMcOHOBqfDxfvPACw/r2BScn/jlxgv+9/z4vPPAA3by9xRBT/c29VSvxhqDPn3ab4+DgQF5eXs3cAnBjOW00GrGDX+sz0T03PycH+9vEal4vEgl0744sNpaQZs3Ezq77rrQZMoQvsrIYMWIEixYt4tFHH0WtVlNQUMDUKVMoS0oiQ6Xi7127bov8HEeOHGHatGkEurvTNiSERwYPRpBIoF073vj8c/G7k54uChq1CYF3iDgINyBy6I81RIjxnSAQQt2Ld1vbmk4ZCwsYMABBq6W5nx/N5XLaBQTwwQ8/kJCVRWpqKm+//TZbtmzB29ubCxcuiNXHmwALFy7kww8/5P5hw3isf3+sOndm8uLFbNm/n5jkZL574w0e7NIFwd+/UhzUcwct4KRSKeq8PDh1Sszpq09vUX3cuR4H4Q3mILyjnBzt29f+mXh5sfKZZ2DpUqY/9RRPTZmCv6Mj37/+Og6CwOajR9l47Bgzpk7l/OXL2NxGOcdmTJ5Mv379GNWrF71mzOCIRMKst9/m9WefZcu+fYzr0UPcdEC8R7u5u1/7orfRAk8mk6EuLxfvB4Zzsupja20FfgzPqW2Op6f6Yk3vvm2qIcaG6N2Q9Ql8+vfg5ITQvr24OS6R0DYyEqW1Nb179yYjI4NvvvmGLl26UFxcjLW1NTt+/x1rGxu6Dh7c8O/DiJSVldGuXTtsLSz48Mkn+WXbNoaPGMHX993HPVOmmLp5jYY+xPiaFBaK1eONKBDeUeNOfeidXg4OCN26YWihCRs5ktljxzJ65Ej2HTpUM9Q4Lk7MVd0E1gmXTpxg1XffMXv8eH544YUqRUWt/f05deHCnS1aVSM4OJjo6Gjo0KFmiLEh11rr1Hdc3zfPnq3UEUpKRAfh3fJZOzlV5N73Hj4cAJlWS4+QEHx37eKpF19EIgi4ODvz3Y8/EhISgqqoiJ2bNjHsoYeM0oRr3aUeBK7jLooWuG0Fwp07d7Lh9985/fHHnElM5IF338X3m2/oFhbGj9u3M3XkSJ565ZWahUbuhHLbBjg4OBAfH18pEELduQX0/9bWwZOSxDwC1XOC5efDhQtiYQ+D6+RkZ6O8jSbm9WJlBWFhNY/7+3PvgAG4eXsz+Z13ePmFFygqLkYAJo8YQcehQ1mzZQsrFyzg5fnzG73ZN8rKjz5ixsiRTG3fXhQGu3YVb+SGIUqGlR/vYOQWFqhry6NWV5EScw5C4yEI4o9OVO8+ejR/DBtG2rlzNJ80iZSUFH755RcC/f3JycvjnXfe4bXXXqsodtLYlJaW0qtXL5ITEji8aBG+etHC359Tkyfzx5df8se2bdw3eDBChw63nav4RpHJZJQUFUFsrHjv1KVrqNNBaIwQ49utSMn1UNf78PVFcvkyHD7M508/LYbWeniIn9OZM9wzbhz3DB5M6bx5eHl5MWjQIFauXImjoyPHjx/nyO7dDBs6FK/Q0EZ9O9eisLCQ85cukZCUxKmDB2kfFkbfZ56hR+vWjG3RgnEREeI94XpEwdsUuUwmjjuGAqGhk+N6+07186o/Zphft6nnILwVDJPGu7uzeNIk4lxcKCotZebMmbz2/PNMmzmTU6dP8+wrrxDs68u620wg/P233/BzcWH3kiXg48PMBQvu+DGmNvQhxtdE74AyIiYv8NMUEASefe01jly6xLRp0/j2228rCmlpS0v54M03mfXee9g1gTX2g716MXfBAvxCQsSNaheXKo/b3ckhr7UQFBTEuXPnxHX8rYQY19W39FGZIAr0+t9LSihUqe66z7sKggCOjvh6exO7aRP7Fy/m2c8/p1P79gyOjMTZyYmV33/PLnd3ekdG3vLLXWuEH6zVag/d8qs0YZKTk5k8YQJLp01D0b07PTw8uDxpEseOHuXj5ctZPm8eY6ZMqdwNuYOp4SCEuq3DUHfSa42mslMbUlpaNbeA7rnZWVk43ikCYX20b083mYwDH33EhZwcfIOCQC7Hs7AQmjenxcCB9BgwgD3HjrH2jz+w0jmFEhIS+HvDBqzValpHRjaJiq2fv/GGKAL7+oo7rHdB/6gLmUxWf5GSG7Hd3yjl5WIV46Yc7tWY6MLH3AIC+GjGDCYuXMiTo0ax68QJjm/axJmiIkY/8ghdu3Y1ST+ykMtZ/thjtA0IQB4aKt4nw8NBEPAQBGa8/jozXn+90dtlKmQyGUUajbi5YDhZ1DuhqrubrhVifL0OQkBdWnrniRzVkcnEkBW1umb4fq9e4r9BQXz++uu8V1rKax9+yP2jRqG0tmbfoUP0a9WK2fPm8eyLL/L00083eFLs+tBqtSQnJ7Nv3z6iL1+mbWAgQ7p351JMDD/++isHduxg8H33IZSXi2GxtrZNwoHSUMhkMjG1hWHfMew31xuefy1XrlotFkI5d+7Oyt9ZHzIZ/QcNgowMtDIZW+3seGj8eAa1a0ff3r0pLiwkJimJkpISLG+DzTmNRsP/5s9n8cKFrHnnHTHs+jZod0Mhk0prL44VFSUK7r6+4t9GTAWjR6PR3D751hsQwcvr/+zddXhcZfbA8e8diXvSeNqm7i4UStGW4hR3WaALC7v8YHEWX2CXhV0WWygs7r5okQrQUndJ2yRN2ri7jtzfH3fuWCapTfx8nqdPktE7kDfvvec957y8ctttHH377bzwwgvcfPPNtNTXs+W337j7zTfZ29rKy++9192HyegTT2RiejrDR43S2rv0c0OHDuWrr746+DhBR71vD5RB6J7B7ggQ9psMwg6kpaaSGhfHjHnzWDF+PNagIG7/xz/46eefeeWPf+Ta3/2OJ59+msGDBzN58uTDfh+/zPCKophVVT2I5Zie5ftvvuH666/nxtNPZ8Hvf+/cBS40NJRj58zh2DlzuvkIu5bPAKGv1GH3i7ZDuVBzH+z79ztPWKtravpOBmFHTCaYMoUIYIaPu8fPmkXx1q387tprGTlkCDEREZw+bx6L3nqLeZMnYwf+/Oij/PXxx7nuuuucK27dwj1Lsges8nWndjMIu6LEWFX7/sXa4QgM5PK//IXjZ88mZe5cmurrCSkq4ujaWtYtX06qryzfrqAoTL/+eu3voJzoYDKbtTLJ0FDPRSXvE059HvK1M7j7cw42gxAtQNgnehAeyMFkck+dSnxmJi/ccw9PvfIKqYmJvPHss0TEx7Pzww/567ffMmXKFJYvX05KN/2937R+PfNPO41JI0bw26ZNLDz1VB447zwt8ys1ldOvuKJbjqu7mE0mrKrqGSD0lUF4oPL8jjII9XO2lhZtgdfR07BPZhB6mzABWltRgA+eeYavPvyQc2+6ifeef57IoCCe+OgjZs+cybHHHUdJeTlvvPFGj12oe2vRIn764gs2v/UWA885p7sPp9uZjEbfGYSNjW37c0oGYedQFEKmTuWTW27hqDvu4Mwzz+SyM88ku6CAWxcs4L0ffuBPO3Z0Wn+1gxYczKrPPiPIrbS4P3OWGLufn7WXQQjtJxJ1lEFotbriCG4Bwj7fg/AgzTvxRAa0tmIYNgzDsGGYzGaef+01WLcO0tNZsmcP559/PpdddhlvvfXWYb/PYc/wihadOBG4BDgX6Lnd4NthKyzkjQce4MSrrtJ2A+znIiIitE1K3Btbd7QyoN/f3OzZIL2jFQN9Uq6qcmUQVlcT3Qv67nWFkKFDeffVV9m9YQPVwJ8eeognHnyQa2+5Berq2PnRR1z5zDN8+eWXfPDBBz3jj2UfztI4GCazueNNSnxlEHrffrgkg7B9oaGkOjZeCNH7eUREkLZrl5Yh0F1jJzCwX2dvuDPpZZIhIW0zCN0volRV+7mjsrBD2cUYsFksfT/IcbCCgmD8eIKsVv4SGgpjxjh3yR5z4om8N2oUj3z0EZdddhlLlizplgvcKaNHU/rRR2AwsG7DBuKGDtVK0vvpApXJZMICbTM59LYLuiPNILRYXPOWW4Cwz2+0oG9WAwROnMj58fGQksLlp58OisLQIUPIzclhZVYW27Zu5ZtvvuGcc86hrq6Oc885h6NmzmRffj6PPfYYaXpGWje58txzueL44zFJkANw9L71NZd4BzP0OUX/6oe/e5JB6GbAAIZdcw23/PorF557Lvvy8jh61Cj+eOONxEdH88Tjj/POu+9291ES7Ng4QsDAgQPJz8/X5h33jRh99SDUvx7Kgq57YNGrB25DU1PPuObtZoPGj2dQQoLndURsLMybB1Yrr992G7ecdx7nP/ggqqoedjJRRzP8PqDF+0ZFUY5CCwpeACQAlcD7h/Xu3ey066/v7kPoUSIjI10ZhAezMqB/v2GDdkGh7+TcUQahPgG79RmobmggKiKikz5V72McNowxjhO59e7NRiMjGfO737EqLY2Fb77JJ598wlVXXdVNRyl0h7xJSUdlks3NWqbpgS6+bDZYtgzmzNF6EMoJ58FJS9Mm1T600UdvZjQaXQHC5mbXHd69b71PQL3nl46yoJqbtQt9/eKuP5UYHyq9JNnd8OEwaBD3NTQQlpiIxWLpngyYsDA44QQApgcHa+cbPaw3YlcymUxYDQbfFR/uwXLv8vz2xo6vzEK93Eu/yHO8ttViwdSfFjkMBlcgOi0NGhoYc8IJjDn9dE6rqmL4K6/wl/vu47l//pO169dz3jHH8PKLLxIQEsL/NTQwZcoU7r333m6r+jAmJPTpfpyHqt0SYz1zSaf/3hcUQHW1tjHlEeoXuxgfCpOJO2+5hXfOPptLzjiDf111FcyYwR+Cghh61llkZ2cz1HuDOtFtgoKCUBSFptZWgr3jBN7JQ963uWtvQVd/vD7v2GygKKh2O42SQaiJjHT2XfdgMEBAAEEnnMDMrCwUm43MzExG6L29D1G7f6VUVXXuPa4oyni0oODFwCCgFQgAbgNeUFX1YDYyET2cR4mx98pARxmE3j0HO8ogBNfAdwRVqurrJYPwYBmNmKdM4TVF0XqXiW5nDgg4tAzCjgKEe/Zof/gHDer4TW02aGpCtdmw2e1SsnIovHcFFt3GI4OwocF1h55B6H4C6p1R6K69IAdovdPi412bEOgBwr60SUlnCwjAOGMGtwUEOLOqutXkyf0+c91sMmHRL8J87VDc3iYl3toLrnufr7md41lbWzE5Mkz7nfR0z5/j4rjwyispLC9n4tChHP3ii0SPHk35/v3Y1q5l5h13sGnTJpKTk7nmmmu655iFB5PRiLW1te0detaS989Wq+++6oeh3+xifAgCJ01iyRdfEDlwoLYJiMFAxKxZ3Hjqqfz1kUd4/c03u/sQhZuoqCiqGhoI9rUwBa7FJaOx4wzC9m4Hz3nHZKKloQGT0Shj52CYzSjp6Zw7axbZWVmHHSBstwGPoihDFEW5T1GU7cBm4M/ADuBKYDigAJv8ERxUFOU1RVFKHe/l635FUZRnFUXJUhRlq6IoU470PUVbHgHCjjII3cu/3NPv3e/3HvgtLa7b9JIV9wxCCRAevPh4lLlzUfppaVVPYzKbD74HofukeKAdvDqiBzlaWjAZjd3bj1KIw2QymbApipa55mv1ub0MwkMJENps2vyjc+9BKAHCgxcXB8cf75cyuyNmNh84y7qPM5lMWBXFdwah+wY/3vNMez+3V3rsnkHoeEy/yyA8gJBx47j3iSc4/cILiR43DoxG4tLTSTjxRHIXLeLThx/m+8WLu/swhYPRaPRd9eErg1A/Z/NTL0LJIPTBaCR12jTC4+Nd87zJxJ9//3t++vFHli9f3q2HJzxFR0dT3dDgGhN1dRAe3jZu4L5YlZfXto3MwSYSGY3aBiUhIZ33ofqaoCD+/X//x6lHH33YL9FRh+4s4BGgFvg9kKiq6pmqqr4L1B32O/r2BjC/g/tPRQtKDgcWAv/x8/sLOtjF2PuCwFc2h/fFnffAX7rUFfhwH/gmk5ZBqJcni4NjMPT7C6SewhwQ4Ptk83AyCA+2j5pjvFkaGzHL74HopUxmM1ZoW4pyqBmEBwq6u2eLSA/Cwyf/vXoMs6+x46sHoX6x5v6zu45KjMGzB6HjfWw2mwQIvSUktC15HzAAjjuOyePH88Fdd3XPcYk2TEYjtvbO2bznIf32I+0Z3dwMdrtkEB6CyDFj+M9NN7E3M7O7D0W4iYqKoqquznUd39gIERGei1L64hVot+/Zoz1O11ErMmgTJ6iXAOGhO8LS/AP1IBwEjAOOB4oURfm+M8qJVVX9RVGUwR085GzgLVVVVWC1oihRiqIkqapa5O9j6c/CwsKor6/HpqoYvVekvYMc+gSnBwO9J1XvgKHV6mo2qp9wOlYGJINQ9GYmsxmLxQLff69dJOg9vA6nB+HBrlTrAcLmZgkQil7LFBaGNTjYs0wSfPcgPNQS49JSrWzZPUC4Z49nibGMHdFLmcxmLL56EOoBQu+xoz8mL0/7qrcoaS+47qvUy/F+Vru9/5YYH6rgYG1H5MrK7j4S4WA6lAxC/fYjzSDctg3S0iSD8FAMGMAZ55zTM7LWhVN0dDRV9fXamKit1bIHvRehvDMIfcUFDpRB6HiezWDgoiefZM4RZMP1S0e4OVa7GYSOHoRHo2X3nQR8BZQoivKK4+cjXE45JClAntvP+Y7b2lAUZaGiKOsVRVm/YcOGLjm4vsJoNJKYmEh+eblncKOjEmOvlWXnc9pbhQOfGYRRkkHY7WTsHB5zQIBWYmw2Q1OT6w79Ys09eOE+NtqbHA8lg7CpCbOcPHU7GTuHxxgcjDUw0DOAsXevNld4ZxC6n4DabJ4X3L6CHPX12smrqmolxjYb7N7tnIckQNgzyNg5POaYGKzR0Z4BwsZGbRMmXwFCXXOzz4zaA5YY63OX0agFCGXsHDyDQSvR9yMZN4fP2F4PwvYChP7IIHT0arfKLsaHZvRobbd6P5Kxc2Q8MgirqyEqqu2c457J7itA6OvnjRs95x29DVlzM7sLCnj7lVc6/bMJl45KjFFVdbWqqn9CC8bNA74AzgM+cTzkekVRpnXqER4iVVUXqao6TVXVaVOnTu3uw+l1Jk2axKbMzI4DhL7KvXz17XD/GXwHCB0ZhNExMZ3zgcRBk7FzeExms7ZJSXx82wbX7kEO73FwJBmEepCjpUUu1HoAGTuHx7kDuHuQY8cO7av3rsXuc051NWx3a1nsK8jhvnjV2uo5/yBjp6eQsXN4nPOO+9jJz3ftttteeb53GaWvXoXuj9ErPsC5qGtVVRk73UzGzeEzBQZiq6yEzEzPOaO9EmN/ZBA6AoQ2m0163x4KPUvaj2TsHJno6Giq9QzCpiZtkznvZAjvDMIDtSKzWKC4uG0GIVDb3ExUaCiKBNa71EGNOlVV7aqq/qSq6rVAArAA+MjxdY2iKBmdeIwABYB7rmSq4zbhZ5MnT2bT7t1tm157Z0F5b1JyMBmE3iXGjlLl6oYGySAUvZbZbMZqt2vlxd5lkibTgXeJ9L7tUEqMm5qkxFj0WiaTCZteuqiPHZMJTjzR9wmnrr0gR0cBQv3xra1aFlRLC0Y54RS9lEdw3WbTzqsqKiApybMHoXdw3VfmBniOne3boaBAe553cMRkoqm1VQKEotcyBgZizc+HXbtc1yX6uPAOGOpffZ2vWSxaK4uD4UiKkAxC0ds5MwhV1ZUIAW03lfPOIOxoM1P38zWveaemuZmIkBCtSkt0mUMOy6uqalFV9X+qql4CxANXAJ3dQfRL4ErHbsZHATXSf7BzTJ48mU16n6b8fMjNPXBvAV8BwY4yCC0W1/dGI5V1dUTHxnbaZxKiM5lCQrAYjVqvIfcAoa8ySXeHu0nJunXarmE4ehDKarTopZxBDvcSY7tdG0sdlUn6WpRy/+r+GPcSY3BmQdksFumjJnots9nsChCqqnZeFRCgjZMjDa5brVog3WxuEyBstdtZv2cPU6ZM6cRPJ0TnMQUFaTuAg2cQEA6tB2FNjZaFeDD0DELp3yl6uejoaFeJsX6d470odaAS4/YqD+12bR5zG3O1TU1EhoTIJmld7Ij+a6uq2gi85/h32BRFeR9tI5Q4RVHygQcBs+M9XgK+BU5D21m5EbjmSN5PtG/y5MncpmcQNjdrzUfj4pwBCY+Br5+Yegc1OupBqCgeqcOV9fUoikKkZBCKXsrkKLnyudGCd4mx94Wbt4MpMa6vh7AwACwtLZhlVU30Um1KjPW5Q1+EOtggh68ehO6LV1arFkABZyDFarNhCg7u3A8oRCcxmUza5lj62HHP5OgouK5n7Op8ZRDabNqYMZs9g+uKwq/btzNy0CDi4+M778MJ0YlMJhO28eO1QMTBBAjbyyA82JYw+mMdPQilxFj0ZtHR0WzXS4zdA4Ttna+5n9u539ZepaHZ7NEnt6apiYi4OL+XmouO9YhwrCMbsaP7VeCmLjqcfm3QoEEUl5fT2txMAEBiotaA1FdvAf0PwsFmEFqtbVYGsoqKGJaUJL0FRK9l1ncxNhp9lxi7By9MJleg4nBPOO1252tYW1owycmm6KUCAwNpbGx0BdcPJchxKCXGoC14gTZ2QkJkJ1bRqwUGBtLU1HTgseOrB6GvHcG9z+H0BV2j0TVnKQrfr13LabNnd94HE6KTGY1GLNo3bXrTthu0cM+Qcr//QOdr+lzlGFM2mw1TYOCRfgQhuk1UVBSrN2/mqVdf5fbzz/ddYuydUej+VX9se8F4s9k15wC1jY1EpqZ2wicRHZFwrPBgMplIjIujsKSkzcpAU1MTl15+uStA6J31oWuv/EtfGXArMc4uLGRoYqKkDotey2eZJGgBiaAg18++yiS9tbeLcXm51hMKXP2mkB6EoncbMmQIe/fudc0levYgdNxH7VADhGaztsOrzmTSMjkkQCh6qREjRrBz505XJYd3ZmBHwXXvig/3r/pz9NdzDxAaDOwtLGT00KGd86GE6AIhISHawpT7OVtnZRDu26eVITvO26x2u/S+Fb1adHQ0u/bu5eEXX6RZH0feC7rum8voY+tQMgjdAoQ19fVERER04icSvkiAULQxMDmZ/QUFzl2G9YG/detW3v/oIyrq6lwnjgezMuCewq+n9Dsen52fz9CkJM8TWCF6EWcGoXuJsdWqlWaFhnqOEV+ZG+7aO+GsrnYFCN0yCC2NjVJiLHqtQYMGUVJSQpO+icghZhCuWbOGJUuW+J6H3HsQBgZ6lKxgMtEiPQhFLzZ16lQ2bNjQfgahzj3oDs6xs27dOsrLy32PHb3E2DtAqCgUV1aSmJjYuR9OiE4UHR1NdXX1wWcQei9Iud9/oACh1eqqmmpp0XoQyqKu6MViY2OJCAtj5ODBLFm3rm0PQu+9CnwF3w+UQeheYlxfT2RkZCd9GtEeCRCKNtKSkrQAoc3G4l9+4fV33sFqs7Fx40YAMgsLQVF45N13efLTT7UndbQ7kX6fPvDdehBm5eUxbMIE2Z1I9FoRERE0NDRgcV8lq63V+ne67wB+MAFC72xcqxXy8lyvCW0zCGXsiF7KaDQyZMgQsvbu7ThAaLWi6rfrF3V2O48//jgff/zxwWUQtrQ471q/Zw//WbyYufPmdcGnFML/hg4dSl1dHaUVFQcfXFcU59i58847+eabb9ofO3pZpdHoulgzGCiprCQxIaFrPqQQnSA6OpqqqirPRV29pN5XcoPbNYuHgy0x1gOMLS1aBqEkRIhebPz48Wz69lsuO/VU/vfLL6gGA7+7+WbW7typPcB9rwJF6XgB1/1n8MwgdCxs1TY0SAZhN5AAoWhjYHIy2/fsYdmaNVxz++0888ILPP7uu2zatAmj0cieggJQFJZs2sTL33/P56tWUasHL6BtkMNr90iPDMK8PIYee6w0HxW9ltFoJC4ujpKSElfwoq4OIiI6zoI6mF2M6+pAD540NWmTpnsPwqYmTBIgFL3YyJEj2bNnjzYH6L08dW6LS6dffz2/bN/uvKirqa9n8eLFVFZWtt9HzT1A6LYivXzTJi476SSOPfbYLviEQvifoihMmTKFDVu2dBwgdO9BqAdE7HYyMjKo0IOL+uN07fUgNBgorqggMSmpaz6kEJ0gKipKCxC6V0Hp1yftZTUdbgahHmyXDELRRyiKwpDBg5k7cyZLNm7k599+44NPP+WW555D1dtd6FmF7kH4Q+1B6Li2qamrkwzCbiBRGdHGwJQU/vnGG5xx223MO/54vvjwQ/792Wf8+OOPnDhnDu8tXcpdzz3HxsxMbHY75z7xBC998AHnnXcedrudGj1Y6L0yrZcYt7ZSXltLQ3MzWfv3M2zYsO75oEL4SXJyMkVFRa4Tztpa3wFC97R7VdV6C7rztarmfnJaXa19dQQ7LK2tkkEoerURI0ZoAUI9EOHeg9AxduwWCyvWrSOrqMjZr/DHzZsJDAzUAoTtBTn0f455R1dcWUmyZEGJXu60007jzffec/2eG4388ssv/P3dd30uTP20ZQtX/OMfVFZXU1JSopUYtxdc10uM9Y21DAbqm5ux2u2EjxzZxZ9UCP9xZhC6lxjrgQlfWU3t9YZuL0Bos8GWLa7v9QB7S4vsYiz6BoOBsUOGUN/UxB9uu40Xnn6a0upqtm7dqv3Om0wHDhC2l0GozzmOQLpkEHYPCRCKNtKSk7FYrax75RXeeOEF0tPTefOOO5g2bRoXnHUW369bx5NvvUVSbCxf3HcfL95wAw+88AKfffYZb7zxBhOuvpoWi6XtiafNRqPFQl5hIXe/+Sb/+OwzquvqSE5O7r4PK4QfJCUlaQFCfTJsbobg4LaZHO47gNvtsGrVgft3up+E6gFCR/lXq9WKWfqoiV5s1KhRbNczAy0WPl+xgpaWFo+xk7lvH3X19RRWVjrH2O6CAmZMn65d6HVUYqyqbTIIiyoqSJIAoejlbrjhBpb98gsZubmsWrsW1WDgwQcf5KkPPsBms9HY2IjdLbNwxc6d/LJjBxm5uQCeAULvecc9g9CRhVtSXU1iYiKKzDmiF/PZg1DPWPKeQ/Rsv0PJIGxthf37XRsyOuYe1W6nqaWFIPfN64TojQwGFLudEyZMICwsjKsuu4wTJk1i5cqVYLWSVVDAkvXr29+Y0btayjuDEFi9ezel1dWSQdhNJEAo2pg8fjw3X3wxYwYORHGsApxx1FF8/PHHTBkzhuiICG684AJmjR3LpKFDufqkkwgMCCAmJobPP/+c/SUl/PfHH9v2HbDZWPTpp9z8/PPklpXxxZo1DElLwyDlxaKXcwYI9RNOrw1+AM+dvdpbVfOVQahnEQYEaGXGoL2myUReWRkpUu4lerFZs2bx22+/gcHAZ199xbn33sszzzzjMXbWZ2QAaAFCxwlnVlER06dM8cwg9A626zuxmkwePQiLKitJksx10cuFhYXxu6uu4vZXXuHoCy7g8VdfJTMzk4ToaFavW8f555/P1ytW8M2vv6KqKpv37mV/WRmrd+4kIiKCsrKy9vtDuW9SAmAyOQOEQvRmPnsQNjRAWFj7AcKDzSDMznYtRtXVebSEKaupITgoiLCwMD9/IiG6mKOS4/ErruDTTz7BYDRyzNixrFixAqxW/vvllzz57rse7cM2bNvmauvSXgahvqAL3P/aa7y2ZAm1sotxt5DIjGgjLTWV5+6807OPgGNynDJmDDs++ohn77qL52+7DRSF4MBA9n/zDWeddRY//fQTJ0yaxBK9Lw54/BH4adUqsgoL2V9WxpacHIYOHNgdH1EIv/IZINRT7H2VGBsM7e+a592/U88i9CqTxGwms6iIYUOHdv4HFKKTjBw5krq6OjZmZfGH++/nrYce4h//+Ae7HVlOqqry1cqVzJw2zZVBCGQXFTFj6lRXBqGvBvPuGy1YLM7y/uLKSpImTeriTyqE/11++eV8u24d44YP5y/PPss///lPzpkzh6+//54NGzawescOzrz+evYWF7M5O5sBkZG88cMPHH/88ZSXl1NZVdV27DjmoRaLhVHnn09OcTFPffIJxdXVJEjmrejlPEqM9fOt2lqIimp7PqYHyNvLIPS+LycH6utdr+lWYpxTUkJ6Sop/P4wQ3cFRlTEkMZG0QYMAmD1uHEuXLmXZypUsX7+e7Xv3OsdPc2srNzzwACtXrsSi91IHny0ucouK2JKTQ25xMUu3bpUMwm4iAULRll7+6B0g3L8fxWIhKSkJk9lMeGio84IrMjSUYcOG0dzczMlTpmgXcm4Dv7y2lpP+8hd+WbuWnJIS8h291yRAKPoCjwChPnY6ChB6977R+VpVa6ePGiYTmYWFDB8+vPM/oBCdRFEUZs+ezfx77uF3CxZwxZln8sQTT3DBn/+Marfz0ksvkVVYyIN/+Qv7SkuZfP31rN61i6ziYqZOmkR9fT1WfXMT9yCHPpYMBuz6KrajNLKoqEgyoUSfMHb8eP58zjl88+KLfPTss1x44YWcMHUqn3z1FaWlpXy6YgWqqrJq1y4qams5Y9o09uTnc8stt1BWVsbw005jb2mpz91bd+3fz+7cXD5auZK/v/8+xVVVMm5ErxcVFeVZYqyqrgCh9zhwzyDMzISKCtf9vgKEbiXF1Na6zgfNZi1AmJramR9NiK6hVzS5bYw1LDmZO++8k4tvvZWte/ZQ29BApSNYftUzzzAkNZXExETtWsm7tYXdjqqqVNTW8tJbb/HYRx+RV1rKql27KC0vlwzCbiABQtGWI3XYY1c8VYXt27WVMbPZcwtzx/36ZiMnTZqkBQjdJs+VO3eyOSeHKePHExYcTHBgIOmJiQwbPLjrP58QfpaUlERhYWHbDEKDwXeJsXuA0LsRdkcbLbiVSWI2awFCaRgverkbb7yRR669lr/eeCMYjVx33XWoqsry1av55Zdf+NMZZzBm3Dg25+SQX1bGOY8/TnV9PalJSURERFBdU+OZDQKu7w0GTrjsMtbu2QMBATS1ttLY2EhMTEz3fFgh/ElReOraaxk4YAAXnHkmAEeNG0duXh4RERHsKSgA4PUlS5gyciQnTpjAjaefzrhx48jMzKSypoa1WVk+gx3bsrIAWL5tG+XV1Xy7ejVDJWNd9HJtSowbG7XrmoCAjnsQ1tRopcju97t/1b/Xs9Xr6lzneXqAMC2tcz+cEF1Bvx5xCxAqwG233cZzd9/N/DlzGD9sGB/9/DM/btrEyowM3n7iCdLS0igoKGg7dux2Nu/dy5Rbb2X95s0s3baNqLAwTpw0idTkZFmY6gYSIBRt6UEN9wxC0AZyQ4Mr8OEeILTbGTZsGFFRUUwaMoTiqiqtObbjvjV79nDz6aez7IsvSE9MJG3AAM6dPZuZkyd3z2cUwo+Sk5PJz893nXBarZy2YAFbMzI8dvGubWykrqmJeXfdxUr3Xe7A926S7g20vTII7UYje0tKGCYBQtHLnXLKKdywYAEGR89ARVFYeOGFLPrgA7Zt28b4gQNJdJRmXTJ3LjeeeirDkpIwADExMVovNfdgPLjGkaKwMzOTdZmZEBBASU2NttGCPncJ0dvpLSscmbKhISFMGT+es884A4BRw4ezdOtWTp4+nctPOIFnrruOmJgY7I4xsj4ry+f8szUzE4AVjh6g3/z2G6effnpXfSohOoUeIFT187W6OoiIcFVP6dxLjEEbY+4VH+1lEFosEBiona+5BQhzS0sZLAFC0RfoiULu40NVobiYC+fO5dNFixg3dCh/euYZTnnoIW6YP58Ak4mUlBQtQKiqnlUfdjv7y8rYX1bGzytWUFlXx6CkJP732GOs/fFHQkNDu+dz9mOm7j4A0QN5ZxDqfdBUVQsQJia6VsjcAoSTpk7l66+/JrC+nvCQEMrLyogPC2PpypUs376dv1x4IYrBwJCkJBpaW3nqhhtAyiNFHzBx4kT27dtHYWUlv27YALm5fPf998w76igmnH02WVlZfPryyxQUFxNisbAxM5NNWVkcM3Iku3fv5oW33+bZf/5TezFfQQ4fAcK8igpiwsIIld4coi/Q+wSGhABwyemn85d//5tWq5XRgwcTGBREXEQEcyZP5twxY7jx1FPBbicmJoYTL76YC2bP5t+33YYz7OcYO/UtLZRXVrI1N5f//fYbezIyZDVa9C2OHcDdszl+d8klDB4+nB+++46LFizg4Sef5OSZM7X7VRWT0Uh0dDSDExJYt3t3mwDhlf/6F4s3b2bymDFs2rmTaWPHUlldzejRo7vhAwrhP4GBgZhMJhpbWwnVA3oBAb4XmUxul8kHChDqO7PqAcKWFtc1kslETmkp5zj6tQnRqymKFiR0m3Ow2WDdOi1GYDRy4wUXcMUpp2CrqGDq0KFgt5Oamkp+fj6tAwcSEBrqMXYKKysBSExIIMxgYHBysqvqSnQ5CRCKtvSTTfcsQX0QNzW5eqvp9wOoKkajkWOOOQYWLyY5NpbCggIagDMWLqS1tZUZI0aAwcCQlBSqGxs9ny9ELxYYGMiZZ57JwscfZ8XmzdQ3NBAaGsrqTZvAsXnPEy+/TGpiIkGKQkVNDRl5eQD8unIl3333HTz1lPZivjIIrVbyKyoIrq0lNjYWLBa25eQwdtAgmTxF32AwQHOz84QzLiaGqWPHUlRdTVBwMAB3nnceJx91FIb6euIdDeWjo6PZtGkTn69YwTWnn87k2bO117PbqWloYJfjpPOzVav4dM0aZo0cyRNPPNEdn1CIzuEjQPj7K66AwEBeufNOpi9YwCcffMD0sWOhrEx7jN1OXFwcV595Jvc9/zyqzaYF1x3zz687dpCamMj5p53Gpp07uePaa7HU1UnmregToqKiqK6vJ9Rk0saOd89oOPgAoVuVCOAKODY0uM7PDAatxDg9vfM+lBBdKSCgbYAQtJJ9k4nJo0drY6aoSLvdbiclJYUnn3yS7xIT+f7vf/dYmCqsrOSE8eMZf+yxFO7YweCUFC2JKDy86z+bkACh8MHHyaaTqromUv2fj95PyXFx5OXn89RLL/F/V17J/82ZQ1xEBBgM/On882nV30dONkUfcd111/HInXey8v33ue2BB5hz7rm88tJLoKpsdezEVdfQgN1ux2AwsCs/H4BtO3dSWFiIard7XKDh/r3VyqOvvUYKMGnUKE4aNYqt2dlMlH5Qoq9wX5gCUBQunD+fFbt2OS/S7jj/fIiMdO0S6cggPGrSJGKDgthbWMjksjIYMADsdu57+22W7dzJ9ClTWLdxI4/+6U/85Ywz4MQTu+lDCtEJvM/ZQDtXs1o58/jjITGR7c8/79ykBwC7nQULFnD2nDk8smgRZRUVxAPYbNhsNgorK8lYvJiVu3YBcPrJJxPa3NyVn0qITpOWlsbarVtZMHGiFsTQSya9z7/0AKHRqD3O1/mZ94ZzFgsEBbmqrwAbsL+sjEGSQSj6isBAz5/18eAIEDoTidzuT0lJoaioiLLSUrKLihjqlUF4yZw5XP/00yx+7jkiExIgOblrPotoo8ekniiKMl9RlN2KomQpinK3j/uvVhSlTFGUzY5/13XHcfYLZrNHJofP+w8iQPi7m2+moKCA26+6ivi4OO0+RSEhPp60pCTteZL9JPqIY489lh9ffZWxgwax+IknuPfee7HZbPzhiSfYsmULU0ePZs7MmUSGhjJn0iRngHB7RgaNjY3UVle7yvt1biec+SUlZBYWcuOzz/LT5s1s2bOHiSNGdP0HFaIzmExaCb3bwtTCCy/ktRde8Fyscp+X7HYSExOZP2cOAxMTySsogLVrnfdt3LuXnbm5TJk0iXmTJ3PV+efLopToe0ymNg3jycqCfftcF2rQZuz8/e9/Z1BCAunJyeQ45qPvFi/mnZ9/Jio0lKDgYIYNG0ZaXByhjgVeIfqCxx9/nFsefJCS0lJqKisprKggMzu77e6q+u+8HiB0zyD0fqx7gFDv1e4I3BdVVhITHk6wZEOJvsI7g1Bntbr2L/AKEA4cOJDk5GR+f+qpvLt0qUcGYVF1NckxMWAwMP/oo5klexR0qx6RQagoihF4AZgL5APrFEX5UlXVnV4P/VBV1Zu7/AD7m5AQz+a83hdU3gFCk8kzqKGqjBg4kGmtrXz53XeYd+/WSpNbW107uOp9BeRiTfQlRiM0NqI4xsjGH37glPPPZ9Pu3ax77TVaBgzgnvvu46QZM1i3cyeVdXVsy8ggMjKSwoICIr3HklsJS35xMdnV1RSWl7MhO5stu3dz/1//2j2fUwh/Cw11nVgCKAqGxkYCyss95yK3DEPsdh577DFM+/bx7H/+w849e5j3l7/w7dy5GKxWtu3bR1hwMOnp6bx0zjkwYoTnLpRC9AWhodpGC+7jpK5Oy7TV+ziBZ7BD7ylttzM4KYncggJmAu9+9BFrVqxg4IABoCgMGjSIHc8/DzExnuWWQvRiJ510Er+79FIGL1hAfGwspxx/PA2qyrtXXgnAutWrmWYwUN/YSLh+vdLS4hkg9M4cdOsZXdvYyA+rVhFiMFDa2MjQUaNIT05um3UlRG/l3hfd+1reuxWZY86ZPXs2q37+mVX//S8frVzpMXYKKyu1AKEeV5AFqW7VU/7rzwCyVFXdq6pqK/ABcHY3H1P/ZTBAcHD7AUJfJcaqqv2rqgJF4Y7LLuPb99/HbDZ7punrj3cPFArRVxiN2kmk4/d9wIABPLJwIYMGDWLasGEcM3s2N517LmcedxyXHXccZzz6KKqqMmnSJPILCli1ezeVNTWu13M7Gc0vKmJ3QQGKorB440byi4oYOXZsV39CITpHWJj21X1OKC/XMqF8BQgdmeshISEEmEykJSby5Zo1/Lh5M78sW0Z2YSFxkZFcfsopTNJXogMCYMiQrvtMQnQFfex494OyWn1nEOoLUevXQ2Ul6SkpWgZhXR3bduwgq7CQtLg45/laeFiY1gfKsZO4EH3Bg3fdxZbXXyc6LIzXP/6YbTt28ODbb7N48WKOnjOHN5YtY9gpp9BitVLb2EhDUxMX3n47l156KS0tLW13/nbLIPxt61bufu01ftqyhXeWLiWnqIj0CRNcY1WI3s49g9Cbfn3vlUhkMBgYmJrKqIED2Z2Xp81BZWWeAUKJD/QIPeW/fgqQ5/ZzvuM2b+cpirJVUZRPFEXxuVe8oigLFUVZryjK+g0bNnTGsfYPYWEdlxi7D3y9xLimBtasAYMBo9mMUlGh9SJwDxAaDK6VgXHjQC89Ft1Oxo4fGI3aippbQPyMY45h27ZtWqmJ2cxFJ5/MxNGj+ff113PihAn89M47pKSkcMuddzL71lt5/ccfXa9nt1NeW8vSLVtoaGggIiSEk486inWZmVx96aVaAF50Oxk7fhAaqn11D3LoPZ98lRjrQY7aWmhsZGByMiXV1YQEBvLJp5+y2dGj8z933skpp53m+dqix5Cx4wfeAUJ37gFC/YJLHztNTWCxMDg1ldzsbCybNrEnO5uggAAtg1Au1HosGTdHTjGbGZGSwhXz5zNy2DAyMzP5z7ffctNNN2G1Wvnbhx9SWlHBX95+m9HXXsvCF17gu99+Y8OGDfz444+ceuutqO47H7ttKrevqIj9paXsLSlh7a5dZBcUyAYlPYSMHT8JCvJM/nHnfs3vHidoaIBNmxiemkp2YSH7cnNprasjr7CQ6vp6BkRGSgZhD9Gb/ut/BQxWVXUC8CPwpq8Hqaq6SFXVaaqqTps6dWqXHmCfEhrqebKpKNofA2ibQeh+oWaxuG7fuxdKSz0v8NxPOL3fQ3QrGTt+EBioTYDuAQ1VJTww0LMvjcFAUEAAf738ciaOHElycjIZu3dz3pw55JaUeKxMv7NsGef97W+kpKQwLDmZebNnc97RR3PPHXd03+cUHmTs+IGvAKE+DtxPQr2DHFlZkJ/PQEd205/POYdPPv+cz9es4bhJk1wnqHqZmOhRZOz4ga8MQp3eEgY8S41bW7U5CUhPTeV/q1dz8p/+RFpyMpNHjPDIIJRx0/PIuPEDgwFsNm5ZsICfPv+c9PR0rDYbe/fuZXBaGnvy8jhn7lxe//FHHrrqKt7/5ReOGT+eqVOn8s4777B4zRqySkvblhoD+0tKsFitrNq1i8aWFr7+5RcGDx7cPZ9TeJCx4ycDB8KoUdr33gFC7wxCPUBYWwsVFYSEhBAfHc3kW27htoceYsrll3PfVVdhcA8oyrzTrXrKf/0CwD0jMNVxm5OqqhWqqrY4fnwVkFHdmXwFCENCtNJj9+AguEqMa2u1n/ULMZtNOwHVdz7WX0cGvuirwsK033v33/f6evj5Z2cGoa+NFpKSkoiLjeWik05iX1mZx4r0iowMqhsaSE1N5fKTT2bunDl8cs89pKSmdv3nE6KzGI3aHOPeY9D9Pv027wCho49aYkICJqORi449lvGjR/Phr79y0cknez5eFqREX9RRgNC7F5R+ftbY6HzI6GHDCDSbaWpuZtzIkdx26aWcMmWK9riAAM/dj4XoKxxBC5Oqkpiayvjx4zn/mGOYOnUqD9xyCwDPP/ww+e+/z/VnncVx48Zx2qxZjBw5kq+//hqAHzZt8jhfU1WVxz76iL15WlFcaXU1J0+fTnlVFSeffHK3fEwhOoXBoF3TgGvOcZ9jfCUSNTdr40VRGDVoEOHBwbz4zjtceeqpPPj737vO1wYPlpYW3ayndBxeBwxXFCUdLTB4MXCp+wMURUlSVbXI8eNZQEbXHmI/Ex/v2UxXUbSTxKOP1n7Wsz1qaz1XBsBz85HWVu0+/Y+IXmKsT6hC9CW+LtRaWlxZtO4XaTq7nRkzZvCn669nSEoKuXrWrcGAarPx686dTB8+nJSUFG698EJt4ty1S4Lsou8ZMkTrdQaHFiAEjGYzX9x7L6NTU7n12muJUVWS9TJJgAkTXFnwQvQlZjNMnNg2QGg2t91UTq/icNusZ/DAgeS88gpVLS3UpqUx2GyGoiLt8YGBcNxx3fChhOhk+s7EjuuS2267jfCNGxmzcCH27GxobiYlOVkrxVcU/nfffYQmJPBJSQkNDQ3MmzaN7zds4Ca9B6Gjj9pf3nmHqIgIUuLjqaiu5t3HHsMcHU2EZBCKvi4w0JVJ6x401Mdac7N2m8HA/JkzuWbOHFpjYzlz8mTXzscgvTp7gB4RIFRV1aooys3A94AReE1V1R2KojwCrFdV9UvgT4qinAVYgUrg6m474P4gNNQVBNS5D974eO1rQYFniXFwsHa7flFmsbTdpGTAAM+dwIToK4xGbQy4/77rJ4/uQXKvZvKzZ89m9siRVG7cyL7SUm2zn7g4Plu6lMCAAB6+7DIqhg51ZUFNmiRZHaLvce/R5J6h3l4PwuZmZ5kkRiOnT58OwBnHHssZSUmei1VJSV3wAYToJgMHtr1t0iStz7Oqei5OKYqWQaiX8RuNKIpCTHAwMcnJUFnpWZIvi1GiL9LPoVpbwWxm5syZ2u/+6tUYTCauuegij8B6REgIACNHjgTg1nPP5aLHHqO1upqAmBiw2diYkwNAdW0tp82ezfpt24iNjZWAh+jb9POs4GAtoA7aOZeqQl6e63xNv09RuPXSS7UNSgYP1uIH+v4GokfoEQFCAFVVvwW+9brtAbfv7wHu6erjEg7eWU/ut+srA2azlv3R0NB+gNBggOjorjtuIbpaWJjvxr3uqfjeWVAWC9TWEh0RgQrc/vvfM3T2bP767LO8/8ADzBk2DE49FVat0sabBDtEX6ePnYEDITFR+374cNcilNGonVQaDJ59bkEbT3qAQ044RX+jj53oaNdcdMwx2sWYe4lxeLirCgS0izn3sePdV0qIviYiQgsKui9C1dVpSQzJyZ5BdQCbjREjRhAeHs6xY8cyYuBAPnzjDY475RRajUZW7t7NmLQ0MvLzOXbaNMpKS7UgvXtFlhB9jT4+oqLAEUAnIsJ1v15p6JZB6HyOe5xA5pweo8cECEUP5903zZ1+e2iotiKnr0yDqwehr944QvRFUVGuk0H3FHtfAcKAAO1END8fMjJQ4uMZFB/PW0uXUvb553zz+OPMmT5d2yEctNeVzEHRn8TFQUyM9n1aGlRXa9+bTFBero03PeNJv721VQKEov9yz77VhYdr40Wff2prtWoO9wAhaC0xZOyI/iIiQgsI6hRFmz/AdX6mjwWjEWw2QkJCyM/PJ3TVKubNmsWVTz3F/FWr2JaVRWFpKa/cdBO/lpZy0ZlnclR6uvRSE/2HyaTNK+701mJ6gNC9Ly5oCUZ6gFDmnB5DAoTi4Bwog1DfxCQgwPPEsrVVu99952Mh+jJ9Vy9w/b4nJLhOEt1LJvVJs67OWQZ2/pw5nDBqFINOPJFBdXWeafeTJ8sYEv2D/ntuMrV/e0uLFjR0zwAJDpYAoejffAUIwXNMtLRowfX8fM+NgVpatF6dkkEo+oPISCgpcf3sPl8EBGgBDX0smM1awFBVicjMBJuNmy65hKNTUrjsX/9i5JAhzB43jtOnT+fa+fOhoYFo9/NBIfqyA8UJ7Hbt3Cwy0rMnrtXq2txR5pweQwKE4uC0l0Ho3vxa3/lY/zk4WMsg1HcwkoEv+hv9dz40VCtXAS2AaDLBli2uE069YbzBwINXX63tfBwfr2VLuQcIZQyJ/qK9IId34DA2FvbudV3EBQV5BghlzIj+xn3h1vt296C5npmrj7GQEC1AqO8mLsF10dfFxHi2PdKvX1RVCxC6zyUBAdq5WWMjFBYCkJycTPL06Tx4zTWMHT+eeZMnaxv86IkRMoZEf9FRnEBf0DWZtNiAnqkeEuKqNJTx0qNIgFAcvPYGvj55Ohr4On8OC9MyO0wmGfiif/KVBaXvpKqvSOsZhPpt7tm3+g7gMnZEf3MwGYSglYgFBHguTFVXu3YNl7Ej+qOOFnT1csmICM+LupAQbbEqPl6C66J/CA2FKVNcPxsM2jnXkCHaXGK3a5m29fVtz9fAOXZuXbDAtSGQ/jphYdpzhegv2pt39Nv187XWVtf5WkOD9CDsgeTMWRycjlKH9fv0HoT6RVlwsDZZtrS4LuCE6E/aC3Lo95lMrk19vMu69AChvnOxEP3JwWQQBgZq48Y7QCglxqI/6yiTQ59jwsJcrWHMZu2f3kM6LEzGjuif9POy4cO13//wcBg92rMPYW2t9lj9Z9CyoGw2V49oo1HLTBwxons+hxBdraN5R789JsYzThAS4ioxlmSIHkX+T4iDc6ATzqAgLUAYFaX1WjMatdvMZm2y1UtWhOhPDhQgNJu1lenwcG286OMpMFALrEvjXtFfHUyAUN8lTz/hDA7W5hopMRb92YHO14xGLQgIcMIJ2rgJDnZtpBUW5rtEWYi+Tg8Q+rpd7x9dU+MZ5DCZXAFCfQzJOZvobw5UYgxa0Nxs9kws0ncxjomBGTO69phFu6TEWBycjjIIDQY47jjXbYMGaSsCoPXpiIrSgh/jxnXJoQrRYxxMgFBVtaa9eiahomgBw+Zm1wmpnGyK/qajEmNFgcRErf8gaFka4eFasKO83BVYDwx0XbAJ0V8cTIAwPNx1W3AwHHss7N6t3RccDEOHavOSEP2Jfl7mTT8H0wOEAwZAaalrYaqpSVuY0p8rVR+iP+po3gFXv8+AABg5Urt91y5tPtLHkugRJEAoDs6BNinxpl/Umc3aRZyiuDZpEKK/0CfFjgKEoF2I2WyuFemICCgokD5qQnj/7rv3vQ0M1G7TA4XgmqeGDHFtwiBEf9PenGMwaOWT3udteu+10FBXAF6I/kYfB97cgxxNTdr4qKhwVVBZrdoirz4nyTmb6G8OlEg0f742thITISHBcxFYztV6HAkQioPnK0Co79TVnlGjZBVaiAMFCKOitAChzabdHhOj7cyakODKIhSiPznQTqztiYiAiRPlhFP0X+0t6IaEaOdj7Z2zBQS4MguF6I/aKzF2Dw4GBGiLUuHhWkAwPFzLHqypcc07EiAU/c2BEoncA+/u53Vms5yv9UASIBQHp72LsvHjOw5exMV13jEJ0dMdTAah3qMzNNR1n82mfU1M1HaU1HutCdFftHehZjS6GsH7YjTCwIGdd1xC9HTtXahFRna8YJuSIpmDon87UIAQtPklMBCOOkr7OSEBVq/WxpbZrJUfy6Ku6I8OVGLsy4gR2pgRPYoECMXBae+Es6PsQSH6uwP1IAwNhTlz2mZJ6U3kExO1792Dh0L0B+3NOWYzHH98lx+OEL1Ge2PnQCRbXfR3ByoxPukk333SzGZX9q0eOBSiP2lv3hk9uuMkh9TUzjsmcdgkuiMOzoFWAIQQbXUUIBwwoONNFE44ofOOS4ierqMgh8xFQrSvvSwoIUTH2hs7gYHafBQS4vt5qamywYLo39prOaZvTCJ6FTmDEAcnIaH9iVEI4VtHAcJJk7r0UIToVSTIIcThCQgAVe3uoxCi9zEYfM87Q4d2/LyEhM45HiF6izlz2k94EL2OnH2LgzNqVHcfgRC9j95nUDKehDg0kZEwcmR3H4UQvY/04BTi8HhvpiCEODgybvqUHnPVqijKfEVRdiuKkqUoyt0+7g9UFOVDx/1rFEUZ3A2HKYQQB89ggJNP7u6jEKL3MZmkcbUQQoiuM2yYzDtCiH6vRwQIFUUxAi8ApwJjgEsURRnj9bBrgSpVVYcB/wL+3rVHKYQQh0HKJIUQQggherboaK1EXwgh+rEeESAEZgBZqqruVVW1FfgAONvrMWcDbzq+/wQ4SVG8t/4UQgghhBBCCCGEEEIcip4SIEwB8tx+znfc5vMxqqpagRogtkuOTgghhBBCCCGEEEKIPqqnBAj9RlGUhYqirFcUZf2GDRu6+3CE6DVk7AhxeGTsCHF4ZOwIcehk3AhxeGTsCHFgPSVAWACkuf2c6rjN52MURTEBkUCF9wupqrpIVdVpqqpOmzp1aicdrhB9j4wdIQ6PjB0hDo+MHSEOnYwbIQ6PjB0hDkxRVbW7j0EP+O0BTkILBK4DLlVVdYfbY24CxquqeoOiKBcD56qqeuEBXvdVtHJlIfqrVFVVrzvUJ8nYEf3cYY0bkLEj+j0ZO0IcHjlfE+LwyNgR4vD4HDs9IkAIoCjKacAzgBF4TVXVxxRFeQRYr6rql4qiBAFvA5OBSuBiVVX3dtsBCyGEEEIIIYQQQgjRB/SYAKEQQgghhBBCCCGEEKLr9ZQehEIIIYQQQgghhBBCiG4gAUIhhBBCCCGEEEIIIfoxCRAKIYQQQgghhBBCCNGPSYBQCCGEEEIIIYQQQoh+TAKEQgghhBBCCCGEEEL0YxIgFEIIIYQQQgghhBCiH5MAoRBCCCGEEEIIIYQQ/ZgECIUQQgghhBBCCCGE6MckQCiEEEIIIYQQQgghRD8mAUIhhBBCCCGEEEIIIfoxCRAKIYQQQgghhBBCCNGPSYBQCCGEEEIIIYQQQoh+TAKEQgghhBBCCCGEEEL0YxIgFEIIIYQQQgghhBCiHzN19wF0puuuu05NTU3t7sMQots8/PDD/1VV9bpDfZ6MHdGfHe64ARk7on+TsSPE4ZHzNSEOj4wdIQ5Pe2OnTwcIU1NTeeihh7r7MIToNg8//HD+4TxPxo7ozw533ICMHdG/ydgR4vDI+ZoQh0fGjhCHp72x06cDhKJ3Gzx4MOHh4RiNRkwmE+vXr+eOO+7gq6++IiAggKFDh/L6668TFRXV3YcqhBBCCCGEEEII0WtJD0LRoy1btozNmzezfv16AObOncv27dvZunUrI0aM4IknnujmIxRCCCGEEEIIIYTo3SRAKHqVefPmYTJpia9HHXUU+fmHXc0khBBCCCGEEEIIIZASY9GDKYrCvHnzUBSF3//+9yxcuNDj/tdee42LLrrI53MXLVrEokWLAAgKCuLkk0/u9OPtbvn5+bz66qvdfRiil+tvY0fGjfAXGTtCHLrDHTeVRRaCwgyEhBs78/A6hYwd4Q/9bc4BGTu9lqqConT3UTjJ2OmYBAhFj7VixQpSUlIoLS1l7ty5jBo1ijlz5gDw2GOPYTKZuOyyy3w+d+HChc6A4kMPPdQvmtD2h88oOl9/Gzt9/fOJriNjR4hDd7jj5g/TMmgJVHhy5ahOPLrOIWNH+EN/m3NAxk6v9dNPcPLJPSZIKGOnY1JiLHqslJQUAOLj41mwYAFr164F4I033uDrr7/m3XffRekhf2iEEEIIIUTnU1UVAEuL2s1HIoQQ4oBaWrQsQtErSIBQ9EgNDQ3U1dU5v//hhx8YN24cixcv5sknn+TLL78kJCSkm49SCAGwe10D/7k1D7tdJn8hhBCdq6VJ5hohhOg1VFUChL2IlBiLHqmkpIQFCxYAYLVaufTSS5k/fz7Dhg2jpaWFuXPnAtpGJS+99FJ3HqoQ/UpxbgtN9XbSxwU7b3v17gIaamxUFluISw7oxqMTonfZ+ksdTXV2Zp4e2d2HIkSP9o9rcjnqjEiOPS+apnpbdx+OEEII0SdJgFD0SEOGDGHLli1tbs/KyuqGoxFC6B45fy8AL64f7bwtLMpIQ42N4pxWCRAKcQheui0fQAKEQnTAZlXJ2dZEzrYmjj0vmuZ6e3cfkhBCiIOhZw5KBmGvISXGQgghDpnN6prow6K0XSSL9rZ01+EIIYToo+qrXRmDr9yZT2OdZBAKIUSvIgHCXkMChEK4aW2x09IkK9NCHEhFocX5vT7nl+W1dtPRCNHzff5sKXecuIfqMkub+6R/pxDtq6+2Or/ftLSOJskgFEKI3kECg72OBAiFcPPQOdncfsKe7j4MIXok1W2SL8t3BQPrq7RsjpZGuWgToj0/vlVBQ62Nwmwt09Z9PDXUSEaUEO3R5xid9CAUQoheRgKFvUavCxAqinKroig7FEXZrijK+4qiBHX3MYm+o7rM6lE6KYRwaW5wBQDL3QOEjuwOCRAK4VtTgyugUV2qjZfWZtdc4x0AEUK41LmNj6AQA011MtcIIUSvIIHBXqdXBQgVRUkB/gRMU1V1HGAELu7eoxK91c7V9fxhWgblhVqgw1fZlxD92Zpvalj8ernz57pK10VareN7m1Wl0XGxJuX5QvimBwXdv3fPGnTvsSaEcLFaVF67twCAUTNCCYkw0ljrGi9Sni/EwWuotVFbaT3wA4Xwlz6yScn2FfWU9pNWSr0qQOhgAoIVRTEBIUBhNx+P6IVsVpU379d+dfJ2NQNQmCUbLAjhrryglS9fKHP+7H5SWef4ft/OJudt3Z1BuH1FvQT6RY9UU+YeINR+R92Dgu491oQQLsU5rnOzuFQzlcUWfnyrwnmbeyauEKJj95ySyd3zMrv7MER/1IsDhDabyov/l8c/rsrt7kPpEr0qQKiqagHwFLAfKAJqVFX9wf0xiqIsVBRlvaIo6zds2NAdhyl6gaXvVzpLVvQsjsri/h1YkLEjDqTOLUBYW659v/WXeoxGLbOjpan7Jv+mehsv/l8eL/85v8vfW8aO8Ja1uZGv/lNG/h5tAaqqRJtfgkIMVJVoY6e+yjWe+msGoYwdcSDlBdrYGTEthJBwIwBN9XZik81A9y9MdQcZN+JwWS29N0jjDzJ2ukEvDgzqSvdrmYMNtf3jXK1XBQgVRYkGzgbSgWQgVFGUy90fo6rqIlVVp6mqOm3q1KndcZiiF9izvtH5fc62JlRVpaq4f2dwyNgR7dEnRL1PWtKQQGeJce62JtJGBRGdYOrWC7X8PVqWSXcE+mXsCG+71zbw3X/LefUurTRSDwqOmBZCYZYWNKwocv2udufYqSqxcN/pmTz7h/1d/t4ydoQ7S4vdGVTXVTjawFz/ZCpBodplS1CIgVOviwP6Z4BQxo04GPXVVr76TxmW1rZjpL+W5svY6Ua9OFCoVxkGBCld9p52u8ob9xeQvcUVs7BaVKpKO/86p1cFCIGTgRxVVctUVbUAnwFHd/MxiV6oILOZ6fMjAFj1ZQ2rv65xZniAlkosRH8XHmMCXBOjnnWbPDSQ6hILGWsaKMhsIXVkEAHBhm69UNNbBUTGmbrtGITwFhyunWaV7mshJtHMqJmhVJVYqSy2UFFgweA4C3PfAKirffqvEqpKrOxa29BtxyAEwM8fV/H4pTkeQcLyAgvBYQZCwg0EBGsDZtDYIEIjHdmEDT0no6Opwcaab2s8digXorss+6CK7/5bzs8fVbW5TzbGEl2mF/49bG60s29nEy1Ndr59pYycbVo7pdZmlW9fKTvAs/2jLK+Vtd/V8vS1+wBQVZUHzsrivtOyaKrv3PHb2wKE+4GjFEUJURRFAU4CMrr5mEQv0Nxop7xAW4WuKrVQXWpl0Jhg5/3bV9RTUegKEFpaet8fMyH8bcrJ4QDO1au6Sish4QaiE0xUl1l57qb9NNTaSBkWSGCIoVs3KSnZ57uHaFWphVfuym/3fiE6i9GkYDJrq83Fua0kDA5g2OQQALb9UkdFoYW4lACCQgzdGiDct1MLxihK/80qET2Dvsi0f5crQFhRaCE22YyiKASFaJctAUEGQiO0AGFjbXdmrjfz5oOFNDuO+/N/l/LmA4W8dl8hbzwgLdJF92pwtK7Y9kt9m/tqyvt31ZToBr0oULj4tXL+fmUuT1yWw9cvl7P0vUrnfV+/XN4lG/0U57g2RKmrslJRaKHa0c9ar0rpLL0qQKiq6hrgE2AjsA3t+Bd160GJHi9nWxO3zdnNA2dnY2m1895jxQAMmxLCxXcnArBpSR2ZG10pvJaW7jvh/PnjKr54rrRNSUD2lkYWv1ZO1qbGdp4phH+FRZlITA8gyzE2asqshEWbmHhCuMfjkocFERRiwGpRu62/jX6R6H3S+9kzpWxaUsc7jxZ53F5e2Nplq4Ci/0kYFMCkE8Kpq7Khqiql+1pJGBRAyvBAhkwIZtn7VZQXtBKTbCYorOsChI11No8goN2uUl1qxWRWUFXPTMbyglaWfVApQUPR5dw3jasutRCdoPUbNDoC7gHBBkKcAcLuy4Ra+l4la76p4ZuXtbmkztF6Y8MPtaz9tqbTszyE6EiBo53F3q1NbRZwJUAoukwvCgzqChxZ7HrvQW/Zmzv3WnxfRhObltS6jiezxWPhrLPHb68KEAKoqvqgqqqjVFUdp6rqFaqqSlqI6NDOVa6Vs1Vf1rBjpfZz6ohA5pwfTXiM0Xl/wqAAoPsyCNd+V8OHfy/mhzcr+N/zruDFqq+qefrafXz5Yhkf/aPY4zl2u+pcvRbC38YfG87OVQ38YVoGm5fV0VBjY+jEEI67MNr5mPiBAc7Sr87OIlRV1WdJmb7ZUEO1KwBSW2lls2OC3bejGZvVNa4//3cpX79c3qnHKvqvhMGBhEUbqSyy8MqdBTQ32kkYFICiKIycHkp5QSul+1uJSzETFGqguQvKJG02lTtO3MNLt7k28qmvsmGzqqQMDwS0AKL+2CevyuXjp0pY/VVNpx+bEO7y3C6EqkqsRMVrrSNam7X5JSBIISRCm3O6M0Col0IXZGqXIoEhnpdV/7xun7PcuKLIwmfPlPDSbXlde5CiX6mtsPLsTfupLLZQvLeV2CQzNqtK1qZGj8We3d3UUqKh1sbyDyulDL8/0f9f96L/5+0FBnVNdZ13rWNptfP3K3JZ+10t5gBtUawwq8VjXqw9yADhK3fm89M7FYd8DL0uQCjE4Tjx0hgANv6kBQt+/3QqBoM26H7/VKrzcXr5V3dlEL5xv6skZdWX1c4JdPVXNcQmmxk1I5SSfa3OHol2u8o7jxTx7I37uuV4Rd937PlRHj/HJmmZHOf/OcF5W0Ss0Vn61dl9CDNWN/Dn4/bwh2kZztVxcAUI7XZX6v32FfXYbDDv6lisFpXiXO0iLntLI1uW1TFkQnDbNxDCDxIGBRAebcRqUdm8rA6AuFRtASoizoTdru3EOiC160qMS3JbUVVtXPzwpnbCqPfeTR7mCBDW2mlttrPi0yrnzsq/ftq2f5UQnWX4lBD2bmmkodZGa4udhhqbM4NQFxZlcmYQ6n/7O1PJvhZK8zwvGJsb7c7AYHWZNo5qKzwv2gqyWpzH98Vzpfz0TiVbfZR7CnGksrc0UlNu5bv/lrNrTQNL3qmgodbG7POiCAhSeOFPedw8Y5fz8b98XNUtO7J+/I9iPvpHCdmbmyQ7XfQ4drtWCeXedix9vHatMOmEcEbNCAVci6mdYduvrjli/JwwImJN5GxrImdrE7HJ2lxYU2494Pj57X/VbFpax2fPlB7yMUiAUPR5c6+K5eybB2AwaLsXBwQpjD82zHn/kAkh3PDPVCJiTQybogcIu37Scs9umnRCOE31duqqbLQ228nZ1sTkE8M5+pwoLC0q+bu1wMi6xbWs/rqGsceEtfeyQhyRuOQAHvtmGAAGA/z+n1pA3Wh07eSlKIorg7CTA4R7tza5vt/i+r6hxkbqCC3I8epd+Vha7ZTua8VoUpg2T9uQKH+3djG39N1KwqJN3PRcWqceq+i/AoIMhEV7bpgzwBEgjBrguj02xUxQqLFLAoR5u10B9T3rtewRfdfvlGFBADx5ZQ7/N3s3Hz5ZQnSCiTN+H8e+nc3OXfNytjdRkNVMeWHHq+tCHK5jz4/GZoPvXi0nc4NWxhXpGDNHnRHJvKtjOfW6OAICDZgDlE69UNM9fN5eHlqQ7TxPqyiycNuc3agqBIUaqHH0hdK/AgweG+R8bPaWRucY1zfIE8Kfdq1p4J75mc4NSfRNFdJGBTFutuc1wjELorC0qmz4obbN63S22gptvL71UCH3nZbVJsjx8u35vP1IoQQP+5JekkG4/odabp6xi7Xf1WB3OyWbdVYkd709mGseS+bm59NQlM7NINSzF0dMDeGcP8aTPCyQDT/WsmdDI0MnBhMUYmDfjiZunrGL9T/UkrOtiZ8/qqS12U5jnY0Pnyzm2T/s92ittOO3Q1uYku0eRZ+nnUQaGDQ2mJxtTSQMCnRmD+omzAlnwpxw5wDqjgChvpIXl2Lm6LOj2LysjhWfVfP1S1qp8bApIQweF4yiaBkg5QUW3nygkLSRQZx2fRzrHu7yQxb9RHSCmev+nsLgscFEx7syOeZdFUtQmBYYDHZ87eyeS1Ul2o6WzQ12Z3CjtkLbFXbiCTHk72lh385m9qxvpCxPK+FMGhpIaKSRHb/VM/nkcLavqGfyyREEhxoP8G5CHL64VM+spxhH9m2E207bsclmgsMMbTKPOsP2X+sJDDYweFwQTfWOnp2OgIaeQaifFEcNMHHXW+m0NNn5ZlE5v35SxVl/iOcfV+cCYA5QiJ7X6Ycs+iE9mLH0vUpnY3g9g9AcYOCcm+Odjw2NNHZJBqEuY00D444Jc256B1p2ScbqBu4/K4uKQgsjp4eQv7uFEy6N4fX7Cvn7FbnOx46ZFcqVDyfz6KNddsiin8rdoS0IJQ4O5PiLYtj4U53zvmGTQ1i/uJayvK5f6DE6pr/yAu38bcfKesYfq/W1trTa2bJcO84Zp0ViaVax2VQUBf73fBkX3ZXg8zVFL9FDA4SVxRbefriQVY52Ku88UkRQiIFjFkSx5N1KBo8NJnVEkPPxwWGGTl2Yqiq2EBpp5P9eHgRocQFdUJiR+IEBbFqqjZNfPq5i75ZG7Hb48MkSQiOMbTKDzQGKFksYcvDHIAFC0W+ccEkMOdsK2pRMugsI0oIcrZ1cYmyzqSx7v5LE9EDiBwYQnxZAk+OPzZk3DiAxXcs0+XaRqw/h4HHBRMSYGDophG8WuXqnnXJNLNqm3kJ0nikntc16OOePrgs1V4Cwc8dOeb6F5GGBVJdaqSzSTjDvOy0TgLAoI9c+kcJ/7ymgdH8rWZsaGTQ2GKNRYfJJ4az4rJr132sr5kMnSXmx6FxDJoR4/KzvaBzpFiCMTwtw9CDs5HFT2MqGH2uZ/7tYSve3UlOmZdPWlFsxGmFAWoDH4+dfG0dErHac42aHsfrrGqafGum8P21UEFIoKfxt7pWxznYVullnRjJiWojPxweHGzs1kwPw6PGsB1Ssra4L3SETtAChXpJ21JlRzPxPpM/AZcrwII/seyH87donUtiyvI7139cyYmoIUfEmYhLNHHteFL9+Wg1AYLCBgGCl03tG++R1vbJrTYMzQFi01xWw/PcN+9s8dc030g+3V+qhgUHdqi+rsSa7frfmXRXLoLFBjD82nGmnRHgEB8Ex73TitU5ViZWYRFdQMDzGdc547HlRTDslgn9ep7UW89641Ds4+PcfhxMYbKC6zMqL/z34Y5AAoeg3ps2LYMysUELC288aMgdqE1dnZxBu/LHWoyfAP5aOcJ5MhkQYiUkykzIskAK3nfwiHH8gLrwjgRf/L4/qUisL/hTPpBM9d5QVojsEO8ZVZwcIy/JaGTUzlIYaG+sW1zLpxHBsjvmwrtLK/N/F8s4jBj5+qgSA5KFaZtQZvx9ATZmVHSvrOfb8aGaeFtneWwjhF3qgIzzGyH0fuJZu9cDbwNFBBIcZCQo1dHrmbVG2NpeMmx3Gqi9rnOO0ttxKeKyJiFht/KaOCCR/Twsjp7sCMiOnh7Lt13oevWAvAH96cSCjZoTy0EOdesiiH9IXaQ0GLZv15ufTGHNU+y1UQiIMnZ5BWFXs6kWlBwHd33PkjFCPRdsxs0Kdx6abfW4UQyYEM+ZoaQcjOoe+ADV8agjDJodw7HnRDJ0U7KyYOv6iGFeAMMRAQJCB1uZuqJbyGq/LPqgif08LKcMDD7gza3ODHWRtt/fpBSXGJ1wczbIPqjjuwmiP5IdBY9r+wgWHGVj7bQ1zr4xxtmfxp6pii7PXIGgBy6gBJo5ZEOUcz2ffPMC5mem0UyKcyQ+63z+dSlJ6IOGOVjfxXovAByIBQtGvdBQcBDAF6AHCzg1yeDepLs9vpdGxCh4SYcRgULjo7kTnCsFVjyQ7H5s6Ioi/fj0Mq0UlIFDaiIqeQR9bTZ2Ydt/aYqe6zEpcaoC26UhOKx/8zbWr95STI1AUxTl+xx4dymnXxwFaUObGf6XR3Ghvk6EiRGd5+ucRGAwKgcGu3zmTWeHBT4c4S46DQo20NNpRiyTd1gAAryBJREFUVbXTssGLc7XMjITBgQSHa+UxdrtKaV4rkXEmzAEGnlk5EnOAgt3u2WM03W0znwGpZgaN9f8JsRDu7nhzMPm7WzoMDgIEhxk9+v51Bvdm9dt/rfe4cItNNjNsUggX351IZZGFIRODnRdkiqJw83NpxKUEED/w0C7OhDhUM0+PZN7kOGcygXumOuDc1Ae0xavAYAOtXZBBqKoqLU0qdZVWBqQGUOdopxGbZGbM0aH8+mk1mRsbydyoZUJNOyWCSSeGs2VZHfEDA/hmUTkRsSYGjw2idF8rjOr0Qxb9zMDRQZz5h3jmXhVLWNSBQ2N6IPvlP+fzyP+G+fVYSvNaKc1r9ciaDww2cOx50R6PO+XqOAaPDWbL8jrOuy2BKSdHUFlkYfjUELavqGfc7LAjylaXAKEQbvSAW2dnEOqlkbqSfa3OhZVQxySu77A666zINtlOBoNCQKCUqYieQy8x7sy+HBWOnjUDUs0cd2E0e7c0UVlswWRW+PuPwwkO08bOuGPD2PpzPZfdn+TMSNFJcFB0pfb6XCYMCnR+HxRqwG6H1maVwODO+btekttKeIyR0AgjwWFGLC0qX79URvbmJme5vT7/Gb0OedCYIM7/cwLTTolwXnwK0ZkGjQ5m0OgDpwoFhxkozuncDEJ9c5+kIYEU7W1xZqcDPPT5UADmnB/t87ljZknGoOgaUfFmTrt+QLv3u2e0xg8KICDY0CUlxmu/reXNBwsBGH9sGBVFFk6+Ioazb4qnttJKWLSJikILa7+tISBI4bK/JBEYbHC2tTlmQRTmAAPfv15OxuoGoiVA2Pv08AzC8ceGExRiOOjrg7pKbc6pLvXv4lTe7maeuCwHgLGzDzx3jJweysjpWsb6pBNc1YRpI498EVfO9IRwE+C4OOvsnVjrKj3/qLxxfyGDxmgDOjhcL7FReHbVKAyyj4LoBcyBCkaT0qn9oMrytSyouNQAQiOMTDgujOUfVjFyeogzOAhw9aMpWFvtB7USKER30zf6aW6we2Qa+lN5fquzxCTEMcf8+kk1AK1NHZ+0GwwKJ14S0ynHJcSR6Ir+ndmbG0keGshxF0Wz9N1KSva5+qRJP0HRW5gDXHNLaISRwGClUzMImxpsPLNwvzPADrDtV616auDoYIwmheh4M2feoAU155wfRWxyQJs5MGqAlrE76cRwBqQFsGRbpx2yEAclcXAAxbmtWC0qP75VwUmXx7TZ/PRw/PR2BaAF+PTAX3eRVAoh3ETEmjCZFefuWp2lrtJGfFoAs85yZQbu26lNou5l0Caz4pc/OkJ0NkVRCAnv3F5q5fmuDEKAmWdEMnVeBBfemejxuKAQgwQHRa8RFNr5O4BXl1qJcuwEG+QIpuvNrK9+NLnd5wnRkwWHGTs9QLhvZzPpE4I59txoHvx0KNNOabthlxC9TWAnZxCW7mv1CA7+7fvhzu8Hjmqb4TRkQkibsmjv+73LLEUv0cMzCA/Vra8M4uK7teuOz58tZdfahiN+TbtdJWN1AzNPj+Sed9O7ffFJAoRCuDEYFOJSzZTubz3wgw9TS5OdliY7s86O5IoHPC/M5l4Zi9EkAUHROwWHG529NDtDTbkVk1khNFILcAwaHcy1j6cwIFX6O4neSy9D7qxAh6qqVJVaiI7XLr7cMzTmnB9N0pDA9p4qRI8WFGbAalGxtHbO2LFZVRpqbM6xA/C7x1I65b2E6Gx3vTWYR77UyuLNnbxJSV2Va8HrjN/HERFrYv7vYjEYIC7V3MEzRZ/VRwKE4dEmZp7uSvCpqzjyxd3szU3UV9s8NojrThIgFMJLfFpApwYI66q08mJ923L3DUhOulzKuETvFRxmoLkTs6Aa62wEhxs6bSMHIbqDe4lxZ2iss2NpUYmK1y7KEga7AuoT3frWCNHbBDuzbztn7OhZtqFeGemXP5DEJfcm+nqKED3WoDHBxCVrf/8DghRamztvQVdvpXT/R0OYf622WdyZNw7guTWjpDKqv+kjgUF3gcEG5l0VC0B5QSubltZSVXr41Ycf/r2Y2CQz4+f0jHMyCRAK4SV+UADl+a3YbJ3zB63esaqmN3t334BEGsCL3iwsykitH1bS2tNYa3Nu4iNEX9HZJcbVjpPWyAHa/JKUHshzq0fx4vrRjJ7ZvX1uhDgSerl8c2cFCKu1MRkW5TnvHH1WFMeeK+WOovfq7F2M9Wud6ESzMyCoKIos8PZnfSxQeM4f44mKN7E/o5lX7izgP/+Xd1DPs1lVj0Sk8oJWCrNbOOHSmB5zjSPRCCG8pAwLwtKqDd6kdP+XXun9DaPcSlZue2WQc9t0IXqr6EQzuTuaD/zAw9RUZyekh0yeQviLHiDsrAxCfac99zlHWlmIviDYkX3b1NA5wfX6dgKEQvR22i7GnVlibMUcqBAYLHNNv9fHAoPu4lICnJvvuG9g1ZHnbt7PnvWN/O2H4dSUWVnzTQ0A447pObveS4BQCC8pw7WgYEFmS6cECPfvbMIcoHj0fRo2uWf0HBDiSMQkmWmosdHS1Dm7sTbW2ojooIm1EL2RvgN3ZwUIayu0AGFHDeCF6I2COzmDsL5aGzuhEiAUfUxgsAGbVcVmVTtlwaiu0kZ4tEkyBkWf26TE3eijQsna1AjQ5rqnusyC0aQQHu0699q+op4967XHv3JHPtlbmgBtU9L4gT2nn7qUGAvhJTE9EKNJYX9GU6e8fu6OZtJGBUkGh+hzYhK1HmdVJZ2zC3hDrU0yCEWfExTSuX3U6iq1LKhwaWEh+pjgcG3s1FZ2TgWGZBCKvkoPZnTWTsb1VVbComXcCDd9MEA466xI4gcGYDBo84X7eHrw7GzumpuJqqrk7W6mqsTC6q+rCY8xYg5QnMFBgHlXx3bH4bdLzhaF8GIyKwydGMy672ppqLZx9h/j/dYbUFVVCjKbmT4/8sAPFqKX0QOElUUWEgf7P/u2qc5GSLisa4m+xWhSCIsyUpDZOeX5tRVWgkIMnZLVK0R3SkoPJCjEQOaGRqaf4v/zKr2PWmikBDpE3xISoc0HlcUWQsL9//tdVWJlQFrPyYgS3agPBgZ1UQPMPPTZULYsr+Pl2/MpzGohfXwwrS12LK3a585Y08DzN2v9CYPDDAyfGsLcK2OprbAyfEoIRrPS487PetbRCNFDjDk6jJpyK6u+qmHTT7Ws+76Gr18uY+svdUf0unWVNprq7R67SArRVyQNDcRggMyNjX5/bbtdpaleehCKvmnWWVFsWVbHpqW1qH4+ma4ptxIeI+NG9D1Gk8KomaGsX1xL0d4Wv79+VYmFsCgj5gC5XBJ9y5ijwzAYYMMPtc7bNi6pZduvR3ado6ssthCTKHlIgj5dYqxLHRkEQN5ubaE3e7MrO1APDoJWKZIyLIihE0OYfGIEYVEmgkONPW5nb5nxhPDh2POjmHWmthr94ZMlvH5fId++Us5Lt+W3e/HW3Gg/YJmL3sBUAoSiLwqNMDJiWihblvnnBNNdY60NVUUChKJPmnd1LPGDAnjlzgKWvV91UM9RVfWAwcSivS1s+KFWxo3os866aQAA3/233O+vXV5gITbZ7PfXFaK7RcSYmHh8OEvfrXTuqPrqXQX859b8I3rdnavq2bW2geYGO9GJMnZE/xCTaCI0wsg3L5dRVWrhzQcKMQe6gn4GA4yaEQrAwDFB3XWYB63XBQgVRYlSFOUTRVF2KYqSoSjKrO4+JtH3BIcaueLBZJ/3FWa38NTvcvn65TIALK127HaVf9+wj7vnZfLOo0XtXrTpK9wJg/xffilETzD6qFCKc1t5+rpcbFb/rRYWZmtjJ1GC66IPCo0wcs+76YyaEcp3/y13ZMx2vDPry7fn89eLclBVlZpyz8WppnobW3+p49EL9wKuXmpC9DWJgwOZcXokW5bVtdtPTVVVmhsPvddaZZGFuBSZc0TfdNFdiRiMCk9fl+uXDNzC7Bae/2Mez/5hP+BqOyME0KczCBVFYfZ5UdRV2bjvtCxqK6xc+0QKVz2czHEXRvPU8pHc/Hwa976XzoQ5PWe34vb0ugAh8G9gsaqqo4CJQEY3H4/ow37/VCozTo1gwZ/iue5vKQD89kU1e7c28e0r5fxhWga3HL2bfy3cx76dWlrxb/+rpiCrhR/frqC+2sq672v4yxlZ/GFaBh/8rZjIOJOk3Ys+K31CMKCl16/4vNpv5ZL5u7WTVz2NX4i+xhxgYMZpETTU2PjoHyX8+fg97F7X4POxNpvK1p/rKdrbwlf/KeOe+Zk8fF42VSUWasqt/P3KXF66zZUJcvn9SV31MYTochOOC8PSqpK9xXd7i9Vf13DbnN2U5bce9Gva7SqVRZJBKPquiFgTF96ZQF2ljW9fdWXg6sF0m1Xl82dL+dsVOc77bDbtnM476G6zqXzyzxKP149LleC6oE8HBt2ddn2c8/vxx4Yx/tgwZp4eyUV3JhIUYsBgUEgdEdQrdvbuVVEKRVEigTnA1QCqqrYCBz/bC3GIJh4fzsTjwwHtZNFoUlj2QdvyL73XwN3vpPO3y3N46ppcWptV1n5bQ0Gm56rc4HHBveKPgxCHY+BoVwDvw78Xs+z9Sn7/VCpJQ1xZs4XZLUQO0NLxO1JTbqVobwsjpoWwP6OJ8Bij3zYMEqInGjEtFIMBfvlYm2c+/VcJ97ybTkWRhQ//Vsy0UyKZdkqERyBk8WsVgNbC4qe3K9rMUbe/NoghE0K67kMI0cWGTgzBaFLIWNVAXaWNyiILp17rulhb953WZy17cyMDUgPI39NMdamVuFRzmw216qqshIQbydzYiNWiesxdQvQ1s86MYv33tR69CCsKW0kZFsQrd+az9Zd6AH58qwKrReWntys4++Z41n9fS9amRq55LJmWRjvvPVYMwCX3JjL26DBK97eSNlLGjqBf9CAEPHrVXvtESq++1u9tV1rpQBnwuqIoE4ENwC2qqjqX2BVFWQgsBDjjjDO65SBF32QwKKh27Y/bCZfEgKoy4bhwDEaFfy3cR2yymbSRgYTHGKmrtBEUYnAGB695LJn08cHsWdfIiOk980JNxo7wh4BAA0//PII/H7cHgNL9rTz1u1we+GQokXEmWprs/PWivUQnmPjLh0PYtbaB5R9UceXDyQSFGqgtt5KYHkBxTitvP1xI7g7Xzq4zTuuZu3/L2BH+EpNo5o8vDmTvliZsVpVvXyln+YdVqHbY8VsDO35r4Lf/VbNvZxOBwQYsLXbsbpWT7sHBeVfFcvbNA3r0SaqMHeEPgcEGRkwLYcm7lc7bsjY1csM/UynPt7BrrXaZsHNVA9NOieTxS3Ocz7vvw3TikrVMp+oyCw+fu9dZqhyTaGbK3PAu/jQHJuNG+NNRZ0aSsdqVrb74vxVc+XCSMzgI8Pmzpc7vP/hbMUYjmAMVXr+v0Hn78CkhzF4QhaIoPba8WMZON9IDhI2NYLdDWM8vtT1Up14bR2leKwFBvbFI16W3BQhNwBTgj6qqrlEU5d/A3cD9+gNUVV0ELAJ46KGH+naouh/Ky8vjyiuvpKSkBEVRWLhwIbfcckuXvb9+IXbyFTFEx7smv1v+M5DwGBOKonDTswP5+cNKzrstgZ2rGsjd3sTUuREYDApxZ/fcdHsZO8JfgkON3LpoEDtW1lNVYmHL8jpeu7eA3z2ewj3zMwGoKrHy5+P3OJ9z/5lZzu+j4k1Ul7p6qhkM2tibdkpE132IQyBjR/jTyGmhjJwWitWikrmhkY+f0sq2DAYtSL7mmxpikszMvTKWkdNDyN7SxMzTIln/fS3rFteQt7uZukobk08O79HBQZCxI/xn3tWx7NvRxJS5Eaz4rJqM1Q389HYl235xbZq1/vta1n+vZUrp88wDZ2UzbHIIlha7s1VMVLyJ8ceGcdJlsT1yB2MZN8Kfpp8SyYQ54RTntLB9RT3fLCpnw4/aOLn8/iS+WVRGVYmVWWdFMmJqKLvWNjDvqlii4k28dm8Budubuff9dMKijTLniLa8MwcLC6G1FcaM6Z7j6URn3jiguw/BL3pbgDAfyFdVdY3j50/QAoSinzCZTDz99NNMmTKFuro6pk6dyty5cxnTRX9k/vDvNPbtaPIIDgKMnB7q/H7gqCDnBifT5kUwbV7PDGoI0ZmGTwlh+BQtW3bVV9W8/XCRMzgYPzCAU6+N49dPqygvsFBb4bnBQnWplblXxlKQ2czZN8eTOiKQsnwL8Wk9N8AuhL+ZzAqnXR/Hv2/UGr4PHhfMlQ8lc+l9SRhNOC/E9E2vZp4eyczTI1FVlaK9rSQPlfIu0X+MnBbKk0tGYDAonHxFLK/elc9X/9E2k4tNNnPHG4P57tVyfv6oipBwAw98MpSvXypj6XuVZG1ylexf+VASR50R1U2fQojuERhsYNCYYNJGBbFzVQM525owGGDc7DCmzY+gtsLqzLSdebqrmuMP/06jtVklMLjnBdJFD+NeatzHy417u14VIFRVtVhRlDxFUUaqqrobOAnY2d3HJbpOUlISSUlas/Xw8HBGjx5NQUFBlwUIxx0Txrhj+l5KtBCd6agzIsnd3sSuNQ2MOzacC/6cAMCM0yJQVWhpsmMOMLA/o4mIOBOW5rZ9nyQ4KPqjYZNDGDUzlLgUMydfHgtogcOOKIoiwUHRLxkM2tiITwtg0okR5O/RAoTX/DWZiBgTF92ZyAW3J9DSZCcoxMB5t8Zzzh/j2bysjvL8VhIGBzD5RFnUFf2XwaBwy38GUrq/ldQRrp7SenDQm6IoBAb37KxB0c28g4F2uwQIe7heFSB0+CPwrqIoAcBe4JpuPh4B8PPPcPTRYO66nhO5ubls2rSJmTNndtl7CiEOnaIoXHJP211UFUVBUbSSZEA2UhDCi9Gk8KcXBnb3YQjR6xx3QTQVha2c88d4wqNdlzsGg+KccxRFwWRGKj2EcBMQZPAIDgpxRLw3KZEMwh6v1wUIVVXdDEzr7uMQXlpawGrtsgBhfX095513Hs888wwREW1P7BYtWsSiRYsACArqH5Ncfn5+dx+C6AP629iRcSP8RcaOEIeus8fN0//2+0seMRk7wh/625wDMnb6hB4QIJSx07FeFyAUPVQXDnaLxcJ5553HZZddxrnnnuvzMQsXLmThwoVdcjxC9CUydoQ4PDJ2hDh0Mm6EODwydkSv0AMzCGXsdMwvHUUVRblXUZQkr9ukW2l/0kWDXVVVrr32WkaPHs1tt93W6e8nhBBCCCGEEEKII9QDAoSiY/4K4j0KpOk/KIpiBCyKokzx0+uLnq6LBvvKlSt5++23Wbp0KZMmTWLSpEl8++23nf6+QgghhBBCCCGEOEjeGYSySUmP568SY1/bF8mWRv1JFwUIZ8+ejSp/VIQQQgghhBBCiJ6vB5UYi45JGbDwDxnsQgghhBD+s2ULFBd391EIIYQQh8c7PiDxgh7PnwFCX/+35Tegv5DBLoQQQgjhN1u2baOipKS7D0MIIYQ4PL42KbHbu+94xAH5cxfjJxRFqXR8r5cXP6koSpXX41RVVS/y4/uKnkAyCIUQQggh/OaRV1/lrDPO4KqJE7v7UIQQQogjJz0Iezx/BQh/AYzAALfbfna8/gCfzxB9jwx20Ze1tkJBAaSnd/eRCCGE6Aeqa2tpaGjo7sMQQgghDo+vDELRo/klQKiq6vH+eB3RS8mAF/1BTQ3k5kqAUAghRJeoqa+nvr6+uw9DCCGE8A+pOuzx/NKDUFGUpYqijPLHa4leyDtA2NQElZXtP16IXsjS0MDmnTu7+zCE6H1aWkCyoIRoX0YGNDe3ubmmoUEyCIXoKaRvmhCHzlcGoQQIezR/bVJyPBDhp9cSvY33wC8rg337uu94hOgEv61ezcVPPCEniEIcqrw8yM7u7qMQoucqLITaWs/bVFUChEL0JGvXatUkQoiDJwHCXsefuxiL/sp74EvzUdFLlZeXt3tfWUkJWUVFtEq5lxCHZNeuXazauLG7D0OIHuuKxx5jb0YGbNniutFqpaahgXoJEArRM1itYLF091EI0XN5B9C3bPG5+HVYcQK7HWy2wz82cdD8GSCUiFBvoar+LQGWlQHRB+zNzGTyhAnt3l9eVobNbidr164uPCoher/Pvv+e/372WXcfhhA9k83GL9u3s3XtWti/33lzc10drVYrDU1N3XhwQggnub4Ron0NDbB+fdvb9PYZRxonyM+HHTuO7BjFQfFngPALRVH2Hsw/P76nOBz19bBhg/9ez3uQSwah6IUKs7LILyqivp3ykfKKCgB2yeQkRFsdnPBVVFZSXl3t3/ez2bR+t0L0dq2tlNfWsi8rS/vZagWgxrGQ29DY2F1HJoRwJ9c3QrRPVdu2YfJ1bni446ihQTJ4u4hfdjF2WAYU+fH1RGdpbvZvHzXJIBR9QGlBAQB7N29mwnHHtbm/vKKC4MBAMjIyuvrQhOj5MjO1eWVU2/3KKiorKa+q8u/7lZZCQQFMm+bf1xWiizVWV9PY0sLm7Gwm33ILm+bOBZOJGseiVL0ECIXoOeT6RgjffAX+3IOGRxonaGpyLqCJzuXPAOGzqqqu9ePric7S1NQ5NfzSg1D0YqWFhQBkbdvmO0BYVcXMsWPZnZnZ1YcmRI+3fdUqAiMiGO4jQFheVUWZvzMIZZ4RfURFcTEAX6xeTXVDA3aLBUNQEDWOoLqUGAvRQ8i8I0T72ssW9FeAUC9VFp1ONinpj5qbsfszAi8ZhKIPKCspASB7926f95dXVTF6+HCq/J0JJUQvt/rXX5ly3XU88/bbPu+vqKmh3N87P8o8I/qIsuJiTEYj1Y7NSJrq6gCoqaoiJjxcAoRC9BQy7wjRPl/j42DLjg+GZBB2mS4NECqKEqQoygVd+Z6irZysLCb96U/+m+RkF2PRB5SWljIiPZ2s3Fyf95fX1DA4PV12lBTCy+plyxgQGUmBI8juwW6noraWqvp6rP5emPJnqwwhukl5aSnjR4xw/qz3wa2priZ5wAApMRaip5AAoRDtc88W1LmPmSOJE6iqlkEouxh3CX8FCB8G8n3doSiKUVGU0xRFeRsoBT44kjdyvN4mRVG+PpLX6dMOcBG2ZuNGiqur/TfIJINQ9BYdpKeXlpcza8YMsvN9/CmzWCivrWXQ4MHSMF4ILwX79jFj0iQKysra/u23WKioq8NoMFBVVua/N5V5RvQR5WVljEhPZ8CAAYQGB1NfWgo5OVqAMD6eBimrEqJnkHlH9HctLbB1q+/7OjODsLVVex3JIOwSfgkQqqr6sKqqhe63KYpynKIoLwHFwFfAXOB14KgjfLtbANkloCNLl8LPP7e708+G7dtp0geaP/gKEEpmh+hpWlthyRLfgXFVpayykknTp1NcWdl24iotpbyujsGDBkk2hxBeCgoKmDF9OoWVldo4c2NraqKmoYHBCQmU+8owPFxyoSb6iPKyMgYMGEB2djZDU1Opz8uD7dupKSggOSmJeikxFuLQdNY1iMw7or9rbobyct/3HShAeCSJRE1NEBwsGYRdxK8lxoqiTFMU5WlFUfKApcAlaLsbA1yoquotqqquO4LXTwVOB1498qPtoywWsFopU1XuueEGnw/ZsGsXzZ0ZIJQSY9EDWWtr+ednn0FtrY87rZTW1DBu4kRKamraBDmasrKw2u0kJif3jQBhczPs2dPdRyH6iIKiIqbOmEFpdTVWR/80XXVpKRGhoSTGxvo3QCjzjOgDyrOzyc3KIi4hgfDwcMJCQqivqoKQEGqsVpJHjKCxpQVVfteFOHjr1kFlpf9fVwKEoq/Lze34d7yjJCB9fLg/v72g4aGOo5YWCAuTDMIu4pcAoaIojyiKsgdYA9zo+HohkAAsBBR/vA/wDHAn0G5kS1GUhYqirFcUZf2GDRv89La9iCPC/v2+fbz80UdQVASFruROtbWVjVlZWG02rC0t/nlPCRD2CX197GxZt44/v/YaNfv3t73TYqG0pobRo0dTXV+P1b3PoM1G1o4dpKamEhYR0TeyOSoqIC+vu4+iz+jrY6dDqkphWRmDRo4kNjKSEq/fq/LiYmKjooiLiqK8tNSv79tt88zeveDYeVYcmX49doD77riDf332GXEDBwIQFhqqBQgHDuS3vXuZMGkSZqORZikzFm76+7g5kC+XLqWyoMD/LywBwl5Pxk777HV1HH/mmbR4LfSydy/ot3V0je+dKah/7yuD8FC1tkJgICiKVCl2AX9lEP4FGAosAYapqnq+qqqfqqraDPjlL6miKGcApaqqdjiaVVVdpKrqNFVVp02dOtUfb927NDVBSAjLf/2Vqvp6mn77zSMQUJGTg8FgICQoiGZ/ZUJJD8I+oa+PnRUrVwKw20fvDFtTE1V1dSQkJBATEUF5UZHrzupqvt22jfnz5xMWGdkn+kHZqqrYnZXV3YfRZ/T1sUN9PWze7PPEUG1ooKCykpSBA0lJSKDAa5OfiqIiYqOjiYuO7hsBQrsdsrJ8ZyKLQ9bnx84BZO/bh9FoJCEhAXAECJuayC0pYd26dZx51lmEBgVRL79vwk1/HzcH8vd33+VXxzmfX8n1Ta8nY6d9+zZv5uft2ynx7sWek+M65/G1EYnOVwDQV4Cwo9doj8UCAQFgNGpZjo7NvETn8FeA8BEgEzgZyFQU5WNFUc5TFCXIT68PcAxwlqIouWgbnZyoKMo7fnz9PkFtbGTilVfyySefEBAQQIHdDm6BwPzdu0lNTCQ4MJCmziqVlAxC0QOtXLuW4KAgdu3c2ea+ytJSIsPCMJlMxMfEUOKWdUtVFV+tWcOZZ55JQEgIdrudVq8S5N7mhx9/5KK//73dPqVCeKiu1haafvhBC46531VYiNlkIiwsjJSUFApycjzuLy8uJjY2lpioKCoqKvx3TN11oVZSopW6SJmLOFI2G7lFRSxbsoRzzjkHgNDQUL5YvZr0Y4/l/PPPJyQkhLDgYBokQCjEQatvaqKosPDADzxUEiAUfdj2dVoXuDL3JImWFi2OoAf0DidA6GvMHM4mJWYzmEywfz8caNO7nBy5xjkC/tqk5CFVVUcBU4EXgRnAx2i7Fv8XLYvwiP6iqqp6j6qqqaqqDgYuBpaqqnr5ER14H7Q/O5uC0lIuuugipk2bRkFsrDawHQMxPzubtLQ0ggMDJYNQ9BtWq5VfN27kgjPPZPfevW1+P8uKixkQHQ1AQlwcpUVFWiNcu52moiI2ZmRw3HHHoZhMhAYF0eBegtwLbdy6VdvJvA9kQ4rOt271ai587jmeXLyYl9580+O+gtxcUhzZT4PS08nJzva4f/++faQNHEh4eDh1/gxydNc8s28ftshIVDnxFAejqAjaKXW0VVWRV17OjKOOwmQyARAWFsbOvDyuuuQSFi1aBKDNOd4lX0KIdtU3NlLsz563Orm+EX3Y9u3bAe2ayKmqSvuqbw7SUYCwoxJjRfHMIDxUra2uDMKGBq1isiN79kgrpSPg101KVFXdpKrqHaqqDgKOA94B5qD1IPxMUZTnFUWZ6c/3FJ42bt7MUVOn8vLLL5OamkpBcbE2oByBgLzcXFIHDSIoIMB/GYTSg1D0cB99+CEjkpM57fTT2V1U5JFVC1BaUkJ8bCwA8QMGUFJcDFu2QF4eO7duZfiwYQQFBYHRSFhQEPW9+WLNamVTZiZlNTXYenmgU3SNZStW8PGPP/LkW2+xffduj/tysrIYlJICwIyjj2b11q0ef/9z8/JIHzqUiIgI6vw5bjpqlN1ZGhv5fskSUs8/n/+8/37Xvrfonfbt8+gD7a5gzx7ioqIIDAx03hYWFsbe4mISEhNRFK19t99LjDMzteMSohdb8dVXNLYztuqbmymSAKEQB1ZRAfn5YLWyPScHs8lEaUcBwgNtUgKe9+tjRlHa3nYoLBZXBqGjOlJVVe65/XYs3skOdrsWUJR57rD5NUDoTlXVX1VV/QOQBJwKfAtcDvzmh9derqrqGUf6Or1aZaXWNNTLhi1bmDplCgApKSm88cYbvPTDD7B2LezfT35JCamDB/svg9Bi8fyjoX/tygm0pkZLNxaiHc/94x/cceWVjJo4kR3792urT25KS0qIj4sDICEhQQsQVlVBVRVbs7KYMGmS87FhwcE09ObeF83NbNq7F7vdToV7GYEQ7Vi/bRupyclUVFVR5lUmvCczk5EjRgBwzJw5rMzIQHVrZp1TWEj6iBFERERQ688gRzcsRFVmZXHNM88wadw4Cv3ZT1H0SXsyMjju+ut59YMPfN6fm51Nelqax21h4eFUNzSQkJTkvC0yLIwa/SLNH1patIunrlZUBMuWuZrd61pbu+d4RO+lqlxzww288fjjbc7nsNm0EuPO+BstAULRF9TVwS+/aN8XFGg9pnNy2L5/P0dNmkRZaSlV+pyjZ+65lxi7f3XXUQ9C9wzCwxlH7hmEAI2NFOTm8renn+azRx7xnFeamthSVMTZ995La339ob2PADoxQOgmHCgH7kDb1fiCLnjPvq24GNavh4wMzwFaVcWG3buZcvTRAKSmpvLjjz/y0c8/a81Fd+0ir6aGtIEDCfJXD8Lt2127OXZ1gFBVYcMG+O03bUVc9G/Z2T77TVRXV7N9zx7mXXstY8aOJb+sjCqvkq+ysjIG6AHCtDRK8/L4bcMG9m3dyraCAsaPH+98bI9vGH+AybC2vJziqipGpqdT0hm7/Ik+Z0NGBh+99RYv/OtflHkFKnZnZzNy1CgA0tPTsQH7ly3T/j43NpJTWkr6kCGER0RQ6+/M2y6+UPvuu++YMWkSp8+bR1VvXiQQXeL7zz8nIz+f71av9tmzMmffPtIHDfK4LSw8HIB4twBhdEQEVZWV/jsw73O0rsqIz8py9bNy8/MHH7D0HWkpLg5ec0kJe4uL+e+SJVBe7nGfraWFxpYWiv3Z81bXHZnrQvjZqv/9j7K8PFBVPvnqK15fuxY1O5uswkKOmT6dz3/8kTFjxmj91m02LTDnXmIMHfcV7MwAocmkZRI2NrJrwwZCgoJ4/qefYNs212ObmjjvkUf4dt06irw2zhMHx28BQkVRLlYU5QNFUT5VFOUyx20PAEXAWsfX94Dv/fWe/ZLFopU+zpgBwcEeJ1r2zEzW7NnDzKOOArQMwsDAQHbm5sLYsdDSQn5VFampqQQHBdF8oPr9g2G1uk58j2R3okPlluHFSSdpJ536Hy9fyssP3NBU9F52O+za5QpWu1mxbBlHjRxJQHQ0ZrOZ6RMnsmrFCo/JqbSsjPgBAwBITk9nT1ERC//zHz5Zvpyt+/Z5BAjDQkL8FyC027XduPxpxQptU4l2FBcUkDxgAMmJiZR0kEG4+Zdf+OG99/x7bKLXqayooKy6mplz5nDMccdR7vW7tTs3lxFjxgCgKAonn3IKnyxerGV2NzaSU1JCeno6EZGR1PlzJbczMgibm31m5uvy9+9n+PDhRMfEUNWTFwlEj1CUn8/Js2axPS/P567X+/PzGeidQRgRAUB8fLzztujISP8GCN3P0Ww2VyZJZ7LbWbV2LfstljYLeZ8vXswbn3wCwD333OPfzYxEn7Rn3TqGDxpEcVUVezZu1G60WKChgUbHWCuqrPTvtYiv+UY2qxK9TG11NcdceSVH33kntLby+ZIlPPnBBxQVFxMaEsKQ9HRWbdlCcXExn376qfY7HhjYtlrQ19hqrwch+K/E2GiEkBAwmdi1cSMXzp/P+m3baHWbI2tKSymurGTCsGEUSR/Cw+KXAKGiKNejBf/SgUjgdUVR/gXcCtwLnA7cDZwE3OeP9+y3cnIgPp5lmzZx/b//7Uqtb2hg24YNDEhIIDExEYA5c+bw3//+l+bWVl5bvpys6mr2l5SQlpbmvwxCVe2eEuM9e7TsxZgYbUUhNLT9zKmaGli3DnzsXiv6iNpanvz4Y8r37Glz1/KffuL46dOdPx9zzDH89ttv2u8PwM6dlBYVEe/YaOHss8/m5+3b2ZGby/7ycnbm5DB27Fjn88NCQvzXML652b/Zr83N2iTqtaLurrK0lJjISK2Uup0AodVq5YprruGSG26g1GvTCdG//PDNN8weNw6D2cyAhATKamq037HiYrBa2ZOfz0i3APodd97JUx9/TFNFBdVFRVisVmJjY4mIjqbWnwFC73nGHyWKBQXa3NKO/IICUtPTiYqOlgChOKDCoiKOmzWL/aWlNPkodywoLiYlNdXjttCYGEBrdaHze4DQfez4Y0G3oUHrYdWRujpu/e9/Oeeuu2jxmj8Li4vZtmcPqqry7DPP8NO33x7Z8Yg+b+eWLYwbM4aTTzqJZcuXazfu3w+7d1NfW0tUWBgl1dXY/dVrHdoGRqqqtGouIXqRzM2bGT1oEPtKS2mtrGRTVhb7Cwr4ZM0ahg4axICEBOx2O7MmTdIChO4ZhCtXus61Oioxdr9P/95g8LztSDIIg4IgNJRd27czacoUBg8ezO79+7XHqCpbN29m3PDhpCQkUCyVUofFXxmEfwSeUVV1pqqqJwO/A/4E3K+q6r9UVV2squo/gIeB8/z0nv3O6mXLyFq5kqVFRVx88cW8+8MPVDsypjZ++SXPLV3KnDlznI9PSkrisssuY8yYMVx33XW8sGYNRaWlDBkyRMsg9McOpu7p9l25SYnFQkt5OeMvuYS33noLwsN9lsk01tay+OWX+dfq1Xy6dKnPVXzR+9Xu38+977zDh5991uZ3b9369cx0CxAed8opfL9nD5SUaCtjOTmUVVQQ7wisR0ZGcv+DD3Lcccexs7CQ6ro6Ut0u4kKDg/2XQejvcpW6Om2VroMMjMrycmKiokhIStJ6Lfrw0TvvEB0SwoWnncaif/+73dd65ZVXeEdKw/q0/338MefMmwdAXFwc5bW1qM3NsHUrtVlZ1DU1eQQ5Jk6cSHJCAls3byY3K4v0tDQURSE8MrJzA4SrVx9ZqeSGDVqQw5GF0obFQl5JCWlDhhAdF0e1/l4+LkAXLVrEerlw7NeampooKi5m4ODBDBs8mF16lpObgpISUr1LjH1kEMZER3dugND96+EoKdHKhzvQUFDAtpwcbHY7K9as8bivoKSEnfv2UZSfT2NzM8sXLz78YxH9QkZmJqPHjOGEuXNZtnWrliBQVwcWCw21tcRGRhIWEkKlP/uTe5dPuldQdYb9+6W/uvC7zC1bGD1sGAkxMWRv3kxuaSl/+MMf+McXXzBsxAjik5MBOHXsWCrLy7Xfcb0HYVXVwQUIfZUa6yXGvu47ED2uYDKBycSmnBzKkpPZVVzMqMmTGTduHNuLi2HTJsjIYOv27UwcO5akhASKJEB4WPwVIBwKfOX28//Qdi7e4PW49cAgxIHpFyiqCs3NqFYr1117Lb/7z3+4/LrreO+99xg7fDgZ27ZBXR3/evVV3vj8c04++eQ2LzVmzBjCw8N58733mDhxImaz+fAyCO12LRPP+zbvPxJdkEHY0tDAtn37qGtq4u677+bnjIw2F4fWhgZOP/ZY7njlFV7//HNeWbLEZwmq6P1+/ukngoOC+OS331w7bjns2buXUePGOX8+4YQTKC4t5Z8ff8ymb7/l+hdeYMXOnQxwy9i49dZbeeqpp1i5cydDhg7F4LbyFRYa6r9djP0dTK+vh6QkbRMjH/0YQSsZjYmJISE5mRJfTbxVlddffJGbfvc75hx3HFt27PD5Oo2Njdxzxx3cddtt/Lxsmf8+g+gxVqxYwXfLl3PmxRcDEBAQQEhQENXFxezOziZrwwaGpqY6d1vVpSQlUVxQQE52trPHWkRUFHX+zuZwHztWa8dtJjpitUJhIZu2bmXG3XfTnJnZdjGpoYH8ykpS09KIjo2lqq5Oy9hdtgzc2nV8/9133H7rrVx7+eVYpfysX9q3bx8jRoygsLSU5LQ0xo0fz5bNm9s8rqCsjBTvAGFYGKAF43XRMTFUdnaA8EjmIbfgTBvNzVBczJqVK5k4ZgxjR4yg0H3nWVWlsKICo9HID198QXhwMN8vX8637WQRWq1WVFX1738P0bPZbDSsXs1nH33kvClj717GjB/PCSecwLKtW7GXlWm/g1Yr9bW1hIWEMGPCBL74/HP/HYd3YKOT2ynZKyqwd1ANIsTh2LNzJ8NHjiQtMZFvv/mGUYMHc95555FfXMzQUaMY4AgQzj76aKr1AGFgoPb3XVVdQfGDKTH2V4CwtVUrLwZsJhPn3XEHTzz/PJv37mWcHiDMz4fSUigtZcuOHUwYP57ExESKj/C6f/PmzXzQzkZjfZm/AoTBgPuSu34V0OL1uFbA7Kf37JtKS7UI+NKlWmlseTmsWsXqzz6jxWplb2EhCxYs4KSTTmL0yJFk7NoF+/ezq7SU5cuXc+GFF7Z5yT/+8Y98/PHHVFVVMW3aNACCg4MPPYPQatWOz52vEuMjDXrYbB1PuqrKGQ8+yKM//MAJJ53EM888w/3PP9/m2NZ+9x3ljY1s3rWLH374gbU7dqC205vNZrNhl8bDvdIb//wn97/4IrfecgubsrMpzchw3ldbW0tdQwMpjl1WAYxGIwsXLuS+N9/kvkcf5a0lSyipribeq9Rr4MCBNDU1MWzYMI/b/Rog7IQMwl+zsmiNj8fu9t/BXWVlpTNAWFxTo2V/uGVMFWZksGHXLs6+4QYmTJvG1nZKLt969llmjRnDJSedxMoffvDfZxA9wvLlyzl3wQJeu+02ktxK7AdER1O2fz/H33cfn3/zTZtdWAESExMpKiwkJzeX9KFDAYiIiaHWV2be4fIOEB7uxVppqfb7HxLCCouFDbt2ces999DqXVrf2kp+eTmpqalExcVRVV+v9Su027W52vH4J+6/n//+5S+EGAx88/HHR/ABRW+1Y/t28vPz2bVvH0mDBnHi3LksXrNGC5bpbDbyy8t9BghjYmIwm12nytHR0VR10Ff2kLmPFX9kENbVaX2hfB1jZSXs2sVv69Yxe9YskpOTKSop0c7zcnJQm5spqqzk+KlT+eKLLzh52jQmDh7MBeedh+p1Hmm320lPTeWvjzzC0KFDKZfgSf9QWsonH33EH268UfvZYmFnXh6jx49n0KBBxMbEsO6XX6gvLaWwsNAZIHzojjt45LnnsLSzWHrIujhA+LeXX+bvixZpP2Rna/OMEEcoMzubEePGkZaSwudLlzJl4kRmzJhBfHw8Q4cOJW3IEO644w7Sxo+nprraVWKsZw52FCDsKPinLySrqlZufChxAqvVGSD8etcuWmw2nn/+eUaPHk1KSgrjx49nW26ulmHY3MyW3buZePTRJCUnU6QHCJubfVeHHMAHb77Jk489dsjP6+38uYuxr//Tshf8oair04KDEREQHw+1tTQUFfHyJ59w9Z//zN233srPP//MU089BcCocePI2LULtaCA3fv2MWbMmDaZHKCVfM2bN4/U1FSmO0otDzuD0Fe2oK8VgyMJEO7eDWvXtv8aFguFlZV8uXgxU6dO1UpB9T5upaXac8vKWP7jj8w96SSMRiOJiYmEh4eT5aMPYX19PccffzxnnHEGl1xyCTk5OYd/7KLLvfbWWwwbPZqrr72W+SedpK0YO34nd+/axfCkJBRH2Zbu3nvvZffmzSzesIFjjjmGV199laEjR3o8ZsCAAQQGBvoMEDb4q1TSzxmEan09p117LSfcfDMTzjpLe32vk+PKykpiYmMZM2YMm3NytL85bv2jtqxfz7SxYwkKDmbExInsLy2l0Ssgmr1pE/f/7W88/MQTTJ8xQ8ope7vCQvDKcLrxxht5/Z57OPfqqz2aSw+IjWX/7t0UV1Xx9erVDElPb/NySampFBUVkZOXR7pj/IRFRdHQ3Oy/hRh/BQgzM7XPHxDApu3befiBB9hRWsrfXn7Z42Et9fVU1taSkJBAdEwM1fX1qPn5EBUFeXmwcyfZH33Ejqwszv6//+Ois8/m688+O7LPKHqlPVu3Or+PS0nhrLPPZvHGjbS4ZTK01NVR3dDgUUoMWnB99OjRHrdFx8b6d9dsP2cQ2qqrqQgO9h0gtFigvp6snBxGjRtHcnIyhSUl2mMzMqgsKCAkKIjTTjqJr5cvZ+jgwXy+fDkBRqNW3gba+KyoIG/vXvJLSnjgoYdIS0xk5syZzvNhz4+nYjvcbGLRrZYtW8Ytt9zC8uXL2bFjh1a1tGsXn27dSkllJTVlZVhra8kuKmKk45xtwbnn8sU33/Dv//2Puxctor6ujrDQUI46/ngGxcfzzTff+OfgfAUIO7FaKicvj8zcXO09du+G336THZTFkWlsJLOggOHjxpGWmsrKjAyOnjMHg8HAyy+/zNy5cwkKCuLJJ58kKiFBa6Wilxi3OHK+9GuKjnYx9rXw5J5BqFdlHez4sdudz1m6dCm33norKSkpXH311QBaBmF2NsTHY4uKYsf+/YyfNInElBSK9OSh3FxtM8uDZK+pYf0rr7Bm5Uo279hBTT8L0PszQPi9oiiliqKUou1YDLBEv81x+3d+fL++Z9s27CNG0JySArGxUFvL199+yz//9z9uP+ccrv3Tnxg6dCjBwcEAjJ4wgYySEoqqqwkODibG0dy6Pc888wynnXYacJgZhL4GvN3u/wzClhZtx+GSEt/3WyxUOVYBpk6dSnx8PK2trVRGREBRkVZimpvLz+vXc/z8+c6nzTzqKFbv2KH1Z/vxR60hvdXKQ3/+M2mBgQwAVvz6Kyt+/fXwj110KVtrK5v27OGV119nyJAhnH/ppfx38WJWvvIKALu3bWPkwIHOlSed0Whk4KhRzJgxg7lz53LttdcSEBDg8RhFURg4cCDDhw/3uD00NJS6HppBWFZcjMFoZFB6OpkFBVgyMmDbNo/3q6quJiYujqlTp5JbXEx5RYXHBj/ZmZkMHTwYAHNAACPT0tihBwDtdigp4a2XXuKq889nynHHMW3WLNa7v4foXaqr2fy///HH++93/i3PysqiqrSUU486CgYO9Hj4gLg41m3QuodszslhiNf4AEhKS6OopITc8nJngNAQGEhIYCAN/soi9B47hzvvWK1aOXFAAJs3b2bu/PlcetFFFHjNP4V5eSQNGIDRaCQoKAiDwUBpcTH/27aN3G3bGHLDDcxYuJDbbr+dgOBgzrzgAr5euvSIAqIS5Oid9mRkkBAVRWJ0NIaAABISEhg7YgS//vST9oDqaooWLyYxJsajfQXAoEGDWLFihcdtfg8Q+jODsKWFT1as4Jjrr8fmozfv9u3befDdd9lfWsrAoUO1jI7SUm3M2WwUZmaSPGAAZ19wATa7nfTBg8FsZmBCAvv1hd99+yA3l12bNzN12DCunz+flQ89xF/vu49//vOfbcbJsmXLOPXUUw/v84hu9elHH7Fo0SIWXnMN77/yCtaiIvaXlfHzunUMS01l99q1ZGdkkDxggPNaaMEFF/DR2rV8t20bFdXVzgxCwsK49qST+K/jfPCIdXEGYXF5OfklJVBXx/3vv09JQ4NHOwt3VquVfz/5ZNtsyT17XIEdH1RV5YGbbmqTrSv6gNZWKChg1apVvPCPf0BhIdVZWWTk5TFu3DjSHOd2R594IgDnnHOOc5NTgMjYWGobGlC9A4R6BqGv85ODLTFWFNfPB8NmcwYI8/LyGDx4ML/++qszQDh06FBKyst5YdUqbn37beLi44mMjCQpLY3i0lJYuZLFP/zAO+5VHa2tPhe1PvvnPynZsYNfP/2Uo264gTVbtzIpPZ0VP/98cMfaR/grQPgw8G/gBbd/vm77N/CIn96zT3nyvvuYc8MNTD33XCIiIvj9I49graxkxZo1XH/hhVx/ySVaCYebadOmsTojg82q6lxJ68h5551HbGwsoAUIm9oLELY3YNvbncjfGYQWCwQHa83fm5vb9j1sbaWqro4PP/yQ6dOnoygKw4cPJ7OiAkpLKS8vx5Kfz6rduzn2uOOcTzvxxBP5fts2rZn9wIGwdSsV69fz2vvv8+Sjj/LmE0+w8MQTyWgnG+qpp55i1apVZLRTuim63p5164iPjiba0a/ptNNPZ8TkySy4+27WfPEFW9esYaSjxNGX1157jRtuuKHd+2fPns3UqVM9bosID/dfgNAf/Z/cZO/bx4jhw3nvvfeIj4mhcOtWV0q9zQZLl1JZWUl0bCxms5ljpkzh1Ece4X9uTeGzsrIY5hb0mT5hAiuWLNF+KCmBtWv5be1ajndcgA2ZMIGGxkYuvvhi/5XyiC7TsGkTFz/1FC8vXkyTI8Pp2w8/5LSZMzHMmtVm3klJTWXZxo3OwEa6W/m+Lik1leKGBnJqahjsCDZjMBARHEytv0olvQOCh3ux5ggQthoM7Nq1i/HjxxMZHU2NV7CjoKCAZLdsr6iwMGbccQcX3norz3/1FaNHjWLbzp3c85e/ADB08mQGREay9KuvPF7n1eee4xtHZmF9B5nI+/btY+DAgVT7s7RUdIk9mZlccsIJpLr9vkybNo3Nmzbx+F//Svkvv/DqV1+R4pU92J7oAQP8u2u2PzMIGxrIrqhgd3Y2n33/fZu7t+3cyQe//sr+8nIGDhpEcmoqhWVlzh6fhTt3kpyYSNrYscwaNYpRjuzJgUlJ7N+6FXJz+XHZMjasXEnG1q0cNX48i266ifDRo7kkIYGkyEiWLV3q8Z4bN27kt99+45tvvuEHaX/RqyxfsoSEiAgyc3PZs3kz0267jQkLF3L//fczfcoUdq9cScaKFYx2O6+bOnUqCcnJrNy8mcq6Oi1AGBYGBgOnzJ/PWq9NcQ5bVwYILRZKqqvJr6ykPjubx997j+e++sqzTYGb7evW8X933cVNV1zhurGwUMs87GDTusK9e3n0xRcpycvz9ycQnay1oYFnbr8d1dfv4P79sHIl1g0b+Oj557ntvvtY/913vPuf/zBvzhyioqJIGzGCmKgoRo4a5fP1zaGhBJhMNDY3a0kWemBQ/6qq2uZU+/a5nnSgTUpAGzN6gLAj5eWu33e3DMK8vDzS0tJITU3F6Dg/NRqNjBo1iocfeYTnXnyRiRMnApCUns7+qiqslZW8+9VXfLRsmbavQ0MDV5x6KvNPOgnVZiMvL4+9e/cCcMsTT/DnP/6Rn5ctIzw0lLS4OK454wxuuPFGVq1aRaM/e2n3YH4JEKqq+vCh/PPHe/YVS5Ys4Y/XX8/TL77I7LlzueSSSygvL2f99u189cMPrNi6lWMWLIBJk9o8Ny0tjSlTpnDf3/7GWLceUQcjqL1djC0Wrem6L75OJDtjF2OLRduVuKUFdu7UNhZxCzw019VhV1UuuOACTCYTACNGjCCzsBBaW7ng6ad59JNPSEpMJDo62vm8M888k+/WrcNywgmoI0awvqyMF55/ngVnnEHqrFkwcSJjZs1ip49sqIKCAh599FFOPPFE/r+98wyL4mrD8D279A6ioChiVxSw94bd2GMvsRt7iSVRMZZY8qkpRo1Ro4lGY+wajS2JvSZWjL2DIlKU3st+P2aHXXBRQBTRc18X18rM7OwMztlzznOe933d3d2FSPiGiIuL0yUjj4wkIS6O9u3bExERwcqVK+nYuzfVtR0BgIWFBevWrWPlN9/QfuBAft60id6dMy+c7u7unu4ZychPP/2UlrdTwdbWlvDccnNk7EQfPJDdszk8172AAEppHVvFixTB78EDXZVVf39Qq3kWFYWDdmLapWdPnkRHc+LChbRruPPgQbpw6w979GDLjh1yZx0QQIq9Pf/cukVtrfguWVhw8ptvuHHhAsdEsZJ8xbThw+kwaRK16tXDs2xZLp46RWJiIj+uXk3nnj0NDuDq1K3LwcuXqavtk0oaGFw6OzsTGB/PA39/SuiFIFtbWBCZW8UFMi5EvSx3bWakpEB8PD/v2UPt2rWxsLDA1sFBV6VYy5PAQArrFTKyt7bmWXQ0vbp0YdVff9GwYUOKaJN7AyBJjB0yhK/nz093njWrVjFoyBBmf/EFjo6O+OkPrvVYt24dT5484ZtvvuGyXsiq4O3g8oULcn49vaTtqcnJfPnxx1y6do3Ro0ezXS9MvVLVquw9cQKfzz+nyYQJrD95kvpa58bLsHd0lIvi5Ba5WcU4Ph6/0FA8K1Zk/8mT8jZ//7QxW1hYGHcDA3kUGkqxYsXk9ANPn3Lv5k08xo7l9q1bFC9RAlQqDq9di7d24cm1aFHOHD7MwV9/ZcqaNfT++msunz0rFxxr3hwqVYLGjenh7c2WNWvSXdI1X19iYmIYPnw4Pj4+ObsvwesjPj5d1ILvli0kR0UREhLCw8eP+XX2bOaMHMn5mze5HRDArl27mDBhAuWqVOF6VBR/XbiAu17hOUmSmDt3Lg0aNOBZdLQsEFpaAmBVpgwxuTWhf5MhxnFxPAkP51FoKBdPnMDFyYkVu3aRnMn3wOWzZ2lZowYbd+9Oc9QG/fMP9+LiIDISjUbDzp07GTRwYLr33dYWobuTSTE6wdvLqT17+OTrr7l8+nT6HamppPr68t3Jk5QdNYp/fX3p1LEjnb/4grE//sjgjz8GoE6DBsyaPdtgajIAjI2xtbQkPD4+/UKxfg7ChIT0DtUXLTxlDDF+mYPw/n2duJ2amnYN/v7+FDOQ+7pSpUo8ffqUkiVL4unpCUAxV1cqeXnx7d69nLh8mauPHsHjxxz96Scu+Ptz2c+PbevX416+PCP79ePZkyeEx8Rw7PZtVh46xPJFi/ht4UJGDx3KnOHDGTx4MIULF+a4gUjDd62OQW6GGAuyia+vLz169MDF1JSTO3Yw7+uv+fTTT7GxsWHsuHHM27mT20+eULVmTbCwMHiOMWPGYGFhwcyZM7P12Zk6CJOTM7ejGxpIGipSkpMKRSAn4L15UycQxsXJIcPm5ulWzcJCQ7G3sUn3pVamTBlu3bkD1tZc9fNj6b59VK9VK93pXVxcKFmyJDv++IMDBw5Qo18/vtyyhU+1jg9AFghv304nSF69epUhgwfzUePG3D50iAkTJrB27drnLn/ChAlcM5DjUJAzIp89w7tBA1q0aEHgsWMM6dqVVf/7H7t376Z3jx7MmjaNoa1bM/STT557b8cBA9jxxx9s3bSJslmchGUVOwPuohyTsU2Fh6cbOGeLpCTuBgVRSruy7lq0KH4hIXL7TEoCPz9qfPIJ5/z9cShYEICBw4fzzaJF3AsOloXEM2e4++gRpfUWHJp27MjNgABO/vYb9y5dosO8eRQpVixdlc3yvXvTvVkzdv76q3wv2mTGY0aM4PSpUzm7H8Fr5fbly6z87TeqNW7M0u+/p2a1avx7+jSLZs+muJMTH/TubfB9DZo2JTU1lRYNG+Lk5ISboRyEhQtz5coVbG1tsdHL/2ljaUlUbjni9PuZVxE5kpNJTU3l8yVL+PbbbwGwdXAgIqNAGBRE4cKF0363s7amQdWqNGrShIiYGKrXrfvcqXuPGcPJS5d4GhAAoaGkPHqE7+3bjO7ShbvnztG0Zk02b9r03PtSU1NZu2YNXw4Zwty5c9PCaARvB1cuXcKrWjW+/ewzUi5dIv7QIUhNZdHs2fz211808/KieNWqFK5fP+09FT08OHz5MiVcXbnh58ehQ4dY+N13Wfo8W0dHouPicq8qtqEQ45wKHfHx+AUH4924MX6BgfJ5rl6VF5SQBcKU1FRsbGwwNzensKsrj58+5cCxY1y5f5/vdu+mqTYVjGnNmkh2dgC4FivGV9u20WnePO4HBVGxUiXWHDhABQ8PuaKmSgWWlnTs25dd+/enm5xd++8/ihUsSEhwMI8fPmTp0qXZT6kjeC08uHmTEV27EqwNo0+KiKB+v378OGcOQzt3pmfDhtQbMIDR8+Zx78kTKpUrR8OGDZEkifIVKvC/77/H9/FjPs4w7vP29mbr1q08jYoiOiIirRq4ReHCxMbH504IbW46CF8Q9gugiY0lKCyMhORkDp0+TfuWLXGws+P6lStpaZT05ym+Fy/i3aABRRwc2LN1K77HjtHRx4eK/fqxa+dOCtnbM3TQIA7pRYsA3NaaHQ4dPMiihQsBWLlyJceOHcvZfQlyhUdXr5KoFbaTk5OJiYlh17ZtbN2wIe2Yv/ftw9rCgg2//JK2bXDfvuzdvBnPMWP4ceNGJGNjTl+7xg8rVuDn50diYiItO3YE5HHaqFGjMr8ItRo7S0siXiQQZmwDLwoxzph38GUCoX4KM62DMCEhgbCwMJz0FmsVPDw8qFatGitXrqS3dvwqSRKrVq3if7/9RlR8PIFhYcRcu8ayP/5gyIgR1Pbw4DMfHwa2asXJCxc4deQInqVK8fOaNYQ+fUrLDz+kas+eULo0H9WujaWJCR7u7nz55ZesybAw1aVLF3ZniBjJzxjl9QW8V4SHy8UAXF05fP48337zDeO6dmVy27ZQr166Q7t06cKPP/7ImrVrMTU1zfSUbdu2pW3bttm+FLPMchCmpBjOKwCZ5yA05CBUfn+ZhVifqCjZPZWUxKeLF9PB3Z3/7t6lVtWqVImPl0VDdAKhPlWrVuXzzz/n42bNCNE6VJSCLPosWbKE9u3bY2JiwneLFmFvYkI5d/e0/aUrVsQ/JITIffswrVSJ00eOMHj6dHrUr8/4fv1wiI5mYKtWNO7Vi/79+1O+fHlCQ0O5ceMGy5YtY//+/Vy4cOGF/2eCrLFu2TKcjIx4FBdHudatqVSxImvnzaNDrVqcPH2av1avpnKbNmBmZvD9dQ1M2HMDOwcHwnNLIMzlAefd4GAaNm4MQHE3N/wjIsDSEp4+5dGjR5zTFqLQz1dasmRJ7gUFwe3bXLt+nfvBwZTUCxs1MTFh5erVdB09Gk1qKr379OGzKVPSf7a5OR379qVF+/Z037qVf0+epOPo0Sxdvpw7169T1suLQYMG8ejRI5Eb6i3hx2+/pX/nzszXJvmv1agR67//nit+fvy9fTuSyvD6oVvJkhQpUIBy5coREBCQFuKhj5OTE/Hx8YwdOzbddhsrKyLDwnLnBgwJhNmdBGoXuPxDQjA1NU0LS7EtUIAI/VyJR44Q+OQJznoOQXsbGxrXq0e9hg0BqJrBaQxgZmlJTQ8Pjq1fT1kHB9QpKTg5OOCzdClcvMiho0fp/913BAYGMnbkSIpr3b8HDhzAWq1mUpcuDPnwQ4p27EhsbCwWmSwUCt4cISEhrFu2jJ4tWzJr40aCwsLY7+vLmT/+4PtVq9i2ejWV7e3TxisKSpTHyDFj6N27d7pcTy9DZWaGrYUF4eHh6RZmckxuOwgDA/l43Dg5dD46Wp5AaqtfKtWXlXxX1jY2GKnVbPnnHwo7OXEvKIgW2tzY+hQvWZLklBS8PDwo7ebGql9/ZdOCBdRt1izdcWVq1qSAtTWnDh2ifrNmaDQart+5w+g2bfD182Nw+/ZMW74cSZIYOXJkzu5RkGvs27KF9YcOER4by4ZWrfj3wAHMTE0ZuXAhLevU4bupU8HYGBtjY5wLFaJazZpp7+3YsSNXr159roiPgoODA+ExMURGRGClXcxRm5hgYmREfHx8Ws7CHJNbDsLISDh3TnbBxsaCkoYDZDOEmRnhT55gZmqKc5EibPf1ZfzEiTx79ozzJ0/iYWwMtrayUFilCgCXr11j/Jgx1Ll5k+59++JoY0NCaiqjhw5l8rJllChcmM4NGjB9zRoSEhLS5ii3b9/G2tycRStXYmZiwrhJk1i1ahUNGzakobZvE7xZNBoN3i1a4OnhwcDRo+nduzfGxsakJiZSrFAhuvTqBUlJ/H36NAs+/xyfefMICwnBq2lTft20iV27d1PG1ZWTvr7MnDmTdevWpUVKZcx5+zJsrawIj4vTiXugE6YNCYQvCzHOjoPQgED46NEjXFxcDI47u3XrRvXq1WmsnQMplChRgvlTpvDPqVOc9fOj43ffERISwor+/Ynz92fHkSN07dmTK3fv8vWiRXhVqEDTpk0JCgrC1tZWPomJCSpPT87Mn0/ks2d4jh/PP2fOUK5cOSwsLAgLC2Pv3r0YGRnRrl077a1rSE5OxjhDDvz8ghAI3wCJiYn8tGIFx3bswNTKinGdO9Nl/HjqVK7M0HbtoH7954ooWFhYGLSw5hbmFhaGHYRKh2dI3MvMQZjZioFezoAskZQE8fE8e/qU737+Gdtu3fhi0yaMjYyIvHYNldb1FPb0KfZKo9XSoUMHtm3bRk8fH6pWrcq9e/eeCw0FqF27NufPn+fUqVN06dLluS8ZY2NjBg0eTO0JE6hQqBCX/P35sEkT5ixbJgtRgYG4+/uzYPhwWrZsia+vL23btuXff/9l5uDBnLx7l7Vr1zJkyJDMbdsCQP7y9Pf3p3jx4gb3b9u1izHt2lHByYk4Fxdc69TBtVgxpn75JdUsLVFXr5695yuXMOQuyjEZJ2k5DZMESEzk3pMnDFAchOXLs/i777Cys2Ns166cfPQIZ2dnnjx58rxA+OQJD86do96ECXwyfvxzQkTnzp1p1qwZe/fupUePHgaf7fK1amFhasqwzz4jPCqKJwkJDGnVil+PHOFxcDCLFy/GxsaGnTt3PteBC94sqamp/LZnD/v/+CNtW5cePfhx2TK8ypShol7u1oxIksS348bR2Nvb4CANwNTUlIkTJzJixIh0260tLYl6HeH5ORU5tIPPmwEBlNOrVm7n4EBEbKwsdCQnQ1QUTwIDqa3Xp0z75BNKV6mCY8mSHDp0KNNUBXXq1GH0woU8jYlhcLduVK1cWe7va9akUYECzI6J4dqDBzSsX59L165h7+DAovnzGde5M1LDhtjfvk2l4sU5d+6cmLDlMUlJSVSpXJmAx4+5cOIEMfPmsWD7dmpUrsykqVN5FhmJZ9Omz43nQE5NUbx4cerVq5ctcRAAlUp2cjx9mjsCYS46CDVxcfgHBlK/YUMehoaSGhSEysREzkuVlERYWBgFHBxw1St29FHnzixbv571v/zC2p9/Nlhgr0KVKtT08ODQ8eMkJSWhVqvplXFhCkClYsyAAUz85BO2HzhA586dMTcxYeqgQcSGh+Noa4tpSgozVq9m5MiRhIeHM3/+fLy8vOjQoQMmJiaZfo8Jcp9///2XXh07cvLkSUhN5e/9+xnQuzdt2ralXrNmaWmDAMpVqEA1vUggU1PTTMVBACMjIyzNzHj85AleysK/Wo2lmRkx0dGvRyDMyXgtMVHODf3okfzvwoXl74xnz+D0aShShKBHj3BydKRo0aL8+++/tPrgA0IePODChQv07d+fFQcOoLl1ixHffcffS5Zw9upVPGvV4uG9exz85x+cXV2pVr06Ldu2ZeF337Fo3jzGTp7M6j17uHvrFoeOHqVOnTrcvnuXZjVqsEPrGHxw/z4XL17EJkPRPsGb48KxY6SmpmIB9OzZkx0zZ5JobMzNW7dY8Ntv3Ny1Cwt7e24FBtJ/zBh6dO7M5HHj2Lx+PX/NmUO7WbMY1KMHAIMGDaJkyZI5vhY7a2uOXL6M04MHpGX91H/+U1LSuwtfVqRE2ab/78wwIBAq+QcN4erqmq6f0WfwxIkMTkig95AhXLhwgX/++QcbGxtq1qyJjYUFtZo25RN/f7r7+NBLa7qyzaAzUKgQKjMz7CpVwn/FClb++Scjhw7l6dOnhDx7RkkXFw7s38/jx4+ZNGkSnTt3Zt26dezYsSPze3yLEQLhayIkJARra2tMTU0ZO3YsV86eZXCnTtyOjaXBiBHU8PTkj6lTwdMTtFb4N4mZmRnxhizuSmNMSQGjDI9HVgVCJQFpdgecyckQH89vR4+iVqs5cOECrs7ORMTEEHzrFs4pKeDiQlhgIPbaMBQFSZKYMmUKFStWpHfv3vz8889U0stRok+xYsXo3r17ppfx/fffs3btWtauWcOla9ew1ncCFCsGLi70NzPjnytXqFatGrZWVhz/6Sc8raxoGh2N98cfM2vWLPbv34+Hh0f2/gbvEQcOHKBbt274+/tjZWVFbGwsNjY2hIWF0a9fP85fu0aL7duxcHFJ60zuP3hAQa1QnFfYFSjwXH6yHGOo7byCQHj38eO0EOOSpUtz884dfvv7b8Z6e3Pizh3Gjh3LhQsX0k3I7O3tUanVTNqxg2FDh/Lll18aPL2trS09e/bM9OMlSaJjy5Ys/OknCtra8v2aNVw+dIhx7dpRumhRgpKT8X3yhCGDBnH1+vW0qtEajUaI6W+AmJgYpk2bxqnjxyletCjWZmZU1HNZW1pacvDkSRJfEvoE0M3H56Xu8IXacCV9bKyscq9ISW44CJOTwdSUGwEBlNNzzdra2soOwqSktKqRT8LDKeziknZMnS5d0v7t7e2d6UfUadqUOd99h4+PD8ePH2fcuHFp+9RFi9LP2xtMTIiPjmbIRx8xa/58Lv/3H7s2bJArB1aoQJ1y5ejbty+fffYZw4cPz949CrJETEwMycnJHD16FH9/f4YNG5ZOrADYvn07pZ2c+GfjRlzq1WPI8OGER0fz9YIF1Kpbl+Y1aqB6gVvg9OnT2RcHtdhYWr6e/J2v6CB8GhSEqakphQoVwtbKiqBr1yhcujTcvQvAs4gI+vTsKafK0bJk7Vo6DxhAkyZN6K1fVEEPr9q1OePriyRJL3VgDPbxYfuePVT28KBL/fqs/OgjLCpWxCI8HMzMaGZtzcDFi7lx4wb79u3j9OnTrF69ms8++4xGjRqxdu1a0QflMoHXr/P08WOiLS3Tii2VLl2as//9xw/Ll7N261aS7txh65EjLFq5kkYZnKEAy5cvz1QMyAwHW1v8Q0Opqy3ICMgCYUQEjq86dsytfOtKiOaTJ7IwePEiODvLOdeqV4dLl3hy7x7Ozs5UrVqVjh074uTkRLXq1Zm5YAFbGjUiJjYWFwcHenXvTtfPP+fnJUtwKV2a/sOG0bxOHUwqVMDCwgIzMzPMzMxo1r49SBJlihXjzz17+HT6dIYMHszN+/f5ZOBAfj9xgiolS7Lqhx8obG/PxStXmDp1KuPHj8+dRQlBltmyYQM92rRhbufOaKZORbK1BVNTWg8bRpi1NZ3Hj6eqqyvd2rfHzMICszJlWD53rlyUpmxZTtnaUlrryi5WrBh9+/bN8bXYWlsz4+efCbWy4hv9dE0qle75N+QgfFkOwqxUMdYXCLVVjO/fv5/t7wRAFjEtLJg1axZmZmZpqW8aNW/OvjlzMLa3p23//twrWxb7DIUp09GggXzdJUowqFYtYpOSiImLY/+FCzTz9ORfPz+6du3KqVOnCAkJ4cGDB9m/1rcEIRDmAikpKfz333/8999/3Lt3D7VazcKFC5EkiQIFClDIzo4Dn3+OTbNmaCwtuebrS29vbyhbFvJI7Hihg1D/9WX7MqtirFbnbLKm0eDr50ebNm3Yvn07rRo04HFICAFXr+KsVkNMDGGPH2NnwK3h7u6Oh4cH5cqVS0tQmlP69etHv379DO9UqaBqVRYPHcpBPz/czcxwrVwZihWj3pkz3Lx4kTNHjtC8eXP27NnzXBVcgcy+XbtITU6mb69eXLl+nSdBQdSpXRsHBwcszM3ZO3MmFoULpxMi8locBG34YU7zBGbE0CQth06O2IgInkVGphVKaN68OZcuXaJevXqk1qvHXxMnsn7YMCZPnvzce0uWLMnx8+dZliGnRnb5aMgQwiMjUQF+/v6UqltXrhju6EjRoCCKxsXx/c8/s+z77xn3ySckJydTsWJFtm3blhaCJyZqr86RP/9k3YYNhEVEMP6TT6jfsCHbt2/n7IkTLOjWjdE//ED3li2fE/lMzcwwzSRkPx05/D+ysbYm8lUdhCkp4Oubew5CIyNuhoRQXi9fnJWVFXGJiSTFxGAcF0eqJBH47BnORYtm+3IbNG7M3LlzmTp16vM7TU1B69acv2QJtevXp2XTpgzr2BFTJZxZpaJ/hw6YOzgwb84chgwZ8pxwJXg1NBoNXbp0ISgoiAd37+Lg4EC5cuVo3rx52jHR0dHM8PFh4aBBuGiflbZt2/LBBx8gSRKlixalToa8xxnRz2GZXXI9PD83qhjHx3P3/n3ctFEAxZ2duX75Mp9u2MDsvn1xK1iQsOhoxnTqhHfTpmlvU6lUNMlCbuCs9gUqIyN27trFos8+Y9SoUVg5OYGLi/wDGBUtSu9Gjfhl7VoOHTzIrI8+QvXRRzwCvvrmG9auXSvyfL4Cc+fOpUWLFlSoUIGxY8dSvHhxwm7e5J6/P1FGRpQtV451a9dS0NGRkJAQajVujHOBAiyYMwdzGxua6D0b+pTPpLrqi3CwteXCvXtM1nNNWZiaEvuqYzb9NpNRIMxuOiVFIExJAY2G1NBQVAkJ3Hv0iBKNGyPZ2xMUGIhTkSLpFtpqNmrE8F69qN++PbGxsXw6cSLjvviC1k2a0GnAAABMChakRAax9fr167hpw5jLuLkxddYsOtWvz4Zff8XS2Jge/fqREhzMvfBwVqxeTc+6ddl08iRffvklrq6uDBs27LlbCA8Px9bWVozXXoEuXbowbNgwmjVrRlBQEGvXrsXFxYW/jh5l8cKFUL060tOnslagjZaaOXcu7l5efDpxItv1C6CVKwdFi4KjIxVsbEBPIH8VbG1sSE5J4dTp06D/na1W5yzEGHQmIpUqywKhJiWFwKdP2bdvX7p+ObuU1osUATBydKSu8nw7OOD0spRtyj1YWqK2tGTcrFkQE8PIy5cxt7bmzPnzNP7sM1yLFeOvv/7CyMiI5OTkfDlmy39X/JaRkpJCw4YN8X/wAAsrK8LDw0lKSuL63r2YODvj7+9P5YQEVMWKgZUVErBdL6wrrzCzsSHOUGUv/VDHzPYZ+jLQ7ySU/ALZHXBq8xoER0bSuEULtm7dSrly5VCZmPAoNJRqsbHw9ClhBQpgn0nC6W+//ZaiOZjEZRtJwrhyZVrZ2Mhf3oojq3BhSjx+TAlXVywWLKB169bs3LnzteXDy6/4+fmx548/+GXKFM5eu8aE4cOpV748a8+fZ+/BgywdOhSnihXTW9ffEmwcHYmOjyclJeXVQ5Ny0UF4/+5d3PRyc6hUKjw9PbG3t2f/6dNER0dTtWpVg+8dMmQIXl5eryzAVqpVixWbNxPq50eykqdEaY/aSeTCMWNoPHYsnbt0wd/Pj9u3bzN21CiMTE25fPkyu3btomLFipiZmWU7X4pA5pfly0mNjaVRqVJ06dgR37Nn2btjB/3r16fRiBGcrV4ddQ6dTK+CtbU1Ua+avzMxEYKD5RxMkL5QVnbbTnIyqNXcjIykQ4aqmIpjyz45mTLDhvEkNJTCOehbrK2tDYuDGTArXJjDx45xYetW6nfqlG5f5Ro1qOzoyPFz59izZw8dOnR47v3nz5+ncOHC6SspC7LEmjVrePL4MTa2tgxo2hTUas6fO0fz5s1JjYggLjSUWd9/T60SJWg3YkS68Y7yHbXixx9xyzD5yE1sc9N9+6Lc0dnh4EFOX7xITa07sHiRIvRZtIiohAQqeHpiFh1NWEwM9rk0WX0RZs7OTF6yRM79mFG0MDWlb/v21B0xAhsLC5rUqYPxs2egUlHzyy9pOGAA69evZ/Pmzc+FO3/zzTf07NnzlcTdd5mkpCS++uorNBoNWzZvJvTRI/bv3k1sXByWpqZEJyRw+MgROtauzbRu3bhjbIyJqSkVypRh1m+/8ccff+SqyORgZ8eF69fT8skCWJqbE/Oq/c6dO3KedHj1fOtJSfIcydwcjIzo/8UXOFpZsWTPHn4CPqpdm9vBwZTM8H1iaWPDwtWr035/9OgRO7ZvZ8XXX7/w49z0chxW8/Ki7u3brJ86leLduzOyRw+sixdn2IIFHN6yhUeBgUwfPhwkiSRTU3Zs25ZOINy9ezfW1tb06NGDOXPmMHjw4KzftwCAkydPEhYWxu+//47vpUuULlOG06dP06xJE06dPk1URATVvb3l6MIMY3JJkujevfvzUXDm5vIPQKFCuXatdra2FLS357///iMuOVkO009KkiMMlbGX/jxIaQsvy0GY1RBjbRvbtncvvT/5BFMzM3744Ydcuz9A93fLCdpiKXapqWBpSUMrK3754gtinzxhzI8/4mBtjb+//yuFeecVQiB8Ba5du8atW7eIj4zEf/lyJEdH1v/zDyFJSRQJDwcjIxwdHOQiAa9x4JgTzG1tZQdhaqpsa7ezk1ccXjTRysxBCDpBMKsrA4bQrqqFRERQtWpVTE1MKOfhQWxSEv89eoTFrVs0L1uWsPj4TPM9Nc1kJfK1UKjQ81/EJUvKlZcrV6bD1auYfvklHTt2ZNasWfTv359r166xf/9+Pv30U9Rq9XsngIwZPZrmLVrQu3dv6pcvT6fx4/nQ0jKtevYglYpBXbtCxYo5dim9blQmJliZmREVGWnQyZotcjEH4d179yhpIP+Gh4cHs2bNokOHDpk+b4ZWiF8Fx0zySgJUbNmSwa1aUb58eQoXKsSI1q2JUqup0aIF/Tp2pEfXrlhaW9O0SRO+1avyGRISQmBg4Cu7g98FYmJiiI2NfU7QTU5O5sGDB1y8epXlw4dTq2lT/gsO5seFC/nr4EEWHj8OVlaYNmiQJ/k7bWxsXr3AT8ZV61xwEN65c4cyZcqk22VrZUXEyZNEGxlxLyAAgEK5OPA2hL2LC00zFHYBZJHdzo4Pa9dm/x9/pAmET5484ccff6Rr16707N4dL09Ptmzf/lqvMT9z8eJFUlJS0vITp6amEhgYyGeTJvHXrFmUd3XFyMGBTX/+yY5Tpzi+ezdfzJiBf0gIT6OjOb9pk06YzkDjFi1e67XbWFsT8aoOwrAwCAhI74bKqbiu7atO3rxJ+48/BuCD1q2p4OmJZ9269OvXj9jYWKzMzTMdr+U6GYrX6ePRqBEbp02jZsmSGHt5yVWWo6KoeO8e53/4gWkbNvDFF18wZ86ctAq44eHhfPbZZ8TGxjJt2rQ3cw/5gICAACZOnIiDgwPu7u5ERkZy8uhRTp46xe21a1l16BBrdu0iNCICK1NTijo40K9HD6r17k01bbhqhWrViAGa53K7cXBxwcnJKV2VU0tz81d3ECYmyj9gOPIjO/1pcjIpjo5EGBvjABy+coWA4GDat2jBsGHD+LZMGUoUKkRXbXXvzJg0aRKTJk3K1m306dWLPjVrItWty46lS6mkpMewsMD7gw/wLlkSypdnsakp0WFhFOnfn8jISP777z/++usvfvnlFwIDAynh5oaPjw8dO3YkODgYd71ij4IX8/XChezes4f2NWtSrUYNSlesyJaBA7G0tcX93DnKly+PaR6kHjOEra0tDatVwz8ignP37tGgalVZIFQchBlDjJW2kFkV4xwUKdFoNMxeupSh3bqBgwMF3sCCU7bRajySkREfVa5MYHQ0D4KC+Pf2bfbt3cumzZuZOHEiTk5OVK9ePV/kvRUCYQ7x8/Ojdu3axMfH89WgQUj160NMDH2io+UVTGNjePpUHpC9hQn5CxcpwuOwMPkab9yA8uVlgfBFDkJDK83KNv2Gn5XcAoZISgJjY4LDw3F2dqZsuXJU8PQkOCyMxYsX43r2LI0XLSI4NDQtz9pbh7U1tGgh/z0KFqTV0aPs+/FHZq5YwaRJk7C2tqZo0aJ89dVXODk50bt3bzp27PhO5yp88OAB+/btw97enu+XLWPtmjW0qFKFrT/9JIvnIK9GGRlBtWryc/iWioMASBJ2VlaEh4S8mkB45ozOeZoLDsJ79+9TqkSJ57ZXq1aNdevWMWrUqJxeae5SsCBffvwxo5o3p/K4cXRt355GyuqaRsOmwoVJTElhy6ZNPAwIIC4ujhEjRrBmzRqOHDnCtWvX3opQ87wiICCAZs2a8eTJE2ZMn86o0aNRq9VIksSmTZuYMGECkeHhePToAc7ODBo5knpNmtCuWTOKKuJqHlVVs7a2xl8rtuUYpY3kUg7CVJWKwMBAXPTyCwLY2dgQAQTfv09lDw8qVKqUd9Xo7OzAzo5mLVrww8SJaZvXr1/P1q1bWbhwIa6Ojpw4fpyrV6+mhesL4MaNG2mhip+OH09AYCAnTp3i0aNHeHt7U7RoUUZ26IBXly4QEgIFClCtQQMG/u9/nDlzhrHDh3P68GHi4+MpXq9ent1HruTvjIuTqwxnlhomOyQloTEx4eSdO8zX/l0GTJgAQFRUFElJSdjb2xMWFmawCMkbp0gR2lSvDlWryt9/hQvLP5aWuAHzNRo++OILSvz6K2vXruWDDz5g//79FC1cmJ9/+onSpUvTvHnzt3Ny+gbo3r07UVFRdOjQgZMnTmAWH48z8L958+jn7c3Pf/9NY09PnLp0YUKbNnQfPZqh/fphZWbGhhkzsKxSRTfeASZPmUJKSkquh6g6ODqmcw8CWJiZEZPdvNEnT0KtWrpc7Pr50F41f2dSEgf++492Q4cyZsgQYhIS2L9zJ7UbNSJVo8HDw4Pbd+8yd/Hi7J03C0iurnLaF0miVsac0gUL6hxrpUphZWlJ9dKlOXroENOnT+fu/fuUKVqUxi1a0Lt2bbZdu0aVKlUIDg6mU6dObNiw4b0zPWSHHTt2sHHjRk6dOIFXyZJ0a9iQ7k2bygUwtd9FY3v3Rv2qxXRyke7t2vFBgwasPXGCU9evcz8sjN7Vq6M2Mso8xFgRD5XfFTIWKcmiQHj9+nUioqL4bto0pBykHXijODpCuXIUdnTkyxIlGDpnDqPHjKFTp06MHTuWBw8eMGjQIBwcHJg/fz6SJL21edjzlUAoSVIx4BfACdAAKzUazXcvflfusm7dOn766Sf8/Pzw+ewzYgMD6fPhh2BvL/9YW8vhT8WKyQ+/kdFbGSZZokQJ/ENCSL58GSOVSle2PKcOQqWhK6sHOS1SYmVFcFgYhQoVYu/evbi4uHD37l1CQkNJTkpi5qZNLPvpJ9atW5e9c79JlA7SzAwqV6ZaQAC7J0wgeMkSHK2tiU9M5M7du5w5e5a9hw/zx+7dnD5z5p3oWLesXs2u/ftp36ULamNjfvvtN86dO0fVcuW4/eABXw8ezOfr1tGzfXvDrtp8EsZja2lJRMaE8TExsivhBe65dERGglItWL9t5UQgTE7m7p07lDJQ5XTatGn4+Phgamqa/fO+LqpWxcXDAz8bGyzq1pVt+vHxYGzMJlNT1CkpPAsK4sDly2g0GiaOH09UVBS9W7akbevW7Ni1iyJFipCamvpOtJusotFoGDZkCB/WqEG/+vUZvnw5PtOmIUkSx44dY9+ePYSGhlLOxQULrdutdqNGLFu8mL4DB+bx1YONrS1Rr+rkyLhqbagia1ZJTiYkMhJbW9vn2odtoUJEuLpy4+lT6jVsyNKlS1/tunOBSnXrEhEZyYMHD3Bzc+OPnTuZ+9FHJBUqhNXDh5y4do0Vy5ax+Pvv8/pS3woeP35MhQoV+P333/H09OTChQuUKVoUFxcXEhISmNynD6Hx8Uzp1EmeGGtdR2WsrKjg4sLqJUuo2qYN8f/+S3xIiG5BKw+wsbEhMrfct7nhIExKIjg6moTEREpkWJiytrbm9u3bLFiwgBUrVqQ58vIUY2MwUAhDyVPo0rQpvnZ2nAwOpmPfvlzy9WX5smVMad+eS0FBbPjlFz777DPu3LmTtlDwtk7scpOoqCiePXvGvj17WDRpEvu3buXIuXPc2LIFB7WaaW3bkuroyMYGDWitHX+YmZtTukIF6jdujKWlJZYGInte1yKfs7Pzc45VSwsLYrLb70RE6EIpQW4fSu7AV60AnpREQGgo3t7eLP7xR5o2bUqL9u3Tdrdq1YoNGzY852rPFbL6vGrDkpvUqMGECRMoYGHByf/9j7jkZGqWLg3W1lQpW5akuDjm9epFx7lzmT59OrNnzyYmJoavv/6aESNGvNeLuSB/R8yYMYO9e/fyJDCQZ8+eYW9lxdmFC5Hc3HRRdyVKgErFsHnzcpyH/HVQqmFDSE3ldmoq86ZO5dKdO9RdvpzSTk6GqxhnTDGWC0VKDh8+TJNatZDeQi3lOSRJTjsGULs27mXL0iAwkE1Dh1Lu44+ZM2MG+/7+m4CAAJo0aULLli3p1KkTXbt2pXfv3nl77RnIVwIhkAxM0Gg0FyRJsgbOS5L0l0ajufbaPjA5mdmzZ9OmTRvWr1/Pb7/9xtwZMyhtakoTpZKOfhEKW9tMQ1DeJkxNTXEqUAD/e/coWaOG3BHeuqUTt1JS5Hwbycmy8Akv7hRzyUGYYGVFXEICtra22GkrFSvOjsjoaA5fu8aqVavo1q1bDu/8DePkJP88fEihK1fA2hoLwNPICM969Rjs4UGDKVOYOXMmLVu2pG7duvl2wHn/3j2Gjx/PzH79WPzFF0hmZvSuV49e/fvTqU0b2a3q4kKrbt0ok89zMtpZWxMeGpp+Y0SEHF6eVYHQ0Ip0TkOMg4J4HBVFI71cMwpKteC3Cm1lcIumTXU5o7Srpma1aoEk4RQQQN9mzSA6mt6NGhEeFUWBSpWYu2ABtWrUoHvPnqxevRpfX19cDYRWv2toNBo++eQT/O/cYeu8eZh6enKwZUsiL15k1Z49TP30Uy5cuMCc3r0JjIpK+y6XJIlhb4l71MbOjkhDTo6goDTX9UvJ6HJ/FQdhSgqPw8IM5u2ztbPju6VLCQ8Pp3Pnztk772tCVbAgXerWZdnSpaiMjLhw6RJN5szBIiYGSpWibNWqVOvVi+mzZonKk8DJEyco7epKL61TZnLnzozu3BkjSSLS3BzncuWQoqLkPlpvoUFlbc3Fw4flhV7AzN0dsyxU936d2NraygV+UlNlF+ALwmkzxZBA+AoOwpDYWJycnAyOWYoXL46Xlxf29vb5Y0zj6Ah161LvyRN6N2iAm5sbnZs3p1/fvnycmgrx8TQaM4YZ06dz4+ZNOnbsyLpffuGL2bORJImvvvqKVatWERAQkG8dvImJiSxYsIASJUrQq1cvJEli5KBB7Ny3jwbu7gzs0IGBH31EakoKqlKlIDoaKS4Ota0tfb29+bBdu3Tnm7VgwRu/h08//fS5bZYWFtkPMU5JSR9JpT9e09+m/5pVkpMJDgujRo0aWFlZUUkv/y1AmzZtuHHjxlsRhujduDGfr1rF6q++wqNJE/lvYG8PdnY4PHvGj+7u4OTEtoQEWsyZw8aNGwkPD8e1WDF27drF+PHjWbp0KUOGDKFLly5p1WPfF9auXcuu7duZNWIE1y9cIDAkhMdPn8pOTmfn59NUKfPntwXtYkjdunW5dOcOAPeDgiitOAgNhRjrFynV36dvJMqmQNihevW30mz1Mkb07cvQBg0wKlWKq99/j5lGg0+vXvx+7hw9evSgdu3aHD9+XDZACIEw52g0mkAgUPvvKEmSrgMuwGsTCH18fNixeTOzZ89m3Jgx7Fu1iurm5rJFu1SpPAvVyg1KFy/OnZAQSjo6yq7HwEDdl1Vqqjxpi4/PnkD4ig7CEBsbChYqlG5AWapUKcqUKYOZmRln/vmHdevXv52ix4soVixtsqGPKiKCLZ9+SruFC1m1ahXe3t64u7szZcqUfOGMiouLw9fXFzc3N9q2bo1Pv36MWryYUVFRcOGCXFlLkuQJWFwcGBlRPsNgKD9ia239vIMw44DyZWS2Ip0TgTAs7IW5Od9aDC2mKFV0lbDjggUxcnTEEcDammnz5lHP05Pdp07Rom5dlixezP/mzyc6Oprff/8dCwsLOnfunD8mpRl4GhrK6GHD+Gn9enx9fQkNDaVNmzYkJibyw3ffcWLfPo5++SWmFSvK37PFi2NjZMRwJydOTJ1Kcy8vJo8Zk73n8A1ibWNDZHS0vCB14gQo+Y9CQ+XBnyGB8NkzuS9RQvuU9qG0nVcsUhLw9Olz4cUgCzLbt28nNjYWHx+f7J33dWFszJjevanQqxftP/iAXTNnYlG9Ojx6BKamFPfwYFTbtnh5eXHo0CHKlSvHs2fP2LRpE+fOnWP06NFUrlw5r+/ijXHy0CEGN2/OJwMGkBQZiaW9PZQpA6amWL7M1aa/0GNlJf/kITY2NjwOCpLHaffvg17V7SyjiByGXLc5cBCGRkW9UIiuXLly/nIQWVuDtTXffvEFXwwZgo2REXh4pC1eTerbl/HLltHF25tPxo7lWXg4DpaWHP3nH1SpqXg3bMj1mzfxbtKEH374IV1xiPzAgH79CPH3Z31ICKdOnSI2NpY/9u+nS+3atGncGLTfHWkjUxubNKF6+fjx8BbkBzY3EJ5pYW5OTExM1k9iKNWS0nYkKVfaTkhYGK7lyzN16tTnhMAOHTpkqcr3m6C2tzd/fP45DTp10o3JFPQc1c716nH2yy+59+wZVkWKUDQxkRFbttC/f38WjhrF2hUruHDmDEtXrnzDd/DmSU1NRZIkxo4dy6aNGzkwcyaVmzShQ4cOxN66RXRUVFpbyi+4uLjgVrw4SQkJPAgO1oURG3IQ6guE+ug7CLOYg/DBo0ccPnyY74YNy5O82a+KcdGiGNvaQokSmEVEyGPchw/p4OTEtbVr2XvvHp+3bUtbHx8CAgLSjUU12r9NXs1l8pVAqI8kSW5AFeCfDNs/Bj4GaPuyctUvYNGiRVy+fJk9u3dz9bvvSLKwoLCVlZwLqFQpnWiWjyldpgx34uNpYWKiS8CrJOFVxI6MHaShVzDsIMxJyEp4+HOJ4EuVKsW1a9fo2rUrd+7ceS6cJV9ja0uRYsU4P2cOQZLE4r172btnD48ePaJLly4UK1aM4sWLv5Hw0Oy0nejoaOrXr8+jR49ISkrCwsKCEa1a8cnnn8sHWFtDo0bp3/QW5dV4VexsbQl/+BB8fcHdXV4oSEnRiRZZQV8gfNUchPHxhEVFpblu3zm0jkMAihTBe/x4vAcM4MHBg1QfMoR169YRERFB2bJlkTQaRo4cye+//07BggVxc3N77SvxudXvrPj2Wzbv2IHdmDHs3L2bhIQEnJyduX37NsUKFWLvqlXYNWmSfmHKxQVza2u2KxM0Fxfd9/hbho29PVExMXKut+ho+fk3MnqxoBkcLLeJjAKhIQdhDgTCx6GhBh2Ebdq0oXfv3gQFBVGnTp3snfc1Ur5lS3ZNn07zmjUxc3KShSu9vDwzx47FrVQpWrVqxcmTJxkxYgTJycm4FytGj+7d+XHVKho0aJCHd5Ce3Go7ACkpKQzo3x+1kRE+Pj4c+Osvfpw9G5N69TBRxjP5bXFRi42trey+DQvTpYTJLoo4qO8gfIUwyacxMS8UCGvUqMGff/6Zs2vNQ6SKFbFxcJD7Hb1xS9u+fWlbsSIaSWLfkSO09vLi1127GNmtG+4ODkzfsAH/deuYvWUL8+bMYeWqVa/vGnOx3QD8/fffnDl2jKubNnHn6lWGrVpF87p1mTd4MMN69JAdli+iatW3dl5kaWkphxjHx8vPufJ/eu+eHNqZcQKu9C2GBEL98MlXyH0bEhZGtYIFsdYf22iRJOmtcdqpCxWiTefOaSHHmeLqiqmrKxWePpUX9czNWSpJjGvQgHL169O8WjXafvYZSzSatNQweSV85HbbUe7nypUrDBs2jEuXLlGiRAnCQkK489NPWLu7p4mrFioVFrGxr/yZecHRY8dYs2QJD/z8dAJhRmetIv5lLOCjuAaVY7LoIOw/dy4+Pj64FCyYLwVCHB11351Vq8qvxYqBRkORR48YbGkJFhaM79CBli1bcuzYMRISEihcuDBz5swhPDycr19Spfx1kS8FQkmSrIBtwDiNRpMuKYtGo1kJrASYOXPmS7+1d+zYQaVKldJyPcTGxrJ3715mz5pFjfLl+ap/fxybN5cnNM7O6ZLs5ndKe3hwJzBQnmzGxsoNVQmjeZFA+KLqRDl1EGpdIMFPnxpccTYyMqJkyZJUqFAhXzjrskXdupCUhFNQEHPt7Yls1ozmc+awa9cuEhISiI6OZvPmzbTXy1HyOshO21k4bx7l7ezYNmYMJsbGnLx9mx6dO2ctRPAdwL5AAZ4FB8vihaurPDB+kYMwPl7+DlFcpMokLbcchAkJhEdF5T8H4atgb49bhw74WVgQ8PgxpUqVQoqLQ2VuzrajR2nSpAlmJiZYWFmxYsUK2rRp89ouJbv9jiEiIyNZunIlm6ZOZf2JE8zr1Qs3Z2fijIxo5eWFZGEhJ003NKi2sYE6dXQDEaO3s2u3sbMjUhEIQRYyjYzkdpBZf5Ex7F5fINTvd/T3ZZWUFAJCQgw6CHv06JG9c70pHBxoN2yY/F1r6P+5ShX6A08CAqhatSomKhW3fvwRM2NjHDUaevfowcDBg6lXvz7Nmzd/01f/HK/adjQaDb6+vlw4cYIhY8fSqEoVHKysaNywIR/WqkVtpUqqWp0vQ5QUbOzsiIiMlCffORUIDYUYv6KD8EVFOyRJomjRojm71rxEpUrLTZiOQoWgSRMklYr9//sf9i4ulFi8mDFdu2JfujQf1KxJoY4dmWxnh0fXrixavBgLJc9wLpMbfY4+M3x8mDdwIGb16lHJ05MTlSrJ3y8VK8q5kl8m5rzFxVssLCyIjYmBmzd19wRw7ZpcIT7josGLBEJD4ZM5cRBmMtd56zAyggxFX15IgQJpz4La3p5yyclga4t7kSKkTJjAoUOH6Nu3L+Hh4fTq1Yvvv//+jUeE5WbbSUxMpGzZstjb2xMcHIzP8OHsnjOHJb//jpedHdbVq6cPI84nedYN4erqSonixdl/9iwYGbHn6FHuXrnCGP2UXxkdhPqCYXZyEGo0BIeHc+n2bf4cNQquXs2fAmFmSJI8H9T2j9MsLQmeMwcvLy8CAwPp168fR/7+m9Bnz6hXrx7e3t5vfH73ds4iXoAkScbI4uCvGo1m+6ucKygoiL59+1KmdGlKliqFu7s7u37/HWu1mu8+/pg+Y8bIgqC5+TspfJQrV45Dhw7pBELQOU+UztCQQGgonCvjyoD+KltWSE4GtZrgkJDnHIQKFSpUICIiIuvnzC9IkjxA0YYh29y4wdGZM0k1NcU/OJgrd+4wfNgwEhIS6Nq1a15fLQCpT54wf+ZMijduDNHR9IiMzNcdX3YpWqkSj4KDZYeBfoGfzATCiAjw908vECrvAV2n+SoOwoiI90sgBDA2xtLbm7JBQelWuDuXK0dBKytqe3lxyteXrn36sGP3bkqWLGnQLfYmSU1N5eLFixgbG3P58mX69OmDRqNh9JAhtKtRg86jR9PZ2xsqVZLzdiruOUfHF0/S8kHOOWt7e1kgDA2VJx6JifLkMyXlxQJhZk52I6P0OW1y4OQICAmh5lsQGpctXvRdq1ZD9epMtrWlbe3aSMnJmNWpA7a2fFqyJJ0aNGDE99/zw/LlbN68mUYZnd5vMUlJSURERKS51hITE/m4f382btuGSpLwXb6ciuXKIRUrBjduyGHC+aBdZAVbJX9nxhyejx/L3w+GogxCQuTFA2WfftVJ/XaUw4iP0MjI9y/XpfZv6dSwIVhbM3vmTHmRUK3GrWBBMDHBpUkTmnp5MWzQIH5evz7Pc8ldv36d0SNHEhsby7Lly/Hy8uKHZcu4fOECHbt25dDffxPg70+X/v3lZ8HGBvKwYnduY2lpSVh4uLygqzyv+ou0mQmEGRemUlPl+VJuOAifPcsfAuGroBeCLFlbM6x1a1q2bMnUvn0Z07Qpg1etokuXLowdO5YzZ85gZGRE165dKZkxlPktZdGiRRw9epTSRYowa8AAkhISaFypEhQpwvTevWXX4DtkKgJwK16cB0FBYGTE7wcP8uD+fcZ06aI7IGMbUQRCyJ5AmJrKvvPnaVqliiwgKwvC7xraMb3k6clXo0ZR0MmJj7t2pdWYMahSUmji6UmvXr3wmTyZz2fOfKOXlq8EQkn2I68Grms0mm9e9Xzfffst/Zs2JTYlBfeaNQkKCWFyt250/+ADpDJl8jznzOvG09OTy5cvp2/M+iHGGa3DWXEQZjW3QEa0oWZ+fn4G3RwAAwYM4KOPPsr6OfMr5ctjVqQIJCZSPjWV8p6emFpYMHHqVO7cucOQIUOIj4/P05X52d99p2sfb0F+pjdNsWLFOH/+vNx2siIQZiZy6DsIczrYBFJiYoiKinprwlLeKObmz4e/FClCw4kTITWVxtWr0/XoUZo1bUqNypVp3aEDAwcOxNnZOU8ud+2aNQwcNAhra2tsbGwIDAwkNCCAKxcucPTgwTSXCvB8Aut8jo2DA1GxsbJT3cFB198kJ2c++Mus7YCu30lJkcXCHIgcj4OD81w0fi2UKUMlCwv5+0mZpJQuTRlXV/5yc2PPsWP0++gjli1fTsGCBalRowZ+fn5IkoRarc60H85LxowYwb79++nZpw82Njb8deAANomJBB46ROCdO7j36SMfqFbL4uA7NKGwsbeXqxjb2soLTkq0xv378v1qKzCn4+5d+e+gCMqGCmPpC+3ZISmJp5GRuBjIrfxeoPy99cL7Ub5HjIxYu2IFdTt0YO/evTg7O1OjRo03f41agq5fp1OFCpiZmNC2dWv6DhjAri1bqOjsTI+uXWlbvTpfjByJOp8IM9nF0tKSR/fvy5EcynhNGXsZSguTmYMQng8x1g+nzCpJSYSEhr77AqE+ksTU4cMZ2KIFhWrWROXmxsbChZm1YQMzpkyhmpsbKYmJVP/f/9izdy+VKlXi8OHDrz1yKiucOXOGSpUqpVVjX/fTT2zevJlT//xD5ZIlmTFwIPXatpWfC2fnd6rfyUgJNzfuBQWBWs2/V67wLCzs+RBjJfxY+V35eyivekVKYmNi6NK7N0uXLk0vDKem8sfZs7StXVv3nnf474okYVq1KtOjo8HEhC0TJ+IXEED9WrX47+JFun7zDT7Tp7/RCMp8JRAC9YCPgP8kSbqk3TZVo9HszcnJpnXvTlJ0NLZ2dvDwofwA2tnJdup3+UHUUrx4caKjowmNiCBtDViZqBlyECoNPeMAE9KHeuWkirFWIDx//jy9evUyeIharc7zldg3hr7QU6AA7WJiqObsTOMZM/jqq69Qq9Vs3ryZxo0b5831Gcib8j7h6uqKv7+/LBAqA8wX5SBMTk6/z1ChhVcIV4mMj8fa2vr9aR9ZRaUCe3u+nTmTef360XrGDI4dOsTA/v3z5HLi4uLwmTKFv+fNw1Slwq1IEbw//xxHS0sObN+O1TtejdnCxoaEpCSSzc0xMjGBmBg5ZFI/jDgsLH0uq5cJhIq4nlli7BcRFcWTZ8/yTCx+7RgS+UxMoF492pQuzd5Tpxg1dCiJSUmUq1iRixcuYG5uTlxsLN8vW0bTZs0ydfS/aa5cvsz+P/6gRqlS7Nm2DWNTUxpWqsRXU6ei9vDAPqPj6R0bw9nY2xMZGyuLvbGxstBhavp836KPoX7HUN5b/QldVklOJjQiAs+3OLQ0L7EoX56hH3zAkEGDCA4N5fTp09SqVStPrqVx69Y0btECUlOxtrBg1ubNbPj0Uzy6dSP88WMcypbN1+H3L8PC0pLYiAh5fqe/KKX/qo+hIiXKvzMKhNkV1zUaNCkphISEvF8CIYCXF85VqqQ5Ns3q1OFLBwf5u0xbGdlrzhxmTp1K69atmeTjg5+fX54u4J0/f55GjRrRvUsXlv7wA6dOneLzzz/HVJLwrlSJrVu2yK7UtzStS25TpGhRTI2NOXnpErf9/dFoNETHxpJmEVF0An0jkfLdYqBIyaczZ3Lw4EH27t3LqFGjiIiIYOvWrdSsXJm/fX1ZMmKE/F6ln3qXsbaGli3ByIgyRYpQJjUVihShRtmyOK9ezW+//UavXr2IjY3F0tKSZ8+e8f3339OtWzfi4uLw8vLK1bye+eqJ1mg0J4Bcu3sLDw/5QTU21uWkeMcGlS9CkiTZRXjlCk3Ual0HqBRcMDQx0+8MMytSkhMHYXw8mJlx9uxZvv3221e/uXcJtRpq1qSIqytHJYmbsbFI1tY4ioF5nlGsWDEePnwotwd9B6F+Hk59shKun1OBMD6esKSk9y+8OBuYenpiWqECx+3tUScnI+WRwG1uYsKxhQsp3bmzXK05IYGLtraYODpi4uGRJ9f0JpEkCStzc6LUauxNTeWFucDA9A7Cc+fkvKxKeFJysuEchKATBZW+6WVtJylJV+AlNRWio3kSEoKTIffVu46TE0u++ALN06fEJyez7vBhfvroI4o7OXH45k1mfv01EyZMYM/evVSpUiWvr5ZKpUpxaft2LNzcSH7yBPPUVDk8vWzZvL60N0I6gTAoKL1AmFlOQkP9jqH8aTkR1xMTeaoX7i3IgCTRfcgQZqxZw6fduvHZpEkcOXYsb65Fr9BKt0GD6Naxo1xczdISB1vbvLmmN4illRUxCQmy6/PRI3ljbjkIs+tcT0wkKikJY2NjgxWX32nMzNL/bm7+XDXffpMn822tWkz/4gsqu7nR9oMP6NajB5MnT35z16nHlrVrWT58OCv//psCBQpQu2pV2tesyVwfH1LNzWXH4HuEysiIfk2aMGzaNCqVKkVcdDS3Hj6kqnJAxv4kY4gxpOXyPHfjBtt27+abb77h4MGDjBo1iqlTp/LPP/8w7uZNyru44Kx8P73rDkIFRWguXly3rXBhvh44kE7jxrFw4UJCgoP578oVWjRrhiolhQ3r13Pj1i02b9hA1549c+9Scu1M+RF9xf99ePAM4OXlha+vL00qVZI3pKTIE6jMchDqd4b6A0olh01WHYQpKbpchQDx8TyOiiIhIQG3l1XLel9xdqZwu3YUDg2FJ0/SuwwFb5QiRYoQHBxMEmCcmCjnF9QfVGb8PklONrward+WDOW9yQrx8YQlJr67FYxzA2NjMDbGqG5d+TtMvwrwm0StpnSfPrrnw8ICqxYt3v2VUT1sLCyIkiTsTUwgMlJeNdV3EBpqKy8LMc7KRC01Ff7+G1q1kvueyEhSzc0JeUHe23cdlZcXpKRgKUkMc3aWQ/Xt7PBu1gzvVq3YuH49ndu3Z+vvv+Pl5ZW3DmVLS2y11aSN36N8twq2jo5ExMaisbND0k9tkR0H4YtcUDlxEIaFvbBIyfuOg7s7j8+cQZ2SQrCfX15fjkzJkmkVVd8XLK2tiYmPlwXC+/flRSnl+c+JQKg/VlPmS1klJoaQpKT3zz2YRUycnflz7Vq+W7yYMX36sPP4cT7Mwzy5/xs9GuzsGNC3LwkPH2JqYZGW5/e9RKViUPPm/BcRwfQePZi3ciU3/PyoeuaMvOig5yDcunUrMY8e0U+p3qvoAtqaA99v385no0fTqVMnpkyZQocOHTh58iQ3btxg5NChVFVSoyjawnuq06BSUa9XL3ZKEompqazeu5cKFSpQu3RptixYQL0BA/i0c2cmTpyIZGxMgQIF8Pb2fuWPfb8FQgF16tRh27ZtfFKlitzRRUW93EGYceVZEUSUSpRZcRBevy5PDBWVPC6OC3fvUq1atVy1yL5zWFvLPyVK5ChXnSB3MDIywsnJicfPnlHc2FgedCoigyKy65NVkSMnyeITEghPSBAOwqzwNqzYZxzkZFxVf8exsbQkIiVFDjPSaOSQL+XZB8OiRlZzEL7oO1E5r3JsRATPACsrK0wNFXh4H1CpdM+jvpPD2BjKl6fHtGlcvXeP7p06YWFjQ68+ffjkk0/eeNVJAZhZWuLi4sKV27fxyCgQZuYgzCy1hVqte09OHYRJSYQ+eyYchC9CkjAqXx40Gpz0HSGCN4qFpSWxSUnyonpSEty+LefyhPR5oJXvwhcJhGp1+ve8rN8JD5c/UxEEo6MJjImh8Hu4yJFVitarx0InJ3B1ZbS7e1ql1zyhTBn51dER02LF5IJQmS3IvA+o1bgWLMj2tWvhxg0quLpy1c8P4uLk3NJ6OQg3b97MkUOH6LpxIxZAikbDnuPHadepE5JazX/37jF01CiKFCnCggULKFiwIF999RWOjo789tNPcP68fE5lDPi+CoQAhQtTd8wYiI7Gs25dzh49StMGDTCqV4+zf/4J1tZUmDePxfPmcefxYwYMGsTTp0/p2bNnjgvRvcd/bQFAs2bNOHToEMmSxDk/P1L1V8QMTcwyJh9VHAX6IociEL5I6EhMlBu+Qnw8Nx4+xN3dPfdv8l1FCKl5SrFixfAPDpZF9dTU5wv86A8alW2Gwoj186jlxMkRH09YXJwQCAX5AncvL45duaKrHJmU9PyCVFYFwuyEGCvnUISR2FiC4uLez/DirGJmxuy5c7m1cyf/69GD8Dt3MH6P3K5vG81btuSvv/7SFcdSnv3MJqyZtR1lopWd8PyMxMfzVDgIs4YkvXeF3N4mXCtU4GZQEKkajfysR0XJggbIbSc4GC5e1L3B0DgtpyHGwcGyY1EhJgb/8HBc3/F8w6+EsTFUqCCnGSlf/u1oO5Iki7wq1fNVr98nlEVFY2NISMCrVCl879+X+yMlHYx2XHbx4kUcHRzYfPgwACcvXKDD5MmMnDGDVEni+oMHuGtThAwbNozOnTtTRivIqgCVWq0zIb3PDkIFIyOws8Ohdm1afvwxRkoBFzc3KFCA/nPmcGznTo4sWEDs/fsUMzKi7Cv0z+/5X1vg5OSEm5sbP587R+1Bgzhx7ZrOQWioirEhByHoOk1tsZF0naghMq56x8Vx09+fcuXK5f5NCgSvAU9PT/7x9dUNNPUHnJcuyXmiFPQLmcDzIocQCAXvCaMnTGDxkiWkqNW6hSRFyMjYTpR/50aIsXJupd9JSiIoPFwIhC+jRAmkKlVoPXQoX06ejCQEwjyjefPm7N+/XycQZnym9VGctYYchMokT7/tZMdBmJJCVEQEiYmJot8RvPWUqVCBAgULcvLkSZ0BIjZW3pmcLOdA1zcsZCcH4csK/CQn6xaPAWJiePj0KcXe1+rfgvyNkZEc+acVCCuXKYPvgwfyM67kYVepiIiMJDAwkFH9+nHk0iUA/jx1itGdO7Nx715OXLyIo50dNpmJv0rbEgLh86hUspM1Y2Ecc3NwdaVsnz58O2MGPqNHU7h8+Zx/zCtepuAdYMyYMYycMgUba2su3rsHRkY8fPyYPSdPsuvUKf7991/5QEM5CA0JhMrELzsCYXw8tx48oOx7knBckP/p2LEjO/bv121ISNDlp0lIePGA05ALSj//54tQhEiF+HjCY2NFDkJBvqB+/foUL16cUT4+RKjV8jOvVsttJ2OFSchc5IDsiRwZk9InJREUFiYEwqzi6AilSuX1VbzXtGjRAn9/f2atXCm3lYztJaNrXf8VdG1HP090TqoYx8biFxmJm5ubSAkjyBd0796dNWvW6Nxf2nFUcFAQX3z7bfr5iBLOmFWB8GX9TgaB0D84WDgIBfkTlQoqVUqbq7gVLkxkbCxPIyJ0acbUai5ev46HhweNatXiuK8vszduZOPevXRu1IierVszbu5cKuqnytKfL4Eu7Yy+gUIsTmYNlQrKlZN/XqG6thAIBQwcOBB/f3/mff45/96+zbYjR/ju11/pt2ABY3/8ke++/dawS8NQ+XLFQZhdgTAujpt37giBUJBv8Pb25trNmwSFhckbUlPlwaciaGQccBp6hew7CP/9F5TPBEhIIDQyEgcloa9A8BYjSRKbN28m+NkzKg4ZQmBkpPzcGxnJTg54XiA0VH0VsucgzBBiHB8Tw8OgIJzfsyqEgvyLtbU1R44c4du1a4n29YWbN+UdigihDeVK26b/CumLlOgXlstKgZ/wcN3vsbE8CAsTBeUE+YZhw4Zx4MABDl66pHv2jYw4d/kyy9avf76dKJFUChkFQsXRpKSIyYyMY8HYWB4GBgoHoSB/oxWeVEZGeJUsyYZjx9BoBcJzV67QeeJEenfpQoVSpQiLimL90aPUrlyZOhUrMrlfP+xtbWlUtapuXHf4sG78BzpB0NgYoqOFgzAPEH9tAQDOzs5Uq1qVjceP02XsWJZv346jjQ0a4MC+faQEBqZ1qPGKgyljiDEQFBTEbwcOoIGsC4QpKURERREdHY2Li8vrukWBIFcxNTWlft26nLh+XbdREQiTktIPCl8WYpydXFBJSc/l77x25w7lX8FKLhC8Sezs7Ni2bRuDBw9mzLJlchswMtI91/qOqJQUXZgJpJ+0qVSEPn1KVGRk1kK9IK1dTl66lClz5woHoSBf4ezsTIOaNdl+6pRcBVySdH2CEjYJhvN5vshB+KLx2tOncPmy7vfYWO6HhgqBUJBvKFCgAEuXLmXC0qWkWlrKG01NefDwIUGhocRGRekOVopoKW1Io9G1D0MCYRYchLNmzaJP796EhIXh/+iRcBAK8jcqVVrkh0+vXizYvp1DJ05AairzfviBmT16MMrbGxUwqW9ffh0/nvXffIOJsTHFChbk4NatfNa/v64tJSXJ/ZmC0r7KlIFr1+R2JATCN4r4awvS8PDywtTIiBnjxlG5TBm2TZnCb5Mn42Rnx/lz50Cj4filS3j27IlG6TAzhBiPnjWLITNmUKtfPzbv2pX5h+kLhPHx/BcQQIUKFUS4iiBfUbtOHfacPcsHs2fLbcLUVOcgzLgiLUnpq98pKMKGIoS8zH2blJQ+ZCU+Ht8rV/D09MzdmxMIXjMjR47kz7NnSQb52VcEQn0hXT8PDchtQ3GpSxKjfHz4at06eaX5Re1G+/7Hjx7xyy+/8Pvx45QqUYJSImxWkM/o2b8/265ehZgYuQq6Mp5SojjAcD5PJWxLkrJXHCvjolRsLA9CQoRAKMhXdOjQAZWJCe2/+AK/4GAwM+NBQAAADx4/1h2Y0UGo717KgYMwKCiIRYsWkZSQwJwtW3j48KFwEAryP8bGoFLRslYtOtepw6nz5yk/dCinLlxgYLNm8jxFo2HK4MFUL1NGN7fJONdR+qqMAqEkQaFCcm494SB844i/tiANM0tLAtasYeakSRxfsoSKrq7U8fSkdpkyXLx8GVJS+HXPHm4/fMjhw4e5cOkSO44do+fChdx//Jhr9+9z8sIF7h0+TEdvb9Zu3ZrpZz0MDCQqIoKlS5dy/MgR9l+8SLNmzd7g3QoEr07tOnVYc+gQ+86elQecJiY6cTBjiLH+irS++1bfQfiyCuBKZ6oIhElJhMXEEBYWRsmSJV/fjQoEr4GCBQtS1MlJTnJtZMTAiRPlQln64obiLtR3EGoFwpTUVP46dozD586BqSlPw8Np27YtCQkJPHnyJN1nxUdH03TaNHwWLqRfv36kpqZy3deX7t27v9mbFghekSbNmnH87FlSkpJ0AqHSJ+jnJTQ2huRkYmNj6dOnDxql7Sj9TFYdhBkXpWJjefDkiRAIBfkKSZLYtW8f5nZ2rDt8WBYIHz9GkiQeBAWlX8DVjtcePnzIhl9/1eXJzSgQZiGd0lfbt9O1SxcWz5vH+kOHSEpKwtHR8c3ctEDwutAKhKhUeLq5sXr7dqzNzLh14gSWZma6RStF2MuuQKjMkZTIQiEQvlFynr1Q8O5hZIS9lRWYmiIlJqZZiMsUKcLte/eIq1GDbX/+SQMvL9q3b4+xkRFGkkSDChVY8uuvlLS2plXduhRydmbIhx8yf+1akpOTCQ0NpW3btvzyyy+4u7vz24YNDBk2DCc7O1JMTUmMjycxPp5tv/+e138BgSBb1KhZEwBnR0f2nDtH2dRUmjdpogszVkhOTi8Q6q9QZ0zCq/xuKCGv0pEqbo74eC4/foyHhwcq0XkK8iENq1Vjz5kzRMTHs+2vvzh19iyXGzTA5Px5qFBB1yb0J29GRpCUxPkbN7CzseHCrVvEpqSw/q+/2LNnD927dycgIICzZ88CcOHCBeLv3uXQ5ctw+TJff/016ps3UZuZyYNUgSAf4ezsTKFChfjPz4/KRYvK+QH1qnMDct+iFQ99fX359ddfmdO6NW5OTukdhElJct/0MgehcqyxMcTHc8fPj+LFi7/2exUIcpOiRYsycMAA5vv4ME0rEHqWLs19RSBUFqNMTAh+8oQf/viDjb/9Rq8VK3RiR2oq0RERWCmiYWoqjx8/plChQhhlKArw259/suP0aY6fPImTpSX3d+wg1t1dREsJ8j8ZBEK/x4/p/uGH2NjayvszCoTKqyGBUKWCZ88gOFh2DSoOQoDChdOnuBC8EcSMUqBDpZIHipaWcoUv7cSsTJEi3L5/n89Xr6ZpvXoMadECR3t7Fvr48OO0aXw1aBC/7NzJH6dP06hyZTAyomCBAhR1dubSpUtMnDgRY2NjOnbsSHh4OD/88AO/ffYZ3Rs0YPOmTWxZsgR7W1vq1q2b138BgSBb2NjYcOzYMYb26sXEn3/mIx8fTp4+zaV7954PMTY1fV7kgOw5CJOSSNB3c8TH8+/t21StWvX13aRA8Brp9+GHrN23j3YTJ/Lj5MkUcXRky65dEBSUNpDccOgQu/74Q36D0nZUKnYePkynli3xLFGCv06e5Md9+2jSpAm///47ly5dIjQ0lKioKLy9vfn8m2/oUKcOq2bMYPy4cYxt105UxRPkWxo3bsxBX18wMSE+KYnvVqwgJSUlvYNQ2+dcunQJgNNXr+omdZJESkICSBKpWXEQQtrC1Ilz54iIiqJKlSqv8Q4FgtdDQ29vzt+9y+6TJ7kfGIh35cqyQBgaKhdESEnhcXg4RTp25IcffuBRQABPwsO5dP8+gU+fcvDSJRyLF8fv2TNSUlPp9cknlC5dmr59+8qpZoAlS5bg6enJlFWrWDtpEoULFIDERGwcHERhLMG7geJGV6lwL1EClUpFIy8v3TxHG2L8UgdhUhLY2kLBgnDlinysfkixiQm0a/fm7+89RzgIBelp2lRu9IprQ62mTOHCnL92jSPR0dz95x8c7t6lVYMGFKxSRVb7g4Lo2a4dS9etY4WPT1rD79SiBcOGDSMkJIRr164xefJk2rVrx5UrV2gxeTLtateGKlXA1pZbf/6JZGyc13cvEGSb+vXrE3H3LskpKdhZW9N8yBBsLSw4vWwZbsHBcqenTNZSUhgxYgQ1XF0ZUL++fAK1mri4OJ5FROBiZ/fCnDaaxES8xozho7ZtMStThuFt27Lz5Emmz5//5m5YIMhFateowd3du+V+IyQEM+CLtWvpXrs2RomJoFbzw65dnFu4EGcfH9ZMmkSjKlWIT0xk9c6dHNu8mfoFC9JjwABqly3LsmXLmDRxIpqkJDZt2sSTJ09ITU3l0JkzfDNqFIPattWFXwoE+ZRu3bszZuBA4q2t2XXgAGdv3cJ95kyaly4tOwpNTdP6HF9fX4oXL86Za9fo2aZNWh7Cmt7e2KvVnL93j6NLl+KpdcRn5Mq1ayz8/nsGpKbyODqaeYsWMWP6dExMTN7sTQsEuYCltTVf9OnDrKVLiYmPp0HFisz+5ReiLl3CumxZSEnhzJUrlCxcGM/KlYlJTOTktWt89uOPGJma8uzZM2pVqcKk5cvxCwnB3saGgIAAGjRowPTp02nZsiXz58+na9eu3D9zhno1ashiSUKC3CYFgncBJQpKpcLCzo7pffrQsFo13f7k5PR5bxVHoL5AqBxnYgIVK8Kff8rbRM7BPEcIhIL0KK4mMzP5Va2mdJEiBAQH07FuXRwLFYL79yloZqZbGZAkvpk2jYbFi1O8YMG0VYXPR47kn5s3mdWlC5bJyXzzzTdMmzaNWlWqYGphoQtZSUhAsrfPu3sWCF6Rpo0b88fs2Tw1N+fiv/9SzNaWdlOmcGrBAqxbt04LWYmPjeXXX39lj6Ul3WrVQkpIwEKtZtLXX/PvtWvsW7EC88RELFJTOXLkCI0aNUKSJGJiYvD19SUxJITIuDi+3rCBsu7urFmxgoDgYLy9vfP6TyAQ5Aw7u7SQYWJjadOoEct37uTDL79kV/XqpEgSl27fxvfbb7lkZET3zz6javny/HnmDB2aNKGcqyvl6tdnrr093QoWpGi5cuxasYKfFy9myuzZuLu7s27dOjp16kTlihV1FcaFQCjIx3h7e5Oi0bBx/358unXjUXg4Q5cto/CmTRxbvhx10aJgYkL3+fM5eusW48aNY8WiRUwIDcXVyYmAp0954O9P7169aNWhA72mT+dyr15IkvRc+OOM77/nzv37DB43DveKFRnapg39Bw7MozsXCF6dT7p3Z8zcudzato2yxYqx999/6TZjBntWr0ZKTubMf//xUaNGfN6vH3OOH2f6mjVYm5vTu0sX6jo4UKh2bT4cPJhJH39M79atUdnbc/DgQTp06MCiRYtYsmQJ/fv1gz175CILSqEfZW4lEOR3lMJwKhWYmTGjSxd5PFekCDg5wd9/68ZaikCohBQrAqGiAxgZ6fQHJaWFEAjzFCEQCgxjbi6veKnVWFhbU7RQITrVqSM3/qZN4fBh3cqASoWxiQldGzRIl0De1MSEv/76S+4gHzzA2MuL+fPnQ1iYbCNWrMViVU2QzzGztqZl7dpQvjy9ypdHY2bGhVu3mPTTTyxv2BBUKn47cIB5K1dSpUoVitnYYN+iBUYqFbPHjWPjgQOoVSqqd+uGi50dX3z5JU3btWPz5s0YGxszYMAAihYtyo0bNxj74YfMHzgQVYsW/Dp3LmGpqcLJIci/FCgg/2irSaotLdk1bRqF+/Xjzo0bBNy+jZOdHWVdXCjr6Ylbaiq3goPZ9OmnWJUqJfcfxsaMnzBB7msA4uMZUL8+A+bOTQsjHtalC9UqV4bYWAgJ0Q1GBYJ8iCRJbJg1C8dSpShmYsJTPz8uXr3K7UePWP/HH/QbPJgnz56x//x5ypUuzYjhw1HdvEm9oUOZN2wYV2/fplmdOozv0wdNmTJs/O03mjdvTnJyMrt372bXrl14enrSrl07SEjg2urVWLq7g4MDXLokcncK8jceHqgLFKBC2bIQE8MPPj40GDCA/61cyS979vAkPJytEydCQgKjBg4k3s+PBpUq0bJzZ7h+HQoX5vLOnfLcJT4eAKewMA7v3MmaHTvo06ePTggxNYWrV+U+p0yZvL1vgSC30BbBQqXSLbiamsp9g5GR7ApU5veGHIQmJnJIv5WV7v1mZvJ7hECY54gRssAwZma6RmxlxcoxY2hUoYLcoE1M5MYcF6fLmaZU9lKS/CorBYmJ8r8DA+UvgVKldOFdGg2cOyd/TtmyeX3HAkHOsbYGNzc5f2dSEpK9Pd99/DEeo0axbds2LJ49Y/6vv1K9ZEm69OtHm0qV+OWLLzj8xx98/OOP/DJrFkfPneN6UBBOxsY079CBbt26MXLkSDQaDQcOHKBGjRosmzuXlhUrok5JAUmij7c3lC+f13cvELw6Fhbyq6kpRmo1ratVw7tPHx6HhNClQQN5X1wcNcuXp2bduvDkidzvxMfrVqg1GvknPl5+jYiQBQ3gh08/lcP979yB//4DUUVSkM+p4uUli+uRkRQwN+fXOXM48NdfTN+wgRQbGw6cPEkzLy+2TZkCajWffvghjm5ubDl4kJO+vnzz2WdgYoKkVjOlTx981q+nQYMG1K5dm9u3b2NmZkbPnj1Z1qMHRlZW8sQtPl64oAT5n0KF5Fdra4iLw9jSkl8nTKDyuHE0qlyZytWqUcvdHczMsDM2Zs7HH8tzFUXoUNyAytwHIDAQ84QEhg8fLv8eFyfPh8qVk/u3a9eEGULw7mBsLLcDrYMQSP98GxvrNAB9B6GiLbi5wZEjcpViZcFWEdyFQJjnCIFQYBhzc7mRagXC1p6eckeqYGYmd356VYyesw4rEzVLS6heXf4iKFFCd0zp0nIi+tu3RacpyN8YG8vPtlKl2NgYW1tbVo0eTcuRI3FzdsbYzIzVw4ahcnOD1FQkY2OaeHlx5/BhCAujRcWKULky6qtX6TFkCA1at+a/c+dQmZunJYMf2auX7IDy85MrfsXG6oQVgSA/Y24uv2r7gnb167Pv4kVWTZ+Oa61ackXjp091RUqUAWdiom71WemHFNEwOBjs7eXjUlLkvqhZMzh1SoQYC/I/FSumr/BtYUETT096LFzIZ199RVxiIt/07y/vi4wElYqBnTszsE0b4iIjMbW2ThPXOzdsSKepU5HCw5n91VfUbNiQ1atX4+Pjg9GdO/ICb1SU3D6FQCh4V7C2lsdSRkaUKlqUI199RYmCBXHo0EE2Q5w8CTExur5FXyC0sdEVmAN5ThQYKAuC2griaU6qEiXgwQNdPycQ5Hfs7dPy3GJuLi++ZhQI4+PTC4TanIVIktxGChWS20yJEvJ79B2EIsojTxF/fYFhzMzkgWfBgvKrv788udLfHxurm3zply9XJm8REfKg0sxMfq+FhWwnVjpNe3s5ZFmtFgNOwbuBWi13lMbGYGxMi3r1eLR5M4UKFSLCzg6Vv788UbO2Tpfnk9RUjJKT5faiVtO0QQNITqbas2dQtar8HhsbXT6PSpXg7Fm5LYm2I3gXMDWV+xHtALNLu3Y0KleOQrVry87zp0/h0SPDAqEy6VKp5D4oIUF+j7+/3L7KlNEtTIG8Lzo6j25UIMgllNQSytjM0hJjU1Na1azJvcBANv3yCy4lS8oTsIgI3WKuSoW5mZncn1hbp4kfKpUK/PyY3ro11KlDq1at5PNevw5Fi8phklevyou7AsG7gCKSGxuDvT3VtPmi09qWuXnmAqGJia5Sa2KifEypUnD0qJyKSZnrgLyvSRMRmi94d3BwSIvQSDNH6AuEJiby3EVfINQfh4Hc/gIC0ocYBwfL4z139zdzHwKDCP+mwDAFCshJRgsXlhV+SUrvVFIchIo4mNFBWLy43IHeuKETMGxs5BVo/U5TkuTJm+g0Be8KVla6hLsFCuBiZoaxoyOOXl7QqpXcBhQhHXQOEKWSl1LFOCJC7mx9feHiRflYJTy/eHFZJLS0FG1H8G4gSfJkzNQULC2RbG0pZGMj9xsg74uLe14g1OYgBOSw4X/+kY8rXFhuI6Gh8j79NufkJE/kBIJ3AUUgtLICS0umDRjA4kGDcPPwwLhAAXkSFhkp9zX6aWEUkUMR1kE+LioKDhyQIzyUcZ21NdSuLY8Nrazy7l4FgtzE3l5+pi0t5Vdj4/TPtyIQqtW66Ch9gdDMTO5jnj6V50hlyuhcifpzHRBjNcG7i2ISyuggTEhIrxMo4cUKSlvTDzF+9AhKlpSLnQjyjHwnEEqS1EqSpJuSJN2RJGlyXl/PO4u1tSxCKBgbP+8gVATCjCsDinW4RAnZpaEvEAYHy+GRtrZv9n4EgjeFlZU8cPTwkF0XIDtlQbdSHR2tEzXUarktKYl8jY3liVlEBLi6yqJiXJxs1dcPp3RxASU3m0DwLuDmJvcTTZqk7zdAF3qi71JXqdK3iRo15N9jYuT25OAA4eHygFPJByUQvGtYWMhtoVAhqF2biu7u1PLw0LUhRSDUcxCmEzmUxd/AQLnt1K4tj/+CgtKK1aWN8+rU0fVrAkF+x8ICvLzk8VTZsnK/YUgg1G83+m2nUCH5vb6+Oie7o6OcI/fRI5HKQvB+IEm6gjwKigtXEcaV1GOGBEJ9B6FighDkKflKIJQkSQ18D7QG3IGekiQJD+qbwNg4vYNQETz0VwYg/QSsQAH5VX+Q+ugRFCsmCx8CwbtI+fKy0OHgoBPV9QVxGxt5sqbvIIyN1Q0uPTxkIf3BA/l9kiSH+h87Jk/g9NuhfkcrEOR3SpbU9RcmJvJgUxlwqlS6kC5FIATZUag/CStUSG4XihhvYSE72Z2cRK5bwbuJEr6oTNDMzNL3OVZW8gKTUnFSGbcpKStUKqhcWa5ObGYm911ubnJ/c/iw3HYEgvcBU9P0+dYtLOR2orQZRSBMStIJIEpudWVsVqCAbIRITRVhkoL3B2Pj9GMspRCQftuB9PMWJQpKmQ85OspplcTcJs/Jb8vpNYE7Go3mHoAkSRuBDsC1PL2q94GSJWUrvoLS8GNiZMFDETf0XYZKR6vsK1QI6tXT5SwQCN5F9EVyU1N5dVm/XZQoIQuAShiyWi2vqikraaam8or2yZO6SV7x4vK/S5USYSqC9wNr6+edSiVL6ipBGhnpFqYUlyHIYkZwsO73smXl9qd/jEDwrqFf/MDG5nknh5OT7AhU+hylH1FEDQcH+Ril4IKVlbwQXLKkvEAlELwPFC2afq6jPPuRkfJ2pd9RFqBAbmsFCujakr29vNBbvLgYrwneH+rWTW9gUNpRTIw8f1H6JEU/ALktWVrqxHYzM5FX/S0hvwmELsBDvd8fAbX0D5Ak6WPgY4C2bdu+uSt713Fze35b06bpy5s3aPD8JKxmTd1+lUqIg28xou28BlQqeTVMn0KFoGFDWQBp1kyerNWpk75tODjIjhD9kBVHxzd33YJsIdrOa8DS8nn3RalSctuwt4datWRh3dk5vQDv6Cj3OwqFC7+Z6xXkCNF2XgPFij2/rXp1ObWFlZUsAiYkyJXB9duOp6euIjLIbUzwViLazWsiY3STJEHLlnK7UMZjFhbyj774V6VK+oIkhuZMgrcC0XZeE/p9iYK3d/qiP02aPH9c/foiFP8tJF+FGGcFjUazUqPRVNdoNNWrVauW15fzbmNhkV7pt7PTOTr0j8m4TfBWItrOG8TWVrcKrVLJokbGdmKosxW8lYi28wZRVqWNjJ7PjQvypE0UUcg3iLbzhlCp5AVclUpuO5aWz7cdIyPh3sgniHbzBlHydCrhxba2zwsaZmYix20+QbSdN4iSk13B0LxGiINvJflNuQkA9JdGi2q3CQQCgUAgEAgEAoFAIBAIBIIckN8EwrNAGUmSSkiSZAL0AHbl8TUJBAKBQCAQCAQCgUAgEAgE+ZZ85YfWaDTJkiSNAg4AauAnjUZzNY8vSyAQCAQCgUAgEAgEAoFAIMi35CuBEECj0ewF9ub1dQgEAoFAIBAIBAKBQCAQCATvApJGo8nra3htSJK0CrnSsUDwvlJUo9EMzu6bRNsRvOfkqN2AaDuC9x7RdgSCnCHGawJBzhBtRyDIGQbbzjstEAoEAoFAIBAIBAKBQCAQCASCF5PfipQIBAKBQCAQCAQCgUAgEAgEglxECIQCgUAgEAgEAoFAIBAIBALBe4wQCAUCgUAgEAgEAoFAIBAIBIL3GCEQCgQCgUAgEAgEAoFAIBAIBO8xQiAUCAQCgUAgEAgEAoFAIBAI3mOEQCgQCAQCgUAgEAgEAoFAIBC8xwiBUCAQCAQCgUAgEAgEAoFAIHiPEQKhQCAQCAQCgUAgEAgEAoFA8B4jBEKBQCAQCAQCgUAgEAgEAoHgPUYIhIJ0SJLUX5IkjYGfYXrHSJIkTZUk6aEkSXGSJB2TJKmygXO5S5J0UJKkWEmSHkuS9IUkSeoMxyjn72Pg/X2U/a/lZgWCXESSpK6SJO2SJClAkqRoSZLOS5LUM8MxRzJpX2YZjnORJGmHJElRkiSFSpK0VJIkiwzHPNC+d5qBa6mvd26313LDAkEuIUlSaUmSVkiSdFmSpBRJko4YOEb0OwLBS8itMZwkSTO177udyefc1u6f+XrvSCB4M+TWGE6vDUZKkmRu4HP+1u5f8wZuSyB442SxHxLjsLcYo7y+AMFbSxMgTu/3e3r/ngx8DkwCbgDjgb8lSaqk0WieAEiSZA/8DVwDOgClgK+RRemMgkY00ANYn2F7T+0+q1y4H4HgdTMeuA98AoQCHwAbJEly1Gg0S/SOOwxMzfDeBOUfkiQZAweAROR2YQd8o33N2JEqbWdOhu2i7QjyExWR28sZwDiTY0S/IxBknVcaw2mJB0pIklRdo9GcUzZKklQDcNPuFwjeFXJlDKeHBLQBtqZtkCQnoDFyHyMQvOu8qB8CMQ57axECoSAzzmo0muc6MO0q2WTgS41Gs1S77TTwABiFbhI2DDAHPtRoNJHAX5Ik2QAzJUlaoN2msBvoIkmSvUajCdOe0wFoDmwBer2OGxQIcpl2Go0mVO/3Q5IkFUEedOoPLp9pNJozLzhPF6ACUFqj0dwHkCQpCdgoSdIsjUaj7+j4A+iundhd0R6r1p5jF6LtCPIHuzUaze8AkiRtBRz1d4p+RyDINq86hgOIAS4gT+DO6W3vARwCqr2WKxcI8obcGsMp7EZuK1v1tnUD7iAEQsH7gcF+SA8xDntLESHGguxSF7ABNisbNBpNDHIjb613XGvgQIYJ2UbkyVujDOc8DTwGOutt66zddjrXrlwgeI1kGFgqXASKZPNUrZE71ft623YiOwpbZTg2ADiBPAhVaIK86rYrm58rEOQJGo0m9SWHiH5HIMgdstqWFDYC3SRJkkAOT0YWOTa+/ksVCN4cuTiGU9gIfCBJkrXeth7AphyeTyB41xDjsLcUIRAKMuOuJEnJkiTdlCRpqN728kAKkDEvzXXtPv3jbugfoNFo/IHYDMcBaJA7TP1cHz0RA1BB/qcOcCvDthba/GixkiQdkCTJM8N+Q20nEbjL820H4DfSC4Q9kSd7Ma905QLB24PodwSC7PGqYziF7YATUF/7ewOgoHa7QPCuk5MxnMIRIBzoCCBJUnHt+X57PZcqELx1ZNYPKYhx2FuKEAgFGQlEzk3zEdAOOSfUckmSPtHutweiNRpNSob3hQEWkiSZ6B0XbuD8Ydp9GdkINJIkyUmSJGdkt4f4ghDkWyRJaoo8MPxab/NRYCzQEvgYcAWOS+kLiWS37WwFikuSVEPb/joh2o7g3UL0OwJB1sitMRwAGo0mHNiPbhGqB7Bfo9FEvJ7LFwjeDl5hDKeQihwmqbSd7sBljUZzw8CxAsG7xMv6IX3EOOwtROQgFKRDo9EcQC6QoLBPm7NmmiRJ373Gz70oSdId5NAVCbil0WguSZJU/yVvFQjeOrSDxQ3A7xqNZo2yXaPRzNA77LgkSX8jO57GaX+yjUajCZEk6RDyILQwcvvZh5zDQyAQZILodwTvGq9pDLcRWCRJ0njk/LZjXvEyBYK3mlwcw20EjmrzqvVACB+C94CX9UP6aWXEOOztRDgIBVlhK+CAXLUuDLDSFkLQxx6I1YZCoj3O1sC57LX7DLEJuQMVOToE+RbtQHAf4Af0ftGx2oqRJ4Gqeptz0nY2IneuvYCdGo3GUEU9gSC/IvodgSDn5GQMp88u5Ly2cwFL5BQWAsE7SS6M4fT3KznWpgKVEQKh4P1Fvx/KiBiHvWUIgVCQFTR6rzcANVA6wzEZcz/dIEM+G0mSigEWGY7TZyNyfo7aiE5UkA+RJMkCubKwCdBWo9HEZuFtGnRtDAy3HROgJJm3nR1AIaArou0I3j1EvyMQ5JycjOF0b5aLmPwBfIJccVzktxW8k+TSGC4jm5Dbzr8ajebBK1+kQJA/0WR41UeMw94yhEAoyApdgFDk1bRTQCSyEAGkdajtkFfcFPYBLTNU7+oOxCHn8HgOjUZzHVgJrBQ5OgT5DUmSjJDzzZQBWmk0muAsvMcZOfn7eb3N+4Aa2oTWCu0BU+RcUM+hzRM1H9gG/J2T6xcI3mJEvyMQ5JycjOEy8gOyc3D567tMgSDvyMUxXEbWIredb3LjOgWCfIp+P5QOMQ57+xA5CAXpkCRpG/AvcBl5lbm79meMNmdAvCRJ/wM+lyQpDHnFeTyy2LxE71TLkfPUbJckaT6y+2km8I1Go4nM7PM1Gs2wXL8pgeDNsAz4ADmBdQFJkgro7bsIlAO+RB6A+iEnt56CnMh6kd6xWwEf5LbzOXLI5LfABo1Gk7HyZBoajWZ6rt2JQPAG0QoUH2h/dQFsJEnqov19r0ajiRX9jkDwcnJxDJcOjUZzBLkqq0DwrpJbY7h0aDSaa2grGQsE7wNZ6IeeQ4zD3i6EQCjIyE1gIFAMOVnoNaCvRqNZp3fM/5AHk1OAAsA5oLlGowlSDtBoNGHaCmBLkVfOwpFFjpmv/xYEgjyhhfbVUCL4EsBT5Db1JXK7iUKecHXUaDT+yoEajSZJkqRWyG1nM5CAbLmf9NquXCDIWwohT7r0UX4vATxA9DsCQVbIlTGcQPAekitjOIFAkKV+SPAWI2k0L0qbIBAIBAKBQCAQCAQCgUAgEAjeZUQOQoFAIBAIBAKBQCAQCAQCgeA9RgiEAoFAIBAIBAKBQCAQCAQCwXuMEAgFAoFAIBAIBAKBQCAQCASC9xghEAoEAoFAIBAIBAKBQCAQCATvMUIgFAgEAoFAIBAIBAKBQCAQCN5jhEAoEAgEAoFAIBAIBAKBQCAQvMcIgVAgEAgEAoFAIBAIBAKBQCB4jxECoUAgEAgEAoFAIBAIBAKBQPAe839RH13eP6f8BwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<Figure size 1296x1656 with 132 Axes>"
+       "<Figure size 1296x1656 with 66 Axes>"
       ]
      },
      "metadata": {
@@ -240,7 +235,9 @@
     "ylims=[0.009, 0.009, 0.009, 0.009, 0.009, 0.009, 0.009]\n",
     "\n",
     "index=[1,0,2,3,4,5,6,7, 10,11, 12]\n",
-    "motif_names=[\"CTCF\", \"IRF1\", \"SPIB\", \"JDP2\", \"RUNX\", \"NFKB\", \"ELK\", \"SP1\", \"NFYB\", \"ATF1\", \"BATF\"]\n",
+    "#motif_names=[\"CTCF\", \"IRF1\", \"SPIB\", \"JDP2\", \"RUNX\", \"NFKB\", \"ELK\", \"SP1\", \"NFYB\", \"ATF1\", \"BATF\"]\n",
+    "\n",
+    "motif_names=[\"IRF1\", \"CTCF\", \"SPIB\", \"NFKB\", \"JDP2\", \"RUNX\",  \"ELK\", \"SP1\", \"NFYB\", \"ATF1\", \"BATF\"]\n",
     "\n",
     "for i in range(1,12):\n",
     "    motif_t = \"GM12878_COUNTS_metacluster_0_pattern_\"+str(index[i-1])\n",
@@ -259,22 +256,23 @@
     "    #print(ymin)\n",
     "\n",
     "    inset_color = \"#4400cc\"\n",
-    "    make_inset(corrected_500M[motif_t][0], axs[i-1,0], inset_color, ymax, ymin, yvis=True)\n",
-    "    make_inset(corrected_250M[motif_t][0], axs[i-1,1], inset_color, ymax, ymin, yvis=False)\n",
-    "    make_inset(corrected_100M[motif_t][0], axs[i-1,2], inset_color, ymax, ymin, yvis=False)\n",
-    "    make_inset(corrected_50M[motif_t][0], axs[i-1,3], inset_color, ymax, ymin, yvis=False)\n",
-    "    make_inset(corrected_25M[motif_t][0], axs[i-1,4], inset_color, ymax, ymin, yvis=False)\n",
-    "    make_inset(corrected_5M[motif_t][0], axs[i-1,5], inset_color, ymax, ymin, yvis=False)\n",
+    "#     make_inset(corrected_500M[motif_t][0], axs[i-1,0], inset_color, ymax, ymin, yvis=True)\n",
+    "#     make_inset(corrected_250M[motif_t][0], axs[i-1,1], inset_color, ymax, ymin, yvis=False)\n",
+    "#     make_inset(corrected_100M[motif_t][0], axs[i-1,2], inset_color, ymax, ymin, yvis=False)\n",
+    "#     make_inset(corrected_50M[motif_t][0], axs[i-1,3], inset_color, ymax, ymin, yvis=False)\n",
+    "#     make_inset(corrected_25M[motif_t][0], axs[i-1,4], inset_color, ymax, ymin, yvis=False)\n",
+    "#     make_inset(corrected_5M[motif_t][0], axs[i-1,5], inset_color, ymax, ymin, yvis=False)\n",
     "    \n",
     "#motif_names=[\"CTCF\", \"IRF1\", \"JDP2\", \"RUNX\", \"NFKB\", \"SP1\", \"SPIB\", \"NFYB\", \"ELK\", \"FOSL2+JUN\"]\n",
     "\n",
     "for ax, col in zip(axs[:,0], motif_names):\n",
     "    ax.set_ylabel(col, rotation=90, labelpad=10, fontsize=15)\n",
+    "    ax.spines[['top', 'right']].set_visible(False)\n",
     "\n",
     "    \n",
     "for ax, row in zip(axs[-1], models):\n",
     "    ax.set_xlabel(row, rotation=0, labelpad=10, fontsize=15)    \n",
-    "\n",
+    "    ax.spines[['top', 'right']].set_visible(False)\n",
     "plt.setp(plt.gcf().get_axes(), xticks=[]);\n",
     "\n",
     "#plt.tick_params(left = True, right = False , labelleft = False ,\n",
diff --git a/src/evaluation/figure_notebooks/figure_5/5a_motif_grid_uncorrected.ipynb b/src/evaluation/figure_notebooks/figure_5/5a_motif_grid_uncorrected.ipynb
index befb487f..3e6effa8 100755
--- a/src/evaluation/figure_notebooks/figure_5/5a_motif_grid_uncorrected.ipynb
+++ b/src/evaluation/figure_notebooks/figure_5/5a_motif_grid_uncorrected.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 22,
    "metadata": {
     "scrolled": false
    },
@@ -128,7 +128,7 @@
      "output_type": "stream",
      "text": [
       "[1] \"new\"\n",
-      "[1] 0.05996\n"
+      "[1] 0.05842\n"
      ]
     },
     {
@@ -143,7 +143,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.054682\n"
+      "[1] 0.056638\n"
      ]
     },
     {
@@ -158,7 +158,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.043417\n"
+      "[1] 0.04011\n"
      ]
     },
     {
@@ -173,7 +173,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.028175\n"
+      "[1] 0.029255\n"
      ]
     },
     {
@@ -188,7 +188,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.046902\n"
+      "[1] 0.043397\n"
      ]
     },
     {
@@ -203,7 +203,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.068782\n"
+      "[1] 0.065572\n"
      ]
     },
     {
@@ -218,7 +218,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.041662\n"
+      "[1] 0.039104\n"
      ]
     },
     {
@@ -233,7 +233,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.044245\n"
+      "[1] 0.043152\n"
      ]
     },
     {
@@ -248,7 +248,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.044552\n"
+      "[1] 0.045231\n"
      ]
     },
     {
@@ -263,7 +263,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.04294\n"
+      "[1] 0.039256\n"
      ]
     },
     {
@@ -278,7 +278,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.039913\n"
+      "[1] \"new\"\n",
+      "[1] 0.076104\n"
      ]
     },
     {
@@ -293,8 +294,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] \"new\"\n",
-      "[1] 0.071423\n"
+      "[1] 0.080554\n"
      ]
     },
     {
@@ -309,7 +309,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.067896\n"
+      "[1] 0.065065\n"
      ]
     },
     {
@@ -324,7 +324,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.06323\n"
+      "[1] 0.059854\n"
      ]
     },
     {
@@ -339,7 +339,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.049646\n"
+      "[1] 0.056512\n"
      ]
     },
     {
@@ -354,7 +354,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.055508\n"
+      "[1] 0.073508\n"
      ]
     },
     {
@@ -369,7 +369,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.064818\n"
+      "[1] 0.091924\n"
      ]
     },
     {
@@ -384,7 +384,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.053462\n"
+      "[1] 0.060287\n"
      ]
     },
     {
@@ -399,7 +399,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.035628\n"
+      "[1] 0.039794\n"
      ]
     },
     {
@@ -414,7 +414,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.049868\n"
+      "[1] 0.053802\n"
      ]
     },
     {
@@ -429,7 +429,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.065197\n"
+      "[1] \"new\"\n",
+      "[1] 0.057836\n"
      ]
     },
     {
@@ -444,7 +445,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.049052\n"
+      "[1] 0.049566\n"
      ]
     },
     {
@@ -459,8 +460,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] \"new\"\n",
-      "[1] 0.073461\n"
+      "[1] 0.028837\n"
      ]
     },
     {
@@ -475,7 +475,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.063404\n"
+      "[1] 0.042206\n"
      ]
     },
     {
@@ -490,7 +490,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.037107\n"
+      "[1] 0.03512\n"
      ]
     },
     {
@@ -505,7 +505,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.056776\n"
+      "[1] 0.033053\n"
      ]
     },
     {
@@ -520,7 +520,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.04542\n"
+      "[1] 0.051769\n"
      ]
     },
     {
@@ -535,7 +535,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.045461\n"
+      "[1] 0.030329\n"
      ]
     },
     {
@@ -550,7 +550,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.043058\n"
+      "[1] 0.035002\n"
      ]
     },
     {
@@ -565,7 +565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.074336\n"
+      "[1] 0.036701\n"
      ]
     },
     {
@@ -580,7 +580,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.046961\n"
+      "[1] \"new\"\n",
+      "[1] 0.0801\n"
      ]
     },
     {
@@ -595,7 +596,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.028773\n"
+      "[1] 0.05945\n"
      ]
     },
     {
@@ -610,7 +611,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.045608\n"
+      "[1] 0.048507\n"
      ]
     },
     {
@@ -625,8 +626,67 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] \"new\"\n",
-      "[1] 0.084209\n"
+      "[1] 0.037939\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.051162\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.038201\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.036564\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.040199\n"
      ]
     },
     {
@@ -641,7 +701,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.061451\n"
+      "[1] 0.047486\n"
      ]
     },
     {
@@ -656,7 +716,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.057083\n"
+      "[1] 0.049345\n"
      ]
     },
     {
@@ -671,7 +731,38 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.042564\n"
+      "[1] \"new\"\n",
+      "[1] 0.105229\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.106323\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.070857\n"
      ]
     },
     {
@@ -686,7 +777,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.060807\n"
+      "[1] 0.066954\n"
      ]
     },
     {
@@ -701,7 +792,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.043311\n"
+      "[1] 0.067887\n"
      ]
     },
     {
@@ -716,7 +807,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.041048\n"
+      "[1] 0.048419\n"
      ]
     },
     {
@@ -731,7 +822,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.045929\n"
+      "[1] 0.065861\n"
      ]
     },
     {
@@ -746,7 +837,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.057269\n"
+      "[1] 0.056354\n"
      ]
     },
     {
@@ -761,7 +852,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.054291\n"
+      "[1] 0.059152\n"
      ]
     },
     {
@@ -776,7 +867,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.05897\n"
+      "[1] 0.057333\n"
      ]
     },
     {
@@ -787,12 +878,82 @@
       "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
      ]
     },
+    {
+     "data": {
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ACggL1UAQDI68DED/AhEZ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\n5hnu9iWbf/NC21PbO6aS8o0XLF49vyTocbgcfEbWx2KZd1qGFd1cXFtw+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7epMmwjY/TthvKBaE5fsI7t0XbJ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\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8QlRCM7NQNe6jtCwz1kcANf/1r8FkMp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\nXESRMqxmh8U4MDdLm42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kpA8xBTFK1DpThBRcaKLxEC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\nsi0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QB3PP6u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\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78SOHnIGHqNZLqRvQnZZyDBDwiBlgMs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\nj2O7sTiEaAGZy/W0THQ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35DfnalT11moH0ZGblkCbOTpQt3x9+6t/PGm3glyHRVGuP2dvm9V4LlS5qX3Yotmw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7x088G6HgA42uKY2Xf2+MXu+q0VbQBwzJY5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\nilJqqUp6sLdj44qlj9y75IoLTsx3l136/Xs60sa/tJ+nr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HGm4Wcr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\nqu1/JJ4vEW8Spj6GnPOQYAWSoqEjTE2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aScCeJrqnKumFGesV75bxejNHCARU4w\nNcmZywEYkBRTYizcDECYEmfJQVCiuO7HR7qs3UPhaFKa31AMgjGiEIPIp2SVx4ixOME6JJgR\n5k8OBHtGwuG45LQXKkivE3kKIHIQT8mUsRThCDIzJHy4q63DHYokpAvn1cSRyHQCQkhWVJlR\nhSYksMiCiWH9pgPtLX2BYDR1ycIJlNKEajDphbQkK4RijIw63qgpRPfeSH27TILNPvUxnL0U\nsAhqnIaPA5GJZwtrO6EA8PX3wvDm+tDRSZDgp12j4dOABBo+DiTNJB9QBfmLmX612vWSKXhw\nuhLjq25AplIQ8ke3psRZvBt8iCVLSdmVr392YvvxwbSs/uK6BRPG3JMBwBtOUgaKSnQCL/K4\nvjw3lpSzLAbGIBhLaatQSlk0KSGE9DrebBTFcevVg96TG2oSykvXmgWDPx1Z2L1zeekMHScC\nACDMlV5Kev/GEn2k9w1ccA4SHUiXzVddz6QA6X2duDcCAM6dA6OGIYgruURteyqzca7wXNCI\nEqcXV7BKbX82k26HXQsBAJnLcc5Z42/nXPGF4wEVLn85AIA+FxmLEGcYjHv3j7QCwILCyddO\nOjczbO9Iy3PHPqCMfdC1/ZbGNcrhe2hgH1OTNZMfqs0rB84AaoKGjwGRqO9juc+NDHl81fWK\noeiOrU8SRm06k0LVV1o+1rZWZHYOxr1/OPrehZULJud+U4uJtec0/PTFbQBw95+2PHP7soyN\n9aH2kZt/92lKUguyzReMy3S5blXjeEDF5TAtn14OX6lrz2m85/ktmYcGHX/J6eHzCMEj3104\nu67wL58cb+3zE8p0Aje/oXjdZTMyuM63HlrviyQ5Dm9/6qoc22lGB4FoSovv5jBqqnYZ9YI/\nkuIxSsqyoEMcQoTSlMQBgKpShAAj9PyGo18e7u/zRFbOrJhc6cyyGjiM1DQNRFMte/1WU/6E\n4knzi4sbzdvqa4+yigB2NCExC3jDKMOOpFmFD7CADAUcdu0ZNB84OWPQN6E8316YY9Z8GBhj\nwWjaE0qYDPNLXbZp9ry0pB4PTBqOx1MeNSeOLQYQBU6Sq6My54ukbKYbi4osTS7nHU9t3n58\nQJLJYzcvbqjIzRnFIFkolj7c409Kis2Ut8CR89+/FFqUtqtLP7sqZwDZJmKrBYAAJ7LUCJA0\nk0NAZaQPYsGqnvxE7fgTALNO+ZVj4pXarZm4NwJJMDnCAtuJfxvLnf3bXSUvfnQUAH58xazr\nLjhnfDjtUCDeOxJJAAzFhKK+x9XuvwCAOP1pc94S7YwkQx+DGgdqpKGjLNIGac9rbQ1P/f1A\nMq2sXdmwYNlUu1lvNYppWY2lZK4/OOSPuSVrMXLaas0AAIwwNUEiEbFg9OwyNuRhRx5CWFOt\nkUiUpSU9wIo659lFBvePHtCGFT7+IGcxA1AaiyODnvLoO58+vN/TmmuwX1W7LJiOBNMRALit\n6ZKXmjfsHWm5dtOjf135Mx5zDNinvfuePvLunuET51fMvWLC+X9r+XjTkYfPyq+/Y8oly0tn\nonE9kevOadzXOrz1aL/2MMuif/SGr5mtPbh2Xs9wuLlnFDgx6oUnbz3bODZ/yihUvhpK7xpz\n9xqK++kYhfBfKFT+Da43STMpsLAaPv/l7D0dyZa+YCCa1glcZb5jfkNOtlkANQqc3ptO/GTn\nnwBgVl7dm6sezLybATv/H3efCPTcu+tPS4qnJiLc1iP9g/64w3xpddGNNhdgUCjSpxSIeqV4\nn+J0PNtQkOuM7L1N+YyhLmxcjHUlSJcNWGSxTiYPz7ZFwBhFBh9O94PBwYAdTSQ/CZs2DwhO\n4+Q5+Q07ho6NeALL1dA5Ip4y7XEE6PfvH9IUdd9fM/V7F1yQob8AgNsf7/NE/MwhJsxFiR4m\nBQEAW2uRuWLUC6L7FZCCTA4Q90dAFRY6hrKmgZoCAGTIz0zpydAmpoRYykv63wUqcXlLj9ju\n/exg73CgqCzvu5PKcjL/REpZOJ5WYjQn95yJJdmvbjz654+PAcBPrjorY6emVTCWHvLHASDL\nYnj6vQO/f/8QANx+0fRr16wY/8vyhZMd7pAE4I7qKyeuw64FNHiYjnxOfbtG/Vg4nabqw/YG\nGjwMAJx/y+z4fC645AAAIABJREFUa7P4LjxpGedcALocQJiFjjIlwnLcLO1FhjwuefZn7cWv\nbTrR6U43VU1eNasyS6e3GHWyosbTSs9gJJaUi3KnzCjI7z4++LfPW3uGubqyebNqC7JtBoOO\nBwrhhDQSiCckpSB7xozifF/7yAc7Un2eyhLXlFm1BTxgkAAAOIy8oWRaVl2ONVOcrm/kQ/cf\nrSlVrp3PXH20y9vnib740dEkxAExBCBgUccMDouuLM9WVeiQiWJKub5j+W5AkV1xi5M3mQwC\nANiUygLd1Kk6f07a0DeYqs7Vp0ZWAqyElML8iXTL5yQSBQCxolQsqAfuPKaEgVGn3bhkaume\nE0PBWOqzg729IxHtDJEU0jEQPN7jq8i352WZGstzH75+wa4TQwPe6KNv7HY5TFajDiGIp2RP\nMDkcjFcW2JdOLaORVmXfTUwK4rwlQv1PTaUuAKDhZhZtZWqSebcBJUQwC40P/utDkSkWPUk8\nW1msHbCYd2sBCDUAAGRIaX6UpYawuRQ7F+LsGcf93buHmwHAwOu4MV87Ay8SRlRKXmre8L3G\nizLn6ofdO2/6/DGVkqcX3bmpb9/7XdtWvPeDt8/9RZk1DwDCUlzLvDzD7wsAnIZ//6r1b9aN\n5zV9tKcr4/8JABihtSsazhg2szb//rVzH3p1l6biRQjuuXL2jNPtgDKlNlZzBh4ApMJ/BRPy\nOdmaT4PGpzyjCKOb+/a/eGLD4Zz2q29e3hMd/mLguYu3tH63/vyGnAoAaKpy5dgMWs5KW3+g\ntuTUraHPE33y3f2UstI827oLp/AzPqDB/TTend77INLSR7AeGAUmI+MsKh8DIgNCiDcizghM\nBTnCJD9QiSUHWbybpUZo4ABL9BJ4G9sbtVxMnNWEs2cgUwnwZhbrgnQvS/totBU8X5LeN8UF\n747R0jEwFagERGCSj4VbWNrDYp0kehK6Xu5v/OwPHxwKxtJ1pTnfWdFQnm/LnDODvtjuE27K\nGKW0IMd85dK6Qx2ekWBiX9twnydq1PEqpcBgJJgYCsRLnNaqQkdzj/+yBz9QCW2qcr76k/P0\n4wIJCGUnBwKUgqSo7YOhq365PpKQJpZm//HO5YU5pw5+JC619PmDMCEZ48VU8IpfrAeApirn\nWz9fc0bQZ1t/QCUMY6CJQdL9F5b24tyz+NJvIVMxII5G21j4OCDMUsM0dJQMfyrOeoFJASaH\nkbEQBCtwRsSbmeSnoaMsNUJDh1nSTQbe5/KWjOez/3dKnPc66f4r8W6Vv1iJsqZgYwlgEXF6\nmnKDmkSmEo1wKc57k/T/nfp2yjuuQIY8ZCwCrAcqMTUOjGFrLY22AYBu/ttk+FPq36sc+hHw\nJiQ6Rs3caBrpcrFrMYt1IN4oznmN9L1FPVvkbRcicwUylQEWgcos7QUAZKnWRI3irOeJ+yMy\n8plyYB1gHplKEG9lahzUBNI7cfYMFj0JWBBnP6+2/o6MfCF/eR52LUSWSiRYQbSzWCcNHkai\ng8khAOBKL+OKVtPAPjXcGu/7hElhAEZ5q2DKNxavwc6FLHoSWapqrFkv3nVOMpns6zkSC/Sr\nUjOnSgTpGxps31tSX1IxBWv8vJKLuIKVxLeThZuVgfdIOgJAgLeJlkKuaDXOnUPDJ5CpRJz+\nlBbtIx+4A/EmZKnQdoGlR5CxCNvrNQiKK1hJBv/BUm7i3sgVrASEkT5XaLwfGCX976hdfwYA\nVrRamP0C6Xie+HZKWy/knPORsQiwgLNnsNQgCxwAkmRSENTkq62f3LXtD4SRB8+6PmMWvaio\n6aaG1Td/8fjzx9d3R4ZeXvYT84TbqG8XS3uUw/ew0FGcvwLpspkSoUOfqt2n+u9c0WoaOQGM\nqi2/FpoeAcQB1mFbHTCi9L41/lxCumy+4lq147kzzjGuYi1LDmQe4qxpwOkyuMupt+ud2Faf\nySjFjoZM2sppw0wl2FxBxkJNLls8MWPJ2OeJ9nlOZVfnZZnmN4zOLDjnfHUc2jFucxg7JmsT\nJEA8LlippSRS/15p62qcM4uGjrHTA+eRoUCY8UflwO1MDqltT6ltT2W+J9K7kC6HRk5o35PJ\nISYFmOTXKCPIkK9b8gkAsOSAvGeUZ790aulHe7oA4PkNR86dXZnRDQ/6Yp8d7AMAs0H8/9i7\n78A4irNh4M/W66e706n3Ylu2bLmBu8E2zfTee0IJAUIoIZSXUAIhEHqABFIgoff2UmzAvWBw\nL5IlWb2edL3ubZvvj5VPsixLsprN+z2/v1Z3c7Nze7ujnXlmZ6YVpz1xw6I7X17RGYhe8dgX\n1yydMinPmWTWeYKxHfs6XvpsayAcn1ua9eJvToLBEX+4XnVvBNbEz/knbZsCAKDEVd9WACCh\nGhJzkdgyitFLOx8i8U6Kt3Ezn+16vkoKKPv+DQAUnwRylAgdJFAu73pEC7ZxM57UQqRE9MmV\nXctmU8ZsACDhOmn7/5C4m+Jt/LF/3Z/M39VdwxgpYw7IMRKqpHRzB/ktBo8iPUZDjDW1uxMZ\nAGSPDyQZAGibldbvD45RFMiKEu4+14NffS97vABgmnusrjh/fzKasZjVaDSRLLz2B8UfBADD\n1FI+NyuRmzYWBgDcscA+f3OwoiKDtoTEaCDFnJtbXGzL0rrYFKJ2RH1NIVdnLCCrikLUHEtK\nSIy5Y34dwxtYPseSmmlKSdKZ6oJtX9f9sKxhU1OooyAp4/SCefXBtuUNP0mqPCejdGne7BNy\nZg5+EhVQu5+3qL/0htjOcgBIf+hu+4VndR8QXF57qJpCHVW+xip/c1iKRqX4pOR8fzxUH2xP\n1luT9baJjtwJ9rwknSmuiNowKCOn5+nuG6aIFJNUBQAsvDEYiG7aVu8PCzqOKStKlWTFpD2G\nJckURYmiYtJzTpvRlGRSY92h46Yb74xt3w0AqXfdbEv8pgC0Xlbq3yVCG8U76JT52tRPRAqQ\nWDuJtYLoA4qldE7tqU+vEHy9/KtXd33hNCT9dvqF5d6GV3d9PtGRd8vU888onMf0WmkNoOHK\nX0c3bweAtPtud1zZ/TQ3kWXSo2yNv7ortnUnAKTe9euea9JQOp0a6Q6bh9dtUtxeANCXTdIV\n9lgsUcf7WpoAgKVpI6sPr/0hkYwtzI1IAgBwNJ1UNM4fD4fEqPbMU0yO23QWSZUikmBk9RQF\nFs5o5U1GTu+OBRpDruZwRyAeEZR4niU9IgttYY9db0nWW3MtaXnW9MRg3sRPZuVNkioltukj\ndbGoqljf/a89unl7aNV6AODzc+wXdP/ubHpqqxra2Vmzx1sXkQR/PDTBnqcQZbe7zqYzG1h+\noiN/irNwnDVLruyerSW4bKX/vU8BQD9pQupdv068zuVmMxZzSIxubNu9vnXXDve+MmeRlTet\nb92VbU5ZkFW2ILMsy9z1eGlTqGOnu2ZH5776YFuJI9fEGbZ1VNt05mkp48qcRePtOdpaHUT0\ng+DqCNS5wy4aCMObo/Gww+DMcIxjiARyiLKWgBIngXIitANjjNN6meYBgALKxOm7Qkein8k+\nE4ii+naQaAtlSAUuiWJNoC0IAUBEvxrYA3EvU3SN9n/XFxL2dXRsa68NxUSGooAi45OzyjJz\nU2zGrhnniFrjb9ntqavyNbqi3iJblpkz7uzcZ+YNhUlZk5MLJznyjJx+t6f2431rPqtZm25y\nFCdln5w3S8/yq5q2VfmbtnVUnZA787zi4xdlT+doVg1UqO3fakv/Ac1RrAmICqyJTj6GRFtI\noJydeKc29gcAQA7HV56mdZABa9Yt+Zri7SS0Tw3X0PZpPRea04JSAEDbJvMLuqMmvYg/3qSN\n0qJtZfyCt3qOIHtu2wfaYlqvnXzfOUULE6+7ot6J/7mCADkmbcK35z0nNexTw/Uk1gEkDowh\nseILqDKoCpGCIAe48Uufrt/6+E9vAMDbpz54an734Mf2iGfmO9dFJWFycuGKC57netR7wW9W\neP75JgDoS8ZlPHpvr5I/88FPr36xXSWEZeiyolSn1dDcGapodBMC6Q7Tq3cu7dkIB4C7/rYi\nMVrzmV8vOWNuH8EbRSWXP/p5YmWI2y849qazpyfeVf27SKQB5AhlKQLGKClqPB43snGaokAR\ngGKAS6KtJa6gvOynur2NnnBMynKaTXqeY2lCiCcYa3GHc9Osx07IWDw9tynk2tJRtdO9LxAP\nu6K+Kc7CJN682bWXoRkLbyx15E9PHT/ZWfjW8oqV2xrq2wMnzswvyUm2mLruR73BWFWTz2HV\nl+Y7j5+aS3csJ/5dRPTSjmOBM1OsBdS4Gq4FooAcIeE6IkfZ4uv+sVH3U2Vbuydy1vzinFQr\nTVEqIRxLxwS5zRu2m/UFGbbpxal3v7pq3a7mQDj+6C+PK813anPFAIAvJOyudwtxOTfNOn9y\ntqSou2o72jyRcEw0GmmaoYw6JhpXGMIGI6LDoi/KspXkJo99ZUhC1UrHGhJtpYxZlD6d0tkB\ngMTaQZVAFdVwLYk00c5ZbP6lSvtKEqwkcpgyZlOMHhgdUJw2GJBEm0molnbOimdetWJrQ3mD\nxx8SspwWs5Ez6Ni4pBAValv9DEOX5NqXzCxwWijFtYZo8wUZMoAxUPu7D4gchrhbDVbTzlls\n8fU+Idzo9rb6QtGYTFO0juEFRSSE6Hg63WYuSE1ONlpU/06l5UsSbaUd02lrCVAUpUsl0UYi\nR0AKkWgTUeK0cw5lylFda0isWVvMluIdQFFECgNRQYkSoQMomrZNWRcxfVP/EwCcP2HBzPQD\n5kBb1bxtdfM2ADitYG6BOf1fu7+o9DUV2TLnZ3QPliBAVjRtrfI1FiZl3TT1nNyk7r42RVH/\nu3z3+t0taQ7Tr86c1uuh/oS4pLz9ffnWqvZkq+HqUyb3nKHxzb3Lt7j2AsBdMy9N/JvQbO+s\nfn7bBwAwN2MyRcGG1t0A8PDcX+YeuICclowAmZYy7rfTuxfPVEPh5tvu17aznnmEsXXPcRGP\n+32hpmDUE1HlAOEMrN7CG6IxT0yOGWnGzBlsLGszZ24LR1Y37IkL6gk5M3MtaQYdl2w1CJIc\njMR3ddRvaNvFMNTiolJrPH3FtvrmznBmsrmsKIUCShvwK8uqyxehKCrdYZpSkGJlPGr7CjVc\nR7EGylycWNBCG/eghmtJuIF2TO9Mv2RZ3ZZGnyfPljo9vTgRwVKJur2zqi3iy0lKPqXgGDNj\nWruzeW+jp8MfzU6xWPSMNv9hVFRb3CGOZYqz7Ium5TosehKuUzrXkVA1iAHgkwAYYHRACMVw\nwCernRtoxzS2+CY13KB2rlND1STupTgL0DpgeFAlijGCIZV4d9C2yR7HhT9WtLV5wjqOyUqx\nGPWs1agTZSUiSN6gQAhJd5gm5jmtZsOPe1u3VLXXtwVYlnYmGVWVAIBKiMsXSbObygpTFk3L\nJQRWbGvYU+/2BGMZDnOSWWfUsbG4DAA1rX6WocbnOE6ckR+Miqu3N3YGonqenT4uzcCzBh0H\nQIJR0RuMMQydajNOyHGY6IDqWk3CdUBzWtceRfNEDAAQUAQSbVaDVbS9rMN57beb62tb/TRN\nTcpL1vOsNtGfJKut7pCskLw065xJmdo6THXtAZ5lJuU79TybqMYaXUFRUnLSrHMmZgqivHxz\nXUN70GzkS/OdPEtrLVuGpto8EUJIltMyY3xa8pFYQkAl5Ie2PR/tW/V57Tq7znLR+CX1wfZP\natYUWDMuGLfovOLjcy1p5Q3ua5/4yhcSyopSn/rV4uwUi3YWKSpp7gxVNLgJQLbTMqXwgAvz\nUPf2mo6ob1tn1U/NVZ5IbEJSXiAebgi3TkzNnpU9ocxZ1HPmK61N8V7ViomOvOZQJ0szN0w5\n++yiBV2tCSIrrctV72YidNKWItAl7x87QoEqklib6tkMROHn/ktxrSGRBqBYOqlE9glE0YMS\nZ4wqZVBBjgBvp43ZRA53LS+Re34iBqN6t6i+7QAAcR+RI0BR3JQHbln53Ft7lwPAmgtf0vr3\nNfdvePXlHZ8AwDunPrQ0fzYAPL/tg0c2vaYScmbh/CU5M1WiPrXlnbaIx2lIenPpH2anT9rr\nbZj73q8A4Pxxi/554u97HiWvECx67WIAKErK2nzZASOaG6/9jTYRVtrvb3Vcc+mhft/GX9wW\n2fgTAKTdfWvXXLIHefLdTdpoAM2VJ09+4MquxYqUQFBs6G4sNHYEfyhvESV19qTMnk/9GqZM\nAooSVfmzmrV/2/FJS9h9Y9lZNYHWj6pXnZI3+6ayc+ZklAKAyxd57sPNDa6AgWevOGmyUc/y\nHEMBFYtLALC5qt1i4HPTrAunZn1Ru+H18q/88dCp+XPyLOlaN0VnzFfuqV/VvO3YtInXTznz\n2LSJbZ7wS59tXf5TfSAiTMhJzk21MjTV5g3vqnNbDPzpc4puPmeGnVakxuZEUdsffUZr5zqu\nvDDp7O7JZ7jMdLGhGbrWhPdHt+2R3R4ASj++kC8cR/F2EmshqmyYukju9IISVaOtIPqFqnrZ\nFwCK5bOdfG4+ZcgkoWqiinzRUqG6FgCAKEQKxX7cIbpcFEXrJhTpSyYBoyOiD4hsmD4/3thS\n3x5s9YT94TiJxYgsAwCt1xGWs1v0WU5LXprVWDoBaLrS17i2Zcfalp1eIXh89jSvENzUXl6a\nXHBc1tSFWVPTjA5FUT9eW7Vye2NDe2BCbnJ2ikW7KxMltbkzWNcWKM6ynzgzf9HUnB/3NG2r\ndjV1BJ1JhjSHSceyDE3FZaXTH2nzRLT7zxmTsssb3Ot2Nde0+hmaSrObtMkkJFnt8EUFSS7O\nsh8/NWd8tgOISsK1arjOF6zxRtw0rZOBoSklzZpjsU8ivl1EFejUC6Q2NwAhcR+RfLEtu+TO\nAADoxuXoxpVQ+lQSayVEMkxdAsxYL6dHCFQ0uvfUuzs8fhC9ThOhKTWu6l1RI6/TF6Toywpt\nuempgkxt2NNS0eBpcYeSTLxNL+k4JiqywTiJClKW0zJjXNoxEzK8odiyn+p213W6A7GcVKuB\nZ80GLiJI4ZjU4g5lOMwzJ6QvnVXAs0xVs3dbtavRFRRifrsuznNMSORCosFmMeSlWWdNzMxy\nmlVCtlS2b9vnau4MEVW1GWSeZWISE4gRHc/kplpnT8ycmNfViunweNzN26Vgg6rEREnldQaJ\nz+AdZfkWvwVaaVtZQ8D4n2W7N1W0BsLx+ZOzclKtOp6JCnKDK7Bud3O203L81NzrTi8zyQ1y\n5YtK5zrKkMlkLqVMeUBzoCok2qw0f0Zi7UzaInbKA15B9+6Kig17WhpdwaJMW3aqxWLggxGx\nsSPY4AoUZdqXTM+9eMlEnmUaO4KbK9vr2wPhoNfIRCiKisl6kTY7kwwF6bY5pZkpSUYgqtL8\nmdL4oerfRXFJ2rKRRAqQUA1lzgM5ogYquGmPM9lntnnC+5o9IW+dFGm36glNFBF0fsnKmHOz\nrOKENJKUnE9xZrVzo+rfRaJNwJop1gxEBYomShSkIGXIpB0zaOdcoGgiuKSdDyeeHUlgMk4m\nalx1ream/YnJOk3c+EvVuwUAKEsxk302ZcwEoVNp/lwNlGuTk3PT/qStywCqKG66oeck23TS\nJH7+m3L1K3L1K9y0x5jsswFA2nyb0v59753mnAsAStMn3cm23a0NmjwgWf4loIpK48fc1Ee1\nGbcuf+yLn/a2HXxuP3j1/MtPLE38GV91hrbES0+0YzplzFaav+Cm/pHJOZeEa+Orz4GD59dh\ndLR1ourbriUDbeq2ujdV12o10gBEpgyZTNrxTPH1qvsHte07Jv9i1btVmwCNLfpl9wx1AAAg\nl3eNLeOm/lHJOOuU373f4g4BwIQcx+0XHluYYato9Dz93o/aTBLXnT717ktmA0BcUtbsaNpa\n3d7gCoaiYkQQzQbeYTUUZdqOK8uZWtR7zsDg19+3P/QkAOjGFea9+beebyn178j1b5NIA1tw\nBeWYQelTgagkWEWEDtW7WfXtBFXkyh6mzAVy9auq5yc6qYROPY4yZnVdC7FW1b2RBPYSKcCV\nPQRE7VqdKP8SJmMpZc4HigXRT6KNSsv/ar8gV/YQpU+VK19SA+V0ynwmfTFlzAKKAQIk1qq0\nf696NoES58oe7FqNZkQd0YDKkUCAtIQ79/lbwlIsw5Rs4Yw8wwFAUIz4hFBADOdY0oqTsiz8\noEZziqqsTfBq4vSJB4c1kipri43rGe7gBckHI7xqvezxAYBxRhlfkDuEHNDoUQVB8QUSf7bd\n95hQXgkAyTdcZT3txK5XKeDSD+hxcP35BW0RFPvlF1hOWAgHIlIIpKA/HlrftFFRFABIMiYv\nyJrB8ElaXFrUpX1QvfLtvd/qGO60gjlcj8UYGoLtq5q35VvTb5xytnZjfbjcL/0rXlMPAEnn\nnm5eeHiPmR8stnWn7AsAgH58UT8r2fzfJqtKS7izPtjub2/NjdIczTWSIJObmW/NyLOk61ke\nCBF7tENiO8q1hhmfnWE94+TE62yqkzYYopJQ7W/e529u8bbm6pL1DFcdbrck2cfbciY4cpP1\nVgCoq6+s8NarhIy35zgEUFydAECbjPH05F2eWlmVcyypk/NKm5tq/PEQTdF51jSuxSNU1QAA\nm2znpk2sDbRJqqxn+aLcie3+tppAa2PQlaxPsqlMqqJnKbpDDYcMbFPYlWZMLrZl5aflR8Kh\nxpCrKeyKyXFKJXmmFB2jqwu3R1VJx7DZ5tQcS2qaIz2mSvXB9vpge1CMxOR4pslpYHW1gRaO\n4fQMl2/NyLOmpxhsLeHOtS0717buEBU505S8MGuqidOvbdnZGHL54qF5GZMXZk2d7Chw1VUJ\nikRTVKrBrtQ1CbvKAYB1JpsWzG6PeGWi0BSVmVVA+9eRSAMQmXbOpnTJQLGgikRwAVFItJXI\nIYq1UKmLPtrkrWzyhmPiicfkF2XYIoIMACxDcSyzp94NAHaL/tgJ6T3Ho6nuTdKWO4gqcNOf\nYNJPhEMgkXpx/ZVEifFz/609Tt53srhb2nIXkQL8Mc9Rpryebx33wc273LUGVld9zTsm7oD+\noBM+um1rRxUF1NbL/50d634cx//Jl53PvwoAhrJJ2S88nnidsSV9XPtTXJGIxJwxvveKGls7\nKjvjPp6jF2SWMRGfEHJTSsRkyzabLYl1AkGNe7x+KR4GIMkZ43mdGQAqGjzvr6rYUuVqdYfC\nMclu0eenJ504M/+SJRNN+t4zTMYl5Zn3f9xa7Tr/uAmXLJkIh+ANCff9Y/X2fa6z54+7+9I5\niadaAUAN7CGRRpCjkj4jpNICrWOJAnGXmQIjw4DoJUqMSVvsY53N4c6WcGdckeKKmGNO07N8\nQ6hdVhWWZjKMyTmW1HRTslzbmMg5tmNPaPlKAOAy0+2XX5B4nUtL6XpMEAAAQtX10e27AICy\n2+2L5iXGpNNmk9jYPRFc9IfN/g8/BwA+L8d56/UH5NZjKInY2CLsrgAAxm4zze1+/Jw2GiiO\ni0pCUBCavX5RUeIxwjC0TCSDjnWYTE6rKdlgAYCo3+eKeDuiXn88TAtihqLjabZDCgctnJ7h\nMkzJacZkuy2Z4vqY7fNoQBSleyiJokodXY9KcGmpsP93p2gG9q/TExQjqqrSEcHE6cNijJgN\nWvQrkWFUErQlnfvE0+zgx7gIsigoYlCMcDQrq4qFN3I0I6lKIB5maUYhqpU3GVieo7mgGIlK\ngkJU7fEvhqJlVQmKUQqAo1kjpz+MAL+qJlY+I6oidc2JD1xOJpVYcY3GsTV96dGiSVxlFNPj\nwe6j4KCFpVhi5jSaohJz3gJATBI8Mb87FhBkkQIK2jotvDEkRtU0B6EoPcunGm3JBpuB1XV/\nUwJSe1fsmU1Loej9Q2r2j77SzmFRlViKoSlaJaqsKjzD6Riu13oSaMQJkdCGig2uiM/E6mel\nTyQVtSQYAgC2MLfDQu9y11JAlaUUl+RPktzedm+kwxcJRES1sooXBQogmpLGpafZzLp0hznN\nbtTupSVVrg+2V/ma5B+2ZYpcVI6359rSJ04eb8/R5utTotGa2gpX1MvSTL41nWzaoQ1v4qdM\nDKRbXVEfQ9OF1sz08RNrAi1/2/np23u/zTanzsssXZQ9IyLF3q9ascdTZ+IMvyo758qJp5g5\ng0rIHk/d2pYda1p21Afb5mVO6Yh6K32Ns9NLF2aVHZc1LcOUTKSQ6lpJok1AsaKpICBLCmtl\npCAv+cwsy0h+Eq4FVeFmPKm0fqN2rCWRBsqcT+mSgbUAzQJRQYkTwaV2rAZVDh/3VekbV8cV\ncXb6pG/OfbrnIa3w1s977yYAWJw9473TH75zzYtvVCyz6yx/WfjrZEN3sHZZw49/3/mpjuFf\nWny702A754t7AeDXU8/tOWk2ABAgaa+cJamykdO3XPdJz7cGGVBpf+Qpbeyj46qLk846pc80\nikr+8t6mN5fvkRX1vOPGP3zNwp6PskGPheUar79dawWk3nVzco+dSpT6Re36f+z+IizGTi+Y\nW5CUoY3+9MQCuzw1q5u3z0gdf1PZuceklYRjYkNHYE97c0OgI6bGih0ZMSXeHOhM5u251rQJ\n6ek5TquB8st7XyCReopPZvIuBEYPjAGETiK4iBwlgd0AQBky2ZLfAkXHFanK17SprrbVE0nh\nk6Ni3K/4xmekzSsqKrBm9PynJshiU9jl/+hLU5tPUmVp1uS0+fPTTY6eA3S6vo4qtz//ilRe\nDQCGs09JP2MpddA8VO5YoCHUHvlmhd0dkxTZPyUv7dhZedZ0M9dHWLT1nj9GN20FgOQbrrRf\net6hfqymG+8Mr9kIAKl33JR8/ZXdJY+Edu7d4hYCRlY/0ZGn7qxQOn0AwI3Lj2Q6Kn2NhJAC\na0bR+CluX0d7xOcXQ4QQIsvJvC2uiD45pON0AJBuTE42JqXa0rRHxwAgIgmd735IapsIAHfc\nrNSFx+nY/RP9W02NnU31wbamkCsixSNipNCcKShCfaQjxWg3cPoCa0ZRUmZqckZVU+UuT02V\nr9mmM9scqcn6AAAgAElEQVRVrsSaFVXilZF2ryqoRJniLJriLMrL7hqc5BWCDSFX6OvvbB0R\nWVWCpXmpc+bkW9MPY2jvSDv3gY/31LsZhv7vPafPnJCeOGcURf2hojUYFQFgXmnWuQ983NwZ\nYhj66z9fmJ9+wOIi9e0BbVGAgoykk+96T3v6fM3zl2kT/CZ4Q0IkJgKAM8l407PLNuxpYWjq\nuVtOXDw9N7FYY0yUV25rDEXjNEWdfEzBtU9+ubvOzTD0f+45vef68CohP5S3alPfz52U+eHq\nyv8u393ujVy8eOIJM/LSHSaTngtGxTZveOW2xnBULM6yX3XK5FN+9543JDAMvfHFK2zmAw54\nTJTF/YtVqCsXk7iH0qXoTlimTZFHpKDatqxnejp59gkPrG/uDDE0tewvF+ceOEqmuTPkD8cB\nIDfN+ofX1n71Qw1FwcPXLDxtTlFiXgdfSFi+uS4QibM0fd5x460mXWWjt7rF5/MH9EqbwyBR\nlCqDqTVqozhTVhKZmMnlZOVd8cT3P+5toynq1buWzirJSDR7Y3F5c1V7OCbSFDWvNIv/6WIS\nqqZ4G7/wA22JGiKF1LZvAIAIHURwAwCTdyHdNa8DkEh9e9OyWKgBiKrwyfkFZ3JJJSTaTKJN\nlKWY0qWAEpN2P6Y0f35gpIFi8i8BOaw0f9EdUAEAJS5X/VVp/QaUOJ26kJ10N8XbSNxNhA7K\nkKk93au0fy9tvq3XqcjPelnpXK/UvZUIqKgda8Ufb+qdbParasdaue6NREClvj1w4UOfaudD\nwszx6W/edwbTYw1RueY1ueLpXrlxk+9Vw/VK/TuJSIm08yGl8cNeydjCq4gcUxo/SCQDgJgc\nv2vtS+WeegBgafqRudfNzZic+AiJe+MrTwM5DECxRb9giq+jOAsoglz3hlz5V+1Iarn9uLft\n2ie+lOQ+VkkozrJ/+NA5Rj3nfe2dxG2zUF4Z/Pp7AGBTkntO2mleOIdLE7Tp/SlDJmXKA4oC\n1ghKHICQSBMwetpcSKfMJ7SuwRVoaGkRAo205LPoZJYhcdUUiPOSLjvNLBbawilZUyh9qjsQ\nq2rqCHXWyrEOPSNyjCoofFhJYiwFWVZpXKpqTc6jeDsoMdW9Ke4rl8NtqhgAAMIYGGM675jG\nUgIJ7KUzTqRtU2RFbWxuCLrKFcElx8MAoFI6Wp9CLBOyzPF0fQdrn6w90TKy/r8LqCB01Kry\nNf1t5yfvVn2fb824bdqF5d66f+35MllvvXHK2VdNXDrIIB86Svjj4eZwh6wqaUZHz24LSZXb\nIh6VqOlGR69Qq+zxym0dAECbjXz+ATFUjxBsDnUQIJkmJ99jdSVZVdoiHoUoDr0125zas23T\nT26D3OmByXxym2s4yXp2SasxQQ2FAYDiecbWfZtIsewQOry6d2oyHhR7JkT0gxwBJQZAdQcG\niAwUC6pMlChlzKE4SzAqBsJCXFIEUTEbOIamJFmNxmWLkdfzjN2i77V2OgCAKgFRD1465aDy\nRQiRKa732oOD5I+HAYCh6INrAK3jOCLF7DpLz8aS4vVLre3Q9wH52YvUNwTFSCAeYWiacQd0\nzZ0URcX1LJlYGJXiVt5o5Y3W9Eza0KMxo5Ku20Sa6qd3tfu4GQ184QFhrSEgQlyJRACA4rg+\nJ9wYWM+OdVlWRREAKJo+YDbLAzvWFZ9fahmxr4AQQmikxHaWaz2tuuICLiPtUMmE3XuVUAgA\n+ILcXqOymsMdDUEXAZJhSmao7v9lITHqEQJmzjjOlp2kO2BKn0PtVJDFH10VKlGT9Uk9HwEB\ngKgk/OTaS4BYedPUlOI9njqvENQx3Hh7bs+H4P3xcH2wDQCKbVnZ5tSwFGuPeD1CQIvVOQ02\nSZE6Y4GYLLA0m2a0Ow1JXfOs9nis3MwZJFWOK5K2rT0bHRQjtYFWAEgzOg5e1Wmnu0YlKgVU\naXJBWIoBQJ/RQX88rBA1LEZTDLaOmB8AbDqzTWfulawx5BJkMSoLU1OKe3bri/WNaiQKAGx6\nKpvc95TrITGqEFWLsgMARzOmvrr7u76ypCiKajxolElPajTxCIWe6rni6f7VuSmKBfagxqAq\nEkUAAIqzNoc76oPtANDrDAlLUXcsYGR14+w5dp0FVIkIHUQKgBQCRp9YYw+IQrEWEu8AotDJ\ns5vC7vpgG0VRvXKLSLHOmN/A6sbZsh16q0cItobdvnjIyhv1DK9ndQAQFCOiIkXleJrRnmNO\nNXL6iBTzCqHOmB8A9AyfbLBKquyOBSRVNrJ6h97q0Ft1DNcR8zUGXVE5nmFy2HQWLR4TlYTO\nmN8XDzkNSXmWdAtvVAkJihFBjguKFBQjFt4oKbKkyibOYOL0PM31uhAOOLw6Xc/VYnoR9lYr\nPj8A8LnZXFbfs64BgLBnr+z1A4AuP7fXwMHEtcAznInVOw1Jsqp0xHwxOU4BnW6ypxhsiWuh\nK7fySm0Q7cG5DTJZZ8zfEHRFZSHd6LDpzTzNacfNLQS8QjBx3A6V8+j5dF3VR2uqyhvccyZl\nzirJTLUbAUBVSYs7tG5Xs82snz0x4/zjJvxU2f7puqo99e5Um3FqUWqqzchzjLYY8/Z9HVqy\ny06YVNHo+WhN5Z56t9nAT8hxpNlNFAWKQtq84a1VLodVP3dS1pUnlz7+1g8fr6tSVfWeS+cs\nmpab7jAbdGw4Jja4gt/8WOsPx1Ptxl+cWvbvr3a+8e0ef1i4dumU46flZjhMLEMLktLUEfxu\nS30kJk0pTLn8pNKLHvq0ssnLMPS7D5zVc5y+opINe1q0MMNxU3Pe+nbP3z/fFoyKFy4qOW12\nYZbTorUla9v8763c6wlGS/NT7rp4lrHqQaXlf4HmuWmPMWmLgdGDElN920ERlJYvldZvAICb\n/ucHvrZoS0Xec+mcM+cVJ+Ywb+wIfrBqrz8cTzLpfnXWtJueXb6popWmqJd+e/KiqTmJ/v2Y\nKP/vxn2+kMAx9AXHl1z26OeVTV6aoj546JyeDzsSAnvqO7WwVmm+s7ze/fWPtdXNvjSHqTjT\nbjXxgijredYXEmpb/Q6rYVpx6tJZhazrK6XhPTVQwWSdStunUfpUAIpEm4nQofp2qIFykMOJ\nEMjalh0vbP9wbcvOqyctnWDPfXbr+wTIr8vOvXrSUq3CFPbs1Ro7RHAR7zY11g5EpvQptGMG\nZcwh0RYSbeRL5rHpXZM/V/ub71v/So2/xcwbBFm6ddr5V0w8uXdQlqjxVWdpC/NoKH2a7oTl\nctVLcvWriYAKEDW++hwSru1OZkjXLVkmV70sV7+SCKgAQHmD+/aXVtS1+bU/F07JfubmE7T1\nybr3KYXEFUuJ1D3eGhiD7sTvlJrX5X3/6I6UyBFxw9XaFHZdOzXl6ha+L9e8Jle/kkjWHO64\n7OuHd7lrzys+/rT8OTevfIYQ8pfjbr5q4tLEB9X2FeKW27tWDKJoSuckog9UCRgdpU8nkYZE\nbhv2tPz+lVUuX/eUMwAwY3z6C7ecqF2SPQU++7r1nj8CgL6kuOCTAxZHkXY+pLZ/TxSBK3uQ\nMhdSnBWUmNKxBqSwGqoi4XogClty++X/im+tageA/957xuyJmYl2qvZMmDsYA4BZJRn3/XP1\nym2NAPDX35w0f3KW2dBVK4Zj4tYqVyQuMTQ1rzTr/ZV7v/mptrbVf+a8cTPHpzn2P23sDcZ2\n1bl5li7Osp8wI+/Jdzd9tq46Jsr3XT53/pTsVO2SIdARiG6ubAtFRbOBP31OUa9fbURgQAWh\nIy+uSB9Vr/qucbNVZzy9YF7P4Tx1wbY1zdstvPGKkpNnpU86goVECKGjSmzHntB3q0F7puTQ\nwxIRQgghhBBCY0wlpMrXuLF5b4WruciaY6QMewI1SQbj/LySY9LHawPCxNruFRaDW7YHv/oe\nANisjJRrLk08pM6mpVAmQ12gbae7psJb3xbx5Ogzzbyp3LvPajSMs+WUOYtKk/O1YFUwKu6q\n7ahs7azp7IjEJJqiFVCybLZxaWkTchwTcpK1bFVC9rV6N9bVVHW0g0IbWJ0nFsi2Oabn5E3P\nz7Jb9ACgKOqmvW07azoaO4KKqhqNLEtTcUkR48Rk4IsybQumZGsPkciKWtHg2VbfXN3RGRMk\nUGlg1XRrUmlWRllBamJBHdW/U3WtVoOVIHQAawSKA5oDiqFtpUT0k1ANW3wd7Zxd3x74oby1\nptXn8kfikszQlKISq0GfkWyeXOBcMDnbqOcIgW37XJsr2xrag8GoYDAwFFCEgCgRp9UwIddx\nXFlOmt1U3x5Yub2xptVHCOSlWU16jmVoUVKicbm62ZucZCwrTDlxZr42q3Zr2P157bpvGjZN\nsOdmm1O/afhhcnLhOUULZ6eXagM397X4Kho83mAkzRRJN4sWXgUiq4w5ENd1ClYLG0o3Cbl5\nJeURzwvbPtzeWb04Z0axLVsLeyuq0hnzf167Xsdwvyg9/aLxS9hAJNEdHfzyW9fjzwGAbkJx\n7r96LFZqNlE6XpDFZ7a+98L2D6y86S8Lb57oyPvt6hc2tu2emzH5meNuKXEcMIxM7dwo/nhj\n4pEXLbSgNLwn17zGTrorMbeE2rle3PSrxHKnXNnDTO75SsP7cs2/2Yl3Mhldq4a4Y4Gv6zaV\n13riMdqSRJ01beqM1PEHP1qntPyvtO2exJ9syW1s8fVy3RtK1d/YyfdrS64CAMhhae/zatsy\noghMygK29J6uefkiDZS5iNKnbmzbffWyxzpj/jSj48YpZ1EUvb5153eNmwHgxilnPzrvenb/\nA+uqe6O082ES7Z7vhNKlcNP/rHauk2te46b+UaVma19OlJWNOxpq69ojgmSxGMom5U0rTtNC\nHYzDRpuMAAByhIj+2M5K/2fLAYBLdTivvwhojuKTtDUjNUQK1nfs2tCyGcSgmTe4RHGCc/zs\n/EU6UEjcQxkyQrJpc2VbQ3sgEBE5vWIysTRNqSqRBTYSlVJsxpLc5GnFqWt2NH2+YV9Vkzc3\nzVqS60ixGWmKkhXS4Y9UNHjMBn56ceq5C8d/tn7f8s11Na2+xdPyphSkWEy8xcCHYqI/LJTX\ne+wW/aS85MXT8z5aU/nJ2qrKJu+pswtnT8xMtRlpmpIV1eWLrN/VTFHUjHFp5y4cnwjbjCAM\nqCCEEEIIIYQQQgghhEZATfXOPZ66mBwvtmXbPYJaVQcAkGSRy4rLvfWSKhfbsksLyvQmsz8e\n3uOpK/fWNbc1TJDNRk5fGW2ns9JKHQWlyQW51jQKqI7Kcm1Z1hRDklpRoy1QyqWnWc8/3RX1\nqoQwNJWZO77N21ITaKsNtBhYnVOgc0LA0IyLRNxp5taIN9uSUpyUVZQ9Pux2VfuaG0Iuh85q\nD4q2Fj8ACAbWXZTSHvE6dJZx9py8rEK/39MS7mwOd4qySALh7BitZ/hWJexPMfMMk2FyZpqd\nGak5AW9njb95n7+ZpRlHlOQEgaEZF0Q70y0eIZCflFGclJWTnu+LBuoC7Q2htqgkuGP+VF2S\nidU3hNpNerOO4XMsKfnWjOzkrEgosM/XVOlr8ovhaCwyzpBq5PT7Qq0xFkycYbwtu8iWle3M\ndvs6avytdcHWqBwPhwNFrM3A6mrCHZJZZ2D1OZaUwqSsvJRcKhQBQgRZbIt6fRUV5m3VDE2H\nLTrd0uOcBptTnwQAjMVS4a79snbDts7qmakTCsxpTs4MABFFbIn71rfusvCG0wrmLSqc9fX6\n2ooGjy8kLCjLznCYWZqSZNVk4MPRuCcUM+n4DKepONf5+vI9a3Y2+cPCOfPHTcp3mvQ8AFAU\nRARpS2U7TVNpDtNJM/MNOtYjBDyxQFCMSDUN1m37OIYJmjj5xLnaTKdOvU3P8t83bv7d2pfr\ng+1XTTplWkr3MoT+ePi5be9FpfjNU8+9+5jLez6tqDR9Ipc/SRSBLbyGLek9A1h3svq3pPKn\nQZWY/Eu4yff1XMe0MeT6sm7j/9atD4nRbHPq0vzZTn3SNw2bagItbWHPSXnHnl4wb17G5ER4\nAwCUhvflyr8SOcrmX8pOvAN6PNYp1jaQ/QGe6I/bfG9/BABcduYBC+KmprzRtO7utS8pRH1w\nzi8WZnXPFu6Lh276/mlX1Lsoe/q/T76XoWifEPLHw4QolH+HU+5gAAKsPWCexHFmM0MnsZzN\n6OSiTWq0GYDQpgKxxRvdVgNEZYyqeUExqCLQHFAcZSlSal5TGj8iisBNuoOyT6d4G9C86tsB\nUkj1biHBSmCN7ITf7CWOz2vXfVG33sqbCpMyT8w9Jok3r23ZXulr2tZZPT9zytmFC07KPbYz\n6N7RuW+XuzYkRRSiTnLkJ+uTtnVWBcUoSzHjbdllqcWTU4uIpLSHPe1Rj1cI+uNhE2VigPFK\nAaveYOL0WeaUDJPDbnZ4Yv6OiLct4iYAnaGwiTOYOUNr2GvScXqeceitmSZnqsUpKZIr4vMK\nAV8w7olGOFWvY3i/GOQ4Ks1qTkuyJuuTkk02hj5oxpFhw4AKQgghhBBCCCGEEEJohPUzs25n\nzN8S7gSAXEuaQ989F7Qgi/sCzdoidvnWjJ6zDg5ynt7BJhvclLlHNhll0OuK8oeQLCbHW8Lu\nsBS18Mae0/cBQFCMMBSdYrSlGuyD3WmPGYmVQFA7vJRBp8vv8RVoCihKEGVfSIhLirYKjoai\nwGrkDTrObtEzNBUUIyohFFAHT9OXWBPRyOn5g1ZjGhQiA1GAHnB27jBRRErX9+yOo0EVBMUf\nBACK49hk+4Dph0sOE9J7DRWKYoGiiSpSjB5oHohKRB/IEW2Gya40rBFYC6gSEb2UIYPiB1fU\nnmdIMNh1Iul1uoLeZ0hiRUAlEJSa27qS9TzfKAoUlezPTg2Eus43va7nrOYUTRNZ7t5pKKzN\nBk/peD4vp8fXYYDusYbZCMGACkIIIYQQQgghhBBCCCGE0ABGPkSDEEIIIYQQQgghhBBCCCH0\nfwwGVBBCCCGEEEIIIYQQQgghhAaAARWEEEIIIYQQQgghhBBCCKEBYEAFIYQQQgghhBBCCCGE\nEEJoABhQQQghhBBCCCGEEEIIIYQQGgAGVBBCCCGEEEIIIYQQQgghhAaAARWEEEIIIYQQQggh\nhBBCCKEBYEAFIYQQQgghhBBCCCGEEEJoABhQQQghhBBCCCGEEEIIIYQQGgAGVBBCCCGEEEII\nIYQQQgghhAaAARWEEEIIIYQQQgghhBBCCKEBYEAFIYQQQgghhBBCCCGEEEJoABhQQQghhBBC\nCCGEEEIIIYQQGgAGVBBCCCGEEEIIIYQQQgghhAaAARWEEEIIIYQQQgghhBBCCKEBYEAFIYQQ\nQgghhBBCCCGEEEJoABhQQQghhBBCCCGEEEIIIYQQGgAGVBBCCCGEEEIIIYQQQgghhAaAARWE\nEEIIIYQQQgghhBBCCKEBYEAFIYQQQgghhBBCCCGEEEJoABhQQQghhBBCCCGEEEIIIYQQGgAG\nVBBCCCGEEEIIIYQQQgghhAaAAZXD495xPUVRFEVdu7XzSJcFAeAvMprw2CJ0NMArESGEEEII\nIYTQzwu2ZNH/YRhQOTy7/7wKACha/2Cp40iXBQHgLzKa8NgidDTAKxEhhBBCCCGE0M8LtmTR\n/2EDB1SiHW9RfTn2iZ09k7WuPlV73ZJ162B2HG5+SkvPGQp7vRVpfTGxF/uE6wV1gKzkaIWW\n2Jrzu0Pl0xNNM3qDKT274JiFJ//i1vte/3ilVxpoH/s9820LAFiyfpOvY8amtP044bP64Xyc\noqgpt/94cAkD+9Y9ftd1C2aUJFuNLM0aLI6iybMu+9U9n26s6/MbJXbN6jIHeRiHuceeev0i\nwzyMB1G2r/jowd9ef9K8mfkZyUa9jmF5szW5eNL0My7+5ZOvflATEAf5HWt++PJP995y5kkL\nJ+Rl2CwmjmE4ndGZnjNtzuKrb7n33eXbDnXiHMHDi2c7+hkZ2iXW/7lE07TOYM7MLznhrMsf\nf+XD9rhyqL2fkWzUPnLZXm8/hYx7t/5yQZaWcsK16wb51Q6+EvvZ3WCuDk5nSMnMn73ojNsf\nen5jTaDPnY50XdpFldznZVu09NmLlw3yCCCERpm6a9XHD/72+hPnzshNdxh0PKsz2p3ppccs\nvOQXt7/26eqQQvr82Ij8O+4nE5ph9eak7MKJi047//ePvfRTfWgIxRh8Td7rmOxe89kjd/1q\n6cJjC7NSTAY9QzM6oyU9p2j2olNvuvuPn63Z0/dBQQgdrUaqIXMoX11SnMj/5Deqj1xhRqwB\nCwBYGSJ0lDisGgZG5+5oCI3Zni3ZUOOj/RSpv9IyhsF8qUPpsyOol5E6vMPpbYahdhegI4YM\nJOJ6s88PHvPnHT2Ttaxaqr1uzrxlwDwJIaGmv2jpWX1Br7fCLX/tuaPjH/2h/6ykSLmW0pJ9\nVz/59IM1ZF1+57N1Ean/HQm+77T00x7YcgRLm7Dk07rhfBwAJv920wGFU6XX77lAR1OHSj/r\nske8ktrrGyV2zfAZ/X/3PgxpjwkH/yLDPIw9bXznyXnFtv4/S3OOc255ojrc35kTrF1+6YL8\nAYuROu2sz/f6D/74kTq8eLb3f+6ho8dwLrHDOpd46/iH39neZxlOd3Td5F1a4TlUOeuWvzjJ\nwidyG3/N2sF8uz6vxH52d7hXB0XzS2962i8fsmIfvEPVpT29c+3ERPqsRd8M5ggghEbV9k+e\nX1SS3P/Vbcyc8dBr6w7+7Ij8Ox58JhStO+u3fxMO+s88UjV5wpaPnj1+wgDHBACcpSe9urxm\neIcfITR2Rqoh0ydZqE/nmUTmprTLjkhhRqoBq8HKEKGjxOHWMGQU7o6G0Jjt1ZINNvxx8EXq\niaL1Q/hSCb07gsb28A6yt3loRxgdWQMHVHr5eJJT+3XHLKBCM+Y364P9ZDVgp+1BPdGKEPHX\n7d32xduv3HbFqclc15Wjt097dW1bPzuq++RkLeWjDd3lGf3SDsowP6759zVlWiacsfi+F96v\naHaLsuhprfzob38YZ+S0t/LOfHYEdz20PSYc/IuMyHFQJM+DF3QVjKLoGadd9eKbn+3e1xSO\nxVVZDHQ0/7Tis0dvvyrHyHadOckz/rvN3WdWoYaPCvVdyTJnnfP0vz/eXtkQjMQVVRFCvqod\n6195/HdTnPr910LeBw2hXjkcqcOLZ3v/5x46SgzzEhvgXFJkf2fThm/evfWCYxMVwi/+ufvg\nhAMEVNT4Ww9cyFEUADD779UGeYfU55U4mIDKoa8OOeTv3L5h+eN3XJrEdj2lmrXk/l7RwxG5\nynpp+uqOnhUIBlQQOsKU6PO/XJC4JEuWXPL8fz/dta8pEhNlIdzWUP7ZG3+9YEFxIsGMy/8S\nH4WKop9MVFkMeFp+Wv3ZA9eflhgAMfmX7w8+B0IOoyYnhKhy8PHLE8moaade9eKbn+2qbgzH\n4qoih32unRu/e+7B30xNM+xPw1z02PIhf3eE0FgaqYZMnypfO0FLXFRm1zb+VN3HULnRK8wI\nNmAJVoYIHWUOt4YhI3p3NOTGbJ8t2cMu6pBSHpYRPbxD6m0eRncBOoJ+BgEVALDkXRY6aPxs\nwjA7bQXPnvsuntp1K8PanlhzyLP8v8emAQCry4kq3YUZ49IeyvBrlra1t2s5cMaJy1oivd4N\nNXyctv/CfqjcOyK7HvIeEw7+RYZ/HFQl+vtFXZNrGVJmv7Gu8VApxVDV/1xQmtjdfyt8B6e5\nsTBJSzD95v/Ih8hHitb8ckrXhJJJRb0vnyN1ePFs7//cQ0eJYV5igz+X1r9wkZaS5hyr/fFe\n7/YT4Yj7dtxwfLb27qTz7v/2+dna9iDvkPq8EocXUOnm2f564lQ//70DhhaO+N1q3L9+nIED\nAFPOJC1nDKggdESpz188XrsYWUPRs5/vOVS6da/93sJ0BV8nX/Nmz7dGO6DSU+X7N2rJKIr5\n2B0bWjH6r8mJKv7xtDwtgd4+87XVDYfKR5W8L966JHEvdPOHdf3vFyF0NBiphkxf1AtSjADA\ncM49O+/UPpV90ttjVpiRbcBiZYjQUeawaxgycndHw2nM9tmSHU5RRyegMoqHdzC9zcPsLkBH\n0FEdULFOSEn8e55178pDZTUinbYf/G5hV3kMhRuDBzWxCCFqvMjAAkDK1H8c8dIebPg1y/1F\nXU8Hn/Kfqj4TfH/tBC1B/pkHDD8Z8q6HvMcuff0iwz8Oq+6do+WgS5qzwSMMlFx56coSLb3e\nsbg5rvR8L9LxVtdJpcvx9Dt5lOBdYd7fW/Fg9QH3tUfm8OLZ3v+5h44Ow7/EDutcumN/8Gbq\nPZt7vdVPhOPFEgcAULTu6j9/rhJS8fd5WspB3SEd4kocqYAKIeTHP0zvukhzbh9OPgNQxdtn\nOAGAZkyvbXlCyxkDKggdQRWvnpe4xt+tHGAUXu2nt1NU1wMid/doCo5lQIUQcq6zq+o77s3q\noeVA+q3JNz9xovYWZyr9vj06YFbv3tQ1GJzVF+wZxEwOCKEja6QaMgfzlv9BS5kx/z9EFaaY\nOACgWevuQ9cMI1uYEWzAEqwMETrKDKGGISN3dzT0xuwhWrLDKepoBFTG4PD239s8rO4CdEQN\nvCj9EVRw3vsX51i07Z+eOPXVKv/o7euCJ1feM90JAHKs9uKL3zs4QbjlhZqYDACldy/qM4ex\nLO2IU8SWJ+sCAEBRzEsXFfaZpuz2mdqGd/v3R8MeB/xFhiDu//7MJ7tWrHrg+y/mOnQDfYK+\n6d8bTksxAIDgXXn2I5t7vhd1faxtGNOudrCHXKsDAHT2xX9bMn3ukjNuuO3e8X55qMXvNszD\ni2f7yJ7taJSM8SV2w72TtY26dz4f/KfiospbJr303b7Xf39mf0Xsy2jUcr1MvOEKbSPS+o9B\nrtQ8BOsfO/nZrW4AWPiHZRemG0dtPwihQVHiDWfe1lWPnf3qqovHJ/WfvuDsZ/55ao62/fKl\n9x6p9YcvSumqPQK7A0PO5FA1uRTZcfoDK7Xt6z9ZtmT/PDb9leeva07Jylt89lWPPfsYO+QC\nIQwENFsAACAASURBVITG3Ig3ZD6+8R/axpnPLgVK98LFhQCgysEb/lE5BoUZ2QYsVoYIHW2G\nU8MMUj/t3CE3ZsegJTsixuDw9t/bPJzuAnRkHdUBlbiHf3X1i0aGBgCiCncsusYrj14jjrn/\ns8e1raZl160Lir3ernn9XW3jN6dkHwWlHWE0a9+25cfvvvni7TfeK9IzfabhLfnahiK5j4Y9\nDviLDMHm+38TUlQAcEx6+P6ZzsF8hGLtf//P2dr2zmevCijdPzpFdy3FocQbB8zniuWbN3z/\nxSvP/emyYwa13/4N8/Di2T6yZzsaJ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VjqyWBYisbkBBE1emqqnN5A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\n23/Mb267/+8ff7u1uu2YX+7ll7eaG/3uxoAKQuhndUI3PAHbngih7px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ZKWu5CRrGCGCgAAERFvySjlyj8EiYhITeTYnxIn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\nv1/xzbbaHgDgWJxm0kZ4n8N7EACMOtVvr5x+6fyyH/dtcjmZHGTGz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AYUScDiil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\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TClz+Y12VaA+du51v8LhgoAXH3G7Mfe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\n3ry+tTow+TwcShS2Hx6jQKNBV8TnuPye5xXNnNsUfvDLZ09Vips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CjhgbgL2yoy52O6Jv6ulH9ReRy08d2Scj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wMWxYVvYGt56oGo/6hmCsfUIO1drxx5FEiU\nK7iEL70Jm4oAC8BUFmpXO/5But6j/Z/zlT+nvv1q/YMs2s9lrORyz0LGAuANQCIs3EW61rKu\nd5Exj6+8FwkW6ttP+zdRfz2TR5BoQ4KFKUGm+JFgxtZynLVaE4OzSA/p38hGDiWuOWfQ5ipI\nl4HslVzmKs2TjQYa6MBWFmhk8jASHcAoIMyUEcRbkKWUy1iGzCUs0qO2/IV6vtB+3bCpEDgJ\niEwjnbRvAwDgjJOEslvp8AGl/hEWasXpi7nsNShtNhJtTAmySCft+Zi0vowspUL5T5WDPyP9\nnwGWxDlPY+f8cddtaDcLd7BIN5NHlP13UO9O4PRizcvYOhVgrE+FBRpYuANImMW9qXZ8J3AC\n/3e46awZlLHXNtT/4Z19O+p75k7JctkM0bja0jv8ya5WWSWnzi1aNbsQALjMkzRChYVa419e\nyuddwBS/2vrKhCjjJVW5f1ib4Aw0t/3UZxdNS1TTsKM69fciVR+NDLnJxsQl03K1IsLhNu+t\nT2+89tSq1t6RMZqhNEMzFArKEU3naBB0d2x5RqsDajTDG40bfz73Srs05gz2XwKnLyJdawEA\naFzecTWXcwbIPtK7fsIwhGBBRc6HOya6+gCAw6wbE1PzJmyr+NpCMJe+KElHrZlX9NQ7ezVR\n58b97Rv3t6eOTM57H9n7uj8eRoBmu8tG4omrNz+zfGtPXUSJ/Wb3K08vu03byCLddLiWhTuZ\nMgJEBgDEG0FyYFMhcsxEgkUqKcKmUWuRWKz37sRPRsa9d3D2hFAjQcyfwAn8CxxsHtDYlOpi\nd6o1Bc/hS1dOPaOmWCvSUf82bf6JXTVJNoV0/JN0vpW6N3Hh3wFhZJv2tcfCjlnfFaHiWkR6\nPgYA0v4Gl75YC/hkSlCpnygPmj81+6V1E9OVNGgN0+rQcPxY4p5QAfDB+cX7jvUTSquL3Y5w\nf3hHos5onDsz2YPyl5PuPqd4cXI/l627/6O2HZSxzqCnJEVRfhzvogYanvzqds5VQwc2p0zI\nZ2l5DwAAanhMwqJNhgFmFLs0E45th7pbe0dSfRF6h0I//MNGQmme2/rkTSu2jfagXF9xRjJ/\nHgBaRnr2eI4CwJe9hwotmZ2jKSk3Vp2dWja9YeMjbzZ9DgDdoQGn3uq2GwsyrNr0OK6Q+1+Z\naLFoMYyTshFC/RE5HJUBQC8JFqMo8mMJX0weUY89S7rfBxLB9ipkKgTgQAlQ3x4WH8Zps/ip\ndybmVydwAt+IuEK2HuqqbR7w+MJxhRj1QrbTXF3injslS0tWF8rvkndew+I+ecdVXNE1nKsG\nJBeQKBn8kqS4FKhNz2lfMWH6b5KmDoA4nLFc4HSapJjFB5EhpyjLrhEqskq+99i6U+dNem3j\nWNFMc0NNeqICwHvbm5KEyvo9banDkkTIhEh5uy4xKdISVo5z2HGiazCxrv9qV5nVJCWzomWi\nXjV1tcSJqwvmAVNZbACUIPAmo871q3nX1HlbAKAt0Fdiy1Ebfq82/xkAkD6Ty1wFkh2UEPFs\nosO18t5bU0Xwxw8WaqXDtSzaByQOnIh06chakVSynsD/cnxXGSpHPXhbfUVzTw4w5rIZtC9D\nOKYMDEcIoYWZs5ZV500ONCi7bmBxH05fLFT+QqtZsGg/k32gBKh3Fx2uZdHeVnHN9x9f3zUQ\nmJzjuO/KBRWTZmjCK1kl9e3ePQ19g/5Il4+tyOvU7Fk45zycnmAUks5gbOQQi/YCpxNmPanp\nR5AuHRnztYA1OnyQ+g8DiZHuD7Rh9dGaP6zdF42r86Zm3XL2zFTToX5fWNOFpVn1cs9QtGka\nwDQm+2FwhKn1GhGKOAOINiRey8LtgJEOABgpT4++cF16OMj1DsVGIgoDbDdJuQ6kM5iQ5AEq\n0YEtWumQyz8/yaaQvvV0cGfy6ElhyLypWc/ffspdz2/2DIeTz+alW568eWUy5/Nbx+Rcx6LK\nnK2HuidsP3NhSfJ+p+HUuZMefG1H0isWAMwGcdG0iVM9LueMiYSKexkAIE5iSe0hpxPmvaAe\nup/0rtc03chYIMx4FOnSucyVTB6Zmu806IRITDnQ5PEMh5M2kQDQOxR647MjjEFuuvnCZVMe\n+f6y31y3uLW9yTfUHR9sGfAwAMAYzcm0rqksyssv5QSJendorjtcwcX8lMRSkw5spb4DAACY\nZ6FWtelPvD5T3nYp0Dh2L5NG9VNM8YMSBADq2w80zkKtUX3Z42/u/nhXaygiX7Ri6vTi+dY0\nCQAicaW+3ru5ts5qlNbMKzpvCRxu826tizb3ljNWnpduwRgYAw7jnvpgJBYqzLx66fQ8uydw\n7SMfdw4ECjKsD1yzqLpkhjYdJIQe7fRtr+8+0OwZHImcOq+Ier4w9a1fFmtcnp2BSwsTtkIk\nxsJdLNSKjhXRzFXUufjVDfWf7m1r7hmZPzWnclK1xrLEZNKwe+hQ6+clOVknzy5cnK386uUv\n1+1uxRhdd2rVrNISh0WHEQqE44favG9s3sthdPKswlmlI5c/+OHgSKQk2/7oD5ZNyXMm+0k7\nBwI763sHRyLb6rpXFAfl7VcClbmsU8Q5T2tfQBZsoqFWAGDBZiAy7duAEC9vv4IpAexaIFQ/\nnLANjA2wuBeYSn37GY1Tz6ZWYfXF978XiMiluY7nbluVTCAHgOFgbOO+dsrYjvqer0oevmsg\nBK/cfdr6PW1fHOx86p29j/x9p8UoGSQhEldGQvGsNNPMyRlnLSwx6HWyqZD6j4DsY+F2ZJoE\nAIAwCBZQQnRwG+nbAADirKdYbBAg0fCrHYLFh0jPBwBAfQe0DBIk2pX9dwBVkGmSMCuxTGLh\nDrX5LwCAsASGHBb10GAz6fkASAzpM7icMxLXNtpP2t/QxtOh3QCAzcXIkMMi3TTcAWoEeAMA\nIGOeMPMxAGDBFvXYs9ohuNyz1GPPgRpSjj4uTP0JIIwkB5d3PlBZ3vfj1MvC5Z03rqCW9CbS\npSNDXlJjiNPmfP1VldKQuZh5d40Om6U5WU8cZshGxvwkIbFqTqFGqABAW99IW4qw2mHWzUrq\ngtPmfP3edG5kLmKDXu2jwVmrSPvfAYCF2uNfrMGmIhpqGW3xSbycy7+QtL7ClABpfwNIjC++\nHhnzWKRLbXxmzBkMAJsm0ZE6FvWMNbKYi0c1SnvlHVdpwxZX5Wpxyn/9pO6shSVGXUKBWN/u\nrW0ZAIAMh7Ew0/bQ95amWfVvftHw679tf23jkeridItR8vqju470DvojBRnWX1yxINVx8RvA\nGKy5563W3hGew2/+8qzyAmfqU3WtA+GYMhKKByNxicoAgBBGyb51JahZ/9GhvZr4UZhh1kww\nkD4zyaawcIey/87ELpUgAAgVdyv1DwONI2OeUHFPYlhsQD323OjHmgMALNqrHPgpkBgyZAuz\nntC6LljcSzrXAgAy5Cb8N6J9ypFHSNe7gAVh+kNc5kljPSuMkv6NLNJFh3YjYy5pe11teg4Y\n48tu5cvvSeoQmeInne+wUIfa8hJfeqNa/zDpfh+wKFQ/xLmXQTIghxHS/xmLdJGBrZwuXd5+\nOYsNIMkpLf8EOD0AgBoig19inRPbKrWOMSb75H23gxpG+kxxzjOjb2GIdrwJmnWesQAAaKRb\n881DggnbEvMBFu1XjzwKAEwe1r6nAqfXIqwRp8PGhGSBxb3adWNxryY1Far1XPZpx/Ppf0eg\njAVHzUz0Ep9ayNBACPUGov5QXFbHCu4Wo+i06A26f0N1+z8c0bha2zLQ2jcy5I8qhDLGRJ6L\nK8Rh1mWkmSoKnVoXGov0UN9eFmpnagiJdi0yBJQQU8NIcmJrGXbOSwq0jxNBOfKrXS9+1rnX\nKpn88dCynBm/nH+NRUzMlxiDzw92fH6gs6FziONwnsscV4gk8pGY0j0YdDsMs0ozz11cauzt\nviEbrrl8artnpH8o7O9sjjcTs8jNE7gzlrmLsuwGied7uqG0mMtarR59LOHRH2xR6h9Mngky\n5mv/tABQOSk96SU7ARghjZsBAEA8516sFTEngMteozb9Ufv71HlFSXpmw972DXvbU0eumZug\nGV4++klQjgBAWIl+3r0/dUxUjb9U//FtMy48/gvLpS9RkmSPGiLtr4+9U0N2qpp75cyCryVU\nlk7PS7VzxBnLv6YQjCXsXpIkVAoyrEuq8ib4hmmwGiXNB7hppPuv9R8BwJlFC188+Z7UMRd8\ndO+Gzj1vNG64ruK0Kj2v1P6cDtdi61Su6GrOMgUkBzACsUEyuF1tex063uSLr9NXzdBXJW7g\nNBhK7sq0eL6QPT4GjMos3MXkIaYEESclOs6JDAAgmJHOraVQNPcMH2739gwGKWN2k05LlQtH\nlWBUthqlSZm2WaUZBp3A5GE2cpiFO5niB84AQAAAEA9qGOlcyFSM7dOSTe0n8N+IDk/gs33t\nRzuHhkOxrDSTXuQ5Dsdktd8XFjg8Oddx8uzC/tH044Kvy7cwj1bAmTBDq9eT3vVc7rlInwEA\nOOsU7KoBElXqfkWHx0ynExqjrwA7qqF1rNnrWwSXsVzRfLEYlXffxOWegaR00vPBhNYNAJg3\nNUsn8rGvOGBbDGJ1iRsAorWH+0a5SRIMAWOaODEEEBYErE90fpds+zDZoZJvGWdBkWNKNGp0\nBPr/PULFOiXZNT5uu6sGOENyfo5d88cIldRh7qUakWzQCU/evOLm328IReVz7lt77uLSaZNc\nHEYtvSP//Lxh0B+xmXT3XrEQALb3HQYAt8GRyqYAwCx3oiV6W0/dZWUnJy2/sk3jVCBZpsRc\ntCs4oLkMLanKTcY/TMDiqgQZv6W2652tjbUtg0adMKs0w2Uz8Bz2BaJHOoaae4bLC5yn1xSf\nOS9X3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fjR56n0UBAMR8F5/lRpnf1yLH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PnYo1VRPR/J7mu7n+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\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jnN6xYJIZSyvCHmTX/EY9f79Mb2ZTnT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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 900,
+       "width": 1080
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "options(repr.plot.width = 18, repr.plot.height = 15)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/HEPG2/counts_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/HEPG2/counts_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.068323)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/GM12878/counts_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots1 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/GM12878/counts_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.085323)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/K562/counts_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots2 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/K562/counts_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F,ymax=0.075)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/H1ESC/counts_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots3 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/H1ESC/counts_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.084564)\n",
+    "\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/IMR90/counts_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots4 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/IMR90/counts_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F,ymax=0.111)\n",
+    "\n",
+    "#do.call(gridExtra::grid.arrange, c(all_plots, ncol=1))\n",
+    "\n",
+    "wrap_plots(append(append(append(append(all_plots, all_plots1), all_plots2), all_plots3), all_plots4), nrow=10, byrow=FALSE)\n",
+    "\n",
+    "ggsave(\"subfigs/compare_top_10_modisco_counts_jan_26.pdf\", device = \"pdf\",  width = 18, height = 15, dpi = 300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[1] \"new\"\n",
-      "[1] 0.118274\n"
+      "[1] 0.020798\n"
      ]
     },
     {
@@ -807,7 +968,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.104777\n"
+      "[1] 0.005185\n"
      ]
     },
     {
@@ -822,7 +983,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.074615\n"
+      "[1] 0.003216\n"
      ]
     },
     {
@@ -837,7 +998,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.06925\n"
+      "[1] 0.00399\n"
      ]
     },
     {
@@ -852,7 +1013,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.094115\n"
+      "[1] 0.004424\n"
      ]
     },
     {
@@ -867,7 +1028,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.049484\n"
+      "[1] 0.006103\n"
      ]
     },
     {
@@ -882,7 +1043,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.068578\n"
+      "[1] 0.004184\n"
      ]
     },
     {
@@ -897,7 +1058,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.056801\n"
+      "[1] 0.006975\n"
      ]
     },
     {
@@ -912,7 +1073,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.070904\n"
+      "[1] 0.00358\n"
      ]
     },
     {
@@ -927,7 +1088,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.061744\n"
+      "[1] 0.004717\n"
      ]
     },
     {
@@ -942,7 +1103,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0.057691\n"
+      "[1] \"new\"\n",
+      "[1] 0.035458\n"
      ]
     },
     {
@@ -954,74 +1116,655 @@
      ]
     },
     {
-     "data": {
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EpbxJDEUMDqho+TFWs8nY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6L8m5e3fvLnaDFvmNCAE6CE\nN+Gblwyq8UX4oDpYZ7fW0VLraFGf+qx+nzvgjTLmis2gGSqcnhjTQoJKMEPFEhnwrXYWhWrj\nqLW7rzBy7fnHN3b9d+MxAC8+cNHiaTnDfLbswW0GLUadcjtnSC+xRP7UFiEkqPg1yy+NM4Ez\nSlBxewN/e3//258f7/MGzEZdbqqdI6Sh3eFw+4164VuLxv7kurmWYccSKvzn+Pr7Nz+t43T/\nXfHQq8c+Wl257ZLVD6657HH1FMTjE4uq2mpaevsuvtF7nheAniRYNx7PSbVNLUi1mfUNrjan\n331u9vSFmVPVqbUA7p9xrbJA2hgSVAD4hIwjcb9p8jSKfQ0STByfm91un2INmGc+AUA3PWBb\nupiNUPe9e71HSgCk/eyeuCsjCl4RYyqx5lNXNQDIAfHYn1XneHUhkHkTMgcTVOZMCMd3nFWY\nOmAfQjC5IKwSnTMt91uLxr7fT3q545JpI8opUchLj7MYdX2h2i2M/Iy44UeGAjDrjHm6dKl+\nlVj2jN/TAqU+Hqej3jbIgRTeKIy6Thj/QwAVjd13PLG+od1JCBZNzZlWmMoRUtHY/dn+mgNl\nLa+sL37hgYumFIxAVOPiJ4Fw/TOjSfxkVn9erX4FDv1SP//fxJgaKP4d9YS/j+NDX4edvUN8\nAz30ylalaM2KuQV3XzFjTFYiIWjqdL368ZGXPyr63Ws7Dle2/enKoNJCzFnsQrH8n4rKwjBe\ncmT4P6mGxoAQEF12+E3i2rKzd+U6AIbR+cnf/164m9G05XA9AIHnrjonWob85XfOZscUdE3l\n9sFuV+1oLuqonJocNNg5WtPx6vri7Ucb23viAPDcwVHpVedMzbl5+ZSMEfoknFwKBxdU2PJY\nlCXF6lrHCeopqU8KeESfKEu7m48uyZ0FgBgS9We/LLdullo3UlcdIBFzNp9xIZd2rty5V06c\nwdnGDnivL4IvIP1j9cHXPz3icPtDbR1YU56RZL3jkrNuWDKRECyblf/h7ioA/1hzcNns/JS4\n4Abl3pLmA+UtALKSbWOyghvWNY6Wb6/9ZVVv0/9NuPDc7Ol3bvjzjesfefa8H10zNjpaU+Ob\njEyp0+036HijfqB5rOyTe4/Tvlrq6wxmfnB6Ykjl7GOINWInnVJ0OT2sOJzAc4l2k0EXzHVj\nG1jHajsXTM5WX9jc6TpU2QYgPz1ufG44oIQzmZK+dz2oCIAzRa+c+bzr5cO/BiC3bPRvvY7L\nWAaxT6p7lxl7AuCS5yvbiFL9alZDRTf55wCop9n3eXiaR71tUvPHtOsQ9TRCFwfIIDqIfYBM\nrKO51AV82vlngGnnBzsrlLiQW1dMtUeW0Voxt2DF3OBfqEzp5oaDAPLtGcGc7xDjX72x1d21\nufGgIloA4OwD5A0TSy7hgn9lTFBR4uXVWEMSi2PYZVRcAY9P8gNIMkbveyYYregFBe32OVNM\n8UxQybWlcSFRWqKyko5Z1dsEgFgLuPjJcs8R2lcbOPRz3bTfgzcAoO7GQNHDbGSp7j2x8iUA\nwrgfCmPuDN8ycRafdYncfQhKzTwNjVPIgbayXc1HEWn5EG9Ac1+XT/I/X7zmzimXK7+kugkP\n0O6i6BRbwaqb8Sff1mvUujIhePTWRRfPK9x4sDYgyoun5pw3XV2KEsRWCE4P2Q+AOkr9O24m\ntkKpYXX/x5swKmkwQUUtq4/JHnQxq57XEYLH7zz3modXtXWHnzYlzvzIrYsGuzwGhYOvoGMI\nPP1p83TfMflSALPSJgSbJA+VA0Rnm5Jc+OOZ10mypFeZGRJ9gn7B61LdSrn7EHR2IffbxD4O\ngOGcAf4AY8NZ8wdsJ7bRaNmgfFlz1oJgfGHbNuptZWUkJJVdeWOHS5GpMpOst0dGas6bmLnm\n91cpxbdHZDMrd+6RGtbIXQepu5HobOAN1NdFBAuxj+fTz+dzvqW8ZjU0BkOUpY9r9gCw6y0F\nceE1b6e3t97ZpriBXVqwYPgDElM6iKDMJ6PaScjGP1xDZRDLL6VDcnxw3lLT0nv2pCx1z8bO\nYFZ0vHUE/8JzBy9jnBVKmHMOkqGiqqHyzQoP0jhT+ZIFFdEtO44FS23LIhFsxJRK7OOJYeCc\ngy+CJMm3/vmjA2UthODB6+bedOFkpeaSJNN3NpU89MrWNzccq2npfeVnFw85FOPFIx88uPUf\nFPTmiRcRkBvGXbC3taTG0XLx6p+suezxPHu61y/+7vUdq7eVU4rvLJt09lmjE20mAE63b09J\n84vrDgsCd9XicbddPG3lpX+g/m658cPA/vtlV43iewhOn2ZKf2fsVD5rhbLId7j9f31n78pt\nZW5vINFmLMxKIPDVte1v6eoz6PiL5xX+7IZ58VYjr1NNdPjgyp8Yjbw9Wi7mU88RFUElEi55\nntwe3o48b/qoFwaq6UcIFkwOv3YnjEo2GQTFP0pNfkZ8VP7g7763WCfwb286riyDCcF3L5xy\n77dnYeTwHJlamKoUulczpWBgdScGYsnfgjkZOVcIY38QrFIo+6T61YEjvxOrXqH+TjLl93c/\n9UlDu5Mj5Nn7li2ZEY6mKapsu+0v6yWZPvyf7e/+9ooR3FiwEkueksIchnCcbQx4vVKXntgK\niSFZMfWm7nrfxovA6SB51VdMLUjRCVxAlHcdb+p1+eJU33ydDs/1j66RZJqbav/JdXMUNWXi\nqOQnv7+Erckzk6w/v2He5QtGyzIIAZ9sEs1Z1N0o1a/k0pcoUYrC+HuF8fdSX3tg331y98Bl\nHjW+CVCvr/ycy4bslvn4Q7LXO2Q38/QpavsCsa1dqcDLJyWo2/2i5PGLAKwm/cD7pCH2tZY0\nuNoA3Db50l/OuYm1f1K7584NfwawqnKrIqi8sO7wk2/vkWTKETJxVHK81dDS3VfV1FPV1PPW\nppJn770gap/0VJKdYreZ9c6wFBGGWR0e7qhQTGZvnrjiT4v+H+uwtnrH/61/FMC2pmJFUAHQ\n2OF6b7t1X+mc5s6JMoVBx+em+uZOLL5y4ZT4wgEsAb8gXr948+MfHihrATA5P/mSeaMtJl2X\nw7t6R3lVU8/Dr26raOx+6LsLls3Oz0uPq2npbet2X/PbVXdcelZOiv1oTcfzaw8F90kvmqK8\npYo7qq5a96s2d/eY+Oxzs2dwhFySf/aqyq13bfhLu6dHCVwF4HT7txY3HKvpaO5y9Ti9kkyN\nesFuMWSnWKcWpJ49OUsv8N6jQxud6bIz+biRJRlonEaqmnre2Vyyt7SltqU33mqwmfWSTNu6\n3WajblJe8oWz8y+aW0BEl3j8CalxHeH0fO6VJH4q0dlAQb0tUuMHgQObiGWUbtrDLuPUV9cX\nbzpcp6wtx+YkKiJKW7d7S1G9LyDNHJt++8XTbl4+Zc2O8pauvr+/v9+kFy5fMMZm1suUFlW2\n//LFzeWN3Sa98N/fXAZlL6bxI7mnCHIg/tw8ok8EFcXi+6i7gRhSuZSz+bzriM7OZ18mVv2H\nOssByI4S9V4hl7pYbtsCgE9fIsVPlnuOSPUrQQRhzO3ElAkqy10HAsUPK3uCAKSmjwOHfg7Z\nz+fdoJv6kNpATHaUyO3baV+t3HuEi58KALJPdlbC20r9PcHFOW8i+nhiziWWHBbYASpTdz11\nN1LRiYATnA68mRgSiSWPhPJmIAfkzn1yTxHtq6OBXsIZQEWAwJDEWQu45HnEVhj590ZZ/T9C\nhAE9pmjAAV8HVU14iM5OjClK4QRbSETpcUbXQPb6RcV4zWrS13jrFVutc7OnR3WbnzFpVeXW\nXl/fwfay2WkTAJCB8oY5Sz6rNejwM+/vGBkqw90LYDXn+2eoxIWq1HZ5HSmm+OreJgACx++/\n4d9CyMTSL4sZz18mU1oTingVxt/v33UbQKWm9XLXAS55HiSP1LYVkpftv8id+5TOfNq57HaB\not9QdzgJhks8Sxj7/WH+FBrfcGRKXzq6VnHkn5yUvzT3RNZ0ius1Afnwir+MiQ/Pvh7e9dJT\nB9+pc7auq955eeFCABDMuvkviUf+IDV9GDJsGKU76w/EksenX0C9bY6PNjT++CE2QhxwZeg4\nnKsCTDiyDbzAxU1UREQAck8RWDn6yP3KqQWpgzkxTM4PB9KpRfQoxmRHpDNmJlnffuiKx97c\n9fnBWlGmCyZn/eamBSMtecKGGjC+MDnOlDiUb4GaXFvaQ/NuBZWlpg/9u2+Xu0NVl4hArPk/\nSzuHz/8O26ihFBsO1KzbVXm0hnQ7JwCIsxwcnVVz3vTcyxeMiT1R709UTZRwuzUPnF6pck/s\noWI8VBRLn9FNexSA3HNEbt/G+lc09SgHUwtTuX6aSQyr8EGggUO/lBrWgNMJ4+/jsy8j+gQA\noLLcfVAs/btUv1J2luum/GaEw2p8s1AqzwO4bfKlapuEva3Hl73/IwBrq3eMSFAB4Ykpg7qj\nbQPJ/2fvO+PjqM6vnzt1u7ao994sy5J7b7iDbYzpxUCAFAIEQqiBQCCUhECAAAm9g6mmGGxs\nbDC4S+6S1bu0Wml7351y7/th1qu1bBKbNyT/X8L5NL4ezc5smbn3Oc85R5Ue104FRiy/js8p\nOcavKDvE7zktvcdlUAFA57FfU6rpH7l4jUKit+Ho/zqm3h4Jpf+ODJUfFSo/4r8DPxShQoLd\nUvOT8tBXQHF08jSkzQGgsL8dt+0mYRtlHs+MuZVKGvPPD3TK2NEwoJR15tbkXn3mSLcCTaEL\n51dcOD/WhUEE8Z+X/xCi9bqnD314984XKISuqznXyOsP2tsB4JqqFZ917awfaj7z41s+WfFw\nc1Pk3a+aAWD17LLbL5qaeIxpY7IS+QO59wOx8WGQw1TKDKbkZ5SuECiGhIfkoa1S27NS+3NM\n2Q04/8rLHvy0qcdJ09SDV885Z3ZpfJawub77ty9+s2V/j90TevHWZaf+tlCZi6HzlRPH6ezl\niYRKbUlamkk75B7dbVdbnJaWcHtlGWpKRebXB3tH7TalfLRLKUNT9/9k1rwZyQfb7ATD7Kq8\nSYUn1/meCqZWZJ5IqEwqPz25NAn1Sx0vAgBlmcSOG/HDAYqn886nzBMIjiJE72mxddu8ADBt\nTFYimwIA1UWpe/+2Br4XKMtE+XhCBekKgdEgiiNYmRwjKm223HssX4fIIB8L6DOUEl8rACRp\n+fPnlr/55dFAWPj5X7748y/mK4EEbn/ktme/Vk57zaKqrsHY0n1MfvKJE83KBAs1bsabUsuT\n8sBnwrfnI10B0hUgxJLIEPY2UclTTqqq+RH/I0AqvnTPF/F/ul5dO/TwkwCgnToh9+W/juyH\ncaRpZPHpXb/Z9crbAKAeW5F+zy0jR9OcUk4Jx9DKjcgbjNi9obiUQUHbgFupnY3JT17XHvP7\nOqd4tpEfWZouzZ/KUYyApXXt23435Yot+3seWbsHALKSdc/+eknpsVXWzsaBnz26MRQRb3xq\ny5d/vjDpdLpy/oVACMpzLXXNJ/F4iS/X435f0zKPe2JOSoutObdbY8Tni58ffuy9vaKEGZqq\nKkjW8Kw3GP36YO/WAz1Pr9v32LVnnLqU+xTx/PpDymN3yeTCv/zyjLhH2RVLx67+3br2Afeb\nXzaeP7e8Is/yp5/NW/PQ+oggDTgC97y8PfEgUyszLz6jEgC+HTh0ycb7/ELoorIFk9Mrle7v\nOdm1VZbCh+vfuGvn88Mh973TfrJ2a9PDb+0OR6UFE/LXLKoqzjKxDBUV5OZe52ubGr4+2Lt2\na9P9V84I3POn+EvILrc4OAQAlEbNFYzc1VNv+rl2xo9mYv8uEIlIIQCUGA0CAJiQL/d1f32w\nt6XPpeaYrBQ9Q1EESCAs9g75Uk2aiWXp584pr2sevPHpLbKMZ1fnPP3QufGZCSbkk53t+1ps\nu49a9RpuGrwg934AAOyUZ2POmQB4+BtgNHT2cjp7OQAAxT3wxk6lavbYtfPPmnacWEGWcSAi\nAgDH0Gqeef/eVY+8s2fDns77Xttx/+s7DBpe8ZtGCKZWZt55ybTyXIvU8aLU9BcAYKvuovMv\njF1u2AZEIqKPeBsBgPg7kLkWEM2N/1N0x2WjYsPpzMV09kph+BsAAESxk54S9/4Sexvl3vfk\n3veA0QERQY4CYqiUGcrkDTt2KOQKnbkkzqaITyyxGQAAIABJREFUjQ9BxAEA2LUfcJQEuhGj\nF5v+jO27KH0xnbsa6YtBSY0SPNhZhzteRapkpvBK4JOllifw4JdA83TO2UhfhjRZQAiJ2uXe\nrdh1AOmLmdJfghQU999ConY6/Qy65KeUoUxxLQMpKNt3YPsu7G+nc86mksrlvo/l4W0k0IP0\nhZQ2FzgTEEyiDuJpJFKAMlUzBWsIFuS2Z7FrP9IX05lLkSYTWANgGUSfPPwN8bcjbT6dfdZV\ny6q/qOuye0J/fncvx9GLJuQr6uTWftd9r+7Y2zzIMfQLtyzdGdmnvAmzskdby07LqFJytrb0\n7osRKnwKUqWRyHEpCMhQGi9SnKLlF5wanJE4oXKCQuUYoeIIe8tMsQyVbF1KnE0BAI5i0jTm\nwaCzy2clQBAgKnkKU/oLqfUZUIRK/Z/ELoEz0sXXKHp0ZBwDA+sBQHbsZo4RSHTGYiIFQI6K\nDQ+CFIBjzhuARRIaIFEHSEGCIwCAGB0wWqRKQ+r0Ecrt/z7kCJH8IEcBABgtYnT/m6Y9koy/\nPti7o6G/w+pR7IJ1ai4n1TCpPCP+Czpd3Lr9mRcb1ierk5xhH01Rby753Xe5cernzuDycgCA\nLz2OXrUGHJ927gCAeTm1iWwKAFxSvviJA+8TIM8cXhcjVAAQa7Bm3tmLL6XDXRKll1V52SFj\nYRJhax4AANzQbDx/ZfwIga93SMMOAFDXVB33uggAgDKPjxMqiaDMNXHqEQAmlX3n0rImIeQ8\nK1lv0HC+E7ph0s3aE7mNTIuueJbHMgkRgialG/9BFMo/hjJjVFxSEzGKwjkVEMEj1t+AXfsB\ngDJPpCwTgeZJsBfbvpTaX5B73mMnP0OZxnkCkeue2Ly3eRAAspL1s6pzGJpqH3BvPdCz9UDP\n8+sPvXDL0vz003AbQ+oMxJlOTF5B+mKgWIVQoZIq4iyX3LcOGC2lL5Van0pckOJjm9S/4s6E\nXfuUWyids4opjNXBSaAT+9sAgM6/KLYfwf8Fis8f8cNh/bFooqX5UxLHx6eWmXi9O+r/omeP\ngCWOOo3qK9LmnYRQ0ebEe2tCUtzy67iVrJrlESACRLH8ilsR7m0+zoHQG4wOOPwAwDF0quk0\nMtUMGi7FqFFW5aMQ13aPhNIfT/Z8j66UH/Ej/i/jByFUSNgmbL+YiD4qqZKd8hziEoSoWJR7\n3iE4Snytsq5iy4GeA21DA45AXpoh3azVqTkKIX9YGHaHbK5AbpphUlnGpLI07NqHPUcgYgd1\nOmINsQY3WQA5TEIDSJ2GTDVOX+xa/nHfh+z2RDu74/+03f+o0NULAOYrLtLNjjEiiKKFmlIt\nq35szvWpbEqppigiyPH56Oza6XtcB9Rqus3dP7u6dsH4/C0Hujfu7cxPT5pSkWkxqADAE4h+\ne6Tvw29aWYa6bFHVRZNo8cjvgWAqfT438cn4qyNdIZUyjcm7gGABIWbb4f6mHicALJyQP8ru\nZuHE/IUT87/HZ0EZx1JJFSPOpwoYHZ2+UKTujg/QFLpgXsWTH9aP+vNz55SPGlkwPv9EQmVu\nTS6cgL8d/ujOHc/yNBuVxVSX6WPjQ+Xm78mpzK3J/cv7daMGp485vb5y7DmizMao5BHqS7Z9\niYcTjIMQGvZfrGzmpP7ziD9MSHu/u2PQE1x6njBzKQDQmlTd7o68NEN5roWhRyZeVMp0xcRj\nZESp9VA8QGxNTmcsGSFUEkBnr4zbtd160dT2Ac+eJuu+VtuCm98uyDBSFOq0eiQZA8CZU4su\nXTim2+ZFCAiBA+1DkowTT0OQ5Nc3NcoY6zXcRfMrEZ/MVt/HjrkTexut9kOSFJaIrDPPTJs4\nC/HJ2HUAcOw5TcI2EuolEQfIQaC1QNEg+oHiEJuENNlIV/Afm2USGbv2YfdhEhoAIsWKOwiB\nHEGqNJRUTqfMAOY/aev0bwXB2NdMvM0kMggAiDUCzYEsENEDgJA6gzKNQ7pCTMiBtqH6Flu3\nzRuMiCadSqNifCEhEBKMOlVRlnF2dc6prpEoSjVm5C4Rqo+tVymtJnH81HH+3PK/rttHCNz7\nyvZHfzE/3v7WN+y79IFP3f6IUafa+tiFhxzt+YZ0A6ednH6c/7ue05xVOGP/cAsmpMnV/fz6\nGNlw/09mlyb0rE0fk/WXX57ROegBAF8o+p8iVABgXFHqSQmV6mMKPMX9HwCmpR9HqKRpzDn6\n1D7/8EF7u18Ibakb+OPbuwGgMi/56RsXxbPfW/pcax5a7/ZHrn9y88Y/nf+vtTj7aEebsnHL\nBZPphHWtmmM+uG+VcCzkEABqilNfuf3M25+L8b5xLJta9OBVs2maAoBkddInKx5m5XA5F4Xw\nUEzHCQBJuguWXudi08KI90ZCD7y+S5BkjqH/ev0COn5z00KqSZPIGKW8/1J82/32h7b7/gwA\nfFlx/lsJYRUAzhfeiG/jQFByewCA4jkmdaQXVTdvZmLc7o84RRDRL/e+j517IepChlKkzog5\nZgg+HOgEyU8ljaFzz3twnfW1TQ0AcNdl09csqko8gssfsToCABARpM92d8gyBoA1i6oS+zwo\nhM6eUXL2jFjfq9QVY0ewaz9lqkl8MJGwTWp5EuQonbXssoW39gz5DrYN/e3jA95gtDjLpFVx\nMsY2V/Dz3R0N3Y7iLOOvVk+szEtONWke+fm8ey+f3tTeNuxwCWE3z6v0+qTSwsLU5GMy2bj5\nDDNyYrJ1AwgeIgfl3g8Ai3TmEiWUHulL+OmviYfuxt5GAABE0VlnsmPvIVEHnbEYmcYBAOKT\nuZlvyn2fyNYNxN8KWATWTJlq6KIrEa2WWp5CxnFM7jmI0cvWDcLea+n0+chQihgtpS8mbBJ2\n1gHNkagd5JDU8ZKSn8RU/TbuLCp1vBLzvqc5EnHIQ1tJqF/u+wgAuClvU8axibshPpXOWAwA\nJNCBHbuViA66+Kp4U5TU8pQSRUACHSBHMKuX+z+Re94BAG7ayyOBH73vA6JQeuxNw8EeEuqT\nh7YBkei0eVTmYsQmKbsRwUNC/di5F+w7SdiaVfPgJw+sfm79oQ17Om79+1e3ITBoeEGUw4Kk\nUbHLphZdu6K2NMec+qVxIXcOAKTX2T37jvOiWSEJ07hzAEDrHWkoocw1svWLxN0oQ/kIoRI9\nRqiwo0sMcYolvs8/hetY5rxFNbqWGidUnBGvNxpU+lvzDaOd03P0aYNBZ0QShoKudK0FAJjS\na5EqTWp5gkRjDadIX8LVPgyIkTVvI87C5F9Mgj1yz7vS0T9jZz2dOgtpMgHLJNQv962LC6Cx\n64DU8iR2H6SSp9HZK5E2m2KTgGAiuImvWe77GHFGOnt53CsvKspufyQiSIGwqOJonqV1au6f\nBvudHDhKgv1EcIMcInIYABBjAFqFVClInRn/8ZLIMAn1kqgT4s54iAHWgFQplK4QGB2JuuTu\nN7BjD5GClGUKpctVlEZEChNvIwkNoKQKJvfck8qS/ivR0GW/6ektPUM+nqUXTSqYUZWNEPTb\n/V/Udb33dfMfDerHrp0/7XjXl3+KR/a99WLDei2r/mj5Qx93bH9k31tXbn7okxUPT0iNvas2\nV3Ddt611LYO9Q75ko1avH0dREP7KZvvgnVSjprYk7dw55RkpxtYr3gYAFY7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/Z8trFIUHJuT59YcefXcvAFw4v8IXij7+fj0ALJlc+OT1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93VOBCKSg9ePXtsSbF5bIWiKQlGhG2H+kJR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\n2GvkVA+fVeqxti+dPX+vm1ctmXfXTVefNGvq4JI8l8POc7zN6SksGzL9m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AirxKEvW+ph1kqMDJeOrtw4+/fsBiNn3qA4uv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\nY6RUAAAgAElEQVSbHxWqLq8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aOEii8YW7+n7eylk57ffERSO1eoaMGCqYUWkyEa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J0kheWIrKtBNaRU15PDZSzDqiaRDCq0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\nkQ8uOxxWAcCQMHnRRb26ssrct59OLRh944PP/7D8yejjP3PaZSX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UHumXJCbznP4dkn9Rs7SMmDWlBhbLsZAnQ2CKh0e56A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i1iqxDS5QbEPoe4XQ9woich/+2L3/JfKK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\n5yWBXhIaABiql6eVSBGP5CdQ8OR8hPBIogE4KrhBgn3Ytg276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JLvlHKJWafIiNdPzI2LnmBwPO53+ge9QSHjWa2QmLSKY4UHwxzvcAU8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\nM+jR8aJFZ9L+y3g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T1RQAYMqeh0jeNLHsjJGT1bbn1fYXwFShcLE05b8guY7kUjkcDid7qHej2rMirVPtfkcc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\nPmahS5AVQQ5IHs7tjqQjXcnBhJat8pa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6m/t1y+0P4GNIsCAEiL2gqrvDC+cmOhkrASjvJEJPpqpZds5TnEGuXj/GmMTU5o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UJHQRNLkK0s5e3InIeMqfdPBoMxLAKR+P0vb/tsTxOH4Izp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JtCdW7nnwzZ0AsGxOxd9uOZcGDXl8kevueafJ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\nsSx2TKtxMIj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BSzdUGOVHLp98+1kHvHrLscYklfXFNYGgWusMrT4Z\n0C/6bVpuhl0vkAABALqIqmnGDZsRWTFU1rm3VYZSMqQmW1+5+ZhP7j1pp0HZxmejctdETShs\nbX5hVF4Hsn4BXa5xOl10jNM8dFrSkXOSjpxjyjtQRERTm2d71gXLZ4vKHJdOMR7VReS+C/+y\n5s1Lbzp9P6Nm1WYen3szAipIKCNObo01S1pEIragz0nK0FeoiEiVp6v2fjd2SR05qEk6skHh\nSZR1ziY5KPS5Nrv0G6b/yB4qALqZw+0rrgo99047ZIw+43vC6P5T9gptUrW+uEZEtIA+uCli\nTmnxPAa16g+jrCTlmYdOU+yNs2bU6vmRjRt8Loe/McxMQAXo49ZsDb0j23lwzmEThomI2aRc\nc9LeeqU/oG4srasP30rlZkQPR+OG5d574aR7L5x05qHjuqnHANDHVLprfWpoHfO2ZitUVmxq\nrLn6/yZMHF+4y7DcR6+YooTTQ+g3loY4IyVxxl0AJEqMqcwR1JqFmq+VmcFBd4ubaCJ+xr3x\nXqP7T5s8RkQuPmb3XYeHsnBHbdKMXoaAChJJa5wlnRZPeyOgkp2cnpUU+kgHtlGJhy8QbHCF\nwu+Dm6ahyM1M0ad7u7yNG/QF1KAe2hmZWZhktkrTTfkAoBtsLKkzsrIeu/9Io/6Y/ULlotI6\nkXCGXIs9KmFXc2r1olDJkmI76E3r+LtsB71jbHml1S6PbFzqqmr6I2Mg0KetLw49Fh69X+OW\ncvuMGZCZlqSXN5XWGem8stOjM6/mZtpPmzz2tMljyX4AAF0kMojSfIXK+m2heInFbDr54J31\n8tih/fYcFcoatG5bk9d/URuitDa/MOoxmSk4QJcLhNO6xMxPUDXPKCtJueZhZykpgxqPGs+G\naL+KOpfxjvGkiaP1gprM8OMAACAASURBVKLIyX8JlTeWdNVkcWwP2EMFCRXe9koscQVUqj11\nIpJkttotSVlJba1Q0TT5bt7GGQs3WSymSeMHH7H3cKWd26zUOUJTJtNTbCnJ1shDZpNy7hG7\nuryByPpKd62qaSKSl5LZz55Z7Kgsc/MyEUC32hLe/zk9xWZMdRER43Vkg8vX4PLZVJ+IKGZ7\nzBNqdaGQiWXo6UpKoYgoyfnmEecGVk0XEc2xPrJxSfTjcatj4Ptrfnzoz7dEZHzuyDeOvCP2\nLwag6229+raYm6NkHDUlzrN5fAFjw/n9xzbugGo2KbuPzP958RYR2VbpcHr8en1kFn4AQPfY\n5mjMzVjvczr87jRr4/1hUWnoxnLC6P6ZqUlG/UG7Fi5YWyYiWyqaTG3UtxTNTEqt8zol1goV\ni8kcUIPCFByg64XzE4hiibFfUeMaFFOS7YBXlbThmvdi36yT9CzQWv3KruxmL7e5rHHAnLhb\noVHef1woZFXn9EZtTIXehIAKEioYCloo5uhpiS2qcteLSHZyhohkGwEVd/QKFU2TO175+cOZ\noeT+H8xYdfjew6ZfdZjF3I4lVvXh0HG/jBbeOd521gFRNeXhHVNykzNzkzOLHZWVbsLLALrV\ntvDOT7sMyzX2TRGR/jmpmalJepbCilrXQH3sjTXwap4yYwND8+ATjXrzwGP0gIrq3BzZPmqC\nYUnr8w1XVBUV1ZeKiDtAHl6gdyqudOgL5sxm07hhuZGHdhqUrQdUKutc7vBi3zS7tdk5AABd\nK2pVSomzcqeswcaPxgYqUSsFdxkeGtXLqp2R9fpi5V37jfi1eKm0vkJFn3MzOnvwiqoiYQ8V\noBvoU5kVi5hivKzX6kIhE/PgqUracNHTPo84L7DqSRFRGzZ0bT97hZWbqsprnTsV5gzs12Ti\neHFl6FG9IDs18tDwAZl2m8XtC4hIea2LgEpvRUAFiaRpoWmJYkpqs2GInvIrJzldRCL2UIkO\nqLz1/TIjmqL7fl7RYx/8ccsZ+8ffNyMHReRknDYYbxLzUrLz7FkSnqEDAN3GeKzdeXB0etwh\nBRn6BtE1Ds9A1S8iosR4fak5QnfMSsogJa0xY4+SMkhJKdRcWyXg1PwNijU0GhsTDHPtmZXu\nuqgMYJE2NZTqhXJXjTvgtVviGmYBdJ6qaX+sLNlQUpuVmjRxt8EZ7X9myzj2cNuw0Os2zeOt\nfutDvZx95smmlNAcFPv4cctqQsPR4Lz0ZFuTJ4ghBaF0E7UOryahNIWpyQRUAKC7lTTdN2Wb\no0lApTg8U2fs0CYbWY8YENoAtareHVmvR0oGp+enWe0Ov7u1DNh6BGV01uD1tdu8QX8ba5oB\nJIaen8ASIz+B5q/TPOV62Vw41ag3DzpGD6hori0imkg707/0GW5v4G/P/DBj4WYRMSnKmYeN\n+/vZBxrTHEvD98ZRI6pJUYYUZKzeUi0iVfXukQPj2mEaOxwCKkgo1QioxPUUrWf30temGCtU\novZQqXN6n/zvvOaffe3bZadNHjusf2acXXOFc1Ck2eN612BMwMm3Z+Xas0Sk1uvwBn1JZsLL\nALqJkWCn+VjXPydVD6g43X7RgiIiphh/0zXnFr1gytkr6pApY+ega6uIiK9KjICKs0pEFFF2\n7Tdi5taFbTweb6ovC32FaFsd5ZGP7gC6Tq3Dc9X07/9YVaL/mJ5iu++ivxy974i2PxUl46gp\nRuKvQFWNEVDJvfx8S17j82HlnHV6YUh+dLbuguxQugmPL6CEU7Im2XjKAIDutq3pCpWoBSsV\ntaEbyxFNX/ANzE1TFNE0MfYDEBF3wOvwu0Ukz56VZ89y+N0t5vIydh7Ns2eHEmWzQgXoanp+\ngljzmDVHkV5QbNmmrPFGvWIfGJpOF/RovlpjQ01Eufn5GXo0RURUTXvr++UmRbnjnAP1GmNE\nHd7sUX1QbroeUIkcVNHL8KiDhNJf6kmT93rBbV+qpTMa2ygm616PiIgmWo2nQURy9JRfyS1v\nSv/hzFUtjkHBoPrm98vvDI9lMXl8ob7FmYPCWI+Sa8/KtWeGK2sHp+fH+Y0A0EnVDR690D8n\nemOq3Ubkl1Q5kqyWtGSz6FPCI3ak1+rX+H47P7K9dff7NHforauSMSbqbPp+KiKi+eqUcCZe\nPYLSz54xMC1XRCrddT41YGspbFNUX2KUN9WXEVABukFQ1a6c/v2fqxr/72tw+a5/5sfM1KQD\ndxnUxgc7psYRGo4KcqKzde8xqmD61YeJSP+c1De/C23UZLW0Iy8rACAh9AStGbbUep9TmuZr\nDQTVelcoQXdhbnrkp2wWc1Zack2Dx+sPqppmUhRpslI5K9eetbG+pNLdZMt6XUXEzqN59qxi\nR2W5q4VmABIolBsmZr4vV2g6nZK9e+SjokROp/NWCwGVlsxavPnbPzdGVb71/fKpB+00fkSe\niFSHl/QNykuPapafnaIXPL5AF3cTPYaAChJK08KlxjWDWv3qYMm3jW0Uix7QqPe6/GpAmq1Q\nqWy6Kf3Hv6xp7du+/n397WcdELmvQBt8gVBAJTm+KZPGCpW8iIBKmauagAqAblPvDIWT8zKj\nF3Rfdvwelx2/h4iIpnrW6nURA68WNLZLCdf4NW9on1JT6rCosylpw0IxFU01KktcVSKSZ8/O\nt2eJiCZauau6MC16DKz3OWu9DuPHTfWlcf9+ADrug5krI6MpuqCq/f2ln799ZJrNYk7s1+mb\nNolIXmZK1KGc9GRjWczr3yzVC9b2bHQHAEgIfVP6PfN3+nnrYk20bREZwGocHv1hPT3FZk+K\nfiL+9N6TAkE1IyXJFF5oaCT4KkjJzkvJklb2UDFiNvn2HD2vQ7mbFSpAF1MDInHkJwhPpzO1\nMJ0unO7VX0vCrxY99/mi5pWqpv3ni4XPXHuEiNSG742NtdqG4w4YVZiXLiI7FRKs6rUIqCCx\n1NhNwio9oakr+u4pqVa7zWTxqYGqiL3fN5XVr9sWum8b2C/t7vMnJtssD7/7+/KiShGpqncv\n31ix28i4IhyBYKhvcb5i0G8Ek8y2dFuKfmsorW/EBwBdoSG8+VNWehsbzsc78Gre0POtkjIw\n6pB5yKnmIadGVeopv/JTsvLsoRvBcldt84CKke8r9GMDARWgywWD6vMtPeaJyLbKhq9+W/9/\nk0b3u/DMjGMP1ytVh6Pkzof08oB/3mLKCM1isfaPd5qIM5w6NTO1remQQTU0t8ZsIqACAN1K\nE63YWSUiQ9P7Zyatq/U6IlN+OcJZH/pltLDvwoB+0YuhI1eo5NkzRcQd8Db4XOm2JmF1I8FX\nfkqWPgWnzuv0BHzJFhJlA11GnwMXmZ/Audk399LIJtZxN2ue0AigpA2LPkNybqgQcHVRH3do\nG4prF6xp+al2xsLNtQ5PVlpyg7PVQXXfMQP2HTOga7uInkZABT3GSO2VFV6bkpWUXu6uiUz5\n9fuKbXpBUeTJqw7bY1S+iLx449FH3PS+vsn8/DWlcQZUVOMJ3xxXAF7PdaNvR5/XmPKL6TYA\nuo+x+VMHNppugS88giXlxdNcf5AuSMnJTwkFlUudLexLHxVBiYqvAOgKs5dtNfYWtlpMe+3U\nv6TKsbk8dAf1yew1/zdptH3P8cbjXaCqRiQUUEmbPDFyc5Q4Ne5F1+ZwZKxVVpjuCADdq9rT\n4A36RKQgJacgJafW6yh2VhhHjbh4RmqMfRd0jZESe7Yxv7DUVR0VUDHiLnn27LzGaYjVQzP6\nd/w3ARCDfr8VcbOl+jU9hZfRIugSX+jZTbFHT6ezDDnF3P8wEVGS2n1P2Bd88+eG1g4FgurM\nxVtOPGgnR3juY2Ie1bGjYe4YEqsd/6Kq3KHH/pzkcEAlOU1EqiICKkvWh24B99l5gB5NEZHc\nTPsJB47Sy8uKmuyz14agGprEbYrvEV/fQ0V/jdgvmRUqAHqAO5x0NTW5jbu0eF9bav4GERHF\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7QwevKeQ/59zeE2i9nl8V/2+LdzVxaL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\nZtQZ8eYZswA81bxB2fS9HX+acG1PNr/zqxW3gHB8+fVSy0OQo+K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MM2aV5lozLXpPIAogFBWVaRaV0lyr4k4zFl9oHNcVHDNsCLuR4hCb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\nMMhJkh3G6T4AYKqvUAf+hiP7gdbxS15AlnoAGD9afoaPHgIAu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oePRt7bAEAoLMi94hJ9jKVu6vhOqtdHIdbSrEN8yVnE5ABAHKOeVM1QcZlzKh1FAERF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\nboZNof6DNHAIAEByIAkAAJkrsa2OBo+RoU2ce4tm9Ymz6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mPip7J1F7B1Z9t7vkemDGdff+H8auxhqvedM3fqn26Jv+a8ru8/tOmwZtee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\nRGc+gmgKAHSTeSXy7Jttc6wsN59d86rU+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cNhRpBp0brHqTiETHDg0RkzJ6aUxch/GnZsDpLH8v8ama+VEyWBSZcqUMFup1\n9EpARadFQAUgUUp2rzzVDhVpKxj0bszXEtj2bMTB4J7/aAaexeUMjzi+qmHLK799SUT9s4sf\nOeLqbuoihCCgknmURATZJn3sK/e3yfGS4f0j0rzK8RJraJUNEVlCy+vyDTn5BjkPrMVjH5rb\nbndLi3qHilG+rNljRUAFAHo30blHXhKpMakLfvKFh5HGRIKH+ZqJCSwoj3Q5XV6MuzGPnOqa\ny5sQcYrPHipIBVT8Vur2gAoThB3TT4x72YCnHso9+dhu6A8ApJy/Vfrfjns4OGOJlKiQ0xc2\ntrmUeTFlBbdkdEWhFFBpVq3U6WbMUy8lwJHwBZOZ3yrHuYnE/T9RwgEVJUGNUZ/Qi5I0majj\ntQNzSjUcLzCxBTVUADpgzt3Sjgoi0o6+ge83h4h0U57y/XiytHmC2bcTFaalb8oyxMnD21eb\nD9VKabZ5lHRehdGyRFzzu0NS1Ddmk59sfPmxpAkHcviy46jqTZLW6Ih+CrqJKGKXoUQ7/NKD\n6cB+T5s74COiImNuxFJLiRJULjLmSQsuLV6byBjPcUQkiPJ+Ig2PXdcAYQ633+r0lhVmK1t+\n1ZSoZL4hW5mdS3sNlUBNfdxrNEUFfFaUhyQkSNjzXpT91kwM7n5LN/mhiMO/Nm37omo5EZVm\nFSCg0v0QUMk8qoBKrNXBLm9AKk3PceFCeZKS/CwNzwki86p2JUsjIbPOZNDoCpSAijfyhVDJ\nCp1vyCkIbWSxeGzD8wYc8G8EANDzKUVN+MJDiFfFszktnztKbNtITGT+VumFlng98bEe0cwj\nLwkPFwtV7meSH6csYOu5r56MuVb+Gvcqw6jh2qL0TI4AQIJYqFJxlMpPhhL9UR9J7aYdjVKj\ntCCrIMeovmxImTwgbE1NQeZEiC2rlbZ27F+1wy8jJvjX3CQ2LSEi0bqJmJBg+mll+k+f2Hpq\nafd2oTFXw/H5hmyL196MGioAHYRDnlqzstOXMxRqBpwq7P2IiETn7nQFVJpCyxBHDGi3DFGp\nldLm8Lq8AamdkxVnUWMiRI9HaIlfJV5XUS46d0ttvmSW+hSfN444LbEg8+6XsjISEWdI/j/A\ny7//58/1vxHRHVMvuvPwizteoISQC4zy/IDARKvPIQVXWCg/GwIqABKb03f/m8u/+3WPyFiW\nUXfhseP+cu7UiCFHeIeKPjsvtHa5Nd01VHadPI+E6HWwFP0fvS/vjJO7pz+ZQpOby/xySJ4F\ngqLHQ0Sk4TXm8Kp0Xq8jImKiUDs/6k2Ehu91E+5Vh9WJqM7ZLDWa3VafEDBokECiWyGgknmk\nLNUUb92c1Sm/0xbmmCKezhqeu++PszQ8x6uGNVJARdp0omw9aenwQijtUMnWmQwaXTgzGN4b\nAaC3CxdtzhsfcYozD6G2jUREfisJXiLitHE27TG/JfSzAyPPmfpxulwiIhY4uC6nEGNs3+U3\nxb0MG1kAej7VTFxRjMv2h3afDO4Xuf1OKSegTDh2P7FtvdTgckZKNQaI0+gm3u/b/xOxIAle\n5tzN5YxM5FbKRhyNJqHpP2kysciUS0SFxlyL196MHSoAHTBp9y2RpuRI0oTXL2v6zZYCKsxd\nRzQ1DR1j1GKTn29Dyto930ryszQaXhBEtzfg9oUCKvGyRCTCuWxV3V/uiXvZmE1LmTu0oCd/\nUrtzvI7LqmCuavK1huuj6HIPvm8Rqu1yNH2vozHqBeEMFsY8ZRrB4rVJcwWCKD9R+eSmQgPI\nTB5/8JJHv966Vy565PYGXvt6Y1V920s3n6j+M9Kq2qGSpTPqea1fDKZ9hwocmJHLFyht2+df\n19/9CBEZR40Y+tlbEVeKrWuZtyn6XYIu0bKaLz1afazWKa/RZMTqXS0R6YUg1RBQyTwBJaCi\ni/WvzxZK5xW1dv2Fx46LOCJVm5fGPQWdB1SkVXhFpjwiKgxtZEHtTQDo9VRlBiKzl3ImeVk3\nCziZ6CMi0hgpJuYL5dgxlkWc0g79g5IjGwAg5UKJCilmosK20Eqd/kWRZedHDCg4edowIho3\nOFZIJqWYVa6+oB18LnFy9gzOWMqXzhKblhKR6KzWJBZQUcpdJ7ieWtqPUmTMo9BAGim/ADpS\nUvDxJTPVx7n8iXIrVFa9m7m8/kBQJCKNhi/Jb5fSiue4meP6W51eIs7rk9/BzcZuXQIsjz+1\nZs4UOVOmGXQOc+0jXscEOSDEaXMoqXxCoMEl/3tRIisRVCm/cgsN4QWXI/M7rBkC6POe+3SN\nEk1R/Lh+338Xbb3ouPCiPasvHFAholyDucVjs6Z7h0qC6v56P4lxNrIUXHRu1tRUZUrMXOL+\n5Uqb7zdbO/h8oXGRsO8T+WzLqoiASp2zRdVuRkClmyGgknmCoUQEWk2UZIsKl0deRBO3dr3E\n4rETkTQGUkZCHYtqynEXg7SRRRkw4b0RAHo7ZU9JVv+IM3y/OVqtmaTIihggIuLjPXhDS8LJ\nUJzMTgIAdFVoaTOni4yUqNmc8kqdiAlHIhoxoODZG45LRdcSxUTRuUdq8mXtesIXTZcCKsou\nwwRuJo+0ucTWU7d4rURUZJR3qBBRq9ceFAUtjwrMAGHMXSc1uPx21eM4fQGnz2d+K/O3mmdN\nH/rJG0wQRZeLiPY/9rx3204iKvj9WTknzOZ4DZ+dRUSU1ORRyjLEwhxjx10Ub9x+itRYtrFG\naiRYXSk5gk65OEpWlPTa2uGXSQ2xdU3oUJQaJwdjn6NJDCXt2ttJQEVJCV5ozFXmB7BRD6Cj\nZqv7ne+3RD31whfrzps9Rkkto9RQkSrS5+mzWzy2TNmh4vjuRyYIsa/Jnj2LEFDpQLTID3Mu\nZ6R+6jPEafnSo8jfJjQuIiKxbVPE9coOFVKl/4Jug4BK5lFe82InIlBKreQmkOY1KAo2v5NC\ne1PCO1Q6bD2RM4OZwqvwCCm/AKAPiLGnhM+fyCvrK8UgEREf569XFpTC1Rynj7UkHAAg1ZgQ\nKnwSM1ehM7RSJz+xlTrdiXkaSPQREZc1IKISDJ8b2pWSmsXvDr/bG/RTaFQshVVExlp99lJT\nQZwfBuhLmLeRiIjj+eyhkeeM/chvpYBTk5ujGT9GOazJl1829UMGmmcenqKOKQ+3guxY24sD\noWSAel33xUpZQEnJWBLrOkH+Fbi4C3q6SB1EaXS1eoN+ozZybkGKnRi1epPWEE75FZof4Eie\nsmAE0Nd9tGSbksA/QovNs3jd3pOmDZM+toV3qORQaJ9Kx+XO0KuIAdFeKTW1I68hTp5P0Iy8\nRg6ohDJnSnyCX1oWL6l1IKDS3RBQyTzKIpHY6+a8ATmgkpXAruRWn126baG8vC56yi+/GHT4\n3RTaoRIjMxgAQC/DAqE3wxhlBpgovTByXPivV+baG/jtYfVV2lHXyrXrtVkJFkkmJgo1nwn1\n3zJvM2cs1fQ/STPwbCWtTRJxHFd0ZbjiqHfrDteKX4hIV1aae9oJynHDsMFJ/2oASA8loBIz\nV6HLK5fTzM3qkQEVIiLic8dEnOJM8rJuFujyuk7G4k8AKquwpZRfUlJcImpx2xBQAQhjglSu\niTOWdtzFqz/sSSa4idJTY0Mp/pRrjrUMUakFEjtLRIT97rZWr90r+CuyS4tNXV5DwwLyZBmn\nj/k8Uaru8Umo76JWbQ/v7WPE9jmaRhVEJvKSlmCq0x6Sal2mUrRVTOCJCtC7fbOqSmpkm/TP\nXH/s5OGlyzfV3PXqUl9AIKJF0QMq2RTap2L3uwQmalLwAgg9geisknNdaLM0/eYox/m8cVzW\nAOauo6CT+Vo5Q6F0vNbZzFShauxQ6X4IqPRa/kBCpVYkqo26OURk0OhNWoMn6LO0j5RYPDbp\nT6z0ulhgyOGIY8QsqKECAL1ewElExOtjzjmG0sWqAt4s6BJbVqovYoPnkeChBGrXh+7q96/5\ni7h/mfzjzt1iyyqh4Qf94c8l/c2ZeL70r39WPrW9/6kcUBlQrj4edx83AGQMUVnaHOt54vWn\np35AIsI1rsyDIk5x5oGGE1cQEccn2u0uFU5W0t5KQ+gC1BcEiIb524iJRBSxh0zCZQ9JY71y\nn19ehmg2xnoGikqWiK4kHLto4QNrmrYT0W1TLrx7Wtcr5AXl4iiki1kcRZR/BfUOabFpqdC0\nRH2VbuK9ia7jCdlrb1ceudre0DGgIs0YFBqjL7hU/nEJQpyaCgC9W1Oba2ddm9S+/ffTj540\nkIhOmzmirsX55Ee/ENHKrXXKxVLKLz2vNWkNRJSnzyYikTGbz6mELVOnutHm9AT6FWaV5LXL\nImieMZVCT8Lg/hZf1R4i4rPNponhCs3akrSV08t0TMleW3Q4adqtPODzJwnuOiJivv3hgEr7\nLSm1CKh0OwRUMo+yMSX2urkES61IWlSZT6VGgTHH4/RFvA02t39p1PKaHH2W3e9CjlQA6P0E\nN8WrMSBNFksMmZcAACAASURBVCSCCX6i+LXrJYGtjyvRFIXY/FOg8ind+DsT/Mbk4jSasZUr\nlI9t73/a+NCTRJQ1ZfLgd19KS5cA4ACxUHxUtbUusPFe5gnPo/FFU/zBUVLbZOhxrw8slM6r\nY9FmIo7TdW3qgeflkbMgJrJDJVSNOTIdrjw2bn37A8+GzbFvYpwwtuiKi7rUSYAMEy4d1+Pm\n2nyhZYixH24Htr1il7Uu1KhVH889cU6uahy174q/SOtXSm+7vujyC5XjyvCP05hi9i3UO9XS\nddG2Rdj3sfoy3YS7uhpQkXao8Bwn7S/Z64hSRkWaCojcoRJ6BmpCcxEBBFSgb1u/Ux5W5Wcb\nzzl6tHL8/DljnvnkV0Fk+9vczVa3VKlO2qGSZ5BfPKUdKtLxlAZUft3WcP+by6vq5T+/08f2\nv/+SWSMHyDvkBr32jHKl9dMFDff+k4j0QwYOeuPZ1HWp72Bu+a8JPn9SxClOSZXpa1MOKltS\npNXwCKh0vx73RgRxKdtmYwdUlEU0Wm0CARVvZECl0JBb72yJ2KESjruEqtYXGnPsfhdSfgFA\nr8cEDxGRJknVPqXkDAnsLxFtlcLeD6OeEqr/qx3ye848JDldAoC+KRwJDi+7Fls3MFe18pHT\n5QaDI6S2LoGBZXcLlRkgfRLmapWlSMpu7xhUi43CNVTUxz0bt9gXLo59ExbK0wvQW7GgS2p0\nNcDZDQLBUHGUxB5uiSQDlOx3t1l9Tqm9s31AJUFMVHJ5hffYiU1LhKal6sv44hkHcPNEVNsb\niWh80dDfWnZThw0rJJVi9bkotOAyT5+t4XiBicr8gE4JqAQRUIE+rXKfRWocM3mgeiiVn20c\nM6hoS3ULEVU32qSAilQuJT8UUFEabV4npaz+5ootdVc98a36j+rqyvrzH/jy/Xt/N2ZQF8ZX\nWTOnUiiAio0siWMeOWLN5QyPOMUXTdMMaiYiMoTTPyoRlENKRq5s2KwuUA/dAwGVzKMNBVT8\ngViDksSHeqRO+RXKVCBt13UGPD7Bb9DIU35Kaq9CkxJQya22N0qpwLg05b0FAOgO0jutJmbx\ngARz2jImz2Cq1gky557A+jvUV2nH3sIXzxCq3pAv1hi0o2/kC6cw68bAtuco6CImBKs/SNcm\nlYQw5lq1Ju5VhlHDtUWF3dAdAIgmoRGjkv5ew/e4gAoLyFOWnD4Jc7XKNEci03/KKuziyNXZ\nWGwEoCKEUlclmOy0G4UfbjHzOvBcl4urq4Mou2y1ImN8l1IKkirg3W7rydaIrSd88fSu3TZh\n0paUCUXDqu2NDr9bXVJFYvHKKcELjXlExHNcviHb4rUr6zWVMJUvgRA1QC9W3SD/oThsVGTm\nQyWg0uqQy9pZvU5S7VDJ1Ss7VFJVl97u9t/60uKOIx+nx3/Li4sXPHJO7Cek2qBXn1ba2MiS\nOOaTAyRcVkXEKb5oKl80NeKgtEPFoNGPKxqysmGzw+92+N05+iSt/oQEIKCSeXRaeQIuEExo\nUJJIZMXilZ/LBcbw1hP5lMfeP7tYaje7Q6vwDLnq66Vi9cpTHgCgt2FiKASiqjbv3S/seU99\nlWbAKfKpOLm/5LPqODQTPKJta7vvDNgp6BQaF0kfdZMf1vQ/mYgofwJnGuD/9XoiEpt+pJ4c\nUBHZvstvinvVgKcezD35uG7oDgAcMGU42dX5wO4g7SCk5MzV6kMj7USm/1rCKb9ylf8mVaBF\nV9HfOH4M8/pEv49EFqiTpyN15f1Iq+H1Bs5o0A0acPDdBujRpEynRByfULLT7iQkVhxFOSsm\nkAxQssNao7S9QX+tc/+gnCglZGJK7LuUcEsXC783W90iY6X55qgP9haPzeF3E1FFdmlFdmll\na7W0YSXiGqlRFJ5GyLV47S3uUEAlVM9VqVUD0Dc1tMqLP0b0L4g49bdLZt154QwiyjLIe9Gs\nkSm/lB0qjhR1741vNrXY5NGUUa8tyc+qtzil0ke76toWrKo6c9bIFH01yHzyHibOUJrI5VJA\npcxc2N9cpBwZUzg4Rb2DjhBQyTwGnfya5435mqcJZwaLf0+LRw6oKHsJzTo5T2uL16YEVJSV\nJsrrorKjpcVjQ0AFAHqvUAhEtUKQ+VqCVa+rL+LyJxDHExOJJeelUWhZTaKfiPiCyXI0hYiI\n+H6z+byxoq2SueuYz8L1vIzkAJAxuphSv4vzdd1CVOZqVZsIg051bJvjtKRNaNWeUS//A/H4\nArGvJFVqL6l+QIEhMuVX6S1/olv+JLUFu2PH9BOl9uD3XtaVd3VqFSBTsdAfUtKEk50yd42S\nMl7CF8+gBHIeBC2tvh1VcS8zz5iaxAhwl5IBSiLqpuy01nY5oKI8n2M+ebnwZYn27eOl257/\nfF2DxUlExXmmi44bf/Vph0RkdFTCJ4Ny+1Vkl1S2Vtc4IlPKRKQ9pNCCS4vXJu3IMYSeqE5v\n/CcqQC9mscu7T8qLIktyGvVaoz48N+sMePxikKKm/PKlJKAiCOKHP1ZK7cnDS1++5cSiXFO9\nxXnNkwu317QS0WfLtiOgkmosYCci4nhOn5/I9TXO/UTU31xcliVPBdS7LAiodCcEVDKP8qh1\nxxyUhMd8CWxkUZbRTXn/ig6nwikLWkMBlVUNWzY1V/nFYL2rRbnDsLz+8Xt/oJjPIratJ08j\nE4OcoYjPG8Pl4IEOAN0l4WrzxOmI+ZSVmAf7ta3rpYam4vSIU3zxTNFWSUTMXZNIQIUFgztn\nnRr3svJ/3Jtz7FFd7ykAZCw+9DrAYg0slRp+Yg+MqCgxbNUmQt9PFzDnHuWjpuw43dRnKAHK\nSNvpiT/9Jw2hjVq9SWsgogJjjlS9uRkpvwDaUcZR4Sl7oeaL4M5X1BcZT92USAjE/cu6ulvu\nj3vZ2M0/kSb+3ZQvjL31RK/rwt41yc62WiLS81ppbnRnW82xA6ck+LOhvinP55grdRJ7jCue\n+vjXl+evVz622DzPfrpm/c6ml285UavK6rM3lOBrYHZpRU4pEdn9rjafoyC0pJLUmcND+S2k\nhsBEq89RaMzNMsh9c0V7or769Uab08dx9Nd50xLpOUDmsrt9UqMozxT7ylZv5HLnPCXlV2p2\nqKzb2WSxe4hIq+Gf+vPcolwTEfUvyv6/a2afee9nRPTr9kaXN2A26uLcCA5G0E1EpMlKMIt4\nnbOFiMrNRWWqHSop6xxEgYBK5jGFBiXKEzmqcGawRPIVeDt967OoTikvh7ctfzHyDil7b2Te\n/cEtjwqNP0RMaHK5o3QT7uYLIzMJAgCkE68j0UeCN/ZF0v+weJkcRMcu+QcKI9/AtSOv1gye\nR0ScoSTBrgn2+ENwFkjq+kGOck+aq3zy76vzbt1ORJqCfPP0w5TjujIs0wZIvqCllbljP4uI\nyzISJ78eMzEQY+pR2frcEwsLJzXGkxWaL+g40t62z3L3a0uJaM4hg284ewqFxsbFRnktoYbj\n8/TZbT6HslYJAIi6sjCl22lDdaFip/IKZ4lIOHXVTmsNEU0vH/9T3SZG7EDq0odK97HYA0te\nH7os1vyAZNmmGnU0RX38lQUbrjszPDxTdqgMzCkdmF2iHCwoCQdUlGkEqYYKqbaqWLy2QmOu\nksIo6tzFq19ttDq9JoMWARXo9TzeIBFpeM6kjzMNq0RN8vQdUn6lZofKL9vk6Omxhw0eWBou\nRzducPGogYU7aloFQdy2zzJlVFkqvh1koo+IOE2ceJukzedwBTxE1N9cXG6Wa5EioNLNEFDJ\nPNkmecBkdUYZlBx143tef7Agx3jPxTOlI25f/DGfJUZARRUpUWqodBQjJHMwmGuff8Ufma+F\nOI1m4Fl88XTidcyxW9j7EbPv8K+8Uj/tJb5kZiq+GgAgLLGUC0TEaUws6GRKQn8izlSum/Q3\nImK+tuD254iIOC7BzGDMXSPdlMseGnlOm81pIzeM9zg8P+Dph5VPbf/9rPHB7USkHzpIfRwA\nUqHx7487flga+5rck+aWXh5Kk6VUjY7mAFZnZyhlAWbHkXa9xbl5TwsRTRwqp7eWxsmeoPeB\nVW9IRwQmkioNDgAQUSKJvNJFlcsr1qgsvHctsdRVPiEgJciaUDR0R1tNk7t114EEVEIza0Gn\ncozLquCLZxIR8zYwZzURkZLtUD3+NJbyeeOIiPlbmUcOjTBGj76/SrkmP9uo0/LNVvnh/+rX\nGy8+fnyeWb6bVJGeI25AdrG0Q4WIqu0Nh5aEE0W0dNyhomQ+dNtG5g9U5i6cHn8gKKqziomM\nOdw+Ioo7vwzQCwQFkVR12mKw+uQ/729s+eqDHT+4A14hFJPu6g6V6nlXsHhln0puvKpyr/yN\nR08eGHF27qGDOSKdlu/1w7+0k3NjqhNjBuzCvo/V12hKjuJyR5EqdlJuLio3yzUaahFQ6V74\nqyvz5IQGJdKmPDWPP9jU5iKiXLPBFFoM4vTEzzwTY3+JOlKSYNwliQIb72W+FuJ4/eHP86Wh\nLDTlpBl0jm/J6RR0Brc9rUdABQBSjdMQcUSMxS2OojWRjyjoIiZK23U5fYFm0HlExBxVckCF\niHg9CV6Kt5CQ+SxExBmKulrkAAAgQZwmVCM6GA6o6CbcyYIuCjiCu15l7joiMoYKCye+Orv7\nhJ+QSdiqkpslj7SV6qwKJcRiNumISGCiNDa2eO3PrG/3xuvwu71Bv1GrJwAgSjCBSYJMh07s\n/+h9zCe/5Fre+q9/zz4iyj1prnnm4dJB3pxFMYvMK5QiH/5ArG00SqjV7ooyeFuwYpfbF+B5\n7rxjxkhHdtvqpDnQoXnlw/L6N7lbq2z1ifRHjdPJwQnmb1MOaipOlzLBBne9Ftz2DKlXNAfC\nk62aQedqBp1LRMHdbwW3PiEdXLm1bledfKs/nXHojWdP1fDc4vV7b/rXD76A4PYGvllddcHc\ncdIF0g6V0qwCg0ZfEdqhsrd9XXplEuCihQ8KougT/J6g/O+lxWslIp2WN+q1Xn+QMbLYPWWF\n4aqrDrdfEBmpFowCZLqv9qz4dOcSIioy5T1x1HXqU3JmggQeS60+OeWX1edUgiuSru5Q8WzZ\nTmKcDYKC1bZvv0tqjx1UHHH2lvMOv+W8w7v0pXCA5CJYqv+L+K3ByqfVl3D6Qk3uKCKqdYQC\nKtlF+YZso1bvDfqxQ6WbIaCSefJz5DUjDrff7Q1kqfIYWkIvfrlZ+hjL64jo8+U7/EHBoNOc\neeQoRixGoFsdKUkw7pI4kbHF6/cuWrtXKohXXpR95MSKEw8fKi0UYo4qsXUdEfGlR4ejKURE\nxBlLjSetinpPAICU4PUk+uKGQDitmRERMRZ0cLo89SkWDD9pOd7ABK96IWE0TF4wrgvvvKag\nS7RuaveNOaM5Q2Hc7nNa7djKFcrHtvc/bXzoSSLKmjJ58Lsvxf1xAOi1Qnvd5GKYRETElxwp\nNYJ73pUaWUalskhyakQlk5LuRvQf/DL43NDqbGmVklqLTY45SUGXVq9d6DyRUYvXWpFdetDd\nAegNOD70xppYkY/YdGX98s44Wflo//YHKaBiOmRC/rwzuno3JZeXyxerb8qkf5sjSvatv731\nk9Pjz8nSKwGV7W01UmNYbv8huWUrGzbXO1tcAY9Zl1A6F5lSmtgbWQ2eiJgvNHemlaMU6sd4\nWDD8KPv+V7my1NTRZTefK0+Szj108BWnTH7xy3VE9OP6faqASgMRDcwpVf6bOgRUlN149c6W\niK9VphFysvRSJL6pzaUOqChB65wsBFSgl3hq7Qfrm3dK7UvGnjyxeJhyiud5QRBj12qSxJid\n6+oOFX1FfxYaqIgut9BqJSJOr9P2C6eM5s1ZFpv857eipMfnP+jFurIoSImd9DcXE1F5VtEe\ne0OtM8rfFJA6CKhkHr1WYzbqXN4AEdU0O0YPDM+jSWEJIsrPNip7dVvskQkcBJHd/foyQRDL\nCs1nHjnK5nMFxE4XGypBFL8YdPg7zQURIxtYp3e2eW547n9rd7Qbk326bPvIAQUv/OWEIWV5\nokP+q4jPn6BcIFo3Mft21U/wmkHndPWrAQC6itMYmOiLFwIh0obySvvaqH1AhXytqsuyKGBj\ngfCaIz53jOHEFRRwME+9f+VlRERMlHKOc6oyy6Jrn3/VVeq76qY8pSk/oeu/EAD0ckWXX5h7\n6vFSW3Q6G+57VGqXP3AHnys/qXRlpaRdJ7XVK6DDQgdjTCY229yL1+0loqHl+dPGlCfxV0iU\nJqGsZQkqzJGnO2ubI6ctGlvleUkp6BK7UEqzx4aACoAsFPUkocdFZJUiHw53rL4pL9d2t8/r\nDxpVWap8AUGKNKt3WigJvoblDxiaV05EjFiVrX5S8fDE+8bp84nXkRgQXTUdz0rbB4mI08mP\ndGlnc+RlqoNKpYRLT5qovubMI0dKAZXNe+RJuoAYlGIkg3JKiajcXKzlNUFRqG4fUImxqlIp\nv1qYY5Syiv0/e9cdGEdxd38zu9eb7tR7syTLcu82xsbGgDE1EErgCy2UBJKQhAAhQIBAIPQa\naoDQjU0zGHA37rZsy5Ysq/dyKld1/W53Z74/9m7vdBa25JiS5N4/3tudLXfWzc3M+733ei3u\nKcXRXlEiraXPNoEE/qPRYO+U2BQA+KBxw6SUm6SXKjnr8YeCnCAQyhxTP3csQmWMCpXidSul\nbedHn4tDQWXZuIKVr8c2c7//BgBghPSJL+MPCDSGoqCeGMsvAMjQiISKhQJFP2KPzf8yJAiV\n7xU0aCH9W8hQPYjFI4wCaYuYtFORvmxM10kzatr7nADQ2uuIJVR6reHuNVmvNOrCBg5Wpz/O\nrtTu9gsCAQC9WpwNHktcYguE61xs/qFj5Ccfww1sRAQ54frHv67rtAIARmhcthFj1Gp2cDxp\n7nVc++hXn//tYnX0djFeq30b+dY3ohdCTIJQSSCBBL4PsGrgXLEqkzAFAkDdTaFd14g7o+YM\ngQGkLYi9AA3FzHLFknAxvl7020EYyfQg08dUFyYGQwkkkMCJQzVtklQIzdscAGFCRbt4AZua\nLDUTelrDW0dXQFNBYlkkIyzLUDxpUdNqufeN7QBw5dIJPwihEg2UCkWrs2VTHoKQg5Ig3/As\n9XaN/mophvDH5vGHLE5fapJaOiSVLpl0SohZLhwRiVz6BBKIIuJJRYdlgeRi0zQQgjRoo4GB\nH+jJQKuSfB1GkJ7c+8Z2lzcolzGP/3KxWNRIKfTbvQUZ0aIZKYNEH6O0ECPoWczkaFOL9Fni\nzhZnz5gIFQCElOnU1wO8h/p6kTo79hiNVB8CqwHEAuVH/BhpILzuFgjxbWYnADAYLZiYE9um\nIMOwdEaBL8AhhCgFhKDLPSAq8HJ16QDAIJyhTu7xDIqyFQnH6Oii8Sr68P9+e/+wPnMgQlEn\nRRYuEkjgPxrvN24AAAUjn51Rvr23elXzlgfm/UKGw4uuGqVMZF5d3qDxmH/zcTZfsRirQmWU\n4MR8FxmDx7Kmn8BJhlhDSUeVVSMSKghQutoEEVolwIfsAXeyUn+ckxM4SUgQKt8XKM83PMO3\nvweEA1aLDeMBsdTZRXs+5xuewemLZVMfijOHOQbSjWqRUDnSYV0+NzomazWHBzSpSWqNUqaS\ns/4QTyjtsbgKM5OkZmZruIMW+/FjcyHSSOjY6ZrHZmWOxr/WHhbZlMxk7Wu3LSvNNQHAoMP3\ny6fX1bZbeq3u17+qufXMsECSDh2RTmTyLhZT6IWOFUL/pjHdNIEEEkjgxMHqAPpBOIoCAaBM\ndLkN5EbxX+qPd8qmvugeJNOJjDEN2uKmxxCKCFkQDk+PyQgz/G9Ds7P7iK0DAPRyzZLc6aM/\nMYEEEvjfBJJ6Ld9RvZa/HyJOEdKKWJ81fp4/6Bym2/iOEBRCX3fsFbfPyJs5zDYnGjMQFQJi\n4xRxg298fkw3StarEAJKAQDqu2yxhEpb35DUBo6rUBm7ejuBBP5bEWU9ueickcm9kMm9EAD4\nltf4hmd/kAeDSIkhAFiH/CKXIIFS+GR7I8cTcdacmqT29g8BQHvfUCyhIqnZYhdJmx3dAJCv\ny5BhtsAQZpqbHCMITY4NpM6lvh4AILZKRv2T6LMFBiWFCgBCChMNDALvpSGH1KuHW/rCWple\nq5dQCgB56YZY23ARL/5umNxZUqLkRpR2ObrUHs9gj8fCEV5aIz7GIoAtEO4DTZGPpbnHHtug\nYyB8rlGbIFQS+I8HT4QPmzYDwBl5My8oXrC9t9rqH9rUdWBZwRyxgVGnFFVZAw5vHKFyqGWw\nsdsGAAsm5WanaKUMlaMxFPIIlDAnNZVKAqUnIYgugRMGYhSU94Awqom/6O6VrNIrGBlECBUA\n6HEPJgiV7w0JQuV7AnfoHqF3DQCwZb9mi6+TVM9kcFto/61kYAtXfa98ZjismHIu0reROGto\n0AKERwoT0pUwGUuQpkBskGEMe4/uaxxWIVLdGi4tzDBpASDdpOnoHwKAxm57LKHSEakNEaeI\nx86Tl+iWY1Mmx6Zb4kApvLcxzJH8/YZFpRGRTZpR/cgNi87780cAsGZ3y+9+ejlOmkSch4WB\nLdj8NZN1NgAgTT7S5BNXg2DdO/o7JpBAAgn8m0AyQ4QCsSL1sLI+CEZto5EiPJqhno64K1Bf\ndBYdXcEM9MfXG8YUiSO5ngbtsV5hSG5k8i4GzkMFPxncFneLEOGv/PrBZmc3ACBAX1742LzM\niZBAAgkkcAwowgGkxNMWd4R6O6Xt5G8pMQaAXouk2xhLNkDkJmSonroaacgOiEXKDGycjFQj\nyFw+bNp86zfhJddHF/zqxknnS4eiHa+/76jzKPX3H7XzWJCx2KhV2t0BAKhuGVw4OVfc7w1w\nXZHlvzSjBo43+h3T2DiBBP7LIbGeI3lSwYh+g98X9BoFw2BBIIEQb3f7pb4OAJyeAMcTiHhS\npSWpxXl0U4998bQ8qVm3Jbz6Gcu/tgz1AoBo9lWoD/dpzREfsNEDaYvAuhsABPNXTG6UUCH9\nm4c1U6SIA0jqbkHJMQnSlFBfWKJndYV9a7NTjh+TIGWlSOkpOdo0gCM8EXo91gJ9BgBwhB8K\nxmdNSZBWD6SP5XD7sJyVxi57pMEJ/HYkkMCPCxu79g/6HABwcclpS/NmiiHhHzRulAiVdKOm\nocsGAG19Q+PzkmPP/dfamq/2tgHA+/ecn52iPYYMhVDqCnmNCt23NTgxKGWMVyAh/vh2ZAl8\nGzpc/YcszQCgk6lOz5t5IpdgVABAY1KvALORZQcaw6ADRDJUJB4lQ2OS9k9JHTfCxQU/JRxC\nLLDqEY4mcEJIECrfB6i7WWRTcMYStuSXsYdw2kLlmdspJShCMgvt73KNzwHvAwCkTAMsI9a9\nQL/g659m8i6WTfwzYHlOWrgDPdxmsQ75RWsCf5A/2BwW+WanagEgI0KoHGwZXDY7GofV1B0e\nu4ihcJKpV74+I7ZrbnR0+fmgM+gWOfBjC1lEQ7BRuvW19TlFE+rcNP28imEriWW5pnf+fK5A\nqCg2lE17JLTrWhq0cFW3Cx3v4+Q5wCioq1Ho3wTfDS2fQAIJJDAiUCQXlPrNcYRK7GodUoYj\n/oirIe4KxN0SfSEt/3m7wTRj2NVirGmQMoMG7ZRzUW5IVDEiVYZs8gMAQBzVoaMIlRerP212\ndisYWZ4uo9nZfdfOVzZd/Ox3VMR0MkFI1/W/P26rlF9erZ6d0Nwk8B+P9Z2VN216XNx+ackf\npZn2DwWkzBA3qKcVeF/sRIvEpNZlJocX4HqtbrcvFJsh3N4fZg7SjGObpAn9m/j6J4/y40I4\n9RTZpHvietpXD38ubb9yePX1E8+TjCmkjpd62uNuQQODo6z1i0VWilYkVHYe6f3NReEu+kBT\nv0AoACAUHkIfzzV3bOrtBBL4LwZSSPrdEQjOH9DvCwAQgmS9ctDhA4DO/qFYQkXy0xY1FmmR\nosbqlmEGiQ0RYiAtwhz0eW1i+GiRIQsATEp9kkLrDHrEkpcxARvGi/4vxLKHDB3BhgoAAEr4\nzg+HtVOmw1AdABBnLY4hVKinDYSguC2GsMLoMkskQqXe3unjgyGBk6ow211mkVCxB1zHsASX\nTBHTI59b96Cr3x7Opac0WgyalqQZ8QoJJPAfBNHvSy1Tnpk3Sy1TLs2duaZ919qOPbaAS1QM\n5KeHeeXq1sHlc4piz62P9CFZKVo4XlCKPeA66YSKQaMQ/Qwd7oBkfJrAmPDg3jc/adkGABih\n6v/71wmk6IUdLEhIGo0jVZZiyVoAoIHB4MYlUkuBkn6vHQAyNeGiqEx1RKESyVYRQRyHhI73\nibWSRgpAkcyAk2cy+Zfi1FPG/i4TGIYEofJ9gAzVixvYOFXaKfR8Tga2RhthVjbtUUnvjNNP\nk1XcidS5AAC8j297i2/6h9D1EWC5bOKfCzLC63oCoR9va7zpvKkAsGZPS5AL2+3lpekBIDdV\nvwfMALC9pvuuK+ZKt5LC6LJTdBDjV/Dw/BuXF86Tmp23+s4d5hpCqT3gSlUlSUOo++Zee1rO\nNKnZb7Y8U2trEyPr9fJRDYYkicykwpSjj84pz5K2kaZAvnAlX/ek0LeO2A8S+8Hwp2WaIZt0\nb3DnFdIAMQxKqLeTBq1AeZAbsTrvP5GAHXT4nN4AQsigVqQmqRM+lgkk8H1A8AvmdcS6h/p6\ngISAUSB1Dk6Zy2SeFTb4ii7YdUDy7NhTY4u4kTI9vNNZA1QAxIQP8B4aU/2NFJGrxaxXRq4f\n00xbIE6PqaMapS0c1sx5OO5Es8f6xIEPAOD6iectyZ1x8Zq7qy0t79avv3rCstF/DD8GcAOD\nobZOAGB0WuXE8h/6cRJI4GSCI/w9u/7pDHoQIAr03t2vnZ43Q/JOiQUFKlX+6uWa787VGimM\nwChBCAAlxL4fx3Q1xF4lbWcma0QjLEqhqrl/0ZRodXZ9Z7jkPMOkIT4/5fnj3FHGYpWKb/0X\nX/8EACBtEVt0FdKVAOWI7YDQ9i9i2RHaeYX81FVSj7q159ARWzsA/LRk8UfNW9qGzBu6Ks/K\nD3NRn6bHVgAAIABJREFUSBUeOhJnbdy9aGQQPiZkp+hq260AcKh5oNfqEau51+8PszUperVC\nxsDxQ+kTCpUEEogAK5BMRzk3DfSDEARm2II+HSlx/ftEhkkrEioN3fbppRnS/s6BcN2hSKVI\nwo7KBjMvEJYJF6zsbxw2uYYYJYpIqABAgT7zkKW5xdlLKB1Tfy65FwJQ7tDd8rlvIIWRb/pH\nNEAFAGK7QcsuKL5W2k/sB6RtXghbOMplDBwPkuXX/XveiDvU6QoTYMfu5aTVA4mPB4BNVZ1X\nLp0AAEc6LLaIYkakWBJI4D8X9oBrfWclAJxTME8tUwLAhcWnrmnfFSL8x83fiJpaKfx4c1XH\nn342V+oGBh2+jn4nAKiVskyTFo4XlOIIeGC0WQGjRbpRY7Z5AKBzYCiOUFn1TcOOwz0AcNP5\nUyfkj7B8lwAA2AOuL9v3iNuE0hWNm/4442djvoosUr4ZtCA2P/ZInLhzwGvnCA8AWVGFSnij\nN0qoUL7ucb7tHQCKdCVs/iVImUZDTjK4XejfJPRvYgqulE28a8wPmUAMEoTKyUGfzbPxQEd9\nl83r5wBAq5aX5pgWT8sTiQ0poxhCMaVqQkBMHqa+LurrBaxgJ9j5ppcAAOlK5DOfjS7DsWq2\n9FdIkw+CFxg1ABRnRf27Xvni0KyyDBnLPPFhpbiHYbBo6loUadbS6zjSYa0oSAGAXqvncFv4\nO1aQoQcAa6SAzjTca8+oDI8Ibf6hVFXSYGTANC21dGpqidQsS5tSa2sDAKt/aJSEitsfEjeS\nRuGXihSpsml/l026d3vDypB/gCBZbub88dnzAEC5dDONWHtTfz/f/JJgXgcxUYeAME6ew5b+\nEg8vAD8+KC/0fin0baSuBhqyA+GQ3Ig0+ThlHpN/qeQscXJxpMP6xtc122q6hzxRlkijlM2d\nkHXVmRPjpDwJJJDASQSx7OQO3UODFgBAmgKkSqeBAWI/JPR8wTc8J5vxFDZOQcpwmQkZqoub\nhhJ3k7QtTWgp5yb2KqlIkNirpCgCAECq8HSdOA7GP0zMgiAyVEDvVwAg9K7BwwkVoW9D3In3\n7HrNy/kNCs0fpl9mUuoX5Uzd2nPogT1vnFc03/Qjt1LFOO+NqHm644NP+v/6BADIS4pi9yeQ\nwI8cXs6/oWu/uL00b6ZWNkKJ3+u1a5qd3QzCz532u1u2PNXi7H29ds0vJ194dMvnDn4kLWP9\nZc41v59+2Xf24Aipc8W1OcG8LtrVkCCxVUqN5CyTYdKKqezfHOqSCJXuQZeUH5Cbqjf/6UH3\nhm+OfT/9WYuzHryOb3gKALChQj7/rTBvDYBNM3HyDP7IYwBABr5h8sPv+qWazwAgTW18dtFv\nv+mpsvqHXjv8RZRQUWcDwkAJ9Zuppx1pC6V7xb6F0UNKRyCUvvDpgUduWNRqdn6+M6wyzI0U\nmUoKlUkpRUxk3M4TQRwYHzuyPoEE/uegzADODZQQV0MMSQBABeJp/U7vTH29Qt8GOnSEhpwA\nAIwcawtx6gKcMgcAAUCGUVMDAAAHmweuOH2CdKLEFqcb1RCpWQQAly+0vaZHdP3qsbhFGx8A\nyEmNECqRrBSJUCnUZx6yNPv5oNlrGVPlMtIWIkWqOEal7pbglmWI0YgvY4E1uWEhi62SBm3S\ndFWIUTNLZhKEHD8pIS58PhaSeOW4Kj2RPcpNjVbTr9hcd/mScgajt9dFh7s5qSe53D6BBL5n\nrGzaHBQ4AFjVvGVV85bYQ+83bhAJlUlF4XK6zgHXpqqOpTMKxJertjaI2SUVBSkiy3JsQsV5\nTP3KiaEw03CwZQAADjT1z4ghlQHg0x1N+xv7AeDmCxM+Ad+KlU2bg0IIARqXlNPs7H6vYf1t\nMy4fpX+PhKiBra8baYYTKsPDWc3esNzkwGDj77c+BzGqJkmhInSu4tveBgAm/1LZxHuiBj/j\nbuDrnxT6NpDBrdR1sWevmQoEjgnVxPGynKxjt/nfRIJQ+XfB8eSxFXve3VgnCAQjlG7SYAT9\nDp8gkEfe2/2TU0vvv2aBImUuUqbRwKDQ/QmT+xOkLQAAJv9SJv9SGnKEdl0lXorYDwAJAQCT\nuVRiU2hgQGQjxbPEMV9JjpFlsFhj4vGHLn/w89hHKs5MkrOM2Eza+fSqfa/+cRlG6OlVlSQS\nNlWSbQIAmz9cepOsGrbiZpIIlYALYmpMvr3ZkDRkPDbYiC0jT47z1ZXQ7HFcuGOV+OSTUtq2\nXjIXAQJWK16I2A9y+26mnBspTMy4W7FxKmA59XYJnSuIdXfItlc29WEm+9xR3ot6O0L7fks9\nbcAomKyzsaECMEt9ZsH8Fd/0D77tLfnMZ3DK3ONfaCxYsbn+gbd2CIQWZSXddcW80hwjADLb\n3G+tq91U1bmpqvP2y+fccM6U418ogQQSGCOI/WCo8hagPNKVyKc9ivSl4n7qbgnt+w31dXOV\nN8uXrEWqMKkZvzxHBeKIikWQOgcAAVAAEHq/lAiVOP4jLEAEIEN1NGiJClY87bHGF0zKPLHS\nWzCvZfJ+iiPKGDK4LbbkEAC29VZ/2roNAEqSct+uXwcAqSojADiC7of3vfPEqbec2IeTQAIJ\njB5PVa18qmqFuP2H6ZffO+fquAbOoOfxAx8AwCWli68Yf8bnbTvXde59dP97l5YuiWM9D1qa\nH658GwBEZvSRfe8uzJk6I63sO3pyrCsSREKlbx1bfhtSmABAMK8Ffpg5flFmkkiorNnTevtl\nc8RM49URmkGvlqcYVP5rf6ZfvlTcQ7zevnseEbczH7gT68MjRllGmtC7RuSYmXHXS2xK+GFM\nM+Wnrozd0zrUu6GrEgCuKl+mlikvLV3yYvWnm7urmhzdpcZcAACsQOo86u0AAKHrY3bCH8Nn\nUiL0rT+BDyS2dOnjbY2tZkd731AgFFbeFEeCCcViIwRo08XPSjKjAB/KfO0COJ5+5d9ES6+j\ntsPab/N4/JxCxhi0isLMpGnj0rQq+fFPTiCBHwJInS0St8S2L5ZQIUN18ZYDJxEkxNU9IXSu\nAEqQIgUlTUJYTn3d/MA2aP0XTposm/EUUmXkRvy0d9b2xEYIVDaE17ByUvUQU60IAC9/fnDh\nlFwGoxdXV0lBzhIXKylUNndXHbF1AEC/L2zp0+ToGaMVDMLppwldq8KveB/lfeED2iJJ1oy0\nEQchKgidH7KlNwMA9fWSwZ3ShVSKcDcleX8dA53ub02fkggVe8Q5fErqOCknBgB2mWsH/Q6B\nEmfQbVLq89KjP3CN3fY7XtmSmqRevSussGEwyhpFpksCCfyYIfp9jYhqS0utrW1iclFprilZ\nrxKFWfe/taMoM6koK2lvvfnVNYfElnPKw1+iY1MmxzYEOzFMLk77ZHsTAHy+q+WGc6ZK6hmn\nJ3CoZRAAFDKmKCaVOYE4vNOwDgBmpo+/YvwZv9/6XIerf0dvzanZY1u+ixZculpw6oLYQzTW\nORygyx32SzxsbTtsHZaAKBEqfMcHAABYLiv/Y1xcAlt+G1t+m7jde8dNNHCcn+DMh+5KShAq\nIyFBqPy7uPufWz/b2QwAy2YX3fvz+WLkmssXevLDyg821328rZFS+vcbT5PNfI7b9xsatAS3\n/YRJX4KSKhBWEG87Ma+l0ghSCItexTCi8L62t/m2t6L3wwrl8gNylinNMdV1Dkt1kzChICzE\nqyiIKvK21XRf/tfVGOOqpvAAKMWgEq2uYxQqw6SDkjOjWHsiNUsZ3kxaAjh2iUospGA6s9Vz\n9FG3L0QoRQjpY+y5nzm4klAqwyxH+MPWti3dB5fkRhhyEuKqbqOcG8mNse4QYJzCZJ/NHX4Q\nOA9x1sYSKoTSdZ17xSKCAn1GrOAGKB+qvIV6O5FMJ5//LtIVS0fYkhu52r8B7yeWHSeXUOm3\ne//69k6B0DSjetV9F0q+5BMLUxZNyXtnfS2hVCTJxgQaGCDOWuo3i0QdAEaKFKQbh/VliQSa\nBBKQwDc+C5QHhOUzn0WaqIkN0o2Tz30tkiRPpUPU20WG6rEhbEVF7FXDhHGsGinTRFJE6PmC\nLbkJqTKpv1/o/Sr2phGOHIASoXs1O+568ZXQt25YM30p0hVTdytQEtr3W1nFndg0ndgPckf+\nHtuMI/wd218EALVMafU736oL3ytFZbD6h/515KurypdNTimGBBJI4DtDr8fyYs0nAJCjTevx\nDL5Y88l1FcuztamxbR6ufMcecMkwe8fMKwHgrln/t76zUmRZHjnlJqmZjwvctPGxEOEXZk/5\n+Ny/Lf/s9sr+uhs3Prb1khdGVL38+0C6UoB1AABCgK/9m2z6YzRk5xufj2tWlmfaWdsDAEOe\n4GMr9t539YLmXvuba8XCbijJMQGAatok6REFu7MPwoSK9rRT2LTouJSrelPcwDFqEu7I3yEQ\nLbtGxkls0TUA8ErN52KZ88/LzwKAayac/WL1pxToy4dXP7Xw1+HrJE0QvB0AwHd+yBReIYoF\nhZ7VY02kj30vEg4Nz0sYF6lYEikTg0ITa9qmZOViFO3oB8YRUOKoIbZ91NtBg3YgHAAAq0KK\nVKwvw2mnIlUmAGyt7np8RWVTj12tlM0en5lu1IR4Ycuhztp2q5xlfnJq6V1XzBW5rgQS+FEB\nawvIAAAAMa+FyLAHAIaZYJ9scNV/EaNM2fI/sEVXSzWLZOhIaPcviLOGO3i7fP47hRE/beuQ\nf8fhblGB12t117SGeyQx/KAkxyQ6HwLAwZaBXzz2lV6jWFsZXsxSydnciIRFykp5ueazuEdq\ncfZEp7GjA5O1LEqoxO7PvYBveFYkp5EumkLMt76BU0/B+lLu8P1Aox6MRm3Yac3i9B37jvaA\n6xhp85J4RYzgBoBfT7nopyWLpQY/X/vgmvZdAGALDJmUeoNGIa0jA8AXu4atDOam6U9gnptA\nAj8e1NrCi9qXl51+58z/k/bv6Tvyq81PAMAHjRv/Nv9GjNDSGQUfbqkHgEGH75y7VqUZNWKd\nighRs+Ll/CHCA8C01JK/zI3a963vrBTVusfWr5wY5kfcUJq67Su/qb9scXie+9Lqg2IZ94zS\nDBmbWEEaGfsHGutsHQBwaeniC4tPvXP7iyHCv9ewYcyEijpSvmk/AMXXxB4izprYl72eeJHi\n0YeorwfElMFICAL194V2XxvbmC3/PaPTUnl4+ZFyPPH7AQAYzGii5kNIlijWGRkJQuXfQpvZ\nKbIp+en6p29ewkR8VPVq+V+uPmViYYq0Do6TJioWf8F3fED6twiD30DfOkAYqbKZ7POY4mu5\nqj8CFaRhEHUekW7B5JyPjJMBQOj8iFh3S/tnlGV8G6EyvSTMKCTrVXlp+q7BcOVI3GxwanG4\nmSg9wQglKYbVhhgUUekJAFh84To7Y5wzWKTZ6K2iy/NTGAYLAjnQ1O/yhWKJE4vTt/DW9wRC\nx2Ubv/r7JeLOHs/gqqYtAPD76Ze+17Ch12N59uBKaSRKbPtpYBAAcPY5UTZFCFISBACJepUw\nFPTesPHRDV37ZmdMaHR0ernAX+dd/6uI2waxVopxCDjngiibIl1twh2jfI+xcPlClfXmph67\nxenjBQoALINMOlVxVtKs8sxUg7qqeUD8oVowMSc25RUAFDLm+pGEKdRvpn4zcG5KeCTTIUUK\n0uQDlkU+k0q+/hnirEG6YiZ9CVLnAJbRoF3oW0sObUXKNLb8DyLDFOSEhi5be/+Qw+XnBAIA\nLMZGnTI7VVdRkKJJTMgT+K8HJcR+CACQJj9KmXCu2P4WAJDgx9oi0VIGAITOD/Dkv4qHhO5P\n4i6JdCVhlQkJhvbdwhZdzbe9DWRY6QdSpCC5kYYcACC0v8PkX4ZkOuA9glhLEgO28Gqu5i8A\nALyHq7439hpIkSzmy71yeHWjowsAnlt068Ulp0ktut2Dsz64Pihwt29/ce1Pnhir7jiBBBIY\nPR7c+1aADyUr9V9c8OiClTd7Of/fKt9+cUl0ENLk6H6z7ksAQAAXffFncSdGSKD09do111Wc\nU5IUzmC/fceLzc4eJSt/aP6N7pDv/rnXnrf6zrYh8107Xn5+8e+/i4fHSRXSttC3jmyppaGh\nYVQxAABMLopWVb+/qW5jVYfDHeD4sNpY8rIYFSLFQ1QISB0TdRwSA+op7wUqMEAAwBXyftC4\nAQDkjOzWb8IegGKFzYrGjffOuVociGLj9DBvLfhDlbfIKu6ggcE47nn0KMkxylgsvbU4SBVL\n4tA3RRXvZZ6sNPR6LFa/kwIdZcdLvZ2hA3+grkakL2NLf4WTJiNFClCBBgaE/o1C3wahfxNb\ncsNuc8ZNT64jlE4pTnv1tmVGXVTc89We1nX72l3e4PoDHReeUnKMeyWQwA8CpAv/WRJXA7FV\nhkW3hBN64imHkwXq6RB6vwQAnLaQLb4u9hA2VCiX7ZFeluZGOdSnV+2fU56lkLGPfrBX8nUY\nl20EAL1anpsanVzvOtIbe83SXJMkbZEUKkfjGIe+DThlNtIWUk/7sL0IM1nn8M2viX01UqZJ\nzmAgBEK7fg5IFjf4zEoOL5C19TnjolwohfPv/sgX5LUq2eqHLpYCVEaEdFQquExWjVxwafEN\nlSTlAkBJjtFW54eRMC7LGPuS40ljt91sczvcAQDQaxTpRk1pjnGY/I4KgvlroW89HaqjAStQ\nHskMSJOHU+cz+ZdJJr0JJPD94L2GsDzlqvJlBfqoX1a2NuWunS87g55VTVvun3udDLOXLR4v\nEioAIBAay6ZMyE8RE0rsEb6k0JAZG10sCcKkjZOIggzD5OK0mtZBALj/rZ2dA66JBSk7ans+\n3hYO+zxnbqIy71vxbsNaAGAxc37xgiSF9rTc6es7K1e3bf/7gl/GLbEeG0gTrjEi1t3Ae4CN\nnCsEiG1fbMtjECr9XhtPBBYzSJlGvZ00aAcSBKwAAGCUOGUeAIDgE38cgfOWbPtCOndo9dfm\nPz0IAMqSosJP3x79k//PIkGo/Fs43B7+O55XkS2xKSIYjC45bfyw1qyWHXeDv+CqmzY97uZ9\nQOGM5Fk3V/wEAOTzw3+sOHk2sVUK/Rtw+7tMwc8AMUhfxujLyOC2OJ/9+RXZ76yPD94UIUkF\nAWBuRZY05ovDrEgzsYDOqNAxw1ULcQoVkVbRydUKZtgiuzRgso26EE+vlp86KeebQ13+IP/I\ne7sfvn6RNJx74dMqgVCI8PMinj/0MUd4BSP7xcRzFYzswb1vbeutrhpsmp5WCgA0FBZQx0ab\n8E3/4FtjAvQQozynGgBanL1Xrn2gydF9Wenpzy3+XZuz9+Iv7/nzzlcODjY9d9rvlKyccuF3\ngeSmmKu9yLe+HnM1rDxnGEX8bRAIfWzF3vc2HOEE4eKFZUtnFKQlaQDA5Qture6+760dGKGf\nLZkwoyz8u+vyho6+SKxkhwZtQss/BfNaGnJg4xSkyQPE0pCN2g9SwceknsqW3QKAQntuBMrj\ntIXyWS8ME6MUX0PdLZSEEGIDIf7h93av3tEsEHrVWRPnTsgy6VQA4PGHDjYPvLPhCMug8+aN\n+/mZE0fzThNI4D8WFIAAAKDoDyL19XAHhnGxshlPM5lnIHWuSLgK3Z8x2efj5JnEtj88HIkB\nNownlh3hS7mauEN3j3hjZCinll0AQIM2rvKXTMEVQueKuMQ5EMsPO947OrueyToTKBGdxNSs\n8r651ykZ+SzDtH0NfUO+IMcTjUKmVcsfm/07O7UBwKDPka42wX8HhCD1dtLgII34ESFWgxSp\nSJMf5x00WlBCg1bghijhEJaBzIAUKQklXwKjR421dVXzZgD47bRLCvQZ108899mDqz5s2nTj\npPMlFezdu17liaBiFTdOOl9axBIoeanmM47w9+7654rl9wPA6tYd7zdsAIAAH1q4aphZ37sN\n6xfnTr9o3CLx5YDD29TjsDh9Q94gxwviOMGgUeSk6spyk8dUSIiNUyXCGACoL7pKKOZIi9sz\nStNjzxIznCVIBT2juqNpptD9GQCQvvU4KTzSkC9YAQCUc4c2L5PGY2/Xr/VwfgAI8KFveoYN\nhv188K26tb+bdgkA4NR50n7qbg7tuSHaDjFAhdE/GwDIWWZCfkp16+DRhxiMREIlKITcIR8A\nJCvjCRWTUtfrsQQFzh3ySfmCDnegttXmu/5XviAvlzHtzbbCECutwPItr4n9vGzy/ThpkngK\n3/wyDVggbCYJ1N26eqdHXOG9bvnkWDYFAJbPLV5+QusdNOSkzhri7YSgPfw3gGVImYp0xThp\nilSsk0AC/yawIZpNwh1+SH7KO0hm4BufPzEZ2WhAPC2iBeswzti8jgx3ApRNf7wszyRW+wFA\nXaf1vD9/pJCxTT3hOaZBo5DS5qeOS/u2yfXUceF1/AAfOsZqlyReGQsQW3ozV3V77C4m43Sk\nykAyHY2Q39g0NWowSwnQMJuC5ElieIxRp0xNUlucPrcvdLjNMqU4Sjy0mh2N3XYAmFiYAjGU\nydTUktgFYtHLyxF0O4OeJIU2agkeZ3QRsQS3BsIFl+V5yXvqhmUASCjLC49O++3eZz7a93Vl\nWyDETyxMLc5Mwhj1271VzQOcQE6pyL7j8jmluSbq7+P2/5YM1QNimcyl2DgNGDn1mQXz13zz\nK3z7O/LpT8TlDiaQwHcKjUx5zYSzFYx8bmYFAADvo5RHiJWx6ttmXH5goBEA2obMZca8iYWp\nS6blbz7YefRFbr4wzJ1Ijl5SfXP4ZWQ44QyO4PLy7+MXyyff+vxGABAE8s8vq2MPpRhU584f\n9y3n/bhAAwNkYCtxNUDQRnkfMEqkMGFdKU4/TdJ/nFz4uMAnLdsAYHHO9DSVEQAuHrdofWdl\ngA990rL1uopzRn8ppC8NO4cLAb5zpVQHIJi/Bn7YkLvHM8IANdyYkgGfPVubyuRdxNc/DYKf\nq3tKVnEHIAbJjbLJ9wEl3JGHT+StjhH/qP7kn7VfAIBWpl5/0VMqVvE93PR7xn8SoTLkCVpd\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Vav69x7Vv6c4t2t260LAMC0zTW448XY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\nmFgapjZVFfWaFOLNri2Xrb0bAKqsv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4YWHxF9vb2iq4yFvoC8rajdgmB7KFKSmGZOJCJGrMnfyfkoTX4XESWZ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\nW+CouTOO/jFXM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Ipoz+fL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\n7hlCCJZMLbIadVYjDwDJtBxJioOhpN0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1tAMDlr/4w3bupL+IvN2Xsu+/PY3KPPeg+u+uA+ALiscsna2uVL374TANbWLH9s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\n0R/rCKr+yXLq4OzQwbm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-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 900,
-       "width": 1080
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "options(repr.plot.width = 18, repr.plot.height = 15)\n",
-    "\n",
-    "atac_annotations = read.table(\"subfigs/ATAC/HEPG2/counts_annotations.csv\",sep=\",\", header=FALSE)\n",
-    "\n",
-    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
-    "\n",
-    "all_plots = get_plots(\"subfigs_new/ATAC/HEPG2/counts_0_%d.pfm\", seq(0,10), atac_annotations,\n",
-    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.068323)\n",
-    "\n",
-    "atac_annotations = read.table(\"subfigs/ATAC/GM12878/counts_annotations.csv\",sep=\",\", header=FALSE)\n",
-    "\n",
-    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
-    "\n",
-    "all_plots1 = get_plots(\"subfigs_new/ATAC/GM12878/counts_0_%d.pfm\", seq(0,10), atac_annotations,\n",
-    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.085323)\n",
-    "\n",
-    "atac_annotations = read.table(\"subfigs/ATAC/K562/counts_annotations.csv\",sep=\",\", header=FALSE)\n",
-    "\n",
-    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
-    "\n",
-    "all_plots2 = get_plots(\"subfigs_new/ATAC/K562/counts_0_%d.pfm\", seq(0,10), atac_annotations,\n",
-    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F,ymax=0.075)\n",
-    "\n",
-    "atac_annotations = read.table(\"subfigs/ATAC/H1ESC/counts_annotations.csv\",sep=\",\", header=FALSE)\n",
-    "\n",
-    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
-    "\n",
-    "all_plots3 = get_plots(\"subfigs_new/ATAC/H1ESC/counts_0_%d.pfm\", seq(0,10), atac_annotations,\n",
-    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.084564)\n",
-    "\n",
-    "\n",
-    "atac_annotations = read.table(\"subfigs/ATAC/IMR90/counts_annotations.csv\",sep=\",\", header=FALSE)\n",
-    "\n",
-    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
-    "\n",
-    "all_plots4 = get_plots(\"subfigs_new/ATAC/IMR90/counts_0_%d.pfm\", seq(0,10), atac_annotations,\n",
-    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F,ymax=0.111)\n",
-    "\n",
-    "#do.call(gridExtra::grid.arrange, c(all_plots, ncol=1))\n",
-    "\n",
-    "wrap_plots(append(append(append(append(all_plots, all_plots1), all_plots2), all_plots3), all_plots4), nrow=11, byrow=FALSE)\n",
-    "\n",
-    "ggsave(\"subfigs/compare_top_10_modisco_poster.pdf\", device = \"pdf\",  width = 18, height = 15, dpi = 300)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.009131\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006504\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006287\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.005524\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.007101\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.008502\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.010525\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.007934\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006681\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"new\"\n",
+      "[1] 0.027318\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.005036\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.005759\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006753\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.007177\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.00474\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.00104\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006287\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.003666\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.00464\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"new\"\n",
+      "[1] 0.019743\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.007579\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.004985\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.005187\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.004783\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.004992\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.007434\n"
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+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006563\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006489\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006791\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"new\"\n",
+      "[1] 0.01191\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.030718\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.007057\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.0072\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.005897\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.008252\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.008337\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006342\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.006183\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0.007378\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = \"none\")` instead.”\n"
+     ]
+    },
+    {
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IyeFjIxGvjrL1gs8Nxty5Zod3t1n1KfedRdfZwpnwwBs4S8mTPnExExiaT4J8Vm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\nmE7bFgN97MiZIYZkEfPTQU2ZggmIDw0qKVlCne0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FzGuYxNEZvfERvfAABhQI57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\nQbSzGk81HcN58kT2EFYOIXRoK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vq4WEPUBZnTlL281pXuaDegkttc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\nEhMtpLikwa8oceO7fDzTLXvD9ebOYTVdbsu99nxXKRF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NyoLtJ9bo2p/tzbNOapiGYT0XhbPB1J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\n1pfXH7ket7i9XDLY9ImDVh2XS5Nn7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TXlAb+vyu1bn7vLaBY1Igop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6ze89vGnGG92vr7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\nd2w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yqurHN7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\n8Yz9bUqwQUS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daGZJHXDOtwXyKWKpYHp5xU+qPZj/7w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UvHwc5A3/6eBvU0RpVfDPVH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\nZWsbNpJFROQYXEZS9ygBJkpBzWoVpxj+Sek+J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qkifx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\nXDIRXfDssh8fmd5y+asWvTs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o67y7kti7sruLeeSGWigStuJoWQIUII6s2Ck4gJQtgQFtyX23OJSK1Y7flzMgk6UlxEREwi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\nAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 900,
+       "width": 1080
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "options(repr.plot.width = 18, repr.plot.height = 15)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/HEPG2/profile_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/HEPG2/profile_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.020798)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/GM12878/profile_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots1 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/GM12878/profile_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.035458)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/K562/profile_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots2 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/K562/profile_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F,ymax=0.027318)\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/H1ESC/profile_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots3 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/H1ESC/profile_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F, ymax=0.019743)\n",
+    "\n",
+    "\n",
+    "atac_annotations = read.table(\"jan_3_2024/mean_modisco_plots/ATAC/IMR90/profile_annotations.tsv\",sep=\",\", header=FALSE)\n",
+    "\n",
+    "colnames(atac_annotations) = c(\"key\",\"label\")\n",
+    "\n",
+    "all_plots4 = get_plots(\"jan_3_2024/mean_modisco_plots/ATAC/IMR90/profile_0_%d.pfm\", seq(0,9), atac_annotations,\n",
+    "                       rc=F, trim_left=0, trim_right=0, first_yaxis = F,ymax=0.030718)\n",
+    "\n",
+    "#do.call(gridExtra::grid.arrange, c(all_plots, ncol=1))\n",
+    "\n",
+    "wrap_plots(append(append(append(append(all_plots, all_plots1), all_plots2), all_plots3), all_plots4), nrow=10, byrow=FALSE)\n",
+    "\n",
+    "ggsave(\"subfigs/compare_top_10_modisco_profile_jan_26.pdf\", device = \"pdf\",  width = 18, height = 15, dpi = 300)\n"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/src/evaluation/figure_notebooks/figure_5/5b_marginal_footprinting_motifs_atac.ipynb b/src/evaluation/figure_notebooks/figure_5/5b_marginal_footprinting_motifs_atac.ipynb
new file mode 100644
index 00000000..db738e43
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_5/5b_marginal_footprinting_motifs_atac.ipynb
@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>GM12878</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>K562</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>HEPG2</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>IMR90</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>H1ESC</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index       0        1                                                  2\n",
+       "0      0  fold_0  GM12878  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "1      5  fold_0     K562  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "2     10  fold_0    HEPG2  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "3     15  fold_0    IMR90  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "4     20  fold_0    H1ESC  /mnt/lab_data2/anusri/chrombpnet/results/chrom..."
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "adir = \"/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv\"\n",
+    "\n",
+    "meta = pd.read_csv(adir, sep=\",\", header=None)\n",
+    "meta = meta[meta[0]==\"fold_0\"].reset_index()\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import deepdish\n",
+    "import os\n",
+    "\n",
+    "def get_footprints(celltype, items_to_filter):\n",
+    "    if celltype==\"K562\":\n",
+    "        celltype=\"K562_old\"\n",
+    "        celltype1=\"K562\"\n",
+    "    else:\n",
+    "        celltype1=celltype\n",
+    "    print(celltype)\n",
+    "    fpath=\"/oak/stanford/groups/akundaje/projects/chrombpnet_paper_new/modisco_jun_30/modisco/ATAC/\"\n",
+    "    ffile = os.path.join(fpath,celltype+\"/footprints/\"+celltype1+\"_footprints.h5\")\n",
+    "    corrected = deepdish.io.load(ffile)\n",
+    "\n",
+    "    ffile = os.path.join(fpath,celltype+\"/footprints/\"+celltype1+\"_uncorrected_footprints.h5\")\n",
+    "    uncorrected = deepdish.io.load(ffile)\n",
+    "    \n",
+    "    return [corrected[key][0]/sum(corrected[key][0]) for key in items_to_filter], [uncorrected[key][0]/sum(uncorrected[key][0]) for key in items_to_filter]\n",
+    "    \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEPG2\n",
+      "GM12878\n",
+      "K562_old\n",
+      "H1ESC\n",
+      "IMR90\n"
+     ]
+    }
+   ],
+   "source": [
+    "items_to_filter=[\"HEPG2_COUNTS_metacluster_0_pattern_1\", \"GM12878_COUNTS_metacluster_0_pattern_9\",  \"K562_COUNTS_metacluster_0_pattern_0\",  \"H1ESC_COUNTS_metacluster_0_pattern_1\",   \"IMR90_COUNTS_metacluster_0_pattern_3\", \"K562_COUNTS_metacluster_0_pattern_2\", \"GM12878_COUNTS_metacluster_0_pattern_1\"]\n",
+    "\n",
+    "corrected = []\n",
+    "uncorrected = []\n",
+    "for i in [2,0,1,4,3]:\n",
+    "    #if meta.iloc[i,2]==\"IMR90\":\n",
+    "    #    continue\n",
+    "    x, y = get_footprints(meta.iloc[i,2], items_to_filter)\n",
+    "    corrected.append(x)\n",
+    "    uncorrected.append(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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g6ZhmIXkAHA7Wrl3Lz59+ylFHHklqmzasXbvWuM3nqtQ333zDLs/XIRER9O/fn6LcXL6fMYN5vrsu7svBpI+73aTn5hLWpo3/oqQWCyQlVWd/hcfH8/wLL1BYWMhvv/9eHfwCWLZ2Le6yMiPYVVBgZP6A8brl53uz2nJyjH+lpfvXVml5GvLESQEwaWx7m8ju73243d6LJuqv0lAOpi/tLVtHATA5WOnpxtwvP987//NVVwCsHnNYp9NJbGwsL77wwj5/t958y3mIeJSVldUIfgHeczhfjTAuLly4kN69e7N06VKOPfZYjj32WNatW9fgjyMtR8auXSzySZgp9zl3LsjLo7SsjLYdOhAcEkKVy0VVAG204Ha5eO+993ABQVYrbmDHtm3N3awm1fIDYGAsbSwrMw7wVVW4MzMpLi7ms88+Y8DAgfz555/GCY/Dwf9eeokIm41nXnuNo48/ngULF7I7N5evvvyy+m7vu/9+3MAuoDg0lA6ewvMAP37/fdM8N7ebXVlZxO7jStmWLVtYtmwZH333HRdedx1z5szhky++4Mq77mLHjh3ceMMNFAHZeXneiY/DgSMjgyW//YbTTJV3u72Tn6Ii7ejTmvibTDbWe6qAgjSGhuhXvmOZmVWr/ioN5UD7UlmZceHJ177GZ208IvXhO+b5bvRUm9mPDiDolJ6ejhu4+557aj7uwdi1y7i4LeLDt2RNcnIyYcDiH36oO3Bb14WCvfXPOm77+uuvWbt2LUOGDKkOerz//vv703xpZQYPGcKxp51Goef7fJ9knPTt2wFo17Ejds+qqgpzs5EAsHnTJgDcQM/evQHYtmVLM7ao6bXIAFhhYSGLFy82MpesViMbKi8PSkqY8/XX/OPii+nXrx+PPfkkAA8//DBr//6bJ/7v/3jv1Vc5/vjjSerYkX4jRgDQrX9/YxdFH4899hhuIDg4mPadO1f/fPmyZU1yZcrtWQKZnJy819/r3LkzAwcOZPiIETz69NNEduvG0SefzN33309QeDgDBgzABWzxpJ7Pnj2bu+++m97dunHyKafw7LPPGgEwMwssPNy7aUBhoTEJ2bWr7gZI4MvM3HMy2ZAnTsoAk8Z2MBlg/ibBygCThubbl/YnALCvq8J7ywDb38eS1qOw0P/GTLWPx7XHOItlzzlsPZZAfvLJJ7Rv35677roLl8vF1q1bKS0tZZtP1sDmzZvZbS6/rH3/9a2N568/l5V52+Z2awffQ9CcOXMYOnQoAAkJCXz77bd0jYtjxeLFe/aH/R0TLRbjothelk2uXLmS0NBQJk6cyLXXXssRRxzBY489xs8//3wgT0daOrcbC0YNbnM0LfANgO3YAUC79u0J8QTAAmkZ5J9//AHAE08+yWtvvgnAtr//br4GNYOWUQPMU++KyEgIC+Payy7jq48+4oarruKTb74hNiqKZ/7zH6xWK+eedhrmyu5FW7bQBvht/nwGjhxJAjBmzBjuvvtusFo5+/zz+WH2bD7/4IPqh3r+uec47rjjKIuL47bbbmPKlCkkpqYCRp2t0pKSuuuO+Wv3ASoqLKTC4dhnAGwPvu2KjaVd//5UAd/++CMrf/uNf991FwCHjxzJihUruP+JJ9iwcSP2hASuPO88yqKiGJCaitVfDTUwAmV5eRATYxwwiouNeid1raeX5uevHzbEib/LteeEQQEFaQwNVQPMtxC+xaL+Kg3Hty9VVYHNz/SqqAjCwvzftj9qB8B0/D20lJYacy8w6hn6zvt8+2Fl5Z4lLTybNcxeuZLuvXvToUMH/vviixwxZgy9evXC4tuffPrZl19+yc6dO/nPgw/y4IMPYgFuOf98DjvsMAAswJixYwE4f9o0Hr7pJmxmPy8qqs5AmzdvHsNPPJHQqCjvY/j2X387tuflGc8xJcV4PgUFEBVl/JNWz+FwcO6551Z/n5mZidViIS0tjfXr19e9wmF/MsDCw40yMXVYtmwZp512Gu+99x64XOzOzmbs2LGcc845fPPNNxx++OEH8tSkpfKMsy7PP6i5k2L6zp24gPbt27PGU1crkDLAVq9cSZDFwiVTp2IJCcECbN+6tbmb1aSaNwBW+8BnBpeiowEjWrpg3jyCXS4SQ0JIS0ujoKiIHz76iDDgvy+9xMCxY9m6aRNnn302TqcTC3Dh1KkcdfrpfDtjBmePGUN2djYL/v6bi48/nt49exqTT4uF8PBwpk+fzrjNm9nsSWkdMmQI4eHhhCcksH37dlJTU/l71SoAwiIiKC4uNq7YhoQYbd5bMOwgTtqydu/GDaSkpBzwfWCx0LlXLwDuf/RR2nh+fNONN3L11VdTUFDA+dddx5ee5Z/TX3+dbGDy+PH0TUvj77//5oorrqBNmza0T0kxnmd5ufH88/KM17GyEiIivK+HtAwNceK/a5dRn853EqqAgjSGve0CWd/lYrUDYEFB6q/ScHz7ksOxZ5DL5TICAaWlxol8fe0rA6yqqsZGOdKKud3Gcdf3/S8rY0d+PlOnTuW1F1+kfXIybs/tllr1cecvXMjC+fOpqKjgueefZ/jRR3PPAw9w/733UgX069WL7ocfzgOPPsqtt97KcePHE1NZSUVFBZs3bcIKJABlQCHw7bffEh8fjwXo0rkzOzwZBM8++yzvPvss9917L5fdfTe43eTm5rJ582Ymn3suF5x/Po+9+SYUFrL4118hKYlhI0cajfTdad3l8mZIVlUZ/8zPWXGxcVHcDH74Cyq7XPpstHAOh4N58+axa9currvuOgYNGoTVU9KmZ8+efPHFF7g9537V9rdGonkuWkcG2ObNm9m2bRvXXHON0R937yY5JoZHHnmEM844g1GjRvH4I49w8xVXKCHgEFHlGad8A2CFPgGw7N27cQGpqakEe+p4VwRQ5uqu9HQSExOxh4VBcDCpqansUACskdQOFLndkJEBkZFUhYayeetW2gcF4Xa7CY+KwllVxdknncSiH38EINRuZ/Lkybz77rsEAVdecQUTJkxg1Mkns2X9eqZddBEDBgxg6tSpdD3sMIiM5NgTTjCWflVVcWrbtkadjYqKGgfEoKAgfl24kO2rVvHhl1/SsWPH6l0R27dvD0D3nj3ZtHEj9z/5JJ+8+ab3gFtcbKShJyf7P/ge6DIFt5ucnBzcsP8ZYLVERERw1llnkZqaysCkJDp37sz48eMhOJiwDh34+ocfOHvsWOx2O7aEBOzx8Xz+9tvMnTkTgO88Nc9K7HYmnHgibzzxBNEhIXtO9kNCjB1+goIgIeGg2iwNqK5MgYY68Xc4agYVVLhWGsNBjKV1/p0CYNKQfPtXZaWx056/E6H6Bmz39Ttm/1Ufbj3cbmMeFRVlzKkyM435lN1uvM9lZdX9Y/Hy5VjcbhYvXcrVd92FDbju/PPZtm0bubm5dO3alVdeeYU333yT0tJSzjnnHKZecQUFPhtDLZo1ixNnzQKgFFizdi0L1q7llTfeAGD6W29h7nleFRfH1EsuoWd8PGeeey5fzJvHozfeyDPPPktIcDC//vILpYWFWK1Wkj0XXu+5917+cdddzPz5Zy4866zqx33n3XcZPn48T953H1v+/ptSYFN2Ng899BDjBg6kMCODjIwMbnviiZoBsfLymkshy8u9m2DVrpfrcBivZXi4ce7REJmX0qS2b9/OuHHj+NsTWL3uuuvo0qWL8d5mZxtJEYWF7Nq5kza+O+/6C4DVZ1ytIwD24IMPEhoaypQpU7wZlQUFnD5hAmtWruT6m27isdtv57fPP+eNr78mNjmZkp07oaKCiE6dvOe+Dof/Tc2kxcn2LPV2YdTRgpoZYAV5edjtdkJDQ7F7MsAqAygAtnvXLpKSkqrnKClt27IrPb2ZW9W0Gv9o4HZXD1ZERxvZQhZL9UEtf8cOzj7nHJasXEkoEBIcTGKvXmxZsQI7EBYaSlpaGna7nf976y3OnjiRyRdfzPFjxhhXAYKD6dK9O19//bVxcIuOrrnFc1KS96Q8Oto4INbe5tNioUO/ftzcr5//5QShoYS1aUNwfDwlpaW4nE6jeJqZqu107jsAtp+vWVZW1sFngHl89NFHxhclJUbAzu2uztoKCwnh6wULjPcjPBwsFqZddRWW7GzcbjevvvoqNpuNNz/7jM8++4wFn3/OiBEj6N61K//6978pKyqivSdgWGOiYn5fXOxdLilNzzcgVVlpTKThwPpmRYX37335FhavqDA+64mJ+3//Iv7UzvI6mI05lAEmDcHM5oqMrFlPzvy6pMToa/5OyupjXydu5k6mCoC1XG63cTIdFuadH1VVeTPs4+NxuVzs3rCB5LQ0Vs2eTd++fdm8eTNffvkljzz1FA63m3CMiXwCRoZXJUaGVtnq1dxyyy3MX7AAgOnvvUcBcM3557Np82b6Dx3Ky88+ixO48+mnadOmDR898ww/zp9f3cTY6Ghjzgjk5uXRo0cPbrzoIggO5oQTTuDp22+nvKKCk046iZCQEEJ8+7vHksWL+eKLL6q/b9e2LZmZmVxxySWEAOVAONA5MREn8CZgztCPO+88/l69mvzdu7EEBbFi5Uruuf9+IkNDjdfPs9u7X+Z81AxYOJ0QF+d97X3n+b4X6IuKjPmx5qwNz+Ew3oewsD1vq6gwkhTM1SbADTfcUB38sgKdO3UyxrucHHC76dmzJ05g5fLltOnTZ8/7rGszL39f7yVr66effuK0006jXbt2sHu3cb7nKQHTq00b7rzzTs7+4Qd+X7SILz/7jAuvuIIzjj+ebdu2MWfxYpJ79DB2YC0tNc5JFQRrmdzu6vOo3Z7a2HVlgBUWFBDtGQ9DArAI/u7MTNonJVWPc8lt2rB+w4ZmblXTavwAWG5uzd0ai4shJITZ8+fzx6xZvP/++2RnZ3PjVVeRl5eHzWZj+44ddBszhrTBg3ng//4PsrJwOBysLy6mb9++xn1lZnpTVu12Y0CNitozEBUU5D2QBQdDmzZ7T0+t67bwcCKjonABZaWlRJgHUvM5RkQYgR5fB3GSlp2d3SAZYDVERBjBP7MOiSkkpMYSxiEjRxoHGLudISedhDszk+uvv56d+fl8+d57fP7TT/y+cCHT33sPgDPOPJM7nniCNrVf+/x87yTE9/WSg2dOlGsHpMzlAuZ74RsAy8kxTsjCw/cs2LyvlO3SUuP9NP/eH/M+fGt4lJQYbfEXOJNDT3n5nhcg9sa8iu/bP+sTDPO9kuu7Y57vZNezjEKk3sygRWmpd5ljRIRxm7ncKijIGHcPtDCzbx/d2+YPNpvRBgXAAof5ntfneFdUZMyHXS6IjKSwqIiMLVvoFBVFYWEhK37/nekvv8w3P/xAj7592eIpxWEaMmIERx57LH/89BPzFywgrVcvEtLSOO2ss7jggguIBeYvWMAJxx9P7969+deTT2ILDeWu//s/IpOS+Ouvv3jm2Wf592OPcd111wFw9lFH8dvChcR06cIDDzzA5x99hHkpywJ07dq1+vG7d+/Oxo0bsVgsuEJDa2zmsGjhQrb99Re33XYbI0aOJAY4/bjjuPfee0np3p1tq1Yx7ogj6N+vH1/9/jvnHXMM69et4+yzz+bZl16iCiPgcbxno6pyz79Y4IN33qFdly6069KFi888k5EjR2K1WgkNCiLcvGDsLxvd/CyZKzdSU70XQTIzjWC2OT+urKx7JYPbXTPYLfXn2YwLu736vOzjjz/mySef5Ifp04kODaWqrAxLeDgffvghn376KY/ccw/zFy+mT0ICloIC473xjHmDBw/GFhbGzB9/ZNn27Qxp146RI0cSHh9f/zb5jLHffvcdvy9cyD3PPWckWAAlJSXs2LHDOPc0590xMca4X1gIxcWMGTGCt998k2uuuYb33n4bp9XKXytWADBq+HDufeYZxnTvTufOnY3zYAXAWiRnfj5//fYbXQcPJtMTLPIdZaZNm0bX/v058sgjKcjLqw6AVe8CGUAZYFmZmQzu1q36c5jSrh2/zZvXzK1qWg0TACsoMA4GkZF73uZ5w1esXMnrr73GlClT+OKLL3jtzTcJAgYOGMB/X32VYYMGGQcfh8MYZEJDwWcQCw4Pp2+nTt77jYrynhRZLPUPsBzE2uzIyEjcQElxMRG1bywpOfgAmM/JXLYnTT2xoTNprNaaV6Xr4nPwtyQm0q2qim7AuMMP54mEBBZ/9RUPPvggnbp354tPP+XrGTP49y23MGzYMAbYbOQDOWvXEp+QQDwYfUOD/v6pqDD6a+36auaEDbxp/3l5xqQiP9/4m8RE72fJlzlJ9T1pqs9kzgwU7K0eTe3dzCorvTs/+VzVqxczWKFJZsti1mzxVxOwpMToD3FxNQPwlZXGSUd8/J7jsxlMbYhd72rXAAsJMQIZdWXwitRWXFwz68R3OZa5vCU21qjTdLB9ygyA7S0DDLTsPJDk5Bj/m8EVc8dCP0H/8txclixaxGeffUZhaSlfzJ9PWU4OvrPIIIuFM04+mdmzZ1MG9O3Th0vPPZdjjz2Wdr16GfOqK66gsLCQiI4dsXouTk2ZMoXBqanszsrilltuoWvXriQNHMjIkSOJ9ASJBhx2GH/+/bdR9sNksTB61ChISGDq1Kl87lk9MG7sWH7ftIkhgwfXeA5BnpMnq9Va4/jfv18/+nfogMPh4J4XXyTRZuP888+nQ4cOEB1N9169+OLzz+nUtSv28HA+mTfPuDhSUMDo0aNp06ULu/7+m2uuuYYzzzyTk887jwUrV9I5MpI7b7mF9Zs2sWTTJn79+WfMGYIFGH3MMQzq1YtNOTmsWrqUHRs3Ym/XjsFduhAVEUF027bk/f03u3fv5uwrr2TcxIl0bdfOuIPSUu9xyzxRLS83jlvmfNjlMoI4VVXG8Wp/Lua0ZsXFRl+PijJes6Ag72tpHl89/cPtdpP599+kdOmCxWLh+uuvJz09nZ7dutE2NZXNOTmMOfZY5s2bx7iRI7n50kuxXXWV8ZrX2vU0NDSUcUcdxdtvvUUmkAJ0aN+emUuXerMETX6+3rlzJ2efeSafPP88KVFRXHjRRQQBMd26cdNNNwGwceNGANLS0rzzEfO5hYVBcTGW0lKOOeYY/vWvf3HDv//N7AULOG7IEB565BGOmTiRSy66iCQgrUcPnn3hBQaMH+9dJu+P02mM76olFlCee/ZZHr/3XsAIauV6fv6vf/2LNx99lCDg/vvv58gjj6SooIBoT0wg2NNfAmUJpNvtJnf3bhJ8EmxS2rWjpKiI4qIiIg+RzUUaZtbvchkHCTOTqLgYSkqoCAtjzbJlfPPNN3zwwQfk5uXx4UcfUQ5cfeWVXHv55SQlJRFkFg0MC/Omf/sG01JT9xwI6spCaUTVATDPbjb7tL8nahkZxvNMSiI7O5v4+Pjq4nnNyjcI4VnCOuzww43i+RERXHfrrVx4+uncfc89AMTFxpJXXg7l5RTb7cz86COjwGlwsPEeh4QovRyMz82uXd6sqooK47MTH28c7M3JdGJizYCCbxptfr4328CcHLjdRqDBPFj7W+rle9Lk6acbNmyge/fuWDyfNbfbzfvvv89vv/3GM/fcU/dgYfbzqCjj5ND8rPoGxCorawY9CgqM282JZW6u8RzNorbmVur+PvvSvDxLYszNSqqZ9WuqqvzXRTQP/rX7YkmJcZu/nfP8Zbf4ZnLtTwZY7d8zP1OVlTUf19zd9ED6XX2yKaX5lZUZ42J9MnUyM43jnr8LfGb/LC01+m9sbM2TP1/7mwG2r98xsxiVAdbw/H2OzWVy5eXG8So+3pjTmPMj3/dt1y5ITKQ4M5O/16xhbU4Oi+fMISM/nw5t2rB45kw2bdxImedY3r5dO44dNYqCvDw6JCQwYPRo+nfqRL8BA4hNTsZVVUVQfLzxWL61bD1tjI6OrjEnDgoK4vtffyUsOppYT/+48MIL93ianXwvKoN3Z9z8fA7v1q26sPj111/PyEmTjG98aojt9fUDTj31VE69/HJjTm9eRAsKApuN4cOHez8nVqvR/oICjjrqKIiKolfnzqxcudK4PS6OwUccAfn5nHjMMZS43RQGBfHxxx9TsGsX1sJCSktL+eabb1g4axaxCQn0GziQCUceycb8fJbMmUNoZSU5ubnVTVx21VVkAQPS0jj28MO5/oYbSPY9phUVeYPdZhCnrMw7dyoqMgIYubne8iuHKnNOEBVlvB4AbdviLi9n8+LFtGnThlmzZrFo0SJjN9HsbE659FKysrLYnZ7OoI4d2bVtGxm7dlEFfPPNN8RERfH2K68YO4nWDvInJlb3w2umTWPlggV079mTM8aN47H//IcXnn+e2+69d5/zhJdeeomFCxdy4YUXMvrEE6trOc3+8UdumjoVYmNZt24d4AmAlZcbnxHzvMyzoZq5zPaSf/yDjh07sm7TJs496yziOnTg1Zdf5t7HHyd940b+2LCBKeecw7/+9S++++47KuPjefjRR+nRowdutxuLxYKlstL4jAcFGX3K/FyXlxvHq9orLUJCjK89nytpPDN//tn7TWws7rw8CrKyiI6O5g1PAKx79+6AZwlkG2PruUCrAZafn0+Vw0GiT4mlVE/N813LltG9X79DYtVWw3xaYmOND2BhIcTFkb52LU899RQfffwxFQ4HpcCYUaN49847+fXXX0k78kgmTpzoXfNtt3sHlJCQPXdICpBgiW8GWL0cSNaCZ6DOzs4mMSlp37/fVGJivAd8MN4npxOCgujWty8ffvghv/zyCwBLlizBZrMxYuRIbnv6ac6eMoVXXniBY445xphAhIUdEh+ufTIDRAUFxkHODHg5HDWvdBUWGpNt83Pgu2yr1hbnc+bM4b///S9XX30148aNo6ysjK1FRfSqvZTW56SporycmfPmceKJJ/Lzzz8zfvx47rzzTv7zn//g9LTxqrPOol9amv9ggjlJNE8Qi4p46KGHWDpnDu+89BLh4eHGBNg84TQD5ub9OJ3Gwb283OhXvoX1MzKMPme17plhacrLMyZIcXH7nzG2t4wl8c8c/3yzcME4BvieHNT+jPvu5lVZaYz5Fsveayn6BlGt1pq7gPl46aWXALj44osJCwvzH4SqvQTSrONRexmkOfmsvQTG6TQ+i2ZNw9qPYZ4wRUYe2idDgcI3+8bt9gZY3e49C2f79j1zV8WiIqOPVlUZ73tQkHfc8tidkUFhVhbd4+NrLvX2t3yxrkBsXW03+1ddGWB+6thVVVVVL92pVvuzlp9vHG8iI71ZmeaSnn0pLzdei8TEgJmXNQrPRVxiYoz5SmWlccKdkODNas7NNV6DiAgq8vN58bXX+GL6dPr06cOgQYNYt20bX33yCSVFRTiAyJAQgoKCKCsvp3u3bowcOZKxRx7JWaefTkJKSs3yBcnJxuMHB0NICDVeabNfmK9/HWUy2qSlGV/sT2Fjc/luaSlRPhes2rdvX7/NG+patus7ZlssRv8rL68Z2LBYvBfRzIul5jzIN+AARERGEhETw7Rp07zvDfDAAw+A3W4EEcC7ksThgJwcnJWVzJ49m5EjR7Jz506++uknZs6cyVtvvcWbb71Fl759OWbUKKpsNmLCw+nYqRN9e/Wid0gIQQkJ3uVrdrvRR3bt8j635h7z/dWUa8j7zsgwxgibrbpecPVtpspK/vjjD1555RUKg4LYvmYNm2vVFRo4dix9LBamv/46ltBQ7rjqKv51003kFBby8Qcf8NYHH+CIjOSxG26gU0KCt1+Y45o5X0tMBJeLMWPHsmzZMoiPx1ZYyI8//sjsWbO47e6793wOtb42d0tdtXo1v61eTWxoKBOPOoqVK1YYr2VUFOvXrwegu/n++14IMecRDofRvogIxo8fb2w05gnqTpo0iUmTJlFaWsq2ykqOHTKEW2+7jSCLhd1uN59+/jnJ0dGUeua/ZxxxBHaLhXYdOjB16lTaDRliHMdyc2vOLcxAdGqq92vzeGb2A985im8Bfi3j3W/FxcUs+f13EuPiOPmcc/jXY4+xY8cO4+IDRh0wCxgBW7ebwoIC2vfuDQTeEshdu3ZhBZI8ATqAtp5M4B9/+IH0nTsZd/bZrfsYTwMFwPLy83nh6adJX7OGvzZt4u9Vq7BYLJx77rl07NuX8668kri4OHA4OGzCBO/Jpt2+75pcASQqKqpmAOxAJwQVFcY/3wNmrYycrKwskhugAH6DiYioOUH2PVkIDqZ9+/ace+65EBfHuYWFxs9DQhh+yilcMGkSF198Mddeey1Tp05l1YYN3P7444SHh3PaCSdw5Q03GINGUyotNSaYiYl79j9zOYuZguw7CJjBmX0FTPZW6ygvr7oOV2VlJWvWrKFDcTF/r1tH3759sefnU1RcTEZGBhkFBcz//ns2btyILSyMXfn59O/cmfETJ3KUJ8AVFhZGWVkZCxYs4MKLLsLlcjH3l1+45aab+N9rr7E+P59Z771H7969SQ4Px+FwMOOLL+iZlsZvv/zClf/+Nz379weMYp+JiYk89thjREdHc/nll/PYY4/xx6JF9O7WDavvkh9g27ZtLFq0iIEDB3L8uHHE2GxYi4tZsnMnscDEE05g1LhxDOrdm7feeovn33+f7l26AOByuSjbsoUI30BDRYX382Je2TIz3uz2PV/ToiLvJNnccAH23HU2P9+YEAQHG5NW84QvP9+4f3PJir/30cwWqSsAdygoLjZeU98xywwumJMtcwyz243Xzfck3ix+C95akHa78R6bf2cGGczMlqCgmgEwMxhRKwNs08aNPPLIIzirqnjgwQd59plnGDV2LG3N8dPPVWA3Ri3HcE8dMHd5OR9+8QXjxo6lre8VVt/PeW6uN1hrXrRxOo0TLLMouvlaBQX5zxg6UE6n93VpScxgk5nduT9/l5+/76Xz5nvq77NrbvqSmGj0uZIS43Ne+wKWJ3DpCAvji2++4cgBA0jq1AlKS3F7LkbFJiQQnJ9PcXExoaGhZGVlMe3mm5k9Zw45wLN33MExJ53E9uXLGTlyJFFmJo15LPQEg7/55hsOHzWKhDZtvMEnn9fF6XSy8NdfSQ0NJS46mgTfMSc/v2ZhfYuFrdu3s2nDBhJ69WLdunVceOGFvPXKK5xz+ukQEYG7pIRlc+fyyLPP8uRrr9E+KsobyA0P915Aqagwvs/LM042g4ON/l9S4g1kl5V5A4cOR93Zc+bY63LVXOaTmWncb131edxuo12+Gws0l5AQ4/kWFBjtN48xZqafx/atW3nggQf44YcfcHjGqg0bN/LlV18RHhbGxIkTGThwICkpKZx06qm4wsOpKC2l+h1PSDD6bnCw0S+Li72v174CkuY4ta+5U1xc/S/A1pqvfv3VV7z15JO08TlRqlZXIXF/BcdrB8BCQ43+Vvt4HhXlLUAfGmq8D0VF3udofs5978+nr1jMYImZTW+OHcHBkJqKraTEuBBrtdIrKopevXpx67RprN2wgVk//sisOXN44803yXQ48O2lKcnJdOrbl7jgYEqCgjhsyBD6d+jACSecsGfAublUVBj9tajICIqawUzzNcvJ8b7mvuOlOd/1PdZVVBjfm33MnIOZFwAqKiA+ntzcXApzcpjxzjusX7+eL7/8krLyckKCg4mMjKR9+/ZcdOGF1SVdJkycyGlXXgm7dnH5r7/StWtXIwsxNpb2XbtyQ4cO3HDddcYY4DtO+zt2mO0tKzPOHzzPaeDAgbz52We4nE5wuVi+fDkDR44kCMjNzSU3N5fuiYk8+uijPPzww9VLaN3AmDFjGNC/Pz98/70xt3a5WL92LWnt2hFhZljVXh4WHOx9/cx2FhYaP7fZjPE6P5/w8HB6de/OT4sXY8nJoVu3bmzdto3Zy5ez5tdfCQsLY0tBAT9+8gmFgNVm47X//Y/jL7qI7qmpWMrKuOiKK0iMjq7Z/30vgpeWGu+ZuTLEbKuZNWp+vvLyjO9b0Pl3c/vpp5+ocjh46fXXGXvqqQD08dl4Ia1PHzauXk1+Xh643RQUFBDrOXaaRfADJQNsd2YmVqiRAdY1LY1s4MaHHiIeeCcykvGTJrXqVQ0HHXVwOp2MGjWKdWvX0jEkhLS0NC665BL+ceWVdGnfvuYSJn8T2Rb0wu53Bhj4D4CZmT6+J5O+WQhuNzk5OXQYNOiA29rowsKMg6G5q6fJc8WSsjIIDaV9YiLfzpjBrVdfzbPPPccrr7xCeUUFbVJTCYuL4+Fbb+WX33/ng48/rl561yTMzI+CAu8JhXnQz8rynngHBRkZiW43DqeT7YsWkZCQQERcHLbERGPy5QkEFBYWMnfuXFLDwogNDqb7iBG4XC4WL1jAH0uW8Mr06ZQWFnLy4Ydjs9lYu2MHm5YvJ9c8qQAiIyKIi4tj+44dlAN5gDn1DAkOJjw8nMWzZ/PSG28wuE8f1q1bx/jx41m+fDm7MjMpAB646SYeeOIJbnniieqU7nOnTAGgS69e/LVjB5HFxTgAc8qzwlOw87HHHqsOfm3YsIHo6Ggee+wx/nXbbWzbsoW7HnnE+AO3m8svv5xvvv22uu0ZGLs3xQFXnnkmxxx5JPc++iiPv/YaQUAiMKJfP044+WR6tW/P119/zbbt2zn5pJPYtHUrZ555JhdccAGFpaXs/PtvrG3bEl5QQPdu3YxaI7m5NetHuVzGJMkMvpifITMTx1yGV1npLVxtBtsKC42+a/5NWZn3qmZZmTGBMAM5Jn81/pqyz5oniFFRdV+Z8TeZhT2zq/zVmDBPXIqK9qzp6FniUJyfz8ZVq1i5ciUOu53tmZl0iIjg8MMPZ/fu3TgcDgqsViIqKrAmJ/PORx8RbLUSD3Rs04aLpk4lynPSs+iXX5g/fz7HHHMMffv2xe1wkL9hA0uWLOHNt99m/po1nDl+POeecw5r1qwhLCmJnK1b+XPTJgZ0786k446jfVoa9913H06fk9Jp112HE1i+ZEmNEze3282WjRtZ8vPPPPXuuyxbtYr533xDUVYWH3zwAf/98ksG9OnDq488wpw5c1i+bh1TLr+cjz77jIULF3LC4ME88cQTxp2ZFzHAu/wfjP5ZWmq8XpWVxvc+ux4THGz8vhlECw31vi9mBqT53prFfkNDvUuCzQlr7eUQ5pLpyEijH/tmPDUns33mSazvCa8nK6O63yUkVAdV3GVlVBUVYSsrM/picrL3OOrb981aiKmpAJSXl7NlyxY2bdpEv+RknOXl5K5Zw9a1a8nKyCA9N5fc9HSio6M566yzyPjpJz7/4AO2bNnCWs9Sl5joaC644AI2bdrE7NmzKfe8z2NGj2bBggXYIyLIA1xFRYQB8cAjDz/MbQ8/TBlGIdxnzOU35lwhLo6NGzZw+RVX4AbSxo3j2zfeMOrUePru+vXrefTRR3n7hx+IAcLtdlYsXWrsxAfeEx1PX7vn3nt54okniAYyMa5AW4Fbp07lhfvvp+uAAWxdv551q1ZRiTFZ7xgayiMPPcSR48ZhLS0lPDycBQsWEBEVRZ7LxREDBhjvS0KCN7M2PNx4TJ8druoMgJkFxuPjjfaWl3vrP1ZVeQPY5nuYnW28TuHhRh8xd7X0zR5tjr5stxttyMpi419/8d1HH/HlZ59RXFxMVVUV5eXlxMfHs23bNmzBwZxzxRUMT0vjpJNOwmq1smXLFjp17kyIWRbEE2C0xsYSHBvrzcqyWr3PKzKyfkHi6OjqC4z14m/HvbrUKh4/8ZhjmDhkiPf2vQXSfAPRtTfZ8f07c0yoqxat7+e79oVX34CWyWr1Zo6Zt9nt/ndyNPtsdHSNi1u9xoyhV8+eXH311bgsFkhOZtXMmbiqqli7fj2zZ85k85YtZJeUkAt8MWMGiUDXLl047bTTGDZsGF1GjaKouJjdGRmMGjuWEH8XUBuTme2Wm2t89iIijPHRvNBUWenNtm7b1ruBRnGxd0mt3W68ljk5Rr+xWnEDm9es4ZvPPmP9+vWsXLmSHTt2UOl2k1VRgRWIAZzAgN69Ofzww7n11luJ8ldHyHyM8HBjqSsY7axd0sb3HGtfwdtafa5fv34UvfUWs37+mf+7/Xb+WrGCT775hlHHHMMdd9zB4sWL+WPrVm6//fYad3P4yJE898ILLJozBwtwxBFHMG/lSnatXs1hnou2fuuURkUZr695bDOzG81xu9bnNK1fv+qMui6dO9Olc2c45ZTq2xeeey7thwzB5XLx0r33Mv2tt6pXYnz/448MnDiRIIeDZKuVK6+8kijfz0J+vvfzUlTkXRZpzlX81bCsHcBtxQGPg/H1118THx3NyNGj/d7+659/csLw4ZTk5OByOiksLNxzF8gACYDlejajiPOpMd6xY0def+cd3njjDVbPmsWXH3/M+OOPN8aQvW1+1oJZ3PsaXHwMHTrUvWTJkj1+/v7779OtWzeGdu9OkDmJN080W9EHacWKFRwxYABvvvoqJ0+d6o2qm8z0U1NOjnGiadYGMTuQOfnx/X1z4ggQH0/vlBQmnncez7z4YqM9nwZlPifPiUiNZWWeHQTXrl/PY488woYNG3jnnXfo0qULzz//PA8/8gi333EH195zT52TOovF8ofb7R66v82qq88C3kCJ54q0IzOTv9auxV1RQYcOHUgxo+N2Oznp6Vx62WUs/v13ALp07sxpZ5zBcaefTnRQEC+8+y7fvv8+2bm51csVRowYQV5ubvXWsr379SMsLo4da9fidruJi4ujX//+jBo3juzMTHa7XKRv20ZQfj49e/akc9++JHTtSo+UFDK2bSO1Rw/at2lDWU4OL779Nu/+739s3raNCiA1KYmH/vMfYhMSOGnCBPJLSzl+0iSGDx/OiBEjWPXTT0RGRvLq22+zpbCQ7hERjDz6aHZt3sxva9Zgj4mhf//+zPPsAvLOO+9w/vnnm699dRAuPTeXpVu2MGzIEFKAYUOHsnv3brZu20YGMLR/f+Z9/rmxDA1wJyQwcdIktm3bxlHDhvHV9Ok4gCCgY9u29O7dm59nziQXI3hmDrGVQI7nZ72Sk2nfvj27d+8mqVMnenfpwu7ycjZt2kS7iAiKw8LomJREUng4jvh4rLm5rF+zhoFHHklEYiLF6ekcO3o0w4YNq5n1EBFBWWEhW9atIzU1lXhPgd4aAVBfvp9Xs96VufusHw3eZ31rQ0RFGY+dn+89GTB3FAPccXFsW7mSPzZsYOemTZx+3HG069SJAoeDl956iz+//57Url1p27s3nTp3pn3btsz98EO2b9/O+vXrKSgowBYezi7PxLhTSAgVFRVkZWVRXlGBC/A3ra8CdgOpGO9hCRAGJISHs6u0lHaxsfTt2ZNdu3axbds2XJ7jT2REBC6Xi5zKSpxVVaRERDD68MP5yafmQj7GRLsUo19YgTKgGDiqXz9vvRjAAYwYNIgpN93ElAkToKqK//3vf9xw771Ee9pYBUQDERjFpvsNGMDSv/7C5nZTDkQEBVHlclHueYxEICkxkXPPPReXy0VVVRX2iAgioqLoMWgQE4cOhaQk3A4HFjNYkJREVVAQVjNQk5JiBLPM467VavzM4SBz5Uri2rYlxBx3fMdUz5bbhIR4T1DMnYddLu8VXTBOos1aNbUzG/dR3+xA+yzspd+a29Y7HEZ7ysqoKiwkMz+fjI0b6datG7GxscYJRGUlvy1axBVXXIGrrIwuXbrQq1cvegwaRKjLRWlxMatzctiwfDkleXkU5+QQExND36FD2b5tGz8tWUJwZSVhGFf33J5/5isQFhpKckoKqzIyiPScDLZt04b4zp3pn5bGnB9+YNuuXcbfhIYay1Iw+knPtDQOP/xwXn3rLbKA26++mu1r1/Lf//6XvKIivlu0iGnXX09YWBhbly8n2m73BhPatuWVZ5/l3uuuww3sAga3b89Tjz9O5969uerii/lz2TLA6JsxGMspHn/wQS6ZNs0Yt8w+FB7OsvnzGX3iiYw++mj+dcklvPXxx/w2fz6Tjj6aDz76qHo5RucOHTj7rLOYM2cOPy5dSgJQiPH5CQFiY2LIKSiozoI4Ytw4evbsSWhoKOHh4ZSXl7N41Soi4uJICg6mODSUoq1bsUZEYE1IIMlup2+/fgw+4giysrIo3ryZEUOGEJ+UhM1u955wJybizsqiqKiIyMhIglJSjD7rb5ytXSLBrLlVuxamj4Yea9955x1+/vlnCjZsYOnixTidTjp17EiPHj2oslpxx8RgKSqiV+fOXHjJJXQaPtz4zIWEGO9TaKjx2TQDIBUVxjhtfhbNz3agZWD41r2C6uwVwJu5l5trPJe4OG9gPizMW/y8dr2muDhv5iDsOU/eX3VtWpKba7RhX/dvBmDN52oGjgoKvMtOk5KM47vDYYynbnd19pM7JYX8/Hzmf/YZzzzzDCt9ducsAqKAuORkjjj8cP5xww1G7TI/GmVOC96gsk89LrfFQl5ODrGxscbFRM/7WlZWxqxZs0hMTCQ/P5/8/Hx2ZmWx6s8/qaiooLi4mBUrVlTXqgMYOGwYwwcNYvXKlaSnpzNkyBCuuPJK0saNI9hcihcfb7xeYWHei5BmhndqqvfcKTq6ZtC3osK7KY5pXyVTzCWwERFQUsKKFSsYfdxxWO12wioqcANPvfgip513Hv3atKGktJQF69bRuWdPwDhOJAGfzprF4cOG8fdffzHKE+ToOXw4qxYtYuqFF/LII48c+OfV3zmf79Lk4GDjdTCXp5sXW7OySN+6leyiIjbt2MHNl19ORnk5URYLVrebkOhoRgwYQK9eveg5ZAib//oLi8VCbGws3bt3Z/DgwSR27EhJTg5hbdpgNZdvm8yLTua8wMxYriNLt1HmBy1EWocOHHXYYbz83nt1zvknjxtHVlkZn33zDb1TU7n1sce4/tZbWb96NUf27ctTb7zBORdf3LQN9+O1F1/krn/+k983bKCDp2aZr+OHDSPEbufL777zZiXvz86qAaauftsgl9TOPfdc4wszeyA0NLAO6g3EzAAr3Z8i+OaEYG91PMyvPSrKyykoLAysJZD7YtYiMSd4vhNVT2ZNryOP5I3DDjMCEHY7OBz885//ZPXq1Tzw8MP0OPxwjjMLrTay6dOn89577zG6e3eGjxnD+nXreP+NN9i0eTNujKvpp598MuHh4WzZsoXly5fjdrs54aSTSI6PZ/Xq1Tz95JM8aWaEAEcdeSQXXnEFlVVV/DV/Pj/88AORiYk8+tprHDluHB3Cw9njU+GTYVb92pkTOZ/JQYe+fav/JCIiglv+/W9u+fe/AcjKyiIiIsKoteURGxbG/PnzAc+ygCOPBOCmW2/l308+ycUnnkifkSOhsJCN2dkUOBxER0fz008/cdVVVxl/43BAUBAPP/QQz915JwC7d+3i5Zdfrn4eb7//PqFWK7///jv9Jk4kPirKu9V1SgoWq5XvvvsOl8uFxeXizmuvJTU1FUtoKEExMQQFB1NSUkJQcDCvvfIKxZs3Ex8XR2KHDjgiIyktLWXGd9/hzshg6NChbN++ne+//ZbomBhSO3YkLy+PvPJy1q5cia2oiIqKCpxOJxEREfz622+UYgTVnn78cfqnpZHWqxc9unUjJyeHn376iaysrOqlK4OHDeOJZ58lrUMHfl28mNdefJF58+YxZMgQxo8fz9Q776SsrIz1Cxbwx59/snv7diaccw6He17bRhcSQn5QEL9++SXzFy7kh3nzSI2KwgUMGTqUcLsdi8XChg0b+OOPP8jyKVb8yN13E+IJlOU6HHRNSuKXX3/FPP2sAuwYG1j069+fPn36UFxcTPvycsKTkrAVF1NpsTChTRtGjRpFlz59iLXbCQ0NJTMzk3Xr1hEWFkZsfDxhXbrgzs5m19atBAUFMXr0aMKSk1m6YQMP33kn21euZMDw4Zx88smcf/75vPvZZyz+8086tGtHZNu2DBs2jCGHHUb74GC++OILnE4nQ4cOZVVGBrs3bmTBkiXM/uEHSoqKCA8L46ZbbmHy+PGM8znR6NG9O0v//JPvzj+fPt9/z2OPPsrcuXOrAyFr166lsKSEd158keG9ezN27Fjat2/Phg0b+GvFCoaddBLLFyxg859/ctFFF5HncHDnddexPi+P+597joSgIIJCQsgtLycKI9jXJj6e3UFBZOfkMKBjR1Kio3ECv69cSfuoKBISEggLC8Nut7O7qorQykqqysv5a9cuojwZjLaQECK7dWNg//70jImhb9++FFsslGZmkpefT5XTycRTTqFnx46Up6dTGhzMulWrWDR7Nm27deOI4cPp2bOndyMLh4OdWVmsmDeP0MhIhg8ZQoR5gtcEx2iXy8UJJ5zAn7//zpVnnsllt97KinnzuOeee9ixcyeVGIGYEcOH07VrV5b88QfrN2ygfdu2DDj8cIqLi/nhhx+Y/sEH1e9dBdA5NZVuPXqQMnAgq5cvZ9HcubRr146bzjuPnmlppKWlsXHjRqpiY4lxuejfty8pKSlEejLkdhQVsWrePKisZPz48dhSUyEkhOK8PN6aPp3RY8bQpk0bnn76aTavXs0rjz5KdHS0Udrh/PNZvG0bl55+evXy34hu3bh88GD6DRjAuHHjGD9hApecfTZnnnkmyZ4aTbNmzap+XW666SYWfvUVF1xwAR169GDzmjWMGT2aQYMG0WnoUMoyM3n2mWf4/fffuQRqZIhXlJVxxx13kJSczCeff050RQVHH300FRUVFBYWEtWtG9dfeSXbV66kd+/e2KOjueSSS1i3bh3Z2dmc+c9/cvm0aXSx29m+fTvlISFEud24XC5mzZrFkiVLKCsrM4LTFgu9evZk09q1FBUWklFVxWEdOmBxucgsLoaCAt4pKSEDY/yIxwg+p8bHc+LJJxMWEoLD4cDhcPDrr7+y5e+/iQgPp3PPngQ5HBw2ejTd2rfHWV5OdnY2f/zxB5179uSoM89k6KBBtE1MZOmvv7Jq+XLOuPpqIpuoVuPff//NrFmz6J6czJRzz+XSSy+lx5AhWIqKjM+OuUQ0O7vmjnBg3G4WxzbVzpYLDfUW1A4ktdvjm/UH+87G8fd8zCBofHzD1NqsKxMwPr5+Sz3NeZbZVjMDxm43gjbm7Z7l8dVZeiUlYLNhsVqJS0jgxPPO44RLLiEvK4sZ06fjdrsJDw/H7Xbz/fff8+OsWXQZObLOAFhDW7p0KTfffDNbV62iQ0wM2bm5DB08mB07dpCVlcX67Gz6dunC8AEDyC8oICoykoULF9aYJ5iSEhNJTU3F5XIx7Oij6dS1K1f94x9sW7PGCC6ZdZ/NLFFzcyuzbltoaM1sX6vVCK74LpWFPfuD3W7889Shq/4s7Y15X56Lf2lpaQTbbFRUVHDWiSfy5bffsjszk3lz51Liufj58ccfe//c83/37t3BZqNz584sWbyYocOGsXjRIkKAbt261Xys/RUdvWemVXR0zXI4dvue2ZpRUbRt25a2iYkMsNk4dtw4XBUVREREsHTLFv778svsWr2at956i/RXXyXOZsPmdOL7KQgLCyOvrAxHeDiXnnAC4eHhrFmzBrvdzunnnUdsp07s/OMPgoKCyMnJYcCoURxzxhkH9jxbI7ebzA0bKN6xgyFTp+51DIuOi2NzRgYFu3fjBqI9iUBmEfwK3xUlzSjPswotoY76kR27dmXJr7/usetqa9MgGWCHiqysLHokJ/PEQw8x9Y479swAMwMZ5oCdleUtPOtbZNb3yr55sC0srE773VlSwrC0NO578UUuu+qqJnp2B8ksbL2vdHvzKpu5hXR6Og6Hg/9+9hlXXXttnbXAGvpq2YsvvsgLL7xA+urVmC3u3r07102bRmTbtnz100989+GHxNntdOjQgXZDh3L5pZdy2ODB1Vfit23bxp9//klOTg5hYWGce/753g0cMjK89VrMwFRJibc4uN3uTTuvvTQ4P984+Ne3MHF9OJ1G/yorMx4/N9e4+pOTs+dVOFN6unHQTkpi7Zw5TJgwgW69erFq9266derEKw8+yEDPzkyAcXWrqsqbqeDvSqwZ3Kvrqp5ZZ8Zur1mE3Lf+jCdTBJfLez9ut3E1uqoKt6f+TllZGUVFRURERfHcRx+xau5cNqxezbYdOygIDeWcI4+kfdu2HHbYYaSnp/Pyyy+Tm5dHVWgoueXl9E1KYvDgwaxZs4at27bhsNtxut2EeSb0FcAVt9/Oww8/7Pclb+g+u2rVKiZNmsSuv/8mJTiYESNG4LRYCKqqYsmSJRQ6ndhdLrp07syQIUPoN24cwwYOxGax8MX772OxWHC5XJx66qn079+f3bt3k5WVRV5eHkVFRQwYMMAodhwW5l0i4nvFMCXFu5zPYjFO/pKTja+LiryZdW3bejNabbY9l7GAtx/sLbXad5lQVZUxGS4oAIuFqvJyKsvLKXI6Se7SBXdWFjfffDNjx46lbdu2bNu1i2lXXcUuYFDbtuxIT+e8c87huNNPpyQri9Ouusr47HmyU6uZKeHmJKeqyhjHzSU9bduyYf16ol0uUrp3x+VyUbx5M7fffjs7duygzZAhxMXFsWTxYqKKi3G5XAwdOpSysjIyCgtxFxdTUFpKeVQU4TYbCW43yampWNxuoqKjKSstZeX27axbvZrC9HTKKyurM5hKQ0KIBpzmCaWHE7BYreyuqqIN0K1rV0aMGEFVVRU/LlrE6r//rs7gDAsN5cqbb+b6u+/2u8NwY1zhnTp1Ktu3bWOlTzZfWo8eXHjhhSR1785fa9fyzTvvUJSXR68hQxh25JFcfdllRNlsUF6O22Yjy3Oc2LZtG927dzdqdJknYeYJnLmcMDra6Cdmlop5bI2O9i7RN09k/B2La3O5jPszl1lFRBj3Yd6vGRDxmDlzJjdMnUr21q3EREfz6KOPMunyy2mfkMCU449n2nXX0WnkSLJWrWJgv35UAHfceCM33XSTcQeefn7FlVfyw1dfcddjjzH+mGPY8McfJKWk8Pb06Xzw7rs8P306p0+ZUjNrx1wi7jsOJyYan7XiYrBYqExIMJbmmc/dbL95zMJYLlxRUQGJiYSay5HM44TPZ8btdrNs2TI2ZWeTEhVFSHAws5ctY/XcuSxcuBCH240zOJhYq5VOnTpx3HHHkZGRwfr16ymrqOCnFSuIqKwkFLCHhNC1a1d27NhBZnExdiDUZsPhdFIEzPz9d2PnQD8aLZvGrO1nLgmvfdHSrOG2v8szawcCAoVZl84fs9ahmXVcOwPMLBBfe1MRU63PSbOrqjLmFObmOW63kWkbFubNfPOdh9X1npmfNfM+PMeKYoDoaCPo7kdD99kNGzZw8sknM2rYMBzp6ZSVlbFp9276d+5MeFQUHYYOZebPP1O8cSNxsbHsKCmhc/fuXHnVVUSWlhIdHU1U27akRkYS3rYtVnNFijmPM+fuvvNFs86hb5H6upbQ5eV5x86qqj0LyvsyN0eqT38xX3+brbpW5rjjjmPZX3/x5H338eBjj3HehRdS4nbz0csvA9B50CAW/Pknxx57LLHh4WSvXctPS5disVqrL+C++uqr3OTZ/fzdV1/l+MmTG24+Xvu57m0c8E0s8J0Ht21bvZN8Xl4eWUFBdE5OpjQzEywW1qans2zuXLZt20ZSt278tXkzv3kCf0m9erHtzz+pcrlw4w0CuoF2Q4ZQ15h4SGaAlZfz4wcfcPEll/DJnDmM2ktA+8aLLmLG99/z3vTpTJg4kde++IJTTjmFjB07GNKhA/c/8wz/mDatCRvv3x3XXccHL7/M5tJSv/Oeh++8k+cffph5c+cyY8YMTjrtNDoNG9YMLW0YjZoBdqjYZwZY7RP/uorz+uMTiNztWe7SojLArNb61ZowD5Q+V4eCg4O59oYbGrFxe7r66qu5+uqr2bhiBemrVtGjb19S+vQhyOWC4GCOPeMMeOEF74HVN1LuqWHQMSqKjr16Gc+psLDmFS/z/fQ9gNcOBNRVRDgy0ltgvKHYbEZbSkuNg6jF4q03VHvJAngnsZ7bevXqxX//+1+efv557HY7/77jDgYOHLhn/zbf27omNmZgpa7nbgYeaj9386TOs9Ms4K2ZBMbzSUoClwuLJ+U+PCfHyIoLDeX2O+6Aq6+G0lIqq6qwtWtHkFkA31No+/TTT+ebb75hTVYWfQcN4oLx4wmNicEdHc1P06ezcOFCqqqqGDNmDCkpKfQYNIjyJtw9MtFzVfaZp55ibI8exKWmVtf8qKqowOUJGAabNQZ9ApD9zTTnqCjjRDgykhSLxVjmm5Dgrc9j1o3w7GhEUJAx4TJ34gTve+cb4IyNrd41CTD+1qzF4o9ZJ6Ou28EIuJknKWC0xVMY32q1EhYWRpinn1gsFm99LmBYSAgpcXEcMXky6enpPPf885x92mnezAt/zM+EL6vVeG7m1u5AD3N3NYzAVHT37rzwwgt7via1lzgkJe15Ap2RYXwdEWG8dtnZ1cvCSlwuduXmEu5wEBsbS2j79pTm5PDTF1+Qk5NDaGgooaGhREdHc8SJJ5JRUsKc997jhx9+4OuvvyYoKIgOHTty40MPMaBvX0pKSnj33Xf57Mcfuem+++p+3RvYa6+9BsDir79m5V9/kdCtG8edeiohNhvYbJwE3DltmvG++C5z89SosURHk+x0QlgY7Tp2NPqE2W98Aw++Kfq+GW5mnwwPr/t4vLfjdFCQd8zxDdpHRRn9qdZJ2vjx41n+119sWLqUm266iWuvvZb5mzdTUFDA2LFjjaLPQFJSEu++8w7bMzO5cPLkPR72xBNP5KtPP+WGG28EjKW6Toylv9defTWnm1fpIyK8gT/f7JXoaOMzaQb9PLuChZivmRm0MD/XnuWp5OdjsdsJjYszXjOr1XhvzEC1+dm12bCEhTFo0CCqq5VGRzPipJPgssuMkzOzrpW5hDcpybsBTUgIrvh48jZvJgwIS03F4nRSmZ/PkiVLWLt2Ldu2beOwww5jyLHH0r5r17rfo8ZSe/MT3xNVi6XuOlb7EmiBL9PB1qva2/MKlGLxJqvVe8EDjLbHx3vbGRbm/fyYt9d1P+ZFTYvFmDO6XET61ittAj169GDNmjXGN8XFxmfepx4vFgt33HGH94KB73P3t0TPXBpnqrUTJ2D0l9qfgbpeJ9+xs3aN0doslvoHS83H8+xKj8XCgP79+euvv+jtyfzN3LWLeUuWcOQRRzBn7lyW/vkniYmJzJgxw5hrm6UlfProP664ggsuv5zPP/6YYyZNapzgl2/76+L7mTSPe2Y7PRfQ4+LiiGvTBioqCImNBYuFkb17M7JvX+M99BxX3Q89BG43lnbt2LFoUXVW8NixY7FYLEQnJrLL37L0Q5nTyZ9//klpUBCH7SMIFB0XZ9TJXbuWSqCnZ5mtmQEWKEXwC/LyjI0J6xjvO3vOF8YdcQQVwB9Ll/LGjBlN2MKmoQDYfggNDcVms1FQOy28LvuK7PvbRQfI9lxVa1EBsPqqvTtafdPWG0n3/v3p3quX92DrO0mzWo3Jb+1gjG9mkql2NpOZ4XUghXttNm8mWUMKDTWu4pWVeSdrZnaNadeu6q3dq9vieX8mnXQSk04+2ThRMjMv/PXvva0Vt9n2nm1htdZ9e+0iwbUnl2aABLy1kgoLvW30TGhDzB0GfScRoaG069iRK6680qjzANU7KFmAiRMnMnHixJqPFxNTHYBpCikpKcyfP9+7PNVcZpOQgBWq6/jUmNjWFhXlDQiEhtYsZm0GhnwnnuZW2nsLVJl8J8/7+pvQ0H3XarFajX9mPRHz+8rKfY4ZFouF4cOHE2yzkdalC2ecdZYxQTYn9L59wtwdqa4TtNDQvW88YL4PtT/r5rJw8z58H9f8Oj6+OiBZfbLtCWBGtGtHt7ZtjRMqT2ZxRGwsp3p2IKqupeKpU9MpMZGLLrqIiy66qGbdMB9HHnUUxVFRRh2YJjbs2GMZdswx/gP7MTHeYs2m4GBvXzY3tHC7vRmk4H1Par/2tV9n3w1nfMXH+78AUB/+AqbmTUFBpKWl8c477zBp0iT+7//+DzswduzYGn336KOP9mYumDxFw08++WQ6zZjBnQ8+yC+//sphhx1GaFQUg/r04ZZbbvE+R38nobDneFn7hDI21tvvzOcTHu79zJv8ZXD6jvFmJjF4X4+QEG9NKN++ZgbmPDsBBgUFkdCli3FcCg8Hp5OQykpGjRrFqFGjvH8XaLWyWqt9zVfrGnfr+rlZSwuarhj8wfA9Ztnte5+r+PIX6GrODUj2Nk/yN3/1N9+sPV6EhxvPqQkv+tWLb5+12cDlYtzYsXz1xRf07d+fpORklixZwo4dO7jjqqvYtGkTeTt20KNHjz3vq9ZYZXc4mHzWWYHznIOCjPfPt2/5zi3Mn5ufx5SUGrtZW1JTq29r37OncVHB3NW+pATsdrr56x+HMqeTBYsW0WPw4BplZvyJjovD6XCw4q+/wGqtXjobaAGwvJyc6gL9/gwZNYoSIBgoB6qcTm657jr69ezJRVdd1WqOxQqA7QeLxUKnzp3ZtnWr8QN/B33fk6g6anzVuN3P11meTLJWGQCrrQkDCHXa25WmA73qY55YBdJAYbV6dwMyX/faATBzFy4zAywoqGbBbqez5kT4QJ7fviaUDTVRNq/gmgEfM+hnTgh9JwsWizFZqGN7dcyJg8NhvH6+W6w3oeqdUvf22P4ma+bybF/BwTXvJzTUf6Hp5v6MBgd7T/DNoJLvuOpvbLVYsNvtvPbaawxMTcVqBhlqB8Aslr0X2DXVUfS0mrn5h6/kZOMx8/PrzgSoXSvF97U3i9Wbu5HWXi4dFGS8X77ZosnJ3iUT5mc9Ntboyzk5EBJS55KcRre3kwgz47Au5mfV9/NrMpfmHojG6tue9sTGxjJz5kxmrlhBsMtFgr+TC3+7RLvdYLczcOBA3njjDe7+97+59447SOnQwVuX42CPLXVlWezv/drt3iCeeX++fdaX+b1vQNIMcoPRR5KTvXWjzGyVQDqOtmZ7e53Ly2v2lzrG3Rqaexfag3Ugc5HwcGN+EKjP3d97XN/svEAJBPkyj5Nm+RmHg7POPJPjjz2WqMhIUlJSWPD771iAvn37MmzYMP7asYOBnToZ44t5PDRfF3N397Iy78W2QAre1r6w6HtR0+xzvsdL39/3fR6+ma0OR83drKVaRUkJi5cuZfI11+zzd6M9x7Hff/+dLmlp1WUm7AG2C2Rebi6xe0lU6NmzJ3lVVdx33308dP/9rN+wgZ9nzgTguOOOI6U5srEbQYCO0IGra7dubN68uX5ZSweZAZbi76RKWgZ/GSGBICGh5tbHVmvNOnYmsz/6BhhCQoyTHN8MnEA+MbFaay5dBW92FxgT1aqqmife+1oiZS7xNJcGtRQteYLry3wf6lmM+fzzz/dmNULNAGdD8vc5MINQdRQarfN+EhJqvl/R0d6NZfxlOvkGcXxvr70Ts7nTYmsTiJ9Dn/5gt9s54bjjvDuh1Va7L9f6PikxkZdefLG6vo2/x2h2iYk1L/hERBjvi3nyZS59ry9zHEpKath2yt7t7US/rMz/rp3QMuYDTcWskaXXoumY/c8TiLcAUZ73ICUlBQtGnau0tDSjdMfnnxNizmV9d5wE79hj1jusPe4GusTE/T/3MLOtA33+1wyWLVlCSWWlkb29DwNHjADgrxUrGO2zkYDVZsNC4GSAFeTkkOpZnlmXoKAg7rvvPsrLy/m///yHUIzdqa+96io+nDHDezG+BQugsHbL0L1HD7Zs2YJrbzs7Atu3byc7K2vvE4o6AmC70tMJCw0lorHWnMuhy1z2aKpVuLWavwCYeTJTqwg3SUl7X3YXqMylUQdydS8Qg5uHgtrv1d6W5fj7O9/ssUBlZtSYbDZv/SWLxQgu1CdrzbwybtpbDSxpWLX72N766N6Csr6ZjhZLzfcvkPqxv81cfHcDj4w8sGNE7SxVaVy+y2r92ddy4dp90mIxsmMPtYu5Gmebh7nBiTkvsFgYMXIkYATAYmJijHpXwFFHHWX8jbnaoa6LWL7/twRmbd8D+Tsx5OUZGXEuF4sWLsQJjBkzZp9/Nmz4cM67+GLatWvHtOuuq/65xbMiwVH73KmZFObn7zUDzFfv3r1xAaXA5Vdfzcwff2T9+vWN2r6morO4/dSte3fKystJ37mT9rWWYjidTmyeyVrHjh1pZ7GwMTOTgsxMjhg+nKNPPplu3bpx07nn7nnH5gTX7eb3+fM5bNCgVhFhlQBnBsMqK2vuhmeesPmenJknOZWVNVOsFQySplLfiV3tpedm8MB3R6WWynfpggQmfwGw/a116TMnqF6GE6gBMGkdzD4VGurd1bc+9ta3W/p4Ky2HGQAzA7UWC5MmTQJg2JAhgLGhU/r27cT4C9bW5lsfUVovlwtneTm3TZvG7vR0Qtxu0tLSuPqWW/j999/p3qsXifW8gPP4668bX9TqM8EhIQGxBDIjI4PsrCw6dO5cr98/88wz+fTTT3E6nZxz/vm89OKLLFy4sLrAf0umM9f91Kt3bwB+/uknZkyfzrR//pNRo0bxxhtv8OSTT/Ltt9/SLjkZC+Byu0lMTiYaKAReeuklAC6cMIGkpCRKS0qY8c03nHzyyWTu2EGblBRWrVjBmlWruOmOO5rtOcohxAyAFRfXDHb5ywAzgwgHWwNM5ED5O5mqTy0ac6lgaWmT7swlAhhjax0Z33v9G9/fVwBMGpvVamR022x7D4A5HP6XtKpPSnPwLehtXjTwfG212di0YQNBPsv/Y83ljbGxNS/81uZbK1Rar7w8lv7yC+97dqzu0L49M2bMoKSkhEWLFjH+vPPqf1919JXgkJCAWAL51WefEQQc5wkM70tkZCRff/01AK7CQqzApRdfjMVi4cILL2y8hjYBBcD206jRo+nSuTN3XHYZAPPmzOHiiy7izbfeAuD+++/n3y+8gPkRMKe5vh+JY489lk8//ZSXPvyQx597DoAkwAWYSajj69k5RQ6KGQAz08BNdQXAzKtrzbhzpxzC/C2B9Mff5iNRUUZQobmKwMuhw3cpWe3gF0BGxr4zZ82gl89ynhrL13VSJo3BXHIaHb1nzTrfoKxv7SRdEJPm5Ls7n+/GTZ7+GBMTY/TrsjLjNrOWne/y6r1lgGm+2zqZ72tFBbNnzwbg/R9/ZNy4cZw/ZQqvegJiZ5599kE/VEhICI7a51nNYO4PP9CpY0d6Dxq0338bFB7O2DFj+PnXX7nhoosY0asXPYcNMz47DofxeQnEmqx1UG7yfrIGBXHzzTfX+Nk777zDmNGjOWnSJL77/nt++vFHLEBcbCx333NPjd895ZRTKCkp4YYbbuDvLVuqf27BCIDdcP31/PrbbxzmWbcu0qis1ppXzEy+E13fnfN8Jxfmz0SaSu0AmLkrJ/jf3t13ImzuNqclOdLYatdSOtAlkL5f17Vro0hjqO+FAj8BB5FmU6sGGFBzGTl4l0ju62TdDKzV3nlRWqx5s2fz0nPPQWUlVenpvPnII0ybNo1XXnmFwSNGcMQxx2Cx27nnkUcoAlK7d+foo48+6McNlAywHZs20bVnTywHUrrGZuPF119n7g8/EGmzcek55/D644/zv8cf56+ZM9m9ciVuc5fqFkAZYPsrOJjTTjuN/OJi2iQl0aNHD0JDQ2nXrh1bt27l62++4corr8QKzJo1i+jOnXnmvvuq//zuu+9mYb9+PPjQQ9h81hTP/+03lq9dy2nHHYclIkITCWk6Vuueuzv5C4BBzcmF+b1IU/HX30pKjP99D+jqlxIIgoKMvuiv0P2+diR1uYw+7Xsyp+CtNKXw8H3XAqt9UUykOdVaArnHRiLgDYDta0l5cDC0bdt4bZUm9evMmUyeMAGAeZ9+yp/Ll5Oen098RAQTzjmHO+66q/p309LS2J6Xh9PpbJB63CF2e0AEwDK2b2fkqFEH/PcJ3buT0K4d/SdMYPaMGdx5221YMFa7uYDI0FBiu3Xj2ZdfZvTo0Q3V7EahANj+stmgbVsuueYaKCiocVOnTp147dVXufX//g9nRQXJyclYIiJ45eWX+f7XX3nxnXfo1asXNs9uEs7sbI4cMYJL//lPunXtSre+fY36NCoqLk2prrpK5kTCDDD4FhJXAEwCmb8i+CJNxexvNptxslVVtWcf3FcAzJfvyZtIU4mJMTLBdu+u+3csFu8FNPVRaW6+fdD369pjqC4mHDqqqpj/44+cc8IJJCQkEBsTw9y5cxk6cSL3XnIJkydP9vtnsb615Q5SVFQURb7LxptBfl4eJUVFtK9nAXy/LBYID+f5l15i1qxZDB06lNDQUBYvXsyujAx2LlvGb3Pncu4RR/DCu+9y0jnnBOxxQZGWA1VH6uzxxx/P8RdeiKO8HEtRUfUuJJMmT+aFt9+GoiL69OlT/fv/ueMOhp18slETxGLRVrTS9CIjjYK2ta/0mplhPjvqVA9k+3PyJtKQbDaj/8XGQm6u9+cBepCVQ1BwMMTFGRsv5OYaY+iB1MYwgwu+J29JSd4xWaQx1a4750/tDHGR5uQvyFV7CaTvbf7+Tlq88vJy7r/+etatXUt0RASzfvyR5I4dmbdiBVF2O2UVFYRFRzdZe2Li4sjKzm6yx/Nn6+bNAAcXAPPo3Lkzl156afX3aWlpxhdOJ4WbNnHu5MlMu+gi5v34I3MWL8YSFsbll1/OP6ZODZjPmgJgB8rfpMBneViwebu5BAKMiUJRETabjfvuvZfIyEiGDR1q3KYrvNJcQkONf1VV4Jui62+C4NuX1V+lOSQlGf97rkT5XaJj9s3i4qZrl4gvc7dRm80ovhwUZHwdEwM5OfW7DzN7DLx9OjhYtcAkcPjbPVqkudTOAPPNBvcXALPba857pUVyVVUxffp03njtNXZv3cqurVurN5ULDwvjjMsv5/rbbiPKE/QKa+K6bjFxcWzcsKFJH7O27Z664526dm28B7HZiO7Rg7d+/JETxo5l+htvAMbyyLsWLyZn5UpO+8c/SOvRw/jcNWEQco+mNtsjt3TBwcauYr4pjaGh3l1G/C298dkB4jLPLpIApKfv+bsiTS0+3ggmmEt7/aWImz9TAEyai2+/q2tXPPPr8vI9bxNpSlarMV6aY+b+ZHmHh3t34lMflkCkoJcEkn0tgTQTFcy5bHy8VjS0UG63m0/ffZcvv/qK3777jrLSUkJsNhxOJ23i4/n3Qw/Rs0cP2nXtSlKXLs3a1tj4eAry8prt8d0uFx++/jo2m41uPXs27oMFBZGYlMSvy5ez+Y8/aJ+YSGZmJvfeey/PPfssTz3zDOF2O507d2b0UUcRkphISmIip599Nolt2jRu23woAHYwoqKMK7qVlcYyMpfLGwDz3TnPVLvQeG2a4Epzqr3LmDLAJNDVVcQ2NNQIPGipmDQ3s6an02l87VtLcW/sdmNeUVZmXDzTeCvNYW/9zmarOa8NCYGICG/dUJGm5i8DzF9Cgjl3qM8yX2lebjcut5s/fvuN+XPnsmbFCioLC0nfuZO/VqwgMiKCUyZNYvTRRzPh+OMpcDjo1L69MR4FyHEzJi6OosJCnE4ntmao8z37yy+Z+f333PbII8TGxzfJY4bY7fQaNQpKS4lMSeGdn34ifccOZrzzDuk7d/LLL7/w7uuvU+HZyf3e669n0LBhHHHqqYwZPpyRo0cTFBraaO+hAmAHKyzMu9TBHGR9d23aVwAsMtK7TCdAPqhyCNtbjQTw9lGHQ/XqpPmZE9faE9igIOMCRX6+8b3GVmku5iTcd/MQMyusLrGxRvaX+bsKgElzSkoyMhF9l4pFRRnBrl27av5uTIzxe/u64CvSGFQEv0XLSU8nfds2whMSKMzOZvb337N47lyW/vUXRZ75XExsLKnJycTExHD3o49y+TXXGIESzzwwtvmaX6e4hAQsGIXoE80yHk3F5eL1554jIimJK2+4oWkfG7xzGaBtx45cevvtxjEiLAx3VRVFGzbwyy+/sGrdOj776COevvNOngbat2tH3759iU9MxB4Rwdjx4xlz3HFExcQ0SLMUAGtIvmvNa2fIuN3+JwTR0d6d9pSGK83Nt8+GhBj901yCAzUnDdqtVJqb2V/NYKzv1d66lkeKNKWgIGMcLSjw9kMzqLW3vzGZ/VjzA2kuwcF7XmSw2eoOIjT1CZ6IyV8AbG8ZYNJs3G438z77jC8/+4ztu3axZvVq8jMzcfksq3YDvfv04cQTTmDouHFMPP10EhITsYCx+qqJa3kdKDPrKjc7u8kDYMt//51Zs2dzzb33Yg+E1ysoqDpxyGK1Et2jByd268aJwcHc8tBD5GVkMGP2bD766CM2bNlC/uLFlJaW8ub//octJITkTp0oLSlh9KhRtO/QgeKcHP79zDPE7OeunTqDbUjm4Jqfb5yQ+abYVlbWvRwnJMQIgOkkTZpb7clDZGTNAJjv7QqASXOz243ggnmFKTnZe6HBd4KrouHSnCIiai7H2NeSG99xNjzcmB8o41aaU+0+62++6q/0h0hTql0WwTcI5nuBTAGwZldaWsrFU6fidDrpkJLC0ePGkdqtG126dcNdVUVkfDy9Bwygh7nDYG2BEMypp7iEBADymmEnyGcefZTwyEiuvu66Jn/serFaq48vFrud+M6dmXLJJUy55BLj81pRQQWwcM4cfvzkE3ZnZBAcHMy8mTMpLCwkNDaWf959twJgzcr3oF9Z6T3pMgvg1iUszBiMW9CHWVqputLH/f1MATAJBJGR3q99DqQ1Ttg02ZXm5huErSsAZtat8x1ng4OhbdvGbZvIvoSH19z0SXWTJBD5y/z2lwGmCwrNLiIigk9mzqRfv36BkZnUiKoDYPXdAbqB/PDVV/zw1VdcduONxO5ngCggWCwQGoodOOK44zjiuOOqb3KXlIDViiU09IDuWmcFDal2wMDfSVddk4ZW/uGXFsJ3lzJ/AbCgIO8/TSAkkCnoJYGqrr5pzg+0s54EGqvVqO8VFwcJCXtm1cbEeOvhijQXi8Xoo8HB3rmqyWo1bktM1AXcADFkyJBWH/wCiEtMBGDjmjW463N8N2uJH8BcoKKignVr13LteedxySmn0KV7d6659db9vp9AZ4mIOODgFygDrOFFREB5uXEV1xx4w8KMnZzAKG5bWGhcTdOyHAlECQlGf/XXPy0WSE1t+jaJHAhzIiwSSEJDjflB7QluZCTk5urkTAJTRMSePzN3NPV3m0hz8N2cLDzcW8ajEXeUE9mbjl260LFHDx6+4w6eeeghwiMiCLXbcXviBBa3GzfgtliwAFbPvMANuOros2Zot6qqCrfLhdPppKKigvyCAuM+goK4/Kab+NcDDxCqixN7sNQrEmn+ssWSBWxtvOaI1KmT2+3e78qB6rPSjNRnpaU5oD4L6rfSrDTWSkujPistjeYH0hL57bf7FQATERERERERERFpaVQkRUREREREREREWjUFwEREREREREREpFVTAExERERERERERFo1BcBERERERERERKRVUwBMRERERERERERaNQXARERERERERESkVVMATEREREREREREWjUFwEREREREREREpFVTAExERERERERERFo1BcBERERERERERKRVUwBMRERERERERERaNQXARERERERERESkVbPtzy8nJia6O3fu3EhNEanbH3/8ke12u5P29+/UZ6W5qM82E7cbnE6wWMC2X4e4Q96B9llQv21Q6sP7RWOttDTqs9LSaH4gLVFd/Xa/ZladO3dmyZIlDdcqkXqyWCxbD+Tv1GeluajPNpOyMsjLg5AQSExs7ta0KAfaZ0H9tkFVVEBOjvpwPWmslZZGfVZaGs0PpCWqq99qCaSIiIiIiIiIiLRqCoCJiIiIiIiIiEirpgCYiIiIiIiIiIi0agqAiYiIiIiIiIhIq6YAWGPLzob09OZuhYiIiIiIiIjIIUsBsMZWWdncLRAREZGWxu1u7haIiIiItCoKgImISOuhoIG0dOrDIiIiIo1CATAREREREREREWnVFAATEZHWx2Jp7haIiIiIiEgAUQBMRERERERERERaNQXARERERERERESkVVMATEREREREREREWjUFwEREpPXQDnoiIiIiIuKHAmAiIiIiIiIiItKqKQAmIiIiEijMLEZlM4qIiIg0KAXARERERERERESkVVMATEREREREREREWjUFwEREREREREREpFVTAExERERERERERFo1BcBERKT1UOFwERERERHxQwEwERERERERERFp1RQAExEREQkUymIUERERaRQKgImIiIiIiIiISKumAJiIiIhIoFEmmIiIiEiDUgBMRERERERERERaNQXARERERERERESkVVMATEREWg8tGxMRaXxlZZCZ2dytEBER2S8KgImIiIiISP3l50NVFbhczd0SERGRelMATERERCRQKItRREREpFEoANZUNKEVEREREREREWkWCoCJiIiIiIiIiEirpgCYiIiIiIiIiIi0agqAiYiIiIiIiIhIq6YAmIiItB6qtyithfqyiIiISINSAExERERERERERFo1BcBERKT1UfaMtFTquyIiIiKNQgGwpqIJrYiIiIi0JprfiohIC6IAmIiIiIiIiIiItGoKgImIiIiIiIiINACXy8WMGTNwK0s24CgAJiIiItKc8vKgoKC5WyEiIiIN4OWXX+b444/nww8/bO6mSC0KgImISOuhK23SEpWVQUlJc7dCREREGsD27dsB2Lx5czO3xD+32822bduauxnNQgGwxqQTMRERERFprTTXFRHZg91uB6CioqKZW+Lf//73Pzp16sSSJUuauylNTgGwpqIJgoiIiOyLOV/QvEFERKRFCg0NBaC8vLyZW+LfL7/8AsCaNWuauSVNTwEwERFpfRQ8EBEREZFmEOgZYEFBRhjI5XI1c0uangJgIiIiIiIiIiINwe0mEqgI0Awwi8UCKAAmDU0ZCCIiIiIiIiKHhD///JPFs2cTBbgDdIMbMwPMfQjGK2zN3QAREREREWmBDsGTJxGRvRk8eDBRQCRQXlbW3M3xKxhIBVwOR3M3pckpA0xERFoPnYyJiIiISDOxA2GerwsLC5uzKXUKq6rCAlBZ2dxNaXIKgIm0duXlEKDptyIiIiIiIq1FPGD1fB2oATCLZwnkoXjhWAGwxuTboQ7BziUBorRUATARkZbCnC9o3iAiItKiFRQUNHcT/NIukCLSerlcOpESERERERFpQgGbAaZdIEWk1ToEBzYRBX2l1XK5YPduOAQL10oA0lgrIlKnoqKi5m6CX9W7QB6C54kKgDUmTQokECgDTESk9aioAKcTioubuyUiIiKyF84AvVhl1gCrqqpq5pY0PQXARFqxZcuWcf6UKZQ2cQ2w999/n3/9619N+pgigIK90rr468/m1VrP8gUREREJTK4ADTCpBpiItEoPPfAAs2bP5vvvv2/Sx50yZQqPPfZYkz6miMghwQyKKQAmIiIS0KqczuZugl9mAMwZoO1rTAqANRVlJUgz6NG9OwDLly1r3oaIiEjDUABMRESkRXC73bgDMA5gLoGsrKho5pY0PQXAGlMAdnY5tDg8g9rixYubrD/OnDnT+/gBuu5dRKTFMpdTaI4hgUD9UESkThYCc5mhGZQ7FM/VFAATacUK8/MByMjIaJLHKygoYMKECdXfF6tIs4jI3pWV1fzeN6CwtxpgCjyIiIgENAuBuczQbJMCYCLSqhQVFACQk5PTJEUYS2oV2w/UrX9FRAJCaSnk5e3f35hjeQBeURYREREvC4G506LZJkdlZTO3pOkpACbSipkZYFUuF3n7e5J1AMpqZTIoACYishcHMilWBpiIiEiLEKgBsOoMMAXApEFpcirNzMwAA9idmdnoj1deXg7ApEmTjMdXAEyamjnuavyV1kp9XEREpEUI1ABYdQaYlkBKo9FEVZrIwoULq2tvFRYUkJiQADRtAKx3796AaoCJiByUvc0dtARSREQkoAVqDbAqZYCJSGtQUlLC4Ycfzrhx4wAjA6xLly4AZGVlNfrjmwGwpKQk4/GVASYi0jh0YU2akzIRRUT24PYZE8NCQwM2A0xLIKVxaFIgTWznzp0A/Pnnn2RkZFBcWEi3bt0AyNq9u9Ef3wyAJcfHEwLk5uayfv36Rn9cEZFDhgIPIiIiAcnlyc6+8YYbuO+++wI2AGZmgDkPwSWQtuZugIg0nPT09OqvjzjiCJwOB506dcJC0wTAzCL47YKDSQAuv/xyXC4Xu3btIiUlpdEfX0SkxfMNbO0tyKUAmIjIwXM6IT8fEhLAYmnu1kgLZwaUgsPCICwsYJdAmm2qVAaYiLRkZgDsnHPOYcOGDQQBMXFxREVFNckukOXl5YQBCZ66Y27PVZDPP/+cfM+OlCIichB8M8AUBBMR2beCAqirFm5hIVRWQkVF07ZJWiWzqLw7LIyg4GBAGWCBRgEwkVZk69atBAMjhg8HjA94UkoKISEhVDbBgb28tJRYICYmhiCLBavn51dddRWnnnpqoz++iMghIcgzfVMATERkT5WVkJ5u/A9QUgIBGISQ1scMKNlsNoKs1oBdAqkaYNI46ruMQaQBPP/889xxxx0kAgNSUwFj55GOXbpgt9ubJABW4akBFhoaSmRkJCFAuOe2uXPnNvrji2islValdn82vzeX6ai/S3NTH5RAZM556zP3VR+WBmQGlqw2GzabUW0qEANgLk+blAEmIi3W22+/Xf2BbpOcTATeAFhISAgVTRAAKystBYwAWHR0NDFADBAL2FVXQZqSJrTSmikDTESkYWmeKg3ADIAFBwcT5AmABWKQyQzKOQKwbY1NATCRViIpKYkgoEvnzqSmphLt+XlMXFyTLYE0M8Dsdnv17pMAYUC8201JSUmjt0FEpNVTBpiISN1qZ8uKNBEz2GW12bB6LlYFYgaYlkCKSIu3bds2jho7lhkzZhDXsaP3hpCQpqsB5tkF0p6URPe+fQGIjYlh8KBBuIF169Y1ehtERFq0vZVPML9XBpiIiEjAcfjUALNajWrIgRgAUxF8OTBmgUXPTnd70MRUmojb7Wbr1q0M6NePqKgoLGFhAMTHxUFwMHa7vUmWQFaUl2OzWrGFh9O2UycAJk2axJNPPokbWLNmTaO3QQ4BLhcUFTV3K0SajtsNGRngucigDDARkQam8VQaQHUR/OBgbJ5dIAMxyFSdARaAbWtstuZuQItWXGz8X1kJoaHN2xY5pBUUFFBUVETH9u2NH9hsLF6+nLDoaLBYCAkJoaCJAmB2ux0sFnr37w9AmzZt6Ny5M0FBQaxevbrR2yCHgPx8KC+HkBCw2/f+uy6XxmgJDA4H2GwHtiSnqso4OSssNL5XBpgECvVBCUT70y/Vh+UguD39x+I5tpuBpZaSAXYoLoFUAEykFdi6dSuAEQCzWMBiod2AAcaNbrexBLIJMmbKy8qqA2BHHnssH7z/PocffjjBwcF07dJFGWDSMPZnspqbawTAUlO9QQORpuZyQVaWEYiNj6/79+pa8uhbz8btVgaYiEh97M8FB42ncgDatGlD165dmT9/PlAzAyzIDIB5gk2BxOVZwRaI2WmNTQGwxrS3Oh4iDWjr1q3YgM6JiX4P9na7nYrs7EZvR0V5OaGhoUYbrFbGTZ5sZCwUF9OzRw/mKwNMGlvtsTYAJx1yCDL75YFeaa3drxUAExFpWBpP5QBkZmaSmZlZ/b1vBpjN3AUyAOeiZlDO3MDsUKLL4Y1JA6k0gfnz5/Paa68RD7Rr127PmnSeJZBNkeJaIwBmio6G2Fj69OnDxvXr2bFjB/PmzWv0tsghxO2ue7xVoEACwcHuRFa7/2oJpIhITRoPJQD4ZoAF8hJIMyhXVFhYvYzzUKEAmEgLk52dXT1oLV68mNGjRzPjq68IDwkhMTERoqL2+Jum2gXStwZYDUFBnHTSSeB2M2LECI444gi+//77Rm+PHCJ27TIKhO/NIXZwl2ZQXm5sjONvonug/a/2EkiTArsiIl45OTXnAb7Lxn3tbczUeCoHobS0FPAGlqxWK1ZPBpgrgANgzqoqykpKmrk1TUsBsMakgVQaWGlpKUlJSdx4440AfPHpp6QACRjFFy1t2vgNgNlDQ5tkF0jfGmA12GykpaUxbOBA0tPTAfj8888bvT1yiPA31tae/Go8lsZmboxzMPU06uqnZmav2Z/VryVQqA9KIDjQOW5BwYEvTZdDnstn1c22bduAlpMB5huUK8jPb76GNAMFwBqTJgXSwP78808AXnnlFQBWLFpU/SEuqaios8h3cEgIjqbKAPO3257NBkFBjBoypPpH7777LkuXLm30NokA3vHY6fSfoSNysMwaHwd77N9bQNfk2exE8wwRET/qyp6tzTfzReOp7Ccz6wvg77//BryZVcHBwdUZYIFYaN7pdGIPCQEUAJMDUVrq/+qBBlJpYEuWLAGMJY0vvfQSc2fP5vQzziAPKA8Pr/PvQkJDm6wGmN8AGIDdzuC+fbEBvRMTSUpIYPLkyTUOHiINrnamzO7d4FOsVKRBuN3eLK3adRjN2w/0fuv6e4vF/2OJiIh/WgIpDSUvj5We8zKA7du3A97i8jabLeAzwGLi4gAFwORAlJeDvx329ncXSJdLk1mp03fffcddd90FQFFREVdffTU24KhjjuF/77zDb7//XuffBtvtTZYBFmK3+78xLIx+ffqQCHRq25ZXn3mGDRs28NVXXwGwdOlSnnzyyUZvo4hIg/M9djfEcbz2MnI/m5soA0xERKQZVFXx4ZtvcvpRR2EerQsKCgBw+C6B9GSAVQXgLpBOp5PYQzQAZmvuBrRq+zsx3bXL+L9t24Zvi7RYW7Zs4dNPP+WWW24hMjKSgQMHsnz5cgDuvv12Tj/rLCzx8Xu9jxC7naqqKlwuF0F1LJNsCJUVFXVngIWG0mfAAC6+8EIuuugiOnXrBsBtt93Gv/71L7Zu3QrAuHHj6NWrF5GRkY3WTmnFVCxcmsO+AmANtSzS934UAJPGlplp9LHU1OZuiUj9uN01LyDszxip8VTqy+nkzTffBCAVqASKPXVAzeWOVqsVmxkAC9AMsFjP+WOhJ3h3qFAA7GDsa6DUQCoNYOKECWzZvBmAjIwMqqqqiI2NBWD8kUdiCQ7e530Eh4YSBFRUVBAWFtZobS0vK6s7AAbYoqN55JFHqr/vlpLC9m3baqSiDhs2DLvdzsaNG2nfvn2jtVVaAU1spakVFRnF7tu0qflz36BXfSa6JSVG8eWIiJo/9928we3eM/Dle3tQkPq1NK769GX1QQkkZgCsrn55EP3V4XAQXI85t7R+7spKlv/1V/X3IfgEwMwlkAFeBN/pdBJnBsAOsQwwLYEMFAH4wZDmt23bNgo3byYZuPPOO4mMjCTGZqNNXBx2oGfPnlCPg7Hdbq8OgDUmx94ywADM4FtUFISE0Ck1lQSge3w8NmDo0KGMHj2aiooKZs2a1ahtlUOEMsCkIRUVkZuTw0lDhrB4/nzvz81juNVavwwws/ByPY/9zspKLr30UpYsXlzzhooKzR9EREwHc6zfy98WFxfTt29fzjnnnBo7/8mhIz8/v7rO17bNm6ndW8wAmBnsstlsBJkBsEBdAnmIZoApANaYaqfhmpxOo25Y7Z+JeBQWFgLGbo82YN7cuTxw9dXGyU5BAUt//pm5n31mXImqq+aWj2C7HQuNHwDbaxF8ME4OU1KqA2BVZWUAPPTQQyyeM4dFixYxb9484uLimDdvXqO2VVqB2pNVFbeVhlBUZGxu44/FwmuvvcYfS5fyom/NQvOEKDjY2BTnYHd88g3cut3s2LKFGT/8wJlnnun9Hc/Sihq7mImIiJdvJu1BeOKJJ9iwYQMfffQRb731VgM0TFqa/v3707FjRwBWLltG7a3FSj3nbuYSyOCQkIBfAhkVE0OQxXLI1QBTAKwx1RUAy8qC3NyaPzMDYP5+Xw4JGRkZnHPOOVgsFmJiYrjmmmt44ZlnOPHEE+nevbtRZNEzQKV268aI0aON5TP16DP20FAjAFY78NrAKisqsO9riaXnagghIUR4dq7s3bs3h/Xpg8ViISgoiHHjxvHVV19VX2kRweXa9yQ2I8MbePBdKub7vcjeuN1GAMwz1mZkZFBeXm78PCsL3G7e+/xzALZs3EiueSx3uYy+5tlSnOJi44KFJ8hf4/7r0wZfJSVkeWqEVjocrFi5kgnHHMOqnTuNZZABOLEWEWkWe9s5d28/38dtn376KUcddRQDBw7k8ccfD8iAhjSuHTt2VH+9avly3D7nXzHR0ZQVFQE+RfBttoAugl9VVYUtOJjo6GhlgEkDqisA5m+ANa8eKwB2SHI6nUyePJmPPvqo+mcvvPACh/XqxZ133mksHfQ90YmMhORkiImp1/0He7LEGjMA5nK5cFRWErK3DDBfISE8//zz3HTjjXTr1csIXGRlQVERD02bhqO0lNtvv736151OJ3l5eZSUlDBjxgzcCmgcWnbtqg5KVDuYGmDp6dp1V/bkk7nlrKykbdu2xMfHc9t11+GurCQnJ4eVW7dSAaxcvpwxY8YYv1xVZYzRkZFGZpbbDTk5kJdn3F7fk7HagVsAl4vdu3djnm4de+yxzJ03jzfeeIPfly5l7apVzJ49m/LychYuXHjQL4HIAamq0pgqzc/fWFvfuUIdv5eZmcmKFSuYOHEid955J2vWrOG///3vQTRSWioL4MzIYPXKlXRNS6v+eVJSEmXmEsgWUAPM7XZTVVWFNTiYqKgoijzZa4cKFcFvTL4BMJ2si4fD4aCqqopQT6Bo27ZtXH311cybN4+33niDsWPGEB0Xx/bt2xmYmorFZoO4OG+AKCjIm0VVTyGerKyK2tkIDaiiooIgILS+RfaDgujSpQs33XQTREcbJ4oOBzgc9O3ViwsmTeK56dPp3q4dPfv0YeX69Tz88MO0b9+eHTt2MGHCBB599FGGDBnSaM9JAoQ5fpaVGZ+Fg7kPX2bQQgSM8Sc7m4svvphOnTox6YILCAUqy8p49bnnqMzOpuOgQTiBu++7j0fuuYc1a9aQnZ1NosXiHZfrKMC8fPlyLrjwQn5auZIUnwDX3Llz+fTTT3nmmWfY4xKYZ/lO5u7dOIAIu53w8HBCIyN59dVXefWJJ7ADu4HY2Fjy8/P5Zc4cxowbBxYLmzdvxuFwGPUiRRpTZqbR92tvECHSQLZu3UpMTEz1RlB++ds45CDPwcyatBMmTGDIkCFMmDCBadOmsWPHDoYMGcLAgQPp0aPHQT2GBL54wA78Ons2K1asoP+4caSvWwdAcnIyubWL4NtsmGdrgRYAM+vY2Ww2opQBJg2qrgywun5XWr0//viDdu3aERsby4cffsjnH3/MsLQ0fvv5Z1546ikunDiRLuHhJFitHNatGxaXy8goAKO2TEhIvYre12b3ZIBVNmIGWLknuLbPJZC+EhON7LawsJrbrMfEcMLEicQAr/znP9x48cU88/DDxAG7d+/GZrPx888/c+uttzboc5AA1RBZBf4mwRp3D2mFhYVMmzaNGTNmcO3ll3PjxRezY8cOPvvpJ1569VU+eOkl4oBkjEnvq++/z4233orFYuHSyy/ni08/JRRYuGCBETwza3LVkfn99NNPk5WdzZw5cwB4+eWXufuuuzh3yhQ++fRT0tPT62zr7qwsgoKCWLVqFb/99hvXXnstBQUFVAFWjKvS1vx8YoB/nHwya+bMIXvrVgb27UuvXr14/PHHG/CVE/HhO45qTJVGUFRUxG233Ubnzp257LLLmDNnDt9+++3e/6gBl0D+/PPPxMXFMWjQICwWC59//jkXXHABjz76KGeddRaXXXZZPZ+JtFQulwuz4vKU884jY9cu+g8aVH3RKi4ujhJ/GWCeeYHzYOuCNjCzPVZPAKz4EMsAUwCsMe0rAHYwabrSsrjd5G7bxumnnkpISAg9evRg8uTJ/OPssxnRrx8rf/mFq88+2/v7paVGHRqrFXyXFMbHH1AGjLkssTGXQJZ7ikbvVwAsJMT7fIKCjGWdKSkQGsr48eN59K67+OnHH4mNjiYauOfWWyktKMDhcHDXXXcxb9YsHrrpJk4YPpz333iDrKys6l1YpBWpvUR8XzU+fKkwvvhTWcmVU6Yw/bnnOP300/ngf//jg/fe4+q77qIIKADe/+AD+vbpww5PLUKzLP4ZZ5xBVEoKA4YOJdFq5X/PPWdcXDDH6joywEo9Y2RlpVE697777+fFl16qvn3VqlXeX/adO3gywJKTkwkLCyMmJoar//lPbrrppuplkWeNGcOML79k6uTJ5BYWcu755/P6U08RWV7OwK5dufXWW3nrzTf3rEkmIhKAtm/fTp8+fejYsSO9evXiP//5DwCffPIJRx11FJMmTfL/hw18buV2u/npp5846qijsDqdUFJCZEQEb775JqtXr6Z3794sXLiwOutHWqecnJw9fjZw8GBmLlnCk88+S3hkJKWeDWmqKitxYxTBD9QlkGZ/tVqtRCoDTBrc/maA6YSs9aisrN7cYNOKFYzq14+KHTv45Pnn+X3GDO684gquu+IK3vnoI9p16mT8jbmjo8ViLAusneYdFHRAS7bMnRkrG3EXyOoMsPrWAPPHZjOCflYrwTExXHnllfQdPZoXX30VW0QEJ554IlZPYenzzj2XOJuNF558kmWLF3PjpZfSLTmZxKgojjzySJYtW0ZmZmYDPTtpVg0xLioDTKqqjA1odu9mxZw5zPn2W0YedhjWsjKCgSLg6x9/ZPLkybzz4YfEde/Ok6+/TpDdzj9uuonrbriBTz75hOnTp4PFQni7dtx1110s/uknvvn225rjt8vF4sWL+fnnn42fud2UeCbHmzZt4rrrrjN+1ad5S5Ys4aVnnmHiEUfw9NNP12h6ZmYmScnJ1d/b7Xb+7//+j3UbNnD4yJE8/fTTDD3ySB557TVe/+orlqenc9czzzB0+HAWz5rFxJEjefi226jMzISsLHZu3swLL7zQ6DsDyyFCtb+kgb388susWbOG8PBw0tPTue2223j33Xdr/E5eRkbdx/X6nFeZt0VHGxdk/fzue++9x/bt2znh+OONuo4FBdU77/bu3Zu77rqLiooKLr744uri59L61D6fyAYOGzyYXkOGMPnaawmLiKDEUwS/tLAQJxDskwHmCrAAmFnzKyIykuiYGIoOsQCYaoA1pvpkgNV1e0nJntk/0jK43biBJ2+/nbKyMqbeeivPPfwwpaWlvP/uu4wcPhyAB+++2zjgJiQYf1daCuHhUF5u/Hw/63ztjRmUKist3cdvHrgyz4RgvzLA9iY2trqg9FFnncXWM8/EkpdnfDZKSugZE8OW1avZmZ1NscXCWUcdhcWT4bZw7lwGDRqEHfjoiy84+ZRTGqZN0jxqn1wdSG1FfwEwnbS1Pi6XsRzR5QK7nbyCAjZv3kznzp1JsNtxl5Xx3gcfcNPNNxMbFcX777/Pp59+yrJly5h6yy247XYGDBiAxWLhbJ+s3Pv/7//2fCyrlanXX89TTz7JYy++SNfRoznssMOM26qqOOXUUwH4e/JkQvBOoF977TUqduzARs0A2PMvvFD99dJVq/jn5ZcT7HbjdrlYt349vc379tGhe3c+nTXLmCtYLIQAk046qboe2DnXXkuw3c7t117LlPPO46677uLCiy/mnrvu4pcFC3jl5Ze5dOpULr/8csLCwoyTO4fDWJ4uUl+1x9KSkgMu2SDidrt59913OfbYY5kxYwYZGRmkpKSQm5tL9+7dOf7443nnuedYt2ABI485BqKi/N3Jvr+uh0ceeYQhQ4Zw0TnnGCszwOjfYWFgtVZvhDJ9+nQGDx5M165dufTSSznxxBN555139vepSwC64ooreOWVV/Ctbvj70qUk+hwnI3wywBb++itpvXsTERFRXRg/0DIEzZ2lE5KTiYyOPuSK4CsDrDHtTxH82oNzQYFxpVpaFrebnUuW8K/LLuOJJ5/kxZdeYlTPnrz34Yecc9VVHHXyyd4lf3FxRvDLYjH+RUQY/3sOqg0pKjoaoHp9emMwC+yHhoc33J3avDF6i8VivGYREdWZFhEREaQNGMDgkSNZuG4dX3z+ORPGj+ejl17ihTvvJB54+cknG6490jwOJnNrb1eBlQHW+uTnV++++OKDD9ImKYnRQ4fSOymJu667jmk33sj5N9/MbuCCf/6T6F69uOSSS3jmmWcY4ClmbNmP3ZitMTG0PewwFq1ezaBBgygtLaWgqIgbrr+++ne+/uorcnNz2emp8eW7lbr5SKecfHL1zw4bOBCATp07k5+fz8Lff2f7jh01l/z4tjEsbI+LaZ9++inHHXccZ555JkRHc8RRR3HpZZfx1Lvv0m/CBGYuWMCEI48k8++/uf766xkwYAD33X03udu3G9nLRUXVGcwie9jXxYSCAmPTHpH9cO+99zJlyhS++uortm7dysmecbFNmzYEBQWRmJjIhg0b+NdttxEGrFmzBoqL9x3g2lc9MHMebl4o82TrVFVVsWHDBo4++mhsTqcxN4+PN2737PDboUMHli9fztixY7nzzjs544wzyMvLY/r06aSnp1NkBs2kxfrogw/22KBm0KBBNb4Pj4zE6XCQm5PD4oULOfrYYwECdglklueCXGJyMlExMZQUFeE+hObEygBrTAdSA2xvP5eAVlZWxn133sm7Tz+Ny+3m3MmTueaaa3j25ZfJs1h46IknjCui0OAZXvsSHRMD0KhrvKsDYA2VAeaPxQKe5wIY2XKeYFhSx44ktW3L254MO9O9Dz3EW6+/zkWXXtp47ZLGVbsGmGl/dndSBljr4nYbn3/f8cbpNH5mtTLrl1/4z4MPcsLQofzjH//gm2++4bXXX6cM4yTruOOO8+4gm5Rk1MY6wDH5hltuYeGSJZSWljJ8+HCKd+yg0mesnTx5Mhd5ssmiIiPZ5XMhYtjQoVx60UWEh4fz5VdfkZKczH//+18GHX44AN26dcPtcBAdHs6kk06qd5uOPvpojj766OrvLeHhPPjKKxx34YUsW7aMyspKLjvlFEJDQrjnmWd48fnnefGBB/j+rbd48b//ZfDAgUYQLCWF4rIyMjIy6NG1q3GyGRFR4+KEHOLcbo2lctBKS0u57777AHj//fcBGD16tHGjy1Wj/EebpCTi4+L4a82aPfvf/iyB9OVZuk5hoZHhlZrKzp07qayspFu3bsaFgZAQI9s2IsJYtVFRAcHBDBgwgE8//ZRhw4axdetWpkyZwnvvvUe7du1ISEhg+fLltGvX7qBeH2keJSUlhBYWcucNNzB+/HhOrKP2XERkJBZg8YIFOJxORnr6biAugfzll184b/JkLEBSSgpRMTG4XS5KSkqINDdea+U0g9lfDocxSd5XHSbfqwr1oaBXy1VSwo61a7n51luZN2sWk885h+OPP56jTjgBW0ICT5kHcF9NGPwCiPHUEmvMFNcKz0ldgy2BrI/aS4RtNiOrLiQEHA4uu/lmZs2axe1Tp/LK44+T2qULQ8aM4dRTT6VPnz5N1045OAd6cuUb9PIdY8PCjICHxt2Wq7iYjPXr+WvLFmZ8/z2f/fADFdnZxIWGQkoKa9evp2dUFE888QRd+vXjmAsuwHbttRxxzDGccdZZNe8rOPiglmpNmjSJkpIS/ve///Haa69x5EkncfaJJ5KUlGRsejJuHO+9/z5HDxpEdEwMG+bM4X+vvELn9u0ZOGgQeCaeSYmJ3H///XTs2JG1a9YwoHdvHA4HNmDKlClERkV5l+DsR5aarzFjxlQv2cHhgNxcHr7xRh664Qb++OsvzrrsMoafdBLXX3kl6StXsn7nTnYWF1O4axdnjB/P5MmTOf7EE7GkpBxQPUpppcwxWn1C9mHp0qU89dRT5OTkkJaWxqZNmygtLa0Odr388stcccUVAPTr29fI6K2oMOZ1EREQFobF6aRPnz4sX7PGuNP9yQDz97XveGpuFuV2s2nTJgC6d+liZH35XsQuKTHaFhQEiYkkJSXxxx9/8PLLL3PVlVfSsWNHHn30UXJycvzWL5OWYfPmzViAHj16eEsc+BERFUUQ8Lenz6S0bQtAkGdMDKQMsOOPPx5KSogGklJTiY6JIQgoKChQAEzqkJVlDHyJifUrrHiwGWBVVU0eLJF9cxcX43Q4qAA+fP55Hn74YcrKy7nzjjv45z//CampATURjPRcmWi0DLDCQhz5+UADL4E8EGYh6pAQgkNCmP7zz/z3scdYMHs2S+fN4//Zu+vwqI4ugMO/9bgrwd1dCxRtgXpLjbpRgdKWulCXr0qNulHqQguFIqXFCrRQ3IqEECBG3Hc3a98fd292EzaQ0BjhvM/DE7KWm83s3LlnzpxZuXgxrz7xBA/ffz/X33wzsa1bK38r+Zw1XrWxjNE7GObvrwyoJQDW+HnN/DutVlYsWsQXH39MTkYGO3bsoMxmw2gwMGTwYP7OzKR5x44Yo6O5/MoruffOOwkNCgJ/fwzAWx9+WKeHOnnyZCZPnqxkEHhleV02cSIL587lySefJCo6mmvy8zmnf38lY83dBgMDA9m2bVv5c6K9aotcefnl3DN9eu0fsMGg1FrMzUXj70//8ePZsnMnd999N6+98w6ROh39unQhKj6e4ZdcwjfffMOCP/5g+m23ccm11/L5vHm0adNGWR6k19O+ffvaP0ZxaqgqS1cIL6WlpVx44YXly8AXL14MKEGC5cuXA0rGbGBgIDlHj6IrLFTO1SaT8rWsTJn4tNvp0rUr73z5JQ6HA52vSbKant+9l0C6n5+YmAhAB3WjKnWiRA2EgdL2MzMBiAwK4tF77oH8fP731FM8/vjjPPbYY7zxxhsMHTqUW2+9FZ1Ox759+3A4HHTp0qVmxyjq3cH9+wFo1apVeXkEX71ccEgIGuDAvn2AElgC0Gi16LRaHI2opIC/vz9lJSW4gIjISELcSRL5eXmnTaaiBMBqQu1g3VuYH6OsTOk41QtwOLkMMO+OXF1zLhqG3a6kOet0uLRaNH5+rJg/n4/ffJMtW7bg7+9PekYGLXr14p1Zs+g/cKDyN29EwS8ArU5HUFBQ3dUiKC7GYrNRiNKxNiamgADufuop7n74YUoKC8k5dIgH77+f1196ic8/+IBLLrmEc887j9a9exPXpo0nnV5dwlxWVnGZlah/ap9Y3Vpg6kD2eI/VamXZTiPldDrZsGEDS7//nnXLllHicBDRvDnpu3eTmppKcFAQ4eHhXHj55Qw/4wz69epFmzZtKHA4CG7WDG1DnzMrnfc/eP999k2dyoBOnUCjoU18fPkF0/Eu0l6fOZOjxcXcPWXKiUsqnCyTCeI9pX3DwsL4/PPPefLJJwkPCyPcblfOgxoND73yCrdNm8ZrH3zA+x98gAWwAGUog8mWHTowceJEpk+fTozXjpXiFFU5U8a7/R1vOblMLAgvNpuNuXPn8uOPP/Lzzz/jcrno0qULnTt35sUXX+To0aP069ePDz74AKPRSEhICFdfdRVkZCjZWEFByi6NVquScVVUBKWldOrenRKzmeTkZNpFR3t+oBrE8p44q04NsMqcTg4cOIDBYCAhJkY5FjUAptMpx+Xnp0x2qFljxcWe18rJISAggNtvu423336bKVOmMGPGDIYMGcKqVauwWCyMHTuWnj178uKLL9ao9qSoP8nuIGgrdxD0p4ULiW/X7pjHRcXEoAX2urMS1QAYKHXAGlMGWFRoKAXZ2ThQjk0NgPXs0YPC4mICAwMb9PjqgwTAaqLyxVLlDjU7W/narFnNM8CqCoA1og/MacVshvx8srOyWLFiBevWrWPu3Lm0aNGCg8nJmAET0LtlS9784guGjhnT6E9eQcHBFTPAnE5P4c//wt12S9z//BrjzqXuzQUC/f0JjI7mm99+Y8vu3Tz/2GN8+vnnfPjpp+iA0aNGMXDgQLKysmjRogVt2rShZ8+exHbvLrtZNaTKfWVNdnTyNQj2LngrGozD4cBiNhOo1SqBGI2GXTt2cOO115KyYwd6nY4BAwbQLCSEtLQ0OnbowAMvv8wFF12EX1mZshzGYFD+jjYbod6z8o1IWGgoAwcMUDLDapCxeOWkSUrw3WY7tq+u4/NN27Ztlf/Y7cpxBwaiN5n45JNPuP3WW5n/+efcct11JCUmcuDAATZt2kRScjIfvvgic954g6tuvJGLrr6arl27YrfbyTx6lG1r1tBn6FC69OhRp8cuasnJbDZS0+eJJqewsJB169ah1WopKiriqaee4t+dOwkEJk2YwM333ceoMWPK++2OcXEQEMB07yxXNUNbDX6Bco7QapUgk05H92HDcAEbN26knVrPEU5ck66q9qlOinmNGQ4fPkyLFi3QORzKuca731WPKyJC+Wq3K5uX2e3Kju7uY+0UFkbi9u0sW7WKJb//zrJly+jfvz9RUVH8+OOPLFmyhPz8fF599VWCfe1mKRpUysGDBAUGEta5M9jtDD73XJ+Pi3RnbR/Yuxe/gAACvFbDNLYAWJjBQAGenRDVAJgW+PHHH1mwYAGzZs0iziuI19RIAKwmapLiXdMaYL6e6/0zRZ1ylpbyxy+/0LVXLywaDXM//JDFS5eybe9eHA4HQQEBDBwxAldpKaMmTuT2e+4hIT4ejVZ7yqT8BwUHV6wBlpGh1Mz6rzP17vZqsVqBRhoA8+bO5Ovbty9zFy8mMTGR3bt2sWP9er756COWrFiBy88Pp8WCP+Dv58dP8+bR68wzld9Vr6+Y/i7qlt1esR+sTqaBr+CW98BWzdKU/rXBZGZmMunyy9myahXRQUGMGzcOm83GggULCA0N5fWZMxl/0UWEtmyp1FrRaJTPnZph7b3UWr2vsfC1WcN/UVfZX9Wh13su8NwGDBrEgEGDAGjdrRujzWYmu7PVE/fu5e233+a7Dz/ko/fewwwY3P/8ASdwxbRpPDZjhmSJnUqO1warqrUoThtlZWU88cQTLF26lMTERIq9loB3bNGCH955h969etG6dWs0BoPSpxcXeyb5vVfWeJ/zK9cjioxUsq0CAugRE0NsbCyrVq3iiltv9TzGVwCsOhlgJpNyXCqnk/TkZHrExSnHd6LaSHq9sklTTo4ycWEyKZliBQW0Dgtj8oUXMvmii5SxR1gYLr2e5EOHOGvMGD778EM+/PBDWrVqRatWrSguLiYhIYFp06YxduzYRj/B3pSlHzlCbEICGp3uuCuyIiIjAchMS6NFixaeOzQaJQDWiJZA5uXmKl/d36t1oqOBG264AYCJEycyadKkej+2+iIBsJqobofqTe20alKUsaqLPVFrtvz9N4kbN9KtWzcsNhuzZs5k8dKl5eu6nUDX4cO55d57ueqqq+jatSvGxnSBdRIqBMDU9lYbHbL7tczuFPBGHwCrpH379rRv354LLryQh599FovFotSfyMpi3+bN3HTTTZw9fjyhgYHo9XrOOOMMnnv5ZXSBgdi1WuLV2hCi9pWVeTJrVdXJAPO1VKfy4zQaybCtB1u3bqV9+/YVCqsuWrSIqy+7DFNpKV3atCEoJIT58+cTFBTErbfeypRHHyWqeXPPUnJ1pv1UURsXK77aa2O8CAoOVv65tQ8P582BA3koOZk1S5Ywf/58WrRoQceOHRk8bBifffQRc95+m4/ffpuHHn6Y7gMGkJ+fz8Fdu4iJjeWy665r0rPOpxTJABM+pKamEhsbS0pKCitXrsRgMDBz5kw2b95Mt27dmDhxItdcdRUmo5GilBRGDRmilMaIjlbO6QUFyj/wTCiWlnp+gBpk8LXhmNeGJRpg7FlnsWrhQqxmMybvx3mf230sgVy0aBGFhYVcOXGi50Z3FrL3mCHz8GF6DBmi3FedUhgmU8UawOqOuTk5yvd+fsp7kJODBmjj58eGBQt4/fXX+fDTT8k8dAiX00mr1q35c/Vqli5YwLnnnst7n3xCbGzsiX++qHWZqanEVqMuVoQ7A0wPhHvV8ITGlQHmdDrJzc5myBlnMOONNwDo1KULL734Ig89/DA6wAHsd9c+a6okAFYT9ZUBJgGwOjd/7lzeffVVnIARsJpMDDj7bIYNHEhYUBCDR41S6nk1IcEhIWTmueP9NlvtvXClDLDGVgOsJnQ6Xfna98joaIaMG8ey9ev54IMPKMnKIuXgQeYvWcLCxYvRAjfdeSfPvf12wx50U+Erw8C7nep0yqD2BBdXGzduJCg0lM4dOvj+GeDJGLJYlFno02TXm/pmNpu5fPx4cjIzad+6Nc1btqTAbGb7hg3079qVWe+9R6czzgC9HqfTicZsRmMyKRcMpzJfGWA1DQqon4fKS3cbO/dEUbPOnbm8XTsuv+MOz9IhjYZXhgyhV9++PHj//bz/4osA2PEMRj949VWenjWLiZdfDiiD9eXLlzN27NgG+GVOIyfKMlTboNemFFU+zldbVwuZi1NWXl4et9xyC7/+9BP+JhPFVit6lMzOqLAwFn/yCePPOUfpv202pR107Kg8OThY6Qf0eqUNlZYq7Uit3RUa6unvKvd7x3HV1Vez+MsvufzSS3ny/vsxm80Uu1yMGDsWP/CdhOByca57GduV7nFr+QSDwVCejWa32chIS6NZixZK5ll1Vf58mEzKZiN+fsp9DodSZsXpBIuFiJgYnn3hBe677z5MJhNGoxFdYCCWggLmfP45//vf/zijc2fadu9OVlERrVq1Ii4ujnvvvZdOnTpV/7hEzblcHE1NpUM1lu0b/P0JCw0lv6CACO+adCh1mJ2NJACWm5sLTifnnH8+AwYMAEBjMHDttdfy0MMPE4ySBJK4b5+nDvmpMPaooVN8lFnPKgfAfHXOasdXncGurwKjlVN3ZSatTtz3xBNMuvlmVv/5J8WFhdx0yy2EhoY29GHVqaDgYJIOH1a+UQMLtZipcKpmgJ1I8+bNefbZZ8u/379/P3N//BGXw8H5F13UcAfW2DidyvKBgADfaeJlZcr9ISHH3m+3K5leBoMyENZqPQNFlcGgfF+pRof6/8TERFq0aMEFF16IHWjVrBmLt26lwjyc04nL5cLldKINClIGoWqRXfV3cDqPH4Dxzp6UunDH5e/vzyfvvcfMF1+kpLSUrJQUzGYzD953H1MeeICAqKjytqDVapXZ8qagtpdAqq/ZyDZXOSFfnw+TiavvuotuPXrQrFkzDqWkkJKSwthx49ibnMyMqVO584oreOKuu+g3ZAgphw+zY/Nm5i5fzpmjRtX/73A6UIuLh4V5lm3Bse3W4YCjR5VgRqXdngvy88lOSaGdr51AS0qUjJ+ICCUIIBo3l0s5N7qzrTYvX868n3/m1fffJ9Bu5z73EqmWLVsycuRIXC4XcXFxhKntx+lU/s5Op/K99wSTRuPJGvVuX2qf6f21GuPT0WPGMKB/f1avWsV5q1YBUAo8/+KLTL322hNuIlZWVoa6tiMzM5N3X3iBFpGR/Pvvv2zcsQOdy0Wz5s1P/J6diPfnRS2gDxWym8OiopTPF4DFgl9YGLc+8ghnnnsurzz5JAX5+YSEhLD+r7/Izsnhs48/5qG77+bmqVNp1rw5Bo0GTXVWqjTkkvpTTFlpKVlZWcR5L2msik5HeHg4+QUFhFcKgDWmDLDMzEx0QLR3RqFGA+7v/YEzhgwhdc8eZbOewEBlXN7E1FoArKy0lOLiYiKqqumgdqhOZ8XOUL2AOdHA7kT1B6rYwaNWB4w+BrEulwu73Y7Bx0DPZrOB3U61L5EqB8A0Gt/BsJp2XGVlygVdIxs8l5nNGBsoWyg4OJhOnTvTqXPnBvn5DSEkNNSzBFJd+vhf2oTVqpzI3e0yv6AAo9GI/lTP3jiBDh068PAjjzT0YRxfWdmxBVsr89U/Op1KcNRkAocDl1ZbsX8rK1MCRhoNFp2OwykpGI1GEhISMKjblbuDXHsOH2bJkiV07tyZccOHo3EvecjLzGTBmjX4h4Rgs9loHhVF/3btWLlqFcHuZaaPPPkkvceO5alp0whz9xF7Dx7kxUcf5d+0NNrFxtKja1emPfQQBQUFPHDDDaxYuZKr3PUKXEBqWhq//PILE4cNY9/evRxITGTHoUP8/tNPdBkxgldef51o99KLvNxcnn3uOSYOG8bQwYOVc5TRCHo9VocDo16PJjvbs2zS5VIyliIjsel0yvJosxny8pT3zmBQBg06HS73Y3Uul/L++Ps3ur64Lg2/+GKGX3yxJ3ip0TTZGcUqVc5mULMOTvScypkQTSTgqjEY6HP22QDEdu+Omms9oEULFq5bx6evvsoXc+bw+7x5ANx4/fUMHzasYQ72dKC2x/x85WuzZsc+xj02Xb9+Pb3698cvIIDffvuNzMxMrr71Vq65+mo2/fUXSQcO4OcrcAbKuUWdcGji44T6ZrfbcZaVUVZUhNlsVsp56PVEJSTgAvbv2EFhSgo9+/TBqG4eotF4AlVZWaRlZrJx1y6Sdu5k99atmM1m8oqK2PzPPwBccfbZTJkyhQFnnKH8DdXsED8/5bzv51ezPqoWzgEarZYff/yR3oMHk5eRodwG5GRlVdzsxqtNulwuTCgFvw8mJVF65AhHHQ4efeop9m/ZgncZej2QUJ3gR23Q6ZSMODVo5x4ndB4wgE9+/VWZJHRPXmdnZ/PMM8/wyeuv8/Hrr+MAmsfG0qdfPwYNGEBAcDAt+/ShqKAAncVCdEwMsfHxlFmtbP/7bwaMHElChw64gB3r13PQvcPyRTfcQLPWrevn9z0FpKek4AKatWx54gdrteS5V9n0cWdWqXQ6HfZGUgPsyOHDaIAYr92fAdDpeP6558jMzCQvL4/58+fjdDrRms0UabVcd911vPTSS3RUszpPcf/5DORwOHjzpZd4/4UXKCkpYfTYsZwxdiyXXH89se46DkeTkvjynXdYsmQJkZGR3H7vvYw4/3w0Oh2Jmzfz9UcfYbbbOWPECMZdfDFG9+4ZDoeDA4mJhAUFEelyoQsPh8BALBYLe/bsITIykoT4eCwpKVjMZrYkJ/PPtm3o9XouvvBC2gYGKkXr/Pz48ddfefWtt8jJyeGuu+7i/PPPJ8RoZOW6dcSGhdG8VSvCY2MJDAxEYzYr26j7+7N3714OHTpEWVkZH732Gl++/z7B7oh9WloaN1x7LWVlZSxbtky5SHS52LdvH7Nee41FX3xBrkbDRy++yJiLL+ZwUhLffPMNSz/7jHnz5tHSq7BsSXExAQEByknL6aS4uJgXXn6Zm2+5hXZq6m1ODj/9+CM5Oh033XQTOp1O2Q7YbFYGGMHBWPR6tmzZwuDBg8HpJO/wYcKNRuV9iInh37172bp1K3q9np7du5MQE4PF6aSwqIhDhw5RWlpKq1atyMvJwVpaypGkJPYfOECzNm3o0bMnPXr0ICQkhOXz57No4UJySkpo0749t0+dSrOEBIoLCvjhxx9Zt3o1jpISWrZowS3Tp9O8ZUtwudi7axe/L17M7PffJzUpiZ79+/PeF1/Q5jQKRDWUoKAgit2Do5+/+IJLzzsP439ZkqDWNHC34y1bt9KzZ89aOFJxPH+uXMl3n33Gjo0bMYWGctb553PTrbcSFBTEtvXr+fmLL1j/558UFxfTpW9f+o8dS99+/bDn5bFtxw7+WreOnRs2EGA00qprV7oMHozVbKa0uJgjO3dSnJNDQXExJYWFZBUVUehw0Do+nuYxMfTr0QN/f3/++ecf/tm2jWyXCwMQp9czbNgwWnftirWkhPV//smmpCTKUAaj3WNjsWk0WOx27AUFlNlsFLl/n0BAo9OR6XCgB8KA8OBgPnzzTX5+7z3OGDyYiy++mMdmzqT4yBFKgOSdO1nxxx8cSEvDGB7OunXr6NSxI19/8w0AX8yZw+MPP8w7s2bx1mOPkXH0KFqgGAgCNn31FZ9/9RUfvfUWERoN973wAoXp6Xz/xhu8/fbbnHHGGYSHh/PlN98w5cknaR0ezsN3382R1FTW/fMPvbp04btvv0VnMHCgpIS2UVH0bNuW3v37M23aNIxaLYuXLuWnefP4fc0a/PV6Lhw/nksuuYTA4GCKHQ52HTrEtp07SUpMJDoykrZxcbSIjsYUEoIxNBRTcDBpaWmUlpSQmZlJVmoqIUYjfYcNo0PXriQkJJCfn8/SJUvYvXEjJcXFnDF2LFddcw0tWrSgpKSEZcuWsXT+fHZv3gwOByPGjePa22+ng6/loXXtBAVkm5QTZYDl5FQvAFB518fTIHBqCAritief5Jq77+bgnj0ktG1LWHS0suFMPUlOTsaSn4+f0Yh/QACZGRlkZWfTtlMnmrVqhdFgUC5ErVYyk5PZsm0bfhERdOzTh7iYGDSlpbj8/SnMzaUkL49Ak4mg6Gh0AQGev2lJidIGdDolAOFrwqI6ExnV4XJ5Jr2cTmXMqC45U2+r6nlefv/9d6675BLuffBB2g0YwNQbbwQgvl071v31Fybg559/Jqx9e3olJNBMDaRpNLhcLjRms3IcpaUQFVW+XNZut1eYOHM5HMqY1X0fJSXog4JOrv/wNUHu/r1cKMEQrVfbstlsrF62jPzsbDp06UKHrl3xDwjABezdvZtNq1djtdtp07YtnXv1Ir5ZM9BqKSoo4O8//mDz+vUYtVpGn3cePYcMqbd2+/fKlVx+1lkVbtNptbRv356SkhJSUlNxAoH+/rRr1470tDSMRiP+oaG0a9GClJQUEhMTsbnbSVzr1gQGB2NwOJjx2GNcesUVxMTGKu3GaDz2PW2o3Qs1GgwGA7179GBFRgYtW7Rg35EjpKemKv2ljxU7Bfn5qFdf69at4+WHHuJAYSE2YPyZZ7Jt9WoiwsN57LHHKCoqYtiIEfX3+1QVQNRolM9MWRkUFRHVti1vfvIJtz/yCN/Nno3dbCarsJB//v6bxYsWnfDHaDUaOnfuTKnZTHJyMmWADpg9ezZ/JyVV+EyczlIPHQJQlsFWQ757ove8iy+ucHtjWgK5Z9cuADr6uO6+0d2nf7dkCXO++ILBgwfzyaefsiktjXnz5mE0Gvnuu+/q9XjrisZVg9T8/v37uzZu3FjhNpfLxaXDhxMQEEBoXBzrV60i9fBhNBoNrTp0oLS0lLyMDErsdgYPHUpaYiIZR4/SrU8fWrRty6pff8Vut2M0GikpLSU2Jobx553H4eJili9bhsYdTdVqNERFRaH38+NgaipWpxMtysVM+bEAmShrVwOAeH9/+vbtS0FRETu2bye8ZUv2p6fjstkIQ9mVyIlnG9B0wA8IB86ZMIGt27axIy0NdW+SMGDKDTfwyCOPcLSggGmTJ7N12zZASRe02WxYLBZW7tuHpqyMy8eM4VBJCfv//ptCQN1bJB547dVXmTR1Kvj5sX//fkZ27cqEc87h1f/9j9DoaG6/6SZ+WLiQCydM4LN58yA7mw0bNnCR+0N1zfTp7N6zh4lDh9KvXz/y8/P5888/+XbePHampTFmzBhsubns37KFqMhIunbtyv5Dh9h6+DAGIARP9NPlPjaN+/92INT9PSgnUZvTSQnKBWREUBCm4mKCg4KIjo4mOTkZdDpGjBrFjs2byc7JISI8nNDQUI4cOQI6HROvvhpzXh6/uGd0+w0YwIAhQ/hq9mycdjsTJk5k6sMP06lrV59tT6PRbHK5XP2rbJxV8NVmT1fvPfccTzz+OKMuu4zlP/zANZddxhtvvOF7tvcELBYLpYmJREREQHg4juxsorp04arrr+edd96p/YM/BdV2m921axcP3Xknm1euJDgoiH4DB1KQm8uWrVuxKD8QP5cLk9HIsGHDCAoKYuPGjRxJS8N7KNOmdWv69euH0+nkr7/+IjUjgwCTCZPJROvWrYmNjSUoKIjgkBBCgoMxGo0cPnyYo0ePsnHjRtJdLgb06MGEIUNo27Ytdrud/fv38+W333LYYiHAz4+xffpwzllnMWrUKP766y/efPtt0ux2Jpx7LiGBgVx59tk4LBaKi4vJyM5mQ2Ii5110EQ6Hg4yMDMb16UPO0aP88MMP/PLLL6RnZFAaGMgfP/xAi7ZtWTh3Lhs3bOCb+fMpASZfdBGTJk3i8iuuAGDjtm189sEHPP7uu8QCDz78MD27dmXUeedRkpnJ5BkzmL9wIVaLhVigZ/fuPPbYY7zwzjusXLMGUGoDBgE93UG/PzdsIAeIi4sjIyMDA3DxsGH069ePw4cP8/uff7IvOxujyYTLaiUMaJWQwLhx4ygtLeW7xYvJLCggxP3aTiAsPJyObduSn59Pampq+TJigEKUPjgYCAwIIDY2loKCAnJyc1EvV0tR0tU7tm2Ln58fu3bvBiAyNpbMzEy0LhdhISH06t0bp8vFujVrCImKYkdaGjofAZiTbbMgfW0F6pIydafR0FDlwt97hzG9/vibkKiZcgaDJ3MwNBTS0pT7T6Lfbqpqu6998I47+PL9930+xwlERUbSo1s38vLy2L5jR/l9LiAiLIyYmBhSUlIocRf2dqF8lgP8/QkPD0drMmHSamnXrh0BAQFYrVY0Wi2OgAB0LhdR8fFEBgZicDopKCqCsDAi4uPxB/z0ekx6PYWFhbTo1AlTcDABAQF0696dFcuXs33jRvJTU3n06acJiY8ncf9+8vfvJ+3wYVq2bMkrr7zC0KFDMUVHk1pQQGZmJs/efTf20lJmz55NeHg4o91BdAOQ/e+/bNy4kR0ZGXw+eza5+/ejMRgwxccTYbPhdDpJLyjAZrGULyXLASKBKXfcwYx33mHh118z/dZbadGiBZGRkVxwwQXExMQw7sorWb5oETfdeCPX3HornVq35rcFC9i0fj1XT5mCISSEj159lZKcHEJDQmjfpw9JSUn06NWLdu3bY3O5cFitHElKwgRYtFq0NhvhkZH4BQeTffQoBenpuEJDiQ4PJzooCIvZTOqRI+zft4+CggKK9Xp69O/PkL59Kc7OZvGvv1KqFmh3/91iExLIN5sx5+ZSORSZ0KwZBpOJQ8nJSravu43ogdvuvZcnX3vNZzuq7TZ7MCmJ7z/7DIfRSFhYGGaLhdz0dJJ37cJcUMB5V11FUGwsf65ezfKlSxnYoweBgYHkHj1KUnIyLdq1o0OvXowePpwOHTvSqkOHigHSxhoQcbkgPZ2bb7mFxYsX8+rrr/P1d9+BRsPC+fM9WWBGo5IwAPybns6Y/spbX4ByvdOiTx+ee+klhg8bxqszZnDbddcRrS5jq7TRR2PmcrlISUrCZrezd9s2gkJDsWu1ZGRmUmo2Y7BaiW/dmnV//83Wv/7iSGIiN95zDwOHDiU4MJC0lBRGu7NzKzsdxwffvP8+0++4g9V791Yr82n2Cy+wdu1aPvr11wq3D2zZkr5Dh/K+e5K2IU295RZW/fQTO9LS0FReku4eX5jDw2kdEIAOOPuss4iOj+erOXMYNGECP1cjwNqYVNVu/3MADKCkoEApHO0eUO/ZupUfv/qKgzt3EhAYSFhsLNdMm0anzp2xFhYy58MPmTtnDjk5OQwePpxHX32V6JgYfl+8mM/efZf1K1cSHhbGiBEjGDRoEIVOJ+l5eWSnpeEoLaVNs2Z06tSJlJQUisrKMLhny1v6+TFm9GhycnL4448/2LB5M7MXLWJAv35cMmoUd9x8M7k2G8889BBdu3ZlwZIlRLqX3Bw+fJj2PXuy+KefcAKOoCDO7NmTDX//TbHTiSkqih5xcezauRODXl8+S/LgAw+wYcMG/vrrL6xlZUSEh7O/pIRlCxZwZvfulAQEcPvEicxbvpxiYMqUKfz87rvccvPNPPP22+Dvz8cff8xjkyfj0mgYNXQoI8eO5dmnnqJFp04c3ruX8PBwxo4ezcZNm8gpKiLUz4+U1FR0Wi0O98ydFSVw1b11azr36cOqtWuJCwtjyJAhzPziC6ICA7lo5Eh69e7N6JEjycrL40h2NilHjxJmMhFmMhEdHU1QUBCpqalgNBISH0+r9u1pExtL+v797N+/n227dnEkOZnRY8YwZtIkTEDSvn188PbbLF+yhDZt23Lv448zaOhQNH5+JCcmMvPpp/n5228xmUxcdsstXDJpEv0HD0aj0ZB04ADPP/QQfy5dytxVq+jRt2+NGvDJttnTUcmRI4wdOZK/k5KIQZntWbliBR3PPLPC4GbPnj08++yzvP76655t6p1OZXlXeDhotQwfPpwDa9ZwKDmZTYmJ/PTZZ7z21Vd89vnnXHfddQ3y+zU2td1mMw4d4oqxYznnkkuY9thjBISEgMPBnvXrWfzzz9jtdtp268aYiRMJCw8vX9Zy5N9/2bN7N36BgbTv2ZP4qChl+YJGg6u0FAoL0agDXJNJGeipNbicTqV2S0CAsnTFz89TyNhmU9qETgdhYThRliOUb9ddVqZc4Dsc2I1GnBqNZydVu10pPh8c7HtW3yuAUAT8uXAh7YcPp2NUlHJMDgcZaWl07dePYmD5d98xbNgw7rzzTjZv3szajRuxFBRw5T33MHHYMK67+WblePz9leyHuDhsDgfz5s0jWKvlrMGD0UVGYtVoePrpp9m1axd5eXk8eOONTBg/HldQEO99+ildu3dn5MiRrF27lkGDBmFyOJTfw+mEmBj+XLuWH374gcjISM4aO5YhgwejsVrBaKS0rIw1a9ag0WgIANpHRxPTrBkad1aIS6ejqLSU0pwcrDk5mEtLCQ8PJzQsDL+ICDAYcBoMpOzfz8Hdu8lMTyfQ35/+gwcT07496PUc2LGDP+bPZ/v27TRv25aBw4YxfPRoDMHBoNGQduAA+3fvZsT559dqmz1euz0tqbuXqhs3+AqAqfdVRQ2A6fWetqu+jt0utZS81HZf++/WrWz/5x/MQElJCXHx8YSHh3N41y5yjh7lYHIyW/ftIyQ4mDNHjmTkiBGYS0rYv2sXe3fvJiUnh7bNm5PQvDn+4eEUlpVRmpdHWWYmhfn5lJrNlJjNpKekUGg2g58ffk4ndqsVl8tFZmYmFqsVCxBsNOKw2XB6jdWdKIEZ712rHVCh3EbHDh1o1rYty5YuxQAUQfnuXkb3v1KUSc0Yr9dyoEwkx8bGcsXFF7P4iy8oLikhy/2YqZdfztc//IAlIICFn3xCfHw8Z4wdi8tmo0+3bpx55pkMu+ACfvjgA36eN48srZYgpxN/oH27duw7fJgSm41goGWLFhw+coTAgIDyYKENZVJaPSvEREeTlZdHWFAQ8fHxNG/enH///ZcjKSmYjEZMJhMxMTGYTCZcLhcGg4Gs7GwsZjMxMTFER0dTUFBAYWEhOYWFGAMDiYqIoF+3bkRERGCxWNi8eTM7d+7EaDRy1oQJnHfBBSR06cL+f//lwO7dHElMxKjX02vwYAaddRb+/v4k7tnD7g0b2L5rF2VlZXTs3JmBI0cyZPhwSktLWfjTT/Tq25e+lZZCqep1TNuYA1i1wZ2dctvUqaxcvZq3Z81i7dq1rFq5kmeeeYbHn3iCYqsVf4eDsrIy9mRkcM3559O2TRt2HjxIADBvzRoGDh1a4TXLhYY2nfqU/0FTHx+kpqZy7rnnotPp2LBhAzqdjv/dfz9vzJzJEYvFM349HptNOa9XOj8PbtOGHv3789EPP9TR0VffWWecQaDdzry//z62X1BLVQUGsmTxYm6//XZ0mZkEBgaSnZNDSLNm7ElNbZgDP0l1GgCr0vF2D7DZlNt9zEK7HA40FosyWAwOPnbrWZdLGRC6XBUbmcWiXJCpv1NkJC6jUbkgcziUCyq7XfmZ4eEViyoXFEBZGUlJScS1a4d/XBwasxlXXh65ubn4+/vj7+/Phh07+G3+fFwuFxMnTqRbt26AUkjRbDYTGhqKMzYWbVmZciwxMZCZiSMggNTCQlq2bMl5fftiMpm4/p57+OLHH/nxxx9pHxzMbVOnMvPFF9FotYwfN45Zs2fz7ksvsX79en5Zu5Yy4OtvvqFjcDCrV63i6ptuYsv+/eQUFOAXEMDIkSMJdTjQeu+cFhLC9qQk4uPjiQ4Lg9zc8vemwnuv1oBQ18pXfs+tVuU9LyvzFHCs/LdT0+h9nGitxcVonU4MVWxnby8rQ2cweC6eK5EAWC3Iy+P7r77iimnT6B4ZSUlpKYMHDuSL337j9bfeYvjw4QwcOJBBgwbxzz//cPvtt/Poo4/y66+/cuuVV6ItLYXgYHYeOkSPHj1QV4+XomRcBrVrx1/r1xNZk91ymrA6abNqkXnvz4naH/pallAd6vMb2w5d6o6P3v1Mfr7Sz7v7qUXLlxMSH8+wLl0APPW2AgKUPisuTikqazR6diFTb6/t+pC1WdNKLfTvcBx/CZSvCxu73RPArKGmPsCtN2oATF0q5w5WVwiAqQHm4zEYlPOt1apcgFVx/jzdnTLjg8pjJO9+w+FQ/s4aDc68POyhoTh1Okx6PdaCAkpycrBptRSazSQeOkS79u3JO3wYW14eeYWF/L1+PX2GDGH8eeexaMUK3nz6aZL37MEFJHTowPQnnyQlJYWEhARKi4sZ1a8fB3bsYObMmaSkpDDmssvIS02lS7t2BLVvz8KFC1n1++9MPPNM7r77bnZmZLDxn39499lnycrPRx8dTaQ7gJucmcnrL73Ec089RXBwMISFYU5P57PPPiPN4SBco+HqSy8lLi6Ofw4d4tU33sDf5aK4qIjwhAT+9+yzLP3hBwgK4qobbyQjI4NdGzZwaOdOLr3lFkyhofiZTJ7PVHAwB5OSiAgOJlTdSMPhUD5rfn7Ke6peyKmTHpV3l1X7V6sVXC4cZWW4goPRV7UErZb7+FOmzZ4K0tPB5aLQYiEkNJQX//c/Zs6cyS2TJ/PBhx9y3TXX8PnXX+PndBIRHs4jL77IA7fdxrq1a3niiSf4/Y8/SC4pUcrvqCQAdoymPj54+YUXePmxxzADM+6+m4dffJE7rrqKvzdtYqt7KeTJGtq+PZ169uTTn36qnYP9D9pFRnLxhAm8+uWXJ3zsihUruGD0aPyAM4cNY8WaNfxdzWy4xqJhAmANQR1gqBcO3tQC874yDlyuikE579Rfq1UJZvn7KydX97LMKsXFKRdp+fnlATDvFNqHrr2WL778knzA7H5KG39//vjzT4b270/HDh1YuHgxQXFxSmAO2J6Vxb79+7n00kuV47Faqx4MOxxKjQUoz/Q41clgoRbk52MtLOSyKVN44NprOXDkCI8+8ADX33cfL772Gnq9np9++okLLrjgmKfO/fRTLhk3DkJCeOrVV3nm6aeJc99nB/73zDNcOm0aoWFh9fkbNWrSZutAUVH5MgZA6Y8DAz316Lxvt1iUvjgjw3MRpAYk4uObRL9Y25r6ALfeqAEwo1H5f3BwxfNydUkArFqkr/XB5SI9KYm/tmzh4okTfU8u2u2UHj0KOh0BcXFQWKhktLqX1+ampxPudCrPVbNvc3OVdhkeroxtQZmUUTN+QQkYqMsIY2I8Y2a1fpSQNlubMjKUthkQAE4nK//4g8suvxwHStaiXqvF6nSihrc69etH2t69bN+9G+x2cnJyaNa7d8UAqdmsfB4cDmV31Eq7np6Omvr44MpzzyV5xw5CQkPZuXMn/3vpJb79+mtMERHMW778P732mZ060aZTJz7/5ZdaOtqTU1paSsvAQGbMmME9XrvbH8+jjz7Kxo0bmf366/Tv2ZNr7ruPl19+uY6PtPZU1W6bXk6sOvPtaxZHXVLgi7pGvPJsu1arXDxFRysn/Mpb9Z6Ij8d5LxErKiriwgsv5IUXXqBN27Z89eWXfPvttwRFRnqOJzycnr16KcEvUGayjjcQ1uk8ATe5yBMqjQaT0cgv8+YxfPhwbrjlFs4cPpyZ7hoVdrudyZMn07lzZ0pKSrjvvvvo4s6sufmmm/jpp5/A5WLZsmUMGjCA19w7df2xZAk333yzBL9E3auc1VR5dz1V5X7PRyFcIeqcWoT5v7Q7tY3LuVzUhEZDfLt2XHLppVVm1qPXE5CQoAS/oGJmGhARHOx5blV9ra/7vGoZVshkleCXqAtq23MnL4wcMYIpt9+O2iIdTie3TZ7M6tWrAdiyaRMXXXQRprAwTCaTsllD5c+Iv78SvA0NleDXaSDr6FH+WLSIfoMHs+jPP2neoQNffv45h5KTaVELmwYZjEas1dn9uY6lp6djAGJqUEP0hRde4LfffqNZq1acN2ECH733Hunp6XV3kPWk6QXA6ooaUFOzwyovEfTmPRjwMfDo1q0bX3/1Fev+/JOgoCDmzZvHlVdcAVoto0aNIi4uTglyGQxK4O14P0uI6tJolFkydZZWr+fpp5+uUNT16NGj/PTTTwQEBPDqq6+ye/duDhw4QNsWLXj99dcpzM9n/fr1nDV2LJMmTWLMmDH06NFDLs5E/ajusr7KAS/1eWqGsLRXUZeMRiX7OizMs9TxZIJg0k5FfVL7ycxMZVlZYWH1n+sdpPW+0FNXXjTlGlSiYal9q8lUPuHw9FNPkZSUxLRp0zAZDEybNo127dsTER6ODpg8eXLVOy6qNBpZ+thElXiXIwCuHj8eAzDirLPQh4Vx4/TpbNu9m5yCAvoNGfKff15AQAClNc0ArwNpKSnlG3vUmL8/Dz/yCH5WK0888YQysaEuIz8FyRmppvR6ZfnM8WayjhcAc1+AjRw5kt5t2yrp5upzvB8rBW5FXVHrwBmNdOrUiSW//sqbb75Jr169WLt2bXnWF0VFYLHQtm1bHrr/fg4kJdG9a1ccDgfDhw1THqO2U7lQE/XheBlgx5so8F7SLkR9CAlR2uuJit2fiDq4lD5W1DXvemSVA7aVs7yqk3kLniXrkv0l6pq6gsfdVsMjInjggQdYvXo1zRMS0Oh0LF26lMS9e2nv3jhGnF6WLljA2MGDaREUxC/uelwpBw6wc+tWpk6dyuU33gjAVVddhdnfn0K9ngsvvPA//9yAwEDMjSAAlp6SAkB88+Y1f7JOR9tevbjp6quZ99lnvP7ww/z544/M++QTcjIysHhn/p4C5NN/sk52Nisjo+L3JSXKTDEoHXdIiGcWQ4ja5OfnCbhqNOWzXyOHD2fkOedw1113eSL6TqcycNVoID6eiRdfzPq//mL2t98CMHDAAKWWksmkLHeQ9irqQ1VL2EFZ8m12V1WsnAGm0SjLGBrBAEScZnQ6T31RnU4J1Kr98InIkl1Rn040rj1ee3S5lPZdeZLBalXGHjKpK+pKdHTF8jRe5/3AwEDatW1bnmSQkJDgycr1bu8yhm3yXC4XD9x0E9nZ2fgBs996iwsuuog/5s7FAVx4223lQdHQ0FAeevRRCgoKCA8P/88/2z8gAHMjWDaY4d7BMf5kMsAAAgOZ9uCDLPj1V1559dXymx1Tp9LzzDP5+scfsdlsyrLiRk4ywE7W8TrLyhlgaofsaxa48tKIoKATp+UKcTKMRqWmgfp/9YJMXRIJyo55R496AgUGA7hc6HQ6nn322fKtyUPVGnRqkWYZPIj64GvHQ1/LGn3VAHNvQiJEvfLOANNqPRNe1eHdN0sfK+raidrYiQJgVT1faiiJumQweLK5vDLAyq+/VJUzwWVZ7mll2cKFZGdnc9GFF3LOhAlsWruWNT/9xP+ef55WvXrRrXv3Co+fMWMGr7zySq38bP/AQCyNYAI2IzUVfz8/wiIjT+4FNBriunRhwT//cOuTT6Km9OiAXatXExMTo5TFOQXIp/9k+eo4T1Qs1FcAzPtxMsAVdU2vh4gIZUMHqHp5jjpAUOt3oKxh//iDD1i4cOGxmTUmUz0cvDjtVe53nU7PbmO+ePevOp0yUyybNYj6pNNV6EdrdJ6XDDBRn44XECgrq3oJZFUlP8LClOCEjA9EfalqIqxygLZyW5frr6bLYmHHP/9w+2WX0bZdO1787DOeev99TAEBXHbZZWTZbHz3889VbxZSCxrLEsijaWnExMSg+Y/Lf1u1asVTTz1FUXExF119NUaDgVYtWxIHuHJz2b9/f+0ccB2SANjJMhiOndVSv69qMFBVHZCaFBoV4r/y8/Oc/PX64wfAvDMU9XouOO88zp0woWIbDw5WdsoRorGoaiDjq98Woi6py3btdk9GwskMtOUCTdQ1tY35WoVQXFxxd0fvcUNVY96AgIrL04Soa74CYL520pU22SS5nE4cNht2q5UP/vc/3nvqKR657TbGDRxIQGgoc9esISQ8nOYtW/Lx/Pm0HTSIV2bNok2bNnV6XP4BAY0iALZ3925at2tXa68XGBjI259/zt7CQpYtX84H772HHzC0Y0euGzcOa6XNBhoTqQH2X4SFeZaKNWumzJCpf2xf6eBVBcDU50iHLOqbmgHmq+Ct+lX9v1pDrKxMdtMTjU9VA9/K9wlRn9SZVu/aSN51ao6nuo8TojbodBAZqQTAKterBc+SXLtdGQdAxUwaWVImGtrxMsC8SQZYk+MsKeHhqVNZOH8+0VFR7E9MLL/PAbz5xRfExsWV33bmyJGs/fvvejm2APcSSJfLVaeZZsdjtVpJ2ruXcWefXauvq9Pp0Ol0mFq35oJrr+VoVhY7d+/m+2+/ZeLw4fQZOpSt+/czcswYJl19daOpDyZnq/8qIMDTkZ7MEkhv0gGL+qZenNlsFevNeGeAqf83mZS2brF42rcMeEV9i4yEqKiGPgohqkevP3aMUN1+Mza2Ym0bIeqaep73xXtiTJ241WplokE0HpUDYFV9L221yUjcsYOHr7uOTjExfPn55+Tn5+Nyubj3yScBsAEL1q9nTC0HfmoiwF3706xu1NQA/vj1V3A46Na3b938AJ0OAgOZPGMGb37zDff/738UOZ18MmsWm5Yu5bUHH6RHQgLfzpmDsxHsyC5Xr/9VWBioEeUTZR14BxiEaAzU2hy5uZCV5bndu3PyLuBsMCjtWAa8oqGYTMomDpV35vE185uff+x9QtS3yjWQqhMACw5WHvcfa3UIcVKio0/cTr03eVK/F6KxqE4NsOhoUDd1EqeUstJSXnvoIc7s2ZM5X3wBJhOT7rqL/cXFrE5M5P6nnuLZd95hxdatDBw4sEGPNSAwEICS6u4AXcsO7tvHLRMnAtB7wIC6/WHuz9l9Dz/Mqq1befS55xg7ZgyjRo7EBNx7/fUMbtOG5x58kE1r1yrPaYBMdxlZ1ZXKna13EVxvfn4V6yoIUZ/UoJbNVvF2dYtop9MTANNolNvUYrgy2BUNyd9faZuFhcfO9KqDW5l0EI1BQIDSl7oHwScMLEREKGMDOHZiTYj6YDAowQGLpeqNRtQ2qfazMiYQDc1XxpevAK3aBxsMvmveiUbF5b7u2LNjBzOfe44yi4XDycns3bsXgLuffpo77rmHkErBzJunTGmIwz2GdwAsOiam3n/+soULAXjkqado26lTvf7sOx97DB57DBwOrBYLv/zwAy898QTvvvIKH73+Op06dcLo789bc+bQrFUrXC4X/upYqQ5JAKw2qZ1rUVHFWdvKW+96i4hQLuCKi0+8RFKIuhAWpgTA1GwZlU6ntNmiIuV7jcZzmwTARGOgFhhXv6r8/SvWZBSiIZlMFbPATtR3+rpQkwCYqG86nRK8Pd5OuyAZYKLxOFENMDUgJm210XM5nZSZzXz/0UfM/uAD/t2zBwAn4B8aSkJCAm9/9x2t2rShX//+DVZbqzr83UsgS9TrqXq2etkymrdqxTT3stAGodNhCgzkshtuYMSECaxauZKfvv6a33/5BQMwrGtXAIwGA8EhIUy86ioeev555b3zngispb+zBMBqk/pHsduVf5VnFaoKcKmPa8QfXtGEqTNgZjNYrZ7bdbqKmWFqBpjLpbRvqf8lGpraZ1YOgIGyTFI2GBGNUU36TskAE42Z97hW+lnR0I6306OviQXRqDhsNv5avpwFc+fy2y+/cPTo0fL7XMCF11zD5Lvvpl///g13kCchwL37eGkDTMraysr4e/Vqzr/iinr/2VWJiY3lsiuu4DL3Mf3y44/s2raNIwcPYrPZyM3J4YO33+b7OXNISEjAz9+fkuJizGYzNrudMRMmcOOUKXTo3h2tXo/mJD7PEgCrS96ZX8dr9P7+yld1yYMQDSEiQlnukJenfF85qKDVem6zWj3tVoiGombaugcXGI2yO5lo/KpTW0kVFKQEGdQ2LkR9Ol5QKyhIWb2gMhg8qxpk+bloCFUtgVQzRyRbsVHZ8vffWEtL+WftWg4lJbFs4UKysrMxmkwMHTOG4CNHaN+tG+dedBGXXH55o87yOp6GrAH29+rVlJaWMnLcuHr/2dV1waWXcsGll1a4bdXy5Xz96adkpqVhKS0lpm1b8nNyOJyczOeffMIXn3xCTHQ0RpOJ33fsICQsrEY/UwJgtak6H8yqtjWXYIJoaBpNxaxFo1H56h289b5wk+LMoqHp9comJGq7jIz0LDX3DuBKjQ/RmFQVAPM1PtBqj93wQYj6FBGhBLrUyQVQ2qS/f8UAGCgTuX5+krEoGoZ33+prYzLZwbzRcDqdXHn22RS5lwWa/P0ZdvbZXHD55Uy44AKC3MsGm4JA9+9S3xlgRQUFvPPii2i12kYdAPNlxOjRjBg92ud9BxIT+WPJEr7/7DOatWhBfl6eBMAa1PHSbVU6ncyMicarcop4aGjFAJgEFURjU3nA66su2Ck6ayiaqKouvvT6YzckEaKh+fkpWd/eATBfbdi7zq30uaIh6HSeTHBfbdBkUvpYdYJXNBiXy8Wsb78lKTGRs887j7j4ePybaDJIgBoAq8cMsPfffJOXHn2UMrOZ+595htAaBogas3bt29Puzju59c47T/o1JABW2yIilKLhNpun8w0L8xQYDwxUCor6+x+7NboQDe1Ey8a8gwoygBCNmdr/SqBWNDZVZc8ajRIAE42TupOpwaB8rTx+1WhkTCAah6goT51a73Gs0ags2xWNgk6n46xzzmnow6gXagbY0vnz6TdoEHqdDp1Ggy4gQKlf5XAoX911ljU2m/JVqwWTSVn6qX4PaACcTuV7pxM0GjRaLVarlYOJicz57DPmfvIJGmD6448zfcaMBvvdGysJgNU2Pz9lAGuzeWp6BQQotZUsFuX/AQEyOyYaJ40GYmKUrC/1Ik3tYNX7Y2OV26QNi8ZObatCNCYGA0RHK+MEu91T50uvVwILEkgQjY3B4HspbkCAsqxMlumKxsS7PmhhofJ/6VdFA2neqhWDRo7k17lz+XXu3Dr/eXqdjoEDBvDZggVExMbW+c87FWlcNVinr9FosoBDdXc4QlSplcvliq7pk6TNigYkbVacak6qzYK0W9GgpK8Vpxpps+JUI+MDcSry2W5rFAATQgghhBBCCCGEEOJUI2tDhBBCCCGEEEIIIUSTJgEwIYQQQgghhBBCCNGkSQBMCCGEEEIIIYQQQjRpEgATQgghhBBCCCGEEE2aBMCEEEIIIYQQQgghRJMmATAhhBBCCCGEEEII0aRJAEwIIYQQQgghhBBCNGkSABNCCCGEEEIIIYQQTZoEwIQQQgghhBBCCCFEkyYBMCGEEEIIIYQQQgjRpEkATAghhBBCCCGEEEI0aRIAE0IIIYQQQgghhBBNmr4mD46KinK1bt26jg6libLZlK8GQ8Mexylu06ZN2S6XK7qmz5M2KxqKtNkG4nSCwwEaDehrdIo77Z1smwVpt6LhSF8rTjXSZsWpRsYH4lRUVbut0dVB69at2bhxY+0d1ekgLU352qxZwx7HKU6j0Rw6medJmxUNRdpsAzGbIS8PjEaIimroozmlnGybBWm3ouFIXytONdJmxalGxgfiVFRVu5UlkEIIIYQQQgghhBCiSZMAmBBCiKbD5WroIxBCCCGEEEI0QhIAE0IIIYQQQgghhBBNmgTAhBBCND0aTUMfgRBCCCGEEKIRkQCYEEIIIYQQQgghhGjSJAAmhBBCCCGEEEIIIZo0CYAJIYQQQgghhBBCiCZNAmBCCCGEEEIIIYQQokmTAJgQQoimw+Vq6CMQQgghhBBCNEISABNCCCGEEEIIIYQQTZoEwIQQQgghhBBCCCFEkyYBMCGEEEIIIYQQQgjRpEkATAghhBBCCCGEEEI0aRIAE0IIIYQQQgghhBBNmgTAhBBCCCGEEEIIIUSTJgEwIYQQTYfL1dBHIIQQQgghhGiEJAAmhBBCCCGEEEIIIZo0CYAJIYQQQgghhBBCiCZNAmBCCCGEEEIIIYQQokmTAJgQQgghhBBCCCGEaNIkACaEEEIIIYQQQgghmjQJgAkhhBBCCCGEEEKIJk0CYPXF5WroIxBCiKZP+lohhBBCCCGEDxIAE0IIIYQQQgghhBBNmgTAhBBCCCGEEEIIIUSTJgEwIYQQQgghhBBCCNGkSQBMCCGEEEIIIYQQQjRpEgATQgghhBBCCCGEEE2aBMCEEEIIIRoLhwPS0sBiaegjEUIIIYRoUiQAVl9croY+AiGEaPqkrxWnOptN+Vpa2rDHIYQQQgjRxEgATAghhBBCCCGEEEI0aRIAE0II0fRIJpg41UkbFkIIIYSoVRIAE0IIIYRoLDSahj4CIYQQQogmSQJgQgghhBBCCCGEEKJJkwCYEEIIIYQQovpsNigsbOijEEIIIWpEAmBCCCGEEEKI6svOhuJicDob+kiEEEKIapMAWF3yLmArxWyFEKLuSV8rhBBCCCGE8EECYEIIIYQQjY0Ec4UQQgghapUEwIQQQgghGgvZBVIIIYQQok5IAEwIIYQQQgghhBBCNGkSAKtLsnxBCCEahvS/4lQlbVcIIYQQok5IAEwIIYQQQgghhBBCNGkSABNCCCGEaGwkE0wIIYQQolZJAKy+yEBWCCHqnvS1QghRf6TPFY2R3Q5paWCzNfSRCCEaGQmA1SUZFAghhBBCCCFE/TGbla8Wi+c2uS4TQiABMCGEEEKIxkMu0oQQonbl5EB6ekMfhRCiEZAAmBBCCCGEEEKIpslqbegjEEI0EhIAE0IIIYRobCQTTAghqqe4GLKzPd9L/ymEqIK+oQ+gSZPOVwghGob0v0IIUfekrxWNQWGh79s1mvo9DiFEoycZYEIIIYQQQgghhBCiSZMAWH2RGTIhhKh70teKU520YSGEEEKIOiEBMCGEEEIIIYQQQgjRpEkArC7JLK4QQgghhBBC1B/1GkxqgAkhKpEAmBBCCCFEYyOTaOJUIO1UNAV2e0MfgRCinkgATAghhBBCCCHEqe1kArLFxZCZCTZb7R+PEKLRkQCYEEIIIURjIRk1QghRf6xW5avD0bDHIYSoFxIAq0veg1gZ0AohhBBCCCFE3TqZ6y6pFybEaUECYEIIIZoOddCrfrXbleUNQgghap9M8IrGpHJ7lKCWaAhmM5SWNvRRiCroG/oAhBBCiDqTk6MsawgIAK3M+YhGSi3ArPcalklgQQgh6p7sGClqW16e8jUgoGGPQ/gkVwNCCCGaLgkiiFNBZqbyD6TNCiHEyarcf57o++reJ4RoMiQAVpekIxVCCCGEEEKI+lOTazC5XhPitCIBMCGEEE2XuqRBBrhCCCFE0ybneiHECUgATAghhBBCCFFzEnAQp7rKm+cIIZo0CYD9F3a7UmC5Oh2mdKpCCNFwpA8WQgghmrYTnetlLCDEaU8CYP9FYSFYrco/X6STFUKI+lVVv6vebrMpkxcOR/0dkxA1IdkIQghRO2qSpCB9rhCnBf2JHyKEEEI0Ii6XEsgyGk/82Mrbmmdlef4fGws6Xe0emxBCCCEahgSzRCPisNuxOxyYTKaGPhThRTLAhBBCnFqKiiA7WwmCVaU6W59LFpgQQghxepOgmagjkyZNws/Pr6EPQ1QiGWBCCCFOLWrgy+n8b69TOTtMCCFEzUjQQJxKpL2KepCbm4vL5eLHH39s6EMRPkgArC5JJyuEEI2Dr/5Y+mghhDg50n+Kxqg62d9VPUfatKgl3Xv0AEADSKtqfGQJpBBCiFOb0ynLGUXTIRdhQghxYi6XsqlNVfcJ0cBknUHjJAGw+iIdsRBC1I2MDDh61Pd96jJHyQATpxppn0IIUbWcHMjMrHjbyfSbkgEm6kgMynI7538t2SFqlQTAhBBCNH75+VBWduLHVTWAlQCYEEII0XSoY4KanMtPZomkEP+BP2A73qZNot5JAKwuSacqhBD/ncsFpaXKzo+1/bpCCCFOnvSjoiF4L330boOVs7lO1D6zsny/jhAnq1I70gH2qpbqNrTcXEhLa+ijqHcSAKtL0pEKIcR/91/6UlkCKU5l0kaFEOJY3nU/T9RPHu/+xhqYEE2GlkYYAHM4wOHggzff5LLLLmvoo6l3EgATQgjRuP2Xmh61/bpC1DVpl0II4dOBAwd46KGHsFmtnhuPlwFWE9L3itpQqR0ZaIQBsPx8KCjg6WeeYe26dQ19NPVOAmB16Xi1aGTHMiGEqB61L9WcYD8d7wFxVa8hhBBCiFPSs88+y8svv8y8n37y3PhfaoDp9WAynXh8IUR1VWpjGhphAKzS7umu02yMLAGw2lCTosug7FpS1Y5lQgghKqruiTknp3qF8mv6ukI0FGmjoiGVldWsTxWijhmNRgC+//Zbz40nqgF2vH7U5QKtVgmASX8r6kCjDIC5XEoQzK3seBPITZAEwOqLd6da1WDCaj1+BoMQQpyOqtq1qTqDVe8aYLL7k2gKsrOVwrVC1LXs7BNvPiL9qKhHae6C3Rs3bPDc+F/bYG1kf8kuf8LN5RVYUjXKAJjX56a4qKgBD6b+SQCsNjgctVNgOSdH+SeEEMKjuksgq/MaJ7pNiMbEVxstKwOLRS64hBCnlWnTpvHrr78CkJ+XR646EXCiGmAnygA73mY5FR7q4ueff+af9esr3lFUpOwmKX2yAGw+2oG9MbYNr0BdcXFxAx5I/ZMAWG0oLPQ9QyYXV0II8d/VVl8qGWDiVFDddilL04QQpwGz2YzFYmHWrFkAtGvXDg2wf/9+5QH/pQaYqhoTbOvWreP6Sy7hwsGD2fjPP9x1111kZmZ6Al9S31kAFrP5mNsaWwZYSXExZq/jPN0ywPQNfQBNhq/Ibm10yEIIcbqraZ3FEz02NFSZsZV+VzQGJ8pOqEytVeNjmYUQQjQlKSkptGjRguuuu678thYtWpB/4ACJiYkMGjTov2V4VyNTzGw2o9VqmTt3LgHu24YMHIgdSEpK4rv33iNQp5MxhQDAYrEAUAy4gGAaXwZY+/btCQ0OLv/+dAuASQZYQ/LuKGXWQAghfKuLQaXs+CTqW1XlEk6WBMBEYyAX/aIOPf/88wDMmTOn/LbHH3+cAJOJfQcOKDd494W+Mr1P1EY1mirHBIWFhQQGBuLn58frr79OTFQUANFAp5Yt+fXXX5l+773HHoc4bVndATAroLYIX8siG5IGKPQKepWcZksgJQOsLtVkUCABMCGE8K2qGmDV6WN97QSlDnZdLmXjEZOp9o5VCF9cLmX354AACAur2fOquk3GDUKIJszlcvHT3LnEA4VAQHQ0R48eRaPR0KFdO/aezBLIY3+I7/+77d27F5fX7bfddhudO3UiJyeHyy+/nJtmzOC3uXMpKSkhMCjo5I9DNBlqBpi3xpYBVtnplgEmAbC6pBZWrGr2wbvwogxkhRDCt5OZVa3OjK/ZrPwLDYXAwJM7NiGqQ23DPgbG//k1hRCiCdqzZw85WVm89dhj5ObmMnHaNDQaDTiddOrQgXVbtigPPFERfG9VLSuvIgMsKSkJgL///ptBAwdCenqF+88aOZL5n31Gh44d+XvbNlr27Fmj31E0PWVWK+Be/hgcDEVFjasGmI/PwOmWASZLIOuSd4DrRNSBrFb+JEKIRsRuh4KChj2G2lhi4ysDTNWYBiaiaTreWKC0tOL3x8tI8P5eAmCiMbFaZRc8UauWL1+ODpgwYQIzZsygS6dOyh02G+3atSPx8GGlkPfxMmWrswSy8nO8HHAvs+zWrZvPCYwxI0aUX0zPmzuXI0eOVMgYE6cfNQPsnVmzmD17NtC4iuC7fIwdZBdIUbuqGwCTzlII0Rjl5UFJSf1c2Lhcys52lU/OVc3o1nQJpDfvvtn7/w6HBBZE7auqrZaWVgwwS+kEcarKyYGsrIY+CtGEzJkzhy4dO9K6dWvlBptNOT8XFdGhQwfKgMQDB46dNKhpwMt7UszlqjAplpSURGxsLEGgjIcMBvD3V74CMRERHElOJioykmefeYaWLVuyePHi//Jri1OcWgOsdZs25ctiHY0oAOZrl0rJABO150QZYMebscjLU3YpE0KIU13lvq6szHf/V1TEC/fey/svveT7+bU0UZCTk8Mnn3yCUw10OZ2sXbuWgoICpU7T0aO+n2i31+4SNtG0lJZCZqaSCVPdIGpNg1jqZ0DdcUwmz0RDkzYoapGlqIiXXnqJvn37smHDBm675RZl2SMoY4fcXCgro2+/fqDV8sTjj5Ny5EjFF6lqEu1EXC5lQiIzs/w1Dhw4QNu2bZWfDRAZCeHhEB2t9MMOB35GI/fccw+jhw1DA3z33Xcn/waIU4PDUeX5Ww2A+fn5odcr1aYaUwZYkY/4ggTARO2pyaCg8mNtNk9nK4QQDe1EF/QuFxQWHvu44mKlZoZ34e7s7GOXfVksOAoKmPXOOzzx6KPHvrb318q3V4dXsODpZ57hgYceYtmyZQD8u3Mnw4YNY/LkyZ7H+vp9MzOVwbcQlblckJ+vBElzcpQ2Xvn+k31dX9+r5RIkW1EI0QTs3buXqy6/nI7h4Tz/8MNs2bKF0aNHc8O11yr9nUajJAaUlYG/P8179eKGG29k/YYNTL7lFs8LnWhiwNcSc+8MMHf9JvW+pKQk2rVrp3xvNFYsVaPTKX2+y8VNU6fy3XffcdOVVzJnzhzOHzKEnRs31sI7Ixqjgn37yP33X5/3Wd1tyGgyoVMDYI1oeXihj7ImEgAT1VedAe3JZoBVdQEmhBD1yXtZQGXqRb/DoRSTLy4+NnO1sFD5qs5+qYF979kwlwtyc9m/fz82QAeeGho2W/VrIvni4/6UlBRswA8//ADAu7NmAbBg3jwc6oxe5RRx7wkJ6ZtFZZXbhJotWDnQq6q0zKbC7dUZW6gXYZJ9Ixojq1WW6Ipqmz17Np07d+bnH37gmmuu4btPPyUnJ4fffvsNf6PRk/EKyqY14eFgNPLGG28wZvRo/tmwgZycHM8L/pdztNd1WFlZGUeOHFEywJzOY+s063SesYH7OO+eMoWrLr2Uw4mJXDVhAumHDp38sYhGq0ePHnTv0YPbL7iAvMzMCvf5zABrRAGwInVc7kUCYKL21KQIvq/163KRJYRoLLyWC5b/v6zMU8NIvU2dPa1MvRjyFQBz37d+925KAQ2wd/du5bFZWbWz7NDdr7pcLjZv2UIxsHTVKvLy81k4fz5xcXE4bTZmuYNhlJRUfL734MXXhZ3TqQT7JCBxeqrcJgwGJVswP1/5vnK7KCyssMymSpIBJk4FldthTo7SvoU4HquVtH//5eGHHyYiIoIlCxbwwgsvMGHcOCLCw9G5H6PW2wKU+ltuAYGB3H///QBMmTKFqVOnKgW+q1sTrHIGWKXnJScn43K5lACYr2s67+NyZ4f16NaNrz79lO+//57CggJefvDBmr8votGzu8/5vyxYwKfvvlvhPjUDzOTnh97dRhrTEshiH0sgc0+z1Q0SAKtN2dkVC4B6d5Y1vSiSAJgQojFR+6OMDOWfN+9sFrsdnE5cLhcuu53c3FzefPNNfvr+ezZv3kxpfj433HADa1at4uDBg/Tv3581K1eyfft2HnnqKdQhQs/u3Tm4f/+xx3G8bLBqyMrKIj0jgzPOOIOU0lKuuOUWrBYL8376icsvuYRXX32Vz3/6idLCwqqDXr4CYAUFSgZcVQFA0bSpn4/gYOXriXZ0VttJdc7zZWXH7sQqGWCiMTneigZxelLrabn7OJfLhc09AWYpLeX6q69m/qefcts112AuLmbFihWMOOMMz/PVCTaXS+lXo6KU7C/vvlWjoUePHgQHBPDnmjX8PG8eGzdu/G9Lzr0ywJKSkgCUJZC+MsC8gnEYDMr9TidYrXTq1InrrruOH3/8kdykJNkhtQl76umnueKKK8q/L88A8/cvzwBrTEXw1Rpg3p+SpMRESkpKWLRoUcVsyiZKAmD/ha/Czt4dXE2K4PuarZACt0KIxsLXhbp6m8tVISjkKitj8uTJtGrWjNGjR/PSyy9z4/XX079fP9q3bs3SZcu4bOJE7rvvPrZs2sT0m27iqquuwhQYyLxffgFAD6xYseLYn3mySyDdX1NSUgC47rrrAFj+119MuvJKBnbrxmMPPYTd6eSmadN47rnnwGym+MgRdm3fzt5duzyDX18BMPU26bNPT+pnISAATKaTr1fn63k5OUpGoveYQDLARGMhY1Xhi8Wi9Fvu2qD/u+ceOoaHc+2113LewIEs+/pr7pgyhU2bN/PuG2/Qs2dP5RrKZFKeX1amTKjpdMo/oxECA4/5MVqtlu5dugDgAB64/36y1exDXxMFLhcWiwWz2ey7BpjX4w4cOADgWQJZ+TE6nRKcCwvz/DyHo/xa8Pzzz8fhdNK9XTtmv/JKxUx6cUopVcsZFBfjVDO73TTA999/r7QpvDLAvGuANaYAmHsJpNoKw8PC2L9/P5MnT+bcc8/l8ccfb7iDqycSAKupmp7kq7sEsqqfIYMKIURD8g4gVe6PvJdCWq3g5wfAvO+/55NPPiE3K4sOHTow5c470QHqYgEryoBh/s8/0yU+ntQjR8jNy2POl18y/txzmT59OgZg7Z9/en6WTlfxeGpy7F7Hn5GRgQsYNGgQV111Fc++8AKvzZqFxmymc4cOXHbFFTiB777/nqfuv5+OLVsyoFcvhp9xBoUWi9KnV7UE0vurOL2of/e6yMxSX9N7AC0ZYKIxkX7v9OR0HlsvU6X2TXY7RZmZzHrrLaylpXz15Zfs37uX2269lfbt2nHbrbdy1WWXeXbVM5lAr1fGFQ6H59xfFY2G6KgoXMCw4cPZvXs3/3vhhfL7fOnSpYuS1VX5eN0F7dXf7dC+fcSYTMRHRyu3+crsDQ5WJj7U+x2O8gSIPn36lD/s0cce4/VXXuHFRx4hpaosNbsd0tIkk7yRKSoqIjAwkJtvvhkKC8mrtOtoEBAKLF26FPCdAWZrRBvbqUsg1V67a9euWCwWVq9eDVC+QVRTJgGwmkpPr7qmh5vdblfSC082A8x7ICGDCiFEQ/IaDKqBH5fLRVJiIlNuv51p06bx9NNPc+GFF3LHvfdy3sUXc8v119OzZ08KsrP5Ye5c7n3oIfTAjZMmcefUqTz76quAEhD7dcECunfvTscOHRg2bBgarZYHHnuMqyZOZP7PP/OLOyOsQgDseEE5L1u2bOHcc89l3rx55X1peno6AM2bN+err77ikUceUQJ3DgdYrbz1zjscOnSI5p068cFHHyk/2v3vzNGjeef998vfB3V79FWrVnmCYifqs51OZbm8LIdoOHURNFKzA3zVkqnpz6xcPkG96FJ3mQTP50HGCKKu1GQyViZuT095eco/9fxns3n+7/6an5nJGd26AXDZpZeybOFCkpKSePLJJ1m9ejVPPvccGru9YjF5Pz8lCFRWduIAGBAREYEDuO7667lq0iRmf/opxcXFSt9ZqT0WFhaSnJysjAW8M8B89NnpiYl0adkSjVre5kRL273vdxdA/2LOHD779FOGDR3KIw8/zFsvvsigQYM4/5xzSE5Opsw7MKLWO62Nuqei1hw8eBCATz/9lMOHD5NdeZdnIADYtGkTAHlZWWi0WoKCg8trgDka0aYgagDM5R5ndOvWjSCgJDWVCCAxMZHk5OQGO776oG/oAzilqJ1jaakn3dWHm2++mTlz5uBMSUHzXzPAZHArhKhN3gO+6nD3Qfv37qXA4WDrH3+wYMECFm3cSACgLkgoAmw7dmCyWunXuTP/mzkTndUKRiMBQUHs2bGD4OBgDMHBEB5Oj4QEEhMTaRUfz7c//YTN39/TXxqNPPfkk2zbuZNZ77zDBRdcULOMF40Gl9PJxRdfjPnwYbZt3szI0aMJCw0lPT0do9FIZGSk5/F+fp5aJXo9LVu2ZOHq1VxxxRVcP348V1x1FQ/fey9vf/EFTzzzDN/98AMfzJ3LD998Q/LBg4wfO5a/f/mFXr16KYN+u105T4SEKK/vPYutDuorF/YV9Sc9XbnIioqq/nPUwJO+imGTw+FpoxpN1efu6gYSvC/IdDrlwtJm89QCq2590dxcJTvBnZ0pRK2oKhtYnF68Al+FJSVkbt2K2WLhg19+IQxI3b+fpb/9BsDMN9/kyksvVepmeWeNGQzK+VLNejIYPLe5z8nHpdHwyCOPEBQZyfjx43E6nXz65ZekpKbSOSJCeYxXe132++/l/y8sLCRE/SYsjJLkZIx6PWVlZQSGhnLw4EHatWpV4Wcdl3oO0GrLz/ljxowBYOzYsbzz5ZdE6vXs2bOHd779ljZt2hAcHMzLL79MSEgIiZs3c8W559Kxb19O4upR1JHUlBSiUYImu3fvZsmSJT4fl7h7t/J1zx5atGmDwWDw7ALZiJZA5ubmotVoiGvWjNzUVDp16gSAP9C/Xz8WbNrEli1baN26dYMeZ12SAFhNVPMEP2fOHEBpYJFBQdV7bckAE0LUh5wcZdAaG6t8b7crxdtDQysO7hwOCoqLyU5MZOWKFTzyxBMUA2Huux974AF6tWvHeePHs23bNkpMJjr27ElqYiL9vZcW+PuDwUCEOhANDASNhjNHjODMM88EIKJ584rFZN0BquuvvJKnn36a5ORkWnfvrtxXOQPMF42GgwcPcujwYW6cMIElixeze9cuzmjdmvT0dBISEipOTuh0yiBbrTcCBAcHs2jRIuX9slqZMWMGrfr25b2ZM9n/778M6tqVIKBtYCD+JhMTzjmHGTNmMGX6dGUzFJeLxLQ0YsPCCHY6lWUdkZGeQX4jGgydlmq6HEGtKdOsme/7nU5PkNNXAOxEu5BVpXImuVpkWaPx/L8qDoeSSWCxVH3cQlSlclZXVRf/lTNxJQPs9KHTgd3Ox++9x8PPPosxLw+AApQlYUFhYcT37cuYAQO48o47lPOpd/ArOFh5DbW+Uni4p50ZjUrfdaKgk0ZDZGQkTz37LDgcJCQkoAXSMjLo3KNH+cNc7na5f+/e8tvatmnDzpUriYuMxOZ00nvIEEqystDrdGzct48jhw4xYvhwz4REdTPAfExu6fR67po6VRlvATdOn87HX3/N6tWrueOOOwCIAN5/7TU69+7N1XfeyTnnnEN8fPzxf6aoc6mHD5cHTKZMmYLFxxLVHt27k7RnD0eOHGHnli107tsXAIPRCIC9EWX97961i3bt2mHz8yM3NZXmbdqU39e7d28Wb9rEoUOHGvAI654EwGpCHWieYOZVq9XidDo5cuQIkerMQU12yJEMMCFEXVEv/MvKlAFmURGYzaRmZ/PHunWsW7cOTUEB/6xaxf6jRwl0OnGhnCwuGTuWnt260bt3b84+++zyl+zfv78SUNPpiI+JgaNHlTvCwpTsE+/C3WqB27Aw5RgCAo4dLLpnzM495xyefvppFi1axCg/PzL37sUeGMiYwYP5/fff2X3oEHc98sgxv+Lhw4cZNnw4oGTkLlm8mJ07dqCLjCQlJYVmCQnHvi9+fsrAtPJss14PVitBQUHcceed3HzttWxbs4ZZs2Zht9u5/vrr6dOnD9OmTeO5l19m2PDh9OzenSVLljDx1lsxOhwkhIdz4403cu9zz6FR33915ry4WCkSLAGK2lVSogRVK1+wVOfi3GJR/j4BAcdefNntx7YRl0vJzvLz47333mPrqlW8/9praDQaNm7ciCMmhleee47+bdvy0MMPV29mvzoZmsfLNFOPFU580SbEf3W8pWSi8XO5lLFAcHDNahc7nSxevJiH7r2X/iNHcuHQoWg0Gi699FLS09Np1qIFUe7ljwAEBSmTQIGBnqxU78CA90RYYKDSF7sDCCek04HTSbP4eDTA7n//5YeffuKxGTOIb9uW6XffzerVqxkzcSIalDqkhUVFLFu2jGu7dWPhwoVkZGURDNgdDma+9hrm0lJatmlTHuirdgaYweAZU3jf5/V925YteeGRR3Ddcw+Lfv+dzQcOMGnUKP5ctYoPPvuMW265BZPRyNo1a+g3YED13gNRJ9JTUtCg7JposVrJQ8mW8s6rHnLGGbz/+ef0atkSP+C8q64CaJQZYDt37GBU374cyM0FwBoYiM5gwGGz0b59e6IDA2UJpPBSOQBWhZCQEAry80lJSaG3+7FZWVmsWrKESy+91PeTJANMnMp8bQ8tGp7ZXB50WrN6NRsXL6Zd69ZotVq27N/PX7t24czLw9/pZN369WShLGkMAs4cPpzBgwczYMAA2vTqRY+2bQkPCVEu/ivt+khcnOfvr9Mp2WQWi2eAq9Eoy828Awf+/hUHu950OtBoaN68Ob179eK555/n7uefJwooAXb98w/XXX89JUD/ESM4o337Ck//+ptvyv8/ZMgQYqKjef311zn09NOEAZdNmXLsz/T3V2ahKwfjvL/X6TD6+zNgwAA+//xzJUDinrl+c/Zsxo4YwTnjx6MzmbBZLJgMBkYPHYoTeHXmTN777DPaJSRw57RplFosvDt3LoasLKKionh25kw69OlTsbaTfKZOjt2uLBW0WJSsO/BksHi3W19ZLTabsmwQlPsCAio+p/LSVnVQ63SyYft2pkyZQghw8dlnExAQwJWTJpEBmIB1QNdu3bjgttsqHEN+fj7vv/8+3bp14/ybb/b8bPf9TofDU7DVO9DgXd/GalWOR11eGxvrOe6TKcUgRE0CWdUcH4tGqrhY+afVKkEqVWmpUnvQ6xxvNptZsWIFnTp1Ysfvv/PAvffSv3t3Fn7/PQavYFZ0dPSxS6+Dgiq+PijjAp3O06+qTCaIjz9xm1L7Ob0ebDZiY2PRAm++9RZ5BQX88OOPtOzYkYx9+wCldmebsDAK8/MpBNauXUv7QYOYPXs2oWFh3D95MnPnzuXdd98lAGjVpo0nmHWic7I6xjGZlMBds2ZKzU+9/tjNAtzBMI1Gw7kjR3LuuedCSQnt27blhptvZltGBhNHjeLqMWO45YknuOOOOwj0sQumqHsZqalERUWR5a799eX336PT65lyySUALPjrL3b/9Rd2q7U8KNbKvaywvAZYIwmA5efnc+TIEbpcfTX5Bw9yFCixWPDz86PEZiMqKorOzZuTeuDAiWuZn8IkAFYT1cwACw4OpsDdwNTHXn755azcuJHMzEyi1QfWVgaYetEm9WRETRQVKSflqoIQNZGRobS/6OgTP1bUDh8npsOHD/Prr7/y9ZdfYvL3p2NQEOHh4fyVmMj6NWsI9XqsGYjt1Ik4o5Gs7GzumjqVM88+GxOgDwykj3tbcUAZsDocygDOYFAGdgUFSvsJCjp2UBgYeOx25dWdxfX+/YDLLruMrdu2odXruW3yZN58773ymhoa4ILzz+fRyZO59dZbCXIPrHfs3AnAmj//RKvR0LtPH3777Te0wJQ77uD+l1469ucZDMogvzLvAbxG4wlQqcsmAQICiG3WjAUrV/LRO+9Q6nLROSSE6667Dn9/f1wBAbz31lt8O2cOO3bu5LrbbsMfyNbrObdvXzZv3syFY8dy6623cvPDD2NwOHDk51MUEEBseDgadYcpb6WlynvU1AfEaWlKHxUeXv3nqBdE6oCzqEj5Fx9fMZjlcFQMyqoXeyr1+e6vVrud1T//zKjLLmN3YiLRWi3vvfEGfn5+3H7HHdzz0EOEh4cTptVy//33l2+FrgEuvugitq1cyZ9r1lQIgLmcTp599lm++fZb/P38GH7JJTjz8/E3GtG5XPz83Xfcf9ddLJg3j149erBr+3a++vJLBpx7LheceSZlFgubdu1iUKtW6LVatN47RlYecLtcSlDQZJLgqqiZE23qIEsgmwazWTlXq+drd9DGnp/PW7Nn8+WXX7JryxbCgDwgCmjRqROzP/+8QvALUAJavs5dlWk0npIMvu6rLveSSYNeT1xsLEePHsWF0v/u3bevfPyzcuVKhvXpg59Ox6K//uKXBQv4dMECbMADd97JPffcw/XXX8/ixYs5dOgQI0aNUn4XP78T1yPT6SpOCIKn1mRZmdInx8QoQTGn05Mln5GhZC1rNBAYiKa4mN5t2/LMo4/yxhtvMOOBB3jnnXeYPn060ZGRDBs4kObt259cnWlRY+kpKcTHx2OxWCgqLua8Cy7AaDSycMIELr/xRvoNHkxZXh7qKPesiRO56tprAU8GWGMJgO3Zswct0KlTJ66cPp3o2FgmTJjAY35+lBQVEdKmDc2aN+doUpKywYVavqSJaboBMO/dRGpLNbOxHA4HGiAlJaW88/5n40YAjhw5QrSvi6yTzQBzuTwDdllCU39croo1X05F7l1A/nMATG27NptnWZ2oO8XFHE1OZu/OnUR36ULnHj3QuFysXraM8eeei87ppG/79tiCgli6di2g7JB09sCBfPTRR0pg3mAgoVkzYrp2VbJd1GwSdYYyMlKp1aHy9/fsSqTTeZYv1GX7d2dX3fDQQ3QbOpToVq1oExJCaHg4L77wAjrglZdfZvqMGbz62mssX76c+fPns3r1apb9/jtXXXYZQ4cOhYwMxo0fz2+//UZYSAgzZsyoWZvXaj0z1OD5ajJ5/u8e4LRu147nZ85UbktLK38JjV7PlLvuYsott5BfVERiVhb6wkLiu3Yl1mjk4MGDTJ48mZdfeYV3332X0tJSnO7PVVxsLFdPncrg0aMxmUwE+PkR6XTistuJiIjAWDkAVlLiCVI2FWZzzQJgXllZgPKegNI/qdldoJw7o6LKNykoyc9n4XffccEFF+Dv709uZia7t2+ne5s2PDp1Kh/Nn08E0PmFF9i+bx/ereild96hEJg5cyYFKSl8OHMmWo2Gu++6i8feeos+vXtjz8pi544dlJWV8ej997P/33/5NzGR4vx8unfvzs6dO/noo494ecYMunTqRNcOHfhqwQKMwCUTJ9K/b1/WrFoFwCtz5tA8OJiSoiKygXhg9KhRfPjhh5hMJpZ8+y27duygV+fOjBs/XjnI0lJP4DompmZ/g5ISpb1LMf26c7wNStRd8qoTUKjt46nJY6t6jtXqWf4uGge7XTm/eQdrbDYlOKMGcbRaHA4Hd998M7PnzaNNt25E6vWMGz2as88+mxYtWjB43Dj0ar3BgABPaYWAgPoNtHvtFJ3QrBkZ7lIMffv04YHHH+fzt99m+YoV2Gw2YqOi+OSjj+jevz+57p2hAS668koICiIcuMq9hK08Y62659SqfufISE9R//Bw5bOi9qdqvTN/f8/PKS7m6quv5uqrr+bvv//m8aee4tG77yYQZVfqPuPG8fO332KsXL8VlHOmRiP9dS05fPAg3Tt04J133iEjIwOTuy/7eNGi8sd06Ny5/P93TJtW/hg1A6yxLIHMzcwkCIiKiiI6JoY333wTAL27rURERxPdrh3/rF+Py2xWdmg9UeD3FNQ4fiOHw1PLoiZvck6O0jGpJ1XvP5K6Ramfn+8dkBwO5flqVL86vDPA1OUGPuTm5qIBkpKSfGZo9PUVAFNft3Ix0crBsLy8Y3/n41Hry4SEHJt2XJn6s07nmeHqvgd5ead2YeHanKH1fq28vKpn8sR/lpGezqfPP89HH32EtawMG9AsJoaB/fuzevVqOkREMHv2bHr37o1Go8HsDmj5ewV8otq0Udp5SYky66jTKf2JweAJgHlfqKhLEDQaZVCrvlZdB3/DwiA0FI1Gw8CRI5Xb8vOZPm0at918M0mJiXQfOJCb77qLeR99xJ3TpjFu3Dh2//svLmDQoEHlLzVuwgTuu/deJl588ckdi3ewQK9XZsRMJs/7cqJziNFY/jkJi49XNglQi6oDbdq04ffff2fd9u18+f77BAUFERISQlRUFL///juvPfEE5ieeIB+l5oQaCgrw9yesdWsG9uhBv+HDmXTDDQQUFip3NmuGNTsbQ3AwWpPJk/nT2GeM1WWFJlPFc2zlC2izuepMJvV56vlU/Z0LCyv2V2VllBw+zNb168koK2PZzz/zy9y5zJ49m8DISP5es4ZUm40QlC3O7UAhsH/fPqICAigpLSUHMKIszX3qqae455572LpmDR/OnMn111/P9OnTCWjdmluuu46X09L45NNPWbp0KXO++AK9+zX1KMHcp59+mlmzZuF0udi9Zw+Je/YQFhpKj44d2fDPP6xYtYrLL7qI8847j4tuu428oiL8oXwXs+UrVtC+Q4fyrAfVtddcw8o9e8hNTmbShRcyceJEevXvr3yW7XYloK3XK8HBkBClL1DfO3WZpbr7pHrO897ZtCaa8JKKY9T0PcrIUN6buDjlfcrNVZaTq38bu13pa2p7jGazKRt3gNIWQkOPfczxxgwnKoKvZlaGh1dv8qG4WHnvfB2HqD3qOSgyEkpLKSgo4PDhw+j1ehxHjpBXVsa/a9bwzVdf8e+uXTw/Ywb3TJ+OJT8fP+9znsnkyS7191e+r89AbUjIMcHj3r16sXHLFu6eNo0nn3gCTCbGDhxIly5dKCwqItq9NN7iTiD4+uuvSWjdmiFDhiiv4127q7bodBUn0Lx5B8C8r4PdG50MHjyY3xYvZtOmTZSWlrJ69Wrefe89xg8ezMfffEPbnj2V13a5lPOcOulzql6jNCI5OTkcPHCAq6+4gnZDhtCuimv/yNhYwkJDyS8ooFXbtuW3l9cAayRF8AvdG1UEVtpcYepjj3H37beT0KoV7Xv2ZP+HH5KalkZzk0mJH+j1TSoQ1jh+E7VgMigdcXVmiex2ZUBstSof8MJCpcNyF2Iup+6ApNEor61G1gsLPVvV1zQA5nJVzI5AyfrKy8sjMDAQi8WCHti1axcuIM9rxvnw4cMwcKDndVTqoFDNLIJjd3jKy/P8zuqOYt4BMF9RWnVnlerseJWRofxMNUBXV1Hf/HxPjZLGJiND+Vr5pOF0KkHVkBDlfVGzYXwNcI83wFf/Xr7eV5dLub8+lrJW0YHXiNms/B7qYFzdRe9Uu8Cp7vE2cE2mpKQkJoweTdGhQ4wdM4bJU6aQcvAg8+bNY9GiRQwZMoSHHnqIPgMGlNer8FcHU2q70+mUIrdq9h8obUGrVZ5TUqIEnkBp6947MOn19T+Yqvx3cRe5DQgIoHv37uByodVouPjiizl69CjPPvccAK+++iqXX355+YVZRGQkf//1F7GtW3t20PsvvM8ZVZ0/oqOVn28weJZOOhzHLhk1mcp3xjxj/HjOGDGiQmbvbffeS+q+fezduxdrYCBFublYs7NxOp0sWbKE7Oxslsyfz7zvv+f5++9n4MCBtG7dGktQEPM/+YTg0FAsQUGM6NKFK667jmEXXljx4qWhqDtuVv5bFBYqfYtau0uVk6O8pwaD8j66B3GEhlZcBlpSUn7xkpyczE+ffUbriAjiY2KwWq3o9XoSjxxh44ED7Fmzhr27duFwOikDDIAhJITftm8nEBjSoweTJ05k4eef8+jDD5PQuzddOndm+9KltG7dmqNWK7kWC4cOHeLll1/m/vvvR6PR0KdPH+bPm0fv3r0xGAxMu/NOKCujZ8+e2Ox2LrjggvJSCBog6cAB/Pz8uPrqq1l4110Ee/3a77//PoN692brtm107dKF+MhINBoN6enp6KxWurRowbihQxk9ejTpNhufv/8+mRYLX77yCp06dWL42WfzxZdfkqPV0iU2lo8/+YSPP/mEcWefzcSJExk1ahT7tm6l56BB6KxWz5IHNSASG1uxSLXLpYwncnKODWiYzcrnwdfnKyvL8zqVlwh5O14WVENTi2D7CmqpY0k1k7a01FPsu7qBHO9AkjrWKyhQPgvqOTsvT3lNg+HY41A38Tje51u9QHa5PH29Op4B5fOjHu/JZoBVtTxSvcD3po5xHQ7P2EcN4v/XAJjdrrS5k81ydzqV1/DO/PE1DnC5lL9VY+hXKysrU/6m4eGedhkUhNXhYN/OnTgdDiwWC7/++iuffvppeeaxGcgHooEWbdrwxAsvMOX668FiwS8sTGk7Wq3y/qp/N4OhYS6QvSf33ddRL774Itdcfz1t27Qpb38ajYZ+/fqRvnIlUVFRoNVS6p70GzRoEC28ghYV1EfmopotV/lnxcV5aoU5nfQfOhRsNs4880z69evHAw88wIQzz2Tk2WeTq9XSIjycSeeeS//+/ZXlkWVlnnqslTPYbDbl79UY+9pGZP2aNZiA/kOGHDcIpPHzo3379uzYsYNYr7GyVqdDgxInaAwK3WPL0ErX4JNvu43J7vIMgwcPxgWs37uX5gkJrFu4kLhmzWh7xhn1fLR1p3Z7qsoXkqWl5ctEnMXFHNq1i5DmzYmIj0ejNoTsbPbv309qaioDBgwg0LuOkLo0R6PxnKjVn+Ed+DGbPZkLNlvF+9zLdJxFRZQVFOAMDCTAezlPFQ1y586d7Nq+nbHDhhHp709KYSE71q9n/IgRZKanExMdjcvl4pdffiEvL4958+axefNm5s6dCyjboWbs3El6ejp7tm4tf92lS5cy9+23+eSTT2jv3tXjyy+/pHNEhLKTmtPpGSy4L/ZwuSA7G6fVisPlQgvKQNXprDgwVdOP8/KUjtRgOKaGCfn5nsGTunOV0eh5HXUwos4IVidzTKUeU1mZ8jOys5W/VUxMhXaRl5rK7t27ie/dm7YdOlTvtWuZxWJBo9GUp6gCvuulqCcI99r9kiNHMJvNyskTPLvmqAPR7GzldarK8lNn3Jo1U14/L095vsGg/G3M5uNfHNSW2kjFVS9A1ewYdZec+lwaqn4GTnaAotYGCgz0vRuhGuA0m5Xf130BXpKTw0fvvUfvoUMZOWrUf/89TNaxlAABAABJREFUqkGr1RIdHMx3ixbRe/x45b3OyuLKK6/EarV6ghoGg/K7WK3KV++sLvWCwt9fGRSrn3d1ZtI708lXsdqGpvYjlZaGazQa7rjjDvz9/fntt9+49tpr0Xq3ca2Wli1bemZZ60PltqTTec5j3ioHetSLNfdGA5qQEJr360fzFi08ATV3f33DDTcoDw0OZsX8+cyfP5/t27ezefNmzGYz48aNw2g0UqLXs3rpUn5dtAh9s2YkHzniqRXVEBwOpS8MDVXaaG6ucp4ODmb/7t3s2rKFvXv3kp6eTn5+PgMGDKBv376ENWvGUasVR2kpQVYrhYWFJB44wIo9e2jZsiVn9unDoZ07sVqtlJrNfPTZZ9hstgoDHfXCLjg4mMH9+nHr/fczpF8/DA4HLpeLMy+9lCeffZZOLVpwzXnnQUwMT95+u9J23Mswh553Hmi1JHj1OzerxesBNBoGeO/a5T6nn3POOcx47DHufP55+vfrx8P3309BUVH5Z/ecc84h7PHHcRYU8MLzz/PpBx9w5vDhBBuNjD3rLM+FNhARGQkWC//u3k1QUBA6nQ6aNeOBxx6joKCABPc454916wi0WnEEBtI6IoIlq1bx52+/8e1337H0t99wAWUomYlx7t8nJCSEDh06kJ2dzcHcXNIOH6ZLy5YMGDAAq0aDraiIsLAwzDodF157rZI1YbWSunMnO5KSOPP88wnwzgBxn0uLi4vJzc2lZUSE8nt4B2FUOTlK+2iME2Q5OcrnWD3/l5Qo/ardrpy/NRolY1Y9N6qPCQ5Wzulq3+MdKCkrU94H74sq73Gs9w66DocnMKbRKO1RfS012189hqqUlnqyQ9Sxi3d/WtXFsMtFXl4eJiDAVz9W6bEV/u9dJqGyrCzPONx7klp9fOV+VP0MqAFAdQWJr/5MHW+pmaI2m/K1uuOTnBzlOcHBSj+lTt5GRFT8G6oZN1FRjW/puXs1jM3Pj6/ff5+/VqygqKiIrXv2kKdO+LpdftllnH322ZQ6nThKSigLCqJDVBT9hg/HEBiotGu13antxPvv04iyQ7QaDT179fJs2ONug71792bxypUUubPZ35k1i48//lg5v/pSX5N+Wm3FrLlgr2kQX+OwtDQmTJhAm/79eef55/l96VICIyNZk57Otx99RPOEBNq2bcvw4cOJiIhg2KhRtOjb1/PZcbmUz15AgHIeliCYb3Y725cvR6PV0nfEiOM/Vqtl0KBB6HQ6tJX6GL1e32gywIrc2dyhxykr0bNnT/z8/Fi5fj2ZSUk8++STREVGsj0rq8m0lVrprTZv3syX77zDv5s2YTKZGDh0KLfceScxfn64TCY+mTuXb15/nX/37MEJxLRuzTMPPECbNm346quv+Oqrr3ABwUFBXHrppVx21120at6cbcuWsXPnTvbv349fy5acc/bZRBsMHC0oICs9nbbNm+MAcrKyyMrKIjsri6N5eVgKC7n22mtp3bMnn86Zw6rVq9m9ahXFpaUUabUkGAyMGjWK+x99lKMpKewtKCAwKIgfPvmErLQ0Bg4fzodffEFYWRkB/v7ExsaSmJyMBkiIiyM9I4P+/foxcuRIXn3ttQrvxUUXX4wGGDN6NF/t3MmAgQOZPHEieq2W5s2asWjRIuJRlkp8uXAhubm5XH/99bTy9+fDjz6ie6dOBEVE8MbLL2PVaCjMzmb7kSOc0akTi377jV0ZGUS4XIwfO5aXP/6YGDWYpdFgLyxkycKFBDmdLFiwgJYtWzJlyhTlxFVWpvwrLVW+RkUpgwOnE6KiSEtK4tCuXRQUFODfujVGp5OOsbHs3bSJtXv2EBsayvAxY2gTFkapVsucTz5h0/r1lBQXM3j8eM6/4ALamEzs2bOHt956i41bthDk70+bNm146q23aNGhA06nkx9mz+aJu++mqLiYPOD8Sy9l5iuv0Lx169poitU2a9Ys3pg5k3tuuYUW0dGkHjlCxtGjHElOJiMjg47Dh3PRyJEM6NmTjMxM1m3ZwoYVK1i+fDkWq5XmCQncc889TJo0CY3Viismhn///ZfAggLCw8MJCQxUTlpqsNbprDgwUoNrFovyNTa2/MKGsrKazSSeKDOprEx5jPdr1uZMhDp41us9S4NrMwCmHquv11SzItXMT1/ZA2oWpK+U/JIScnNzKTlyhOatW6OJilICRgEBbFm9mjeefx67VsvUyZPp27cvJquVeT/+yHP33kt6Rga3TJtWbwGw1q1bs/r339GqM3bugadGo6mY0aPRKH8LdSJBDYCpKcygfI2LUwIPDkftbIRQH3zNvHtdbN1www1KUEgNxqqqCJw1qKio4x+Pd1aTetGtZtGof2P3gEoTEMDo0aMZfc454O+PPSuLkpISQuPjlfav1WIpLWXdX3+RkZWFth4HMPfffz/+TidnnX02/QcNIiAwEJfFwqJff+X9OXPYn5JCuNNJSUkJBTYb1sJCtIBepyMsLIzQ0FB+/+MPQNmCPANlOWIoUOD+6goPZ3VREd+6/+Y2lKWKV116KU/fdx8HDx4ks6QEm9WKJiiIgcOG0bZtW08BYYfDsymI0cizzz6r3Hb0qOciuvIOpsdT+f11t1OTycTUqVMZeumlxOt0xEZGejK/UZYqT5s2jY3Ll3Prbbdx6403Ku1ADZxUfl2tllA1qO0OegQFBSmbQbjPMX3UXcjcWeAXX3UVF40bx2WXXcaB4mIObttGgMHA9sREilJTWb9+PSaTiQNpaUSFhREfH8/A7t3ZuX07P/zwAzqdDoPBQFZWFlqtlnfee492nTtTmpVFcU4ODvdxtm7RgnY9e3Lmuefy159/cnD9eg4cOIALaNe2LRdccAHnnXcerfr3pyQnh29mzyYpOZnCzEym33svPcaOLa8/tGTJEmWHtHqyZcsWjmZkYDCbOZqSQnp6OpExMXRs0YKu3bqh9/Nj38aNrPvtN3777Td2HzxIr44diYuLIz4+nqCgIA4cOIDVasVgMOAIDaXvGWcwunNn2rdvT05+PikWCx07dcIvPx+n04nOawc8Z1YWWrVPt9s9/TSA0UgJEOBwYM3MRBMbS9HRowTabErGr/ey39JSpe0EBSnjj5ISz1JWUO4LDKwYmPI+z3r1rSkpKbRu25abzzuPD+bMYWtSEgl6PdHR0VitVkzen4+qAmB2u9IWw8OVn1NaWnEsUlJybD0qvV75DBoMyvNKSpSAkzpZfvSo76Cf96oHNYsrKwt0OlwxMWg0GlwuF66iIrQGQ/ln2m63s23bNhITEwk3m4lv1gyDXk9xaSkphw+zatUqWnbuTGzz5ujtdnSBgQQ6HOzYvp1CIM9sJlyvJzA8nOHjxyt1KOtLWRmZSUns2rULc1kZEVFRdG/ViieffJI5CxbgKimhdfPmREdGMrBnTy58/HH8/f3RarX069dPCapHRyvtQs3CA2Xs6O+vvM9GY+O+APYuJePVt6pfL774Yl5+4w1GjRoFRiMXX3wxF1988bG/U2ho7Y6Ta8o7AOaLOyjWuVkz3v7uOyVYW1ZGcXExX3zxBStXriQ1NZXnX3iBYpSdvdu2b0+r5s3JzMwkJiaGMWPGMGL0aPbt309QYCDa8HBi4+Jo361bnf96jZpaKzQ0FGw2Nm7cSEKvXgSe6G8CPDpzJprK4zqNRgmANZIaYAX5+Rj0evyOM44xGAwMHjyYWbNmASj1TnNy2LNtG517966X46xrGlcNUpz79+/v2ugu5q5yOp1079CBsvR0pZhgdjaJ//5LdFQUI0eO5O+NG9mVnMwZ3box8brrcBUX891337HXvR2tTqvlxhtvZOTIkfz8888sWLCANJsNLaDuOxAfF0dubi5WH8v4cr0eBxAWGkppaSllNhuZgAPQ6XSM7N2bMUOHUgpYc3L4fv58jhYXEw5kuR8XB8TGxFBUVESXnj2ZfuedzJ07l8NHjzK0f39WrFjBwfR0BvXqxR/LlwMwZPBgXnnlFcLDw3nnnXd49733yAAWz5/PnZMmcbi0lEigfe/eRLRqxfz581FP03c+9hgffvklhw4dok1AAIWlpRhRBu8GlJoi0QEBaMLCKElLI6R9e845/3wcdjsLPvwQu78/t193HZ179mT/oUN8/tZbZBcUVIhqhoaE0HHAAOIDAjhaWEiARkNgaCgFZWXEBQZSWlrKxp07STl8GD3KcowCIBjwFVLp3r07iUlJWEpLaZ6QgMFgYFNyMmVA79hYMo4eJTAggDFjxlDkcPDXihXYtVomXnopa5YtIzUtjX59+zJ9+nQ2bNvGrLfewllWhiE4mG+XLvWsv69Eo9Fscrlc/X3eeRy+2izAqj/+4I3HH2f9X39hRYkE64DmCQn4+/uzLzFRSVl13w6QkJDA2WedRYsWLViyZAkb/vmHiy+6iH79+vHejz+ycds24t3vYc+ePbnu7rsZMX48WxYtYueOHWTn5mLU69FqtQS0bMmwQYPoFBdHYWEhjuBg2kdFodPpyCkqIrWwkB7du6NxLyexpqdjDAxE4zVQAzwD2piYihdo3vVb1GLc3jNZ6pJhqN42076or6sWTA8JUV638uxoVaq79NDX8avPV4uXqsug0tPBaMQcEEBJfj6WsjI0mZmUlJTQsk8fjC4XuXY7Go2GDRs28On//sfaP/8EYMgZZ/DIo48SFx3Np19+yYezZhESFoa5tBSr1UpgQACRLVqwf+9eenXtynOzZnHG8OFVp0PXcpvF6VSW6AYHewZHVquniK3drlzEh4X53tmuPjIL65rFUrGIuZ+f8nevtCQdPz/lsXFxynvmLqhfvhz0ZNt8Q1M/C+rOUUVFnu3Wy8o8M7tqLSejUfn91QC8uoS7ivosJ9tmoep2O2LgQJK3bMFmt2MyGgkNDcVkMnEkJYXoqCg6duyIXq8nplUrArVaOnbsSHzz5pw1enR5llpOTg479++nIDOT8IgISux2nGYzlpAQBrVuTUxMDDk5OWRmZtK2Vy8ICsLhdBIaEFBxKV9NA/NeGxlUu34ReDJGVRERyvvunaGTmek7E1ftR00mz/ItddZeDWqA8hruLFDg+JkK3scTG+vZzdVk8pwL/P3BbMZut6MPC1P6mOJiz3LpoCDlfOMeM5aVlWG325k9ezZr1qyhWbNmtG/fnmbNmrFn/372HjjAtg0b2JaeTkx4OGcPGECPHj3w8/Pjzz//5NfVq6mc1xDg74/FYkGr1dJ12DBaJySQtGMHm3bsYN7y5QyvYrKhtvvaWydP5qePP8ZIxTGAqgRPTbhBffrQsWNHtmzdSklxMWlpaThdrvKxhLWsjIzCQg7l5xMPREdFkZWdrezCGxqKn9FIcXExke5MUI2/P6kHDhAREYHL5aK4uJigoCDatm2LMSaG/ampbN26lUCU2m9mlIsTo8lE76FDaRsdjaFZM1q3asWoHj3Iz88nLCyM+PbtyT1yhNjYWALCw0nct4+4qCiCwsM9WfsuFyW5uSzftYsJEybw+6JFrJk3j8LCQlbs2MHeffuIAkaOHct3v/9Oaz8/nnnmGR5+9lnGjRnD4w8+yGeffUa+RsOfv/xCz549GXPVVbSOiqJv585o9HremjkTm8HA3ffdR1FqKmvWrqVIr2fE4MFkJSfTY/BgAlwutFotuSUlhCckoMnPp6CgAIvTSURQEMnJyeTl5ZEJmAoKSElJIaBdO4xaLbvWrmXd9u3s3r6d9rGxBLjH0FkFBTQzGEhNTWVLWhrN/PwoLikh2GQiLi6Otv374+dysWrVKg5nZ+MAz47tbi7A4n6/j2mDgMtgIDg4GHtpKWaLhbuffJKHnnrKZ9ur7Tb7559/MuXKK8nx7rNQMqGcLhdnnnkm1193HeMvuQSNXu/5XHuXWlFLqzidnkxBh8NzvjkVqP2ZVqv0aQ6H8s9oLN852aXVKkGK6Oj/dn5oTNTxIZTXZHPZbOSWlZFRUMCaefNYsXw5eTk5xMbGkpycTOKBA8e8TMeuXVm5a5fPH1EX44NGyevcbLfbadOlCxfddBNvv/32Sb9kx6AgJt5wA/9zB5Qa0vSbb2bZvHnsrDxuruT777/niiuuAGDd2rVcOWIEk267jRcbwe9QE1W12/8cAAPYtnIlrePjCe3YETQati5ZwhszZ7J+/Xq6du3KJZdcwqRJk9DGxUFmJmazmb82bqQkP5++ffuSkJCgDMaCg8lNTGTxH3+QW1JCx/h4epx1Fs1MJkoLC1n4xx+UlJTQLj6emJgYDh85ArGxJOh0REVFEREXh8HlIjc3l99//51DZWWMHjOGvn37ojGbPbsl+vlx1Gbjow8+oH/z5vTo3Zv80lKahYcTXjklUN3GNy9P6UzdxWA/++wzNmzYwIMPPkibNm3KH15cXMwPq1dzzZVX4szKIrhtW6KAm+++m1umT2fSpEncN2kSM2fOZEtyMmYgICCAfxYu5LqbbiItORkHSmHrt2bPpk+7drhMJuZ8+CGXTptGrHtp3d41a3jy0UdZ/eefFKIsYbhyzBguvPBCIuLjadOsGUlJScxZtozdO3dizM/HbrORkZFBYGAgfn5+5JWUUAaMHjiQfv360aFPHxLCwijIz8dqtZKUlESbNm3o378/2dnZ/P777yxZsoQOHTpw5ZVXMuC88yAzk4NJSSxduZLt//xD+/btufHGG5X3MSaGpB07eP7RR1m8ZAlDhg7l8quv5sJrr8XP5YKiIg4dOsRPCxaQl5nJjffdR5suXWrUgE+2zVJUBCUl7M3OxgYEGwyYSkqIadMGrdlMVlYWu3btYndqKglhYbRu3pxegwaVz8Q7nU5mzpzJrFmzKLPZaNOxI5MmTyZap+NQejoL5s5le1ISDjwFio0GAzqdDpfLRYHVipOKAyl/Pz/Cw8NJcwd1dFotgRERtOrcmcPbt9O2bVumTZvG6MsvZ/+hQ6xcuZJJY8cSERysXPCrNRnAM+MfE+PJYAgO9mSl5eR4Ms5ONjCiDrTCw5XPh/q18mApP1/53Hh/tvLzlcGIGojIyVGeo17s5eYqr2MyeU7q8fE4XS62bNnCpk2bWPrLL+QlJhIdHU14XBx7Dx2iXVQURUVFfL9mDZXmg9GgLCO0OJ3koVzY9ImN5eqrr8ZoNPLuu+9SUFhYXpR60qRJPD5rFmaLhQ0rVrB66VJSk5MZO3YsN0ybhv4Eu9LVepu12Tzvb2NbZlFfrNaKwa4TBcBiY5UMgaAgZVCsLpk5VQNgxcXK76UuwaoO9bNejQBOnQxws7MpLihg+ZYtrF+2jDR3lu1V11zDFZddhlFty94XI9516gIDleM2GJR+Qe23vDNAHA7lcWoxd2/p6Uo/cjJbeZvNnp3SAgKq32YqB2ojIpTPr/o7xccrv+vxZoS9A2DeGxioy9fi45V+taq6ld68L44qP04NkKvUsgU6XcXPW0SEcl9BgdL+Cgs9wTgoL6SN2az0UX5+ONPT2btnDy3bt1fKW6jBPeBwWRnrFi4kOy2NEoeDC6+5hs6dO5Obnc3MGTP4Z9s2Mo8cITQykqlTp3Lp7bejqeICtbb72sLUVLavXYsrLIyYVq0I0unI3r+fHfv2kXnkCEVFRfTq1YvuQ4fSPiFBOde622x+fj46nY7g4ODy3WNdFgsHMzJYOm8eWw8dokurVrSKi2Pl6tUcLSujWXAwNouFMiDdbKZzaCg2mw27vz/hQUEU5+Wx78ABUktLCYuMZOjQoWiBaIeD/Px8NH5+5AC/L12KPieHo/n5UFaGC8hEGYN4f/LNJhNFVisRQKuWLcnKy8MvKgqDw0H24cOoC+OMgLpAWxMVxbDhw/nz55+xoUw+N9frsdntlKKcM9WzUj4wtl8/Nm7aRCbKhGqgXk+q3U4oSvBQq9FgdrnIB5wo5+cYlInX0LAwDBERZCclkdC8OSXFxeR7tVGn+3Fq8A88QUmDTkeXLl3o3LUrKenpaF0usouLCQsLg9JSoqOjad68OTabjaCgIAoLC0lLS2Pb9u0YDQZ69OjBueeeS8eOHbFarWRkZGC32wkKCiKseXO6DRhAaVoaR3Ny0IaGkpKUBDYbZwwZgkn9PIeGYisuxunnh6mKOma13WYPHjzIK/fdR/c+fegyZAhGnY7Nv/2G1WplxIgRniXZwcFKP6lmvBYVKf1DaKjS13iPBZ1OT5mPU2XyTC1rodEofba6qYpa/kCtAepyeSbI4NQPgIFnTH6i8Y3NBg4He3fsYN1vv9GiRQsybTbCo6LwDwxk5NixPp92WgTA3JnfJRoNgUYj27dupe/Yscz56ivPrqAnoUtYGOdfeSUvv/9+LR7sybnl0kvZtWkTfx08eNzH2e12HnzwQW688UZ69OjB5Asu4IcFC7hp+nRee+01TwZ9I1enATC1fkd5fRmLRRmoG40Vd9Jo1kz5Xl2Ope7U6L1u3vvCXC3IXnlZU1lZxcLbeXnK18BAZXCovpZ3XSC1qLDdrgzO1HXr3rOwRqNnpjY7WzkZVL7IrTxY9EUtGpubS9tBg7CkpLBp717iO3ZU7k9Lo6CggLU7dxLXrh1+fn50jYjA6nJxxYUXcsNNNzH+7LPx864lodNVrInhcODKyCAvL49VO3cSHhPDiE6dlAYZEeEZfKuDXffyR6fNhqasDI3RCJGRuBwONOpFR3S0cuJQC5UGBSm/r7pLnF7vqXXg56f8nMop9d71xdwzihQVYXG58PMeCLhcSlswGJTXMpuPe3FW68EE93t4zIYJJpPy+6gDe7X4rLtwaHk9AYcDbDYKSkrIycigTfPmns4gOhpbTg7bNmxgw4YNNGvXjjPHjSPIzw+jy4XLYKAgJYVNmzaRlJqqLGGxWtm9ezeHCwro0asXOoOB3Zs3Ewjs378fi8VCWloauXl5+IeFUajXk52dTf+EBKbdeScffPABgdHR/J+9+45vqzr/OP7R8N4rdpy9Q8hOCISRhEBYDaPsPQot0FJo6a/sMgplFSiUsvcIlE3KnoEACWSQRfZ2Esd7D1mypN8fV9eSFdmxEw/Z/r5fr7xiyxrX8tG55z7nOc855LjjOHTSJHpHRjJmzBiWL1/OgQce6K91ZmYP5eX5s8SCs8eaYhbShMbZV2ZwwbyADawb53Cw8ptvqK2tNXblM18rNxev18uqnBzWb9nClkWLsNvtVLrdxALr1q2juqaGQpeLRLeb6upqKqurKaqpYXdhITXAsKwsDh83jpyiIop37KBv376Ul5djsViYNWsW2dnZREVF4Y2MxFZfz4YNG/BERJCRmEh0dDS9evXiuOOOI9JXrLx6927e/uwzqj0epk+axIGTJzfuQwIvalsQNGyXNtvTBe5WBv4C8oHBBmjIZmkIgJkBFXPg25N2RvL1VS3JymzzAa5ZcNs8P4PRv5p1kQI3Y8jONr43g035+Q2BlEaqqox/Zo06s05mUxcvnbEpR3AALCXFn6FpCtycIhSztlRUlH/jG6vVP6Yy23Burv983JzcXH/QMJC5oYD5vge+316v8Xew241zYeD7aC7jr642Hmceb3W1cQ6wWPx1qcAYX6WnG++NGbA0azgGL6sy2wi06MK0zftah8N4zwPHLGZN28BNm3yFtBt2ZzRrRtnt/gkpj8cYM5orGMyaqGb9sFD1K80L2b2dm80MvYwM//0KCvA4nWzbto3n33iDkQcfjNfjYfuSJQwZMIDtdXUUlZRQUVHBu+++y8SJE+nduzdVVVVEOBzM//hjCoCI6Ghu+etfOevoo0lPTye2f39+/vlnTp0+ncw+ffhxwwbWz5/P3Llz+cNf/0pJQQH/ffllRo8ezZGnnEISxo7oObW1rFqwgE8/+ohiq5XZs2YxccAAnnjiCbIPPJCjZs+mqKiIW265hQSPh5kHH4zbbmdndTUHpqVRUFBAfK9eDM3OJiIigurqaiJ69SLZ6yUrK4vE5GT6ZmdTXFyM0+nkgCOOINrMVDTbmFkGIFhmpvG3Ky42/ibp6cbfPXD5X0qKf4l04PscSlmZ8fMW1M5s8zZbX29czwROQFZXG8fjcPg/T8ETlOYYvqtOCgUL7HPi4/3nv4gI4zNoLms0M3HNz1p3yJB3uYz23tKauIHj+HYc00IXGdd6PHzx2mu89/bbPP+///HCnDls3LiR22+/ndzcXLKaqu3cAmPS05l1yik89OyzbXjA++asY46hJD+fL1asaNXj1nz3HVdefjnz165lzquvcu5JJxn9S5iXUGnfAFhTzECSmV4bfNFRXGx0UMEf1pISfzAiuEBwWzN3yHC7jQG5eXJraiei4KUNofTq1ZCtsbmykuK1a5ly1FHG84datgVGQCIqyn+itlqNk7MZjAncndFkznSYHVd+vvH8mZn+1wi1m2HwjE5lpT8QVV9vvGZKSujO0Nz9KXAgYB6HOcBtBx0eTGjNSdGs5WXWv+rVy/ibVVYaA42A2h57PL85GAl1kebx+DeCSEqivrSUefPm8d7rr/PV8uVcd/nlPP7II6zPyyPZYiG7Xz+W7txJksdDJEa6vg044aSTeObxx42aF/Hx1NvtRJSV+YMEvgC0w7ebmdVqZciQIfzxj3+kX79+XH/99f4MyuRkdhQX88lHH3HywQeTmZnZONumoADi4vAmJLDo88/ZtHIl1153HTbg+uuu47d//jOxGRnMffppnnvuORYvXYoTCOwBLMDQoUNJSkoiJiYGh9NJTFQUqampeDweZs2axeSpU+k3YABWr9f4HJlZCmaGj8kc1JWU+IMlwZ/hrCzjPmYQtrlBYG2t8RotyMBSAKwdmIN8U1MBMHPJo5kBmZjoX84BPSsA1godNsA1z6/m+SRUYKYrMzOnzIBrcrLRdgMnBFsTADMnpaxWfxDNbMPmLq57u3jd190V9zeAWFpq9Jut2VDHHKdER7foYq5D+9rA80dLL5rdbqM9mOOzvTEnIlvymQj++zgcxvEFF5Svrzc+b80tZauuZtuKFWRmZlIbF0dqSor/3JqWhsvl4vY//pHfXH45QyZP9vfFsbHGOMisJRaQ6deQLWjuxB6YjWju6moyPzexscZnxhwnJSUZ/Xfg8nVz4jUhwXhds+RDZKTxN3K5jPZm1vMB47HmOdysZ2UWA09I8F/ImeeZwNpibbwLdLtkiFdUGO9bqICxGZQPfs+7m8AAWEKCfydQc/MYs881r6m6UwBsX1RXG5+RvaxogG4cAPPVFfxw3jx+d955AOwO+PHhhx/Od75SKftqfFYW0487jkdefHG/nme/1dRw8vTpWKOjea+1v1NZGe7KSk789a/ZuHkzLz3zDJmZmQw5/PCwDp431W7bd8sOs9ByZWXoDrmp4FZSkjHw6YgthSMjQ1/MNrMTDtB43Xyo+/juN2ToUIYEFs4L3IGsvNyfWRU8iImLM75PSDAu5EK9lpnKbHba5sDGYmlc8DqQ1brn1tKBx2fO9DYlMnLPYqOxscbvtbedgbqS1FR/4HFvIiIalvA2MJfjNMWs2WO28VDtzWptFFC0Z2Qw65RTmHX44Q23XXjFFazeto3BmZkker3URESw4JNP2JSXx5XXX09CbCwv/u9/2L1e6urq+PS776hyOJg4ZAiHzZ7NHX/6E8W7d/Po009z9913hzzU9evXc+8NN5CZkMC2rVuZcPjhVJSV8e+UFN566y3Se/Xiphtu4O0ffuD23/+ecVOm8Mr//scnL7yAB3AlJlLucPD3++/nxVdewR0TQ9GWLTjj4/ndRRdx0kknMfGQQ9i1dSt2X420PqNHN95l0rzIMzPwSkuNwUxsrPFeRkT4g+l5ef6dEM33NTA7IibGfz+7fc/PT3PCfKaj2wv1eQw1iRP8ebJYjL954JIt6Tzm3ycqyvh8d7cLD/P3CyzI3FqBRZybC161dNnOvg5Q93dgm5BgjF9aU0Mo1DglXMTE+DcUamm7Dd5hd28C6zzuTfDfJzo69C6QdvveM70tFgb6NiSKMSe2AkTY7fzjH/8IHUAJLnwf+LXH43/twPcs+HjMnYqDz7MREcZYyNzxERq3j+AgaUyM/znMZfLmeCC4HZpLfgPZ7cbzBz5vuPdRERHNj93NdhHGF6ptIvD3C85Ybepnob7vKfZ2rdITVFayZvVq/nTZZfTJzub71av5aeVKbrnlFsrLy/njH/+43y8RLkXwv3z7bRYvWcKsfdlUxlfG598PPcS06dM5/owziAP+88ornHrSSUYf1IWukTpmz9qWnshNNlv4FlyMifHPJuxtKWQwr3fP7LHSUv8sn9kB22z+mVJz+WFTjSrwpBz4PofKOmovNlv3mr2H9g/Apqb6gzqtERnpD74mJBCRkMD48eMbZixjXS6OPv54jk5P59gzziAxMZE/XXMN/5szB4B6q5VJY8awduVKfnj4YZZ8+SUrtm+noLKSmJgYjjnmGNLT04mJieHAgQMpKCnhtrvv5n8vvsht11/Ph/Pn4/F4eO+dd7jjD3/gxBNPJCoqitraWgYecAB33nMPFowaIL8//XTOu+oqJhx6KBaLhddffJG3nnySzMxMJlxwAZfeeCNRpaXG75KY6K+lZ87EORzG5yzUwL221l93xWJp3P5aMpNnBhfDaNtuaYFQu+uZQlywNaIAWHjqzp9Bs68OFQTb246kwfe3WLrmRdreJtW6oqSk8A3Q7Y/A82ZwX7m3IG5gwDZwqa/b7c/OMpkTgKHac+DFuHk/c6JqX4NQ+/K36o5Bga7Yf7SFwF0hTT31vZA9eKxWLrjgAmpqa7n0mmuISk5m2rRpzJ8/v81ew2qz4Q6DANiFF10EgMNcEt0avhIJQ+PiuOs//+FfTz6JrbiY2669lm3LlzN+/Hi8vXuTmprKhAkT2vjI2143Hnm2E99ytIY6ZYFCDXbNTtbMXAlm3mZ2zubMVWDn3N0Gj7J/g7m0NGOAGThAC7yI9GUPmgGlV159lcrbb8dusVBYWEjfvn1Zt307y7dt46G//pWEmBgefuopTj/9dCICl9L4limcecEF3H7lldx7331UAY+9/DInnXgio9LT+ctf/kJkZCR/v/NOBk6dSnZSEjEuF9deeCG33HQTCYMHN8wWX3DZZVxw+unGwDolxZ/96HQaX2dk+Hc1hOZTss2fNZU51xI9tZB8Vxb893Y6/RkwiYn+AFhgv2tqaV0MkbbS3EXW3gIKgcWazfGBLtqkPbV0GW2o2wMDYIHq6/dcPtjSMa25eUS4Z19J+GguA6yp+4X6Xrovj8dYap2UBJGRLP3pJ3Ly8jjv0ku59tZb2+Ul7XZ7WATAYmNiqKmtxbq3CbhQLJaGeu+/+cMf+M0f/sDXH3zA+SedxAMPPggYS0d79+7Nrl27wr5IvgJg+yr4Dxsfb1zomxlegQEw876hGlxgp2yxtGgdtvRwERGhCx6bm0CEyFxLGDoUysro78skHDVhAqMOO4xzjz4ab0QEFvP5And2BLBYGDlyJE8/8QSPP/44WYMGcf7554PLxdChQ5n7ySf+4r4xMWzYuZOo6moSzWBDcHZHcnLjpbIpKf5ML/N3a4kw71ilA5l1E/fWJqxWfx0QkY5gs/nrEwVfgLUko8Zc9m3qzrV7pPM114c6HI0njVqy9NxuNya19jX7xmrVxIW0TnA7C/V9Z2yMImFhwbx5fPraa5xxxhkcMHIkXrudD+bOxRMZyd8feoiodlrCZwuHJZBeL9HR0WRlZfHAM8+0yVMeceyxjb63ALt372bjqlUMHzu2TV6jvSgAtq+CO8/ERH8goKmaH6EywMz6CCL7yywy3NRsaUyMf7trM2vGbscSqv2ZbdlXny4xMZEbbrjBGIwGzvKa9VB8MsxaGpWVxszv3gYZNlv3XGog7a+5IuLBGWDm9+np+1aPSaQ1IiP9NTrNzWla2+7M+we24YgIo58PNZYQ2V/NZVrV1DS/aYO5E2tg24yI8E9QdKHaMNJNBGeABbZv82d7K50g3UN9PQ6Hg6svvJDcnTt58403SEtLo6ysjNKyMo4+8UQS27F0UHR0NDX7suywDTkdDkpKSznvD3+gz/DhbfKcEZGR9JkwgbXLlvHBq6/Sp08fZhx5JCcdfjivf/IJEw47rE1epz0or3hfmUW3U1ObT+cO7ICbGrSaO+JoRkL2h5me2pSoKKO9Bt6nqSCCGRQLvnALXLILTS8jTEhQNqO0j4wMo2hxcOC2JRkGFouW00jHMHdattv3LevQzFIw27nZpuPju2f9Kel8Zhtr6rzeXOA1VFZNTIwxVk5K6t61/iR87G0Ds+D7paQYdWOlW/vXTTcxoU8fcnfu5I4776TCbmfFtm3U+God/vqss9r19ROSkqg0dyftJKtXrQIgq413Qf/qm29Yt307R86axfDhw7n6j38kIiKCXx99NA/ef3/nZ741QWek/REcLQ7MOgg1GFCml4Qbu91ol/X1jYvemm3V42l8AVZf75/RNW/LyFBWjXQccxlYS/pTtUvpbBERey4fa40wHTxKN2TuVmmxGJmLwcz+1OWCwsI9bw+1I2VH7OYuEkrwksfA9mlOhKm2Yvfk8bB99Woqa2v5dsEC/vnPfwJw4w038NsbbuCi667D6XRiB77+5huOO+GEdj2cxKQkCjdubNfXaM6OHTuYdvDBJAC9Qu0SvB8SExP92XNuNzfccAOXXHIJN9xwA/dffz1vzJnD+PHjOeSww7j4ssuwhskktAJgbSk4ABZsb8sW1AlLRzPra5SUNL7QMtuvuakDGBdwdXVQVeVftmixaGZXwo8K3Eq4iIzc+/KxpgQsL1cblg7R3Pm8qbpfTQXARDpac0XwVQOsW/N6vVRUVBAfH89LjzzCP26+mVqHAw9Gbaoft2xhQP/+YLMRCUT6JqVOmD273Y8tMTmZivLydn+dpqxesYIE39dtnQHWiM0GaWlk9urF8//9Lx+++SaXX3EFO1eu5MOXX+bvt93Go08/zYknnth+x9BCunJtT8EdrOp2SLix20Mvgwxsq+bX8fH+LDANeKWzBdebCTW7G5itKNIZzMyv+nojGyw9HXbv7txjEtmbUOOCUAEwm81/e5jM7EsPtq+7QErX5fGwYtEifn/llaxbvhzi4oisruaQ6dNJiIxk8ODBHH/22QwYNKjTDjExOZnKTgyAbVm3DoAKoN/Age37Yr7ECktKCieedx5Z2dls3bIFW0QETz3xBOeddBKnn3suJZWVpPfqxZ133klmYiLWyMgO3ehHAbC21NwSyKaK3Vute9b4EOlIMTFG0fpAoQJgVqu/vSoAJp0tI8PIRqyq2nMZQ0yMkXUTuKxXpDMETjK0drmNubMvqK+VjtWrlzEuCB4bBFMGmHQV+7ITqYQlp9PJxnXriIyM5KPXXuNf99xDamoq11x+OTU1NRx+xBGccPHFWO32sPhbJyYn46itxeVyEdEJuzlvWb+emJgYvlyyhF7mZmUdIT6eg048kYO8Xqiv56hjjuGyCy/k09deA8BitfLWCy+QYrNx1llncd9LL3XYRIoCYO2hrMyY9TU/dOb/oQJgvXoZ93c4VK9GOkdsbOhBrplhY85amAXEFQCTcGC1+meLQm3+YC7ZBbVT6VyxsVBR4c+oCQwaNCcpyXhMXZ3asHQ8cydTc6Ompmg8IOEiVJArsH2qrXZZXq+XvN27ydm4kat+8xt2bdnS8LOjZs/m4aeeIi011Zj4NDfgCBOJyckAlJeWkt6RASifnI0bGTRiBKNGjerw1wYadrFOHjqUt7/6ipXLl7P85585fOJE/jtnDvPmzeOVV1/lhx9+YOSUKVz5+98z6sADiWluk8H9pABYWwrsUJ3OxrUUamubzgAzi+SqSL50BpvN2AmnrKzxRVmoJWZmZN7t1gBCOp/ZBs0AWOAAt7kdUUU6khkAM8/xVmvzJREiI43gg3lf0ASZdI6WnOcDA7si4SIw49ZclaMAWJdTX1fH8//5D2+//TYrfvwRG9AnO5s7br+dmJQUDhwzhvHTpmExx3xhuPFGki8AVtbBATCPx8N/7r2Xn374gV+dd16HvW6zYmIYO3UqY6dOBaeTm4YN46/33suzjz7Kom++YeFnn/HxG28QGRHB4CFDmHnCCRwzezZTjjyyTQ9DAbD21Fzm177cT6S9xMQY67arq/3ZYMEBBHMJJCgAJuHBnGQwN2Xo1ct/MRaYRq2NGqQzWa3GrtFmn7m3AFh8vH+DksA+VyTcBAZmNSaQztbSZY5qq2FrzcqVrF22jJeefJL169djt1goLSmhb58+XHj++QwcP57zzj+fZHPH2i4gKSUFMDLAOorH4+HPl1/OW88+y9EnnMCdDz7YYa/dYpGRkJ5OBHDljTdy5dVXU5SXx4uvv07xrl0s/e47Hn/oIR5/6CGsFgvH/upXnHnhhZR7vcw66ihSUlP3uQ3oqqAttWTnsVBLH2JijNnh2Nj2OzaRvbFajZkTMwAWEdG4iHhgBpjLpYK30vnsdgjc0cZm8wduAwO4CoBJZ4uP939tsxl9aFMCxw6xscbEhBkQE+lIzV1cJCQ0Lp8QEWEs262oUMaidI5Qmd+BWV8JCcaScnNzEuk0Xq+XHTk5/LJkCe76elYsXcpn77/P5o0bASPL6+QTTqCmro5jzz2X2cceiyUqqktm95sBsLKSkg57zT9fcQVvPfssV1x1FX/797+xdIVgYVwc6UOG8H+33AKAp76eNUuX8vjDD1NWVsb8+fP55MMPcQIRwODBgxlxwAH8/fHH6du/f6teSlcFbakljctm23NnHZut8UWcSGcJzprJympc/8P8ucdjBG5FwlUXHCRJD9FUQNacIAscS0REaHwgncusq+NwNL4tcMLMFBdnBG0VAJPOYLEYJT2qq/ecpLVYjIkIc3m5dCq3y8VhI0fi9vUrVouFSVOncuHvfseBU6Ywefx4ImNjG68+6aI6OgOsqKiId194gfPOPZe/3X9/1wh+hWC12xl98ME8/vrrAFRVVfHJW29hd7nYtGEDq9etY+WaNcSbq0BaQQGw9hSqwUVF7RkAEwkXgW021Ekn8HtlJEg46+IDJunGmmqbdnvzmWEinSE62hi3BgbAmutfW7vbqUhbionxT9AGFkJX1ldYsUdG8p8nnySrf38i4uPpN2AAGZ1QIL4jpPiKue/avr1DXu9f992Hu76eC6+5Bks3SlaIj4/njEsuaZPnUgCsrfXubRQTr631DwCys2H3bmNGLD7e6JhtNg0QJPxYrcYgwekMPcANLDauAJiEu+TksNoJSARouk1GRxsBMI0NJNzExxvnf7N+XXAwITVVYwIJP4FtUmOBsHPyRRd19iF0iOy+fRk1fjxPPfggab16kRAXR3xkJBEpKURFR2MBI0vLasVisWCprzf+t1qx2GxYAGw2LBYLHo8Hl8uF2+XC5XRS73BQX1+Ps76ezZs28en//sfPP/zAWWeeyZjJkzv5Nw9fCoC1NYvFP1sWmGabkmKkigfWqBEJR2lpxkWYuUwnOtpfgNligYwM42e6SJNwp7qKEo6ioowxgctl/IuJMZaYJSQY/a0u1CQchcokSEoy/g/DnddEGmrb6rpLOpHFYuGme+7h0l//mv/77W/b9bWGDB3KX2+9lT9ef71WQjTD4m3FOn2LxVIIdEz+nkhjA7xeb0ZrH6Q2K51IbVa6mn1qs6B2K51Kfa10NWqz0tVofCBdUch226oAmIiIiIiIiIiISFej3DgREREREREREenWFAATEREREREREZFuTQEwERERERERERHp1hQAExERERERERGRbk0BMBERERERERER6dYUABMRERERERERkW5NATAREREREREREenWFAATEREREREREZFuTQEwERERERERERHp1hQAExERERERERGRbk0BMBERERERERER6dYUABMRERERERERkW7N3po7p6enewcOHNhOh9JNuVzG/xERnXscXdzSpUuLvF5vRmsfpzYrnUVttpN4vVBfDxYL2Ft1iuvx9rXNgtptm2plG165dCkAo8aOxd4Dxxrqa6WrUZuVrkbjA+mKmmq3rbo6GDhwIEuWLGm7o+oJcnON/7OzO/c4ujiLxbJ9Xx6nNiudRW22kzidUFQEkZGQnt7ZR9Ol7GubBbXbNuVwQElJi9twtsUCwMf/+x/ZAwa099GFHfW10tWozUpXo/GBdEVNtVstgRQRERHp4urr6zv7EERERETCmgJgIiIiIl2cy+ns7EMQERERCWsKgImIiIh0cS6z5qiIiIiIhKQAmIiIiEgX59YSSBEREZFmKQAmIiLdh9fb2Ucg0jZa2ZaVASYiIiLSPAXARERERMJFKwNf5r1VA0xERESkeQqAiYiIiHRx2gVSREREpHkKgImIiIh0cVoCKSIiItI8BcBEREREurh6BcBEREREmqUAmIiIiEgXpyWQIiIiIs1TAExERLoP7QIpPZQywERERESapwCYiIiISBenGmAiIiIizVMATEREuh+LpbOPQKRDKQNMREREpHkKgImIiIiEi31cxqsMMBEREZHmKQAmIiIi0kWZ4TJlgImIiIg0TwEwERHpPlQEX3oQt9vd8LV2gRQRERFpngJgIiIiIl2Q0+ls+FpLIEVERESapwCYiIiISLhpQTZjYNDLrQww6UgeDzgcnX0UIiIiraIAWEfRshwRERFpQ4EBMGWASYcqLoaSEo1vRUSkS1EATERERKQLClwCqSL40qHMjEMFwEREpAtRAExERLoPXYxJe6qshPz89n2NVrRhl8uFxfd1QxH83FwoLW374xIRERHp4hQAExEREWmJykoI2HmxswUue2yUAVZb2wlHIz2SxbL3+4iIiIQJBcBEREREuiDVAJNOp6xbERHpQhQAExEREelKfMEup9PZsATyhRdeoKCgoPOOSURERCTMKQAmIiIi0hqdmfXidEJhIVRVNcr6Ki0t5eKLL+684xIREREJcwqAiYhI96HlONIROrOdmcXu6+v3WPZYUV7eCQckIiIi0jUoANZRdFEmIiIi+8scT1gsOOvqGm62AMlJSZ1zTCIiIiJdgAJgIiIiIq1hBqG8XigrA4+n7Z+7KeZrWSw4HI6Gm2OAFAXARERERJqkAJiIiIhIa5hBqpoa419lZce/ttVKcXFxox9lxsR03HGIgFY4iIhIl6IAmIiIiEgLLFiwgP/85z97XvR3ZBAgIPusqLCw0Y/sdnvHHYeIiIhIF6MAmIiIiEgLvPPOO0YAzGSxGP93ZAAsYLllSUkJFsBcCBlYE0yZOSIiIiKNKQAmIiLdhy76pR1VVlbidDrB7YbaWn8ArD001ZbNAJjXS1FREcnJydQBbsAVUBOM3bv1eRAREREJoACYiIiISAtUVFTgqKvDW10NpaVQX2/8oDMywLxeygsLSUlJMW4GXIEZYGAE6kREREQEUACs42gWVkSk47RnZo70TF4vFRUVQECmVWcGwABnXh6pqank5+czYODAPQNgbbk7pYiIiEgXpwCYiIiIyN4EBMDqamqM28wAWGfweCgpKSE1NZX4+HgSkpJwOp2UlpZSW1vbcB+RdqUJXhER6UIUABMRERHZm4AA2FdffMGkSZP4efHihp+15eu06OdeL6WlpQ1LICOjonA6nZx88sk8+OCDxn0UABMRERFpoACYiIh0H8pGkHbi9XgaAmDz589nd14eZ5x2GgcffDAb1q3r+APyZYClpKSAxUJEVBS1tbVs2bKFXbt2GfdRDTARERGRBgqAiYiIiOyFo7YWp8sFQG5uLgC1Dgc7du7kheee6/Dj8dTXU+twEBcXB0BEVBS7d+/G4/VSVVXlu5MywERERERMCoCJiIiI7EV5WVnD19u3b2/0M4dZc6sj+LIc63yF+KOjowF/AAwgJyeHN954A6dZrF9EREREFAATERER2ZtGAbCcHPr369fw/fp16zp8+a0Z3IqMjASLhcjoaDy+Y9i4aRN/vvZarr7qqg49JumBtOxcRES6EAXA2lPgoEADBBERka7D4/HX0Kqupiovr9GPD5k6teHr9evX4+3g5YZmBlhUVBRgZIAFW7Z0aYcek4iIiEg4UwBMRES6j4Ad8kT2S36+8Q+gvJzKoqJGP+7dvz/fzJvHH37/e2odjg5fblhXVwf4A2CRvqWQgYoLCzv0mERERETCmQJgIiIiIsGCgqgVFRUE3pLRty/Dhw8nIyMDaIc6YE0Fcc0aYIEBMK+XiBABsKqqKhx7Cczl5OTs33GKiIiIdBEKgImIiIgE8Xq9uM0lkOwZAEv2Bb4ifRlYbRYAa2H2YnAALCpEAMwKFBQUNPkcCxcuZMCAAaxZs2afDlVERESkK1EATERERCTIOeecw4ABA4xaYBgBMEvAzxNTUgCIjIkBoK4jd4IkRAAsRA0wgJNOPJHy8nJuueUWtm3b1uhn69atA2DXrl3teqzSjWm5uYiIdCEKgLUnDQpERES6pPnffYfH68XrywKrqKigCvACTiDJDID5Mq/qOqIGWMC4olEAzGZrCIBZLUaYLiE+HoCVK1dy/3338fw//sGFZ5zR6Olyc3MBY6mkiIiISHenAJiIiIhIE4p9SwgLHQ6qgDygGEhKTQUCAmCdlAFmzciA6OiGAFjfvn0ByMrONn4OeF0uAOrLyigpKuKYiRPZuHZtQwCsurq6Q49dREREpDMoANZRlA0mItL+1NdKG8vZsgWAsooKUnxZXwDJycmQmIg9KQlohwywvbTlhgywhATj/6goSoC4rCzKgKT+/QGwAFu3bvU9pZe5b7zBL8uW8chddykDTPad+loJZ14vlJWpnYrIHhQAExGR7keDXtkfAe0nxxc8Kq+oIDMzs+H2pKQkiI8n0heAcvoCUh3CYmkIgEX7apBFRUVRByT360ctkJGVxT/uuosUYNumTYCxfNPcFTI6JoaC3FzSgerKyo47dhGR9lZVBTU1xv8iIgEUABMREZGeK1TmlsdDVGQkAO++9RYej4fSsrJGATBzyWFUVJQRWOqIJZBmYC4gABZ4HACXXXYZ119/Pff/859ccsklpCUmsnvzZgDcHg81NTUAREdHU75rFxFAXXl5+x+7iIiISCdTAKw9KQNBREQkfDkcUFKyZ5aA14vVagyRPvnwQ15++WUWLVnSaAmkKTo6Gg/g7Igi+CarFafT2fD6AAm+TLRBgwZx7733kpaRAUBKSkrDEkev19sQqIuKiqIwLw+AKl9QTKTVNNaVrsDpBNU6FBEUABMREZEeKnfHDkaMGMEvK1Y0ur3e6aTW4eComTOJAG66+WbqXC6j7leQ6OhovLRhEfyWBBTsdv8SSF8A7KSTTmLu3LmMHDnSuI9vN8iUlBRsvofVu92UlZUBsDsvr2GHyxpdGIpIdxKQLQtAUREo01VEUABMRES6E2UjSCt8/NFHVFZV8fTTTze6vcpXE2vk6NENt3mB2hBBrqioKDx0UBF887bIyIZaXubSx+joaE466ST/fX0ZbKmpqQ0BsMqKCgoLCwHYvHlzwyCwWnVyREREpAdQAKyj6KJMREQkrJjLHN0eT6PbKysqAMjy7aQIRgAsJydnj+cwM8A6tAi+1bpHBlhIycmNlm1WlpdTmJ8PwKZNmxoGgcoAExERkZ5AATARERHpkSy+5TGeoABYlS8AlpSRQaxvl0UP8Pjjj+/xHGYR/A6tAQZUWK1UW63Y7fam7xQbS2pqaqObtm7ZAkBJcTEA6WlpCoCJiIhIj6AAWHtS1peISOdQ/ystYC798wS1F3MJZGxKSkOA6f7772f8+PHMmzeP1atXN9y3oQh+R2aAAZVuN25fcK45M48+uuFrC7Br1y6AhmWRgwYN0hJIEelegmuAiYj4NDNtKCIiItJ9lfsKwnt8xeBNZgAsISmJAq+XeiA1LQ2AGTNmNLqvmQHW5jXAQgm4qHM4HA31v5pzxLRpDV9bMYJgYAwAU1NSSE5OpqSgoM0PVXoITTaIiEgXogwwERHpPnQxJq1Q4QuAvfnmm/zzn/9suN3MiEpITMQFVMIeSwlNZhF8V3tkgDXTnh0OR/P1v3wsVitbNm/m1VdewYI/AGYFsrKyiI2NVQaYiIiI9AgKgImIiEiPVFZaCkAscPt11zXcXlVZiReIj49vqA/WVADMYrEQERFh7Mq4LwHYujpwu8GsQ9bC56irq2tRAAyvl+joaBJ9xfADB379+vUjNi4OR01NKw9aRCSMaQmkiDRBATARERHpkSrLywEjKyoJwOGAvDwcRUV4gYSEBLy+C6mmAmAAUdHRxq6MQcX096q2FoqLIT8f8vL2fv+A4FhLl0Caj4lLSgL8tb8Ahg4bRmxCAnl5eSxZsmTPxwYtDRUR6XY8HvBtfCIi3Z8CYCIiItIjVfgCYCaPwwEeD9XV1Q0BsL1lgAFEREXhdDpbnwFWX9/aQza0ogaYeUyxiYlA44Hf4KFDueDCC+mVns7f/va3PR+Xnw8lJft2jCIiXUF5OVRVGdm4ItLtKQDWngIHwqpLIyIiElbMGmCmMl+wp6qqCovFQkxMDOPHjwcgxbeEMJSGDLCCgtZnge2jmpoaYmNj935HMwMsORnYMwA2fORIDj/sMDZt2tT4cS6X8b/DoUwwEelaWnPd1UF9toiEBwXARESk+zAHvZp0kBaoKC/nwFGjSPNldxXm5gJGACw2Ph6LxcKHH37IZ599RkxMTJPPExEVZQTAAJzOtjvA4HYc8H11dTVxcXF7fw6rMdRLCAjg9evbF4Ahw4eDxcLAAQPYtm0b9QEZaZ66Ot544w1Wr16975lq0v2prxURkS7E3tkHICIiItLRPB4PpcXFnHHqqcyYMYMzzjyTorw88nbs4KdFi4iLjwcgPT2dY445ptnnasgA6ygWCzU1NfTu3Xvv983IAJeLSJu/+tfDDz9MSkoK6ZmZ4PEwcMAA6uvr2bFjB4MGDQLgjltu4Zl//xuA71evZvCoUe3yq4iI7Lemit6HmkRo6j4qmC/SIygDTERERHqcovx8XC4X2dnZpKenA1BYUMA5557LL7/8QnxCQoufK9KsAQYdlhHT4gwwmw2io7HY/XOeaWlpjBw50viZLwMMYPPmzQ33WTB/Pgm+umGLFy1q24MXEWlLJSWwe/e+PVZZjCI9igJg7UkdqoiISFjatX07ANnZ2WRkZFAPFBUVNfzczABricjoaBwOh/HN/taTaeHYocUBMFNAdkPD44ICYHffdRd/v+02AMpLSzls+nRsVisb1q9v+euIiHS04AzcfbkG03WbSI+gAJiIiIj0OLk7dgDQu3dvkpOTsUdFsXHjxoafJ7QiABYTG+sPgDV1EbUvu0TupQZYi4rgh9AoAGaz0Tszk6iICNZ++y1P/v3vAJSVltIrK4sBAwawYd26fXodEZGwp8CXSI+iAJiIiHQfGshKC+Xm5OAFevfpg9Vqpe+AASxdurTh5/GtCIDFxsWRU19vZFmFygDzeKCoyFim0wa87EMGWIA4c3mnzQZRUdhsNkYOHIiZI+ZyuaisqCApNZWhQ4cqA0yapj5Xwlkzkwh7va+IdEsKgHUUdaoiIiJhI2/HDqx2O2m++l8DBg9mzdq1DT+Pa0V2VWxsLDU1NU0HwMwxgMvVuoNsYuzgcDjwer37HACL6N0bEhONAFhEBFitDO/Vq+HnWzZvxgKkpKUxaPBgtm7ZglfjGBHpKlrTX6lvk/bg9UJNTWcfhYSgAJiIiHQ/GtBKc9xudm/bRkp2NtaMDEhMpN+wYTiBct9d6uvrW/x0DQEwqzV0AKytdhfztevq6mqAfQ6AYbdDYIZbUlKjANe6NWsAIwDWOzuburo6Stooe01EpN3sz7lf4wZpS9XVUFamIFgYUgCsPakjFRERCT8OB0uWLGHU5MkNwaBBw4ZRDJhhrzqzplcLNAqAORxQWtr4Dm08HqiprQVaHwBzJSdTFOoHMTGQlUWF79sNvky41PR0emdnYwV27dq1z8crXZTHs/+bOoiEO12vSXsw+063u3OPQ/agAJiIiIh0Xy4XFRUVlBcUGANSr5ecTZvI2bWLaUce2XC30aNHA2AOVWtasVyxIQBmXkj5AlT7rYkLs33NAFu3aRMbtm4N+bNHH3uMY48/HoCNvqL3qWlpZGVnYwHGjRvHF1980arXky4uL8/4J9JV7K3mV6g+1bxNgTBpSxZLo52lJXwoACYiIiLdk8sFhYWcd+KJHJCZyZLPPoOCAr77/HNcwPTp0xvuOmPGDKxWK26gCChp5RJIp9NJvRn4stna9NcI5PV6mTdvHtD6AFhaWhoDBw4M+bOsrCz+/eijAGwyA2Dp6WT36dNQHP/ee+/dp2OWbkxBAwknoYJZaqPSCRYvWcLYceO49De/6exDkSAKgImISPehga4EcrvZtWsXS+fPB+DTDz4At5v//ve/DB8+vCHrC8BisVBcXMxHH32EC5h94oktfplYX8F8hxk0s9sb36GtCjJ7vdx5551c/ac/NXrdtpLuK4S/ZtUqANIyMuiVmYkFiAFGJCb6l3VUV0NVVZu+vohIhyooUAaYtIv1vlIC77z9dicfiQSz7/0uItItVVcbF2lRUZ19JCIi7cPj4X//+1/Dt0VFRUZA7Oef+b+778YSVJw+OTmZE044gcLCQtLS0lr8MmYgqjo6mvi2KngPIS/I3njjjYav97kIfhOioqNJTkoiv7ycKWPHkpiUBOXlWIFkoLSkxKhnYrVCuW+7gMBi+iIinWlvGWDBfWorMn1FWiPft3xc2UbhR3+T9qT0Wwln5eVQXNzZRyEi0iacTidPP/10490bfQGw8ePGMX7cOAoLC/nll18oA6bOmNHkc6Wnp+8RHGuOGQCrqauDyMh2Pec7Aorzt3UADKuVSy+9FDtwzjnnGIEuq5Ub/vIXwFcIX0XRRaSz5eb6g/CBWlLzqym6VpM2lL97N2AEW7xqW2FFAbD94fEo/V9EpDPsbVcdDTZ6nKeffprLL7+cJ598suG2zRs3smLlSk466SQyMjIoLCxkzZo1eIAxY8a02Ws3BMCa2u58X9tj0ONcTie17RkAs1i49tpr+fzjj7ngssvAYmm47fLf/Y5du3bhDQ6AlZXp8yYiHSdwGXZTlIQgnawgPx8wgi31yjQMKwqA7Y+KCuNfXV1nH4mISM9RVQX5+Vq6II04qqtJBzYtWtRw23vvvAPAiSeeSHp6ekMAbOCQIcS34dK9RgEwi6XdLrgqKysBMJ89JiambV/AasVisTBu3DgsZnDNV9C/T58+1DocFPiWddxxxx1ce+21/PDFF3hra1v/efR6jSyO5i5iRUSCBU6ABfc7+9P3KlAmbaggYAmkqxW7Skv7UwBsf5gzEOowpatpjzarzl06ijnpEJwF5nLt2badTiNDJfCxaqvdUmVxMRFA3rZtDX/jue+9x+QpU+jTpw8ZGRkUFRWxatUqxowb16avvUcG2P4sw2lGlS/r/PdXXsmFF15IL1/R+nZhBtciIgCYPHkyAB998AF4vTz19NP89403OOPMM1m+dKlRTLqiouXPb74nrXmMhB+NgaWjBZ77A4vYw541wLze5rPBbLY9Ny0RaQNmBpgFo0SDhA8FwER6orau4VJbC4WFELA0R6TDFBQYmSSFhXsGt0pKoKbG3+aLi437Sbeze9cuABYvXsx3n31GeXk569asYcbRRwOQkZGB2+MhZ8cOJk+Z0qavvUcGWFsJulir8AWLpk+fzksvvYTNl53VphITIXADAF8AbNy4cYwYMYLXXn2VtWvWNHrI7p07jS9aM8hXLTER2RdmAMwMXO1v1ldUVOszd51OY9yhCTUJ4dtvv2VHTg4x0dFYUAZYuFEArD011ZFWVxsXbME8Hg0IpWO09YytGfjaW10mkfbQ3NIrMxihLIVuLz83l8xevYhNTeWxBx5g+fLlWIGx48cDRmF7ABdw8MEHt+lrBwfAvvnyS6YOH05Nfn7j4rf70A4D64pV+gJgCYmJ+3fAzYmPb7w7sNUYKlrsdk6YPZslixczZvRoAO64/XYAinwz3a0K/mm8IyKt5XIZwSeLBcxl2oF9yd52gWwr5rhXE78SxOv1cvHFF5OUkMApp5wCKAAWbhQAawslJa1L4S8v3/OCzeOBvDztyicdo60vPMzAly5oJFwpANZ9OZ0s+fxzvvvmGyZOnsz4qVPZsX07y3/8ERswbtIkACZOnAgYAbBJvtvaSvASyKuvvprtGzfy2bvvYrVamf/tt40f4PU2XT/U620IJM2fP5+4uDjmzZsH+JdAJrZnACyUzEzIyODwadOwYizpADjppJOAgABYa4S6UJWuy+XSJJi0L7fbyOCurW28bDFUAKwpoZan+zb7aFVf1JaZvtKtLFmyhB3btvH3229nim+yzRlm9cKrq6upra3t7MPoNAqAtZW97Qa5t07VbISKEEtHaOuZMTOgqwCYhBtlgHV/tbWc8+tfE42RpdRv2DB27NjB/E8+oU92Nhl9+kBqKv1Gj+aduXO548EHSUhIaNNDCM4AK/JNZr3//vsAPPLII40fUFZmTHiFCBh4vV7mzp1LbW1tQ+Ds66+/BvxLIDs8AGazgdXKxEmTsALJ8fH89/XXyRw8mKTERIpDZbWbXC5jGXIwnS+6l8JCY3MSkfZi9pfR0ZCS4j+/h+pLmqoB1tY0tpAgP3z5Jb2AWccc07CZjCvMaoAlJCQw3pcd3xMpABYuzM5bhRilIwSesNviIsR8Ds3+Srgw23hTAbCiIg1cu4GKigrOO/dcqn0BltPOOIPBQ4bgdLtZ+O23XHjhhRAZaVwwxcUx9aSTuObaa9v8OMwA2I033khpwKYLq1atAmDjxo2NH2BOdoXofxcuXMjlV17J3XffjSUoy6BhCWQbB/BaKiomhq8/+YS1P/zAtGnTIDaWtPR0Nm3YwKGHHsqnn37acF+3282TDz7IruXLjYBf8GyzAmAisjfl5f5VNh4PCxcuZObJJ1NZWxv6/G4GvFp6fg8cK+ztMZWVxpLHujp/UL+qSjvZSiO1vt2aU4YPx+YrJxBOSyBramrwer1s2LChsw+l0ygAtj908SRdVWvSxU1NpcoGPl4BMAlXoXaHDKMBieybzz77jE8//hiAfz30EMcfcwyDBw/G7Il+c/nlHbJUJSkpiUMPPZTS0lJeffXVhtt3794NwIYNG6hryRIIr5cyXwBt27ZtDTfb6+uhspIqMwMsKanNjr1VIiMZO3ZsQz01rFbSMjL47vvv2bZ9O+effz5ffPEFADfdcAN//7//49ZbbzXuG3wO0RiqewgVbKiuVv8q+6a+vnFZmepq/yobj4drrrmGb7//ntdffz10BpjXu2eZmb31NS3NFK+sNMreBGfvlpc3/zjpXrzeZq93HLW1REZEYI2MJCIyEgivDLCffvqpsw+h0ykA1p5asw5dtTCkI7U2A6y6GkpLG89yVVcbs2AKgElnaUl/2dzAVjU8wkt9vf/v5HIZm8UE9E+7du7kkksuoXT79oYLovXr12PFqOs1atQoAIYOHYobmD5tGnHJyR1y6BaLhTfffBOA/33wQcj7FBUVNfr+wQcf5P/+7//2uF9lZSVejKWQ5vsRWVtL7vr13HnzzUDnZYARE9P4e4ulUUH+SOD8X/+awq1beffxxwGYN38+tW73nueHwHOPssG6luD+NPhvW16u3XZl3xQUGP17iM1t8nfvZteuXXiAV155pekAWHNj0aZKgLRyPLBs2TKy+/Rh/fr1xg0K+PYcFRXGcu8mzlu1tbVERUcDEOHbRdkZRgGwH3/8sbMPodMpANaeNKCTcNXaDDBzMBF43/JyYxbMvM1mU5uX9hMYHGmNltQIkc7n9RoXPubywZoa429uBt29Xi494QTefvFF/nXnnaxfvJgty5bx0IMPYgPSMjMZNnEipKTQv39/3nnnHZ5//vkOLSuQnZ1NfHw8P/70E1aLhZEjRmABMjMzsQAFQXWyHnzoIV544YU9nse8X+DukRavl7fffrth0Gaz2drpt9gLmw2SkyE21tgd0mqld3Y2AA8+8ABz338fW3U1Z51wAtU1NVz9xz+SU1PD7f/4B5sDllu8+uqrHDByJFdccYXxe2rypGvTuV/aQkmJ/2u3u3F95fp6Fv7wAx5g/PjxxvKtpia4zOCZ1dqy5ZAtyADbtGEDd999d0P2jDnh8dVXXxl3CLMi59J+HGVlRuCzifZS53D4A2BhmAFmZqZD412mexIFwNqTMsAkXLW2CL45uA01QxYYAAu8r0hbKigwshADtaa/DDUIVn8bPpxOXnnlFTb88kvj2+vqePuNNxgUF8fKVauIB5597jmOnDmTs2bPprqsjJnTp7Nu40aiMzLAN9t65IwZxMTEdGgAzGKxMGLECLzAwIED+dWvfsXQIUP4+x13AHsGwELyeikoKMCLseNjrW9wWudwEBsb27D7YqdmL8bGGkGwrCwArr/xRj768EPOOeccJk+ezPRp01i3bh1TDjqIa6+9Fjfw2FNPccF554HXy6pVq7jwwgvJ272buR98wPMvvEBucI006VrMAKZVlxWyH5xOf99WXNywFNLtdkNxMcu//56o6Ghmz55NQUEBdWZQIdQSSF+AvlktzABbvnw5kydN4j+PPcY111yDy+VqCBwUuN3G+NfMAKutNeqESbd13XXXceTMmZTl5ITMVHTU1hLty5Y2A2D1YZQhWJmfTxZGxnZwZnpPoTNVe9qXnfZ0QSYdobVLT5prlwqASXsz29S+DCqbm9lVfxs21q1YwfU33MAJJ51EbW0tl1x0EcuWLePjuXO55OyzqfPVjwq8RNmVm8v0Qw/lpptu2vNCJzHRKHzvK0DbUUaOHAnAsGHD+Mtf/sL8+fP51QknAI0DYGZ2V6hLLvN++fn5VFdXY8XYstzcAfKlF19st+PfF+lpaUyYMAEwgoBz5sxh/rff8s477xAZGcnNN9+MG9i2fTubN2zgvvvuw+v1smbVKiZNmcJf/vY3Zhx8MNu3b+/cX0Satre+srlJMpGW8HqNdhQf33BTTk4OF1xwAYMGDeKZJ59kwYIFTBg/nsGDBwOQa2ayBGaQms8TmCW7nxlgd999NxE2G/+46y7W7djBkbNm8fPPP1MH3HHvvTz21FNUlZUZAbzSUiOTTWPhbutb3+7Mh06YwJyHH97j547aWiJ9Y49wXAJZUVSEBUgECnvoUnUFwNrTvmSAmaqqmi46LtJaHo+RQWPOVHg8/gvG1mSANRdEUABM2ktTbbS1NcCUARa2Xn/5ZQDKysr43e9+x4dz53LRRRdx2WWXYeZwlQE2X7913rnn8tCDD/LZBx8wevToPTO97HZITe3wC/Lrr7+ev/71r1x33XUNt/XKyMCKUQjf4+sfy31Fky34shsCFOTn48UIhFVWVmLDyAarrKwkJjqaWbNmdcwv01LmzLZvoG+1Whk6dGjDMs0777yTd997D4BRI0cyZ84c0tPS6J2RwSuvvcblf/gDVVVVzD7+eObOnYu3tFQZFOGgNf1jYAaY+lVpLZerYXw696OPyO7Th5KSEq6++moWLVqEw2Lhtttv55fVqznt17+mb9++AOzYudN4fPASaq+36b5/bzXAgorwFxQU8O6773LZb37DxRdfzKPPPstPW7fy45YtmAs2b7ztNi4691yeffRRVplZzFrW3W2Z57ay8nL++te/7vFzR2ANsDBcAllWUkJ8XBx2em4GWMetDeiJ9mUQYD7G7HyDC86K7IvAejpJSf7ZMY9n/wNg5s/MgJoCYNLW9qVNBS8rVwZYWPv688+ZOGEC23fu5NVXXyUTKCguxoqRpp+YmsrpZ53FebNm8e233/oDTGagxDfI7GxjxoxhzO23+2uZARF2O17gueefZ+CgQVx6yy3k5eU1/Ly6uppEs5C8bwkkgKOujtxduxplgCUGFJwPG1FRxt8hLc2og+PxGJN4yckQEYHFYmHmMceQlZlJfX4+DuD2yy8Ht5uU3r2576GHOOGwwzjpkkv49SmncNvvfsfBBx/MjHPPJdp3ESFhJjiIoDIesq88HmPDhKgovF4vd993HwAPPPAAHy5ezJOPP86sE07gr3/6E2N79+a8Cy5gna9/3blrF4wYYQSbLBbjX6iJrtZkgJk7PMbFgc3GRx99hNvt5pxf/xqLxcIFF16INTqa77//nvXr1zNv3jxuv+suHrjlFhb6ios//dRTzD7nHGMiRlmR3Y4l6G9aWFhIRkZGw/d1Dod/CaRvYsgVRksgy0pKGDp0KMtXrKA4P7+zD6dTKADWnrxeIyjQ2iVmGkBIewksCG63G7NurSmCH6otmz9TBpi0l/3JAAu8rzLAws7bb79NQnw8mzdu5KYbb2Ta9Ol8uGgR58+axYOPPUZWcjInnngioyZOxJqWBrt3c/DBBzd+ksjI8L7I8HobljouWrSISzGWN4KRAVZVVdUosJWXl0dcXBzO6mp27tyJDfjiyy9xAyOGDAm/3zUlxZ9VbE7axcU1uktUbCyf/vQTCz76iBNPPNFfxN8X4Jo+fTp5O3ZwyUUX8dTTT/PU00+TdM89LFy8mOQO2slTggSPS5trdxrDSmvU1/uzds3i8XV1HHXUUSxZv55M4MWXXiI2IYHzL7mE6Oho3nzvPWPMarXS17dMcueuXcZjvV5jDBoYiDUDYs0JzgAzl08G/OzDDz/kgOxsxviWXWK1ct5553HeeedRU1NDUVER/fv3Jy0jg+fuv5/Vmzfzu8sv58exY+k/fLiRiSzdijWo5MLCb77h51WruOTSSxkwYACO2lpifefAcKwBVlZSwtSDD2b5ihVsXreusw+nU2gJZFsKvsBqzYBBpL0EntADb7NaQ8+WNfUcof6HPQNgatfS1vYnqGq2x1DZjmqrnSo3N5czzjiD2ccfD8CUww5j7Jgx3PTb39K/f38eefZZbrzxRkaPHo01IqLx+bRXL//34ZYlFHze93p5+aWXAKj3LfMJDoCZqqqqKK+oMJZ1YtTAMQdqbgjPDDCLpXG9nSb0GjCAUy680B/8ysryF6q2WolxuXj9mWdYtXIlzz/3HDu3bOHyCy7A0UN3qeqS1KdKoMCL/ro6qKw0ViIUFPjHjr4A2IYNG1i3fj0eoBAoBk477bTGWaAREWCzkZCQQFpaGhsCN8+w2fxj2ubO9aHaaKgaYL6vF//4I0dPnerP+gkIfsTGxtK/f38ALvrd75i/fj0PPv88AMcddxxLFyzwH09zSyLr642d1fX56RLqA5YzeoF3n3mGp++8k6t/+1vAyACLCSqC7wyTXUKdTic1VVX0GzqUGTNm8Mxjj1EWkLHeUygA1pbKyhrvUra3AFigpjpndYbSHIcDcnObDxAUFDTeShr8s/X7GgALZNYVUwaYtJem2lRrM8D25/Gyf4IuShYuXEifPn0AsAEjhg9n/JQpxg89HiOTKHBZY3CAJbDWUGxsOx74PggRADvttNOYeeSR7PTVrNnly1ywAJs2beKzzz4DYOeOHQCMOvBAwKgPZsEYZHuAhISEDvgF2pFvOQjQeOMC39/QYrGQlpbGcccdx79uu43vPvyQi087rSFwKB2oNWNRjVslWF2dsbSxutr4vrjYCICZAW1fcKy2rIxjjj2WK6+8EvNMf8Y55/Dw44/z6KOPNvn0kydPZvHixbz40ku8/vrre45pA/vhlmSRWyyNV0V4vZSXl+PIzWXEiBGN79cUm42LLr4YMOpDnXrqqXz04oss+/hjyM9vHBAMVFRkvE8K9oc9r8dDSXFxw/fR/fvz0RdfABDpO6fVORz+GmBhtgSyZPduLEBKejqX/f73OCoqWLFiRWcfVofTEsi25Ha3TQZY8A59LZhZlR6qstL43+EIfRFYX7/n7jjmv9YEwAIfH/g/GLvegPFcwUt+WxMEFmlKS3YhDdTSOiC6UOswVTk5uKqrWbxzJ6+99hoVubmkATfedx/HT53K0P79iYyLMyaSYmKMZXXg71OCd3m0WIyaU2733re6DwdeL3369GH58uUA7PAFugAu880ab9+2jV05OUQAo0aNavi5GQDzAnFxcV27Tw0MgAVKTDR+ZgY9CwuNDRDsdv52220sXrSIqYce2nHHKfsmVJ/qdhu74qWldY3PqjTNrLUV6u/o8fjLa4A/2FNebvzzqa2oMLJjSkrweDy89d//suSXX4gGxhx6KP/717+YMGFCQ+CgKVOmTOGuu+7ihpUrsQKzzzyThKgo/7l+Lzs71tXV8Z9//YurzjqLqCbGDOtWrcIODB4zZu/vjY/FYuHxxx7ju+++4/X//pff/uY3AAzo35+49HQuuPpqLjrnHOPOkZHGGNocN1dXG8vHzYC/+V663boWDBNlpaW4AiZkRo8dy4c5OWQB6b7lro7AGmBhtgSyfMsWAFLT0+k/fDh2IGfrVpg+vXMPrIPpTNRazRUND/5ZqEF7U4IfZ9IuItIcs93U1Rkn0fJy4wRqds7BO4maAxRoeQAsVNsMdVtwAMztht27jeyzqqqmZ75E9qa1GWChZn4D79uaHVBlvzkcDo489FAOnTqVKy67jDdfeokfv/iCv1x1FX85/3xGDRlCZEaGEfiKjzc26jBlZEBCgr+2VGKiv++Kigq/7C8ImQEG0LdvX0pKS6mpqWHHjh3EREcTeM/S0lKK1q/HAhzoywCz+P6BEQCr7eq7Q1utxt8tVF2vmBjjIs9cygRcfPnlfD9/PlMnT+7Y4+xpWtuumiqCH/wz8J//ld3S9eXnG/9MLpc/uFVaaqw4cDiM+wQt+frvW29x+OGHM3ToUB555BE2bdrEUUcdxQ033kgFUADMnD2bKVOm7DX4BUYAzBvQ1t55//09+95mgmD/+c9/uPHGG3nZtwPxHmNUj4f1vsyY4ZMmGZMyvtpje3PK737HA88+azwtkHrggZR7PKz5+Wcuv/hipo0axaO33YbX48FVVcWjjz7K2u3b/dd8BQXGPzBuy8/3T3jvi9rava8WkRbZ7gsgHTByJH/4/e8bapJ6gBrfapvAIviR5hLIcNgF0uvlC1+2Wt/+/ek7aBAAO7Zu7cyj6hQKgLVWQYE/nTe4Qw1VZ6m5GYiWZoCJBKqoME7UHo8/0OVw+FOoy8uNdurx7LmV/N4CYK2tnRC4RMmsA1Nfbwx8zEFRRYXxr6jIGAAXFfkHRmbdg3BTX+/PbJPO11wbbGkQK7Btp6Q0Lpgr7cbj8fDsI4+wKzeX8ooKanfsoHd0NH++7jr+dOutxgx3YqIRyLJY/AEuk81mBMDMc2l8vFE7qisIOv+bSz537NhBTk4OBxxwQKMAWFFREbt37wZg5MiRmK0zu3fvhgywquDl7F1RWtreA5fmeSoujgEHHxw2u3x2S9XVRvAiOEC1L0sgQ92vu+0QHWqc1JOYdWVLSvxLHN1u/7iupKTh+4KCAn7zm98w5fjjOe/Pf8YDDBgyhJvvv5+Dpk9nQ2kpV/z1r/zniSc4YsYMLr300hYfxtFHHw34L2S/X7iwcQ2wpjJlfX+7wsJCAGpqaoz7hriG27xhA7aICAYOHmwE6Ftag9FuxxIXxxW33YYtM5Ply5ezbONGvl+4kItmzyYlJYV77r2XSf37MyAtjTvvvZfJhx7Kxg0b9kx8CJ7Q9nr9t7WUed7QUvL99vozzxAREcGcL77g5kcf5U9/+hNgBMDKSkoAcNTW+pdAhlEGmNfj4d///jczjjmGg6dPJzoujl4ZGQqAyV643UYH2VRWVnAG2L7WAFMGmASqrfXP/LjdxomsqMg/WxUTE3owlpdn3CewgGhgQfyALaPdbjfvvvsupWvXUrV5c9PHEjzItQetoo6ONl6zuHjP4JvXayxvcjqNARIYg+7q6vDLDisoMN5j6Vwej9GWAoORgRcfrckAC+yfzbZfU6NAZzuqra3l5OOP5+4bbmDUAQdw7z330Dszk48+/5y/3nsvMRkZRjH7Fs6qdxlm+wsMgPmWQAJs3bqVXbt2ccABBzR62CeffMKGDRtIS01tVOvrhuuv54svvsBDNwmAtUZEhJbN7auaGmMcYE6W1dQYQYqmJm/r6vYpSJWXl8ell15KRUWFcUPw8wfuPt0dFBQY72t3ETgxGorb7b/+MVVXNx7jBUxkfv/99/ztb39jzpw5HHzMMbzy+ecUVlRw6mmn8eH8+Xz8ww9UA2XAG+++y633388VV1zBvHnz6NWrV4sPOzo6moULF3LN1Vdz7DHH8MLrr3Pfffe1OOGgvr4eC2A3x7HBwS2nk8K8PHplZvo37milW2+/nby8POx2O1HR0QweM4annnqK9+fO5d777mPKxImkJCczfOJEUtPTOfHEE3ng7rvZtm2b/7jN68D6euNfWZnRBkNNBlZWNj+m0TXlflk0bx5vvPgiJ5x5Jll9+4LVSnx8PJWVlRw5cyalvmubujBdAlldWUlVdTWTp00zNnWw2ejTty+7cnI6+9A6nGqAtYYZOQ+1DMwUHABrbuDWUzLAamuN2dueuH7d4TCWe4QKhLpc/qwp86K+rm7PmXFzY4W4OH+gyOtt+NobE0NdaWmjnXI2b97Mzp07GThwIP0nTcJiDlQCArivvPYag9PSOGjyZO79xz+47bbb6A2kpaby13/9i/PPP9/Y6jfULHCIAFhlZSXr165lcr9+jR8TE7Pn8gozGGaejF2upmvDtERFhfE65nMEbrG9P1TDrHM5nXssowiZaRusmR2d9lBUBNnZ+36M4aKuzggWZmaGTV8bHR1Nkm8QOGPGDC7805+48JJLjG3he8LnylwSHpQB9tNPP+F0ufYIgD340EMAHDVzZsNtFowNAvoPGIAXOPnkkzvk0DtdaqoRsAmTttwuWlPXx+k0xhPBF+nBuzA7nca5z2o1zrFgTJpVVzfeNCI52Z9JYt5eW2uci80gRFCfWZCfT3RNDYm+Cz/Tvffey9y5c5kxdSoXXHABeL3U1dURERFhzLKbz1Nfb1y42+3G69fU+Osd2e1Gn9ARtY7297ze3YIIpaXG+cM8d1RXU2+zYYmIwGaz4c3L4/333+fNefNY+dVXxMbGMn36dEpLS7HZbGzYsIG6ujrS09PZnZdHbm5uw1MnjxzJ8nnzGpZ0mx5//HE2b97M4Ycfvl+Hfsghh3BI//7ce++9eD7/nPsfeICrrrqKuMBxd6iMvbo6XL6J5erqauO+8fF88NlnDE9NNYre19RQsns36a0Iyu1VbCx4PFhiY7nwssu48PzzcUVHY01KYuXSpdxz7bXcdeutPHTrrRw0eTJJw4aRYLez8PPPmTx5MmeffTYzzfOD2w12O0VFRcTGxhLrcBh9RE1N412Swf91d2u7Heyhu+8mITWVf/z7341uj4+PJys7m/WrV+NyufB4PP4AWBgVwS/yLatNTU9vuC27Xz+WrVrVWYfUaRQAaw2z42jNkpvWZICZqbvdKQDm9RonV5vNOLl2B4WF/lo1zTELv8bHG4PWmhojQ8ocOPrSr4mKMgYfNpvxmKio0MuzgmYcVy9ciD0ykt/cdBPxFRV8+tVXvP3eezzwwAOs2bSJGIytpO+66y5OmjmTzIQEbr7pJnaVlHDKrFlcfvvtpAATx47lq5UrG563uKSEiy66iJ++/ZZLf/c7bBERjDO3qw9ojwsXLmT17t3878UXeeWVV/jDddcxZ84cLv/tbzn80EM57YgjWLNmDbc//TSXnHYapx5zjPEcKSm4qqpw5Oezfft2oqOjGTp2bOP3Zm+qq42BfnKycUxVVcbgPTPT+LqiwqgdFBHhH/gH/728XuNnsbFNv259/f4F5robM4DaUe9JqHT9lmSABd8f9swA6+iBoNttzM4mJIS+wKuoMNrivgZuzaX5Tqe/XlYns1gsPPHIIzzUuzeX/OUvxmc8MCO1uwu6AMvMzMRqsfDRRx8BxjJHgDGjR7Pql18aHjZ16lTjYb7vhw8bRnJSEvn5+cSaEyfdXVSU8a+rqa9vVMOsSWZdn4QE41+wsjKjn42LM743M5Lj4hr3H2YmSFoa2GyUbNjAG2+8wWV//Su1FRV8//33zJg5k9joaDweD7t37yYpPZ3SoiL6JSXtmSXe1BIpr5c+WVmMz85m8fLlRoDSx8z8MrMT6+vrOfDAAznjjDO458UX/eMGMzPFzGJxOPzL52JjjX6ruNg4r8fGGmOmigrjvN6aNm9mlZvLqWtrjbZktfrLRJhjmqaYE5JNTWBC1y1MHnxtUleH1+tl2Xffcc8DD1C0cSNFxcXkeTyccfTR7NyyhSVLlxKTnc1JxxxDaXk5b7/9Nk7fBf2QwYMZN24cOYWFDJ8wgfPPO4+TTjuNreXlHDZtWqPJWdOVV17Zdr9PWhqzL7qIGx99FC+wfNkyDvPVZQou8/HDDz+QPnQoK779lreefhqAr7/+ml+tXk2vvn056fTTyUhJocDXHxcWFtLbVyepTVgs/s97bCw4HET4xgQTJk/m9ddf59tvv2XNmjW8+eabfPXWW0TU13PQgQfy3Xff8eFHHxEbE8PgwYNJTkmhwm7n54ULGTZkCFNGjyYtO5vfX3opqYmJjccBgbV5ZZ+46upYvGABp5x9NqkB/Z8pJS2N0pISan1LyYMzwJzBk7mdoMh33ZmekdFwW2afPhR/+WVnHVKn6T4BsMpKY2DQnqnyZsfRkgywwAutluqsAFh9vfG+hXrvKiqMn4f4sLf4uaH7dLputzGwcrmaDpzU1RkD18B1+xER/tnY4ALAvsHH5vXr8Xg8JFRW0jsryxioRUSQk5PDztxc1m3ezAdvv43b7SZ72DD+99//4sYoHGoDjjjlFH5YuJBJkyZxzm9/y88//0zh0qXccsst3AIkAHGAE1j8ww+AcYG1cuVKTjjhBD7++ONGv8Z7zz/PO88/TxGwfdEi+gwY0DBb7HG7Oe300ykG0oDZs2fzva8w5FPPPMNTzzzDddBwfN98+CFrf/97TjznHHZVVnL11VeTv2kTZkjq91deySWXXGJkSERHs2LNGkoKCph28snYwHgvrFYjiOX1+tPtbTa8djt4vVjMz5w5yK6rM4IJ5n2DA2Dl5f4lcIHtO/Bz3RMDYOb7F6rmjnnREphl1NrZdIfDCIonJxvvbUGBUZMrOtqfkWC+duCMmd2+566m5usHmDdvHl9/9x3nnXkmI3v3bnzf4B1KTYEXMvX1/iWS0HbnlIoKoy+or4f0dP9SpOho47bAIO6+MI83zGp8JERFcds994Quet5dBZ//fd9HRETQu3dvFi9eTAYwevRoVq5YQVxcHEOGDm14+EEHHdSofaakpIDXS5zZVnpSELEzud3GuSewBl1zPB6jP7Namw7amJsjmX1bZaUxligvbxwcN+txmRvcABs3bsS6fTtDJk0iPz8fq81G7ooVVFVV8eOPP5KamsrSpUt59733sFqt/PLLL7zw7rtMHz+eP//5z9x7772sWbu24VDOP+88zjvvPMaNG+c/vsAAfEAbrK6uxgLsys3lzTffpP+4cQxPTiY1NZXc3FwsGEt7d+/ezdeffkpNbS0vvfwytz3xBNHBGeH19eBw8Pnnn/PBBx9w3333EQvcd9tt9OvXj3MvvhhiY8nfsIEtmzcz8eij+WnFCg4//HAsFgtOp5OtW7eyYcMGMjIy2J2by4wjjzQuSuvrqdq1i/j4eON9jo01zjdRUUaQ0JwocLmM22pqjPsFf6YqK40+OXBHWvPvZ3K5wjMAVl5uXA8F/C09Hg+fffYZLz3/PGu//ZZe2dkMnDyZyZMnk/PTT3z88cdsLyggPTmZaVOnMnToUHaVlLB04UJiYmK47957Of/KK7FUVUF0NHnbtlHsdrNg6VLOmDaN5ORk6N3bOIeVlUFUFIPT0jrm942KYvSkSRQXFzM0LY3FixYZAbAQpRDOOPNMyoEk381WYOWqVYyfOJH/u+EGAArNVRdAQUEBYw85pH2OOzraeM8Cxhq2iAhmzpzJzOOP56qrrqKsrIyCggKGjx6NMyKCJ+6+m61bt5KTk0Purl3ExcUxIC2NrWvWUJaby9qyMh57+GHOvewyItPTKS4uZvjw4Uzo3Zuxo0eTETyp0JrN2noyj4dln3xCbU0Nhx95ZMi7pKSlUe9yNQSZzACY1WrFZrVSHwZjs2LfsaUFBMBSMzJw1NTwww8/cMghh+zzct+upm0DYOYa8ZZeMJozLC0ZzHk8xiAg1H3r6oyTVX194xNVWzMbb2uyDpobNDVVL8w8wdps7R8AMwdsERFGxoyZTWMO+Mx6I3u7yG0qM6SpwJfLZXS6nf1B83iMAVF8fOjfr74eT2kp1pQUvDYblsAOzOk0HlNezk/r15OYlERNYSHxVivVLhe9Bw8mf8MGvvjiCw488EA++OADli5dyqGHHsqUo4+mb2Iic+fOZfHixeTl5VHlG5jZbTYmT57M9OnT+fzzz1m2fDkFGMEks/U7li4lDohNTORfd9zBzTffzA8LF3LRRRfx7LPPNtQ02Lp1K4MHDwZg2pFHMnPiRK6++mp2FBfjTUrijaee4rgjj2TCccfx4osvctMllwAQAwwdMgR7ZCTz167l+eef58r/+z/SY2KoKS3lvn/+s9HbtGXLFg4++GDeeustPvroI77//nv+O2cOHuCoo45ieL9+PPb44/zj8cepBfr37895l1/OxGHDWLpiBY8/8QSPP/EEANFRUTh8MyVZ/fuTnpJC4e7djBo1il4jRzJy2DB++vxzoqKj8dhsfPnttwzLzuaqa66hLjmZkb16UbRzJy6XiwEjRhBns5EduMTNDGKaFxgOR+NBQOBnLoyyaRrZ27Li4M+rw2H0Xy2ptWRmGmRl+d8X870xg2O1tcZr2+1GJmNKiv9iwu023rPSUiOwaLcbj3M6cUdEYKuo8Gffme9taSm1dXUs+flnDjv4YKwpKcbPfBeJN910Ez+vX88/77iDMYcdRnlZGT/98AOzTj6Z5cuW8e6TTzJ48GCio6O577772FlYyOsvv8wf776ba8ztxoGdOTnkrlvHit27Wf/dd1x99dXs2rWLiUcdRbnTSXJyMpHm729mZNrt1MfHQ0QE9ogIqioriXY6sScn7/n+mzN8kZGN33+Px78M2Ok0fv/iYuP77Gx/P+l2Gz9zOo1zWlpa4+dxOv0Xc8EBJbPdhsEgq5HMzJ5XMNr8vERE+P/ePtnZ2ZCby5DBg0kMUVD5vXffbQiA/etf/6KgvNz/eY6ONtpRd6uZFo7MLCWn0/g8NzH2rMzJIaFXL2Pn0sAyGU4nnvp6KnbsYEVODsNHjiQzIQFLTQ0b8/LI6NWLgk2bWLBgASeefjofvfsua9asYeTYsVR6vURWVVFdXc33339PZWUl5eXl7PItLUtLS6PY7D+COH3/7vj736kHaoFly5dz4UUXUR0ZSWZmJlW+XfxenTOH//73v7z88stUu9188vHHPPLQQ9jcbu684w6ye/fGU1DA7NmzefLttxte4w9XXUUpMDIlhVmzZrF82bKG53t1zhxKAHNK6Q9/+APXXX45rzz3HO/Pn09ccjKesjKS7XZWr1lDDfDxxx9z8cUX8+8nn6QeI1C8fOdO5jz6KM66Omox6kXZgBEjR1JfV0fO1q3UYSwRzgBGjB7NlGOPZd3ixSyZP5+BAwcycuxYimtqmDJsGLNPOYVCi4WS9etJTUhgyIgR9Bs/nghzYtI3RigpLCQ1Pt4/9q2tNf625mc6cEwbmD1nZiZ3YjDBWVHBLwsW8MIzz3DiKaewpriYTRs28POiRaxYu5aamhoGpqczLDOTndu2MX/FCl547jl6AX2ys/nnP/7B6b/+NUl9+jReMmuqqjJ+v6QksoYMISsxkQMPOsjY7dvsowInxjpYamoqw4YN46effoKrrtpjTG+OsQMFfqrvvffehq9X/fILqSkplJSU0Ks9N10Jvu7IyPBPAhYXk5ySYgQXo6KIjIvjmmuuCfk0TqeTiJgY5q1ezRuPPcbrzz6LA7AkJ1NSVoY5FZgQH0+FzcZB06YxIDOT4enp9B4zhm9++IHp06YxbcYMMrvLap025HE6+cc//kFSYiIzfJsvBDOXFe7cvh2AGHN8a7EQERGBKwzqzZoBsIyAv3FqRgYW4PDDD+fcM87gz9dcw+TDDuukI+w4bRcAc7uNCyGPp3FEO1DwRZiZCWDWIwj+udfrrxFgpkKHShcPTKturcDaQy6XURDcZgsdAW1BBtjc999n/AknMKBvX+OG1mRGWK2NC036AmAejweruaNfdLQxu5OS0uhEW19fb3TUrV2rHjgb5nIZ39fUGJ1v4ExBba1xQVpUZKSVR0Xh9XgoLSggNSHB+Nt4vf5lZ/4Da/y1L1vnm7feIiEhgUmzZ3fecg6HA29FBSX5+URmZHDPv/+N3eGguKSEYePHU1ZWxi8LFrDo66+pc7uxZmWRGR+Pt6yM6Ohoeg0dSlpsLNs2bmTtxo14APM39wLVGBlXDb9dfDxHHnYYn3/+OW++9RZeICY2lumHH86wAw8ka8QIBmZksGvrVt78/HO+vf9+Rg0ezM0330ziiBFkZ2eTlJREcnIyaWlpREREGLOcwJFHHklhYSFHHnlko7Y7aNAgVqxYQUlJCTOmTzcGKsCgAw6A6GhuvOmmhgvziy++mFkHHshBU6YwZdw4vvn4Y5wWC3HZ2Tzx5JM8+uqr3P6nP/HGhx/y4/LlpPl+z+sfeog/XnUVVt/f/YorruCKK67g1ltvZdy4cdx0003MmDGDM88+m9KaGiwWC0cddVRDkeffAueedhqvPfccS5YsYdasWQw58EC8NhuvPPEEKQkJ9B8+nJULFrBgwQLqnE569e6NNSYGS20t06ZM4ZuvvuJ3l13GbozBcHDHdtTMmYwYMYJch4NV336L3W6npq6OSpuNrIgILvvjHyExkT79+5OVmsryL7/k5+XL8Xo81CUlMeOoo5g4YgQ4HLiTkpj77ruMGzeO7P79G5YxdRi32+g37Xa8GRlGIctAXi+L/vc/ho0ZQ4ov+El5ub//ioujPCeHmMRErAkJRrDUDALGxOD1eikvLychJgabWRQ5IsIIxvj6PE9ZmVEfzmKhqqqKZd99x7Dhw9myeTMbN24kOT2dY2bOJMa3tKW6rIw7b72VN954g5kzZzJu/HjsNht5ZWUM6t2bkpIS5syZQ15+Pgnx8YwcOZKiqiqOmzEDh8PBiy+9RBlw7LHH8vDTT/PPu+5iV04OvQcN4pdt20gL6ot/f+WVbFy9mj/9+c9sWrCAW2+5hSefeIJ/Pvkk8UA+kAK89vrreIDI2FiKa2pIiohg+MCBxMfHM378eG644QZWr17NX/7yF6JiYjjzyiu55667GJGRwf0PP8xOh4PKykoSExMZmJ7O/A8/pKamBmtCAseccQa9e/fGarWSt3Urf//znxk0ejSzjzySmP79iaqoMNqO10tddTXb1q8nMzOTGLebKF8/aS7Beeu114gAsmJiGHnAASQnJRn9cWAfbfa1ZsDFYsFbX8+O1avJHDqUJcuXM3H8eGLMJVUdJTCbrqeIiDCy/CIijP41oH1mZmZiAaZMmRLyoeaW6ni9nHPOOcbf2Jwwiojw12eSdrXz559Zvnw5P//8M8nZ2fz3vfc4eORIeg0aRGKfPnzzzTdsW7aMXZs3ExkZSeKIERwyZgzukhIjWFVZSfmuXZSVlmJOqdgwzpluwIExPgC4+ZZbmjyOUQccgMVioX///owePZrs7GyKy8qwWywMGjSI2Lg4lmzfzmlnnomnro6vv/2Ws849l7WLFlFTX885F1/MpWedxZjJk7n8qqtIS0tj6Q8/sHbNGtKSk3ng1ls597zzqMLIhlmxZAmbN20CoAhIB267/XaCt4SxACWlpbz99ttMPPhgNq9dS2VZGZddeilZWVn06tWLrVu3csPDD/P+iy9iAYoxCo6f/qtfsfTLL5k4YQLHX3op7739Ng89+STlQHJSEn/405+wA2ecfjrpvXrxxOOPEwGMGjaM1evWkZWezr9vv52xY8eyMz+fxQsW8MEHH/DCY4+RZLFw3sUXszsvj3W//EKZw8GSL7/kkSeeoAjo5fs7AGC1MmLoUM444wzKLBZ+XLyYJZ99BhiTi2MOPZTDR43ilPPPZ97ixaxdv554ux1rWRl9+vUjLi6OAosFj9dLxbZtRHo8RA0YQNmOHVx0ySX0GzWKyA7avfTbb7/lqlNPpdhXhPudd98lD2M8dMjkyVxxySWMHTuWs485hihfPdm8igoq6+roHRNDdFISdnOMEBlp9Dmhlmz5ltqSlOS/LTBgYo4/Oylz/rDDD2fum29SX1/vHwd6vdS7XOTs2AHQaOddc3rTC4wYMYJnnnmGadOmceyxxxJht+OlcbCg3dls/vfQrNMUOEFrLoE2N5yIiQGHw2hnNpuRPTZxIldfcQVRUVEMGTuWspoaVn//Pa9/+imlOTlEREfz0VdfsaimBitGwNwKvPv446SPHMnKgCxRMaxctozFS5bwj7vvJqWJ9mAGSrf7VsNEB0ye2yMiwqIIfrFvgjdwCaRZ484GzHvrLea99Ra5PWDS0uJtxS85efJk75IlS/b8gdvN5gULeP/999myZQuTZ80iJiWFM844A5vdTm5uLp999hk/f/QRZaWl/Orccznx7LOJ83igspKamhrWlpSwZeFCUlNTOejoo0nMyjI+4NXVRhaBw0F9RQVWux1rr17GBVtdndFRmzvkWSxGh2G3Q1kZXqC0upoUm81YHpWWBlFR/Pjjjyz47jv+fPnl5KxezaeLFnHsySczMC6O7+bN4/QrruDdF17gsBkzeO7tt1m+fDmPPvoo7l27wOPBZrfjzcriu7ffZsLYsQ0X8nl5eUycNInYwYPZtH49ucuXkzZkCPVFRUSlp2M3s9PMApFmlgNAfj6nnnEGdo+HF155hbioKJx2O5ddcAGfLV/Ony64gEvPP5+CggKGDBlCTO/eeGNiKNi9m7LSUp576SX++c9/sm3bNgYMGMDyb79ld04O048/nuWLF2P1epkya1ZDkML03dtvs279ek445hj6HHigMVivq6PI5WLDtm18/cYbpKenc/LJJ9N76FCorsbtdvPB55/z4ksv8fOCBZx88snMOvZYjj/2WOwxMcz96CMef/lldubnM7Z/f4b16cNZZ51FWV0db33yCe8/8wwu38VaUp8+HHbwwVz2xz8yevLkhoDOHg3VYlnq9Xont7ix7qXN3nfvvbzkm+0pKy/HiTEojcEYgK0vL8eDP5jSr29fBhx0EDFeL4k2G/UeD5u2bsVVXU3vAQPol5lJ//79GThwILt27aKkrIx6q5Uou52ZxxyDu7qaEdOnkxwTg7uwkE2bNrHD4WDSkCGkxMc3bguA2+2mcPduegHWhITGg4394XAYF2Pma5mfMXOpWG4ur732GjOOP57slBSIjCQyI4N0jMFrAv7gXirw5fLljBwzxgiG7C+vF299fcPOJFgsRpZMXZ1x0VdYSFVlJdu3b2f4xIlEmMFgILesjK2rV1NWV4erooLU3r2prKxk0+rVlJeX89Zbb1FVVUVdwAzMhEMPpdpuZ9eSJVT7ssHygCiM4IgnKYl4p5P82lpi8Q+anUCk7304+/e/51+PPRby12nrNgvgKCvjg5de4q233iIpKYmF27bRJzWVU085hT4jRvD+++/zw9dfU+7b0jiuf3+uvOYaRiYlsWnjRpb+8gs/r1hB8c6dANjS0vjDueficDhYv349v+TnU7ZxI26PhwNGjuRXv/oVK3314YqKiqiurqaouJjKigrS0tJwu90U+GaU6oAa33sHRt2DjLQ0UlJS2L17NztLSzG3dygFkoGoiIiGOiIpyckkJSWxzTd75gEibTbqfYPy9+fP58bf/55lv/xCBMbfoxY4+dxzeeD667nvvvuY89prALw1dy5jR4zggJkzqc/Nxeu7fw0QC/SbNIm/33UXV155JZEOB8ccdhgjR46kpKSElStXUllZyYqAungmN8bf3RxY52EMpqMwllQ4fd9H+o4tGqjC6FesQAnGBWUVEA8cduihrCspoWzXLty+81h8XBwTJkzg6KOPZsOGDXz300/kBOzMWh0XxylTp3L08cfzwVdf8fn33+P1eulntxOdkMDUyZMp9Xj4Zf16SrZto762FjfgAiYecABHnXUWN952W8j2ta9tFppvtz1afr4xTvHVH/r555/5aOlS/njOOSQHnO9efPFFYmNjOfPMM40bzIsg82LUbt/3UgTdXFv3tX+5+mpef/TRPW5PSkykoqKCfGBA7964i4oYOnQow4cPZ3thITnbtpEaH09KcjKJyclkpKbSa+BAXOXllJSUkJCQwJtvvklSWhrTjjyS9LQ0Rg0dyvr16xl1xBHMmDqVitxcoqOjsSYnY6upITYmxjhfB48BKiuNc3d09H61i9L163n+6ad55LXXiLVYKN+9uyFAUAz0jY6m1uGgGIiLi+Py007jN5dfTp/Ro3nunns49dRT6TNsGBtWrWLlsmWcfvrpDc/t9Xq55d//xllQwKWXXUZVVBSpqakM7t+f2u3biYqKwtq3L16vlxdffJHc3FxuvPFG3nnrLSw1NZx+8sl4ExJ477nnOGLqVDIyMli+fDnDhw8ntlcvf5ZW8EY7iYnGhE9NDdjtLF6wgNVr1+Lt1YvhCQk43W5+WbGC77//njVr1jQEjSIjI3E6naSlpnLI0UfzxcKFOH1BEw9QFhGB3eUiPSKCfJeLFKAcoy83xwde/AGWy2+6idv+8Y+Q73tbt9mC/Hxu/s1vmD59OuPGj+fLL76gpqaG6667rqEIN2D0I2lpRr/kPxjjPTPLRWRlGWNFMzsuIcF/ndWSDWOcTiMA1gkTH+8+/zy/v/RSnn/mGYZPnszQ/v2pKi9n0NChTB43jhXLljVMTl9z9dU84itm/ue77+ayK64gMTGRf/7zn3z+4ousW78egKdef50Tzz67w3+XZgWWqnA6jeQEsy8wVzlUVzcOYmZlNfQbXq+XBQsWkJmZSWlpKSNGjGDVqlXUuN3MauJ37cnjg0fvvpt7br6ZRTk59O3XL+R9VixezPFTpnDhH/7Ay489xgvvvcexp5wCwIGpqRx/+uk84Ks711nu+MtfePaRR8hxuRomz3/89ltOnTGj0f1ynE7s3aT8S1Ptdr8DYG63m4tOP52v338f8KVXVlVhwdhNzpuUxLqcHKLdbsxkfydgz8zkkpNPZt3y5SxatIgajIsED1APnHXaaUTZ7WzcuJH1eXlYamtxVleTmprKTTffzLq1aykqKmL16tWUlZXRd8gQYm02VqxYwdTDDuPggw7ivffe4+dly0jt1YvDpkzh1IsuYtPWrdx23XVEAiccfzw//vgjhaWlxPXuzb/+9jfuuececnbsYOiQITzzzDNM/NWvqKmt5aQTT8Sbm0t1TQ0ffvABH3/3HX+45BL69u7N3//+dz799FPsdjv/feMNdgN333EHT955J56kJFKAMrebBatW0adPHyy+LJzAoEfN1q30GzyYKN97cOstt/DuZ5+xZvFiSoBEINI30zRo4ECOOO443nv/fSry8hpOtk5g2jHH8PTLL3PE8OGU+2YIzBNynxEjOOqUU3CVlVFbUcGKtWvZtnw5JRgXowdMnEhqXBypiYn876uvqHQ4SMKYqYwGhg0dytixY8nJyWHxkiXEREfjcrmMYA2QHBlJRmwspWVlDBk8mD4jRpC7aRMbt22j2uUiHuMiOTs1lQsuuIDU1FQ+/vhjfvzpJzzA2998w+HTp4dse209WPjo3Xf54X//o7CwkP79+1NdXc3RRx/NhKlTSY2NZVdeHukpKcZOiiNG+GfGwJ91YZ5YMjKMk0+oz1JsrH/garH4d0ECYyBRVGScvHzB2T2YFz7tNZCorDT+ZWcbx2+2zehoYwAUEcH8FSuo3LqVlKFDufeWW1iwYAEx6ek8/8ADzDr33Pad6fN4/LU6SkqMYzLrRblcxvtnLgcrLfUPClJSjOMqLDQySn2Dt507dxIdHU1ubi5jDj8cS3w8tZs3s9k3i7+zuJjcnTsZd8ABjD76aCxFRSxeuJBffvmFoqIiMjMzyc3NZfz48WC1Mv6ww8gaNizkobd1m120aBGXHH88pSUlDB40iPr6+oYZTTCCK9GxsRwxciRRUVH07duXhcuXs3zjxoZsvYEDBzJu3DiGjhpF6e7dfPbVV+zesaOh2Hb2yJEcMXYstrg4vvviCzbu3MngPn2IjY6mT58+RMTGEpuaSlpkJDt37qSgrIzMIUMY2a8fcX37MnLUKEYlJ7NlyxY+XLSIoqIiKjdvJi8vj7898gjTDjqIvC1bSD3wQNw1NSTZ7WzfvBlXbS1xqan07teP2qIiSktLycrKwmazUVhYyI4dO5h43HHsWL2a2//2N8aOHs3vLr2USpeLXoMHNyzbzPbtsrdu61YSIyOpTU7m3zffzBNPP81dt93GQ48/Tt727WwoLyc+MRGvWTvO4TACDuayouhoHnr0Udb89BOzjj2WEy65BG9VFdW7d5Oamkq108mnH36I1W5n7OjRbN2yhfz8fEYfdRQxVisxdXV888035OXlkZCQQGxsLOMPPZShkyaxaO5cXC4Xq1at4r333iMqO5uB6ekcf+yxbC4pYdVXX7FhwwYKCguJjIhg2rRpDBs2zPibDhjA3B9/ZNXXX1NWVERCfDwnnH46kTEx2Gpq2FRSwtpFi6iursZRVcXIESOYOnUqKSkpbNu2jYULFxKfksK3a9a0aZttrt32eEVFRv9tt/szrjMzjbbWXNa6OQkQEeHPVG/PEg9dWFv3tV+89x6bly1j7NixjDnkEHbm5jIoI4PorCyqdu1i965dDB06FLfbbUxsmsEBMM5N5gWo1WpM3gRsYOPxeBqyZxuyON1u4zxl7uQKxjk5L884B6am7rkE07zoTUryF8rfF+aya7sdb0UFq3/8kcjISMrLy/GmpjIwIYHxEyZw1Bln8Pjjj5NgBjdSUvxjmdhY49hDlbzIyDDOzcHtNzfXuHgP2I2sWR6P8Rqlpf66XGadrl69/AGduDj/pkNmQMesH2luOJSWZnxfXo7H46GyshKLxUJCQgIWi8U4L6SlQXQ0yz76iCVLljB16lRGjRqFy+UiMi6OQq+X0rVr2bZ1KzExMfTp04eEpCTqamupqavjx4ULOfDgg5kcdGFpavMJMrM9gH/JrrnJj1ljzuHwt6XCQv/4MjHRX6fWZvPvELp7t/F+JiUZzxUV1Ta7a7ej/A0bGD9iBBagAijcuZPvvvmGs88/HzvG5NUHn3/OvE8+4dobbuCaK6/knXffJcfhwB44Bs/N5e677+att97i/a+/ZkDQLpZhJ1Qd7Npa/y7yYPQpwbeZ5xcw2oHF0mR/0pPHB2cfeyzbN25koS+7K5TC/HzGZWUxdORINq1bx8KNGxngq+05tlcvjvzVr3jkhRc66pBDuvqii/jmk09YafbdwKa1a5k2alSj+81fs4ahQbtUd1XtFgADuP6yy+iVlMRRs2Yx6ogjWDVvHuvXrWPp0qXkFxTgstm45f/+j95ZWUQlJrLsxx959oUX+OrLL+k7cCBnnHwyo0aNYtgBB7Dkl1/4/N13+ezzz4mw243dZCZN4oBBg4hOSmLhN9+wafNm3BgzLuPGjqXP8OGs3rqV6MpKhgwZwmeffYbb42HggAGcfOaZrNy5k+VffEF+URGRGCmyJaWl5O7YwcixYznnjDP4++23U1JaSjVw7e9+xxPPP4+1vp5yjKwBG0bqtBNI8s2INSUPo5NNh4ZaCJUYs/6jRo3ii5deMuqA+Ha6+eijj7h09myc+Nejm7UbXnjkEYYMGcKGDRtYsGgRA0eP5tlHHqHUl3VRD8TFxmKz2Tj2qKN49/33qcaYkfrjLbfgKC1l0IQJRFRX88LTT/Pz6tVEA70yMhg5ciQTpkxh9sUX89lrr/HVF1/gqq2luLiYcePGMX3GDI6dPZtNZWXMf/ddFnzzDStXriQyMpJ/3HMPZ5x2GlarFY/Hw8fLlvHdl1+ya9UqZs6cyUUXXdSwFK/Q6eTDzz8npb6elJQUDpoyhdjevY2BS309a7dtI2fLFg6aNYvUJlJL23ywUFHhnz1MSTFO6h6PcYKvrTV+bg5K4+L8gyuLxRjQVVcb/8zdLc3ni401Bg61tcZ9MzMbn5DMQYW57NfpNAYm6emds0zI3DGxd2/j2MyBuhkAs9uNwU9xMaSn46mo4OEHH+Tk885jSGqqMfDsqAGR19tQk6mBw9F4lyZzp7/gJczl5f6aUuag2Aw6mhehRUX+5cdmwdyyMmMgbda7stmM780lgc1kvrV1m62qqOC6Cy/knHPO4fAZM7AkJkJFBeW1tdSUl7N182aGDRtGhm/gjssFbjf5+fns3r2bQaNHk2S+d8nJUFuL1+HA6/WyY8cOnE4nw4YNM36ntDSchYWUApm+mkWAMYPochntwWo1vg9mBobNwaTDYXwekpP9G30EF6f1ePx/M7MOofn5TE83PmspKcbfzpzp93iM3zM+vmHgv2jRIn788Ueuvu4643WzsiA/n1q3mxirlaKKCnZt3cq4Y48N/bcL/D2tVv9nwFySGPh9ba1xPBER/guDmBijjRYU+LMQqqr87Qn8F7XmrLo5ADWX9+fm4vV6WbduHcnJyfQeOND/nvr6m5qcHFYuWcLw4cMb70bk61fdZWWUlpYaae4BAeCGYLwGuB2nuNi/jNisO9iSAJhZe8+sIxaq7psA7TA+MDMnEhL2nIDyBU7weo1ggtXq7zdsNqO/qqz0l+9ISvKfRwLZbMZYIrAf8niM/sF8XH6+0YcEl5Yw1dYafWBbjR3MHRJNKSlQWsrmzZvJHjOGmOhoYyIqIsJoi+ZO1r7d7Brt0GtKTw9dt7C5zZeaE1y02zyfmJsFmCVSHA7jWKOijL7YnKQ0d+X2eIzfNT7e+D3i4vyBIqfTv9FLXZ3x/JWV/n44MdF4nDkpl5TkDziZhfMD64aF0OZt1u022kNsbNPLzs0yJOb93e7Qm92YWrvBTTioqGDyyJHk7t5NOUZ2tg0jscCGcW22q6AAi8sF6enU7dpFZWUl6Qce2HjcWFdn/C3r6vZYodGl1Nf7N4SKimpcf9XcDdv8HO8lu68njw/G9enD1EMP5cm33mryPh6Ph4FRUdTX1xMdG8vmqqqGLKtJffpwyJFH8tirr3bUIYd0znHHsTsnh28CJkHLS0s5ICiL+MorruDmhx82Vo118U0S2jUA1jBrYJ543G7/Tnjm4M+8UPF4jJMG4HA4iMrIwFJdbdweF9dQZ6q2tpaoqChqamr8y+JSU3Hk5fHNwoVMmDmT1IgI7LW1WMwZDV/2SmVlJbVAxuDBWHyDl5pdu/j+iy9ISUlh8nHHYTHratjtUFmJs6SE195+G1dSEr+9+GI+/PRTLjnpJK7/85/pPXIkn737LosWLOAvt97K/E8+YerUqZx++ulYLBZ++9vfMmHCBLKyshg2bBgJQ4dy2NSp9LbbyauvJw1/AAzguvPP57777jN24Nu9m0OnTSPN5SIuLY3yvDzSUlNx2myMnDiRuc8/779o9J24PeXl3PHXv5I+bBijJk5k5syZxoesro5P58zhX//6F1OmTePOwKVZDgfuwkJeeeUVDps5k2H9+xu3m/XazAGaeXFnBhbMWmN1dVBTQ1lVFTH19URlZxsnTjNQlJpqHKNZI6y2tvFg32bbczbTHNyFmt0MbqjtsJys2S2sg4MtZhDELG7tdhsDpago/8WkORh1u433ITU19OAinHZdMQe8ZuDRDPSZadXmbKAvANawvXhCgjGo7cgAWFtxuYzBbEpK48Fdba1/Mw2zTbrdxmfBHFSas2ctmLlu8zbrchmf06SkPZcXuFwNAWUSE43jC54BT031z9hnZhqfaXOJQ6BQ2YhFRcZr9O5tvCf5+Y2DOm3N/F1jYhoX/DYDtmafGBVl/Dy4ILS5JMYXAGsoHm5mAjRVp7KgwPh5S5Z5tER9vTG4TEtrvLOlmRGUn99Qe42kJH/fYrEYj3O7/dmZ+fnGZ9EMbpqz+yUl/udISfFnAphtNDCzUwPcjmdmppqbREDLAmDmUnBzE4lQy+AEaKfxQWuYuwEGFko3M5fNvqqmxp8pVVLS9LgncFxSU2P0g031V23NfD1T4NJCc4xnZnMFBsBiYvzBoeCNm9LSjN/XzMzqKOZEZOCFflObdAUGhrxe428XPHbzePwZ6WbQsa7OHwAD//jADGA2o9PbbHdVUcH0SZPYuGlTQwDMjlG+4MO5c9m1bRunn3WW0R6ysvyTvuakVzBfHc5uzQzga3wQksPhYFBMDNfccAM33HNPs/ed3Lcvubt2ccCkSXwV8PsePHAg46ZM4ek332zvw23WEcOHM3DYMF756KOG27xeL31CtP0Z06cz+8QTOfcvf+nIQ2xzTbXbtrlyDZ6VtNmMEz0YnUrgLIIZyHG5iDaDYmZBv+johp3NYmJiICGBePOP4hssRPfvz3EDB/qfKzp6j8h8QnY2CUG7+sUmJXHMMccY35gnNvNEGB1NZGQkF19xRcPF1uwTT2RdYSGpgMXp5LzZs3G5XESkpfHbc89t9Hrv+5Z/NsjMZOXy5cTX1THYV9D2lZdf5sV33sHj8fDKq6/i9XrJyMjgnw8/TA3wxdtvc+jRR/P+nDkcdNBBpPTvT0Rqqn/211zqBVgTE7njqaf2HBBFRXHcqady3Ekn7blUIjoaW1QUF//2t8ZsYkVF45lN82LfzNIwdyEJeG6iokg2L7DMnwWe5M3sKPP+ycmNB3PmxbL5vsfGGgGHDioSuofmdqA0L1BNZnsOfGxw3Q2zHdrtoTNjTOES/AL/38Jc1mAyA+OBM7rmrGLwbV1NRETomilmrZVANlvjbBnzPeqMAZG5U2tTPwv+zMfFGf/MOjE2m3/ph9lHm3UUIyONixkzmylYYKDLZvPv+themvpdzc/O3iZugttl8A6fTbXb9PS23XnXbvfX1zMFfs7MguaBx2P2S716Nf6cBfYp5gUo7NnXmIEv83XMZXRdLVDdXVit/sxUU0s27AkeN0n4Cg6qBBayhsbLiqKj/ZOCoQTebi5f6yjBY5PAulp709QOiGbb7+ixgsXS+L0L/psECuwbLZbQY1IzWy3wvr5xcYPoaGNysKM3GhE/i4WHH36Y//znP/zp9tvpn5XFsIEDSYyPZ+qhh8LkyXsfBwTq7sEvMALTGh+E5vWyfdUqLMDAIUP2evdY36TG8HHjGt0eDkXw3XV17Ni2jWknnNDo9j020vKtTvjm22/55ttvu3wArCkd0+ID31yz2GKg4JN84Daw0dHGxbk5UxZ4gg0+wZmCd4kE44RkZvQE/7EjI40L4qCOLi093Zjt8c1yRZgz8dB43XQwr5eRI0ZASQlJSUlQXs4pp5zCKRdcwFuvvMKSDz7g1TlzAH/hzIOnTAGLhVN8BfMa1uSbxxc4A9hcp93cEonAC8rgv0F0dOPZyOaCUq0JWAUOOIIHieZAIZwCQj2NmUnkdDY+AQbudNrdAmD7w25vejlKuAocjAcP2AODZsHLdQMF/507azlA8PGFWnIDey6xNNvu3oJb+7IkZ3809/nZn90Tg/vopgKn0v6s1j3baXFx8xMwofS0vrY7a+3fvqPsrY0FtuFQfWmox5eWGrf3hECCxRL6+kM6jsXChAkTeO6554zxjcPB4kWLiAxcKhw4Jgh4XI/VxOZjPZ7TCWVl5PiWCw5qot5voBKnEzfw+z/9qdHtkZGR1IXaVbUD7dy8GafLxaAQO9e/M28e6ampRLjdDBw/nuyAcbDb5cLWla55Wig8Q75Wa+MsrZa+8ampTV/gWCzNpyQ3tQQvIsIfHTePIyvLX/+lKb6BwspVq6g0d/Dyejl95kz6vfMOzz//PAUFBdxwww0kjxpl1MsyT55VVXv+zm3ROYdjB6/gV+cyZztraxsHdAMDYMH37+nZCN3wRACE70VZoNb2F4GB2rg4o28Nx35Quq/g7MPWCJxwULuV9tZc/xocxA1uz6HaqNl+Y2M7L9NferwB/fsbbTuwferaQ5pilrcpLQWvl40bNwIwePjwvT705fffZ8uWLYwZM6bR7bFxcdQG14HsYFs2bMADDA0RAJs6Y0aTj8tZtIhBhxzSNa4RWiE8A2D7ai91pPZZcHS8qSyBwKUOvoFC7+xsettsDYWeLRYLhxxyCIcccoj/cb17+2u0mIWQRTpKbGzjHRSh8UDX3PBBGWDS2ZrLAPNl2wKhL8Ti441Ab1sucRTZm8AAmN1u9LcVFS1vhz19wkE6TmsywALHAE2NB8zbe0L2l4SHprK6gvvR4LGExrICxiSpb5OzN954g48//ph58+YxdMgQ0pvYpC3QxIkTmThx4h63x8bFURm4W3AnWLdqFfXA8BYE8i678kqefeIJwNg48KZ77+XYU081NsrqJhQC31dNBcBgz5kys2MNtUV0IHXA0hliYpoPHgfWAQncbVGkozU3AxXqIiu4H05O1gSDdKzAwuhmYXtoeWBLkw3SUcw2FmqJe2lp6Czx4LIkgczJYwXApKOEmvwK/LolpWSk5zA3KPGdZz+bO5dJkyZx6aWX8udrr+X1L7+kxGbjva+/xrIfWYOx8fHUVFe31VHvk+/mzWPAkCFkt2CTp9v//W9W7NyJOyEBL3DbDTdw0q9+xbply1i5dCk3/uY3lPl2X++qFADbV6EKBoa6OAvsZJsKgAVm3oh0hlBF4UPNkAUXZdYgQjpScHtzufy7jzVXz8P83tw1UqSjmEumzT6zNYPo5rJrRNqazWZsvtFULVmzr3W5jCxGaL6NJiYaJUPUdqWjNJcBFmoJpCbEerbaWigrw11eTvnWrdx/111szsvjnU8/xQlMnDKFz77+mrT+/ffrZWJiYzt1CWRVaSk/LljAUcce26L7W+12Mvr0YXtBAd8uWcJxM2ZQvnEjMydO5LjJk3nphReYO2eOkS23t+SeMKUA2L4yBwqBQmWABXa4TWXNFBfveV+RzhaqPQZmgKm9SmcIbnehBhXaPU/ChdXqnxyzWPavjp1Iewu1UVRTzLpKzbVR1VqSzhZqHBAYAGtBRox0TwVbt/L6668z86CDOGDwYNauW8c9DzzAh999x11PPMHCH3/ksMMO2+/XievkDLD7b76Zmro6Tj3vvFY9Ljo6muGTJvHcG29wxrnnNvrZgi+/NMqOVFX5b+xCq4O6Vw2wjmYOFMzONXBpQ6gBQagoaUxM67aaFukoVmvjNhsqA0yko0VEGFmzNtue7TOYggcSDsyNRqB1AQGPp8vOrkoXl54OwUtcmrq42ZfsRpH20NSSx+AMMI0Jeqxrzj2XcQcdxOkXXshR06aRU1JCWlQUZ55xBuvWrePUs88ms08fDj/88DZ7zbj4eGo7KQDmrq/nvy+/zOwzz+SQQw/dp+eI7tWLR+bMoaimhtwdOxh8wAHMffttsu+6iz9edRU7t20jLSqKn7/9ltrERM4855w2/i3angJg+6tXL6NjdbuNk78ZCQ21I06ogWxyspFOXl+vDlnCS3NLIJUBJp0lJQVKSoyi99XVoScQzLZpbuAg0pnMAFh9fcuDBHa78Tiz7pL6W+lIkZF7TjIEMwNfZmBMbVTC1d6K4Ev34vUa59uICKMPq6qCiAg2b97MW6+/zluvv84N116LDXhqzhyOPuww0jMyjH4vVImj/RQTF4fD4cDtdmPr4N0Uf1m2jKrqao467rj9fq5X3nkHgJycHEqqqrj/6ad57ZVXqKmpwe07D9QA/3fNNXz4zjuMnTAhbMuOKAC2v8yGbLcbHzZoetvyULNnZlFc87EinSU6unHAINQAQQEw6Ww2G2RkGF+bNWkg9OyuOSGhtiqdKTraWCpg9puBmeOhJCUZu0VaLEYb1wSZdIa0NCNw29TuZeZYV1mKEi72VgtU2Yrdg9drTA5FRhrf+86tZeXlfPDuuzz6978TZbNRHR3NIaNHk7NxIzabjVqHAw9QjxEAOeKEEzg7aGlfe4jzBYFqqqtJSExs99cL9ON33wEwddq0/X4uq+9zM3DgQN577z3eeecdbr/qKoYOHcqI0aOxWCy8/sor1BcWctHpp3Pvvfdy7MUXG3+vykqIizPG8GEwnlEArC0FptoGD26dzqbTx1u7K5RIe0hJMdqpWZOuuQBYfb3/xCPSWQJn0lqy4YhIZ7DZjP7SLIi/twCYWVsJ/JNrGh9IR7Pbjdn7pgJg0KVqvkgPECoAFiopQQGwLq22qIiFn33GsjVrWLZmDbnr1pGans7i1atxlpVhAQYOGMCI9HQWzJtHWr9+2J1OnDYb51x9Ndl9+3L22WfTr1+/DjleMwBWVVnZ4QGwRT/8QGZWFgMGD27z5z7ttNM47de/Ns4Ddjt4PFxw3nls2biRO++8k9/+9rf8cc0atmzeTGVZGRUVFWzbsYNDZs3imGOPpW9mJlWFhYw88ED6jh/f5sfXHAXA2kNlpTHYDex8m9vp0byI00WbdCaLZe+pv4EDiA5O4xXZgzmINQMLpthYY3mkLs4kXKSn+7+22Zpvm4H9bEyMkZmrCzYJR4HtWG1Uwk1TmSbBYwYJGx63mw/efJP0Pn1Yt3492X36MP2II5j32Wc888ILFOTlseXnnxvt4jdsxAgWL1vG1EmTOO+CCzj05JNJi483MqhtNiOQH1jGpYPFxsUBUFVRAX36dOhrL/3xRyYdfDCW9vrdrVZ/32+1Mv7YYxl/7LEcfcEFnHbCCTz0wAMkJyaSlZVFaWkpBx10EF+98w4fzpkDgAuIAE4+5xwOmz6dI3/1K6w2G1aPh0ybzdiowvf+tSUFwNqS2bjcbiNtvKXriGNijKBZdHT7HZtISwR2kFFRxonD3O48+OftsE5epFUiI40Tb1KS8b2ZLWOzGbeVlhq3h0G6tUiDvQUKggNgERHqb6VzNNV3RkYafWxhof82uz108XyRjtJUBljwz9SfhoVXXnqJgyZMoLS4mGWLFvHOO++wavHiJoMTcbGxjBgxgqjhwznloov49cknU7F7N6MOPxxrVJQxWRQV5T/HxsZ22O/SnLiEBACqA3dM7AA527dTkJvLQftY/H5/JCYl8en8+Wxcu5ZBAwYQFR9vJANFRFBfXs6P8+ZRWVcHyclcdvLJzH39dea+/jpewIkRoDryiCMYMWIEuQ4HVrsdamo4/ayzGDxyJDHJyViBzN699ym4px6gLYVab94Sdru24ZXwY7EYsyZNBcCUASadzWqFrCz/9+np/kzawPaptirhpLXtURdr0pkyM6GsrHHNxbi4PbNoLBYjMJacrBUN0jn2dt0VH6/srzBRUVHB3ddcQ3l5eaPbkxMTOezII+ndqxcXXXop7772Gu+98w6XX389p/361ySnp/v7GosFDjzQ/+CYmA7+LVrGXALZ0QGwrz//HIBDjjiiQ1/XZLPZGDl6tP+GqCgA7CkpHH7qqQ03f7FsGUlxcRTl5/PjJ58QGRlJYVERc+bOZf4PPzTKNP7gzTcbvcakqVP598svM2jo0FYdm0ZV7SlUR7y32h8inSkwM6Gp9mvSRZmEm6BU7AZqqxJOmsoAs1q1wYiEH5ttz6Btc200TLIupAey2/27lwb3s+ZSOAkLiYmJfLF4Ma+8+iqjx4xhyIgRbFmzhuNPOw1bwJjtxilTuOHBB7F04XFcvC8DrKq5eoptzOFw8OJjj9Gvb1/GHXRQh73uvhjlC2L2GTiQcVOmNJxfrnvoIXbt2EFhTg7L5s+n75AhFFdW4qyowOJyUVRVxftvvklaYImJFuq6rSkctSQDTDs+Srhrbutzs06YzaZZNAlvTRXIF+lsTWWA7a02mEhnCW6zoYK4arsSDjIzjTGsxdK43WrMGnb6DhvGjXfc0fD9qDFj9ryTxdKlg1/grwFW04EZYKcddRQbVqzg8j//uWu9f0Hj9T79+tGnXz/GH3aY/0av1zjf2Gz86fbbsexD/UlVrGxrZqG9pqjOl4S75OSmC+JbLNCrl7E9uoIKEs7UPiVcNTUYNQdxCiRIuImPN5avxMdDYmLoXaC1M7SECzPwFdgmVQpBOkm8b+fHKrOkjNcLLlfzD/J49j4W8HiMDZ8CVpbV19fzjzvuYNmCBfzq1FP5v7//fX8OPTwFBLf3JfgFygBre4mJRqOuq/MPcjMy/IVC4+KMNcper3bMkfAUFQW9e3f2UYjsv5QUzfpK+ImKMiYanE5jrBAXZ9RaTEyEkhIFEiT8WCzGxFdTVMdWwlHg+V+TYtJJsrKzSUlL44Fbb2X+V1+RGh9PTEQEsZmZJCQnYwOwWPAAFq+XSIcDry+oVR8RAW43brsdj9WK1+vF6vVidbvxOBzsyskhJSWFOrebX9auZemSJVSVlpKclMQDTz/dUH9MGrN4W1GPymKxFALb2+9wRJo0wOv1ZrT2QWqz0onUZqWr2ac2C2q30qnU10pXozYrXY3GB9IVhWy3rQqAiYiIiIiIiIiIdDVagyciIiIiIiIiIt2aAmAiIiIiIiIiItKtKQAmIiIiIiIiIiLdmgJgIiIiIiIiIiLSrSkAJiIiIiIiIiIi3ZoCYCIiIiIiIiIi0q0pACYiIiIiIiIiIt2aAmAiIiIiIiIiItKtKQAmIiIiIiIiIiLdmgJgIiIiIiIiIiLSrSkAJiIiIiIiIiIi3ZoCYCIiIiIiIiIi0q0pACYiIiIiIiIiIt2avTV3Tk9P9w4cOLCdDkWkaUuXLi3yer0ZrX2c2qx0FrXZTuL1Qn09WCxgb9Uprsfb1zYLarfSedTXSlejNitdjcYH0hU11W5bdXUwcOBAlixZ0nZH1RPU14PHA5GRnX0kXZrFYtm+L49Tm5XOojbbSRwOKCkx+tz09M4+mi5lX9ssqN1K51FfK12N2qx0NRofSFfUVLvVEsj2VlAARUWdfRQiIiIiIiIiIj2WAmAiItJ9eL2dfQQiIiIiIhKGFAATEREREREREZFuTQEwERERERERERHp1hQAExERERERERGRbk0BMBERERERERER6dYUABMRERERERERkW5NATAREREREREREenWFAATEZHuw+vt7CMQEREREZEwpACYiIiIiIiIiIh0awqAiYiIiIiIiIhIt6YAmIiIiIiIiIiIdGsKgImIiIiIiIiISLemAJiIiIiIiIiIiHRrCoCJiIiIiIiIiEi3pgCYiIh0H15vZx+BiIiIiIiEIQXARERERERERESkW1MATEREuh+LpbOPQGTfOZ2dfQQiIiIi3Y4CYCIiIiLhwumEoiKorOzsIxERERHpVhQAExEREQkXHo/xv8vVucchIiIi0s0oACYiIiIiIiIiIt2aAmAiIiIiIiIiItKtKQAmIiLdh9fb2Ucg0jbUlkVERETalAJgIiIiIiIiIiLSrSkAJiIiIhIuLJbOPgIRERGRbkkBMBERERERERER6dYUAOsoquUhIiIiIiIiItIpFAATEREREREREZFuTQEwERERERER6Z5KSyE3t7OPQkTCgAJgIiLSfWi5uYiIiASqre3sIxCRMKEAmIiIiIiIiHQPbjcUFBj/i4gEUABMREREREREuoeaGqivN/4XEQmgAJiIiIhIuNFyXhEREZE2pQBYR9FAVkRERERERESkUygAJiIiIiIiIiIi3ZoCYCIiIiIiIiIi0q0pACYiIt2Hudxcy85FRERERCSAAmAiIiIi4ULBW+kKPB6oq+vsoxAJTf2oiDRBATAREREREWm5khIoLlagQcKbxdLZRyAiYUYBsI6iAYKIiIiIdAcul/G/xrciItKFKAAmIiIiIiKtpwwb6eq8Xqip6eyjEJEOYu/sAxARERERkS7I61UQTLq2igqorgabDaKiOvtoRKSdKQNMREREJFxoJ1MRkY7jdhv/q88V6REUAGtP6khFRDqW+l0REREREQlBATAREel+FAgTERHpmVozBtB4QaRHUQBMREREREREupfg+nQKdon0eAqAdRR1uCIiIiLSnWh8K92FNnMQ6REUABMRERERERERkW5NAbD2pFkxERERaQ2NHURERETahQJgIiIiIiIi0nNp8kGkR1AATEREug8NYKW7UFsWEWlb6ldFejwFwEREREREpPUUUJCuzmzDassiPYICYO1JHamISOdQ/ysiItIzaQwgIk1QAKyjqCMWERGRvdF4QUSk46nvFekRFAATERERERGR7kVBLREJogCYiIiIiIi0ngIM0pWovYr0eAqAiYhI96HBrYiIiLSWxg/SRq644grOO++8zj4MaYICYO1JHamIiIiIiIhIj/DUU0/x2muvdfZhSBMUABMREREJF5o8ExHZP63pR837qu8V6REUAOso6lRFREREREQ6hq6/RCSIAmAiItL9aNArItL+1NdKuAtso821V7VlaWNetamwpABYe1KjFxERkX2hMYSIiEiX5XA4OvsQJAQFwERERERERKTnUQ0waScVFRWdfQgSggJgIiLSfWgAKyIiIiaNC6QDBS57VAAsPCkAJiIi3ZsGv9KVqL1KV6L2KuGopVldBQVQX9/+xyM9Rl1dXcPX5eXlnXgk0hQFwNpTS4suiohI+6iogN271QeLiIj0dMFjgcDgl8YJ0gZqamoavlYGWHhSAExERLovcyCiga2IiEjPo/O/dKDAAJgywMKTvbMPQERERERERKRN7EvQS4EyaQM1NTXE+L5WBlh4UgBMRES6L4vF+F8DW+lq1GZFRES6lOrqapJ9XysAFp60BLI9afAqItI51P9KV6W2K12J2quEs+D22Vx7VVuWNlBTVdXwdVlZWecdiDRJATAREek+mhrAamArIiLSM2gjMukktQFZX/n5+Z14JNKU/2/vvMOjqNo+fO9ukk3vBELvHalSFBBQQMSCWEERFBELiL6IjfoJoqhYUNT3FZUuoKiIoiDSi4CA9N5bem+7ye75/jg7u5NlE0IISYBzX1euJLuzs2dmzpx5zu88RQlgCoVCobj+UQawojyTnQ05OflfU31WoVAoSg815ipKgJz0dOffF86dK8OWKApCCWClhRpUFQqFovTRcoApFOWZ5GRISirrVigUCsW1ic0GFktZt0KhINtRBbJK5crEnD9fxq1ReEIJYAqFQqG4/lGLEAqFQlFyqDFVUZ6IjYXERNf/xQmBVH1aUQJkZ2cDULNmTSWAlVOUAHY1UQOpQqFQlDxCXBwuVpTPKBQKhaJkUWOrojxzqST4BgMEBoKPT+m1SXFdk5WZCUgBLO7CBex2exm3SOGOEsAUCoVCcW2RmirDxazWS2+rQiAV1xraBE0JCwqFQlE8iuoBpsZZRQmj9wCz22wkJCSUcYsU7igBTKFQKBTXFjab/H05hqsychUKhUKhULhjMMgfIcBul0VJFIpiYs3JwQ5Ur14dI3BOJcIvdygB7EooThiOQnE9IgRkZCiRQVH2FOQ9o39d9VOFQqFQKK4/rvT5npQki5KosDVFERkyZAhvvfWW83+LxYLJZKJCVBRGIEkVuCl3KAHsSkhLKzwMR02yFDcKGRnyflCrZoryhnsI5IULoNzRFdcKyo5QlCU2m8vjtiBUH1WUJ9wXwQpb9NJe13uA5eXlf0+huAQzZsxg/Pjxzv+tFgs+ZjOBwcEYgNTU1LJrnMIjSgC7EjSjoCirBGogVVzP5OaWdQsUisLRj8GqvyoUivJKXl7h0QW5uaW32BQbK3+Kgs2mvGYUpUtW1sULWpeab6n5mOIqY8nJwcdsJig4GCOQlpZW1k1SuKEEMIVCceVcaoVYoShrlNGruFZQffXGJi5ORhcURHy8DNEqKTIymPruu9SrV+/K9nM5YplCURKkpFwchVOc3KCap7jeW0yNw4oiIDz0E0tODmazmeDQUOUBVk5RAlhZkJsrVy0UiusF5TKuKGvy8uTky12MVVUgFQqFAiwWrBkZrFy50vWaEJCWxtQ33iDt6FFyr9Q7VtkAirLAU78rahVIDXdbwcNnli1bxqFDhzh16hTHjh27zEYqrkdyPHjrWq1WGQIZFIQRJYCVR5QAdjUpaMCNj5erFu5kZMgfheJaQ+vrKvxBUVZkZhaer0ZNzBTXIqrfXl/k5RX9ml5qu6I+by0WuW1iIu++8grdu3dn+/btF32HCUgojgdXWfRRu73436sKoVyTXCQ0aAuvcPliV0HbXqJv9O7dm4YNG1KzZk3q1q1b9O9RXLd4Ere0EEhvs5kAPz8VAlkOUQLY1eRyH7BpafJHobhWUUaloixITJQCWGGovqlQKMoSIWR4Y2HeAPpJ/aUErqLmn01MdC6u7tixA4BYTegSgqlTpzo3TyxuCGNpj68xMcUPA42Lk8VQFNcMP/30E35+fuzfv9/1on6xqzABrKjeXwV4gGVmZpKdna28eBQe8dQvtCT4GAyEBAWpvlMOUQJYSVBQiI2acCmuZzIyZKJefT9XHmCK0sA9R4fFUnZtUSgUiqKgPR8LS3BfkFdLYfsDmRQ/MfHibbSQxtxcxo8fz9Zt2wA4f/48ABlpaUz98EO0lPqJMTGFf2dBlKa9q31XYeexMFTO0pInL08Ki1fJBvzkk08A+PXXX8nIyOD333/P/12eBLBLVX50x2j0uM8GDRrQuHFjzpw5c9FH8vT3q+KGJCUlBS+kB63VkY/OarFgNpvBaCRYCWDlEiWAXQnaIJqS4rkiz+W45KoHsuJaIy1NJuotruu5QlGauPdNJdYqygvuE3k1pl6fFOVa6m1BT2OU/jXt75gYPn/7bdo2b44lPT2/iOb4+9zJk3w1YwYgJ2raZD7WIXhlATauEQFMjd3lj9xc2deugiCUm5vLv//+C8CaNWt49tlnueuuuzh29KjnD1wqH5in1w0GKYC5jb1CCM6dO8fJkydZv379RR+PVUUfbnhSU1OpAEQBGQ5P25ycHMy+vmAwEBwURJoSwModXmXdgOsCm026Yvv55X+9KKV4Ne8xtYqgKG2SksDbG4KCrmw/ygNMUR65VBhETAxUrAgmU+m1SaFwJyvLc05QDSWAlS1ZWdI+Cw6+8n0V5flYkFeLhl4gs9nAaiXXYmHS228DsHzBAvx8fNgbF8fLo0ax5Icf2LZmTb7k9lHA+ZMnIT6e+HPn5FcBdiDJkxdZUbicZ39entzex+fi97Kz5eJaQIC0qT2Nz+qeKH9o/fIqXJtNmzaRmppK5cqVWbduHZUqVQLg6OHD1OnQ4eLvLW4bHH0tNjaWlStX8tjw4cToBOHnn38egD59+tCxY0deeeUVLpw+TZWQEAgMLN53Kq559N5d6enphIeHk2u1EhgUBEYjQUFBnFYCWLlDeYBdTS7HIFAeYIrSxmp1hUeUFMowVZQmRelvhRnGSrBVlDXq2V++SUkpuDhRTg6cP1/0556n8SYtLX8uq0t4gOVaLAjt+1JTISGBDRs2ON9fvGgRY8eO5b2JE0lJSeGlF17gm2+/Zc7cuRh16TrWLFnCuDfe4KgjJ9imzZsRQGJCQtGORc/lJpVPSJA/7sdnschzYbOx7tdfOXvggPOtTH2OxysZtz0JjFphgrS04odV3uho5/UqPFOXL1+OyWRi7NixZGZmOqsvHjp40LWR+3PeZiuaKObuAQY89dRTjHr1VS6cP8/hw4cBGDZsGN6OjyxevJguXboAkHDggOw3yva9YXEXwEAmwdc8wIKCgkhXAli5QwlgV5OieIBpaA8No7okilLiSqoo6VHhOorSwN2gvVxU31Rci6h+W35xTHY8LiRlZEhvprw81/uechNp22nY7S470E1MuHDhApGhocyfPz/f6ytXrsTLz4/7Hn6YlStXcvrMGSzp6Xz44Yfk6MS7jh07snPHDnrdeSeZqanM+PprZs+eDUCVqlUJDQ0tHQ8wbVtN1MrNleckJQWMRnIDA3m0Xz96de8OaWl89+mnhAYGckBLgF4ckcWRm+ciAcxmk7mr0tPltUhKKngfWVmu/bhzo1eW1M7rVTgHy5cv55ZbbqFnz575Xj+4fz+//vorNpsN4Z4QX39PFtAmIQSbNm1yCcqO++706dMAZGdlOQWwV555htsaN6ZKVBRGo5GqQUGEIO9JQC1k3MCk6jy4NQHMarXi4+Mjc4AFB5NezgrcnT59moULF5Z1M8oUpbZcTYQouqB1Iz84FaVPSRor+hU05VGjuBoIAbGxF4eKXW65c+UBplAoSoqCCiDl5ro8u+LiID5evlbYeKNNoO12rO7P5+xszp86xZuvvYbJZuPlV1/l86++4sSJE/zxxx98O3Mmrbt1o8vtt6ONcD7AV19+Sd3atbm7b18AatStS8UmTXhl/Hg+eP99APbu24cBqBAVRURERPE8wPRt1c5JQQts+nQfVqv0+oqPd3p+7Tp2jDcnTiQLmY/MlprKm6+8QgXg9iZNpPhnt3P8+HEyMzM5fewYTevXZ8aMGXz++ef89P33CF3VS0CKWwkJ8hpkZpKTk8POnTupXasWe/75R25TlEIqKSlyP55ITLyxK0teJQEsLi6OHTt20LNnT2rWrEloaKjzve/mz+eZoUOZNm0a/v7+fPPNN64Pav3My+vidjn+njx5Ml26dJG5vQwGZwikFi6clprKkSNHMJvNVI2MZNmyZRxyeCVG+vkRbjazd+9e9uzdy/kzZ1xCmuKGIlXnwavlALNaLNIDzBECmVHOPMBat27No48+Kj1rc3Jcizk3ECoHWEmiuVF7Oxxl9Tm+PHE5VUsUNzbJyWA2g79/yeyvJN3V9atnqv8qrgba5LA44SlXWh5doShtLuVVm50txQNHjhFFGZOeLq+Fj48UUjx5UcXHk5ubixCCPJuN14YPp06tWox45BFyc3OJPXqUqvXr892cObz82muMeeUVhrz0EnEJCbzy+ONs2LgRgDBfXxJycnhtwgQmTZjg3P2wF1+kYY0aGA0GevTowR/Ll5MWH0/HXr2wBAaSCQTXqgU+PjRq1oxG1aszZ84cdu3eTXh4OF4+PoSEhpJWXE8FzZbQbN6YGHk+IiPzb6d55phM8lw5hCd7Vhb79u2j3Z13YgE0S+fIkSPkWK1olvRjjz1GjT/+4KE776Rzp06kpqaSdOQILw0ZQiYQBDw/YADjJkxg6z//cODAAar7+eElBLN/+oncpCQOHjxIQmIi8cB/hg2j75130q1nTyqGhxMaGsrJkyexWq3Ur1/f1W733GvuuckK8gy7UbhUDjAh5PPbPVeyBw4cOEC9evXw8vJixYoVAPTs2RODwcDq1atZv349W7ZsYdm8eQD88ssvGIH58+fz1FNPuTzATKYCx8fXXnuN9957D2904bVGI2+++SZpDjEgNTWVw4cPU7duXUyAydsbb19fsNsxmUy0bduW7xYs4LsFC0gF/jd3Lo899pjzOw4fPsyAAQNYuHAhNWvWlMefl6fyhV1nnDpxwvm3PgTSx2wGLy+CgoLIyczEZrNhKgc5Z+12OwkOIf/UqVM01kTlK80HfY2hBLCSJC5O/q5cWf7WC2BFnWipCVnpY7NJ400TLksDzfAramLd7Gz5c7kCmMUijVB3IfZKV+s8TdBMJmUEKq4OmnHtbsxergdYUV5TKK4WOTlyPL5S0SolRfZdLy+ZLFxx9dGHJrqjPc8rVy6woNHZs2d5+OGHqVChAlarlb937yYLOLNtG3/99Rdx8fEkG42E2u1kA2+/8w6/rVrFqTNnsKel8dqrr3LnnXdSv359TqSnYzMY6NCwIT7Ae1OmcHv37pCTw++//0616tX5o3FjAoEuXbrwz7lzpAHB2kTH4RVTr149du3eTYUKFQAICg4ufq4adw8w8GwPaOfHbIasLJYuXcq8efNY56iwpwWuaWfxxyVLyAEmvvEGe/fu5eulS7nrzjsJANatX4/Zx4cpU6awb98+6nTowLm9e/nf9OnMnjOHRMAKVESGu+QB1SpVcop8BmDXP/+w759/mDhpEgCREREcycjAZrczfvx4nnvuOcLDw/OH1Fkssv1G48W2lSdxrLQRonj24pVwKZsyI0MKxQYD+PpKD8m8PAgPd25y9uxZZs6cydixY5k2bRrDhw9n+fLlREZG0qpVKwBatGhBixYtMBgM/DFvHnbg0OHDGMCZHB8h8t2vubm53NunD0MeeYS+Dm/I1atXA7IPpKenY7VamfT228ycNcvZnlkzZ/Lnn3/Ss3t313HZ7c5+3bp1a9atX4+3lxemvDy2bt2aTwD76KOP2Lp1KxMmTGDmzJmu8FolgF1X7Pr3XxrUr8+hw4elACYEVqsVX4cHWGBwMF7Ifqb3YCwrdu3a5fx7xYoV1O3TR4Zr3mAoAexqokIgrw3i4uT514TLK8VikWJaYddeWyEuigBW3L6Rlye/x98f3AfdknRXd89fdynPR4XicilIALscPIVAqnFXUZJoXg7aooO+v9rtcgLkySvGfR+F/Q9y30Ko6tGliXYdCnu+2+3Oa7I3Lo4l33yDPTeXZs2a8f3333Py1ClOnjpFgL8/C775hlmzZ/PdggU0bNCAtm3bsmX/fgb27cvA4cP54+efGfXSSwB88/XX3Hnnnc6vqd2wIQDPvPEGM995hx49ejj7W7NmzcDLCxvg6+VF97vuolNQEDa7nSFDhsgdOASw1q1b88PixTz44IOAFMBiz5wp3rkpbCzVvOIqVJBjudEIXl7s37+fYcOGkavrx61vvplt27ZhBSz+/oyeNg0fYMiQIZjNZlq3b8+HU6YwduxYfvnlF0aMGEGH++6TdlxwMKSnc1unTuzcto2wsDDGfvABlfz8GDNmDBaLhUeeeAL8/fn5229p3qYNB/fsISUlhYMHDxIfH8/PS5bQoEkTwqOiGDNmDO+88w4jR46k3z33kH7uHCEhIcTGxtKpUyfpMREYKEM4NcqDAJaWJvOrmUxSqCsNLmVT6vPgpaU5Q1RTUlLYsmULPXv2ZMSIEfz444+AnKTb7XZWLF9OzzvuwOh2vzVq1AgDYEOKm0ZwiQtuYmxCQgLrN2xg14YNdOzYkajQULKzs+nYsSNbN2wgIyOD73/4gYkTJxKCy/vw+++/JwtoVLu264sdlVcB+vXrx5YtW/jkk094YuhQ9ms56oD333+fL7/8EoAffviBTz/9lBvLv+bGICsri8MHD/LywIEcOnyYFEc4pEULgQQCw8LwQnoUlgcB7NSpU86/3335Zc5s2cIbb7xBZHT0DTV3UwLY1eRSAlhhrsI3UCcscwoz3Ox2uWoVHFz0a5KYKA3MqKiC96n/22iUq3VeXhd7odlsstJTUfGUj8vTKmxxQiBzc/OH9+rbCEoAu97RDNeyCLvSJkjF8QArTEBQAphCjxAy0fXlelWlp8ufwEBX7iGTCSpWvHjbkhCttH5b0lV8FQWjjReJiVLE1HmuANhsNowXLrB161Y+nT6deX/9RRSgl0LGjhmDn58fHTp0oEGDBrRu3Zrfly/noeHD8U1Ols9XqxXCw3lsyBCa1KvH+fPn6XnXXfK9wEBXTiNg0qRJvPzII04PLr3wsnnnTiJ9fTH6+ODv78+4ceNcDXFs9/jjj9OzZ0+n50xQUFDxkzUXNpZqoetWK2kpKXw3dy7nU1JYOncu5rAwtm7fTvqJE/hERBBUuTK7du0iKSmJoKAgevTogRUwV68OsbEMGTSIpwYMwGQy0f+pp6RtptlOjrZ37d2brp06AfDQQw/h5eUlvTFAbuftTZ8+fQCoVbWqPB82G3a7nYcffpi23brhFxXF1m3bmDhxIm+99RafvfUW3kjBRbPSRowcyYjx4/HV39PlIRn6pcIRSxr9s7Sg79RszezsfKkMRowYwezZswkJCclXTe/rr7/m2LFjWOLjua99e5e47PBUady4MUZAGI1gt2NAF8qotcfxnE9PS3OG0O7atYvu9euTnJxM8+bN2bphA2lpaWQ47OTgqlVJPXuWAGQ4rQGoHR0t+4jB4Crc4O9P1apV+eGHH8DLi8b16/PLunWAFD8mTZqEEXjvzTcZNXkyixcvZlCPHq5zoULXrwt+XLSIUCHo2LEjc+fOJebCBYTdjsVqlSGQQEBIiFMAKw/ExsYCUgAygjOM97zNdkPN3ZQAdjW5HCHAPaTMYpED5A3ollhmeLpeqanygW02S7ftS6E95Aub5OgnLdqDUEui6O6FlpJStOSsGpo3W6VK+V22C2onFK2fZmfLNoaHX3we3D3A7HZ5jHl5KjynrCiKF2Jx9pmZWfR7oSQprkHvPq5q/4eGyv6skuCXH5KS5PUozDvqaqMP07mc8CFN9NIvNrhPhEsi3Nz9NSWAXV3cxw/9olJMDCDz/AwfPpw9e/c6N9Wuyq+//sqFM2f4+uuvadq0KU8//TTe2iKSnx+RkZEMGDRIjqdeXq7+4+UFJhMtbr2VFgEBrsm3G0ajkQoNG7rapeU8Cg6mdni4tAcK6schIZi8vamkE82CQ0KcOWw8Hn9Rctp6WljQpQJZvGgRb4wdSwrQpmZN5sydS8Vq1ahYtapzuy5dujg/+t577xEWFpbvWWaqUEFuq/duCgyU9pKPj/w7LQ28vAiMiHAtMsqTlv+5GBwsn202G0YfH7p06ybPZ0wM7Zo04ddFi4g5fZpvp09n1s8/k3T2LCDDKT+ZOpX/zp3Lf6dM4ezZs5w4cYKqjRvTpksXWtWvL9tXmqk1ygr3Coye0PqobszKy8tj2a+/AmBOTcUXGDBkCOnp6SxYsIA1a9Zwb/v23H333fL5YLVCRASYzVSqVIkWzZrx8MCBmG02hr/2GikpKZw5c4bgSpUI0WxPgyFfXrtDhw7RXQgsSUnUCAvD18eH995/n3igYsWKvPTyy4wcORLNcg0EmmnXMitLvqh5/mVlOQXVhnXqsHjhQqaOHMm/hw7hk5bGitmz6datG7MXLmTu3Ln5BDBhMPD777+zdOlS0tLSmDlzpmts8ITFIm2WihVvKJGiPGOxWBj13HO0uOkmunTpQlRUFDHnz2N1zNk0D7Dg0FCMUPz8iiVMnCNdk9bbcgBfuOEWhEtXAMvNlQNlaU+eygq7/fKT4GukpUljJiLi6rRNcTGFGXhFHRiKMqHWGwsFVUoq6v40rxyDQRpyNhvnzp3jh4ULGfjww4Tqv8NgYP/+/QghOHfgAN07dMCgecJc6oGqrdh5cnP35AGWlSUNTl9fz+EAZZF37XpCCFfiZfdrp/dSKEkxQVelrNQpie/WT8wMBpcXmKJ8UJwCByVNcYuDGI2yjxYkel3OhEX/mcLEL4fXSrkIubpecfSD33//nVrNm9OwRo18b8+ePZvX33gDX7OZ+vXqcerUKUaNGsVt3buT6efHzTVrQsuW3Hvvvfn3q3kGZme78gF5eeVPEG8wQEjIpduoF4EMBrn4pVFYSIsmEOgSHwcFB5OdnU1eXh5eXiU4PXC04fH77mPVqlUYvbywWa3S/nDbxp1Ro0Zd/KKnOYSfnzwX2n4qVcovdOkFMP13BQa6zru3t1wc0SpGOiaslYKDeWP0aN749FPSTp8mwGAgJyCAzydO5IOPP2bgoEGyWWYzSRYLmcCUkSPp88QT1L7ppkucnFJGCFmtMiysSAnpi4T7gqobu3btIvPoUSw5Odx2223MmjWLr776Cj8/P5KTkvjx+++Z88EHbN6yhfp163L++HEAvvrqKwb27o23EC5xODERjEYMISEs/+MPeY+kprJi7VpWLltGu/btefiJJ/j4gw+chZnSdB5g+/fvZ+rUqXhnZxPl709kcDDZjoTg1atXJ8Ttnps7Zw5tb75Z9i0vL2nbavdMVJS8VzMzZegxMPXDDwFoVKcOHTt2xGAwcN999/HOJ5+wbNkyNm7cyPHUVNZs3kyMQ0QHCAsLY9q0aReFejpJS3N5wSm7uUzIzs7m119/5cEHH8RgMHB8zx4MOTkMGTIEPz8/KlasyIXz58lx2DJmx9gc7OhTqe5VzMuI2NhYqoaG8t6YMfzxxx8sWrkSXyAnOxvfGyg/XckLYIUZfFqcfEnlWirvXE4OMPfPXUo8U5Q8lzMZdnPHdlIU93f3EEj3z+TmSgPMkwHq7jqdlZXPJXvBggWMHj2aUzk5fDdjBpv/+EMamYmJnMnOpkmTJgCEAItnz+b222/Pd9zJyckYDIaL49QL8GjLyMhgx6ZNdL7llvweYNox6Y0FPfHxcjtPBSMUlyY9XXqdeHldvMLvYaW1RND6QFkIYAXlF7mcEEj9tkoAuzGw212FQDSRKCfH8wQ6Pr7494w2drmP5Vqi54iI4hds0P7OyJBjvZY30stLCWBXEyEgLo4zZ84w+OmnsQO9evSgRo0aBAUFceTIEX5ZupSGDRrwxRdfUK9ePVJTU6W3kpYCISfHlfjaYJALEgaD63rpbWEfH5dIU1LPwqLsR/d8Dg4JwYD0VAgPDy/UAywlORl/q1UmT3YPgfPQ14UQrFq1CoBatWvnF7+KSoUKhdvU+vcK2k7vTaeNA9q2mjgWGSnvNb3HhmOb4GrVIC+PAG9vRo0dS5UqVbBYLPTu3ZsKFSpw8OBBRo8ezQdTp5IRGMi4shDACvP+18ao9PSiC2DJyXKfbmG/Tux21q1bx4oVK0jIyKDVHXfwzDPPALB3717atWlDuMN+aNWyJTt27sTH2xtrbi5G4O5evfjlq68wADXMZp4aNow2zZrR78knMSQmXmx/2u2uqAk/P0hNJVQnXG375598fVDzvKlSuTI/L1lC6pIlhAAhISFEBQZyKiEBAYSHh3PHHXfk+6rGjRvLP7SiB3qBQLPRTSZuvvlm58vPDBnCyJEjnQLIPXffzScffsjTjhx8yUDtxo154403GDJkCGPGjOHDDz8kISGBefPmYTAYWL9+PUFBQTRt2tR1j2nHrigTXn/9dT6bNo2Vq1bRtWtXjjm8fms1awZmM1FRUew4fRqLJoA5xpcgR99MKycCWHxMDLUiIujXrx+PPvoodT//nC8mTyY1JeWGEsBKPgj5wgXXwHQjU5SV34I8wDQDojzkEriR0B4s589L48DTexoJCfInI8MlWCYkFK0KYlEEMCHkb/cJmc3GsWPH+HL6dKyZma73DQaSTp9mzJgxNGvWjFeGD+fg3r0cO3YMgH82baJ69erO3RiBffv2IYQgVXe/VoyMJCIsDIPBwJEjRzh69CijR4/Grh2XzsjdtWsXL774Io8+9BD79+/nzPnzsvlWK3/+8YcMpdBcxjWsVvmjDyXRViTd4+Ozs4s/IU1NdU08SoLyJpQUlnD2ahlIRfHCuhpVQB3hKQV+9+V4Z5aGB9iVhLppE+DrlaSkkvP00lIFFEZamrRHtPE8I0O2wdN5dg9NvxwKmmxrXu9aey8HfQXpvDx5LDZbfi+h4rRVcUmOHDnCjq1bycnO5tNPPwXA7Eja/r+vvmLqhx+yatUqHujbl9mzZ9OgQQOMPj5S/NLj6ytFrrAwKd54e3te2AK5kGE0lpxXTjEIcohhhXkq2Gw2Dh8+TFh4OE8//XSR952kex5Hu3nSFRlv7ysXe7V7tVIleV30aPs2GKSHXHCwKwpDuy4Gg8sDx8+P/v378+STTxIVFYXB25tGjRqxePFi1u/YwdMOEajMKMyD9HLQ5e3KyMgg180ue2vCBB7t14+vvv2WH77/nqFDh7J8+XJ+/PFHmjdvjj0vj773309oSAg7du6kdq1a7N69m1YtW/LJ5Ml45+UxYsQIQkNC6NixI+Hh4fS//34MsbGeF191518TLUN1BaXOnTmDEAIBrF+/nlSHB5gmUmk9KDQ0FIPufCQmJlKjRg2E7rXIyEhXaHFBmEz46e7bMWPGEKRbRG7TtCmdHVUsv54xgyU//siOTZt48eGH8cvN5YN332Xq2LH8tHAhFStWpG7dunTp0oV2rVvjazYzcOBAYi5ckDtT432ZsW/rVioCmx0Va48ePkwOUK91awgKolKlSsTFxJDjmPf4OvqE5gGWVk5ygCXGxMi8kYGBGCIjqd+gASAXNUaNGkWLFi047vDCvJ4pWQ8wbdC4lIFb0t4emZnSqCitaicahT1I3AWwy139vd4TNGu5GkqzTPOl0FV3cYaXaei9anJyXA9lLfzQZAKrlZ8WLsRisfDoo4+6PpudLfetKeuaF5cmfunvBbudY0eOsGTOHPz9/Rk0aJAreSuwa+dOWrdrRxQQZrXySP/+YDaTkJDAo337kpWdzbvvvoufnx+zP/2UtevWUbdFCzZu3EgA4PAVwwi8O2UKv/76K+sPHOCj6dPp2bMnkY7jvAB069aNs458F0N696ZmzZrOPjl71ixef/ppLIAZuTLy+/bt/D5rFnvOnOH9MWPwDQzk4/ff5+7HH+f3tWtp27YtFdwFrYQEziUmcvrwYaKjo6nZtKnLw0ET5jTvicJCp7UqU1FRcizQvOIul4wMeS31kxBNoNPGGG2lsaDCBaWJp3H0ao0blxLAMjLk/RAWJgXIkJD8njcFoYn9niaGVqurYqq2raff7ngStzzlpSnpc5WcLPtFcbyctcpdRmPJPctsNin4hIeXvZeQEHLszMlxnZ+8vIuvu7uXqz7fld7jVvOC1IQFT2jjtDbuaH34UotLBfVx7Rh8feUzLCioYEFD209B+7LbnUJcXFwcUVFR7N+/nzqhoZgh/72tF+y0/Wnf6+lY7Hbp0RYWdvXziKamyuf45YyDWui+e0ENi0We16ioMvUInjx5MvNnzkRLQPHUk08yadIkpxdTgwYNqFChgtPDA5BeQ1arPAZ3u6YoopbBUOb5fZyhOtpEzcMi7ZQpUxg9ejQAK//6C4B///2XujVrEujl5dl2FYLTp0/LfQO2snxm6r29NLT26l8zGFw2W8WKnsdP/b3l6yvvgfR0DL6+1GnZsmTbXRz0czIhZD8sqoCiL3rkICUlhVatWuFjMvHa8OEYgoP59ddfWb54Mc88+iiT3n+f7MxMujz0EPffeScGoFWbNiyaNYtaoaFYLBbWrFlDgwYNCHZ8FoDMTFq0aJGvimI+NHtZQxMudWNvsONamUwm0tPTiY+LY8vOnQwaOJC6tWphAFq3acPPS5Y4J77B4eFkO8ZWAdx2220XfbWXIx9foTjasWb1amITE/GKjJSvOUIrDbm5LFiwgO3bt9OpUycMRqPLRs3IwJCRwctDh3Jg+3Z+WL+eJk2a8H/jx5Nz8iQHTp7ks3nzWDF7NqPfeIMnX3qJX3/5hbZ16hAcFkb4pbwps7Lk9S/Ie8/TM1hxEQcOHGDH33/jC2xcvRrGjePYkSNUio4mMDAQ8vKIiooiOTmZNMfcxdsxZwlxiKHlxQMsITaWuo0aSZHfZHK2LyUpiS+++ILMzEzGjx/PnDlzyrahV5mS7fVFHVjt9pIzxoVweY6URmhlWpp80F3KqLwSDzBPVQLLEqtVDpCX2w6L5eKJnN0uB/6sLDnwlicBzN1jKCbGeS1sKSlkJSZiBjZs2MCyZct45b33qGQ0yvPj48PatWsZNmwYAjh48CD/GT2a4OhoRFIS69evJw2w+fmRcfo0u7Zvp03LltzUrh11a9bEJASbNm3i008/5Y/169Gknvnz59O3b18yMjLYt38/f6xdS9WICLyyspg5cyYPP/wwBj8/Ppg2jYMHDvDlF1/QsF07SE2lTu3afD1jBnVatWLNmjUEA0EBAcRkZmJEVjPau28fduC5554jODjYmfgTIO/sWUKQBmtMTIxTABNC8O6778rz4tj2n+3bsQNLlizhry1bCAwIoGrjxjz9/POEjRpFTEYG9/Tvz9z33wdgzpw5zJ8/n7S0NE6fPo3NUcVn3rx5dOnXL//9oCUr95SAX0MzJlJTwdfXuYJn0HJl5eRIQeZSwkJ6+sWr8PrCBnl5LoEuJUU+QMrCmHd3h9ePN5fy0PL2vnhcysiQx1ZYieZLhUBqicAzMvKHKFxqXNaEH0+TDP13eXmRFBdHuCPHTUZGBllZWUQ5DN+EhATeeustJk2aRHBo6EUimS0vj7+WL6d769bSYDQYXAUutHFIE6V113THjh20bNmyaCE7mlBRnHFbO7+XK8p5aLOTrCw5kdGHzpUV7v0mM1PerxERrr6jbafPJxgX5xJ5tLw+WqENKPjY9d+pnduCFqTc21ZYH09Ph4AA7JmZnDl+nKotWmDydM20dhYk1jqSOn85axZvvfkm0z75hBdHjKDrI48wb/r0/G3Vp0TQJzw3GOQ59PHJfw6sVnlesrOvrgBmtcrrqHnFaOGml7pXtIIaRmP+hSbNyy02VvZXf3957TIzpRhRSiLuxxMn0rF2bVJTU2nSpAk9HMmrDQaDTBsQECDbpR2vlqTd1/fKctyWcRqAS3kqHDx4kIULF+Z7LT09nc633cZdd9zBglmz5IseBLBTp04BMnm8rSyjGzyNy5ey1wvqd/rXw8NdHu9lnc7B3T7QbFuz+WKvWbtd3sfae1rBAMc9dy4xkWWzZ5OZmclH333H6bNnCQJGjxhBImAFRj3zDO+OH4/R1xc/Hx+Wfv890yZMIDQ0lOGTJslctBYL5kqV6Hn//UXzdta8uzQHB713ubtgYzCQ5XiGtGrblg2bN3P06FESk5IQwPETJzAaDLRyeGFpnw6KiiLLcc3+++WXPPHkk57bcqlxx2iESpWoX7ky9bXX3O6BgIAAOt9xh+wbFoscJ0JC5JiXnY3BYODLL7/kC8BLK/LgyHc29OWX+eCNN5j8zju88c47ZAFafeFqbduydOlSogqqOp+a6srLq59vZWe7PPschQUUBTNw4EDsQNOmTfl3+3aEEJw5fZoatWrJDQwGKlasiBE45fCe8nHMI/wCAjAZjcWvsFuCCCGIj40lslMnZ78OcQjKJ44fd1ZS/e2338g5dw6znx+GgsTTa5yrK4AJIQevgICLvFw8TnQslvwTT5tN3ryhoQVPJooSTqF52VypkGS3u/JwREc7B7jU1FSefPJJpkyZQr169eS2xUl+q/8efduvtN36we9y2yOEXMW43ITaubly8A4IyJ/INS3NZSRoExjt2msx9qWEEAKDwzBYunQpu3fv5ty5czRu3JjBgwc7XZq3b9/OiBEjOH7iRL7Pbzxxgv++8w6Hdu1i0aJF7Ny5k/r165OQkMD/vvqKVatWERARQVpcHCdOngRk7H8AsrTyrFmzMAGBAQF4eXmR4jA6fYG/t2zhUFwco/7zH6a8/z4GwBwRwX+efZb+jz/O31u28NLIkVSuWhWfSpWIiYlh2KOPymS7ZjNERvLSSy8x/MUX6dm7NxHAk4MG8fZHH5Hh5UXe2bOcj4+nYa1aBFSrBsg8CZoAZnS0ccTTT/PWjBmuZJ12O1u2bOHo0aM0iozkREIC4WFhJCUnYwcW//gjOcDP331Hi27dmDRlCt5paSycN49fvv+eMy++yNGjR3nt9depUrky586fp8999/Hoo48ycuRI+j/2GOZRo7i3Rw8aV6/OsmXLaNq0Kbd07Ejzm25izf79pKSnU7tWLSw5OZhtNtp36UK43c7JkycZOXIkhw4dwmq1YjabCaxTh1bVq7Nv924CKlbkpo4defvtt52JRnfv3s3IkSOJiYnhow8+4I5mzZyegHlGIzt27KBh7drkkw5yc53eQ3aT6SrEkRcBTwauxSI9J9zDdzX0+WgCAvJXc9QezAUJYPqJfEFVRQvKPaYJGklJcgxyfGd6ejrJyclU14wyqxX8/LDGx/POe+/RqVcvunXowJo1a5g9ezYPP/EETz32GLN+/53urVoxaNAg/ty8mc9mziTaYGDlypX8sHgxzZs35/EnnsDsJor88MMPDPrPf/ji7bcZNGoU2Gzs3r2bypUrE3nTTc7KX4BTtNu4cSMdO3bk448/ZsQLL0jRMyzs0gaxp/yAl0I7v5c7OczI8OwJZbG4JjvatUlPl9enLAwa90phWj9JS/Mcfmi3Q1YW9txcV2JgLSw6K8t1fguruKs9P7Wcje59ODtb7svd+He0VQjhFD4PHDiAKT2d+lWrgtXK119/zegJE6jVti0r5s4l/sIF6jZpkn+CWVhyaLudc+fOMX7cOEzAhAkTAFi4cKEUwNxxF8D0HoxJSVCxIhaLhb/++otenTvLpM+XU0EY5BihTyTuCf1xaJNYRy4ykZBAhs1GkC7cXo/dbiczM5Mg/ec95Yi022Vb/P1dIlsp5iYJCQlh8ODB+V/Ue6GEhMh+GB9fNC/XawTNE8AZAunWZxs1auT8W+shhw8fBuDPlStdG7p97vSpUzz/wgsA9L7nHt5xLISVCZ76ttks+2JxFrMiI112q1bN01OfLk0KEt3j4y9+viQny3FCK6rhIC0tjWDgzWeecV7bFp07883MmdzWqBHzZ8/mwoULvDh+PH4Gg7wfHEnna4SHM3XqVLkj/Rjk5yd/NI9+TaDXCwNmszx/Pj6u+83X1+UJ7slGMRjIysrCBrTr0IGNmzdz4vhxDLrrGRwURIOGDQFX7p9QnQDWqVMnmWvLwbq1a7FobS/KvMR9G4NB2mNJSa6FU09jWFiYM7TXpC0aJiW5+pDdToPISL788kvGjx/PriNH6DdgALmJiSQlJfHWtGk8O3Agi5ctIysri7y8vPyJ/E0m+f3Z2fkFMH2qIk/OCop8HD10iGcGDaJR3bq8OmYMcbt3Ex8XR12HqIrJRFR0NH7AhrVrEUAdhx5gMBoJDg4uFyGQMTExZKanU6tOHedroQ4BbLUjR+MLL7zA9OnTqV21Kvfecw9f/vJLmbT1alOyApj7wKpPIqkXqjxNoBISXNUtNHU/I0N+LjvbVbHGHW1VoLABKj5eDqBBQa6wA0/V0y6Fh4nJpEmT+PyLL5x/z9JWwDSK4wGm/57CVreLSlaWaxXgco1I7fxebm4fbVKSlZVfANO8MWw2Vq5cSYd77yU4IEAOxpGRVz9cw8HPc+bw3f/+x6uvvkpMTAxDn30WAH8/P35esoQff/yR7t27s337djZt3kxoSAhdu3Rh27ZtTJw4kZycHJ596y06de3qFI38/fz45ptvqFy5MitXrmTChAlknz1LcMWKvDVqFK2qV8cYHEyQtzfVa9dm/4EDHN2zhxUrVpCamkqbNm1o1qwZ4eHh1KxTh5pt29Lz7rs5d+YMtrw8qtesKftyXh61GjRg0Zo1pKenUyk6mjq1a/OmtnrlyFdw//33AxBUpw4+Dtd1srMJDAuD4GBCo6MhM5NvvvqKnYcP877DKG3WtCnD+/TBlpjIiy+/zMQZM7ig5R8QgtmzZ+Pn68uIESMYPnYs7dq144033uBUbi6fjx1Lx06d6Ny5M4SF8d7UqZCby8N9+9L7nnvo1asXzVq1Iiw0lE3btpGUk0NUxYoYbDYmpKczb948fAMCWLZoEd9nZWEB/t6+nSkzZ1IByHb86Guj+nh7c/PNN7Nr1y5MJhP33nsvQgh27tzJheRkNp48SbVq1Th/4gTrNmxg7969xMTEcOLECZKSkoiMjMQAPN+/Pyt/+43IyEhWzJjBux9/zM4jR4iKiGDxjBk0b96cvLw8DF5e/Pnbb3w9fTp39O/Pi//5z1Xtqx6x2/nzzz/ZcegQr06YgEEz1BISwG7nzJkzREZGYsrIwMfhnp0vH1tmpvwxmSAggNzcXFmCWxOrcnPlPRkR4TKgoOCwQW3Mcrx/9uxZjh8/TmxsLPXat6dFixZyLLdYIDqavLw8unfvzt69e1m9eDHbN27kdGIi2/ftY5fDeJjwwQd0bNGCY//+C8BGx+/XX32VOv/7H5s2bwZg0KBBztVQgLHjxjF/4UL+WrHC9aLBwIEDBzAiw5tqtWtH/dBQ7uzVCwFUaNaMKePHs+bnn3nttdcId3ijrVixAm/gw3Hj6H/33VTw85P3oMPD7J3JkzmdkIC3tzfvvfee03PTXQATQmDIynIZ/kbjxc+s4gpgmniUmpp/kcIxYRCOHFLFEkRKCs3zVyM2llyLhf3791O1alWZcBtZhGPLX39Ro1YtspKS+Oijjzh66hRvffwxDSIjOXHiBM2aNSM5OZlp06YxcOBAKlevTm5wMPsPHaJnz55SLNNPALWJVl5ePnHt6NGjvP/66xzctYvMzEzat2/PvffeS+fOnclISmLR998zduxY7rnnHl588UU6tW5NCNCta1cmTZrE9OnTMQBbt26lQf36ZAP/Hj9OrYgIDu/Ywfvvv8+zzz4rvQcdHq56hBBMnDgRo9FI3/vv58effnK+t23rVm5u08Z1DI60EXa7nVPHjlGrWrX8toXNBkLw6quvMm3aNJYvWkSPW291ea16eZGQkED7du14a/RobmrShMbNm2P09QUhOLhzJ/j40DA8nMScHCJq13bt291rND4ea1YW8+fPZ+3atZhMJjp268ZfW7dyfNs2Tpw8iX/Vqjw+aBD39OnD5MmTueuuu2jfvj1Dhgzh782befLee+nQujX33HMPoT4+mENDOX36ND6pqfh7exMcHOyyOXJzEYChNEWmgsLd9Hast7e8366jimzBDi/RNA+eCgVN3g4fPowBKYj98ssvchHO7Rnxy5IlANSqWZMff/65bKIawsMLzimqLcwUp116m9VolKJHWeJYJFi9ejUHT53iuVdecb2nf7bk5ckFEcd9dvLYMc4kJWFNS2PVn3/y7cyZtGvbli1bt/LQgw/y/PPP06BBAynkpKXxxBNPuPajPc8MBldhjqCgghfjDAaXGAZO4cwpFGljjdEo5w/auF1Q2hSDgZdffpksm41hw4cz7eOPGfnKK2TjyvcVFByMf0AAbVq3Ztv27QCERkZic+w72s1TvW7duvnbWxz0kTOFhUFr8zK9KKU/dyYTprAwJr37rtynLv9lcHAwY8eN444uXdi2cydBQUHMmzePunXr8tNPP2FOTCQqKoo+ffsW/P3aWJuVJc/1DZQIvSikp6fjm5ZGjcqVqVOnDt7AgX37iI2N5dboaOd2FWrVwgf4a9kyTN7eNG3aVL5hNBIUFER6ORDA9uzZgxFooBV3wCWArV+3DoDxI0cyw7EI98vSpXx5vRbaEY6QpqL8tG7dWnjEbhciJ0eIc+fEhe3bxSv9+4uTBw4IERcnxLlzQsTGCnHunDi4apWY/8EHYv4HH4iU+HjXZ5OS5HbnzgmRnu7ab0rKxa+5k5Dg+qzdfvH7Fot8T2uL9pOaWvA+HeTm5ooNGza4XsjKkp+NiZFNj4kR0eD8eaRTJ2c79u/aJQb16CH633+/yDp61HkMNptNrF+4UNjPnhUiO9u177g4IS5ccLVNa2dm5iXbeUm081iEY76ItDT52QsXhN3T+fX0mhAiOy5OfDlunFj08cfin5UrhbDZ5BtJScJ25ox4vk8fEQ2iQdWqYs2SJUKcP1/gvoQQAvhHXEZfFZfoswtmzBA1fXyc165TnTri1//9T2QeOSJWzZ0r2lat6nxvZL9+InHPHiESE4X15EnntUlJSRGTJ0wQD956q/hp+nRxctMmIRIThYiPl31Eu4ZZWfJL4+Jkf71wQYjkZOc9IxISZP/Q98/kZM8nIjdX7kffdzTy8vL3F/19od+39pORIc+7o1/gON4zW7Y4+4w9Pl4EBQSIsYMHC3HunMg5flxUDQ0VTzzyiNi2ZInwBfHeSy/J/Qkh96fdb3ri48XWn38WUQaDMIAY8uij+d/X+pnjx3LihFgxc6Y4fuyY2Ldvn5g2bZoYcN99Yt2CBeLounVi8aefis2LF4v1CxeKsYMHi9bR0WJg9+6y7RcuyPPjdrz2s2fF+CFDRHUvL9G+ZUvx9NNPizGvvy5ijxwRB//+WzQKCRHRICIc56FhcLD4ctw4UTc4WESDqGIwiMq6+7199epizpw5nq+TKPk+q/HTnDmiTeXKIhpEJIg/Z81yHuORtWvFq48/LqoajSIaRG1fX/F///mPWPzpp+KuFi1Ek6pVRZiPj7i5ShXROjpafDNpkhjcq5eo7uUlXrj/ftHvnntEq1atxMO33y4+ee018cH//Z+YN2+e+Hv1ajHv/ffFzj//FEc2bBC7d+8Wn3zyiXjkkUfEfffdJ26/9Vbx5qBBIuncOfHmiBEi0mBwnqcAEBW8vMRjXbuK3StWiDVr1ogWLVoIQAQHBzvPdzSIxqGh4t42bcSYp54SRhBBIOr4+YmaPj7C37GND4gox9+VAwLE/40bJ3q3bJlvLK4A4uzWrSIuNlYk7dsn3n3pJRENIhBEg6Ag4WMyiYe6d3duD4gQ3edffeEFkZaSItrdfLO4qWpVUc1kEve0bi2+mTRJjB08WHwzaZLY+vPPzs8Con2jRuJ/EyaI03//LSyxseLdd98V7dq1E+Hh4aJmtWpi3YIFIvPIESHOnRNZJ0+KY8eOia0rV4ohAweKahUrij5t24oBt98ubm/dWrzwwgtizrffismTJ4s333xT7N+/X0yfPl08++yzYv/+/c6+kJKSIr6ePFn0atlSNK9QQbz32msiz2p13v+2M2fEawMGiFp+fuLWFi3EF2PHihz3e7ME+mxh/fb5Bx8Ubw4aJKaPHi1WzZ0rHrzlFjHxuedEy4oVnedb66+efkIc51f7v2l4uKjh7Z1vm0jHNrc3bSpef+IJMfv998WmdevEc/fdJ4JNJtG+enXxwhNPiGVz5ohpr78uXh88WODoR03CwsTgXr2c938dPz8RDaKSo68Z3L5f/7Plp5/EW//3f6KSo68+8cQT4tChQ6JmeHi+7T547z1x+vBhcWH7dvHPr7+Kzz77TCz9+mv53htviD+++ca5rQlEzcBAYY+LE5lHjohT+/aJd159VSQdPCjGPPOMqAji1//9T4icHJGTkiKefeQR8cuXX4pdW7eK6sHBwgtEOIjXBg4UOcePC/vZs+LlZ565qI8P6tFDzJsxQ7wyYIB8zcdHPNa1q6gE4ovp08WMGTPE3vXrxZr588XOdevEjh07xHdz54rJw4aJFlFRIhrEzVWqiMpGo6gAonJIiHi0c2fxfJ8+4tZatUQ0iFDd/aHd74907y6aRUSIcEc7moSFiXu7dhVGo1FEOsbZ1wYMEPM/+EBs3rBBfDphguhQt66Iuwr9tsCxVns2a8+1zEwhrFb5bE9IKLAd1zpJ586JaBATx4+XL1itzvOwce1aAQhfxz1hcFy/MU89JYJ1z83MI0cuOn9P3H23aF+9uut8Kkq8z6YmJooRAweKVY57NBpERRD//PKLEOfOiW1Lloh727QRQ3r3Fgf++kus/e47MfG558T/DR0qnrrzTuezNcBtjBvx0EMi5/jxi+3HzEzn3ECcOydtOL3dZbdLOzIrS87DLJYrO2FZWa55hDuOOaZITRUiMVG0qV7dOSaHOZ4PHRo0cG6XdfSo2PHbb0LY7eKLsWNFbV9fYc/Ly79Pd1u5uOTlyfuhKFitcg7hfq61MSchQc4tzp932tr2s2fFSw8/LIJBtGrVSvj4+DjHWz/d8/GhW28VB3ftEhtWrxYPPfigmPf+++KPb74RP02fLmZPmSLOnTghkg8eFDH79hXYvKthH1wL7N+/X0SD+PGzz8T5f/7J/2yfNMm5XeyZM87Xb2/RwrUDm010b9JEPHDnnWXQ+vxMnTpVRIKIP3TI+VpWZqbTVq9dqZIQ586JDYsWOY8l4dQpIYQQGzduFLm5uWXV9GJTUL81CE+r+QXQpk0b8c8//1z0+oh+/Yg9d459+/aRlZVFdk4OjRs14umnn6ZDhw5Ur16dlStX8vLLL5PkULhrNmrEn3/9xZ/LlvHbjz8SUasWZouF22+/nVtvvx2Ttzfbt26lQc2amIKC8ImKwuRJgYyNda1qaAmw9aSmQmYmdiE4eeIE0dHRMrTNz0+6fJrNYDIhhGDy5MnY7XbGjBmDAfhs/HhGTpzIIwMG0KFDBx7o2ZMAm42s3FwswcG0qlYtnwtdWGgomzdvJrh+fW5u2ZJzu3eTBMz5+GM+mz2b2jfdxK5du4jZuZP58+bRpW9fZzhQ7N692HJzEXl5VKlXz+WtERRUsDu1lrwwIcG5QnEwJoYjx4/TtGlTatWqBUI4vYbw9b049EVL2urjQ57Fgt3X11lyVyQmsmX9eubOmcPRY8c4kJLCgrlzMfv7s2HjRvKSkzm9fz+/bNnCkKFDGTlyJLNmzWLqlCkEZmdz5vx5soBwLy++nDkTS0AAGadOsWPzZhYsXMiTTz7JurVrOXb8OK+PHs2LkyYV2PcMBsN2IUSbAjcogIL6LNnZHN++nT179pCSksIDDzwgExk6sNvtWCwWDAaDKwl95cqyr2VkyP6jX/nLyJArwXo3YkdVRGcOpJQU13UNDJQrXVoOIoNBvp+XJ1cptfevBM0l3WRytcXLy+XNExYmV5m8vSEsjEGDBrFi1ixOnzqFV1CQ0z298a230rFePaZNm8aqVat4esgQZv3yC91bt2btvn3cUr++9B6qXNn1Pe45BRwrVjPmzGHO3Lm888473HLLLfnbGh9/8TG4549KTPTork9kpDPhKOHh8tpo4Wxux22326WXiNs+jh49yg+//cbu48e5t2tXuj/0EBEmE8f37+fvv//m/PnzWCwWrFYrHTp0oPv992NyryKlo6T7bGpqKj8sWsSkYcOoV68eXbt2Zd68eSQlJ3Nb585UrVqVJUuWkJGZSZXKlenQoQMxMTFs2LjRuY8UoN+AAVjOnmXV6tWynUhrCaBO7drk+Phw+uBB58ppAuADBCOLKAQAFsffuYAd8AdCgBjHvkY88QT9+vQhPDyc/82Ywby5c6UXBxAP1KhRgynjxlG/alVeeP55Rrz0Eq1atiQiIsLphTD0tddYv2IF/27Zwl9//cV9Tz/N3e3a0f3hhzl/8CBnTp7ks2++IbBSJY5t3kynzp2dx5kLeDvaU8NsJtNiwQt48IkneLBXL7r264cdeHf8eKoGBNC4Sxf+XLiQU2fO8NOiRc792IAxY8cSGhDAu+++S1xKCllmM3kWC+FAoL8/a//5h827dvG/t99mz9692IHQoCBS09NpfvPN1GzVijXLl5N28iRGg4Hg4GBSUlNJACIBk9FIr169iIuL4/jx4yQkJmJ1nPMcZA4+f8cxWQBvb286d+7M8ePHST5xAj8g3XHeg4AqNWpQoUED+t5yCwsWLODAwYM0adwYX19ftu/YQWS9evyzd2++cA+N4vZZ8Nxv8/LyGHzvvaxavty50q7RuVMnHnzwQY4fP86ePXvYv38/DzzwAO3atWPXrl0EBATQu3dvQqpXZ8POnXw3axYrv/8e7HZ633MPzz3zDMuWLaNSpUokJSWRm5vL2nXrOHP6NKlpaaQh++RdAwaQm5DApnXryNB5oVmBkcOG8dqYMRhtNnIsFj54/32OHj1KREQEVatWZciQIcydO5fhEydS0ZGjZc+hQ8z5+msGDRrEq6++CmFhJB07xodffcXb06YBEGk2M+/LL5k6dSp79u4lpF49Th45QjhgNJk4a7NRAWhUty6rNmwg5vRpbm7bFoB+w4cz69NP+XvzZia8+SZLVq/GGwgwm8mxWAj29cVut1O9VSuCIiL4/bffqAhkOfrJQw8+yMHDh9m6ezf9+/ShYsWKfP7f/xILVELeg3Xr1OGoo0IwQI/u3dmwYQOZ2dkYHP0sD5zezbnIPuYDBCK94J5++mluu/12zsbH89UnnzD05Zep4ghPstvtrHF4KDdu2ZJ9e/dyLiuLu3r3ppbDE2L1nj0smjePM3v3kmO10qRDB6oFB3P82DGWLFmCQN5/JqBlhw588PXX+ULw9JS4faDlIYPSyS1bXsjJoVe7dsQJwZqNGzHk5nJg/Xru69OHgBo1OHHqFFHIcSkF2Z/q1qkDAQF0aN2aOd9+yycff8yh+HjeGDoUk8mEqFSJJtHRdOnYkc/nz1ehVg5Kus+uXLaMoQ88QHZODkaDgejoaM6dP4/JaKRx48bs27cPf3//fGOgnsaNGvHmJ59gtNs5vm0brVq1Ii8vjzZtPDTRZJI5O9PTXZ5K2vwrI0P+vpJceJfLhQvSxoyIgKwsevfowc5//iEDGebkhRzPkvRVJQ0GmcpGs1mjo/N7emn7DA0t3VzFmi0cGirnZ1r6mtBQVw5K4Yjm0XJn+vmRk5qKb7VqJCcns37TJk4eO0bHevUQQjDghRdIOXPG+RUZyHHcE7VatmTjjh0e3ytp++Ba4c/ff2fgXXfx4+LFtLvlFqrovL7emzGDxx3h8narlZr+/uTZbDwyaBAfffut3EgIHu7UieS8PP78+++yOAQnTz31FNuWLmXPkSPOcGIhBLXMZpJzc+l+66384LCB161bx0P9+rF40SLskZHc1q0b77zzDq+//noZHsHlU1C/LREBbFi/fvy7ZQstWrQgKCiI7Oxsvv/+e+ekKiQ4mNS0NMLDwpg6dSoZGRkMe/FFZ0iAFcgJCCAkLw9hsXDrLbdQsWLFfCEBNrMZu58fd3XogE9YGJlC8OADD9ChZk2OnztH3JkzrNmzh7Px8VitVt566y0aNmzIsm+/5cTRo6xevZqd//5LpYoVGTlyJFWqV6dD27aYAwM5lZXFf//7X9555x1Axod/NGkSezZtYsmyZcQjB9AKjrbkIieFUUCwvz+ZutCili1a8O7MmdzcsiUjBw7kA0cloTRcFfiigacHD+atTz8FPz927dpF9xYtwPE9O/fv5/Thw7Jkb0AAdl9fln/3HYeOHycX2H/mDPf17o1vWhqtbr2V3LQ0fH19GT9+PAt++olEwGw2M2zYMF5/9lm+/PBD6tSpw+09ejB80iTWrl3LY489RlhwMP3uvJOctDRWrlzJV199xZH0dIY++ig3N23KnLlz2bF3LwYfHyoGBBCfnIwJaRzbkMa2n68v5kqVOHDyJEajEbvdTs/mzakUFcWtnTsT0bgxH0yYwMk9e8hA5rfyMRh44YUXeH3sWHLS0xk5ciRzlixh4W+/cdddd3nseyVu4FqtLsHEE+HhUpDSQsI0UeVysFrzl8zOyZEuziaTK7TMHS1E19e3ZF1OHSE0gHy4anmAMjPld1aoQF5eHmkHD8pwJD8/Z06MHo88wl6HWFLR4d6/7dgxvNLSZN4hTbjSC2DFnTSkpsq2adXB3MNLtDxWfn75c+KEhUmDBVy5kLS2gAyhzsyUhoTNlj8UzMdH7s/TOXfvJ1oIYBFy4pV0n93x99/c3aEDVatU4bfffqNChQrExcUxd+5c5s+fT0pKCt27d+fNN9+kmiOvW15eHt999x2BgYFUr16d1r16yWNMTOTs8ePs3LmTjh07kpWVRVJSEs2aNQNktSct99GRU6c4HxeH0WrlSHw81QMDMZvNGI1Gevbsid1k4uy5c+zcupVDaWl069aNdvXru/J7WK3k5uWxdNMm9q1bR0J8PBMnTpRCl5ZXJzBQXhNdiIrNZiMvLw9zQAD2kBCeGz6cQT170qFHD1e/9feXn42LIzY2lpaOfAxRlSsTd/48MchksT/98gsxZ87QsXt3Ivz9+X3nTurUq0eD2rVl/3WEdRAcTNrZs7z88svUq1ePdu3a0aVLFww+PuRlZxOTlER0s2b88ssvLJo9m8H3388dd9wh+1tuLsePH+e92bMhPp5HH3qILl26QHAwmTExLPvtNw4ePEhcXBz16tUjonJlIgIDadasGVWqVoWKFbGeP09ORgaJiYkkJCTQsGFDYuPiWLF8OS1atKBeixZMevddNuzcSf1atbi1fn0iIiK4/6mnMIeGsnjePH7+5hs2/P031uxsKlWsyLhx47jvvvswGAys3bCBf8+dY8TLL5dony2s35KURG56OkePHuXvv/+mS5cuBAYGyjLc7tW9QPbPChXk+OueoDc9nYQTJ4isW9c1tmZluSYCBgMiKopTW7awb/9+GtSvT90OHSA1layUFDZu3Eil6Gh8zWYyMjJo1qyZTDhstcp+pPUFrXAAkJuby7wFC+h8663UbttW9unERNkmrR8KgQgO5offf+fUqVP06tSJJtWqIYTgo48+4tWpU/EGwpBCZazBQKQQ/G/6dPo89BDCapV9AJi3fDkP9eyJQFbY1YQnA9Dx1lv5dOpUxo0bx9wVK8gDOnfuzGFH6IIBOHP6NCYvLyZ+/jlvT5pECPBE//707dsXX19f4uLi6NGjBydPnkQIQYUKFQgKCuLs2bNs2bmTdh06sG39eqKjozl06BB2ZIU1Hx8fGf5hNlNPyx9SubKr+mtR0BYd9PnqtHyhun5w8uRJMjIyOH36NDVr1qTxLbcUOgktcfsArvxZdi1isfDh6NG8PnUqUVWq0LpZM7b98QcgFy4MSNG+eevWPPrCC7zx1FMADH/tNZ4ZOJBmjrCaZGDrH3/QrFkzTubk0KZOHaZMmcLgV18tk8Mqj1wN0fbCv/+yZ88eWrVqRWRkJGfPnmXWrFls376dljffzLBnn2XLli3ExMQQHR1Np06dOHb8OHVq18bb2xvvGjXy58LUo1U2zMpyLfZmZ0ubVhOTygotNNlRnbd7587s27OHdCAyJITQgABu69aNjz/80GVjOBLXFyiACUfoeVmE62rzjrQ0Ob56coTQKp+DtOX1Rbz0z9WAADK9vDi9fz+L5s2jeoUKdOzYkX379lGjRg3nQv+hQ4fIyckhokkTHnjoIY/NulEFsFn//S9vPPssm7dto0bz5qz94Qf69e8PwOxly7ijVy+5oRC0rlyZCzExTP70UwYNG+bcx5C77+bfI0fYduhQWRyCkzt79MAUH89v69fnC3VtGhlJYmIiTw0axNtTpkBeHgnJyVRq2pRpo0eTCrz59tv079+fefPmld0BFIOrKoBpXlZ60tLSiImJ4c8//+T48eO0adOGvn37OstGf/755+zcuRODwcBbU6dSqUEDsjMz+fStt/j4vfcwGgwMGDDA+dnU1FROnTrFsWPHyMzMJCUvj3MpKUQhxaVgwB4QQFiVKlw4fJg0pJgUCeQYDNSJjubee+9l5cqVHDl6FACzQ9BIsVpJAWrWrMmpkyeppDsOTexatmABf/70E4sWLSJXCOzA/40ezXPPPUeVqlVpUL8+QggOHzlCrOO7f/nmG+556ikikUasVucqGrlqtm7PHixGI3PnzuWNp59GIL0WmrVqxZ4dO3hl1Ch27N3LH7//jl56yXK0S8usZQAaNmzI4ePHeWHoUG7r1o3x06bx5+rVROHyHAgBMo1GmtSvz46DB3FPg3xb585UqF6dn7/7jjybjZo1ajBk9Gge6tuXQIuFpKQkJk+eTKNGjWjVqhV1GzbEbDJhNBqZM28e+44fp1u3bvS49VZZOtixcpKamsp/p0wh0GikTp061K9fX3qnOVYw8kJD+WrmTJ5xrBh64qqu8Lp7E4E0evPyZAUyb29psF8vaA/OihXlwzU7WxoCQrhEJF9faVR4efHjmjUMe+QR58efHDSItz//XBo+UVHyHIE8ZxaLNCKuZi43zQPPva9ohoxWKS4rSxpsWpJVzZDw8ZGTa00ADAoqXGzUC2maMOhe3MEDJd1nbbm57PnzT5o2bYpXcLArUTTS48IuBF6XEk31kzn9cYErgW9GhryGFSvKa6z3JAwPd/URd7y8XPlPtPMdESHbqSWKttnyG6vZ2XJbzeNRa5P+ntQMVZCGudY37XZ5PTThApm886eff2bPwYMsX7qUUVOn8mSvXkx6/31XoleLxdVHtH1qxmKFCi4PQy3fV26uHMvy8uRr2sRdGx/0aEZ0bq48foslv8ATGSlXcN1zcWmCqr5QgZ7QUNlnNfz9Xd6kZrOrCh1AZiaxhw+zdu1aevXqRVBEhCvHxyUm81fFwPVgHzjRiyAgj1PLxSMcxVv0+T+11/QFXWw2KVIHB7v6WXKyK6l1ZKRsg3a+NE9o7bp78hwHZxJ+cnPlvoxGeU/oJ0n666X3FNAqXAL79++nZY8eBJrNNK9Rgy5duzJm+nSyT50iwOGBjs3G0KFDiYyM5KWRI2nerBkWpPD17rRp9HvgAdLS0giPiMDoOFd/7dnDtM8/Z9asWYRYrSyYM4eQsDDuGjDAeb8uW7aM3Kws7r3lFpkQWvN+z8rK77lhs8nXtEqLeXmuKpLamKARFpa/wqs2Cdaup3Yt9c8G7XWjUX6HpxwzmpBmNLom1yDH2YJywDpQAlgJYbGQdOQIr7zzDt/On48PrnybCUD1atXIOnOG5q1b8+YHH/BI164AbPr3X2pGR9OjWzf27ttHCvDJhAkMHjyYhWvW8OKAAfy1ejU3delSJodVHrmqfdYTEY4rqYkmGtriIHi2DyIj5fuevN01b6Ui3KOlRmoqj953H+vWrpVewH5+nDl+HKO3t3x+aM9CzYutPN/n2rPTUwJ9vf0RHe2qYK49IzX7yf243AVOfWXQsrAPyhnTp09n586dzJgxQ75gs/HuyJFM++QTTmZn4+PrC3l5VHbYLst37qSZw4EF4K4WLfh31y5+//tvmrdr53x91IAB/Lx8OUfcbcZSpuPNN1M9IID5y5fn88Z9tGtX1q1Zw+h33+WFF16Qz2JfX5rddhv1goPJyczk3127aNq5MyvWri3DI7h8rq4AphkuPj7yRvXkMaERGemqpqgNRLrBU6Sns2/zZoJCQ6lRtar8vFZeOCfHabzl5eWx6p9/2LNxI/VvuYUaYWE0btQIr4AAjv77L/PmzePXX3/l0cce46lXXyXMkaTRnpHBkSNHOHv2LGvWrCE7OxuTyUR0vXo8MWgQC7/+mjatWjF6zBj27t3LyFGj6DN0KHXCw8FqJSkrC5PFwqmTJ7npppsASEpKws/Pj4SEBNq1b08sUsg6vm0bQ0ePZvuKFRcJYACvjRvHS2+9xT333MOWpUvJQ674am7/VsdvE/DqqFEMGTKEM2fOcNddd5FtsfDkwIHMnDULASQB8xYt4sFu3ZyD2aLFixn9+uu06daNfk8/zcmtW2ndogW33norFouFhIQEvv/+e/xCQuhyyy3Ur18fg8FAWloaCQkJ1KhRA5N2DbKy8k/AwDUpdPPcyDdh1aNNiv39XeKBzeZ58uFGiRsLerGncmXZt0wm14RVCz9MSZF98zpKdJsP7d7VCwzgeig6RI+zO3fS1jGYz5s7l659+3oWwMqSwgyZwgyJS5GVJe8Bk0n+LqLBd1UN3LAwl8iioTdk/f2lsKdVwcvNlX/rj10TALQJrV68MRjkfambyDuP2WqV931mpuu+9/GR72mJXh0hr5cMg7Db5T0WEiLPryY4uZdr166pFu5ttcrv8POT3+t45uTm5pKdnU2wQ8jKi4zEGBeH0d9f3uMOj7R8q71aaLKncUt7VgUGek5arxcDPQnl+vOnHYdmuOrLvPv5uSYYNpucpPj6uq5v5cryvGjijiY0+/p6npjo+4nZ7BIfLjGOXRUDVxtjzGbZL/WJfjXRVLs/9cLklSCEy5NWXwXSZnOJZJ4qT3vCZpN9RBPP3dHCsvUCmNvzMt3HBx+jEXNOjstbIi7OlUDazQuucpUqWIA+vXrx+axZmA0G+TmDwSUgOwo1AFKEy8nxnOagoPOjHdOlnr96Dy/NO09fLVUIee20AkbawlJ0tMvG00LQLxX+5kjHgMHgCkEqSKDUcVXGWqtVXpsi2CfXDZrHc0QE3Xr1YuPq1fkEsC+/+IIxzz3HiJEjeXHCBMYNHcodd9xBz/vvB4uFtORkMjMzqVpzdn0AABe5SURBVNumDSFA61atCG/YkPU//8zhkyelt6UCuMr2QXi4vN8yMvIv7ug94728XBUsc3Ndnu0aWtGQSy1mlrcE2ampXDh6lIkTJzJ7yRJ8cIQ+as8V/ZgUFXVtCGCe7E1NyNKEPHe0Z42nMVezs7QiHvq5UCFclwKYVlHay4v09HRnCo7k5GRCQ0PJOXuWDq1bU7l+fek15eD2Dh048Pff7IqNpYKu8MWTPXuyZs0aDqem4q2zfye9+CLTvviCWKvVGV1RFrSsV4/2jRvzxU8/5bO1MmNj+eSzzxg8bBgVAwNl3wsMZNTEifz3gw+cIbOZwOTPPqNr165sWb2aJwcPLt1w52JQUL8tmSe7ZiAIIf82m+VNpRnv2iCrH0x9fV2eKLob1GA2y8oJoaHyAoSEuHJ1gdyHzYYX0KN9e3rccou8+bOzpUGXkUHdunUZP3484996S6566NpnzMqiQYMGNGjQgNtvvz1/CBfw3NCh4O3N0l9/5cSxY9StWxevatVkO728CHes4GviF+CsYFWtWjWmf/YZ28+cYefWrURHRzNn7lwyT57kqREjWLF5Mz/88APVvby4r08fprz1FmHA0qVLaVahAkOHD2fyuHEYkdV0vAID2bttG/369cPk8A5oWLcuf/zxB/v376fPQw8x+eOP+eTzz6nXooUMH9RNyh5+4AEe6tsXQ2SkPH+9e8vzlJODGahSpQovvfSSPAd5edLItdkIrl3bOQg4J4n+/q7Jk3a+vL1dBr9WqlwLX/NEUJD8noAA137Lyrh0H4C0G9h9guip5PL1hGa42O0Xu39rv4WgatWqvDh8ONM+/ZQOHTq43i/DgfwiKlQouMqTVgmxOLkc3D8TFlb2A77RKO8nf3+58qeVuPb1zW/waNfX031WoYJLENCLAPp7QP+3dsw+Pq5qTHrvMD2aOFOU49BP2CMiXCuXKSkuTyz98eivsdsCjre3t8xH5+iXXloVJu1B76kCcWBgwYa+0VhwLj6DQfaFlBTZDk/Hq3kqaeKpbJRrUq954ehFH5NJvu8ucGrXQvOqK0wo0lbttXNXlnl3tL6hGeMBAfK8xMe72qUJ0yUVbmIw5L9vjcaLr6/ReGnxS2u/5j3hiaAgacvo7xW3cTEoMDC/96MeD4uQvy9bRmSVKlSJinKJtlqVNU/f4e8v93MJr9R8ny0kf2E+tImXFnbufo0MBs+LCu6Tr6JMkvXbO/L5lJmNUEoVqcsVWp8Sgg4dOrBJl/pg66ZNVK1alTtatKBKzZqY/PyclaO1zwSHhBAcHOyMTti+YwdxO3bwWI8eeF2vi4jlES0HlyM9gFNoB7nIoInpGp6uTVHvu/IkfgE48p+NHTuWWY5cgh7tVe1vTxEg5QV/f/kc92RbaLZJQXZWYfZXZKTsE9p1Dw8vf9exNNAWYB0i4vfff+98a9OmTdzVpQtLFy/mUFwcH8yfn++ji379lTVr1uQTvwAefPBBmjVrlk/8AggND8eWl0dmZma+fNOlTWZKCoEhIRc9xwMqVuTNiRPlP8JRkTUwkIEDB7J27Vqef+45Aq1Wnnv2WYYNG4YBmQOyWbVqtLnnnvI1FywiJWNZ6E+k0egyFrUcLQUZEmbzxYqzj49rZdPTZFXbv6baa0aZNtHIyMgfTqEfxA0GaSD6+LhW0PUDvzbZ8vfHHBlJw0qVpPClqeP+/pc00O+//37u1yYvycmEhoURmpvLnDlzWLllCw888ADi3DmiK1XiQkwM2rf36tWLF4YNo0v79vj5+VG7fn3w9aVFgwauCarDWK/fqhX169eXBqyXFyPefDP/MaL/V3eMXl7SWNcmfOAKE3FP4O4J94ek/txqk+LC0Lz5FOUHrT/bbK6VMXD1Dy0PAvDqq6/y0ksvuYoCaGhenWWN1o89calJ7OVQlEnz1UYTVLRQY1/fy/dSNJkubfT4+LjGTE/blvSkRi9WefIWMJmkiKAXDQrzYtbecxfA9OOkl1fxxyU/P7nP1FTPHoHuQoz+O923c6ewa3Mpoago43FpoT82raw9SJFPL45dqwa4j8/Fdox2zJoHvG4cvQgPrzdv3tzluadt40l80vD1vXqivCeBKySk4Htf8z65UspTH75R0AlgWvLzUa+8wrPPPotf1aogBNWrVy/4XnX0908+/pgjR47Qq1cv/tq9m6EPPHBNTpCuWfTXJyJC2vnaa2WR06o0cfSzCg77obKWm8w9n5fWHwtbOC1rtOJSBVFcIcV97lvWC7plhTbncQigfy5fTs2gILKysli7di13tWnDwh9/pFKtWnTr1i3fRyMiInjggQcu2mXvxx+nt4f+FBIejgkZMVZWApgQgozUVIIu5dihaSVA06ZN2bp1q/x8ejq977iDh4YOZc1ffwHw8ccfM/eOO0q3UEQJUTICmDbxdF99LK73TFEMYX9/p0LpRBOJMjPlyron40mbpOhv/qio/BMvDbM5f9U8k6loDw+9sesYZKOioujfvz/ExGAwGJg5cya7d++m0223sT8piQ41a4LRSJMmTVzt077LfcIdHCyFrIIMCn34UECA5xVb7Rzrz19RH4z+/vKBeq0bNJGR1/4xXClaP9DyFWnoBTAHxuBgfLX8UPr+rSYppYcW5qgfIw2GqyvKlZfcHuDKcaFRkKjgfl/rCyaU9D0fEFD8c6Q9hzyJCZqHUnkQXK8ER3jBRZ7BN0JomZZPqzABrCA00Uv7rLvXRlk+uwrr7zfqZOp6IiWFPrffzuLvv6fHzTdLr1oofOzVCQwP6ZJot+zePV+lcsVVJDTUlbJBw2QqvlByLaLz/l6wYAHN3SvHamOqdo60nIOKGw+rldzcXC5cuMCQe+5h944dPPTAAyQkJLBk9mwG9+rF7+vW8eqECUUPWyzAZguNiHAKYNWrVy/Z4ygi2dnZ5OXmElxU7283DAEB+AUH8/qzz7Llr7+4vWdP/ly+nI8mTSI5N5eX33yTsGLuuywoGQvUYCj9JOGFiWuXOyEpzBCPiJDiQGbmxQZoQXgQwAA5+XIIC82aNZMV1wwGqrdtK73M3L0StBVQTxOgwtoRHu6sTFXsSUZBVQpBnvvrITRQCTcu0dN9xcKToav1w4yMy5/MKUqGkJDCxe/rHU9jkqe+qPNkcP6vX8woL/j65q+I5841ZEwUiNHoKo5wo6APr9EEMA0hPBc6cMfdC7c8CWCK6xPduGnIyKB3796unH1F8WL01Ce1XHjXu+dRecDf/5r0xLha3HPvvXJh12LJL3qBGj8V/LNpE4P69ycpORkfb2/q163LI/37k5WTw5rHHqPb7bdj9/Hh2WefveLvCo2IwAAk6XOklzIpKSmYgJDi2pWO1Csdb7mFU0eOYPP3p1mDBrz/zjsALF66lGeee470tDRWLV3KV/PnU6du3ZI7gBLmBliCvUJMpovzkIWEyMHTPSm8Hk+rY57CxPS5lrQQB7vdJYBFRl5+m0sinEStiNwYFJQbSd9/9WFjnoQFRelyI08kinrsnjzAfHzy50IpL6jcONcf+nxv2uRLP6Y6KuwWin7CpsZbRWmgeQvl5ck+qi9YAZcON/c0tmrFD651T1bFtUFRx0g1lt5YaMVa/PwQRiOrV61i5MCBnE1OplnTpnz45ZfcXLs2hIYivL2Znp3Nmn/+ofnNN1PRU5GByyTMMZdPLksBLDkZAxTbAwyQY7nRiH9AAERG8sSgQcz47DOeHjyYhQsX8n8vvYQRWcF61vTpvPXRRyXU+pJHCWBFRe/WHxBwcRl7yB+uoH9NH37jCf324eFSWFPeSYrSoqAkoFp/1vdbNSFTlCWeKjEWJQcYyDE1K6t8hXQqrk/0HmDguZ9eKm+iPgRS26cabxVXm+BgV9V1PcXxANP2FRZW/hYeFNcnnry8CkuCr7h+0S0iDX/8cc4cOUKbdu347/z5xCclEQ58M38+ffv1y/cxA3D/4MHcP3hwiTUl3CGAxWsVlHNz5dhY1MI1JUCKw/M8+EoiuAwGV9VRg4GxU6bQvWtXOvbsyZi33ybx7FmWL1/Ol19+yW+LFvHGuHH4hoZit9sxafZQXl7+wlBlhBLAiounC6c3VgsKgbzUPn18brxwEUXZYjR6FsC01z1NwMpD0nvFjYf7JErfP/V4Gp+1IiYqR5HiaqP1Uy23qKfxsqgCmP5vNWlTlAZFCTUvyrgbEHBj5Z9SlD0F5f+81HaK64vMTFmcKCKC+KQkfvruO4xGI1u3bQMgHBg+YgT36fIVXk2iq1UjIjycnVu2yBcSE6UNEBRU8OKANi8roQJBqQ4BLPRKC4Lp7h1vf3869e0LgE9AANEhIQyKjqZ27do83K8fd99yC2np6cSlp1OnVSsGDRrEH99+S/8nn+TugQOxZWcTc+wYVZo0KfV7UglgxcVTh9Qbq+7x5lCwwZuenv/zCkVpYjJ5roKj9Ud9/3SfkCkUpYn7uGuzeQ5FL2jlV4lfitLAZJLe3GazNMS1Fc/LQRtj9SHoBRXGUShKEk99NSXFlRojLy9/1eiCPMCUjaAoS/T9T7+IoOzX65bclBQyExNJjo1l0cKFrFu3jsOHD2MXgh//+guj1UpmQgLVqlWjdtu2pVaMx2Ay0bx9e3Zv3uzK0Q2FRzDExsrfhVUCvQw0AazYOcCKguMZ0blnTwaOGsXX77+PFs92cM0aXlqzBl9gzdq1fB0ezuSXXuLY8eMs27iRZm3bYtKuR17eVb82SgArLiaT9NSKi5P/m81ytSspqWBXcU9eNiCTiisUZYW//8XhDuB5VUJ5gCnKC+5J7QMDXWOpMm4VZY0mtnp5SbGguCFgenHBbJYFalSOTsXVpCCxVhO9hJDeFRoqJYKivKBygN14WK2QlYXw9ubJvn1ZtXq1862w0FDqN27MTc2bc3PnzrKao/Y8LuVK1K07dmTVsmXEnD1LJe277faLx9u8PJe2UEJYLRZmfvIJ3t7eVCwhQc0jWoik0cg7775L4wYN6NGuHYvnzyc6OprhL77o3HTwvfcikCGnd916K7UaNGD2b79RJzoae1ISRl9fGT5fQh5w7igB7ErQ3zwRERdXGCuqB1hB2ysUpYGvryy24J6c0X3CpjzAFOUJ9wUFvQCmoaqVKsoaLy/Izi5eX9R7gGn/K/FLcbUp6NleUNijJowpm0BR1hSU58tTTkXFNU9GUhK5sbG8OGwY27dvJyU1lTTg1rZt+eSrr6jXoAEm92dmGeXY7tCpEwCbN2zg/i5d5IuedAFPOcavkJ1btrB71y4mTZt2dT3AwClYGQ0GBjryqA175RUwmUhLS2P58uWsW7+e4PBwNuzZw01VqgBw4tAh+nTpQpf27fnl55+pWLEiXbp0YejIkdRp2dK1f5tNzk+v8B5WWSmvlOBgl0Fa3BBIhaKs8fRAKEwAU31ZUVZERckVpsIEBa2falXMlLGrKCv0iV8vF7vd9TnVhxWlSWSkHGsvlS9GbyeoPqooT7inQtD/rfrqtUV2tlzgTE4mLzubQ3//zfrff6dDvXo0adyYH1atIrpJE8ZMnUqazcbyLVtoeNNNF4tfZUirm2/G12xmy6ZNrhdLaZH2wJ49AHS9665S+b6LCA+HkBCefOYZvvvhB37fto2dZ88SWbkyX373HS+98QbLVq3COzeXRT/8QINOnajSpAkz582jU6tWTH3zTVISE8lKSSHl0CEZbXeFKA+wKyUw8OIkn54EMPdqehq+vq7wMyUqKMqS4GBIS3P9X1gIpM1W6u7DCgXg6ndmc/6VMv14q/VdNaYqyhp9Tgtvb5enoqe8i+6Yza7t1IRNUZpoi2KXCt31VCVaoSgrCspD5z4nU3312iEnhxM7djBjxgzWrVvHsePHnW/ZgATgw48/ZsSIEWXWxKLgYzZzU4sWbFq/3vViUWzUEoi4ObR3L75+ftSoVeuK9nPFVKiAAWiuK/Z376OPcu+jjwKw+cQJsrKzCQsPB+DAgQO8Pnw477/zDrO/+oqgoCCOnzhBo4YNuf3++3lt7FhM2dkyVPIyz5HyACtJtJOfmpo/QaheNHAnPFwKD6Ama4qyJTDQVd62IPTGxFWKy1YoikREhGvsdK+Q5+cnczIqFGWNj49rrDQYZN8s6tgZHOwS0NSETVEWFNbvvL1VmLmifFFUkUuNp+UaYbdDbi5H/v2XEQMG0LlzZ7777juq16vHsNdeY8qXX9J/yBAeGDqUI8eO8aIut1R55u577+Xo7t1s3rxZvlCUCtGFaQNCSO84i6VQL/Mj+/ZRp2FDjMXNRVpKmP38nOIXQKNGjfj5zz/5ee1aoqKjOXj2LANGjiS4QgU+e+cd2tWpQ/cuXThz8uRlf5dy4ShJ9AOqzSaNA5CdNzOz4M/5+0vBTE3YFOUJH5+LvcL0fVx5gCnKGk1I8CQo+Pi4xl1l7CrKkoAAOY5qBuqlBDCTySXuenvLz6kFMkV5Qot+iIlxvabGWUVZ46kPegp71OZnivKB3U7W2bOs3ryZXxcvZvkvv1CpUiVOnz6N2WzmyaFDee7NN4muWtX5kQFl2Nzi8tgTT/DZxx/z0Ucf0aFDB1ffzM2V/dPL6+JnfUaGtAE8haNnZEB6uut/twT3lpwcpk2cyN8bNtD/+edL+GhKB4PBQPvOnflj506Sk5OJjIwEu53Z//0v69atIyY1lYCQkMver5rBXk2KagwYjdITTKEoa9xXzwIDCxbAlAeYoqzR+qNmzPr4uLxv9f1TTcwUZUlgYH6v2UutwoaEuKpIBgbKNAnlKJeJ4gaioLHT2/vifuztDaGhkJJytVulUHjmUs99zatWLeCWC754912SExI4smcPq1atIjcvDx8fH+o1bkxIeDg9HniA5155hYrR0WXd1BLBLzqaoS++yMSxY1mxYgU97r9fvhEfL39XrnyxAKYt5OpDIXNyCnesAVb9+SdvvvACp48coX23boyfMqUEj6T0MZlMUvwCMBp54rnneOK554q9PzUClCQFxZ67v6ZcxhXllctJaKsMCEVZYzZL75qgIPl/RITLeNAbwmq1V1HWaH0ULr144O6pcJ0Y/4prlMqVZUGR7GzXawV52vj7y3FZ2bmKsqCwEEijUfZPRflACD6aMoXMlBQqV67M408/TYubbuLeAQMwu+fWvl4wmRj0n/8w5/vvefb55/mxRg1adO7sfPuvv/7ixD//8PhDD+GrLYJppKdLO8JgkOOxhzH2y+nT+XPlSnIsFrb9/jteQL/Bg5k6Y8ZVPrBrDzWDLWm8vAqv9uTlVbTktwpFWaEVZvBk4OoFMiWAKcoag0F6y+j/9+RlU87zHihuMAoSwEwmmT5BeSwqyhvuz3tPz39Piw8KRWkTFQVZWfK5r++nnqqdK8oOg4HtJ08SIATGoKAbZtzw8/dn8R9/0LttW+675x663n03ptRUMjMzWbx+PaHA1MmTefDBB+nYsSNZWVnExcWRk5PDjgMHsPj58fBtt5GSlITVaiU2NpbExEQ2b97MufPnnd/z7ODB9H/mGeq1bl1mx1qeUTPYkiYqyrVSpk26AgJcroohITKk7Aa62RXXGGFhUgDz5DVjMMg+bjQqUUFRvtES4/v5lXVLFIr8FPTs9/OTOT2UAKYobwQFSe8Zo1Eu8roLYGazK2xXoShLvLxcORT1fVIt2pY7goqRu+l6IDo6mp9++YUXn3mGf1asICcnh8qVKzPkvvu47777+Pbbb/n6m2/4+ptv8n2uZo0apKenM2zRIgCsgA8Q4O9PmzZtqFmzJvf06UP7du2o37Rpsaoj3igYxGW4KRsMhnjg1NVrjkJRIDWEEBUu90OqzyrKENVnFdcaxeqzoPqtokxRY63iWkP1WcW1hrIPFNciHvvtZQlgCoVCoVAoFAqFQqFQKBQKxbWGimFSKBQKhUKhUCgUCoVCoVBc1ygBTKFQKBQKhUKhUCgUCoVCcV2jBDCFQqFQKBQKhUKhUCgUCsV1jRLAFAqFQqFQKBQKhUKhUCgU1zVKAFMoFAqFQqFQKBQKhUKhUFzXKAFMoVAoFAqFQqFQKBQKhUJxXaMEMIVCoVAoFAqFQqFQKBQKxXWNEsAUCoVCoVAoFAqFQqFQKBTXNUoAUygUCoVCoVAoFAqFQqFQXNf8P0Y3ZdyaPSgVAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1224x720 with 35 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, inset_axes \n",
+    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
+    "import matplotlib\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig,axs = plt.subplots(5,7,figsize=(17,10),sharey=False,sharex='col')\n",
+    "\n",
+    "for i in range(5):\n",
+    "    for j in range(7):\n",
+    "        axs[i,j].plot(corrected[i][j][400:600], c='black')\n",
+    "        axs[i,j].plot(uncorrected[i][j][400:600], c='red', alpha=0.1)\n",
+    "        if j != 6:\n",
+    "            axs[i,j].set_ylim((0,0.006))\n",
+    "            axs[i,j].set_xticks([])\n",
+    "            axs[i,j].set_yticks([])\n",
+    "        else:\n",
+    "            axs[i,j].set_ylim((0,0.03))\n",
+    "            axs[i,j].set_xticks([])\n",
+    "            axs[i,j].set_yticks([])\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "        \n",
+    "plt.savefig(\"poster_plot_marginal_footprints.pdf\", dpi=300, transparent=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_5/5b_marginal_footprinting_motifs_dnase.ipynb b/src/evaluation/figure_notebooks/figure_5/5b_marginal_footprinting_motifs_dnase.ipynb
new file mode 100644
index 00000000..c293d91c
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_5/5b_marginal_footprinting_motifs_dnase.ipynb
@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>GM12878</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>K562</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>HEPG2</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>IMR90</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20</td>\n",
+       "      <td>fold_0</td>\n",
+       "      <td>H1ESC</td>\n",
+       "      <td>/mnt/lab_data2/anusri/chrombpnet/results/chrom...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index       0        1                                                  2\n",
+       "0      0  fold_0  GM12878  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "1      5  fold_0     K562  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "2     10  fold_0    HEPG2  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "3     15  fold_0    IMR90  /mnt/lab_data2/anusri/chrombpnet/results/chrom...\n",
+       "4     20  fold_0    H1ESC  /mnt/lab_data2/anusri/chrombpnet/results/chrom..."
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "adir = \"/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_dnase.csv\"\n",
+    "\n",
+    "meta = pd.read_csv(adir, sep=\",\", header=None)\n",
+    "meta = meta[meta[0]==\"fold_0\"].reset_index()\n",
+    "meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import deepdish\n",
+    "import os\n",
+    "\n",
+    "def get_footprints(celltype, items_to_filter):\n",
+    "    if celltype==\"K562\":\n",
+    "        celltype=\"K562\"\n",
+    "        celltype1=\"K562\"\n",
+    "    else:\n",
+    "        celltype1=celltype\n",
+    "    print(celltype)\n",
+    "    fpath=\"/oak/stanford/groups/akundaje/projects/chrombpnet_paper_new/modisco_jun_30/modisco/DNASE/\"\n",
+    "    ffile = os.path.join(fpath,celltype+\"/footprints/\"+celltype1+\"_footprints.h5\")\n",
+    "    corrected = deepdish.io.load(ffile)\n",
+    "\n",
+    "    ffile = os.path.join(fpath,celltype+\"/footprints/\"+celltype1+\"_uncorrected_footprints.h5\")\n",
+    "    uncorrected = deepdish.io.load(ffile)\n",
+    "    \n",
+    "    return [corrected[key][0]/sum(corrected[key][0]) for key in items_to_filter], [uncorrected[key][0]/sum(uncorrected[key][0]) for key in items_to_filter]\n",
+    "    \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEPG2\n",
+      "GM12878\n",
+      "K562\n",
+      "H1ESC\n",
+      "IMR90\n"
+     ]
+    }
+   ],
+   "source": [
+    "items_to_filter=[\"HEPG2_COUNTS_metacluster_0_pattern_1\", \"GM12878_COUNTS_metacluster_0_pattern_9\",  \"K562_COUNTS_metacluster_0_pattern_0\",  \"H1ESC_COUNTS_metacluster_0_pattern_1\",   \"IMR90_COUNTS_metacluster_0_pattern_3\", \"K562_COUNTS_metacluster_0_pattern_2\", \"GM12878_COUNTS_metacluster_0_pattern_1\"]\n",
+    "\n",
+    "corrected = []\n",
+    "uncorrected = []\n",
+    "for i in [2,0,1,4,3]:\n",
+    "    #if meta.iloc[i,2]==\"IMR90\":\n",
+    "    #    continue\n",
+    "    x, y = get_footprints(meta.iloc[i,2], items_to_filter)\n",
+    "    corrected.append(x)\n",
+    "    uncorrected.append(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1224x720 with 35 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, inset_axes \n",
+    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
+    "import matplotlib\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig,axs = plt.subplots(5,7,figsize=(17,10),sharey=False,sharex='col')\n",
+    "\n",
+    "for i in range(5):\n",
+    "    for j in range(7):\n",
+    "        axs[i,j].plot(corrected[i][j][400:600], c='black')\n",
+    "        axs[i,j].plot(uncorrected[i][j][400:600], c='red', alpha=0.1)\n",
+    "        if j != 6:\n",
+    "            axs[i,j].set_ylim((0,0.01))\n",
+    "            axs[i,j].set_xticks([])\n",
+    "            axs[i,j].set_yticks([])\n",
+    "        else:\n",
+    "            axs[i,j].set_ylim((0,0.1))\n",
+    "            axs[i,j].set_xticks([])\n",
+    "            axs[i,j].set_yticks([])\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "        \n",
+    "plt.savefig(\"poster_plot_marginal_footprints_dnase.pdf\", dpi=300, transparent=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_5/5d_heterodimers_coordinated_effect-ap1-tead.ipynb b/src/evaluation/figure_notebooks/figure_5/5d_heterodimers_coordinated_effect-ap1-tead.ipynb
index 50095f8f..a75520d7 100755
--- a/src/evaluation/figure_notebooks/figure_5/5d_heterodimers_coordinated_effect-ap1-tead.ipynb
+++ b/src/evaluation/figure_notebooks/figure_5/5d_heterodimers_coordinated_effect-ap1-tead.ipynb
@@ -26,7 +26,7 @@
     "import tensorflow as tf\n",
     "import os\n",
     "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   # see issue #152\n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"2\"\n",
     "%matplotlib inline\n",
     "matplotlib.rcParams['pdf.fonttype'] = 42\n",
     "matplotlib.rcParams['ps.fonttype'] = 42 "
@@ -38,20 +38,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/GM12878/negatives_data/negatives_with_summit.bed\"\n",
     "regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/IMR90/negatives_data/negatives_with_summit.bed\"\n",
-    "#regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/K562/negatives_data/negatives_with_summit.bed\"\n",
-    "#regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/HEPG2/negatives_data/negatives_with_summit.bed\"\n",
-    "#regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_SE/IMR90/negatives_data/negatives_with_summit.bed\"\n",
-    "\n",
     "genome = \"/mnt/lab_data2/anusri/chrombpnet/reference/hg38.genome.fa\"\n",
-    "#model_h5 = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_PE/K562/nautilus_runs_may18/K562_05.13.2022_bias_128_4_1234_0.5_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
-    "#model_h5 = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/K562/nautilus_runs/K562_02.17.2022_bias_128_4_1234_0.5_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
-    "#model_h5=\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_SE/IMR90/nautilus_runs_apr12/IMR90_04.09.2022_bias_128_4_1234_0.8_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
-    "model_h5=\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/IMR90/nautilus_runs_apr12/IMR90_04.09.2022_bias_128_4_1234_0.4_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
-    "#model_h5 =\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_SE/GM12878/nautilus_runs/GM12878_03.06.2022_bias_128_4_1234_0.8_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
-    "#model_h5 = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/GM12878/nautilus_runs/GM12878_03.01.2022_bias_128_4_1234_0.4_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
-    "#model_h5=\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/HEPG2/nautilus_runs_jun16/HEPG2_05.09.2022_bias_128_4_1234_0.8_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n"
+    "\n",
+    "\n"
    ]
   },
   {
@@ -88,8 +78,13 @@
     "    counts_for_motif = np.exp(pred_output_rev[1]) - 1 + np.exp(pred_output[1]) - 1\n",
     "    footprint_for_motif_tot = footprint_for_motif_fwd+footprint_for_motif_rev[:,::-1]\n",
     "    footprint_for_motif =  footprint_for_motif_tot / footprint_for_motif_tot.sum(axis=1)[:, np.newaxis]\n",
-    "\n",
-    "    return footprint_for_motif_tot.mean(0), counts_for_motif.mean(0)\n",
+    "    k_l=1000\n",
+    "    if counts_for_motif.shape[0] > k_l:\n",
+    "        print(counts_for_motif.shape)\n",
+    "        smallest_100 = np.argpartition(counts_for_motif[:,0], k_l)[:k_l]\n",
+    "        return footprint_for_motif_tot[smallest_100].mean(0), counts_for_motif[smallest_100].mean(0), smallest_100\n",
+    "    else:\n",
+    "        return footprint_for_motif_tot.mean(0), counts_for_motif.mean(0), None\n",
     "\n",
     "def get_footprint_for_two_motifs(seqs, motifs, model, inputlen, batch_size, spacing):\n",
     "    '''\n",
@@ -157,124 +152,237 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
-      "/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/IMR90/nautilus_runs_apr12/IMR90_04.09.2022_bias_128_4_1234_0.4_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\n"
-     ]
-    }
-   ],
-   "source": [
-    "model=load_model(model_h5)\n",
-    "print(model_h5)"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "NARROWPEAK_SCHEMA = [\"chr\", \"start\", \"end\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"summit\"]\n",
     "inputlen = 2114\n",
     "regions_df = pd.read_csv(regions, sep='\\t', names=NARROWPEAK_SCHEMA)\n",
-    "chroms_to_keep = [\"chr1\"]\n",
-    "regions_subsample = regions_df[(regions_df[\"chr\"].isin(chroms_to_keep))].sample(1000, random_state=0)\n",
+    "regions_subsample = regions_df.sample(10000)\n",
     "genome_fasta = pyfaidx.Fasta(genome)\n",
     "regions_seqs = get_seq(regions_subsample, genome_fasta, inputlen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/logs/checkpoint/JAN_02_2023/model_dir_atac.csv\",header=None)\n",
+    "cell_type=\"IMR90\"\n",
+    "ndata = data[data[1]==cell_type].reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8/8 [==============================] - 9s 672ms/step\n",
-      "8/8 [==============================] - 4s 531ms/step\n"
+      "0\n",
+      "(10000, 2114, 4)\n",
+      "79/79 [==============================] - 38s 485ms/step\n",
+      "79/79 [==============================] - 38s 485ms/step\n",
+      "(10000, 1)\n",
+      "(1000, 2114, 4)\n",
+      "1\n",
+      "8/8 [==============================] - 4s 488ms/step\n",
+      "8/8 [==============================] - 3s 485ms/step\n",
+      "2\n",
+      "8/8 [==============================] - 4s 485ms/step\n",
+      "8/8 [==============================] - 4s 483ms/step\n",
+      "3\n",
+      "8/8 [==============================] - 4s 483ms/step\n",
+      "8/8 [==============================] - 3s 486ms/step\n",
+      "4\n",
+      "8/8 [==============================] - 4s 485ms/step\n",
+      "8/8 [==============================] - 3s 486ms/step\n"
      ]
     }
    ],
    "source": [
     "motif =  \"\"\n",
     "batch_size=128\n",
-    "full_footprint_1 = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)"
+    "k_l=1000\n",
+    "motifs_footprint = {}\n",
+    "\n",
+    "for i,r in ndata.iterrows():\n",
+    "    print(i)\n",
+    "    if i==0:\n",
+    "        mdl_path1 = os.path.join(r[2],\"chrombpnet_model/new_model_formats/chrombpnet_wo_bias\")\n",
+    "        if not os.path.exists(mdl_path1):\n",
+    "            break\n",
+    "        \n",
+    "        model=load_model(mdl_path1, compile=False)\n",
+    "        print(regions_seqs.shape)\n",
+    "        full_footprint_1 = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)\n",
+    "        regions_seqs = regions_seqs[full_footprint_1[2],:,:]\n",
+    "        print(regions_seqs.shape)\n",
+    "        motifs_footprint[\"control\"] = full_footprint_1[0]\n",
+    "    else:\n",
+    "        mdl_path1 = os.path.join(r[2],\"chrombpnet_model/new_model_formats/chrombpnet_wo_bias\")\n",
+    "        model=load_model(mdl_path1, compile=False)\n",
+    "        full_footprint_1 = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)\n",
+    "        motifs_footprint[\"control\"] += full_footprint_1[0]\n",
+    "        \n",
+    "motifs_footprint[\"control\"] = motifs_footprint[\"control\"]/5    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8/8 [==============================] - 4s 530ms/step\n",
-      "8/8 [==============================] - 4s 532ms/step\n"
+      "0\n",
+      "8/8 [==============================] - 4s 477ms/step\n",
+      "8/8 [==============================] - 3s 477ms/step\n",
+      "1\n",
+      "8/8 [==============================] - 4s 477ms/step\n",
+      "8/8 [==============================] - 3s 476ms/step\n",
+      "2\n",
+      "8/8 [==============================] - 4s 478ms/step\n",
+      "8/8 [==============================] - 3s 480ms/step\n",
+      "3\n",
+      "8/8 [==============================] - 4s 479ms/step\n",
+      "8/8 [==============================] - 3s 480ms/step\n",
+      "4\n",
+      "8/8 [==============================] - 4s 480ms/step\n",
+      "8/8 [==============================] - 3s 481ms/step\n"
      ]
     }
    ],
    "source": [
-    "#motif=\"AGGAATGT\"\n",
-    "motif=\"CAGGTG\"\n",
+    "motif=\"AGGAATGT\"\n",
+    "#motif=\"CAGGTG\"\n",
     "batch_size=128\n",
-    "runx_footprint = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)"
+    "\n",
+    "for i,r in ndata.iterrows():\n",
+    "    print(i)\n",
+    "    mdl_path1 = os.path.join(r[2],\"chrombpnet_model/new_model_formats/chrombpnet_wo_bias\")\n",
+    "    model=load_model(mdl_path1, compile=False)\n",
+    "    full_footprint_1 = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)\n",
+    "    if i==0:\n",
+    "        motifs_footprint[\"TEAD\"] = full_footprint_1[0]\n",
+    "    else:\n",
+    "        motifs_footprint[\"TEAD\"] += full_footprint_1[0]\n",
+    "\n",
+    "motifs_footprint[\"TEAD\"] =  motifs_footprint[\"TEAD\"] / 5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8/8 [==============================] - 4s 523ms/step\n",
-      "8/8 [==============================] - 4s 520ms/step\n"
+      "0\n",
+      "8/8 [==============================] - 4s 484ms/step\n",
+      "8/8 [==============================] - 3s 483ms/step\n",
+      "1\n",
+      "8/8 [==============================] - 4s 482ms/step\n",
+      "8/8 [==============================] - 3s 482ms/step\n",
+      "2\n",
+      "8/8 [==============================] - 4s 482ms/step\n",
+      "8/8 [==============================] - 3s 483ms/step\n",
+      "3\n",
+      "8/8 [==============================] - 4s 481ms/step\n",
+      "8/8 [==============================] - 3s 484ms/step\n",
+      "4\n",
+      "8/8 [==============================] - 4s 483ms/step\n",
+      "8/8 [==============================] - 3s 484ms/step\n"
      ]
     }
    ],
    "source": [
-    "#motif = \"TTGACTCA\"\n",
-    "motif=\"TGACTCA\"\n",
-    "motif=\"ATGACTCA\"\n",
+    "motif = \"TTGACTCA\"\n",
+    "#motif=\"TGACTCA\"\n",
+    "#motif=\"ATGACTCA\"\n",
     "batch_size=128\n",
-    "irf1_footprint = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)\n"
+    "\n",
+    "for i,r in ndata.iterrows():\n",
+    "    print(i)\n",
+    "    mdl_path1 = os.path.join(r[2],\"chrombpnet_model/new_model_formats/chrombpnet_wo_bias\")\n",
+    "    model=load_model(mdl_path1, compile=False)\n",
+    "    full_footprint_1 = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)\n",
+    "    if i==0:\n",
+    "        motifs_footprint[\"AP1\"] = full_footprint_1[0]\n",
+    "    else:\n",
+    "        motifs_footprint[\"AP1\"] += full_footprint_1[0]\n",
+    "\n",
+    "motifs_footprint[\"AP1\"] =  motifs_footprint[\"AP1\"] / 5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CAGGTG 1054 1060\n",
-      "8/8 [==============================] - 4s 523ms/step\n",
-      "8/8 [==============================] - 4s 519ms/step\n"
+      "0\n",
+      "AGGAATGT 1053 1061\n",
+      "8/8 [==============================] - 4s 479ms/step\n",
+      "8/8 [==============================] - 3s 478ms/step\n",
+      "1\n",
+      "AGGAATGT 1053 1061\n",
+      "8/8 [==============================] - 4s 478ms/step\n",
+      "8/8 [==============================] - 3s 479ms/step\n",
+      "2\n",
+      "AGGAATGT 1053 1061\n",
+      "8/8 [==============================] - 4s 479ms/step\n",
+      "8/8 [==============================] - 4s 481ms/step\n",
+      "3\n",
+      "AGGAATGT 1053 1061\n",
+      "8/8 [==============================] - 4s 481ms/step\n",
+      "8/8 [==============================] - 3s 482ms/step\n",
+      "4\n",
+      "AGGAATGT 1053 1061\n",
+      "8/8 [==============================] - 4s 482ms/step\n",
+      "8/8 [==============================] - 4s 483ms/step\n"
      ]
     }
    ],
    "source": [
     "\n",
-    "#motifs = [\"AGGAATGT\",\"TTGACTCA\"]\n",
-    "motifs=[\"CAGGTG\", \"TGACTCA\"]\n",
-    "motifs=[\"CAGGTG\", \"ATGACTCA\"]\n",
+    "motifs = [\"AGGAATGT\",\"TTGACTCA\"]\n",
+    "#motifs=[\"CAGGTG\", \"TGACTCA\"]\n",
+    "#motifs=[\"CAGGTG\", \"ATGACTCA\"]\n",
     "batch_size=128\n",
     "#puu_runx_footprint = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)\n",
-    "puu_runx_footprint = get_footprint_for_two_motifs(regions_seqs, motifs, model, inputlen, batch_size, spacing=-4)"
+    "for i,r in ndata.iterrows():\n",
+    "    print(i)\n",
+    "    mdl_path1 = os.path.join(r[2],\"chrombpnet_model/new_model_formats/chrombpnet_wo_bias\")\n",
+    "    model=load_model(mdl_path1, compile=False)\n",
+    "    full_footprint_1 = get_footprint_for_two_motifs(regions_seqs, motifs, model, inputlen, batch_size, spacing=-6)\n",
+    "    if i==0:\n",
+    "        motifs_footprint[\"AP1-TEAD\"] = full_footprint_1[0]\n",
+    "    else:\n",
+    "        motifs_footprint[\"AP1-TEAD\"] += full_footprint_1[0]\n",
+    "\n",
+    "motifs_footprint[\"AP1-TEAD\"] =  motifs_footprint[\"AP1-TEAD\"] / 5\n",
+    "\n",
+    "#puu_runx_footprint = get_footprint_for_two_motifs(regions_seqs, motifs, model, inputlen, batch_size, spacing=-4)"
    ]
   },
   {
@@ -294,31 +402,31 @@
     }
    ],
    "source": [
-    "import seaborn as sns\n",
-    "sns.set()\n",
-    "sns.set_style(\"ticks\", {'axes.grid' : False})\n",
-    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
-    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "# import seaborn as sns\n",
+    "# sns.set()\n",
+    "# sns.set_style(\"ticks\", {'axes.grid' : False})\n",
+    "# matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "# matplotlib.rcParams['ps.fonttype'] = 42 \n",
     "\n",
-    "#plt.plot(puu_runx_footprint[0][400:600], label=\"AP1-TEAD-heterodimer\", c = \"green\")\n",
-    "#plt.plot(irf1_footprint[0][400:600]+runx_footprint[0][400:600], label=\"AP1+TEAD\", c = \"brown\")\n",
-    "#plt.plot(irf1_footprint[0][400:600], label=\"AP1-only\", c = \"red\", alpha=1)\n",
-    "#plt.plot(runx_footprint[0][400:600], label=\"TEAD-only\", c = \"blue\", alpha=1)\n",
-    "#plt.plot(full_footprint_1[0][400:600], label=\"background\", c = \"black\", alpha=1)\n",
-    "\n",
-    "plt.plot(puu_runx_footprint[0][400:600], label=\"ZEB-AP1-heterodimer\", c = \"green\")\n",
-    "plt.plot(irf1_footprint[0][400:600]-runx_footprint[0][400:600], label=\"ZEB-AP1\", c = \"brown\")\n",
-    "plt.plot(irf1_footprint[0][400:600], label=\"AP1-only\", c = \"red\", alpha=1)\n",
-    "plt.plot(runx_footprint[0][400:600], label=\"ZEB-only\", c = \"blue\", alpha=1)\n",
-    "plt.plot(full_footprint_1[0][400:600], label=\"background\", c = \"black\", alpha=1)\n",
-    "plt.xlabel(\"200bp surrounding motif insertion site\")\n",
-    "#plt.ylabel(\"Total counts\")\n",
+    "# #plt.plot(puu_runx_footprint[0][400:600], label=\"AP1-TEAD-heterodimer\", c = \"green\")\n",
+    "# #plt.plot(irf1_footprint[0][400:600]+runx_footprint[0][400:600], label=\"AP1+TEAD\", c = \"brown\")\n",
+    "# #plt.plot(irf1_footprint[0][400:600], label=\"AP1-only\", c = \"red\", alpha=1)\n",
+    "# #plt.plot(runx_footprint[0][400:600], label=\"TEAD-only\", c = \"blue\", alpha=1)\n",
+    "# #plt.plot(full_footprint_1[0][400:600], label=\"background\", c = \"black\", alpha=1)\n",
     "\n",
-    "#plt.ylim((0,2.5))\n",
-    "plt.legend(loc=\"right\")\n",
+    "# plt.plot(puu_runx_footprint[0][400:600], label=\"ZEB-AP1-heterodimer\", c = \"green\")\n",
+    "# plt.plot(irf1_footprint[0][400:600]-runx_footprint[0][400:600], label=\"ZEB-AP1\", c = \"brown\")\n",
+    "# plt.plot(irf1_footprint[0][400:600], label=\"AP1-only\", c = \"red\", alpha=1)\n",
+    "# plt.plot(runx_footprint[0][400:600], label=\"ZEB-only\", c = \"blue\", alpha=1)\n",
+    "# plt.plot(full_footprint_1[0][400:600], label=\"background\", c = \"black\", alpha=1)\n",
+    "# plt.xlabel(\"200bp surrounding motif insertion site\")\n",
+    "# #plt.ylabel(\"Total counts\")\n",
+    "\n",
+    "# #plt.ylim((0,2.5))\n",
+    "# plt.legend(loc=\"right\")\n",
     "\n",
-    "#plt.savefig(\"subfigs/ap1_tead_coord_effect.pdf\", transparent=True, dpi=300)\n",
-    "plt.savefig(\"subfigs/zeb_ap1_coord_effect.pdf\", transparent=True, dpi=300)\n"
+    "# #plt.savefig(\"subfigs/ap1_tead_coord_effect.pdf\", transparent=True, dpi=300)\n",
+    "# plt.savefig(\"subfigs/zeb_ap1_coord_effect.pdf\", transparent=True, dpi=300)\n"
    ]
   },
   {
@@ -344,7 +452,7 @@
     "matplotlib.rcParams['pdf.fonttype'] = 42\n",
     "matplotlib.rcParams['ps.fonttype'] = 42 \n",
     "\n",
-    "plt.plot(puu_runx_footprint[0], label=\"AP1-TEAD-heterodimer\", c = \"green\")\n",
+    "plt.plot(motifs_footprint[\"AP1\"]], label=\"AP1-TEAD-heterodimer\", c = \"green\")\n",
     "plt.plot(irf1_footprint[0]+irf1_footprint[0], label=\"AP1+TEAD\", c = \"brown\")\n",
     "plt.plot(irf1_footprint[0], label=\"AP1-only\", c = \"red\", alpha=1)\n",
     "plt.plot(runx_footprint[0], label=\"TEAD-only\", c = \"blue\", alpha=1)\n",
@@ -355,15 +463,51 @@
     "#plt.ylim((0,2.5))\n",
     "plt.legend(loc=\"right\")\n",
     "\n",
-    "plt.savefig(\"subfigs/ap1_tead_coord_effect.pdf\", transparent=True, dpi=300)\n"
+    "#plt.savefig(\"subfigs/ap1_tead_coord_effect.pdf\", transparent=True, dpi=300)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fccf0445ee0>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "plt.plot(motifs_footprint[\"AP1-TEAD\"][400:600], label=\"AP1-TEAD-heterodimer\", c = \"green\")\n",
+    "plt.plot(motifs_footprint[\"AP1\"][400:600]+motifs_footprint[\"TEAD\"][400:600], label=\"AP1+TEAD\", c = \"brown\")\n",
+    "plt.plot(motifs_footprint[\"AP1\"][400:600], label=\"AP1-only\", c = \"red\", alpha=1)\n",
+    "plt.plot(motifs_footprint[\"TEAD\"][400:600], label=\"TEAD-only\", c = \"blue\", alpha=1)\n",
+    "plt.plot(motifs_footprint[\"control\"][400:600], label=\"background\", c = \"black\", alpha=1)\n",
+    "plt.xlabel(\"200bp surrounding motif insertion site\")\n",
+    "plt.ylabel(\"Total counts\")\n",
+    "\n",
+    "#plt.ylim((0,2.5))\n",
+    "plt.legend(loc=\"right\")"
+   ]
   },
   {
    "cell_type": "code",
@@ -375,7 +519,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -389,7 +533,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/src/evaluation/figure_notebooks/figure_5/5e_heterodimers_syntax_exhaustive_imr90.ipynb b/src/evaluation/figure_notebooks/figure_5/5e_heterodimers_syntax_exhaustive_imr90.ipynb
new file mode 100644
index 00000000..377abf20
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_5/5e_heterodimers_syntax_exhaustive_imr90.ipynb
@@ -0,0 +1,3107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyBigWig\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import deepdish as dd\n",
+    "import os\n",
+    "import pyfaidx\n",
+    "import random\n",
+    "import pickle as pkl\n",
+    "import matplotlib\n",
+    "matplotlib.use('Agg')\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import argparse\n",
+    "import json\n",
+    "import one_hot as dinuc_shuffle_main\n",
+    "from tensorflow.keras.utils import get_custom_objects\n",
+    "from tensorflow.keras.models import load_model\n",
+    "import tensorflow as tf\n",
+    "import os\n",
+    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   # see issue #152\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
+    "%matplotlib inline\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/GM12878/negatives_data/negatives_with_summit.bed\"\n",
+    "#regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_SE/IMR90/negatives_data/negatives_with_summit.bed\"\n",
+    "#regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/K562/negatives_data/negatives_with_summit.bed\"\n",
+    "regions = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/IMR90/negatives_data/negatives_with_summit.bed\"\n",
+    "\n",
+    "genome = \"/mnt/lab_data2/anusri/chrombpnet/reference/hg38.genome.fa\"\n",
+    "#model_h5 = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_PE/K562/nautilus_runs_may18/K562_05.13.2022_bias_128_4_1234_0.5_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
+    "#model_h5 = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/K562/nautilus_runs/K562_02.17.2022_bias_128_4_1234_0.5_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
+    "#model_h5=\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_SE/IMR90/nautilus_runs_apr12/IMR90_04.09.2022_bias_128_4_1234_0.8_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
+    "model_h5=\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/IMR90/nautilus_runs_apr12/IMR90_04.09.2022_bias_128_4_1234_0.4_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
+    "#model_h5 =\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/DNASE_SE/GM12878/nautilus_runs/GM12878_03.06.2022_bias_128_4_1234_0.8_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\"\n",
+    "#model_h5 = \"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/GM12878/nautilus_runs/GM12878_03.01.2022_bias_128_4_1234_0.4_fold_0/chrombpnet_model/chrombpnet_wo_bias.h5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def softmax(x, temp=1):\n",
+    "    norm_x = x - np.mean(x,axis=1, keepdims=True)\n",
+    "    return np.exp(temp*norm_x)/np.sum(np.exp(temp*norm_x), axis=1, keepdims=True)\n",
+    "\n",
+    "\n",
+    "def get_footprint_for_motif(seqs, motif, model, inputlen, batch_size):\n",
+    "    '''\n",
+    "    Returns footprints for a given motif. Motif is inserted in both the actual sequence and reverse complemented version.\n",
+    "    seqs input is already assumed to be one-hot encoded. motif is in sequence format.\n",
+    "    '''\n",
+    "    midpoint=inputlen//2\n",
+    "\n",
+    "    w_mot_seqs = seqs.copy()\n",
+    "    w_mot_seqs[:, midpoint-len(motif)//2:midpoint-len(motif)//2+len(motif)] =dinuc_shuffle_main.dna_to_one_hot([motif])\n",
+    "\n",
+    "    # midpoint of motif is the midpoint of sequence\n",
+    "    pred_output=model.predict(w_mot_seqs, batch_size=batch_size, verbose=False)\n",
+    "    footprint_for_motif_fwd = softmax(pred_output[0])*(np.exp(pred_output[1])-1)\n",
+    "\n",
+    "    # reverse complement the sequence\n",
+    "    w_mot_seqs_revc = w_mot_seqs[:, ::-1, ::-1]\n",
+    "    pred_output_rev=model.predict(w_mot_seqs_revc, batch_size=batch_size, verbose=False)\n",
+    "    footprint_for_motif_rev = softmax(pred_output_rev[0])*(np.exp(pred_output_rev[1])-1)\n",
+    "\n",
+    "    # add fwd sequence predictions and reverse sesquence predictions (not we flip the rev predictions)\n",
+    "    counts_for_motif = (np.exp(pred_output_rev[1]) - 1 + np.exp(pred_output[1]) - 1)/2\n",
+    "    footprint_for_motif_tot = (footprint_for_motif_fwd+footprint_for_motif_rev[:,::-1])/2\n",
+    "    footprint_for_motif =  footprint_for_motif_tot / footprint_for_motif_tot.sum(axis=1)[:, np.newaxis]\n",
+    "\n",
+    "    return footprint_for_motif_tot, counts_for_motif \n",
+    "\n",
+    "def get_footprint_for_two_motifs(seqs, motifs, model, inputlen, batch_size, spacing):\n",
+    "    '''\n",
+    "    Returns footprints for a given motif. Motif is inserted in both the actual sequence and reverse complemented version.\n",
+    "    seqs input is already assumed to be one-hot encoded. motif is in sequence format.\n",
+    "    '''\n",
+    "    midpoint=inputlen//2\n",
+    "\n",
+    "    spacing_per_motif = spacing // 2\n",
+    "    \n",
+    "    w_mot_seqs = seqs.copy()\n",
+    "    \n",
+    "    motif = motifs[0]\n",
+    "    start = midpoint-(len(motif)//2)\n",
+    "    w_mot_seqs[:, start:start+len(motif)] = dinuc_shuffle_main.dna_to_one_hot([motif])\n",
+    "    #print(motif,start,start+len(motif))\n",
+    "    if spacing > 0:\n",
+    "        spacing_per_motif = spacing \n",
+    "        motif = motifs[1]\n",
+    "        start = start+len(motifs[0])+spacing_per_motif \n",
+    "        w_mot_seqs[:, start:start+len(motif)] = dinuc_shuffle_main.dna_to_one_hot([motif])\n",
+    "    else:\n",
+    "        spacing_per_motif = spacing \n",
+    "        motif = motifs[1]\n",
+    "        start = start + spacing_per_motif - len(motif)\n",
+    "        w_mot_seqs[:, start:start+len(motif)] = dinuc_shuffle_main.dna_to_one_hot([motif])\n",
+    "    \n",
+    "    #print(motif,start,start+len(motif))\n",
+    "    \n",
+    "    # midpoint of motif is the midpoint of sequence\n",
+    "    pred_output=model.predict(w_mot_seqs, batch_size=batch_size, verbose=False)\n",
+    "    footprint_for_motif_fwd = softmax(pred_output[0])*(np.exp(pred_output[1])-1)\n",
+    "\n",
+    "    # reverse complement the sequence\n",
+    "    w_mot_seqs_revc = w_mot_seqs[:, ::-1, ::-1]\n",
+    "    pred_output_rev=model.predict(w_mot_seqs_revc, batch_size=batch_size, verbose=False)\n",
+    "    footprint_for_motif_rev = softmax(pred_output_rev[0])*(np.exp(pred_output_rev[1])-1)\n",
+    "\n",
+    "    # add fwd sequence predictions and reverse sesquence predictions (not we flip the rev predictions)\n",
+    "    counts_for_motif = (np.exp(pred_output_rev[1]) - 1 + np.exp(pred_output[1]) - 1)/2\n",
+    "    footprint_for_motif_tot = (footprint_for_motif_fwd+footprint_for_motif_rev[:,::-1])/2\n",
+    "    footprint_for_motif =  footprint_for_motif_tot / footprint_for_motif_tot.sum(axis=1)[:, np.newaxis]\n",
+    "\n",
+    "    return footprint_for_motif_tot, counts_for_motif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_seq(peaks_df, genome, width, shuffle=False):\n",
+    "    \"\"\"\n",
+    "    fetches sequence from a given genome.\n",
+    "    \"\"\"\n",
+    "    vals = []\n",
+    "\n",
+    "    for i, r in peaks_df.iterrows():\n",
+    "        sequence = str(genome[r['chr']][(r['start']+r['summit'] - width//2):(r['start'] + r['summit'] + width//2)])\n",
+    "        if len(sequence) == width:\n",
+    "                vals.append(sequence)\n",
+    "\n",
+    "    return dinuc_shuffle_main.dna_to_one_hot(vals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model=load_model(model_h5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NARROWPEAK_SCHEMA = [\"chr\", \"start\", \"end\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"summit\"]\n",
+    "inputlen = 2114\n",
+    "regions_df = pd.read_csv(regions, sep='\\t', names=NARROWPEAK_SCHEMA)\n",
+    "chroms_to_keep = [\"chr1\"]\n",
+    "regions_subsample = regions_df[(regions_df[\"chr\"].isin(chroms_to_keep))].sample(100, random_state=0)\n",
+    "genome_fasta = pyfaidx.Fasta(genome)\n",
+    "regions_seqs = get_seq(regions_subsample, genome_fasta, inputlen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "motif =  \"\"\n",
+    "batch_size=128\n",
+    "full_footprint_control = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size=512\n",
+    "\n",
+    "def simulate_motif_set(motifs, model, regions_seqs):\n",
+    "\n",
+    "    print(motifs[0])\n",
+    "    print(motifs[1])\n",
+    "    motif1_only = get_footprint_for_motif(regions_seqs, motifs[0], model, inputlen, batch_size)[1]\n",
+    "    motif2_only = get_footprint_for_motif(regions_seqs, motifs[1], model, inputlen, batch_size)[1]\n",
+    "    #total_sum = motif1_only+motif2_only\n",
+    "    \n",
+    "    #plt.plot(motif1_only)\n",
+    "    #plt.plot(motif2_only)\n",
+    "\n",
+    "    # -/- orientation\n",
+    "    data_in_spacings = []\n",
+    "    for spacing in [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,20,40,50,60,70,80,90,100,110,120,130,140,150]:\n",
+    "    #for spacing in [4,6]:\n",
+    "\n",
+    "        puu_runx_footprint = get_footprint_for_two_motifs(regions_seqs, motifs, model, inputlen, batch_size, spacing=-1*spacing)\n",
+    "        data_in_spacings.append(puu_runx_footprint)\n",
+    "        \n",
+    "    # +/+ orientation\n",
+    "    data_in_spacings_rev = []\n",
+    "    for spacing in [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,20,40,50,60,70,80,90,100,110,120,130,140,150]:\n",
+    "    #for spacing in [4,6]:\n",
+    "\n",
+    "        puu_runx_footprint = get_footprint_for_two_motifs(regions_seqs, motifs, model, inputlen, batch_size, spacing=spacing)\n",
+    "        data_in_spacings_rev.append(puu_runx_footprint)\n",
+    "        \n",
+    "        \n",
+    "    #all_counts = [x[1] for x in data_in_spacings]\n",
+    "    #all_counts_rev = [x[1] for x in data_in_spacings_rev]\n",
+    "\n",
+    "    distance = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,20,40,50,60,70,80,90,100,110,120,130,140,150]+[-1,-2,-3,-4,-5,-6,-7,-8,-9,-10,-11,-12,-13,-14,-15,-20,-40,-50,-60,-70,-80,-90,-100,-110,-120,-130,-140,-150]\n",
+    "\n",
+    "    #total_counts = all_counts_rev+all_counts\n",
+    "    #distance = np.array(distance)\n",
+    "    #total_counts = np.array(total_counts)\n",
+    "    \n",
+    "    #coop_score = np.max(total_counts)\n",
+    "    #coop_distance = distance[np.argmax(total_counts)]\n",
+    "   \n",
+    "    #print(motif1_only, motif2_only, total_sum, coop_score, np.max(total_counts)/np.mean(total_counts), coop_distance, total_counts,distance)\n",
+    "\n",
+    "    return [motif1_only, motif2_only, data_in_spacings, data_in_spacings_rev, distance]\n",
+    "\n",
+    "    #print(motif1_only, motif2_only, total_sum, coop_score, np.max(total_counts)/np.mean(total_counts), coop_distance)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "annotations = pd.read_csv(\"addons/imr90/imr90.counts.tomtom.motifs_string.tsv\",header=0, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pattern</th>\n",
+       "      <th>Num_Seqlets</th>\n",
+       "      <th>Label</th>\n",
+       "      <th>Match_1</th>\n",
+       "      <th>q-value</th>\n",
+       "      <th>Match_2</th>\n",
+       "      <th>q-value.1</th>\n",
+       "      <th>Match_3</th>\n",
+       "      <th>q-value.2</th>\n",
+       "      <th>Match_4</th>\n",
+       "      <th>...</th>\n",
+       "      <th>q-value.5</th>\n",
+       "      <th>Match_7</th>\n",
+       "      <th>q-value.6</th>\n",
+       "      <th>Match_8</th>\n",
+       "      <th>q-value.7</th>\n",
+       "      <th>Match_9</th>\n",
+       "      <th>q-value.8</th>\n",
+       "      <th>Match_10</th>\n",
+       "      <th>q-value.9</th>\n",
+       "      <th>string</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>metacluster_0.pattern_0</td>\n",
+       "      <td>12878</td>\n",
+       "      <td>FOS</td>\n",
+       "      <td>FOS_HUMAN.H11MO.0.A</td>\n",
+       "      <td>1.022300e-03</td>\n",
+       "      <td>FOSL2_MOUSE.H11MO.0.A</td>\n",
+       "      <td>1.022300e-03</td>\n",
+       "      <td>FOSL1_HUMAN.H11MO.0.A</td>\n",
+       "      <td>1.022300e-03</td>\n",
+       "      <td>FOSL1_MOUSE.H11MO.0.A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004283</td>\n",
+       "      <td>FOSB_HUMAN.H11MO.0.A</td>\n",
+       "      <td>0.004283</td>\n",
+       "      <td>JUNB_MOUSE.H11MO.0.A</td>\n",
+       "      <td>0.004283</td>\n",
+       "      <td>JUN_MOUSE.H11MO.0.A</td>\n",
+       "      <td>0.004393</td>\n",
+       "      <td>JUNB_HUMAN.H11MO.0.A</td>\n",
+       "      <td>0.004393</td>\n",
+       "      <td>ATGACTCAT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>metacluster_0.pattern_1</td>\n",
+       "      <td>9160</td>\n",
+       "      <td>CTCF</td>\n",
+       "      <td>CTCF_MA0139.1</td>\n",
+       "      <td>1.493190e-12</td>\n",
+       "      <td>CTCF_HUMAN.H11MO.0.A</td>\n",
+       "      <td>8.496500e-10</td>\n",
+       "      <td>CTCF_MOUSE.H11MO.0.A</td>\n",
+       "      <td>3.108420e-08</td>\n",
+       "      <td>CTCF_C2H2_1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000024</td>\n",
+       "      <td>CTCFL_MA1102.1</td>\n",
+       "      <td>0.000237</td>\n",
+       "      <td>ZIC2_MOUSE.H11MO.0.C</td>\n",
+       "      <td>0.139479</td>\n",
+       "      <td>RARA_nuclearreceptor_6</td>\n",
+       "      <td>0.166742</td>\n",
+       "      <td>ZIC3_HUMAN.H11MO.0.B</td>\n",
+       "      <td>0.166742</td>\n",
+       "      <td>GCGCCCCCTAGTGG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>metacluster_0.pattern_3</td>\n",
+       "      <td>6646</td>\n",
+       "      <td>FOXC1</td>\n",
+       "      <td>Foxc1.mouse_forkhead_2</td>\n",
+       "      <td>3.633420e-03</td>\n",
+       "      <td>FOXG1_forkhead_1</td>\n",
+       "      <td>6.010030e-03</td>\n",
+       "      <td>FOXJ3_forkhead_2</td>\n",
+       "      <td>6.010030e-03</td>\n",
+       "      <td>FOXL1_MA0033.2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006467</td>\n",
+       "      <td>Foxj3.mouse_forkhead_3</td>\n",
+       "      <td>0.006467</td>\n",
+       "      <td>FOXJ2_forkhead_2</td>\n",
+       "      <td>0.006791</td>\n",
+       "      <td>FOXO4_HUMAN.H11MO.0.C</td>\n",
+       "      <td>0.006791</td>\n",
+       "      <td>FOXO4_MOUSE.H11MO.0.C</td>\n",
+       "      <td>0.006791</td>\n",
+       "      <td>TGTTTACAT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>metacluster_0.pattern_4</td>\n",
+       "      <td>4786</td>\n",
+       "      <td>FOSB+JUNB</td>\n",
+       "      <td>FOSB+JUNB_MA1135.1</td>\n",
+       "      <td>3.365410e-01</td>\n",
+       "      <td>FOSL1+JUND_MA1142.1</td>\n",
+       "      <td>3.365410e-01</td>\n",
+       "      <td>FOSL2+JUNB_MA1138.1</td>\n",
+       "      <td>3.365410e-01</td>\n",
+       "      <td>FOSL2+JUND_MA1144.1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.336541</td>\n",
+       "      <td>JDP2_MA0655.1</td>\n",
+       "      <td>0.336541</td>\n",
+       "      <td>JDP2_bZIP_3</td>\n",
+       "      <td>0.336541</td>\n",
+       "      <td>JDP2_bZIP_1</td>\n",
+       "      <td>0.336541</td>\n",
+       "      <td>FOSL1+JUNB_MA1137.1</td>\n",
+       "      <td>0.336541</td>\n",
+       "      <td>TTAGTCAT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>metacluster_0.pattern_5</td>\n",
+       "      <td>2157</td>\n",
+       "      <td>SP1/KLF</td>\n",
+       "      <td>KLF12_HUMAN.H11MO.0.C</td>\n",
+       "      <td>9.172640e-05</td>\n",
+       "      <td>SP3_HUMAN.H11MO.0.B</td>\n",
+       "      <td>9.172640e-05</td>\n",
+       "      <td>SP3_MOUSE.H11MO.0.B</td>\n",
+       "      <td>9.172640e-05</td>\n",
+       "      <td>SP1_HUMAN.H11MO.0.A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000545</td>\n",
+       "      <td>KLF3_HUMAN.H11MO.0.B</td>\n",
+       "      <td>0.000545</td>\n",
+       "      <td>KLF3_MOUSE.H11MO.0.A</td>\n",
+       "      <td>0.000545</td>\n",
+       "      <td>SP4_HUMAN.H11MO.0.A</td>\n",
+       "      <td>0.000663</td>\n",
+       "      <td>SP4_MOUSE.H11MO.0.B</td>\n",
+       "      <td>0.000663</td>\n",
+       "      <td>GGCCCCGCCCCCTCCCCG</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Pattern  Num_Seqlets      Label                 Match_1  \\\n",
+       "0  metacluster_0.pattern_0        12878        FOS     FOS_HUMAN.H11MO.0.A   \n",
+       "1  metacluster_0.pattern_1         9160       CTCF           CTCF_MA0139.1   \n",
+       "2  metacluster_0.pattern_3         6646      FOXC1  Foxc1.mouse_forkhead_2   \n",
+       "3  metacluster_0.pattern_4         4786  FOSB+JUNB      FOSB+JUNB_MA1135.1   \n",
+       "4  metacluster_0.pattern_5         2157    SP1/KLF   KLF12_HUMAN.H11MO.0.C   \n",
+       "\n",
+       "        q-value                Match_2     q-value.1                Match_3  \\\n",
+       "0  1.022300e-03  FOSL2_MOUSE.H11MO.0.A  1.022300e-03  FOSL1_HUMAN.H11MO.0.A   \n",
+       "1  1.493190e-12   CTCF_HUMAN.H11MO.0.A  8.496500e-10   CTCF_MOUSE.H11MO.0.A   \n",
+       "2  3.633420e-03       FOXG1_forkhead_1  6.010030e-03       FOXJ3_forkhead_2   \n",
+       "3  3.365410e-01    FOSL1+JUND_MA1142.1  3.365410e-01    FOSL2+JUNB_MA1138.1   \n",
+       "4  9.172640e-05    SP3_HUMAN.H11MO.0.B  9.172640e-05    SP3_MOUSE.H11MO.0.B   \n",
+       "\n",
+       "      q-value.2                Match_4  ...  q-value.5  \\\n",
+       "0  1.022300e-03  FOSL1_MOUSE.H11MO.0.A  ...   0.004283   \n",
+       "1  3.108420e-08            CTCF_C2H2_1  ...   0.000024   \n",
+       "2  6.010030e-03         FOXL1_MA0033.2  ...   0.006467   \n",
+       "3  3.365410e-01    FOSL2+JUND_MA1144.1  ...   0.336541   \n",
+       "4  9.172640e-05    SP1_HUMAN.H11MO.0.A  ...   0.000545   \n",
+       "\n",
+       "                  Match_7  q-value.6               Match_8  q-value.7  \\\n",
+       "0    FOSB_HUMAN.H11MO.0.A   0.004283  JUNB_MOUSE.H11MO.0.A   0.004283   \n",
+       "1          CTCFL_MA1102.1   0.000237  ZIC2_MOUSE.H11MO.0.C   0.139479   \n",
+       "2  Foxj3.mouse_forkhead_3   0.006467      FOXJ2_forkhead_2   0.006791   \n",
+       "3           JDP2_MA0655.1   0.336541           JDP2_bZIP_3   0.336541   \n",
+       "4    KLF3_HUMAN.H11MO.0.B   0.000545  KLF3_MOUSE.H11MO.0.A   0.000545   \n",
+       "\n",
+       "                  Match_9  q-value.8               Match_10  q-value.9  \\\n",
+       "0     JUN_MOUSE.H11MO.0.A   0.004393   JUNB_HUMAN.H11MO.0.A   0.004393   \n",
+       "1  RARA_nuclearreceptor_6   0.166742   ZIC3_HUMAN.H11MO.0.B   0.166742   \n",
+       "2   FOXO4_HUMAN.H11MO.0.C   0.006791  FOXO4_MOUSE.H11MO.0.C   0.006791   \n",
+       "3             JDP2_bZIP_1   0.336541    FOSL1+JUNB_MA1137.1   0.336541   \n",
+       "4     SP4_HUMAN.H11MO.0.A   0.000663    SP4_MOUSE.H11MO.0.B   0.000663   \n",
+       "\n",
+       "               string  \n",
+       "0           ATGACTCAT  \n",
+       "1      GCGCCCCCTAGTGG  \n",
+       "2           TGTTTACAT  \n",
+       "3            TTAGTCAT  \n",
+       "4  GGCCCCGCCCCCTCCCCG  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "annotations.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_0-metacluster_0.pattern_0\n",
+      "FOS-FOS\n",
+      "['ATGACTCAT', 'ATGACTCAT']\n",
+      "ATGACTCAT\n",
+      "ATGACTCAT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_1\n",
+      "CTCF-CTCF\n",
+      "['GCGCCCCCTAGTGG', 'GCGCCCCCTAGTGG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GCGCCCCCTAGTGG\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_3\n",
+      "FOXC1-FOXC1\n",
+      "['TGTTTACAT', 'TGTTTACAT']\n",
+      "TGTTTACAT\n",
+      "TGTTTACAT\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_4\n",
+      "FOSB+JUNB-FOSB+JUNB\n",
+      "['TTAGTCAT', 'TTAGTCAT']\n",
+      "TTAGTCAT\n",
+      "TTAGTCAT\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_5\n",
+      "SP1/KLF-SP1/KLF\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GGCCCCGCCCCCTCCCCG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_6\n",
+      "NFIC-NFIC\n",
+      "['TTGGCAGCCTGCCAA', 'TTGGCAGCCTGCCAA']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "TTGGCAGCCTGCCAA\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_7\n",
+      "CEBPB-CEBPB\n",
+      "['ATTGCGCAAT', 'ATTGCGCAAT']\n",
+      "ATTGCGCAAT\n",
+      "ATTGCGCAAT\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_9\n",
+      "NFYB-NFYB\n",
+      "['GGCCAATCG', 'GGCCAATCG']\n",
+      "GGCCAATCG\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_12\n",
+      "ELK-ELK\n",
+      "['ACTTCCGG', 'ACTTCCGG']\n",
+      "ACTTCCGG\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_13\n",
+      "LYL1-LYL1\n",
+      "['GACGCCAACCAGCTGTT', 'GACGCCAACCAGCTGTT']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_14\n",
+      "RUNX-RUNX\n",
+      "['CTGTGGTTT', 'CTGTGGTTT']\n",
+      "CTGTGGTTT\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_15\n",
+      "NRF1-NRF1\n",
+      "['GCGCATGCGCA', 'GCGCATGCGCA']\n",
+      "GCGCATGCGCA\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_16\n",
+      "ETS1-ETS1\n",
+      "['TCACTTCCTGTTT', 'TCACTTCCTGTTT']\n",
+      "TCACTTCCTGTTT\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_17\n",
+      "ATF4-ATF4\n",
+      "['ATGATGCAATC', 'ATGATGCAATC']\n",
+      "ATGATGCAATC\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_18\n",
+      "ATF1-ATF1\n",
+      "['ATGACGTCA', 'ATGACGTCA']\n",
+      "ATGACGTCA\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_19\n",
+      "TEAD4-TEAD4\n",
+      "['GCATTCTTGCG', 'GCATTCTTGCG']\n",
+      "GCATTCTTGCG\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_20\n",
+      "IRF1-IRF1\n",
+      "['TTCACTTTCACTTTC', 'TTCACTTTCACTTTC']\n",
+      "TTCACTTTCACTTTC\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_21\n",
+      "ZNF76-ZNF76\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_22\n",
+      "SIX1-SIX1\n",
+      "['GGATCAGGTTACA', 'GGATCAGGTTACA']\n",
+      "GGATCAGGTTACA\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_27\n",
+      "TYY1-TYY1\n",
+      "['GCCGCCATCTT', 'GCCGCCATCTT']\n",
+      "GCCGCCATCTT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_29-metacluster_0.pattern_29\n",
+      "ARNTL-ARNTL\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_31-metacluster_0.pattern_31\n",
+      "SRF-SRF\n",
+      "['TGCCCTATAAGGG', 'TGCCCTATAAGGG']\n",
+      "TGCCCTATAAGGG\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_32-metacluster_0.pattern_32\n",
+      "IRF4-IRF4\n",
+      "['CGGTTCCGATTCCGGG', 'CGGTTCCGATTCCGGG']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_33-metacluster_0.pattern_33\n",
+      "RFX2-RFX2\n",
+      "['CGGCGCCGGTTGCCATGGCAAC', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_34-metacluster_0.pattern_34\n",
+      "ZBTB33-ZBTB33\n",
+      "['TCTCGCGAGA', 'TCTCGCGAGA']\n",
+      "TCTCGCGAGA\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_35-metacluster_0.pattern_35\n",
+      "REST-REST\n",
+      "['GCGCTGTCCGTGGTGCTGA', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_36-metacluster_0.pattern_36\n",
+      "ZNF76.1-ZNF76.1\n",
+      "['GCGGGGCATGCTGGGAACGGGCGG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_1.pattern_0-metacluster_1.pattern_0\n",
+      "ZEB1-ZEB1\n",
+      "['CAGGTG', 'CAGGTG']\n",
+      "CAGGTG\n",
+      "CAGGTG\n",
+      "metacluster_1.pattern_1-metacluster_1.pattern_1\n",
+      "JUNB-JUNB\n",
+      "['AATAAGTAGAAAACCACGC', 'AATAAGTAGAAAACCACGC']\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "AATAAGTAGAAAACCACGC\n"
+     ]
+    }
+   ],
+   "source": [
+    "#motifs = [\"TGGAC\",\"TGACTCAT\"]\n",
+    "#motifs = [\"AGGAATGT\",\"TTGACTCA\"]\n",
+    "values = {}\n",
+    "\n",
+    "#for i in range(annotations.shape[0]-1):\n",
+    "#    for j in range(i+1,annotations.shape[0]):\n",
+    "for i in range(annotations.shape[0]-1):\n",
+    "    if 1:\n",
+    "        j=i\n",
+    "        label1 = annotations.loc[i,\"Pattern\"] + \"-\" + annotations.loc[j,\"Pattern\"]\n",
+    "        label2 = annotations.loc[i,\"Label\"] + \"-\" + annotations.loc[j,\"Label\"]\n",
+    "        motifs = [annotations.loc[i,\"string\"],annotations.loc[j,\"string\"]]\n",
+    "\n",
+    "        print(label1)\n",
+    "        print(label2)\n",
+    "        print(motifs)\n",
+    "        #list_motifs = [[\"CAGGTG\",\"TGACTCAT\"],[\"AGGAATGT\",\"TTGACTCA\"]]\n",
+    "        #labels = [\"ZEB-AP1\", \"TEAD-AP1\"]\n",
+    "        values[label1] = [simulate_motif_set(motifs, model, regions_seqs),label2]\n",
+    "        #break\n",
+    "   #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def define_cooperative_net_pos_neg(motif1_only, motif2_only, cooop):\n",
+    "    total_sum_th = motif1_only + motif2_only\n",
+    "    total_diff_th = np.abs(motif1_only - motif2_only)\n",
+    "    total_diff_th1 = np.max([motif1_only,motif2_only])\n",
+    "    total_sum_th1 = np.max([motif1_only,motif2_only])\n",
+    "    \n",
+    "    pos_coop_score = (np.max(cooop) - total_sum_th)/total_sum_th\n",
+    "    pos_coop_score1 = (np.max(cooop) - total_sum_th1)/total_sum_th1\n",
+    "\n",
+    "    neg_coop_score = (np.min(cooop) - total_diff_th)/total_diff_th\n",
+    "    neg_coop_score1 = (np.min(cooop) - total_diff_th1)/total_diff_th1\n",
+    "        \n",
+    "    return pos_coop_score, pos_coop_score1, neg_coop_score, neg_coop_score1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define_cooperative_net_pos_neg(values[\"ZEB-AP1\"][0][0], values[\"ZEB-AP1\"][1][0], values[\"ZEB-AP1\"][3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define_cooperative_net_pos_neg(values[\"TEAD-AP1\"][0][0], values[\"TEAD-AP1\"][1][0], values[\"TEAD-AP1\"][3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.scatter(values[\"TEAD-AP1\"][4],values[\"TEAD-AP1\"][3])\n",
+    "#plt.axhline(values[\"TEAD-AP1\"][0]+values[\"TEAD-AP1\"][1], color=\"blue\", alpha=0.3)\n",
+    "#plt.axhline(np.abs(values[\"TEAD-AP1\"][0]-values[\"TEAD-AP1\"][1]), color=\"red\", alpha=0.3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.scatter(values[\"ZEB-AP1\"][4],values[\"ZEB-AP1\"][3])\n",
+    "#plt.axhline(values[\"ZEB-AP1\"][0]+values[\"ZEB-AP1\"][1], color=\"blue\", alpha=0.3)\n",
+    "#plt.axhline(values[\"ZEB-AP1\"][1], color=\"green\", alpha=0.3)\n",
+    "#plt.axhline(np.abs(values[\"ZEB-AP1\"][0]-values[\"ZEB-AP1\"][1]), color=\"red\", alpha=0.3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "motif =  \"\"\n",
+    "batch_size=128\n",
+    "full_footprint_control = get_footprint_for_motif(regions_seqs, motif, model, inputlen, batch_size)\n",
+    "values[\"control\"] = full_footprint_control\n",
+    "\n",
+    "# values_new = {}\n",
+    "# for key in values:\n",
+    "#     if key==\"control\":\n",
+    "#         values_new[key] = values[key]\n",
+    "#         continue\n",
+    "    \n",
+    "#     values_new[key] = []\n",
+    "#     #print(values[key])\n",
+    "#     assert(len(values[key])==2)\n",
+    "#     assert(len(values[key][0])==5)\n",
+    "\n",
+    "#     values_new[key] = [values[key][0][0].tolist(), values[key][0][1].tolist(),\n",
+    "#                       [(x[0].tolist(), x[1].tolist()) for x in values[key][0][2]], [(x[0].tolist(), x[1].tolist()) for x in values[key][0][3]],\n",
+    "#                       values[key][0][4], values[key][1]]\n",
+    "        \n",
+    "    \n",
+    "import pickle\n",
+    "\n",
+    "\n",
+    "with open('addons/imr90/coop_matrix_self_new.pkl', 'wb') as handle:\n",
+    "    pickle.dump(values, handle)\n",
+    "\n",
+    "    \n",
+    "#json.dump(values_new,open(\"addons/imr90/coop_matrix_self_new.json\",\"w\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#list(range(1,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.scatter(values[\"metacluster_0.pattern_0-metacluster_0.pattern_1\"][0][3],values[\"metacluster_0.pattern_0-metacluster_0.pattern_1\"][0][2])\n",
+    "#plt.axhline(values[\"metacluster_0.pattern_0-metacluster_0.pattern_1\"][0][1][0]+values[\"metacluster_0.pattern_0-metacluster_0.pattern_1\"][0][0][0])\n",
+    "\n",
+    "#values\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_0-metacluster_0.pattern_1\n",
+      "FOS-CTCF\n",
+      "['ATGACTCAT', 'GCGCCCCCTAGTGG']\n",
+      "ATGACTCAT\n",
+      "GCGCCCCCTAGTGG\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_3\n",
+      "FOS-FOXC1\n",
+      "['ATGACTCAT', 'TGTTTACAT']\n",
+      "ATGACTCAT\n",
+      "TGTTTACAT\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_4\n",
+      "FOS-FOSB+JUNB\n",
+      "['ATGACTCAT', 'TTAGTCAT']\n",
+      "ATGACTCAT\n",
+      "TTAGTCAT\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_5\n",
+      "FOS-SP1/KLF\n",
+      "['ATGACTCAT', 'GGCCCCGCCCCCTCCCCG']\n",
+      "ATGACTCAT\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_6\n",
+      "FOS-NFIC\n",
+      "['ATGACTCAT', 'TTGGCAGCCTGCCAA']\n",
+      "ATGACTCAT\n",
+      "TTGGCAGCCTGCCAA\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_7\n",
+      "FOS-CEBPB\n",
+      "['ATGACTCAT', 'ATTGCGCAAT']\n",
+      "ATGACTCAT\n",
+      "ATTGCGCAAT\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_9\n",
+      "FOS-NFYB\n",
+      "['ATGACTCAT', 'GGCCAATCG']\n",
+      "ATGACTCAT\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_12\n",
+      "FOS-ELK\n",
+      "['ATGACTCAT', 'ACTTCCGG']\n",
+      "ATGACTCAT\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_13\n",
+      "FOS-LYL1\n",
+      "['ATGACTCAT', 'GACGCCAACCAGCTGTT']\n",
+      "ATGACTCAT\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_14\n",
+      "FOS-RUNX\n",
+      "['ATGACTCAT', 'CTGTGGTTT']\n",
+      "ATGACTCAT\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_15\n",
+      "FOS-NRF1\n",
+      "['ATGACTCAT', 'GCGCATGCGCA']\n",
+      "ATGACTCAT\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_16\n",
+      "FOS-ETS1\n",
+      "['ATGACTCAT', 'TCACTTCCTGTTT']\n",
+      "ATGACTCAT\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_17\n",
+      "FOS-ATF4\n",
+      "['ATGACTCAT', 'ATGATGCAATC']\n",
+      "ATGACTCAT\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_18\n",
+      "FOS-ATF1\n",
+      "['ATGACTCAT', 'ATGACGTCA']\n",
+      "ATGACTCAT\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_19\n",
+      "FOS-TEAD4\n",
+      "['ATGACTCAT', 'GCATTCTTGCG']\n",
+      "ATGACTCAT\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_20\n",
+      "FOS-IRF1\n",
+      "['ATGACTCAT', 'TTCACTTTCACTTTC']\n",
+      "ATGACTCAT\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_21\n",
+      "FOS-ZNF76\n",
+      "['ATGACTCAT', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "ATGACTCAT\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_22\n",
+      "FOS-SIX1\n",
+      "['ATGACTCAT', 'GGATCAGGTTACA']\n",
+      "ATGACTCAT\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_27\n",
+      "FOS-TYY1\n",
+      "['ATGACTCAT', 'GCCGCCATCTT']\n",
+      "ATGACTCAT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_29\n",
+      "FOS-ARNTL\n",
+      "['ATGACTCAT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "ATGACTCAT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_31\n",
+      "FOS-SRF\n",
+      "['ATGACTCAT', 'TGCCCTATAAGGG']\n",
+      "ATGACTCAT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_32\n",
+      "FOS-IRF4\n",
+      "['ATGACTCAT', 'CGGTTCCGATTCCGGG']\n",
+      "ATGACTCAT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_33\n",
+      "FOS-RFX2\n",
+      "['ATGACTCAT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "ATGACTCAT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_34\n",
+      "FOS-ZBTB33\n",
+      "['ATGACTCAT', 'TCTCGCGAGA']\n",
+      "ATGACTCAT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_35\n",
+      "FOS-REST\n",
+      "['ATGACTCAT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "ATGACTCAT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_0-metacluster_0.pattern_36\n",
+      "FOS-ZNF76.1\n",
+      "['ATGACTCAT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "ATGACTCAT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_0-metacluster_1.pattern_0\n",
+      "FOS-ZEB1\n",
+      "['ATGACTCAT', 'CAGGTG']\n",
+      "ATGACTCAT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_0-metacluster_1.pattern_1\n",
+      "FOS-JUNB\n",
+      "['ATGACTCAT', 'AATAAGTAGAAAACCACGC']\n",
+      "ATGACTCAT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_0-metacluster_1.pattern_3\n",
+      "FOS-TBD\n",
+      "['ATGACTCAT', 'AGGGGGCC']\n",
+      "ATGACTCAT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_3\n",
+      "CTCF-FOXC1\n",
+      "['GCGCCCCCTAGTGG', 'TGTTTACAT']\n",
+      "GCGCCCCCTAGTGG\n",
+      "TGTTTACAT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_4\n",
+      "CTCF-FOSB+JUNB\n",
+      "['GCGCCCCCTAGTGG', 'TTAGTCAT']\n",
+      "GCGCCCCCTAGTGG\n",
+      "TTAGTCAT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_5\n",
+      "CTCF-SP1/KLF\n",
+      "['GCGCCCCCTAGTGG', 'GGCCCCGCCCCCTCCCCG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_6\n",
+      "CTCF-NFIC\n",
+      "['GCGCCCCCTAGTGG', 'TTGGCAGCCTGCCAA']\n",
+      "GCGCCCCCTAGTGG\n",
+      "TTGGCAGCCTGCCAA\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_7\n",
+      "CTCF-CEBPB\n",
+      "['GCGCCCCCTAGTGG', 'ATTGCGCAAT']\n",
+      "GCGCCCCCTAGTGG\n",
+      "ATTGCGCAAT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_9\n",
+      "CTCF-NFYB\n",
+      "['GCGCCCCCTAGTGG', 'GGCCAATCG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_12\n",
+      "CTCF-ELK\n",
+      "['GCGCCCCCTAGTGG', 'ACTTCCGG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_13\n",
+      "CTCF-LYL1\n",
+      "['GCGCCCCCTAGTGG', 'GACGCCAACCAGCTGTT']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_14\n",
+      "CTCF-RUNX\n",
+      "['GCGCCCCCTAGTGG', 'CTGTGGTTT']\n",
+      "GCGCCCCCTAGTGG\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_15\n",
+      "CTCF-NRF1\n",
+      "['GCGCCCCCTAGTGG', 'GCGCATGCGCA']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_16\n",
+      "CTCF-ETS1\n",
+      "['GCGCCCCCTAGTGG', 'TCACTTCCTGTTT']\n",
+      "GCGCCCCCTAGTGG\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_17\n",
+      "CTCF-ATF4\n",
+      "['GCGCCCCCTAGTGG', 'ATGATGCAATC']\n",
+      "GCGCCCCCTAGTGG\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_18\n",
+      "CTCF-ATF1\n",
+      "['GCGCCCCCTAGTGG', 'ATGACGTCA']\n",
+      "GCGCCCCCTAGTGG\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_19\n",
+      "CTCF-TEAD4\n",
+      "['GCGCCCCCTAGTGG', 'GCATTCTTGCG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_20\n",
+      "CTCF-IRF1\n",
+      "['GCGCCCCCTAGTGG', 'TTCACTTTCACTTTC']\n",
+      "GCGCCCCCTAGTGG\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_21\n",
+      "CTCF-ZNF76\n",
+      "['GCGCCCCCTAGTGG', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_22\n",
+      "CTCF-SIX1\n",
+      "['GCGCCCCCTAGTGG', 'GGATCAGGTTACA']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_27\n",
+      "CTCF-TYY1\n",
+      "['GCGCCCCCTAGTGG', 'GCCGCCATCTT']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_29\n",
+      "CTCF-ARNTL\n",
+      "['GCGCCCCCTAGTGG', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GCGCCCCCTAGTGG\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_31\n",
+      "CTCF-SRF\n",
+      "['GCGCCCCCTAGTGG', 'TGCCCTATAAGGG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_32\n",
+      "CTCF-IRF4\n",
+      "['GCGCCCCCTAGTGG', 'CGGTTCCGATTCCGGG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_33\n",
+      "CTCF-RFX2\n",
+      "['GCGCCCCCTAGTGG', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GCGCCCCCTAGTGG\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_34\n",
+      "CTCF-ZBTB33\n",
+      "['GCGCCCCCTAGTGG', 'TCTCGCGAGA']\n",
+      "GCGCCCCCTAGTGG\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_35\n",
+      "CTCF-REST\n",
+      "['GCGCCCCCTAGTGG', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_1-metacluster_0.pattern_36\n",
+      "CTCF-ZNF76.1\n",
+      "['GCGCCCCCTAGTGG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_1-metacluster_1.pattern_0\n",
+      "CTCF-ZEB1\n",
+      "['GCGCCCCCTAGTGG', 'CAGGTG']\n",
+      "GCGCCCCCTAGTGG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_1-metacluster_1.pattern_1\n",
+      "CTCF-JUNB\n",
+      "['GCGCCCCCTAGTGG', 'AATAAGTAGAAAACCACGC']\n",
+      "GCGCCCCCTAGTGG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_1-metacluster_1.pattern_3\n",
+      "CTCF-TBD\n",
+      "['GCGCCCCCTAGTGG', 'AGGGGGCC']\n",
+      "GCGCCCCCTAGTGG\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_4\n",
+      "FOXC1-FOSB+JUNB\n",
+      "['TGTTTACAT', 'TTAGTCAT']\n",
+      "TGTTTACAT\n",
+      "TTAGTCAT\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_5\n",
+      "FOXC1-SP1/KLF\n",
+      "['TGTTTACAT', 'GGCCCCGCCCCCTCCCCG']\n",
+      "TGTTTACAT\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_6\n",
+      "FOXC1-NFIC\n",
+      "['TGTTTACAT', 'TTGGCAGCCTGCCAA']\n",
+      "TGTTTACAT\n",
+      "TTGGCAGCCTGCCAA\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_7\n",
+      "FOXC1-CEBPB\n",
+      "['TGTTTACAT', 'ATTGCGCAAT']\n",
+      "TGTTTACAT\n",
+      "ATTGCGCAAT\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_9\n",
+      "FOXC1-NFYB\n",
+      "['TGTTTACAT', 'GGCCAATCG']\n",
+      "TGTTTACAT\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_12\n",
+      "FOXC1-ELK\n",
+      "['TGTTTACAT', 'ACTTCCGG']\n",
+      "TGTTTACAT\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_13\n",
+      "FOXC1-LYL1\n",
+      "['TGTTTACAT', 'GACGCCAACCAGCTGTT']\n",
+      "TGTTTACAT\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_14\n",
+      "FOXC1-RUNX\n",
+      "['TGTTTACAT', 'CTGTGGTTT']\n",
+      "TGTTTACAT\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_15\n",
+      "FOXC1-NRF1\n",
+      "['TGTTTACAT', 'GCGCATGCGCA']\n",
+      "TGTTTACAT\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_16\n",
+      "FOXC1-ETS1\n",
+      "['TGTTTACAT', 'TCACTTCCTGTTT']\n",
+      "TGTTTACAT\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_17\n",
+      "FOXC1-ATF4\n",
+      "['TGTTTACAT', 'ATGATGCAATC']\n",
+      "TGTTTACAT\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_18\n",
+      "FOXC1-ATF1\n",
+      "['TGTTTACAT', 'ATGACGTCA']\n",
+      "TGTTTACAT\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_19\n",
+      "FOXC1-TEAD4\n",
+      "['TGTTTACAT', 'GCATTCTTGCG']\n",
+      "TGTTTACAT\n",
+      "GCATTCTTGCG\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_3-metacluster_0.pattern_20\n",
+      "FOXC1-IRF1\n",
+      "['TGTTTACAT', 'TTCACTTTCACTTTC']\n",
+      "TGTTTACAT\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_21\n",
+      "FOXC1-ZNF76\n",
+      "['TGTTTACAT', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "TGTTTACAT\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_22\n",
+      "FOXC1-SIX1\n",
+      "['TGTTTACAT', 'GGATCAGGTTACA']\n",
+      "TGTTTACAT\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_27\n",
+      "FOXC1-TYY1\n",
+      "['TGTTTACAT', 'GCCGCCATCTT']\n",
+      "TGTTTACAT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_29\n",
+      "FOXC1-ARNTL\n",
+      "['TGTTTACAT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "TGTTTACAT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_31\n",
+      "FOXC1-SRF\n",
+      "['TGTTTACAT', 'TGCCCTATAAGGG']\n",
+      "TGTTTACAT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_32\n",
+      "FOXC1-IRF4\n",
+      "['TGTTTACAT', 'CGGTTCCGATTCCGGG']\n",
+      "TGTTTACAT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_33\n",
+      "FOXC1-RFX2\n",
+      "['TGTTTACAT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "TGTTTACAT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_34\n",
+      "FOXC1-ZBTB33\n",
+      "['TGTTTACAT', 'TCTCGCGAGA']\n",
+      "TGTTTACAT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_35\n",
+      "FOXC1-REST\n",
+      "['TGTTTACAT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "TGTTTACAT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_3-metacluster_0.pattern_36\n",
+      "FOXC1-ZNF76.1\n",
+      "['TGTTTACAT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "TGTTTACAT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_3-metacluster_1.pattern_0\n",
+      "FOXC1-ZEB1\n",
+      "['TGTTTACAT', 'CAGGTG']\n",
+      "TGTTTACAT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_3-metacluster_1.pattern_1\n",
+      "FOXC1-JUNB\n",
+      "['TGTTTACAT', 'AATAAGTAGAAAACCACGC']\n",
+      "TGTTTACAT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_3-metacluster_1.pattern_3\n",
+      "FOXC1-TBD\n",
+      "['TGTTTACAT', 'AGGGGGCC']\n",
+      "TGTTTACAT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_5\n",
+      "FOSB+JUNB-SP1/KLF\n",
+      "['TTAGTCAT', 'GGCCCCGCCCCCTCCCCG']\n",
+      "TTAGTCAT\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_6\n",
+      "FOSB+JUNB-NFIC\n",
+      "['TTAGTCAT', 'TTGGCAGCCTGCCAA']\n",
+      "TTAGTCAT\n",
+      "TTGGCAGCCTGCCAA\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_7\n",
+      "FOSB+JUNB-CEBPB\n",
+      "['TTAGTCAT', 'ATTGCGCAAT']\n",
+      "TTAGTCAT\n",
+      "ATTGCGCAAT\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_9\n",
+      "FOSB+JUNB-NFYB\n",
+      "['TTAGTCAT', 'GGCCAATCG']\n",
+      "TTAGTCAT\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_12\n",
+      "FOSB+JUNB-ELK\n",
+      "['TTAGTCAT', 'ACTTCCGG']\n",
+      "TTAGTCAT\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_13\n",
+      "FOSB+JUNB-LYL1\n",
+      "['TTAGTCAT', 'GACGCCAACCAGCTGTT']\n",
+      "TTAGTCAT\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_14\n",
+      "FOSB+JUNB-RUNX\n",
+      "['TTAGTCAT', 'CTGTGGTTT']\n",
+      "TTAGTCAT\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_15\n",
+      "FOSB+JUNB-NRF1\n",
+      "['TTAGTCAT', 'GCGCATGCGCA']\n",
+      "TTAGTCAT\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_16\n",
+      "FOSB+JUNB-ETS1\n",
+      "['TTAGTCAT', 'TCACTTCCTGTTT']\n",
+      "TTAGTCAT\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_17\n",
+      "FOSB+JUNB-ATF4\n",
+      "['TTAGTCAT', 'ATGATGCAATC']\n",
+      "TTAGTCAT\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_18\n",
+      "FOSB+JUNB-ATF1\n",
+      "['TTAGTCAT', 'ATGACGTCA']\n",
+      "TTAGTCAT\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_19\n",
+      "FOSB+JUNB-TEAD4\n",
+      "['TTAGTCAT', 'GCATTCTTGCG']\n",
+      "TTAGTCAT\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_20\n",
+      "FOSB+JUNB-IRF1\n",
+      "['TTAGTCAT', 'TTCACTTTCACTTTC']\n",
+      "TTAGTCAT\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_21\n",
+      "FOSB+JUNB-ZNF76\n",
+      "['TTAGTCAT', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "TTAGTCAT\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_22\n",
+      "FOSB+JUNB-SIX1\n",
+      "['TTAGTCAT', 'GGATCAGGTTACA']\n",
+      "TTAGTCAT\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_27\n",
+      "FOSB+JUNB-TYY1\n",
+      "['TTAGTCAT', 'GCCGCCATCTT']\n",
+      "TTAGTCAT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_29\n",
+      "FOSB+JUNB-ARNTL\n",
+      "['TTAGTCAT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "TTAGTCAT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_31\n",
+      "FOSB+JUNB-SRF\n",
+      "['TTAGTCAT', 'TGCCCTATAAGGG']\n",
+      "TTAGTCAT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_32\n",
+      "FOSB+JUNB-IRF4\n",
+      "['TTAGTCAT', 'CGGTTCCGATTCCGGG']\n",
+      "TTAGTCAT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_33\n",
+      "FOSB+JUNB-RFX2\n",
+      "['TTAGTCAT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "TTAGTCAT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_34\n",
+      "FOSB+JUNB-ZBTB33\n",
+      "['TTAGTCAT', 'TCTCGCGAGA']\n",
+      "TTAGTCAT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_35\n",
+      "FOSB+JUNB-REST\n",
+      "['TTAGTCAT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "TTAGTCAT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_4-metacluster_0.pattern_36\n",
+      "FOSB+JUNB-ZNF76.1\n",
+      "['TTAGTCAT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "TTAGTCAT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_4-metacluster_1.pattern_0\n",
+      "FOSB+JUNB-ZEB1\n",
+      "['TTAGTCAT', 'CAGGTG']\n",
+      "TTAGTCAT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_4-metacluster_1.pattern_1\n",
+      "FOSB+JUNB-JUNB\n",
+      "['TTAGTCAT', 'AATAAGTAGAAAACCACGC']\n",
+      "TTAGTCAT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_4-metacluster_1.pattern_3\n",
+      "FOSB+JUNB-TBD\n",
+      "['TTAGTCAT', 'AGGGGGCC']\n",
+      "TTAGTCAT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_6\n",
+      "SP1/KLF-NFIC\n",
+      "['GGCCCCGCCCCCTCCCCG', 'TTGGCAGCCTGCCAA']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "TTGGCAGCCTGCCAA\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_7\n",
+      "SP1/KLF-CEBPB\n",
+      "['GGCCCCGCCCCCTCCCCG', 'ATTGCGCAAT']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "ATTGCGCAAT\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_9\n",
+      "SP1/KLF-NFYB\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GGCCAATCG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_12\n",
+      "SP1/KLF-ELK\n",
+      "['GGCCCCGCCCCCTCCCCG', 'ACTTCCGG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_13\n",
+      "SP1/KLF-LYL1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GACGCCAACCAGCTGTT']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_14\n",
+      "SP1/KLF-RUNX\n",
+      "['GGCCCCGCCCCCTCCCCG', 'CTGTGGTTT']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_15\n",
+      "SP1/KLF-NRF1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GCGCATGCGCA']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_16\n",
+      "SP1/KLF-ETS1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'TCACTTCCTGTTT']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_17\n",
+      "SP1/KLF-ATF4\n",
+      "['GGCCCCGCCCCCTCCCCG', 'ATGATGCAATC']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_18\n",
+      "SP1/KLF-ATF1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'ATGACGTCA']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_19\n",
+      "SP1/KLF-TEAD4\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GCATTCTTGCG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_20\n",
+      "SP1/KLF-IRF1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'TTCACTTTCACTTTC']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_21\n",
+      "SP1/KLF-ZNF76\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_22\n",
+      "SP1/KLF-SIX1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GGATCAGGTTACA']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_27\n",
+      "SP1/KLF-TYY1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GCCGCCATCTT']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_29\n",
+      "SP1/KLF-ARNTL\n",
+      "['GGCCCCGCCCCCTCCCCG', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_31\n",
+      "SP1/KLF-SRF\n",
+      "['GGCCCCGCCCCCTCCCCG', 'TGCCCTATAAGGG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_32\n",
+      "SP1/KLF-IRF4\n",
+      "['GGCCCCGCCCCCTCCCCG', 'CGGTTCCGATTCCGGG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_33\n",
+      "SP1/KLF-RFX2\n",
+      "['GGCCCCGCCCCCTCCCCG', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_34\n",
+      "SP1/KLF-ZBTB33\n",
+      "['GGCCCCGCCCCCTCCCCG', 'TCTCGCGAGA']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_35\n",
+      "SP1/KLF-REST\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_5-metacluster_0.pattern_36\n",
+      "SP1/KLF-ZNF76.1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_5-metacluster_1.pattern_0\n",
+      "SP1/KLF-ZEB1\n",
+      "['GGCCCCGCCCCCTCCCCG', 'CAGGTG']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_5-metacluster_1.pattern_1\n",
+      "SP1/KLF-JUNB\n",
+      "['GGCCCCGCCCCCTCCCCG', 'AATAAGTAGAAAACCACGC']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_5-metacluster_1.pattern_3\n",
+      "SP1/KLF-TBD\n",
+      "['GGCCCCGCCCCCTCCCCG', 'AGGGGGCC']\n",
+      "GGCCCCGCCCCCTCCCCG\n",
+      "AGGGGGCC\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_6-metacluster_0.pattern_7\n",
+      "NFIC-CEBPB\n",
+      "['TTGGCAGCCTGCCAA', 'ATTGCGCAAT']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "ATTGCGCAAT\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_9\n",
+      "NFIC-NFYB\n",
+      "['TTGGCAGCCTGCCAA', 'GGCCAATCG']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_12\n",
+      "NFIC-ELK\n",
+      "['TTGGCAGCCTGCCAA', 'ACTTCCGG']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_13\n",
+      "NFIC-LYL1\n",
+      "['TTGGCAGCCTGCCAA', 'GACGCCAACCAGCTGTT']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_14\n",
+      "NFIC-RUNX\n",
+      "['TTGGCAGCCTGCCAA', 'CTGTGGTTT']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_15\n",
+      "NFIC-NRF1\n",
+      "['TTGGCAGCCTGCCAA', 'GCGCATGCGCA']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_16\n",
+      "NFIC-ETS1\n",
+      "['TTGGCAGCCTGCCAA', 'TCACTTCCTGTTT']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_17\n",
+      "NFIC-ATF4\n",
+      "['TTGGCAGCCTGCCAA', 'ATGATGCAATC']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_18\n",
+      "NFIC-ATF1\n",
+      "['TTGGCAGCCTGCCAA', 'ATGACGTCA']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_19\n",
+      "NFIC-TEAD4\n",
+      "['TTGGCAGCCTGCCAA', 'GCATTCTTGCG']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_20\n",
+      "NFIC-IRF1\n",
+      "['TTGGCAGCCTGCCAA', 'TTCACTTTCACTTTC']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_21\n",
+      "NFIC-ZNF76\n",
+      "['TTGGCAGCCTGCCAA', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_22\n",
+      "NFIC-SIX1\n",
+      "['TTGGCAGCCTGCCAA', 'GGATCAGGTTACA']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_27\n",
+      "NFIC-TYY1\n",
+      "['TTGGCAGCCTGCCAA', 'GCCGCCATCTT']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_29\n",
+      "NFIC-ARNTL\n",
+      "['TTGGCAGCCTGCCAA', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_31\n",
+      "NFIC-SRF\n",
+      "['TTGGCAGCCTGCCAA', 'TGCCCTATAAGGG']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_32\n",
+      "NFIC-IRF4\n",
+      "['TTGGCAGCCTGCCAA', 'CGGTTCCGATTCCGGG']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_33\n",
+      "NFIC-RFX2\n",
+      "['TTGGCAGCCTGCCAA', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_34\n",
+      "NFIC-ZBTB33\n",
+      "['TTGGCAGCCTGCCAA', 'TCTCGCGAGA']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_35\n",
+      "NFIC-REST\n",
+      "['TTGGCAGCCTGCCAA', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_6-metacluster_0.pattern_36\n",
+      "NFIC-ZNF76.1\n",
+      "['TTGGCAGCCTGCCAA', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_6-metacluster_1.pattern_0\n",
+      "NFIC-ZEB1\n",
+      "['TTGGCAGCCTGCCAA', 'CAGGTG']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_6-metacluster_1.pattern_1\n",
+      "NFIC-JUNB\n",
+      "['TTGGCAGCCTGCCAA', 'AATAAGTAGAAAACCACGC']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_6-metacluster_1.pattern_3\n",
+      "NFIC-TBD\n",
+      "['TTGGCAGCCTGCCAA', 'AGGGGGCC']\n",
+      "TTGGCAGCCTGCCAA\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_9\n",
+      "CEBPB-NFYB\n",
+      "['ATTGCGCAAT', 'GGCCAATCG']\n",
+      "ATTGCGCAAT\n",
+      "GGCCAATCG\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_12\n",
+      "CEBPB-ELK\n",
+      "['ATTGCGCAAT', 'ACTTCCGG']\n",
+      "ATTGCGCAAT\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_13\n",
+      "CEBPB-LYL1\n",
+      "['ATTGCGCAAT', 'GACGCCAACCAGCTGTT']\n",
+      "ATTGCGCAAT\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_14\n",
+      "CEBPB-RUNX\n",
+      "['ATTGCGCAAT', 'CTGTGGTTT']\n",
+      "ATTGCGCAAT\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_15\n",
+      "CEBPB-NRF1\n",
+      "['ATTGCGCAAT', 'GCGCATGCGCA']\n",
+      "ATTGCGCAAT\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_16\n",
+      "CEBPB-ETS1\n",
+      "['ATTGCGCAAT', 'TCACTTCCTGTTT']\n",
+      "ATTGCGCAAT\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_17\n",
+      "CEBPB-ATF4\n",
+      "['ATTGCGCAAT', 'ATGATGCAATC']\n",
+      "ATTGCGCAAT\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_18\n",
+      "CEBPB-ATF1\n",
+      "['ATTGCGCAAT', 'ATGACGTCA']\n",
+      "ATTGCGCAAT\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_19\n",
+      "CEBPB-TEAD4\n",
+      "['ATTGCGCAAT', 'GCATTCTTGCG']\n",
+      "ATTGCGCAAT\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_20\n",
+      "CEBPB-IRF1\n",
+      "['ATTGCGCAAT', 'TTCACTTTCACTTTC']\n",
+      "ATTGCGCAAT\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_21\n",
+      "CEBPB-ZNF76\n",
+      "['ATTGCGCAAT', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "ATTGCGCAAT\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_22\n",
+      "CEBPB-SIX1\n",
+      "['ATTGCGCAAT', 'GGATCAGGTTACA']\n",
+      "ATTGCGCAAT\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_27\n",
+      "CEBPB-TYY1\n",
+      "['ATTGCGCAAT', 'GCCGCCATCTT']\n",
+      "ATTGCGCAAT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_29\n",
+      "CEBPB-ARNTL\n",
+      "['ATTGCGCAAT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "ATTGCGCAAT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_31\n",
+      "CEBPB-SRF\n",
+      "['ATTGCGCAAT', 'TGCCCTATAAGGG']\n",
+      "ATTGCGCAAT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_32\n",
+      "CEBPB-IRF4\n",
+      "['ATTGCGCAAT', 'CGGTTCCGATTCCGGG']\n",
+      "ATTGCGCAAT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_33\n",
+      "CEBPB-RFX2\n",
+      "['ATTGCGCAAT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "ATTGCGCAAT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_34\n",
+      "CEBPB-ZBTB33\n",
+      "['ATTGCGCAAT', 'TCTCGCGAGA']\n",
+      "ATTGCGCAAT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_35\n",
+      "CEBPB-REST\n",
+      "['ATTGCGCAAT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "ATTGCGCAAT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_7-metacluster_0.pattern_36\n",
+      "CEBPB-ZNF76.1\n",
+      "['ATTGCGCAAT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "ATTGCGCAAT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_7-metacluster_1.pattern_0\n",
+      "CEBPB-ZEB1\n",
+      "['ATTGCGCAAT', 'CAGGTG']\n",
+      "ATTGCGCAAT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_7-metacluster_1.pattern_1\n",
+      "CEBPB-JUNB\n",
+      "['ATTGCGCAAT', 'AATAAGTAGAAAACCACGC']\n",
+      "ATTGCGCAAT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_7-metacluster_1.pattern_3\n",
+      "CEBPB-TBD\n",
+      "['ATTGCGCAAT', 'AGGGGGCC']\n",
+      "ATTGCGCAAT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_12\n",
+      "NFYB-ELK\n",
+      "['GGCCAATCG', 'ACTTCCGG']\n",
+      "GGCCAATCG\n",
+      "ACTTCCGG\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_13\n",
+      "NFYB-LYL1\n",
+      "['GGCCAATCG', 'GACGCCAACCAGCTGTT']\n",
+      "GGCCAATCG\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_14\n",
+      "NFYB-RUNX\n",
+      "['GGCCAATCG', 'CTGTGGTTT']\n",
+      "GGCCAATCG\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_15\n",
+      "NFYB-NRF1\n",
+      "['GGCCAATCG', 'GCGCATGCGCA']\n",
+      "GGCCAATCG\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_16\n",
+      "NFYB-ETS1\n",
+      "['GGCCAATCG', 'TCACTTCCTGTTT']\n",
+      "GGCCAATCG\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_17\n",
+      "NFYB-ATF4\n",
+      "['GGCCAATCG', 'ATGATGCAATC']\n",
+      "GGCCAATCG\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_18\n",
+      "NFYB-ATF1\n",
+      "['GGCCAATCG', 'ATGACGTCA']\n",
+      "GGCCAATCG\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_19\n",
+      "NFYB-TEAD4\n",
+      "['GGCCAATCG', 'GCATTCTTGCG']\n",
+      "GGCCAATCG\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_20\n",
+      "NFYB-IRF1\n",
+      "['GGCCAATCG', 'TTCACTTTCACTTTC']\n",
+      "GGCCAATCG\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_21\n",
+      "NFYB-ZNF76\n",
+      "['GGCCAATCG', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "GGCCAATCG\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_22\n",
+      "NFYB-SIX1\n",
+      "['GGCCAATCG', 'GGATCAGGTTACA']\n",
+      "GGCCAATCG\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_27\n",
+      "NFYB-TYY1\n",
+      "['GGCCAATCG', 'GCCGCCATCTT']\n",
+      "GGCCAATCG\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_29\n",
+      "NFYB-ARNTL\n",
+      "['GGCCAATCG', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GGCCAATCG\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_31\n",
+      "NFYB-SRF\n",
+      "['GGCCAATCG', 'TGCCCTATAAGGG']\n",
+      "GGCCAATCG\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_32\n",
+      "NFYB-IRF4\n",
+      "['GGCCAATCG', 'CGGTTCCGATTCCGGG']\n",
+      "GGCCAATCG\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_33\n",
+      "NFYB-RFX2\n",
+      "['GGCCAATCG', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GGCCAATCG\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_34\n",
+      "NFYB-ZBTB33\n",
+      "['GGCCAATCG', 'TCTCGCGAGA']\n",
+      "GGCCAATCG\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_35\n",
+      "NFYB-REST\n",
+      "['GGCCAATCG', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GGCCAATCG\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_9-metacluster_0.pattern_36\n",
+      "NFYB-ZNF76.1\n",
+      "['GGCCAATCG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GGCCAATCG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_9-metacluster_1.pattern_0\n",
+      "NFYB-ZEB1\n",
+      "['GGCCAATCG', 'CAGGTG']\n",
+      "GGCCAATCG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_9-metacluster_1.pattern_1\n",
+      "NFYB-JUNB\n",
+      "['GGCCAATCG', 'AATAAGTAGAAAACCACGC']\n",
+      "GGCCAATCG\n",
+      "AATAAGTAGAAAACCACGC\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_9-metacluster_1.pattern_3\n",
+      "NFYB-TBD\n",
+      "['GGCCAATCG', 'AGGGGGCC']\n",
+      "GGCCAATCG\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_13\n",
+      "ELK-LYL1\n",
+      "['ACTTCCGG', 'GACGCCAACCAGCTGTT']\n",
+      "ACTTCCGG\n",
+      "GACGCCAACCAGCTGTT\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_14\n",
+      "ELK-RUNX\n",
+      "['ACTTCCGG', 'CTGTGGTTT']\n",
+      "ACTTCCGG\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_15\n",
+      "ELK-NRF1\n",
+      "['ACTTCCGG', 'GCGCATGCGCA']\n",
+      "ACTTCCGG\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_16\n",
+      "ELK-ETS1\n",
+      "['ACTTCCGG', 'TCACTTCCTGTTT']\n",
+      "ACTTCCGG\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_17\n",
+      "ELK-ATF4\n",
+      "['ACTTCCGG', 'ATGATGCAATC']\n",
+      "ACTTCCGG\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_18\n",
+      "ELK-ATF1\n",
+      "['ACTTCCGG', 'ATGACGTCA']\n",
+      "ACTTCCGG\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_19\n",
+      "ELK-TEAD4\n",
+      "['ACTTCCGG', 'GCATTCTTGCG']\n",
+      "ACTTCCGG\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_20\n",
+      "ELK-IRF1\n",
+      "['ACTTCCGG', 'TTCACTTTCACTTTC']\n",
+      "ACTTCCGG\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_21\n",
+      "ELK-ZNF76\n",
+      "['ACTTCCGG', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "ACTTCCGG\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_22\n",
+      "ELK-SIX1\n",
+      "['ACTTCCGG', 'GGATCAGGTTACA']\n",
+      "ACTTCCGG\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_27\n",
+      "ELK-TYY1\n",
+      "['ACTTCCGG', 'GCCGCCATCTT']\n",
+      "ACTTCCGG\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_29\n",
+      "ELK-ARNTL\n",
+      "['ACTTCCGG', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "ACTTCCGG\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_31\n",
+      "ELK-SRF\n",
+      "['ACTTCCGG', 'TGCCCTATAAGGG']\n",
+      "ACTTCCGG\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_32\n",
+      "ELK-IRF4\n",
+      "['ACTTCCGG', 'CGGTTCCGATTCCGGG']\n",
+      "ACTTCCGG\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_33\n",
+      "ELK-RFX2\n",
+      "['ACTTCCGG', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "ACTTCCGG\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_34\n",
+      "ELK-ZBTB33\n",
+      "['ACTTCCGG', 'TCTCGCGAGA']\n",
+      "ACTTCCGG\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_35\n",
+      "ELK-REST\n",
+      "['ACTTCCGG', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "ACTTCCGG\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_12-metacluster_0.pattern_36\n",
+      "ELK-ZNF76.1\n",
+      "['ACTTCCGG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "ACTTCCGG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_12-metacluster_1.pattern_0\n",
+      "ELK-ZEB1\n",
+      "['ACTTCCGG', 'CAGGTG']\n",
+      "ACTTCCGG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_12-metacluster_1.pattern_1\n",
+      "ELK-JUNB\n",
+      "['ACTTCCGG', 'AATAAGTAGAAAACCACGC']\n",
+      "ACTTCCGG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_12-metacluster_1.pattern_3\n",
+      "ELK-TBD\n",
+      "['ACTTCCGG', 'AGGGGGCC']\n",
+      "ACTTCCGG\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_14\n",
+      "LYL1-RUNX\n",
+      "['GACGCCAACCAGCTGTT', 'CTGTGGTTT']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "CTGTGGTTT\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_15\n",
+      "LYL1-NRF1\n",
+      "['GACGCCAACCAGCTGTT', 'GCGCATGCGCA']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_16\n",
+      "LYL1-ETS1\n",
+      "['GACGCCAACCAGCTGTT', 'TCACTTCCTGTTT']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_17\n",
+      "LYL1-ATF4\n",
+      "['GACGCCAACCAGCTGTT', 'ATGATGCAATC']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_18\n",
+      "LYL1-ATF1\n",
+      "['GACGCCAACCAGCTGTT', 'ATGACGTCA']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_19\n",
+      "LYL1-TEAD4\n",
+      "['GACGCCAACCAGCTGTT', 'GCATTCTTGCG']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_20\n",
+      "LYL1-IRF1\n",
+      "['GACGCCAACCAGCTGTT', 'TTCACTTTCACTTTC']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_21\n",
+      "LYL1-ZNF76\n",
+      "['GACGCCAACCAGCTGTT', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_22\n",
+      "LYL1-SIX1\n",
+      "['GACGCCAACCAGCTGTT', 'GGATCAGGTTACA']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_27\n",
+      "LYL1-TYY1\n",
+      "['GACGCCAACCAGCTGTT', 'GCCGCCATCTT']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_29\n",
+      "LYL1-ARNTL\n",
+      "['GACGCCAACCAGCTGTT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_31\n",
+      "LYL1-SRF\n",
+      "['GACGCCAACCAGCTGTT', 'TGCCCTATAAGGG']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_32\n",
+      "LYL1-IRF4\n",
+      "['GACGCCAACCAGCTGTT', 'CGGTTCCGATTCCGGG']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_33\n",
+      "LYL1-RFX2\n",
+      "['GACGCCAACCAGCTGTT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_34\n",
+      "LYL1-ZBTB33\n",
+      "['GACGCCAACCAGCTGTT', 'TCTCGCGAGA']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_35\n",
+      "LYL1-REST\n",
+      "['GACGCCAACCAGCTGTT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_13-metacluster_0.pattern_36\n",
+      "LYL1-ZNF76.1\n",
+      "['GACGCCAACCAGCTGTT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_13-metacluster_1.pattern_0\n",
+      "LYL1-ZEB1\n",
+      "['GACGCCAACCAGCTGTT', 'CAGGTG']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_13-metacluster_1.pattern_1\n",
+      "LYL1-JUNB\n",
+      "['GACGCCAACCAGCTGTT', 'AATAAGTAGAAAACCACGC']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_13-metacluster_1.pattern_3\n",
+      "LYL1-TBD\n",
+      "['GACGCCAACCAGCTGTT', 'AGGGGGCC']\n",
+      "GACGCCAACCAGCTGTT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_15\n",
+      "RUNX-NRF1\n",
+      "['CTGTGGTTT', 'GCGCATGCGCA']\n",
+      "CTGTGGTTT\n",
+      "GCGCATGCGCA\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_16\n",
+      "RUNX-ETS1\n",
+      "['CTGTGGTTT', 'TCACTTCCTGTTT']\n",
+      "CTGTGGTTT\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_17\n",
+      "RUNX-ATF4\n",
+      "['CTGTGGTTT', 'ATGATGCAATC']\n",
+      "CTGTGGTTT\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_18\n",
+      "RUNX-ATF1\n",
+      "['CTGTGGTTT', 'ATGACGTCA']\n",
+      "CTGTGGTTT\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_19\n",
+      "RUNX-TEAD4\n",
+      "['CTGTGGTTT', 'GCATTCTTGCG']\n",
+      "CTGTGGTTT\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_20\n",
+      "RUNX-IRF1\n",
+      "['CTGTGGTTT', 'TTCACTTTCACTTTC']\n",
+      "CTGTGGTTT\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_21\n",
+      "RUNX-ZNF76\n",
+      "['CTGTGGTTT', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "CTGTGGTTT\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_22\n",
+      "RUNX-SIX1\n",
+      "['CTGTGGTTT', 'GGATCAGGTTACA']\n",
+      "CTGTGGTTT\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_27\n",
+      "RUNX-TYY1\n",
+      "['CTGTGGTTT', 'GCCGCCATCTT']\n",
+      "CTGTGGTTT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_29\n",
+      "RUNX-ARNTL\n",
+      "['CTGTGGTTT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "CTGTGGTTT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_31\n",
+      "RUNX-SRF\n",
+      "['CTGTGGTTT', 'TGCCCTATAAGGG']\n",
+      "CTGTGGTTT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_32\n",
+      "RUNX-IRF4\n",
+      "['CTGTGGTTT', 'CGGTTCCGATTCCGGG']\n",
+      "CTGTGGTTT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_33\n",
+      "RUNX-RFX2\n",
+      "['CTGTGGTTT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "CTGTGGTTT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_34\n",
+      "RUNX-ZBTB33\n",
+      "['CTGTGGTTT', 'TCTCGCGAGA']\n",
+      "CTGTGGTTT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_35\n",
+      "RUNX-REST\n",
+      "['CTGTGGTTT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "CTGTGGTTT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_14-metacluster_0.pattern_36\n",
+      "RUNX-ZNF76.1\n",
+      "['CTGTGGTTT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "CTGTGGTTT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_14-metacluster_1.pattern_0\n",
+      "RUNX-ZEB1\n",
+      "['CTGTGGTTT', 'CAGGTG']\n",
+      "CTGTGGTTT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_14-metacluster_1.pattern_1\n",
+      "RUNX-JUNB\n",
+      "['CTGTGGTTT', 'AATAAGTAGAAAACCACGC']\n",
+      "CTGTGGTTT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_14-metacluster_1.pattern_3\n",
+      "RUNX-TBD\n",
+      "['CTGTGGTTT', 'AGGGGGCC']\n",
+      "CTGTGGTTT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_16\n",
+      "NRF1-ETS1\n",
+      "['GCGCATGCGCA', 'TCACTTCCTGTTT']\n",
+      "GCGCATGCGCA\n",
+      "TCACTTCCTGTTT\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_17\n",
+      "NRF1-ATF4\n",
+      "['GCGCATGCGCA', 'ATGATGCAATC']\n",
+      "GCGCATGCGCA\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_18\n",
+      "NRF1-ATF1\n",
+      "['GCGCATGCGCA', 'ATGACGTCA']\n",
+      "GCGCATGCGCA\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_19\n",
+      "NRF1-TEAD4\n",
+      "['GCGCATGCGCA', 'GCATTCTTGCG']\n",
+      "GCGCATGCGCA\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_20\n",
+      "NRF1-IRF1\n",
+      "['GCGCATGCGCA', 'TTCACTTTCACTTTC']\n",
+      "GCGCATGCGCA\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_21\n",
+      "NRF1-ZNF76\n",
+      "['GCGCATGCGCA', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "GCGCATGCGCA\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_22\n",
+      "NRF1-SIX1\n",
+      "['GCGCATGCGCA', 'GGATCAGGTTACA']\n",
+      "GCGCATGCGCA\n",
+      "GGATCAGGTTACA\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_15-metacluster_0.pattern_27\n",
+      "NRF1-TYY1\n",
+      "['GCGCATGCGCA', 'GCCGCCATCTT']\n",
+      "GCGCATGCGCA\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_29\n",
+      "NRF1-ARNTL\n",
+      "['GCGCATGCGCA', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GCGCATGCGCA\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_31\n",
+      "NRF1-SRF\n",
+      "['GCGCATGCGCA', 'TGCCCTATAAGGG']\n",
+      "GCGCATGCGCA\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_32\n",
+      "NRF1-IRF4\n",
+      "['GCGCATGCGCA', 'CGGTTCCGATTCCGGG']\n",
+      "GCGCATGCGCA\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_33\n",
+      "NRF1-RFX2\n",
+      "['GCGCATGCGCA', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GCGCATGCGCA\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_34\n",
+      "NRF1-ZBTB33\n",
+      "['GCGCATGCGCA', 'TCTCGCGAGA']\n",
+      "GCGCATGCGCA\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_35\n",
+      "NRF1-REST\n",
+      "['GCGCATGCGCA', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GCGCATGCGCA\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_15-metacluster_0.pattern_36\n",
+      "NRF1-ZNF76.1\n",
+      "['GCGCATGCGCA', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GCGCATGCGCA\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_15-metacluster_1.pattern_0\n",
+      "NRF1-ZEB1\n",
+      "['GCGCATGCGCA', 'CAGGTG']\n",
+      "GCGCATGCGCA\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_15-metacluster_1.pattern_1\n",
+      "NRF1-JUNB\n",
+      "['GCGCATGCGCA', 'AATAAGTAGAAAACCACGC']\n",
+      "GCGCATGCGCA\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_15-metacluster_1.pattern_3\n",
+      "NRF1-TBD\n",
+      "['GCGCATGCGCA', 'AGGGGGCC']\n",
+      "GCGCATGCGCA\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_17\n",
+      "ETS1-ATF4\n",
+      "['TCACTTCCTGTTT', 'ATGATGCAATC']\n",
+      "TCACTTCCTGTTT\n",
+      "ATGATGCAATC\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_18\n",
+      "ETS1-ATF1\n",
+      "['TCACTTCCTGTTT', 'ATGACGTCA']\n",
+      "TCACTTCCTGTTT\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_19\n",
+      "ETS1-TEAD4\n",
+      "['TCACTTCCTGTTT', 'GCATTCTTGCG']\n",
+      "TCACTTCCTGTTT\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_20\n",
+      "ETS1-IRF1\n",
+      "['TCACTTCCTGTTT', 'TTCACTTTCACTTTC']\n",
+      "TCACTTCCTGTTT\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_21\n",
+      "ETS1-ZNF76\n",
+      "['TCACTTCCTGTTT', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "TCACTTCCTGTTT\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_22\n",
+      "ETS1-SIX1\n",
+      "['TCACTTCCTGTTT', 'GGATCAGGTTACA']\n",
+      "TCACTTCCTGTTT\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_27\n",
+      "ETS1-TYY1\n",
+      "['TCACTTCCTGTTT', 'GCCGCCATCTT']\n",
+      "TCACTTCCTGTTT\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_29\n",
+      "ETS1-ARNTL\n",
+      "['TCACTTCCTGTTT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "TCACTTCCTGTTT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_31\n",
+      "ETS1-SRF\n",
+      "['TCACTTCCTGTTT', 'TGCCCTATAAGGG']\n",
+      "TCACTTCCTGTTT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_32\n",
+      "ETS1-IRF4\n",
+      "['TCACTTCCTGTTT', 'CGGTTCCGATTCCGGG']\n",
+      "TCACTTCCTGTTT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_33\n",
+      "ETS1-RFX2\n",
+      "['TCACTTCCTGTTT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "TCACTTCCTGTTT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_34\n",
+      "ETS1-ZBTB33\n",
+      "['TCACTTCCTGTTT', 'TCTCGCGAGA']\n",
+      "TCACTTCCTGTTT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_35\n",
+      "ETS1-REST\n",
+      "['TCACTTCCTGTTT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "TCACTTCCTGTTT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_16-metacluster_0.pattern_36\n",
+      "ETS1-ZNF76.1\n",
+      "['TCACTTCCTGTTT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "TCACTTCCTGTTT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_16-metacluster_1.pattern_0\n",
+      "ETS1-ZEB1\n",
+      "['TCACTTCCTGTTT', 'CAGGTG']\n",
+      "TCACTTCCTGTTT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_16-metacluster_1.pattern_1\n",
+      "ETS1-JUNB\n",
+      "['TCACTTCCTGTTT', 'AATAAGTAGAAAACCACGC']\n",
+      "TCACTTCCTGTTT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_16-metacluster_1.pattern_3\n",
+      "ETS1-TBD\n",
+      "['TCACTTCCTGTTT', 'AGGGGGCC']\n",
+      "TCACTTCCTGTTT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_18\n",
+      "ATF4-ATF1\n",
+      "['ATGATGCAATC', 'ATGACGTCA']\n",
+      "ATGATGCAATC\n",
+      "ATGACGTCA\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_19\n",
+      "ATF4-TEAD4\n",
+      "['ATGATGCAATC', 'GCATTCTTGCG']\n",
+      "ATGATGCAATC\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_20\n",
+      "ATF4-IRF1\n",
+      "['ATGATGCAATC', 'TTCACTTTCACTTTC']\n",
+      "ATGATGCAATC\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_21\n",
+      "ATF4-ZNF76\n",
+      "['ATGATGCAATC', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "ATGATGCAATC\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_22\n",
+      "ATF4-SIX1\n",
+      "['ATGATGCAATC', 'GGATCAGGTTACA']\n",
+      "ATGATGCAATC\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_27\n",
+      "ATF4-TYY1\n",
+      "['ATGATGCAATC', 'GCCGCCATCTT']\n",
+      "ATGATGCAATC\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_29\n",
+      "ATF4-ARNTL\n",
+      "['ATGATGCAATC', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "ATGATGCAATC\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_31\n",
+      "ATF4-SRF\n",
+      "['ATGATGCAATC', 'TGCCCTATAAGGG']\n",
+      "ATGATGCAATC\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_32\n",
+      "ATF4-IRF4\n",
+      "['ATGATGCAATC', 'CGGTTCCGATTCCGGG']\n",
+      "ATGATGCAATC\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_33\n",
+      "ATF4-RFX2\n",
+      "['ATGATGCAATC', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "ATGATGCAATC\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_34\n",
+      "ATF4-ZBTB33\n",
+      "['ATGATGCAATC', 'TCTCGCGAGA']\n",
+      "ATGATGCAATC\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_35\n",
+      "ATF4-REST\n",
+      "['ATGATGCAATC', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "ATGATGCAATC\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_17-metacluster_0.pattern_36\n",
+      "ATF4-ZNF76.1\n",
+      "['ATGATGCAATC', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "ATGATGCAATC\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_17-metacluster_1.pattern_0\n",
+      "ATF4-ZEB1\n",
+      "['ATGATGCAATC', 'CAGGTG']\n",
+      "ATGATGCAATC\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_17-metacluster_1.pattern_1\n",
+      "ATF4-JUNB\n",
+      "['ATGATGCAATC', 'AATAAGTAGAAAACCACGC']\n",
+      "ATGATGCAATC\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_17-metacluster_1.pattern_3\n",
+      "ATF4-TBD\n",
+      "['ATGATGCAATC', 'AGGGGGCC']\n",
+      "ATGATGCAATC\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_19\n",
+      "ATF1-TEAD4\n",
+      "['ATGACGTCA', 'GCATTCTTGCG']\n",
+      "ATGACGTCA\n",
+      "GCATTCTTGCG\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_20\n",
+      "ATF1-IRF1\n",
+      "['ATGACGTCA', 'TTCACTTTCACTTTC']\n",
+      "ATGACGTCA\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_21\n",
+      "ATF1-ZNF76\n",
+      "['ATGACGTCA', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "ATGACGTCA\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_22\n",
+      "ATF1-SIX1\n",
+      "['ATGACGTCA', 'GGATCAGGTTACA']\n",
+      "ATGACGTCA\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_27\n",
+      "ATF1-TYY1\n",
+      "['ATGACGTCA', 'GCCGCCATCTT']\n",
+      "ATGACGTCA\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_29\n",
+      "ATF1-ARNTL\n",
+      "['ATGACGTCA', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "ATGACGTCA\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_31\n",
+      "ATF1-SRF\n",
+      "['ATGACGTCA', 'TGCCCTATAAGGG']\n",
+      "ATGACGTCA\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_32\n",
+      "ATF1-IRF4\n",
+      "['ATGACGTCA', 'CGGTTCCGATTCCGGG']\n",
+      "ATGACGTCA\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_33\n",
+      "ATF1-RFX2\n",
+      "['ATGACGTCA', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "ATGACGTCA\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_34\n",
+      "ATF1-ZBTB33\n",
+      "['ATGACGTCA', 'TCTCGCGAGA']\n",
+      "ATGACGTCA\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_35\n",
+      "ATF1-REST\n",
+      "['ATGACGTCA', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "ATGACGTCA\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_18-metacluster_0.pattern_36\n",
+      "ATF1-ZNF76.1\n",
+      "['ATGACGTCA', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "ATGACGTCA\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_18-metacluster_1.pattern_0\n",
+      "ATF1-ZEB1\n",
+      "['ATGACGTCA', 'CAGGTG']\n",
+      "ATGACGTCA\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_18-metacluster_1.pattern_1\n",
+      "ATF1-JUNB\n",
+      "['ATGACGTCA', 'AATAAGTAGAAAACCACGC']\n",
+      "ATGACGTCA\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_18-metacluster_1.pattern_3\n",
+      "ATF1-TBD\n",
+      "['ATGACGTCA', 'AGGGGGCC']\n",
+      "ATGACGTCA\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_20\n",
+      "TEAD4-IRF1\n",
+      "['GCATTCTTGCG', 'TTCACTTTCACTTTC']\n",
+      "GCATTCTTGCG\n",
+      "TTCACTTTCACTTTC\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_21\n",
+      "TEAD4-ZNF76\n",
+      "['GCATTCTTGCG', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "GCATTCTTGCG\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_22\n",
+      "TEAD4-SIX1\n",
+      "['GCATTCTTGCG', 'GGATCAGGTTACA']\n",
+      "GCATTCTTGCG\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_27\n",
+      "TEAD4-TYY1\n",
+      "['GCATTCTTGCG', 'GCCGCCATCTT']\n",
+      "GCATTCTTGCG\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_29\n",
+      "TEAD4-ARNTL\n",
+      "['GCATTCTTGCG', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GCATTCTTGCG\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_31\n",
+      "TEAD4-SRF\n",
+      "['GCATTCTTGCG', 'TGCCCTATAAGGG']\n",
+      "GCATTCTTGCG\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_32\n",
+      "TEAD4-IRF4\n",
+      "['GCATTCTTGCG', 'CGGTTCCGATTCCGGG']\n",
+      "GCATTCTTGCG\n",
+      "CGGTTCCGATTCCGGG\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_19-metacluster_0.pattern_33\n",
+      "TEAD4-RFX2\n",
+      "['GCATTCTTGCG', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GCATTCTTGCG\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_34\n",
+      "TEAD4-ZBTB33\n",
+      "['GCATTCTTGCG', 'TCTCGCGAGA']\n",
+      "GCATTCTTGCG\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_35\n",
+      "TEAD4-REST\n",
+      "['GCATTCTTGCG', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GCATTCTTGCG\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_19-metacluster_0.pattern_36\n",
+      "TEAD4-ZNF76.1\n",
+      "['GCATTCTTGCG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GCATTCTTGCG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_19-metacluster_1.pattern_0\n",
+      "TEAD4-ZEB1\n",
+      "['GCATTCTTGCG', 'CAGGTG']\n",
+      "GCATTCTTGCG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_19-metacluster_1.pattern_1\n",
+      "TEAD4-JUNB\n",
+      "['GCATTCTTGCG', 'AATAAGTAGAAAACCACGC']\n",
+      "GCATTCTTGCG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_19-metacluster_1.pattern_3\n",
+      "TEAD4-TBD\n",
+      "['GCATTCTTGCG', 'AGGGGGCC']\n",
+      "GCATTCTTGCG\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_21\n",
+      "IRF1-ZNF76\n",
+      "['TTCACTTTCACTTTC', 'GCGGGGCATTCTGGGAATTGTAGTC']\n",
+      "TTCACTTTCACTTTC\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_22\n",
+      "IRF1-SIX1\n",
+      "['TTCACTTTCACTTTC', 'GGATCAGGTTACA']\n",
+      "TTCACTTTCACTTTC\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_27\n",
+      "IRF1-TYY1\n",
+      "['TTCACTTTCACTTTC', 'GCCGCCATCTT']\n",
+      "TTCACTTTCACTTTC\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_29\n",
+      "IRF1-ARNTL\n",
+      "['TTCACTTTCACTTTC', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "TTCACTTTCACTTTC\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_31\n",
+      "IRF1-SRF\n",
+      "['TTCACTTTCACTTTC', 'TGCCCTATAAGGG']\n",
+      "TTCACTTTCACTTTC\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_32\n",
+      "IRF1-IRF4\n",
+      "['TTCACTTTCACTTTC', 'CGGTTCCGATTCCGGG']\n",
+      "TTCACTTTCACTTTC\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_33\n",
+      "IRF1-RFX2\n",
+      "['TTCACTTTCACTTTC', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "TTCACTTTCACTTTC\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_34\n",
+      "IRF1-ZBTB33\n",
+      "['TTCACTTTCACTTTC', 'TCTCGCGAGA']\n",
+      "TTCACTTTCACTTTC\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_35\n",
+      "IRF1-REST\n",
+      "['TTCACTTTCACTTTC', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "TTCACTTTCACTTTC\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_20-metacluster_0.pattern_36\n",
+      "IRF1-ZNF76.1\n",
+      "['TTCACTTTCACTTTC', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "TTCACTTTCACTTTC\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_20-metacluster_1.pattern_0\n",
+      "IRF1-ZEB1\n",
+      "['TTCACTTTCACTTTC', 'CAGGTG']\n",
+      "TTCACTTTCACTTTC\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_20-metacluster_1.pattern_1\n",
+      "IRF1-JUNB\n",
+      "['TTCACTTTCACTTTC', 'AATAAGTAGAAAACCACGC']\n",
+      "TTCACTTTCACTTTC\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_20-metacluster_1.pattern_3\n",
+      "IRF1-TBD\n",
+      "['TTCACTTTCACTTTC', 'AGGGGGCC']\n",
+      "TTCACTTTCACTTTC\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_22\n",
+      "ZNF76-SIX1\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'GGATCAGGTTACA']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "GGATCAGGTTACA\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_27\n",
+      "ZNF76-TYY1\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'GCCGCCATCTT']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_29\n",
+      "ZNF76-ARNTL\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_31\n",
+      "ZNF76-SRF\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'TGCCCTATAAGGG']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_32\n",
+      "ZNF76-IRF4\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'CGGTTCCGATTCCGGG']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_33\n",
+      "ZNF76-RFX2\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_34\n",
+      "ZNF76-ZBTB33\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'TCTCGCGAGA']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_35\n",
+      "ZNF76-REST\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_21-metacluster_0.pattern_36\n",
+      "ZNF76-ZNF76.1\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_21-metacluster_1.pattern_0\n",
+      "ZNF76-ZEB1\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'CAGGTG']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_21-metacluster_1.pattern_1\n",
+      "ZNF76-JUNB\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'AATAAGTAGAAAACCACGC']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_21-metacluster_1.pattern_3\n",
+      "ZNF76-TBD\n",
+      "['GCGGGGCATTCTGGGAATTGTAGTC', 'AGGGGGCC']\n",
+      "GCGGGGCATTCTGGGAATTGTAGTC\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_27\n",
+      "SIX1-TYY1\n",
+      "['GGATCAGGTTACA', 'GCCGCCATCTT']\n",
+      "GGATCAGGTTACA\n",
+      "GCCGCCATCTT\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_29\n",
+      "SIX1-ARNTL\n",
+      "['GGATCAGGTTACA', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GGATCAGGTTACA\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_31\n",
+      "SIX1-SRF\n",
+      "['GGATCAGGTTACA', 'TGCCCTATAAGGG']\n",
+      "GGATCAGGTTACA\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_32\n",
+      "SIX1-IRF4\n",
+      "['GGATCAGGTTACA', 'CGGTTCCGATTCCGGG']\n",
+      "GGATCAGGTTACA\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_33\n",
+      "SIX1-RFX2\n",
+      "['GGATCAGGTTACA', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GGATCAGGTTACA\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_34\n",
+      "SIX1-ZBTB33\n",
+      "['GGATCAGGTTACA', 'TCTCGCGAGA']\n",
+      "GGATCAGGTTACA\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_35\n",
+      "SIX1-REST\n",
+      "['GGATCAGGTTACA', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GGATCAGGTTACA\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_22-metacluster_0.pattern_36\n",
+      "SIX1-ZNF76.1\n",
+      "['GGATCAGGTTACA', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GGATCAGGTTACA\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_22-metacluster_1.pattern_0\n",
+      "SIX1-ZEB1\n",
+      "['GGATCAGGTTACA', 'CAGGTG']\n",
+      "GGATCAGGTTACA\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_22-metacluster_1.pattern_1\n",
+      "SIX1-JUNB\n",
+      "['GGATCAGGTTACA', 'AATAAGTAGAAAACCACGC']\n",
+      "GGATCAGGTTACA\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_22-metacluster_1.pattern_3\n",
+      "SIX1-TBD\n",
+      "['GGATCAGGTTACA', 'AGGGGGCC']\n",
+      "GGATCAGGTTACA\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_29\n",
+      "TYY1-ARNTL\n",
+      "['GCCGCCATCTT', 'CGCGCCGCCCCGGTCACGTGAC']\n",
+      "GCCGCCATCTT\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_31\n",
+      "TYY1-SRF\n",
+      "['GCCGCCATCTT', 'TGCCCTATAAGGG']\n",
+      "GCCGCCATCTT\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_32\n",
+      "TYY1-IRF4\n",
+      "['GCCGCCATCTT', 'CGGTTCCGATTCCGGG']\n",
+      "GCCGCCATCTT\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_33\n",
+      "TYY1-RFX2\n",
+      "['GCCGCCATCTT', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "GCCGCCATCTT\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_34\n",
+      "TYY1-ZBTB33\n",
+      "['GCCGCCATCTT', 'TCTCGCGAGA']\n",
+      "GCCGCCATCTT\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_35\n",
+      "TYY1-REST\n",
+      "['GCCGCCATCTT', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "GCCGCCATCTT\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_27-metacluster_0.pattern_36\n",
+      "TYY1-ZNF76.1\n",
+      "['GCCGCCATCTT', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GCCGCCATCTT\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_27-metacluster_1.pattern_0\n",
+      "TYY1-ZEB1\n",
+      "['GCCGCCATCTT', 'CAGGTG']\n",
+      "GCCGCCATCTT\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_27-metacluster_1.pattern_1\n",
+      "TYY1-JUNB\n",
+      "['GCCGCCATCTT', 'AATAAGTAGAAAACCACGC']\n",
+      "GCCGCCATCTT\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_27-metacluster_1.pattern_3\n",
+      "TYY1-TBD\n",
+      "['GCCGCCATCTT', 'AGGGGGCC']\n",
+      "GCCGCCATCTT\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_29-metacluster_0.pattern_31\n",
+      "ARNTL-SRF\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'TGCCCTATAAGGG']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "TGCCCTATAAGGG\n",
+      "metacluster_0.pattern_29-metacluster_0.pattern_32\n",
+      "ARNTL-IRF4\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'CGGTTCCGATTCCGGG']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_29-metacluster_0.pattern_33\n",
+      "ARNTL-RFX2\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_29-metacluster_0.pattern_34\n",
+      "ARNTL-ZBTB33\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'TCTCGCGAGA']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_29-metacluster_0.pattern_35\n",
+      "ARNTL-REST\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_29-metacluster_0.pattern_36\n",
+      "ARNTL-ZNF76.1\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_29-metacluster_1.pattern_0\n",
+      "ARNTL-ZEB1\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'CAGGTG']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_29-metacluster_1.pattern_1\n",
+      "ARNTL-JUNB\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'AATAAGTAGAAAACCACGC']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "AATAAGTAGAAAACCACGC\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metacluster_0.pattern_29-metacluster_1.pattern_3\n",
+      "ARNTL-TBD\n",
+      "['CGCGCCGCCCCGGTCACGTGAC', 'AGGGGGCC']\n",
+      "CGCGCCGCCCCGGTCACGTGAC\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_31-metacluster_0.pattern_32\n",
+      "SRF-IRF4\n",
+      "['TGCCCTATAAGGG', 'CGGTTCCGATTCCGGG']\n",
+      "TGCCCTATAAGGG\n",
+      "CGGTTCCGATTCCGGG\n",
+      "metacluster_0.pattern_31-metacluster_0.pattern_33\n",
+      "SRF-RFX2\n",
+      "['TGCCCTATAAGGG', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "TGCCCTATAAGGG\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_31-metacluster_0.pattern_34\n",
+      "SRF-ZBTB33\n",
+      "['TGCCCTATAAGGG', 'TCTCGCGAGA']\n",
+      "TGCCCTATAAGGG\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_31-metacluster_0.pattern_35\n",
+      "SRF-REST\n",
+      "['TGCCCTATAAGGG', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "TGCCCTATAAGGG\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_31-metacluster_0.pattern_36\n",
+      "SRF-ZNF76.1\n",
+      "['TGCCCTATAAGGG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "TGCCCTATAAGGG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_31-metacluster_1.pattern_0\n",
+      "SRF-ZEB1\n",
+      "['TGCCCTATAAGGG', 'CAGGTG']\n",
+      "TGCCCTATAAGGG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_31-metacluster_1.pattern_1\n",
+      "SRF-JUNB\n",
+      "['TGCCCTATAAGGG', 'AATAAGTAGAAAACCACGC']\n",
+      "TGCCCTATAAGGG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_31-metacluster_1.pattern_3\n",
+      "SRF-TBD\n",
+      "['TGCCCTATAAGGG', 'AGGGGGCC']\n",
+      "TGCCCTATAAGGG\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_32-metacluster_0.pattern_33\n",
+      "IRF4-RFX2\n",
+      "['CGGTTCCGATTCCGGG', 'CGGCGCCGGTTGCCATGGCAAC']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "metacluster_0.pattern_32-metacluster_0.pattern_34\n",
+      "IRF4-ZBTB33\n",
+      "['CGGTTCCGATTCCGGG', 'TCTCGCGAGA']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_32-metacluster_0.pattern_35\n",
+      "IRF4-REST\n",
+      "['CGGTTCCGATTCCGGG', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_32-metacluster_0.pattern_36\n",
+      "IRF4-ZNF76.1\n",
+      "['CGGTTCCGATTCCGGG', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_32-metacluster_1.pattern_0\n",
+      "IRF4-ZEB1\n",
+      "['CGGTTCCGATTCCGGG', 'CAGGTG']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_32-metacluster_1.pattern_1\n",
+      "IRF4-JUNB\n",
+      "['CGGTTCCGATTCCGGG', 'AATAAGTAGAAAACCACGC']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_32-metacluster_1.pattern_3\n",
+      "IRF4-TBD\n",
+      "['CGGTTCCGATTCCGGG', 'AGGGGGCC']\n",
+      "CGGTTCCGATTCCGGG\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_33-metacluster_0.pattern_34\n",
+      "RFX2-ZBTB33\n",
+      "['CGGCGCCGGTTGCCATGGCAAC', 'TCTCGCGAGA']\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "TCTCGCGAGA\n",
+      "metacluster_0.pattern_33-metacluster_0.pattern_35\n",
+      "RFX2-REST\n",
+      "['CGGCGCCGGTTGCCATGGCAAC', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_33-metacluster_0.pattern_36\n",
+      "RFX2-ZNF76.1\n",
+      "['CGGCGCCGGTTGCCATGGCAAC', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_33-metacluster_1.pattern_0\n",
+      "RFX2-ZEB1\n",
+      "['CGGCGCCGGTTGCCATGGCAAC', 'CAGGTG']\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_33-metacluster_1.pattern_1\n",
+      "RFX2-JUNB\n",
+      "['CGGCGCCGGTTGCCATGGCAAC', 'AATAAGTAGAAAACCACGC']\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_33-metacluster_1.pattern_3\n",
+      "RFX2-TBD\n",
+      "['CGGCGCCGGTTGCCATGGCAAC', 'AGGGGGCC']\n",
+      "CGGCGCCGGTTGCCATGGCAAC\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_34-metacluster_0.pattern_35\n",
+      "ZBTB33-REST\n",
+      "['TCTCGCGAGA', 'GCGCTGTCCGTGGTGCTGA']\n",
+      "TCTCGCGAGA\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "metacluster_0.pattern_34-metacluster_0.pattern_36\n",
+      "ZBTB33-ZNF76.1\n",
+      "['TCTCGCGAGA', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "TCTCGCGAGA\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_34-metacluster_1.pattern_0\n",
+      "ZBTB33-ZEB1\n",
+      "['TCTCGCGAGA', 'CAGGTG']\n",
+      "TCTCGCGAGA\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_34-metacluster_1.pattern_1\n",
+      "ZBTB33-JUNB\n",
+      "['TCTCGCGAGA', 'AATAAGTAGAAAACCACGC']\n",
+      "TCTCGCGAGA\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_34-metacluster_1.pattern_3\n",
+      "ZBTB33-TBD\n",
+      "['TCTCGCGAGA', 'AGGGGGCC']\n",
+      "TCTCGCGAGA\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_35-metacluster_0.pattern_36\n",
+      "REST-ZNF76.1\n",
+      "['GCGCTGTCCGTGGTGCTGA', 'GCGGGGCATGCTGGGAACGGGCGG']\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "metacluster_0.pattern_35-metacluster_1.pattern_0\n",
+      "REST-ZEB1\n",
+      "['GCGCTGTCCGTGGTGCTGA', 'CAGGTG']\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_35-metacluster_1.pattern_1\n",
+      "REST-JUNB\n",
+      "['GCGCTGTCCGTGGTGCTGA', 'AATAAGTAGAAAACCACGC']\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_35-metacluster_1.pattern_3\n",
+      "REST-TBD\n",
+      "['GCGCTGTCCGTGGTGCTGA', 'AGGGGGCC']\n",
+      "GCGCTGTCCGTGGTGCTGA\n",
+      "AGGGGGCC\n",
+      "metacluster_0.pattern_36-metacluster_1.pattern_0\n",
+      "ZNF76.1-ZEB1\n",
+      "['GCGGGGCATGCTGGGAACGGGCGG', 'CAGGTG']\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "CAGGTG\n",
+      "metacluster_0.pattern_36-metacluster_1.pattern_1\n",
+      "ZNF76.1-JUNB\n",
+      "['GCGGGGCATGCTGGGAACGGGCGG', 'AATAAGTAGAAAACCACGC']\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_0.pattern_36-metacluster_1.pattern_3\n",
+      "ZNF76.1-TBD\n",
+      "['GCGGGGCATGCTGGGAACGGGCGG', 'AGGGGGCC']\n",
+      "GCGGGGCATGCTGGGAACGGGCGG\n",
+      "AGGGGGCC\n",
+      "metacluster_1.pattern_0-metacluster_1.pattern_1\n",
+      "ZEB1-JUNB\n",
+      "['CAGGTG', 'AATAAGTAGAAAACCACGC']\n",
+      "CAGGTG\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "metacluster_1.pattern_0-metacluster_1.pattern_3\n",
+      "ZEB1-TBD\n",
+      "['CAGGTG', 'AGGGGGCC']\n",
+      "CAGGTG\n",
+      "AGGGGGCC\n",
+      "metacluster_1.pattern_1-metacluster_1.pattern_3\n",
+      "JUNB-TBD\n",
+      "['AATAAGTAGAAAACCACGC', 'AGGGGGCC']\n",
+      "AATAAGTAGAAAACCACGC\n",
+      "AGGGGGCC\n"
+     ]
+    }
+   ],
+   "source": [
+    "#motifs = [\"TGGAC\",\"TGACTCAT\"]\n",
+    "#motifs = [\"AGGAATGT\",\"TTGACTCA\"]\n",
+    "values_fresh = {}\n",
+    "\n",
+    "#for i in range(annotations.shape[0]-1):\n",
+    "#    for j in range(i+1,annotations.shape[0]):\n",
+    "for i in range(annotations.shape[0]-1):\n",
+    "    for j in range(i+1,annotations.shape[0]):\n",
+    "        label1 = annotations.loc[i,\"Pattern\"] + \"-\" + annotations.loc[j,\"Pattern\"]\n",
+    "        label2 = annotations.loc[i,\"Label\"] + \"-\" + annotations.loc[j,\"Label\"]\n",
+    "        motifs = [annotations.loc[i,\"string\"],annotations.loc[j,\"string\"]]\n",
+    "\n",
+    "        print(label1)\n",
+    "        print(label2)\n",
+    "        print(motifs)\n",
+    "        #list_motifs = [[\"CAGGTG\",\"TGACTCAT\"],[\"AGGAATGT\",\"TTGACTCA\"]]\n",
+    "        #labels = [\"ZEB-AP1\", \"TEAD-AP1\"]\n",
+    "        values_fresh[label1] = [simulate_motif_set(motifs, model, regions_seqs),label2]\n",
+    "        #break\n",
+    "   #break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('addons/imr90/coop_matrix_all_new.pkl', 'wb') as handle:\n",
+    "    pickle.dump(values_fresh, handle)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_5/FOS_simulations.ipynb b/src/evaluation/figure_notebooks/figure_5/FOS_simulations.ipynb
index 127aa6af..1ab48590 100755
--- a/src/evaluation/figure_notebooks/figure_5/FOS_simulations.ipynb
+++ b/src/evaluation/figure_notebooks/figure_5/FOS_simulations.ipynb
@@ -3,6 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "92299645",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,6 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "aa5015e6",
    "metadata": {},
    "outputs": [
     {
@@ -336,6 +338,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "9d31abfc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,6 +402,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "4053513a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,6 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "5980fb41",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,6 +436,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
+   "id": "0585d7a3",
    "metadata": {
     "tags": []
    },
@@ -475,6 +481,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
+   "id": "823dd882",
    "metadata": {},
    "outputs": [
     {
@@ -553,6 +560,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
+   "id": "be437a4d",
    "metadata": {},
    "outputs": [
     {
@@ -631,6 +639,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "b47c5edc",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -638,6 +647,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "74df790f",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -645,6 +655,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "7c0e7639",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -652,6 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "36b73f37",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -659,7 +671,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -673,7 +685,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/src/evaluation/figure_notebooks/figure_5/Untitled.ipynb b/src/evaluation/figure_notebooks/figure_5/Untitled.ipynb
index 838599d9..6988d473 100755
--- a/src/evaluation/figure_notebooks/figure_5/Untitled.ipynb
+++ b/src/evaluation/figure_notebooks/figure_5/Untitled.ipynb
@@ -2,1368 +2,42 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "import h5py\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "    \n",
-    "def import_tfmodisco_motifs(tfm_results_path, trim_threshold, trim=True, only_pos=True):\n",
-    "\t\"\"\"\n",
-    "\tImports the PFMs to into a dictionary, mapping `(x, y)` to the PFM,\n",
-    "\twhere `x` is the metacluster index and `y` is the pattern index.\n",
-    "\tArguments:\n",
-    "\t\t`tfm_results_path`: path to HDF5 containing TF-MoDISco results\n",
-    "\t\t`out_dir`: where to save motifs\n",
-    "\t\t`trim`: if True, trim the motif flanks based on information content\n",
-    "\t\t`only_pos`: if True, only return motifs with positive contributions\n",
-    "\tReturns the dictionary of PFMs.\n",
-    "\t\"\"\" \n",
-    "    \n",
-    "\tdef trim_motif_new(cwm, motif, trim_threshold=0.3):\n",
-    "\t\t\"\"\"\n",
-    "\t\tGiven the PFM and motif (both L x 4 arrays) (the motif could be the\n",
-    "\t\tPFM itself), trims `motif` by cutting off flanks of low information\n",
-    "\t\tcontent in `pfm`. `min_ic` is the minimum required information\n",
-    "\t\tcontent. If specified this trimmed motif will be extended on either\n",
-    "\t\tside by `pad` bases.\n",
-    "\t\tIf no base passes the `min_ic` threshold, then no trimming is done.\n",
-    "\t\t\"\"\"\n",
-    "\n",
-    "\t\tscore = np.sum(np.abs(cwm), axis=1)\n",
-    "\t\ttrim_thresh = np.max(score) * trim_threshold  # Cut off anything less than 30% of max score\n",
-    "\t\tpass_inds = np.where(score >= trim_thresh)[0]\n",
-    "\t\ttrimmed = motif[np.min(pass_inds): np.max(pass_inds) + 1]\n",
-    "\n",
-    "\t\tif not trimmed.size:\n",
-    "\t\t\treturn motif\n",
-    "\n",
-    "\t\treturn trimmed\n",
-    "    \n",
-    "\tpfms = {}\n",
-    "\tcfms = {}\n",
-    "\twith h5py.File(tfm_results_path, \"r\") as f:\n",
-    "\t\tmetaclusters = f[\"metacluster_idx_to_submetacluster_results\"]\n",
-    "\t\tnum_metaclusters = len(metaclusters.keys())\n",
-    "\t\tfor metacluster_i, metacluster_key in enumerate(metaclusters.keys()):\n",
-    "\t\t\tmetacluster = metaclusters[metacluster_key]\n",
-    "\t\t\tif \"patterns\" not in metacluster[\"seqlets_to_patterns_result\"]:\n",
-    "\t\t\t\tcontinue\n",
-    "\t\t\tpatterns = metacluster[\"seqlets_to_patterns_result\"][\"patterns\"]\n",
-    "\t\t\tnum_patterns = len(patterns[\"all_pattern_names\"][:])\n",
-    "\t\t\tfor pattern_i, pattern_name in enumerate(patterns[\"all_pattern_names\"][:]):\n",
-    "\t\t\t\t#if pattern_i > 2:\n",
-    "\t\t\t\t#\tcontinue\n",
-    "\t\t\t\ttry:\n",
-    "\t\t\t\t\tpattern_name = pattern_name.decode()\n",
-    "\t\t\t\texcept:\n",
-    "\t\t\t\t\tpattern_name = pattern_name\n",
-    "\t\t\t\tpattern = patterns[pattern_name]\n",
-    "\t\t\t\tpfm = pattern[\"sequence\"][\"fwd\"][:]\n",
-    "\t\t\t\tcwm = pattern[\"task0_contrib_scores\"][\"fwd\"][:]\n",
-    "\t\t\t\t\n",
-    "\t\t\t\tpfm = trim_motif_new(cwm, pfm, trim_threshold)\n",
-    "\t\t\t\tcfm = trim_motif_new(cwm, cwm, trim_threshold)\n",
-    "\t\t\t\tpfms[\"%d_%d\" % (metacluster_i,pattern_i)] = pfm\n",
-    "\t\t\t\tcfms[\"%d_%d\" % (metacluster_i,pattern_i)] = cfm\n",
-    "\treturn pfms, cfms"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ffms, cfms = import_tfmodisco_motifs(\"/oak/stanford/groups/akundaje/projects/chrombpnet_paper_new/modisco_jun_30/modisco/ATAC/K562/modiscolite_crop_500_1M_seqs/modisco_results_allChroms_counts.hdf5\", 0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27134632"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "def _plot_weights(array,\n",
-    "                  figsize=(10,3),\n",
-    "                 **kwargs):\n",
-    "    \n",
-    "    fig = plt.figure(figsize=figsize)\n",
-    "    ax = fig.add_subplot(111) \n",
-    "    viz_sequence.plot_weights_given_ax(ax=ax, array=array,**kwargs)\n",
-    "    plt.show()\n",
-    "    plt.close()"
+    "27134221+411"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seqlogo\n",
-    "import pandas as pd\n",
-    "from modisco.visualization import viz_sequence\n",
-    "from modisco import util\n",
-    "from matplotlib import pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": false
-   },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_3\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_4\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_5\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_6\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_7\n"
-     ]
-    },
-    {
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WGWPWGGN+EmP9IGPM28aYj4wxS40xn0/FuJ2Zdze9S5G36KD3PMZDS09kY6gxrXlbL76YfOXqxx6TnonFKZ6h7XJJ3s6770Lfvqndt5I67ryzfdXOS0ul3MJPf9q+Y6ekRDypK1ZI2DpZ9u2Dv/1NvKjJcsop4nE98sjkbXe7JcT+zDMyOSSuEyFfwyvl5bDi95KzFQ/jgtG3tr9QqaLkKB0WW8YYN/B34FxgFHC5MWZUi81uBZ6z1o4HvgL8X0fH7ezMWjeL6saD87WKfcUUe1ufyRdsXUAonJ7S3L//vUyDd8qf/iQNc0vSNEvb7YaePUVwdeuWnjGU9hMIwIMPJp+rNWiQiJUzzuiYN7SgAHr3ltyuyy5L7v8+9VT7xrzxRgmZd+uW2AsXj5IS+N73pLZXUVEbG+3YIa63VCxOScVY27dLD0QSjHvYF6StjqJ0MlLh2ZoErLHWrrPW+oFngS+22MYCUad6V2BbCsbt1Ly29jVsixNTY7CRo3sf3Wpbt8vNsoplKbdhxQpJRHbKVVfB1VenLnTYFj6fhGxeeim94yjJ8+abyf+fwkKprzZgQByRkQQulwiXadNgUhKpjA88kPwkkPPOE09eqry4paVw/PHwxBOp2V/OULuBhEIL4OifSn9DRelkpEJs9Qc2N3u9JfJec34BXGmM2QLMAL4ba0fGmGuNMQuNMQt37dqVAtPyk+rGatbtbZ1E6nP7OG3Iabha/NlC4VDMZtUd5c9/Fk+FE/r1g7/+Nf1CK0pBARx7rBSeBPI3vNLJZgw99FBynlCARx8VoZXqkgjFxZL71bt34m03bZIZgskwfDg8/XTqvbhFRXDOOXDTTandb1bZswBMgimjRf2h+9jM2KMoGSZTZ/rLgUettQOAzwNPGNO6xbq19gFr7URr7cTeTs6QnZR5W+a1ytcCGNlrJMf2O5ZS38GKpj5Yn/K8rZoaCas4rZM0bZoIoExSWgp/+Uvkmp+v4ZVQKD/ttla+82bU18OMGcmZcsMN8IUvpD6/L0qXLpIHlqg0xNNPO59wBWLva6+lz+7SUvjNb2DKlPTsP+PsngfBBCq812QIaW8kpXOSCrG1FWheYnBA5L3mfAt4DsBaOw8oBHqlYOxOyZvr3ozZD/H4gccztu9YwrZ19vG8zfNaJc93hGefde50OftsSYj3tV1/NW14vZInpmSf//wnOe/UCSdITmA6vaEFBXD00VKkNB4PPZRcYvxTT4k3N5X1vVpSXAwvv9xJJoNUzCFhGLHn8dmvq6UoaSIVYmsBMMIYM9QY40MS4F9usc0m4EwAY8xIRGwdunHCBMxcM7OVoCr1lTKx30SGdR+GP9z67i9sw6zak2QcJA5PPeUsHOR2S65LuhLiE1FQIAn5SvZ58EGojl+D9wAFBfCvf6XPM9SckpKmNlGxWLkStiWRRfrVr8JZZ6UmvywRXbqI1y3vqXJwbio/TZpBK0onpMNiy1obBG4EXgNWIrMOlxtjfmmMuSCy2Q+Ba4wxS4BngP+yuVpNNcvUB+r5dPenrd43GMb2HYvb5WZot6Gt1lssczakJm8rFJL2Ok649FKZHZhNCnXyUtbZt0+qpTvl+9/PXH4fiOC6//7YocInnnBeMb6oSHIZM2W71yvlLM48MzPjpY2QA7dhlyPSb4eiZImU5GxZa2dYa4+w1g6z1t4Ree82a+3LkecrrLVTrLVjrbXjrLWvp2LczsiHWz+k0NtaPdQH6zmq11EAHHfYca3W1wXqeHXNqymx4aOPnIeD/ud/km/Dk2ryOce8s/Dii87LHvTqJVXWMym2QJLwv/rVg9+zFh55xHkB1h/9KDMereaUlkph4bw9zsMOZ9loyQelE6MlenOMtze8TZ2/9fzzAWUDDjSlnjxgMs+vfJ6G4MF3i3M3zsVa26rKfLK89Zazi8/o0dJcV1Huv9/5LMRbbklvrlNblJZKS5/mLFrkPPTZtavcXGQjZF5eLl7kvCTUKMVKY+SaHkSi2Yrt4V99ocFB1fq4uOCK9NQxVA4d8vVeqdMyffV0grb1FMDxfZtKYo8tH0uBu/XUv4ZgQ6teiu3hpZecFaX8/vc7VsRR6Rzs2yfeUCf07AnXXZd571CUljlizz7rPDH+Bz/InnepSxep55WXuAvAOhArMc57HabDQgugHe0QFKUFKrZyCH/Iz9KdS1u973P7OHHgiQdejykfQ12gtffL7XIzd2MSiTMxCATkbj8RpaVw+eUqthRYsMB53tzNNydXYiHVtAx5z5rlLF+rtBT++78zk9DfFh3p+ZhVXF5w0kDbSV6XouQpKrZyiMXbF1PoaX3VKvIUMa7vuAOvuxZ2pWth11bb1fhrmLlmZodsWLjQWb2syy9vX/87pfPx/vvOKq+7XPDtb2fPq9WSYNB5h4RcCOFlOzeyQ7gd/NGdzFhUlDxFxVYOMXvD7FZ5WCDhwTHlYw56b3Sf0W3uoyPMmiXFKRNx+eWZT3BWcpNZs5wVvz399NxK8l62zHkh3muvzXOxk226HpV4m4o5kKYer4qSbXLo1KdM/2w6/hgVlAs8BfQp6XPQeycOOBFX6yL87G/cz9aqljVlnfOf/yRu0eN2w+TJ7R5C6URY6zxf65vfzC2BPn++sxBi374wVrvIdIzep5LwcuOkyryi5CkqtnKEUDjEwu0LY64b1XtUq/eO7Xcspd7WVy6vy9uhvK01axJvM3688zY+Sudm7Vpn7XmixWdzybP11lvOwp+XXea8DpfSBr1PSFwdfs+H4M5CGwpFyQA5dOo7tFm6cyleV+xs8+MHHN/qvTHlYwjFmOFT7a/m9bXtK2MWCMD+/Ym3O+OM7LTmUXKP+fOdCahzz809gf7++862u/ba7HVI6DT0mAg2gcu8fhvscTA7R1HyEBVbOcLsDbNjhhCjbXpaMqzHMILh2FevWetntcuGLVuczSq74AKt2q4Ic+c6q6919dVSpypX2LcPKioSb3f44TBkSLqtOQQoGeysjtaK30KgKv32KEqGUbGVI0xfPZ3GUOziVmP7tk4YcRkXh3c/POb2FbUVVNQ6uJK0YOPGxJXjPR6YMCHpXSudlNmzE29TUJB77WY+/NDZrMjLLsut0GfeYgyUn0HCEhDbXoWgg9iuUwrLU7cvRekAWkE+B7DW8sHW2M0IG4INHNnzyJjrJh42kZW7W89dL3QX8u6md7lo5EVJ2bFhQ+JQz/DhUl1ePVtKfT2sd1BD99hjpUhuLh0z8+Y5y9c699zcsjuvGfU/sPNNCNbG2cjCsl/CuN9DjJzUpLloR+Jtns5i4TflkEHv2XKAlbtX0lZf7oFlA/G6287lKvK0vj2v9lcza13yocT16xNfgAYP1vpaivDxx86KfB5/vPMSC5nCabmK8eMTb6M4pNeJUNgv8Xar74P9y533VFSUPEDFVg4we8Nswm30DZvQr+2Y3djysTGFmMXy2trXkrZj5crEM8uGDNGq8YqwZo0z4X3WWbnnHXIy63bYsPTbcUhhDBz908SzErHw7qXSU1FROgkqtnKAGatnUB9sXUnU5/ZxwsAT2vx/x5QfQ30gdgXSLfu3sK9hX1J2OLkAHX547lQAV7LL1q3O+gpOmpR+W5LBWtizJ/F2kyerFzflDP6Ks+3qNsGi7yUIOSpK/pASsWWMmWqMWWWMWWOM+Ukb21xqjFlhjFlujHk6FeN2Bqy1vLf5vZjrijxFjC1vu5piWUEZ3Qq7xVxX6C3kvU2x99sWW7Yk3mbUKE0YVoR16xKH4vr1y61CpuCsvAnAqafmnu15j6cIjrjBWfuedY9ISFELnSqdgA5fNo0xbuDvwLnAKOByY8yoFtuMAG4Bplhrjwa+39FxOwtr966NWfIBoD5YH3MmYnOOKT8m5vs1/pqkS0Ds25d4m6FDk9ql0olZty7xNpMnS3J8LrF1q7Ow5qmn6o1FWhh9OxT0crbtRz+CtdPUw6XkPak4lUwC1lhr11lr/cCzwBdbbHMN8Hdr7V4Aa23ydQk6KXM3zsW0MR262FtMr+L4J6UpA6fgjlG/JmzDSTeldlIlu5+D/Fbl0MCJJ3TKlNzzDm3dmlhEFRTojUXa8BTByS84824BLLoJlt2hgkvJa1IhtvoDm5u93hJ5rzlHAEcYY94zxsw3xkxNwbidgldXv0ptIPZJZGSvkQn//7i+4yjxxS5vvbZyLTV+Zy54a53lp+RaorOSPZwUBR0zRnpp5hLbtiUOfw4d6iwfTWknPY+DI28Ct4PprCDFTt+eCnVbVHQpeUmmnOQeYARwGnA58KAxplvLjYwx1xpjFhpjFu7atStDpmWXORvnxHzfYDhx4IkJ///Y8rGEwrFdUkXeIuZvme/IjmDQWcgk1y6cSnYIhZzlPg0YkH5bksVJYn///pocn3aO+V8oHkDCQqdRdr0Lr4yI5HHVaWkIJa9IhdjaCgxs9npA5L3mbAFettYGrLXrgc8Q8XUQ1toHrLUTrbUTe/funQLTcpvN+zdT7a+Oua7UVxq37EOUod2Httm2p9Zfy1vr33Jki9+vYktxTkWFs/6Y5TlYwHvt2sQh88MO02M97bh9cMqL4Emi8WSoQfK4Zk6AtY+IlysQ+xzqaF8xZoErSjpIhdhaAIwwxgw1xviArwAvt9jm34hXC2NMLySs6CC9tnMzd+NcPK7YRfwtNmFyPEjbnuE9hsdcF7IhZqye4ciWRPW1FKU5W7cmFlvG5FY/xChOqt73768h84zQdRScPtN5ODFK1aew4NvwQh/J6apcJOIpUAP+Kojl7Q/Wgn+/eMRqN8Kqv8L0Ua23U5Q00OF2PdbaoDHmRuA1wA1Ms9YuN8b8ElhorX05su5zxpgVQAi42VrroNJN5+a1ta+1mVPVGGzkiJ5HONrPcYcdx/Jdy2OuW7l7JQ3BBgo98a8cPp+zsImGVhQQsZWI3r0hEEjcbzPTOEnsHzZMi/dmjN5T4NRXYM75EEqyL2KoTkpErHsEMNBlOPQ4FrqNg4Ie4jULNUr5iP0rYe9i2PcJhNSjpWSWlJwGrbUzgBkt3rut2XML/HdkUSK8uf7NNtcN6jqoTa9XSyYPmMxzK56jLtD6RFXoKeTDrR9yyuBT4u7D63UmpIJBZ+EjpXOzdasIqXj07y/h6VwrguskHVRnImaYvmfAGbPg7XMidbXa42q3UL1alo3/SLWFitIhcuye89BhecVytlVva3P9hn0bKPmNs1yGsA3TEIyd8VvVWMVd79+VUGwZIzlbiQRXoguscmhQU+NMbOVieNpJA+r+LedTK+mn9wlwznx48wwIVKn3SelUqNjKEtM+mhZ3fciGYnqq2oPTJHm3O7HYqqjIzTwcJbMEAomPlX79cjMU58SD26dP+u1QYtB1FJy/Bj76Iax/QgWX0mnQ+shZYvrq6RkbqzZQS50/sXBzUnzSSXKx0vlxIraKinJvRp+1zor3FhSk3xalDbylMOl+OOMNKQ2RbPJ8qinMwSm1St6hYitLrN27NqPjPbv82YTbHHZY4v18+mkKjFHyHn/sDlMH4fHkXrubcFhC5onINZF4SNJ7Cpy/GkZcD+5CcGVKARvwlEKP42T8i3ZkaFylM5Njp8JDg7WVa9usjZUunl/xfMJthg1LvJ/Vq6FePfuHPE76HXq9zoRNJgkEtJ5cXuEuhGP/IKJn5M3g6wGeLmkaqwhchTDgQjjtVTjnA5ndqCgpQMVWFnjk40cyPuZ7m99LuM3IkYkvjhs35l5jYSXz5KtnKxh0JgBzze5DnuIBMPZXcNFOOPFJ6H2yCCNPFxxXoI+Fu1C8WEWHwTG/hAu3wCkvQJ+Tcu9OQclrNEE+C7zy2SsZH7OqsQp/0I/P03bdhmHDJM8m3mytDRv0HKQ4S3wPhXJvNqLTYzfX7FYiuDww4AJZajdBxTuwZz624j1s1aeEw4ZQ2AOEcBHCEMZisLixuDEu8FKPLR6Iq9ck6H2S9GnsOUlPbEpaUbGVBVbuWpmVcV9c9SKXHX1Zm+sHD058EV2zRitrK85qrTlJos80TuvJ5ZrdysFs2QIvvDCIt9/+KsuWfZVNm8DnCzO873qO7LOEEt8+Cr0NFHobCIS8NAQKaQgUs2nvkazYNpq9VYX06gVHHAGTJ8PFF8uj6i0lXajYyjDbqrYRyFID1X8s+0dcsTVkSOKZWvX1kiQ/NnEnIaUT42S2XjCYe6LF43Fmk5MZi0pm2boV/vlPePhhyR11uQ7OHw0EXCxZO4wlax0knwI7d8ry/vtw331yTF9+OVx5JUxSR5eSYjQzIcM8uuTRrI09Z+OcuOsHDXKW/P6f/2hx00MdJ8nvfn/uiS2Xy9lFVMVWbmAtzJgB48ZJmsNPfwrLlkneaKom6oRCUqR3zx6491446yxpNfWrX0FtbWrGUBQVWxnm35/+O2tjV9ZXEo5z9SsshBIHRevfeMNZFW6l8+Ik+b2iIjdFuZOZhvv3p98OJT4ffADHHQeXXgpLlqRWYLVFc+F1550wYEB6x1MOHVRsZZilO5dmdfwZq2fEXT94cOJ9zJ+veVuHOkVFiRtMb92am4nmTsTW9u3pt0OJzapVcO65cMYZsGhR9rxLdXWwb192xlY6Hyq2MkhlXSWNoezWTXhm2TNx10+dmvhi1NgIn3ySQqOUvKNfv8RJ8lu35ma7nu7dE2+zaVP67VAOxlr4/e9h/Hj1niudD02QzyCPL3082ybw5vo3464/+2y4/36oqoq/n+efh6OPFg+HcujRv3/iMOL27bnpAe3bF3YkKAq+NrMNHg559u+HSy6RZPWOhgoLCuDww6X9mM8nEzUaGmDzZqisTI29ipIsKRFbxpipwD2AG3jIWntnG9tdDDwPHGetXZiKsfOJl1e9nHCbQk8hpgNF+hpDjYRt23lZFbUVWGsxbWQJT5ni7GT3yCNw++3ttVLJd/r3T5yPFQxKCKisLDM2OWXQIPj44/jbbNokvwO9mUg/mzbBaafBtm3tK5g8eTJceKE8HnmkJLfX1R08OcMYEf6NjSKkP/4YXn0Vpk9XD5qSGTostowxbuDvwNnAFmCBMeZla+2KFtt1AW4CPujomPnKnro9cdcXe4u56+y7OjTG9NXTeXX1q4SJLbi8Li/r963n8O6Hx7ahWCrJL02QWlZRIa7+887TatuHIv36ibcgEbt25Z7YctKWautWmU2pYiu9rFwpN3hVVcnNAB03Dr75TSnVUFAgQqp5yLqtY66gQMKU48fDRRfJ67fekpvHf/9bbhAUJR2kwrM1CVhjrV0HYIx5FvgisKLFdr8CfgfcnIIx8w5/yM+ne+J3cZ7QbwLfOe47HRqnf1l/3tn0DlWNseOAPrePuRvntim2AM4/H1asSHzi+eMf4fTToUuaWpUpuUtBgQjzmpr4223d6kzcZJLBg8X+eF6UXE3u70zs2QNnnilJ6E6/6yOPhD//GU4+WUKEHckJ7NpVHj//eTjpJPjTn+D734cXXmj/PhWlLVLhk+gPbG72ekvkvQMYY44FBlprp6dgvLxk0bZFFHraTmAxGE4ceGKHxxlbPpZgqG2VVBOo4dU1r8bdx9lny4U0EXPnag7EoUzv3om3WbUq/XYkS//+iYuybtnirHCr0j4CARE5e/Y4E1o9e8Jjj8FHH0kdrJKS1E6+KCuTMg+PPCJ1vE44IXX7VhTIwGxEY4wLuBv4oYNtrzXGLDTGLNy1a1e6TcsoszfMpiHYdtyl1FfKhH4TOjzOoK6DsMQ/e83eMDvu+uOPdxYiAvFuJfJuKJ2Tww5LvM2770J1dfptSYb+/RMXNt22LTdrhHUWrrtORI2ThuYnnCBdKy67zFnJkY7QpYtM/Jk1C269VavIK6kjFWJrKzCw2esBkfeidAFGA7ONMRuA44GXjTETW+7IWvuAtXaitXZibye3zXnE9NXT8YfaPrOEbZgx5WM6PI4xhhE9RsTdpqqhii1VW9pcX1AAYxyaMm2asxNmOlGxlx2GDEm8zfz5uXfBcpLcD1LjSUk9Dz8Mzz7rLDH9a18T4dOrV2Y9jcXF8OMfa0hRSR2pEFsLgBHGmKHGGB/wFeDAtDtr7X5rbS9r7RBr7RBgPnDBoTQbMRQOsWh7/DN3IBxgeI/hKRlv8oDJcdd73V7mbpwbd5tLLnGWHFxbCz/4QfYETygkoQUl8wwfnlhIRXvY5RJ9+zrz3M6alf0bic5GXR388IfOhNZ//Ze0z3GS0pAOSkvhnHOyM7bS+ejwadBaGwRuBF4DVgLPWWuXG2N+aYy5oKP77wws2bkEryt+gsHQbkNxuxyUtnbApP6TKPG23Xen2l/N62tfj7uPb3zDeV+7J56ADRuy00+usRG+/e3Mj6tIjksiQW6ttFrJJXw+Z22p5s3TsgCp5q9/deZVPPFE+Pvfnf2d0km2hJ7S+UjJPae1doa19ghr7TBr7R2R926z1rYqLGWtPe1Q8moBzNkwJ24IEWDCYR3P14oypnxMQuGWqLhp797SMsNJCMhamYLdnho5HaG2VnLGVq7M7LiKMHiws/yZN97Ivfyn8vLE2yxcqBfbVFJdDXfckVjADhok9a/0u1c6Eznm4O+cTF89PW6bnkJPIcf3Pz5l443uM5q6QPwzWkVtBRW1FXG3+eEPnZ/wli2Du+7KXB+zUEgqlP/qV5kZT2nNscc6C8fNm5e9/nZtMWlS4m2qq7VHYiq5+25ndaweeURCeIrSmVCxlWbCNsz8LfPjbuNz+xjXd1zKxiz2FtOnpE/cbQrdhbyz8Z2420yZ4mx6f5Rf/lJmn6U79BIOSxHEc8/NPY/JoUTPnrIk4oMPcq846OmnO7uReCf+T0RJgr/9LXF3ismTZUnnjENFyQYqttLMyl0r22yNE6U+UJ+SmYjNGVs+Nu76an81b6x7I+42xsDNNzvPmwiF4ItfFC+X09IR7aG2Vi6Wa9akbwzFGU7qEe3dK0Vyc4nJk50l7r/4ovTtUzrGhg3OvJt33517wlxRUoGKrTQzZ+McQuH4meNdC7rStbBrSsc9ceCJeFxt3x5aLG+sjS+2QKZeO02UB8nbOussObmmYyZXba0UQ8y1pOtDlTPPdHZxvP/+3CrRMWqUM6/ojBmpLZ55qDJnDrgTzP8ZMULa8OTa7FVFSQV6WKeZGatnUB+M7zsf3Wd0yscd13ccxd74cZLNVZvZW7837jZdukgxwUQnyuZUV8Mpp0i+S6pCiuGwCK3LLpNQpZIbOA35PP98boWG3G4pXpmIhgZ45ZXkbjiU1syYkVhsn366tkhSOi8qttKItZZ3N8VXBi5cKWnT05Kx5WMJhOLfuhd5i3hv83sJ93XzzTJdPhl27ZKL2dNPdzw5urYWPvtMLuzTD9mGT7nJmDHOZqHu2QMffph+e5LhzDOdeVGmTcu9Kvj5xttvJ97mvPOyX+pBUdKFiq00snbvWgLh+IKnxFfC+H7jUz72gLIBCbep8dfw5rr4JSBAQi6XXpp8BefaWrjmGrjgAqioSJwc25JwWDxjf/gDHHMMLF+e3P9X0o/XK82BnXD//TKxIVeYMsXZrLc338y9Kvj5hpMeqtqPUOnM5JBjv/Mxd+NcDPHP0mEbTpjM3h6MMRzZ80g+3vlx3LFnrp3Jn/hTwv394Q8SCmpPLa233pJq4z/9KXz969L01euNLd7CYfEieL0wcyb87//C0qXJj6lkjtNPl0kRiUJAL70krVpyhcmTnR3PoRD84x9S6DeXQqH5QiDgLAyr5R6auOoq+c4KClovpaVSUHjAABg4UHqUpiuvsLpamrKPdLh9SYn8XoyRUH108XjExl69pD7fEUdIu6/oZxgwQGa+d+Z8PT11pJEZq2dQG4gfQwuEAwzrMSwt408eMDmu2AJYW7mWGn8Npb74Z7reveEXv5ClPWHB6mq45RZZRo8WT9kll0jpgKIiOSHX1sLcufDkk+JN0FYp+cHJJ0ttpEShttpa+M9/4Etfyg3R0rev5CQ6EVwPPyyFe7MpCPz+5MP5uYDfLxfcRDW2cuGYSAXWikBZvFgK4y5ZAhs3wo4dsIRy+rIz7v/fQTmPP972ep9PRJfLJd9pfb0cl+XlIlyOOgqOOw6++lXnImzzZsmFfestuXHaulVSQYJBOT+vNuWU28R2J8rR3bat6ea5sLDpePb7Zazu3eV3OWeOPO9MGJujGYkTJ060Cxfmd6H53n/oze663XG3OarXUay8IT0l0B9e/DA3zbwpruArKyjj+Uue5+xhZyfcXyAgeVhr1uReImtK7XEaM8q1LyFLdm/ZIjPJnJT7GDkyu5XZ6+oOHvsLX5DkbScsXAgTUtfoIWnq61NcFiFDx0s47GyCTV1dfpZ9CAYlH3HmTHj9dUl3CAZFSNTWZqeNWUlJ/AkJ27eLvf/+N7z3ntjp9TrLTfR6xZsW9Uj16yfiz+sVwezzyWcOBJqWPXvkPLF5szwmGifXTq1OMcYsstZOjLWuk9xL5B6b9m+ixp94rvtxhx2XNhvG9h2bsG1Prb+Wt9a/5Uhseb0STpkyJfn8K6XzMmCA3I1u2JB425Ur5e556tTseDJansQvvFDuop14a3/8Y7k4ZcO7FQhIza8rrsj82B3F5ZJzR6JSG9u2wbD0OPlTTigkIv2xx0S0uFxys9H8M8a6+ejTR8Jo0eWII2QZNAi6dpXfRDT0ZoyMEwzKY0ODeJzWrpXf0caNTcvmzQd7DmMdz42Ncvz+8Y/iXfJ6DxZkLe3t2VN6VJ52GowdK+KqvFyO//p6Gc/lEqHldsvz5mFAa0Voh0JNniuXSzxa4bB4zrZulWb1s2eLZ+2zz9rxx8gT1LOVJp5c+iTfmf6duIKryFPEXZ+7i+uPuz4tNtQH6im7s4xgOL7/fkz5GJZc57xw1Y9/LNWgc6lJr3q2yKrdv/mNtE5y4t06+mjxBGTau1VXB/fcI6HsKJWVcpfuNBfx449lskamc0vq6mTmZ0oL+WbweBk5Ej79NP42f/+7NJVPpsxMpqmqkpDy734nf5N4Hhq3W9pCnXWWTBI65hgRGY2N8tUXFiY/6ShKKCTjh0Ii0IqKRLjMnAmvviriZW+kqk8gAHfeKXm30LbNxkge40UXSZpH377yey4tTf/fJCr6wmHxDn75y+kdL13E82x14nS07DJzzcyEni2v25uW5PgoRd4i+pb0Tbjdyl0raQg6L/n+y19KcqMWe1SiJONxWb4cXngh843LGxulEXJzevRIbhbcd76Tea9uQwM88YR4NPKVL3whsUB9443cKnzbHGvh8cehf3+49VbYubNt0XLMMbLtvn0ifG69FSZOFGFVVATduokXq71CC0T8dOki+4qKoUGD4Oqr4dFHJT8MYNEiEbp33in2tmXz6afL8fX66/D974vXraBA7MyE+C0tlaWsLH+FViJUbKWJtzckLixTH6jnmPJj0mrHmL6J2wAVeYv4cKvzIkgFBVI3p1evzj17RHHOkCEy49QpN9/srClxqqipkTFjhVeuvVYuXE6YN0+8BpnsyRkMws9+lrnx0sGZZyYOv86enZs3cNXV4pm6/no5jtry6I8YAe+/D/PnN02m6No1s5MaXK4mIbd6NZx6qoiotmwuLpaiva+8AkOHyu8gF/8GnQG9VKaBnTU72VO3J+F23Qq7UVZQllZbpgycErdtD4jom71hdlL77dNHZg46vUgpnZ9rr3UeGty5E370o44XvHVCY6PMsHrkkdjrL7ggOfF0ww3p7f3ZnJoaCdvvSXw6yWmc5Hnu2wcPPpi579Yp3/qWeN3iHauXXAIffSRhw+Li3JhZ+aUvJf7OH3hAhLAWk00/KRFbxpipxphVxpg1xpifxFj/38aYFcaYpcaYN40xg1Mxbq4yd+NcfO7EtzPH9EmvVwukbU+JN/4vKRAOMP2z5EuzDx8uJ6FszSyLku3xFeHSS5ObeXXfffDyy+nP/auqgvPPb7vWU0kJfO5zzve3caN4LtItFOvqJAfn//4vveNkgrIyCWcl4te/zs7svbaYOVO6VsQLeQ8dKkK+pCR38s38fsmRS1Tf7Mtfzs8ZoPlIh8WWMcYN/B04FxgFXG6MGdVis4+AidbaMcDzwO87Om4u8/q616n2x5/b6sLFCQPTXzJ5TPkYGkOJk2OW7FySsL1PLI47TvJvsiV4ioulX6KSfcrLYXySzRC++U3YtCl9IcW6Omlcvjt+BRauvjo5L+306fCXv6QvxygYlNl5X/96evafDX7968QelN274U9/yozH0wmzZye+GbjootwLvXm9Ek5MxLJlmQ3nH8qkwrM1CVhjrV1nrfUDzwJfbL6BtfZta230kJ0PJO4lk8fMWjcr4TalvlKO7Xds2m3p36U/bpP4dsvn9rF4++J2jTF1qhQh7do1szlcRUWSh5NLVckPda67LrnSCA0NcM456bm41tZKsq+TSc3nnJO8R+XWWyUBOR1hr7o6sakzlVg57zxJvE7EL34hIblcCCf27Jk4kX3TptwrwGwMPPNMYq/VRReJwM0VcduZScWlsT+wudnrLZH32uJbwKuxVhhjrjXGLDTGLNy1a1cKTMs8lfWVbKvelnC7oA2mdSZilGjbnkQ0hhqTzttqzvHHS8Xk/v1lSnM6MUY8Wg88ICdm7VuXO1x4YfLJ45s2yUm/ttZZWxcn1NTAU09JDpATfD6xPZljKRyW/1NZmdqLbV2dhCnXrUvdPnMBY8Rrlci7FQpJ2HfXruyHFK++OrG9zz8vVeJzqRQOiFi/+245V7YV3ty0CQ4/XP4udXW5IXA7KxlNkDfGXAlMBP4Qa7219gFr7URr7cTevXtn0rSU8e6mdyn0JFYboXCIod2HZsAiOH7A8Qm38Yf8TF+dfN5Wcw4/XIrtXXll+vIAioulHcWHH8o4Sm5RViYn+WQ9nG+9JUnUFRUdP+HX1cFPfiI1m5LhmmuSD4fv3Suh06VLO+4dCAZh/35J2Hda1T7fOPtsKeKZSNTu2yeJ23v3ZnbmZ0sKCqSQc3Fx2zZbK5/rqaeyVzG+La67TkqtnHhi26Krvh5+/nM5r95+uxzLDQ1yLGbys9TWyk1SnvpZEtLhoqbGmBOAX1hrz4m8vgXAWvvbFtudBfwVONVaW5Fov/la1PS+hfdx44wb8brbDuKHbZiJh03kvW++d/AKG4bGSmjYCY0V0FAhz+t3QO1GqN8K9TshWAU21GKJugRcYFxg3AeWF6pDfGVDBR6XCzCyHDhzyOswlsO7H56y1kFvvy29uaqrU5PXEu0F9uMfyzT4tM720aKmHWLZMpmV1Z4QWPfuUil9woTkK7X7/XKsnX++TMFvi7Y+vrVSI2n58uTGBTke77lHGgi3Z2ZXXZ1UAT/nHEnAb4vOULx3+XIpnulEnPbvL0J8wIDM5oVGK7ZfcYU0UF++XGb3bdsW34N13HFyfvrc58Tzma1ZfoFA6zyyJUskz/CZZ+R4jVeQtWdPOOkkqb/1uc9JDa+CAvlNh0Ii2oqKkjsPh8Pynfr9cuhFK89XVEhJlddekyryn36ae6dWp8QrapoKseUBPgPOBLYCC4ArrLXLm20zHkmMn2qtXe1kv/kqtsI2TGV9ZfyNQn7KGrfiq1oBez6E3fOhZi0EqsHlBZdPBJMNQ9gP4Y5Xf6wMwYEIjXGDq1DGwEI4BLaB4gvWUFx2eIfHihIMyg/7lltkRlhNTfI/opIS+ZFed514K/r0SZl5bZMjoiVpcsjuqVNlpmp7woIulxTO/e//dnbBivZfW7xYpuBHCzq2RbyPP2OGTLho7w3CFVdIeDsa6k5EMCgz3aZPF6GWyKvXGcQWSF2nyy5zJsgLC+U7veiizIiXqHflC18QT3304zc2Squbu+6S4y3eMdKvn4izyy+XgqaBgISq05ViEQyKeC0slLZZL78s+ayxqK2V4+2FF+Q32tAgoifRMV9UJKI3ugwcKLXFogVQvd6m/ojRNkOBgDzu3CmteDZulN6I0T6JlW1cKnPt1OqUtIqtyACfB/4MuIFp1to7jDG/BBZaa182xswCjgG2R/7LJmvtBfH2ma9iKyY2DHsWwqZ/wvaZUL1KxA5hCLZxe1cyGMpGQmG5LMUDoGQgFPaDwj7g6wHuiGAyEY+VcTV5uawFG4DAfmjYJR6yus1Qu0meN1RAzTqoWgHhiJ/+ihQf4aFGCOwnVL+P11+zPPlcGS+91huXy4KFmjoP1h58wi8qDOLzWhoaXYwfXcU3rtjDxRcG6Nm3C3i7gack/UlaOSRakiKH7P74YwlddCTBu2tX+K//knpc3bvLSbugQD6m3y8fw+OR8M2f/wwrVjjbb7yPb62EUzrSo61XL8n1+cEPmlqyNE+yDoWaGv++8ILk1Xz0UcdtT5osHy+/+Y1U9Hea63TKKdLSZ8iQ9PSnrKuTY+y22+Dee5vy8Fp+/EBA/m533CHtk4yJf5y73eKpPeEECaEeeaQIlL59m7xF0Wbdxhy8hKOnctv0vKBAjvtdu0SwrFkjonDpUql9uH9/bLtjYa3kbb37LsyaJRGJbduaPE8ul3wPDQ3pO234fE1jBQIyVi6FYpMh7WIrHXQKsVWzHlbcKSIrHIBQA9gY82y9ZdB7CpSfAeVnQtmRiMcpAMYDbp94u1KBDYsd4YD8otxFULdFvGtDLk/8/wNV8rlq1slj1acS3myshMA+WR+sgWCdjOX2yWeIzIgMhtws3Xg0K7cewbLNI/ls23AaAwV43QH6dN3NuCGfMHLAKsYOXkb30mZnDRsUL58Ni82eEvnefF1FhJ3xekR0poAcEi1JkWN2n3WWnLxTkfQ+eLDkRg0eLBeaPXtE0K1YkXxyeqKP/9JLkg/Y0fC3yyUCYdIkudB27Sq2Ll0qMyRff11yk5KhM4kta6U22/TpyYnyCy+UhO6ePeVC3ZEK7eGwhNO8Xtnn737XOrwW7+Nv3ixepOeegw8+EFuifQudUFoqIbohQ+SGIuod8nrlzxP12gYC8h1t2iTeoYqEiTjt/LOFQ9jGvezduZ/NG+rYsjnAls1hNmx0sXp9ERu2lLCtogu793fBYHG7g7iwGBNZsJKZYsFisNYceAyHXQRCHspK6ujXq4ZB/WoYNrSOYUMCDBhoGDjQw4DBhfQd2AVP2WHtMD77qNjKNI2VsOgHsPk5CAdjCyyArqNg9O0w4AIRQJ5SSFDtPaPUb4ftr0HlItizAKo+g2A1uIvlBxVqEAEEIoBKD48sQ6GgXDxwhb3A1xN83UQcebqApwiI3sZF88git29Ebt/CjSLaAlXg3wf+vdC4RzxyjRWSwxYVfY27UuuVyzHR4pgcs3v5cgmh5NoMp0Qf31oYPdq5pyyTZENsvfWmxVrxvHg8By8lJeKhKS1tn8O5sVEE16xZyc/mGzdOwrZXXik10kIhsSdeHlE43NTA2eeT3oWPPSb5Qm0VLnX6nfv98MknEs5+/33JQ1q3TsbxeJpyllKd8O9yNeVPRcfo2RO2b4/znxoqYO8S2L8C9i+H/cvkfNq4S26OXd4DN8gSKQlCyA+EwVuG9XZnf2Ag9bacgOlO0PoIhHwEQ14CIS9uVwiPK4DX7cfjCuKlCq/dQ4+CjXhCu+XGPBwUJ4LL23QdiI51aXXT+HmEiq1MEqiC106A6tUSxouJgckPweCvRHK0cqgiXt028cStmwZVq8S2YIxb/NJhMOhS6He2iEZfDwjVRzxPhbKki3AAgvVAZKxwELwpjCvkmGhxTA7afeWVMjU+002n4+Hk48+ZI8VQc206f7J/uoYG6ZG3Y0fTEvWOTHu1L72CO+P+/52mnCPLYifBRUNbDQ1y6HXvLiHUfv2avDV9+8oydWrbHqhwWPKL7ruv/d/3yJEikEeNEk/iEUc0Ca9QSATOpk0ihJYskbDbokXOjsuO/FyixWk3b27KVfp0tZ8Plu5m9foGGvd3h8au2LBMVHK7RTxFZ/OGw01LVPAWFEjuav/+Ur1+xAj5vpvnU7UKszbsgk3PwYanRFyFGuXcGWo4OCfYeKDsKOh2jERYuo2FsiOgoJc4A9wFcr4NB5o5EZpNtjLNX0u6iPwT+RKNC4xXIh7hUOSGer8IvX2fQNVKse/sd9r/pWcRFVuZZPlv4ZP/jZ/UPvTrcNz/SSgsl1j/BHx4HWBFOMWi52Q44XHJIcNEvFSdjBwULY7IQbv37IFhw5rySHIBpx//ooskxJVLBSvj2R4ISGh1wQJ45x0Ja23eLF4Pl6tphl28iuFer3QC6NLlYE+WMU1Jz9Gk/p0744dajWnyuDj5+//jH9KHMJrDlCuk4ufiD/mZuWYmDy56kFnrZ+F1ean2V+PChcftwW3cDOgymG8e820uPOJSehcdhrUHhxWjOV1JsflFWPlHqFwonqJQDDVb0BMGfhmGfxu6jY7cNCPXJ1eGvUvBWhFyvm6ZHTdFqNjKJMt/B5/cHl9sDbkCJj2QWrH1r76S+J5uLqlOrRcpF8lB0eKIHLX72WclYTxXqlQ7/fi7dolQjDdFPtO0tH3fPvj3v2HaNKk95/OJGGorB6p3bzj2WPECDR4sHpH+/UVg9eghCf3RBOVoYnZzooeYyyXbWiu1sHbvbvLgrF8vSduLF4tXLdZ+2mL1arj4Ygm/5dvx0pJQOMTcjXN5+KOHefHTF3Eb94E2bl6XF2MMHpcHl3FR4xfVWuQpImzDjO4zmm9P+DYXj7qYHkU92mfAh9fJDXQsgRVl+LVw7J9lYlVnP69nABVbmSRYC2+cBPtXxhdcE/4Cw66WO4dUJL8/naIZeu4SCMU5y437HRxxo7iDUxUqTIlQdMEVKZrCkqOiJSE5are1cMYZMuMpF/qwJfPxH3sMbrgh9y78CxZIKZT33hOvR1sepiFDZKLCBRfI7NCSEhFTRUWJ29C0l+hsS5dLvFrLlknunlOslUkK3/uelAbI5ndfUJBczqG1lsXbF/Pox4/y5CdPEgqHqPHXYCNhtEJPIQbDN8Z9g5+c9BPKS8t55pNnuG32bVTWVx4QXQAl3hKC4SBTBk7h2gnXcv6R51PsdVhsLByEZx2kp1xWn/qUj1w7n2cQFVuZxr8flvwU1j0idwzhNuIQpYfD0T8TT1eoUUJy7RVeqfJsHXcvrPqr1P0yrtjhxC5HwODLYOhVUDJIZh4alwi19jRHTJXtqUqSz1HRkpActnvLFvGm5EI4MZmPb60Udpw3LzfCiTt3ipdw1qz40/HHj4ff/16q8odC6SmVkG6CQXjkESlk3NiY2fy5aPj0q1+Fhx5KvP2rq1/lng/uYd7meTQEGwiEAwcEFogny2VcnDr4VE4dcmor0WStZfmu5UxfPZ3Kukr8La4ZBe4CQjbEyF4juXjkxdx84s0U+xIIrwU3yjUonmfr8G/CcX+L5L0m0Yk9Hqm68U91KaIMoGIrW9Rtg0/vlqTEQFVT+YKWuAuh5yTocxr0+5wknLuLJHkRIwLMXdDx0gZOfgTRA7x2M2x+ATb+Q8o7BGvBUxwpYdHsx+spkXpg3UZD6Qh57DJcEuY9pSIgbUj+XzgItDzeWtpkD15n3DJD8x8OcsNUbDnbLkt2v/22FIrMdnPlZD/+nj2SeO1kun26OeooCdHF8xA+9JAU04xW6M536urg8celz+Xy5fKZ0iG8PB75zkpLZWLHddfB8OFtb79y10p+9PqPeGPdGwTCbU2GEgwGl8Pzt8UStomT1oZ3H85PTvoJ3xj/jbb3veUVuQbtnifJ77GiFt5uMPAiGPFt6DZOIjI2LOf79kzeyrWb5wyiYivbWCszLDa/ANtmwN6lkcRDt5RSaCVAkNkfXUdFipr2kWKmJQOhaAAUlct6d3GzoqYuudjacFNh03AgUjKhQoTfu19ObGtbB7h/L+xbLkVQq1fLlOHqNVC/TYSYyyuzTFxumko5RGxwecHbtakmlq/rQe2EMO5mdkdsD9bK9GB/ZAnsi293YTlclKB0uFNyXLS0SR7Yfeed8OtfZy805PG0b+r94sXSviSbQtHjEaERbwbd5ZeLKMlWm5h0s3Mn/OtfIiiXL5e8sfr69nkdXa6mDhVRgXXFFeIVbOunVFlfyT+X/5P7F93Psp3LCNrgQR6sKAaD2+WmV3Evynxlre8pHdAYbGRP3R5qA7UxxwDwuXx0KejC18Z8javGXcXY8rGYWMY3Vsr1Z/2TUuYhUC03wmF/5KY+argLuoyAbmMij2NlVmJh70jZnmKaOpsEkb4kzVu/EbkOwUGzELE03Tz74B8OQpcqtjJDpxJbLbFhqF4L+z6WGla73pMaVoH94v1yF8pdCFYO6FADzZrtpI8bXLCvHeMYoBAoAYpbLNH3yoAugAdpf95yiZTYarXUAlVADVAXWWpbPK8Htu1MXS+fPBAtMckDu6OFLGfMyHxZBZdLShPsbOdN96OPwvXXZ0dwFRZKCYWtW+OLxauvlmr6nVVsNaeiQkTwihVSxmHpUkmsD4VEmLpcTT+JaPkEvx8OOwyOPlryyI4+WvphjhzZ9s+n1l/Ly6te5oFFDzBvyzw8Lg+1gdh3CyXeEob3GM4dZ9zB50d8PrbwSZL1e9dzxzt38PQnT2OxNARbJ5F5XB58bh89inpw9firuXLMlQzrMaztnfr3yk3//mWSX7x/maSO1O/gQDQleuN8oKh0QMoZuYvB173Z0rWpcHV0IRxpAxdZgrUyZnTJ5M1zBlGxlWn69m3/Gb0AESVdEYFSFnneM7J0RQRMS7FiIkuY1sKlEdgPVAJ7Is/3IyIm+pgjCcAdIlXHch6Ilpjkid3BoMw4a08hy/bicslsuw8+gMM70P7z7rvh5z/PrFAsLISxY6U0wgknSK2stv6EHo8U6DzuOCnfkDN05JyYBBbYTS+q6UIjBfjx4SGIDz+FNNCP7Xhwnni9vhuM+mEhDRHvT4m3BE+MwtP+kJ/jBxzPr07/FVP6jZGC0IGqpiVY3fTcv08KNPsjXTeCdQd79W30xG2RiIULjJuKYIi7t1fwt50VuI0rIuQifX0iF4DqYAPhyMFx++Tv8ItTbxevlNMUFGvF1ubCKLLs3b+OFVvfZWvFYopCNfTyGMqLutO7qAelvmLMAftDYrfLhRSvdjWJMF83KOgdidj0jhS77t5CvHWXchTpbsuWBlRsZZoMnVhSjsuVWwVukqG8PHEHYqfkiWhpRR7ZHQzCF78oeVzp9hS5XNCtmwiteDk4Tvnd76RRdiYEV0GBTCyYM0e8VWvWSLua9evbDsUaI7MP77pLfhaFhTJjMavk6Tkx4IIPj+kOL7/S9KYNSlpG3SbpN1u9lvLADobbPVI81IYiObbRhDkbSaPoJWE4bwl4yiQh3VsWaTvWXXJcTTNxgmkmwiITrRorqW7YzdLKDSLSgrXyGG6EQE3EYyS/78HnvsOAPuM79Pn31O3hH8v/wQOLHmDVnlVYa2kMNWIwWCwGQ6mvFI/Lw5VjruQb477BuL7jUuLRy0dUbOUzeXQBPYh8tRvy1/Y8szsQgBtvhCefTJ9wKSyEgQPF2zN0aOr2e/fdcOut6RWKxcXSwHj69IO9VNZK3tL//I/cXwSDsXOWjJHejFOnwnnnSVX1+vqmNjvpxO+Xv2lRkdQCKy9P4c6zcZzXboRtM6UK+673ZdKOcR3csgxkpnb56ZLz1G1MpHVZL1kXaqApj8nVNPnH5Utu8lPIL+G8qCcpissLrgJJR6nbCt3HtO+j+mt55bNXuG/hfczfMh+3y01dQH6gBe4CjDFMGTiF5buWU+OvOVCuwmM8FHgK6F7YnW8d+y2+NuZr8UOZnRAVW/lMnl1AD5CvdkP+2p6ndj/2WFMuVCpNKykRofH44yJcUs3bb8OXviQeplCKSwIVFcF3vwu/+U38GYUrVsDTT8tnrKgQT1h1dezvMeolmzBBliFDJH+pd29ptWNMU0HTKLEOqei+jZF9ejxQVSWzNnfskDIfn3wizbY/+kgKnuZtA+3GPTD/G7D9jYi4inFX4PLB6Ntg+DVNhapzrTtIAgKhALPWzeLBxQ/y6ppXD1S4j1LkKcIYw3cmfoebT7yZ8tJyQuEQL6x8gVvfupXtNdsPqhFW4C7AZVwM7T6Ub0/4NpcdfRnlpalU3LmJiq0cIRiUu7zKSjkh1tTI0vx5TY1ss3evPP/Hc85OLJd/JcaMGNO67JXfLydTl0suQF27yom2SxeZkdN8ib7XvXtTdWnH5OmFH8hf2/PVbiS5+Wtfg7VrOz5TsbBQKqn/8Y/S/iWdEY1NmyT/bOXK1MywLCwUAfPYYxJmTYYdO6RNz/vvy+OKFfLZPR459zQ0xM8SiDaV7tevdbsel6upVU+0XU+012JlZfxDKtnCoAnJ5HG+9Bew/NeRPKQ2GHE9TLhHvFR5RDgc5sHFD/Kn+X9iTeUaAEItPqfLuDAYBnYdSP8u/WPmq4HM0ly/bz11gbpWZStcxoW1lq4FXblk1CXcdtptDCgbkJ4PlWXSLraMMVOBewA38JC19s4W6wuAx4EJSIr2ZdbaDfH2ma9iy1qplL1ypUxN/vhjybOorJQTlM8n+RPDh4tbf/hwOPJImUjXsyeUlclJr6hITrzFw/piEuQ6hPuUU7O6KV8pei5q/hhdotPH/X45AdbXi7u/ulpm5KSsqnSeXvittRinhVlzzPZ8/c6jWCutfW68UUKMybbJid4MfOtbUl6iW7eUmxgTa+Gf/5RK83V17QuJejxyXrjmGvjVr+Q8kAq71q2zLFpSz4YtjWzeFmD9esvWLW527/Syt9JDfVWBZPi4GzCuMOCKJF+7cEVn31gpZWBcRm7gjDmQk2NM0yy/QEDs7tlTRNuAAdISqH9/qQafMjJ5nO9bBm+fE0lyb6NMf9FhcNLz0GN8pDhoCirIprEK+/KK5Xzv1e/x9oa32ywpkW76FPfhnqn3cNGoi/C5U9BBJUdIq9gyxriBz4CzgS3AAuBya+2KZttcD4yx1l5njPkKcKG19rJ4+81XsfX001J12Ji2f+vnnAMzZ8rzUKhzFB5sRRYv/NZaqhqr2Nuwl23V25j20TR21+2msr6SqsYqGoIN1AZqqW6spsZf0+pubvsfoG8CL8WOEuh3c9PrYm8xXXxd6OLrQqG3kGJPMd2LutOzqCenDz2dkwedTPei7nQv7I7X3SxbuXKR1CyL1iY78CGiSbFBSa4t6CHrDzSai85air62MOhCZ1/Q5pfaWBGrwKxtVjOHSG6JafZoml6HA5IwbNzNpoK3EK7uIhh0cVzzGhvhlVfgnnukLU1BgQiYloU8jZEbE2vF+3rDDXDVVeKZyQZ1ddKj8K9/lVCa3x+/+KgxTZXdr7oKbropfgJ/faCen7/9c/Y37KfKX8X+hv3sa9jH3vq97KnbzZ6GyuSNtoC/FGr6ylLfHcIesG55jC7WBa5g68VbB6U7oHQHnpL9DOjSk24FXeheUEbXgi6U+UrpWlDGXec9hC9VTeszfW4J1sOWF2HTC7DjDQ78LoJ1HPSbLToM+p0D/c+HrkdD8cCmvC5sUy1CJ+IixYVBN+3fxFNLn+KhxQ+xo3YHDYEGwjHKCXldXoq8RZw44ESO6HWE40KsLdlTu4d5W+axft96wjYcU9SVeksJE+bikRfzzfHf5JTBp7R7vFwh3WLrBOAX1tpzIq9vAbDW/rbZNq9FtplnjPEAO4DeNs7g+Sq2du+Wwo2LF8Nnn8kEnGheg7UirgoLRZCNHSs1XoYMEbe9z9d0go42goWmcGDUOxV9L/rY/P3mTV9jPQ+HW+/X7Za7ao/HEggHCIQC+EN+AuHIYyjAJzs/Ye3etdQH66n111IfrKch2NDm8v7V8xx9X6dOO4UCTwGFnkKKPEUUegqbFm8hpd5SCj2FFHuLOWf4OfjcPnxuH16XVx7dXioqNzL43qM6/sfLAruH+ejpSlCR8ZwF0NNBczknM76iszaX/gKW/a9TM1vREIb3nzac8Wp674wr6M0CjmMx45nHiVTSnSAeSqhjLB8ziQUcy2JGstJx3ciaio34izyEwiHCNkzIhgiFQ4RsmI37NrGjdifBcJDGUGPktxAgGA4QsCECkd9DIBwkGI6uC8p2YT9u4yYYDrJ/0xC2LJjAzhVHsHv9AOr2lYKxYF14C/2UD97D4WO2M/K4bYw7eSslBV48Lg9et48JfcfhdrlxGzcu4zrwvLpqA0MfmpLw8xW6C+le1J0eRT3oXdybLgVdKPGWUOwrlkdvMcXe4jZDQlHCNiw3Jv5a6oJ11PnrqAvUURuoZU/dngM3MDX+mpgX7uZcMvISpn1pGjX+Gmr9tdQGag88f+WzV5i/ZT61gdoD5xZ/yI8/6Mcf9hMMH6xY23Mz5DKuA+eMAk8BBe4CSnwllHhL6OLrwo9P+jGlvlJKvCXyGFlX6is9+ObIhmHvR7B3idwk7f1Yijw37JR17kIRWNG6VN4yKOovBakL+0VmH3aV0gYFPSJlD7rK+66Cg29mjOvgchA2JMVIA/tl1uE78W9aKkPw3Mh7uX/R/azctRKDOVDCoiXR+mC/OfM3nDv83JTNJty4byN3vHMHTy59ss0aYS7josRbgtftPTCjsc3irDlOusXWl4Gp1tqrI6+/Bky21t7YbJtlkW22RF6vjWyzu6395qvYakkwKHkd0dyG6FJdLXlZ0fysqiq5M45GsKICqLhY7t5LSppyJ+Bg8RV9Hq3cEBVUUXEF8hgKNYU5amubcjii2847J3UHd3tOiO2luK4n3+sRpqsxdDUuyowLd7MfqsFisAw8/176lPShT3EvSr0lTT/mqBsy6lw68H9beJtasvJuWPWng97yW9gdgp1BqIpx/bFATRj2Rpab1vaHsq2ttnMDbiM1YD1GLHFF3mteXi362mOkYrXLuA4s0U9gIjLEddB3Yg662ww3Ow+EI++/3beOXm14XXcFoc/6tr+aZMnk8XL72/CLOR3fTzJYIIgHNyFc7Q3dFAD/PhFM1KMZqccULToZapTnk+8XD+IBondkrhbvucQD6fJywKt50LEfWVbdA5/9pX02R/e2ukP/PavY2x38vayVUGPdZqjbcnCdLX9lpK7W3ohQ2i8hyUCNJNyH/c281dHZitExo57jZo/GI4VF/XuJV+z6KxUF/GN/nHYDyHnglMGn8Oszfs1Jg05y9H20h4raCv74/h/524d/oy6YOM5ec0sNJb78mmQAeSS2jDHXAtcCDBo0aMLGjRs7ZNuhhrUdSwZ2eixk8o7DiU0WmzX3c0e/82yRr3YDuWN8OnLfXuwXP3yUrcraHfzO8/XcApm16SD0O8874omtVEyf2AoMbPZ6QOS9WNtsiYQRuyKJ8gdhrX0AeADEs5UC2w4pOnp85uIB7sQm4ziAlHpy8CtzRL7aDeSO8emwI1dblHTws+bruSWr6HfeqUiFO2ABMMIYM9QY4wO+ArzcYpuXgasiz78MvBUvX0tRFEVRFKWz0GHPlrU2aIy5EXgNSTWZZq1dboz5JbDQWvsy8DDwhDFmDdKh7ysdHVdRFEVRFCUfSEkVNmvtDGBGi/dua/a8AbgkFWMpiqIoiqLkE/ld1EJRFEVRFCXHUbGlKIqiKIqSRlRsKYqiKIqipBEVW4qiKIqiKGlExZaiKIqiKEoaUbGlKIqiKIqSRlRsKYqiKIqipBEVW4qiKIqiKGlExZaiKIqiKEoaUbGlKIqiKIqSRlRsKYqiKIqipBEVW4qiKIqiKGlExZaiKIqiKEoaUbGlKIqiKIqSRjoktowxPYwxbxhjVkceu8fYZpwxZp4xZrkxZqkx5rKOjKkoiqIoipJPdNSz9RPgTWvtCODNyOuW1AFft9YeDUwF/myM6dbBcRVFURRFUfKCjoqtLwKPRZ4/Bnyp5QbW2s+stasjz7cBFUDvDo6rKIqiKIqSF3RUbJVba7dHnu8AyuNtbIyZBPiAtR0cV1EURVEUJS/wJNrAGDML6Btj1c+av7DWWmOMjbOffsATwFXW2nAb21wLXBt5WWOMWZXIvhylF7A720a0g3y1G/LX9ny1G/LX9ny1G/LX9ny1G/LX9ny1G/LX9sFtrTDWtqmPEhIRQ6dZa7dHxNRsa+2RMbYrA2YDv7HWPt/uAfMEY8xCa+3EbNuRLPlqN+Sv7flqN+Sv7flqN+Sv7flqN+Sv7flqN+S37W3R0TDiy8BVkedXAS+13MAY4wNeBB4/FISWoiiKoihKczoqtu4EzjbGrAbOirzGGDPRGPNQZJtLgVOA/zLGfBxZxnVwXEVRFEVRlLwgYc5WPKy1e4AzY7y/ELg68vxJ4MmOjJOHPJBtA9pJvtoN+Wt7vtoN+Wt7vtoN+Wt7vtoN+Wt7vtoN+W17TDqUs6UoiqIoiqLER9v1KIqiKIqipBEVWynEGDPVGLPKGLPGGBOrmn5OYoyZZoypMMYsy7YtyWKMGWiMedsYsyLSEuqmbNvkBGNMoTHmQ2PMkojd/5ttm5LBGOM2xnxkjPlPtm1JBmPMBmPMJ5Hc0YXZticZjDHdjDHPG2M+NcasNMackG2bEmGMObJZru7HxpgqY8z3s22XE4wxP4j8NpcZY54xxhRm2yanGGNuiti9PNe/71jXHyetAPMNFVspwhjjBv4OnAuMAi43xozKrlWOeRRppZSPBIEfWmtHAccDN+TJ994InGGtHQuMA6YaY47PrklJcROwMttGtJPTrbXj8nBq+T3ATGvtUcBY8uD7t9auinzX44AJSPu2F7NrVWKMMf2B7wETrbWjATfwlexa5QxjzGjgGmAScpycZ4wZnl2r4vIora8/TloB5hUqtlLHJGCNtXadtdYPPIu0M8p5rLVzgcps29EerLXbrbWLI8+rkQtQ/+xalRgr1EReeiNLXiRQGmMGAF8AHkq0rZIajDFdkVndDwNYa/3W2n1ZNSp5zgTWWms3ZtsQh3iAImOMBygGtmXZHqeMBD6w1tZZa4PAHOCiLNvUJm1cfxK2Asw3VGyljv7A5mavt5AHF/3OhDFmCDAe+CDLpjgiEor7GOkX+oa1Ni/sBv4M/A8QsxNEjmOB140xiyIdK/KFocAu4JFI+PYhY0xJto1Kkq8Az2TbCCdYa7cCdwGbgO3Afmvt69m1yjHLgJONMT2NMcXA54GBWbYpWZJqBZgPqNhSOgXGmFLgBeD71tqqbNvjBGttKBJeGQBMirj/cxpjzHlAhbV2UbZtaScnWWuPRcL9NxhjTsm2QQ7xAMcC91prxwO15FFoJVLc+gLgn9m2xQmRHKEvIiL3MKDEGHNldq1yhrV2JfA74HVgJvAxEMqmTR3BSsmEvPD6x0PFVurYysF3DwMi7ylpxhjjRYTWU9baf2XbnmSJhIPeJj/y5qYAFxhjNiCh8jOMMXlTRy/iscBaW4HkDk3KrkWO2QJsaeb9fB4RX/nCucBia+3ObBvikLOA9dbaXdbaAPAv4MQs2+QYa+3D1toJ1tpTgL3AZ9m2KUl2RloARvsqV2TZng6jYit1LABGGGOGRu7ivoK0M1LSiDHGIHksK621d2fbHqcYY3obY7pFnhcBZwOfZtUoB1hrb7HWDrDWDkGO8bestXlxx2+MKTHGdIk+Bz6HhFxyHmvtDmCzMSbae/ZMYEUWTUqWy8mTEGKETcDxxpjiyDnmTPJgQkIUY0yfyOMgJF/r6exalDQJWwHmGx2qIK80Ya0NGmNuBF5DZq5Ms9Yuz7JZjjDGPAOcBvQyxmwBbrfWPpxdqxwzBfga8Ekk/wngp9baGdkzyRH9gMcis1hdwHPW2rwqo5CHlAMvyrUTD/C0tXZmdk1Kiu8CT0Vu5tYB38iyPY6ICNuzgW9n2xanWGs/MMY8DyxGZjx/RH5VNX/BGNMTCAA35PJkiljXH6T133PGmG8BG5G2f3mNVpBXFEVRFEVJIxpGVBRFURRFSSMqthRFURRFUdKIii1FURRFUZQ0omJLURRFURQljajYUhRFURRFSSMqthRFURRFUdKIii1FURRFUZQ0omJLURRFURQljfw/RoVpMLO1DGsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_8\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_9\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_10\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_11\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_12\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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g6Y36BoulD5z4XzhrCYz/GZRNg9JJMOgCOOafcNEeGHh+7GN37hTSGYm8U+my/VkgDd2fgAu2/E3x0mOPJfZo2e1CC6q0NPWPDOEP+qluqw7rFUV7uQ4q3/y/2J6OwpEw4wX4Vguc+w2cswq+dQCOfwWKxiR1+HvuST58qKYnDGAFvg6RB5hMmCeagFOEy2Ox7anUKwxBjE2xaN8qqm1TJeCErXHONYrqVlHIEfKeZ4NV9avC/8+Wd/eL3V/Q1T2OOX1OvtzzZVaOm1U2P5KaoQEiTN04PxdnkzQtbuGJ2t26O7MDZZLwv+fVzD67l5A3trY+lnTpeiq8846QdEiXriTslAXVC/BEnfui6hQy6/cthPZNaCpiCobC+Zug6nzhwTKqPBnmYiFoecJrMCWG/MWaX8efsCxlMPaHQq/syAeg/Ojkz1sOCm9cuoacOzJxuN1w//2xja1p0+Dtt+HAAdi0SVQKL18O3/pW6h+7rXkbVpM17NnatD97GkNp074V9v5XP1dv+HVw3moYepW4DsxF4mEqgCGXw7mrYOT34h5+9WpYmYEDr8eLkfa+lb5YqTvGyj8YgF0vpG7AQXfFWgx2PBs/0T4eSVb6hnK2DrhT9MjEQJblsLdMQqKmvQZ/MI3vRcWSvUsUz1MaB3uC1vURUeBUkdIXpM2UQDCA0ycMxOq2DCuotz6eurEJYoG8LU61+yHE4W1syXL3iiPNZJ04h7333vS9WskSSo4PsbV5a/Ll1Bsf1K6cbf3hzMVCYNSUQK3TVADjfgzjfqJ8vatJTFp6k4tkgKP+ChfXwBF/gtG3CM/Z6Z/DeWuhcETi8278PLPfKxhxlzz3nPBA6nHrrfDll6JYwWYT4rMWi9CJ+ve/4ZVXxPNkWde0LmxogVDR3udMLEeRU7Y+JvIV1Yy+DY5+AkyF+saHwSh+/2mPiGsgBr/5TeLqw17F1n/EN3DiESv02Ph5+kZRLJmUYAB2PJdeMQPo/+Y67HeL8I0n4KHTm/lgVt9ZrxifrCYrOw7syOiY7Z52GjuVnrfqtmrcvp52i8Zhx7/S82xC1uemVNjv2o/cfQ3WdGRQJNG5Uyzs0sWZvarVg8nhbWw1LUh/cI3DunVC6iGXOL1OTTmu2WBma3MSF7W7vluyQjWYn/QW2Pomv7o3FQjvVsXxkde2Pq6/rdEGp3wII28SCtym7v5EklFM6sUT4JyVUH5M/M/c/LfMfrOowf6vf9UPcf361/DQQ8LAMujcIYWFcOGFMG+e/vt6rKpfpZiwbCYb65qykyCcFkEf7HxeaxRXng1TH0ouL89UAFPuh4EXaN5qbYWPP05dpPeg4dwLrRlUx8XyXG3/Z/bHmKYv0je0IKmqY4/fg8snJnqL0ZKVarT1TeuxRnWHMEgGRcJ8OqyoW6Fp+2M32xXhyoNKMCBygtOt9D6IRCfFZyRCu/N54uorJiIL3s/ewOFtbG17Kn6uVp+pcOYiuMIF326DY/8Ftn4JD/vKK+lJCKRCdHJ8CEmSkkuS3/4MmpynYdeIHoUpJkBjcsDxL0ee7/y3fojvuOeh36lgLtQ/jsEIllI4bW7sHnFd+0X5ehbYulWIzKo57TS46y5haMWjoACmThVhyGT4quar8CoRoMvfdXArEmtna18zl8AJr0YM4WQwOeD4lzS9Et97LzXP30Fn1wvZP6bf2f09ZzkeuvXJnCwSo6nvrMfW7d02GUzUdmSeJL++ab3C4+T0OjNecHxd83X4mGaD6N/n8XuSV5LPNRl5Ng8u9R31mCSx8A7lbqWMLMP2pzOrdj8EDVU9Dl9jK+DtLq2OMRBOuV8YWuUzRKNYczEMuxou3An9T4t5WFkWlYS5NrZW1K3A253YbOvuI9Xh7UguSX7P60qDSDLCUX+LbQglIjTRtm8Fj07lyJBvw8BZwqOVCIMdTnwT3QT4ujmiMi4LvPaa1utSWip00ZJtCl5QIPSgTjop8bYbmzYChJuFewIevtqbQkFDttn6d6EsHs2U+8CQYrNHEAb6EX9SvPTcc8mF0Y0HLyVFyfZ/Zr+gY+87Wbtew/i7VeazbcCpqOuoCxsvyJH8rUxYVrsMb5RHLiAHMq4cnLdrHr7uEF2/ArEQ9gQ8fLYrsdh0j5ALz2YPUd9ZjyRJSEgE5EB6oeTmpdpx5jDl8DW2Gj4VTZD1GHULjPuRWLVHx4mMNhE6OfldKNVvzLpliwihxMJohJ/9TGzj94vtZ8xI/fQXVi+kyy8mh36FEW9bwuRQXwd0qrRbqmal3vw5mlCn+r1va7OazaWiuWoyYSkAo1k0+x59q/a9vW9mbeB6/nltPtG996beWLqgAJ56Kv42bV1ttHmEDIZBMmDpLjpY1XCQQh1GtP0irX1FwnsyBrHmeDYYdm34aWcnfJ2g2PKGG6ClRcic7N+fXhP2rNG5Gzw5yJ+rfj37E03Twszu1SSp66gLe2K9AW9WjK1QH1ezwRy+BzL1bIXChWaDmUn9JoU9Mctrl8fbrWeQg1D/Cbk2jHNFfUd92JC1mWzpqcjXvJeTArRDkcPX2Kp5TzRZVlMxA456OL5xYCqA0z7T3Wb27Nh5KsXFokLrN7+BkhJheI0ZA599lnw4KkS0m3x8xfhwSHHz/s3xk+SbFghPXTQT7hKeu0zZ/ZLWOzDh57GN2liYC7WVjsGAcMlngR07tCHEsjK45RYh85AqgwfHf39d07rw7+ML+sLhmT2texJXY/Xvn/oJJWJqGRhUE/bw75CtSeHzz2MbrSYTfPGFaN9TWirWMuXl8PTT4t5JNgcuqzTOQ1igWUSWoTEHEgT1H/WIp6S2vTYsK+OX/exqyUzYNCgHw/IBZqMZo0F8343OxrRbV9W014QXnHaznelV03FYxH3W5mk7+AUorQcxJzML7G7bHf6/UTKml7dX+0F6lbj/gxy+xlbDXP3Xj3lKrNTjIRmEoTXhLs1br74aW7bh5Zdh1CiRYB2NwwE/+pGofEsGp9cZTl4sMBcwdcDUsMvfZDCxrTmO6nD9J8qeW6YiKJue3AfHo6sJOrYrXzM6YMwPUssBCqEOv7SsIuHlWjQapj4MxzwLg2J/mYsXa8NXmaiVFxXFf39t49pwyNdhcoSNYavJyvYD2+PtCg0NYuKO9dAj3vayDC/8UptgPfaH6f1OIaI8Ym++GVv25C9/gaOP1t4DhYVw8slCm67HqfkgvbL0eLRtAHJQHVD7gaLII1dUt1UrZGV2tqbQcDvG8UIGloQUvgfsJntyRT06LKtdFh73AsEApw49NXxcm8l28PO2Gj47pJO7o3tXBuVg6iryvo70tOD+Rzk8jS3PAX1dnEGXQMFwYUwlwlQgZA9sEc+D2x1b9fqOO+DUU2Ov+AsKRK7XsGGJP3pN45qwp8RkMHHikBPDA6NBMsRXJz+wCsUk0O+kzNq0hGheoTVSh16e/vHMKgum8Yv4SZZDLheim+PuhFE3iqT9Y5/TenAQjb7V+UQ33ph8rlaqLK1ZitsvvuPBJYPDCb0S0sFJkq/7SPldFo8Da0XWDv/JJ/p24KxZ8L3vxS4+KCyEn/5U3Cc9hixD0/zE2xWPFbma9oHJHTeZxGijTRThFI9N7pieZtFOKB62fjD1L6LQYdStpCX+C+xsURpXmfZHXN+0HlN3lbPb58Yf8CveS4dF1YvCeUTegJcTh5xIl69b3NTr1Ohv9Tg17yXOAyybBiNvhKpzSfe3yhXRRRHegDd1z1bTQm0URY3BKqJJFccndnIc4hyexta+RfqJwNP+pp3k4yEZYdK94aerV+uHoYqL4YEHtKt5NXY7PPxw4o9dUbcCr194Jtx+N8cNOg65e3br9HbGT5J3qTRLKs8S0guZcmCFtrJz+P+l9n3Go/bD2OXugy6G454T4ceQCKCpAIZeASe+rtl8wQLl85EjYWCSc2g6rKiP6KGNqxhHsVWEbJ0+58EpUW9ZrXze7+SspZXs36+fs2gwiFBhoipPhwOeeSY755IUnTviayBJBpj2qDDkT3oLLtgKI25IfNy6j+K3kyqZABfugNO/EJInp30mvMzx2Lck/oRUNh3O2yC8lEOvhKkPwsyP0rq/1SKWai2rVFnXuC4sJVFoLaSiQBj3Tp8znMuVKvN3zw/nlQ0qHoTNbKN/oVj8BuQAn+/OTtpBWshBMSbGRILJv4MzvoSjHoETXoeZnya+BnqQ6DCsJ+BJ3eBumq+MoqgpGAqz1sOpH8HMOTBrU3Jai4coh6ex1fCZNu+h7wlC2TwVjDZhUHSzfLlI+FVzq06utx4mE5x7LgwdGn+7RdWLwu0pzAYz5Y5yBpeIxCEZmcXVi/V3lGXoUg2afaYJ2YVMUXezNxVAxXGZHxdEvtaBGAmv5hIhyaGXY2dywIAzoOq88Etev5mdqojIpZeClKNFZVAOhoUbJSQm95vMkJIh4fd6vCIxGABfq/K1ynPAnGQBQwKWL9dfcFx6aeJwa4j+/eHss7NyOonZtySOJ1uCmZ/AyBvEtWQpFf9O/7um+lKBLIsm0bHodzKctRSsA8BS0n2vHA/nrYnvYWyaHztfq3AknDEfbBWRELy5EPqeAqenLpeiToZu6WoJL+jSYVntsnB+4qCiQQwtEYNcUA4q+iUmSyAYYOO+jeHnE/pOAMRiJsTaxrUZnXNGtG2KHyGZ8R8Y/5Pu7gyFYlHa9wTRFiuRN6gHkGWZ1q5WxWspNySvn0vMUHrJJDh3NRQME/eAuQQcg8Wips/UNM6493N4Glv1n6C5CEbdInKMUiZynPnztflaVmtyuk0hDAb4xS/ibxOdixAatKIHmU37N+knyXv2o0kELkhg2SVL6xrl88qzs1eF0r4RpBgl9NMeiT84mQrguP+EV/cbaiZqQrlnnZVeYnwy7G7dHQ6fFJgLGFU2ijHlkb6CG/ZlIKSZDl312tBqxbFZO/zSpfpCsffdl7yxVVQktgdyUyAQfUy9hVeIkTdC+XFaz5CpQHiP+hylv59rb+yQt8EqvBjmQmU1gMkO9irhRYtF/aex87WO/ZduyByTTQgGD7s69nF1aHY3K54bJWNGbXvWNEbGh5FlIxlbEQmdRhtNybJp/ybMRjEmGCQD06tE3un0qunhTg0SUuKcyFyxf0nsnMp+p8Dgy3SuKzvYB8DEe3J/fglo92iLx1LybAV9YtyOxfEvgalYKaBtMAqj8/iX6W0h1WyQFWNLkqRzJEnaIknSdkmS7tZ5f4gkSV9IkvSNJElrJUk6T+84PULAK9oHRGMww+CL0/PwRFXxLdWRjLn8cuGxSharFa67LvbE5Pa5FbH08X3HAzB1wFQM3T+nUTLqt8Hw7NOKliYh0poQzwHwqW7OfqdmL4TYshbdOJdjkAiXJJIrMDnExAns3jdM48U6KsacmQ3WNq4NJwYbDUZGlo1kcr/JGLvDne2edtq62nJ3Amqc1driA2vfrB3+iy+EpEk0J5yQeph2wgQ44ggSFwikUyTQEOW1aVqgv7+5ROQ+xRThtYnQtR77lsSuwJ1wV+z7wmgVIXG9XqFBH7Rv0d9v0EUi9ydW5wdzIUz/R9Ihqg5PBwFVvpnVZE1b/iEQDCgm6gl9JzC+Ynx4EdLsbg6HGJNlac3SsNeqwFzAxL4TAZjUbxIFFrGylSSJpbUxdLxyYcRHH7dhrn6rHckkrptY1e6mAiE7VDA8N+eXJPWd9VhNSuM9JemHltWxQ97DrhWeWL3SY8kgPFzd4/X/EhkbW5IkGYHHgXOBCcBVkiRNUG12D/CGLMtTgSuBg9dZsnOn9iLoe5IIr2RAWxvs06k0vumm5Ff0IXw+uEDbAQVQJscbJSNTBwiX67iKceFBJmaSfNCrWjBI2dHs6dyh852ekFyhQTK0fKOv9D8yyRLC0AAG1LVUKUK9AwbkzqsFQlsolMTrCXgY0WcEI8tGhn9Dh9nRs217XHuV3hFr36yVZsuyyFtUc911qX/HZjNcc01WTis2AS+4Y6zWj/hD/G4KBoOYMIbqCIQ1L9f3ltkrhbEVT1bGaBNeKjUd22Pfq9P+nliQ2GATYaskqOuoC8uThJCQ0ja2drTsCE/cDrODUWWjGF46HHv3IslhdrBpX2qN2b/c82W4STJEFp3jK8aHhYM7vZ0srF6of4BcGPHRhvyBGLmYI28Ea4IFrsEsQtUhcm0Y6lDfUR9eEIZQezvj0rJavxLTaBfe23jXq7lQLHSykUvci8jGbHgMsF2W5Z2yLHuB1wB13b0MhFxAJUDmCnnp0rFVawT0OzWzsndg5UptNVtJiShzT5XiYrj+ev33VtStwBcQCb0F5oJw+HBs+VikbpdNp7eTr2t0ckbU1VGSIfky8rcHwCuS/uOTY1SeLUlUuGWL5q/ReLYkA4y5LfkKFks5VMyg5sBARah3woTcNktesndJOKQbCAboX9CfkX1Ghn8rT8DTsxWJ3lalceUYJIyOLFBTo+2cIEnw7W+n5t0F0ern2msTb5cRHdv0UwcMVhhxfWKPqblQV/5F5BfqTNYjv0fC8IhkEF6NsmnK19s2ojtclx+bXK6pyQ6jvp94O0QVmnqi9Qf9abfsWd+0Pux1NxvMDC8dzvA+w8P3QFAOplyRGC3e7PK5GF02GoCxFWMVXrIvd+dA6ywRchBcMZrjjvtJEoaxWeSahvL3cm0Y6tDQ2aBJRXH5XOG5JyEta/Q9e4MuTq73rmSAId9K7rMOEbJhbA0E9kY9r+l+LZrfAtdKklQDzAF+oHcgSZJuliRphSRJK/bpuYmyQfsW8KukDgael3FbjeXLhfRDNGedlf5EfuKJ+q8vql4UlhGQkcP5P6PLR4cHGRmZhXt0VnTq0IYcSN77pE6sj0fBULKqmtyms+odcFZqfRyNDhj7Q3Y2jVS8XFWV25Yx0V6ryqJKJEliRJ8RYSHHLn9Xxi1LUiLoUw7OpgKy9VutWCE8UtFMnZq6oRWiOAs6u3GJlVMy8ILkPd1FY7Qhn1jaQqO+n5xCv9GqDaO0rtP37o6+JfkFh6lQeJwTUNdRFw4jRss11LanZ2ytbVxLZ3dVmj/oZ3if4QwvHR6+Bzq9nXzT8E3Sx3N6nYqwZIWjIuw5K7QUhqt9QUhYpCuamjbOPSDpjE0lE8GRZDxdDogUiYNEfWe9QmcNhHZZozPJeeBADPmh0bclJ6BtLoLRt0ee5zp3swfoqQT5q4D/yLI8CDgPeFGStLO8LMtPy7I8XZbl6X37Zi+PREHLamVjS4NFVEZkyNq12krEb31LeLfSQa+qEVDkILj9bkaVjQKg2FqsaEy9af8mbSWO0aZdBfly0LfKMSi2TEOqeFv1W56MvDE11XuDEQZdRHWzsiCgslLkyeUCp9fJflekV+SIPqKsucKhrDhbURevRDzLyAEUxpWUpKUZz7PZ/dj2xl2aBcf556ffkDpXFaJhWjfEMGBuBUuS15YkwYhIRTJ+t9DDUlN+tMgDSwaDWTSGj14cHVgOqAxAg1X0HU3GUwBiwTHqloSb1bbXhpXZQ211ggQ12lvJsrRmadhL4va7GVw8mApHhaIxeyoCpKvqV2E3R4zW6GR7IOzlAqEsH52c3yO0bdTP/x1xQ+xCHzWmAhhze+LtckR1W3VYiDn0O5kMpuS1tjp0hLVtA6A8BQHt0okifwtyI+4cx7OXC7JhbNUC0Q1LBnW/Fs2NwBsAsix/BdiA7KkopkKbyl1dNi2+Hk6SbNVZzJ5ySvrH06tedPvcihVdibVEMegML42ssCVJYkeLKkneXqUVMO3KwQVnG0DW7PjW9TrVhpLQB0uVgJdWT6XipWHD0jcGErFh3waFARxK4pUkiaqiqvDrO1p2xG+xlE0MJhShrGTztZLwbG6sHacJI557bvrGbK5EZsMcWI6mKtlaAX2PT/4YRhuMujnyvGOrfnXsiO+lKNooK6/xVp2q1YHnp5ZrajDC4EsTeoR3te4KyzQURoW8Ui7972b9vsiY28fWB7PRjCRJ9C+IeBa2NMdI/tdhae3SsDEIMK1SGXI9qjJS8eIL+HpeSb5tozZ6Ihlg5PVgTGGwcQyG4vFZPbVkif6to0PKSanI+zrBp1P0M+zq1LofyHS3EfvfIBsz4nJgtCRJwyVJsiAS4N9XbVMNnA4gSdJ4hLF1cBpXde5WPi+ZmPzqPg7VqhB9ebno/ZYueqGXdU3rcETllg3vowxfhCZzEDeIRjDTXKRNslVXZmYD+4DUQnzxaFunFZ3scyRplQabCvCh9FiUpSitlgprG9eGJy2bycb4isjAObJPJJxpNBjDfeNyjsGiDB1728hWmfWWem2e3qTMnca5o00njDjwwvgip3qYCqF0SvcxYyR6D03BAwWicnD4deL/QT+4dUJ4gy9J3gMXIugTit1xiJ5oy+yRGySdBHmP36OoYhtUPCj8/2Glw8L/7/B0JF2VO2/nvLDXpcBcwJT+UxTvH9H/iPAix+138/muHhY3bV6ujJ6AkAlJtUesZIRBF2bvvFIgOmQcKpbwBXzJebbaN+vnQI/4bmq50Sa7QsfyUCdjY0uWZT9wB/AJsAlRdbhBkqTfS5IUulJ+CtwkSdIa4FXgevlgqM0FurQ90EomZJwcHwhAsypyMH26NocrU1bWrQx3YQeY0k85yBw54Mhwr7CYSfI2pWeHpoXZ78pu65+dKkcQA5faG9d/ZuoDF4DBREBSugzVOUbZZHnt8nAendVoDYcRASb2ixjGJoOp55LkrRXK7861N2siinv2K0O0I0b0QCgwXYIB/dY3Ay9IXbJEMolWPiAWB+rQZPH45MNH4WMaYMDp4v961b4gtOxSxeQQIrZxiPae9y3oi7X7Xk6nsfPW5q3hqkNAoTEXqiAEEe5LVnMu2nhy+93cv+B+pjw5Jfx4cMmDCs/Xx9s/Tvm8M0KvAfWAM/R10OJhtImE8oNAKDfLbDCHDdcuf1dynq22jVoPlqlQ5DemSsFQMJemvl8vJCuxHlmW58iyPEaW5ZGyLP+h+7Vfy7L8fvf/N8qyfIIsy0fIsnykLMufZuNzU8bTrPW49JmasURBTY02VHLMMdkPgyysXhhOjrcarZoV3ZjyMeGwYswk+UJVMm/DZ/F7DqaDqSB7sg+63ofzk0s01kNVCJFLkz86fBGQA4wsi3izxlWMC09CTm/6LUtSxjFY6cjytZGNhsnBoMS+dmWe5THHiIVIr8S5S39B0P/U1I9lsgsvE4geoervc8Bp6VmdISO4bQOaobpgeHpGssEsDMo4NHU2iU0xUFlYGc7bave2hz21yRJtQBkwhJXeAcaVjwsf2x/0s6EpsbG1vnE93qh80KAcZEfLDtY1rQs/drbsVITl3X43u1rSC4GmjCyLa0vNoIvSW4AeJDX1kICtxWihxCpyDWXk5L5HvQVHuj14A13p3ZO9kMNLQd57QLvCLBqtv20K7Nql9ZDMnJn9XKAPtn4Q/r8v4OOhJQ8x9rGx4cedH99JhyeSTL6qYRXBoGrg73uy0uA8sCK5VbcthcqNbIUQAdw6oYuyNPQ0ujEZld9HrEKETJFlma0HIol8bp87rPYPIlk+NNEE5EDPNc0tGKKVetBryp4i9a2VmE3K0MnxxyfuB3rQaNuoTR8oGpV+VXJZd+Jvu04YsfLc+NpasQgZDG0btR75ficmbnQdi+LYY54sy7R0tQBCyLSyqDKsW2U1WmlyNqX0UdE6cw6LQxE+H95neDhE5fK59LUBVby+QdvrNBleW/9aWvuljLtWu9CUjN2pD2mQhXziVOnyd4U9g0aDkXJHefi9PW17Yu0WoXkFmgrn/qelp5tlKoL+p6e+Xy8kzaLsQxRPs2qFKaVmRMRg926tavYEtaxrhrR2tYYHLRDVQYl0b4JykAXVCzh12KmRFyvPhE0PRqoF5QDsfVOIM8bLKblUlUj/SpyVerJJ128PSJB4bdAmlNorM8qxs1iU512XI8W32o5aRTVoia1Eocg8os8IhZegx4RN9URMm+ZrPZ4psmvfcCxGL24i7tzp0/VFonsFHdu0E1nF8akl8EYjB8S+eoZruj1CQ+HM9m1aw6r/6alV40bjd8dcCTa7mzEZTAQCAcwGM4OKB4Wr0cxGM3UddYrijkQsq10W3t8oGRV5WtH/h+SqcufunJv0Z0fzyY5P+MVJCfqgZYPOnWKxGe3FKZ0sxtuUCiS6yVY6Rgo0dDZgM9lw+VxISAwoGBB+L6mcLb1KxAFnptehxRAVTj/E6a1DYW7wNKOwuC2lWVHP3rEDXFH6bQaDSJDPJu9tfi+t/d7foqpVKJumTd7c/NfshhKTzQFLWOEW1KoQl07O6Fz79Vde8nv3avtZZoO1jWvDvduAcPPpEENLhirySva79uP06sgQZBtJ0rbnqZ+rbbeUIrv3DSMgK7/bRA3VDyrO3Vp5kv6nZ9BiSupezKkmFMegzHPinLu1ryWhlxWTOJN+bXtteFEgSRJDS4YSiKp4TFVrK7rvoTfgVRT1DC8djtsXMUq2NsfQJ4siVfHTEKnoeGWEq05rsFfMSC/HFNIz0DKkvqM+nPsblIMMLhkc7je5z5VE3p5XlcAsmTITuS4amXibQ4DDy7PlPaCcvC1lojonwwt6715l7s+gQULMNF0xRz0+2fFJWvst2KPq/WYwQdkxsC/q9ZbVooKkdGp2XBGefSJUlUqZcyyMdqXOVsnkjCYvtQFQXy9+K3Vz6kx5f/P7ipDuAdcBrn5L2QzYaDASiEpqWli9kHNGxU9ezgoVM6Dm7cjzpgWJw2e2/nGN430dffH6I6twSYJcSeVlhU6d3BO9noTJYi7s9mpYlYuBPlO7jboMEjjVlYiSARxD9LdNhjjekrqOuvDEGpSDQoC3e/HkC/hSqkh0+VyKFi++oE/hFSuxlWA2msNFP56Ah33OffQt0L9w/EG/okVPKrR72gkGgxhy7Wp112k9pmXTMy7C6kkaOhvC3sjQb2Y1Wenyd4nvUQ6GQ8sa/E7tArlotLgn0vXSxfHEHkocXsaWp1k5EFrL0897iEJdiTh8uLZtSaakm9Ozef9m7Ysjb4CWVcr+bUtvhjMXgiELg4K7ofvmysINor6pK2Zk5FoPVciFjOOamtwkyb+75V2FaGN1ezXV62O08EBMJK+uf7VnjK2qc6D+40g7DXetyAtSt4iJRh1GBkUoudVZitcf+b0rK8U9kMtqz4xw7dW+Vjgss2O667SJ8MXj9VsCpYJH5U1wDO0OS2V/AqrtqA0bP76gTyHV4Pa72duu873FYNO+TTjMDto9wmta4ajQTNKVhZVhPUC7SVQknlpwqu7x5u5IL4QYYknNEk4cEqM1R7Zw7tZGDsoOTpJ7utR31oelNbwBL4NLBmM2mOmiC5PBxAH3AY0wcxh3vXBeRM8tJeMzHGR7a0lzahxmYcR9SuPKmp1Yn56xlU2vFpB2XzKnz6lNkh9yuXbDllWw7Ul9Re1U6WrIihELaMO86SaadlNVpWyKvG5dbsQzo5Xjk2V57fLsn4gefU9EM4Bt+XtG3QSa2vspjjl8eO6KD7KC2ktnr8rc6nbXaVf15dMzM4qCPiESGU3pxKw1D1dT214bDu15A17K7GUUWSKh1R0HdsTaVcPr618PG1oATc4mCv9YqHhE68u1edp4ed3LMY/3zuZ3UvhLtPx3w38z2j8p9DymhYdWGEzdQaBfQb+wkWwxWuLnbbnrtKH0komZefbMaRSX9EIOL2NL7d619MmKREFrq/L58OHZncDbu1IvuY5mSY3KK2ayi3Y36qrB1f8PGj7P3OByN5C11Yhaibkgg/AJwtiK9rZ0dkJjCm0fk6G9qz3cWy4VekzYtHic1gNT/SaZ9EhUyz4MG9aLk+NlGbwtyteKx2feYspVqy1vL52iv22ydDVq0xyKJ2TuLYvBzpadYY+s1WjFbDRTaisNv59UNVo36mT2oBzE6XMqHur7JJ4A6d42rVdtROkIRvTReZSO0O6fglcubdQeU2tF6vpaB5mdrRGh6yJLEWX2svA1YZAM8bW23PXanLWy6Zn1Hs6C6Hhv4PAKI6pXgwYr2bA321W5xYMGZXei0SS5p8h7m9/Tus/H3gnb/6l8TQ7CosvgxLeENlCscvVECfAd27KYoxB141oruj1m6d+4Y8ZoQ7xffw2DB+tvnw4f7fgorf3cfnfP5JVIElTNguo3CBtYARes+x1M/p3IP0qR5k6ll7i8vBenWfg7tF6skgmZT4rOXWg0tgoyrBJw1YmJKtomKT86JyFEUKrHhxo6lzvKw68nJWrZzfaW7Sl/frwEfHX7sfEV49l4e4xm4sDIv49U9HPcdkCnSi7bqD2mxeOFAX4QqgrTJdqoLbWVUm4vDxdJ+IP+xJ4tdQFTaW9uI9Fz9Na1Z25Qt+GQjFmRuO5Uefkrstz18YvdX2heK7IUUWwt1n2o+armK+1BC4fD0Ku1E0zQBwsuhK9vAM9+EVoKeER4JPT/+gSKzEGPvjp3pjgGZax2P1LHoz9nDnRksR/35zvTbw+ysiGx1lBWGHO71pje8ncRaleHnZOgxVmqeF5a2ouNLVed1ltUNjV9odwQ6qpBx6D0pSRCuGu1hmHp5MyOGYfoBPiQR6ufo1/4tVTC4+lU13oCHm3aA0L7Se35ndwv/vcwqa9ykt/avBVfIMvJtNHIsijCiqZkQmZenYNAdHulPvY+lNnLwjlc6vZLGpx7lB5iySDugzyHm2dLdRMnG0JMoAfl6vQAkZkl2/32oiUCQLRQeOGSF8JVQ2pu/uBmmlwR8UH1/mGmPgR739KXUqh+A6r/K1bRfU8URplrL9TN0Q4oejQvz1i7SYN9IJmEukDY1pMmwbKo3rTvvgtPPpnRYRV8XavTJilJPtr2EUdXZVAVlyx9TxQig9GJrLIf5p8HZy8DQ2oSCG3uEsXzfv16cRjRXae99zMN94Ew4qIpGpuFSsQ6bXjTNkB/2ywQXdrf1yFCw5VFkRZfHr+HLn9XWIw0FtVt1YoCkVRY1bCK6VXTFa+tbliN3WzH5xHGktlgVjSc1mNa1TRmb5sdDlVajVbWN61namWOEtb1PKbFY9MTtD2IRBvU/Rz9KLYWh79Dv+yPn+6g7rXrGCyu32wKXR+iHF7Gljr2m+yqM46h5fWbCQSVx82kAbUey+qUXeuHlQ7j4nEXx9z+iRVP8OmOSEekzfs3I8syktqLZy2D456DJd+JVKYpkKF5mXikStOX3W114kw0CeQENDgGZeWmPfVUWLEi4sBpbYXly+GkkzI+NJBaErGaxdWLs3MSiZAk4d3a8AdlLmP7ZvjyQjj1QyGxkeSCpMOtNM56teyDu05bwJENA8ajUld3DMw838S1F4JRv49kSivMmwy+gE8hnNy/UAg+DywaiISEjIzNZKOuo07R51OPTPoRzt0xV2NsLa1ZGvaugOilGN36R48JfSdQYC6g3SvyPALBAMtql+XO2Ap5TP1R3rPC+N9TbyMQDNDhjbj5+xf2R5IkCswF4dd3t+2OfQB1RMNepS0aOUzprWvP3KB258qBjCuQ2t3FmE3KlWdJSYyN08Ab8LKnVZmUOr5ifIytBVMHTFV4vYJykOq2GLIDgy+FMXdkP+G2bg4Jk+QvbYCr5cgjEY4hWRH502sj8+ST6YcS1Tlg6WoBAaxpXJP2vikz6vvoDgFN82HuiSLhW69Ywu/UtFFy+5TXT7ZD6VnFXa8NR2damSwHtcKw9qrMr9culQFn65+zFi6NzkZsUecb0sTqW9A3LHRqMpiS0tpatGdR2uehl/bw+e7PFR76QDCgaGStx/iK8Qrvmsvv0k3JyBpdjdrFiSOLyaA9wD7XvnAbMRDSHABF1shiKq6wrfp6tVfxvyLdkCmHl7GlVvENuDKWKPAHTBgkpaGQzX5w65vWK1z2BgwcOeDIuPuMrxhPgSXiujYbzfH7jh35AAy7JjODSy006tyt39cwE4pHZ6V69MQThZBpNP/9L7jT6JMKSmNrW7M2CddqtFJoKdR9GFWej2ZXs2b/nGHrC+N/qi8S27IaPhwHq34CB1aC3yUSfVtWw6qfwQdjFZsHg8rfJdve3azibVFqIZkKM/dA+V1oJpWCYZnn66irJh1V2tzTLFHXUYfJKMZIo2RkQKHw9pXbyxWK4skYWyvqE7feiYVeU/ZlNUrvujfgZXhp/DSF0eWjcfmUHvuc9iD1taNJc8hCO7iepKGzIWxsWY3WsMBsH1uf8DZxVeT9qgRme1XOijkONQ6vMKI6du45QKY5QIGgEUl1jGwKOa6sW6kojy6wFCRc0Y2tGKuYxDs9nSytWcql4y/V30GS4JinxISz6/nku7Mb7WAugZkfQet6WH6r8mbb/TJMuCt7lTj25HuyxaO8HKZOFVWIIfx++MUv4NFHUzOWfT745BO45BLxfE/bHuwmO+5uyQq7yc4Pj/0hk/rpV+S8tv41Zm+bHX4uI+PxexR9FHPKhLth2z/1f/OAC7Y/LR4JCKpa9fTa5HjQGjD2SuEtysQw8nd0Vw1GhUyykbPoa1M+tw8kV56CaI+F1WQN52xFi5F6Ap6kWvakIhGhRp2A3exq5oBbmSdaVVSFMUGvvZBGVHQFZUNnA+2edt1Coozxd2ojJVnScuwp6jvqw1ERs9FMmV0kIEc3o25xt+juC2i9ro7Bmber+h8hK54tSZLOkSRpiyRJ2yVJujvGNpdLkrRRkqQNkiS9ko3PTRlbX5CiBlRP5l6EoGxAUnm2slDgGGbx3sWK1ZkkSYwpHxN3nzHlY8KTPYim1QurF8b/IMkAxzwJp84R+StxvVxG8f7w78CF24XQaNk07UCz6/nMq7GiSbfxrg7f+x4UqGzv558XfS5TUf93u+HOOyPP1zSsUfSSM0gGbp52M9dOuVb3cfG4i3GYI9+1w+xg0/5N6f5ZqWNywIz/ZBxGlmXlRd9rk+NBx9iqylyE19ep0xdxYGbHBP3QZI6q2+o66sJ5USaDKawSXuGoCIfjvAFvUoaU2qOUCr6gT3EPLa9bjs2sDMcmWnCGGFeh7MlnN9tZWZejil9/h/I6yobHtIep76wPazoaJSPldmFk9S+IeOgCckCR2xdGlrXFHP8jfQ2zQcaeLUmSjMDjwJlADbBckqT3ZVneGLXNaOAXwAmyLLdIktRP/2g5xlIuXJqhBEZvc/oNQrtRJ8dDdicadf6Cy+didNnouPuU2cswG8yKhNL1Tev1k+TV9D8VLtzOssU/ZsiB+Qzw7OmWh5BE6MVcBEOuhBH/B2VR1UDF40R+SiAqx6dzp+jB2P+M9Dq+qzFlLz578cVw++3K1wIBuOACWL8+Oe+k0wnXXCN6Y4b4uuZrvFEDTpe/S9OEOpqRfUaGQzQAsiyztnFtwlBxVqk6Fyb+Ejb+KW1BW7Vny9ib5xiNt6iSjNed/k5tiNuahWFOrR7vGJSz5sR72/eG86IkJIWxFS2qHK1dpcc+5z6MkjHskTdg4LThpzFj8Azd7VfVr+KT7Z/g79ZBNBvMVLdVh5tWL9m7RCEjISExvXK67rHUTKuaxvzd88PGotvn5uuar5k5fGZS+6eEr1MZ4s2Gx7SHqeuoUyzUQ56tUEgZwGayUd9Rz+hy1TwUcAnjMlrP8hDLWcsl2QgjHgNsl2V5J4AkSa8BFwHRanM3AY/LstwCIMtyk+YoPYG1TLnS8BzIOJ6sDiFmE1/Ax64WZfuHAnOBIlkxFkNLh7JxX+Qn8Af91LTXMLgkiYvfVMCVqz/D5XPRcOf+SPm5uUTcPHoGmyTBkMtgx7NKb9bqu+HMRWDIQvlzFo2t8nKYPh0Wq4r/9u6FSy8VchAOR2zD2emEBx+EDz9Uvv5NwzfKz3GUYzLEvs1G9BkR7kUH0OnrZGXdSq474rpU/pzMmfhLUYm4963kw8hRqO+DXPSbzBoab1Fl5qFufwea8J6lNLNjgnLxAmDtm5W8RT2iq2iDcjBsbJU7yvH4I0mOMYttutmwbwMFloJwq55CSyG3HX0bl4y/RHf7z3Z+xqLqRbR5hBFsN9tZ37Q+bGx9vutzRSpFoaUwZlhezeR+kymwFIQ9Mb6gj3m75vGLk36R1P4p4e9Q5gLa+mevbVkPsbt1N8Hu8TsgB8LhwwGFA8IGtFEyUt+pY2z5dELph1jOWi7Jxl07EIjuUVDT/Vo0Y4AxkiQtliTpa0mSeqDbrg6WchQDop4uSooYDEFNCCUNTUhdNuzboNGzSZQUGmJChbIsOmGSfBSBYIC97XtpcjbRJZmFVkzpZNEqJ55nbMgVYFQZRC2rYc/r3QnEGZLl2P8992hDiQDz5sGMGdDUpK1QdLmEoXXjjfD73yvf8wf9moloaEl8BfFBxYMUHkgQ3rEeR5KEDMjgy1ILKRodMPjbSEalQZmteyAn+FU/qrkkc0kRtQfKaCMruVXq+8bSR3+7LBAdHvQFfeGJ1mF2KBpINzrjy7VsaNqgvKYlwoaTHsNLhys8Zy6fi/VN6wHh6dVLmFeHB2MxrmKcpvl1suNgyqjD0+bUtOp6A9HjlzfgDYcRy+3l4cT5oBzUFzb164TSs7hAPtTpqcwKEzAaOBW4CnhGkqRS9UaSJN0sSdIKSZJW7NsXp+IhXazl2hwid3oNnkMYDdqVS7YmmpV1K8OrjBAT+01Mat8jBxyJKSpE2untZFltcnpZqxtW4w/6kZFTm/j7nazfIHfFHSI/LtP8rSyHT84+Gyor9d9bv170uLztNli6FGpqYNMmuP9+GDECXn9du8+25m2axPZEMh1GgzHsQQixuXlzSn9H1jCY4fgXRQ6XqTB++xqDRRScHPM0nPQGBoPSsAj05gW9OlRq6ZN5oqW/Q3l9mwqVXo50UXsZLVnUlVERnUju8XvCISRQlv43u5qR4yxSl9ctV8g0uH1uhpUOi7n94JLBitCVP+hnae1SQLQPUoujuv3uhHmrIcZVjNPkj3X5u5JK8k8Zj8rYMhXlzAuZK2o7It+LL+CjxCautzJ7GWajCId6A179lj16ofQchbwPRbJxJdQC0bGpQd2vRVMDvC/Lsk+W5V3AVoTxpUCW5adlWZ4uy/L0vrlQRbSUaQfA9i0ZHdIgaT1bqSRYx2Px3sUKzSazwZx0Ls+4inE4LBEPRVAO8uWeL5PaN7oZ7Gc7P0vuZAEMJiFkql7RB9xCmdzXlr7AncGiPW6GSBL87W/63i2Ari546SU47jjRO3HCBPjTn4THS4+1jWsV+mYmgykp41jtrfT4PTQ5D06kHYAh34bzN8PEX3RXEzmE98dcAsYCURE34W6YtQmGXwNobRVXFhyZOUNjbGWh5YO/U2VsFWUu5ijLWj2wLBaJqImWHbGb7Yrwd3QzaiAcItRjVf0qxXOTwaTZPxqL0aJ5f23jWkCImarlUcrsZdjNyXm5i63FFFmUHiaL0RI25rKKT21sFXKoqSvtc0acHNEezXJHeXhs8wQ8+k29fTqh9EOoJ2SuycaVsBwYLUnScEmSLMCVgLpz8rsIrxaSJFUgworxsyxzgbUMVCEbDqzKKK5uNvo0ycHZ6rOn1oSxm+2MLR8bY2slY8rHaFaf6xrXxV2Rhvhg6wfh/3+49cM4W+owLoZuU9t6+GiaEJT0qb6ggDdxmNHo0PeaZcisWTB+fHYSulfVr1JU6ThMDkb2SVyNo/Z+2Uw21jWuy/yEYjFggLCO4j0KBsGU38Ile+HHLvhNG/y2DX7shEtr4YjfQ+GQ8PamLuVveiCJjk4HDbW3yJwFb5FPla9jLszckxvo0lqxOQrLuHwuRehPLY0QCicBYRV5PWRZ1jSMjm73E4vBxcpc0pr2GvxBPwurFyoUzQHGlCXn1QoxqmyU4nmHpyM3elvqXEBzkViAHiLIskxLV8RgjPZmltnLFB7G6IblYdQaW0ZbL0/e7FkyNrZkWfYDdwCfAJuAN2RZ3iBJ0u8lSbqwe7NPgGZJkjYCXwA/l2W5B9UbuzGYtavY9k2JK7DiJPkV29vxBZTVJu2xF31J4wv4NFU/QTmYtPt8VNkojfvcF/Qp3MR6+IN+ltctB0TVz6Z9m3D7UkiYrjgmdrmvcxd8OBZW/gjaNok8F89+2PkcfHRk/OMajED2E4EkCd58UyTDZ8pXNV8pQx4SjCxLbGxN7DdRodrc5e8Kr+xzQmMKLZIA6hF39sbu/+tQgPIe2p98v+KeR60FlI3QnF9ViWYqIlMNP/xObbV0joytuo46RX5otIglQL+CSGWlQTLEHEfqO+s1qQ8jShO3rFFXWFtNVnYc2IHFaMHhA4c38rjk2UWJFwtRj8ueX67Y3+6VMT2exWaoIdTGlqnwkKpEbPO0KTzz0d7Gcnu5Iq9ub1sMz1YuQun/I2TF7JZleQ4wR/Xar6P+LwM/6X4cXIpGQnPUTNCSRHuUS3WSAV/pFn4z+TEZ/Xj9EddIa2uG5whs3LcRq8mKzxu5WN0+d9xE02jsZjultlKa3RGb1mK0sLJuJYOKY3dh/6b+G8wGM12ICclmtvFVzVecNvy05E/+iAdg8eX6RmzALYyrnc8lfzwADDlbJQ0dKsKFV12VXvgrZKhFV3+C8BYk6iEHoiLRZrKFPQuegIevar7ix/w49ZM5SBTTTiOR8vBYodaDjixrFdizYcB421AYV9noXygHtDkw8XqNZkBdR50ibKjOI4z2TgXkQEzP1vqm9ViNVkXOVjKaWBP7TeTtTW8T7F5QGSQD65vW88g5j/DIuY+m9LeouWuxeCjJQZxbY8SXHlI6Ww2dDdhMtvCcU2GPXAPljnKF51M3zSFWKP3Q+QpyyqEVUM4GpVOUz9s2ZJzE6LAojYrmLPjsVtWv0oT8KhwVCg9IItQTfae3M+y1isW8XfPwROWJOL1O5u6Ym/RnAkK3yRG/Ci9lcjxoXXihqDBM1cNltcLIkdDa1arJY7GZbEkpVY/sM1LzW6vzXno7JbQqnu/fL1T5ex16ob1sGFtB1URrKiLzHEOdc41XtJABdR11Co9UtIglRJpRg1j0xTK21jWuU3jCbSZbUt744aXDFTmmTq8zt97dXKBOR8lh5WguqO+oV1RuVhREjC27SZkaEr2IDyMHUFyz2Qil/w9x6ASUs0XpFDDYogZHGfYvhwHpi9wV2TppdUXCk9kIoSypWaJpaKzOPQgjy9C2ERq/AF+rcF0XDGNyxViFcRWUgyzYsyDu536w5QO8AS8GDBgMBvxBPx9u+5A/nfGn5E9ekkRV29wT09Js0icFr9bbA0RT2JgY4Gptnt6jj0KfPvDQQ8n1SXQ4hETEO+/A6sZ1OMyOsFYQiAkqGYb3Ga6oxgJRgu0P+uNqdPUmylAmB7e1gdcLpl53+kGEERR1PZmyICkSVF1PpoLMK9Hk0LlGk7tWPdGLrIHFymu3r0M0o+7ydxGQA5q8rBDL65YrRH0tRktScjXD+wxXJMIH5EDS1dO9B9UYlWFXhp6mvrNeoWcWakINonNJoaWQ1q5WIJLjp1j8qw0rUwEZh9L/h+h1Q2HOKR4jKiSiV6J1H0LfGWmXqRY72iEqIXjvXrGqz2SiuXXarYqLHWDW6FnKjfwuWP8HEZILNUENeMQgb7Rzd1cXgwYNQu53arhP28xhsY1KX8AX1qAJtcfwB/1s2b8Fp9epaG6dkLKjYOKvYMOftMKMSRM1KepOPDGIa2hBrNwvSYLf/Q6OOAK+8x1hw+oZXSaTUJi/5RZhmBkMsHbTWsVkBdo8lFgUWgpxmB0Kz5jVZGVb87ak25IcbCpQrjAaGrJXlZtV5ID4oRVzQDYc/CpjSzKSsWGkd81nsxdYFLvbdofDREbJqPFslTvKFekFu1t26x5HrYkly3JSqQ/DS4fjCygvmHVNOSwSyQVqY+MQk32o76gPi9dKSArVeBBFEyFjy2ay0djZqBTJlgOqVI9D6+/PNYefsVU0Rpuz0ThfvJamsVXqUK7qd+4Uk3RRBpp2R1UdxVFVR8XeoHE+LL5aeLLU3iM5CP4ORpvgd6YacL0FZRfD0Y/HdW2vrF+JxWjBE/AQCAQIdE8gdrOdJXuXcObIM1P7IybcDTXvCGHTVCs+DRZR1RhqrSIHcrWo13DppXDSCX7+/Xg1jzxZTluHBZPRTzBoQMbIdd+u4wc/68/4KRHjc2ntUkWeioSUtMo1CHHT6JwvCYm1jWsPGWOrL8ocjl06xUq9Al0DJguTQk4m2qDONZ+bCSy6U4XVZKVvgVJ6J7oZNUBNR43mGEE5yO7W3YrXXD5XXI2tEFVFVQqPGAjxVI/fwyEjHqAe4w6hfC0Q3vRQNwuryarJ2+tj6xMWPTUZTNR31quMrSCKVcwhZmzmmsPP2CoYopUQaFklkhvTVPwtL1TWue/alWNRxw1/hvW/F72okiHghr1vQ8M8uCy212feznlhg8FisuAwO2h0Noq8rZ1zUze2DEY4+X0RTnTXQdCTeB8QRtaYO2F7VMWQHKBHVkpyEHY8R99vfsr/mwQ/f9TJ7qbBtLpKKbA6GVJejc1hgY1e8H4fjvgDmAtZUbdCcRiH2aFtZxGH0WWjFcaW0+dkVf0qrph0Rdb+tFzSl/0YCBDszobdtQvs2RX8zw66HtJsGFvqG96QuRdKryAk2UOmGEqPNrYkJNx+N5v2RRqit3vaFdVoegri1W3VGA1GhZOvwFKgaLQeC6NBND2OVqe3m+xsbd7K5IR79xLUv9chZmzsbtsd/r/ZYFbIfYAIJUejvQYObc9erjn8jC3JIPK2DignR3Y+D2N/mFavxPJCZbLgrl1gybD7R0y2PJaaoRUi6AFv/GSyD7Z+EF7ZTOg7gRJrCZ/u/JSAHGD2ttk8eOaDqZ+vowrOXQVfnAOtaxPncJkK4KR3RKg32tjyO3NfRt2+BRZdDp07wpWUEjC8327ldqE+cTuegd0vEzzuOY1Mh8lgSkpjK8TkfpN5f8v7YemIoBzUNCHPGv37py7/kIBSWrHgpQthYbW1iVB6zu6DtMmlARN9zCQPmsoxIfmK3BRD6dFdC5w+J3d/drcih0pGVuSQtna1anIK1zet1+QYVhVWJXe+wJCSIZpWQOub1h86xpb6J0/2t0ozxzTb1LRHvJUGyaDoIABK+Q9fwKdVkdf8vdm8B3rmO8glh6fpWXWeVr9mxzNpi2YOrdijaFhcXy9yerLOrpdg9V2pG1oh4lSGeAPecL6FhMQJQ07gpKEnhQfPbc3bFIKdKWEphTMWwIjvCq+VOnFUMorXS4+AMxdD5ZnaXnWyP7dNXVvWwifHQuu6xLprIQJu8Daz64vLNZOML+BLSvYhxKiyUZqcOLWURNZoaBADY7yHHnG2r3zrcSzFSldWnX7B2sFFMqAxuLJmwEQfM8kqrFSOKQ6c4vaJCQaDGm0sl89Fh7cj/NC79zfvV7aVWte4TqPtl4zOXAh11aLT59Tti9h7UU2nyY5XaeaYZpvGzsh5yMjh3pghqooihnOXv0tbkar2ZGX1Hjj0qxoPT2Or/0ytXk3HVuF5SaOx4Yh+OxWSAbIMWW/t2LkLln0/i9V9SlbUrQj39Su0FHJ01dFMHTA1HAKwm+0srtaI1SSP0cLu0T9n0C4jbWP/H/SZCgVDoWgsDL0KzloC562GPkeI7U1F2ptVrWOTLQ6sgrkndeeHpT6Z7ejy4Av6KLGWUGItodhajF/2ayq6AFpahIjqd78Lp50Gp54Kl18OW78ejS/gVxyjpaslNUHZg8hwnRzoHfoFawcZvSEvBwO5HMyNLlwOSunVzdOTRd3lYGntUkWoEYSHPFkm9p2o6OcalIOiIrF/bFHptMnFMTVhs0PLQDjgjqTDBIIBjWerb0Hf8KJSRmZnq6oJjGRA6c06tP7+XHP4hREByo/VN1pW/ADO+BIMqZXsDu+3S9PuZd06qEregx4fWYYl18bOeSoaBSNugKFXgG2A8AI1r4BdL8CeV7QFATp8tvOz8MQelINMHTCVImtRuEKp09vJpzs/5exRZ6f9Zzy/+nlqPZ087XTw83MT6EiZi7UrQ78z7by6mHQ1wbwzwJ++7P8ZDnjvyvcIRJX/DykZokgo3rVLNLX+/HOhzaVu6eSYM4Ng1RzKRri44w4YO0YkJSfbA+5gM3y4tnJz0SKYObOXhRIz8WylRI4mmiTu5VRZ05iEsLMOW/Yr+8qqdbEcJkfSFbkg5B/sZruiPc+m/ZuEJzYesUK2Pd0qRuPZOXTCXk6vU1Hg4/K5eGjxQ1hMkZt3feN6xa2zun618iCSKk8xr7Gl4PA0tkx24VFpW698/cAKkUReeQ4Yk48DDu+7C6+q5eLnnwuvhTUbpTQ17wile021i0k0C55wt/h/dL7ZgNOg/BiY/GtYdIU2R01FdL6WL+hjVNkoDJIhXO4dlIPM2TqHh896OK0/QZZl/rnynwA8seIJfnb8z5Di5bWYi7Rh3UT9E1M/KVh8pbanVwijHQacDlXng2OQkNdo+BTq5ggjrRuDBOeMOkf3EH4//OpX8NhjQgrB70dzrQC4nBJsm8mubXDPArjgAnj66Wz8kT1DaamQxIiWe1i2TCjy9zpjSz0J56DnprhXczDZB1xAFhpnR7Fxf3rh6u0t28P/9wf9ipwfALPRnFQlYohhpcM0Y0Kzuzl12ZmDhmo8SzYdoRfwTcM3ilZjMjJPrHgi7j5bmreoXlEbm71R1fjgcXgaWyAMqrZNaPRxVv4QzlsT39hS6SlVltZrNIWWLxcr/YyNLVmG1b/UalVJRjj5beh/WuwWHuZC8d4Z82HBJdCgrwTv8XsUq9JRZaNEVREiDLC0dikAO1p20O5pT0oVXc3S2qVhjZZ9zn2sbljN1MqpsXcwFWlX8Rl4n3TZ/SrsX6bTv0uC4dfBtEcj5xJKyht4Pkx/ArY9CWvvjZs/19UFF10kPDzJiKSGcLvh3Xdh9WrYoh7PejGVlUL2JMSKFb2xItGAxuuUi0nR78zNyj7bCw5gx4H04r3R4cea9hqCcpBCS0SN3+136+ct+jph/1dChLlzJwS9YCllROEk3D6X4hhd/i52tOxgSv8p2uP0NtR5wJ7e3I1dSTr5oeqQsSaM6OvMVyRGcfgaW4Mvge1Pab0azt0wfxbM/LhbAVdFMKDwagAYDDIVFUpv98qV2WluTPNScGk1bTj+xW5DK8GKTzKIbU5+Bz4/S3eTZbXLFL35jh14bPi9EwafwLLaZcjI2M12FlUv4rzR56X8Zzy98umwm7rL38Uzq57hiVlxVk4GIxhMSoNL3eg1E4J+WPVjrRFrMMNJb0O/U/X724XCmKNvgSHfgk9nCFkLFZ2dcMYZsHZtaoZWCK+3F2tVxWDECKWxdeCA6BOai/SYtJEMaAyuWJ7NTPB3khPPlr8j8TYpsqd1T1r7RTcjHloylLcuf0sRAiy3lysT5Ns2iwKf+o+E19jvVHjr+xkLmD3QSKNsEPdW35OwW4qY3O8QqUdUL3p97WKcOQS6QGw/sD3xRipkZGVFqsGqNK78HXljK4refxXkiorjuvuM6Qy0+xbB4qtgxoti8g3dRL5OMbHOP1ezy9ChSmOrvV0kyQ9MrmOLBq+3O/yy8QGt92T4dVB1QWJDKxpTAZz8ru5b83bNC1cROcwOZgyaEX5vetV0Ci2F4YqkuTvmpmxsefwe3tjwRthNHZADvLT2JR4951HM8TyIRrvS2HKml8iry963td+rZIRTZkPf4xN/tyYHSJVw9nL45GjFW7IM114La9YI71a69EoF9jhMmACffaZ8bdkyERLtNUiSkBWJztnMphEfPmYODDjIybnuaUvP2IrujydJEheNu0h/Q78Tlt4ENe8KL5Yc0M89Czg50wbggZY3oGO2GINzpJqfdcwqj7+/Q/ydh4CxpRajTRZFlwtzkdK48nWClGO5nkOIw9fslAww/Fqt6zdE7Qfw7mDY/Ajs/1ootq+4DT4cJ1zfKsaO1R5i7ty0ihuBbm+ItxXqPkaxQrb0gemP6XtdEhGj4e4HWz8Iu4RNBhNHDjgy/N7UyqkK7afZ22an/LEfbP1AkSwe4uPtH8ffUS0R0bEte0mn636j9WhMuR8qkjC0wudnBmtfOOUDxcvPPCOMDj1Dq7gYvv998X59vZC7WroU/t//S98w7y2MGQM2VROG998XXr50aM+BDQRoO0Xkwtjyd5CTlgc5ONd9rvRKp9X9PHXp3A1zjoC97wgDN9n7198pJAEWxDDgeiPmEuVzf+chkySvzrdLFkX40VSI4pr3d+ZeG/EQIivGliRJ50iStEWSpO2SJN0dZ7vLJEmSJUmano3PzZhRN8e/GPwdsPZXIlQ0bybsepFYoYGpU7X5WW+/ra06SxaTCZHTYFAddMwP0l8t6DTc7fJ3sb4pUijg8rkUbWZGl40OhxdBhBzautpIhX8s+0c4vBDqt9Xh7eDx5Y8nOF+VcejaC8kM8Mng2qt8XnqEELU1p5iIazSLFlDd1NbCT34CTp00oBtugD174OGH4fTTYcAA6NcPjjlG9GTculX8mxONth5gwgRtMvycOen3CM1KcYkeRtV94G3N/mfkKl8lB+fq9KaXs6bJ2VHTuh4+OhKcu5S9aFMhR1I3OcFSqnzu6zhkjK3ajtq09tvavDXyRD1e51ob8RAjY/+mJElG4HHgTKAGWC5J0vuyLG9UbVcE3AkszfQzs0bJBCgcAW0bMj7U9OlicvBE5c7Pm5f+hPHZZ3BR/w+VORqSCcbeqWs0pctnOz5TeJ0KLYX865t/KbbpY+sTVnY2GAx8sPUDrp1ybVLHb3I28XXN1+FjXzjmQl5Z/wqd3k7m757PAfcBjZ5LGLX3zl2bvQqXgKok8KiHwJBeb8zo8/z977XVhpIETz4J11wDhTEckiGP0M9+JqpYzz1XVPIdSkybps1Pq6uD6mrh9UqV9evFMbOOelLw5iCR2d+Rm/CRpznxNimS0GiKQ6e3U5HQHsbdKBaovnZ0F6jWCuh7EvSZIu67rnpoWgStqw9dyQB139lc5e3lgBZ3S+KNdFB0zjAXaSt9A11571Y32Vh6HQNsl2V5pyzLXuA1QM/3ex/wZyBHypRpMvGXqeU+xWDqVO1E43LBl1+mfqy2Nnj+eaBeFUIceH7WB/AHFj+g0Fdp97Tzs09/pnhEhxm6/F08uDj5tj0vrX0pbMx5A15uP/p2fAGRr2EymHht/Wuxd7YoFYxx1WTPWxBdgVg0BipORNEGIA2amuDFF7W5Vs8+K3K4Yhla0TgccPTRMH9++h6hmhoRnvT3cOV1cbHw1Kn5979TLxJwOuFf/0q8XVqo73dva/ZX4P4c5au4azTV0Jng9XsVJf+pohY2BUQR0YILwasjElw0Gk58Ey7eC8f9Gyb9GibeDUc8AGd8AZfUCc3AQ6yJM9BtbEVX46UWATiYJBUS1iG6SEIsYlT3Ua6EqA9BsjFzDQSiYzI13a+FkSTpKGCwLMupJ/zkmqFXgq0y48MUFOiLmD7zTOq5J0YjrFjUpF3FDro4Zt5VuqysX6l4HpSDeAIexUPdymPT/k0kyxPLnwjfyENLhjJlwJRwKNHpc/L4sjihxEJV2birRptvo4ctxfK3MbdlxYh98EFtjt6VVwqF+IIU7Hm7XYTk/vzn+Nt5PNBVqvxbG+jP2LEwbJg4zrBhcPHF8Npr+qHNbHPssdrXXn459eMYDEJpPyeoE5m9B7JqwADdk0wOvBqu2qxOYNE9EdNhwz6dqMDul6B1g1ZSZfh1cO43MOhCcR9bSiKLJ5Nd/C62/kJy5czFYC3XHrs3YypUenHcDbFzgnsZ6Xo3q9ujipbMRaL6Mhpfa/on9T9GzhPkJUkyAH8FfprEtjdLkrRCkqQV+7Le7ybWhxrg6CfAmIF3q3tynzFD+9a776ausfTkkzCwZIfWsKg8O6t5IAdcBxRerWTxB/3sakmsS7C2cS31naJZqVEycvG4iwG4cOyFYW/XrtZdGiXqMEWjlINVqJQ6EZc2wNVy5JGIQZdk7OqWZXjuOWUYubgY/vnP5DxaagoK4JZb9N9rbYUf/hDKyqBfoAEJOfyopAGXSyTn+/0iR+y99+Dmm6GiAq67Lq0/L2lmztQmye/dCx9/nLynzeuF11/PQcurEGpjy12XE2X2nOSCuevIphG3tmFt4o3ioLl3A12w6qdaSZVJ94hx1lSQ+F4zF0KfI+G8dWDPfCHcY5iLlN5Md702P7AX4vXrqCwniaK4wlSoTfNw9cYGqQeHbMzctcDgqOeDul8LUQRMAuZLkrQbOA54Xy9JXpblp2VZni7L8vS+fftm4dSSpPJMkTuQTvWQsUBM7sApp2i1tQIBkceTSqL8X/4iWgApchds/bQJmBny79X/TnvfZ1Y9k9Q2oRvZYXZw/pjzAbhgzAXhPA9/0K/JEQvjGKQ1OHUqQTPCMUhUFGbImjXa8OEPfoCmjVMqqJPNZRmeegqGDBEeU5cr+euqo0MYYK+/nv75JMPRR+srxv/mN/rK+XoEAnD//dk9LwXmUuXzLBswYVJuMp0E7rqshti0KuCpsatVteja8pg2qX3wpaLLRSrpGkYrWCpgpr4Qc6/EVKhcDAc96RcG9CCZeDc7PFEDkFEnQdm5O+1j/6+RDWNrOTBakqThkiRZgCuB90NvyrLcJstyhSzLw2RZHgZ8DVwoy3L8/jE9zdFPprEKMYA1ktx99NH6eTbPPZecBERoRd/UBMP77lSqRReOzHr8+61Nb6W97/tb3o/7vj/o58U1L+LvXun4gr6wftdJQ08Ke9R8QR/PffOcJlQJCENIPbEkaDuUMn2Oit1zMgXee09pTFgs8POfpxY+VBN9LXk88O1vw09/GjGcohk4UPRevP9++Mc/4I9/hDvvhJEjldsla/CkyxFH6Cf2r1snJC4SaYd5vUIyJadNrNWJzO763CTx6ojdZuWYWfSWKBKcuzFIhpgPNRqNrq2PKfXrzKUiNyudvFijGQqHpb7fwcJUpI08ePYfnHNJgQ1N6ReI+dQeYXX1fOeufEViNxkHlGVZ9kuSdAfwCWAEnpNleYMkSb8HVsiyHH9W7i30OQKO/RcsvSH5cmNzEZz+Rfjp5Mn62kpdXSKM89xz8Sffjg6htwQwtnILimTDgmFkO+q7qj5BM+g4JFoRz90xV2FAHT/o+LCAqc1kY3rVdJbsXQKIxPn5u+dz2vDTlAdxDNK6pZuXw5DLY7coSpWikcnlgSXgrbeUhszJJ2d8yDCBAJx9thAIjQ5JFxTA1VeLcOP48cKgj76+3G5hdO3ZI/osvvCCUHXPJTYbjBoFm3UWy9dfL4yueNIWnZ3iXskpGmOrISvXgIbOLLUAMFiEGCiIBVfQkzjH0NY/Kc9a/0JtfuPvT/29rmEFcP+C+3FFLQL72KK+y9YN2jzTyb/OrFAgC8VLPYY6PA3CkC8Y2vPnkgIK+YYU0SySjXalse3uzjE8lH7HHJGV7D1ZlucAc1Sv/TrGtqdm4zNzwrArhSbM+vvj9rwDhODmzE/EZN2NxSI0kxYt0m7+xhtwzjlwxRX6bXxcLrj00kieSrFdlVVvr9J306aJy+fCo0oKNkgGjDFCFEE5SCBqheIP+ml2NVPu0E9ifXjJw2FtLavRyvSq6SyvXR5+/5iqY1hRtwJvwEuHt4OHv3pYa2zZq7TevNZ13RNPtoytcVmZaHeqHASXXZZerpYed90V6bUZYupU+OADKCmJ/TmhvoTjx8Mf/gD33ivOK9d861siuV/txaquFvIXr72mv+hwOsW+jTmIvimwVgi5gVCIR/YLlXO9yTITnLu6FcQz9JoZrBFjC4S3JNHkdWmD8vkr+ikSoZzKEAXmAn518q9iHva51c8pWrsccEdZ73teUea+Ge0w6pasStX0auyVWi+Os1p0K4lHkoZxrtCEglOkrr2OquLu6jBzMXijDO5c5UMeghwapRI9yYS7RcnulkfFRaK+eQw2MFrg+FehQlt6deWVoi+iXlL8rbfCoEEikT40Qfr9wiPy05/CggWRbS0mVbzHYMlqrsaSvUsothbT7hFGXbG1mOcvfj6cxK7ms52fcdkblym2X7BnAZeMv0Sz7Z7WPczbPS/83BPw8OSKJ/nnyn+GXwvIAYVY6pxtc2jobAhXKgLCCDLalUrvreu0yvKZkIU8uJYWbXjuzDMzy9cK8cUXomAiOjR35pnwzjvCmEpWrcLhEI/ZPVAPfMEF8Mgj+iHDDz+EH/0I/vY3oUFnNovvzuMRHrovvtDuk3XsleIejs6n6dqf2NhKdVJ01yfWGUrmmGovU/vmrHlLqluVLbBKrCUxthRU2CvYTsTYauiMMuoaPlNWIFbN6n5+GBlb6qhI5w7hco53oyZpGOeKdNXjQ2zYtyFibNkrxSIjhLuOhLnQB9nY7CnyxpYaSWJbwQNs8N7I2M4fMqJgPl6/FSSwGL2saL+dloH3cpy1mAqd3WfNEsKUeng8cNZZIvfmD3+A0lIRVvn+92G7qg+oLKtvziSF/t4ekODCNcDVAT7d8Smd3ogR4w/6FW161Bw54EhF5WKHp4OPd3ysa2z9ct4vNa9FN6iNxT3z7uHZi55Vvmjrp+z34msFfzsY9b79NEjWgI3zvW7bcTR201x8vshENWhQ5qcmy0KNPtrQGjtWGFrp5oJlpTl6AqZPj9/O7tln4aOP4Ne/FjllW7aIIpKce7RC2KvQTACuPVA0QnfzMKlOiq7axPkq6mPqHVcvlN7/DNGsPUPqOpR5ZaX20rjb9y1QFpNE90ekfZty46GXZ99b2JsxOcSiONrgat8sKjMNRQfvvBKg0MpKg837N3PmyDPFk4KhsH9J5E1ndeLIQTL3wP8AeWOrm44OIaL45JOiVN1oHI3T+RGDy/cwuGwvHr+VHY0jae8qo6BAGE7HHw+33y7Cf6HJZdgwobelDitF89//ikc8vH5VSZfflVxT04QrBGG0zdk2RxFvl2WZoSWxV8sVjgoKzAVhb5SMzCfbP9HdNt3E+1c3vKo1tuyDtBWITQthsNbISwt1iXos4nyvtQeUTQ0HDEi/J2Y0X30F21Tz1/3361f79SYMBnFPvPhi7O+htlYsMg4KjiqthMiBldD3lIyFbRW0bxaTb6aoQ+lt67sn8MwNmf1uZQJ3X0f8ytyqIqWYoD/oFyryBLS9RvscmfH5HXJYy4UeYIj2Tb1eET960R3CZtI3kGRZ1qSfKELJhSMQC5nu6l7vARECz2IKzKHK4duIuptgEP7zH1FO/8tfiv50brdwpsgyVO8fyuKtJ7Ji59G0OMsIBIRIqccjQh7XX69dxf/f/2Xe162+VaUv49yTlao5EDeXOilyXMU4pHjuCGBK/ynKc+ysZ59TKYY0b+c8zc2YLC6fi6/2fqV8sc8U7YZ1c0TvuWzQvjXjnAKP34osR767kpLEVXfJMHu2suBi5EjhOT0Ueid+5zuZVWLmFNsACKrCPa3rkje8k8Vdmx1dPHWFV9tG/e1SxOP34PIpc1MVYXwdBhYNRIryCtpMNuEdc9dqqyQdQ7JynocUNtX317Ype8U8OUIdOp7UbxLVP6rWfay+ZTWWqAWE2WCmxBa1v65cTy5Liw8dDmtj68ABOOkkuOMOIRSpl2dlNkN5uQj56S16O3Xm/CuvzHyBvKV+rHKQde4iWz/Xkr1LsJuVA+Pxg49PuN8Jg09QVCnZTDYW7Fmg2OaRrx/R7FdoKdR/qHsf6u1fNk2rmt/4efbEXTt3ZNzsNhA0KlSajEZti7B0+PhjUYkY4o470m/h09OccsrBPoM4GK3avL+seSBU12U2dIbUSurtW7KSt1jfWa/xYKg9V2oqHBWKfYySURhbgS5lZFYyZMerd6ihNjD9nd39IXsv+9zKBXNlYSV9C/rqPkaVjVLIPfiCPva2R4Uh7VXaHMWWNbk8/UOGQ2Tozj51dSIMWF+vTW4ePVp4p264QfR683qF98psFs1xn3xShAFjldGPGQOTJokKsnTZvX9YdxVSt5eoQ0dRPk0+3fEpnZ6IlVhoLuSYgcck3G961XQKLYXhJPl2Tzsfbf+IyyaIEregHAw3nQ4xvmI87135XsxjnvfyeWxviSSsLaxeiCzLES9b6WStYdW5UxQxZGPF2LaRtMRso7CYvBikiHXV1ZWd5PhqZe4yY8YcGl4tEEbhnXcKgV49OZSDjrVcGfZq25SdqjmjXekha10HxeMyO6a9ElxRE1rQC11NIhyaAXUddZijJkaTwZTQs1XuKMcUlcoQkAPC2CpWJdanstpIMs/0kCCqOj1Mx1aw6rQX6QXIskxrV6vitf4FsdudmQwmbCabopfi7tbdkQ308iEPrOyW6zlMCiVicFh6tjweOOMMkTcSbWgdeaRIWF+9WlQHVlaKSdNuF/pBRqMQbfzLX8S+b78Nffrof8Z992VW+r+raTgKVeuAS9y0WWD2ttkEoxLuJUli6oCpCfc7csCRmh5an+74NPz/hXsW0hWVX2IxWLh2yrWMLh8d83HV5KsUA367p52ltUsjH1A8QSnuGqLm/eRa9ySidV3GHo0+BS1IUb9VTY22ZU06BFTzi/0QG6t+9KP4ifIHFXU/VH8n+FJo8xALdU5l8/LM+y46dHIpW77J7JhAbXutogm11WilwhG/8KTCUaHwbnv8Hmrba8XCR3EfyVnJhxT07pwnBY7B2rBv80r9bXsBbZ42DCozYEBRfIO7yKpM9lck2NsrlTIlIBa0WUqBOZQ5LI2t226D3buVvdq+8x1YvFg0AHY44k+WhYXi/XPP1RdvBFF1OCD+NRuXuo5R2lYPNe9pL2Q1CZowdwRhxwFlDL3L38W4isSr72Glw5BVK9YmZxNNziYAnlzxJE5vZIC1mCycO+rcuMc8b/R5irBEl7+Lf66ISERgsuu309nxTJYU9WVo+DTxZnG+11H9t+MLRAxGr1fIQWRKicpZ0KBTtNObKS8XHuJ0E/qzYbDGpEAnnygbixm1R6d9U8ZhauEtUVmt9R/rL0JSoK6jDo8/MgkaDcakjK1oA80X9AnPRsFQ7f2Y7dZahwJ6eogHlmfHkM8B9R31WEyRG9RkMMX1bAGUWksVzxudUcayrb92cdG+SWuAHoYcdsbW6tXw6qvK/Kzvflf0nHM4Usu1stlEc189JEmUs6fr3eryOYRXJ5q9byZO5lY3YVY1Yl7kRpOvNbR0aFjdPR6SJDG2fKziNYvRwvzd83H5XLy/5X3FQCwhccSAI+Ie8+iqoxViqQE5wBsb31BMApRO1u54YCV0Zcn62P504sEwTnPrweV78QaUFsXazPr7AiKfMJrXX0+tx2Zv4Be/SD9/MadesUKdcE/jl5l7S9Ur+JbVmeculUzQipjuW6yVhEiR6rZqRTGLhJSUseULKMegnS07RYqDVbXv/q8heIiE/7KFXhht/5Je6+Kt76xXCFlbjVbK7fpC1SHUQtbNrij5D4NJNBKPxl2X92xxGBpb994rwoghKipEL7l09YfiTSTf/nZ6FVk2mxBAZdDFylYXLauhY3uMvZLjU5e21PfoqqOT3n/GYGXuQYe3g4+3f8zbm97GqNL9OWPEGTHbfoQwGoycMlSZTW2UjHyw9YPICxXHITpBqdj81wyqEqMGv4bPRA5MmhgNQfoXKw2/117TL55IhbPPVhrr778f6TBwqDBkCFxySerVuTab8DbnjIIh2iTzfQu08gWpotbVctVk7tkqmajNW2xdk7HI8Y4WpYc7KAcTT7T2ck21cThBumS8cuM9r2W/wrO3UzxG+3t3bO+18g8NnQ0KCSCjwUiZvSzOHmgMcrffrRCo1o0CNC/L6Dz/FzisjK3WVvj0U6X2z89+lrtFh8kEr7ySWq6NJAkD8De/ASrP1ibFb/hDRi7pOU5lPyubyZZUJWKIYwceq6ki/HTHpzy+7HGFEVdoKeTS8ZcmdczLxl9GgTlilXZ4O3h82eORDfocAWYdq3X3y+lXJZpUIoOr785ITuKU8cqqzNmzM68cvOQSpTEvy3D33ZkbcT3NE0/Ezm3UQ5JEYcpf/5q7c6J4vLZqav/XmTd5tldqX9v/tfa1VCgepw0ZykHYt0R/+ySpblNWYHgD3oSeLavJqsixBMJpBAy8UPn9NSXpKUyQ+nBIYS3X92Rmeg3kiPqOeoVYtYQUswVbiMpC5TVuM9lo7IwKJZZM0vmgT7KU9nHoclgZW199pcwDKS4WoqS5TDw+7TShAp6s58xuhzlzuj0a5UdrjYnqN0U5eRru+bYAbFdFIY2SUTc5PhgUyt5Ll8K8efDJJ7BwIVhbjtQ0H63rqOObBmXCrjfg5ayRZyV1XmePOlsRSgT4quaryA1ceoT+oO1rh/X3xTeSYqlqqr/XvW+K1VeayczfPva/FEdpTNbXCx02dZJ7KjgcwriKvj7ffFP0RHSm6TBwZZbmkxalpeKck73PbDZhrOZUp6tkvHbw97YIvaiMjjtB+1rdHNF7MV1MDm3zbIC9b2d0XMUEibhnS22lCfdTJ0i3dLWIXM4hlys9OHIQ1t6beBETJ0R/SFI4SvtazbuZXQM5orqtWiHlEJSDCT1bAwoHKLTWTAaTssdm2XSQVCvNxvmJ843/xzmsjK3Fi5WT1MknZzYZJsvvfgeTJycu23c4RM+4yaEUJYMJxtyhSi6UYdEV2uT5JDBLcI4DZgyaEX6cMeIMjqo8ChCVmA8/DDNnQlGRaLty9tmiefEVV4iedzdfOgH3ullYm2bQ3zuDsY4ZDC8docjVAhhcPJh+Bf2SOq9BxYN0latfWfeK+E/hiNj95TY9JHICYrnpY31PE36u9WIsvhK8rWnl7Zwx6TONxMEvf6kMWadKRwf8+McwdGhESkKWRTPnf/0rdYOrs1MYPenS1JS+sTZ9Ovzxj4kXHQ4HPPaYkE7JKda++t0YamdnlmdUdjSaYbXxcyBDI0JPPmLvWxmFEhWtdhBNqNWpAHr0sSkNP6NkFCrijirhMYxm+9OiMXEvDaPlhD46ld0N87Sv9QLUTah9QV/CUHKFowKLUem9q++IMrZKJ2pleVpXZ7W376HIYWVsNTQojauKiux254iF0QjvvRepdNTDbhdVkjfdpHpjzO3ajds3wVf/l3I1ksMAswfCkhuXhB9vX/4uH31gZ+pUOO44+NWvYP58Mal2dkJbm/LR0WpGfv0NPE8soelPS6i7bwl737+B8nY/lR2EHz9+aYeIByX5+PEbexX7l7V6CPz21+LEJQn6nqT/R8kBWHxVdzsj1YDu64TNf9Pfb/Rt2tc8++CTY8S/gdRWYYU2J8erorGrV2tV4JMlEIhISHz8sdLLI8tCw+qKK2DjRvFbxfoMr1cYZbt2iTzAK69M/NnbtkGnqiKpgf4MHSoqJMvLRTP13/xGeD+T5c47RT9Eu10bupck8fqf/gQ33pj8MdNGkqBApxdi3ezM8rZKJmqT2ds3Z9ylQBhxqi/Ns08kyqdBRxCNN7nYmlz7H/VkbDVZIz0WJ/5C+ffLfvjyoszz1g4lyo4Cgyr9o2MrdPW+hMvadqUn1xvw0sceP+ZfZi9TGFu+gE/p2SoerzWu5SA0KVMtDjcOK2NLLTTp82VH6TsZ+veHFStEjpjVKkKYhYXiUVkpJtSHHtLJH7NXQuU5OmGvt2DZzd1GRpyVuC+2+2PbNuFxuO46YRi4XBFPjMUCp54qzve+++DPf4Z77hHtiUJNlmVZeF98n/+SuoeC1D1M+HF7ioKuP/4axf51D8PPPo2a9AbOiq2a3bIKPj0O3HtFaNHXIVz2K38Ia+/R38dSKryGau+Wqxo+mtpdWu/U/25j5Mz99rfa0NdNNwnx21Q9qG63yNkC4dn64gsRjou+hmfPhokThSf0T38Sv2drqzC82tqEvMkjj8Cxx8KIEfDSS7E/T5bhww9h/HihJVfhb0BCDj8qaaCrS8ilHDgAX38NDzwAU6cKsdV33038N0kSlJ7+T8be9X9MP8aPxSKuf4tFVF6uWgU//GFq31NG6PXua5iXWRKnXhgRYM8bmRlcfY7QGnEAW/+RlkJ5nV/b/y7RJBuiX6HSYy0hRYytId/Wqqi3roGF3xL30+Hg4SqZoN8LcNuTGct1ZBuFbAOi/Y7aa6Wm3FGuKHzq8ncpPVuFI/Tzs7Y/0+vV9HNJVhTkJUk6B3gUUTL2rCzLD6je/wnwPcAP7ANukGV5TzY+OxVGjBADe0jItL4+S8bWgAHQmEiYT3zZvwP+Hw6+8MzkAGWMYzNHdy5H0mtv0tgoMoWnPii0oNSrw90vw4FVcMIrUDRauGlDCfW+dpGoueUR3XOZNw8uukhM6iGHkNEoKsBuuAGOPloYXnZ7RCcpEBAGmckEzc1icv7LX2BHT7S+6j8z/vttG2D2RGGYmgrEij9RT67JvxPfobpPU1cjLLgI+p4Aw/9PGHrmUjGA7FsMe17RPdzJJ4v+hdGyD21tonXNokVQVpacAnxnJ1x4odJrdNRRsGYNnH++aHIeHULcuVPIjPz+94mPrceWLeJ337gxtdBk6D7atk3sn0iW4plVz3Dr7FuRkRlz7ddsemcxQWcF/fuLsHWPU3YUVL+hLEuX/bD7VRh5Q+zQdTz0qtEAdjwLw65J75ggJnC9YpC62aTTAaHWj6LkH6BvQfwm1CGqCrXNqGs7uj0kkgRHPwnzzxNCzCHqP4ZPj4eZH4viFJ1WXUB2hIoPNiUT9HM/d78IU37X8+cTB0UTabT5eHqoPZsyMjtbozTVDCYhgeFStcCom81h5t9RkLGxJUmSEXgcOBOoAZZLkvS+LMvR3VK/AabLsuySJOlW4EHgikw/O1VOOEGEZUKTxJdfCi+AWjwyZZIwtKIpwMX5zE68Yf/+whosHiO8MFsfVw5gIEKKH00V4YtBF0HRGGEUNM2Huo9EWxsVX3whJvPo/JsjjhDJ1wMGROQG1KKSRmNkUhw0CL73PeEV+/vfgV8k/eenR9EYkQeg/vuj8TuFxy9ZTHY46W2YN1N/gty3OKUwjSQJb8+3v600WrZvh2nTRJHB0KGxtdecTvGYNUt4QdUMGSI8kC++KHK5/P7UdLf0Pnf2bBGOdLm0Cw+bTZzL9OnQt3sebm6GlStF7le0jZqoQvKvX/2Vez6/BxkZg2RgV8suTnt9OktuXEJRgn58OaNkglicqDWAdjyTvmFktImKNLUq+oEV4GuNbWQkQq8iEYS3bOOfYeLd2h6icajza8OIiVr1hBhYPBADhnAXCrfPrQxH9T8Fhl4lFiXR91XrWnhvGIz8ngg3WiuiPCCS8AbVz036b+i12Prre0fd9WJx3Df56u9c4vapJBvQNqXWo8xeprl2qltVhlXJRK2xFfRC9X9h+Hf08yX/x8mGmXkMsF2W5Z2yLHuB14CLojeQZfkLWZZDI8XXwKAsfG7KTJ+ubM8TCIjQT8ZCkf1zVLocfdwp93WXlcf4ydo2wIY/wtfXw/JbhMaNjqHV5irmssuUhtasWbBkifD8pSLCajKJHLQf/CD5fdJGkqDqfDLtY6ih4liY+nDmJf/dnHOO8HCpVdNra0W47wc/EPlTXV0i5NfaKgyspiZ48EFhjOkZWiEMBqHKXlsLzzwjihksZpniQh9FBV6MxgAgYzQEKXR4KS70YjbLHHusSDyvjZoT330XLr9cfH60oXXGGcLwbm6G554TFZE33ige/+//ic9tboZ33hF/bzxkWea383/LvV/ci9vvxma0Ma58HJIkUdtRy7Snp7GrZVf8g+SK4vH6FVIHVoI3RuPTpI47Vv/1bU+BX8eoTwZzYbdgpg6bHhKFHbHQqYKr8aMo+QcYWDQwqVOpcFRgNUXCZEGCGs0ujn68W15DdSMEvbDtCXh3MLw3HBZdDkuuhc/PgDeKYMGFSZ1Dr0aSRChNj41/ylzLLUs0dDZoQsmJKhFBhBHVRpoiZwtERaLeXLXj6cNWAiIb5uVAIKo5EjXAsXG2vxH4SO8NSZJuBm4GGDJEp51Ghthswhvz9NMRo+vf/4Y//CHDAyfTR0VvpZNKDNNoFS74T47pHljTi3/e+9/7FIZWaanwlKQr6grdeUr9+6fs4UuI2ogdfDHUvJ39uP+YW0UIdtWP0k/k7dYKkiR4/nmR+9SsLPZCluE//xGPykqRb2U0ijDgtm0pfJYcxN7yKVcMeZ0rfvAZXde3saVhMuv3jqF6XxVenwmL2U9lnyYmDdnG+Mp1FBSbod+p0PptKLqUDRslrr1WaXQXFAjj6rzz4ndTCElcXHihMMzmzhUGoOY0ZZkff/Jjnln1DC6fC4fZwa9O+hXfn/Z9Jj4xkUZnI03OJqY/M51F313E+L7jtQfJJY5BsXOI1vwSjn4qPU9Un+nQtBDNPbrjX8KjE49YUiUA/U+FXS/o7OOFr2+Ak9/R5nXJQV1ja7cPRZ/TZNq0hCi3l2M2mHEToxkxiPHqtLkw9yRw7tK/r7oaDr0eVMlSfpxYAKup/RAOfAMVMw66d6e+s17RVBzQrQpXU2wt1vTI3e/ar9yodJLwtPpVY/X+r8X3UjYdYlW+xrsHDmF6NIAqSdK1wHTgIb33ZVl+Wpbl6bIsT+/bN/GPng6/+IUyydjjEfIG7e3p/cbpVJqlTdEoOHMRmJOrGlIjy/Dyku8o5Aj+9rcs6Yw1NIgPiPWId1KxHuqBeMCZucvpGH2zmFyNDq1GTDyMdhh5s9AK6qZvXxFmi6cTVV8Pn30mQotJG1pBv0iyfXcwLPo27PoPuGuwmTo4YtASrpnxH35x4R/5zWW/5xcX/pHrT3qW6UO/pMByQIS1ql+Hr79LIChxwQVojO6vvxbyHoWFyVXpGgxi23POgWUqgWh/0M+sl2fx1IqncPlcmA1mJvebzLEDj2VN4xp+d+rvsBqsBOUgB9wHOPqZo5mzbU6SX0SWkCQR7tBj98vQuT32oBDPQ1VxjH5Ir6sBNvwpgS5cnAFlwJmxQ4UNc2HNPcpQY8AjKuA+P1Oz+S5Vrn4yTahDVDgqkFSLx3CCvOKgZXDuSqiaBcYMRNPSFT31O8U901NVUNEMOF0rnBxi6fdia05lIKycKg2dDRrJnv6Fib9rg2TAYVauzts97Ur9xfJjQI5RELL0xvjte9KQNToUyIaxVQsMjno+qPs1BZIknQH8CrhQluWD1iipqkqEa6InwhUrROinsTE146mzU0xQPUrJBDhjPtgGpBb6MjrYbvopXUFlxdHll+e44W82MTlg6JXotu7JBiOugwu2div3xzO6JDGQFo6G0z+HY/+p2WLGDOHxySTxW2EEt22E2ZPgm58LXbHoUETRaCFlcepsOGcFzFoP56yE0z6FcT9RKjr7O/jvf0Xbn+g56L33YPTo9Axvu12EoEN0+bsY9sgwPtrxUbi1iz/oZ9P+TVz2xmVc+vql3PXZXRiiLDqnz8msV2Ypm5BnwMsvizDpvHmwf3+cDSvPJOb19PUNsQf+eAKNFcfHnmg2/QX8MfIWAp74OYd9T9C2A4pmyyMwfxa0bQbPflFZOWeiyJVSUaM6TDJNqEOom1FDlIq8GqMNTnwDpv0VLGXJGV2SQYxtVbPgoj2KhUyYriaonQOb/gplOgNYCfDfUnjNAq+ZxQLl87Ng9S9h18uZS3Ekom+ca6Bjq/CcqnPw/J1Cdb+HqO+o14QDq5LMnyy2KBf8ZqNZ2SOxYGjsnqBtG4SHVm/BEugSRSv/g2TDj7kcGC1J0nCEkXUlcHX0BpIkTQX+CZwjy3L6TeiyxO23Cy2pOXMiSb5r1ojQz+9/L5K+Q6t29Qrf6xWSEQ0Nop3Ik08eBK9nnyPhwu3wzV2w87nu1VuMG9tYIEILx79IR/1ZihYyknQIGVohxv6wu+dajkqoHQPh1A/5Zv3TrFj6C75bXoTJvVcYXnIAzCUw8HwY/C2oOjduKGDGDCGke9ppIi9KXfQYC7NZ5Hy9/nr3C02LYP653eGg7knOaIcJdwkdtpDRrScNUHGiqLqUA7DzP7DuN9z7S2VC+4wZIoE/1f6F0YRy1Dx+D1OenBKpTuum0FKoUJ0G0cQ8EAzgjTJcbpl9C0NKhnDu6HPjfp7TKXpFzpkDDxv60y8YCWE30p9bbhH3riSJ772yUniwzz1XVOGGHTN9Twbzk/qh6ZZvYPmtorouJNIY9IlJcf6s2CdXMFj8Jnq5KUGPyFM6dY4w6EOhFL9bqNev+kmc4w4Tv3G8UHfTfJidOBzbqHIQJ9OEOoReM+oObwf+oF8TlhIHl2DUzQSGXEfz0qex730Mh7wHb8COQfIDMrJsRJJkJINEu+0MrMfcR9EglbJtoEtUim57AlrXiVBloAse8wqPX/E4kS9nKRUTvRwQ36tztygkapgr+qCaCmD4NUn9rWnjGBL7GgDY8ih0bIMTXhOhXqMVNv0N1v0mt+cVRU17jSJvz2K0JH0NlNpKqeuMeDMtRgv1nfWRilZJgvJjRRW9Hqt+KgyyfidFvLV+pwgzruiJJOCeJ2NjS5ZlvyRJdwCfIJaIz8myvEGSpN8DK2RZfh8RNiwE/tvtfq6WZfmgZUJKklj5XnaZqMwLhVPa2oTw4k9+IibIm26CKVOEdyIQEKHGjz8WOTfr1x+ccw8EhH7Stm0FbNv2D1p23cIw6WVOH/kqFYX1+AIWJGRMRj+ra47nq4bv0FH6bYa5CiktVSqay7L4m1PpW3fQ6XOEaCLcvjn9YyQISzQ5mzh99t20dLXwhH8IS7+7Hgs+IVRotKWkwzR5svi97r8fHn1UGOaxVOVNJmFoXXyxMOQHDEDkd8w/R5l3U34snPxudwl9Ak+BKcpVNeom9hdfT7WqSOihh7ITSnZ6nZzz0jnsbYukcFoMFu45+R5NA/MQ/oCfH3z8A3a27AyHIb71xrd49VuvcuFY7RARKiT4Z7cDrLMTXkLH86GKxuzZIxL7X31V3Mdh+s6Ib7zsegHat8CM54U3uW2D6DTg2ht7HxA5O/W6qamwb5HIvTztMzAXiXzBpoWw6FvxW7pIkggl7nk1/mcnQJahVbVADMiBhD3xQpQ7tM2orUYrTc4mhWdElkXF9/PPi1Zpu3bZMJt/iMHwQ4zBNo4btYSq0jrMJh+dXYWs2n0Uu5rHY7VKuFyiSnzSJOGdNOx4Gr75GSBHvLp9jxeVjZVni9QKv6tbJNksvGOyDASEgWywiNfaNojcuVwjScLDWRen6rxuDrxTJZLpuxqEt64HUavHW4yWhOrxIdTXioREfUc9U/pPibxYeXZ3f0ydAS/gEguWyb+D0beI13Y+Jzx+/6NabFnJ0JNleQ4wR/Xar6P+f0Y2PiebWK1iZXzPPUL40eOJeKgCARECmttLqpADAWHkPf88fPSRGENMJnHOgcAkRoz4E2+M+hOD+u6n1NGKN2Bhf0dfdlXb2bZNTFAFBSJXrbJSTP4h/vhHoQSeShXiQWfcT2DVj9PrNVZ+LJwdO/brC/iY9fIs2rpEJefW/Vu59eM7+deF6Q/QBQVCdPTWW0Vj8jffFFpcIS+jzyf0uS65RCi8H3FE945Bn2jNpDa0Tp+n78VKhMnB4uVK+ZNBg4SGV6adFNq62pj5/Ew27d9EV/dq3mF28KNjf8S9p9wbd9+FlQuZ+MTEsOaPy+/iyjev5JkLn+GaycIDESou+OEPxfelNliHDIGzzhJdGkpKxPYtLUIq47PPRIpASIRXgaUP2AcK70csmpfChzrtcuJRdS40fRHbs9G+Gd4fITwxATd0bE/uuAMvEEnWsUKRSbA/ACYJAlGRQF/Al7RXw2K0YDaYFQaX2Wimtr2WqqIqfD5ReBSq9A5Vu5pMMGyYuL6nTSthypRzcTjE611d4jf66isRZVizRoR/v/wyiGHlD2HnvyPe7IKhMPMTUZ1pLIhcvJYkNHzKpkFRjGrRbDPoItETMBBnnPJ36oZ5e4K97coFg1EyJlWNCFpNNn/Qr61I7H+KMHJj5mfJsO7X4nEYcPiJXURhMAhj49pr4eabxcDc1ZW82rctNSdHWjz/vPC0+Xxi4DIYRAXYd74DZ54pWqe43eKcjcYKJKkinF8eDAqjUpJEEvZrr4kCv1/+MqID9be/iZL+MWN6pnVRVhh6Jay8M/X9TAUw/qdxN/nBRz9g476NBAkiIeHyu3ht/WvMGDSD7x31vdg7JiFsOwS4u/vRhZVWXykBjJTSSsEWFzyAeIQ4AbgjKhHVYIYTXk3P0OqmtlYpf1JZGRGvzYTr3r2O1Q2rKbIUYbGK8OAxA4/hvtPuS7jvgMIBfHjVh5zx4hnhMJQ34OU7b3+HKf2mMKnfZL7/feGNjk7qr6oS7aUuukgIxgYC2kVDR4fwFtbVwRtviE4IGqpmicIDsrii7n9a4l5wQa8Ih6XCgNMzbuhbFwArED0F+gK+pJpQhyi2FrPPpWw/U9dRx44dQsS3rS0Sqh42TMiHXHNNZEEbqwjjggvEGGyzwebNsPjll0X4O2RoFY6Es5eKcH661Xzpap0hWmhZLOJRXJxgzBxwBlm9prJMQ6fWI5ysd7OysFLxvMvfpT1e6ZHxcwwPMw5rYyvEhAmwcKFIlH/uOXjlFZkurx+rxUiX24DPJwwWq1U8urrE3HrLLWIASYZgv74YmqIGpwTaXIGAUHJ/883IBDNxovBslZQoB6tkkrAnTxYJ0GazCMFs2SI+IxAQYat584Thlk4Ol9MZv/Iu65iLhPt53e/irxoVSGKgHnxZzC2eX/08L659EZdfyBRcd8R1vLjmRZw+J3d+dCdT+k/hmIHH6O+couyFDQ8DSLDPiUAwyroYeyfYkmvuHQt1YVa2Fgt/PO2PfPeRL8UsCxhkOH3nFxiuT66YYQaweADsLlW+Pv6pM7jlwkZeeUVpaN15p1gomUxaTbNoQvfGiBFin1tv1dmo6lyh7p1NSZGSCblpvGvrB6VT4ECK/bCiqPN3/+5R14LD4lC0YElEH1sfhbHlDXj5Zr2TG74vtONCRtXPfy48XIl+pxA2W2QMOvJImFT/T2iJusdHXC+6OSTRMDtV2ttF2HPTJtE66h/G/lQElLmAY8eKvy0YFPdSVZXI9Z06VcwjV14Z8VhTOFzkI/XSvpBquYaAHEg6jFhZWKkQtg3IAa38h8EoQqmNn2XjdA958sZWN5IkWtRUjq3h6ykXsXrLAYwHjuGGQX+gyjYKg0GUx0+cKPIIUlGmaOxsZMYvC9nVug8JiSJrEV/83xyOirPPc8/BW29FJpgpU0TLl4KC9D1QoUFszhzRdHrfPmFsbdkiBoxHHhGDhcGQ2OgKBsXKtb1dGIWfxsiDzBnjfixi/O1bSEpzzGgTXqEYE8qq+lXcOvtW3H43JsnE6cNP57FzH+PT7Z+ys3UnLr+L814+jw23bdAvj86Fztgw1fN+p2bk1QKxSDCbI1W3jY3JTYKJmNhvIhOXakV0U+HIBvGIZg7TeOklpaH14otigZCqNpzdHsOD1+/k7AstShL0OwVqP8jucQHG/gCW3552KLHWDz7VLZNsE+oQ5Y5yiNJ97fJ38cRdM2lpiRj03/++SFHIRMPPVFgJrYZIHk9XgwhLGRIc9O0BWhV/BQa4OkAgIGRaHn1UCDvbbCJS4PPBooENjBsHY8cKsWGHAx4yiHunqUmMm1u2iIXqJ5+Isfnaa1UfM/gy0ZGgl3l4AsEAnV5lYqMv4Es6jFhmL8NqsuKOqijUGFsgtBH3L+61BmdPkje2ovhgywdc8/Y1OH1O6BPE3Wc3L5g+4MfH/Zjfz/w9xjRWUzXtNcx4dgYNzgbM3e0/2j3tnPKfU/j02k9jJg6//76y5cvpp4tJMhuhvqFDhS7SKaeIqkq3W4RbbrxRhFkuvlgUDxxxhLaCzmwW5zVnjjjHDz8UA1OPYzAJ4+nT4xPfyEaH8ArFaBK8z7mPc146JzxwWEwWnpj1BCaDiX9f/G/OfflcXD4X7Z52Zr0yi69u/AqzUdXKJRfCtm9WgDeqnDpZ/a84E80Jrf3xuncDwpres0dMGFOnJnfouGTZ4AwicaPh35puB5dckmVPqrlI5MLtW5jFgyLC3Y3zM8qv0mXwpbDs+2nvXusHt+qyS3aSDaERQG0eyYGaMsXl/Ne/ZmZoATD5t6LtWMiDve0pUWE76PyU2hNpCbJsWaRtWSiX7+ij4bbbxOsGgwi5Wyzav8PnE/tJkjDgV6yAp57S+ZhRN4kii1xVT6dJk7MJi9GiqEb0BrxJh5LLHeWaylN1BTIAgy4WlYd58sYWiIvsJ5/8hH9/8+9wCOmGI2/gP6v/Q6evk0eWPsKnOz/l3SveZWBxci0tAHa27OT4fx0fdteOqRjDlH5TeG/Le3R6OznzxTP54KoPmDlc22R51iz4/PPIiv6LL0QvvGAwOwbX4MGi8fA998Djj4s53+OBrVtFtdeDD4pBZPJkEYoJGVm1tULxvFfQ50gYdQts/2fswUwyiR5sk/WTMLv8XRz1z6PCv5FJMjGtchqPLX0svM3AooFsP7AdX9DHmoY1nPHCGXz53R7QwykZLyrXQjQv7S6VTjCDxVnRDyhtpH9JI9XNQ8Ov/exnQmcr4yKJRAZnisbmqhXQOZNwZaHdLjy+OQlZj7kdWlZn1zAadJEQsMw2pgIYeJEQqU2jk8ROn3YvjXJ4wAuefeIRFgcNdFf7magyqwahgBVZUnpvjNmI9JVOhDO+gM/PFsnksg+WXA1jboOJ94h7wejQ5m+ptbleUV57r3/1bW64KTK+2u1Cl+3445XdE2IZi2azsqfujBki4qGhz1Qx/qj7BGaCrxNa14jKSnc9dO7W3+7NCuHJl4yiWtM2QMiSFAyjwe3HIhmI9ufazfakHQpl9jJN2Hmfc592Q8dA8R0097QgZe/jsDe21jSs4VtvfIu97XvxBDxYjVZ+MuMn3Dj1Rk4fcTrXvH0NLp+Lb+q/Ydzj43hq1lNcMyVxotbm/Zs54bkTaHG3ICNTbC3m42s+pm9BX6b+cyrbmrfh9Dk5/9XzefPbb2p0hW66SeQPvP++GBBWrxYDQchdnazKdzQej1JLyWaDv/xFJOD//e/wxBPida9XbOt2a5XBQ0iSMMK6urLkFUmXqQ8BAdj+rNbgMtpFWfXMT0UYUYejnz6amo6a8HO/7Gdh9UIWVut7OfyynwXVC7jhvRt47qLnsvVX6DP8O6Jxbejv2vSgKJNOZGwl4NeX/o4fvfpcOIH588+FkT15sphE0sHnS3/fWMyfr0zmnz49My2wuAy8EOTvZveYpgKRfL/3LdJtrxWT0bdA3Ydp9dk7paiAdQVjxJNAF/idXFcUhI+ng3OvOKatUhg6tn5gsIqqMoMFCELAw2UOH18VlYmQXqALeeAWdhZ7ad8XuTYfe0zkyGVsHJcfLYR6N/xBVCVKRtj6OGx9QvQ2rTxHtH8pmSBaMCGJsKMkiW29LYrDNXeUccMzz+GKqhBYsEDkXGXiidPNnZUkGH0rrP9d+qFqW3/o3Alb/g61s8G5R0i6OIZB2VQhh9O3DPZFxXX794OLd4mKZtkniirc9dC8ElpWUb/nSySVYVVkSV6BudxerhG2belq0d94zG2wfH2v6Ql5sJDkg9HKIAmmT58ur4jXkTcL3PnRnfx92d8Vr4XKmkME5aAiLg1wRP8jWHLDEhwW/TvzrU1vcdWbV+HrVik2SkbOGHFGWIOm09vJu5vfVbx/1/F38YcztE0an31WJJkGAsLVbTSK9ijf+Y6oSiwpEcaYLIv3JCnS7SYQEC5wk0l4o15/XeRQxCIQEEbdF1+I8OCqVZFKR4NBeNUsFlHBdtZZQiTypJOSzF+LlYmd4fW3rnEdd392N+cY93GbcStGOShWc4EuEcY5+kml1lQUP/30p/z1q7+m/dnvXvEuF427KPkdUg0j+t1CHqArapVeeTac9Fb83K0E+So+v4mhd/uoj6rUrqwUOSuVlakbNF6vyGEZlKi9fIp//29+I0SGQ1xwAbzwgsidzAmLrxaCuekaRrb+Wo9Kw2ew4NL0PWZ6xwRhTLxdCZ4UtZkMFhhzJxSNEMaLp3uCdFTCuJ9C5VngGCzU7GV/d46jIfIvMhAUny8HxXOjHYJeFn5dxLnnKtMf/v1v+Na30veaulwqA8jXIaQvdvwLDqwQ2lomO3IwAAGXkM0NGYZyQBiDcgCZSAv7eetP49JH36HdJfLUiouFzES2FwuRP6IW3h8Zv0VNLIyFMOxq2P1Ct3fRL36j6U+AfUC3IGqKiby+Tv61+j/8cO5duHyRBerY8rFsviM5/cI9rXuY+MREkXLTjdlgpvn/NVNkVRltvk54q29mbXhi3QchMu09nCUkSVopy/J03fcOR2PL7XPzvfe/xyvrX1G8bjKYMOpUEMnImrYGg4sHs+C7CxhWOkzx+vOrn+f6965P67xumXYLT57/pOZ1nw9mzxYyEJ9+Ku4rQ3eiptEoNJpGjRKGl8UijCKvV5Qpb9smyt7tdmF0tdoSSxSo8WPEhxkbXcQwmcQx+8WplMuiseXxe3hr01s8tPghVjeuDr9uAG4YMoXrJ3+b4ybditEWu7JmdcNqZvxrRjhnQUJiZNlIrp2sznAVBOQAjy19jFZPa/i1cns5m27fpNGciUk6A0LjfCH+F+21G3QRzHhBTCgxPHYKXtF+7rJRMqeeqszJKysT8gjHHpv85NjZCd98I3L8mhLN+yn+/U8+KUKcoVDP9OliIZAzTbjGL+HLC9IzjAxWuFJPMT4Ab/fVeFeSQ4Kr40gH7PgPrLgjhYpchJE+4kbYEeUJrpoFJ70tfh9DZhbHkiViERathXbaaXDffcID7vWK3y9eiNHlEvsHg8Lb/qtfKd9vbhYe/4ULYcemNsxdmxlauokjh6+jqrQWm9mNxeQhEDTh8dno6Cpm7d7JbKoZT6s8HlvZYBYvMSiu/a1bhVZbzjynn8xIL5RmsIhUiNBvZa+ES3T6UKbIfV/ex2/n/zZcTQhw/KDjWXzj4qT27/R20ufPfRQNqQsthay8eSVjysdod1j4Ldj7NmktZKrOh1MTFJrkja30yZWxtXn/Zs5/5XxqO2rDE63NaOP+mffz3aNihxHWN67n/FfPp8MrBmIDBhwWB89f/DyXjr8UgEXVi5j5n5n45cgFKCHpt7BATODRzTslJJ48/0m+P607+VVHu8mPkR2MZBuj2cZo1jGJ9UxiH33xY8aHGQNBzPiw4mEkOziCNYxlC6PZxokkdzOlRbxrKQvG1pLqJTz81cN8tP2jcJsXs8HMxeMuxu1zi9flAFajFavRygVjL+DuE+5mUn9lMkWzq5mJT0yk0Rn5bh0mB5vv2MzgksHqjw0zf/d8Zr0yK7waNBvMHDngSBbfsFibMK9HugPC3ndFnkp0IYC1L0x9EIZeJbx4pgJ93aFgAF7Tef1qmeeegx/8QFnpB6Ii9fbbhXHj92uNG6dTTJarVgmD6OWXE/cbB1L++3ftEqGdUOWkJMHy5UISICv5QJpzCcJb/cEbr5liDAacKXpR6rHyRyLsFTUuJIWpCC6PI0chB+Gjo7pFMZOpyC2AIx8AX6toiB2awPufDqd+2L1NHOM9yQq/piZhXD37rPidQp6ugQOFIT91KpxwglggWq1iG69XiNCuWCH6za5ZA0uXCq96SDPwo4/ggQfENWA2C0PfahXX6bhxolJ86lTh+TSbxb5dXWLB+c03ohBk1Sqx+PzHP0TEIGRw9ekjqgpHjUq/n6nGCxfN3nfgq++kJsRssIoE8/qPhJdb9glj+MxFUDwhsV5YnN/rxkZ4TnVpXTT2It698t2kTk2WZcz3mQlEVVmWWEt478r3OGXYKdod6ufCwktTDyWaikQh1MA47bEgb2xlQi6MrRW1KzjhuRPwBX3hRECzwcyj5z7KTUfdlHD/tY1rOfnfJ4ddp0E5SFAO8qfT/8S0ymlc/PrFCrfsoKJBbLh9Q8yy6qAc5OwXz2Zh9cKwGrPdZOe+0+7jpzN+mnvF1GzSv3/8BOk0ja2gHOST7Z9w2RuXacK5SZ9aQX/e+PYbnDTkJAJygJP+fRIr61aGw7gOs4O7T7g7odI5wEWvXcRH2z5S7Pt/R/wfT8x6Qruxsxqal4sci45tcKxOudKrNqFHIxmFflDBENHguniM+H+fqaLn276FYnXo71QaXQaraFtSeQ5UnQfWMtFWKOgBXxvUfwYrdXqNXS2+9+efF9VXaoMLhO7at74lQsV9+oif8MAB4b0INbOOJu5PKQf1tZF2/Fus3CWTMBgdA0UYy1oBksQxx8DKlRHdpvHjxYSccZVbLDb9Bdb+NjVvkdEBMz8SEhJ6tG2Gj49KvfzdVgmXJvBitKyBTxO0HApRNAZmbRQhwer/wvr7xHVptIpw1ejvi0mtaLS4hsLe1O5794MxicOWV0cugrY20R7p2WdFMQ4II8jlEkZ8LELdLlwuMaysXg3nnSf0r0J5htOmwV13wfnnC0PNaFQmtqsJ5aDabLBjhzDMPv4YLr9cXFuhBcSFF8KPfiSMwq4ucc2HzkeN0yk8cDabKBx69lnRKUIXWRa/U/NykhY6NRXBRbuEgbb2N6JNk8EqxoCqc2D4ddD3JLD17W5qLRMO+UoGeG94zN/rzBr4THXJfH/a93nqfL2SSn1KHiih3ROx2IosRTxzwTNcMekK7cbBAHw4Fjp3JH18QPSYvHBHYgHbvLGVPrkwtva79vOfS4YruvBOboSzU/j9t5bD+6puD4aCAu49RcYV1cXdYXaw/KblTOirLzcQorWrlYlPTKS+oz6ccBie/K94PPvaTYmMIsjNhZuGsfXQ4od4+KuHFR4oi9HCmLIxXDPlGgYUDtDdb0/rHl5Y8wI1HTWK8O/QkqFM6jeJOdvmKJI7q4qq2PnDnVhNiWMIte21jPnHGIVRDfD2FW9zydiLhVG06yWoeVeEoySLGCjKjoJvL4YDUSNcv3LYPFcksXqaoX2rGIycu8Wg7HfCBdvA3l1m7+sUE+S2JxA94tJoVxQialJcvhyuukpcFs40D1lQEHVbBbpESK7xc2icJ4xNbwvcGoBoKa4+FnjnLDEQB70iN83dIP4u2Q+WMjaP3sxRx/VRhHxuuklowqVjcIlOC3E2CPrg/VHJV49JJmHknvJe/O2+vhF2v5x83o6xQKzoB12QeNtl34edz8c/ttEOp38OFccpX3c3iOa/B1aJalfnbvAeAAyiuMReKSZ4ow2M3XlQAY/4vXyd0PBJREPqdoO26WI3MrCXwazhCFZzJCuYTgdFuLHhx4SdLmx0MYzdHMMyjmANE9lAO8Uc2beWljZjuFjiJz8RuXw2W+YeTqdT6LY98oiQQTEYIh6qcePEY/x40WEj9HldXeJeWbdOeMs2bxbPLZbYfU8BaNsoihCSMYxNBaL4Z3SUCq/fSaBhMR075mPYvxCrZzMmuRVZshMomggFI8BoRTJZwj1cZb9H/F4BH5aG15Fk8SVO3A0boyR7JCR+c8pv+M2pcZJ6VQz52xBFyx+L0cKfz/gzPzruR/o7NH4J889LXgbD6BA9YCvPTLxt3thKn5zlbCXRViVVRv/IyI7SYKRsVpZ58dSfcmVFGXTuAleNqATxNIG3DZBFs1SDAQJ+1nd5OXa3G68MSBJBWcYgGdh/2UOU9DtOVJvoJUTnKOk82xeuLINk0D/XuZ/KGI1ioCorg4oK8a/JBNdcY6PaGhm9irzwqwVwQoIewCAG949HwcMzwBO1KOrbCSO6f4IQ167TimnG46NRMG+48rXbWksY8YcjoGWlGEzloBA0nPgLKJksBphQjlU8pe5Al3gYbaLZcdFo7TZ+lzDodr8MB5YTxIAc9GKUdQTPDBZhQET/wTrJpoGASD6/+24x2YQKI+JhNIpcQJsN/vAHuPmaalj5Y9F812AVRlP5UaLSr+pcKBwVUdRWhNS605clkzAK/B3C41I7G6b8lpdfFgZWtME1bRq89JIITSUj9BsIiL9r/XpRph+X+rmw4OLkJgWjAy7Y0l0FFwdPs/A0JJMPJhmg4gQ448vkvNu+dph3mpjM9SZyowMm3SOuxRDJjINmoAiwIxIiQ/nxge5HJ+Ak64WW0Xyfp/gP1+NFLISuvFJ4kLIm/+FrF30pnXuo3tnJp18Us2a9ndVbB7KtZiD724uwWXwYJRkkGSG8LyHL0OU1Y7P4GFHZwKQRe5k6sY2f/i2Bcbz8diHGnKgyMeSFNBhZtkyE6t99V4RAq6qEB+7442H0KJkBFe2UFzRiDTYgd+1HDvgJBv0gyxiMJjCYMNj64Df154B7AA3NfTh9zhA8Umt4zvIGvDx8yi+4beQM0RDb2yKMbneDmLcCbmFUhwqQJCPTVi5hs6sTk8EISHiCfn4y5TL+OPM+KBymn//35YVCNy1RSF0yiZZXp30Sf7vw9nljK216ohpRlzQMGI/fg79lLWz+KzR8htHXgc1SAKVHQMUxQjCxdLIIkRgsgEFcbMIKAYMJr8+Fz9MCzl3QshrpwCocRz0ItjjNYXuZseX1ihDP0qWioezatWLFJ0mwvXMA5X7l4N5Z2J+//LQBr1fs63SKPJ2NG0W4qs0xAOO+LHv2IFJamS0Kgb9KUGjoXuVLIuF4wBkZ9WFLxH7Xfv604H4WrHmc46x+xjoKOHPwMYwursRgLhT942wDoO8JwrOWRPKzLMNXXwe5/+97mfe5jP/AQCxmI2aTsGYCARECGjoUTj0V/u//hPFiqHlL5KQEfeLadgyG41+CPkcJj4ghDZn6gFfsi+jreeONwuAKXYoGgwgjXXKJCDMVForrKHT5yrLI6dm/XyRUv/22kLlI6vb4/GzhmYs3KRgdMOGumBpuGrY8DmvuSuyRNNrh3G9Ek+okkGWZHfs3UvHNHZS0LEcKhUAls/jNj3kGhl+t3CkHi86s31fAcXzFUiLeuD/+UYQPM9IaDPpgwwOw5xUhqWC0iSTsoVeK+8TWTxgVQS+ergB79xrweMHjkbr7zcqYTdC/v0yfPojv2GgVC2lLn/gGsrcNPhgtDJlYqvJGO5w2D/rOYNEiEcY3GsW9V1YmCgRCYqvpsrZxLdv2zof6T6DlGyRPE+eVFGOrOhtKJ0HxeFGxah8ovh9Dd6hf6lbzlwPsbN7GN/Urxd/S1QTuWmYe+3vKSobH/mDnXhFOTChEbYdZG0TLo2TIG1vpc8gYW7IsViu7/iNc63IABl4Ax/xT6CEZ7OEJIyV8nYkn6l5ibDmdwq0fUlD2eEQ7oJ/9TKy+iooiastGo3gYDOLfUJ+xQED86/dH5CoSDSZeL1is2nOt3iNjMqF52O1JpsGl+r1W/xe+/m5kEi0YBudvFgNwDtjv2s+fFv2JJ5c/iTfgRZZlrCYrnoAHi9FCub2cB854gKsmXZVS14MufxfPr3mePyz4QyQ8EJQwdA7hpAHncev0W5kxcjKDBulMdv8tUfYWPONL0Rct3WbBOmzcCFdfLXJuojIBwoweLR4FBZE8nPXrRVVuNEndHp274aMjhdSAXo6NwSpy6s5bm1xFKIhw6ezxwtsdy4gz2mHk92D63/XfB5xeJ8vrlrNk7xLe2fwOK+oi4+Q5DvhJhZ1xBSX4y46mdMo99OkXo59nPHrJ2PKPfwjjKpRTeOSRQhMrk7ZltK6DOVOg20+FZIKrkmyDkWSRQFyce2DuKdBVr20qbiqAk94Jh86am0WIf8ECsXDweIQkyvXXi5zK0OLDbI4fVg0ExL4+n9i+cP31mGpfF9ek7BM9J6f/Q8hLxCq2SUQwkFzPyk1/hbX3xvYcGx1CsHbSL5SvB/1CZDfkefO1gbdV3KPjdPJSNz0ayV8LIfsBgxB5tZSIPFlLH2FgW/tm3HMz58aWJEnnAI8CRuBZWZYfUL1vBV4ApgHNwBWyLO+Od8xDxthyN8I7lSh86ZftB2tyDT0zopcMiFdcIRTIQ/kKt94qciBMpsxWoMGgyIv48ksx0W7bFkma7dNHVB3d/+wACjojg19XaX8+f7kBn08MLH6/8JLV1IiE7rY2YRSWxetOkvI10CBWa74OxOBtgJlzofyYjCqGBJHBe1vzNm6fczvzd89HlmUCcgCL0cLlEy9nSr8pvL35bVbUrcAX9GExWnCYHFx/5PX8+Yw/YzHFtly3NW/j4a8e5qW1L+EP+vEGvNhMNm4+6mbWNq1l4Z6F+GU/NpONoSVDueOYO7hl+i3KKts5R0LrekR8CZj2iDAaEvVyTHHykmUhf3LffSJx3myOtFqJR0iE1+OJVDcmpGM7zD0ZPPvFhBTCaBee6pmfigE7FVx18NnJIrVAnWNldIiQ6/EvhL2Qsiyzs2UnX9V8xZ8X/Zn1+9an9nndlFhLeOCMB5gxaAaT+k2KaYSHep4Wl+jfA1u3yIrFksEgFkUlJUnKJqQ4tvh8QlPwgw8iBtfEieIaKCwUGlmp4naDvX0+VL8pQt6uWuFBGXgBVMyAwhHI1gqw9EEKCQjLQSAI7w5JcL2iyIcMEQgG2NK8hVX1q3hz45vsaFjBq1VGxgfqwWjDIAeRCgaLPL0+R2r27+yMNMlevx527xbXfWGhTHExVFXJVPSVMVtkbFYJm1XCYJDweCQ8HrEwPXBAhCFbWuDxC6czoXJDRPvqqL/BmDvE/+MZWtkwNkEI0n7zM62Hy2iHI/4I434UeW31L0XLo64mcW5V58K0x0RRkaVU3CuVVUpPbf9+UL018lySgG5pE4NFGLkGM9luFp9TY0uSJOP/b+/Mo+Sqqzz+ubV0Ot2dlez7MiGCBhIQDBhASGBCRBCBYcmwHEQOMqBhRhkiDgPKHEHcBmeOHBRHCUKMSjAIhmDYPBoSkpiks3UWsnVnT5N96XTVnT9+v+qu7lRVd9V7BWn7fs6p0+9Vvf6+W6/e/b372+4PWANcClQD7wE3qurKtGPuBs5Q1btE5AbgalXNMGWhkTYTbKnCvFtcDpHEEUBh0PVwzv/6GVYV2aPlZQ+7zMLZiJbD9Tmmyp4kwdajj7pZOKla1oQJMHOm+6zQnEg1NTB0qHuYpvKGVVa6VAB797pCvkOH1ufFSeX9qa9vRVLMQq7rvlXw18lurFFqevPwO+C0r7sWkMQx39LZrCuvxcILaq/ewxdnfZGXVr/UguHZmfb5adw4qmlLV9XuKs756TkN6Uzy5eYzbuapK56iLF7mHljzboHd8xtn8g26DsZ831U8NOHWIGxOK74/NymqyspdK1m0bRE1+2uoOVDDu6u2sGx+N45XXQLvT4BDvSFSj0QSRCQKySjJRIRe/Y4xaWKMKybFufBCNy4QXCCzed9m3tr4Fus/WM+uQ7uo2lPF8p3L2XXYTbXsFYUf94Qry0ElgkiEBR3PpLLPTYwZMJZP9f9U/mum1u1zY1dqF/rEoVH3MB85BUZ/p8n9J4+ceC/GI3FO73k6o3qNYnj34XRo1oKa0ASb9m1ixc4VLN+5/MTf92BPfjpyJwsWOJ+qrXVdVf37u8S0/frBpbf0oaS26e+S6NmbrYu2N7REJxIuCNi713XVTprUirFUBXT3qLrK20O+t/bgQef/kya5ruWLL3at1keOuPdTlbxUYufU6gbxuMun9eyzbjmy9BMcOrCBxe+/ysbt89m8YwEf7F1DqUBMoEMkQmk0TsdIjEFdBtKzvA89KvoysMsQYmt/3DSdQWlvjl+1hb9u+StT505lzZ417D+2v2H2cnP6ReFjJbA9AesTHeha2pUze5/Jk5c/yYhTRpywJE59sp4ZK2Ywe91s5lXPY13tOkgKJGOQjEMi3ritwqh+pzF24NlcO+rzXDTs/Mb1dY8fgI3Pw+ofukk5sTK06xgYdA3S4zwoH+xafJJHXYuVu1Dw8shWzUhVdde9ro4mFd/UknMiUFo7m24b7yN2dAMAiQ6DqB3yA46ecgWqjb9l98rPUrrnVZJSikoHZPjtRM7xyagTx/z4t9Q95IMqwaU3WflYBgM9LT1fC6DYwdZ5wMOq+o9+fyqAqn4n7ZjX/DHzRCQGbAd6ao6Tt5lgK8WehS4j8/a57sESLXPjtXqOc8tNdD7NZ/zFj9fyNSWJ+qbOGLyQodDOUEsKbGtLFFAg/vnPbhHruXOdg1RUuMJwwgS3oPWgQa4GXFfnnC2Vl0mk8RWNugJx+3Y3Q27qVFeDKy11gdKMGS5zfaoAFXHnSuXqyWR26tjUeSORVtSGC72umoStr8L7z7rcOKj7veOdXHqGiuFu4GunEX4ZlDggrtWkbq9rSZl3c4NcbQIe/wD+52AZiUSCY8ljRIgQj8aZPGoy5w08D8mQZvZo4ijTK6ezoGYBdb6boiJeQbeO3Xh8wuP0rujNo+88yrzqeQ255mISo6KkgvHDxmec5ZlIJli6YykLahaQ1CSK0iHSgXgszj3n3MOUsVPoXdEbtr/hsntvfcWNjZGIa57vNQ66fMKntBjsxpJF4rjxi0nQOtdCOPvshnMeTkB5Putw1sfheDnUlYMoxA9BySGINu2y61x3KvtL1mQRyUyZQPco7EzgJrKccO4Ys1ZcwOdmvtl60cHA6UAdsAjYe+IhS+66my7TG9OKRBQG7nd/W8u2Cjia1ljx2NHv8PTRBxr2zz3XjbNMkfLLfGj1/wQYW3P4sBu79/TTLh1Eaanz6yNHXMvayJFu1mAquXMqz9bGjW7W4ObNjSki1m3dxbzqeby96W2enP9kk+Sc+dCppBP3nnsvFwy+gOff+BvTtn2jIJ1cTLv8ZXYd3sF/z5nJpg6vFKxTEi1h8qjJfO7Uz/HCtz7Lb6a71u7yDgeZclslj95fBfur3MScoztdmRQpaRwUH4n5ikGi8bVnYUOrb30iyqO3fJOHydGAkInOuOBoX+aP+1PNdunDiD5rGdRjM1Pu2sGk8XvdsI3EUddCLBHfnZ8KTv2km/S1A1Y+1jQw7tALrgl33GKxg61rgYmqeoffvxn4lKrek3bMcn9Mtd9f74/Jmj2wzQVb6ccdWAd7l7gcOAc3wJEa191Yt9vdHJG4H7wahcRx96BR/I2QNj6kkCUK8rE1H91WaiYSbip0ZaUbIL9unQuedu9urNGINNY0U8FQakmgY8dcF2Hv3m7JoN274S9/cTrr1rlm8ETCFbSlpa47sGvXxrFgqZpbXV1jLerwYadz6JDT/30Ls/RDua6adLPD9ixw438OVLka5OEaN96gIeDWxoA71tmNQ/Djee7fBU/sbSp7ydBLmHb1tIaln3LxxoY3uOl3NzVJnQFumnd66ouKkgqeuPQJvnTWl1pspdl6YCt3v3I3s6pmNdG4/uPXM/3a6WnfX9333T3PDUI+sLZxZu6x3X58o69wpHIDRUp8LdX5QFLhK7ugXwwGxNz8g9KLX2FE9xEM6TokeyLZZQ+jlY+wOwFrj7tWg3p1f6vroerFO3n9yDAi+/sidRV8+qI4//mNoQzuNIyO8dKGn1lSZbW7aN6mJFsP1LB5/wbqknUcTx5nx6FtLF79Ab/Yen/Oa7ftCejTbGz89nLo+/Uc//Rw03vu3XddDqgmNB/s3kJ6l2PHXGVmyRJYsQK2bHE+1L27q4iUlzful5U1HWOZai1Kja/cu9eNKzp0CP6j5TR1oQ1krq119ldVuXJmyRJXPhw44OxKlSfRqKuMDRjguh/POsulc7im8kQ7PnbKxxg/bDxj+491FYcMVO+vZl71POa+P5f39zarCay9DPYPhh1nQF05vXrDCz8ZzPkDz6c01vK4vn1H9/HOpnfYfdg9FmuP1LJkxxJmVc1qks8qJjEuGnwRN55xI+MGjsvqBweOHWD2utk8t+y5E7qfLy67ly+8OZHIH10y2469uzDsmjEkkkJSIiQ0ghIBkQYXUIWG9eA8EUl9oERJctmuGaBRUIFknMvOGEOPii6gEYQYkowSjUaIRiIIgvj7IVUmqSqKkkgmSSSSaCTB9NqvkdgzEOo7wvGOlEe7MaxnfzpGy+gYLyMuHYhHY8RjMSK+ZctpR1BfMQQliXK8vp7jyePUJY7y2kP/TmlpnrWKFmgzwZaI3AncCTBo0KCzN23aFMi2gsg0S6c1uakKoZAqZDrFsjXPwrtQgn79oumeJPdAJt+UPL9YJg1VPUEnDN18NbxQ+DdBGJrt3QeKpXmSXNdi+VaLGgWULSeNra34rYphazHKK0VP6KINg1zBVhhThWqA9DVOBvj3Mh1T7bsRu+AGyjdBVZ8GngbXshWCbflTjAdqNoKWXMWy9UO6BsVKkB9Y9yS5BwoKXlqhUSzdAoXC0Qlbs737QLE0T5Lr+pH5QAHfv73bWhTN7Kv8Fo0wQrv3gBEiMlRESoAbgFnNjpkF3Oq3rwXeyDVeyzAMwzAM4++FwC1bqlovIvcAr+FSP/xcVVeIyLeAhao6C3gGmCYi64BaXEBmGIZhGIbxd08oGQdV9VXg1WbvPZS2fRS4LoxzGYZhGIZhtCXCHyFmGIZhGIZhNGDBlmEYhmEYRhGxYMswDMMwDKOIWLBlGIZhGIZRRCzYMgzDMAzDKCIWbBmGYRiGYRQRC7YMwzAMwzCKiAVbhmEYhmEYRcSCLcMwDMMwjCJiwZZhGIZhGEYRsWDLMAzDMAyjiFiwZRiGYRiGUUQs2DIMwzAMwygiFmwZhmEYhmEUkUDBloh0F5HXRWSt/9stwzGjRWSeiKwQkWUicn2QcxqGYRiGYbQlgrZsPQDMVdURwFy/35zDwC2q+nFgIvAjEeka8LyGYRiGYRhtgqDB1lXAL/32L4HPNz9AVdeo6lq/vRXYCfQMeF7DMAzDMIw2QdBgq7eqbvPb24HeuQ4WkXOBEmB9wPMahmEYhmG0CWItHSAifwL6ZPjowfQdVVUR0Rw6fYFpwK2qmsxyzJ3AnX73oIhUtWRfAfQAdrcBzWLpmq1ma3u3tb1//2Lpmq1ma3u3dXDWT1S14BdQBfT1232BqizHdQYWA9cGOV8YL2BhW9A0W81Ws7XtaJqtZqvZarbmegXtRpwF3Oq3bwV+3/wAESkBZgLPqupvA57PMAzDMAyjTRE02HoMuFRE1gIT/D4i8kkR+Zk/5p+AC4HbRGSJf40OeF7DMAzDMIw2QYtjtnKhqnuA8RneXwjc4befA54Lcp6QebqNaBZL12w1W9u7re39+xdL12w1W83WLIjvuzQMwzAMwzCKgC3XYxiGYRiGUUTaTbAlIhNFpEpE1olIpkz3hWj+XER2isjyMPS85kAReVNEVvoljr4akm6piCwQkaVe95EwdL12VET+JiJ/CFFzo4hU+jF+C0PS7CoivxWR1SKySkTOC0FzZNpYxCUisl9EpoSge5//nZaLyAsiUhpU0+t+1WuuKNTOTPd9a5buKlD3Om9rUkQ+GZLmE/4eWCYiMwtZ0SKL7re95hIRmSMi/YJqpn32byKiItIjJFsfFpGatPt2Uhi2isi9/tquEJHvhmTrr9Ps3CgiS0LQHC0i76bKF58DMqjmmeKWpqsUkZdFpHM+ml4jY/kfxL9yaBbsWzk0A/lWDt2CfSubZtrnBftWXnyYUx8/qhcQxSVSHYZLqroUOD0E3QuBs4DlIdraFzjLb3cC1oRkqwAVfjsOzAfGhmTzvwLPA38I8TpsBHqEfB/8ErjDb5cAXYtwn20HBgfU6Q9sADr6/RnAbSHY9wlgOVCGG6/5J+AfCtA54b4Hvgs84LcfAB4PSfc0YCTwFvDJkDQvA2J++/EQbe2ctv0V4Kmgmv79gcBrwKZCfCKLrQ8DXwtwL2XSvNjfUx38fq8wdJt9/n3goRBsnQNc7rcnAW+FoPkecJHfvh34dgHfP2P5H8S/cmgW7Fs5NAP5Vg7dgn0rm6bfD+Rb+bzaS8vWucA6VX1fVeuA6bilhgKhqu8AtUF1mmluU9XFfvsAsAr38A2qq6p60O/G/SvwgD0RGQB8FvhZS8d+lIhIF1wB+QyAqtap6t6QTzMeWK+qm0LQigEdRSSGC462hqB5GjBfVQ+raj3wNvCFfEWy3PctLt1ViK6qrlLVgpMbZ9Gc478/wLvAgJB096ftlpOnf+UoT34I3J+vXit0CyaL5peBx1T1mD9mZ0i6AIiI4Ga3vxCCpuLyPwJ0IU//yqJ5KvCO334duCYfTa+brfwv2L+yaQbxrRyagXwrh27BvtXCMzWQb+VDewm2+gNb0varCSGAKTYiMgQYg2uFCkMv6pvgdwKvq2oYuj/C3awZVwUIgAJzRGSRuJUFgjIU2AX8n7guz5+JSHkIuuncQJ4Pgkyoag3wPWAzsA3Yp6pzguriWrUuEJFTRKQMV6MfGIIu5Ll010nE7cAfwxITkf8SkS3AZOChEPSuAmpUdWlg407kHt818/N8uqVycCru/povIm+LyDkhaKZzAbBD/Vq7AZkCPOF/q+8BU0PQXEFjJf46AvpWs/I/FP8K+5nSgmYg32quG4ZvpWsW2bdOoL0EW20OEakAfgdMaRbVF4yqJlR1NK62ca6IfCKInohcAexU1UVh2NeMcap6FnA58C8icmFAvRiu2f8nqjoGOIRrjg8Fccl7rwR+E4JWN1yhPRToB5SLyD8H1VXVVbim/TnAbGAJkAiqm+E8yodQUwyKiDwI1AO/CktTVR9U1YFe854gWj4g/gYhBG0Z+AkwHBiNC+i/H4JmDOgOjAW+DszwrVFhcSMhVGY8Xwbu87/VffgW74DcDtwtIotw3VV1hQrlKv8L9a9iPFOyaQb1rUy6QX0rXdPbVizfykh7CbZqaFrLGODfOykRkTjupviVqr4Ytr7vPnsTmBhQ6tPAlSKyEdc1e4mIhJJTzbfupLoiZuK6goNQDVSnteb9Fhd8hcXlwGJV3RGC1gRgg6ruUtXjwIvA+SHooqrPqOrZqnoh8AFu/EIY7BC3/mlqHdS8u5A+TETkNuAKYLJ/eIXNryigG6kZw3EB91LvYwOAxSKSaa3avFDVHb7ylQR+SnD/AudjL/ohCwtwrd2hDDr23elfAH4dhh5uxZNU2fobQvj+qrpaVS9T1bNxQeH6QnSylP+B/KsYz5RsmkF9qxW25u1bGTSL5lvZaC/B1nvACBEZ6lsgbsAtNXTS4WuCzwCrVPUHIer2TM0MEZGOwKXA6iCaqjpVVQeo6hDcNX1DVQO3wIhIuYh0Sm3jBl0GmvGpqtuBLSIy0r81HlgZyNCmhFnr3gyMFZEyfz+Mx40zCIyI9PJ/B+EeXs+HoUsrlu46WRCRibiu7ytV9XCIuiPSdq8iuH9VqmovVR3ifawaN9B3exBdaHhgp7iagP7leQk3SB4RORU3CSWshX4nAKtVtTokva3ARX77EiBw12Sab0WAbwJPFaCRrfwv2L+K8UzJphnUt3LoFuxbmTSL6VtZ0SKOvj+ZXrjxKWtwtY0HQ9J8AdcEf9z/WF8MQXMcrol4Ga6bZwkwKQTdM4C/ed3l5DmjpxX6nyGk2Yi4WaNL/WtFiL/XaGChvwYvAd1C0i0H9gBdQryej+AKlOXANPwMrxB0/4wLMpcC4wvUOOG+B04B5uIeWn8Cuoeke7XfPgbsAF4LQXMdbgxnyr/ymjWYQ/d3/vdaBryMG9gbSLPZ5xspbDZiJlunAZXe1llA3xA0S3CrhSwHFgOXhGGrf/8XwF0h3q/jgEXeD+YDZ4eg+VXcM2YNbuk6KcDWjOV/EP/KoVmwb+XQDORbOXQL9q1smmH4Vj4vyyBvGIZhGIZRRNpLN6JhGIZhGMZHggVbhmEYhmEYRcSCLcMwDMMwjCJiwZZhGIZhGEYRsWDLMAzDMAyjiFiwZRiGYRiGUUQs2DIMwzAMwygiFmwZhmEYhmEUkf8HiBZi9YwyHCYAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "27134882"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_13\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_14\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_15\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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b9k1MgHyq+eomCLcjVQYFAy6DoddBxxGgw+Ar2HOTTqOh50nShmX9K7D0XtiZeEffqioRW4lYB8aPl4mmSxdpVZEoRUUSqLpggRQFTbRW1b33JlfN/tBD4Yc/hPPOk4DX1jFqeXkS1/Hoo1JrrD2ZTm1YfE/qugQ4zO9+lx2xT1pLyYVssMDZZUfjDqobonc6Nz0SxYpp5/c4bJiELqTkxqkVhYVw++1SeubRR1O7L0N2Y8RWKqmeDxX/Szz7MK8UDn8ZSg4CX1Hs7ZQHciKzyYBLYb/vwVc3iiUtAR55JLHhHXoo/O9/IpiSweeT5dZboVcv+OlP7b1v61Z46KH2WV6OPBL++1+ZhPPzZf/ROOUUabTs8Uiz2X/8I/F97aJxK6z4mzPJEd48sXQqBQ3lyTWsjkJ5OfzrX85kdiold/9ay8XIad58UwKWk2XAACkB0KOHjHX1apg7V0qJpJsFmxdQ4C9gR9OONuvWbl9LIBRoEzz/beHTT6UVl1U9rS5d5GapuDg94yoslCSd1auB/6Vnn4bsw4itVDL3+sQvsD1PhknPSwNhb47993l8shz0F+h9Jsy4GALbLd/W3CwThV2r1sSJzgit1hQVSeNYreHaa623v/vuxC0vSkkD5VtvtW+Ja7krvu8+Cfy/9FIJzk2YFX8FkjAV5XaBIdeKpbOg7+5zypsLDRWw7kVY/oCIryS5777kUtuLiiRm77LLYPjw3YHIoZC4dP71L0nCSNZFqTX8+tft/D4QoX3ppXJOlJTIBTwnRz63uVmsm4sWSWPsd95JX3buvPJ5NAajH5w8Xx7Lty1nVPdR6RmMy7j7buuYUqXg7behc+f2BcG3l4ICePllIMWWNEP2YmK2UkXtaqj6goRqKQ37PzjiFcjplJjQao2/CHocC99ZKhYQC156yX7l5UMOkRgZJ4VWC0VFUjH8b3+Lv92OHWKuT+Ri3XKne8st7Xd5nnSSBPaPHp3gm4MNsOzB9lm1/J3gkH/Bmeth+C1QNBA8fvAXy+LJgcJ+MPRncPoqmPSCWEXbSU0NPP54+9yzxcXw5z+LZez3v5fYt/x8SZvPzZXjfvDBIuw3bxZXZV5eu4fKtGlSzLI9XHCBlDf585+hb1+xTLRYOgsKoFMnGduECWIFXbdO/j/pYEbZDJpC0c2KGv2tDZKvqhKrlhWXXgqjRsUu55BK8vPTv09D9mDEVqpY9S+Js7JL33Ng9J3gcyCS05sn1pDjPo7rhtRa7tztWAe6dZP4pVQIrRaKiqSUww9+EHubl1+OHYAfjcJCqcU1cWJyY8/PF1fntGkSD2KbNf+mXVatwgFw6nzof7HE6u0dr9cabx748qHvWXDqQug4MvH90f7+fn37Sir8T38qxzjecS4qEtfijTdKyYZu3dq3z9tvb1+s1t13y/+zQwd7bqYOHeT/99lnUkk/1czdNDfmutrmWr7Y+EXqB+FCXnkltsu/haIieOCB1M5R8TC1twzxcERsKaVOVkotV0qtVErdGmV9P6XUx0qpeUqphUqpU53Yr2vRYfjmMQjbNBF0ORgOfcYZodWCxw8FfeDIN0FFVyeffw6bNtn7uBdeaJ9VKFGKiuCvf42dpfj3v9u/yColQfy9eztzp+vxiHibOlVcT5boMHz9u8TjqrpOhFO+EsukN4GBe3IgtyucOAt6npjQLoNBaaicqIAZO1ayUffbLzFLVUGBpM8vXJigeAWWL4fZsxN7T14evPGGBNS3JzOtsBCefVZcl6miOdTMhh0b4m7zbc1IfOQR63Pz9tuTs5YaDKkkabGllPICDwOnACOAC5VSI/ba7DfAS1rrg4ALgARDsrOMzR/Zb/pcuB8c876zQqsFXz50OQTGPxx19V132bu4XnqpuIDSZZrPy4Pnnmv7+urVcqG1yz33wKRJzpr3PR5xS779tvWdNltnQaBtoHNc+nwXjv1QXMmedoRUKo+4ko94Hfa/0vbbPv448TiqU08Vi09JiY1jEYWcHOjeXSxcRx5p/33/+U9icWV+v1gkTzghOatHYaHE/Fm5utvL0i1LyfPFVwtLty5FZ0t7B4dYy36Wv/vSUrGspuOG0HIg2UI6g9qSJZuOawycONoHAyu11qu11s3AC8CZe22jgQ6Rxx0Bm/aULGXF3yFoJ3JXwZGTwZfCtBl/obii+p69x8vV1WLZsqKkRC4u6TTNe70SXL03Tz9tP1D5/PN3u7WcJjdXYrcsszjXPS8xW3bpMAwO+48zwttXAOP+KmLbBv/9b2LB5sOGSbxfsvWLPB5x5731lhSgtMOzzyYWV/anP8l4nbgQFxXBFVdIH0anmV8xv01PxL3RWlO2M5sq+SbPc1xkuc1F1pukh5ZeZ8ksdkh2H1rvrnmTDWOtqGj/d+ISnBBbvYHWtu+yyGutuRO4RClVBrwL/CzaBymlrlZKzVVKzd2SibxrJ2jeDpumYCswfv+rIkHPKU4K9RfBwY/tEb81dao9S9Xtt2cm2HRvkaQ1PPaYvZIE3btLXI7ThQxbU1goE/xxx8XYQGvJEsSmCcaTC0e9BR4H/SC+AjjyDfB3iLtZKASvv25/7szJgcmTnXXZ5OfLGKxutpctS6wx9sknS5soJ8+FwkJ45hnn27XM3jibukB8U7Pf63cmSD6LLAVT/N+xtLpec40LrFoGQxzSZUe8EHhaa90HOBV4VinVZt9a68e01uO11uO7tTdyNtOsfR48NiK4/Z1g7F9ECKUDbz6MvmvX0zfekOyzeBQV7e56n2lmzrQfT/Tgg+kJVi0slFIGUV1o1fMSqxY/7kHI72nv3EkEf0eY+HTcTWbOTKy0wQMPSLJAIokKVvj9Yn267bb42730kv2yHz17SqxhKkR3fr4Izva4T2Mxq2xWm9f8nj1P5LrmOr4q/yr5naXLAgNJ72dpp8PifvywYfatogZDpnBCbG0E+rZ63ifyWmuuBF4C0FrPBPKArg7s232s+Ju9gOgDbnekzY5tfAUw6EeAzGHvv2/9lh/8IH31haz45z/t9W0cOxbOPDN91rguXWIUY133IsRI4W9Dr9OkKG2q4vZ6nAADvh9zkxdesN8T8+STpURHKgRMUZGU55g4MfY2zzxjv+Dq66+nLh3f75ckjj/8wZnP01qzbOuefYcUipL8PTMxQjrEtPXTnNlpFlBbKyEP8bjoImeFv8GQCpwQW3OAwUqpAUqpHCQAfvJe26wHjgNQSg1HxFaW+gnj0Lwdaldab5ffS4SPL82FWSLibsUK6yKmHg/86leZS6NuTSAgqd92LBoPPJBeS1xRkfSHbMPa/4K20VfEWwCHPZsaodWCvwgm/F0yFfdCa3jxRXsB54WFEtuVSvdsQYFkkEazGK1aJXW87HDOOTBihLg8U0VhoRTh7d8/+c9at2MdSqk9Pz+nkAGdBrTZdsHmBcnvMEtYvNjaPXj00ZkJdTAYEiFpsaW1DgLXAlOApUjW4WKl1F1KqTMim90E/FAptQB4Hrhc74spNVtmiLvOiqHXxyzHkFIihVLff9/aYnXGGe4p0jd/vj13zaGHStuVdCfZtHFZ7lgKzRa34y0MvQ5UChVBC8oHI3/T5uV58+xnId5wQ3ouasXF0WutvfyyPcHt9cL996enXYvPJ1mvyTK/Yj6+vWI3vcrLgT0ObPP69sbtbG/cnvxOs4Cvv7a+EYiWTGMwuA1HLkta63e11kO01vtrre+OvHa71npy5PESrfUkrfUYrfWBWut9s4NU5ScQsEjpUh4JjE+kfpLDvPqqtWXrRz+Sgo5uYPp0e66j3/wmMwKxjaVn/UvY6ofpK4aRv5KM0VTjzYNBP5S4sFa8+KK9Y9uhg/SITKVVq4XiYnHP7S3snn7a3lgvukjataQDvx9OP11qhiXDl5u+pLZ5z7mjIdjAIX0OIX8vC3i+P5+Fmxcmt8Ms4auv4sdqFhe7Z54yGOKRRYU2soBN72NZLbzHCanPPoxDICB1jeLh9SZW9yjVTJlifZHt1AmOPdYlpWPWPGuvoO2Qn4j4ThfKA8N/scdL//2vnBNWXHddeuNicnIkNqyFdevstedRSloBpasJMYjgsgrst2L6hultyj74PD4O7HFgG/diU7DpW9O2Z9Wq+OtHjrQfb2gwZBI3XJr2DUKNUGOj4uaQn8VtoZNq1q2zdslNmGC/X2Kq0Vra7Vhx3nkuGXN9GdTvnR8SBY9f+h2mMlZrb7x5MOiqXU+/+cY6+BhE+Nx4Y3pT64uL9xQwn3xiz5V86qk2q/s7iN8PZ59tvV08olmq+hT3Yf/O+1Mf2FNNNIWavjVB8lYu7l69RGAbDG7HiC2n2DZXLmbxyOkMPY5LrzVjL1avtr5onXKKO8o9AKxfb891dO217gjmZ9scaZtjxX4XiODKILNm2bMEXnSRsyUO7NLaPTR9ur2iq7/5TXqtWi0kc8GvaqiipqltHZYhXYZQnFtMgb+tyo3XQ3Ffwkps5eYasWXIDozYcorKz6yrhfc7H3QCfUZSwOrV1tW3zz47tVlciTBjhvWFfuBAGDw4PeOxZOsse90Dhl4P/gyoglaWtJkz7QmYG2/MjIBpLbam2TDkdOsGBx6YsuHEJZmbkwUVC8jz7/kBCsXo0tEA9OvQtnpq2c4ymkMJlNHPUqys1T6fEVuG7MCILacof8861b/vWel1G0XBquxDXl7ywb5O8tFH1sVXL7/cRRNu5WdYxu3ldoWOI9MynHjYETA9e2ZeyAYCsNJGRZXvftclruQEmVcxj8bgniacwpxChnaVH+Kwrm07def58li6ZWlaxpdJrLJfm5rsF7k1GDKJEVtOoDVUfWm9Xdc41RrTxNdfx18/YIB1pmI6+fhj623OPtslbk+tYYfFAQbocyaEbUSlp5BQSIS3Fd/9bmJNn1PBkiX2vt8rrnCJKzlBpm+Y3sZK5VEeBpeIyh3TYwzevUrFhHX4WxEkb/W919W5p/CywRAPI7acILDd2j1YPDijsVotWGX3DBzonjvF2lqJ2YpHbm7mLS+7qN9gz0084PuZcSG2YsUKey2NLr88PeUe4jF3rrXgKymBgw5Kz3ic5stNbW/UmoJNDCoZBMDgksFt4rbqAnVR2/vsa1iVdVixwlSPN2QHmb/67wvUbbBuINztCFu9qVOJ1rBpU/xtBg50TzXmVaus72zHjXORJa7qS+ugd39H6HJwesYThy9tGGJLSmD06NSPxYrPP7dO7z/jDHslLNxGY7CRjTVts1eVUnQtkIr/g0oGtSn/ANF7Ke5rHHhgfDG1apV75iuDIR5GbDlB/XrroKGeJ6av6XQMqqutrVbDhrmncnxZmXW23GGHucSFCBIcb1XUtvdpELbZ3C+F2AmOP/10dwiYGTOst7niiswE8SfLki1L2hQtBehd3HuXwNq/ZH8aAm3vKJZvW86+2IijNWPGxLeshsPW1m+DwQ0YseUE9Rusi1h2z3yV0B07rLMMR4xIz1jssGGDdebkySe76M7WTnB8/0vAn/mS13aC4y+7LPMCpqkJ1q6Nv43XC4cckpbhOM78ivltipkCDO6y2zfeKa8Tub62J7lSinU7bFR6zWJGjbJ2IS/49rSKNGQxRmw5Qe1aCMXxZXn8kNs9bcOJRXOztQGutDQ9Y7HDunXWdXYmTEjPWCyxGxzvAhdiOAzLbdTfPTjzQ+Xrr60tl8OGWYtytzK7bDZ1gbb9aEZ339N/27dD3zbb+Dy+fT5IfsAA6+/2zTetM5YNhkxjxJYT1FikdeX3ji/G0kQgYC227ARNp4tvvomfadSnT2aKbUbFTnB8breMl/4Ae8Hx++3njnIaX39tnW02fnx6xpIKZpbNbPNaob+wTbmHoV3a1mOpba7d54uber3Qv3/8bV5/3V3zlsEQDSO2nKB2Tfz1BX3tNSZOMXbu/t00aVm5j/r2dZFFo3aVdeX4knHS1inDLFtmHQs3bpw7alZt2mSdADFpUubdne0hrMOs2Nb2Rs3n8e3hRgQp/+DZK5s5rMNMXz89pWN0A2PHxl9fWyv9U92SRW0wRMOILSdoKI+/vrCvK8o+ZBtWmZN9+rik8TRAQwVEib3Zgy4TXGHZqqiwDnyfONEdNavWrrWO2Tn88LQMxXHWVK/B62mbatcU2l32oYUhXYZEbduzqHJRysbnFs47z1pMP/aYvW4IBkOmcORSpZQ6WSm1XCm1Uil1a4xtvqeUWqKUWqyUes6J/boCHYbmbfG3KegL3syn+NmxWrnBmgHiOtq6Nf42ffq4KDi+odw6y7D70RnvhwgiYq1i4Y44wh31i6ysmz4f7L9/WobiOPMr5rcpVgpisSot3DN4clDJoDaWLYCaphqqGqpSNkY3cNpp1oJ7yhR7TdUNhkyRtNhSSnmBh4FTgBHAhUqpEXttMxj4JTBJaz0SuCHZ/bqGYD1gEdxSPNgVF9mcHOv4F7e45bZvt44Z2n9/F4mt+vXWGamdx6RnLBasXm19HowalZ6xWLGxbQmqPRg50lo4upW5m+ZS29zWHNOruFebulqDSga1aekDkOfPY0HFvp2Ol5cHZ50V34odCsGPfmSsWwb34oRl62BgpdZ6tda6GXgBOHOvbX4IPKy1rgbQWlc6sF93oAPWLsLC/mkZihV+v/VF1sqalC42brQWUoMGxV+fVqzi9vK6u8KFCFJSIx777ece9+yWLfHXj8x8i8l2M23DNHSUSsctbXpaU5Jfgs/TNhukMdjIvIp5KRmfm/jhD607GUyZAnPmuMc6bzC0xokptTfQevoui7zWmiHAEKXUdKXULKXUyQ7s1x2EbYgtF7gQQVpfWMXqLFmSnrFY0dBgfcHv2zYbPnM0WJhgiga5IjgeoNwixNAtpRS0hioLD1mvXi4qapsgiysXR319dGn0sv19OvRp81pzqJlp620UTctyjjjCukYgwDXXZO7cNVY1QzzSdf/qAwYDRwMXAo8rpTrtvZFS6mql1Fyl1NwtVre0biEcxNKN6AIXIkCXLtbbLF3qDreMncrlJSWpH4dtGjfHX5/fA8vzJE1YWS979nRHSY0dO6wF93772bsIu40tdVui1tcq8BcwvOvwqO+JVv4B4KvyrxwdmxvxeKRLgJW1e8UKuOUWaVCdThob7bXAMnx7cUJsbQRa2xj6RF5rTRkwWWsd0FqvAVYg4msPtNaPaa3Ha63Hd+vWzYGhpQEdwPIwukRsKSUX0nisWeMOsWXHFeCGAO5dNFmYYPJ7uuI8CIdh58742/To4Q5rUXm59cXVqgaTW1mweQF5vrYH2e/xt8lEbGFM6RhUFMG+sWZj1HiufY1bb7WX5PPww/Dyy+kTXMEgVFbCd7+bnv0ZshMn7l/nAIOVUgMQkXUBcNFe27yBWLSeUkp1RdyKqx3YtwuwY61wsH/Zaz2srShxGFgwlbUcG3P96tXuiNexY9lyjdgKNUHY4mKX3xu8mVcwVVVitYqX3dW/vzssW+Xl1udi770DFrKEeeXzovY7bA41xxRbQ7oModBfSO1e/Tfzffks2bKEsT0tClJlOV26wF13wW23WQupq66SbhhHHQUFbStmOEYoJMk8hx8ufw2GWCR9WdVaB4FrgSnAUuAlrfVipdRdSqkzIptNAbYppZYAHwM3a60t6iVkCR4/lv3wwg52801CaAGM7BO/pcyaNe5oRJ1Vlq3GCvBYCKmiAa6otZZN1qLycutClW5qL5UI09ZPIxBlXgiGg/Qsjm5+HlQyKGpdrpAOMa983w+SB7j2WnvhEKGQWJpefTV1Fq76epkvDznEOunEYHBk9tdav6u1HqK13l9rfXfktdu11pMjj7XW+kat9Qit9QFa6xec2K8rUD7rFD8nxVaSDO25PK6LqK5OJpBMY8e6ZnXY00ZDubWLsMAd0fzl5dYlNaxczemiqso62NlVcXsJ8FVF9DirnsU9o9bTgtjlH+oD9czeONvR8bkVv18KmNqxVjU3SzP1n/1M5jUnK8zX1kpPxtGjxRtgMFiR+VvtbMeOZSvgnjSVgd1XW1o2Xn/dnhsvldiJzbAqdJg2mndYb5PXI/XjsMG2bdYXne6Z75kOiHUznqAuKHCRdTMBGgINVNRWRF23f+fYFVq7FnRtU3+rhVllsxwZWzZw0kliTbL73T/1lLR0WrMm+YbVdXXyGTfeCBddZN1KymBowYitZPHkWLdpqf0mPWOxwcDuqy1Fyrvviok8k9iJGXJNPR0dxDIuL9eG7yMNWAkYgM6d0zMWK4LB+MKwqCjzNwXt4evKr6O23oHYZR8AlFL0Lo4epLZi2wrCVvPQPsS//51YP8wFC2DwYLjkEilvU1OTmKWrtlZisu66S8qNPP54wkM2fMsxYitZvPnWsTi1qyWI2gX077bW0jUzc2bm0+ntWLZcc1dpp8m4xx2l7q3ElseT+e++hUAg/gXRZ8OD70bmVcwjGGp7zuT78hnRbUSUd+wmVvkHn8fH6upvjz+rTx94663E4ku1hsmTpRDud78LTzwBK1eKu3H7dik1UlMj2brbt8v8snWruAuvv17c6/fdZ+ppGdqHC3KOshylILcrNMTpmly3QQpaejN/wc31N3PQQTA7TohHIACzZsExx6RvXHtjp7XQxo0uCeYOB60TTl0QHA/WYqslU9EN2YhWlq1sFVszN8ykPtjWdJzjzYmZidjC6NLRvLfyvTaV5z3Kw/yK+Zbv35c4/HD4619FCCVqif/oI1lALKQTJkjgfUGBiK/aWpg3z7pdlMFgFxdMqfsABX3ii6369Tha/iFJzjkH5s+HpjjGtscfh3HjpOp8JujWzdpNuGaNxGJkHDtuxCwSW04GEieDlYswW8XWF5u+iPp6IBywFEtDuw6lwF/QpiBqbXMtczfN5dwR5zo2zmzgqqsk6eOee9of+lBbCx9/7Oy4DIa9cccVINspHBB/ff0Gie1yCSefbG25ePnlzMZE9ehhXVz1m29cEiQfzi6xlS3WIqusSbeMMxFC4RCrqlZFXdccao7akqc1g0oGRe2RqNFMXz/dkTFmG7/5jbTpSWU9LYMhWYxlywk6DEWKm8aY/Rs3u0psjRplLbaCQfjzn2Uiy8Qk5vNBx45QXR17m/XrJa6iqCh944qKUqS1FU8ShW31nJ9B+F4gerCLlcBJJ36/jCeWqAoG3TVeO6yqXoXf46cpSgxnaWFpzLIPLQwqGRT1vQCLt0TvtbivoxT85S8wdixcfbXMCdkoxA37Nu643c52CvcDbxxFosNQty5947FAKTjxROvtHn00sxczq4KVZWUuyUi004ZHO2iCS6Kwrc8bRMWxwgUC7hEwfn/8emvZKLbmlc+LWb4hXtmHFkoLS2NmHdYF6thSlyU9ZVPAxRfDF19IV4FMtJsyljVDPIzYcoKCvhDFtL8HFR+6JxgGycaxSp2urob//te6sGSq6Ncv/vqyMne0FkL5bPi83PHd+7xBlIottoJBlxxTxLoZ77C6aax2mbtpLrXN0dPZDig9wPL98co/5PnymF8xP5nhZT0jR0pph+OOg8LC9OwzL08E3qxvT6kzQzvIsqnKpRT0tbZcbJ4KwSQr6jnICSfYE1G3354ZsVVbC4MsEqvWrHFHw2SUDW+8k5atJPB5gniySGzFG0t1tXvKVNhl+obpbTIJQco+jOw20tZnDO4yOOrrDYEG5lV8O9r2xKO4WMpCPP643LClKswgP18E3Q03iMA7wForG77FmJgtJyjsK6Ud4lH5uStKP7TQrZtkG86YEX+78nL45S/hj39MX2xUOCy9xgYOFFdSrKy0piapkzMifmmi1OPxYRmzFagBX5puteNg5UYMh0VwuUHE5ObGrxIeDEoGWiLFLTPNnE1zor7eGGzkd5/9jr/M/IvlZ2yrj95WNhAO8OS8J7ll0i1JjXFfQCm48EI4/3ypk/XLX4ol3Ik+iYWFcl7+4hfSqzFTGduG7MIl97BZjq8Q/BYzfmMFNG9Py3Dscttt9gTUI4/AsmXpq9bd2AgXXCCFC60sVx984IKMRDsFSxsrUz8OG/g88d2IIAUd3UD37tbFbbduTc9YnGDh5oUEw9GDDDWa8tpyVlWvsly2N22PuY9vqtzTrcINeDxw1lmwdCm89hoccYTMKUVF9gui+v0iqPx+GDIE7r4bKirgV78yQstgH2PZcoquh8Gmd+JvU/kZ7Pe95PaTV5pUgHRrTjpJWrNYVUQOh+Hss2HxYnuV3ZOhthbuvBMWLoS+fa1dWp9+CpdfLpmLGSOvu3X6U8Mm6By7FUu6KCmqwqPix49VVrqjP2LPntahcBUVMMCi8opbmLx8csr3EdZhttZvpWtB15TvK2sIB1G1qzlx+BJO/McyQjVlfL3Ez/SvejFlzkHMWHoQ1XWdCGuFzxMkrD2EtYeCnAZG91/GSePncMRBazlk7HYKu5RC0f7QMBL8w1xhrTZkB0ZsOUWvU2DzRxCK00NmwyvQ62TwJ3E7dHb0BrZ78Jy9FC2lpLTDjTdam9c3bJBiqK+/nrrA07o6eOcduP9+eb7ffvELr4K4Qa0aa6ec/J4QthioS7JRe3YqR1u4PMvLpTxIpunZ0zrbdMMGOPTQ9IwnWT5b91la9vPW8re44qAr0rIvV1K7WhKSNr4D1QugYSN486TWXbAerw4yJg/GHAY/OWz32wJBHw3N+eT6m8jxNbcV+uVAuRKBFfkscjpBh2HQ4wToeSJ0OST7UmQNacERN6JS6mSl1HKl1Eql1K1xtjtHKaWVUuOd2K+r6HY4KIs29GWTcZvn9tJL7QdEf/CBCC4n4h72pkVoXXTRbiNRr17WQmrzZulpllFySqz7I9athXDmuyb37FROczB+QNY6d+hCeva0Lmy7apWrknzjkq5MwalrpqZlP64hHICyt2DGpfBqd3hnFHz5c9g4GerXyW8zWAuBnXF/p35fkA4FNeT6owitXeg9P6tpK2yZBl//Hj460QgtQ0yStmwppbzAw8AJQBkwRyk1WWu9ZK/tioHrgThd+bKYjqOsM87CTbDueRh4ub3aTGkgPx9+8hN48EFrKxLAlCkiuF57zbm6MnV18O67EtDa+sKpFEycKPuMx6efwrnnZjCLTinwd4TmqtjbNFZIEkWGv/euxVsJBOOPYc0asShluj9iXp6I7XhtWDZulPM2kYbEmWJbQ/TAdqeZXbZvTrFtqF0Dyx6E1U/Jc9vZ3kpqI3YcAR2GS29bXz5488UCFg7IbzVYD8GdULMSdiyGnStiW7B1AIKZv5kyuBcnptODgZVa69UASqkXgDOBJXtt9zvgXuBmB/bpPjxe6DwWtlq0zPjmUeh/YcYvuq254QZ46CH720+ZAmecAS+9JBe59l7owmGxXDz1lDSTjWahOPFE+OST+ELwX/+SFkQZDVbN7RpfbDWUu6L8g8ejKc6vYXt955jbVFTI95LxyvxASUl8sVVWJqVJ3C62NtdujlmM1GnW71yflv1kjIZymHMtbHpXflM6jshRXig9BkrGQZeDodMBULCfvCcciAisOOZzHYJgnZjbfQViydqxFKrmQPV82PQ+BLY7/T807IM4IbZ6AxtaPS8DDmm9gVJqLNBXa/2OUmrfFFsgcVvbvoj/46/+Chq3uONKFqFHD7FuPfqo/WauU6dC//7wpz+JKzIvLzHLUm2tXCgvuQS+/DL2docfLmUI4omtqVNdkJGY3xNqVsReX7/RNf0RuxRviyu2ystdUpkf6SJQVhZ7/bx5qU/acII3l7+Ztn01h5ppDjaT43NB/Q4nCQfEkrXoTnkcb57tcjDs/0NJSNI6IqpaHw+bx0Z594yxze8pS/ejIFQnbdi2TIeV/4SyNxL/Pxm+NaR89ldKeYD7gZtsbHu1UmquUmruli1Z2Hai+5Hyo7Zi+V8hkILApyT43e8Sr1dUUyMNYI86CubMEWvIzp2xt29sFJFVWQl33CFB2PGEFsCBB1q7N0MhePbZzFW6B+RuOR41y8VF4QJ6diqPu37FCvcImL59469fv94FQtsGH6/5OK372+fitgK18MERIrRC9bGFVrdJcNrXcOxUCdfwd4CcjnsJLQfwRESYNw96HAeHPAFnu6O8i8GdOCG2NgKtp8Q+kddaKAZGAZ8opdYCE4HJ0YLktdaPaa3Ha63Hd+vWzYGhpZkuE+JnI7aw6vH4d2UZoKBA3HnticOaO1diq3r1gh//WKo3b9kiwqqhQSp9L1okgm7iRLFW3H+/vYtkTg4MH2693RNPpK8OWFSKBhC3sGk4ALVr0zWauPQt2RB3/apV7onztVPWYeHC1I8jWWZvTG8c1fsr30/r/lJK8w744HDJLAzFML37CmHSS3DMFOg4EvxF1i3UnMTfQTITDYYYOCG25gCDlVIDlFI5wAXAroIyWusdWuuuWuv+Wuv+wCzgDK31XAf27S68edDVRh56sA4W/kbu1lzEKafA6ae3vwVOdTU895zEc3XvLpayggKJuxk9Gv7wB6nVlSgnnGDtoly0SGKNMkZ+T2vL1VaLcv1pYkD31ZZiaunS9IzFir59ravZf/qpe9yesSjbGccXmgKmb7CIHc0mZlwMO5dCOEZqqr8DnDAdep9u6l4ZXEvSYktrHQSuBaYAS4GXtNaLlVJ3KaXOSPbzs46h14HPhj9u5WMQqE79eBLk8cel0KmbOOooeyFuDz5oXaA1FQSDiNiySnrYMt0VArt3502Wgvqzz9zhnuvXz1r8z56dmnIkTtEYbCSQ5rIfy7ctT+v+Ukb1QqlfGI4TI3Dos1LryucON73BEA1HYra01u9qrYdorffXWt8dee12rXWbksla66P3SatWC71Phzi953YRDsDMK8TK5SKKi6Wsg5uyuw491LreEohQtFO+wmkCASC/l/WGVV8CmVcwPTpVWMZkzZ6dGeG6NwcdZG21mjvXHb0cY/G/Vf9L+z5rm13w5TnBknshFOdH3WEo9DjeVX1nDYZouCM9al/CmwP9LwFlI15g81RJHQ7aiPNKIxMnSjkFtwiuLl3EDWlFU5NUxE+nSAgEpEYYHUdIXZ547PgaPA4c1LzSpN4+uPQby+5CX34Zvwl0urATs7Vpkz0xnimmrLIoFJciFm7OgmA2K2pXAnFKZnQea11Q2GBwAaZdTyoYfA2s+TeEbEwCMy+Dk7+EooHOZ8wkQUuB0R/+UILcM82118piJaSeeAJuvTV9lTUCAbjpJjjnnAKxbtXHqXEUDkDdGrkbT4YkWzYN773UVmX2jBWJbYVSkrX6xRfxt3vrLbj4YncIxL2ZuX5mRvY7eflkRpdmvh9nUli1NmtyOGv9tR5J9p71wEWZt14b3IcLptN9kM5joKC3vW1D9fDRsRDYAWkqemiXiy+Gxx7LjIVr732efba9IOhgEK68Mj0xPHV1klW5q71Nl4Ot31Q2OX78SRrweUMMGhR/G62lhpUbOOooa+H3n/+4w+0ZjRVVceqvpZB09WJMKQMuB1+cO6fNH0Hzduf2l5TQgrhWOMO3GiO2UsWQn4HXZh2FhnL46HhrN1QGuOQS+Oc/0yu48vPFldma4mLJlrRTkmDqVHj//dRa5EIh2LZNMix30f1I64zE9S/Fj0FJE5MmWW/z73+7Q8BMnGhtqfz4Y3datcLhMHUZqqmXrl6MKWW/C8DfKfZ6HZZsxaALTlSDIQ5GbKWK/hcn1p5l+0L4/CwI1EDYXTEIl14q7rmCgtRf0AoL4bjj4L332q778Y/tuwcvvVQKXqaq9lZdHRx//F6CrmScVJSOR9Xc2P3V0sikSdbH8s03M98fEWD8eOvvMRgUV6LbmlIv3Rq/hoZC0TG3Y7uW4pz4Wc9VDXHaR2ULHi9Meg68cUo6bJkGH50k9biCSQbvJRkPaTDEwgVT6T5Kbgn0+x6sexG0TbdRxYfSsf7IN6B4CPgTrBmjw9JANQVcdBEcdpi4FhcscN5Nl5MjFq0nnpCm0tE49lj7Yq+hQcTQggVS58tJ6uulGfc33+y1ovOB9qyT616CQVent+jiXowbZ20l3LwZli+HMWPSM6ZY9O1rT/Q9+yyceip07Jj6Mdll7fa1FPgLqA9EPy8GlQzigZMeaNdnh3SIs148K2bPRYWitrmWohz3tAZrF92PgGP/Bx+fFNuCtXUGTB4IY/8i867H377+s1bxkHFiIQ2GeBixlUoO/CNseCWxbP/69TBlAhxwFwy7QbIa7QTOB2ogsBM+TV1ps/79Ydo0ePJJaV4dDCafBaaUiKxTT5XejF26xN7W64XLL4eHH7ZnsSorg9NOgw8/FIuZE9TWSsbjhx9GWemzESQPsO55GHAJeDLXOXv4cHtlMp55Bn7/+/Z1FnAKpeCAA2CGRU3YqVPdYYlrzVflX9EUjH2gJ/WdxGlDTmv35/co7MGm2k1R1xXkFLBo8yIO7Wuj0LLb6XYYnDANpp0PDWXRS+Y0V8GsK2DJfTDsRnFB6hD4i13Tl9Tw7cWcgamkoDcMugY8CRbb0yFY+GsRXcsfksbV0dyLgVqxZFVOg7k/hbeGSKPrFKKUBKCvWQP/93/iimpP5l9OjhSrPPFEuUi+/HJ8odXClVcm1rdv1iz4/vfFEpesi6muDv7xD3jooTgb2QmS3zoDy9oLKcbng/33t97u1Vfd0brn6KOtrZrNzTLeTLRtqqmJ/vq0DdMIxQgnyPHmMKr7qKT2O7DzwJjrAqHAvhG31ULnMXDaYjjwPgmajxUfuXMpfPFDeLUEZlwEG16Dug2SDRzYKe7GREM1dNgVBYkN2YvL7gP3QQ64TXohtudCv2MJzL9Flq6HSvG+vO4SeN+0TWrQbHjd+fRnG3TtKr0Of3nTdl55fifPvZjPp7NKyPGH0BrqG3yEwru1fEFeAL9f09jkpX+fei44u5qLL81h8KhuEpdhk1Gj7JUCaM2rr8KSJVIPq3v3xK00gYC4JS++GN5+22Lj7kfCprfju3N1GDa8Cv0vBW/mOj4fdph1W57162HDBhgyJD1jisXEiWKdjNfoHODuu+G889LfSDuWwFtQsSDme/J8eQzuMjip/R5QegDTNkyLuq4h2MD0DdP58YQfJ7UPV+HxwpCfwH7nw/K/w4qHRDgFo6jdcAA2vSsLiFuxaJDUxOs4Um6MOo6U8hIen3gRPD75fYaD0r821Ai1q2HbHNge+7s0GKwwYivV5HSWO7F5N0MoiUCnrTNlyQQNm2HbF7BzCexcATUroG49NFZSoENc1iWfy36qaPqhjyVlQ1mxaRDLN+3P1pouhMJeCvPqGVS6miG9VjGy7zK6dagSc8nSZljcLMcovw8UD4TiYdBhMJSMl4kwilnlgQekX2J9AsmbS5fCyJESE3b66eK6tBP/VVMDa9fCd74jwsOSknGgcgAL/+rSP4ubg8yJrUmT4IUXrOPvHnoI7r03fbXLWti5EzpEPK3HHGPP7bliBXz+ucTrpatOWF2dHJ97793z9Z1NO+MGqWutGVRiUYPDghHdRpDny6MxRmD4nE1zkvp815LbBUbfAaN+DeVTYMXfYfMnkqASahChtDfhgFi9di6Vmx2DIY0onWF3RizGjx+v587dR7r6hEPw7hgRK3Za+aSai+KMQWuZjDa9JwH72+ZIUKo3VyrdR5vEHEXtbib7vRi+GaT20ueft88bd+SRcM01cOaZEnfWYa/Qqbo6EWKzZonb8LXXrGt87RpHqBFe6WwvUeG4j8USlop4EqtA3os0ixfDIYdYi638fAmWL7bR8tNJKivFEtnCiSfCBx9Yv2/8ePjkE+fi9KyoqYHevdta3aatn8Zpz53Gzqbo5jiv8lL7q1rykujp9/7K97nglQvY0bQj6nq/x0/9r+vxpSIZw65/OV3XmHBAbgrLp0DZG7Bj2W5XY7CWtMy9eaX2ig7Hw85xdct1O5vGmgaUUl9qrcdHW2csW+nA44XDnoEPjpC7LjdSvQBWPwNr/ysTkw7tWaIgWrkCfwepfF84QP52HAGF/WWC8+SI2V55ZBIMN8vfpq2w/WuoXSXm+drV0LCJ3ROhtlUz54EH4PDD21dL67PPZMnPFyvX8cdDt24Sx7Rtm7Sqef552Lo18c/Gmwelx4sr0YpFd8BR74A/M9liI0aIILESWw0N8Pe/S1JEuuqt1dXBPfdI0dgWLr8cZs60rv01d64kchx3XOoD5mtrxZ0eLWZrXvk8mkOxM5E75nVMSmiBZDMG48Qf5fpyWbFtBSO6jUhqP1mBxw/dJsky+i654dm5AnYshu2L5MaxZhk0V0vWsCdHlrg3O1rmrVCTfL6/g8xxJWNl6TBC5r1ch1OeDfscRmyli5JxMOLWSGNVlxQv1Ro2fwwLfiWTUbg5dp8xbz50P0oabfc4XoL/Pf7dfR29efabwfY5QyY7Hdotyhq3SEzEhlflzrS+LO5HjB0rQumNNyQwuj00NMBLL8niKAMvg8pPo8eRtKbyM7kQdJmQkWwppeCCCyS7M2SRMXv//XDddekZF4gl8bHH9hRb3/mO/e/6xhthzpzUi62mJvjb36Kvm1E2I6Z7D6B/p/5J779/p/5xsx1Biptmtdjq0UNMq06igKImKG6CDog33xNZNBJjGwRqgZrIEmwCtkSWGO7ZzZv3NMcaDBFMNmI6GXUb9DxRhEumqVkJ7x0En50B22aLAIwmtHqdAsdMgXOrYdILMOhH0GGIuPo8OZDTURa7QgtAeSUdO6eTlEvw+KGgl+xr7IPwnRVw+krLj3noIfel+gPQ8xT7LXm++nlGrZ0XXGAvYWDrVrFuJRIn115qa+Guu9pa3Dp0gENtVjFYskTOj1RWwK+vlw4LscqfzNkYP17KCQHk8/joUhA7jbeuuY4vNiaQTeJGnBZaIIKqBtgELAMWAQuAecB8YCGwBFgPVCPCyw6lpiiqITpGbKUTpeCw58TtpjKkEvJKYcMbIrS2L4perwagxwlw+gqY9GJEIOaKqEogc7Bd+IvAlw/F1nUJevSAO+5IX2yObfxF4sqww9aZYl1MUTFaKw45xH6h2HvusbaAOUFjIzzySPR1V1xhP1D/ttukKGsqSkHU1cHjj0tbqGgEQgHW74idUeHz+Dig+wGOjCVe+QeNZsYGiwJlBoMh5TgitpRSJyulliulViqlbo2y/kal1BKl1EKl1FSl1H5O7Dcr8eXDsR9Yd7NPBd4COPwVqT0TrCVmPYoxf4QjX4fiwWKBcjE//zkMGOBCC9eA78dvoNuamZdlrIaPxyNNvu1k7m3fnvom33V1cOGFsa1FZ5xh35UYCsF3v+t8j8xgUOrM3Xxz7G2WbV0WNx6rwF/A4JLkyj60YFWra+nWpbg1Ecpg+LaQtNhSSnmBh4FTgBHAhUqpve3j84DxWuvRwCvAfcnuN6sp6CWZaP5OSPBAGvAWQq/TYOYl8d1W/b4HQ67dnRHocvx+sS6kuyyBJX1Ot+9KbK6Gz8/OWCPyiy6yf/xefll6JqbCnVhXJ6U5olbnj9C5s7QasktZmbj6nBSIDQ0i+uJZzOZXzI8rcJwo+9DCyG4jyY3jxg+FQ2yqiV5lPivIJtdcNo3VkFacsGwdDKzUWq/WWjcDLwBntt5Aa/2x1rplep4F9HFgv9lN59Fw0mzI7SoxTKnEWwgDLoUD7pBswHgccEfGsuPaS+/eMHly+jLlbJHTGTqPtb/9ls9h2YMZsXAddVRi7sGrr4aqKmebPodCUjw1nrWohR/8IDHX8VtvwR//mLzgCoclBuy888SyFY8vNn5BbZzvsiHYENf9lwiDSgbFtaL5vf7sriRfUSHJPMksdkl2PxVJln0w7LM4IbZ6AxtaPS+LvBaLK4H3oq1QSl2tlJqrlJq7ZUv6q6KnnQ5D4OQvpapxqoLmvfkw4hcw4REJRLeaeKKVeGgvr/WQek/tXuyL0COOkJiiTPTwy4t1ndv/ChG6dll0O9Qs353hmSZ8Psn0s0tdndQoS7YvZmsaG62tRS2cd17ipXvuvltKR7RXcDU3S1mQSZNgyhTr7a3ipIpyiijMccZ6PKhkUMyWQAD1zfV8Wf6lI/syGAztI60B8kqpS4DxwJ+irddaP6a1Hq+1Ht+tW7d0Di1zFPaFUxfAwCslpsopPLmQ2w2O+Z+0DFIKigfJEs91Oe8XsYPmE6Ux2SyixEwnP/sZnHtuegVXbq7EjEWl95mxS2lEQ4fgw6Nh26zkvoN2uCMvuSSxoqVffSWFYZ1wJ7bEaX3zjb3tO3YU61aOjf7srXnlFalEX1VlXaS2NQ0NMrbRo2HhQuvttdYs27Ys7jb7dXQubHVA5wE0BGIL9KAOMm199JY+BoMhPTghtjYCfVs97xN5bQ+UUscDvwbO0Fo7aD7ZB/DmwoS/wVFvSqZiMvFSyic1r/pfBKcvh+6Ht1rngcP+G9+KVvEBfHWjXLCd9BOlAaXgqafE8pGODMXcXBg6VAptRiW/VOLkEqmhFayFj06Atc+1T3AFamFTjBS5OJx4ov2sxBaefVYsYjt3tu9UCQahuloE0FtvJfbeX/yife145syBMWOkK0BDQ/zg+dpaWR5+GCZMsO8h2rBzg2VA+vBuwxMYdXxyvDmU5Mcvqrlwsw2VaDAYUoYTYmsOMFgpNUAplQNcAExuvYFS6iDgn4jQqnRgn/smPY6H07+BCY9Cfm8RXXbjubyFYs3q/R04dRFMfFLihvam0yg4bmokVixGX76Vj8H/DoXKjyKiKwEzQGvy0h8s6vGI4Lr00tQKrvx8aYg9bZpYWmJywG3yvSSCDsEXV8PC2+T4h2zcm4QDsu2Kv8G0cxLbH2IluummxOPePv5YykdUViZWXLapSYLXDzpIBFCi9OkjQi9RgQiy3/PPh4ED4cEHxWrV3CxWuoYGcWnOmyeCrrRU4sgSyWicXzEff5wG4z7lY3T30YkPPA4DOsUyrwpVDVUx2wYZDIbU40hvRKXUqcCDgBd4Umt9t1LqLmCu1nqyUupD4ACgPPKW9VrrM+J95j7VG7E9aA3VX8Hqp2Hj21JR3Zu/p5Uk1CiFRTuNgv2vhL5nRxdY0WjcCrOvFEtWOBDb3dVxFAz+Mez3PfDkifjzORhfZqOHX3vQWiqQ//znzqf+FxSI2+2vfxXrliVTDpGebe2hcIDE3A24VL4jb9Fuk44Oi/XL44P1r8DiP0pfSztEOa5VVSJi2nO8uneXFkpnnSXPY4m2+nqxQP7nPyJidkRv6beLeNPT0qWSmejE95uTAyUlYqHbsqV97dxa3nPHx3fw+89/T1hHN/cV5xTz5JlPcu6Ic5MY8Z784M0f8NT8p2Ku75DbgXcueofD+x0ec5uEcVtvxHjsa2N1wzghu8aaBuL1RjSNqJ0gFe0k9sYLlALFkcfNQCWQ6M3q3u0kdq6ARXfChtdFyMVrJdRhOPQ8SSq9F+0vJSzwREpJKBFhHotAmnB4d7V6jw9esggUaqfYamHBArGAVFcnn42WmyulJp58UlyVttn0Pkw7z1bPx5j4O0Gvk6HrYdBhKKCkr+SWaVDxP2hM0GAc47j+6EdiGWxvIdDSUhG4F1wAPXuKBQskiWDDBnjmGalEX1Vl7/Ospqfzz7fXKDwdtIz1uGeO46O1H8XcrkNuBz69/FMO7HGgY/u+f+b9/HLqL2P2Yszz5XHf8ffxs0N+5tg+9zkBA9kzVjeME7JrrGnAiK1Ukw6x5RSlpdGDTxo2w/qXYfWT0q/Pkxfp7WdxfuR0libULUvhwIjoygVvDuCNNKFuFHdYYyXUfLO7CXV97CrbQNJiC3b3r/vtb+WinGgWndcrIuuyy+D3v5em1QmhNbwzCnYuSfCNKSTGcV27FoYPdybTsLBQBJfWsHFj+z7TanpycrzJ0jLWbn/qxtb62CVWfB4fVbdUUZzrXMHgycsnc+nrl8Z1FZ4/8nxeOPcFx/a5zwkYyJ6xumGckF1jTQPxxJbb6m5nJ07VVsnkiZtfCkOvlaW5GiqmQuXnUPkJ7Fy2O+4o1LCny7G5Gqq+lMVpHIr5ys2F//s/qX7+j0c0j/5TU1WlCAahqSn6Mff5NPn5Go9SXHIJXH+DYnB7C34rBeMehM/Pci7TM0X07y/WoueeS77NTV0drLRucZkU/fuLS/ff/25/Q3In2d64nR2N8X2jBf4CR4UWwOCSwZZB+XM3ZcnNq8GwD2IsW27CrXcJ4aBYu3Yshfp1sHO5WKfq1kfKO+hIPNfe428Za5T/VzggNb1yOksyQNH+0GEYFA0QC1nJOOnFaGt8AahZJWNqqoSmbdC4BRo2yfiatogoDOyAYB1aK+atG8enS49kxoqJzFs7hpqGYjSKPH8jI3ov47ChMzh00BccM/IjfKpZGmb7O4g7L7cL5HWH/J6y5HSBQVfH//60hvfHSxyeG4hjMSwrgyFDnI91aw92TvetW2HwYGknlEm0hk/XfsoZL5wR18I0qvsoFv14kaP7bgw2UvSHorj1tvweP3W/qosbvJ8Q+5q1CLJnrG4YJ2TXWNOAsWwZdpMKl2cB0KUZcpEzytvqrwJCQLDV3yCwA6gG9FZgK7Cg7efuHV8GIqKq5sLWL2DLZ2J1a6yUchfKA+GQiLgW61tuVxFHBX0h90DI7YLK6cLYA7sz9ntd+LmnFtRsKZmhlLxfByHcFwIdoXG0iLfmbfK3aRvUroStMyRrEK+4Swf/KP4xarFufXxy/Lg4F9Cnj7hMn3rKHdYiK7p2hRdekOD8TAvEeRXzaArGzx4d3tW5sg8t5Pny6JTXiW0N2+Jus2zrMg4odaYBtsFgsI8RW982UhFbVh9ZnKa0VO6KGjZLLNmKh8VK5cnbHWS/Cw2dxkCX8dDtCMnQLIiUfws3S+YeiKjy5ETiyWwQDkRizoKyD5RU4vf4oaFCxN7W+NXCd9H9COk9uf7F+P0pXcBdd8Hzz2eH2AI46SSpEP/005kVXNPXT6cpTqkOr/IyutTZsg8t9O/UP67Y0lozr2KeEVsGQwYwYuvbRmlp9gTzD+oKH58Kmz+KZEpGrqKtGzx3PRSGXg99zpAAfG+eLK3Z+3kitAiraBT2laX0aPufN+HvUm6joU3dX1fRvbtkDl58cWqaTqeC+++XVjpr1qTfc9HSssmqLU6Bv4DBJe0N/ovPiG4j4u6/NlDLFxu/4LIxl6Vk/4Z9l+3bJbxg27Y9l1ttvLegQH6PSknVmtZLfj506iTW6dJSSajp2RO6dNlz6d1bmtBnM0ZsfdtwIpg/HfEPTdvg7REiTGLVADvgtzD8JrF0ebyp6y9pRSwxFg1fIRzxGkw92vXWre9+V3ogvv66O7L9rMjLgzfflCKr6RSISsmFoDnUzIadGyy2VQwqGZSScRzQ/QD8Hj+BcOzMhpkbYrU7MBjkd75gAcydK8uCBbBqlViL8/JEIGlNJLkILqeUHsS+ed+RX8oPfiDvC4el4XwoJBbzqioRbFu3wuLFUiA6HJbs79xc6dmqlLy2YkUaD0KKMGLLJWyq2UTnLh3J3xY7k0mXdo/X1XDfYsdiKT0Rr7fgoKuTa23Umtd6JNnL0QMXxQ5O3oOuB8Owm2DZ/ZmJ30ogy/Of/4SPPsqM2LKr6VszapS0EbrkkvS5E4uLYepUWLJlCfm+fGqaa2Ju2xBoSJnYGlQyiHx/PoGm2GJr2bZlaK1R7Tm4hn2SDRvkd/7CC7BunViiAoE9fz8ej8Ryjh8vPULHjZOklOLOFYTyZPvm5t1tu7xeeY9fwZ9anWpay9IivFo+2+8XgdXQIDURv/kGvvxSepHOnSuhxtmOEVsZorKukk/WfsJ737zHlFVTqGqownejj7qAZA2FwiG8Hi8hHUJrTXFuMQ2Baob9YzRnDD2D4wcez8Q+E8nzJeEiczNdJkK3SbBlRmxBMvNSsRKhJVMwGdLcNJsD7oBNb8P2JaDTFBilvFDQD85cbfstxcXSwPnEE9MfC5XXzlP77LOlmv2NN6bewlVQAB98ILW+npo3L2bV+BZyfbl0zLOZZZsgg7tYl3/QWrN+x3r26+RcI2xDcoR1mDXVa1i4eSELNi/guo45lOyIPSfs7FzAmwue5YDSAxjedTi5vgTbgUX45BO4806YPVsEUEsB4p2tEmmPPhpuuEFiIltKwRQWtu1L6vW2//famsJCWfr0gaOOkt6k+8p9gRFbaWJN9RomL5/MtPXTmLZhGlvrt5LjyaE+KFeDPF8ezaFmRnUfxdnDzub4gcezrWEb76x4h8nLJ1PTXENYh1lUuYgllUt4cNaDNIWaGFIyhKP7H83EPhM5e/jZFOakoQNzOvDmwNHvwap/iQWoviySZdjKelTxIbzaDfqdI3FbncbINqhIr8gEWn/mlToguBLA44NjPoD3x0qJijgp+86gIKcTHP9pwu88/HC4/Xb43e/S554rKJC+he3lRz+SO+RUjrmgAN54Aw4+WJ7P3jibukD8Omr9OvRLzWCAgZ0H0hCMr4j9Xj/zK+Z/q8RWfaAeunSgYJtFu43S1PdyXVO9hg9Xf8g3Vd+wuHIxS7YsYWPNRjzKQyAUIEyY3/5ctu2Y25ExPcbg9/hZsW1FKxd1PeqN75PjzSEYDtK1oCuDuwzmgO4HMKRkCIf2PZQJvSbgiTP/Pf44XH997Buorl3Fot2//25x5YSYSgSPBzokeQ/tJkydrRSxs2knzy54lls+vEV+7GlkYKeBvHDuCxzU8yB8nhTo6UzUrKmaB988IsVW6zdI3atwYM+4J+WVlkIlY6WnY8lYqdvl7wT+IsAjYqx1ZuHuN8vTPcYceax8Iv5esLiDbE+1+7p18N5YaLbZv6a9+IrhxJnQaWS73q41XHutZPulw1r0f/8nFf+T5YUX4Ac/cNYq5/NJ8/EPPpBG2i0c+OiBLNgcpYRJK84adhavnf+ac4PZi5J7S6hurI653qu8/PqIX/PbYxw4uC6vXbVi2woemvUQzyx4BoWiNiDtsvJ8eQRCYqYJ6RB+jx+vx8uY0jHcMukWzhh6RtLzZiAUYPm25SzavIivyr/izzP/nPT/pz18Z9B3OH3Y6YwuHc2o7qMoyikCJAg9Xvjur38Nd9wh7j2DfUy7njRQ11zH9A3T+d+q//HOindYVb0Kv8e/y3LVgifSSLrAV0C/Tv3oVdyLvASy5TSa7Y3b2bhzIxtrNhLW4aiFDItzigmEAxzc+2DOHHomxw44ltGlo3ftPykyPckGG2D7Aqj6Sv5Wz4ealRDYKWUdPD5ASbkHHZQsRcKSlZjTBXJL5G9ORxFoLQtKLEwtS7BuzxpbVr0N29taaPtimHoUNO+IH6PWLjwiNI+ZAl0nJvVJ4TCce65k/KXSWnThhXLn7ZT7YPZsOO00qWifbOxZYSEMGCDHoFev3a9rrSn4QwGNwdg78ODhtqNu486j70xuEHE46NGDmL95ftxtju1/LFO/PzX5nWV6HohCMBzk7RVvc+/0e5lfMZ9gOEgwLL+pAl8B+f58/nzin5nYZyLXv38909ZP2+NmuDinGJ/Hx7UHX8uPx/+YnsU9be977sa5/GHaH5hfMZ/1O9ZHnZc9ykOOJ4dAuJnSgq4cUDKQ4Z16M7i4GwWKiPU+DOjI/KXlccvzXY81QR1kbd12ltduZeH2CtbWVUtAuYZADEt597yOjCoZgHr/H8x8/2Dq66NfD4YPh+nTJY6qoMD2IfjWY8RWivnx2z/miXlP7PpRe5SHQn/hLmGjgeZgEz3y+3FS9ysZm3s+hYH9aGiQO+76evlbUyM+6paloUH85C0BhS1/ITLPqTC1nWZR2fPfVJa+AIRRyoOKrNcqQEDtnkjyVUd+1uu/HNPnNEpKdqfVduyY4IXNhZMsICKsaSs0b6OxbiNlWxZQsW0xW7cvp6FmDTnNVZT6oMQL+R4f+b4c8r055Hp9+JUXr/JIiQnljfz1yeOcTpDbDTZPjZ1BmFcKZyeR6Vm/CaYeI70iQw5Fo3tyILc7HP8xFDsTlB0MwjXXSA0upwVXQQH85Cdw772JeYDtsHMn3HyzBM+3x8rl98vy+9/DdddJjEpr1lSvYdQ/RsW1YhfnFPPodx7logMuSnwANrno1Yt4/uvn427TtaArW27ekvzOXDQPVNRW8OjcR/nbF38jEArskaSQ483B5/Fx66Rb+b/D/o98/+6s5Wnrp3HN29ewdvvaPVzAeb48tNYcN+A4bp50M0ftd5RlUsG8jbN4Ye7foHl75AZti9xAhRpBS72+jjlFTOo2iIN7jSO/eKC0ScvtBv6OkuzjK5C/3vxIoebIjaNM+Lv/7npM5PiGCYdDLN6ymM/XTWPdzg27bzbDwcj+g5Gagc0cVNyNjV89yB/uzSUQkGvP3pSUwBVXSGP5jh3l2lNU5PxvMx7hsNwkFTvb3SplGLGVYuZXzGfl5k2sXQurV8OyZbChDLZXy0nc3Az+nUMpat6fUaPkzrhbN8mw6N5dBE/nzrJ06CB3zz7fnvVIWtcogT3Fl9YQCIb4YuMXVDVU78okqa2DuloZw86dEsNyx9VjGdwz8dSOQCjAospFzNk4h/OPv4FOO+ILgprOhXwy83km9J5Aj6LUpZJordlUs4kFmxcwr3weM8pmsKBiAZvrNuNRHppDewaa7t95f6obqqlq3O228yovXo+XPF8ew7sO57C+hzGu5zjG9BjD0C5DnWtvYkWgFmZcLLFoyWYpegul5dGRr4slz0G0hj//WYJrnRJcBQXw0ENw1VXOfF4svvwSfvMb+Phj+U1ZWboKC+U3duGFUui1d+/o27229DWueOMKdjbHjgvqkNuBDy79gIN7H5zE/yA+90y7h9s+vm3XjV80/B4/m/9vM53zkyxclGGxFQ6H+fsXf+fuz++msr7ScnsVI5dbYz0+r/Jy5tAzeeDkB+jXMUbc3YxLYe1/2B2esNfndpsEJ0yLrArJdk54GpIgFIL334e//EVKL+Tnt81EBGnfNX48TJokmYiDBsm1qqlJbsBgz6xCO4IsHJb3BwLyWKndZR9qanZnJM6YAf/5j/P/91SQcrGllDoZeAhp0PKE1vqevdbnAv8GxgHbgPO11mvjfWa2iK1gUCbiyZMlgDDaiXrOOXDPPdCv3+51ubnpDzgMBkXEWREKh1i2dRlzNs1h+obpTFs3jZVVK8nz5dEUaiIQDuBVXrTWDOkyhAm9J7CjcQcfrfmIhmDDLvN5ob+QQChAYU4hY3uO5Zj+x3Bw74MZ12scJfmJC4DmUDNLtizh6XlP88jcR+LWE0oFE3pN4N7j72VMjzHtGr9tNr4Ns34gbstEa3F586Rp+IRHYb/zU5rK8+mnUvS0urr9oquwUG46nntud6B5Oti0CZ58Et57DxYtkt9ty2+juVnu5MeNk8zGCy+UO/p4/Oaj3/CHz/+w16t7zq1e5aXygsfpnFvYZt1uF1Fry4UnynZR5utW3/Gra2dzyed/oykU+7fh83j54MRfc1SPkRETeFjiHz054M2V82fX4xyxuhT2b9t1IUNia2fTTp6Z/ww3/e+muHOAV8kNlN3QCY0mEArE7QAAcFz/4/jLSX9hTI8xe66o+lISerYvgtrV0kbM4wfljyiJQuh/gcSVdhwBBX3AXwy+IrGghwNiidKh3R0v4rH4Hlh6r63/W1S8hXD+7tCIpib5LcyZI7/tBQtg7VpZl5u7uz5WS1eJggIxGuxdgLRLF4kJy8+X31TLEgzuXhoaoLy8bZHUbdtgy5bd80lOjizRLG9uJKViSynlBVYAJwBlwBzgQq31klbb/AQYrbW+Ril1AXCW1vr8eJ+bLWKrrk5OLq9XTqBoh/NPf5KA35oaEVhuCzrcVLOJZxc8yx+n/ZEdTbHrfDmNQjG+13gePvVhRpeObpPC/Nm6z5hdNpsZZTOYVz6PDTs3ENZhPHgItyq10DKZepSHLvld6FHUg64FXemQaz+VpSnYxNaGrVTWVVJZV0lDoAGvx9vGQuBRHsI6TEleCSO7j+SwvocxtudYThl0CsW5Dtq6A7XwzaOSiRmskfixmHfgSiZsT45kZQ69Vhp8p4HGRrFwPfSQ3M3aFV2FhfJb+dWv4JZb4vwm7rzTmUj5OGigku7UUIyHMJ2ppjPbE/qMlSPghR/H36bP4U9x+YGXt3eYtqhrruOROY/EFQwN2zvQ9PmPyfH7CQTkO2tZGhrkOz31VLG0h0K757QWV6rXK3/POtue2Prs093nbUudpZbHIFaNYFAEb8vFOBTaXTgzHJZza2NwIR/UPsCMHS+itSLsbXuyeXUunfzduW7SVQztOtTeQduL2uZaXl36Kh+u/pBQOLTHXAMSe5fry2VQySB+MekXnDviXHJ9uZSVSTmFigqpXbV+XYhQbQWeUDW5VJPvrWb0iBr69qylKK+OorxaOubvoDivmnxfLbn+ZnL8YTy+HDz+XJQvD+XxiEUucqiVaiXIWxvQIgdTR/7RaHwb/osKx75Z094C1LAbINgIoToJwwjVyw2eDsF+F6LDYcorc1m6sgMbK4vYtr2QLVX5bKospGJrPluqcqnansOOGj+1tV7CWuH3hVERL6dqNW6t5UnLOSAeGQ8epSkqCtGxOEBJp2a6lTTRo2sDvbrX0a2kgS6d6rjs/45q13eZblIttg4F7tRanxR5/ksArfUfW20zJbLNTKWUD6gAuuk4O88WsQUyIbz7LsyaJUGFixeL265FWGkt/u9Ro+RuedQoqSNSWiq+6KIiUe/NzTLhhKLHNtpGKbmTaJkY6+slBqy6GnoN3M7cTXOZXTabj9Z+xLzyeTQEGkDRJrjXq7yEdZgOuR3oUdSDboXdKM6xLyiC4SDbGraxuXYzlXWVuyxee9ci6pDTgYZgAwM6D+CIfkdwRL8jmNB7ArfdNJbFHZsIxZnTR1bCad/AUetg8DZiOAoSpyofpvWDDwbCJ/2hMYZFUAF5QXh5agn9F28iEA4QCAVoDjUTCAdYt30dS7cupTHYSH1zPQ3BBhqDjbv+tiwNgcjz0O7HRTlF1DTt4IjcAEf66xjrqWE/Tx05kck/iJdKTyfW5vRjbf5QNhSOpCC3A/m+fPp27MvIbiPxe/34PX5yvDm7Hns2vo0ON9EyPYcjM19TqJmmUBPK3xlvTgfCOozWYXRkmz0eo9G65XGY7Tt8vPLqAB5/cijV1bnk+EPU1fsIh0UIe71hCvJDNDV76FnaxHXXrOH8c8rpUKTxeXy7XLUtcTEKCIdCPLDwZWoDDdQFG6gNNFAbqKc20MAXmxdTXh+7D2Bryv8EPeJXZKCiEHrebP1ZAzr0Zky3oRT5C3Ythf58ioLdGf77zZy241HwI0V1fOz5uFMn6N5XJoacHDEX5OZGVIwX/D7wRhZfJF5QtVxUW125PArUXFnawceLj+LYP3wSdxtblwU7Te1LS5PqWtEUbOLVpa9y3/T7WLFtBc2h5qiB5wX+AopzivnrKX/lvBHnOVK0de32tfx8ys+ZsnIKjcHGqO7Glvnw6nFXs/rJO3j9xdjzY9euYrXZmxbhkY5YqJZ9qS9+iFr9ROwNc7vCOYnH9dXXiwGipVJ8KAShrx8ktOzveD2hNkthbh0FuRbW+70scG4m1WLrXOBkrfVVkeeXAodora9ttc3XkW3KIs9XRbbZGutzs0lsRaOpSeahigoxl27dKu0JKivFhbFzp5yULQHygcBuf3dBwe42Ba3jtlqq8iq1+46zpRJv68D5QEDuTltclnl58pkFBVD7vUnMKIvfOLlnUU9OH3I6Zw47k6P2O8qR2l1aa1ZWreS9le/xypJXmF02m+Zwc9z3NP69M7lbY6exu4k7j4I7Ey9h1X4UsQ1dVpwNpK76AOX0YA4TmMdBbKMED5oubGMcXzKeuXTH3iTenAO5v7K3T4XaJdr8Hv+uvz6PD5/Hh1d58Xg88jdinQzpkFgudJhgOEigqZpAoJYg0KwhqMG2o3p7P3hwXdxNGhvl9x2XNAiYKXPu5aRvpKtdMOSlKZBLUzB319/GQB6Boz4n6Ou2y9LUchPYuuUK7OlJVGpPyxVEHm+aAhVTZBvCoDQq4jYVHRlGh4LocAjCQTy6CY9qxqua+Eiv4W7v0rj/nyJ/Eb895rdce/C15NhtMJ8AczfN5Sfv/IQ5m+bE3e6gbofw7FGzqKhg11JeLn0Fd+yQOd/nk6/Y79+tt1vcbX6/zNX5+bvna79/t4Wodexuy9+WmN29k6haWurU18t513Ktafk+H9MTqPDK9dUH5CrIU/K3ZfEr8KnIvYKCHKXI9+WS6/GR6/GR5/PjVT68Hh8+jx+PR6FQKKXwRP4C8hglN2ott3haHuvIjdsfctfS0RPdylAVgpKLm8Ry73KyRmwppa4Grgbo16/fuHXr4k9e31Zamnq2773W33c6WnnYPe+SHksaLl4AoVD2HFdNGK/FbbQbxqrRzpQqsUsyP6w0k+o5AFxyDthsLeSGsaZrHC3sC+eAW8bhFPHElhMVLzcCfVs97xN5Ldo2ZRE3YkckUH4PtNaPAY+BWLYcGNs+STLnnVtO2rSNw4nG2zbwet1xXO05Ur3Wm6QBq3MgVvZYynDJb8MO+8IcADbOgSwaa7rZF84Bt4wjHThx2zgHGKyUGqCUygEuACbvtc1k4PuRx+cCH8WL1zIYDAaDwWDYV0jasqW1DiqlrgWmILfMT2qtFyul7gLmaq0nA/8CnlVKrQSqEEFmMBgMBoPBsM/jSOM8rfW7wLt7vXZ7q8eNwHlO7MtgMBgMBoMhm8hs+VqDwWAwGAyGfRwjtgwGg8FgMBhSiBFbBoPBYDAYDCnEiC2DwWAwGAyGFGLElsFgMBgMBkMKMWLLYDAYDAaDIYUYsWUwGAwGg8GQQozYMhgMBoPBYEghRmwZDAaDwWAwpBAjtgwGg8FgMBhSiBFbBoPBYDAYDCnEiC2DwWAwGAyGFGLElsFgMBgMBkMKMWLLYDAYDAaDIYUkJbaUUiVKqQ+UUt9E/naOss2BSqmZSqnFSqmFSqnzk9mnwWAwGAwGQzaRrGXrVmCq1nowMDXyfG/qgcu01iOBk4EHlVKdktyvwWAwGAwGQ1aQrNg6E3gm8vgZ4Lt7b6C1XqG1/ibyeBNQCXRLcr8Gg8FgMBgMWUGyYqtUa10eeVwBlMbbWCl1MJADrEpyvwaDwWAwGAxZgc9qA6XUh0CPKKt+3fqJ1lorpXScz+kJPAt8X2sdjrHN1cDVkae1SqnlVuNzCV2BrZkehE3MWFODGWtqMGNNDWasqcGMNTVky1j3i7VCaR1TH1kSEUNHa63LI2LqE6310CjbdQA+Af6gtX6l3Tt0KUqpuVrr8Zkehx3MWFODGWtqMGNNDWasqcGMNTVk01hjkawbcTLw/cjj7wNv7r2BUioHeB34974otAwGg8FgMBjikazYugc4QSn1DXB85DlKqfFKqSci23wPOBK4XCk1P7IcmOR+DQaDwWAwGLICy5iteGittwHHRXl9LnBV5PF/gP8ks58s4LFMDyABzFhTgxlrajBjTQ1mrKnBjDU1ZNNYo5JUzJbBYDAYDAaDIT6mXY/BYDAYDAZDCjFiK0mUUicrpZYrpVYqpaJV0HcFSqknlVKVSqmvMz0WK5RSfZVSHyullkTaPF2f6THFQimVp5T6Qim1IDLW32Z6TPFQSnmVUvOUUm9neixWKKXWKqUWReI852Z6PLFQSnVSSr2ilFqmlFqqlDo002OKhlJqaKu42flKqZ1KqRsyPa5YKKV+HvlNfa2Uel4plZfpMcVCKXV9ZJyL3XZMo839dlrtZYIYYz0vclzDSqmszUg0YisJlFJe4GHgFGAEcKFSakRmRxWTp5F2SdlAELhJaz0CmAj81MXHtQk4Vms9BjgQOFkpNTGzQ4rL9cDSTA8iAY7RWh/o8rTvh4D3tdbDgDG49PhqrZdHjuWBwDikldrrmR1VdJRSvYHrgPFa61GAF7ggs6OKjlJqFPBD4GDk+/+OUmpQZke1B0/Tdu6302ovEzxN27F+DZwNfJb20TiIEVvJcTCwUmu9WmvdDLyAtDByHVrrz4CqTI/DDlrrcq31V5HHNcjFq3dmRxUdLdRGnvojiysDIZVSfYDTgCestjXYQynVEcm2/heA1rpZa709o4Oyx3HAKq31ukwPJA4+IF8p5QMKgE0ZHk8shgOztdb1Wusg8CkiDlxBjLnfstVeJog2Vq31Uq11thQ4j4kRW8nRG9jQ6nkZLhUF2YpSqj9wEDA7w0OJScQ1Nx/p+/mB1tqtY30QuAWI2sHBhWjgf0qpLyPdJdzIAGAL8FTEPfuEUqow04OywQXA85keRCy01huBPwPrgXJgh9b6f5kdVUy+Bo5QSnVRShUApwJ9MzwmKxJqtWdIHiO2DK5FKVUEvArcoLXemenxxEJrHYq4ZvoAB0fcCq5CKfUdoFJr/WWmx5IAh2utxyJu+p8qpY7M9ICi4APGAv/QWh8E1OEel0xUIoWmzwBezvRYYhGJIToTEbO9gEKl1CWZHVV0tNZLgXuB/wHvA/OBUCbHlAhaShK40hq/L2HEVnJsZM87mD6R1wxJopTyI0Lrv1rr1zI9HjtE3Ecf487YuEnAGUqptYi7+1illKvr30WsG2itK5HYooMzO6KolAFlrayZryDiy82cAnyltd6c6YHE4XhgjdZ6i9Y6ALwGHJbhMcVEa/0vrfU4rfWRQDWwItNjsmBzpMVeS9/iygyPZ5/HiK3kmAMMVkoNiNwtXoC0MDIkgVJKITEwS7XW92d6PPFQSnVTSnWKPM4HTgCWZXRQUdBa/1Jr3Udr3R85Tz/SWrvSUgCglCpUShW3PAZORNw1rkJrXQFsUEq19IQ9DliSwSHZ4UJc7EKMsB6YqJQqiMwHx+HSxAMApVT3yN9+SLzWc5kdkSWWrfYMzpJUBflvO1rroFLqWmAKki3zpNZ6cYaHFRWl1PPA0UBXpVQZcIfW+l+ZHVVMJgGXAosisVAAv9Jav5u5IcWkJ/BMJDPVA7yktXZ9WYUsoBR4Xa6z+IDntNbvZ3ZIMfkZ8N/IDddq4IoMjycmEeF6AvCjTI8lHlrr2UqpV4CvkOzkebi7ivirSqkuQAD4qZuSJKLN/UhrvZeUUlcC65C2ehknxlirgL8B3YB3lFLztdYnZW6U7cNUkDcYDAaDwWBIIcaNaDAYDAaDwZBCjNgyGAwGg8FgSCFGbBkMBoPBYDCkECO2DAaDwWAwGFKIEVsGg8FgMBgMKcSILYPBYDAYDIYUYsSWwWAwGAwGQwoxYstgMBgMBoMhhfw/Ba/VEKSYBZMAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_16\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_17\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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0k2oAXl8P+fnKyWrKTTepSXHHDnjrLZg1CzZtssaOU0gJn3xb5WtZhoDOE6D3ddDvRsjtDUHvaa0nYVftdk5ug/I3YO9sOLnVwv23MTY+APVxmlwQkNsH8vqCzQVBH9TsAU85qZqAv+nQJgKy9T6M5SfLOew5TNe8rgmw6kw2bIAnn4xtXunSRVUwfuc7aonQ1bzOyBANzte4cbBokWpqf/fdsFX/JMOina0UZts2JTBpZWSkZ0+48UbVBX7oUOVgNV7iavjxlZTAiy/Cm29CZaV1+7eCBaULkC1M2HX+OubumMvdE+9OnFFNePRROGhBZ5icHHjwQXXlKKXqQwkquhWJdu3U31GjVBPae+9V+X533AF7I6++mmPPDCX1YEWeVk4vGPsH5WQB2LPVCRpO/21Mx9HQbgiM/I1qWrz7Ndj8W9XHT2OMk9tgxz+sjWrl9IS+N8Kg70LBUCUiG2w0udgc6nhWl8CuF6H8TahJncll/s75hpLfXXYXH+36iFtG3ZIAq04TCKim8bHk7X73u/DEE6eXCa3AZlPz0kUXqSXFV16Bu+5S9mpOoxtRpyhSqo7pn31mTfXh5ZfDI4/AiBFqey2FjBuoqVHRrx07VJuFOXOM7y9eXyuP10PHP3fE12gSF4hmzle+K5+qe6sQSWg6fPAgDBwYe07FddfBM8+oiazByYoWr1c51b/7nSoXby2HrMXjF6iH9/pA/eHYjBJ2GPpTGP2wiliFc6yM4K9VTsOaH0GFgdL8b0S3m7SipQMoJXw4GY5+hiXVhz0uhzGPQPsRKgrpMDC5+EOTS9UO2Hg/7DUxudwSn8ml6Nki1u1fd8Zz4eYWgOvPvp6ZN86Mix2R+Nvf1DwcjZi1EOoC8Ic/jH0uaQ2PRyXSf+Ur4fUgU9TlsATdiDoNWbhQLf3E6mh17Aj//a/quD5unLqaMeJogRqXk6PW5l9/Hd59V4Wgk8nqytXkOHNaHSelZNuRbQmwqDm/+EVsfcl69VLNZF99Fbp1s2ZydLnUdn7zG9i5UzWdjprtT8SeN9WpCK7eDKN+C4686B0tAEcOZHWC816AS5dB3oDYbMt0DixUfSJjdbRcHWHKf+GCmdBpnIpIGnG0QI2z50DHMTDldbjgXchK3uTi9rrZdMj4WvvCXQsJJrCVkcejlv6icbRyctSF8u23x9/RArWPCRPgiy9UiopGYYmzJYS4QgixXQhRIoRo1sZWCNFXCLFECPG5EGKjEOIqK/abqUgJP/1p7O14vvpVKC1VuT6x/sjy81XFSkmJyglKFot3L8bjbf0fI5EsL09QR+xGbNoEb78dfWn0wIEqsX7yZPU/t5r8fOjXDz76SEU7TeM9Dpsfjq2dy+jfK6eoYAg4LfyQznzoMkk5cQO+bd12MwkpYd1PIRDj5NL7q3BNKfS6RjnLseDIh55XwldKoF9yJpelZUvJdjRfV3PanDhszbNtgjLIhgMbEmEaoNJJormAKyxUS3sXXRSf+SQSOTnK0fr8cxg/PnH7TWVidraEEHbgSeBKYDgwXQgxvMmw3wBvSSnPAW4Gnop1v5nMnDkqUT1asrLgvffU2nnHjtatzWdlqcq3555TrWdyWg8wWc78kvmGklhrfDXML5mfAIvO5Be/UMnr0TBkiKro6dIFnE5r7WpKXh68844K9Zti08MQRl3bMOf8VS0dOnJBxCGwbnOqbY9/CgbeZv320529c1SierTYsmDqezD5FRXZCuOgRIU9C1ztYcJzMG2+inolkFnbZ4UtuhFChHXCvAFvwuYXtxv+8AfzaQm5ubB8ucrbNLqaYSUOB3TqpFZphgxJ/P5TDStmuwlAiZRyl5TSC7wJXNtkjARCabu0B/ZZsN+MJBiEn/1M/cCiweFQztpll8XvSiY/X0kPLFgQfSVLNHgDXrYc3mJ4/IqKFSQyJ7G0FJYtiy4nYcQI+OQT5RyHqzCMB7m5qgDia18z+AZfFZQ8E7345Tl/hcE/sjaaFQlHHhT9UztcjZFBWP8zVVQQDcIBF86B7pepaFQ8cOZDtwvhogWxLS2b5P0d74fNzbIJW9heq96AN2Gte554wnyyuRAwY4aqNk/kHB2OvDxVrdixY3LtSDZWOFu9gMaXSpWh5xrzEPBNIUQlMA/4cbgNCSFuF0KsFUKsPXw4xuTbNOWDD1R7l2iw2VSp/+TJ8b+SycmBc89VEbREOQdr960Ne5Vpt9kRNE+Er/XVUn6yPBGmAUpTK5oKnFGjYNUq6NAhcf/LBnJz4eWX4RYjhVWlL0CY/7MhEuloNRDJ4SosTJwNySDS59v3QaglTxQIG5z/FnSdbDwvK1ocOdDpXBVBE/H/QZQeK+Vk3cmwr43pPoYeBT3Cvrb18Faq6uNbAet2w5/+ZD6qdf/9cMEFyVl9aIrdDl27qnNFEuqVUoZEJchPB16SUvYGrgJeFaL5GoKU8lkpZZGUsqhr18RrmKQCjz4afVTrxRdVRCsRSZCg9jN1KrzxRmJ+REvLllLra16q7rQ5yXU2PwHYbfaE5W2dOKGWbc0qxbdrpyKEBQWn1ZoTTW4u/PvfqhAiIjIIW/8SXWL86N8n3tFqwJEHRU9Cv+mnnztwQIUf43EzSzxsOHAg/L6KH40+qjXxxVBEK0GTiyMPuk2FyW8QtYNvkEhCpjZsTOs3jUm9w1eTZNuzWbJ7STxN4/nnzV/ATZ4M/+//JTZHqzWystTF+f/+b7ItSR5WTO97gT6NHvcOPdeY7wFvAUgpPwGygSTXtaUeFRVqKSka/v53uP76xP/A8vJU4vy//x3/fc3bOe8MyYcGvAFv2CUAt9fNh6Ufxt8w4Nlno3vfq6+qiFayHK0GsrPV8nNER33ffPBH0Wi68BIYdndyHK0GHLlw3r+hYHDybEg2ngo4EuXkMu7v0Of6xB9DR55KnD8vvpPLjOIZ1PqbX8TlZ+Uzuc9kpvWfRo6jeYio2lvNe9vei6ttzz9vLqqVl6eqxpORo9Ua+fmqorKtYsUUvwY4SwgxQAjhQiXAz24ypgK4BEAIcTbK2Wqb64Qt8PTT0V0cT5+uxOqSdSWTn68qFL/3vfjtIxAMNNPAaaBLThcCwfCXf4t3L46fUSH8/uja8tx6K1x8cWqE+m02lZj/r39FGLDlD+ajIlmd1dJToqIhLWHLhgveU3lHbZGdTxOVcnu/6UqkNF45Wq3hzIe+N8HA+Ewu3oCX1ZWrw75W769nfK/xjO85PmxFokQyd+fcuOWF7t+vNA7N8LOfJW5lIxoa2v20RWJ2tqSUfuAuYAFQjKo63CKEeFgIEeq6xM+BHwghNgBvALfJVFVTTRI+nzrRma1k69FDvS/ZIeP8fHj88fjpqmw8uBGnLXyJ3ujuoxnYcWDY147VHuOAO8KyikW8957541ZYqMq5k33cGpOTo+RCLr64yQsni+H45+Y3OOG5+Of3GMVmh7z+MPL+ZFuSeII+2PkvCJr8kub0gAn/Sp6j1YAzH8Y9DrnWTy6rKlbhsofPIM9z5dE9vzujCkdR5w9fFOLxedh5bKfldoGKUJnJ4ezQQS0fprKzleic1FTCksULKeU8KeUQKeUgKeUjoecekFLODt3fKqWcIqUcI6UcK6VMzNpOGjF7dnTJ1a++ap20Q6xkZanqtniwrHxZ2CVEm7Axuc9kinqGFe3FZXexsmJlfIwK8eqr4ZWSW+L555NfJRSOvDz4z3+aPLn972e2XTFCj8uhx2VK6DJVcObB2W0waaRyNhiQS2nGpFdT5/jZs2CK9ZPL+zvex+0NH7Ed12McoOaQwZ3CL0EbaV4dLS+9ZG4J8Ve/atvOTKqjFeRThL/+1fwJ+9prVVPiVDlpO52qsm769NbHmmX+zvlhry7zXfmM7T6W83qdFzavwu1189Guj6w3KEQgoMqazTBxIkyblroh9YbeioBa197zrrkeiPYcmPhSaiwfNiWBcgIpQ/Ffzefb9b4WOp+XOv8vmxM6jDqz0MEC3t32blgleKfNyUX9Lzr1+IJ+F4R9f62/Ni4SEEePqqbTRunWTfUjTIWUBE14tLOVAhw4oFobmCE7W/XNS6VlKFD2/POf1m5TSsknleGTe/1BP6MLRzOm+5iwywESyUel8XO21qwxn9z+0EOpPSmesQxRXWI+V2vw7eAssNQmywiTe5PR1B6A41+Ye489GyY8k9yihnA485WUh0Xsr97Pvurwko85zhwm9p546vEFfS8g3xX+/7Fm75qIy4zRMmuWOWHjBx9MfpGNpmX04UkBFi82H526997Uc7QasHpZc/vR7RH7kAWCAfp36M+obqOo8YWPuVdWVXKi7oS1RoWYMwfqTMyzZ52l9G/SZmI88CGmEquFDUb8OjWjWm2Rg4vNR6eG35v8PK1IWLis+WHphxHzQGt9tYzrOe7U4wm9JkRMhM9yZFmeqvDSS8bbtblccNttqZNOoglPukz5Gc2cOeaWELOyUrvqxOqy4+Xly8NKOwAM7jQYm7DRPrs97bPahx2T48xhVcUqa40KMXOmOW2t++6LfyseS6mYAQETZZa9r0t4qxVNC+ydY24J0ZYFw36Wus6yhQUXM4tn4vaFj9oW5hXSLuv0evrgToMjXvC5vW5mb29agB89VVWqbZdRLr3UvL6fJvFoZyvJSKl6R5nhppvalhLv/JL5EaNWjRPjR3QbEXaM2+tm0W6TiVUGOHwYysqMjy8shBtvTCNnK+CFI+HL4iMy8sHUXUJsa0gJB0xOLv1uIt4ioqlAIBhoURam8RIiqEKckd1Ghh0blEFLna0NG8xFqb75TSWKrElttLOVZHbtMh4ubuDee9vWj2tF+Yqwz+c4zsyrmNxnMrYwX+mgDMalYmjBAnOO0z33WG5CfDnyMUQoiw9L5/OgYFD87NGYw70L/CYnl+H3tglnef3+9dgiNELPceQwrf+0Zs9f1P+isPMLwEHPQSqrKi2xbds2pd1nBIdDNZNPm7SENow+RElm0SJzUarx41Vz0bZC+YlyPL7wJwyn3cnowtGnHp/T/RzyXOGXP0qOlUSMjkXL22+ba630gx+kdmJ8M/bONXeyHvkbJR6qSQ0OLsJUlKrzeMhrG5PL3J1zIya1O21OJvSa0Oz5SX0mkZ8VPpfNLuyWdavYuNG45MPUqdFJBmkSj3a2ksysWea0VH75yzQ7YcfIiooVYdWbQSWxNg7tjy4cHTGJNceZE1EpOhrMSj6MHauuQtOKyneN6zO5OkH3S5V4qCY1qJxlrpfl2b8EW9uYXGYWzwyr2wdKzqHxRVwDE3pNwOv3hn2Px+dhxtYZltj2uQn94FtuSd1CKc2ZaGcriQSDsNxEn+SuXeHqq9uWcN2CkgURRQc7ZHdolsRaHwivkl3jrbG0aWxxsbmI5NVXp44emiHqDkGNiWWR7peqHC9NaiCDcMjE5JLVFXpd3Sac5eO1x9lxNHIfnIEdB5LlaC6C17OgJznOyM7osvJl+IMm9OgisNOEIP2FF7at80E6o52tJLJpk7nxt9wSXe/EdGZxWeQk1lHdRp3x2G6zM6DjgLBj/dLP/JL5ltlVUmIuT+LrX09dEdOwHFquKtOM0vtacLVrfZwmMZwwObn0bzuTy6Ldi8iyR/5un9/3/IivndPjnIiv2YWdNXvXxGRbXZ0SNDWCEG0rpSTd0c5WElm40FzJ7g03pGY393hxyHOIozXhZx4bNib1mdTs+YYWG+HYdGgTXouiL6WlxvW1OnSAYcMs2W3iqNpubgmqxxXxs0VjngMLzbVY6nND6vSxjDPvbnuXam94OYx8Zz5T+02N+N5LBlwSUZurzl/H3J1zY7KtpMT4HN+rl/FEek3y0c5WEpkzx3gDY4cDisK3/8tYVpSvaLFJ7Dndm19lTuw9kWxH+CTtbEc26/ats8S2LVvAa9Bvu+wy842qk87JLcZb9LQfnjptXTSKvXOMN54WDujcNiYXI70MwyXHN3Ber/MiLiX6gj5mFs+Myb5t24yPHTZM62ulE9rZSiI7IqcNNGPCBOMn90zho10fRbwCDcogY7qPafb8mMIxEZcI6vx1LC83kcfSAlu2GB/71a826TeYDlRtNz62x+UgdOJISlFlYnLpPAGCbWNyKT5STL0/shPql36GdB4S8fVxPcdR64ss8rvr+K6I0XhD9hUbL5gaNizNUhPaONrZShLBoBLFNMpll7WtKkSgxZ6GvqCPgR0HNnt+VOGoiJOhN+CNOczfgBkx0/HjLdllYvGUGx/b52sQpgm4JknIINSbmFy6X9ZmqhDn7ZzXYhL7iK4jIupvgSrK6ZrXNeLrWfYsFu4yKSTbiEOHjEs5jB3b9s4J6YwlzpYQ4gohxHYhRIkQ4t4IY24UQmwVQmwRQrxuxX7TmQMHzAliXnttmlWzxcjJupPsqdoT8fWBHQeGnRQ75XSKqLUFsHbfWgLB2IRpfD7jSaygcivSioAXfCeMj+90btxM0URB7QGIkFcUlj7XmhOvTWNmbJ0RsWJZIMKKmTZlYq+JEV+r9lbzzrZ3ojWPWhOdsQY2v9bUpDAxO1tCCDvwJHAlMByYLoQY3mTMWcCvgClSyhHA3bHuN90pKzPuPOXmwvDhrY/LJFbtWdVimXVLifCR2vaAqljcdMhkpVYTKiqMt9Po3DkNWyvVVBjvb5jVJb62aMzjKTOeQ2fPhXZtY3Kp8dXw+YHIIlYFWQVM7jO51e1M6z8tYl4oKLmaSHp/rWGmqb2OaqUXVkS2JgAlUspdUkov8CZwbZMxPwCelFIeB5BSHrJgv2lNWZlaSjTCpEnmfoSZwKLdi6iuD5+vle3IZlLv5pWIDUzqPQkRQTk7EAxEbP9jlJIS4wKlgwal4bGrLjWeg5U/EALp9gEzHE+ZWko0QpdJbeb4LS9f3qKT5A14W0yOb2BCrwkRC3cAAjIQ9QWdmciWztdKL6xwtnoBjdd7KkPPNWYIMEQIsUoIsVoI0ebrxHfvNp4IOXRoGjUvtogFJQuQhL86zLJnhVV4buDcHueS7wovq1zrr405b6u01HixwqBBadi3zLPLeMJ0/iB06meK4d5tXLaj3VBzS45pzKxtsyJewIGaV3oVtL7mP6b7mBaT5H0BX9S9WM1UF6ZdR4o2TqJmSQdwFjANmA78WwjRoekgIcTtQoi1Qoi1h81kj6chW7caT4QcPLht6WvV+GpaVHiu8dUwqnBUxNdHF46O6KgBfLzn46jD/KCikkavQNPy2FVth4DBD5g/qM3oM6UNJ7cab7NUMLjNHL85O+a0OC+c0+MchIE1/2xHNv069Iv4en2gnre2vBWVjWbycrXsQ3phhbO1F2isY9s79FxjKoHZUkqflHI3sAPlfJ2BlPJZKWWRlLKoa9fIFR+ZgBnZh7aWr/Vp5act5mu1y2pHh+wOEV8f0nlIi+XdARlg5zETPTGaYKb59OjRaXgFesKErkWHkRChd6UmSVSbmFzaSL5W+YlyjtUei/i6Qzi4uP/Fhrd3Qd8LWnx906FNEduMtYSZPKy00+5r41jhbK0BzhJCDBBCuICbgdlNxryHimohhOiCWlbcZcG+05Y9kQvtmtHWqk6Wli2lxht5GaSlBHgAh81B3/Z9I74uEDHpbZnJwerWLerdJI/6I8bHtousSaRJEjUmJpeCtjG5fFDyQYtRq1xXLhN7R64ybMrUflPJc0aues52ZLO0bKkZE9X7DBbegLn8Lk3yidnZklL6gbuABUAx8JaUcosQ4mEhxDWhYQuAo0KIrcAS4BdSyuiV39KcYNCcdECPHvGzJRWZt3Me/gjq5QLRYnJ8A+f2iCxH4PF5YuqTaEZcNi3lOqSJD5jTO352aMwjg1BvYnLJbhuTy4ytM6jxRb6Aq/XVUtTTuIr++J7jW3TequureW/be2ZMBMxFtsyozWuSjyXxfynlPGBek+ceaHRfAveEbm2eBo0tI32t8vPbVtWJN+Bl46GNEV/Pd+W36Eg1MLnPZGZvnx1RUyeWikQzka20PHYtiD42o4WGvpok0KCxFTBwDB35beL4+QI+Vu5Z2eKYzrmd6ZjT0fA2h3UZhi8QOWlKIqMqxOnZU6UdGDk3bNwIHg/kRQ6waVIIXUaUBNxu43k8/fu3rXDx+v3rybZHjqVLJGMKm7fpacrowtEtlnlXe6upOFkRlY1mIltpl68F5lq3iHT8gBmM3238mOT1N14IkcasrlzdolQDqJ6HZrDb7Azv2nK+28m6k5QeKzW13bPPNl5Qs22bTpJPJ7SzlQS8XuNCl716GdfjygSWlS2jrgXdn3p/PYM7DW51O6O6jaLWH/lE4rA5oo5umXGgjFacphRBEzO47omYWgS9EEFjrhm5vYzrcaUxc3bMweP1RHw9y55lSDm+KRf2vzCinh+olIcFpQtMbXPoUOPz/bZtaRo5b6Poy9Ik4PMZd7bMJExmAvN2zsMbiBxZ6d+hP3Zb6yf4rnldyXHkRNyW2+tmQekCvjH6G6ZtNDPBpWXzcDMn4Bb6yFnGO92h7qCFG7TBLenoBRsgaGJyaSGCnEm8+PmLBFqQwgjKIIt2LTItRFp2vAy7zR6x12KNv4a/r/47d46/0/A2zYggHzwIMSjYaBKMdraSgJnQr9OZhu1eoiQog6zZt6bFMUbytRoY3nU4n1R+EvH1JWVLDG+rMWYc4LQszzYj5SADQJxFMS11tAAyOJpjKirpxHAULE3ZcmgLR2pbrq71BX28v/P9uOx/x7EduL3uiCLLTXE6VQXzvn3Gtl9erpYeNamPXkZMAmaiHW3J2dp8aHOLUasse5ahSsQGWmrbA3DIc4hDHvOdo8w4W1VVpjeffMwoihsVz9QkBjP5drbMn1yeXPNksk3g2XXPmho/dKjxsQsXGkum1yQfHdlKAmbyeOz2jJ8PT7GsbBmBYOR/TrYjmzHdW0+Ob6ChbU+1N0KPRXs2KytWcv3Z15uy00x59ubNcOWVadayR5hwtsxULmrijxnnV9iJe2TL8iVgMLMMHI38gtW8tuk17plkvBD/3HNh6VJjS4RvvAG33grt2kVvnyYxpNMpIGMwk2Dt97eddfl5JfNaTGqv9dUyqlvkNj1Nac0xq/ZWs2jXIsPba6BXL+P6WTt2GO+BmTKYiWz5I/ea0yQBU0vACZhcLHe0wOgysD/oZ797fxz2b46NByNL2YRj5EjjFYmrV6dpEU4bRDtbScBs/6u24GxJKfl4z8ctjslz5dE5t7PhbQ7tPLRF500i+bD0Q8Pba2DQIONLiaWlaRjmN6O95C6LmxmaKLCZmFyCPmihV2C6898t/022CYBy+r7Y/4Xh8cOGqRUNI0gJM2ak4RzTBtHOVhJwOo07UG3F2So5VhKxqqeB1nRtmuK0O+nTrk+LY8pPllNVby6xavBg48ektDQNVeRzI7c6akbV1vjZoTGPzYlhByrDna0X1r+QbBNOYSZ3bMwYc4U1r7+uxE01qY12tpKAGWcr7ZagomR5+fJWNWvMJMc3cE73c1p8PceZ02pErSkDBxo/Lnv3quOdVrQfieFcnpPF0IIumibBCBOTSyCzJ5ePK839ruPJ7B1N2wVHJicHLrrI+LZXrGg7eb3pjHa2koCZSEdFhfGQcjozv2Q+Hl/kyzOjbXqaMrnP5BbVo91eN4t3Lza1zdxc1UbJCFKq9kxpRcFgcBjsAeLepZ2tVMLMMqKnImNFaSurKqnzp8738pDnEF6/8UrRW2+FggJjYwMBeOKJtnNhnq7oasQk0KGDcfmH8nJz1W/pypztc1p83e118/MPf869i+41td06f12LPcyCMsi/1/+bv1z2F1Pb7ddP9SYzQnEx9Gl5NTO1yB9o/CTsLk2MsKnGGK4OxuUfPOVgz8zJ5cnPki/50JTXNr3Gd875jqGxV11lTiLoL3+Bn/40SsM0CUE7W0mgSxfjLRnq61UvxQ4d4mpSUvm08lO8rZwgJDJulUUn6k5wrOYYnXI7GX7P0KHGna133oEpU9KoYWz+QOMnbPfujD1hpyVZXTAs2hqsV70UXR3iaVFSeGvrW8k2oRkvb3jZsLPVrh1MnAjLlhnbdnU1/P73cP/9xqPumsSiL0mTgBBQWGh8fGVl/GxJBV7Z+EqyTeDVja+aGj9qlPHl3fnz00xnK6eHcf2sQA34tPxDyiAEZJuYXGoyb3IJBoPsPr472WY0Y3XlalPjb7vNnOP0j3/oxtSpjCWnACHEFUKI7UKIEiFExHUeIcQNQggphCiyYr/pTL9+xseWmmscn3Z8UPJBsk3g3W3vmhp/1lnGtXAqKuBIyx1DUgshIMfECfuguZw3TZzJMzG5uDNvcllQugCZglWW9YF6U07gNdeYc55qa+E3v1ErIalKW3YGY3a2hBB24EngSmA4MF0I0axGXwhRAPwU+DTWfWYCw4YZH1tcbHzZMR0pP1GebBNYu2+tqfGDBpmrAHr33TTTwskbYHxs5bvgS8e+RBlKgYnJ5WTmTS5m2+Mkkn+u+afhsZ06wdix5rb/73/DoUOpe0jTslesRVgR2ZoAlEgpd0kpvcCbwLVhxv0O+DOQOiUiSWTYMOOSACUlmVtpUny4mEAK9Nfz+DzUeI3/k0eMgDoT3+RZs9JMC6e9CU2z/R+Zq4LTxJf2w4y3XHKXZJwExKLd5rtCJIqZW2eaGn/77eZyPX0++MpXVJQr1fB44M47k21F8rDC2eoF7Gn0uDL03CmEEOcCfaSUcy3YX0bQv7/xKsMtWzK3JcNLX7yUbBNOYUZxOjcXxo0zvu2VK9NM3LTd2WAzqCRffxhq9rQ+TpMY8vqDw+DkcnJLRjUT93g9EXuhNiXHkUOuM9eSW7bdWEuJipMVSBMq1d/4hvlq9K1b4Z57UuvirqYG5s6FV82lxmYUca9GFELYgMeA2wyMvR24HaBvXxMq1mlI//7Gx65dm2YnahPMK5mXbBNOMWPrDMPVQgA33giff24swuX1qsnmuuvM9cZMGh1GqrY9QYNx/z3vwbCfmevNp4kPef2Njz26FlrQoUs3Pt7zMTmOnBbbdIHS7XvsS49Ztt+ADHDXvLtajdJn2bPYengrI7qNMLTdrCx4+GH4xS/MOU/PPgvjx8P06cmvgvZ6YfdupR3WlrFiZtwLNFYR6h16roECYCSwVKgkl+7AbCHENVLKMxJlpJTPAs8CFBUVpV6Go4UMGGB8/drvhzVrYOrU+NqUDLYf2Z5sE05hVnH6qqvg1782Pv7hh9V70sLZ6joFAiYSLPbNhcG3g6u99bZkF8apoXGGkj/A+LGTfji6BrplxuQye8ds6g189gv6XsAPxv3A0n0/+dmTbD68ueVBQhUEGXW2AL73PXjgAfORqjvugLPPhnPPTZ5WYyAAJ0/CpZeaS7vIRKxYRlwDnCWEGCCEcAE3A6d6E0gpT0opu0gp+0sp+wOrgWaOVlujk3FJJ0AlWKfiOnwsVFZV4gumTnnKiboTrfZnbMxZZxlXeQbYtAnWrUvd5NUzsGdDJxPrpEc+jp+46fUH4BZp7KYBl8nJZc+70EokKF2YvW02QdnyD8xld3HxgIst3/eF/S9sseUYKJHlt7e+bWq7Lhf87nfmI1R+P1x+OaxalZwlxbo6JVs0cWIadtGIAzHPjlJKP3AXsAAoBt6SUm4RQjwshLgm1u1nKma1thYtSrNqNgO8/MXLyTahGbO2zTI8VghVnm2mKvGBB9Ko2KHP15TTZYSgD3Y+rVv3pAJmtbYOLlIRrjRnz8k9HKo51Oq4bEc2E3pNsHz/U/pMId/VujDW5wc+p8ZnbhL47neNS800xuNRDteLLybW4fJ4VIrF2LGwa1fi9pvKWHIpKqWcJ6UcIqUcJKV8JPTcA1LKZt03pZTT2npUq4HRo42P3bzZeH/ZdGHWduOOTaIwkyQPKgfLTHRr6dI0EqnteQWmpojtj0MK6hu1STqYmFxObCYTjtuC0gXYDbSZqvXVRtVntTUm9JpgqLI625HNsjKD0vAhXC74v/+LLv8qGIQf/xh++cvEXOi53WolZupUOHEi/vtLF9JJ1zrjuOYa4z8eKWH58vjak2g2HjTY7yaBLC1bamr8RReZz0V44AHVXiNVOZVL2G6Y8cgWQO1+2DvHuPq8Jn70vgbsRs/MEg7FaXIxE2GLkRlbZ7TYzL6BngU9DUWgzDKw48BWlxFB9Xmdvb1ZHKJVvvlNFSky2rmiKU89BV/9Khw+HB/h07o6FdH67W/hW9/KvJWYWNHOVhK55BJz+TszZqT2SdoMJ+pOGEpkTTSHaw4TNHFQzEpAgOqVuH9/6uZunZokhYAeV5h78+Y/GO+rqIkf3S/BcI9EgIoZ8Wm7lKB8O3/Qz4qKFYbGTuo9KaZ9RUIIwajCUa2OC8ogs3eYd7aEUNIJWQYVWcLx4YfQp4+64KuqsibS5fWq7bz4oir8evTR2LeZiWhnK4kMHGiu99Xbb5vLD0plXt2QuoIrS8qXmBo/fbq5fIpAQL0nFatz3G64t3HDrT7XgaOd8Q2c2AAnNlltlsYs+QPBYWJyqXgbDERlUpXP9n5maAkxx5HDtP7T4mbHxf0vNmTHsdpjlJ0oM739AQPgoYdik3Oor4e//Q1694bHHlPRqJMnzWs5VlWpoq133lFVj3feqaJmmvBoZyvJXHaZ8bENVw+Z0PLgneJ3km1CRF7b+Jqp8d/6lvl8uvXrlRZOKgkP+v2wcyc8+WSjJ7tfCkGTXuGmh8CXwg3a2grdTUwugRrY9aI5uY8UYu6OudT6Wq+odNgccUmOb2BSn0nkuVr3hGzCxoKSBVHt4557YMwY4x1IIlFdDfffDz16KPHUp55SfXjr61WuVXW1OufU1qqLsJMn1eODB+H115W0xKBB6sKxoiI2W9oCwoyabSIpKiqSa9dmfh79G2/AD39ofHlw0CAlIZAs3RSryP9DvqH8imTQu6A3e+4xp4j+ox/BCy+Ya7SalaWO5cCB0edhWInbrSbxXbuaOI/vD4eqYnMbu2QxdD0fbDGeEczyusHoTLxkIsyGnuM5/5a9AZ/9EPwGJ5f8QXDVJuPq81Zj9NhBs+N39j/PZtvRba2+zWlz4vm1B6c9Pt/Lg+6D9Hu8n6EUiUsGXMLCby+Maj+HDsHw4XD0aFRvb5GOHWHyZCVPlJur5iaPR80Pa9dCeYytbFPU5bAEIcQ6KWVRuNfSQV4xo7n4YrXmbZTSUvjsM7jwwvjZFG/q/HUp62gB7K3e2/qgJvzyl/Dyy+acrfp6+PKXVZQr2SrPHo8STwxbpj3wu7DpQXM99FZ/B67eknhnS3OawovN5c+5S+HoZ1CYXpPL0Zqj7DphTF/grM5nxc3RAijMLyTflU99bevO1qo9q/AFfFHZ060bzJ+vzgNW6y8eP666XWisRTtbSaawELp3N3e18Oc/wznnQDsTqTSpxMYDG3HZXHhbORE4bU7uHG9d51J/0M9Ta55CtlLmbhd29lbtpVe7Xi2Oa8zAgarU+aOPzF257dgB3/8+PP98dDo6VuB2w2uvwVtvRRgw+Puw6QFzG/WUq+XEUQ+ayx3SWEdOIWR3hxoTk0vxn6HTOeBMn8nlo10fkWXPwhto3bGc2jf+SvlFPYtYUNr6EqHL7uKTyk+Y2i86m8aPh5degttuyzzB60xEO1spwJVXwjPPGD9Jf/CBWjtPV2drecVyhIHllrHdx/L4FY9buu9Z22ZRWd2y0FWOM4cVFSu4eeTNprZ9//3RqTW/+aZafXruucQ7XG63qnC6syWf1tUB+k2H3a+YE78s/j/ocz10HJdR/ffSip5XQskzGNbR2vcB+GvSytl6p/gdQ82n8135nN/3/Ljbc1H/i1hStqRV58/j9fD+jvejdrZA9Wd1u5WOVtqIJbdRdIJ8CnDVVeaEMaWEX/0qPlopVtCaXXN3zjWU03Ber/Mssug0Y7qPaXVMtbeaD0s/NL3tKVNUWXU0vPGGSrRP5ITpdqsk/RYdrQbO/t8olgQlrPia+QR7jXX0vAqcJiYXJGz4VdoUOEgpDf9WpZRxTY5v4Lze55FjIO8tIAO8u+3dmPf33e/CH/+YvMi4xhja2UoBLrzQfIXhyy+rTuqpqNXUUh+soAyyZu+aVreR58yjqGfYPMOYmNxnMg5b6wHdhbvMJ64KAX/5S/T5V++8o65UE1Gh6PHA44/Dz39u8A3tz4aOY83vqHYfLLsG/Kmbo5fRFF5ovsJw18vg2Q2t9BhMBTYd2mRItR1AIhncaXCcLYJxPcYZbsdTcbKCQ57WWwy1xk9+ovSt0r1wKpPRzlYK0K6d6l9lppBJSnVFk2paTR4PfOc7kV/feniroSVEm7AZikKZZUzhGHKdrV8CHvQc5GiN+VKfL38ZRoyIXg9t7ly49lqVpBqPKJfPp7b74INq2dMUI+6LLv/q0DJYerV2uJKBsx30uBxzGloSVn83Lfpczts5z1CuFsDIriMNzT2xUpBVQPf87obGuuyuqKLo4bjjDpg1S2k3pqoeY1t2BrWzlSLcc4/5iMjatSrfJlXW6hsE7laujDxmWdkyAsHWr0Rr/bWc3eVsC61TjOk+Bl+g9ZLBbEc2Kyta+CAREELl32Wb6HLTlEWLoH9/Fb2sqTEvNhgJt1vllI0apfqsmabnleAwsyTVCO1wJY9h94DD5ORybC3sflXlb6Uwb29925CzJRBcNOCiBFikMKpS7/a6LdUcvOwy+PRTVXQVyxxkNXa7Or+9Zk7CMKPQzlaKMHWq0jcxyz33qChIKlBdDf/zPy2P+aDkA2r9rZfO9MjvQZYjhr4UEehV0MvQ1W11fTULd0engTN2rOpB5oohJ7yqSuVSTZwIGzbE1qbJ41FChLfcono5hpV3MIKwwdg/mT9xN3DK4UqPfKCModtUcEUxuay/B7wpMrmEwe11s/nQZkNjC7IKmNxncpwtOs20/tMMRdBBpSwELVyyHT5cVTnffHNq5HHl5cG558LmzWpebKtoZytFEELlz5j9cdTUqDyfZEe3PB6lJNySUyClZOUeY9Gisd3HWmNYE4QQDOk0pNVxEuOJt+F47LHYepg1sGmT6r34ne+oJcaG1hot6XkFg2pMfT2sXq2KKQYMgDlzYreHAd9UcgLRcmgZLL4Maip1lCtRCAHDfg52k5NLoAZW3piy0a0lu5eQ7TAWvqn31zO+5/g4W3Sa8b3GG2rbA2qu+Xz/55buPz9fdRt55x3o3Dk5TpfLpZYNH35YzUP9+yfehlRCO1spxLe/HV3C+8cfqyhIslq/eDzwv/8Lixe3PG7X8V2GQv4OmyOuV6ETehurSNp9fDfV9dGFlAoL4V//sk6sdOZMlQ/Wvr36+/jj6ur18OHTbTWOHlVtM/7zH/Vd6twZJk2Cf/zDQh0eYYPxT4M9hg92ZDXMHgTbHlMn8qAJOYlIBOrj00g5Uxj4bUw1pm7gyMew5s6UdIxnbZ9FVX2VobG5zlx6FPSIs0WnGV04mjq/sZw3b8DL/JL5cbHj8svVnHDffWouSsTSosOh9nPLLVBSolZfbNrTsMbZEkJcIYTYLoQoEULcG+b1e4QQW4UQG4UQi4QQ/azYb6bRsSNcd110X8yXX1YRjEQ7XB4P/O53yrFojeXlyxEGEnXznHmMKbQ+Ob6BCT0nkOds3VnIcebwSeUnUe9n+nS1bGdFhKuBQEDlxP3ylzB0qFKSbtdOTaRdukC/fnDrrTB7dhy/Cz0ug45jiKlxcdALGx+A+WPh2BrlKEVzpRH0K0dg7xzlwGnC4+oIva5TzrJZdr8MX/wq5Ryu93e8b3jsuB7j4mhJc1x2F4M6Gfs+egNe3t76dtxsyc2FX/8aKiuVHldBgTmpITP7yc5W57BNm1RkrWdP6/eTrsTsbAkh7MCTwJXAcGC6EGJ4k2GfA0VSytHADOAvse43U/nZz6K/+vjHP5TeSqIcLrcb/v53pWhvhAWlCwy16akP1DO6cHSM1kVmdOFo7LbWQ/wer4clu5dEvR8h4JVX4jOxJZ3z/g12Cy6Tq3fCh5Nh5deg7BWoP6Ycr5bkCvy1KiJWUwk7/gmLLoaVX4f6w7Hbk8kM+xnYojxmO/4BW/6YMg5XybESw1Etp82Z0OT4Bi7oe4HhsduObONk3ck4WgMdOihpmiNHVB/XKVPUUl+7dtFXL+bnq/PVoEHqPLBvH7z9NgyOv8JG2mFFZGsCUCKl3CWl9AJvAtc2HiClXCKlbFj4Xw30tmC/GcmECSrBOtov/yOPqO7t8RY8dbtVNO2++4y/Z2nZUkPj7MJOz4L4XRKN6DbCkA5OQAZiDu937Kh6mKVCoqqltB8Ow/7XfB5QJPZ/qPopzuwMC6fC1j/C0TXKGavdpxyrqu1waCVsuA/mj4H3+sD6n6l+fprW6TIhpJUW5eSy5RHY+VRKFDh8UPKB4bE5zhwm9p4YR2vCc0HfC8h3GZNKybZns3h3K3kYFuFywde+piLkBw6oC8If/lDldTYsAbZvr5ywggLlUBUUqOfy8lRlYWEhfP3r8M9/wvbtarnwrruiK/JqK1jRrqcXsKfR40qgJenv7wFhz2BCiNuB2wH69u1rgWnpyd/+BtOmRZ9n88tfwrFjSkcpK0v9OKwiGFTaXo8+Cr/9rfH37avex4m6E4bGDu08NK56OLnOXLrldWNf9b5WxxYfKabOX2c4ETccRUWqh9mtt2ZYD7NR90PFW1C9A8PtYIxw/At122TiC6Yxxrl/g0XTIBDlF/HzX6ro48j7wZYFBiLE8WDG1hmGqpoBan21CV9GBJUkLw32YKvyVvHe9vf46tlfVboNBw/G2TpFR1RkpCE6EkRw0F9IWV1/9tETLy6C2HDioyuHGcBuelOJ42AA3kbdzHLwoMp/aGMktDeiEOKbQBEQtq28lPJZ4FmAoqIiC2fv9GLCBBXiXbw4eoX4P/0J3n1X9d0bPFhdncSK260aZt90E2zZYu69K8pX4LK7DLXpSURLjdHdRhtytrId2azZu4YL+hlfEgjH17+u/md//WvyK0ctw+aEqe/AB+NV5Zom9ekyAbpMgYOLiSphHmDrn6DyXZjyJuQPBqcFk4uJ9kBeCZ9Wfmp4fLe8brTPbh+NVTExpPMQw+r2oARapZSIBDla4bAh6cEBetBCG5BYKSw03gg4g7BiGXEv0LgjXO/Qc2cghLgUuA+4Rkppsn9E2+P//i/2xOrt25VswP33qzyuluQCWsLvVw7C738PY8aYd7QAPiz90FCz2DxnHuN7xb9Ee1KfSThE69catb5alpUvs2SfDz6oHNWMWlJsPxyK/mndcqIm/pz7f2CPcXKp2g4fjION96s8rmCUk0vQr/Lvtvze8FtW1YLLYVzE7rze1vdYNYJN2BjZbaTh8TW+GnYc3RFHizTJxApnaw1wlhBigBDCBdwMzG48QAhxDvAMytGKvRFUG2D0aNW2JRZhTFCRsccfV6rhzzyjlherqpQD1RKBgBp34gQ895zKI/vzn6NXMzcqEGq32eNaidjA2O5jyXW17iD4gj7m7pxryT6FgOefV7IMqexwmS7QGPQdGHCrdrjShY6jode1YItxcpFB2P44zB0FO58B7zHwVbUu5REMqHHeE1D6nKpI3WqwygaY7QF3vbFIWLYjm2n9phnettVM6z8Nm8HTrJRS5YgWFsbZqiST6Z8vAjE7W1JKP3AXsAAoBt6SUm4RQjwshLgmNOyvQD7wthDiCyHE7Aib0zTiiSdid7Ya2L1blf126aJ0ml54QSn6NkS86utP983bskUlv197rdJquuMO2Lkz+n0fqz3GAbexsHSNr4bhXZsWs1rPmMIx+A3qO31x4AvDY1tDCFXA8KMfpabDlZOj1J5NU/SESr6O9QSuSQxFT1h3rDy7Yd2PYUYXWPpl2PUCnNh8OuIVqFd//TVwYouSklh2rSqGWHOHKoIwwXtuCBpcAnXZXUmLbAFM7j2Z/Cxjy6y1/lolAXHggFpms/pmlnjYIKX6fG0QS3K2pJTzgHlNnnug0f1LrdhPW6NrV6VE/rOfWSfnICWsWKFuDeTmKqfO54uPbMTKipVkO7INCZoW5hWS44x/t9K+7fsabpHhtDnZcGAD43pak2QrhCow6N1bVXOmStJ8bq5ysF94IYo32xxw4RxYMB48e0BGuaykSQzZXeHcx1Q1p2VyDhIOr1C3Buy5yqmTPkv2s88P+01E12t9tQmJlEdiQq8JhhtlA6zbt45aX21C5kBNYklogrzGPN//vhKH++wz6xoSN6WmJr5J2wt3LTSsxD6me2ImRiEEZ3U6i02HNrU61hvwsqx8mWXOltq/cqLPOw++8hWlAh9tTp0V5OSo5eYf/CCGjWR1gi99Ch+el9oOV3bbXMZoxqDvw64XlXSGiURuUwRqLC2eWFWrLhgLXK2L1wVlkN7terfeY1VKZWP9MbUU6j125v26I+A9qpY/pV/dgqG/MoCqxLWpCw5hB+EI3XfQy55FjgCHI0sJOguBkt5ofP80tf5a1u9fz5S+U6L8D2lSFe1spThCwBtvqJyrWJoRJ5MFpQuQBqQBHMLBpN6TEmCRYkKvCYacrfpAPXN3zuWeSfdYbsPkyVBcrHRv1q9PfAeA3Fzo1Anee08VU8RMdpfUdrjsOXB921zGaIYQMOUNlXPlT4/J5St58HJ3YMqzhsaPKxwD1SVQtUMtV57YAO5SqD+qcsb81aGIm1AROJtD3UcqhyroVU5VDCztAVt9ISdMhLYtg0BA7UPYlUCwPQe7swsTu7beu1WTfmhnKw3o1w/++1+44YbUWXIyitvrZvfx3YbG5rpy49aAOhwTek3gjU1vUGOg0e5nez9TZdlx0P/q1g2WLVNNY3/4QxVljPdxdjjU0vGvfw2/+IV1uYHAaYdryWXqJJcqshD2POiuMxrOIK8fnP9fWHFD9NpbCSTbBjcXACNvbv6ivxaOrobDq+Dwx3BiI2w6qCovhQMCdRCMUAhvz4XcXuDqpCK0rk6n72f3gJxCyOoKznah6FXDzQaIUE5UQEW6ZEA5afXHof4Qo2sPMLruUPOoWf0RqN0bcubcodthyOkar3+fJoloZytNuPJK+MlPlGJvshpOR8Mnez4hx5mDr771CIcv4Itrm56mjC4cjcPuAAMXrlJKio8Uxy15XwjlTF9xhRKLffJJ9bzVy7sul+q9eeWVqtVSnz6tvycqGhyuNXdA+ZvJd7jsOUqIc/gvk2tHKtLzShj6E9j+Twik0eQCSoKi7HXYOxtOblURIn/NmdGoxqXX9mzoMBo6nqs0x9qPgvyB4CxQzmZDHqewqyiXLSu6fpJNCfqVoxf0c0oA2OZU0bTaAyrqdmwdHFsb+740KYl2ttKIRx5RLRbWrlXVg+nA4t2L8XiNT+B92sXr7N+ckd1GGmrbA8rZWl6+PO6Vknl5qn/Z/fcrmYg//Uk5XB5P9AK3oNptBIMqJ+uee+LoZDXG7oKJz0OXSbDuJ+pkY7AowTJsLnWCvWCmjmq1xOhH4PBKOLo2cvQnlaicBZsfgZObQrlUoYu5YJhkdEc+9P0aDLlLOVr+GuVIOZo0o7c542evLZTHFY68PurW7cKUaIWkiQ9W6GxpEoTdDnPnqh5WzjjOC1byQekHhlWUh3QeEtc2PU3Jd+XTOaezobE1/hrm7ZzX+kCLKCiAu++GvXth1izVd6x3b6WB1a5dy4K3Docak5en+pndfLMqsjhwQLWCSoij1ZjB34crv4BOE5qf4OKJPRd6XQNf2akdrdaw2eHCuZA/AEQaTC6rvgHH1qilwYh5gQLG/AFuOATjnoBO45RD5Wqf2O+hUWx2ZZsmI9GRrTSjfXsl21BUpE7ErYmTJoOGXoz1/nq2Ht5q+H2JUI5vyqhuozi421h7jJUVK0/nbSWof5kduCh0+ztQQR82141kJ2exiZGUMJg6sgliIwsvvahkjH8DQ6p2MoxtnE0xtjelag9vBqv7l7UbAl/6GMpeg7V3qeWUeC1ZOfLB2R4mvQLdL47PPjIRV3u4dAUsKIKavTEnhscVI9+dYXfD0J+qJeTktHDUaE6hna00pEsX+Phj5XAdPpxaDpfdrqrbANbsW2NYXyvXmcuEnvHvidiUyX0ms6RsiaHoW52/jrITZQzoOCBhjWKb0pc99GUPEXq5W0c8+pcJAQO+CX2+CqUvwObfq1wuq5ZOHPmQ0x1GPQx9vx552UYTmewucNnH8EER1B9OLYdL2FW0UgaNOVv+uvjb9E53qLN6LrDBLXGS4tAkDT0bpSk9e6rcrUsugbIyqEvAvNIa2dnQowcsWaIeLy1bSq3PWIWTw+ZImMZWY87pcQ55rjyq6qtaHWu32Vlevlw5W4WFSXO4EkI8W2o48mDoj+GsO6HyHdjxFBz5RCUj+6vBgEyIwq62Jf3Q/TKV5F14Uai8XhM1uT3hirWw+BJwl0EwBSYXWzbk9IBLFsHOf8GOf7ZedFHytGqSPfI36rGznfV2We5ogekG4cGAcozrDqhke+8J5Yz63KG/1eo5s/w3D/VbDOmINdYGE0I91yBb4chVjrAjXxUbONuFbh1Cf/PVb9Wepx7ndFd6d9mFsffpTBO0s5XG9OwJ69apKrbly+MrTNoaublKoHPWLJVvBDB/53x8BhvU1vhqGNF1RBwtDM/owtEEgsauIt1eNwtKF3Dr2Fvj13LCrKNgdfQpQXi8HpaVL2PW9oXM3raNqwZez/d6DmBccC9ZVVvAvUv1z7NncSq1VAbViT+rC+QPgs7nQe9roesUHcWymtyecMU6JQlxaHlyq0ntudDlPJg6S53Iz/mzqqDc+ic4uEz9ZiLJVhT/VfVv7HkVnPU/0O2CkCBpQDkGthjXF7ML4+RwNSIYgJOboXoHeMpVBWbVdqjZo2Qk/G71O7E5UTIUQXUTNiVfkdMNsrtDp2w4ZtBx7tYZrljGKQFWgqG5JvQ36FX79bnBd1LdvCfUc3630i7zHoOqYqg9qERhA/XKpmtK2qSwsJApOlkXFRXJtWt1GawRgkG4914lF5AMhys3F777XaVA3pCv5Q/6KfhjAXUGQ/nd87uz/+f742dkBIIySO4judQHjFVg9cjvwb6f74ufQRnqbEkp2XhwI/NL5jNj6ww2HdpEtj2bam/1KcHbdq521AXqGNp5KNeffT1fHngx53ToiZ0AINQJJbuHqnJMVTLt+MkgfHEv7HgyOQ6XPRcGfhfGPR7eMarZBxVvw945KjoqbMqRiqgZJqBgMHQ6Fzqeo/p5FgxVzqUMqB6OjeUfhFN932KRf2gQR33LRFL+JUvh4FLYPx+ObzhdKRmuIMDZXlX8drtQfa68/ioKaM+FYO3pxuAN2mA2h/pc8YgAB/2h/2HgtLp+Q/RLBpRjmtfP+v2mCEKIdVLKorCvaWcrc1i1Cm66CY4fT4zTlZOjEvbfeAOmTTvztXX71nHRyxdR7TWmTP2lQV9iwTcXWG+kAUY8NcJwIn+WPYuyu8vont89PsZk0Mn6kOcQH5V+xDvb3mHRrkUEZABvwHsqh89ld2ETNnKduXi8HoQQp5xzp81JtiOboAwyrf80rj/7ei4fdDm92vVK5kdqnQw6fmdweBWsvAm8xxPjdNlzlBMx5Q0onGbsPcGAkoI4/DEc/VTJWHjK1DKzPUed7P0ewi9TCxUxdXVsLmrq6nx62cvV6UxRU5tdvbfBuZCBUE5ZfaOlvYPq/1byL+Of31HQSqUl0KkIJjwL7Ycr59KRryO8SaYlZ0sfmQxiyhTYuVM1N376adVLMR799hwOdbvtNvjrX5XEQFOWly833IDVLuxM7j3ZWiNNMKHnBMPOlsvuYkX5Cr4+4utxtir98Aa8fLznY+bunMt7xe9RUVWBy+7C7T2dAC8Q5LvyyXPmcfu427lt7G0M7DiQfdX7eHXDqzy19imO1hylzl93ylGfu3Muy8qW4Zd+uuV245qh1/DlIV9mar+pumFvoug6RUlobLhP5UI11raykoa+ggNug3P/ak6iwWaHjmORHcbi7XcntbVKj9DvOYqs2oHNvQO7ZxvOuu3Y/Uew+49iD57EIaux44H64wTqapAcoKFljw0/NuqxiegS1qWEIC6CuHAgEMKgc22kfdLYP0PHMSrqFq+8J10AYBk6spWh7N4NDz8Mb76plhm9xhvPR6RBffyGG+Chh2Dw4MhjL3vlMhbuXmhou+2y2vHKda9w7bBrYzcyCp5e8zQ///Dn1PqNJfP/4Nwf8OxXjPVmM00aRUaCwSCLdi/i2XXPsqJiBYdrDmMTNvzB5hVsDpsDKSV92/dlSOchdMuLLCtxtPYoJUdL2HViF0DE7QWDQTrldGJi74l875zvcc3Qa7DZkiwdmEbHL2rcu2HTw1DxZiiPzoLJxeYCbND3Bhj1kFrqC4PHo9IV9u9Xt4MH4ciR09F8r1ddYAqhou6/+IWq9WjfXuWS5ueri8PcXBWZdziUZqHDLnEID47gMez+YzgCxwjWN7TWOQ7ekyD9yKBfOZkNjaiDfvU/sIWWHEPOogg1osbmOhUpE9mdkM5OBBydCNg74qMjfpmF368qyuvrwV9VyaDjdyH2f9B6wUhOL6Uj1uvqUOTOr6JbVijeN/B6nIpNbknD770B4r6MKIS4AiUDZAeek1L+qcnrWcArwDjgKHCTlLKspW1qZ8sa9u2DRx+F118Ht/v0ZGQUp1M5WTk5MH26mrxaE8WUUlLwxwI8PmM6SrnOXDbfsVlV+SWBVRWruOr1qwxVJAL079Cf3T811u/RNCl+sq6qr2L+zvnct/g+dh3fZajBeKIQCHq3681vLvgNNwy/gc65xgRrrTUitY+fpdTsg+JHofx1lRQd8JqLdjXkQ9lzoN90OPsXSkm9BXbvhoEDz3zObldOk5Qqmh8IBU0GDYKSkvDb8fnURWjjf78Q6mLSZjude2o1DTY27Lvx/hv2a7ejqgoPr4KDi+HAR6oq1J6tjAz6muek5faFbudDu7Oh3TBVQJLbU1UDCqGWNc+QtxGNlkKdkZcf4xLZQjtbUW7cDuwALgMqgTXAdCnl1kZj7gRGSyl/JIS4GfiqlPKmlrarnS1Cv8YgzSpBCFWbnOoeH/rb8Frj9ze6u3GTnbffzWb+gixKd9upqRHk5MhTjegJ/amtVc8P7B/gisvqufGGOsaOCZx5HjnzgbqaCjVl3XpkBxNeutiws5XtyKbm1zUJVY9vTFV9FV3+0sVw5aTT5uTg/x6kY05H641JsZN1IBhg/f71zNs5j5nFM9l+dDsumwu3L7I2ll3YyXXmMrzrcIZ0HmLJUl+9v57SY6VsPryZ6vpqpJQEI5TI5zny8EkfAzsM5Lph13H1kKuZ2HsijkTks6TA8fv1r2HTJnXyBnVib3xyb3y7+27o2LG5GY0/RqT7px5LSY53I53cb9PBM58sfym2YA1BkaMGSEKrchKbrCVoy6HeMZCTuVdwrOBGalxjQYhmNjR+3Pj+xOu6c/JQHUfocsatmgJqyaGGXKrJp5p21JKDq4tqp2DLy8GWl4M9LwtHXhaO3CxsLgfC5cTusuPIsuN02XC6BE4XZOcoiYNTTphdPbbZ1McSQqhpTzRM1fLU/xopQ1OyPPW/DwYkdbUSn1fdvPWSoDdA0Osj6PUTrPfi83h55PWBtO/U5Lvqr1ERRU851JRDdYmqSKzdB3WHQrl0dcpxbcgjU/859VxWN6QjD+zZCEeuWp51hOQYHHkqR8zVIbRs2+CINczptkYJ78HTOWkNVYh+j3K2S59v+YvZGO1sRbXxScBDUsrLQ49/BSCl/GOjMQtCYz4RQjiAA0BX2cLOM97ZukHAO8k14STt2MEQqmiHFxcuvBRQzRB20IGTUW/3vyPg2zcKchp7cREIIBjceRiff3vu6VkLcXquAJo8aOG56On5dBEeXw3CwHZtgRo+vuhHDOk4AEKxHSmD4N5FsGIGADXBIH4JIrsQZ/dpBCVIAVKq/4nM7Uuww3CQkiDy1N8eA79myu4DpTPUpC9EqEjbduqxegRC2NSnEgKBDZfdiT3keDR8WhH6nwsE5ceqeHvbaj7Yv4TVR9fhDXrVFTmR8yzy7DkIIbih5yXc0vdyhve5BnLj0xdo18kd/HfHC7yx/Xm8gTpq/J6IETa7sGMTNmzCzvjC87l6wNf4Uo8p9MgqaDa2aaQDwGa3hb0I6NwFXOG62rQSmWmGZ4+58QawF/QkGDS2lBQ3X917UkkV+KrUMqPNpU7o7Yaok3osJKh7QzKQgLdHN9wlW/H4PHi8Hjw+D26v+9T9p9c8ze4Tu6n111Lrq8Ub8BKQARwyQFe7pIMN8myQb4M8oe7nidBjG7SzQXsbdLBBQejmEkpgxQbYxClVrdD8AUGpLuNDfqT6iyAA1GCnBgeXZNWTbSQnLbsQro+TdE6Sibez9TXgCinl90OPvwWcJ6W8q9GYzaExlaHHpaExRyJtN9OdreseFfRwNKRhRsYGnDfsm2S7CoBg6IQqTv0IBAKBPHXSbDrFCiFoOMaNTxoN2wEYXPUZZ7mt+1/7JCw1WrDkaseI64vpWdDTsv03Y+NDsPm3LQ7Z7YMSg6knB/zwLaMtzIbeDeP+ZnAw5k4khYVK72vjQ61+PjNMmPkj1gx8F2ytq4d36Ajn953EHePv4EuDvpSY6FGIoAyyvHw5/1r7LxbtXoSRucxus3Ow5yHjOzF7BW72+H34I0uPHYDYGcObQx/3lBPe6LEMnlZdAqFek6fnFBmaX6R6cOoliVDOcNONNr1/aueNX29uYuDnVdjymzvLMRNNZN1ib3XWtllc99/rLN1mqiEfzMyoFqSRsyWEuB24HaBv377jysvLY7ItlTH7f0/WElsDZu0NBuNjs+jZHWHiqlYWFiL3WXsVpfojmpub0+34id/+Fn5rrRMQLQ2rUFYhHjC+tcQcN2s/YdWvT5Lvyrdse2cQzcVAumA2YhaHzxfN+Tjt5pYM7vCglxE1Go1Go9Fo4khLzpYVNaJrgLOEEAOEEC7gZmB2kzGzgVtD978GLG7J0dJoNBqNRqPJFGJOsJBS+oUQdwELUNIPL0gptwghHgbWSilnA88DrwohSoBjKIdMo9FoNBqNJuOxJJtVSjkPmNfkuQca3a8DtOS2RqPRaDSaNkeS5ZY1Go1Go9FoMhvtbGk0Go1Go9HEEe1saTQajUaj0cQR7WxpNBqNRqPRxBHtbGk0Go1Go9HEEe1saTQajUaj0cQR7WxpNBqNRqPRxBHtbGk0Go1Go9HEEe1saTQajUaj0cQR7WxpNBqNRqPRxBHtbGk0Go1Go9HEEe1saTQajUaj0cQR7WxpNBqNRqPRxBHtbGk0Go1Go9HEkZicLSFEJyHER0KInaG/HcOMGSuE+EQIsUUIsVEIcVMs+9RoNBqNRqNJJ2KNbN0LLJJSngUsCj1uSg3wbSnlCOAK4HEhRIcY96vRaDQajUaTFsTqbF0LvBy6/zJwXdMBUsodUsqdofv7gENA1xj3q9FoNBqNRpMWxOpsFUop94fuHwAKWxoshJgAuIDSGPer0Wg0Go1GkxY4WhsghFgIdA/z0n2NH0gppRBCtrCdHsCrwK1SymCEMbcDt4ceuoUQ21uzL43pAhxJthFxRH++9CaTP18mfzbQny/d0Z8vfekX6QUhZUT/qFVCztA0KeX+kDO1VEo5NMy4dsBS4A9SyhlR7zCDEEKslVIWJduOeKE/X3qTyZ8vkz8b6M+X7ujPl5nEuow4G7g1dP9WYFbTAUIIF/Au8Ip2tDQajUaj0bQ1YnW2/gRcJoTYCVwaeowQokgI8VxozI3AVOA2IcQXodvYGPer0Wg0Go1Gkxa0mrPVElLKo8AlYZ5fC3w/dP8/wH9i2U+G8myyDYgz+vOlN5n8+TL5s4H+fOmO/nwZSEw5WxqNRqPRaDSaltHtejQajUaj0WjiiHa2koAQ4gohxHYhRIkQIpzqftoihHhBCHFICLE52bZYjRCijxBiiRBia6j91E+TbZOVCCGyhRCfCSE2hD7fb5NtUzwQQtiFEJ8LId5Pti1WI4QoE0JsCuXGrk22PVYjhOgghJghhNgmhCgWQkxKtk1WIIQY2iin+QshRJUQ4u5k22UlQoifheaVzUKIN4QQ2cm2KZHoZcQEI4SwAzuAy4BKYA0wXUq5NamGWYQQYirgRlWfjky2PVYSkjfpIaVcL4QoANYB12XQsRNAnpTSLYRwAiuBn0opVyfZNEsRQtwDFAHtpJRfTrY9ViKEKAOKpJQZqWMkhHgZWCGlfC5U6Z4rpTyRZLMsJXSO2AucJ6UsT7Y9ViCE6IWaT4ZLKWuFEG8B86SULyXXssShI1uJZwJQIqXcJaX0Am+i2h5lBFLK5cCxZNsRD6SU+6WU60P3q4FioFdyrbIOqXCHHjpDt4y6GhNC9AauBp5rbawmtRBCtEdVtj8PIKX0ZpqjFeISoDRTHK1GOIAcIYQDyAX2JdmehKKdrcTTC9jT6HElGXTCbisIIfoD5wCfJtkUSwktsX2B6mH6kZQyoz4f8DjwSyBsF4sMQAIfCiHWhTpyZBIDgMPAi6Fl4OeEEHnJNioO3Ay8kWwjrERKuRd4FKgA9gMnpZQfJteqxKKdLY3GJEKIfGAmcLeUsirZ9liJlDIgpRwL9AYmCCEyZilYCPFl4JCUcl2ybYkj50spzwWuBP4ntKyfKTiAc4GnpZTnAB4g03JeXcA1wNvJtsVKhBAdUSs4A4CeQJ4Q4pvJtSqxaGcr8ewF+jR63Dv0nCYNCOUyzQRek1K+k2x74kVoeWYJcEWSTbGSKcA1obymN4GLhRAZpQEYiiAgpTyE6twxIbkWWUolUNko2joD5XxlElcC66WUB5NtiMVcCuyWUh6WUvqAd4DJSbYpoWhnK/GsAc4SQgwIXcXcjGp7pElxQgnkzwPFUsrHkm2P1QghugohOoTu56CKOLYl1SgLkVL+SkrZW0rZH/W7WyylzJirayFEXqhwg9Dy2peAjKkKllIeAPYIIRr6714CZERxSiOmk2FLiCEqgIlCiNzQPHoJKue1zRCTgrzGPFJKvxDiLmABYAdekFJuSbJZliGEeAOYBnQRQlQCD0opn0+uVZYxBfgWsCmU1wTwaynlvOSZZCk9gJdD1VA24C0pZcbJI2QwhcC76lyGA3hdSvlBck2ynB8Dr4UuVHcB30myPZYRcpAvA36YbFusRkr5qRBiBrAe8AOf08aU5LX0g0aj0Wg0Gk0c0cuIGo1Go9FoNHFEO1sajUaj0Wg0cUQ7WxqNRqPRaDRxRDtbGo1Go9FoNHFEO1sajUaj0Wg0cUQ7WxqNRqPRaDRxRDtbGo1Go9FoNHFEO1sajUaj0Wg0ceT/A1rEv8WRy6s1AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_18\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_19\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_20\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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JyBmlJB1aCyOndISckUr3qLFMnWMFn0R0CD4fnHuu8R6tQ4odrynDw0giDcltf9H4+TXSEOb6R2IuztIlxrmqqrGKysZKBAKJZHv5dqSUQdf8oYwVRowWXyMs+mXS3P3z5qmE9oSVVBd9p1bMkRgHwgZ5J8KEZ+DM9XBuPpy1CSa+Cz3Pjk05/8BytVKKUx4jYspWKKFQPZe6MxtO+grG/FkJRh40tNrAnqq0jgZcBdNXQUa/yN7nLocNfzX+fAtzI/j2W1iyxHiDPl4W7FpAjbvZW9jgbWBJ4ZIw7zgEWHxVeI+2PRUmvgdTZqvuE85M/evI5lTnZ2Y/OO51mLYCUru2eQgvvqjyqgySXDz08DXAqrvir/xsTSSLcW+dCjcabWxFUk1YXwyb/2V8Dm6Mi+J1JeuCukLYhI3dlbuNOqikY3m2omXD32OrNDEAv7+5HDsxA3pg8W8iu9H3vRiOelapGjcJKjaRNRi6T1Oinuv/osJpkaz6pIRl1yXO0JISFv9af7yUPJi2TN3AYm255EiHrEFwxmr47qS2t1/3cHxJq1Hi9cJ118VW4dqkHG8W83cH6wi5fW5m75zNXSfcZd6gZlI0OyAToHNzSu0Kp8yCzIHqvIkGZ5bygp2xVlWZVazW3KyyEu68M7rfOz0dxo2D7Gw1F61fr7TUDlm2/Cdxc0xrNj2ZtAgJa/4vvmIbg1lbvDZIO89hc7C2ZC19O/RN4lEZh+XZigZvLWx8NGlerbffVnIPCWPLs20blqld4KSv4eiXVA9EZ3Zo2wphA1eO8uyMuEvdAHIiUDPfNxOqNhFxKXLOSJjwNJy9DS4oUp61E96HLpMje/+ud1S1n9Z49nQ4dQGkdY+/t6XNob6nKXPCb1e3F7Y+m9AbwTvvRK6LlJqqwo0LF6qqs7o6Zaxt2qQS63NzjTsut8/N9rLQktlDNq9DykCBjY6Vk9pVeUCzhkZvaDVhd6meoKf+oPomavDoo+q3i4QTT1SSEKWl8MUXaj769FNVqDN7tiqysR1qdxRPDax70HhZl0hwV8CGvyXnflKzA/LfSp6hp8Gyvcuob5GXW+epY01xHJpl7YxD7dJILjteS5qvXUqlyByPIGR0A/oDE0GYAVO7qFBF15OD85PC4cxUN5DTFkHukWHGl0osNhL3evYwOGMNnL4EBv0Wsgaqm1VGX+h1IUz+HM7fC91ObWO8u/XHO/6/kN4n0HLHIBxtfGdr7kuoV8vng7vvVrIibXHmmarV3fPPw3HHqR55drt6DB0KjzyiFgZ//KMxx7ameA1pGi1n6j317K0+lLQFAuz5COr2aL9mcyqPlqtj/D0thU1dcyfPDHmpvDwy/TSXC15+Gb76Cn72M+XZ6tCh+ZGaCqecomRC5s83rwG5KWx6PHqJBqNY/0jyPEsr7zI+VytOlu8Nlt/0+r0sLohehLm9YhlbkSL9AQXx5CSK//ijmhwTxr6Z4Q0dZwc49UdI6RJ9I1KbTYU5psxWhpIWxXMDXqY26HUunL4Mskeo6sDWDVmbxkrrrnoijviT9n7KV+l78bqdCt2nxtxfTpdwjWu9tbDr7YSuPN9/v+2keJsN/vpX1fsuO1s9tMjMVD04779feT/ibXy+uGAxHo2bg8vhOjTFTdfcp399HfkEZA4wtHm4lmH/5JNt53726aO6VFx8sfpN7WF6uGdlwYQJqrLxkBAy9ftg85PJ8Sz5PbAtsV7rg7jLVYFXOwohSinZWrY15PnVxdrh70MRy9iKlKLvoqtWcebAqPvgrM0qpHXOdjj6eUjvHdPwTz6ZQK8WqNw0b7X2a8KuDKX0XvHdEBzZKjSXorEU3vjPtr1aw/6oPE7OTLCFuQscHC8dRt0NJ7wX+tqOV7SLAIQDjnmxbS+U0ez+SH3PCeTxx9v2ar3zDtx0U+TGU2am6sO5dKnygMTK9/nfHwwxtJR/qGmsCVKVPySoyVf977TIHg4DfhVfg3YtWl2nUsILL4SXj8nJUYu8IUMi/71TUpRna9YsGD48juNNBMVzkufV2vtN8ioS8t+JbL5MIHur9+LXqP7eW70Xt6/9hDrjwTK2ImXzU5FVqwg7HPFXOL8QRtyhusCndlUr1f5XKuPr+P+q/KYIKS9XTYCjvTb79IEjj4SBA1WYJ2JqdioRUz1G3Kk+V9y5SzalS3Xsq8HPe6rURBiOblNhzIPR35QcmdDjDBh+Z/Nzfh/sfFN7pdf/F+DqFN0YRhDp+WYQe/fC2ja6Y/zhD6rbQGaUdmdaGvTrB6+/HvPhBYUTXA4XzoAH04+fuTsPMSX53R+AXjX7+CeNDVXrsHhxeMPaZoPPPlOGU1RzR4D0dGVwdegQ8yGaz9ZnEnqNBbHl3/qLWbPZ/FRypXw0WFuyFpfGwj3NkXawX+KhjmVsRULDfuXZagthhxM/Vlo3joxQQ8CeokJRvS9UOUsRGlwffRTefd+ayZPVjXPjRpW4unKlSmK99FKISLJk81PoVkhl9IeR9xjn6bGnqJyvlhR8FhoObEl6bzjhw9gThx2ZMPq+5sT5A0v0NbWG3BR5PppR1ORD1fro3uPMgd4/g0HXQb/LovagvvNO+OTmSZPg4YejN7SaSE9XhtrNN0f/3sqGSkpqg/u9pTiaQ9frStdprorbLTte1Q4fdToK8iaGDy8bxEsvhc/VevRRGD8+dm+kzQadOsGMGRHOOYnGUwV7ZxJ1H0B7mpoDnTmxj914AErmRf++9F7Q+XjIGhL72JUblABuNGQOhOF3wJGPw6h7obPxMeI1xWuo92ifkIdLw3lL+iES8v/bdkinydDqNqVtb4s9ReksnbYIvj1OxdDD8PzzkYUQMzPVavSYY5rd/ukBeyQrS+3ngQci6IW46z39XKGjngERxhCKhdbf17YXwqw4BUz+InZD6+CY6TDpE5gxUJXFa3m10ntDThJiIbvei/weIBww+Do44i/qPTaH+izCoSoZ1z4Y0Qr6+ef1b74ZGfDxx83nUqxkZsLf/qY8HtGwfO9y0pxpeBpVzpbb60Y4mu/gDuFgy4EtDOusk//XnuiIfghx0PVgi9NbHAFut8rP08vXGjtWyX/E+3unpiqJiCuuAOLwappCwQx1rURqo3c4QlU6dzpa5VvZnCqndOUdsPfL6Mbe84m6PolQ/LP7aUq7ML2n0nm0OQLaYPcoQdRoDMb8tyMvunHlKj3BAb9Sx2t3qbDriLug+HtYcbOqajSAljmZTaKmADXuGlYWreSy0ZcZMk4ysTxbkbDr7baTKI96NjJDq4kmg+uU2ejHFGDfvrbDOwBOJ3zzDRx/vH5+RVaWCinOnw9duujsqHaPvvGXOQC6nAR2g42tljTsD98cut/l6jjCeb4ixZaqJpMDy7WF/Xqdl5y8ih2vRtanLLUbnL0ZjvibkpJwZSsj0hn4d/D1cN5u5TEJw+bNUFCg//q998aXb9USlwv+85/o3rOoYBF1nubrr0tGlxBPVsueam3StW2hT9M4Mkc7z1HYoM+FCcmlmTkzvLfpxReN+70zM+GJJ4zZl6HseC3CtBCbKlg4bSF0nqjmbWem+jdnhMr/nPxldJ6uHa9EVmhlc8GJn6hFfNYg5VVzdVCe+ZTOcORjMH2lKv6JlJ2vR1Z0kz0cztmh+rs60pvPWZtD3eO6n6YkfHqeE/nYYVhVtOrg/512J46Ad1ciD5uKRMvYagtvnapUC0enY5QREG3+kD1FCX4Ovk53kw8+aFu7RgilfzN2bNuTpN2u8jC+/14nJFQ8Rz+MMfT3zX3/zGLPR4FVnwY2p3JlGxXWc6SpyaRsufbrnY+N34MWLTX5ULur7e3s6UqnK62X/vfhSFeT88kzlQyGDm++qe/lyMtTob94qwkPHpJDVaxFw5ydc/C2SGQenjecvjnNn6fGUxMieBqWoiJlREfzCEc0+3nidjWntCbvxMiPP05eekk1n9fi1FNh2DBj9bJcLpJr4LYe21MDpT9E9t5xj8Oga9S1pPWlODLUIvukLyIraHGXqy4VkXD8W8qo0buvODOVwXfKbDUftEXFetXKqS1Su8KUuaqASa8C2+ZU38nEt6Hj+Lb3GQav30tBVfNqzyZspLSocN9YujGu/bcXDLmkhBDThBCbhRDbhBAhcs5CiD5CiLlCiJVCiDVCiDOMGDchlMwPnwguHOqiiPWm7MyEsX/Xbavx0Udt6+A88ICq+Ir0huhyqYTlDz/UeLHwC+0Vnz1gmEQr8xAtO17TX/X1vzL+pPzWCDtU63Th7hhGB8ws9nwUztGpEDY48SPlGY2kGtSRpSZPp7ZOwwcf6Cu/33238UKV0YSnpJT8tO+noOeO7H4kI7sEi+Iu2LXAiEMzn7Ll2iHrLpMju2HGiZTKs63H3/8ee16eHhkZJNfALWrV/H3vV5EVIQy8BgZdHVlaSO44VW3eFgUzIht79APQY3rb9xWbU80DJ35ImxPH7vfblnuwpynjLSU3sgvfkQEnf6s0CGNk64GtQTmYXr8XW4tFfVVjFZUN7axRawzEPY0KIezAM8B0YARwqRBiRKvN7gXel1KOAy4BogwkJJHCz9VKSI9ht6pwTjzYXCoM2QqfD5aFiagB9OqlhCNjqRCbOBGmTm3xpJRQPFv7Db3Px/RG0NKv21YEgNH3K80sI7E59EPEYbxBplEwo23tnVH3Q5cTI9f9sjlUqOGEUOu6vh526qQQpaSo9lBGhZQOHk4Us05hdWFQ6XemK5PRXUYzoceEg6EGgPyKfBq8SWq5Eg0V67Sf73RUQhLjd++GRp1UoUGDlCDtYU/BJ23nMXaZpCpDI41WODKg7yUwpI0KkD2ftB2+7HU+DP9j5EVIjjTlGR37t/DbFX7RdgjxqGcDaRpRVMQ6s+Hkr2h7lajN2pK1QXIuAhGkJJ/mTGNdic51cwhhxJr1aGCblHKHlNINvAuc22obCTQtq3OAQ0fyufBzdI0MR5aqaos3rGVPUe7i3LFBT69e3XbZ9WOPxVaaDcpAe/bZFje/6m36Hdt7nqvrGTGMqk36YcqO4+OrANLD16jv/reZ7MVrjfTrhzSbSO0amIijDVmnQt7xIa2LVq5UhrcW06cnsOG5DksLlx6UeQAVYhjWeRjDOw8Palqb5kxjddEhIICopxqf03p9ag6LFunPF1dcEV3V8yFLW5WAwgbHvRF9tMKRoQyeVL2EWGD/j+H3YUuBY16I/vp2ZipDL6O/9us+N1S2YbBkD4M+F0Uv3mxzQHpf6PPz6N4XYFXRqqAG8z2yegSFEb1+72FRkWiEsdUTaDmDFASea8kDwC+EEAXAV8BNWjsSQlwrhFguhFheGmmDNjOp3QMNYY5j0G+irhzWxZYCI4PVzefODd+3bNw4OOusQE5EjHTtCldeGfijaqN+vlS3KbEPEinhtL3MCCECypDWWZHFmp/2cTd4W0T4aHF3q9zQ9phj/hz7cTkyVEVVCxYu1Pd0XHONvkJ8ovhx949Uu5u9EHWeOmVs5Q0PSpJ3+9ztX0le+rWFcwHSeiTkEObO1c/Xuvpq5c08rGkobbvfa7/LVaukWBA2GPWA9mu1e8JHSQCG/C72ilSbA8b9Xfu18p/a3u+RT8ReeOTMVO/Xu3+EYXHB4oPVhwD9c/vTM6vZhKjz1LFsbxshnkOARCXIXwq8JqXsBZwBvClE6B1DSvmClHKClHJCXl5egg4tDEXfhqkOEkoY02lQ5rDNAT3OCnrqiy/CKzw/80z8IZ6sLPjnPwOTbG2+9s0ga5BJhk4rSr7XEdsTagI0ogKxNbYU/VLoWHuHNRRHsXELr2npAn29L1Cr1n6Xx/dbZPSDnmcf/PO777SNrexs1e8u2czNnxs0EWe6MslJzWFg7sCgsGGDt4HZO3VC4O2FsF5U84VMQRlbWhxzjPG5Wu2S0h/DXz82J4x7NPZ0BXsqDLhSW+eu9Mfwc5gjU6UIxBopsTmhx5mQMzL0tZL5+oY+qCKvLifGF8p2Zqs8tyhZXxqsKTgybySDOg4Kem55YRse/0MAI5IECoGWZ1avwHMt+Q0wDUBKuUgIkQp0Bkpoz+z5RF9pt+vJJlSqNd9UfD5YEsbRM3w4HHGEMcnLDgdceCFQtUX7guwyOTESCHoVQnknmJfPYnOoG6BW4mhDsRISTBR7vw4vMTLu0fg1zpxZMP5fB7WBluo4g848UyXNG52vFQ0+v48NpRuCnhuYOxBQ5eFdM7pSWN081SwpjEL+ISmY5EWN0Liva8xg505tz8r06cn9rRNGyffhvUsDrwF7nAto4VBdRBb9Mvj54tnhc8WG/zH+ec7mUhXbc08Pfn7vV+GNrfH/Usnx8eDMhLF/UdIWEVLjrqGsvrlCMtWRytDOQ0l1pvL1tq8PLrS2lm1FSololwq5kWGEZ2sZMFgI0V8I4UIlwM9otc1uYAqAEGI4kAq0gzhhG5T9pP/akJvAbvBSsIXxtnZt+FysG26IPVerNdnZcNttqJwpzQ2GmS+B4K2FWh1l425T458IwqGntq6XzGwWpWHyOVK7QM8zjdE4S+kEXU+hsFC/0vWUU5IfQty0fxPOVp93bLexB//fWsS0rL4saOJud9hcYbyoMfboi8KLumb3KN1K0FNPVVp9hz37vkU3B1fYYMxDBuTgupRmWlqrbJqiMJ5XWwoMuy3+npg2h1qcZreodJB+pSWoR9YQ6DDaGFkfYYfe50W8+fqS9UG5ly67iwG5AxjScQhpzuA5v+XC6lAk7m9XSukFbgRmAhtRVYfrhRAPCSGaFM9uA64RQqwG3gGukjJZXTgjxNcAjTr2oM0JPU43via+BfPm6edrORwqmTWeXK3WDBsG1IZpjmu2vlbFOn2DrttUc0KITXQYo/38/kWJa1Rbu0tbWLWJ/r8MH2KMBnsmDL6BjRv1c3QmT9Z+PpEsLVxKy2ki1ZHKuG7jDv49vvv4oCqmNEcay/e243BDuHwWj/ml7XvKems6qIVQGn2HPd56fZkXgK5TjJtnJND/iua/3ZVQH8ZY6Hk2hlV7CwcMuLr578oN4VVsB10bU66VJs5spccYIWtL1gZp6EkpGZA7gAG5A4IKY1x2F2uLD+0keUPuoFLKr6SUQ6SUA6WUfwk8d5+Uckbg/xuklBOllEdIKcdKKb81YlxTqdqk703pfJyq7jCRL7/Uz9c69VTwG6zCYLMB9UXaL2YOMHYwLer26Bcb5B5h7tidj9UOzxXNCm8AGUnJD+EnvME3GuddtNmgxzQKC7WrDdPSoG8SVC9aM2/XPGo9zWF8l90V5M0a1WUUGa5mT0Ctp7Z9q00LobyKWoQzAgyisKynZn7e8OHJrzpNCGXLwlfaDb7euJ6vjjS1vyb2Lw7vnR96k3HV3nYXDPp1899hc0EFDPxVZHp9kdKqqj4cy/cuD+oOUeepo1+HfgzIHRBkhNV56lhTvMa4Y0wCloK8HhXr0L379zjT9LDa+jB9iH/zG5XYbiSpKVI/XygRlVL1e3WS8wdjXMmnDh1Ga0/C+xfp5+wZTdEs/XyO7OGQanDBiN+jG0YcP75tId1E8MPu4Bw+t8/N8LzmXpXD84YHiR96/V5m72jnSfJ6TYTDpSwYxM6S/pritccc006bRRtNyQLl3dLCngbdpxnrwXd1aDH29/q5Yq6OqueikbRcuO2dqT+35000zqvVRBQe+GWFwVWGWSlZpDpS6Z3TO6gAxuP3sLiwHS+kIsAytvQoX6V/o+11nqlhLY8HSnRKB4SAadNMiGBKL7rJu/HmEURC7S5tY6vT0caFz/TIm6ivL7b95bZFRluj0w0gLAfCJHf3vcT4CdGZxfbt2h6NceOMDVHHQr2nnl2VoW2Lumc294Eb1nkY9Z7gm+fKopW06wwFva4EZcvA03bD8HjYVjxI8/mBA+NvOn1IUDQLpE5uRvdpsVcf69FSp694DqDjPux1jvFjt0zyrwjjEer3S+M7F0R4v5BSsqVsS9BzvbJVQZLD5qBjWrD8xiGhoxcGy9jS48ASND0qKZ0hI/bWBJGwc6d+ZdDo0Sa5/P0e/bJ0s/O1QD+MkjnIfGPP1UFVl2qx5Wn9pGY9LiiCy2ToIxx6xQEA/S83pU2SnnJ8//76QqeJYmXRyqDEWYC+OX2DqpEyXZlkuoLDPl6/l92VYb7LZNPhCO2bW+EX5uYlArsPaBeCDBpkavpp+6Fqi/5rA65UjdyNpGVormZHmLF/ZUJnjMBcLv1QH6bLfJ8LEtK5QIvi2mI8vmAjc0inZs9vn5zg++yeqj0h2x9KJOdbPhSo1KnM63qyytcyUXdq0yb9yW/yZOOqEIMI5z1KQFm6LlkDImvwGi+Dr1PVgK1DeQ3FsO5hGHmPcQ2wW9N4QL9nmbMDpOlUS8ZJgc4cPHCgKcNFxZKCJTR6g72No7uMDtlucMfBLN3brF/htDlZWriUvh3aQdKZFjkjtc/nxlKVutApyi7dUVBU2V3z+X79TBuy/eD36hc8IcwVbfY1qAbUmkM7jA8htqR+b8ArrmGkpPdJSD9OPdYWryXFkUJjIKogEIzKG3Xw9WGdh7FiX3PT7lRHKlvLtjIiLzHdFozmf2E9Ez2eKv3qoI7jTfe0bN6snzNz9tkmufxtTn2DK1rPThPxGlqg337CaLpPR7caaNPjarI0uiqhieqt+smzeRPDa2/FgV6ouo+5jtuImJM/5+AkDCqscGT30BDc2O5jg/6udlfzwx4dvbb2QKej9F/b/mLbCuOtiSJkXV6bq/l8j8SI1yeX2l363uHsoebqCNbs0Ddqcse23a8wHqq26Avmdjmx7cbUJrKmeA0NnuYUjQxXBgM7Nq/0RuaNxNEifUIgDumKRMvY0qJqi37VSqdjwqjKG8OKFdqyD0LAcceZNKjNia6xYeZk0BYJamOC3QUDr9U2evxu+H66aUYPVVv0DdquJxlXIdUKvbYt3eLsq24ErRNn053pmivasV3HktbiWpVI5uW30fsumdgcKudTKz9y5xvRX2sRhqylBJ9f2yXeSadA8rCieqt+3mPeCeZWCFRv1d9/3sT4hYrDjr1F/5zqdqr5/W7DsKRwCe4Wx2YXdgbkNle+D8gdEKS1VeOuYVXRqkQeoqFYxpYWDUXoJovnDNd+3kD0KhFHjDBxASZs+pNRuP6QZmN0FV44xjygvwqsXA/zzzOnOrFqk/5+u08zJafC79d31OVqO0ASxv66/ZQ3BIdd/H5/UCViE8PzhuNqVba+cf/GoLLxdkf/X2ob0L4GWHWnKYnyHp8Tm9A26JNdDJEQqrfqF8F0O9W0Bc3BsfWqIHtMj77xczRUrNOXr+ma3H5cK4tWBv3t9rlDjK2WOZoSeUhXJFrGlhYNpdruVXt67A1KoyA/X/v5444zuUQ7RcewqQ2tCksYZirHt8aZrVpd6LXrKJ4N358B7jJjddbKV6FZjGFPhWwdqYA48Xr18wLtCUiRC8eywmWkOoJzIht8DUETcRPDOg8LCjcCpNhTQtr8tCu6noSuF3nHq+A+YHgFrtfnwCa09/k/kRxfuQ78OlXFXSaZO3b5Kv0qyE7Hmju2XiViSifVlSJJ+Pw+dlUE31c8fg/dMpvd6v1z+4fkba4vCaOJ1M75X7jMoqexVHsVlDMCvCaFkgIcOICmFg7AmDEmN4vVq7Ks2mzioG2QiErIlgy4Kny1acl8+GIkVKwyzgOh1yap41H6K+I48Xj0b7KmFGBEwaKCRdS6gz193TK74dDw8HXN6BqkIg/gkz6WFuo0fWwP2JzQ74pgaYAmpA/mnWe4mK5f2kBou8X/J4ytCp1cn9SuwXpYZlCpY/hnDjS/ElCvyrvzxOglbQxke/n2EI90l4wuQbp5ndJC49vlDeVUN5orkWIW/wuXWfTU79X2bOWMNP3mv327vuyD6S01sgZrP1+1Sd8FbzaJqEQMGs8GJ7wXvkqnoQhmHgM/XgLla1RSs55R5HOrgou6vfr702vj0XGcflgzTtqzFNWcnXPwtcphG95ZO3wvhKB/h+AiijpPXfvO2wIVstY7tytWw9LrDA1ZO2xepNSeu/4n1OP1pBc6H2/+3FaTrzP2sbEXH0WC36NfgZkz0vx+t2FYW7w2yLACQq5jIQTds4IraNOd6awvPTS9W5b0gxZ1OjXx6T1ND2uVluqHCgfr2EKGkT1c3QBaTwAHlqpVkAlaT20i/bEZuFHJTtjgshafucNoOO4NWPTL8B6GvV+pR+5YFYrodpqaxOypKim1dhfs+0ZJSpQu0N+P3k03Z4xp+RwOh77B5fMlL5QopQzJ5QDokdlDt+9hj+webNgf7D34Pv97Mw7POFK7wNBbYPMT2udY/lvgyoWxjxhyU3TYvfj92teR35/80LGp+D3QuF/7tZzh5hod3rownSFGmJsrVpOv5iKvRoVrx3Gm67qFY1XRqhDvtVZO5sDcgeRX5B/82+v3srZ4Lcf2Mjn8agKWsaWFXo/AtJ6mu30PHNBeabpc0LmzqUNDZn/l0Wk9ORxYbKquWFhiNbaikp3QyGXpcyHU5sOa+9quQixfpR6bn4pizBbYXNo33A6jQp8zCKdTP0E+mTffBbsXBPVKa+L9De/z6eZPNd/TOmcLoKC6gJKaErpkJi8vpU1G3glb/61v0G95WlWTnfCBWuTFMffYbT6kTiCjthY6dIh51+2fmp3q+9MyOnLHmmt01GwPjK1hcOWONTdSUl+ov5DLTq5W1ZLCJfhbzLsuu4thnYaFbDcibwSzdza34Krz1LFi3wqu4ZqEHKeRWGFELRp1BIjSzRGXbIleztagQVBnbrpYoOG0hlvN74Fy83u3aZJEHRiG36bETE1P0tdxMZnYANxm0/ds1UQp9WQkH234SPP5em89lY2Vmo+WPdRa8v6G98081PhxZsOYv+gXZADsmwlfj1P/euvAH0HYSSOXUAhIc2lPIMUGyOG1a+r3hTE6TK4ubyjWN6hMKn45iLsM3bnF5C4obbGuZF3Q36mO1CCNrSaGdhoaUizTWhbmUMEytrRoLNN+Pi2GnndRUlqqbWwNNVl3DwgI7OkkTe75NDE9Alvj1vktEsWoP8HkGeDMMb4/IagbrlblmS1FhZFMQgh9cdxk3nzn5M8xbF/fbPvGsH2ZxpAbIO/48Ll5Ndth3lkwayIUfgbuCuWlcVcqw8pdqfICvfVQPA9+vFhzN3lZ2vk7e/YY8DnaM8k0OhrL9CtL03uZP7aWBIor17Rc0Eio99RTWhd6LmpVGw/IHUBKq/SVLWVb2nf/Ux0MuXsIIaYB/wLswEtSykc0trkIeAB11q+WUl5mxNim4K3Sfj7F7Dge7NXJo+7bVz9x3jDsKZA7Trsp8u73VVJvNFygE459Owr9ivqixAmb6tFtKpy5Dr4/S1X3+AxIXLanq35oI+5S2kqtyRqowpc280QHO3eG3RptBAsKYFioRz8hbD2w1bB9LSkM09y7vSCEKsj4cpTywOgZBaBC1QsuVP/PGqJyC+1pqoF7zU4oXxk24bpH7l52HwhtY7QjTNu+wwK3jtHhzDY/PcJdpu2dT+1K2N/aqLH9Gsn/WYOTl4MLbCjdQJojjWp3swe2zlMXkiAPythqXSzj9Xspri0Okok4FIjbsyWEsAPPANOBEcClQogRrbYZDNwNTJRSjgRuiXdcU9HrwG6ip6GJIh37pEsXSEnEtdHrPO1VT20+7E+CoFxdO2kqnN4Lpi2Ho59TPcViTWx1ZKh+h6Pvh7O3Kk03LYXnrMGmuzL12rQk6+Zb567TzL+KlQN1Bwzbl6m4cmHK3OjOqeotsOcjlUi/+wMoW95mZVu/PO3O49u3a3esOGxoLNO/xkySVjmIu0y72jFrsPlVkPXF2udE1pDES+q0YG3JWvytvH2pjlSyUkKbcfft0Jd6T/BvlGJPOSTb9hjxjR8NbJNS7pBSuoF3gXNbbXMN8IyUshxASqmTFNUOCCcmaHJPRFBhRC0S1r+s21Rt/R+ADX9X4YpEoqcTkwxsDuj/Czh3Jxz/FnQ4QiXXhg0x2sCRrQzY9N4w7lG4YB+MuEN5thr2ab8to5/pK8++Or2ad+xIzs338y2fG7o/iQzJDWm3ZA+BqfPA1cmccDUwuNtWzUrn9etVkvxhS2OJtncpa7DJKtEEiq007ilZg0z7nQ+iN7ekdU9ewROwYu8Kaj3BJ1zPrJ6a26Y6UslOCfbu13vrWVty6BlbRvzaPYGWUf8C4JhW2wwBEEL8iAo1PiClbJ8JFX4vygbVMrrMXw2U6aQoJaxfXe44/RXyvm+VsZXIflq1u9Tq08yWFr8DLo9x0k0BBlXCUGAkkAE4AS/QAGzxw6Yq2AJU7wkM9rvm938zVWe/nUzPqxg0SPv5jRtVMUZOjqnDhzBj8wzD9/n++vcZ1cW8qk5D6TgOzlwLs6dC7U7DhU17d60iLS200GbxYkhLYKOGhKNbXd4DbCYbHXpV0aldzQ/jNej4NFLykir7oCU4PKijzmQE9MruFdS+y+1zs7jg0GvbkyhfogMYDJwEXAq8KITo0HojIcS1QojlQojlpXouHrORXn0XawJcr5WV2s+bLvvQhM0epn2FhGU3KBFPM9CqGCpfaX4jbJ3vPCIagfXAx8CfgbuA24A7gfuB/wIrAD3R4yXfaT+f1t308613b+2b7JIlCQpZt+KH3T8Yvs/vduh8v+2VtO4qXN39NOM86Y4MyBlJr3Mew6lxjz1wQD0OW3SNjs6qAX1Sxu5ivsGjpy2Wntwc2E0HQjtmjMwbqbv9kE6hVZtaWnztHSM8W4VAS02EXoHnWlIALJFSeoCdQogtKOMrqIZTSvkC8ALAhAkTklNuIH3oNqFOgLGlV3bf0fyWjM0M+wOU/qCtS1M4Q+WHdD7e+IlKSwun7KekKh2bjt79NMX8yteePZXeVn0rB0pxMVRVJaAgoxWF1TpK+nFwKIYbcKTBiZ9A0SxY8Xuo2xObmrw9Q107Y/8O/X/JoDC5WT/8ABddFN9ht1v0jI607trPG4lbx4pNyNjl2s+nJE97blvZNqoaQ1NRHlv0GE8ueVLzPT4NuZNtZdtwe924HIdOF3UjjK1lwGAhRH+UkXUJ0LrS8FOUR+tVIURnVFixfdbACBuGV4lEoWbu8WiPndCQTrepKlSoZWwBLLwcztpkvLGl5dny1atQYpa+m/mQRs+OTDXfldm/v76w6ZIlcPbZph/CQfZW7Q2pOjKCGncNfr8f26HWAFAI5d06c4Na4Ky8HWp3q9CyniI5BDS7pDKyRj8AA685eJ0OGqSfojRrFpxxhsm9V5OFntFhhDRNm2NX6IydZ/7Yevm1Caiq10MvVcAnffii7Bs1J38O0wZNM+KwEkLcM5CU0gvcCMwENgLvSynXCyEeEkKcE9hsJnBACLEBmAvcLqVsn45r4UDX2Ir1ZhChoeX36+cNZZifm9+MsMGwP+r3B6zfC/POMbR3G6BfnVMyP/p9JWIiNQI975HLfFfmsGHg1dGMnTUrASK6LTBTgNRI7a646dZNWTyRPmw26H0enLMVrm+EJ6phHlAK1KDC2LVAObAceK4Wfl8HP9sPQ28ER8rBfYnSEsaP1z6szz5LfgNy00im0ZHMsfXm5wRU1evxwy7jUgUOtRQBQy4vKeVXwFetnruvxf8lcGvg0b4Rdv2KROk3tTGyz29HCL9mw9iEt08Z+CtY8yf910u+V42YJ75nXJjPnqotqlo8B/r8XFXvRYoRGl+JQM856Opg+tB2O4waBcs1Wg5++SU8EqKWZx57KrWVNbNcWSHNaPXYXbk7RE3eho3N+zczdYBOIUKiiUcxtgJYHHjEQteunPawZNGi0HBiaSksXQqT9NI1D2X0PPSJMDr0DB5nB/PHljox40QWOLXipyLjOpEsKTgEdPRacLiuZWJH2NH1bPndpiY1+vx2bMKPT8PYMrtCOQRXB+h9Eex6R/+iLfwCFv4CjntdVdZEUj0XTlqj4wQo1litFH4JxySvesZU9H5XRxSGZRxMmQI//RQaTtyxA3buhJH6eauGoiXR4LK7uG/yffzx+D9GtI+bvr6Jfy/9d9BzfvyHZt6WSUycqIoitHK3nn0Wxo6F7OTdi81Br8DGmYDcDL12Y2YbPEmWMNJjX42OHEUMrC9db9i+EsEhlsiQAIRA92txx1O21jZevwMhtC+SpDQGHvuXto3Lgk/gi2FQ8oNqHaKXBATq9Zrt+q93m6JtsHkqoPTHiA75kCJcemCCSrNPOEE/T+e55xKnv7R8X6h7LdWRyvDOkfeuG9NlDOnOUC/r/F0xhKEPU446KrQgoonDMpQoJboXmdPkBLVkGjxhC72S02Xe6/fi9hlXWV7ZaO792GgsY0sLPSVnt05Vi0FIKXQdHUkhvReMuFM/d6uJ+r0wZ4pqJbLrLZWn4Klp7tnmq4e9X8PSa+GLMN3mOx+r3/R5+4vgNklQNcF6UgcJZ2wlSOH5mGOgQafl5fvvJ8bI31e9j1p3qFXn8/sYnhe5sTU8bzhODSN1W9k2Qyf5Q5mMDH0x2/p6eP11/fPhkCSs0WGyZZlMg0f6wkgYJecu8+NuYxfMfulnf52592QjsYwtLfTyZfQ0UwzCbvMhpfaFEM5hZCoj7og8mbNoFiy6Ej7Mha+PgO8mwTcT4MOO8P0ZsOtdfbc6qDCiVycru+Bz87w9/0GtgKN5hCPSfdTXgl1vwk/MhJiXB7k6qSslJbBgAURZJBQ1y/YuI8URKuzl9rnpm6NjGWgwvPNw6jXar6Q6Ug/J9h5m8bOfgUsn4v/gg8bPNXpyNgkhmVI+yTR42qFn69vt3xq+z6+2ftX2Ru0Ey9jSIqWT9vP1Ol2iDcJu8+HXyNcC09vk6WNPhRM+0Pc4aSH9ULMDKtZA9VbV9DQSnJmQrt22AV+dyh87nDwUSdZ0a2LqVP25/667jPd0VLdSLli4ZyHVjaFyBj2zemK3RX5j6JTeCZeGHInX79VUrf5f5aqr9D2WxcXw9NPGVaJ6vbBihTH7iolwBo/ZC5qkerb8+mPHysfdVIFRRI/Qz7eoYJGxxwN8n/+94fs0i8MtQm8MerIBtQWBikRzboRtebaSkrcF0PloGH47bHwMfCYn8fQ8B7b+R9sDtu4h6HupueMnFEEyNd3ABpf5+PWvYcaMUCMIVPL8woVw8snG5fPU1EBWi/z/OTvnIDW+hxFdwoScdRjQYQBrStYEPVfvrWfOzjlcf9T1Ue/vcGToUOjTBzZv1n79oYfg17+GdAOKjN1uuPZa/bFMJ6meLZMMnoivb4OJatxQ96gZfUqX7V3W9kbtBMuzpUV6L+3nG/aZ2qndbvMhdX4Sd7IdOqMfUE2Y28rfipcBV+r3DKvdFfBuHSZJJeEqX8Ml14Yjhglx8uTwhvwNNxh3/tXUqP0dPALp15yEbdgY311HFCoMo7uO1nz+UOylZibXX69vTNXVwTnnxO/dqq2F3/8etmyJbz/xYdKCJhLvzgfZ+pXcsWKEoWWCeHAklNXrNP6Ng53lOw3fp1lYxpYW6b3QXJFUbwG/ecaWEGAT2hdClUm54REjBBz1H+V5MsPgavIm5o4LX5K96q7weV+HEsKuHx+O1diKAbsdLr9c3+DauhX+9rf4c2/q62HePPjkkxb7PrAVm4aHIcOVEbZfmh7ju4/XDCUW1xZT2XBoVS+ZyaWXhs/FW7wYbr459mrU2lp4+2146aXY3m8YSV/Q6JAkg0eNnficlKqGKlM6RNR6ElQubQCWsaVFaheVq9SainWRaUnFQZpLuy67XKfjREIRNjj+Tegy2TiDy5YKmQObRUiFgAG/0v+eG0th6W+NV69PBsKOlrtdkdiKiKuuCt8L8eGHVbK8nmxAW/h8Kh/okkuCn19auBShkzAWTSViy/ekOULzC9Od6azYl8zkoQBdk9jZoMXYXbrA0UeH3/zll+GVV6I3smtrYdUq+N3voj9Ew2knC5rQsZOVhEtSFqtfb//atH1vLN1o2r6NxDK2tEjJA6FR+WZygjxAdpq2C+tAe2luZHPA5Bkw8u5A0nwcOQn2dKUMP63VTbDfL8InkOa/DTvfOvQNrnA5IwkOlY4f37aY5cUXqwpFvWbG4airg9NOC71xz989nxp36N28zlPH4I6Dox5nWOdhePyhB1jnqWsfitNFRcmpfJVSjd2CO+9suxfizTfDbbep3y+SKsXaWuXNOukk/VZQCSXsgiaZBk8SPVt6ivomsmi3dnK8XdjJScmJ6KHlAQeYmz/XzEM3DCtBXouUPP3yrOptkDvGtKFzM8rZV9Ej5PkSc1UnosPmgFH3Qu8L4IeLoWZndInztlTVemfi26rpdWtyhkFaj/ACqCtugk5HQ85wbS/kocBBAV2Nm4GnOqFtNYRQicx//7t+9WF1tRJB/eYb1cQ6kgRqt1uFwM84Q4UjW7Ng1wLN93VK70SaM4oK2AB9cvrg8YUaWx6/h9k7Z3P3iXdHvc/DlTPOgN69YWMbjoEXXoBly5TuWrduygPasljC71dGtMcDV18Nn35q6mFHR1sLmgSJB4fgrdXPTTUbd2XCm1FrFcAAnDXkLK6bcF1E+7h/7v0s3RtcVey0OXF7k53QHBmWsaVF5gD9Fg/lK6I3tlK7Rhzn75Sp7cLaZ1yXA+PIGQHTV8HW52Dbs8oQFXYl09AaW4p6LaUj9Pul8oyF63U4+HpYc5/2vgD8Hph9Mpz4sTK6IlGD1tBgSjqOdO2VprtCXwbDJG6+GR59NPw2BQXKC/bCC3DBBcrgsuncz2pqYP16OPts1XuvNY3eRnaU79B875BOQ6I8eoVN2Oid01tzv+0ijNiOEAIef1zpbrWVm7VyJQweDOPGKaP8tNOUV6y+Xkk7PPsszJmTRD1APcIuaKqi67dqJJ4qNRcmA3fic1K0qgbTHGmcNvA0pg2aFtE+vs//nmV7lwUZbh6/J8QAa69YxpYW6b303fcHlkOfi6NrvhxFU+Qu2dpG2b59Ku8lafIPetjsMPQGTl78ISMzh/DvMadC/rvKuPTVKi+WKxd6nQu9L1Req0gY9FtY+0D4bTyVMGcqjHkYht2iVqlaK1W/XxltxbOj/XTm48zWMbYSHzfu2FFVCj79dHhtrcZGuPJKePNNVW02daraXgh12aSkwJo18NRT8N57+onYq4tXk+ZMw9MY6ok6stuRMX+OEXkjNI2tBm8DhVWF9MxOrBHbnjn9dBg9GpYsiSyNaOVKVcl4SGFP0/a8eyqBJJ0LnorkjAsq7zXBbNq/KeQ5l93FgNwBEe9jcMfBpDvTQ5Lif9pnXHNrM7GMLS2EgKyBUKnR6LL0B1Pj7d1ztV1YxcVqFdlWjkUyqHXXMj9/PkudqTx1wYfYBhswGzszYfgfYcPfVbsfXSSsuQd2vweDfwf9Lg/8PoE7hz0F9s6ELf9up8ZWB+1cwIbET4ig8nj+85/Itv3uO/XIzVVtf7KzlYdkzRrYs6ft9y8pWKLZRifDmcGYrrGH6id0n8DXW78OqX5y2V0sLVzK+dnnx7zvww0h4MUXVbJ8rMUP7R5nlrax1Wi8FEHENCaxzYzJnVBaU15frpmX6ZO+qIytAbkDcNhCTZadFTvxS79uTld7wZCjE0JME0JsFkJsE0LcFWa7C4UQUggxwYhxTSV3nPbzFWvMNbY6FGmGZbZsaScJpxrM2DwDP37qPHW6OTgxMewPkedUVKyBZdfBR52Vt2vBhTB3GnzcHRac3z4NLdAPJdQnJ27cqRPcd5/qoRcp5eUqj+v99+HLLyMztEAltjZ4Q11odps9pkrEJkZ2GanZkLrGXcPCPQtj3u/hyqhRKtfKCBHTdomelEwSPDwHSdJiCkhIoVdL1pas1cy/rPfUR9WOq39uf83iF6fNeUjobcVtbAkh7MAzwHRgBHCpECJE+lkIkQX8HmgHJUER0OkoFQLTomiWecNmHiBFI29y06bwpfnJ5Lnlzx38/8srXzZux85sFSJ0RHHn9zdC2XIongMHlgRCBe2YlC7azzcUJa1i6fe/b7sy0QiWFGpPBQ2eBoZ1jjDcrIHee/3Sf8hULiWaxx5TRpdez8RDGpfOgiZZSuyQtMUUoLxqJopzt2Zt8VpND3Z2SrZmT1Q9emX3otEbetx2m521Je2/96kRnq2jgW1Syh1SSjfwLnCuxnZ/Bv4OHBry3zmj9KtFCj4Ftzkqo50yD+DUcOaUlrbD5FNgf91+lhQuQQQkID7a+JHmBREzg68Dp06n5MMBvdZQjfuTppSfkgIffABp0RcDRkx5fTmltdqr+xRHCh3TYk8eHtxxMHUe7cKK9aXr8fmTWHbfTnE6lVdSryn5IU2qTuVd/b7kaW01lqgin2RQtyehxtbSvUs1Pdi9c3pHtR+HzUGn9NC+xbXuWlYXrY75+BKFEcZWT6Bl4KCAVlmHQogjgd5Syi8NGC8xdBilf7PbNws0VKqNoFPmAV3ViV27TBkyLt5b/15Qs2C7sPPNtm+MG8DmhBM/iq4R9qFEWnc0tcpqtidvMgYmTlQyENGEE6Nh+d7lutIO0eRxaJHiSKFzuvYN1mFzsPlAshr1tW86d4ZvvzU+nJj08KTegsZ9IKFGRxANxYkZW0svsmoLidQYW7FXuwp4aKehUe9LK+zok75Doh2X6RllQggb8DhwWwTbXiuEWC6EWF6qVSueSFK7hFcxrzEnRtyzY6Fu9dY64/t4xs3zy58P8iJUu6t5YcULxg7S+WgY95j5fRmTQUpHJYvRmqotydMACnDjjUq2wWgPl92uehXqeZ+O6HZE3GMM7aw9kUspWVp4aJSKJ4MxY+Ctt4z7zdPT4Y9/NGZfMZPWXfv5mnxT2681o3Gbrd6WGCV3rXtYzbboqunjQErJ9vJQvUQbNkbkRd9oXi9F4H8ljFgItPQH9go810QWMAr4XgiRDxwLzNBKkpdSviClnCClnJCXl2fAocVJVhgF6y1Pg8d4Jd7+eTt1y+5/+qkdNKRuQX5FPlvLQpUqZ++cTVWjwWHWwdcF+jKa4OHSW/kmAldHbaOqbo/praHaQgh49VXo29dYyZHMTJizcw5ef+jNJsWewrhuOsUpUTCh+4SDoe2W1Hpqmb9rftz7P5w5/3z44QclYKqVPxoJNpv6nd96Cx580NjjixpXJ+1rqXpr+E4VRqGVjlK9NTGipjaNz+etTZiK/O7K3ZrXYbornUEdB0W9v5F5IzUrEotrizVDle0JI4ytZcBgIUR/IYQLuASY0fSilLJSStlZStlPStkPWAycI6VcbsDY5tLzTG03LKiWMVoncpw4HV7d9mmrV7ev8uw3V7+J1BDncdqdfLzxY2MHEwKOfUVpoBk5QTqz9XXQEoGro47KtUxuEm2A1FSYPRuGDYvf2+F0qmrH776T/FSkrY3jtDsZ3jmySsTKSnVNLFoE8+fDwoVKYLOkBEZ2GUWqQ7uiZMFuAytmD1OOPBI2bIATT4w+lJyWBgMGKE2u89uDykZKR21jq3ZnYtIThIbCUkMxcbU6i3xw7adNisy0Zm3JWpz20HuoXdhjShcYkDtAs/dpmiOt3fdIjFtnS0rpFULcCMwE7MArUsr1QoiHgOVSyhnh99CO6XEGbHoSvBq5M55K2POJ6u1ncLhn6FAoLAx9/scf209FopSSF396kcZA3oHT5sSPH6/fS427hueWP8dVY68ydlBHGpwyG+ZMCSR5xrGSEQ6lv3OSeQ1SIyI1T19NsnorZPRJ7PFo0KOHMmJ++1uVOF+nI+ofjvR0JZ75+efQqbPklG2nsKMiVHi0Q2oHju4Z2iG5qAhmzYLPPlM6XoWFqj1MWlpwZy0pleiqP2sK4uIxZKbXk50NWVmQ4gIEHN2jjQ7MFoBKlv/2W6UO/49/wP79SrxWK81BCOXJcjiUwvz995tbYBEVegsav0flbZnt2dbTf6orgKzovTtRoZf3WbkOOkYpHBxFJ5Qm1hSv0UwXaPQ1xmxsaTWul0jWlqxlXPf4veJmYYioqZTyK+CrVs/dp7PtSUaMmRA6HhW+/H79X6HXeYYbW+PGwdy5offgqiqVJD8ktk4mhrKmeA1l9c2igBJJij3lYGhoVdEq9lXvo3uWTr5ErGT0Vi2CFl0B+76JrRm1PQ2yhsDJX+vncySKzIH6oq3lK6HryeH7u7UmhgkxElJS4LXXlKfj5puVoRNJQ2qbTS0Qrr1WtQJSPfVsfHLJJ22+t7wcnntONTYuLFSesdaNrHWPobEPPLMYN+BLg3KUwXXJJfCvyFqxWbgrEWXL+d3kpVx/xEZWrXHxwoxTeHfBGVTWZmO3+fD57aS5Gph25DyuP+drTj6+FHuHwXBggmqjldYt2Z9CX/oBoGaH+caWXsVj1ZbkGVvlq6FPY3ShzCg6oTSxuGCxZrqAz++ja0b033v/3P6a4cJady0ri1ZyxRFXRL3PRGEpyIfDZocuk9RNXYvK9VD6I3Q5CTRcpbEycqTyBGj1K/vmGxg4MPlte15d9WqQxEP3zO4cqG9uMWMTNt5d9y5/OO4Pxg/uSIcTPoDNT8Lqe8HfEHkJtz0D+l4ERz1nWkVpVLhyVCNtLe9p5Ubw1kXW97GJGCbEaPjNb2DSJOXtePVV8Pkk1dXQOlyRmSnx+QTnnw833QTHHhv5GDt3wiOPqHZAQjR70hpjzGVuCr3X1yvj7V//im0/hz3SD/sXw843oPALpTTuSAdvHUJ6GJcDz/7yZZ79Jfj9gjp3Ommueuy2FtdeIVBoU+esrxEcmWrBcML76JZZm01mP9DQeQKgYh10Pi66/UW7oNFdTK2CHtPMXUyldYe63aHPV6xRx2Vy3tia4jWaz3fN7KrpoWqL3NRc7BppJBLJkoL2LeFpGVtt0ftCKF2g70FZcjWctQEwztgaNqzJAxDKrFlwxRXQoYNhw0WNX/p5c82beAPVNALBWUPOYmfFTr7epsJy9d56nl/xvDnGFqiJe9gflKG76XHY85HK5dJK/LSnqRtJl8kw7Fbocbo5xxQrGX2VW781B5aQyBLtNnGXQ9FsBu//nMenruYfxxTy7YpjeXX+r9m1vw9V9Vmkp9TRJbuUy457mwuP+5qMjrngHgHbz4Tup6mcOx08HvjLX1TIyutt23OWmqrkCjIy1OKksVF5vsrKQj1gBz9COyowaTc0lMDGx2D7y6o6z1ff7NHXEQW22SSZqXpeZb9qtAzgboQ9HybP0AJI7YbudVS5TjWo18gD0iXaBY0jCzwazZ+rNqj5yhmFgnC0Y2cN0Ta29i8yvbrb4/NQWK2RDwP079A/pn0KIeie1V2z96lW/8X2hGVstUX30yCcCGLdblj3Zxh5b3QeiDAMHaqfCL9gQfJzIebvmo/H13wnzErJ4swhZ7K3ei/zd80/2Ch0d+VuthzYwpBO5sU9vR1GM3rlcnqmHcGsk65DbHsR6gvVJGZPUyGEfr+AAVcoOY/2SM4IbWOrckN0q14z8FTDtpdg+4tK+8uWAt5qQE0eZ4z9gjPGfqH//qpiqNoERTPVdZSaB30vhXF/D9psxw4lM5Gfr3/up6Qo/a+pU+Gss1Q43etVOUR+v7qf2+1KBb2wEL7+WuUczZsHFRWGfBuHF54qWPtn2PqMWozoySA4O6iOGp2Pga6nKO+KLVV5hv0e9T53BZQsgP0/woGl7aK44yBCQFoPlRDfmrKV4HcDJk6qmf1UTLw1+xebXw3ZcWygVVkrY9Nbq76P7Oi1riJl84HNpDpSNfsixiL70MTA3IGaxlatu5by+nJy09qnMq9lbLVFRh9I6aRu4HpsfAwGXqNWClqNDaOkY0d1w9BahVdWqjDLsNi7mcTNiz+9GHQB1XvqmdR3Evvr9uNvEc7zSR+vr3qdv0z5i2nH8qfZf2LT/k1sAp7Zfzk3nvaDaWOZRu5Y2POxhu6OhAPLoevkxB9T7R7Y+KjydgD4ArE8f4yuoSbPcN0e2PyvIGNryxY47jhlEGl1SejaVeWJ3XijymNMT+dglwU9aYL+/VVC/+WXq2vp/fdVaHJT+178Jo6yn+D7M8BdqcLwrckZpRrBdzsVUjqr39+RHl6OpONR4L1WGWHeeti/UHmdi9tBi6TsoTrG1rLovFqxkDNC5V+2pmY7aOQzGUr2iEAoWMMLuW8WZA4ypaoeVJseLVx2V1ztuEbkjWDWjtCWeWnONNaWrGVS30kx79tM2neb7PZCr3PDr0CkF+adBd5Kw3rq9A/jZX355dgqwoyg0dvIp5s+RbZYKQ3PG06mK5N+HfqRndLsEnf73Lyy8hVNeQgjmLltJv9e+u+DOi53zLqDn/ZpSwq0a7KH6rv0936V2LY9fg+sexi+GArbnlc3WV+Yk82WorwGWYOhwxj1WdJ7he9n2cKD0mRolZeHXjo2G9x+O2zfDn/4g+rXmJODZjsrLex2tX1aGlx2maqofMFgvd1Dku2vw6wTVO5Pa0OrwxEwdT6cvgT6XQbpPZTx5OrQtu6bzR7IQUxTcgs9zoBJn8FZ7UCxv8MRaMog+BqUwKiZ5I7VlxA6YLLyefYQbekJUB4vE/W2VhatpNYdauSlOlLj6hIxtNNQTfmHRl+jroHXHrCMrUgY8Cv9ptRNVG2CbydGbnB5w1tLkybpO8nee8/4FIjKCPs1f7X1q6AERafNyXlDzzv49+mDgvOhqt3Vpih2F1YVctGHF1HvbY451XvrOevts6hsaOfNp1uTNRjdnJLS+YlrKVKzA74+Etb/TeXtaHmxbCkq4XnMw6oq9KIqOHsrTFseuEkvhbM2wc/K4OxtMOHfSozWmROyq8ZGmDJFGVqt7fH0dKWddd99KifLCI2v9HT4xS/i288hT9FsWH69dtL2sFvhtB+h8/EBL1aceajCpuRVsttB+XTOMP0Fzb5Z4VNF4iVrsL73bO/Xygto2thD9BdrpT+YmiC/uGBx0KK8CSllXMbWgNwBuDSKmxq8DSzd2367Q1jGViR0HB9Zvk/VRmVwuferXBctvHXKpfvDRWF3ddppSrdGiz17YP36tg8nGiI13l5Y8QLV7ubPlupIDTKwzh5yNlmurIN/N3gbeHXVq4YdJ4DX7+Xcd889uGqyCdtBA7CsvozLP77cNG+aKWQO0q9YKluhqhXNpmozfHOUStrV8mSldoMjHoELS+DET2DEHZB7hPJ2ONJVkq8rR/3ryFDPZw2EQdfBcW/A+fvguNchu1mw9OGHVTK7lqH1/fdKWFPvGoiVZOc7JpX6fTD/Au1zbdw/YcyDgd8uyaXOZpA1GDSUxwEomWuuonrWYH0tvdIfQZrYAzWls37eZ+N+VRxhEnoio/Xeevp16Bfzfvvn9senI8mk14exPWAZW5EgBAy7JbLqjaqN8Glv+Ok2qC9SSajuCvXwVKs8mE96wd7wPblPPDG8WvzjjyvdLSNwu5WGUltUNlQyNz84/8Lj93BUj6MO/n1yv5MPCp2Cytt6Z907mlorsXL37LvZuH/jwQsuzZl2UKW40dfI9/nf8+9l/zZsPNNxpOlXJPk9Kr/GTCo3wczjVLVhawkNYYMRd8M522Hozc1GVaRej6bQkiMN+lyqPGDHvMLu3fDYY9rh8H//G0aN+h83jMxg67PaSfA9zlDtsBwGW7btiazBYTw8P5q7oMkcoB+KL19lblsuISC9t/7r+W/ry2LEQZUPKhorNF9Lc6SR6Yr9XOub05d6j/bNcXv59na70LaMrUjp9wsiLsP3u1X11qe9YNYkWHABzJkKn3SHtQ+Ap6LNXeTmQu8w18jHHxsXSvR64Zln2t7uo40fhfSlOqbnMUHtGDqldwrpzC6lZPaO2YYc6zdbv+GZpc8cVCXOcGbwj6n/4JZjbjkYx6/11HLnrDvNzd/S66mk93xbZA7Uf23HK6b04QTUDWjO1MA52er8dmTCqT/CyD8p71W8icR2p9pP30v48EPtxf6ECXDxxZahZQpbn9M2tkbdHz7H7nAgJQ/d1jWN+6Gx1Lyx7ang0qmQk15VvWkmnY7Sf23X2yCNN7bWuSHdqe2c6JWtL/8SCSmOFHJSQ9MSQEU5dldqSF20AyxjK1JSOkLfS/STDbWQPqhYrapxylZErXZ++un6BlVjI/z5z/p6QpHidqvy+C1b2t72ueXPHZR1ABVCvGD4BSHbnT307KC8rhp3DS/+9GJ8B4rK07r4o4uD8rTy0vO4Zvw13DPpHlIczfkHpudvFRUxd8ccxAMgHoCNJRuU9VAUY5/Friejul1pUPCJfggkXtY+qDxaWobW1PkqsdggSZPmfafx9ttoNly/4Yb205LqsMLXAI0HtF/rND6xx5IMhAir8cau9/XV1o0gM0yO0o5X9dNOjKDbafpey4q10KghSxEna90EyQO1JJYG1K3pna3tiXDanKwtaZ9J8paxFQ0j7jS8NU84TjtNtRjR46mntFXmo8HrhVtuaXu7vdV7Q9SAbcLGqQNODdl2+qDpZLiaV8oSyZdbv9TskRXxcfq9nPPOOUHVLRnODJ476zkcNgeZrkweO+0xMpzN45bVl3HZx5eZ4laWUnLrzFsP/n3nd3fGt8OuJ4FTx7vQeAAOLItv/3ps/pd2iOOo5yBnuCll8VLCqlXar511liHqKRatEXZ0PfOxhvg/7qbENCN6tIM8sNwwffN2vWNu1W/XKfoV7QWfmRtK7HJi+LZzW//TZsFWtCxrIGhR3IRAMDJvZNz719NurPPU6arWJxtrWouG7KGqUsdsIboAJ56ovfpvorFR6Q9Vx7goqquD559X/Rbb4u21b4e0V9DTSzmhzwkh/aucNiczNsfek/zu7+5m04FNB/O0BIJx3ccFJedfecSVQb0YG32NzMufx9NLn455XD2+2/EdW8u2HpSd+G7Hd/GVHXc+PnxV0uanmlW5jUTLEO18HPQ+z7Q8lvp6fY9txzBt7CziwObUrwrc+3VskjVR9eA0RhInLrqfqh8uLVturrHVZbK+d8ldHugWYRIZ/cJfyzteM1w8ebnOV5nuTGdwp8Fx739Ul1HYNI7Z4/ewuMBkOY0YsYytaDn6OXNXIS3o1Al69Ai/zfvvK5FTXwyVyx4PPPBAZNv+Z9l/QgyoY3seS35FPjvLdwY99lXvY1inYCOs2l3NM0sjSAzT4Jut3/DMsmeCPGOpjlSeO/O5oO3sNjvPn/V8UK5AraeWu767y9AqFSklf5j5h6CQaqOvkdtn3R77Tl054cMchTPQzTmJBy1ByyE3Kq0kk3A69c9Xr8kaj//TDL1Fu8hnzf+B30T5gfZC3gnhX9/+snkyK3nH6VccA2x+2pzFFKiVTd5E/dcbimDfTNAJ+0WLlLBVZ1dCiLhkH5oYmDuQFB3ZitXFq+PevxlYCvLRkjUIht8OG/8ZXuzRIE47DV56KfzC86KLYMkSJeAYKXV1SugxkorGr7d+zc6KUPXluflzGfPcGM33uDUqXH7Y8wNbD2yNamWzrHAZZ71zVkipb4ojhUs+ukTzPaKVUVLvref4V45n601b6ZPTJ+Kx9fh629fkV+QHPeeXfubvms/KfSsZ1z1MuCIc3aaqwgot/G5VyTriTvOTmXtMN7VNkNOpFhL794e+tmoVHH20aUP/b9P/CnUO1eYHV51WroNFVylpDkcU/fKibYqcbLKHhq9x2vGyqrg1A2c2pPXUVrGHwGLKRN9H9+lKY03P4PvpNjjjVIzo8SuB07bCTg1HXof6GsYOPxnidCJOyoYjfg71Goc7YUMJ3BLf/s3AMrZiYeSfVIWYGTLuqcHVbJdcAu+8Ez5UuHkzTJsG332nBCDborYWfv97+OqryA7poXkPaT7f6GsMknmIhPvm3sc7P3snom0bvA1Mfm2ypqZKRUMFFQ0VEY/r9rmZ8MIEim4rwhZHUlBTrlaTV8thc+CXfnzSR4O3gdtn3c53V3wX2867TYFd7x7sPRjCpseV8KTZaAiQGs2558Irr4RGMV9/HUaOjOw8togSRzqc8h18PTbUi7LnQ6gvgEkzApWnEfwA0TZFTjbCBp0mQMk87dert0HFOrWNGYuNblNg+0var/ndsOkx1SLJjMVUlxPDp7/UbFd5a/0ujzt9wCbgk7fi2kWb9K6CRS/rvdoA75k7fiwYckYJIaYJITYLIbYJIe7SeP1WIcQGIcQaIcRsIURfrf0cMthTlFCj0V3T7RkhE9ikSartSFssXqwMs9ra8CHFmhp49FHV8icS/H6/oQrwn27+NOJtT3jlBM0ky1gprSvlwg8ujGsfMzbPoKCqIOi5JukLiWRRwSKWFcaYzJ53Yvjeg95aWHOveTIQTcRalRVFwvRFnadpGlQvvaTds9fCIDL7w9R5SqTW1ioMs38xzOgPGx5p1gU83Og+PXwayMpbw4f74qHrFHCEqXja+E/zeiXmjAzV0GvN6nva3sYiZuI2toQQduAZYDowArhUCNG6pfdKYIKUcgzwIfCPeMdNOl1Phh5nhk5YMWNTPeZaYbercF8kBtcXX8AJJ8CiRaGSENXVqsfcxRfDgw9GflSvrHoFv4HJrQ3eBn7c/WOb27244kVW7AvNsxIIslxZbT70RPM+3fQpc3bOienY/dLPbd/eFpSrdfrA04MqY+o99dz27W0x7Z/0Hm17lba9YK7aNcSu+xNFSGnqqG8ZMCA0Ud7thksvjb/K1kjaKmYdORLGjVM9Hk85Bc47T+VCfvop7NhhWLtU48gdC2dtVEnb9lYWr7dW9cb8uBssux52vgU1+WoR4K7Qz2nye8K/3l7ockJ4z03pj6ry14z2PV1OCL+Q8dbAugfNWUwJG3ScEH6bhmLY8A/zF3P/o4h4y+KFEMcBD0gpTw/8fTeAlPJvOtuPA/4tpQyTsQcTJkyQy5cvj+vYTMdTBTOPDXRvj0cYTqhGr6ctgezQfKZly+Dkk6O7AU2ZAldcAd26qZYoX34Jb78dfuLXOhWG/XsYmw8Y20h2av+pzLoitGt7EwVVBYx4ZkRQWyCAFHsK1024juN7Hx/ROJ9v/pwPNnwQEursltmNjTdspENqh6iO+4P1H/Crz3510NjKcmXxwc8/YG/1Xm7+5mZq3GqSSnem890vv+O43sdFtX8AFvxchXTC0f10OPHj6PJr9EI7wha6mh1wFYx/Onp9rSjDR2tHS445RrtTwrRp8OGHyQknSglr1qjxv/pKhem31XajG6HGZBFd6U5oOM1uV8fu86mk/wED1DX8xBPgira+Jpx6cTzzt5Sw7xtYfrNKkg5nxDsyVNuyjkdBWjfloXFkqAo+b7WSJyn/SRnqenpel0V5rGZ8bl8jfJAdfr7OHavEfKO5vkD//G/5uT/KUyKqethS4PwC1WbH6LE3Pq4842E9dwImz4Cup0T2+f0eeFfjhP4dkKwWtV27xq53GCdCiBVSSk2r1ghj62fANCnl1YG/fwkcI6W8UWf7fwNFUsqHNV67FrgWoE+fPuN3RaJJkGway2DmUVC7J/YeV85sOHUhdNDWH5ES+vePTKIhHlqfCvWeetL/anCoFLALO977tN3lXr+Xo188mjXFa0JytUZ3Gc3q61aHSFDo4fa56fdkP/bV7At6PsWewin9T+HLy76MeF8+v48BTw0IUifumNaRkj+WUO+tp8ujXYJCnsf2OpZFv1kU0b6DyH8bll6nn7fVxNBbYMyfIzOIvHXwvo7V4sgIFdsVdjhzvWpxEk3uSrS5OpdJHnoI/v537fTHY49VnRJyclS/xHhpbFQVuHr9FgsK4F//gjfeUAsbt1ttbxR2e4zVlmYZWy33se9b2PYs7J2pZCLaOv+iJbWrfo6XziEJm/7n7t9PYrNBSgp06aKM2YEDoU8f1YVg6NAwem1fjlZFAeGY9Jla1ETTqDkSg2feeVD4Wfj99DoPjn8rutytSMau3Q2fD9WuQG6JPTWw8B8GGg2fDyL9SrbiIx3DsD0Y1wkmnLGVUOkHIcQvgAnAo1qvSylfkFJOkFJOyMvLS+ShxU5KR2UopXWLQRLCplaIU+bqGlqgzsFbbzXmhhMNevpUAkGKPSWihxY+6dPV3Lpj1h1sPrA5xNBKc6TxwtkvRGwcgdIBe+aMZ4KETkEl9s/fNZ9/LflXxPt6f/37HKhrXrG77C6uOfIa7DY7ma5Mzht2XpDuy5riNczfNT/i/R+k59mReUk3P6kMs7a6Evgaw99YcseHJs5Kn2qUHmXHg9bFHZFw331KVFfr3F68GAYNUgZQdXXs3RLq69Xj/fdhsEYhbEGB8gIPHqyEgktKlLHV0tCy21W/xiuugGefVVWTBQVq2/JyKC2FwkLYtAneeguuu05VVbZUw49FniUhCAE9TodJn8LPDsCxr0C/X0JGfxBOcGYjHZnINrtn2JWB4MxRc2FqN+h5Lhz9QkSGVlkZvPACnHqqaldWhPb5VERX8vNViHbjRpg3D159Fe6/H373O/W9Z2YqY/1vf9P43rtPa1srcfGvwH3A+HBi7/Pb7kFZ8KmqGo00XSDSvNaMPuGV7JtoauFVu1M/b89bqwytOVMjG9sicWFEIcRU4GlgspSyzVbjh0QYsSXucvjxUij5AXwR3KTsaZDeByZ/rhk6bE15udLcCidyGi+tT4Vz3zmXGVuCjSKbsDF1wFTOHnJ2RPt8fdXrLN8X/DvahZ2bjr6JJ6Y9EfT8V1u+4mcf/CwkKd4hHJw55Ew+veTTyD5IC6SUHPXiUfy07ydkq7rvNEca8381nwk9wucyeP1e+j/Zn4Lq5sT4VEcq665fx8COqqfhj7t/ZNp/px0MJQKM7z6eZdcsi8pABGD2VCiOoJeksMO4x2DQNcob0bK7gfSrcEHxXFj4S/1+nOfsgC9HacuYdJwAU2arvB5bHEK+Eay6778f/vlP/QLf9HRl6Pzyl3DkkcpLlZqqPBut8XqVsZSWpkKAH36oDKTSQPu7luf5u+/CNdc0e71a4nLBhReqhc6YMeraE0KFB9sqaK2rU/tLT4f8fNV79LXXoKIi/Ps0SeaK31vLrp2fcf+XV9LH5eCOIy4i07NfnVt+rzoHbS6lEZc1UOWdpvdWau0pbSvU+v3w2Wfw9NOwcCE4HMbm66WmaoSpSxfB3NPb9t5lD4XTl4EzTFJ7E+G8xy09PO5ylQ8XyYLqhPdVXnC4cJ7fo6oov2ydJq0xNkQYSgwgbND/Shj3T/U7C5taiNlcKpl/w9/Dyx9Znq3g1wwwthzAFmAKUAgsAy6TUq5vsc04VGL8NCnl1kj2e8gZW6BOiB2vI1fdpS4+X3WIDKW0Z4AQiGG3wci7o3JTX345fPCBsaGNoGNr6XF219L50c4hQqbZKdl8edmXnNCnDYHAAO+vf5+rZ1wdkn/VKa0TJbeXHPQGFVQVMPI/I6lqDBX+SnOksfGGjfTtEFsR64q9Kzjx1RM1Kxu7ZnRl042bwuZvvbH6DX735e+CEuOP7H4kK65tTuCXUtLriV7srd578LkMZwYzLp3BKf1Pie6Ad/5XJSdHGsrJHAgT/q10uvxuZXRVbYal18L+NkKZl0nY/iosv1F74swcqPLDMgfE3iMxkhAHMGuWqqitqwu/qHA4VKjopJOgb18VZszKUu+rqIDiYliwAH78Udt4k1I9fvMbeO+90G2EUB6SP/9ZebSys6P6tJrU1CgDLSbvdBJvQhtLN3L8K8cflFnpkdWDJVcvibuZsJQqH+7GG5XeWjivZUaG+r2POQZ69lReq/R0ZSDX1CgjetkypTV4QCNdLOQrkn4V+nJHUPbabary+NnT9EPq3lrVY3SjZsAm1Oj4diLsX9j22DYXTHwXup+mHVL01Kh84e+nQ/2+0Ne1xq4rgBmD2w4ltsSRoSqlU/PAXanU7iMphrGMreDXjOgbJ4Q4A3gS1Un3FSnlX4QQDwHLpZQzhBDfAaOBpjNit5TynHD7bO/GVm2tcmNv26Yeq1crl3Z+PlRXeTn/qM8444gvOHHofLLTqmjwpLJi51F8veYM3v7xEnBk0qePyi044ggVwhg8GMaP1z/nCgpgyBDthGIjaHkqvLP2Ha794togTw0oj07VXVUH5Q7aYn/dfno93iskST3LlcXnl37O5H6TATjmpWNYVrgsxPvksru46eib+Odp/9Tcf2VlIIF5m/pNGhubc20cDuWdcLng5aqLWFH3MX6CwwI2YeO8Yefx0UUfae7f4/PQ58k+FNU0h0EyXZn8e/q/uXLslUHb/mX+X3h4wcNBBuqYLmNYdd2q6Lxbnmr4qEt0EyKo5NqUTuCpjDwE2DQhbnsJVtysveIVNiWIOfpBcOWqydeIXC6Nybi6Gu6+W0mTCGHOuS4l3HMPPPlkqKHVpYuqIhw9Wj+3K1F4vWqOGTJU/9zpkCOx25VQrNOpPDmDBinjZORIGDFCzRmxNPfeULqBia9MpKKhAoFAIrELO10zu8ZlcFVVKaN6/nxtL5bDAeefD2ecoaRvevVSv1Namr4ns6ZGvV5WpqqxZ85s1ifUvMUt/a3SvIpE6qDTUXDcW8pr13LB4a1T18vSa2HPx/rvb32eb3sJVtwSWQQEoNupcNR/lCiqv1E1pfc1wKo/BXS7wtzDtQyemcfBgQS0tLGMreDXzGjSawTtzdhqqlD67DMVeti6VV3cTTcELW9T0wTY1J6kyQho/ZXb7WqlJoQyHsJx110qr8Ssm1ATk1+dzPzdoTlHk/pMYt6vdEQBdRj01CC2l28Pes4mbFw++nLeOP8NAF5d+SrrS9eHvDfdmc7tx9+ObMxiwQJl0K5cCevWKcO2vl79DlKq79jvb34Iob5bmw3ILME74Qm8fg9CKG9Fp07q5nrTecdzyREXaB77Sz+9xC3f3BLk1UpzpFF6e2lQs21QzboH/GtAkGGZ4czgo4s+CurhGBELfhaYwE2+PltOiNtfh+W/CxhcOuN2OxX6XKRW26ld1aRvcwXCmA6V4+L3gHSr0IOvIaYE2rIyFfr75z/Vb9mWflwkZGer6++bb2D69FBDKyMDVqxQxShRVwwaQEWFMvTef1+d3/v2KSNpW203usrIKyFBnfNNlZx1ddC5M2zYEHnvyQ2lG5j48kQqGisA1dtUCIHb58Yu7HTJ6MKSq5fQO6d3VJ9x82bVFaOkJNR76XSqXLf/+z9lVMXjUWzylD37LNyu1UWrZD58f1bk3mNhh76XQt9LlJe3sRT2fKS8wm3to/V53ngAPgkYTtGQ1lPlXTUegOotkb1H6xrLf1cZiEYXQUQydjgsYys5tBdjq75eXbCPP64mQ49HGU1NOBzKvT1qFAwbpnI7Bg2C7t3VhOfzBSprAjd+KZXbe+dONaGuX68moAUL1KTY1s9RXQ29e7dtlMVC09h63qhUeyp/m/o3bjn2lqj2+4dv/sDTS58OSXpPd6ZTdkcZKQ7tUGpVFXz0kcp1WbxY3XgaGoK//yYcDujXT4WUUlLUxO31qm1ratT3rRWaClchVuOuoeujXanzBt+VR+aN5Jyh2o7Zt9a8xZ6qPUHPdUnvwr7b9kWnXF/8Pcw7J/ETYuUGJT9Rt6tt71hqFxVeSOuuvF2OTGWoeWtUlW7pj/rtSbTG1sDtVh6Qzz+HGTOaDRCvN/z1kpamDKamvKnp05X+1dSpqr3VzJmh73n0UbjhBvXeROHzKVmWJ59UYU+XSzukJoSqtBs+XM0zubnqc6Wmqu+ork7NDVu2qAXJ9u2h53WkU/36kvVMfGUilY1qkmmq3i2pLWF18Wq8fi92YScvI48lVy+JuAVWUZHytpWXhx7L8OHw9ddqAWSkR7FpMRaC9CvvsVtHpsJItM7zOadD0bfJGdvXAB92jtyzZuTY4bCMreTQHoytV16BP/5RTWYt3d1CKPHC3/0OTj9dTWoOR2yaQPX1zcm+S5cqt3lbPPecOi4jE0ldLnUcAM8se4Y7Zt0R1PgZlJdmydVLGNlFv3JSi2+3f8vPP/h5SD5WliuLN89/k3OHnRv0fGkp3HuvKsF3OEJvPh06qBDD+PFKTHLIEOWhamgINm6b/m3KlSkvVzehVauUd+yrr1RoVu8SuPTDS3l3/btRfVY9/m/S//HQydptjzSRfvi4OzS2WUsSH1oTot8H219QeSjeOnMMvhh1eIroymaGsp2BbGUQGxhBLZm4ceHAQxoNDGYLQ9nCQLYzmK30I/9g7qQfgcvmw+cPndgrKqLrLxovs2fD1Vdr5yw5HEor7xe/UFV1vXurecbjUXOFVljQ7Vbzic2mXi8qUukN//2vMlYjqeZcV7KOE1454aChBdAjswcbb9xIjbuGEc+MOPhaNAaXx6M+x9q1oVGAk05Sx5ee3nbhgaGsvFNV9calkRgBWtdYweew8PLkeZeW36wEkqP1rhkxth6WsZUckmlseb2qQun990NDDcOGKaX2vDy1Akvo5NDi+AYPVmE0o0hPbzbexjw7hrUla0O2yUnJofzO8qir6+o8deQ+kotbY1I7Y9AZfHn5l4C6nh5/XMkB+HzNxh8oD9RZZykj86ij1GvxfP+1teq9+flqVd2aqoYqOvy9Q0gOWaw4bU4a7mmIzru16V+w5p7oJRiiIdyE6PdB4eew7iHl8bI5jVOvv9yY3URLAylkUoNPoy2sz5eY67mxUXWF+Oab0PklL0/1Lb3pJnU9ZGXFf0xVVcrT25bHTsvQSnOk8eOvfzzYXH3W9lmc++65B4tN7MJO5/TOLLl6SdgClnfeUXNq6wVi797KAEukkXuQqq3w9Rjl6TETzQWNR4XXW/eoTMTYoEKRn/YJX01o1th6HObGVhJMhfbP449rG1qnnALLl6twVXZ2cgwtUKveV181LtyRkdHcwie/Ip+tZdoFo6f0PyV6GQNUuHBU11Gar83eOZuqxip8Pvj1r5WhVVcXbGgNHKjCI2+8odoRNeVzxPP9Z2So70/L0AK46rOrDDO0ADx+D3fPvju6Nw2+zvj+m9Fgs0Pv82D6T3DOdpjwNPQ8S4ULbSlKjNeRBSH6Sza1jTNHVXHZXJB3Ahz5JJy5ES71K5XnJJBCI9lC22Bc14bOpRE0NKhQ5tdfh84vxx+vzvNbb1Xnd06OMXNMdnbbc8Xa4rUhhla6M51HT3v0oKEFcOrAU7n5mJtJd6rz0id97K/bzzEvHcOuCn3V5aee0vbE33df4vUDD5I9GLKGtL2dGdicMOh6dX0kg5ROMOKO5M4v/2NYnq1W+P3KY6KVgF5QoEqP2wv//jfceae+NlEkpKfDBRcoQ0YI+PO8P/OXBX8JydfSq8CLlIfnP8yf5/8Zty/Yu5XpyuTp6U9T++NV3HFH6Gc5+mglCZCREVl/SCMoqyuj86OdDTW2QOmF1d9bj8PWljhkC3a+Cct+Z04/xHhaamQCXQKPzkAK4AC8gccBoAQoBSoIzbcvLlax32gwaOV7770q8b6xVQTlrLNU8YuZLYL++ld4+OHQ+eWUU1ROWjLaE3244UMu/vBi/K0q89Id6XTN1DaKC6sLQ65lh83B3CvnhsjC+HwqTUGrVVhVlfLeJY3CL+DHS5LjPXZXwKe9ze11Gs675K2FT3rpa/DFQ5QdAwDLs/W/htcbOgk3kaTFuC433gi/+lXsK8OUFBg7VuWmqRwnyYs/vRhiaIES9pwyYErMx3rawNM0FeVr3DU8u+w57rlH22h87TW1Mk+UoQXw8IKHNQ0tgcBpc0b00MIrvbz000vRHUzfy6LvkxYp0cg3tKYG2AEsBr4APgLeC/z7GfADSn2vHO3CxjFjYh87Tm6+Wduo+eIL1TrIrCbYUipFc62F3PPPJ8fQ+mD9B/z8g5+HGFoAdd46dlbs1Hy0NrRAzRGTXp3ED7t/CHq+sVH/PpqMzxxEjzMjU1U3A1cHGHF38rxLjgw44uHo2gJFgnBGb2j9D2B5tjSYPBl++CF0Jfbss0rFOmlubw18Pjj3XJg7NzoPV2qq0q9ZtkwlnAOsKlrFCa+cECRz0ETPrJ4U3FoQ8nzEx+n3kf1IdkjSPYDLn4334Qr8GknLXm9iDa1ady09H+8ZFE4ByHRm8uLZL3LmkDMj2s8LK17gvrn3hVQy9s7uzc7f78QejSJ7weew8FJjV9/2NLg4BpdoMlefBo69apUKSWsZVhdcoML0keQ5RUN1tbrWtDw8icoXa8l327/j1LdONXy/AsHa69ceLKSRUhlVWkbmzp0qLSOpFH8P359pXv5SWO9SnZKBSJZ3ye+BT/tCg44oaixEOrdIqaqXPZVKLLWDTk4HqPxVREDJPvBwZIIrR6UrOHOa/+/IDD9XmEg4z1YU8Yz/HV56SSVh19QEa/vcfLP6+6qr2sGKLIDdrrS/7rxTGYORGFwZGTBxompj0tKF/9qq12j0hnq1BIIzB0dmZOgep83OxN4TmbVjVuhrLg9pnWqoLA2NJyxcqHJZEmVwPbX0KTz+UNE0m83GhSMujFjM9dfjfs29c+4Neb68vpz31r/HZaMvi/ygep6llNwr1kT+nrbQ8b79rzB2rKoGPO88VYXYUhbk44/V4uW225QUhM0WXzFGdbU6fz/8UOldlWgUmC5froRIE2VwrSpaxQXvh2rLOYSDUweeyvju4yPaz9z8uSwpWIJXNutMSCQnvXYSS65ZwoDcAQgBP/uZqopsbWg+9ZRS6k/qfNr1JMg9AvYvxnBdu7b6hTrSlXdp1Z3GLqYi9S7ZnHD0c6rVnBHGpj0dhv4++LmafChfCZUb1b/V26C+QCn4Czuk9VItnjqmQplGsULnbHBkg/SqylFfHfjcSsXeWwv1e6GuUKnj1+1W+aVp3eL/LAZjebZ0yM9X4nv79oWWTJ96qsr7OPpo5XkxQhemvl5NRPFMOl98oeQoysu1y7ybtIf+/Ofmm0gTUko6P9qZsvqy0Pc50nj3Z+/qaktFyrPLnuWWmbdohiB6Ft5I+X+fDjEW+/dXjWbz8mJTwY6G6sZqej7eM6S1kMPm4PoJ1/PU9Kei2t+5757L55s/DwlJ9sjqwa5bdkWXu3VgOXw3KbKeZm1hT4fxT8OgX0f/3sPEs9VEVRVce63Kl2psDDUGnE44+2w4+WTlCRs2TF3zPp96zW5XD79fPef1qkNJTVVzyMKFSkPvww/VWK+8oioNW3vURo1SemJZWaoAxkxW7lvJSa+dRJU7uBIu1Z7KGYPP4MOLPoy4EMbn93HiqyeyfO/yoEWKTdjomNaRJVcrg2vZMiXx0Pr6djiUht7o0ckRkj3I/qUw+2RjvVuODLgognwsnxs+6w0NBsq8ROu5/ul22Pqf+D6/LQU6Hw+nzAJ3GWx6HPLfUfI1Nqfy4gmbEkTufwV0OkZp9Pk9zfIbthTVwi6SFAfpV+/1NQBSFerYU5W4cpKwpB9ixOuFN99Uqu319WqF2pJu3eDMM+Hii5VYX16emrC9XjVxNE3GNluzwrnXq1bRrbWfPvxQac1sruymkodjPWbsfMDP+YCfM4/J1JBJCo2MZwU/40Ou4A2y0J4Anp+cyTZX6GtZjXDHQkjVEf+MlIpU+Odx0KhxLYyvy6boZ5X86U+h4YacHFU6Pnlycwseo/D5mr1mD817iEd+eCSkh2KaI43l1y5nRJ5Os1cdvtvxHRe8d0GI8ZbpzOTpM57mqrFXRXew21+B5TfFNyHa06H/L+Do52N7/2FmbDWxapVq4TNnjvpbrzejEKo69sgjlQBnUwsZt1u9p6pKdZpYv15bLNfnUxII774banj07avG79zZmH6MWqzct5LJr00OOSdBhbg33LCBTFd0q8fimmKGPzOc8obyoOebDK7Fv1nMwI4DufNOVdTT+nN37qxkMIYNS7KHa85pqgF8JC18IiG1G1wQYXhu9wew6CrjvEuDroHxT0b+HumHudOgZEH0bcIAsKlm5GeuVY3rP+qsPktruZ9z88HVSXn04skZbadYxlaceL3w7bcqj+Obb5pb9LSeTO12pRszeLBSkc/OVgZXSkqz8nxdXXNPxR07QtXQZdf4jK1DGin55hsl9FhREbr6HzJEeQUuuaS5R1pGRvShl9ra5lywL79UxnJlQyU9H++pma82ovMI1t8Q2kqoLfzST9dHu7K/fn/Ia10zurLnD3siDkseZNkNsOO12CZlmwtyx8GpC2IPIybL2JJ+JUWhx54ZavIWtkBOh0NJU7hyVSKys0P49wfYvVstfN54AzZtUudZvK2ChFDe75aq9w89pJLx3e7gfdvtcOGFyvAbPFhtG0+OqNutHhUVUGL/iZNeO0nT0Ep3prPk6iWM6qIt0dIW8/LnMf2/00MWKi0Nrn45Azn1VNUwWsvD9cgjcP316jvQ6oEYLU1afRF/fzU74KuxxgiN2tNUA+vup0X+niXXQv5/4zO4hFOFRE9bGP017qmGr8dB3Z4ohV4FOLNg2grIGqRO2pnHQNXG0ErLSZ9C99NB+qJLzP+4W2TNrwGwwWVx9vaKEcvYMhCfTym9z56tVrAbN6oJuq5Okprmx+v34Wm04/PqT+wteyE2NKi/e/VSxsQXX8RwUEbdALsl0dDr2lVJXqMM0xdeUGXyVVXKsG19sxs7VjXwHjFCqcgPHapaJEFwb0SbTT3271cG7qpVzW2SFi9uDvvcO+deHlv0WFATaVCq+Y+d9hi/nfDbmD7WPbPv4bFFj4VUeGY4M3ji9Ce4Zvw10e3Q74XZU6BsWXQhReFU7XXOWK00dqLEL/3M3DaToyecS6eq0Jw2d+eOOEv2x6TDpnZQCRVroXK9mqQr1kHtLiX66K1RoYLfSajUOJ87psKXl6sJvCmvw+9RD08F1OxUeR22FDXBn7URUvNC9+NrVD3vGkqhsZSSgip+WJTC6vXpLFzVk3XburO/MpP0lEaEUMchJchAm2YhAAFIQX2ji4y0Rob3L+a4MXs4cnQ1xxzVyMCTLzw43I4dKuw/f36zOnxL+vRRhtcJJ6gwY9++zdvZbOr8buqUIKU65+12FcLct0/1Qly8GD75RM1VE54/ipVFK0lzBmf9e3wenjnjGX5z5G9i++0CPDTvIf664K8hC4hady1nDTmLGZfOwOtVoq2vvaadX9q7t8o//dWvlJGYnh6dJ9vrVcZxSopqyfTAA6pbRMTsnQkLzo8vXG9Ph5H3wKg/Rfc+vwe+nQjlq1Vv0VhIyYMz12uf35HQeADmnq5EjCP5DoRTzSdT5kBOiwR3vw/2fgm73oHCL0F61PXnd6vFT+8LIG8SdBilPIDObKVmH5QvG5hLhIAZg6PrphGtoKpBWMaWCRTXFLNs7zIW7lnI3Py5LN6+DsoGQtkgKB8AnnTwptIzfRDdUvvRKzeP3p26kJuZRmqqmjgHDVKPjh3jLJ44TEM7UqpV8Pvvw9tvqzCuw6FWq1ryHEKoG43L1dwXz+1W22p5JhwONZnnF5XT64lempWSqY5Uim4rIic1NonrneU7GfGfESFGHEBeeh4FtxbgskcZF/W5YeEvYO9XkfU3s6cp8cZTZkU9Cde4a3ht1Ws88sMjVDZWUuOuIdWRik3YkFLikz48Pg/pznS6Z3Xn/yb9HxePvFi332UQFeth5xuQ/5YycBzpzfkb3U+HvInQ+VjI6KcSjW0O8LZukt1iQj54vrV63e5S3q6GUpVA2+lYZdSVLlA316qNyhjz1YEzF/KOV99XRl/V/De1i3q4cqltSGPTZgdV1TYaGm00NAoa3XZcTh+pKZLUFEl6uo8hg310zGlUScCN+9WqvH6fCu+0Cp9s2aKkH958Uy0stBYXTR+xVy8lxNuhgzIoWp7nNTWqz+qOHdrvL+nbhaqq0pDnU3zQywAhcwnszgGvhqe5Y2Yeububb5bvvqu8WI2N2lWKGRmqSOm441SO7PjxypBsbZCCet7hUIuo775TPSaXLFELLIhhGlr/N1j3cIze41R17k76JLZJvaEUvhwF7v3RhzPt6XDqD9BxXNvbhsPXqDzou95W/0fnOOzp0OkoOOGD8POKlFC/F3/FJqr3bqa+ZBuiehNO924cVOGgBqetEUdaLr7UvvicnfGTEmhu71QGnT3Q5F56Di6khGwkZf9XCNnqpIhF48sgLGMrTqobq/nL/L8wJ38Om/dvptZTG9JUGSA7JZssl6qoa7oxtcYhHGSmZDIybyTTB03n1uNuDVlphsPtc7Np/yZWF61mxb4V/LD7B764dQXdNO65B7KdPPLh7xnffTxHdD2CwZ0GR5eUHQkJMvSkVGXimzapG8qqVarNx86d6ibT5ClsuX3rvxsamhtWjxypPGLDhsGizDt4esnTNLRq2yEQXDD8Aj686MO4jv3oF49m2d5lIc9nODP4+6l/54ajboh+p1LCpidg7X3BCaYtETYVOux/FYx/QiWPRsjWA1t5YvETvL7qdSSSem89Nmy47C6uHX8t/zfp/2jwNvCn2X/igw0fHPzuMpwZ2IWd64+6nhuPvpFe2b20B1jyW8h/U3nqWk6WI/8EI+5Un8+ZqYwkI5F++PZ45UVDBq/eJ30GPc5QFU42FzgSq+4tpZJi+fJL9e/69bB3rzKq7HZJXZ3Eq2XJtMJma859qqtTOVHDhsH3m9qH57qJ6mr4xz/giSfUtdpW78a+fZUOblpacwPu+noVIt26VVtSA2KYhqSEHy6OfDHThC1VaXZNW6YWDrFSsxO+OwkaiiIM5wkld3DSl9DlxJiGbPA2sK1sG5v3b2ZD6QY+2PABw50+/tbRS9/GnWBLwSZsynPtq4ec0TD2b9BdXzpk0yZl/M6YobysxcVq/s3MhGOOUdGIQYOUB7d7d+iS5yM3q5pUWyV2fxV+nxe/z4/0+5B+H0LYEHY7Nrsdm82Gz5aFhxzKa7LZX+akqAj27FFqAcnq7mK6sSWEmAb8C7ADL0kpH2n1egrwBjAepS19sZQyP9w+k2lsbd6/mbn5c/k+/3sWFSxiT+Uew9XEm7AJG/079Gdin4lM7juZKf2nHOwxVlJbwuqi1Vz7+bXkV+YbNqZAMLrLaP41/V+M6TqGjmkd49hZ8kOY1WSynYHUkkEjKbhx4caFE0/gf25SaaA/O+nMgZD393q0G2WNlThtTnz+5rCMx9/IVY6ZdKyaTEWFWlU3eQxcLnVDaxniaArrtP74G2zv8HXKr7DLlKCzyI+bXO9IrmhYTm6uyvHr2FHdHLt2VWHlNhOGG0ph/V9g94fKg2JzqXCazakMh9H3Q/bQiL5Hv9/PM8ue4cF5D3KgPvR7ioWeWT15ZMoj/OKIX7QYyAvv6uSTXFSrjMJok2ejyulAhRNbG6lT5kDXk1VI0xFoM2T42G3nk0gp2VW5i6WFS5m/cyHfrtjK1k12KB0OReOgIYdMe2ey7V1IFdl0yMggK91FSoo6X0aPVouJ4cNV3ldcVbwJWEzV1Cj5mueeU0amy6Wei3f3GRnqetXynLWJ9MOKW2D7y5F5uOzp0PkYmDRDLRLixV0O885RUgnhJCEcGUr0+OSZbV7nUkpWF61mTv4cVhWtYk/VHvZU7iG/Il/TedCES8D4FOhkBzcOSlP70im7P71zenNk9yOZ2n8qw/KGtRgHpk9X4XEI/v5ffVXlyLrd6nc2UseuPWCqsSWEsKO0ok8FCoBlwKVSyg0ttvkdMEZKeZ0Q4hLgfCnlxeH2m0xj6//OzuS/A2ppnXZlkzBxN5y/CabsgNwoizZKMuDbgfDRcPipO8hW85jTB8eVprD9lDFsKN1ElUYiK4BD2Ei3u0hzuEi1O7Hr3Ji80k+Dz0O9102dz41Pxy3d0ZXBqA69uG3EdM7pc5RKJLanqgnEmaX63zmzAgnHHYLf3A6MrXi5NO9fzMo7mYpySEkFmwC/hJt+m851Fw+kUyflOWuqNG1aQTcZV0IE/91E08f3+yVbyjbj9nkOPieECn+MfP51Mh95zJgP0gHIABqB0Jz8UKqrITOTBm8D76x9h4fnP8yOih2amwpEm/lYUkrdRckRXY/g3kn3ct6w85R3tfBLWPJrVQ7urQcCk33HCcqz1eNMZQgJuzoP21qqRmtsnboQSubDvm+geqsquxc2SOmivANZg5S2WVp3FUJM6aSS7O2pgR/arm7I0g+f9opu7Fb5JJv3b+a99e+xYNcCtpVvY2/13hB5lAxnBsM6D8Mv/awrWReiBZfqSKVnVk+GdhrKSf1O4uJRF9Mnp0/kx6SHycaW3++ntK6U6sZqqhqr2L2vnvnfO1i6MIUNS7tRsa8zwu5B2L0IbEivC+m3IaXAJtRU5XJJ7DaB3y9oaBD06AEnnqhaIJ10EgyIRyB+x5uw4iYVTtOq0hMOtbAZdpta2ISJHPiln3pPPetL17O/bj/l9eU0eBuoaqxif91+DtQfYH/dfvbX7aesvoxGbwMnOat4ILOMXOEDIXDix48dKexIYWNd159T2uNndMvpzZBOQ0hzpB28Tl9d+Sov/vQiG0o3hIg0m0WntE4M7zSKNbfPxNuYEpKXt2WL8lBKaUwRRHvDbGPrOOABKeXpgb/vBpBS/q3FNjMD2ywSQjiAIiBPhhk8qWHEJN74Z/7TxvKUUKPINuQGjuh/BhN7T9TPH5KS5nwVGfivRCnvwv66A/yw+wc2lG7UvCU2/nQRW5cMDtqdzwfHHqvcvk2/VpPnB2DySfqT8Q8Lmkdp/Uu33AcEGy75+Wql25Tc3sSwYXD//QS/SY8ozuuxY2H16ubKxsZGtfL65hs4/XS1TWVlc2jSbldVpk5n9O5qt7vZQ+b3wx8+e4B385+ggciTZvY9imbYuCgDut8e2T4yHR24rPdd1Poq+aTwGfzST4O/OY5jx4UdF5d1+zMvXH1DxFWT9Z56/v7j33n0x0dx+914/c0lu1muLFIcKfzxuD9y7fhryU3tAAeWqgqskvlQvUVtaHOpPI2sgSqBNmOASr7NHABpPZTRL+zN3q8mo6fJCBK2QHjSB++FunW8Eg6cW0RJbQmldaWU1Jbwf3PvxV27j0xZTyebJM8BeXb16GmHdBs4BTgBh1Bq0C6bHYfNjtPmCOSueXFLH14JCAfT0n2kiODzsNaVx8qjP2Zp4VLm7JzD0sKllNaF5lEZQY+sHhzb61hO7ncyR/c8miO6HhFZLl1LDLrGpjw/kjlFG9resDVeJ9R0h6qeUN0TqnuANwX8DrD5wO6GjBLIKoTsQvWvK9iV9fLJn7OsYA31vloafLXU+2qZX/wZ9b5qvNKLX/qRenlJQJYN/tABLsuCfk7wSPX7H/DDZzXwaIWdfX4Xua7OnNBtGqn2DNIcGaTbM3h240O4Y5JSCGagE05JU96lWj/82AArG8NLsI6v6sOK7N0AZDdA30ro0AA2P4zfB8cVwFGF0KfyYOZjm0hgW0dY3gN+7A1ru4JfQFka7OoAtQFn8IVlxzE4fSE//ggrVijvlt2uTqcxY2DaNKVTOXiw8uR36KBOJ738Wj1sNuUhczjU2vHAARWaTBZmG1s/A6ZJKa8O/P1L4Bgp5Y0ttlkX2KYg8Pf2wDa66+/2lLMVEUatAAu/UlovjQeUK9lTpR4dxkDuGMCmbjaOLHCkBkTgUgNibk7UZdPkYhFKqG7rsxENPfmRlcxfOzbk+c8+g3P09Ez1DFONHA3DMXDV3diocsE2bFCVXMXFUFiodNAcDrUKS0trlvJIT1cClE0GWpO4ZdPD52s2qJr+39CgJoTq6maj6wP3b6gd8gr4bdCQC/W5YHPTv3cmw7r1pXd2bzqmdSQ3NZcOaR3IScnRNXwavY1UNlZS0VBBeUM5B2oPsKdqD7srd1Pvrccu7JQ3lB/UQxrWaRibDmwK2Y9N2LjyiCv5x6n/oHN6bH0ZC6oKuOnrm/hs02ea3q4bj7qRp894OvhJKVUSec02qN4OtfnK49RQokKk7nLV2sNTrXJpmjy1NqfyMEifMrLwKYPLngaeGlon+IqtMX0kQ0h3pIe0cGpiaKehnD/sfKYPms6EnhMizq90+9ws2rOIL7d+yWebPtNNObjnxHt4+JSHoztgg66x1/47lFnFW8i2QY4Nsm3glpA7+GryepxMblquOs/TcslyZWFrI4zsX/8PKjc8Trkfyn1Q5odSH1T6lVFc6YcqH1T54Z1dg6DTtoiPNRzZNsi1Qa2E/REYBXe/cgNn+58hwwPpHhUdaSLTDTkNqkAhWiTQ4ICqlGYDB8AnoM6pnrtVbGPJ7oEh703GfF5ZqQo3du5U8+qePWpRXVGh5sOqquZcw5SUZv1DrTm16SGE+remRkUcMjKUJuN338V8mHFzyBhbQohrgWsB+vTpM37Xrl1xHVtCSabREY7WmeKH09Dt9DtP4lce1djhrv2YJRySMEZgoIg/eDI/t5RSd4x2+Z2302ssqhP9gQfgwQeNG5rIPUFNoXrDxj5E5pbDaexosMKIFhYWFhYWFhYmEs7YMqJAchkwWAjRXwjhAi4BZrTaZgZwZeD/PwPmhDO0LCwsLCwsLCwOF+IWXZJSeoUQNwIzUdIPr0gp1wshHgKWSylnAC8DbwohtgFlKIPMwsLCwsLCwuKwxxCFSynlV8BXrZ67r8X/G4CfGzGWhYWFhYWFhcWhxOHXdtvCwsLCwsLCoh1hGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiZiGVsWFhYWFhYWFiYSl7ElhOgohJglhNga+DdXY5uxQohFQoj1Qog1QoiL4xnTwsLCwsLCwuJQIl7P1l3AbCnlYGB24O/W1AFXSClHAtOAJ4UQHeIc18LCwsLCwsLikCBeY+tc4PXA/18Hzmu9gZRyi5Rya+D/e4ESIC/OcS0sLCwsLCwsDgniNba6Sin3Bf5fBHQNt7EQ4mjABWyPc1wLCwsLCwsLi0MCR1sbCCG+A7ppvHRPyz+klFIIIcPspzvwJnCllNKvs821wLWBP2uEEJvbOj6T6Azst8a2xrbGtsa2xrbGtsa2xo6QvnovCCl17aM2CRhDJ0kp9wWMqe+llEM1tssGvgf+KqX8MOYBE4QQYrmUcoI1tjW2NbY1tjW2NbY1tjV2vMQbRpwBXBn4/5XAZ603EEK4gE+ANw4FQ8vCwsLCwsLCwkjiNbYeAU4VQmwFpgb+RggxQQjxUmCbi4BJwFVCiFWBx9g4x7WwsLCwsLCwOCRoM2crHFLKA8AUjeeXA1cH/v8W8FY84ySBF6yxrbGtsa2xrbGtsa2xrbGNIK6cLQsLCwsLCwsLi/BY7XosLCwsLCwsLEzEMrZaIYSYJoTYLITYJoTQUsQ3a9xXhBAlQoh1iRqzxdi9hRBzhRAbAm2Vfp/AsVOFEEuFEKsDYz+YqLFbHINdCLFSCPFFgsfNF0KsDeQxLk/w2B2EEB8KITYJITYKIY5L0LhDW+RurhJCVAkhbknE2IHx/xA4z9YJId4RQqQmcOzfB8Zdb/Zn1ppPImmvZuLYPw98br8QwrRKMZ2xHw2c52uEEJ+Y1cFEZ+w/B8ZdJYT4VgjRI1Fjt3jtNiGEFEJ0TtTYQogHhBCFLa7zMxI1duD5mwK/+XohxD/MGDtqpJTWI/AA7CjB1QEo8dXVwIgEjT0JOBJYl4TP3R04MvD/LGBLAj+3ADID/3cCS4BjE/z5bwXeBr5I8Lj5QOdE/96BsV8Hrg783wV0SMIx2FFiyH0TNF5PYCeQFvj7feCqBI09ClgHpKNyZb8DBpk4Xsh8AvwDuCvw/7uAvydw7OHAUJQE0IQEf+7TAEfg/39P8OfObvH/m4HnEjV24PnewExgl1lzjc7nfgD4o1m/cxtjnxy4vlICf3cx+zgieVierWCOBrZJKXdIKd3Au6iWRKYjpZwPlCViLI2x90kpfwr8vxrYiLoxJWJsKaWsCfzpDDwSlkgohOgFnAm81Na2hwtCiBzUJPUygJTSLaWsSMKhTAG2Syl3JXBMB5AmhHCgDJ+9CRp3OLBESlknpfQC84ALzBpMZz5ps72aWWNLKTdKKU0XqdYZ+9vAdw6wGOiVwLGrWvyZgUlzW5j7xxPAHWaN28bYpqMz9vXAI1LKxsA2JQk/MA0sYyuYnsCeFn8XkCCjo70ghOgHjEN5mBI1pl0IsQrVN3OWlDJhYwNPoiYjza4GJiOBb4UQK4TqnpAo+gOlwKuB8OlLQoiMBI7fxCXAO4kaTEpZCPwT2A3sAyqllN8maPh1wIlCiE5CiHTgDJTXIZFE1V7tMOXXwNeJHFAI8RchxB7gcuC+BI57LlAopVydqDFbcWMghPqKWSFrHYagrrUlQoh5QoijEji2LpaxZXEQIUQm8BFwS6sVmalIKX1SyrGoFefRQohRiRhXCHEWUCKlXJGI8TQ4QUp5JDAduEEIMSlB4zpQrvdnpZTjgFpUWClhCCV2fA7wQQLHzEV5d/oDPYAMIcQvEjG2lHIjKoT1LfANsArwJWJsneORJNCD3B4QQtwDeIH/JnJcKeU9UsregXFvTMSYAYP+TyTQuGvFs8BAYCxqYfNYAsd2AB2BY4HbgfeFECKB42tiGVvBFBK82uwVeO6wRwjhRBla/5VSfpyMYwiEsuYC0xI05ETgHCFEPipkfIoQImGacAFPS5Ob+xNUGDsRFAAFLTyIH6KMr0QyHfhJSlmcwDGnAjullKVSSg/wMXB8ogaXUr4spRwvpZwElKNyIxNJsVBt1Zp61baL8EoiEEJcBZwFXB4wNJPBf4ELEzTWQNSiYnVgfusF/CSE0OpzbDhSyuLAItoPvEji5jZQ89vHgRSVpaiohSnFAdFgGVvBLAMGCyH6B1bel6BaEh3WBKz+l4GNUsrHEzx2XlN1kBAiDTgV2JSIsaWUd0spe0kp+6F+6zlSyoR4OoQQGUKIrKb/o5J4E1KJKqUsAvYIIZr6mE4BNiRi7BZcSgJDiAF2A8cKIdID5/wUVH5iQhBCdAn82weVr/V2osYO0GZ7tcMRIcQ0VKrAOVLKugSPPbjFn+eSuLltrZSyi5SyX2B+K0AVQRUlYvwmoz7A+SRobgvwKSpJHiHEEFQBULIaUzeT7Az99vZA5VJsQVUl3pPAcd9BuVs9qAvjNwkc+wRUSGENKryxCjgjQWOPAVYGxl4H3Jek3/0kEliNiKp4XR14rE/kuRYYfyywPPC9fwrkJnDsDOAAkJOE3/lB1A1vHfAmgYqlBI29AGXUrgammDxWyHwCdAJmA1tR1VodEzj2+YH/NwLFwMwEjr0NlYvbNLeZVRGoNfZHgXNtDfA50DNRY7d6PR/zqhG1PvebwNrA554BdE/g2C5U15p1wE/AKWaMHe3DUpC3sLCwsLCwsDARK4xoYWFhYWFhYWEilrFlYWFhYWFhYWEilrFlYWFhYWFhYWEilrFlYWFhYWFhYWEilrFlYWFhYWFhYWEilrFlYWFhYWFhYWEilrFlYWFhYWFhYWEilrFlYWFhYWFhYWEi/w/89ojAi1zG6AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_21\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_22\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_23\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_24\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_25\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_26\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_27\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_28\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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eeSIgdekijuj5+ZLM+OuvxUR98GDg94L1vdNOk0AUo/3kdNvL3w+/NzFnoePf9xY/BOveC92WI1nySzW5EpKbSN4pEGFFuWHnH+J4nr488LuXG3bm4aOU5AL7vSnk7dTfJ6EWnDweUlqCM8yMz0VZkLlaIqsLih+eEGNZedxeNykvppDvzkdDQ6FIiUvh47M+5tKOl4bXfqQEc6Y/SuWWoyH1wzHLHXcYN4mFw623Ru4sHgs7d0rEY6WtdoG//5ZoKKPQvV87/4T0JWEIWhrUGgC9voTT58OQhdBvDNQ/J+qM0m+/bexYUJEm9OWXjW3vwIHgn61YIY7RRk7Yldm/SlEq/Fesx/BLqHjnnaG1kJom486mTdI3+/aVyGKHQwTvAQNE27l5s5iqw2H+fPjvv0oMSFj2pOSxM5vMtZJrKpSgVf8cGLYZ2j8GqW3AngBxafJypkiCz8ZXwGlzoOubEvl4NLDhUyhK1/+samc4Y4Vkhw9X0ALZN+0EOGMlpLaP6HSW7FmC029MzCrMsvy2wuSoyiB/tLFypSTZrKxk02vWSGRetPmqouHBBys/IswIf5/yBPw+ygsL7y4rWRGMemdAr5Fgjwd7EtiK69dU6yr+D9hgycOw/v2wzyUnRyZIo3xiILQgnJ4On3xirIAe6re5917jFwNKIdqfysoqfQTNCitWiFN5MPNh1aqStLd169CJZks+e/11SYI6ZEjoXGkjRoQv1GoaNGwoAt6hQ1H8LDnbYPPI8MyHifWh+fVQ+2RJQ5C3GzZ/LZomFcbAtOxJcZwPRtc3oMWNotkKhc0GNpdUjKh/NkzoBQUhVh1mU5QFSx/VH8OSm8OgKSIsRoM9XrLynzod/u4a9tf+3fSvjwmx/HaLirGErRA8/XRkPhXVq0stsfT0yJILlvDcc8ZFJYXDgQOSrqEy25wxQxzxTWXrj1CwL/jnNid0eV2yUjuC1FFyFtcE6vySCGWzLpN6ahXw8ceVWwnmnXcqT6O+eLGJi49IymkcY6aF8rzySvAxJSVF+keLFmHmrkKEri5dJEda3776gvnixfKq6PZUqwY33CACdXKyLPri4qS/vvwy/PRTmIL2yhfAW8GKMb4G9P4Kap8iJ1a+bl+dQbJgWvqolNMKRt4eSfAZTIPW5fXwBK3yOFzgagCnzS4WuA5W7kIApL01b+oLkXYXDJwIjhgLxWo2GeNO+Sfsr/y57k8f5/gSDuUdYmfmTupXqR/bOR3nWGbEIOzdKyvQirRMNpv4VSxbJia5JUsk182iRZJHKNz8bSWCTzhapjp1xMdq/Xppa8UKuOsuSTQYCV9+aVJx2RA8/rhxjvhBWf1qCK2WBn1/ltV0MEGrPM5kqDMQTptVVj4kCEVFIjBXlkk2L09ysRmpRQvF++9Xron7eOPwYRFY9MaUuDjxp2rePHxBq4SEBCkWPWmSfl3NZ5+tWKvVv7+YJZ98Uub6pCSpQZqQIDVQP/hANP0NG1ZwMvn7JfpXhVjBpXWCM1dIhJw9IbBAsrOKaG26vAq9RwU35699m6BRjm3uhZY3RyZolWCPB1dDGDQVNIcsBGI1OUP4x9i9GzZ8ou/w3+l/4uBfooWPBZtDrjMM3F43C3aX+VDbNBtacWBBnD2OqVumxn4+xzmWsBWETz6peJ+aNUXQGTVKnNrj42V1Gh8vq81vvxXhqzRLdgi++KJiwczhkISomzaJb1eLFpIxun17eP55GRNuvTWsy0Mp8fuIZKKOjxczR7QJgPfulZqLppK9BTJD5BLo9JwM8o4IJFN7AiQ1gT4/ECpy6d9/K9ckO64SXSUKCiR/WmWauI83Pvss+OLmxRdFYIq2vmhiInTqJNr48uzaBePHh9ZGXnml7FOlighZeqSkQOPGMp51DWV52vJt6BOt3hMGz4T4miLUhMKRBA0vEJOZ5idcuPMk2lHPVJnaHjo9G95iKhj2eEhuKsepbNKXFRfM9sPVAFreFtnYVRFhHmvR7kU+/lr1U+qTFCf3N6swi/EbIi/78/8NS9jSweMR80yo1WCDBhIV1Ly5DER6pKSI78X8+aKNCkY4gk9cnBSpveIKGVj9B+WSAtSvvirRhRUxZ0749fD695dBNidHFl15eeIEHo4QWZ6ffpJSNKay6YvgK8yG50Obu0VbFSkOl5g8Oj4VdJcvvzTWF60iPv88vBxqRvDHH5WvBT2e8HpFC6mn9ezcWUp4BRN0wiUpSXyzyvPzz6EXR2edBR9+GJ5W3OGQPj91qkSs6rLxs+ApGOKqwYA/pP+Fm37AkQRpnaHzi77bd/yKvlZLg5O+BVuUUqt/263viv04kbL5G4lE9KfzS6JpOwJM3jzZx1+rZ4OepZotsPJthYM1fOowd25oc0nVqmUCVFxc6GPFx4vqfcECWTnqMWdO6Ena6YQJE6RgcEUDclISPPSQaLpC8fnnFZu7XC4xTYwbJ8WD7Xa5nvh4KYy9dausisPls89MNrEpJep3vdWuIwV6fhrbateZLDlrUloGfFRQIGbnynIbysmByZMrpy2ATz8NX7AbPly0r3l5Ujpo7FhJTfH/mSVLgj/7n38evUbLH38T5KhRwRdxdevCN99ELuQlJkraiYCFU842yArmkKlJlK8zyCAYCmcStLwd6g0t27ZtTHH5Hz8anCN1S41aGdgitOkawdbvAs2w9kRocD7YK5hwTOKPtX+U+mslOZMY3Gww+e4ybURGQQbbM7YfkXM7VrCELR1+/TW0lunjjyEtTYSgcHA6ZUX47rv6n//+e+j2HntMCtWGOygmJUmS0oFBSvgVFcGPP4Y2LTgcUsT6pJP0201MFG3aRx/BOedUfE67dkm0ZbjUqye+cJddBt27h/ml/f8F99Xq8IQxq11bHHT7IGBzMH+ZYLRpAxdfLKWUWgbKbhXy118VC/rlcTjE96ZFi8jbys6GaWGUQGvVCpYuFcGsaVMRIKpUkUi5FSvEOfx4rzcdjEmT9ANRuneX+2aUbFBeANq/X+67HpomPqKJod0QdXE4RKP/rL+FbdtPwTVWza+Hat0qNh0GbdQFJ30nPlhKwd4gmpQOT0SWCqEijPCNigR3DuTrBPfUO1Nf21UJFHmKWLh7Yel7m2ajW71uNE5rXLrNYXNYflsVYAlbOvz8c3DfmzPPlOLOka5EExPhggtg0KDAz0I5xrdtCw88EPnqMylJVq16g+m0aaEnPU2TDOHdu1c8GLtcUv6jX7/Q+/35Z3jCiN0u6SjWrROz3Icfigbn77+hfkXBLtt/0c+3k1ALWt1ujK+DzQE1ewds/uGH8DQ/KSniD7hwofz/6aei9Xj33ch+4++/D6+9uDgRctLTJZJw8WLxnbv66vDbGj++YsGuVi2py9ihQ2DKAqdTnpPbbhMz9/9HfvlFX1v+0EPRCTzhMHZs8AXhZZeJn2kkAnt5kpNlQecjvAczIdoTocvL0Znvy2OLg3YPi0+TXv68ql2gSpvY2jjSZK7Rz/PV9AqIi0IraAALdy8krpxGLc+dR/ta7elRv0fptuzCbMatt/JthcIStvzYvj14FHpSkqjlo/WtSEoSIaa8oLZnj7Sph80mk2qk0UklpKZKyLY///wT2mx53XUwdGj41+lyiXYuVF6g77+vOAoxLQ2WLxefs6QkOf8qVeS4p5wimrEhQ0IcYM8E/UG42XUYWpLD5js7KiU+TRWZENu3l4ivyy+Xe5aaKi+XS+75pk3habk8HsnFVBFNmkgE2W23lUWXJSeLYPTee2IeDmfRMHJkaMEuIUHOp2rV0BqapCTxTbruuorbPJ4oKBAh15+aNWXhZpYf45df6vc5u12E3lD9NRycznLjS/YWyNmiv2PLm0EzwPzlcEGbe2D3P/panpa3HRmzn5FkrAL0BMnw82EZjb+/VsMqDYmzx3FSg5NILBdJOnlLJfo1HINYwpYff/0VfMK45ZboBZ8SXC649tqy93//HXz1ecEF0KxZ9IOxyyV5cxo18t3+11/BTYjJydENxPHxwR3z8/Jg9uzQ39c0caBv2lS/7bg42f7jj0H8f9x5kBmk/k/L2wLDy2PB7wFZsqTiKMSUFNEQVa2q74zsckmetr//rthZec6cis1O8fFyrKZN9YXmkkzkH38c+jhKVWxC/Owz0XCEoyVJSpLgkxNPrHjf44XZs/WF2rPOMi96NTNT/Er1uPDC2AUtkHFraIkb1b5p+s7bmgPaPx67VqvsgLDzL32/zPpnieb5WCZ9WaArhOaAxBARViYzdu1Yn/xaJ9aVztu5Tmec9rLJK7Mgk20Z2yr9/I4VDBG2NE0bomnaWk3TNmia9rDO5400TZuiadpiTdOWaZp2hhHtmsH33+s7sjoc8OijsQ9SycnwVLmAth9/DK5leuaZ4JGO4WKzwcPlfpGsLNi4Mfj+TzwRnUDpckmJEX/BDiR/UEXHfOQR6NWrYk1LYqIILQHHO7RAX6Cq3iP6TMth8vvvFSe//e47qFEjtJBkt0vQxciRoY/1228Vp+z46CMJzAglqLtcItCHCnJYuzZ0Ox07im9dJDneEhMlZ9f/FxYs0P+9hg2LvX8HY9684ObJp54yrt3S52v/DH2H9ToDjY2gc7ggW8cJP7lZdM73Rxu5OwiIskxuWlbLsZIp8hSxZM+S0vfx9nj6NOoDQKfancgtKpssLb+t0MQsbGmaZgfeB4YC7YBLNU1r57fb48CPSqkuwHAg0MP4KKCgIHgeqHPPjcwBOhQlg6DbLWHUevTpE4aPUhjEx/v658ycGVygadhQBKZIk6OW4HRKSgh/pk0Lbbbs0SN8QdbhkPMMaGf/f/oDUvPrK0xGGiv//BNa2Lr5Zjj55PB8c1wu0RZcdVXwfcaPD60RueQScbwP53dMShK/uNat9T//77/Q33/zzciFc5tNTKrBAjiON1avDnSO1zQxjZvFggX6i8ZWrSRfllGUajP3zdDfoenVsWc7L4/y6juQ1zn1mKggUCF6Y5gjuXLqTOqwYNcCH3+tBEcCXep0ASAlPoUarhqln2UXZvPX+r8q/RyPFYzQbPUANiilNimlCoHRwDC/fRRQsuxIBXYZ0K7hrFoV3BTywAPBUzdESslxli0LLsCNGBG90ONPeZPhhAnBfafuvjs2/xGnU3yqGjTw3T55cujIx1dfjcxJ2OUSYaRevXIbd/2tX96i7hBTTQtKye8YDKdT0nBEohFNThZfGL3foqhIKgcEw+EQM10kfoUJCZIDSo9//gn+vPTuLdrIaJ6Z5GQ5z/8P6GkHW7c2NzJz2jT96McLLzQhX5qnALI3B263OaHBMGMbzNutn1G+2omSIuJYR8/nNNycZOX5pQ58p4X5Ct6B/938r0+Kh9yiXE6oc0Lp+861O/vsP2XzlMjP9f8JRvSC+kB5F+8dxdvK8zRwhaZpO4BxwJ16B9I07SZN0xZomrZg//79BpxaZCxbpr84ql5d8kyZ0Z6eEOJySdSjUY6z5Sf6ceP0s4A7nZI7K1afNBBNTgleb/Dwc5BM+yeeGPl4rGmiDSslS2dGi6sqpS1MZMOG0JPmVVdFF/GVlCQaKn9WrAj9G116aeTRbXa7RMnqpYWYEURhASKgxRJJ16iROIgf72zZEritZUtzs/EvWqS//aqrjMvpVUr6Mn3tcZ1TwWuwU1r2JolK9KeKvzHlGMWu8+N48iIXuPIjqeUYfCX8x7o/KCoXjJCWkEZaQlrp+36N++Eot5jNKsxiS/qWCNr+/0NlOchfCoxUSjUAzgC+1rTAp0cp9YlSqptSqlvNmjUr6dTKmD9f39w1ZIg5NeEWLNDXGpxyijntZWZKNJweZ55pTBsJCb7C1rp1oc2vzz4b3eCfkCCBBjVqACgpGOtPjd7Bs1kbxLx5wQVFu12uLxr/mJQU0Yj5C3Lz5gWfpDVNajNG057DIXXxyrNnj6SM0KNxYxGUY1FapKTAPfdE//1jAaVgn47VqyQPmRkcPqz/uzVsqO9TGTMH5+nXQqw7xFgTIkjEo55JLbmJse0cKeJrBG7L2Wq6K4QehZ5CH38tED+t8nSp04WkchpFy28rOEYIWzuB8tUsGxRvK8/1wI8ASqnZQAKg81QdWebM0d9+8cXGmRDLE8w/7LzzzHGcDeU0e/PNxl1jec1LqFqIrVqJYBmtBk/T4L77oEbKgcDaaQA1+0VXiDYCpk8PnhYh1qiv6tUDE8ZOmxY8E/k550i0YzQ4nXK+5U2zM2cG18pde60xZrB+/UoE5uOTgwf1BdLWrc0TthYt0u/np51mkjbtwGz9RU2tAcbbLN3Z+sJWfC1j2zlSpHUMFKzcOVCYXumnMn/nfBIcZQ+pXbPTt1Ffn3061+nskxbC8tsKjhE9YT7QUtO0ppqmxSEO8GP99tkGDALQNK0tImxVvp0wBErpZzgvMbGYQbBIr7PPNqcO3erV+hqzpCRx4DaK8v5C06cHd46/5Zbws/DrkZgoQmLjGtv1M1NXPcH0UPDp04N/dt99sQnNKSlw772+22bNCr7/o4/G1p6mwe23l72fPDn4b3fDDcYICx6PCHnHK3l5+ouJZs3Ma3PRIv3ox759jUn5EED2lsBtmt2cBKOeQn2/pmM95UMJqe30zaSZEZTfMIh/N//rE22YFJdUmvahhLopdX0c6EH8ttTxEKxgMDE/oUopt6ZpdwD/AHbgC6XUSk3TngEWKKXGAvcBn2qaNgJxlr9GHWW/xq5d+qu+bt3MyYWzd69+BFubNrEXpA3GkiX6xbX79BEhzKiVdvnJJZTPzxVXxCZslbR1Wt8gWWGTDAy70qGwMHgajdq1JS1CrPToUaatys6W5zRYe5066X8WLgkJ4tPz2GPyfto0fR/G9u0lAa0RJCUd30lOi4r0F05mZY0HCaDQG1t6BxY+MIY8nYcytZ3kwoq2PE8wlIfAAtRa5D5Nv9SJwK/JBpdVUjRglXb6WsKto6Fa19hqu0bIH+v+wF3O567IU0TnOp0D9mtXsx1zdpSZhXKLctmSvoWmVf+fF0T1wxD9iVJqnFKqlVKquVLq+eJtTxYLWiilViml+iilTlBKdVZKTTCiXSNZulTfZNK1a+wCgR7LlukLNxWVvYmFpUv1tw8ebI6Al5+v7xwM0LOnMcJdUhKcPWiHrHj9STA3EeCyZcEnzQsuMMZkU1QkZmUIbh4COP98YxYFaWllQtvWrfr7XHSRcWlQwBih9Ggl2DNgVtZ40O9zTqeJxcALDgRuq9oFQ6s2lGBz6BxX6Wu7QmGQA7nhJNTQd5LfPkbfVcIkCtwFLNvrG2Zt02w0qNIgYN8+DfuglftNbJqNKVusqER/rAzyxWzYoG9i69XLuBQM5Vm+XF/V3727eZqtYFqYM880dvIsYcmS0MKIEcKW3Q4ntDkAXj+VnWaHuNTYGwjB3Ln64fUgWjsjfseUFCnvA+Jzp6eZBNFIGWEiiosTwS09PXiQxllnGRO1WoIZwSBHCw6HvnYw2HNjBHraz3btgj87MeEtAo9OlI+roTlO3bY4fS2WXtqXY5V6ZxEgUObvhUOLwhcqY4zCnrdzno+/FkDbGm3RdBw1u9frTnJc2eCTU5Rj+W3pcJwYumNn9279Qb97d3Pa27hRX9Xfo0fgNiPIzNSPfExJgebNzWlz+fLg2pYLLzROY2hHZxZxpspEYKIKYd48fYE5OVk0okZx0kny//Tp+s9McrJEBhpBXJyknFi/XgRlf6HA4ZBi00Ziih/RUUJcnL6wlZlpXpt6WXNatjQp52feHrAlBBaAT25mjh+VM1U/I33enuMnIrHZNbDzD3D7Rd4svg8GTgrPlHh+kAK/34WnbXxhxgtkFvg+pAt2L8D2v0BBVwWYdeG3Nb/h9XqxmeF8fIxi3YlitumUdLLZzHNkDVZ8OpxCxNGwdq2+lqlv34pLv0TLunX6kXMNGkhZGqOwazpSsi0uctNChKxerb990CBjtQgli4B16/Q/P/VUY7VDTZtKW3qTc5cuxmtIzDSpHWmqVNEXkNeuDZ3oN1qUktQP/tSta6w2spS8XfpJRs3yl0xuqq/Zyt5kTntHgtpBSgscmCM1KI3OXabD9K36kT9K558eXuVl2tYKiqr+P8PSbBWzY0fgtqZNZWVvxiC1e3fgNv/M60ayZo3+4N6jh3lmy1Wr9Lf37i0TkFFOwg6bjk1GsxPoSFsBETrNbtum75DTr5+x2poSM/ZO/4QqxQwebGx7+fkiDOhpQvv2jS5J6/9XUlLkfvlreDdskEWO0X0vWBqS+vVNSjWh568F4DKg1pgeyc30S9pkrJI6jMcDNgc0ugg2fwXK78GZfzucsQxsJhXVBDLzM8l1B8kvEwGjlo7ilKYm1qQ6xrA0W8XoJR5s2NCcSESAAzpjVLt2FRc0jpYVK/Qnz27dzPHXAplQ9OjTx+A8Ynqh0spNxA66ETjNerz6vyFIzT8jtTVOp0QiBtMoDTA4nVFysgRT6AnnQ4aYG0l3PFK3buC2zZvN8dvas0d/cWiac7y3SF8FGlfNnPbia6K7iDo0D4qCSJrHIh0e1zfD5myBmcPBAGEoGKOWjjLkOBM2HnVxcEcUS9gqRm/irFXLnHxXAIcOBW6rU8c8k0ows1A7E6tcBNPEnHKKwffVpjO7eAtNjd7ZcbCB7qRms0Hbtsa3t2WLvpDjcBhverbb9XPOgXk+hcczeq4IK1eao2nKzNQfQ0zTmutljgd9vyoj0DRI0JFed0/UN2ceqyQ3heY3iD+cP7vGwYI7TRO4flr1kyHH2ZO9B68ZtvJjFEvYQoSQjIzA7bVqmZP2wevVTxZZq5Z5WaX1NHeaZt4gnJmpr6Wz2yV7tqHYEwnQYhWmmypsbdrXTPfZaNfOHI3Fpk36Gds7dDDeh0opfZ/C1FRzInOPd/QWNLt3B9eMxkIwTbxRedEC8LrR1TSZmaagis4Akr9HilQfT3T8H9iC3MdNX8DUM6EwQ7SLBrJg1wJDjqNQTN482ZBjBbBlNGz7CXb+BVkbTPfPNQLLZwsxr+lpfcxyKj18WIQ4f6fm+vXNEe5Af2Bv2FAEAzN8cDZv1o9ma99ertvQ+5pYWwQun4goBYWHIMGcMh6b9jfTndh69DCmjI0/mzfrO8H36GG8NvTQIf38UC1bip+R5bMVGa1bS1/wD0QZN04y8Rup5Q0mbJnlKmBoLq2wfSa14v7ud0N3/Aot7wB7GA9oQu0Ic20dAeKrQafnYelj+uk19k2FcR2hyytQ/xzR7AXT7nkLwVtx4r/0/HTy3MZFTI1aOopTm58a3Ze9bqiZBvvTfbenajDvJvlbs8l+yg0pLaDeGdDiJvn7KMMSthCnUqczcKBq1MgcM+K+fTJh+U+eDRvq728EeoVp27Y1L9/PpiDBQT16mHBPXQ1lkPEfS/J2myZsrd/TQjfSsn9/c1IZrFmjL2z172+8k/X+/SIM+2smW7UyR5A83unTR18g/usvSbORamA6uEoXtmxOdAUuf8fucAhb+AkS+LLufWhxa3iHiDE1QqXR+i7Y/Q/snSwZ+f3J3Q4zL4Xk5tDyFqg3FFJalkUs2hyQuRZ2jYf1H1XY3MglIw09/YmbJkb+pbw9sOpl2PQlvOeFgEdJBabFAMhYCVnrYd17Uiqq7QPQ+JLIqwuYhCVsIQOUngBQy6Tapjk5+u3pOdIahV5enyZNzNOkbdqkb97q0cMEYcTVQF81mbNF6iOawModHXSbNCsvW6jITqM5dEj/+WzVyrzI1eOZDh30hdR//jFeeA2m5TTNdcbm0Fdu6QkGRuJIDtRsZW+CA/9B7UFHzQQbM5oGfX+A8V1lPAsmxGZvhMUPyMsWD3FpiHY/PaKEr2PX+pc1FlLiUnQTmpYntzAXt9/57cvZh1Kqwu8Conlb/yEsfVhMo9Ekqi35zuHFMPdGWP0qDF0U+XFMwBK2CL4aNEsQCVbCwyy/iqIifa1IjRrm+YitWqXvs2WKQ76rYWAGeYBDC6Du0PDMChGyYY++mtosH7hgZY/qmxBhrxe8AdC58/GdEysotWtLMVP/bWFis0mh9z/+8N1eUAAffQR33WVcPwymwTIrqlocuHUm0vx9kFjPpEYRc9Fmnai55c9Cjd4ijB0vOFPg1Knwd3coPFixEOItiNpEui8n0Lk3LSGNz87+rMLv/r7md75f+b1PPcV4RzzrD62nVfVWob+ctQGmDYPcreDWMZlGgycHDi8x5lgGYAlbiPCjJ3ibpXoPNvCZNZEdOiRmIX9NU9265rW5fr3+9iZNTGjMmYrugL9vBrTJNUXY2p0RqIZMSjInLYJSEtLvT82a5mQFD+az1aaN8W0dC2xcNZPW77XGozxUT6zOznt3Eu+IzOnw3HNh8uTA9CvvvAN33mncuQYbs8xKXExiHf2HMHcHVO1sUqNAwwtg+y+B5qT9M2D/LKh1sin9/ojhqg9nroBpZ8HhpYEZ+2OhuLRPXlEe6w8GDty9G/TmgnYXVHiY1IRUxq4bS0ZBWbSZTbMxZfOU0MLWgTkw5XQoyqbCOpQJtaRYtzNZipLn7ZEca0E1qWaUTYiO40TXGhvBJiyz/FMq26/i4EF9LZ2ZZks97YimmWSa1TSIrxG4/eA8cJgTPpdTEGhPa9LEnEktP1/fDNS0qTl1BQ8d0tdKGpn1/1ji0cmPoooHiXx3Pp8u+jTiYwwapC/A7twJP/9sXESpUvrjmV4SZUNIrKc/0Zmd0b3eGfoFmwHmXGe+GfNIEF8NTp0OTa8GuwHjmt0Fbe4t9V+bvWM2CU7fe+qwOejTsE9Yh+tcp3OAc31uUS5/rv8z+Jf2/QeTT4WiTIIKWrUGQN8f4cJ0GLYV+v8Kvb+Gk76DQVPg4iw4ewO0exgSjt5ByhK2CK7dMU31XskcOKDvgxOBJSRi9HzE6tY1sQCvnm+WJw+yIhj0wyzeml8Yj1KBknizZub4xpREr+q1Z8aC4OBBfWHr/6O/1vqD6xm7dize4okgpyiHJ6c8SV5RZFJ148bBNYP33mtcMuPUVP1jBTNDx0x8dX0/opyt4DFR4LHZod1D+kJH3k6Yc61x5qijCZsDenwAA8ZCWqfw6iT6Y0+Cql3h5HHQ9fXSzZM2TSKn0PeeuZwuTqx3YliHreGqQZIz8Hymb51euljxIXcHTD0j+O9Us48IUQP+gAbnQ1yqCNhxaeVeqRKkkdIcOjwBwzZDj0/AWSWsc65MDBG2NE0bomnaWk3TNmia9nCQfS7WNG2VpmkrNU37zoh2jcLh0F8NmiUYBBPugvlyxUqwEh7Vq5vTXrA2mzUzL0M+dYcU59vyY8dv4Q/65++By1Tgy4/03DTi7IEX0qyZOT5w6en6Ws+mTc0xW+ppQZKSTHSyPop5aNJDFHl8B4JCTyEfLvgw4mM9+qh+5YQDB+DGG/Vz70VCTg488IB+H9u2zaTxTLOBMy1we/Ym/bI6RtLiRoKaibaPgYX3mJpp/YhSZxAMXQJ9x0DVLiJwOKsAOpOLZpfPbE6o1k0EtSELoPYAn93+WvcXHuU7CRW4C+hcp3PYp9WxVseAbR6vh7UH1/pu9Hpg+vmBQQ4g59nzczjlHxGinCnB842Vx+ESYazJFXDOZgmUOIqIWdjSNM0OvA8MBdoBl2qa1s5vn5bAI0AfpVR74J5Y2zWSYMKWXroEo9rTwyxNmtutf32mJTpEvzRQs2YmOljX7KOftXrTl2LbN5D03DQc9sAfq21b84QtPc1khw7mBHH4+4IDVKtmoqB8lLJ6/2rGbxhfOgFpxX6BOUU5PDPtGXKLIpvIzz03+O/144/wySfRC1zZ2fDmm+KErxftu3u38clvS9FLr3Jwvn4ZLSNxVoGm14AW5KZu/KxM4DqWVgp16ojKuqKXzQb1h8AZi+HaIng2E8Z6YCWwofi1EvjVA//LhGuKYOgCqDtIvltynH37yC3KZfWB1QGnEu+Ip05y+Ka5Po36YPMTKxSKKZun+O646gXxtfLXitoTYeC/krIhGq0dgCNRTK4DxkLj4dEdwwSM0Gz1ADYopTYppQqB0cAwv31uBN5XSh0GUErp5DM/ciQk6As6elm0jSCYsBXryjYYwTRmZkVbFhbqt9mokYkZyNM66UfpZK2TsGgDSc9Jw2YLHLyNLptT2l66/vZWFQT4RIteAtxq1czTvB6tPDjpQQo98kyVmFNsxSkFirxFvDv33YiO53TCPfcE10bedx98+23k40B2Nnz2GTzxhLyvoeO+uH27ib9fok5IbN7OyvGb6vhEaEf4jZ/CPz0hc0Xo2onuvKOntqLeaqci8oHlwA/AC8BTxa8XgJ8QoSvYz1G7NrO3zybRGfhgtq/ZPqLT6FavG8nxvtJ+blEuf64r57dVlAkrX9JP1Nr3Z6h2YvSCVnkcLtGQHSUYIWzVB8qLJTuKt5WnFdBK07SZmqbN0TRtiAHtGkZaWnDnVTMckBMT9RdaZjmxBtNsmVX3MT1dP8t47domarZsDhG49Fj7bnGkizEczqmqa72oZlLt3fR0/edTb1I1qj1/qlUzJ/LxaGX53uX8u+lfvMVlQLxeL8+d8hwup6wWcotyeX7G82QXRvZc3XJLaD+7W2+FV1+F3NyKhSO3W/Z7/nkYMaJsu14gw9KlJhYQT22DbjTwoUrIb5RYF054MfTknLECxnWGOdcURzBmi4nTnSv5nA4tgmVPwNjm5p/vUcrETRMD/LU0tLCd40voXKezT+qHEmZsm1Hmt7X+E/0vt7gJavU3NqjJpACpaKgsB3kH0BI4GbgU+FTTtDT/nTRNu0nTtAWapi3Yv39/JZ2aCB16vhT79pljOqlVS99/wixNWrBB20xhS097V7OmOe2VUm+Ivilxy7fRZbQOQnpuGl4VePPMMsump+trXvWeWSPQe+arVfv/lT3+gYkPkO8us7ud3uJ0Tmt+GqnxZenePcrD23Pejui4NWvCY48F1/AqBc88A926wZIlEt3qX6kgJ0e2L1gguc9eesn3c71KFIcO6QetGEKNk/TzWu39NzIn+TADVAJoeZtkDA9Zj1GJoDXjAvgpFX6tD2ObwU9V4O8TYc3rUFB5c87Rxl/rA/21kuOS6VavW0THaZLWpHSBUh6v8oqZ0uuG1S8Hpq5wpkHXNySlw3GKEdPtTqB8925QvK08O4CxSqkipdRmYB0ifPmglPpEKdVNKdWtpukzsy96Wom9e81RvQfzf9mxw8RovUokPV1fg2WmQz4AjS7W9xPx5MKyJw3TbqXnplHkCRTqzBJ+0tMrNzpQT+uanGyecH60sXj3YomgKlZfJsclc2ePO9E0jdu7306CQxzzcotyeWnmS2QWRCbF3H9/xVrJ1atF4GrTBh5+GMaOlTxdv/8ODz0kJuTevfXz2TVvri8YL14c0WmGT/Xu+n6RO/8AFcGAphegEg42O/Qbox8go4fySt3U/L3mO/EfA+Q4Ye2BtQHbvcpLl7pdIjqWTbPRunpgoXCv8jJ1y1RJMqongLe8BUPrbB6FGDF8zgdaaprWVNO0OGA44J/z/zdEq4WmaTUQs6LJiVgiQy//05495kwwdrv+RLlvnzlmy8ou4XH4sP52s8xspaR1kNI9eqz/0LCV6+GcqhR5AoU6s4Sf/fsDfyu73byC0HoLDLv9/49m6/6J9/totRIcCZzS9BQArul8jU8Yu8fr4fVZrwccIxRxcfDTT+GZ9bZtg3ffhWHDJFfXuefC++/LwiwYHTvqO8lPm2ZSkENyEPNb+nIoDDIYGE1SYxjwpzH5p/6fMashuv5aRd4imleN3LTau0FgDbE8d56UAjo4V9/K0Oaeo8rkZwYxixJKKTdwB/APsBr4USm1UtO0ZzRNO6d4t3+Ag5qmrQKmAA8opQ7G2raR1NOpLLFunXl+DlWrBm7bu9eciESHQ3+iNEvYSk/XP7aZ0Y+ltLpDf8BVbph5iSG5dw5k1cDj9dVsmSn86FnUq1QxLzrwWBW2PvkEvvxSBJmVK6PrS/N3zmf29tmlWq04exy3nHhLqWN83ZS69GrQq3T/PHcer89+nfT89Ija6dFD8muZETBy4on6/W/GDJMiEjUtuL/kplHm5tsqT+0BcPJfxgtc0Zo3o8XMBIg6TOzo0vU9bFGtBfZwUi740aN+D918W7O2z0LtmRyY7iGxLjiOvrxYRmOI3kYpNU4p1Uop1Vwp9XzxtieVUmOL/1ZKqXuVUu2UUh2VUqONaNdIGjcO3FZQYJ7Tup4ZYc0ac7LIB9NsmTVZZ2XpT9hmmdl8aHJZ8FQPB+fDvJtjFrgOZQeq6IIlkzQCvejAqlXNMznrTdRHi7CVkwO5VQIno73UZsQIKX1zww3Qq5cIMq1bw4MPwsYwA1Lvm3CfTxZsm2bj+q7X++xzV8+7SIkre5g9ysMrM1+J+FqefRYuvth4gat5c/3+N3Omse34UPsUdKeTLd8Z6i9Z8XmcLEkw7S5iNkvZk6DZdaUZ1iuNPXvKSgFU9ApFmMf46zR9P6ue9XtGdfpd6nYpXZz4no6i8OCCwC9U6x5d0eljjP8nXhgVU6+efioEs/wc9CKGNm82R9gKptkyK49YsOjHSslAHl9dyjsEY8u3sOYtiUiKkiJP4IOSlmae8OPvIF3Snlmh/HrPSjhju5ls3AjXXCMO5rW9e9BQPq867CE3V4SxzExJh1BUJNrpt9+WnGQ9esBvvwVvY/b22SzcvdBnW6danWiS1sRn21mtzvJ5n+/O5+25b3MwNzJlvabB55/D2WcbJ3C5XOL60LZt4Gder1y/KRrtGr31nZszV0PmGhMaDEGdgXD6XEjrKAJTpNjiwJEC3d6GnhUXYD6WyS7M1q2H6HK6dM2B4dCuZruAsj0gi5KiQh3/xoTa4SUtPcaxClEX07ixmAz9J8yZM2HIECnkbCR6EUNKicClN1DGQpUgGtqDJhlyPR79idksM1sA7R+G/f8FL9a67HHJv9X9A7AlhO7oOhmo3TrO8S6XeWZZPaEqNTVwm1HoaUKPVI6twkKJtnvppeD528I5BsD8+XDFFcHzWA0bPcwnUamGxraMbbR+N9Dh13/lnleUxwU/XsDUa6ZGdG42G3zzDVx4IUyapJ8MOFySkuCsYjmwXz+JZvTvh999J/5fhj8/tU8Obi5c+qhkOq/MSLO0DjB0Maz/GJY8IKla9PI6lUdzSvbyemfI2JBQuUFaR4JZ22eR6EykqMB34nPYHBFlji9PnD2OhlUasjl9s8/2PHceeW4vyQGLOUXEWshf6kiAQ1jY4LIjnyTQ0mwV06mT/mS5dKk5xYWbNtXXpC3Q0bLGSvXq+tcWTe68cPB69YWtSjND1T6l2IckRIObv4LxXYuFsnxf06InX5IcHl4GUwJTwnm8gd3GtPxh6AsYZgquetcSTFtpJsuXSzTeyy9LHzRC4AsmzLw791325/o6xykUe3L2sO7QuoBXRkFGwL7Ttk5j1b5VEZ+TwwG//iqO78nJkf+2cXGyoPr0U/j+e9nWq5e+k/zkySY9q84UEbj02D1BFjc6pipT0WzQ6lY4Zwvuzm+RmXgybhVPblEVcgpTySmU/4u88eTYmpPb9AE4bSb0++n/haAFMGHjBLILAlcfuUW5dKjVIbyDeD0yXnryS3/jYCkjDrt1zIV5uyXfWSSELWhB0ALXlYyl2SqmRQt9n5tZs8wpwdKhg74mbe5cuOACY/04atTQN3GZ5Y8WTLNlpkASQLd3YNIA/dpbJWStg39Phvia0OjCslw9OVtg2xjI2az7NX/neJBrM0sY0RMyzEzDoHfswsLKrXgyeTKcc054mp7UVBHKkpLkOd++PfLCyw//q1vSNWIu/eVSlt6yNOLvaRpcfbVo0a+/Xq5fqdAO7SXj0tChEhxQ3g+0Rw/9AIGiIvjoI7jjDmPGtayscr6Yza6D/bPArZOJfd7NcOq0So04UwomToTnnqvBnDk3kJh4A3ZyaV93IamuDJz2IrLyUtiwrzWHCxqSny9R6SNGSK3KSvExPcLkFeVht9mx+9VU7FG/h26EIrm7YOdYODAPDi+W6hzurLIcZ8oL8dW5sKgav2s2lKZRXqezsshGK6efBvTgPKlpeJxjCVvF2O2ibVrrl24kO1vU8b166X4tajp10heAZs0y3mRTrZp+Sondu2VANtpPLJhmq1Kp3h0aDJNEhhU5Xxbsl9QQYeLxBkqNZgo/waIDzULv2IcPV56wNWmSmLr0fNVK6NVLnOEHDxZhq/y+TqcIL8uXi7bo559Da6dfn/V6xHUOg7Fs7zKW7V1Gp9pBovMqoHZt+PNP2LBB/Ku+/loCZ+Lj5ZqUkr7cvj1ceaWkgmjaNPA4zZtLv9/pn/EQeOMNuO22qE4vgMOHywkl9c8M3tcOLYAVz0H7R003Jyol5tInnpBI3hKzsYy3Lv7L7Bf0uzt2yPeeeEIEriefNC5lzcqV8jvWrVtJ/qth8P6Z7/P+me+H3qkoCzaNhI2fi/+dZg900SgflFRwgIs5wMUtEN83byHU7CeFwwsOwuL7fb9fsF+2u3TKPh1HWGbEcnTvrr/9++9DD/zR0CBIOqglS4wXthwO/VXsgQPm5PU6oibE8pz4NtgMdrYDlI550szr0xNyzBTu9MxPhw6Z1155Nm4UASJYf+vZU4SPiRPhkkvEYT4uTgIGSl5JSaIZ7tkT3ntPJtz77tMXIpVSPD3t6YDtGhpV4quEfJWPSCzPjWNv9N0QbmHhcq8WLTXuf0Bj6TKNPYVVmZ/VmqmZXZif1Zq9hWksWqwx4l6Nps10vr9PSs9edpm+q8Lu3SLExZoGIitLErSW4kiCOqcG/8KqlyTPkompIA4eFO3gzTeL/2s09WZzc+X10UeR1x91V9dP27BXq03v3tClizyjNWvCSSfJczl37lGwONXDUwhr3obfGsCShyF9qdS7DOYLq4c7S76zdxLMvR5WPCvmXX9Wv2poSbWjEUuzVY6ePWUV7D8I/fUXvPiisW1pmhQuXrbMd7tS8M8/MpEYSUpK4Op+82ZzIuj00gQckcEkoRb0/wWmnRPanBghDlugfcZMB/Jg0YFmoZfp/9Ah8zPIu91w3nn6WiiHQwSnK6+UhUO451ISHPL005IS4uyzfT+fuGkiHq/vjxdni+OSDpcwrPWwCo//0syXWLDL19Fy6d6l7MzcSf0qxSv1GJ0jq5JOVdLD/0Lt2qAUl1wCH3yg38efegouvTR6U6LbLaban3/2+6D59bBvur4pEQX/XQSDpkJKC8NNitOmwfnni4BlRBqWgoLwFqPp6ZJ49vPPYX/eHtxxOu0roNwtOXBAXvPmiQk4Ph4uv1yEV73gqUpn33/yW7mzDMlNCEgEuDsbEuoGRoNv/Bw6PRv+sRJqR+i3deSxhK1ynHCCrJL9ha2NG2V1rJeLKxa6dw8UtkAGsKFDg0cRRkPVqqUL3lLWrNFf+cZKsJxMHk8l+22BrLQ7vwJLHopsRRYCuy1QsvJ4zNNuVXZ0oF6lrEOHzHlWyvO//0lf89fkxcfD339Lf4nWlzE5WRY38+eXbVNKce8/9waEqWuaxmunvUatJJ2yEn6kJqRy/g/nk1VYNpMqFE9NfYrPzjmyaQO6dhW/UD2/t717xUfsm2+iM2nl54sGMkDor3+2lM3RFbaQjPL/dIdeI2XfYCZFrzuiBdIff8Dw4cZbIEKRnw/vvCP50tzu6DSFHo8Ih9nZ8OGHEuRQmdegy+o3JGI71P13pEDNPpDaToKR4tIAJc7uh5dJ8e8Dc/RzrHV4DBY/5Bsd6s6GeTdCz89DFxUvIVjus++OgmSAQbDMiOXo1Cm4b8dbb8UWll2eEtV2jx76k8fEicanmtDTVuzYYc4EarNVvjYmJK3vgKZXGJNZ2haHPTEw/b+Zwo+esGWm/1TduoHb0tONfybLs38/vPaa/kTzyy/SV2L1c7HbfRcw49aPY0v6loD9+jfuH5agBTCw6UDi7L7hg4WeQr5d/i3bMrbFcroxo2miIQ+2wPntN/jxx8gn9+xsuOUW2KRXcM3mgI5Php4wvYUw6zKYd4PUynPnQlGmmJEKM+T9lq9hfHh1+aZMkeus6Dq6d4d77pE6k0uXwooV8N9/YrU4++zInq85c2Tx/cwzcj+MyMxfVGRO5HtELBwBy54ILmjVPwcGToIL9kGf7+GE56HZ1eIf2+BcaHkrdH1NSiddeBhO+haq+v2Oza7RnyC2/gA7xhqnSTvKsIStcqSmyupXj5EjjTOjlDxnHTvqO6dnZEhpDaPIzdWvAKGU1F4zmmCaLbMyrIdFt/eh/lmxCVz2JEg7AUfdQAfb3FzzzGzBUjGYRa1agW16PCaVeinmgw/0t994I/Tvb1x0bslvpJTi3gn3klPkO7CnxKVwZ487wz+eZuPmE28OELg8Xg+PT35c3lRy+ZXy7Q0fHrrk2O23i3Y93IVkTo6URPr22xA7Nb8+PF/JrT+IQPV3V5h/Kyy6B2ZfBWNqwpzrJNKtAtaulajVYEKKwyGm5w0b4N9/4YUXRLDq1EmCDPr0EdPd119L4vbXXtNfbJTno49g4ECxFAS7b3XqiObwjTdg+nQR7FasgNmz5fs33ywR8EcV6z+GDZ/oWwCq94QzlsJJ30CdQRI9GJemH0XoSIK4VNFaNroEBv8H/X8DV8Oyz094Xj/h7OyrYO/k41LgsoQtP4YP189zk54Oo0bF7lCelyedDcRZMtjxXn9dMmEbgaZJ2RI9AWhV5GmBKiSYsGWUZjAqbA7o+wN0ey+6Uh72RGgzAk6bjSM+8AFJTzfPzKYnbBn1bOhRrZq+FsusigOFhaI59hfmatSAN9/Ud9iPld/X/s7OzMBQPZtmY2jLoREd64auNwQkOS3yFvHTqp/YfHhzZOVXQql/w/3+njITS58++mbhEvLyRJj944+Knclzc+Gxx+Cuuyq4IfYEMd2HYw4CyFwrZX02fi5pBcI093u94ncWbFxp2hQWLZKI1ObNxW9VT/B0OGShnZwswufatVJGSY8XXhCndj3hTtPke4sWiT/su+/C3XdLgtn27eXVq5cIWm+8Idq19eslDUelJXwOxr7psGiE/r1v+xAMmizmQmeE+TBsdvHNq3cGnLkC6p4m21vdCdV7gOZ34coN08+Fde+LhtNrosmgkrGELT/OPjv4pPn887Gbi5SCV1+VvxMSxDyix4QJxkyoRUWiNm/fXl9NvmCB8VqSlBR9ASEriBtHpdL8WhiysNhBN1k/MqY8jioQXwNOHg8nPAs2u24R8YwM8wbMYIKPWZq0atX0Na66ZiMDmDFDv1/dfrs5fnBe5eX+CfcHaLWcNifXdr4Why0yV9amVZvqJoB0e908NvmxmM41VjRN0heEEliLikRoeeghiVTMyiozU7vdIswsXSrpON5+O8yGm10t+etM5MsvpRyTnnw6dKho7Nq2jSxfVkKC7P/FF3L88n3s999lDtAzV/bsKQvXzz6TRXTJcYL1UZdLXi1aiBlz61bxgTsieN0w81J902Hnl6HD47EHM9ic4KwC/X6V95omi1+dgtUor/jY/tNTcnm5c8JLenqURzNawpYfJU7yeuzYAc89F104Mcj3Hn/cNzhp+HB9E4nXKw7DsQooRUXiV9CmjX7HnzvXeI1T1ar6bWVkBG47IqS2gbPWwil/Q9OrRehypIAztfhVRcr4NBgmfgnn7YLaZfUWa9UKvD6PxzwzqZ5wl55uTh1NEGFLT8jRC+YwgjlzAjUFmiZaAaOLNAOMWTWGPdmBDrYOm4Obu90c1THv6XkPyXG+Eo3b6+bXNb+y4dCGqI5pFJdeGp7W9YMPoH59uOgieOUV0cA//7xoxzp3lvxnYWNzQL8xohE2gYMHJfmo3tg1cKAEGSUnR99HkpLkPnz5pbxfuVKiBfUErTvvlCS0bdpElwg1OVnMjt98I1q4Smfz1+Iz50+Ty6HV7cbmRSsvtCXUFI2ZI0gkWMYKCab4p5eYN3O2idBVmAGF6fJy54gG7OB8MUMfxVjRiH5omtQX++Yb/RXTK69Ip2vTJrLIOo9HVMZvveW7fehQUUvr8dVXIpxFm8k4P18SJK5aJWZEPdX3zJmhfTqiIS1Nf7upuZrq1Ik+xN4ONAJcyPIjD9gOFPwO/B6wexq3Ep/wPnn5vhJJTo451QZq6fhqm6lJq1ZN/9lfuVImG6MFoMmTA7WrrVqZJ0w+NOmhAK0WQLOqzWhTo43ud5SSUP316+WVkSELGYdDJuYGTc/D7bkx4HsF7gIen/w4oy8cbfh1hEt8vETMPfRQxQurktQz//xjQMPVukKXV2Hxg4ZFApfwzjv6i5umTcXx34hnNClJUkmApA3RE7RGjJB7a1R7V18d+3EiQnlh6SOBqRjiqkGPj8M3BUdL1c5w2n/w7ykiROlFL2asgAV3yMueACmtZIGs3JC3B3KPbCBKuBgynGmaNgR4G5m2PlNKvRRkvwuAn4HuSikTqgAaw4UXispYz4zn8Ujky9y5kUWv5OfL9/wnsaZNJVJQL9NzYaGsSidNiq4z5+fDrbfK3ykpsoI6fNh3n5wcmTzat4/8+MFIS9OPljOr8DUQWy4jD6BfmUeXNNJx5GcBviuyrCz9qM9Y0RO2CgrMi+6sVk3ftLx+vTyTRgtbehqzYOZ1I7j/pPtZc2BNwPYrOl7h8/7AARg3TrKRl5g64+Pl2S4qkv81TQQup9OFp/HX2BpOo2ZNWYw1bSr97rTmp5l3MWFyww2iKa90v8mWt8Gef2HXePAaE2HhjavDRx/p+7uOHGns4jE5WRzcly8P7G+DB4ulw8j+UOmZ5dNX6Dujt3+USjN8pXWEoUvhv4slcWoo53hPPqSbpGI3mZiFLU3T7MD7wGBgBzBf07SxSqlVfvulAHcDc2Nt02wGDQqd7HPlSkm++Ouv4XWO7GzRlq1fr//5eeeJCl9PQJk9WyJlrrwysk6dnQ033eSrTWreXL/Q9V9/iebLKE1C1ar6988/z9exSlUOY9MpbmqWmbRqVZnk/SeXnJzgWsRYqFtXfyJbv94cbZOexqBxY/Mmntu6h65Vs2ABPPywCFhxcb5uA3r3pVTDsuwCWHYBe4EsF8z1iobunchLJRpOfDx8/DFccUUl53HSNOj9FYzrCLk7QcWQRdnugo5PMW3vg7pa+vPOk9xiRj+jDz8cKKQ6HGJ5MMPMXansm6avTWp+HTjMMQHr4qovUYvbfoR5t4hQZZBwTkIlRwMHwQjRtQewQSm1SSlVCIwG9FIvPwu8DJgYQG4MSUniSxXKTDhxIpx2mmgzgvnqFBaKdmzgQMluHIxzzw3twHrffZKiIdzQ+5wciS766Sff7SecoL//xInR+6HpkZqqf0/27jU3H1VlkRYkm7dZmru0NH2fGyN/s/I4nfoRbNu2mZNrS2+RESyi1UyWL4cBA+Q1ebI8w9He49xc6a9m+blFw3nnidbeaLeBCnEmw+nzoVqX6FOv2BOhyyvQ7kE++kj/d3n6aeMjV3NzYeHCwO3XXHP01DeMid3/iGBTnpQWYDsC4ZGaBo0vgWGboeMTkFhPzIWRRI7bEyXtSO1T4eS/4TIVPAFqJWOEsFUf8XApYUfxtlI0TesKNFRK/WVAe5XCiBEV+8TMmiX5WkaNEn+ozEwZBDIzpZN+8YXk0iqftVqPAQNCC3Y5ORIyvG5dxUnvsrNFyLr88sDPOnfW9ymaOdNYXyO7Xf96tm8/CrIjG0Aa6Xh1us4Gk/yg09L0Aw78TcJG0rx54Dav15xr1BMkMzPNqduph1Lw8ssSUTZjhjyjwUy0HTqI4/R110nI/g03iHtAjx7mlzMygg8/FEHaKEE27HEjoYZoLhpfGpnApdklaOXk8eKsjSQi9f99Wrc2J2/VnDn613j99dH70h5V6JW8SWkZXvSfWcSliRnz3B1w8niKGl9HrrMdHhVHvjuJ7IJUsopfOYWpuL1xFFCdvJQBqA7/g2FbYdBEqHf6kbsGHUx3kNc0zQa8AVwTxr43ATcBNGrUyNwTq4COHaFdO/1VTXm2bBFz3SOPwCmniFYnPV1WxuFOhg6H+Fa9/nrwCSYjQ6KCfvtNBK+EBF+BpqBAJsNPPgnucN+2rWgm/DVkeXmi3fKvGxcteXmy6vPPy7Rpk4mardq1Y65BFy5VOYybQAlh9Wq5t0Y7yael6U+OmzbJc2oGnTqJEO7PhAliGjOy7FL9+lI6qjyLFplzL/0pLBQn6KlT9Rcydrskzbz4Yjj9dBEMS8pO2e3S50qeabtd/Ht++glGjz4KsoHr4HJJKpjevY05v4jM2DYn9PoMavSUwsbeouBlfWxxgA2qnSiJNJObALLw1HNHuOQSc0zcc+cGLhAdDlm4Hh/orSqiWDX8UieCWoU2uCz0RODxwIQJGu+805fJk/uSkAAej5e6yRupW3U3ic483F4HOQVJbNzXikKq4XbL833zzbIIatIk8sswEyPWYjuB8qUzGxRvKyEF6ABM1TRtC9ALGKtpWjf/AymlPlFKdVNKdasZKhNfJfHcc+GrpQ8elHDjzz+HMWMi1zrcckvFq83sbDj1VElCOH68DDwejwg1n34qk2AwQQtk9R1Ms/Tpp8b5HOXl6d+3TZtMTN4XSeLIUITx/bS8PRTqhLRv2mSONiYtTf+0V6wwr2xP+/b65qYpU4w3X/bvH7ht4ULz/WG8XnEXmDxZ33H8iisk/9HIkbJf1aryXJckwExMlEVFlSrySkqS6OK334Zdu+CBB46CZJU6nHCC+G/FYk602eQ+jBsXxZdb3Ajn74M+o8XcoznF/ONIFhOQMw1aj4CzVkukWrGgBfLM6z0XAwaYc68PHAgMFqle3dxyWZWKMy1wW+6OivMP+hNRUejgN8/rlZQjtWqJAP3332XuODk5NjbsbcmMNf2ZsPx0Jq8cxNwNvTiQWa3UmnTggFQBaNtWxhX/RdyRxIi1wHygpaZpTREhazhwWcmHSqkMoEbJe03TpgL3H83RiCWcfrpkFDDLPFSehg3Ft2v8+IrlgUWLotNCpaSIqn316sDPxo83ZgDJy5NcMampkpesPLt2mV/MuDJISAiuaTKDtDR9jeDGjTLAmJFhvWVLmbz8tR9Gm5xBMmx/952vEJedLX6OgwaZZ567+25JceB/jdWrSz3Grl2ju7cl5qWnnhKt99HIlVdCvXriL5qTE1lka1ycjItTp0rEZUxpVwCcQFIRxAG5QE4BqJcRF19fVnM1HtcX+OsJOgTmlDWEo6rGqxnUPgX2z5B6lSVkLAet8rNCLVsmz+XGjbFFzZYseGfOlD58tLiuxDyMKaXcwB3AP8Bq4Eel1EpN057RNO2cWI9/JNE0yedSWREnTz9tvvPqmWfqT15ut5ggY61/p2miJatWLfAzpaTg8PGA3jOxebM5z0pamn505+bN5tVIbNlSv80DB4wPBOjeXf86nnvOvIFy5kzxqfQ/fs2asGSJ+G/FKsQmJRULI0cpgwaJmaxmzfC1Qi6XZEhfsqTctcVqvi8C0oF9QDb6lq1isknGnRuoPjYj5QqIhsV/gXjwYOUHb5hG7VMkgXN5lLe4bFLl+W299Za4xyxfblx6Eq/36DLlG7JmVEqNU0q1Uko1V0o9X7ztSaXUWJ19Tz4WtFolDB0qaRvMiMLyp3t3Md+Y2ZEHDw4+ibzzTmzarcJC8QfZvTu4s+qWLdEf/2hCr1htTo45nTs5WV+ztWmTeZrCRo2CR9lOnWpsW/XqiXbL/7mfPl2CS4zOzO/1isbJX9BKSJBrq1XLuP5upG+bGbRrJ6a5u+6ClBRFcrKepCPb69dXvPCC/C56VQ0qg0LiUDrTlln3uXfvwAWwx1OxL2+lEazIebjFz6t1Q9est/Qx/ZQQwYgyvYJScO+9UnMzLy+41tDlEhea++6D778X8/Uff0j9yauvNjZPpFlYGeTD4KOPxIG8MqKjPv5YHOHNkshDHXvXLimQOmJEdGHNbrd0HJBB3OkM1I6sXg0nnRT5sY82WrSQgrX+7Nwp/jtGYrNJUWZ/BcKOHeYtAux2Ga/1ku3+9JNoSFNTjWmroEAqJcyaFbiqHT5c7rOR/jh//y1pLPy5/37J73U0+lkZjjsH0pfD4SXUPDCXVwfM47lO2/h5zrm8N/F2dh6qT25hIikJ2bSrv4r7znidUzrNQ0ttC8t6Q/Vukv27SptKDU6JoxCbjuqrJGjBaHr00Nf2f/SR+L4d6YhE7+5dtHinBZvTN6Oh8daQt7irZ0WVwsthj4O2D8Kql3yz/GdvgjVvQpu7itMvVECw9ArfhdYc3H+/zHnBNNhduoiAdd55suhKTPQd87xeGTM0TbTu77wjlhWz0uLEwjEQrHzkqVpVnGSNMhGFOk6XLnDbbeaZE5OSxJE+GC++GJ0pMSdHoilLJudmzfSvYd68I5DF2gSCaSDNWvHqBed6vTLAmEWnTvrb//7bOIHE7ZbIvX795JnxN3Hv2yfRgEY9M9nZkozY/3hVqkjyyuMid1IwMtfBwnvh13rwUxpMOR0W3QebR0LmKuLt2Vze5xtmP92bbe804sBHNdn8VlP+euBMBrafjObJhkPzYd27MP82mNAHfkiq1OCUKqPew5Ec6DRoVsLk+HhZoPr39e+/Nzf1Srh8u+xb9uXsw67ZUSienPIk2YURShptRkgtS3+WPQ77ZkjtQRP44w8RWvUErbQ0MfPPmFFWPzgtLXBxabOVVUdp0kRcD7ZsEeHsaMMStsLknHPEpBirEORywQUXhN7nueeM9UHwnxjPOiv4KjA3F+65J/KVQV4evFSuSFOzZvr7zZt3fCQ2bdlS/1mYOtWcVVUwAXnJEuPbKmHIEH1n+Px80fQaFVDx7bcymf30k357M2aI+TsjIzbtck6O5Jn666/AeX/gQPP8344oSkmpnL+7wfgTYN17kLdbTERFmaLN0ByQ2gGaXAFd34S+P8OAP+GUCdB/LPT5ATo+Aw3Og+Tiju3OlrQNsWSEj4L27fV9TpeamKX/pZcCn8uSSNYj6Xxd6Cnk/gn3+9T5LPQU8urMVyM7kDMZur6jk/9MwfRz5fkpMnZQ27UreDWDDh0kp+Tw4bL4iURj6XLJ3PnVV1J5xaz6qtFgCVsR8N13MihHq+FyuWQC+/zz0PslJMjKySjtlv9AcfrpoVfw33yjv/oPRk6OCHDlO07TpvrmyhUrKsf/zWyaNdP3l5o3z5yw8A4d9AeOf/81z7zdt29wn7APPjBGqMzMlMSRIIkpX31V/9mcPVvqDU6YIO1Gco9zcmRwHzYMHnxQFDH+nHLKkTcJGU7uLpg8CGZcBIcWSqbwEuEosT50fArOXg+X5MBpM6H7+9DqLmh0AdQ/E+oOhgZnQ+OLof0j0OtLOGMZXJILQxZC8xvDMzEZSPv2+hP01KmxB/cEo0cPcX3wF/Jmz5bEtkZq6iPpUx8u+DCgoHqeO4/XZr/GgdwIVd7Nr4Zm1wUKXN5C+O9CWHyfFIo2SMt19dX6v2OXLhK8Ur16bPNfcrJot8YGeI0fOSxhKwLi4qRA9bnnRm5ucLlEUv/pp/Ccmvv2lcRssZo1EhNFHVuefv0q/t6115Zl0w5Fbq5o6ub6VbxMTdW/TrdbVi3HOs2a6WtCVq0yx+enaVP9wWf+fPP8+zp1Cu6cPnFi7JqgvDwR2spz663iCKu3oNmzRzTM55wjKRvy80Xbpacpzc6W16ZNknC4eXMRTEF/pVyt2lGSAT6UqW1cZ9FQTToF5twAq16BLaPFPOj/vQNz4c82YgbylJuQkxpDn+/hnPXQ9qGy0izOKvIKdhNsDohLBUcS2BOgWlfo+nqll0JJSNAPThk92tyUDB9/rD8Wf/mlBFuU5DyMhdxcccUIh+zCbJ6a8pSPsJVYXMvQ4/Xw1JSnIj+BE9+ShLM6OQTZ8An81hDWvA75+6EoCzwhIlfcuUEFs1U72jJzZuD4kZoqLgpVQjyGkZCUpJ/D70hxNAwvxxR2u2h+wq2NpWkycdx6K3z2WWQP0dtvw1VXRS9wJSaKFs3fbOlwwKWXhlbPejwiVE6cKAOJvyYhN1e0EldfLROfHvXr62+fNu3YTwrYoIH+StrjMSeRXtOmwX3EzEpN4nAEr6fp9Yp5JRbtltcrk1h5SsyJoTTIU6bAGWeIH9u118p5/POPrIinTZNn/p57xNemeXOJWCr/W+lNinv2HEHzdlEWbPwcJp8OP6WCXuBBKpC+VDRU+6bCps8lYmzeTTC+C/ycBlOGwqaRkL4SJg8uNvOVm9GqdxfNVMMLipOIxqg6d6aI8FXJnHxy4Di6daukDTCL5s1F26+34PnuO9F+zZkTXX/IypIi72eeKel/wuGVma9QWCzsaGic3uJ0vEoG1QJPAV8u+ZJtGTpRIKGw2aWeYP1hYNf5Xd1ZsOxJ+KUWTD4Vlj8F28dAxmrI2Q452+DwUtj0lVQIGKdf3uKFsY/oLuJGjjQ+uOho8sHU1FGaoa1bt25qwYKjO0PEf//BQw+J30xRkW/kndMpr27dpO5ar17RtaGUtPH++5H5ByQmyiBw7rn6n8+bJxNaOCrwbt1E8OvZU86noEDyorz8sgwUwc576FBZqfhzySWS08vojhU2oXJrRNAfatfWd8x98024807joqPcbqkS0KCBvslw61Z9B3ojePppeP55fS1WQoIEROjlVKuIkoCKp4oX4P633eORQJFvvjHeL6ZpU8lRVp7TT4cffjAuwjIsDi8V7dSOX6UGoNsAu6wjGap2gf3/4ZOwqkprKQbtPMK2UgP63uzZ4sPnP3adeqqUMzNzgv3wQ4mOC6ZNHjRITNX9+0tfdbkCNfxut5x7fLwk8nzzTXn2Si6/otuwP2c/Td5uQm6RdIwkZxLjLh/Hm7Pf5Pe1v6NQOGwOLmh7AaMvHB35RSoF6z+ExfeD122oX156Tip1b99NfpGv1HrCCbJYOpqEo2jQNG2hUiqgOg5YqR/KiCILcl9gJrCQroxnCFM5mXTSqMphBhZNYUjR33SZvgR663x5715J6FMBmgavvCL+JC+8IB01lPkmMVFWfT//LP5hwejeXSbJcIStBQvKInL0aisGo0MHfWFr1ixjHRcLCo6MH1jjxvrC1vTponExauLOyQkdMDF7trHCVnZ2WS62/v1lMsjMDNwvP19MdK+9Frm/k9st/lnBsNtF63X22VL0OSvLGJ8cTRMz5Hvv+Wqypk+P/dhhk78PFtwJO/8Ab4EkkdQjuRnU7CemP2cV0SK5c8SxPWcz7JsOOVt9v+Mujhj0T4/Q8Wl989AxSK9eEiHuP3ZNmiSm4iFDzEvfceutkoblmmv080L9+6+8kpPFD7BdO1mslhT/Tk+XCiCrVomfWTRJnp+e+jQeb9nDWzWxKv0aiW/IpM2TyC7Mxu118/va31m5byXta0WYhErToNVtUP9sWPIQ7Pgt9HMaAXM29CLOWRQgbD366PHhyxsKS7NVwpFICRzhvd+4UZK//f67CCslKmubrSzx5V13SU22cJIOvvWWHM+MiBqlRHs1YoT+8bdsEWHFCLKyIpzsDdJsXXWVRLz4k5Iig6hRg0dJQeYmTUSL5c8NN4hAZFTZnpycshVmTo48S3rZ5EGewy1bgpuMgx3/ySclp1sJoW57drZodz//XISwaJ5Xu11+j1atJBfP0KGBk/Vtt4m21ozyR6XsmSwRXt4C3xIpIFqpJpdBvbOgVl+pE6g8sr38M6uUCFWaHTx54pe16y/Y8p1EFzpTociv0OnZ6yClZWTnGnZx4YoLC5diUN97+WX43/8CNUx16oggY1TS1fJ9oTwrV0rAxd69xkcfh7oNW9O30ub9NuS7ZeWR6EjkmVOe4f6T7kcpRcM3G7IzS/Lv2DQbA5sMZOJVE2M7oYxVsOI5ySqPvbh4eJi/leYQHz97IjS9mqfGPM0LryT5KAxsNrnPZhedrwxCabYsn60jiaZF9GreQmP0Dxrr8xvwQfaV3MoHXM433Ot9la8yh7EzJ5UXXtSoWk3n+zoqmKuvNtd3qmPH4Bqsn38OPoFHypEK2e/RQ3+AyMoyNiVDSbResHQa48YZqyksP3kkJYm/SjDcbjj//PAjsvLz5d68807455OcLGb0nTvFpNm4sZxXcnLouTshQUzVLpdoImbOhMWLJUCkS5dAv5+PPxYtrmklPjaNgmlnyWRVXtCyxUG7h+C8neJ03uBsiKsKDpeY/fwvUtOK/aVcEF8dGp4rKRvO2wXNbwgU4gAOzhOTUCSEXVy48h0wg+Ui3LMHTjvNGAEoOzswgKOE9u0l2e6774qmywjzV0JCxcd5cNKDuD1lv6NXebm287UAaJrGPb3uweV0lX42a8csZm+fHduJpbaDPt/BhYeh7w/Q9Eqo0k4EKFtCcXBFavGrCtic8ne1E6HVnTBoCpy/F7q+yr/TkgLG6/btja8ScVSilDoqXyeeeKKqVGrXDjct37H70uGqq5SKizOnqdxcpZxO/c9791YqIyP2n83tVurLLyP8UoT3KBhz5ypVpYr+Ye64Q6ns7AjPS4esLKVuuEH+fvRRpWw2/fY2bIi9LaXkfn78se+2//1Pqfj40Lftoosqvl63W6ktW5RKTY3ptiuvV6nfZ6xXcRddpy6/4YDq1k2pxo2VqltXqYYNlWrdWqlLLlHqnXeUmj5dnkN/li9XKjEx8DxcLqWWLFEqJyeycwpGfn7xH7smKDXapdS3+L5+a6xU5kaligx4WJRSqjBLqYknK/Wt5tvO2BZKFWYo5fWEfyz/cw31CheD+p5SSv38s1JJScHHl4MHy93/CMnJUeqFF8I7rbw8pT74QKkuXaSfuFzhj5Px8fIcNm+u1HPPyTkHY8XeFSrxuUTF0yieRmlPa+rc0ef67HMg54CKfza+dB+eRnX9uKvyer3BDxzL3JeGUk1QqiVKtUCpxijlCr5/EzYFbL78cmPmgqMBYIFS+jKNpdkqIZIsyEoFP06s369kXnnFvMRviYnBfYnmzDFGs5WTA6NGxX6caDjhhOBakDFjjHGQdzjgl1/k7969g5u4vv/emHxbOTmidSzP8OEVR9H+9JNEBepFroKY/tLTxY8lIyPw80jQNHhr400Utv+CPf0uYf58MWXu2iVleNaskVQAd94pWiw9DUiHDvDMM4ERj7m54hP000/BryVcsrOLU6LkH4CZl/iWQwFJuzBkESQ1Mi6qz5kMPT4WDUN5sjbApAHFIfthPihR1rurLM4/H048Ub+fzZ4tedv++y94EI8eOTlSaPqss8SPKBwSEsSXa9EieQ5few2a9ptD7aYHSEwUB/mEBHnFxcmrRQsJXnr2WXGS37BBXDpCBZvc/ffdpeZDAJfTxYheI3z2qe6qztAWQ9Eo04auPbCWvzfoOM+WEEuppXRgC7Ae2ABsBUKY+QsJdKaLNHHpsYolbP0/p3ZtcXI2K31AsDwnSknm8FgFLq+3kp2byxEfH9y0t3u3+HXEyoIFcOiQ/N29e3CB6vvvjTGnapqkVihPq1bh1bV97jmJxlq5Uia47GxxrM/NFRNds2aBUYDRMG3LNObulMRus3fMZvrW6B6A++8Xfzf/Zz8/X8yOQ4dKHrPs7PDNHG63XPOmTRIkMWAAsOYNcWwvj+aA/r+LuUWvVEosVGkFg/4VgUsrN7kdXgJ/tYet34uvV0UJKs/fA5cp39dRhKZJtGqwqOYDByRC8YILxISclye/TbA0Nvv2SY3Opk0D+0C41KkDfc9fzuZBvUm/rgF7DmWxf7/4Wm7ZImv6vDxJ9fDrr+Jf26JFxcf9bvl3TN0yFVXOV0opxeeLPufq3672eeW587DbyqSXnKIcLhtzGe5Izcgm4CBwwC8sPPZTAYWD5SAfLcGcRcK9n1FEP8ZE7dr6qbORyaVRo+giY/Rwucp8eEaODJ5luW1bESaiFfTy8iSq7MEHI1QWGuSkC5LQ8NNP9T+77DKp/RVtZvKsLJn0f/ml7LRq1JCVtx6xpoDweEQAvvrqwNvw9NOiuQpHe6ZpkgupSROZyObM0XfsL0+4t10pRfsP2rP6wOrSbe1qtmPFrSvQoghy8Xrh7rsl8W+oYrjnnCOTdqtW+trM+HjpXr/9Jlmrp00ruSaF+qW+lMgpT4PzoPcoc1Mx5O+D2ddI1KLHrwMm1IJm10LTqyC5ufh5Ka9oxjQdNYO3UISzn4N4nocriBnY90pYuFCE2or8Bhs3Fu1wSR1Oh0MWMjNmiAZy0SL9XGuRntaALwcwfZssAB7p+wgvDHohsgP44fV6SX4xmTx3bM6Et3W7jffPfD/wg0qci05kAYs40Wdbr14StV6paVdMIpSDvCVsRUuswpYRbRnY3o8/Soi9EaUnatUq67urVkl+rmAOq3//LavPaNTIubkyoe/ff+SEra+/Fmddveuz28WpOxytkB7bt8sEUd7yPGRI8CSyI0aIaSzaaLqcHDHzzZ8feBu2bBHh2KySKOHe9h9W/MD1Y6/3yZyd5Eziy2FfclH7i6Ju/6ef4Prr5fpCaVvT0kTgSk4W80dengjFW7fqr2XqpO1m94fNwOt34/r8IGVwzEYp2DtZklEeWgR4Ax3oNZsIXFU7Q9Wu4GpQ7PwcJxowT55kqU9fArvG6bcThrClFGi24H2vXVtFXFyZya1lS9HmduwoZt9Qi5b//pO+YUaR+0iGhMmbJ3P292dT4C7AozwkOhLZeNdG6qbopL0Pk/9N/R9PT3s66u+XYMNGxiMZJMfFEG4b47w3YoQEx5TXZMXHi2bRrHQdlYnpwpamaUOAtwE78JlS6iW/z+8FbgDcwH7gOqVUyLWuJWyF0ZaB7SklQlFJgtZocblEtV9Sdd3rlUkp2CTdrZvkm4k0mqegQLRmt9wi74+UsLVuHXTtGnyQv+mm6PJQZWXB7beXpZYoOa1XXoEnntA3ayUlifkyWk3anDmy8i/fXnm6dRMtghmEc9sLPYU0fqsxe7IDpZq6yXXZcs8W4uzRj9i7d4sZ6bvv5Lk1IkKqZd2NrHurc2DC0kFTofaAyA4WayqG7C2w8VPY8i3k7hSBSnkDtV6RklBbt2xPURGMHy/avvnzxS9pS34dahN4DXuoTV0Cj+FyiQYqN1f8mdq3F82h3oJi6VLRQO7fb1xEqaaFb+LyKi/t3m/H2oNrsWt2PMpDnD2OS9pfwlfnfRVV+26vm8TnEw0zAV7Q5gJ+vuTnincMRozz3pgxsqj3z9s3fXp4ZeSOdkxN/aBpmh14HxgKtAMu1TStnd9ui4FuSqlOwM/AK7G2a2EsmiYDY9260Tsrulxi0isRtEAcq9uHyKm3YIEMkpHa7L1eSQNwpGnRIvS5jxoVnfCalyd+WP4ESzcBIvC99VZ0eaiys8VBNxR33WVyDqoK+GD+B2QViLezTbPRqEojbJoMYZkFmXy04KOYjl+3ruTy2rChrBxXlSqR9wdNk+/Fx0P3PjX0UzGkLwNvhA9GrKkYkpvACc/DsC1wcTacOg26vQstb2WnoxZ5qngi1eyi1bLFSxh/yTThrAo1+0LbB0Uzd9ZaGO4OELQ2b5bghBo14IorpH7gihWy4KrDHjQUDruibRvFRRcqbrpR8fwde7jnHnE0v/ZaqW5Ro0aZP5XbLT5VU6YEfwZPOAFWr4aLLzbGB9XliiwX4OgVo9mRucNnW6GnkJ9W/cTq/auDfCs0j09+XFfQcjldpMSlhHw5dHwBf1nzS2kfOhL07q2/iHn22cgCGY5FYtZsaZrWG3haKXV68ftHAJRSLwbZvwvwnlKqT6jjWpqtMNoyob3t20VTc/BgZId2uSTJ5wcfBJ7uAw9IAstgQknv3lKDMVztVm6uRJtdf33ZtiOl2QLRCM6bF/zzO+6AF18MX1DJzi7zI/I/rawsySYfTICrVk1+w0gnm1WrfIVivduQny8lg4L5jMVCRbc9Iz+Dhm82JKtQRmSX08X7Q9/njvF3lJoUq8RXYfuI7VSJN6YOlNst1Q5+/snDuD+L2L7Tid3mJc5RJI7KCihOY+f2OCkoclCnZhEnn2Jj+GVOBg4sjoQc3w0O+6kEq3aFwdMji0L8LgKftDB9qJRSPPLvI7wy8xUUihZVmzHj6n+pk1hVhEF7fJnQVYFPXHa2BB2MGiW+T+Wf0fh4qQZw0UUyvjRqJNppr1c0VzZbmRbJ65V7n5goz9yaNaL5GDVKgi/C6aKTJomWctkymdwjqXuZnCwLmgcflL6rF83qT4G7gEZvNmJfruQzdNqcxNnjyCnKwabZOKXJKUy6alL4J4EIag3eaMD+XF9n2mqJ1fjgjA8q9FFctX8VL/33EgXlok/j7fE83Pdhnj756YjOpRQD5r1TThFrhj9Ll0rGfbOi4ysDU82ImqZdCAxRSt1Q/P5KoKdS6o4g+78H7FFKPafz2U3ATQCNGjU6cWtFXrVHkuNU2AJZHfbqpV+iRY/ERPGX+Pln/RQBf/4Jl18e+nj/+x/ce2/FAonHIyvnTp18TQUR3YZgDqEhgghC8eST4jweSoP1zz/B0xCUJy9P9i2vHQTf62vUSASqYLzyiviRhSu85uTIJDh+vH575fn0U/G7MNo3pqLf74GJD/DevPdKQ99bVW/FmtvX0Pq91qw/tB6QbNp39LiDVwYboDjP2QrbfoEdv8DB+WCLRynF7oNprN/dlIzcVArdcTjtRSQl5NCi9iYa1j6I3a6Jj1OVttDwfGh4HhxcIOV5/M11PT+HxsMlOWk4GCxseZWXW/68hW+Xf0tuUS52zY5Ns1ErqRYzr5tJ47Tw1TpLl0q9wuxs335ZrZoILbffLkJUtPVQCwulf23ZElpTrndeL7wA4//2UFioER9nIyvL93lzuUR7WVAgvmEPPAAXXhjZpP/6rNd5cuqTpfUK+zbsy4r9K0jPT5c2nC7+vvxv+jUO31b29ty3eezfx3z8E8tnjK8IpXwzypfgcrrYes9WarhqhH0upRgw702dKqk1/MeQZs3k9zJSe56XF56wbBRHjbCladoVwB3AAKVUyLgmS7MVRltmtYf4bl1yiTh4B5tYbTZZsV57rZSLCebgWOJvESqSrSTlQK9eocvcZGdLdNiGDb7bI7kN+3P20+b9NhzKk5wKXet2Ze4Nc3XV7uGwYIGs1kJlrU5NFe1RnTrBc1Z5vSJEtW8feM/LX9+VV4pfXDCcTvGtatMmsAiuPzk54hd2663B2yuP2y2mlV27Qh83EpKSQt+7HZk7aPVuq9JorCRnEm8NeYsbut7AZ4s+Y8TfI8gukgMkOhJZf+d66leJoH5QCUpJAeeVL8DeqaABnhgiAmxxYpK7OBsmnASHFoIqZxKyJ0D/P6BGb3CGIRmH7bNFhcKW2+vmsjGX8df6v8gtyiXeHo9HeXB73dg1O2kJafx33X+0qdGmwqZWrYKTTgrMn9alC0yeLP25Mic8f/5e/zdnfncWZDbg4Sa/oe3tTEaGCG8pKTLJ9+wpjvgV9Rc90vPTafhmQ7IL5RlMiUvhuwu+Y86OObw267VSzVL7mu1ZfuvysKJmswuzafBGAzIKfG9qvD2enffupLorRLHUcrw26zWemvpUqRBYcowbut7Ae2e8F+4llmHAvKeUBNusXRv42fnny3hkhBm4oAB27AhdAcNozC7XsxNoWO59g+Jt/idxKvAYcE5FgtaxgNILMYs27OwopHNn+HLCXK57eAlVq8pqw+WSgTMlRQSrfv0kXPr990NHkrhc4msUCqWko23frj/xFhWJYHDZZYGCViR4lZcLfryArIIs7MUh7msOrOHRf8PMYKhD164V+/VkZEjepsxM/WCBvDxJ+qlXs8+fCy4IrSEoKoIzz6zYd8vtFi3hXXeF3q88DoeYhI0oT1JCRRPxgxMf9PFbUSgu7XApAJd2uBRvOR8lt9fNg5MejPwkMtbAhN4wdSjs/keiB4MJWrZ4iKsGifUhvkbwAs/eQtFyaTbo93Nx6Z1yQ64nH6aeDhs+krQKFTlBG5T3qsBdwJnfnVkqaDlsDlpVb8Vrg1/D5XThUR4O5R2i12e9WLx7cchjKSVJb/211iecIOkv0tKOrKA1cvFIzv/xfLx48FbZypuZJ9HiwpG8+66kZXn1VVlodO0anaAF8L9p//MpoRPviGdoi6Hc2s13BbMlfQtjVo8J65ivzHyFQo+vc5OGxtCWQ8MWtACu7XytT9FqgAJPAV8s/oJtGdvCPo6RaJr4o+o9F7/8IovJWBMK5+ZK8FLPntEfw2iMELbmAy01TWuqaVocMBwYW36HYj+tjxFBK7BI3zFGgbuAs944Eef/HGhPg+u5RH5d9UtUJqijleV7l3PyV/1419OFT2f8yvTpMsm+/DJ89pkIRVOnivYkHC67rOLVyqFDYrN/+WXpLBkZZckxZ84Ujc8ff8R2XS/MeIGFuxdS5C1CK/6XW5TLe/PeY+LG6Aq22myiFq+IZcskYeKPP8pgkpUlr9xc0VQ1bSom3Io49dSKUzBs3y6Owjk5+v4q+flw+LAIZZE68F90kTiTG0FysvzewVixbwW/rvmVomJncofm4IpOV5AUJ9JeUlwSV3S8AocmWskibxG/rv6VlfvCzCirFKx5E/7uKuZCdw74F9mNqwaNLoLeX8H5++DiHKljePZaOHcbXJQhr/6/QbNrRAjzx9UAhiyUz2zlVibKC4vvh/FdYOefIoAVZpiW5TG7MJtTRp3CjK0zSrUdKXEp/H3F39zV8y7Oa3MeiY5EFIqMggz6j+zPf9v+C3q8hQslgau/YuPNN40VyCNFKcUz057htnG3SZJPzY5ds5PnzuO2v27j2enPYkQk/raMbXy84GPyiwXzBHsCd/a4E7vNTv0q9enfuCyrc05RDneNvytAiPLnQO4BXp/9ekBeLb2M8RWhl1EeZFHy0MSHIjqWkXTpIrn79J6RX36BPn1EYxppjUuPR8bT99+XBb4Z/qXRYlTqhzOAt5DUD18opZ7XNO0ZpE7QWE3TJgEdgZLMftuUUueEOubRakbMKcxhyLdDWLhrIXnuPDQ0FIpERyIfnPkB13S+xvhGK9mMuPHQRrp/2p3D+YexYSPeEc8fl/7BoGaDoj7mjh0SuRduSZlGjcRxPjFRsi3PnBl6/3Buw8xtMxn89eDSQSzZmYxbuUv9gFLjU1l1+yrqpdQL7yTL8ccf4pcWbkRNx46y+gcpjlxRtnn/6+vdu6xAdShatZJw60aNRCtZIniNHy/Z09PTw2vPnwkTxK8smsjHEjRNTJLr1wf3jzl55MlM3zq9NHN2oiORhTctpG3NtqX7rNq/im6fdCv9XW2ajQGNBzD56smhT0ApWDQCNnwaWEoHoO5p0OV1SGkpQpC/ZkqPoizJCJ+/X3Jbbf4KLisnOBUcgv8uhgNz9FMuJNSCmv2gzmCoOxgSG4h2zEcA1ESblrsN/mipfx46Wq/DeYcZMHIA6w6uKzVtJToSmXTVJE5qeJKcnruAbp92Y82BNaXaRJfTxZiLxzCkxZCAY/74ozxH/s/9oUNQNUj+U7PxeD3c/OfNfL/ie3KLciVyNbURKNieuR2P8uByuri84+V8eOaHPtnWI+Xiny7m1zW/lt6reHs8W+/ZSu1ksXJM3DiR8388v9TEmORM4oVBL3BXz+Dq5Nv/up3PF3/u49gO0KBKA7bdsy3i5L3Tt07nzO/OLD2HEhIdiSy4aQHtavonDwiBge4zXq/4+c2erZ+qw2YTF5VHHhGjUUmpIz2ysmQM+ftvCYxYtSqmU4saK6mpQaTnp3PKqFNYc2AN+e58Eh2JOO1OsgqyUChcThfPD3yee3rdY2zDlShs7craRdePu7IvZx8KVZovJsmZxL9X/UvPBtHrZTt2lBBwM6joNhzMPUjr91pzME+WOknOJN44/Q1mbp/J6BWjKfQU4rQ56VynM7Ovnx3xAFxQIH5psQgfofC/vnfekUEonPY0TdTpJ50kGq1Jk0TFHkl7etx3n5hior3m5GTxd2vdWv/zfzb8w3k/nOezwu9YsyP/XBmY1fW0r09jxf6yhyvRkchvw3/jtOanBT+B+XfApi8DBa3EetD7a6jRAxwxeOsWZUPudkht67tdKdj5B8y/HYoywB1CQnemgquhONHbE0Toc+eKoFUUIuLET9havnc5PT/rSb47v1Rw1dBonNq4VDAoodBTyLK9y/CoMpWohsaIXiN4/fTXffadP1/8Ff1N31OmSKmuimpqGk2+O5/zRp/H9G3TSzV3qfGpLL1lKQpF5486l/pBuZwuBjQewC+X/EKCI0g+lRAs3bOU3p/39nk+z2x5Jn9e9mfpe6/yUv+N+j654UJFzW7L2Eab99oEaLUicYz3J5ijvE2zMajpICZcOSH8gxnsq5yfD6efLs9RqNxoXbrIfn36SBCDyyULx337JPv/rFnw++/6i0dL2KqAo03Y2pezj75f9GVrxlYKPYXE2+Pp37g/bw15ix6f9iiNGHE5Xdzf+36ePvnpqMqH6FJJwtbB3IOc+MmJ7MjcgUd5SHAkoJQqXWGlxKUw6/pZdKjVIarjRyIgREqo2+BVXgZ9NYiZ22aWmqOapjVl/Z3rOZB7gGbvNCsdmF1OF3f3vDuqEhsXXSRaJDO6lP8xd+0Sx14jik+H054eHo/UQpwzJ/LzSEyUrO1nnqn/udvrJumFpACTS5w9rtTXzudclCdg33h7PDmP5ugLzjv+gJnDAwWtKm1h8H8iZMWQILUUrzdERIQH9kyANW/BvmliXgwleIWLX5LRmdtm0n9kf7wqdtPklZ2u9EnQqZRoaFes8H1mOneWdA3RJtiNhsN5hzn161NZvX91qbDicriYdNUkejeUbL2zts9i8FeDyS2uC5noSKRtzbZMunISVRMjU8X1+7IfM7fNLBVek+OS+ePSPzi5yck++70y8xWenvp06TklOhK5q+ddvHTqS/6H5OWZL/P45MdxOX19Loo8RWwfsd3HX8vjkUXT4sWSembRIjG75edLf9Q00WYnJEB6mzfY2vIx6T82sNllXMwtyuXAAwfCv3YTAsMKCyUf27hxR74CQKxYwlaMbM/Yzkmfn8SenD24vW40NBpUacCK21ZQJb4KY9eO5dKfLy3twC6ni+s6X8c7Q98xRuCqBGErqyCLXp/3Yv3B9RR5i4i3x3N+2/Pp1aAXj/77aKkwWTWhKgtuWkCzqkEqMIdg927xSzJDQAh1G1767yWem/6cj0D82yW/Mbj5YACemfYML898uVTgSnQkRmU2HTcOLr00/JQZkaB3fV26SNSoGYT7WKWny8S6e3f4GdddLhG6H388+D53jruT9+ZHES3lx1097uLtoW/7bizKgrHNoOCA34k1gDOWgSP1CKhk9sGef2H3BNj7LypvF24cOBwJaMoDyiP+XZpNIhw1u0Q2evLFD6zOqWL2rD0IEspC+tccWEOHDzr4aKlAtBopcaEloUJPoW49vvt638drp71W+n7lStGa+j/3XbtKNGJcnPlO8nN2zGHw14PJLcwtDZiwa3b6NepHy+q+ptb1h9YzY+uM0ntiw4YrzsWkKyeFrbl/a85b3PvPvT6FoW2ajVquWvi5R+FVXvbl+Loqa2gsuGkBXet29dmeW5TL+oPrA9qr4apBjfj6/PabpIaZMwc2biwzq+XkVNRnFdRegSvJi9crglqjhtC1UyJfvdkqaKLkAEyKwldKohDvvFPmB6PmiIoinY3GErZiYN3BdfT9oi+H8g6Vds4kZxILb1pI6xpl9o/HJz/Om3PeLJ2wk5xJnNvmXEadOyomnwDAdGEr353PgJEDWLpnaakWq3ZSbdbfuZ7kuGQGjBzA7B2zcXvdpXl4Ft60MCrfpr59K/a/ioZgt2HOjjkMHDWwdNLQ0OjbqC/Tr51euk9eUR6N3mrEgdyyyTctIY3Vt6+mTnKdsM+hsFB8C4L5QcWC3vW99x489FDlawr9OXBAnPbXrau4TEpioiR4vfvu4Ptk5meS9nKaz0QWLTZspD+cTkp8OcFi/Uew6L5ArdbASeIvZYRGKwbS89Pp+n47bAW7eeLEq7i65UA5V69b/MHsSZDUEFyNiusY6udKWbR7EQNHDQxIH5Acl8yoc0dVmGdJKcVzM55j+tbpPlrDREci9/S6h+cHPl+6mFy8GE47TSb98s9A9eplebY8nujzbBUVSf/aulWCaMozZtUYLvrpopifFw2NMReP4by254Xcz+1143reVaolj5YmqU3YfM/mCvfbvl0CDj77TN4H8wtNTpaApaSksioT+fkibKxeHXycMCQhtEFyxKFDcM89ovVWKnqhKyVFoks/+kgsDpWFJWxFyY8rf2T4z8MDOrGGpqux0lPT106qzYa7NphT/BNifsjdXjdnfHsGM7bNKHUU93ea3Z21mzbvtyGzQJauDpuDBlUasODGBRGFIYP4cpx9duUkxdySvoVOH3YqzToOktn5sX6PBeRhmrNjDl8v+7p0UrFrdppWbcrKW1cS5wh/8n3lFUnQarQApHd9e/dKIW4zikNH+ljl58PDD8Mnn8jE6PbLYpCUJD5tX3whglkozvnuHP5YH2PYafnjtTqH3y/9vWzDxL6w30/iT+sEp82KLKO7CeS78+n7RV8W714sgTfORN4Z8g7Xd72+4i+XY8bWGZzx7Rml+cdK8O/bFaHnMA+ymLyi0xV8cOYHpSWTsrIkg/xXX+lnkD/nHMnd17kzNGwYmEHeZpPvlWSQT0gQAWvNGpgxQzLIL1/u+2xO2jSJwV8PDjhvh81BUgW5y3KKcnRL4Uy6clJIrfatf97KRwtjKw1Vwm+X/MawNsN0P8vPF0H100/1a3V27y7+cv36iRaxpLxReZSS6cPlkpqRixbJvfz3X/m7ZJ9wyMuDRJf+XDSgv0LTyoqIV6kiPrpt2siradPIksRu3y5jxQcfSLtud+iFnKaJgFVQIP6pd98tEeKVXdzaErai4OMFH3PLX7cYcqzU+FQ23LUhuoy9gLdObWx7dTJmRJnxvAS3183JX57M/F3zKSyu3xZni2NIiyFc0ekKn33n7pzLu/Pe9RFG6qXUY94N86iTEr72p8THY/nyqE876HHL4/V6qflqTQ7lH/LZbtfsxDv0NQF5RXkBgnW/Rv18tGAVkZ0taRGMVl0H66aXXy4RYf7CTczt1Q6SZb8CNtGUz7mOX7iAXdTDgZuezGU4o7mU73ES5ET37oVatUjPT6fOa3UCIrGS45JpXT2IJ3051h5cGxB1FW+PZ8/9e0hLSJMbOTrON7koQPvHocPjQbVElYHH6+Gc0ecwZfOUgEjnny/+mTNanhHWccavH8+FP13ok8gSxHz+6qmvcluP2yI6r52ZO+n4YUcO5x8OON7Zrc7mm/O/8UkIvGmTpIn5qti1S08T43BAy5bi7Fy1qghjDkeZCWnzZhkj9u8P/G5JX/hh5Q9c+9u1PqZOGzbqVanHW6e/FbSfl1DgLuCef+5hV9Yun4VyoiORkeeO5OL2Fwd8Jz0/nWovVzNE6wqiQT/80OGA7StWiLCwb5+vkGGzSZ9/6ikZ/p3O0Emg9SgoEEF4505ZGH73XeA+Sokw9sMPYolYv15Sxezw1KG28h0XghURdzpFk+31FtfFrCOC16+/hp8l3uuF936dy/2jvqd9wfXkbe3I/v0ieNrtIti1bClm7G7dxIG+XuQGF8OwhK0ImbBhAkO/G6qrqbJVFPqNvoarRdUWLLhpAakJqRGdy8JdCxk2ehgH8w6S787HptnwKi+JjkQe7/84D/d9OKxzCjhHr5f2H7RnzcE1Pts1tKBauDx3XsBKMMmZxI57d8hEFiZ//im+TUYJJCUr4vKc9d1Z/LX+L0OO/9bpb3F3rxB2Lz+eeAJee804jZPLFVwTuG6dCK9Ga7eUv+NJZaAU9/5zLx8u+LBUywoSmDH6wtFhCRt/rfuLS8dc6qPNTHAkcFv323j9tNclku+n1EBhq/coaHqVYZcSKUopbvrzJr5b/p1kdLfF48VbaqpyOV1MuXoKPeqHzg7s9rpJeymNQk8hic4yR6kiTxEXtrvQx7k9EmZum8mpX59KXDkTq8frIacoh2/O/4bLO14e8J3CQvFj/PVXyce1caP0VYcDcnMVbnfFz5jLJfvn5UmC1PbtRTv+xuw3eGLyE6V+siVUS6zG8luXh+3isDNzJ50+6lRaTaKEREciLwx6ISCy/MGJD/LqrFcDjhPOGKyU0hXSJl4xkVObl6l7N26EE08MzMjfsqWkNqhZ07jAg6ws32Pt2CE5qr74QsacggLfhZzdLtr0Eo1V27aitY6PFwEtL0/Oe+VK0UiuWRNYbSISkeOX1b9wxS9XkOfOI84exyN9H+GpAU8ZF3xmMJawFQHlf9wS/B3iK8LfYR5kdd04rTEzrp1BraRaFR7Dq7y8+N+LPD/9eZ/ImiZVm7D58Gby3HkkOZPoUKsDYy4eE3F5kt6f92bOjjASNYVB9cTq7Lh3R9jh00pJzq1NmwxpnsREX/X5H2v/4JzRgWncHDZHhSV5Ct2FPhnJQX7/XfftCtt/69AhMZMYZUqsyMnzzDNlEDYqD2ZSEmTnVP5gtj19G63fax3glF3DVYM99+0Jy/fR4/VQ5/U6Pv53IJPnujvX0SC5Lox2EpC4tOtb0OoOCdMKl7DL59jgstCVkJ+d9iwvzXypVBtVy1WL1097nZv/url0W5X4Ksy/cT6tqrcKeaxNLWuQl+GbzVED2hwAWwzD/c4USNfp4s2dtUjYWfF9UEp8ribM2s2IUSPJ23IC7ZL7EE8qBQWyYIqLK4uga9lSElN27CgasCpVZFwc8fcIPlv8ma7mbsa1MwKcziti4a6F9B/ZX/d4N3W9iddPfx2bZuNw3mEavdUoQHPapnobVt9RcTbirIIsar9WO+D5blujLStvW4mmaXg8Ul5m+3bf/tysmaQ4qFq14moV0ZCVBc88I4KWx+NrsmzUSJJSX3WVjNv5+bJPYmJwrVpurhwjLk4E7IUL4csvxRfLX4gMxsjFI0uT0pbgcrq4vsv1vD3k7aNS4LKErTD5cvGX3D7u9oDOoOcQXxH+DvMg/kJ1kusw87qZNExtGPS7OzJ3cOGPF7J833KfLM+Tr55Mh1odOOPbM5izYw557jwcNgeJjkRGnTuqQsfOEl6b+RoPTnowYJUVb4/3Wbnq4VGegEEJYFjrYfxyyS9ha9lGj4YbbzRGu9W/v5QGAVHzt363Nftyfc2uSc4knhv4XIXXtz9nP8/PeN7H+dWu2endoDfTrp0W9vXdfbc4Z4YboReMhARxGH3xxeD7LFokfhtGCXcpKZDpis6MGDW1a3PJewP4ZfUvPtrTBEcCj/V7jMf7hwhd9OPZac/ywn8v+GjHnDYnF7S9gO8v/B5+bwY5fo7JdYdAn9EQF4Hm2aDC0P7jjsvh4o/L/mBg04EMGz2Mvzf8TaGnEA2NWkm1WHLLktCC/5GYhMKcR/5e/zcX/3wxWYVZaGgkOBJ4cdCL3NXzrrAmz42HNtLi3RbYNFtAXxx9wWguaHdBVKc/ZtUYho8Z7rPNq7x4lZcNd26gebXm3D3+bj5Z9InPc5XsTOatIW+F7VN33e/X8dXSr3yiQ5OcSXwx7Asubn8xv/0m5Wr8x8VlyyQwwAxBa8sWGT8OHvQ1WTZtCh9+KOMrxB5Rmp0tglc4dQ/fnPMmj/37WKk5vSQNUb4nH5fTxfltz2fksJGxB58ZjCVshcEbs9/gkX8fCcjVk+hIZPQFozmnTZCE9153WaRQSYi2Zser2Th99IXM2D7bx//ErtmplliNmdfNDAhJBgkpfuzfxyjwFJR2yOS4ZD4880NaVGsBiEngkUmPsGD3gtJjx9vjObnJyXx93tfUTKoZ9Do/X/w5d427y0fr5tAc9G7Ym2nXTAtrwLt/wv18uOBDH6HL5XRxVaer+ODMD8I6hscjq7VtMZbncrkkSWfv3qKmP+PbM5i8ZbLP75jkTOLjsz7m8k6Bpg49bvvrNr5c8qXPoOpyuni4z8M8MeCJsI6xe7eszGMNBEhKksGwRgXufn36SGK/WElMhAceEF+OsDEgQmnZ3mX0+qxXwEInwZ7A1hFbw9IGl7A3ey9N3mpSWkKlhERHInNvmEvH3d/D6tdAlfPgtsXBsK2QGL7/oRHC1vj147ngxwtKrzvBkcDlHS/ns3Mk9Oxg7kGav9O8NKLQYXPQrGoz5t84P7iWvU7lC8oV+Y4qpXjhvxdKNfV2zY6maRLZ53RxTqtz+PLcL8PSjufVry3OTOWwK4gLrTyskAI7eHV+0sTqtdm6Zi5t32+rW0Jn/wP7A/JiBaMkuaz/cWon1WbbiG1ce1VcgA9V8+awdKk55Y8yMkRbdeiQryatb19x90hKisyxPVaUUjw99Wlem/1a6fySHJfMf9f+x7k/nMu2jG14lReX08WgpoP4+eKfK1xAVyZmF6I+Lvh++fckOBKoEl/F5/VIrzs5p0ocrH4dFtwN086Fv7vDb43hxxQYHQ9jasCv9eD3RpK/5/dG2H6tzxjHf7S2F1DFBlVsGlVsNpJsGgWFGUzaMM6n/X3Z+2j6dlNG/DOCXHduqaBVUr/vjnF3MOSbIQz5Zghnf382K/av8PENK/AU8M/Gf6j7el2+Xfat7jX+svoX7hx3Z4Cfg8vp4ocLfwhbLfvCoBdoWKWhT72t3KJcvlr2FU9MCU8YsdvFlyOW1ZLLBTffLIIWwNtz32bGthkBAnOj1EZc2vHSsI/73MDnSmvtlZBblMuL/73IjK0zwjpG3bqiko+ler3LJb4TFQlaAC+8EFtbJWiaaNIqm9vH3e4j3JYwuPngiAQtgNrJtTm1+akB9eAKPAXcMe4OqV/ob072FsKcq4trI4ZJQmyF5+ftmOcjaAFUiavCm6e/Kf49SlEtsRojh40k0SEdxe11s+XwFoZ8PSR4jb09e0TQDecVinCPUYGglVOYw7DRw3hhxgul2vjGqY0Z0HgAiY5Ecoty+X3t73T7pBs7MndUeN8Sd+0j0Y3PK1ZBCyDeQ8BxE93A3r3cN+E+ijy+qR4cmoOrOl0VtqAF0LF2R1pWC1xkZxdm8/7893U14SVO5mbwyiuBRZ+rVIG//oLU1MoXtO4af5ePoJXoSOTHC3/khDonMPHKiaX3Orcol0mbJnHa16fpWlqORizNlj9FWbB9jGSY3jcN3NnFZTLypB5Z1S6Q1lHy2yQ1lv8TakvGac0mL+UVDZc7B/L2QN5OyN4s/6cvh8OLpXBtMQt3LeSkL07SrfKeHJcc0nSVW5Srm+/l/t738/Lgl0u/O3HjRIaNHqa7Mvvq3K8iVr+v3r+abp920/VzeOaUZ7iv931hHeeNN+DJJyPXANnt4py5aJFEvSzctZB+X/bTvb5/rviHvo36RnT812e/zlNTnipNhFpCtcRqrL1jbdiRpRddJCvESB3YExPlu6NGhf+dq6+WyMRoneVdLgm1vvrqCL8Yo2Zr0qZJDBs9LOBZSnImMe7ycT7FfMNl2pZpnPX9WQH+NS6ni7HDxzIocyKsfTcw11bL26DLK9GngAim7fLTbP276V8Gfz04pqi2esn12Dpia4V+iCExOYff5sObGfz1YHZm7SwN8KmTVIeFNy+kakJVBn01iIW7F5Lvzsdhc5Acl8yfl/5Jn0Z9gh+0kjV3i9tXo8+leboldBbdvIg2NdpEdLxvl33LLX/dEvBspsSl8HzaHh590OVjRtQ0cVw3I8que3cpmVWeoUPh++9F2KosPF4PV/12Fb+t+c2nmsdDfR7iyQFPlu6npwluV7Mdk6+aHHHwmRlYZsRwcOeIaWH1a4AqW+E6UqDt/dD0iuKisLlgiwdHhCoZ5ZVsz95CEdrs8bpO8CCmxuqJ1XlryFsV5rFSSvHJwk/4a/1fPuZKl8NFx9odGXPxGOLscTR8syFe5fUbmL0Ma9KP70+5FwoPQ2G6/F+UIf7DNrucN6p46VO8/NFsgMbbmxbx6JrpPmOyF0WB18OMM56jb8PeEFcV4muIQKqj7lVKipH+919kCeySk8WPoWlTyMjPoM37bXzqj5Xcx8HNBjP+ivHhH7iYQk8hTd5qwu7s3T7bnTYnJzU8iclXTw7LfysrS4TC3bvDX53abOKUunJlZNqq3Fxpa/v2yOfJhAQ44wwpNxQxMQpbPT7tweI9i3HanGVfRVErqRZb7liHlrcdCg7K81mUXvasFhyU98ojpW/wABpoDhQ2Gk//kf2F+cUaW3m5lZsuNdsy9+qJMKk/ZG8F5adOaHwJ9PgMbM7IU0GEIWyt2LuCEz4+wZDyOZ1qdWLprUujP4CJwta4deMYPmY42YXZpUJltYRqzLhuBo1TGwOi0Rn01SDWHVxXumhMsCfw4qkvGlNj1oDrO+nzk5izY06AYNytbjfm3zQ/4lMqcBdQ89WaPhGzIILDTR3vYdRVLwY4kffrJ8XjjTYlDh8OP//sG83drJmk3TBCUx4O2YXZtHynJXtyfMdvp80pVQ78fsK8okDBN94ez+KbF/sUqD8SWMJWRXg9MPEkSF8mAlEJdU+HPj/IoOsw9snbmbmTC368wMcJHqT2W5O0Jvx37X8hfa/Ko2fnBkqd50ee8zmZu/6h8PAKyN4IBZLXxeGqxyXtLiCpzsmQ3ATiq4uGzpEE2GQSUgpQZf8DMqFpKKX4cfUvZBRkSfkQrxu8BeDJY3jXW6iSGJ72JzNTkvOtWhWeViYpSQaefv3k/fj14znjuzN8hR8FDi2ex6stJ3t7c7ZtE7+EjAx5leSn8XhEVW63l6nMPR4Jdz5YYyybup8PWvk+otCwcV3GNk5oVo/GjcWnokWL4JE5GzdKHpiDBwNTVPjjcEho96xZEmIdKcuWQa9eFWdy96dOHVi7NsoM3zEKW7O2z2LFvhVSuDljhTivZ2+hj+0w7e3ZkjG9SqsyTbKrISQ3B2ey+FrZ4qSPag7AC54i8Bay8sA6Zu5eIsJZwQERzAoO06HX65zU4kwR1iacBDlbZBFUnoTa0PV1aHCeLDicYSYGqkDYyirIou7rdQM0pqFSrpTg9rp1y+dc3+X6Uh+viDFB2PJ6vZz9/dmM83OVAIJq4fSSizZLa8bimxdTJSHKtPMQ8/VN2jSJ0785PUAwdjldjBw2kovaXwSeAim3lL9HLCFet6QW8RYV/1/8XikxX9ucjJg7kvdWjcPtV0bJYXMwutsurrqwZkDAy8CBUmzZ4SD88joVsHSpuGH4jxdPPikJVc3wEyvPgdwDNH+neWnC7Fiwa3ZmXz+b7vW7G3Bm0WEJWxWRsw3GNg/MvdN/LDQ4O7JjhREOrhTU3Qx7PRLOLb4lCo/XTbOUOkw7/VHSVB4U7IfcHcU13GxiztTwGyQUJav2V7eu4ulNK3FqttLteV4PXeK9zNELfjxjmZhES9IMG3R9QsXh7uXJyZEUBgsWBDcpxseLeW3CBFF/l6CUYtVaNz/9KEn41m8oTpJot9GqhZ1BgySEvFUr8eWtWlWEqaKiMm2TfzcouR0e5cZuV8TFiVC4bz9s3gQDT3aGnZgPJBBgwADx6w0WNehyialg2rTYTAbffQc33BCewKVpImBNnw6dOkXZYKwO8ukrYP4tcHCBCE3ubBHeG10Mre6E6ifKhKY8IljZE4q1qxHgKShbSJWPOCw8DJNPg4zV4NF58FyNpOZgw/OgVv9iTa9W7DLgKAuKUV7Rgv0QRON9maLQU8igUYOYu3Ouj+k/zhbH4/0fD8sc9eacN5m/a75vxKY9gS+GfRGRX2IpBgtbeUV5tHy3JTuzdkZ+LjokOBJYfstyWlRvEd0BYry+1ftX8/C/D4vAVHBAtP5FmdTSCvigTgJObyEk1Yek5uCqB/G1pJRSXLXiBYBTBCzNIefidYMqYlfWXu6d/RF57lxw5xUvUvOJ8+bx5dULmTKtKZddJovP8nmuqleXhKbXXy9jVyRjUHmyskSD/sknUL8+XHNN4Hhx+eUSUQ3Rt+NPZmbZgi4jP4NOH3ZiW2ZglFS8PT6kD7FS0p/8tY0JjgSW37q8NJissjFd2NI0bQjwNmAHPlNKveT3eTzwFXAicBC4RCm1JdQxK1XYUgqmngH7pvv6cNToDX1/BmeKvMIhzAilefmQ2fAyODAHcrcDCs1Zhd6NT8FVdyCktgdXfTHDOVMAm3TIUg1TOQfX0odSY/GeZRzMPywdvSRSsjCDk1Kq4MpcDnunQNZ6mWQSakHtU6B6D0hpAQl1wJkq7TmSyiY1pQCv/P9bgzCFLUKGu+uhlGSdvvtuGUhyc0UTlJwsKRQuuQTefluEpfKMGSNV48uXtHA4RFNWr578HWmWZTMoKIDnnpOEp3FxZZm1U1LkvB99VGodGlFi4uefJS9Ofn7wOSUuTlauM2ZIssioiVXYGlMzsCh0i1ug+/uRC1XR4PXA6ldhxbPF2oggNe80m/hsVmkr/cPhEgHOnSPasQNzAjVkxahLvVz888X8te4vH+1UoiOR67tcz7tnvBvWqabnp9P8nea6STj/vOxPBjYdGNZxyq7JOGFrW8Y2Tvv6NDYc2uCT2sCm2ejVoFeFfo65RblM2zLNRxDV0EiNT2X8FePp1aBXROcjB4jx+jLXwZxr4NBCsCUUu5d4oM290Ow6qNIS3PmAVwQqe0Jg8EVFlLqYFF938WJgxw4Z85YuDVyAJidLYuizzpJSPbVry3hZoqEvSRFRoqX3eGQxt2eP5Lz64w9Jv5ObK7dh1iwZL/bs8W0rKUnG1ltvhdatZTwpKckTDjk5sqhNSBBXkXffFe3c/pz99P2yL5sPb/b5vRPsCXw57EufJK/B2HhoI4O+GuSjJdbQSEtIY9o10+hYu2N4J2kgpgpbmqbZgXXAYGAHMB+4VCm1qtw+twGdlFK3aJo2HDhPKXVJqONWus+Wtwg2fQXLHpMs0+4cSjtQs+ug2VVQ7cTiwdgr/kd2nVVs2JofKFZTlb09eTzUGyLHN3qS0XMqTQSSyr1cxf8nIHGqJS+HDRx2eWnF6drdxS+PgnOLj1We31zwU3RRIm7szOIkZtKHQuJowQbOYBxVSdfd/0cu5Kr4H/F6tdJ6bA4HbNggK8H4eDEbGsqTj8Ozz0f11VwSmcSpLOUEALqwmFOZRAIhnNYy0yElMgfQZcskGeHWrTKolmjxnE55nXYafPwx1NIL9nPnystbKMKHp/h/5fV94YUaQdT2B+bpbNSKNalQ+oCt/wC2fCWfeYvvQUpLOOk7WXQojwg2FfWJWLWuOdtg5fOw+Ws5F3/n+WhJqM2IpEv5ZOEnAUEADao0YN0d63yyvVdEsHI8Sc4k/rvuPzrX6Rz+uRkkbE3bMo1ho4eRVZgVUPomZOocP1btX0Xvz3sHmJUSHYm8PeRtbjzxxrDPCYj9+la/CYvv9d1mi4PhBWWFwcMhonmh7PlUSlLb/O9/IiTp1UgEEYo6dZJcXElJYgFQqqwQ9cqVMh7oabtLboPHI4vd558XH1O327et2rWlrmWbNtCli7TXsKEs2hwOOU5JDcNNmyR4aflyySK/aFFZ7rDtGTs46fOT2J2920dDq+cQXxH+DvMlpMSlMOHKCdEJ6DFgtrDVG3haKXV68ftHAJRSL5bb55/ifWZrmuYA9gA1VYjGj1g0otcNeybB7gmwaxxkb5LVCkoEreSmkNoBEuuK5impsfiQOFLEoVyzy0pZuSWyMXcHzAqi3k9tLysnR3HPSG0Hza6GOqeJbwoUT3bFE4Nmp8RfqvT/Fc/LBFERtwFhZu6NmXjgC53tfX+GRjpRj8GiiyKo/bhqlUTQjB4tddUSE0VN3q6dCBXdu0veq1q1yjRJJYJZsKewZJy22+V4OTlSqy1zzTi6ZJwZ+IVgmrxYry9jNdw7HL5YVvG+fniwMYVT+J7hLKEzTtwMZDIX8yOd0XesVneCpjdGRXJ9kdbtzNkGW0fDlm+kT2h20c4m1hUNc9UukNoWkpoUB1skFvvBFAt8SsHYFlCgU0NUj1Ba18IM2Pg5bPpCtMC2BHBnEZB1PhiOFOm3iXWh8aW8kVeDJ6Y8ESAcJToSmXz15KgmhCt+uYIxq8cEpMuomlCVRTcvoklak/AOFKMwopTi7blv8+i/j+pG63181sdcecKV4Z1LMfN3zueUUacE+LW5nC6GdxjOh2d+GH5upViFLU8BbPtRXnsmA5oIWAl1oc4gaHAOpLSCxNpiMvTkB7qjoMEfrcJ/NkH3+dy0SZKMjh8vC0mnUy4vOzt8uVjTRCtWUuy7VSsRwvwpGU+/+UYCblwu+W5OTsV+p/7ExYlmy+OBgqT1VHugLwdzD/poP+Pt8QxqNog/L/0z4szwz0x7hpdnvqwbGT92+NiQRcWNxmxh60JgiFLqhuL3VwI9lVJ3lNtnRfE+O4rfbyze54DeMeHIF6IuxVsEGavEryR/twhPOVshd6esVAoPFZv3Slb6xT5UJWkgbPGiIfArAUNCbTh/j2jQ0ldA7jaZcPL3yLHzdktH14p9teKqinpZc5Y7vl16QKmmwSPtb/5KUlXotRcOMQ9Q+TDtbDFVlpybpwAangsNLyzXhq1Mw1HerwGJdmTt27DunfDOueYAGDwVEEFq0yapG7h1qwxMO3aUOchnZ5eVmvB6y9Tu9uLbWaJ293jK3ufkyHdSU8Hd5GcOt7mIJA2cGsQVv2on1aVKYhrxtjjiHfE4bQ6cNjt2mx275kDTtNLcTzZN4yT3Bnq7N4Z3fXpEKvxMuAVWVJyt9KJdsKYIEouvy6lBvAb105qS6Egk3h5HgiMeh2Yvvj4HtuIklVFfX81+MLi44LfySh/L2iABHVnrIW9XcUTiIYlE9BQhfUrJc2MrfikPeNxAsUOyZpc+W77/RdIXCjPE9L5vhjjvZ28s7pvFpiNsIhQm1CleiLWFGn2gzqmQWBulFI3eaqSbP+qhPg/x0qkvBWwPh4z8DDp82EH3uM8PfJ5H+z0a3oFi7OtZBVlUf6V6QPoZm2bjzdPf5K6ed4V3Hn5M3TKVId8MCShIDjD7+tnhC6hG+qR5PTJOZ2+E7C0y/peM23m7oDBTXDDia5S5nWj2srlAsxUvxMuN1bv/1g/OqOD59HhkXFu8GObPFxPd+vVlRaZL/LxKtNjx8bLY7NtX/Fe7dJHAHlsYBpSCAgnyWbMGVq8WDdvKlVLz0O0ue2la2VgaHw+NG4v2q2vXslqKb66+m3fnB47pLau1ZNHNiyoMEtFDKcW5o89l7LqxAZ+d1OAkZl4/M+JjRssxI2xpmnYTcBNAo0aNTty6dWtM53ZEiMTZ/GhtzwBNk/a/SrwHwO77dodduzAU4dzOUH0m1npdwY5te6Zy8w/vule/FmQs12fmfStu4Kjse8Gu26xnJaJjG9DXj/frM4xKfD4D3Hkrob1w2jJrDDB9bAmTUMKWEflhdwLlY90aFG/T22dHsRkxFXGU90Ep9QnwCYhmy4Bzq3wquy6ZGe0ZMAipp47fn8/Mzhvs2Mfq/SyP6YPeUdr3zLpuQ45rQF8/3q/PMCp10q+0piJq76h+VkzGiOXyfKClpmlNNU2LA4YD/vq8scDVxX9fCEwO5a9lYWFhYWFhYXG8ELNmSynl1jTtDuAfJPXDF0qplZqmPQMsUEqNBT4HvtY0bQNwCBHILCwsLCwsLCyOewwpM6mUGgeM89v2ZLm/84GLjGjLwsLCwsLCwuJYonK9bi0sLCwsLCws/p9hCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiZiCVsWFhYWFhYWFiYSk7ClaVo1TdMmapq2vvj/qjr7dNY0bbamaSs1TVumadolsbRpYWFhYWFhYXEsEatm62HgX6VUS+Df4vf+5AJXKaXaA0OAtzRNS4uxXQsLCwsLCwuLY4JYha1hwKjiv0cB5/rvoJRap5RaX/z3LmAfUDPGdi0sLCwsLCwsjgliFbZqK6V2F/+9B6gdamdN03oAccDGGNu1sLCwsLCwsDgmcFS0g6Zpk4A6Oh89Vv6NUkppmqZCHKcu8DVwtVLKG2Sfm4Cbit9ma5q2tqLzM5AawAGrPas9q73jvr3j+dqs9qz2rPaOXHuNg32gKRVUPqqQYmHoZKXU7mJhaqpSqrXOflWAqcALSqmfo27QRDRNW6CU6ma1Z7VntXd8t3c8X5vVntWe1d6RbS8YsZoRxwJXF/99NfC7/w6apsUBvwJfHa2CloWFhYWFhYWFWcQqbL0EDNY0bT1wavF7NE3rpmnaZ8X7XAz0B67RNG1J8atzjO1aWFhYWFhYWBwTVOizFQql1EFgkM72BcANxX9/A3wTSzuVxCdWe1Z7Vnv/L9o7nq/Nas9qz2rvyLanS0w+WxYWFhYWFhYWFqGxyvVYWFhYWFhYWJiIJWwBmqYN0TRtraZpGzRN08uCb2RbX2iatk/TtBVmtlOuvYaapk3RNG3V/7V3riF2VWcYfl4ysSajxlsD6sQmtSqKSIytpDWmbWKLiRJRKVgsKOoPpbYmvaENiCKC17b/FGp6QWLqLYoKrVMv2P4x1sSZZOIE8TKaiTqR3mwreH37Y60Dp+OcTAr7O2P0e2Aza58znGfWPnuv+da9bpl0ebBvb0lPSxqsvmsifdU5TdKzkh7ugmtE0pY69vCZLvj2l3SvpG2ShiV9OdB1dNu4ygFJb0laGeWrzlX1PhmStE7S3sG+y6tra0TeJnq+d2dbs4Z936r5+1BSo7OwOvhuqvfnZkn3N7lDSAfftdU1IKlf0qGRvrb3fijJkg6O9Em6WtKOtudweaSvvv69+h1ulXRjlEvSXW35GpE00IRrF775kp5qldcqa31ODbY/1QcwjbLI6ucpC64OAscG+hYDC4ChLuXvEGBBTe8LPB+cPwH71PR0YAOwMDiPPwDuBB7uwvUcAQ7uxndXfb8FLq7pvYD9u+SdRlmo+HOBjsOAl4EZ9fxu4IJA33HAEDCTMl71UeALDTs+8nwDNwJX1PQVwA3BvmOAoynL7XyxC/n7JtBT0zd0IX/7taW/D9wW6auvzwEeAV5p8vnvkL+rgR81+b1N4vt6fRY+U89nR17LtvdvAa4Kzls/sKyml1OWp2r8uu7OkS1bcBLwgu2XbL8L/I6yDVEItv8E/C3q8yfwvW57U03/Cxim/JOL8tn2v+vp9HqEDQyU1AecDtw+2e/uaUiaRSlA1gDYftf2P7qkXwq8aPuVYE8PMENSDyUIei3QdQywwfbbtt8HngTOblLQ4fmedFuzJn22h22HLAjdwddfryfAU0BfsO+tttNeGixfdlE+/xz4SZOuSXwhdPBdClxv+536OzsDXQBIEmWlgnVNuHbhM7BfTc8itnzZJRlslcBje9v5KIHByFQiaS5wAqW1KdIzrTYP7wT+aDvS9wtKITjhrgQBGOiXtFFlx4NI5gFvAr+u3aS3S+oNdrY4lwYLwomwvQO4GXgVeB34p+3+QOUQcIqkgyTNpNR05wT6Wvxf25rt4VwI/D5aIuk6SduB84Crgl1nAjtsD0Z6xnFZ7Sr9VZPdzh04ivJcbJD0pKQvBfsATgHGXPdNDmQlcFO9V24Grgz2dSSDrU8JkvYB7gNWjqsZNo7tD2zPp9RwT5J0XIRH0hnATtsbIz6/A4tsLwCWAd+VtDjQ1UNpFr/V9gnAfyjdUKGoLES8Argn2HMApdVnHnAo0CvpO1E+28OUbq5+4A/AAPBBlK/D32ACW3qnEkmrgfeBtdEu26ttz6muy6I8NSj/KcEB3ThuBY4A5lMqIbcE+3qAA4GFwI+Bu2vLUyTfJrgyV7kUWFXvlVXUXoKpIIMt2MH/1m776mufGCRNpwRaa22v75a3dnk9AZwWpDgZWCFphNL9u0RS6JputTWm1dR+P6UbOopRYLStZfBeSvAVzTJgk+2xYM+pwMu237T9HrAe+Eqk0PYa2yfaXgz8nTKGMZoxle3MWnvENtJN83FC0gXAGcB5NaDsFmuBcwI//whKZWCwljN9wCZJE+0X3Ai2x2qF9UPgl8SWMVDKmfV1CMjTlF6CxiYBjKcOGTgbuCvK0cb5lHIFSuVxygbIZ7AFfwGOlDSv1ujPpWxD9Img1lDWAMO2f9YF32dbs5EkzQC+AWyLcNm+0naf7bmU7+1x22EtI5J6Je3bSlMGBofNKrX9BrBdUmu/0aXAc1G+NrpV63wVWChpZr1Pl1LGFIYhaXb9eTilwL8z0leZdFuzPRlJp1G68lfYfrsLviPbTs8kqHwBsL3F9mzbc2s5M0qZcPRGlLMVmFfOIrCMqTxAGSSPpKMoE3EiN24+FdhmezTQ0eI14Ks1vQSI7rbszFSNzP84HZSxG89TZiWuDnatozQNv0d5cC8K9i2idFtspnSbDADLA33HA89W3xANzjaZxPs1gmcjUmasDtZja/S9Up3zgWfq9XwAOCDY1wv8FZjVpe/tGso/yyHgDuqMqEDfnykB6yCwNODzP/J8AwcBj1EK+keBA4N9Z9X0O8AY8Eiw7wXKuNdW+dLk7MCJfPfV+2Uz8BBwWKRv3PsjNDsbcaL83QFsqfl7EDgk2LcXZZeXIWATsCTyWgK/AS5pKk+T5G0RsLE+7xuAE5v27u6RK8gnSZIkSZIEkt2ISZIkSZIkgWSwlSRJkiRJEkgGW0mSJEmSJIFksJUkSZIkSRJIBltJkiRJkiSBZLCVJEmSJEkSSAZbSZIkSZIkgWSwlSRJkiRJEsh/AYi/yZdJbHhyAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_29\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_30\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_31\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_32\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_33\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_34\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_35\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_36\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_37\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_38\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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8Xga+/hoOHw68P/NMeOEFEaZKIyUFLihe3tvLt99KwtY6nr0otP+vrt5LVpYIeWvXwtNPy3YffhimkYOLYHwr+P0SGF4EXyN/72lwHhFVrTMLdk+AmefBLx3ivwAGg8FgOKbZengr249sB2Dypsk43c7oO+z6KVSZoWxwxiRIP1N8nsNhS4EG50PvHwAVfhvgu5Xf4cGDUgqH1cHkjZPjPJuK45gXtmrVgsceC6SBcLvhrLNgw4bo/luFhZCXB1dfrSFvZ+CLRpeEmhTj4N13A5qlxEQYNSo2QctHcc1XUZEIk7fcIlq70jR2ubkS+fhA8Wxnq16Cqb0lrUUsyVdduZC1JvaOGwwGg+G44Me1P2K1iMbJqqzM3DYz+g7L/x267nQaBjVPluCzaNhSoO6Z0PaRsF8fyj/EmgOBdSq7KJvvV38f0zlUBse8sAXw4IPiZO6rk5idDV26wJ13wp49+LU/Ho/4N+Xni7aoTRtYMGtPqKqyTp8ymRD37RONmo9zzy2j/5gXjwcuuwxGjhShMJgGDeD88+Haa2Hw4FC/NSgmlC17BlYOK+lkWKsHdHsHTv9RVLZd34LqXYI2MGZEg8FgOObZP0sSdcfIF8u+wOkRbVauMze6r1TWBnnI95HWCk64J77gs45Ph/1q8qbJOLxrtcUr6kzeNBm3xx1b2xXMMZ3U1EdyMkyaBD16BAQNtxu+/FKEqgsugJYtRdt04AD8/DNs2ybbtal/RNSaPhKqljxADMyaJY7vhd6AxptugipVyn5OTzwBv/4a6vzetq2YSQcPFq2XxSJCWUICTJ0q3y1dGtTI9u9h7euhtvLGl0HHZyGlMViSwPuEgscFLW6B7A2w/BnYNb7snTcYDAbD0YXHBbWqwoEjgc+qKpg5WB7Gk+pDyyFRhaGM3AxWZawCQKHwaA9j14xlxPkjsIRLkbSnmFmv86tgiTNBqSW81mLkqpFkF2VTxVGF9JR0NhzcgELx564/6dmoZ3zHqACOC2ELoEMHGD9eBJHc3IB/t8sFY8ZE3s+iiiVVU6VcsrF1oaBk/aaVo/9Nbs5TgI2UFPHXKiurVsHbb4f6nb38sjjf2+3yl1QsQvbcc6F/fxEwb78dKTk0//9CBa2u/4UWN4ItjJ3cYpO/6p0l7cXat8t+AgaDwWA4esiYDX9cAe+6wRX8hddnFyB3K6x8Hla/JNVRml1Xoplx68Zht9hxeVzUS63HruxduDwuFuxaQPeG3Used/uogFVFWaHegNDk4LEQRjhze9xM3TQVgEJXIee2Opd3/nyHfFc+P6376W8Rto4LM6KPM86QxKYNG4aW8omExQLK5iDEZFaaT1MYQQtg4ZZueLQMopNOit1BPxw33xyaP2zYMDGJJidHNk1arfL9NdfA8OHAsqfAHdSJTs9HFrSKY0uFNveV/QQMBoPBcHSw4X/w2wDI31X6+ubOk0CpP28L+/VXy78i15lLakIq/Zr1I8GaQIGzILKv1MFFgdc1u4MuxZk+Rubvmu/XpFVxVOGsFmeRkpCCy+P62/y2jithC+CEEySh6LBhkhKhShXJaRVMaqqYFAcNglHjG4b6M+VsiVxCIAqZ2bX8r6uWzRIJwPLl4vvl8XZh0CC4//7YHe1TU+H6a/NhyxeBJK01uonwFIug5SNWm7rBYDAYjk7WDYfFD4RPDGpLgeSGYA+zYLnzSnyUU5TD3J1zAShwFXBVx6tItCXi0i6+W/ldyTaKDoXWGW5wXvikpWXgh7U/kOeUPvZs1JMeDXtQ4BLlwq6sXezM2hlt90rhuBO2QExsDzwA67dlkXPJmVwyZC2DB4vgcuONUux5wwbx3WrXMVEyxvvYPgqc8aehd+vApbbbA8768fLxx6FarTfeiC+iESDl0LhQp/8294MlsWwdMhgMBsM/j/2zYOkjJQWn+ufCeWskgeh5a+HiffLX4paobjSTNk4iwVsQOj0lnTOanuEXcA4VHGJNRrEI9uxNYA1adxpeGL+/VgRGrx6NW7tJtCXSv1l/qidVp3ZybQCsFisTNkyokOPEQ4UIW0qpgUqpdUqpjUqpx6Jsd4lSSiululXEccvL9B0T8TSZTvJZr/gzu3/yiZjaGjYM2rBKm8DrvVPLVGE8JSEwoLOyyp4TdNQo8TMDcYhv1KgMjeyfFchrYkuDhhcFHOENBoPBcGzjLhIfrWCNVtX2cO4qSRZapY04nvsSiSbWgZPegIt2Qr3whZ+/Wv4V2UWyrpzZ7EwcNgdta7WVw3ncjFlTzDk6b3toUHtSxRSJ3nFkB3uzJWu93WL3+2f1atRLDuvMC69pq2TKLWwppazAu8AgoB1wlVKqXZjt0oB7gfnlPWZF8eXyLwHJMqujST+NL5PaTCB+TvtmRN42MfyA6dBoBSC2vw0bpBZivOTkSLSkjxtvLGP6iIw5gdf1B4GnYuzkBoPBYPgHsOHd0ESiqS2g/ywRsiIl7banyfrWewyk9wv5qshd5HdIT01IZUBzKadzdouzsSgLhe7Cktnk3QX41kSgwqwrP6//GYs3k3mhu5AT008EoF/zfiTbZR2fs2OOX+v2V1ERmq1TgI1a681a6yLgOyBcrvNhwMvAX3uGESh0FTJtyzRAkp+t2L8i8saNLgIdlJtj9cuS2DMcF++Fq3Xgz0uXpktJcYh2a/t22LIl/j6vXh3q2H/hhZLWIW6CnfgT65RJU1cmtn8PWevK5PNmMBgMhjjY/Bns+FFyWQXnlvI4YcWzgTXM4pCM7faqEC49Q3FsyXD62JCPZmyd4U9k6vK46N2kNwBnNDuD1ATxBd56eGuor1RxBUdpxx5bF75REf4ClpnvVn3n99c6oeYJ2K2ikejRsIffad5hczBj64zSz7UCqYjUDw2AHUHvdwIhMZ5KqZOARlrrX5RSD1fAMcvNtC3TJFLCVYBSitGrR9MpvVP4jZMbQtUOcMgbObF/Jmz9FppeFbOjeLdmC0Pef/QRPP98bFGRPjIzQ329yu5oHzTIlY1o5Q6AiOkswAJXF0sQpzVk/CH1FQ8FOZdVBebdDHhE2Gp8ObR7BKqWUIIaDAaDIVayN8HGDyUn1hFfWTlg4V3im6vdImw1OBda3S4mxGBBp8NTkFQvPlcSS6gj+zcrviGnSCIZk+3JNK7aGICeDXuS7xRTpUVZ+GHND9zd/W7ZyZpAyNrjLgz14SpOhEh/QR7gD+Yd5Pdtv/s/XZ+5nhovB3yufX3MKsziyWlPMrBleJNoZVDpebaUUhbgDeCGGLYdAgwBaNy4caX269sV35JVmAWIJP71iq8ZesbQyDuc9CrMOC/gTLjwTqh+IlTrFFNG+U6Nl1MlKYvcQpHyR42Suojx4CmmELKUVS9prwoF3krsRYfkSSfaOUQc5MU6lLcbZl8BB5fA8DCmyWC19davJNig8eXQ87N4em8wGAyGoiOw7EnY/IkIVO8V0xQVt77sGAt7JkH980JTPLQcEn90uTXgv+LyuPhi2Rdo70P8wfyDqOdKPsDnu/J5avpTAWErsW6oNst5pMxJw30MXzDc3w8QM2KhuzDstkv2LsHj8fhNjpVNRRxlFxDspt3Q+5mPNKADMEMptRXoAYwL5ySvtf5Qa91Na92tdu3aFdC18Lg9bn5a9xMQSOO/O3s3Ww5Fse2lnyEOhL5L5imS3CQ7f4psUnQGBrRScH3vz/1mv1274PXXS69nGExiMaE/J4YyhmGp0TXwet9v8SeRC8e+6fBzGzgwD9y5lFrOR7vFOXPbyPIf22AwGP6ppKdHfx+OI2vh57aw+WOZRz1eK4LFIX64zW8SIarx5SLUAKBlrdo/E//8XPPkcqdb+GzpZyECTjSyCrPYeHCjvEltEeg3wN5fS2oU4qSEX1gUNJqfN/xcruPFQ0UIWwuAVkqpZkqpBOBKYJzvS631Ea11La11U611U2AeMFhrvTB8c5WPLxcIQKOqjbB70/3/sPaH6Due+lWomtN5RDQ5vw2Avb/JoHcXiqboyGpYEmoxvXfg21iDNLXPPguLFoWW3ImEywW1a4duO2uWlB2Kmzq9Aw7/eTsgs5wxC/v/EK2fKzs0b0q1E6HnF3DOChi8CQYthZNeh+QgraXnqHDhMxgMhr+FvRuXYnlWoZ6FKRsnw9690XfI3ghTeoh1wpeYukY3WZ8uzYRTv4Wub0v04Ckj4IItMGA2NL0aUFAYFGXV9LroprsYeG/Be3Ft/8rsV+RFUl0pEeRj54+h1o848Xg8AUEuRt77M76+l4dyC1taaxdwFzAZWAOM0lqvUkoNVUoNLm/7lcHIlSPJdeaSYElgYMuBJNmTKHAV8MWyL6LvWKU19PgkIKj4ODAXfusHo1JhbLr8/0t72PhByGZ1q+3j/vsDpXTcbsnt9eef0bVU2dniVH/eeaFleEaMKFmEOiYaXRzq8L/mjei5wyJEWAJQkAEzzw3N1VLrVBi4CM6aDU2ugmodILW5mF1b3Qnnr4W+E+TJxmAwGI5j3p73tl8zNGzmsOgbu/Jh+kDvfO3VJnX8N/SfAY2vFHNgQlWwp3pfVxFhqvapcPL/oMenoZqsOr0j1haMBY/Hw7J9y+LaZ/Tq0fJCWaTwtI9906MLftHWIWDc+nExa9h8zNw+M67ty0OF+GxprScAE4p99kyEbftWxDHLitaaUatH4dEeEhMSubLDlXyy5BMA1h5Yy/7c/dRJqRO5gSZXiHlw0d0ls+5qT6COVDgS03n8calPuHOn+Cjm5UHfvvL3+uvQunUgj5bFAkeOwCOPiI+XxwOXXw7ffy/7zp4t+6dFiNSN3I86ULcf7J4EeKSo9IG5ULs32MKolC8OetL6ppgt/s9bQ8v+NLwATv06sg+Azzes7lkwcAH8ekacnTcYDIZjg3xnPu8ueBcAq7KyaM8i1mSsoW3ttuF3WP0S5O/G7y/b7R1odkNsPlf2VEhpEuonZatSrv7/uetPPEHR5VZl9adXCCanKMcvCB0qOESeM0+2a3QJrN4gZXpcOXB4uZg2w3FxMY1fsbVo+d7lWLDgwYMFCz0b9aRz3c4lmvlkySfku2TtdrqdHMo/RPWk6nGcddk4bgpR+1ixfwW5ReIoVeAq4NRGp9KqZitWZ6zGZrExft14bj7p5uiNtLwZUhrDH5eKyTBcqQMfyipZcVvfA11eIhWYPBm6dxeNlY8ZM6BrV+jUSRKV2mywb59ovYLN2LfdBhMnyr5aw4svSlRjahyVdnJzIaXLa/Ik4c4HNPx+MfSbIdGBtjhCJPdMDtjda/WILmgFY7FCQnXoNy32YxkMBsMxxJfLv8SjPdgsNqzKSpG7iBf/eJEvLgpjZXHlwto3AutNze7Q/Mb4nNuLa7FKq2QSMRIdwMKUBv/GbrHj9DixW+zc3f1ubu16a4kt75l4D5M3TQYg1Z7KlE1TuLDNhRIhue4tcHkDqpY/Bb3Hlqkc3Jg1Y/B4hdDUhFReP+v1sMWv92TvYexaSV2RbE9m8qbJXNnhyriPFy/HXbme71d9T5FbhIP2tduTYE3wJ17LdeaWbkr0UW8AXLgDTrgPbU3CpdJwafF+92gLTqqglR3qDoBBS6DLS/5d27aFqVOhevWSdRmXL4dffoGffoJ580r6C/bpA9WqBd6//bb4bsVqTszPh6VLgaptoc2DAZOoKxd+7Q1bvxZVdTinf2cYW6cnKNKj13fx3yTh6m4ZDAbDMY7Wmv/8/h9ynbnYlA2LsuDWbr5f/T0H8g6U3GHvr4Qs2Sc+XyIFQ6nY0kLTPoSb04MpJd3CV8u/wulNip1kT+KStpfQumbrEn+XtruUFLusDTnOHL5e/rU0UaNbaCT8nimw/3fxfY6Dg25Ym7nW/z7flR9WqwWS3DTJa8HJLspm1KpRcR2rrBx3wtY3K77B6XFiVVbObnE2AGc0DSRem79rPtmFsTnpbdhahX+98R9SbsjirJem8uSoFxn2w1M8O3Yo17z7NbVvO0C3hycyac4JJfK3de8uglXbtrFppVJSoE4dMS1+9FFofq6LL4b580uPTszJgVWr4OyzvR90/DfU6hmw4bsL4M8h8EN9WDEUstZDYaY4VB5ZI2HGxfH5ftU+LbSGZKxURCSkwWAw/MOYunkqmfmZANRNrctFbS5Cef+FdTrfNzOQsiG5MdTqFX/+n7SWof61mfNDndTjYLtTSuP4KHAV0LVe17Db9m7cO8SfasLGCTjdTrFwtHss1A967r/AeTiufk3KBUeQ0NaqZisctvDpjHo27IktaN2ZsmkKbk9ZIs3i47gStrYc2sLunN0ApCSkcEYz8Rfq1biXP3V/gjWBSRsnRW3H7YbHH4cTT4Rvv4X8AhvTl3fnlXEP8MzoYQwb+yTfzzmPzKwqLFoEl10Gp55asp2GDUXL9Pnn0LKlxuHwkJZShNXqxmLxkJJcRHKym1q1NC+/LE7yAGedBWeeGUgFUVgon91+O2zbJibGYI1Ydjbs3i3Ft3v1Cko3YbFB35/FV8sapJFyHoY1r8DPJ8CYWjCmNvzSDtb/N/JFOeHekoEDBoPBYAjL0JlDySnKIcGawPWdr+dfJ/6L1IRU8l35vDn3Tb8Fxk/WWvy+WilNQq0KsWJLFvcNH1u+KFmIOkZ+yAEVlGW7ba22EQWc1jVbY1WBUHybsjFr2yx503IIIfbMwgyY3F3+d0Vw0XGF9nlUDv66jCAKlEh0TO8YknvLoizM31X5VQSPK7XCdyu/8zvz5RXlUTe1rj+3Vs2kmuzJ2UN2UTafLPmEy9pfFraNrCwRbFasiC1lA4hGadGi8N9ZPHlc3P5jLn5pGLv3V2HW2tPYtr8uHo8ivfoheredT8u6G1Ct7wL9ICD5x775Rny8tm4Fp1Oc6r/6Cr7+Gs45B3r2hFq1JOv8okXw448RUphYE6VUw/p3Yemj3mzDZbiJ6w80xawNBoMhBtZkrGHxnsWAOJVf2eFKWlRv4V+fXNrFyJUjue7E6wI7BZcPiSXqLqq/lZeM2dE1SInpEdv4Mhu/o7lFWaJmY1dK0b1Bd37d8isgpsSRq0bSr3k/qbnY4SlY+bw3RyOQu03yNnZ+BZpfL2ZFZZEgNKsDNv7P37ZLw9Qg2SstIY0+TfpE7IvNYqNdrXYs3bcUkCCFH9f+yKmNwmhEKhAVtQDz30i3bt30woUVm4or5YUU8rwSsUKRkhDQ5hS4CnAFDbqsx7JIc4SG+RUVwWmnifmvsJg8cs45Yp5LTxeBzKexCk5aWuJSZ8yFWYNFeneH8ZEKxpIozvZXBGyF+/dDv36weXN8KSCSkiJsn7cb1r+DXvceLrcbj0dhpRBQuHFgtbix2BxY2twrQtrK57zOmgqucsVWVyseVBjvzaVPyc2Z2hzq9oeEavG3U5YxH64vFdGO6Uvl9eVYOx/Tl4rry998Pv/64V98s+Ib3NpNk6pN2HrfVgCuHnM13638Do2mZY2WrL9rfUB7tPghWPemCBzJDeG89eGjx30UjxwPxpYWyGl10hvQ8rbobRVrM9MN9TeDT/dWxVGFUZeO4uyWZ0fc9Y25b/DEtCf8WqUaSTU48PABOT+PCyZ2hqw1JWvnWpMhvQ84aongt39WSAT8H/lwzi7I9l76RFsi6+9aT6OqjYjEQ1Me4s15b/qF22bVmrH53s2ln38pKKUWaa1LJGyH40iztTpjtV/QAske66uTFI6357/NU6c/FfLZ3XfDypUBQctigSuvhGHDJOFocAqGnBx46SX4738lpcOhQ8UOsP49WPJQ9EjGYMIk/6xTR7RW998Pn3wi2i1XlIcUiwUcDrj00pLfZWTADz/U59NPX2TJkhdo32g9HeovpFbqPrRWZGTXZcXuU1izswU9eihevOl9ejqClb+lTDKx1lfUWjIcbx8N1SxwOOjGqwqsel7qOVqTRANXpW2gzqLx/zIYDEc5mw9t9gtaFizUSanDPRPvASC3KBebxYbT42Troa18vfxrrj3xWtmxzumwcQS4siBvJ2T8Dun9ymZRCM6zuPwZSWlkdcT8wDwuB+wKirzTfr4zn56Nekbdp3fj3iRYE/zCVpG7iIW7F3Jyg5Nl7u4zDiZ2EzeW4PXEnQe7J0Zs98ccyAvaPMmWFFXQ8vVlxOIR/pJ9e3L2sOPIjlL3Kw/Hzer08h8vx7X9p0s+DRG2li6V/Fg+06HVKvmuBgwI7+Du++yBB+DGG8Uh3s/mLyS7fHFBK6UptLhFnBgtdsnuvuVLOBjBBgkkJMC778I998DQoZoxozUJCS7cLvEts1ol4rGg0MY558BzQy10Cqq3rTUMHw6PPirCmGjiLCze2IbFG9uEPeasWfAetTn1Tjt4vKkjXLlgj5KzJZb6itmbYN4NcGip3GDvBn2XVM8bSeMWp33nYfn88DLxZejwRORjGwwGw1HCzT/djNsr7HjwsGD3AhbsXlBiO5d2cc+kewLCVt3+hAghy5+Cfr3AEiECPIoJkHaPwuqXZZ515cD0QTDgD7EaxMDIHCjQ4uOstaZx1cZUcQTN/0WHZN06uEQEQzx0caRT5CogwSpR+wWuAn5a95MIWyDWirPmwJRTSwpcUfgpVx76E6wJeLQn0J4Pj1P6sv936Yuy0MOWToEz398Xt8fN1M1TuanLTTEdsywcN8LWj+t+jGv7zYc343Q7sXsLbg4ZAgVByqUPPxTfrZRSMh0kJYHdDnPmeD84vAIW3BYqaFVtD52GQb2BYir0DgA8bhG+Dq+AZU9IXqxwFB3hBNf7fH3J23x2bj7Lt3dkyaYTyCtMJjGhgBObbaBLs2UkOKzAnVBwFyTWIicHLroI5s4N9T876SS44go4/3yoWlUEssOH4YcfJLnqihUwb13n0NI8O3+SbPFl1S7tHA+zrxRtlXbLE1aD86HxpVI41Zokie9Qkrcsb7tov7aNFIHLYDAYjnKyC7OZsW1GzNsfKjjEtM3TxLfJlgxtH/QKSfmQuQA2fggt/k8SlhYnWjLqtg9Jfiufc/zh5TD9LOg9Rh5qwwldQemAnq8JA5OBrqLEOK3xafLF/t+lf3t/FU2Zu8Cfh9Gm7IxraGd1fqHXDaQfg4sLN1XbwNnzJYdl9qbo7jW2FKjang8638uKrP3+j89pdY68cGbBmtdh7euAVaxD3r6kW5P4up6VnW4r1D8HanVn8AmVW/DmuPDZyi7MpspLoVoXSxh1qaeYrXjC1RMY1GoQ69ZBly4BgeTii+GLL0oXtILJz4ekRA2TT4aDi/FL7fXOlgFucUQXVFy5sPzfcNJroZ/v+AHm3yyDOhaTpMUBFgeui48wYIAIWj6z6BlniPauShUxN/qKZvsoLBRn/IwMuPYaD7PvqxK4AWueDGf+Fv6mh+j+A2dMhlkXBvpf5QQ4bbRE3NhSIqu2PUVi698xGpr9K3L7R4ufiOnLX9uXY+18TF8qri+VdT7zbpFov8Q6UiWjWseQbe6ZcA/vLHjH/95msYWkIQApgVMUVKC5Q+0OrLhjhbxxF8CEEyFnY8C3qd0T0OFJeQiNtIYUn3+v1rDrZ/jj8tB1Q1mh6bXQ+SWZyz0u+QxEiFr1Qsl2AAoOiBJh98TYoxutKSE+yCFoD4VrPsKz7Hls7gO4PVaUcqOxYVVOnLZ62E4aSkKra8L/lvumw6yLZI0obV20pUBCLbhwa2z9jsJx77M1ceNEUu2p5HgTuF3W7jK+vvjrEtt9uOhDHpryEAXuAixYmLRpEoNaDeKTT0ILPj/zTHyCFnhrGu740Ru+6x2gNbp6nyRiaMyWAp2eC7zXGhY/IE82xQe3oyZUaSc3iysXstYF1MmeQvAU8vTTkp3eJ2g98gj8+9+h+buK43DIX2oqTP3VAiuvg00fiYYrc4GUkbC1DC8cRVNpB9/wNbpBv9/EKbI0XwRLgvw1Ch85ajAYDBWOKxd2joPNn4sfqa9CW1VkPgSZl5b/W1wrml4NnV9CKxs/b/jZ34zdYueRXo9QP61+aPMeFw9NecifLHTDwQ0cLjhMtcRq3ujxiTCxq7c0nIbV/4E9E6D13eK/6rMMoOR1cNLQYBqcB20fFu2PT4Ok3bDlc9j6FSQ3ktyJPh8xd0m/YQCOrIVfT5f+BAmJoKBaJxE8lRKB7PDSgJAYRmuVmSlR9SNHWli0aAiJiUOombiFU1vNJsWRS3ZBGn+s680RZyMKC+GUU8Rv+vbbg2SueP2hXbklUklUBseFZmvwt4MZv348ACn2FP476L9hbbMr96+k58c9/Y7z6Snp7HlwD+3aKdZ6k9M2by5O8sEFoWNmSi844LUnWhxwwTZIil5cMyKL7i8maClIPwPa3C91D4Mz8FodEuK79k3YM4k9B+vQ4uE9fk3deedJvrB4Sv4AcGQ1TOoWGNTVOorN3VZKQ8WfsiyJouJVFjh3tRQnrcjIxkp6mr75Jk2NGtC+PQwcCHXr/n19+UdrGSqrL8fa+Zi+VFxfytrG3t9g9hUieLgiaGVKHMsGVzmZsXUG531zHrlOETIapDVgx/07QnJV+bhs1GWMXiMFm5PtyTxz+jM8etqjgQ2yNsC0M6EoM1SosNihTh9IrCtarqIj4khfWCwjvU8jpbWkXFj9YuzCSTCDN3sFv8P4lQiO2tDiJjjhnoCfLcic7i6EDe+Ko3/+bn8/tJZk3Q8+KEFewW4tVatKCbvERPl8+/bQUnfJyUFR/1u/hfm3lFRAWByyLibWFeVA3g7YNyPQ5+BrUg6Oa81WkbuIXzf/6n+v0fRu3Dvstu1qtwsxJeYU5bBi3yq2bOng/+z668Pfp6WSvyfU0b3h4NhCbcOxe5LkGfHdHLY0OP1HMeX5zG7WYm3X7S+1tLLW8Ondk/2aOotFIibjFrRA6ig2ugy2jxJh6fAKmDkYTh8n/gWxCky+SMum10BSg4pPIVFGCgtFCP3uO/iMdOoS0MztJZ1PpH45qaliXm3cWCI9hw4tWYbJYDD8g9FaNFVrXw9dyNNaQePLZO7yCTiuPDi4UDRfeyb6XS2GzRzmF7TsFjvXdbourKAFcH3n65myeQpZhVnkOfN4dc6rPHjqgwGTY5VWcN5qWPEsbHjfmx+xSJzB9/4atk0/iUEP+EpBx6ehRheYc53X7BajlsdRR2rqurLwCy0NBkOvrwFL+Bq7dqDto9D2EalWgqRUuvhiqQ/sE5oaNoR//QsuvxzatBF/aa2lu4mJovAYOVLcXvb6XNOOrC4paFkc0PL/oONzXkuJ1dtXLYFWSx6GHWMhRmf88nDMLwm/bfktxCZuVVZa1mgZdluLstCtXjdmbZfMtk6Pk09nTsVqDQhbffsGMrfHxb4Zolr2JQxtdUf06L1IuPJhzjVBglYKnDUbUluBrZSO2dOg2omM39iJIq+295xzoGbN+Lvhp9vbsPsXKPIKTPumw9RTocurUPt0r+AXpMaOpq6tcUpkn68K5NJLJU1HnTqSib9Pn5K/6W+/yc1++LBMAPWQOzopCVq3FjNytyIpFr7DW7FiwwZ47TX4z38q/RQMBsNfyZpXQwWt1OZSC7Zqe+8cFzSB2KuIia7O6aJpWvUSGw9uZM7OOYFNrHau7nh1xMMNaD4AlzsQgFToLmTM6jFc0eGKoOOkwUmviwZp08foTR/jyd+HxZaC8gkPyiICGBoaXgStboM6YZQNDc6Di3bBhvcoWPkiCa5sLLYkr7ZPiTbIkiAJRts/CYm1xVKy7OmA5qrxZdDj09LdYnxKhlP+h8cDl1wi861Pm3X//ZJOyWoNzMuOYpbQrl3FovDMM6INA7yCVpBKLK0VDPhdfMPCrSv2KtLfjv+GqeEVMBXJMS9sfbPim5B8Wj0b9Yz4NAESyTBv1zyK3EUUuYsYs3QKVuv9/u+rVi1jRw4tDqidHbWhVo+ytbPt21C7+CkjILVl6YKWD6uDrTsCUnyLFiUd4eMioRqcOVVs9r7zO7wCpg8UB/fWd4sfli1VVM3RnrpKU2OXUoE+OF/XypWSe2zsWJgXpJXaSzpjxsg2SsEHH4gGq3t3mDhRVNLPPQcvvxy4+dPSRKN55ZXQrVvgKQtkEsjMlAz9n30WuVKAwWD4h7L/D9Eg+eanOn0lJ5Q1KXpQk+9huu3DvDzp/pCk2dUTq9OhTocIO4LD5mBQq0GMWSOTVU5RDkNnDQ0VtnykNIFOQ/ne2oHbxl7B+72u5YrmvQEP2KtBtfZSS7E0k4wtGWfre6kx9hka2B2svepzrK5cEY6qnSgCpq8NjxtWvRjwu0ptEZugFXK8FD7/FKZPD8y1//sfXH11bD7RPkHs9deRAtaHluPXUCXWhQGzxecsmu+vPQ3SWkO/GbH3u4xUiLCllBoIvI3o6D7SWr9U7PsHgFsAF5AB3KS13lYRx46GR3v4ad1P/gKYibZEBraIXFIAoHeT3iTaEv11qfbl7cGuPfjKSEZLGhqV4AjEah3F5m8tg4pszasBoSYxHRpdWNJkWAo2W+CmKyyMUMYnHmp0gX7TRcBy5QS0d7nbxFExVnaNh9Z3RL5hS6lAD6KFeuQR+PRTUU+73QGtVIsW4ld1RoJst2aNZPsHyR2WnAyvvgqvvBK4+QcOlBJIDkdgAij+lNWwIdx2m+RTGz069tM1GAxHOVrDwjsDgpajJvQZH5cG3m1N4tsV35JgTSDJloTT4+SajtcEHvo9LsmKvnuCuJs4j4DFwb+SsplgtWOzOgDF6ozVrM9cT+uarUscw6M9PD7tcQ554I6F33FRnzf9OaTi4fNln5PvymejC0YfKeSKDteE33DvlFCH+bYPgbLHdSyPBx56KGA67NEDrr02epBWOFJTgTnPBjncK+g9GhKqxpbw1eoQs2wlU25hSyllBd4FBgA7gQVKqXFa69VBmy0Bummt85RStwOvAGFE9Ipl3s55BAcA2C12Tm9yetR9utbr6i9KDWBJyaSgMCBsZWSUsTPOrMBre1VCCm/GiisPsjcG3re+q0ym5i5dYOdOeT1/ftl8VEtQsxsM3gDzbpSnDE9RaJbiSCi7qKfdueLIuWeq5D0JN1FEi2hEim2feqqY9nw50Xr2FCFo8OCAb5WPpCTJGTZypDhnrlkjEZk+Qevqq2HEiNhufptN/i4zgZEGw7HDkVWhc26HZwKpEGLEarHyy9W/sC9X5i6F4pyWg2D3ZCkEvWscoLwP0YHJ+DwNo+o5yHMXQHIDEupdTqvqLcIeY9y6cezPlVxT+c58Plv6GUO6Domrny6Pi6enP+1//+ivj3JZ+8vCpkli3/RA2h9bmqTeiVO4W7oUvzsLwPvvxy9oAVCQ4VVmeGlwvkRBWuLoTzwauTJSEZqtU4CNWuvNAEqp74ALAL+wpbUOzsY5D7i2Ao5bKr9u/pVCd6E/s63T7eTEuicGNijIkNIwGbPFuU67cThq0aFKHdZlZ2K12imyHQBrITjlUn3/PfTqFVqaJyaCb1DttaFHI5Yiok2uLJOT/SWXiOo2JweWLBGtzoABFeDUnVBdHPUPryL7z5dxZPyI9rhwuhOQ81U4bPm4VVXcTW8gpcu9ou6d0BGy14twtuBWUemmNCnpYBmcpA9CohoP5Vbj1FNh1y7RPiYniwA1eLCom60R5sdu3cT2/9RTcN11gVQY1avDe+/Ff/OXabIwGAxHJ8ERa9ZEaDmkTBaJPk37BN7k7YZZ58ChJV6BJfxaYFFwXpIvqnwbHPgGLB+V2E5rzaNTH/W7y+S78nl6+tPc2PlGf1LuWPhmxTfkFOZg9a5VmfmZ/Lj2Ry5ue3HJjff/jr/6R3pfr19YfKxbF2rZbF1SYRcbO3+SiE+816rFzTFnwv8rqQhhqwGwI+j9TqB7hG0BbgbCFjpSSg0BhgA0bty43B278+Q76ZQeqE3TvHpzcZY/vEps8Lt+FgfGYk8UP9ZwsCgF8YNqch3Ptbew1OuLM3q0lMeJm6R6gdeFmZSq2SpN0ILYijCH4cor4eGHRdgCcUhcuDC+iESPRyIZg1m7Vq7NDz+0JzPzCywWTc2kHbStv4YEWxF5Rcks396JAl0bpxOaNRNN0HOPTEJNPhmKDkDBfpjYRaJH2twrocL2tJIRisVu7ge+eoO9e0XQstlEY9e8eWzCjy/L/6+/Bkyqjz9eTl82g8Hwzydnc8CEmJguJr8ylCL0c2Ce190iN1CBIzFdnNeb3yAuJlZHoCzZ9jHip3tgbkSf1gkbJrA7ezcA9VLrsS93H3lFeXy1/Ctu7HJjTN1ye9w8Me0Jcpw5JFgTsCgLOUU5PDL1ES5qc1FJP+eizMBrR824tX0g7hjBzRYVlfFhNWNOwIRoTYa6A8rQSOXzlzrIK6WuBboBfcJ9r7X+EPgQJM9WeY9XM7kmF7a5MPCBxw0rX5AsuJ5CSa4WpsBzI0shjZIA9sP+z9h7y3AeWiu25awsmDxZStkUFzaid6Y77PpFtFoHF5Yxf0QxPKU4kEXQjjmAj/81mCve+4n8fHnC6NMHJk0SjV1p0Zb5+RKlV88rPxYUwJNPiho41AdM0aRJY1T9xlgTwZoHSVlwwCuar10rjuhDhzaWEg0zBkHuDrlxlj0myfrq9odGF0Pt0+RG0h4oOij+XV52ZDbku7lXUuiVv+65B5o2je/G3b8/9H3XrmXMpWYwGI4drInIg7EWH6XShIqIFgkLnLcGfjsLXN4kURaHRG23/D+Zy4P9wJRVHtBb3QbN/yV5sn6/pESrWmsemfoIOc4c0hLSuLf7vTz/+/PkFOXw5G9Pct2J15XIUB+OkatGcqRAsrN2qNOB3KJc1mWuY1/OPsavH1+ylI0KatNTREiN2xivy6mH0ynM2wrIgjN2rLhuxB3tf2hJ4HX9gV7L0dE3eVdEQqNdQHCp7Ibez0JQSvUHngQGa60Li39f6bgLZTFf5U3eFlyaR1klQjC5YUnbrSuXK64IdYx/4onQpGuxkJ/UJeDI7nFK8rUyqF5DyNkc/fso2rHzTxrH008HBJLFi6FVK/FROnhQksYF+zc5nfJZRoaY11p5/QkPHpRaiu+/L9fE45Gsvp9+Ktv++ac3T9VnUldx3TrYtg3efFM0T36bfWpTOGcFdHpWhCpbFXDmSA6UOdfCT01hbB34oS780g6WBhL8TVo2EItFfs+EBIkmjDdvWFJSaJUAn9avosmuHjS+0suY0NZgMPw11OgqPkkgWvdgjU44Is65nkAAEYjLxVlzxeRlTYzscG+xSVRjSlMpFF2MqZunsu2IxJppNPf1uI/UBGkruyibkStHlnKCAef6HGcOqfZU7jr5Lu465S6S7cnkOHN49NdHKZH8PC0ofVLOZspiqalbbR9dmi7xKy0eeijUhytm8oJi7VKbly3w7C+gIoStBUArpVQzpVQCcCUwLngDpVQX4H+IoLU/TBuVi9ZSEibjj9ASAWmt4OT/wWXZks39vLVw6SEYtBQaXep/iqleXSRuXxTa6tVw002QF2Put9xcmLykb2jh5k0jQrO8FycxhoV47euhjvdx8vjjEjablCRauiNHRCtUq5b4cP373/DWW/DGG/D005JjrE6dQARJfj707g2bNsnrhATxk5o+XXJU1aolQk+1aoG/pCRJ/nnnneKc/mhQUmQsNmj7EPqSTPa2GsUW2/+R40qn0JVETmEVsguqkl1QlZyiKjg9CRxwdWBL2jCW6hfIKxQhxqdti5fq1UNln88+q3iBa03GGtIf9KCehcRhDuYu+CHuNtwRYg5OOw0GDZLfaebM0KLpBoMhiEgRQZN7wrQBsOBO8QMqOuT1R/JJAN4Sac7s8PtD9Hm7cD9+d5WT35Ok0OESf4ZDWUooArTWPDzlYXKduViVlas6XIXD5uDWrrfisDrIKcrhsWmP4fZED1Qas3oMmXkiRLq0i0vbXcqVHa7077fzyE4mbZwUulP6maKZAzGNFs9QHyMf/d8t/nX10CHxJ87KCn3Qj0ZREWh3kIRmrxqfY/xfSIWU61FKnQO8hVizP9Fav6CUGgos1FqPU0r9CnQE9nh32a61jlpiuyLL9bBxhJS38dt1E+HUb6QItMUW/sdxZokwNOtCOGs2mZlimgpegK+7TvI0KRXe5OR2y6L3yy8irLl+vxG2fhmI0uszHtL7le7kXqKIswXwiDB40R5JMBeO0pzsveUJ1q0T4WjZMnEkL02ITEkRTd9DD4kg5tPyzZwp5rd46kbm5AS0UEuXSjb7UaNkPrTZpC8dmm2n0wkHqJqSg8tjI/NICrMXNyIzpwYJCYHfxOMRAW/HjrIlnn3hBUlI6jv/P/6Q/FsVkQ3eoz10+7AbS/cu9aciaVatGWvvWltqiLbWMGWKmFznzYMtBXVJ16HZ7H3pLWw20VYWFECHDvD771HMqUdL6ZTK6suxdj6mL2QXZlNUpyY1s4JW4/T0oDTiUTiyFlY+J9HStxwM1DQEqWv4nr+Tos3yFEh9wPS+sOUrb0obJS4PVTuUYd72HasdnL0gdkErAr9t+Y3B3w4m15lLij2FGTfMoFv9bmw5tIV277WjwFVAakIqH53/Ufj8XMi81OqdVmw+JFaSC9tcyA9XyEPggC8H+KuvdKjdgeW3Lw/4bmVtgImdAukfWtwMJ70VWUMXZS0aZdPccENgHWncGD7+WB4gnc7wwWhZWfJwP3Uq9M2oQlqiVwBu/yR0Ghq5CkkcuRrLQrRyPcd+bcT8fTC+ZUB9a3FA/5niiBjLYHfl+p8ovv4ahgwJFUbq1JEMtnfcEWpqdDhEw/PMM4FEl/rIOnH+Di6zM2iRJJyLVCwUSt601pSA4Njkauj+YWyhq+Eqvwexe7fYzT/4qIA1q6ykJNmwWGQfj0f8sU46SVIpnHUWtG0b0KBccYVotcpS9mffPrjmGpgzR55UEhLgqqtEe9ihg1zLYE2NUiJAZGTIPi+8AJs3B3JmzZsnUYaRIhAjUVgoubj27JHzbdgQpk2T/2Px//J4pJ/htv3fwv/x4JQHyXfl06hKI3Zn78ZutfNor0d5ps8zEdvcskUE9RUrAvloEhLEVHvmmXJ9UlLkuJs3S3//+CMggEa9vY+hRbjS2jB9OWr6sj93P30/68vGgxtxazcKhcPq4PHej/Nk7ycjJ6t25sCie2HbN96UNB5ASfb39L5Qq6doRLQb8vfC/ukShVjgFeAuOQA/t4PCDECLO8hpoyVDfLR8W5GEre4fS6qEGHypolH9peocLjwMSPWTmkk1UV5z3sGCg/4kqg6rg5wncsL6bg2dOZRnZzyLRqNQVHFUweFdiwrcBWQXZvu/e+PsN7ivx32BnaeeLul6QBQYg5aJubO0FBBh1qHx42UNKCwMmBLr1pU8h5dfLvOyrzbiunWSFWDyZFkDtv+3EY1qenMZtb4TOr8aWRCO9JsE9aU8HN/C1rInpaq5L9Fmr2+lflMZnyqeekr8jYprf9LSJH9V9eqyKK5fLwUzg9EaWHiPaNp8jvmOmpIQNKVp5HDV4gOk7lkyGWjvqGzzgEjzpQlcpQhbPi4ddSljFk/jg74/0qVGH5SS86tXL5BBf+JEiWr0CTi7d5fNhLdrlySz80USnnkmjBkjglIs6TU8HlE/t28vQhtAu3awYEHZIls2bhRB5sgRaTsxEZ59Fu69VyaC1NSSQlxurphh16+HG26QdBrB7M3ZS6t3WpFTlEOSLYkfr/yRC767gAJXAUm2JJbdtoxWNUsm1ZsyRWqGFRSIljQ9HV56SQTRggLRpgZHTLrdImQlJUk6j3vvhVWropzsMbIIV2obpi9HRV+2HNrCaZ+exv7c/SRYE7j7lLv5ZsU37MzaSZI9ietPvJ7h5wwvmRMqdwdM6SFBNe4Cedhucz+0e0y0H8paci1wZom1I3cbLL4f+k6Q1ECTe4RGrje5Ato8KA/uHqcIYcrqrS9YAKOrhT+Zfr9B+hkxnXckhv85nLsn3h3z9g/1fIhXz3o15DOPx0PiC4k4Y/QdTrIlkftEbkCozVwolUOCozXPmgdJdaP7TUVYhzIyxI1l7Fh5wM6OYq31UaUKjL7zLAZ0mCofVG0vmsdIa6ERtkpSIcKWxyVO1UWH5H1aaxi0pFzqW60ly/izz8bnJJ+S4tU2OLPhlw6Qt5OQCI76g6DT81DlhIDjvLJC0WH4qVgajIv3wfgTvJXWvTS8QPwAbGmi+QoOldQemSS+L1ZrKMzA2pW1i8ZvNsaDh/a127Pi9hVhnxjfe0/MiPn5ciinM87oTC9nny3aGLdbtFEzZsRnhvQxfrwIfz4h+LrrxGk/Wo6tYHJzA8fduBHOPVcEQZ82KTFRIjYvukiEw5QUEb727BHhcOJEcfyHkuvH4G8HM2njJJweJ32b9mX69dO5avRVjF4zGo/2cHL9k5l789yQ67xzpwiQPmG2f385jsNRMoN9ONxu6V9UgfMYWIQrvQ3Tl7+9Lz+u/ZFrxlxDvisfq8VK+9rtub3b7WTkZfD8rOcpdBeSYE2gQ+0OzLxxpt9BHI8TJp0MR1aK1iq5sQg6SXVjT2LpypFSYyBmyJnnSpb34DQMjlqSiDmpgVgonNkScb5/Zvg2Tx8HDc+PfMwYTF1pL6aFlKErDZvFRuGThViCJunnZjzHszOfjbkNgDfPfjNUu7XsaVj7RqBmpL2KaO4anCfrTvG11pUPo4p9VmwdOnRIzIOjR8NPvxSAx4bDbkMpGS4ul8yB55wjPl7nNnwSx8aX8K+nF2yDlAipo4ywVZIKEbYyF8C0foFQ265vQ8vb4s50G465c8W8c+BAdEfq5GQRQp57Dh54wPth9iaYfDIUHaFEyGyVE+Smtdgk6eqhpZRIene1hgPz4bf+AfMoAErU4q3vhRonyUB358PhlbDuLdgzuWQ7xbjt59v4aPFHeLSHZHsyP1zxAwNalMxb8sUX4uTuO/f8/LL5SdntAfPr22/Lk01ZeeklKWDqE7jatZP6iCeeKMcobuJ0uUSYSkgQAfqZZ0K/e/ttCRKwWmW7SM7pPnwZ6oNNnmNWj+HaH66lwFVAoi2Rby7+hm71u7Hp4CYGfj2QQnchSbYk3jjrDW47+Tb/fn37wuzZ0o/0dNGaVSlD3fKo/MMX4b+kDdOXv7Uvb897m/sm3xfyWbA5TGuNO6hSRVpCGuvvWk/dtLqw6iVYOcwrCCgYuEBq/JXHfOcuhGVPwcb3Aat3bYnlOqjAds1vhK7/jWyCLEUgmHPabHp/1huPN6LeYXXQsErDEtvtydlDnlMmQ4uyMOayMVzY9kJArlvjtxqzM2unt3eKhlUalvAfLXAVsDt7t9/PtHWN1qy7e11gA48b/rhM1hZ3kLnHUVvyhjW/Qaw3WES7uPVr+U2CiSDgrNq/ig7vdqRqUXtmXr0YV5Gd5GSZZxs2DBpK+2fBjPMC63zHf4vmMpx2LUY/5rJy/ApbGz/0OsbnSfLSSw4EioNWAB4PTJgA9/17O9vXpGNXDmy2gH/TCSeI4/ltt4UxiWVtkFQU+btLL8AcTGJ6IJP6oaUSTuzMiq8NH8UG1t6cvTR7uxkFrgIUCo2mc3pnFt+6uIR2a9s2aNMmIFh88ololuLNTdWoUaB00JAh4nBfFs2Wjw8/lCStRUUBIa5ePbH9X3yx3KQJ3tqIc+dKAek//hAhqcSt4HHjOrCceTMz+WlCEmMmt2RnRg0SbEVYlQePVrg9VpSCM0/eyOWD93P2IBt1OvQBYH/Ofuq9Xg+PV6C2KAtJQb4EBa6CkIVizZ1raFOrDRkZcl182exHjYILLqiEJKv/4EU4GJdLfNq6nBTaRvNmGodDJufu3SVytkcPccCNmObuKDgf0xfh+9Xfc83oa3Bq0fRblIWmVZuW8D06VHCIA3kH/L5FrWq04vebfqfOr90hd6ts1PwG6PpOXDUNo+Jxwv6ZuDd/QcHWb0lUFqzWBEB58zcWQZXW0OwGaHwJrPoPbP5cckApK1ywRZzvw1GKQHB6YW9+3y6+UqkJqbx7zrv868R/ldhu0sZJXP795WQXiRDSplYbVt+xGqUUUzZN4eKRF5PrFNV9yxot2XD3hhJtaK1p9GYjdmVLNqdkezJTrp1Cr8a9gq6FGxbdA5s/rZB1yEefz/owa9ssAN4Z9A53nXJX+P21B0bXDFh6khvDeasrxI85Xo5fYWvBHbDhfXmd2kJMiBWcxn/7ke00f7s5bo+HyeevoV7CCTgcErlY6uLoyodF98HWLwI3aDiUVXwI6pwOZxQLwXXlwpLHJJWEssQ+2IOFNi/3TLyH/y36H1ZlpVZyLXZk7SDFnsLPV/9M36Z9SzTRvbtknvd4oEYNceaOV/syZoyY/Hwm2S++EFNdPI72bneoqXD7drj1Vvjtt9hs/3a7aOWyspDJ/8A8WD88ULPMYoe0NlClBYd1Rw57WpGTn4LDXkSK7QB1bEuw5a+DrPWSh+fSTFBWOr3fiRX7V8R8Hg3SGrDzgZ3MmSMq8iPeaKlNmyQnWYXzD12EfcyfLxrjadPkd96QXZd0ZJHaSzrNEvdSWChNKSVjyuWScRoxxdlxLuAcLX0Z/udwHpn6CPkumRiS7cnMuH4GJzc4Oez2N4+7me9WfEeeKw+7xU6dlDrsqLsH5bMcdHtXitxXMLf/fDsfLvqAdmm1mHf9VFKsNlljEqqHPtjn7oCfTwjMz/XOgt5j4xYIFhbA6XuS/Ncl0ZZIxsMZAdNpEC6Pi1qv1OJIoUwkKfYURl8+mrNbnE2X/3Vh2b5lgPhivXDmC9zf8/6whx82cxj/+eM//prBvRr14o+bSub8Yv8fMPc6SQMRpQxRCGHWIYBpm6cx+LvBFLoKcWs3VRxV2HH/Dn/pvRIsegA2vBfwzW5xC5z0ZunCtRG2KkjYmn0VbPtOXlc/CfpNK3OJm3Boren3RT+mb50OQKc6nVhy25LwhTujkb+HrGXPcXjj5zSyulA+9aenEBzpkkW93cMSMhyJggzY/Ck7l79EXU+WVIpXFhHitEuEzSZXSYhucv0Su2fkZtDkrSbku/JxWB28OuBVnvjtCXKKcji5/sn8+X9/lthn1SpxJveZ7Xr1Et+p5OTY/Iry8sSp/c8/JcLR5ZK/iy8W813LlgG/o+KCa16eaK8cDnEkv+CCku0fOiTfffltPhMnebB4EnEkiCbK7RZtVvPmcOmlUkOx+0k5MPdfXpV4AaQ1h07DJKDC4/TmukkumUXalS+/ldUBOVugajsW71lMtw+7+dXvIIXQi+PyuPzbKBQTr5lIU/fZnHRS4Lr++SecHH6NKR9lXYSdWWKiz94ArW8v+f0P3uja5IZitqnaVvwla3TzT35aizZq9WrYsAGefia0L40aaux2yc3Wtq2Yglu1gk6dZCzcequkGsnPl7a6d5eqDv36yTaJiYGSUpmZIpj9+qv4gfg0qX48LtESZ62F5teFOZ9GInAn1JSQ/+odJUdf9S6Q3CD8NfoHCjhHS1+2Ht5Ks7eb4bA6SLAmUOQu5OO+j3BNnXqQu13GXe5WMUtpF2gPTiz03pjBigInVmUhx+0iq6WFVOUVtjo8De2fiuxCUoaUAOPWjuOqMVf5fckubnsxIy+NkkR09Suw4rmAuS29H/QeLY71MUaj998Jv+Ur/5xxadtL+f7y7yPues/Ee3h/4fv+yMSOdTry1tlvMfi7wX6tlsPqYPv926mTUidsG9uPbKf1O60p9OaFTLYnM/366ZzS4JQS22Ye8LBxzkxSdw+nnm02aY6DFLrk3BJsheQVVWWPswdZde+i+an9qV2npEOtR3toM7wNGw5uwKZs2K12NJq7TrmLVwe8WmJ7QEob/dQkUBwbJBVFy1uiC7RG2KogYWvuDbDlc3ldpY1EKUQzI0YrtRDmZvt+1ffc+NON5LvysSgLDquDl/u/zJ2n3BlXN10eF91HdGfx3sX0a9yTKZd+IdlmHbXiEg5HrRrFFaOvwAIs+9cEOtRsITdxUkOwRPcSf2jKQwz/cziF7kJOb3I6v1z9C7VfrU2Bq4BkezKTr53MaY1PK7Hf+PGS9sGnmapTR3JlXXihmBjT0kId531+UnY7DB8ugQZ5ebIADhsGX30V8JGqWxc6d5aFtkMHEbp80YeLF0tesOXLRSDzmdyKsyFzA70/7c2+7P1YCmvxSp93GdzyMlJTRQvnN1lqLZE1mQslUrR2L4lCsqaUeu2K4/K46PBeB9Zlim9Dqj2VjwaHz3UzY+sMzvvmPP/EVze1Lpvu3kyrZknslnJn9OkjudrKY14NSzyLsLtQsvmvfwcOLpIFwl4FbtoHh4I0srWrwaJPxIk4a7Vo+46sFn+K89axclNdhg+HkSMDdSxPOAEmLatLNe+9V1Atnalf7CU7OyCMrVsnGqmZMyXh7uHDsn/HjlKt4IQTRMCKlhPNN0aTkgjSYL7j1WBaRKAakg2HgqKzaleDxZ+LieLwKlnos9aI+f+8deJsXd5rG4njVNgCWJexFr1rPGz5kuSctTROrga1eojAXqePmOgsDpnflBU8ToqcOWw+sBZy1sOBhTTZ9xNJzoOAW+bS8zdEnk/jdJzembWT9u+1J6swy1+42WFz8P6574c16fnPdd7NsH1kQOBy1IK2D0PTa6Rv2oOUwvF46zAmwEhxKF9aCKfugHxvV9IS0vjxyh85s9mZEbu9Yt8Kenzcw++7lWJPoUGVBqzPXO/fpm+Tvky/YXrU0z95xMks3C3rsULRp2kfpl8v+xQWwjffyPy9c6fcXz4f1+qpR2hY6wBWq4cd+2uRmV0di0W0zAUFMvcXD2D6fNnn3PnLneQ6czmlwSks37fcH7m9/u71Yf3TAKkOs/L5UN+x1ndDp+dA2UtquZzZ8H0xecAIW2Vk1cuw4hkxz9mriroynpDUYIr9CIcLDtPs7WYcLjiMw+pAKUWBq4AUewrr715P/bSS2qNIPDHtCd6c+yYFbtn/yd5P8njvx2PeHyQ0utP7nch15qLRNEhrwJo715DmKN1sejD/II3ebESeM4+0hDQ+GvwRl7e/nMtGXcaYNWPQaE5tdCqzb5oddv/ffpOC0vn5oQvaGWeI0NW6tSyEubmSFuGnn8RfyudTFTwE8/KkRuPChbLNgqX55GXZSXRYsVgUWovA5XKJ/1XnztCzJzzySMl+Ldy9kP5f9OdI4ZHAhGh18HSfp3nstMdCNy7YDz82DESCnjFJkt6WgVfnvMqzM571T3D1U+uz/f7tWMMIbVprOr7fkVUZkqMhyZbEHSffwZmu17jssoB268034ZZb4jOvBieMDUusi3DWOpg+SPIMuXKg3kBJNVKtk5hFLI7weW1ceXLvWZNw7V/Io6/14v33RaNYrRo8/7zk1vF4ArnTik+8Tmcg4jU7W8bUpk3y+7dpE0jxEVckrCsP5t0ohejdBZDSBDr/BxpcIP1VVikZVbxRd5H3fO1yLVKaRD7GP1DAqci+5OeL5nLtWhGWV66U6N4/F4S2c9mlGo8nkKMuMxPatsjk+f7nUTdxBXaVCw0vlAg3ZRUtRSwO7u5CyNkkWeFd3pDeBudL6h9rUsmkl3E4Trs8Lnp81IOle5eSaEvknlPu4c35b/ofTJfcuoTWNVuHb0drWO1z3C8gxNRWtT3UPNmbULUQ8nbBvmn+xKHPZsILByExIRWtNWmONDbfuYvVqywsXy5z5vz5kkKhqEiuaUIC7L+sPe60rVgtFlwUoHGTZE/CoiwUuYsYeelITkq+kGXL5AF2zhx5uCkokPvMagVnm2/I7H0jNpWAsnjQykXmwweZPSOFG2+UezM3VywNl14qeRh79xbfU1+tXKtVxsDs2RJtOHJkINrbR74zn0ZvNiIzP5O0hDS+uvgrRq8ezTcrvsGiLFzU9qLI2kOPC6acCoeWBdIigaz5re4QM3JCTRFo83dLjWSf5SvM71wWjl9ha++vUrzTV9Km7wRZQCOZ+eIQtm788Ua+XfktLo+Li9teTL3Uenyw6AO01vRv3p8J10yIqYszts7gnK/PId+Vj91ix+lxkmRLYvr10+nesHtMbTjdTk768CRWZ6zGqqzYLDY0mvNbn8+oy0aVuv/jvz7OW/PfksnClsyBRw6QZE9i2uZpXDTyIrKLskm2J/Pbv36L2KfsbCn/8/HHAQf0aNF7CQny5ysTFI5Z22Zxxmdn4ClMYkCji/nfoM+wWS0kJkq+r2g+cVM2TuGiUReR58wjyZbEJ4M/4YEpD7A3Zy9J9iRu6nITbw98O2Dy9bhEDZ3vVSe1vBVOej2yCjrC5LzNCe22QZ53uKTYU3jj7DcY0nVIxL6OWzeOa8Ze4w/lTrIlMf+W+Xz8YkdGjAgIXFdfDe+8I9qb1NTIAkZ2tnwnRb4jX6OYFuHCgzCuubdEiQfaPQodnok7fcpdd4kGKi9PTLezZ4ufXzxO/xMmSBCGzwdvyxaZzONNXsv0gRKW7y4QTcmZv8a+iMfKcSpsrVol2upx40QTe8klcP31ksYkLQ0sDepi2S/3jadOOlnrAv46Vqs8pBWs/pyUNbejfP5NV7kjz9mlsesXiZbztVWjG/T8Qkzc1kQRnMMRxbz0xLQneHv+2+Q580hPSWf7/du5f9L9fLL0E4rcRbSo3oIVt6/AYYtiGjy0HBbeIeZ4S0KxqPLwFHhgt60mnP0n69bBFx/UYtz3VbDZAtVKWraUKGyfRaGwENZvzWXV1n14PCIMTZrspm66lfx8GDPKzufvNGLPHrE25OXJ3NqpE9SvL/enyyVR90u2bGPffrdYGIoS+e8L9bn33sD8dNNNUv4tlhyJ2dkyfIr7+D4/63le/ONF8px51Eyqyb6H9rFi/wp6fdLLP5fPvXkuJ9Y9McJFOgCTTpK5OZIPdDSMsFVGnFkwJj2QQLTuALGRRzIlRnu6CfoRZm+fzYAvB5DvyifJlsSiIYuomliVFv9t4X+6+faSb0tWSi9GRm4Gbd5tw8H8gyTbk3muj+Q9yXXmkp6Sztq71lItsVqpp3nfpPsYsWgEBe4C+jXrx/rM9Ww7so1kezLvnvMuN3S+IeK+245so+lbTUM+82Uh1sUcHGsk1iDz0eiFWJ1OWUhHjs3n468P4TxUlwS7BatVblq3W3xwLrtMfGw6dw4vNKzOWE2Pj3qQXZSNRVlItCVyU5ebeGfQO6Vej4enPsxbc9/CpV3YLDb6NulLr8a9yMjNYMTiETg9TuwWO21rt2XOTXNISfAKVIdXykJcmCljpvHl0O0dmZStiaFlnSKMlVcOwuOZkGhPBg2pCUlsv/h1HHlbIXuj+HTl7xLfAu0CDR4UbTYeYafLg8JCocfDvW3O5LX+L/PuN5159DErTqdc26QkiawcOFBylNWrJxOi0ynm1enTRSj55RfREpQ7g/y2kTD/lsBicFl2mSK6qlULCNVPPikLcrxlkP7zH0nP4XbLJJ2RUYYITXchjEzCr1E49Ssx4VQ0x6mw1a6daLO0Fj/MTz6J/1BkrYfJ3UVA8hRClzeg1RARjMKVVivN/WPfTBG4XLkBE1OdPuKkXv9cSbdjSQC0bJP5J/xWLN2Nd/7/buV3XP/D9RR5inBYHdxzyj30btKb7KJsbvrpJsn5ZUlgYMuB/HTVTxFP0RcNvXX9Iez7xtEx5TM61hOnc5fbhsXiwW51sj+7AXN2X8OBlCup2uRELrhQce21co8XFYlgc911cPfdMq8WFgZ8FZWS11qL9nf/fvjyS3jsMYnCvuGGgFtHly5SCeXcc2WO8WmTLRbZ3+0O3Gt//CGBKUuXBgStBx6Qz8pSQcTHvpx9NH27KQWuAhwWB3d1v4v7e4jTfv8v+rM2cy0AJ9c7mT+HlPQh9lOYCTPOhcMrQk2KpRHBWT8ejl9hC+D3y2DHGGRyVXD+RlH/l+aHE+HJpsBVQLO3m7E3R36UU+qfwn8H/ReQ0geTN032R09svXcr1ZOqh21ea80Zn5/BnB1zcGs3ZzY9kynXTeHUT05l/s75JFgT6N+8P+OvGh+5DAUwacMkLh51MfmufBJtiSy/bTnrM9dzxegryHXmkmxPZvGQxZxQ64Sw+/f5tA+zts+Kfi2C+PLCL7n2xGujbrPjyA5O+/Q0th/ZDm47d3Z+hCd6DCM1VZGSUromYnf2bjp/0JmMvAwSrYnUr1KfrYe3kmhLZNgZw3ig5wMR973ou4v4cd2PMZ9PlYQqbL0v6HdyZsHat2D9u3Kjapc4QqefAdU7Q5W2kFhH/Cq0R7bJ2QTTxG+iSEOGG2jxf7DzB6p5ckhJbSKTe53TxO8ksa5M7hYbEiruIr8wi4PZ28W/KWMONU98ksQUcb7esUMKdo8ZI+bYnJyAaj4SaWnRfdmA2BbhnM2ShNenGeg1UhI4xlkDbdDLE5i2qh9OdwI9e4oZIV4ftK+/ljQqOTnS9cOHy5B7TGv4pR1kr5ffr9Gl0PPzyJq6stZSO06FrbFj4b77RPCvWlUKo199tQjWhYVy79ts8r/Vit8twO0OPIw5HJBkPQSLHoTt38q9ktoSGl8qwUJV24umy+MCPDCulbfIcxh8D8muXFjzGqx+1evjVRC75sO7CK/NWEu799r5H0ItyhISBVjkKqLAVysQeKzXY7zY/0X/e1+qoLfeEoHF4RDBpmtXcbXo2FHTrHEeyfZsXB4HmUfSWL7Sxrp1YpLdtEl8FJcuFWGoZUupfZqSElu1DZDfYOxYuPnmgMvHiBFSlSLWBNAgJXTGjJFz6thRBMfy+pS2e7cdaw6s8b9PsiX51z6Xx0VRUMHp5894nidPfzJyYx4XrBgqCVdxB2o4FseWApZE6PIKtLipfCfA8S5sZS6AX/sGJNzU5jBwsdjFozl6RBC2igsnqfZUvy+OR3v8OU0A2tZqy+o7V4dt3pdmochdRIIlgVcGvEKDKg3YcngLT017yv/k9ECPB/hP//+EbWNtxlq6ftiVPFceNouNPk36cFu329Ba88RvT7Dx4EZAnK7X3rmWqolVQ/bflbWLhm9GcDaMQGnardUZq+nzWR8O5h9EobBaxKx54QkX8vlFn4etzxVMdmE2XT/sypbDW7BZbFzb6Voe6vkQXf7Xxa9J/OKiL7i03aUh+2mteXDKgwz/c7i//ESqPZWu9buWiA7ddmQbWw9vxaM9WLDQplYbZt44k1rJtYIbhMPLxBR9aCmuQyvIP7yaZFxYLDaUsgLa69AaNPEDoMRx1+OELq9DyyGSwTpWjZC7MDQ6qegIHJhH9s5VzJ6tmDK7ARPmdWXD7qYopbFa3Hg8FlweGw1r7uGsbosYeNoW+pxWSJ2+D0c+TqyL8M7xMOdqmbC0W5IGtnnAmyG6mPktgnByILsmF7z+E8t29yI3VxaYzz4LpLQorbRSdrY8ybdqJQs5iGZv7NgylGXK2SoazLwdMi/UOwtO+VD8OSy2UL/OsiZBPE6FLd/HCxZIFO2yZRLkkJ8PVapomjRRVK3m8QoaCo9b+R8KDh2StC3Z2RIAA4C7iIMb5rJn2e/Yjswn0bODNNseqlS1oq0paEsy2pKA8hRgcediLdzmT/fg8jiwXVtskfU44eAiXLsns231uzRyZ2BXFpTFhqiZXWL5qNNHsqCnnwFpLfFoD3VerUNmfmDuU95/Ic0XS1K98vaVtK/TngMHJMp6yRJ5WOjcWXIKnnpqICF0uKTQbrdoj5QSv82XXgqY+tavh5o14zOjZ2eLNtznK/Xii2Lij1cjdcIJcnyQXJLDh8cu8IVjyqYpnP1V7D6yNmUj94lcEmyhmk6nU4TR+fOlEsiebYfoVfs9ejf/kXb1lwOg0DjddpZs786MLVewPOcGmrVw8PLLZe+/j+Nb2AKYdxNs/TZgTqzaDvrNkKfZWGsoXa2ZvHEyA78eGNehPxn8CTd2uTHks8+Xfs4NP93gf2+32EOSXea58vyhugCjLxvNJe0uCWmjyFVEjVdq+KPYQKJTfDe/W7tDvjuh5gmsvWttSBvPzniWYbOG4dEeFIqm1ZrSskbLEucwe/ts8lwirNqUjYVDFoa1mc/ZMYeBXw30+3gNHzSc9xe+z9K9S7Fb7fRo0IPxV48n2R5+dcwryqPTB53Yengrbu0mNSGVp3o/hcPm4NfNvzJl0xS/CXDE+SO4vvP1gPisXffDdYxfP97vlF47uTYr71gZNpxZa82VY65k/Lrxfl+59JR0fr/pd5pWa1pi+2mbp3HlmCs5kHcAO1DNnsjL/Z7nhs43o6wJXodb73j5/TLYMVpe1+oB/X8vmy+Q1hL9t/J5ieqzV5OCuXVOl78qrdGWRAoKbeTnaxwOC0mJLiwUSU23jNmSWbnn55H9XeJZhHO2imZgy+eINs4tmrqaPbwavzaQUFU0fh6nlJnKWiPmGy9uj4UvXW5ee00mQptNJv6zzhIfkY4dxQcrIUG6UlAgflnLl8sEOnmyBFf07RswX1x5pZRlsttje7LOzvYuCu4C2PA/WPemBEeApHNIPxOqd5IUD0n1xHSlPeJUn7NRhLRgjLBVKnN2zOHZGc8ydfNUMTB4bOBOwOpJ4cYuN/DQaffQvHZDbLbAYVwu8U8cPlycquvWhdNOk8jcXr2gSWNNgjUXm8WFx+1GWWy4PHYKnEls2KiYNUvqg44eXVIYWbxnMZeOupQth7cAkGK18/SpD/Bgr0ex2dPC3q+vzH6FZ6Y/Q6G7EIWiRY0W3Nj5xhLbLdu7jB/W/oDT40Sh6JTeiQX/t5BTutlYvVoeGM4+W7RC8WqCzjlHyoKBOJ+PGyfm+XiYO1dcEHxlwDZsEA1ZvJx7rmjpQH6PSZPKZ0Js8mYTtmcFigmn2FNKPJjnu/JDtFsP9nyQ1856DRDT9SOPSHoXm02E2dNOk7mic2cRTm0WF8qdA8qCtqbidFk4cEACC6ZPF2G2LCXngjHCVtERMR3k78WvfbBXhZb/J2G3lkQxKyqLN5+SvUT9Jn2V5P5Yf1DEeYWiWmK1EuUNXB5XyNNPenI6Ox7Ygd0qjphHCo5Q45Ua/lILIOUngp+QNDpE2LJZbBx+9HDAtwg45+tzmLhxov+9RVn8EXfBfQn2uxp2xjCeOv0pAHKLcqn3ej2/Ji7RlsiqO1bRvHrJ7JnvzH+Hx6Y9Rp4zD4uycHaLs0sEAAydMZRhs4aJn5Sy0SG9A6c1Oo18Vz7frPhGctEoKzWSajD35rm0qNEiZH+Px0PTt5uyI2tHyHkHa6WCbzSAX67+hXNancPMrTPp+3lf2R6wKQtzel3OiXq/RI0l1ARHDcDqNR9bKPQU0XP+ZFbmHkYBRVpzS/3mjOh5ozcn1EnkJtTl3sn38c2KbyhwFVA9qTrpKelsOLiBBEsCJ9U/ie8u+Y4GVYJyLe0cJ/ndPEUyafceC7V7e32+wghdy5+Flc+V/ByLCHHuXHm67jVSwsCtqbGnonBmRU91UpZF2OOGA3Mg4w/cB5dweP9cbHk7qWIBX2FfhceryasuKR+0dywnpkti4cyF7Nywi8lTE1i6Ko0VW5qzcU8T9h6sjtYKi8UDKNwehd3qpkGtTFrV38KJLbbwyqirmTlTzB5HjojQlZoqzrmXXy4Tq69+mo+kJDE5zpsnFQbef18W4qVLpRJCx6Yb6Jw+lYZpy6mXuIJq9k0kkiGXRJKwoHDjIRGli7Aor+nQXg3aPSL5uTRiXranyQNcu4dCr9vOn+WaaLeMjYIDEiXnzJH/8/dKPbfqXaBOb3F1+AcLW26Pm1t/vpUvln0RUug4yZZEgjWBPGdeyOdVHVV5bcBr3NL1FvbulajTHTtEA3PzzWJ683hiNxv7irJXDVLmO91Ohs4cyutzXyfflU+qPZX2ddqzZK/kRmxevTmjLxtN29ptQ9pasGsBfT7rE5JIdNlty8JGHHq0h/bvtWftAXmwTbYnc0PLRxlx7TM4vac7aZIIXPHy3HOi2SookBQ7GzbEb0Y/cEAeaIIrf1xxRfza4RkzRODyPfS8/774jpXFlBjsAw2QnpLOlxd9WcJ9Zm/OXm766Sb/uKnmqMbOB3byv+EpPPWUaEfT0+H77yVdkC8IKxby8+OvfhKOShe2lFIDgbcBK/CR1vqlYt87gC+ArkAmcIXWemu0NitU2AKpRziluzxxB5VIQVnEDyC5iSyIRQdh7zQoCHWU+6Ttx9wz8R6/tqhtrbasumNVWH+qvp/1Zda2WWg0yfZknjr9KR4/7XG01lw48kImbJiAyyPO22c2PZOzWpxVoo3x68cze8dsXB4XCZYELm1/KV9f/DUgg7PfF/0CSeZsyTzd5+kSSTMPFRzi5dkv+wW3VHsqq+5cReOqjXll9is8N+M5v8aqU3onlt22LOylO5B3gIZvNPQfL8mWxIL/W0D7Ou0BGDJ+CCMWj4h6+YOxKitLbl1Cx/SO/s/u+uUu3l34rv+9zwRZnGAhNMmWxOZ7N5NuVWQvfgS2jQKLHUfdM3C0vV8ijyz2oBBr5W8dNG6Pm1xnjjfUP4lEexIJXg3jBws+4L7J9+H0ONFa47A56N6gO3aLncV7F3O44DBaayzKws1dbub9c98PFHnN2QoL7xITpNUhiT0bnAd1+kK19l5tiXfh1fiFFEZGSEty7mpJDFrRlHERzszLZPifw3l97ut+Yd0C2BW0q96SR09/iovbXYHdHnQ++2bA0kclCstRA5rfBM3/BSlNRfjwOL1CkqKwUGGxQEKCxmrV0k2LQ/rr/b+wUASn55+XCd/jCUz8LVtCrVqi7SoogK1bxZnel0U+uKLAHXfAu+8GTt9/SbSWBy9Pkfw+lgT5jZSkH1Ern4cVTwcaCtZwaS0qu31e82N6OuwN43gbcsAw/AOFrTxnHp8s+YQXfn/B79caKxYsdGvQjdMyP+L95zqQn+/V0rvLr3EYt1YifvNceXi0B7vFzskNTibVnsrKjJXszdkrbgXKwqCWgxh7+VgSbAkcKThCm+Ft2Jsr55JgTeBfJ/6LEedHnu9mbp3JOd+c49eyJ1pSqD5iP/t2JePxiA/biBHxCziHD4v5fedO0ZD16CFJm30VMGLB45E6sEOHyv1it4vGuFu3+MyAOTmiefTdfzYbvPaalFyzWEpPal1UJL9rYqKm0/udWJmxEhDh9NUBr3LHyeGz/Z/x2RnM2DbDv+1DPR/ivcuf48AB+f6xx6RPcUcoVxCVKmwpcV5ZDwwAdgILgKu01quDtrkD6KS1vk0pdSVwkda6ZIbHICpc2AIxr8y6UOoSunNL3dxHhq02LTYV+BeW1IRUvrzoSy5sc2HY7efsmMOALwf4b7YkWxKr7ljF1M1TuX/y/f7PayTVYMf9O8Ka1bIKs2j0ZiOyCkXfm2xPZsT5IxjYciAnDD+BA3kyulLsKbx37nsRE+k9MPkB3l/4PgWuAqzKSoc6HZh14ywav9k4pIxDaekJ+n/Rn2lbpgEiLJ3X+jx+uOIHnp/1PMNmDfM/bSTbkzmz2ZklVMD7cvYxf9d8v0avZlJNfr/xd9rWbssHCz/gwSkP+q9LraRarLh9RdgcYa/MfoXX5rwmfmrKRosaLVh22uU4Vj8PaCl6esmBiOdRGgWuAoaMG8KXK76Ma782NcXvK1nXYfp08RnYsiGfjvXn0aXhHzSusoIajq2k2XbjsOVjtVrQ1mRQNpQ7F4+7COU8EigvomwiqLm8eYZ6fg5Ywvt9LX82gnYMScp6RSCsfHf2bubsmMP49eP5/KIvQjadsP4XejTsQY2kGmGb+nXzr1wz9hr25wackRVKMjxrHaKpAOhWrxsTr5lIrZRaMLY+FOzxXqwH4aTXolzNCHhckrLhwHw4sgq0G7ejEau2t2TeqpZMX9ic2YvS2bM/EbdL+WVrR4KHVs1yOOOUnfTuvIlWZw5m+nTRdO3fDx07aapX91C7NtSqqahVS5GQoLBatb/Wqdut/NqSAwcgc+8R+raeSNGu2SQ611G3cW1S6zXBkVoLS3ItVGItlD0VlBWtrLDxY9Tmj0qeU+0+MGBG+PP9K4UtrcW/9cBcyVPkPCKatoRq4t/a7sGS++ye7NXUueQhtvAAw5aO5pn1cwARwNMTEvmkyzkMbHSylLBx1PTmMLOCslLkKuL15aMZuvgritwuPGg43JhaXywjNzeJ/EIHjz8OT4lCPi4BJScHkpLdDJs5jOdmRbg/IlA9sToL/m8Bq/au44Lvz0VhBQ02Tyr/l7+BelVrU6UK1K4tQorP6d9XneLpNeex8MhEFAoPblpk3knml8PJzpbvBwwQIb++Nx1jNI1QUZFoXmw2eXjo31/M8Dk5ouF68EHJwZeYKNsmJQWEHbdbtINKiZZn4kTJffjGGxIV7AtMuOgiEVQ6dw4kGvXlr9Najl9UJJ/t2CH7Dx8uprenn5Y2iopEa3bnnaK5a9s2IFSBtJWYKL5eU6fK+f/np1Hc9NNNfiVGki2JfQ/ti5gfcuKGiVw++nJ/mpxkWzL/d3gfH76bSn4+NGsmkdgNGkRPj1McX4Ll8lLZwlZP4Fmt9dne948DaK1fDNpmsnebuUopG7AXqK2jHLxShC0QM8iaV2H1i15fjAj5TZRdJoRap3LJgWqMXz/ev5g0rtqYLfduiVqW56T/ncSSvUsAEU5OrHsiaw+sDcnk+99B/+WmLpEjIN6Z/w6PT3vcPxCTbcmcVO8k/tz9p9+k1qpGK9betTZiX44UHKHRm438gmKyPZnuDbozf9f8wJOXNZG9D+0t4UAfzA9rfuD6H6/3t+OwOriwzYUhflJVHVVZetvSsH5PAC/98RLDZg0jz5mHQpGWkMbTpz/NMzOe8auQk2xJjLtqHP2b9w/bhsvj4sT3T2TNgTVoNInWRM5pcCLft2iAZfcEMbXVPx/aPihRS9rpTVTqjYVWFlj5gvz+xVhYlMilh9PZl7OPAncBDquDjnU68sqAV0rkzNFa896C9xizZgyF7kLsyk5SQhK9Vy/kly9bybVOLpm0L6gBr2bLI+bFoN9Pa0To2vCBJEAsPCCZpuv2F2fumt29RWx1QEPm044pG3wrj3UuDcsLYXb7//Lw1If9mkkfe16Fut7+7U2BekG+9A3SGjD0jKGc2uhUDucfZuisoUzdNBWXdhEPSbYkbu12K08270KtdS/JA09CDTjhbmh2nZyXOz8QaIBFivaG+X1KECUnjs+UGOwH5Psi15nHgt0LmLVtFv+e8e8IDQDaAh4rKA3KDRZNv2b9uL3b7cz/5ixefSEtuNkI7XhNqlp7fx+Lv0OlKbb+UmHr8CqY0CHw/qx5UCsop17dugFNHUTU1rkn98KVIcIWtbpjP3tOTOXLXB4Xbk/A4uAuSmDoUMWIEWIeathQFvABA0S7k54eiF70eALRjUqJ5mf+fPh+2kY2nnQpGw5uIM+ZR4I1gYZVGjJ80PCwc92YNWMY/udwitxFWLCQaE/k3MMT+P6NHv5tnEXWUoN8QOYH3xytNditNpxFVj79FN5+W8zXNpsIBX36SMoMX24ruz1gBl27VoIMli6VZKNZWdLehAmiwVm6VISonBz5iTp1CuQ0s1pFSNq0Sfwe168PRDODXKcXX5R0EEqJYKSU7H/iidIXh0M+z8yUNlaskDktKSmQymXPHsnpN2JEIOrUF3naqpUIPUrJfhs2iBN7QgI4kotIfrIx+3JlXFmVlRs638BHg8M8lHjxaA/1Xq/nf+BLsCZwdYerubXup9x7r1yPlBQJPBg4UPy22rSRfrlcoQlWbTYRLFeuFLPo4/HlEQ9LZQtblwIDtda3eN9fB3TXWt8VtM1K7zY7ve83ebeJqIKoNGHLh7tAzE47xkpelYJ9gBY/martoG4/aHELzuQmpL+WzqECCYFSKL695NuwpVeC+W3Lb5z15Vm4vSZLm7KFLFQd63Rkya1LwprKfDjdTjq818HvJwYyIIPbnHLdFM5odkbUvvxv4f+4/Zfb/f5bDqsjZOG9usPVfH3J11HbKHQV0vTtphFNAwnWBH697ld6N+kdsQ2tNdf9cB1frwgcq3pidf+1Bbipy018PPjjqH1ZtX8V3UZ08xdGBVh9x2raVq0rWo/Dy0XzkbVB8vQk1gFHbRFELN7V1+OSRXDbSH/gRLedNhblB36jaztdy6cXfBp1cn1r3ls8OOVBv8Yu0VmXTzttZ+Gfdtatk8Sd9erJ/zVqiHkrNTV0cXC75a+oyKs1yZTweT+526TIa+Z8STORu02cut2FYt6yJgRMXljBdQS0m/PXpfOzJTSSrnXN1rSv3Z7m1ZuH+BvmOnPZdHATK/evZNuRbaEn6UwEu1wji7JwW9fbeLH/ixGLwq49sJYbf7qReTvn+T87p+U5/HLNL6yZv57nH1hA2/pr6NBoBRf23+mto9jAW3fS+xtpLb5eHreUN/EVmLVXg9Z3ye9rsYkZ0l4dHNWDylt5ffM2fQZbPi3RP7Uh9L1VWelctzMn1DqBhmkNQ+7Jg/kH2ZC5gaX7lnIw/2Bgp8IUxp6cw9y5siCmpYlAUKMGVK8e0HhE+p0zM+Xv3tpBglCXV6FtkK9XjMLWjHf/Q9/qQWHwxYXQGDVbp7f7nZ4t59Cl6VLOG5RPat2Wcj0TanivbXX5fZQVNn8e9toCkHYC1D1TXDdSm4s2K6FGoB1bklfwtMKmT2DLZyXb8Gr8tJZoxtmzYdEi2LxZNCsZGYHcT3a7XNOiInmfng5NmsCE1Euh3Rh/k2c0PYPxV40P8X0tzujVo7lm7DUB/1BnIp923Mn6ZTV58UUpG9a1K7RoIVql2rXFb6r47+zxhP7O99wTepwdO6SA+urVIkxt2CBCi9MZMJtardJ2s2Zy3I4dJZ9VMDt3ioZ2yRJxfl+xQgQyl0t+ZqtVBKxWraSO7SmniH9jMEVFsu+yZdLWokXSP6czkEHeZpPr6qvY0bmzCDPBFBRIP3x1SP/8U/ridHoFTrvcE926SQ3TRidu5Oq5rUPWpcW3LqZd7XYRfx+QIuV3T7zb/75ZtWZsumcTSimOHBGhdM4cuS+3boXdu0XAtNkCwTe+DPvVqonA26yZlIorL/8YYUspNQQYAtC4ceOu27YVm/QrmwiPmsWvUbS8V5H201qX2C+Wdoofu3g7pi/xHdu7c6X9zvH2JZJ2Qz0Xx/lEbBx23reTuql1Y+qbWvkcatXQEmuxq0ZvLGfNjKmNkMMXa8j2fDkdbyqI3Q/sjvmaBFP8fCxD4zifYJfBIHx1kv1tBtk9dr7q8WseAXLSIPWDcP0Ca+B5LKSN4u3sTYEPX4Rnaobpy7p47sMIJwRkPXEkJP9U5CZKU+3FRqRmPB5dYiyXZW6xWFTM3ayIU6qgy/KP6MtfPedW1PlEwpgRDQaDwWAwGCqRaMJWRTxqLgBaKaWaKaUSgCuBccW2GQdc7319KfBbNEHLYDAYDAaD4Vih3P73WmuXUuouYDKS+uETrfUqpdRQYKHWehzwMfClUmojcBARyAwGg8FgMBiOeSog2BG01hOACcU+eybodQFwWfH9DAaDwWAwGI51jg6PVYPBYDAYDIZjFCNsGQwGg8FgMFQiRtgyGAwGg8FgqESMsGUwGAwGg8FQiRhhy2AwGAwGg6ESMcKWwWAwGAwGQyVihC2DwWAwGAyGSsQIWwaDwWAwGAyViBG2DAaDwWAwGCoRI2wZDAaDwWAwVCJG2DIYDAaDwWCoRIywZTAYDAaDwVCJGGHLYDAYDAaDoRIxwpbBYDAYDAZDJVIuYUspVUMpNVUptcH7f/Uw23RWSs1VSq1SSi1XSl1RnmMaDAaDwWAw/JMor2brMWCa1roVMM37vjh5wL+01u2BgcBbSqlq5TyuwWAwGAwGwz+C8gpbFwCfe19/DlxYfAOt9Xqt9Qbv693AfqB2OY9rMBgMBoPB8I+gvMJWutZ6j/f1XiA92sZKqVOABGBTOY9rMBgMBoPB8I/AVtoGSqlfgbphvnoy+I3WWiuldJR26gFfAtdrrT0RthkCDPG+zVFKrSutfzFQCzhwFLRh+nL09+VYOx/Tl8prw/TF9OWvbsP05ejvS5OI32ity/wHrAPqeV/XA9ZF2K4KsBi4tDzHK2MfFx4NbZi+HP19OdbOx/Tl+Dgf05ejvy/H2vmYvsT/V14z4jjgeu/r64Gfim+glEoAfgC+0FqPLufxDAaDwWAwGP5RlFfYegkYoJTaAPT3vkcp1U0p9ZF3m8uB04EblFJLvX+dy3lcg8FgMBgMhn8EpfpsRUNrnQn0C/P5QuAW7+uvgK/Kc5xy8uFR0kZFtWP6cnS3UVHtmL4c3W1UVDumL5XXRkW1c7S0UVHtmL5UXhsRUV5bpcFgMBgMBoOhEjDlegwGg8FgMBgqkWNW2FJKDVRKrVNKbVRKhctsH0sbnyil9iulVpajH42UUtOVUqu9JYvuLWM7iUqpP5VSy7ztPFeOPlmVUkuUUj+Xo42tSqkVXh+8hWVso5pSarRSaq1Sao1SqmcZ2jghyBdwqVIqSyl1Xxnaud97XVcqpb5VSiWWoY17vfuviqcP4cZZLKWwYmjjMm9fPEqpbuXoy6ve32i5UuqH0ipARGhjmHf/pUqpKUqp+mXpS9B3DyqltFKqVhn68qxSalfQmDmnrH1RSt3tvTarlFKvlKEvI4P6sVUptbQsffGWRZvnux+9OQ3jbeNEJaXVViilxiulqpTSRti5LZ6xG6WNuMZulHZiHrtR2ohr7EZqJ+j7UsdulL7ENXaj9SXWsRulL3GN3SjtxDx2o7QR89hVEdZSpVQzpdR8JTLDSCXBfRVHZYY6/l1/gBVJnNocSaK6DGhXhnZOB04CVpajL/WAk7yv04D1ZeyLAlK9r+3AfKBHGfv0APAN8HM5zmsrUKucv9PnwC3e1wlAtQr43fcCTeLcrwGwBUjyvh8F3BBnGx2AlUAy4gv5K9CyrOMMeAV4zPv6MeDlMrTRFjgBmAF0K0dfzgJs3tcvl7EvVYJe3wN8UJa+eD9vBEwGtpU2BiP05VngoTh/33DtnOH9nR3e93XKcj5B378OPFPGvkwBBnlfnwPMKEMbC4A+3tc3AcNKaSPs3BbP2I3SRlxjN0o7MY/dKG3ENXYjtRPP2I3Sl7jGbpR2Yh670c4nnrEbpS8xj90obcQ8domwliLz/pXezz8Abo/1Osfyd6xqtk4BNmqtN2uti4DvkNJCcaG1ngUcLE9HtNZ7tNaLva+zgTXI4h5vO1prneN9a/f+xe1wp5RqCJwLfFTatpWJUqoqMuF/DKC1LtJaHy5ns/2ATVrrbWXY1wYkKaVsiMC0O8792wLztdZ5WmsXMBO4OJYdI4yzUkthldaG1nqN1jquxMAR2pniPSeAeUDDMrSRFfQ2hRjGbpT7703gkXK2ERcR2rkdeElrXejdZn9Z+6KUUkjk9rdl7ItG8hkCVKWU8RuhjdbALO/rqcAlpbQRaW6LeexGaiPesRulnZjHbpQ24hq7pcz5MY3dClw3IrUT89gtrS+xjt0o7cQ8dqO0EfPYjbKWngn40lOVOufGy7EqbDUAdgS930kZBmpFo5RqCnRBJOmy7G/1qmr3A1O11mVp5y3kZg+bxT8ONDBFKbVISeb/eGkGZACfKjFpfqSUSilnn64khsWqOFrrXcBrwHZgD3BEaz0lzmZWAr2VUjWVUsnIE1qjePsSRFylsP5CbgImlmVHpdQLSqkdwDXAM2Vs4wJgl9Z6WVn2D+Iur2nok2hmrlJojfzm85VSM5VSJ5ejP72BfdpbR7YM3Ae86r2+rwGPl6GNVQQeSi8jjvFbbG4r09gt7/wYQzsxj93ibZR17Aa3U9axG+Z8yjR2i7VTprEb4drGPXaLtXMfZRi7xdqIa+wWX0sRS9jhIMG8wmWGY1XYOupQSqUCY4D7ij0pxYzW2q217ow8nZ2ilOoQZx/OA/ZrrReV5fjFOE1rfRIwCLhTKXV6nPvbEDPG+1rrLkAuYnIoE177+mDg+zLsWx25UZsB9YEUpdS18bShtV6DmCmmAJOApYA73r5EaFtTBi1mRaOUehJwAV+XZX+t9ZNa60be/e8qw/GTgScoo6AWxPtAC6AzIly/XsZ2bEANxATxMDDK+5RfFq6iDA8KQdwO3O+9vvfj1RjHyU3AHUqpRYiJpiiWnaLNbbGO3YqYH6O1E8/YDddGWcZucDveY8c9dsP0pUxjN0w7cY/dKL9RXGM3TDtxj90wbcQ1douvpUCbWPtfVo5VYWsXoZJtQ+9nfwtKKTsyML7WWo8tb3tec9t0YGCcu/YCBiultiKm1TOVUmXKgebVBvnUzz8gAzYedgI7g7RzoxHhq6wMAhZrrfeVYd/+wBatdYbW2gmMBU6NtxGt9cda665a69OBQ4g/QVnZp6SeqK+uaFQTVWWjlLoBOA+4xruAloevKcVEFYEWiEC8zDuGGwKLlVLhardGRGu9zzvZeoARxD92fewExnrNEn8i2uKoDvvh8JquLwZGlrEfIBU8fHPL95ThnLTWa7XWZ2mtuyKL56bS9okwt8U1ditqfozUTjxjN4a+xDR2w7QT99gN15eyjN0I5xTX2I1ybeMauxHaiWvsRrgucY9d736HkbW0J1DNez5QCTLDsSpsLQBaeaMLEhDz0ri/oyPep4WPgTVa6zfK0U5t5Y2kUUolAQOAtfG0obV+XGvdUGvdFLkmv2mt49LgeI+fopRK871GnFDjitjUWu8FdiilTvB+1A9YHW9fgiiPZmA70EMplez9vfohvgBxoZSq4/2/MTIBfVPG/kAMpbD+KpRSAxHT82CtdV4Z22gV9PYC4hy7AFrrFVrrOlrrpt4xvBNxlt0bZ1/qBb29iDjHbhA/Io7GKKVaI0EeZSlk2x9Yq7XeWcZ+gPi59PG+PhOI2xwZNH4twFOIk3C07SPNbTGP3QqcH8O2E8/YjdJGXGM3XDvxjt0ofYlr7Ea5vj8S49gt5TeKeexGaSfmsRvlusQ8diOspWsQoetS72YVP+fqCvS2P5r+EJ+Z9YiE+2QZ2/gWUdU6kZvj5jK0cRqiRl+OmJaWAueUoZ1OwBJvOyuJIWqplPb6UsZoRCTKc5n3b1U5rm9nYKH3nH4EqpexnRQgE6hajuvxHDKJrgS+xBulE2cbvyMC4zKgX3nGGVATmIZMPL8CNcrQxkXe14XAPmByGfuyEfGB9I3f0qKxwrUxxnttlwPjEcfjuPtS7PutlB6NGK4vXwIrvH0ZB9Qr43VJQKpjrAQWA2eW5XyAz4DbyjleTgMWecfefKBrGdq4F5kz1yOl11QpbYSd2+IZu1HaiGvsRmkn5rEbpY24xm6kduIZu1H6EtfYjdJOzGM32vkQx9iN0peYx26UNmIeu0RYS5F17U/vmPmeMqwB0f5MBnmDwWAwGAyGSuRYNSMaDAaDwWAwHBUYYctgMBgMBoOhEjHClsFgMBgMBkMlYoQtg8FgMBgMhkrECFsGg8FgMBgMlYgRtgwGg8FgMBgqESNsGQwGg8FgMFQiRtgyGAwGg8FgqET+H/fDDCoTUKzIAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_39\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_40\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_41\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_42\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_43\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_44\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0_45\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_3\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_4\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_5\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_6\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_7\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_8\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_9\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_10\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_11\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_12\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_13\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_14\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_15\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_16\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_17\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1_18\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for key in ffms:\n",
-    "    print(key)\n",
-    "    _plot_weights(ffms[key].transpose())\n"
+    "27134632+250\n"
    ]
   },
   {
diff --git a/src/evaluation/figure_notebooks/figure_5/amr_ad_values.ipynb b/src/evaluation/figure_notebooks/figure_5/amr_ad_values.ipynb
new file mode 100644
index 00000000..77ee5fbb
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_5/amr_ad_values.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = json.load(open(\"/oak/stanford/groups/akundaje/amr1/pho4_final/lite_data/preds/ets1_gm12878.json\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13675"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(data.keys()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13675"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data['AAAAAAAAAAAAAACCGGAAATGGGTCCTACCATCT'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data['AAAAAAAAAAAAAACCGGAAATGGGTCCTACCATCT'][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.008304882'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['AAAAAAAAAAAAAACCGGAAATGGGTCCTACCATCT'][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.8181924223899841, 0.8155251741409302]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['AAAAAAAAAAAAAACCGGAAATGGGTCCTACCATCT'][1][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat.ipynb b/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat.ipynb
index 64159ad3..e2e5f4b2 100755
--- a/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat.ipynb
+++ b/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,9 @@
     "import imageio\n",
     "\n",
     "from plotnine import *\n",
-    "\n",
+    "import os\n",
+    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   # see issue #152\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"2\"\n",
     "import matplotlib as mpl\n",
     "mpl.rcParams['pdf.fonttype'] = 42\n",
     "mpl.rcParams['ps.fonttype'] = 42\n",
@@ -317,9 +319,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:1059: UserWarning: genomicsdlarchsandlosses.bpnet.archs is not loaded, but a Lambda layer uses it. It may cause errors.\n"
+     ]
+    }
+   ],
    "source": [
     "model_h5=\"/oak/stanford/groups/akundaje/vir/tfatlas/models/production_run_1/fold0/ENCSR124AIG/ENCSR124AIG_split000.h5\"\n",
     "\n",
@@ -328,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,9 +382,8 @@
     "               np.zeros(output_seq_len*number_of_strands*encoded_inserted_sequences.shape[0]).reshape((encoded_inserted_sequences.shape[0],output_seq_len,number_of_strands)),    \n",
     "               np.zeros(encoded_inserted_sequences.shape[0]*number_of_strands).reshape((encoded_inserted_sequences.shape[0],number_of_strands))])\n",
     "\n",
-    "    plus,minus = vectorized_prediction_to_profile(predictions)\n",
+    "    plus_fwd,minus_fwd = vectorized_prediction_to_profile(predictions)\n",
     "\n",
-    "    footprint_for_motif_fwd = plus\n",
     "\n",
     "    # reverse complement the sequence\n",
     "    w_mot_seqs_revc = w_mot_seqs[:, ::-1, ::-1]\n",
@@ -385,17 +394,16 @@
     "               np.zeros(output_seq_len*number_of_strands*encoded_inserted_sequences.shape[0]).reshape((encoded_inserted_sequences.shape[0],output_seq_len,number_of_strands)),    \n",
     "               np.zeros(encoded_inserted_sequences.shape[0]*number_of_strands).reshape((encoded_inserted_sequences.shape[0],number_of_strands))])\n",
     "\n",
-    "    plus,minus = vectorized_prediction_to_profile(predictions)\n",
+    "    plus_rev,minus_rev = vectorized_prediction_to_profile(predictions)\n",
     "\n",
-    "    footprint_for_motif_rev = plus\n",
     "    \n",
+    "    counts_for_motif = np.sum(plus_fwd,axis=-1)+np.sum(minus_rev,axis=-1)\n",
+    "    plus_tot = plus_fwd+minus_rev[:,::-1]\n",
+    "    minus_tot = minus_fwd+plus_rev[:,::-1]\n",
+    "    counts_plus = np.sum(plus_tot,axis=-1)\n",
+    "    counts_minus = np.sum(minus_tot,axis=-1)\n",
     "\n",
-    "    # add fwd sequence predictions and reverse sesquence predictions (not we flip the rev predictions)\n",
-    "    counts_for_motif = np.sum(footprint_for_motif_fwd,axis=-1)+np.sum(footprint_for_motif_rev,axis=-1)\n",
-    "    footprint_for_motif_tot = footprint_for_motif_fwd+footprint_for_motif_rev[:,::-1]\n",
-    "    footprint_for_motif =  footprint_for_motif_tot / footprint_for_motif_tot.sum(axis=1)[:, np.newaxis]\n",
-    "\n",
-    "    return footprint_for_motif_fwd.mean(0), counts_for_motif.mean(0)\n",
+    "    return plus_tot.mean(0), counts_plus.mean(0), minus_tot.mean(0), counts_minus.mean(0)\n",
     "\n",
     "def get_footprint_for_two_motifs(seqs, motifs, model, inputlen, batch_size, spacing):\n",
     "    '''\n",
@@ -429,9 +437,8 @@
     "               np.zeros(output_seq_len*number_of_strands*encoded_inserted_sequences.shape[0]).reshape((encoded_inserted_sequences.shape[0],output_seq_len,number_of_strands)),    \n",
     "               np.zeros(encoded_inserted_sequences.shape[0]*number_of_strands).reshape((encoded_inserted_sequences.shape[0],number_of_strands))])\n",
     "\n",
-    "    plus,minus = vectorized_prediction_to_profile(predictions)\n",
+    "    plus_fwd,minus_fwd = vectorized_prediction_to_profile(predictions)\n",
     "\n",
-    "    footprint_for_motif_fwd = plus\n",
     "    \n",
     "    # reverse complement the sequence\n",
     "    w_mot_seqs_revc = w_mot_seqs[:, ::-1, ::-1]\n",
@@ -441,21 +448,21 @@
     "               np.zeros(output_seq_len*number_of_strands*encoded_inserted_sequences.shape[0]).reshape((encoded_inserted_sequences.shape[0],output_seq_len,number_of_strands)),    \n",
     "               np.zeros(encoded_inserted_sequences.shape[0]*number_of_strands).reshape((encoded_inserted_sequences.shape[0],number_of_strands))])\n",
     "\n",
-    "    plus,minus = vectorized_prediction_to_profile(predictions)\n",
-    "\n",
-    "    footprint_for_motif_rev = plus\n",
+    "    plus_rev,minus_rev = vectorized_prediction_to_profile(predictions)\n",
     "    \n",
     "    # add fwd sequence predictions and reverse sesquence predictions (not we flip the rev predictions)\n",
-    "    counts_for_motif = np.sum(footprint_for_motif_fwd,axis=-1)+np.sum(footprint_for_motif_rev,axis=-1)\n",
-    "    footprint_for_motif_tot = footprint_for_motif_fwd+footprint_for_motif_rev[:,::-1]\n",
-    "    footprint_for_motif =  footprint_for_motif_tot / footprint_for_motif_tot.sum(axis=1)[:, np.newaxis]\n",
+    "    counts_for_motif = np.sum(plus_fwd,axis=-1)+np.sum(minus_rev,axis=-1)\n",
+    "    plus_tot = plus_fwd+minus_rev[:,::-1]\n",
+    "    minus_tot = minus_fwd+plus_rev[:,::-1]\n",
+    "    counts_plus = np.sum(plus_tot,axis=-1)\n",
+    "    counts_minus = np.sum(minus_tot,axis=-1)\n",
     "\n",
-    "    return footprint_for_motif_fwd.mean(0), counts_for_motif.mean(0)"
+    "    return plus_tot.mean(0), counts_plus.mean(0), minus_tot.mean(0), counts_minus.mean(0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,9 +513,8 @@
     "from tensorflow.keras.utils import get_custom_objects\n",
     "from tensorflow.keras.models import load_model\n",
     "import tensorflow as tf\n",
-    "import os\n",
-    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   # see issue #152\n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
+    "\n",
+    "\n",
     "%matplotlib inline\n",
     "matplotlib.rcParams['pdf.fonttype'] = 42\n",
     "matplotlib.rcParams['ps.fonttype'] = 42 \n",
@@ -526,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "tags": []
    },
@@ -550,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -561,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -582,12 +588,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "Text(0, 0.5, 'Total counts')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -603,38 +619,48 @@
     "matplotlib.rcParams['pdf.fonttype'] = 42\n",
     "matplotlib.rcParams['ps.fonttype'] = 42 \n",
     "\n",
-    "plt.plot(puu_runx_footprint[0][400:600], label=\"AP1-TEAD-heterodimer\", c = \"green\")\n",
-    "plt.plot(irf1_footprint[0][400:600]+irf1_footprint[0][400:600], label=\"AP1+TEAD\", c = \"brown\")\n",
-    "plt.plot(irf1_footprint[0][400:600], label=\"AP1-only\", c = \"red\", alpha=1)\n",
-    "plt.plot(runx_footprint[0][400:600], label=\"TEAD-only\", c = \"blue\", alpha=1)\n",
-    "plt.plot(full_footprint_1[0][400:600], label=\"background\", c = \"black\", alpha=1)\n",
-    "plt.xlabel(\"200bp surrounding motif insertion site\")\n",
+    "plt.plot(puu_runx_footprint[0][350:650], label=\"AP1-TEAD-heterodimer\", c = \"green\")\n",
+    "plt.plot(puu_runx_footprint[2][350:650], label=\"AP1-TEAD-heterodimer\", c = \"green\", alpha=0.5)\n",
+    "\n",
+    "plt.plot(irf1_footprint[0][350:650]+irf1_footprint[0][350:650], label=\"AP1+TEAD\", c = \"brown\")\n",
+    "plt.plot(irf1_footprint[2][350:650]+irf1_footprint[2][350:650], label=\"AP1+TEAD\", c = \"brown\", alpha=0.5)\n",
+    "\n",
+    "plt.plot(irf1_footprint[0][350:650], label=\"AP1-only\", c = \"red\", alpha=1)\n",
+    "plt.plot(irf1_footprint[2][350:650], label=\"AP1-only\", c = \"red\", alpha=0.5)\n",
+    "\n",
+    "plt.plot(runx_footprint[0][350:650], label=\"TEAD-only\", c = \"blue\", alpha=1)\n",
+    "plt.plot(runx_footprint[2][350:650], label=\"TEAD-only\", c = \"blue\", alpha=0.5)\n",
+    "\n",
+    "plt.plot(full_footprint_1[0][350:650], label=\"background\", c = \"black\", alpha=1)\n",
+    "plt.plot(full_footprint_1[2][350:650], label=\"background\", c = \"black\", alpha=0.5)\n",
+    "\n",
+    "plt.xlabel(\"300bp surrounding motif insertion site\")\n",
     "plt.ylabel(\"Total counts\")\n",
     "\n",
     "#plt.ylim((0,2.5))\n",
     "#plt.legend(loc=\"right\")\n",
     "\n",
-    "plt.savefig(\"subfigs/ap1_tead_coord_effect_chip.pdf\", transparent=True, dpi=300)\n"
+    "#plt.savefig(\"subfigs/ap1_tead_coord_effect_chip.pdf\", transparent=True, dpi=300)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f23f42ed0d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f878472e910>]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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2fy0K11TX0EJ9o5nI0H6a1mHQS2gI12Z3pFFRUUFubi4JCQkAJCQkkJubS2VlpU2/LVu2sGDBAvR6PSEhIcTFxZGVlWW3bejQocTExODl1T6/pkyZgr9/61+Go0ePbl08p7q6WxssPFN+UQ0Ag8O0Dg09ioSGcGF2RxrFxcVERERgMBgAMBgMhIeHU1xcTEhIiE2/qKgo632j0UhJSYndNkd98MEHDBkyhMjIyHZtNTU11NTU2Dx2pa8v3Nvx01UAjBkWYqdn79LrkdAQLq1v1rvspl27dvHKK6/w17/+tcP29PR00tLS+rgq4UrqGlow6HX09/e237kX6fV62T0lXJrd0DAajZSWlqIoCgaDAUVRKCsrw2g0tutXVFTEuHHjANvRRWdt9uzbt49f/vKXrFmzhuHDh3fYZ/HixcyfP9/msZKSEpKTkx16D+H+6hvNBPh5oevLOdE7YNDrUCxyyq1wXXaPaYSGhhITE0NmZiYAmZmZxMTE2OyaApg1axYZGRlYLBYqKyvZunUr8fHxdts688033/D444/zxz/+kWuvvfay/YKCgjCZTDY/He3GEp7p/U+Os/nzPPz9tB1lQFtoyEhDuC6Hdk+tXLmS5cuXs2bNGoKCgqynxi5ZsoRly5YxduxYEhMTOXDgADNnzgRg6dKlREe3rlnQWduePXt44oknqKurQ1VVNm/ezKpVq5gyZQqpqak0NjayYsUKay2//e1vGT26b1ZcE+5hbWYuAAG+2u+N1UtoCBfn0LdoxIgRNtdWtHnzzTettw0GA6mpqR0+v7O2SZMmsX379g7b/vGPfzhSnhAOCfDTPjTklFvh6uSKcOExwoIDtC4BgxwIFy5OQkO4tQPHy623rxmu7em2IKfcCtcnoSHc2sULL/XluuCXY9DrUWSNcOHCJDSEW2toal14ae2zM/Hz0f6Yhl6vw6LKSEO4LgkN4dbqGloIG+iv+USFbQwGOXtKuDYJDeHWmloUfLwMWpdhpdfpUBQJDeG6JDSEW2tuUfD1dp7QMMjuKeHiJDSE22poMrM7t9Spllc1GGSWW+HaJDSE21r4zGYACsvqNK7kAr1Oh0V2TwkXJqEhRB9qPRAup9wK1yWhIdyS6qTHDeSUW+HqJDSEWzrX0KJ1CR0y6HWcLq2jpOKc1qUI0SUSGsItfXuqwnr71vGOrd3SF+obWy82fPbPX2hciRBdo/0lskL0gufX7gLgt49OIeYq7eeculRbeAjhamSkIdxaRKj2M9teTH9+5UCNFxAUosskNITb+fJgkfV2SJCfhpW0pz//jdN62VkhukpCQ7idr4+UAeDjRFeCt2kLC4kM4aokNITbGdDfF4D/+vGNGldyeTLSEK5KQkO4nbr6Zvr7e3P9mHCtS7ksvWSGcFESGsLt5BXVEB7iXAfA25GRhnBREhrCrZRUnONwfiVTJgzWupROSWYIVyWhIdzK6dJaAMaOCNW4ks5JZghXJaEh3EptfTMAQf18Na6kczL7lHBVEhrCrdTWt845FRjgrXElHWubSFGVNTWEi5LQEG7l/U9OABDg55yh0UYyQ7gqh0IjLy+PpKQk4uPjSUpKIj8/v10fRVFITU0lLi6OGTNmkJGR4VBbTk4Od999N7Gxsaxevdrh1xSiI5U1jUDrFOTOqO36DJkeXbgqhyYsTElJYdGiRSQmJrJx40ZWrFjBunXrbPps2rSJgoICsrOzqa6uZt68eUyePBmTydRpW3R0NKtWrSIrK4vm5maHX1OIS7WYWxc3WhQ/RuNKLq9t91R1bRNn65qsFyIK4SrsjjQqKirIzc0lISEBgISEBHJzc6msrLTpt2XLFhYsWIBeryckJIS4uDiysrLstg0dOpSYmBi8vNrnV2fPE+JSVbWtowxnm2/qcn78m4+0LkGIK2Z3pFFcXExERAQGQ+s8PgaDgfDwcIqLiwkJCbHpFxV1Yd0Co9FISUmJ3TZ77+3I82pqaqipqbF5zJHXF+4lv6j1MzA4rJ/GlTjGLGuFCxfkFutppKenk5aWpnUZQkMVZxt47q9fodfruHrIQK3LEcJt2Q0No9FIaWkpiqJgMBhQFIWysjKMRmO7fkVFRYwbNw6wHSV01mbvvR153uLFi5k/f77NYyUlJSQnJ9t9D+Ee3t6cC4DFojrl7LZCuAu7xzRCQ0OJiYkhMzMTgMzMTGJiYmx2TQHMmjWLjIwMLBYLlZWVbN26lfj4eLttnXH0eUFBQZhMJpufyMhIh/4BhHvw92n9++eGayI0rkQI9+bQ7qmVK1eyfPly1qxZQ1BQkPXU2CVLlrBs2TLGjh1LYmIiBw4cYObMmQAsXbqU6OhogE7b9uzZwxNPPEFdXR2qqrJ582ZWrVrFlClTOn2eEG1UVeVfX+YD8PT9k7Qtxo7oiEB2Hmo93jZogGscsBfiYjpVdc8TxgsLC5k+fTrbtm2TU3TdXGOTmQXPbAZg0+8TNa6mc2bFwvynN9k8tjDuau6/M0ajioSwZe93p1wRLlxeVW2T1iU4zMvQ/iv3963HNKhEiK6R0BAu7+lXd2hdghAeQ0JDuLzqutaRxpLEWI0rEcL9SWgItzFmWIj9TkKIbpHQEC7NrLTONzXjxiFyUZ8QfUBCQ7i0nP3fAzAyOljbQrrJTU9iFG5IQkO4tD+8uw8Ag5NOhe6oxmZF6xKEcIiEhnBZikXlqqggAG6f6NoXfdaca7bfSQgnIKEhXNY/Pz3BycKzXBUVhK+LzzdVc851rjURnk1CQ7isfUfLAJgyYbDGlXSfjDSEq5DQEC5Lp4OBgb7cM22U1qV0m4SGcBUSGsIlWSwqx09Xc3Os0brutqtJeehmfvfzKQDUSmgIF+EWizAJz3O6tJb6RjNjhrnetRkrHrwJs6IyKSbCep3JJ3sLufOWq/D2kr/jhHOTT6hwSSe/PwvAqGjXC40brolk8tjWRczaJjA8cbqaDVmHtSxLCIdIaAiXU3G2gT+8sxeAsIH+GlfTc4rOnNO6BCHsktAQLmfTjlPW234+sodViL4koSFcjkVm3BBCMxIawuWcPT8V+riRgzSuRAjPI6EhXM7h/EpGRQez4qGbtS5FCI8joSFcSkOTmeIz5xg3cpDLTx1yKZnpVrgCCQ3hUhY+sxmAQcHuc9aU/vwMvTsPlWhciRD2SWgIlxQdEah1CT2m7VoNkNGGcH4SGsLl+PkY3Oog+KiLFpDa8NER7QoRwgESGsJl5OZVABB3wxCXnW+qI79+4CZuHR8FwHv/PqZxNUJ0TkJDuIy2q8BPl9VqXEnP6u/vbZ1WBEA5Px+VEM7IodDIy8sjKSmJ+Ph4kpKSyM/Pb9dHURRSU1OJi4tjxowZZGRkdLutoqKChx9+mDlz5nDnnXeycuVKzGZzNzZXuKoz1Q2UVNQD8JOEazWupucF+Hlbb5+VGW+FE3MoNFJSUli0aBEfffQRixYtYsWKFe36bNq0iYKCArKzs3nvvfd49dVXKSws7Fbb66+/zogRI9i0aRMffvgh3377LdnZ2T217cKFHDheDsDzP72FEaZgbYvpBRcf12i7eFEIZ2Q3NCoqKsjNzSUhIQGAhIQEcnNzqaystOm3ZcsWFixYgF6vJyQkhLi4OLKysrrVptPpOHfuHBaLhebmZlpaWoiIiOjRfwDh/OoaWnj53X0AXHNViMbV9I4B/X1JXTIZgKZmReNqhLg8u6FRXFxMREQEBkPrhVQGg4Hw8HCKi4vb9YuKirLeNxqNlJSUdKvtkUceIS8vj1tvvdX6M3HixHY11tTUUFhYaPPT9hrC9bWNMgC8vdzrgr6L+fm2bltaxn4sMsGWcFJOPUVoVlYWo0ePJj09nXPnzrFkyRKysrKYNWuWTb/09HTS0tI0qlL0ptLKel5M3w3Ay4/fpnE1vcvft/Xr+F1JLaWV9RgH9dO4IiHaszvSMBqNlJaWoiitQ2ZFUSgrK8NoNLbrV1RUZL1fXFxMZGRkt9rWr1/P3Llz0ev1BAYGMm3aNL766qt2NS5evJht27bZ/GzYsMHhfwThvF79+z7r7eGDB2hYSe+7eJp3s5xBJZyU3dAIDQ0lJiaGzMxMADIzM4mJiSEkxHbf8qxZs8jIyMBisVBZWcnWrVuJj4/vVpvJZGL79u0ANDc38+WXXzJq1Kh2NQYFBWEymWx+2oJHuLYDx89Yb7vTtRkdads9BfDyu3s1rESIy3No99TKlStZvnw5a9asISgoiNWrVwOwZMkSli1bxtixY0lMTOTAgQPMnDkTgKVLlxIdHQ3Q5bZnnnmGlJQU5syZg6Io3HTTTSxcuLAHN184M0+bUqNt9xTAsYJqmloUt5uUUbg+neqm38zCwkKmT5/Otm3bMJlMWpcjuuBMdQM/eS6b664O44czR3PNVaFal9Tr1m3JJWPbcQBeemyqS66BLlybvd+dckW4cFptZ00lzfCMwAC4/foLX9InXt7OkfzKTnoL0fckNITT2rq7gPCB/lw9JFjrUvqMKTzQ5kK/wrI67YoRogMSGsIpnWto4eh3VdwyLsqtr824lF6v46nkC9cieXnJV1Q4F/lECqeUm1dBi9nCjdd43llwUWH9+dFdMQDUN7ZoXI0QtiQ0hFOqrGmdfykiJEDjSrSROHUEAHX1EhrCuUhoCKfT0GQmLWM/AAODfLUtRiM+3gYGBftb1xARwllIaAins+9omfW2Jx3PuNSN10SQm1cp81AJpyKhIZzOxu0n0evgzWfitC5FU8MHB9PQZOZEYbXWpQhhJaEhnEp9YwtH8itJmDKcyFDPnrBvhKl1rq0nX9lOi1mmSxfOQUJDOJWi8nNYVIgd7hkX83VmmDHIenvP4VINKxHiAgkN4TRKKs7x+MufARAR4tmjDAAvg54nF10PwH+/vdvj5uISzklCQziN7fu+ByB0gB+Dw/trXI1zCPC/sHZ4Q5NZw0qEaCWhIZyColj4338dJjDAm7XPzpTZXc8LuGjm27N1zRpWIkQrCQ3hFB757ccA1Na3uP26GVfC56LwPFFYLbuohOYkNITmyirrKTpzDoBxIwdpXI1zGWoMsp5F9dv/3UPWzu80rkh4OgkNobnsXa2/CAMDfFi5ZLLG1TgXX28Df3jsNiJDW6dTKSiu0bgi4ekkNITm9h0t49rhofztuTvxllld29HpdKx5ejq+PgYyP8/jbF2T1iUJDybfUKG58qoGBofJ2VKd8fbSM3ZE6667nANFGlcjPJmEhtBUScU5qmqbGBTsr3UpTq9tnY3X3/+GbbsLNK5GeCoJDaGZqppG/uOFrRj0Om6O9bx1M65Uv4uu2Xj53X0aViI8mYSG0IRiUflR6kdY1NY1wK+KGqB1SS4ldICfTGQoNCGhITSxJ7fEenukSQLDUU8mT8Sg11FxtpHH//AZR/IrtS5JeBgJDaGJzM/zGDTAjxeX3sqkmAity3EZt19v4tcP3GS9/8tXd2BWLBpWJDyNhIboc6dLa9l/rJw7b7mKa4eHyhXgV2hSTASP3DPeen/dlsPU1csUI6JvSGiIPlPf2EJuXoV1ypCZNw3VuCLXdefkYYyKDgbgn5+e4N5n/6VtQcJjOBQaeXl5JCUlER8fT1JSEvn5+e36KIpCamoqcXFxzJgxg4yMjG63AWzZsoU5c+aQkJDAnDlzOHPmTBc3VWjtFy99yq/ScgD40V0xBAd65vrfPeWlx25jyoTB1vsVZxs0rEZ4Ci/7XSAlJYVFixaRmJjIxo0bWbFiBevWrbPps2nTJgoKCsjOzqa6upp58+YxefJkTCZTl9sOHjxIWloa6enphIWFUVtbi4+PT6/8Q4jeV1JRb729YPrVGlbiPp5MnsiO/a1Tyv/4N9ls+n2ixhUJd2d3pFFRUUFubi4JCQkAJCQkkJubS2Wl7VkbW7ZsYcGCBej1ekJCQoiLiyMrK6tbbW+//TYPPPAAYWFhAAQGBuLrK3+duqLa8/vc598+kvdW3aVxNe7DoNfx+L3XWe8vfy2HM9UNVNU2aliVcGd2RxrFxcVERERgMLRO0WwwGAgPD6e4uJiQkBCbflFRUdb7RqORkpKSbrWdPHkSk8lEcnIy9fX1zJgxg5/97GftDpzW1NRQU2M7kVvbawhtqarKy+/u45vj5QCMig4mwM/bzrPElZgUc+HCyG9PVfCT57IBWPP0NKIjArUqS7gph3ZPaUVRFI4ePcratWtpbm7moYceIioqinnz5tn0S09PJy0tTZsiRacqzjby8Z7T1vu3jDVqWI17Cgzwxsugb3fq7ebP8/jp3eM0qkq4K7uhYTQaKS0tRVEUDAYDiqJQVlaG0Whs16+oqIhx41o/pBePILraFhUVxaxZs/Dx8cHHx4fp06fzzTfftAuNxYsXM3/+fJvHSkpKSE5OvtJ/D9HDLp5c7+n7JmEwyAl7PU2n0/F/LybwbvZRTpfVMjQikKMFVRw4P7oToifZ/QaHhoYSExNDZmYmAJmZmcTExNjsmgKYNWsWGRkZWCwWKisr2bp1K/Hx8d1qS0hIICcnB1VVaWlpYefOnYwZM6ZdjUFBQZhMJpufyEiZy0hr35XUsH1fIdB6LGPKdYPtPEN0lUGvI3nWGJb/6AbujR/DMGMQhWV1bN0lizaJnuXQ7qmVK1eyfPly1qxZQ1BQEKtXrwZgyZIlLFu2jLFjx5KYmMiBAweYOXMmAEuXLiU6Ohqgy22zZ8/m0KFD3HXXXej1em699VbuueeeHtx80Ruqahupq2/h0d99AsDcKcN5YM61GlflWW6dMJjMz/P466ZvuXX8YPx8nXpPtHAhOtVNFx0uLCxk+vTpbNu2DZPJpHU5HkNVVeY+9aHNY394/DZGmoK1KciDHc6r5Om0HQC88/xd9PeXExCEffZ+d8oOZtGj3s7Mtbn/z9/OkcDQSMxVF3Yh3/vrLaRl7NeuGOE2JDREjykoqeH9T09Y7z+x6Hq85MC3ptoWbgL4aOd3nC6t1bAa0Rd6e+eRfKNFj1BVlaXnj2HcHBvJxt/N5Y6J0RpXJaZeN5iUh2623v/V+d1Vwj0dK6hi7lMf8t9v76LF3DuzH0toiG5RVZXDeZXW4xjREYE8fu/16PUyc60z0Ol0TIqJ4IczRgNQW9/CnCc38tWhYo0rEz3txfTdPPnKdgC+PFjca6dcS2iILtu6q4C5T31oPdhqCu/PS49NlSu+nVDyrDG889yd1vvPr93F25nf0mJWNKxK9JTvy+v4/JsL10T9JOFarh8d3ivvJefhiS4pr2rglfcurFOt1+tIe+oOuXjPifUP8OHxe69jyxf5HP2uin98coJPvi5kWdIEJo6RhbBczTvZR/nbR0dsHjOF9+f/3TGSuBt7b9kBCQ3hkMYmMxu3nyTuxiE0Niv89MVt1rabro1k+g1DJDBcwLRJQ7j9+mgSf9m6O7GyppGVb+7k/15MwNe7dX65vKKz+Pl4YRzUT8tSRQcUxULOgSLKqurbBcajCyYQf3Pvr1EjoSHsKq9qIOXNLzldWsv6LNsP6tP3T7JZ00E4P71ex/M/vYUPt59i1/m12u9Z3jrjw/8sm8JTf2zd3fjGf8ZJcPSx/cfKuHrIQAL8vFFVlSP5VRz5rpKGJjO+3gY+3VtIfnFNu+e9uPRWrh0e2ic1SmiIDp2ta8JiUfls3/e89eGhdu03XhPJ8sWT8PYyaFCd6K7xo8IYPyqM46ereOlveyksqwOwBgbAwy9slfU5eklVTSN//Pt+xo4IJb+4hgXTr0av1/Hsn78E4LVf3sF7/z7G9vNrpVzKy6Dn6fsncv2YCOobWxgY6NdntUtoiHYqzjbw499kt3v8vx/5AYWltQT18+UH46M6eKZwNaOiB/KnX03nX1/kseYf37Rr33O4lNABfgwzBsla7j2kqUXhR6kfAa3/vgCffF1I2EB/a5+209c7kp4ST2CAD95erbuD23Yr9hUJDQ9hsaiXPQ32RGE1J05Xc66hhZHRwfz69S9s2tOeugNTRCAGvY6xIwb1Rbmij915y1WEBPnx/NpdQOtxqq++LSH1LzutfX4wPoq5U4ZzzVW9uxtEVVX+9WU+IUF+jB8Vhk4Hfj6u/6uqrqGF597aSW5eZYft5VUN3Dl5GP/6Mt/62NpnZxLUz4eGJjP+vl6crWsmJKjvRhUdcf3/CQ+jKJYODzjn5lVQVtXAbdcNRlXhVNFZRpqC2Xu0jK8OFbPli3z8fb246drW2X+ra5uob2ph+OBgsi76kLb54YzR1De2MNQYxFBjUG9vlnACN8Ua+dn/G8ef/vEN984czVff2i5k9vmBIj4/UNSju6xUVUVV4XRpLV8fKWXv0TIOHD/TYd9fJE0gv7iW/cfK+I/54zArFgrL6njjg4NMHmvkR3fF8Orf9/OLH16Hv48Xer2OAf1tV/psalF4+Z293HBNBNMmDbFps1hUSivrrcdxjhVU0dSitPtDSVVVis6cY3BYfywWFYuqto7CVNXmu6mef3xD1hHe/ffRdtvz3qq7+PZUBYpFJcDPi8iQfoSHBPDIPeM5U93AoOALIw+f86OJi0cjWpEJCzWgWFRKKlo/dFfixOlqHn/5M/Q6uPHaSMaOGMSbG22PN0y4Ooz9x7p3Uc8wYxCvPnVHt15DuK62X3YAf3xvH//eVWDTfvWQYCJC+jH7B1cxoL8Pg8P6d3nX1fLXcvj2VEW3a76cN5+JY9+xcnJPVRA20J+Mbcetbb/+yY28kL4bxaLy4NxYvL30vP5+6y66MUMHcuS7KgBrSP7+b1/z6deF1uf7+hhoara9zmXC1WHMmTKc59766rI1pT11B/0DvAkdoH0AdMTe704JjQ6cqW7gxfTdHC2o4ve/mEpDo5nRwwbi5+OFqqqUVzew6q+7eDL5eoZEXvgrXLGo/OWDgzS1KDyUGMuB42coKq8jYcpwfLz01JxrZkB/X97ceJAPt5/iT7+ahik8kCPfVaLX6YiOCKS+sYWSinrrcPSb4+V8X36Oz/YVdlLxldPpIDl+DF8fKWNAfx+WJI4lPCSAT/cWcs2wEMJDAnr0/YRrslhU9hwu5e9bjzH9xiGs+b8D7fr4eOlpNlsYHNaf5FljGBUdTH5xDZU1jbSYLcydMtwmVI7kVxIRGsBnezs+yWJUdDAPzo0l+6vvbFZ91IqPt4Hmlu5fBPlfP7mRUdHBThsWbSQ0uhAa356qYPlrOe0ev3Z46GX/KooMDaCkov6yrzk4rB/fl5/DOKgfxWfOARAREmD9YjlqaGQg35VcmHRuSWIsWTu/4+n7J1FWWc8L6bsZFR3Mwrir2bj9JEsSY22CTYju+OCzE7z14bdX/Lyfzh/L8MHBlFXV8z8bvu6wzxOLrid2+CDrLhhFsfDWpm+Z/YOr6Ofnzf0rswCY/YOr0Ot1bNpxCmg93XRIZCCLnv0XAMsWTuDbvAq27T5NZGgAvt4GyqrqaWhSeOGRHxA7YhAf7znNH97ZC8CC6aPYuquA+iYz40eG8X15Ld+Xn2NUdDDHT1e3q3OEaQDjRoZRV99sHYXF3zwUH28DjU1mtu//np/Mvoa4m4ZSVF7H7txSYoaFMHakaxwPlNDo4u6p0sp6fr/haw7nd3zQqre1HYhsM2iAHw/MjWXKhME0tyjo9TrMZossriP6XPrmXP7v4+M2j9135xhyT1Wy92gZAHfeMowfjI3i13/+oqOXsAoO9OXxH17PhKvD7M5X1mK28H15HcPOH2NTVZVms8V69tCmHacIDvS1XjekWFQMnbymolgwW1R8vQ2YFQuqqrY7hby6tonmFoX8khryi2qYfkN0u5FC214B6+vaeV9nJ6HRjWMaikXlSH4lQf18OFvXxNbdBXgZ9PTz86ZFsXDidDX3TBvFZ3sL6RfgzZ2Th+HjbUAH7Nj/PfuOlXPbdYOJCO3HvqNlfPDZSUaYBjB6yEDuuzOGPYdL0QE19c3MvuUq6hpaqK5twtfHQGRoP+obW/D1NsiV1sLpmBULJwurGRU9ELNiwcfbgKJYqG8yAxAY4APAoZNn2H+8nL9vPYZBr2f6DdHWEfvP7h4nn20nJKHhhAfChfA0dfXN+HgbrGcBCedl73en7NsQQvS6/udHHsL1ydhQCCGEwyQ0hBBCOExCQwghhMMkNIQQQjhMQkMIIYTDJDSEEEI4zG1PuVWU1rliSkpK7PQUQgjRpu13Ztvv0Eu5bWiUl7fO9JqcnKxxJUII4XrKy8sZOrT9muNue0V4Y2Mjhw4dIiwsDIPhyq5CLSkpITk5mQ0bNhAZGdlLFToX2WbZZncl23xl26woCuXl5cTGxuLn137BJ7cdafj5+TFp0qRuvUZkZKTHTUEi2+wZZJs9Q1e3uaMRRhs5EC6EEMJhEhpCCCEcJqEhhBDCYRIaHQgKCuLRRx8lKMhzVryTbfYMss2eoTe32W3PnhJCCNHzZKQhhBDCYRIaQgghHCah0YG8vDySkpKIj48nKSmJ/Px8rUvqtqqqKpYsWUJ8fDxz5szh0UcfpbKyEoD9+/czd+5c4uPjeeCBB6ioqLA+r7M2V5GWlsbo0aM5duwY4N7b29TUREpKCjNnzmTOnDk8++yzQOefaVf/vH/yySfMmzePxMRE5s6dS3Z2NuBe27x69WqmTZtm8zmGrm9jt7ZfFe3cf//96gcffKCqqqp+8MEH6v33369xRd1XVVWl7ty503r/xRdfVP/zP/9TVRRFjYuLU3fv3q2qqqq+9tpr6vLly1VVVTttcxWHDh1SH3zwQfWOO+5Qjx496vbb+9xzz6mrVq1SLRaLqqqqWl5erqpq559pV/68WywWddKkSerRo0dVVVXVw4cPqxMmTFAVRXGrbd69e7daVFRk/Ry36eo2dmf7JTQucebMGXXixImq2WxWVVVVzWazOnHiRLWiokLjynpWVlaWunjxYvXAgQPq7NmzrY9XVFSoEyZMUFVV7bTNFTQ1NakLFy5UT58+bf2yufP21tXVqRMnTlTr6upsHu/sM+3qn3eLxaLeeOON6p49e1RVVdVdu3apM2fOdNttvjg0urqN3d1+t51GpKuKi4uJiIiwzldlMBgIDw+nuLiYkJAQjavrGRaLhXfeeYdp06ZRXFxMVFSUtS0kJASLxUJ1dXWnbcHBwRpUfmVeeeUV5s6dazONgjtv7+nTpwkODiYtLY2vvvqKfv368Ytf/AI/P7/LfqZVVXXpz7tOp+Pll1/mkUceISAggHPnzvHGG290+j129W1u09Vt7O72yzEND/Tcc88REBDAfffdp3UpvWbfvn0cOnSIRYsWaV1Kn1EUhdOnT3PNNdfw/vvv89RTT/Hzn/+c+vp6rUvrNWazmT//+c+sWbOGTz75hD/96U889thjbr3NWpORxiWMRiOlpaUoioLBYEBRFMrKyjAajVqX1iNWr17Nd999x+uvv45er8doNFJUVGRtr6ysRK/XExwc3Gmbs9u9ezcnT55k+vTpQOusnw8++CD333+/W24vtH52vby8SEhIAGD8+PEMHDgQPz+/y36mVVV16c/74cOHKSsrY+LEiQBMnDgRf39/fH193Xab23T2u6qzbezu9stI4xKhoaHExMSQmZkJQGZmJjExMS41bL2cl156iUOHDvHaa6/h4+MDQGxsLI2NjezZsweAd999l1mzZtltc3YPP/wwOTk5fPzxx3z88cdERkby1ltv8dBDD7nl9kLr7rSbbrqJzz//HGg9Q6aiooJhw4Zd9jPt6p/3yMhISkpKOHXqFAAnT56koqKCoUOHuu02t+lsO7ra5gi5IrwDJ0+eZPny5dTU1BAUFMTq1asZPny41mV1y/Hjx0lISGDYsGHWOfJNJhOvvfYae/fuJSUlhaamJgYPHszvfvc7Bg0aBNBpmyuZNm0ar7/+OldffbVbb+/p06d55plnqK6uxsvLi8cee4zbbrut08+0q3/eP/zwQ9588010Oh0Ay5YtIy4uzq22+fnnnyc7O5szZ84wcOBAgoOD2bx5c5e3sTvbL6EhhBDCYbJ7SgghhMMkNIQQQjhMQkMIIYTDJDSEEEI4TEJDCCGEwyQ0hBBCOExCQwghhMMkNIQQQjjs/wM+C0lmTS/wdAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat_syntax.ipynb b/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat_syntax.ipynb
index 3954d722..8dde4943 100755
--- a/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat_syntax.ipynb
+++ b/src/evaluation/figure_notebooks/figure_5/sample_predict_notebook_newformat_syntax.ipynb
@@ -3,6 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "4355dc7b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,6 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "7879b7f0",
    "metadata": {},
    "outputs": [
     {
@@ -337,6 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "0c5a65f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -465,6 +468,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "dfbd62dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -474,6 +478,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "5ca6d571",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,6 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "78374282",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -536,6 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "b5b5d10b",
    "metadata": {},
    "outputs": [
     {
@@ -575,6 +582,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "6edaf38d",
    "metadata": {
     "tags": []
    },
@@ -615,6 +623,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "18bf3c2e",
    "metadata": {},
    "outputs": [
     {
@@ -651,6 +660,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "123efc01",
    "metadata": {},
    "outputs": [
     {
@@ -698,6 +708,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "475b2af9",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -705,6 +716,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "ce149e91",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -712,7 +724,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -726,7 +738,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
diff --git a/src/evaluation/figure_notebooks/figure_5/viz_modisco_motifs_for_debug.ipynb b/src/evaluation/figure_notebooks/figure_5/viz_modisco_motifs_for_debug.ipynb
new file mode 100644
index 00000000..838599d9
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_5/viz_modisco_motifs_for_debug.ipynb
@@ -0,0 +1,1398 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    \n",
+    "def import_tfmodisco_motifs(tfm_results_path, trim_threshold, trim=True, only_pos=True):\n",
+    "\t\"\"\"\n",
+    "\tImports the PFMs to into a dictionary, mapping `(x, y)` to the PFM,\n",
+    "\twhere `x` is the metacluster index and `y` is the pattern index.\n",
+    "\tArguments:\n",
+    "\t\t`tfm_results_path`: path to HDF5 containing TF-MoDISco results\n",
+    "\t\t`out_dir`: where to save motifs\n",
+    "\t\t`trim`: if True, trim the motif flanks based on information content\n",
+    "\t\t`only_pos`: if True, only return motifs with positive contributions\n",
+    "\tReturns the dictionary of PFMs.\n",
+    "\t\"\"\" \n",
+    "    \n",
+    "\tdef trim_motif_new(cwm, motif, trim_threshold=0.3):\n",
+    "\t\t\"\"\"\n",
+    "\t\tGiven the PFM and motif (both L x 4 arrays) (the motif could be the\n",
+    "\t\tPFM itself), trims `motif` by cutting off flanks of low information\n",
+    "\t\tcontent in `pfm`. `min_ic` is the minimum required information\n",
+    "\t\tcontent. If specified this trimmed motif will be extended on either\n",
+    "\t\tside by `pad` bases.\n",
+    "\t\tIf no base passes the `min_ic` threshold, then no trimming is done.\n",
+    "\t\t\"\"\"\n",
+    "\n",
+    "\t\tscore = np.sum(np.abs(cwm), axis=1)\n",
+    "\t\ttrim_thresh = np.max(score) * trim_threshold  # Cut off anything less than 30% of max score\n",
+    "\t\tpass_inds = np.where(score >= trim_thresh)[0]\n",
+    "\t\ttrimmed = motif[np.min(pass_inds): np.max(pass_inds) + 1]\n",
+    "\n",
+    "\t\tif not trimmed.size:\n",
+    "\t\t\treturn motif\n",
+    "\n",
+    "\t\treturn trimmed\n",
+    "    \n",
+    "\tpfms = {}\n",
+    "\tcfms = {}\n",
+    "\twith h5py.File(tfm_results_path, \"r\") as f:\n",
+    "\t\tmetaclusters = f[\"metacluster_idx_to_submetacluster_results\"]\n",
+    "\t\tnum_metaclusters = len(metaclusters.keys())\n",
+    "\t\tfor metacluster_i, metacluster_key in enumerate(metaclusters.keys()):\n",
+    "\t\t\tmetacluster = metaclusters[metacluster_key]\n",
+    "\t\t\tif \"patterns\" not in metacluster[\"seqlets_to_patterns_result\"]:\n",
+    "\t\t\t\tcontinue\n",
+    "\t\t\tpatterns = metacluster[\"seqlets_to_patterns_result\"][\"patterns\"]\n",
+    "\t\t\tnum_patterns = len(patterns[\"all_pattern_names\"][:])\n",
+    "\t\t\tfor pattern_i, pattern_name in enumerate(patterns[\"all_pattern_names\"][:]):\n",
+    "\t\t\t\t#if pattern_i > 2:\n",
+    "\t\t\t\t#\tcontinue\n",
+    "\t\t\t\ttry:\n",
+    "\t\t\t\t\tpattern_name = pattern_name.decode()\n",
+    "\t\t\t\texcept:\n",
+    "\t\t\t\t\tpattern_name = pattern_name\n",
+    "\t\t\t\tpattern = patterns[pattern_name]\n",
+    "\t\t\t\tpfm = pattern[\"sequence\"][\"fwd\"][:]\n",
+    "\t\t\t\tcwm = pattern[\"task0_contrib_scores\"][\"fwd\"][:]\n",
+    "\t\t\t\t\n",
+    "\t\t\t\tpfm = trim_motif_new(cwm, pfm, trim_threshold)\n",
+    "\t\t\t\tcfm = trim_motif_new(cwm, cwm, trim_threshold)\n",
+    "\t\t\t\tpfms[\"%d_%d\" % (metacluster_i,pattern_i)] = pfm\n",
+    "\t\t\t\tcfms[\"%d_%d\" % (metacluster_i,pattern_i)] = cfm\n",
+    "\treturn pfms, cfms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ffms, cfms = import_tfmodisco_motifs(\"/oak/stanford/groups/akundaje/projects/chrombpnet_paper_new/modisco_jun_30/modisco/ATAC/K562/modiscolite_crop_500_1M_seqs/modisco_results_allChroms_counts.hdf5\", 0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _plot_weights(array,\n",
+    "                  figsize=(10,3),\n",
+    "                 **kwargs):\n",
+    "    \n",
+    "    fig = plt.figure(figsize=figsize)\n",
+    "    ax = fig.add_subplot(111) \n",
+    "    viz_sequence.plot_weights_given_ax(ax=ax, array=array,**kwargs)\n",
+    "    plt.show()\n",
+    "    plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seqlogo\n",
+    "import pandas as pd\n",
+    "from modisco.visualization import viz_sequence\n",
+    "from modisco import util\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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guusk3qtnT7G8eDwSb1GZoiIRDKmpkun4+edSCdukrIPJLez3i8hbsMB5sXPlCWY6WrBAWv849UFY7bHkrYdvjgKvaVBXOZQLWgyDHg/LPe9KlYVUKPjyAAVrx8CyZyDv9+q3DSJm6+qrZaHiRAEczPV14AAcfrhY552IybHs3CnHEG7WcrRxqAwJGltny8msew9mjwhdaNXrCkc9Bc1OlwKkVYmr2vAegIItMP9e2Dy+5m1rmXSXLZPMw0itojp0kMDUM8+UAngZGWV9x9aulTot330X2mRS3aW/b58U3duyJbRJIDVVYqIefrisnkywlBZKfPVVsa7tqCH73mSMjz8uVecjJYBTUiQe7/DDpYdjSoq89759Yg1YtCi09zX9vPPz4U9/kgQMp1LlsWgNk0+VBtLBJsBkdYGTP4f0VtVbsELBXyTuxhUvwMKHqh6XodgaPx4uvdS5VpRg7ufLL5cSD04UjVD7sWgtmYs//hhZD0ObNjIXZ2aWtfPKyZG2S6HiUBkSNFZsOZWCrTC+Y2hCK60l9H4Vmp4ausiqjPcAFG4T8bfth6q3qWHS9fkkI2zZsvBunh494I47YNgwyerSWkRWVezfLw/6detkYnz+eclMM6G6MV5zjTQrDsWdd9tt8Pe/lxUhDJfCQhGXr70m1sKqJv7aPuvFi8VKF64FqF07OScXXSTNcouK5LNPSpJ+gV6vTOpay/cTJkgF74kTzQPGTa+bP/8Z3nrLuVYtqOZYNoyDn68JvvVOxxvh2GfDs2TVhi8PctfC1HPla3kMxNbOndCpUxBZmHHA9PpyumiE2o9l7FjpChHuAislRfqYnnsunHOOVOavLN5SUmRB+NlnsgCePj24GEqHypCgsWLLqcy8EjZ8FHw7jsNOhpO/qN5NGC6+fFj8D1j61MF/q2HSfewxsZ6EOkE1aSIxOBdeKDevO8hnSlGRCL7HH4fnnqt9NVfVpR+qMMnIgPffh1NPjYzIqkx+vlhxBg8+2JpT0y3s80ms2PLloU9oDRvKebnkkpqFb2VKLZCBgGSlvfGG2Wq8Nn77TVyiTrU4lHLQsXj3wxftgss2TqoP/cZCk5MgKQoXVmUCPvAXwpw/w+/vlv3eQGxddRV8+KGz4zRNrq/8fFlY1GRNdgI1HcuOHSJ894cRjpeZKXPpn/8sc2tmZu1zss8n4i4pSeoSPvGE2X3qUBkSNFZsOZG9i6RwqT/IJ8bhI6VBrKeKwJ9I4ssV69aMSyv2Y6xm0l25Eo4+OjRLQ1ISjBwpbjePJ/xm0bm5Em/x5z/LKqs6qrr0Tz5ZaukE01C6UyeYNAmaNYtMu5nqKJ3IzjsPfihneKzpFv7HP0IP8ne7pdL+00/LOQrnvOTmSnX3666DX36pfjsTMXbssRKvFampKz1d3Mbdu4vgV0rc0kuXivgO1VJz0Phm3wTr3ja/55MbwKCZkNEO3BHuoF4bvjxper+8pPR6LWJr7ly5dyJtaWzeXO6vlBR54K9ZI+79UDG5Zh54QCzkkbRqZWZKL9NWreS+OnBAPAAbNoR+Hdf0f+ecIxblUIXvJZdIk/jU1KpjTE3Iy5P7fsQIsRTWhENlSNBYseVEJp0IO2cChp+/8sAJ70GLM2KzwgXwFUDhVvi+P+SXmFOqmXQvvlgapQZb5qFBAxEOnTqZW0xMyc0Vd+CIEVXfzJV/N2mSCJlgJtnTTxf3ZXp68Ja4UMnPl2rYzz0nP9cUe9ayZWhuhLZtJRauefPIWeoCAXkYf/yxtG6p6lqpbTr64guJpQnXNeJ2SzzLLbfAaafJg9ztloc6yEPK6xXxPHu2JH188UVwruUKx7JrrpR2Mc0+9GTB4FmSRehOMd9pJPHlwW8PStZiDWJLa4nTnD8//IemyyUlY267TazELlfFzzw5Wb7++KOIgW+/DW7OqW18OTnQsWNkRGOzZtJg/ZZbRGSVn1e0lgWMyyXV4F9+WebBYD6/6rb9+WeZl0IRix07ypx5xBGRu+9zc2HePLlvN22qehuHypCgqUlsobV25OvYY4/VdZbNE7T+IEPr9zB/rXtPa29u7Mfq92qdu17rjxvKOKpg3TqtU1O1llvG/NW8udZr1mhdUBC94efmav3ZZ1onJR28//L4fFq3bx/c+Pv0kfePB7m5Wl9//cHHUZ4nntA6LS3483L44Vrv2KG11xu9sU+YoHVycs3npDJer9atWwd/PJVfF1+s9Z49Wu/dq7Xfbzbmfftk3CNHmu+nAl91M7/Xx3q03j5Na18UbwxTvLlaTz2/xk2++ELrjIzwzonbrfVDD2m9a5d81rWdF79fttu9W+vHHtPa4wnhnFTBNddUfV0G82rbVuvvvpN5LS+v9n2WHsv27VrfdFOI11c5BgzQWqngx929u3yePl/tYw4Wr1frbdu07tgxtPOSKABzta5a09jeiLEm4IM5NwcXIHvkE9DqbPBE2PRjgssDqU1hwBRwVb3Cfvzx4C1aHTvKaqd16/DdhjWRkSEr5e+/r9kcPnq0eWA9SJbkt99G3hpnSkaGlIYYPLjqvxcUSPxcsCv0Y44RN1/DhuLSjQYZGeJymjw5OBfF66+Hl8KelSUlCV5/XYJ869UTy4IJ2dky7r//XawqTZsGsePd8yB3nfn2vV6EBsfE3nVYFZ4MOP7tav+sNdx7b3iWxnbtZC647z657rKzaz8vLpds16AB3HOPuJU7dgx9DCChEB98EF7M2cUXSyZu//7mLrjSY2nSBP71L5lXGjUKbf/LlkldLa2D+79jj5Wg9nr1omOh93jk3M6aJVnEhyIREVtKqSFKqRVKqdVKqfuq+HsbpdQPSqn5SqmFSqkzIrHfhGTtW1It3pSWZ8MRIyUYPl64UyCrE/QZddCftmyRwPBgMk+6dBHXTOPGYkqPNhkZkj03c2bVMVWFhdKc1fSBkZ4uLsdoBMIHQ3q6uOSq4o03gq+ndcIJ8NNPIkRMRUiopKfLBD9jhkzwtVFUJLE0oT7UTzhBGlUPGxbeecvMlEbTy5dLORIjVr4EAUP/Y6Pe0P6K+CysqsNVveibPLl615AJl14q4qRr19AXLhkZUoJkwQIJ0g+Vu+8OvQtBZqYItdGjRdSHulDJzJSFyIoV4kYNlscfD14s9usnLsxgFh+h4PGIOJ45U+J7DzXC/miVUm7gJWAo0BW4VCnVtdJmDwEfaa2PAS4BXg53vwnLyufNrVoZ7eCEd6MfDG+CJwNaX3DQr598Mrhg8owMWbnVqxc9y0lVpKVB584wZszBf5swITjL3OjR0KJF7GK0aqIq8ViakRmMMOnQQQJqsyJYvqk20tIkNuTrr2uf5MePD70Y69VXS/xZ06aRSWBIThZB+uGH8NBDtWzsK4D1H2DUGUK55X53RzHLIhRqKDXx4IOhCWCXC957D0aNEoER7lxQWmrlpZfEehnsvZmTI9bvUFqLtWsn4vvssyNj6U5NFcvWV19J9XpTNm2SYsjBHEPPnrG970uteFOnikA+lIiEju0NrNZar9VaFwMfAOdU2kYD2SXf1wPCyCdJYPI2wP5V5tv3etEZroRSKq22d+4UC0owK6l33pGHXjyESno6nHGGZNiV5/XXJTvIhL594ayzopt1GAxVPaQ+/DC44NiUFAnSDTXrKBxSU6U226OP1rzdc8+Zn6PyDBggwcfROLaMDHF9XXllDRtt/FSqvptwxF2Q2tx8+zgze7Zka4bC889LxlykrcOZmXKP/+9/wf3fmDGShRos9euXuZUjPSekp4vF/frrzbb/5z+DW/hmZ4ugi/V973LJPr/6KrohJE4jEnd1S2BjuZ83lfyuPI8CVyilNgHfALdX9UZKqRFKqblKqbk7nF7kJBR+HwumN3TjE6Bpf3DFwM8WIs88E1xswKWXSvxUPIVKZqY0ZO3USX4+cEBcIaa8+qpzhFZVBAJibTEtIgoiZEpT0uNBZqa4cE48seq/r18vMT3B0qOHFFmM5sMkIwNeeaUGl8+K56WMSm2kNIEej8Yu0zgChBITCHD77XDttdGLd8zIkN6s99xjtr3Wkm0abN22pCSxCjVtGt34xhdekEVDTezZIxb3YBa+b70Vm5CBqnC7xTvw8iHk44rVx3wpMEZr3Qo4A3hHqYOXb1rrUVrrXlrrXk2aNInR0GLImtHmNXZ6v+qsuI1KaC0uANMJqnlzeSjFO84JxJJTGuv0xRdl6eS1cdFF0L59fCYnUyZPFoujKSeeKHEu8Qr0LyUjQ2JequL114MP+G3RQj6LWBxXerpcR10rB0/kroN9hn2LOlyN+Uos/hw4IO7fYM/LmWdK3bZon5eMDHGln3tu7dvOmBFa8c9335XabNG2zqSny6KhW7fqt3nppeDe8/zzpTxEPBeO6ekyp552WvzGEEsi8djYDLQu93Orkt+V53rgIwCt9c9AKtA4AvtOHPavkP6DJrS9WOK1HMz8+cHF0Dz7rHNMxh5PmWVr1Cgz91RKiqwwYxnTFAoffGAeQ5OcLNvHw31YFQ0aHPw7v19EejD1rVJTJeA3lqv2jAxxJ1VYI279DuMptssdzojNNOTTT4O35hx9tLi4Y3W9paeLIDruuJq3e/HF4OPOHn9c3JWxWqRkZMCUKVK7qypef93cytiggWzvhIVvRobE7h0KRGIqmgN0Vkq1V0olIwHwlevFbgAGACil/oSIrTroJ6yBde9g3Hj2qKcd7074/HPzB2C7dhI8ampBigWZmbBrl8SdmHDJJc4RJdWhtQSSm1obrrpK4jacQlWf7+TJwfeofPxxcYvGItO1lNLA3wpukR2/mPU9bdwXkutHa2hR4b//Dc5VnZws12asLSkZGWJ1rG6hd+CA/D0YC13v3uKijKVYcblk8VBVgs+GDbBtm/l7PfaYs0IhnDQHRZOwxZbW2gfcBnwLLEOyDpcopR5XSp1dstk9wI1Kqd+A94FrSgqAHTqsHWPWA7F+D0h1vgv1gw/M06QfecQZmXuVCWZ1ftNNzrdqLVlivrp1uWTSdfoxvfBCcIHxXbvCrbfGRxinpMDQoeXcIrtmmf1jl9vA7XAlX4716+VaC4b775c6S/FwwWdnSyuwqgg2c9HtliSfeFjpk5OlTMNZZ1X8/YQJ5p9r/foScO8ULwM4fxEbKSIS1qe1/gYJfC//u4fLfb8U6BeJfSUkxXuhyLBiZrsrQTk3KB6ktlblZsjV0aKFFPpzklWrlB9+MHMfHHaYFPt0Ol9+ae7aHT7c+UJr505Jxw+GMWPKWu7Eg4wMCTzu0CEAuWvM/qnFmTWWV3Aab70VnCWofXspfBqvh2pGBtx5J7z55sF/e/bZ4FyIt98uc1q84jYzM8UF2K5d2e+CyT6+vcrUNEsscHCobx1i3xLz2jkdrga3A5VJOb7+2twi9MADoaVUx4IFC8y2u/DC0OrvxJoPPzR3uT3xhPPF1rvvBmd1GDZMqlPH24pavz48eMca6WdaG6lNnVXepRa0Dj6G7tlnY+vSrYrkZLGSlmfZMli71vw96tUTF3W8Y50yMmDkSPm+qEh6IZqQmiruz0PFkuQ0rNiKBfuWSJue2mjc13kFDavg/ffNVoMZGZLi7SSTdSlawzrDDiojRsQ/W6829uyRh4cJgwcH2W4mTgSzYne7JY4o3g9CkDH85YYFZvWyGhxlnqHsABYtCi5zr2dPKfcSb7Hl8cBJJ1X83bhxwSX5PPRQ/IU8yFz0wAPy/bRp5l6Da65xxvgPVazYigW755kFyrY40/Fiq6BA2i2YMGhQ6O0vos2mTWYWt3r1pMq50/n2W3P32ciRzrdq+XyS8WrKRReF3k8uGiQfmGdWX6v+0Y6/58szdWpwhTNfeME5i63KQeHffGM+Px12GNxyi3OsQqWi6fPPzRMVHnrIGYuRQxUrtmLB7l/NtmvcRxo/O5jZs80nzwsvdO5DfckSsxVh167BVWOPFx99ZBZInpQEp5wS/fGEy4IFwcX53X23s661JO9GpHFGLTTpJ71HE4SJE82TMDp3llhHp9SlKz8Ov988jADEQu8kSoXjZ5+Zid+uXc16kFqih0NugzrOAcMWPdk1VK1zCCtXmq0GlZIChk6ZaCtjmrnXrVtseziGyizDxLcTTwy+UW08+Okn83F27Fhzwce44DNU6GmVm204F62lAKgpN97oXLfVb78F59q8/XbnWLVK2bgRdu822/bMMxNjHqvLOPRRWIco2g1+g6e6KzkhSj4sW2Zm6amtkGC8mTPH7GF+zDHON737/bDdMNn1vPOcH38G4uIxDcK+6SYHinqTsAFIqOD4devMz4nbDTfcEN/M0JqYOtXchXjCCc6sBbVihfnne/HFznHnHqpYrRttDqyWCbW2GlvZh4soc3AvRJAVoQnnnefsm3v5crPtevWK7jgiwcaN4nIzCfY97zznr3ADAXNLnWMf6iY19SC6YQOfNoPCIKpdVosLLvMzbZq5pWrQIOdatUDEvGmrsdtuc55VC2D1arMFY3a2tBWyxBenrQfrHv58jHqeZR0efKOxOLDK0CM6bJgza2uVYhqH1bFjdMcRCVavNnOJdOgghSWdjmlWJcCQIQ59qLsMVxqmoiwUIiK0ACQoaNIk82DsO+90pjUIZJr95RezbTMz4ZxznHmNLV9uFgpx+unBN9m2RB4rtqKN39DunpQFyoF3dDkCAdi61Wzbtm2jO5ZwMY0HcrJ1rhTTFW6fPsGluseLadPM1x2Ofaib9jk0CTFwCD/8YLZd48bOTsJYudI8o/Kii5xbY2+RYY/zc85xVvLIoYoVW9EmUIRRVpIrxawuTxzZtMnMglKvngPdOpUwFVvxrg9kwrJlZivczp2d6Q6pzDffmFkek5Lg5JOjP56QcBsGxu03NBXHmW3bzIOxL7jAuQIFJF7LlOuvd65QWb3abDsnZYQeyjg8eqMOYNp82uXByN0YChGK3Vi1+DSS9CdA/Rq369xZHv5OdiOaPgwSYZJauNBsu6OOcn68ltYwfbrZtj17invEkcI+u4tUkK/t/t/1M7Q+z9wSFiemT5fP2SRA/swznZ2EMXGiWVFmt9u5bbq0hpwcs23btInuWCxmJMCjJMFxJWMkogLFlMZGRJwIxW6s2tqZYn/tpp7OnZ3boqcUU4tVIrjdTFe4iVCcdedO83i6k092qNACaHCMmYDasyC6cVsR4scfzRuC93N4F1zT9jZHHeXcosw5OWYLp8zMxLBmHwpYsRVtTFO7/UWgoyS2IsTWfc0oKK79zu3SxdkrWzC3ujl1si0lmDi61q2jO5ZIsGGDuYA680wHx9Q1OBr8BiJqz0LHW7VA6tKZxNG1bevgc4LcLzt2mG170knODSNYs8ZsDuvQITGKMh8KWLEVbVyGT46i7aAdHOgAFBSnYWKl69rVmdk75TF9oO/ZE91xhIvpRJoIcXQgZSxMcLkcXsstrblZWQffASjaFf3xhMmGDWbbnXSSs+O1duwwd6WfccbBLX6cwtq1Zp9zp07RH4vFjIiILaXUEKXUCqXUaqXUfdVsc5FSaqlSaolSamwk9psQJDcws1jtXeL4GlsFxWZLVqdbtQAaNDDbbsmS6I4jXIqLzYRtq1bmbVbiyYYNZnFB3bs73MWrFGT/yWzbde+aZy3HiW2GkQiDBjk3oBzk+jK1avftG92xhENurtn137GjcwXjoUbYYksp5QZeAoYCXYFLlVJdK23TGbgf6Ke17gaMDHe/CUNWZ7Nq0vkbHV/6odBrJrYSwYJy7LFm282a5eyHenGxWRB/ampClHFjzRozsXXSSc4P9qfJCRjFa65+1dFW7YLiVGOhfuqp0R1LuJhaTrt0cXZyjNdrVr6iZUtnJyodSkTicuoNrNZar9VaFwMfAOdU2uZG4CWt9R4ArbVhc5E6gDsFUpsabKghb33UhxMOxT4zFZUIN/cxx5hZ4JYsMctcihemYisRzglIDSQTjj8+AQJ/Gx4HHoOLLHcN7A+ikmuM2birtZF1pEEDaOLwjmMbNpgV+Ozb19mLk+JiM7Hl5Pi5Q41IiK2WQPn1wqaS35WnC9BFKTVDKfWLUmpIBPabONTrWvs2IJlJDibJbRYt7mRLUCndupm535Yscf4K1yTz06mBvpVZb7jeSITK/rQYCgHDDIvlz4HXMN3PFKNFXu1s3NXa6F7p2NH5lcpNCwB36OBsMV9cbBazZcWWc4iVId4DdAb6A62AqUqpHlrrveU3UkqNAEYAtKlLxUEa9YatU6i1tMOO6dDyLMdmJ6Ummc2kps1q40nXrmYxTKtWOdtd5fGYrcATQQCDee2gVq2iO46IkNIQmg+CzV9Ra2HjjeOg53+kk0SkON8wTXVszWp9467WRtdPImS7mrYbO/xwZyf5+Hxm932iLLIOBSKxZt8MlL/NWpX8rjybgPFaa6/Weh2wEhFfFdBaj9Ja99Ja92ridHt0MNQ/0sydsPEzR1eRT002C9xw+uoWJIjXpM2Lzwdff23e3iPWJCebjc20Yn48KS6G/fvNtk2Y6eHwO8CTWft2/kKYdQP4DJsPxpD1O9saLUxat3Z+vKap5bRDh+iOI1ySksws2olw3x8qROLJPgforJRqr5RKBi4Bxlfa5nPEqoVSqjHiVlwbgX0nBvW6mW1XsBn2LY3uWMLA1LK1dq1zxUl5Dj/cbLvRo80b8MaaYMSW0wvNbtliljllmknqCJqeZl5rb/N42DHDrD5XDFm5tYvRNdaxo/PdVqY16VpWDoRxGMnJZpa3RFj4HiqELba01j7gNuBbYBnwkdZ6iVLqcaXU2SWbfQvsUkotBX4A/p/W2vnFZSJFVhfzhrOrR4HXmU/2hpl7SPbU7iNctiwxCukdd5yZAPn+e+cKFVOxtWWL860OGzaYuWzbtEmgh4hyQeebzevtzboBtLMq6a7dbmbm6XyQr8JZFBebV8F3uuU0OdksltSpi8RDkYj4rLTW32itu2itO2qtnyj53cNa6/El32ut9d1a665a6x5a6w8isd+EwZ0MDQ1rDWwcZ1YMMRgiFCjbqelqUpNqF42rViVGjNCwYdLOojZ8Phg3zpnFGtPSzMTWzp3Ozq4C2LXLbIxt2jj/WCrQ8QaM+57mb4J594DPOSmwm/eYmXnatYvuOMJl61Yzy1vDhs63zJtattavT4z6eocCDg79rWN0ugn2Lak9JqNoF2yfCk0HgCtCEZoRCpTt3GwVAV37mFatcr4VBaQmkKnF6pVX4KKLnFew1eOBxo3Nik5u3uzsitJer5mIat06cUpZAJDRBpoNgJxJZlar1a9KQdSON0BS/C846RxRO82aRXkgYVJYaGYNatNGknyc7BJt2NDMCrx6tRyLLWwaf5wbjV3XaH2eeRr4vLsc2Zy2w2FrjSbenBxnl0soxe2GSy4xWyHOng3TpjmzV6JpMK9pDat4YSq2MjMTMMuq96vBdYiYdxds+coRFi5/wGzRV69elAcSJj6f2eKqSRPnW047djQ7ljVrnJ1VeSiRAI/EOkJyfWhyotm2+5bChg8lQ8lBJHu8NMzYbbTt5sr5qA7lmmvMV3233+5MsdW9u9l2v/3mTFdoKaZiKykpMcR8BdJbwlH/ALeppUrDzMtg89dxz1D0+Ws3oSTCA70u1aQzrWn2++/WquUUEm3KSmw63Qgewzo6C/7qyBYeHQ4zSyKdNs35cQ8glaJNJ6PVq2HMGOfFQHTrZua2XbHC2YkLwYitRHi4H0SXOyA9iGJUOgAzLoFFj4mFy6THahQwsWwlJTn/fq9LYqtBAzM3otcLu83Wx5YoY8VWLGl5lrkrsXA7LH3aEW6E8nRvvchou48/Ns/8iSdKwVVXmU+wDz3kvOD/zp3NxNb8+dEfSziYFmpMqHit8rjc0O89cAdjatCw7F8wqZ/0T/XFXi3XJbFlgmkNq3hjWkT299+jOgyLIVZsxRJPOrQIolPR0v+D3N/NBVoM6NF6sdGDfcqUxAiSh+DE1p49cP31zumXmJsrQe8mD7qFC53pBi3F1DXoZFdorTTsCe2vBleQ0dd7f4Ov/gS/vwe+gpiGGAR07SfG7XZ+nJPp+BJBaIE0yzZh6lTnLRAPRazYijVd7zdf2QaK4cchjrJudW62ykhEFRTAL79EfzyRoEcPOPJI80n244/hjTfiL7iKimDxYkm5N3VtfvWVcy0QprFYpk14Hcsxz0iGogoyGdxfALNHwJcdYcUL0ksx1Jp8Ab/Egu2t3VLtdtWubk2Dz+OJadutRBEmpnPWl1/Gf66yWLEVexr3ln5pphNt/ib4aZhjBNdxHeYYF5QcOzYxiuopJaUdgkn1vusumDUrfvFbPh9s3y61wpKTJRXchE8+ca5719R94/UmuNhKyoQBP0ByA4zrb5WnIEdiOj9tBvPvhR0zZX7w5ULx/qpju/zFULxXvuZvkgScKQPhmyNr3Z3LVfuH7fU6P2khKcnMumUaOxhvunQxK0Uzc2ZixKHVdWydrXhw7PNSc8dvuITaMQNmXgEnvBf3JtVNsnfSuTMsWVL7tl9/Dc8/H/0xRYKjjoJzzxUxYtJPzO+Hs84S612nTrHN+PH5YN8+OOkkKQQKssqdPLn2/5082bn1g0zFVlGRfP5ObhBeK+ktRHBNOh58Iapffz6s/p+8ALI6QaM+0PA4SGkgPRkDPnn/3HWwaxbsmgPefUHtxuOqfZ7yep2ftGAqOJzsai9Px45mn7nPBz/9BEOHRn9Mlupx+FqkjpLRFjr/ObhA2U2fw/QLxXUQiG/QyiWXmAUpb9kC06cnTozNv/4V3AMjPx/69ZOJLFYWvPx8WLdOWg2Vb6p78cVmq9y8PJg3L3rjCwdTsZWTI4Ir4anfDQZMMWtUbcKB1RLTNW8k/Hw1TLsAZlwsLYCWPAFbvw9aaIGZGzEQcP59bmrZMm2GHm+6dTO3rH/0kXMt2ocKVmzFix6PBFfkEGDLNzCxJ+RvkCDZOHHOOearxAceSJw+di1awN13Q3oQxsMDB+CMM+CZZ6JfViE3FyZOFCvcunUV/3bGGeaxJi+95MyJNz3dTGxt3JjgbsTyNOpVTnA5M+jJxLIFzi8xYJoxuXFjYrjd6teHww8323bixMQ4prqMFVvxIikbjnoSPEG24ziwWuIstk2JW8Pq7t3N3WZz58KCBYnzcLz//uCzKLWGxx+HCy6QVXGkxaXPJ0LukUdkH1WtZlu2lJcJH37ovFphIKnsJtfJhg117MHR6DgYNFOC5oMqCxEbMlPNlLnTCxk3bmxmEc3JSZxM6osuMvMybN0q2ciW+GHFVjzpdBOkNifo0+DLlaD5n6+EA6vEtRguQQTgKyXxTaYBsfff7+ximuXJyIAXXwzOulXKxIlS8+rVV+V4wxVdXq+8z5dfQq9e8J//1Lz9hReaxTH5fCIOnZa80KaN2We2ZUviPAyNqd8DzlwKbS8Fd3zjMivTptEGo+2cXs8pO9vs/vD7JSYyEShNkDHh4YedadE+VLBiK564PHDqhNCD3jd9Dl92gZmXw/7lIrr8QQSzBHxQvE+ylBY9GtSuhw+XHnUmTJvmvL58NVl2LrtMhEsogmv7dhg5UsoxvPSSxEjt2xdcPMuBAzK+L76AY46B88+HZctq/79zzjG3OI4e7bwYm+xss5i5RHoYBoUnHfqOhn7vi+U72NIQUaJTM7OuEStXOt+C3bSp2XZbtkR3HJHiqKPM40y//VbmJ0t8iIjYUkoNUUqtUEqtVkrdV8N2FyiltFKqVyT2WyfI6iSTazjug81fSsHDKQNh4SOwfapUmvYeECFVvLdMVBXvlfpdexfDiudFqH3WQqpUB8Epp5hl7ZVyyy3Osm7V5kZ79VVo3z70DKsdO+Avf4FWreDaa6W0xMqV8pnt3Xvwq6hI3DDvvgu33ipp3RdeGJxI7d3bPGW9sFASApxWf+eww8y2y8mJ7jjiSquzYdhyaNQ7csHzwaKSZE7q+RxdhlxnZBFat8758ZmtWpltVz75xMkoJfGapljrVvwIe+mklHIDLwEDgU3AHKXUeK310krbZQF3ArPC3Wedo+UwOOIeWP4s+MN4+u2aJa9l/wQU1OsmKebudFkl+/NEbO1ZIAUSwyA1VW7yL74ws5DMmiUCZsQIs6y5aJKXB5dfDhMmVL9NSkpZMHo4gb9798Jnn8kLxBrYrZt8BmlpIrLy82HVKhFo4eB2w6BB8OmnZtv/978iCJ1CUZHEbW0w8Fr9/rt5A+6EJK05DJwGm7+CxY/DvmUQKIx+f0R3OqChw3Xwp79AZjtaz5drtbaH9MaNspgIxSIcKzp1ghkzat9u+XJx0SUCF1wgZXZMsig//BCefRayDFv0WiJHJCxbvYHVWuu1Wuti4APgnCq2+zvwT8Dha584ceTj0Pq8CMZraNi3WOp5bfocNo6DLRNg589hC61SHnssuD51998vFol4uq/y82HcOBFStdGqFfz4o7m71ITcXBGeU6bIBPn991J0MFyhVcoll5hPpPv2wU03OSd2q7BQHoYmLFyYOJW+Q0a5xMo1ZK4Ir5bnSpsfVxQC1jyZkNIYjnwMzt8Kx70Ime0AEcAm8ZkbNji/sGmXLmbW6nXrnGWJr4nTTze3KPr9Mm875Z4HZ40lmkTi1mgJbCz386aS3/2BUqon0Fpr/XUE9lc3UQr6vglNTgi+b1qc6N4dTjzRvE1HUZEU1otn1fWNG0VgmNKjh7S4cfJqvTzBlOUAWelOnx7/ulW5uXJeOnc2exj+9JPzXKBRpWFPOPkTOHs1dLkNUpuCK7kktisEX7c7DTxZUn6mQU/o+waclyPWrKTsCpu2aWNW6HPNGucWzC2lTRuzuMYFCxKnuGm9esFZeV99VdykTojZ9HoPnSzJqK9DlFIu4D/APQbbjlBKzVVKzd0RqaV+IuHywMnjocVQcMfZ12bIk08GVz199Wq44orYPygDAdnn0KHBC4tTTpGMQNMA7niSnAwPPhicOLziivjGceTni6D98ENo29bsepo5M7ZV+x1Dekvo+S+xPp27GU54H7reB436ilXcnQ6ebEiqV/HlyZI4rLSW0Ho49Pw3nP4DXJQHQ3+FNhfK/FMFLVqYWU4KCkRwOZk2bczu4blznS8cy/OXv5hb4P1+KYIc7wUWiNv58svjPYrYoHSYTaCUUscDj2qtB5f8fD+A1vqpkp/rAWuAUmNhM2A3cLbWem5179urVy89d261f67baC0B64seiZjLL6JcVvGaOfFEsziI8px/PrzzTmwsRj6fxE6ddJLEYpQS7KW/fr3ERG3c6Kw6VZWPIy8PmjULzjx//PHi0oy1Bc/vF/dT9+4iun76Cc4+2yzbcPly86KOcaFZM9i2Lbb7bAykAsklrwBQBBQD+4AQXWMN6/nZs6/2tfmzz8Lttzt3UbJunVirTRZ78+ZJNnAi4PNJnb1gsg3vu08WZpEMkwiG3Fy45x4YNSoxelGaoJT6VWtdZQJgJCxbc4DOSqn2Sqlk4BJgfOkftdb7tNaNtdbttNbtgF+oRWgd8igFXf8f9P9aVqWhuAmiRerBudNPPRV80Punn0pvwWj764uLJU6sZ8+KQisU2rYV98KwYc5xK1Zl3cnIkEbZwazMf/4Zbr45tnEqXi/s3CkxJ6X7bdPGPBbru++c4QqpllgLLYCdSCDHWmA5sBJYD+QQstACaL7PoPYIEovo5Bicli3NF0oTJyZOXKDHA3/7W3Dz8D//KRa8eFi4Cgrk/h01Kvb7jhdhiy2ttQ+4DfgWWAZ8pLVeopR6XCl1drjvf0jT9FQ4YxFkdYlvZWnlEjdE/4nivqjESSeFZmGYMgVOPTX4OlSmFBaKW6NnT7FGRYK0NHF3PfFE/N1Y6ekSEF8VI0eax9KV8s47sXPxlorgXr1gbbkyTi1bmsfKfP+9sx/sdYm2mNVCmDHD2e635GQJBzDhhx8SKy7wuuuCS1DQWuolxrrXaGGhWBivuip2+3QCEYnZ0lp/o7XuorXuqLV+ouR3D2utx1exbX9r1QqCjNYwdJ6kYrujlIlULUpiQBqfAGcsgBaDq93y6adDK+kwd648cGfOjNyD0++XldM770CfPmI9iSRKiZiZNAmOOCL2ZviMDGk98sor8MYbVW/TsCFcf31w2aIgJSrOOy+6D5nCQind0KsXbNpU8W9JSebBvtOnO/vBXpc4lrlGtbZ2746PQS8YunUz2+7nn+O/oAqG9PTgLdq7dpU1tY9FjbSCAli6VMIWDrWFksMTdS2AiKzjXoSz10LH68XKFVXRVSKyDjsFBvwgaeeZHWr8j4EDoX//4B/uIEHzJ58sQZubNoV3E+bmwi+/yIN8xIjoBn6feKJMHB9+CF27Rr9+WGamFP189lkpgHrllTVvf//9oaXif/edxKbt2xf5CTg3FxYvlgKs1eXADB1q1lZl1y6H9+MzLVeeAJxSf5Gx63zKlOiOJVzOPNNsnsrNdX4LosrccUfwFu2dO2VRumpVdGNR8/Nh/nzxhJjUBKtrWLGVSKQ1h+NegrPXSV/FiIsul7xn09Ng4FTJVmrc2/i/x4wJbyX4zTfQoQM8+qi0yygoMLOw7N8vZvDFi8WtViqCYkFpBefFi+GTTyT4NtKiKzNTYq2ff17E6I03mj0sWrQQU30o1p+ZM6FjRxg7VibJcNuwFBSIeLvrLpnYawqAP/VU85i4//3Pwa6erVvFVxOpVzBEcr9a03fjx8YPYtMCm/Hi5JPN74lx45xfFb88jRrB1VcH36h971444QRYtCg691NeniyCTzstceqXRZqwsxGjxSGdjWhK4Q5Y+TJs+hT2LxOh5M1FUpAM8WRCwAspTaD5YDj8NmhwdMhD+vxzSeWNxA3Vrp1YWIYPl4kgPV3ieZSSbKc9e6Tf1+efS/zOnj3BvX80Ln2tZWX/1ltiIdq7V6w0wVjrUlPl+FwuKTtx6aVw0UVm1p7K5OaKaAqnJ9pRR0kvxS5dREgGYy0rLpYg4zFj4IEHas8y1Fom5gYNzGK3mjSRbMZDwp0YjMkiChf3EUfAihW1b5eaKtaSeHeKqI6iIonbMmk31r49LFmSWO7EDRvkXIVipUpOhr/+VV5JSaF5KspTVCT3/8MPwwsvVJ9w4FAZEjQ1ZSNasVUH+HLFl/x1wm3897hLOS3Vi9r+A+RvguLdUrSwNJtRawgUicBKbQ4Nj5FWQU1PFatZhLj8csk2jPSK0OUSwVUakxUusbj0N2yQQNuvvhJrW06OCA6PR45Ha5mA0tPFetWxo1jKTjtNXJPBugSqYvJkKacQrgAeOlRE37BhZYKwqofQ/v3S7mjNGrH2vfee2UMays7Jn/5knj06YYKIcqdXLw+bOIut22+X5uombz1qFFxzTfAWllhx9NHw229m286aJW7vROLvf5c42lDv+datxWrcv3/oojkvT6ycd9xRexyfQ2VI0FixVUfZlruNG7+8kcnrJpPvzSfNk0afVn1469y3aFOvDQT8ULSzrKeaK0ksWO7oBtnn50uQ84YNzk7N103jUAcJ8OLhAFn48ODGTwZ5pBJGOtC2bbV2cL7mGokti5QAPuIIEV89e4qVIDVV4uN27RJxOWlSaD0lS6eju+6SlbCJ+3LoUPjgA/Mss4QlzmLr88/FRWXiIjzmGJg6NX41nGrj/vvhmWfM5qfLLhPhkUjXl9criQCrV4d3KQwYIO19evWSuSMzs/oaan6/WNJTUiS54KGHJBzBBIfKkKCxYquOobXmzQVvMnLiSAp9hXgDZf4Wj8tDijuFJwY8wW3H3YbbFZ8aXevXy4QbrGsvlmgiYDZyCrXcx3l5IpAqZ/85jdLD+PJLKUNh8mB3uSTYvmHD6I4t7sRZbO3cKaU5TNxvIA/6jh0jPoyIMGkSXHih2fXldLdodcyfD/36RcYLkJYmsZTnnisJBvXri6gCcRXu2iX37Oefi8gOtpSEQ2VI0ES7qKklhqzZvYYTRp/AHRPu4EDxgQpCC8AX8JHnzePByQ/S89WeLNm+JC7jbNtW3FeJNkHVVTIyxKSfKOfjxBPNHxKBgPR7c1Igs5MtuqHSuHGtBtQKvPCCc5MXjj/e/PoqLBSrcKIUOC3lmGOkQnskCjAXFEgC04gRIrgzMiQUwuOR79u0gVtvlThVJ7QBciJWbCUIvoCPp6c/TY//9WD2ltnkectmsfSkdHo260l6UtldlefNY9H2RRz32nE8MPkBinyxvwOOOUZit5xSbf1Q58gj5aGRCMG+DRrIpG7Kf/8bfsZkJKmrD5xTTzXf9t13ndu2JytLEnBMefFFZ51TUzH/yCNi0XZq7NyhhBVbCcD8nPl0e7kb/5j6Dwp8BQR02VMlzZPGP077B7/e9Csvn/kyaZ6yJ6lGU+Ar4PlfnqfLi12YsSHIBoYRYNAgMS87zaJyqE4+Z54pgbOJIICHDDF/WOfkiOByQlp5cbFYEesiAweax2Ht3i3i3ikipbL7c9Agc8/s/PkSKO8Ui6XpZ+rxiEWqQYPIJNtYQseKLQeT781n5MSR9HujHyt3raxgzQKxaH00/CPu6nsXAFcfdTUTr5hIdko2LlV2avN9+WzYt4GB7wzk+vHXs78otkVwTjtNgqazs52RMZaaCp06UXeKTgZ5HHfcAbfc4nzBdcMNwZV0eOIJ81Y/0cTng7/8Jd6jiA79+wfnTrv/fucIlMrjPvvs4AL477jDGcKxuFgycE1p2lTK0Tj9fq/rOODRZ6mKKeum0PGFjoz6dRQFvorBBW7lplFaI2ZeN5Nhhw+r8LeT257M3Bvn0jyzOcnuikVSCnwFjF00lvbPt2f8ioM6KUWV446T1jydOsXXjZWRAYMHyyq1zhSd3Hpwv8raeOYZuO8+Z03AlVfePXsGpyMPHBCRE882IHl58NxzkolbF2ndWuqtmZKTI+Ui4m1xLCiA11+v+LsBA4KzcC9ZAl98EX/BFYqY79ZNyrAkQghBXcWKLYexp2APl31yGWe9fxZbc7ceJLRS3Cl0atiJhX9eyFHNjqryPTo36sxvN/9Gj8N6VHArAhT6CtldsJtLP7mUYWOHsTU3+Ad1qHTuDAsXSpuZWD/klZJ9Pvus9P/Lyort/p3I3/4G//ynMyZgj0dcHeVRCu68M7hrZfRoWLkyftaUvXvh8cfjs+9YcfvtwYUF/P3v8U9eKCqS+KXyuN3m3RhKufNO82zMaJCXB//+d2hthAYPTpyYzbqILf3gEAKBAHdPupsXZr2Apvpz4lIuUoKok1XsL8ava37yXHnklYw5ZwyuGPr4vv5a3ET790d/1ZuRIQVCx4yRr1Ejzqn5oTJxovSlzM+PT8ZVeroUMR0/XloMlWf3bvldMNaErl1h9uzYxwnm5cFZZ4nLHKJwih1yfe3bJwV4gxFQF14oTdPjUXcrNxduvlmK61b+WNauFatPMMdy881iGY7HsWzeLOU0iopCP8XffSclHOJtbSwlMzO6PWxjiS394HDmbJ5D42ca8/ys52sUWgABHaDAV2D8qk1oAbyz8B2yn85m8trJkTqkWjnzTFmdPfSQPGyjsdrKzBQ31DvviNswqkIrgRkyRFwkPXrE3uKYni7xY7/8crDQAqmdNXhwcDpj6VK47rrYPkzy8sSiVSq06jL16kmgfDB8/DHMmxd7F1xxsfQtfe+9qv/eoYOIrWB49VW5X2JtrcvPlwKr4X6GAweK4KpfP76JQsnJ0m5r6tT4jSGWWLEVRwI6wK1f30rv13uzpzC+1T/zvHmc/s7pXDLuEnyB2Jg3UlIkgHbDBmk+fdhh4bv33G55gPfoIZPihg1w3nk2E6c2WrUSa9Bjj4lFKNyeaLWRkSEr9G++EStBTX0f77gjeCvVRx/BP/4Rm/itvDx5mP/f/0V/X07hlluCv1fPP1+Kz8bKehoIiGX07LNr3u6uu4KzUmktC5Rt22J3LHl54sKMlDA54QRZlBx5ZHziNjMypNbZsmVSIuhQICJuRKXUEOB5wA28rrV+utLf7wZuAHzADuA6rfX6mt6zrrsRl+1YxqWfXMrKXSsPissqT5IriYu6XUSnhp3C3ufGfRt5b9F7FPmrXxqlelJpnd2aD4Z/QM/mPcPeZzD4/dJY+oMPJNsmP1/EU35+9TE4ycmSsVZUJILhnHOkN2PP2A5dcIibJ1y2bZMH0GefyeceyQy/zEz5mJ58UtwxJs21AwFo3jy0ZtqjR4uLNFouxYICmD5dHr6V63zVVTciiMho0kRi1IKhUydJlKlXLyrDqsD+/dCnT8Uem1V9LAUFUrA1WEto+/ZiratfP6xh1kpurrQLuvfeir+PxCn2+WTB+9JLModGu1ad2y1z9n33iVfDCdnpkSSq7XqUUm5gJTAQ2ATMAS7VWi8tt82pwCytdb5S6s9Af631xTW9b10VW8X+Yv7+09/598//ptBXWK3b0KM8NExvyOSrJtP9sO4R2//aPWs59a1T2Zq7lWJ/9ZGeaZ40buh5A0+f/nSFYqk0i00/QQ2spAvz6MlqOrKY7mygLYWk4EKTRgFdWEk3ltCRNRzPzzQlyKexQU/BoHDQwzASrFsnCQWjR8ukGKqVqLRhdaNGMrFfcUXw4uexx6Q+WLCuG7dbWrP07Rv5FXxRkbSk6d276gd1XRZbACNHiggINmC8Xz9ZVEUzpi4/X0IVfvyx4u+r+1iuvVbCDYJNrOjdW8oqROtY8vPl+j3//IPHHslTXOp6X7w4elX/MzLk83rtNee2cQqXaIut44FHtdaDS36+H0Br/VQ12x8DvKi17lfT+9ZFsfXzxp+57JPL2J6/nXxv9cuoVE8qnRp24vsrv6dpZuRrQe0t3MvQ94aycNvCGseR5kmjXko93j3/XQZ0GCC/rGv+uEjOWA57GEaKvDx4bew2fprQiMnfewgERMQUFFRt9UpLk9VrYaFYpM4/XwJyTzwx9Mtn0ybJZg0lTiYtTVzK558fuYdibq402b300uobbtd1sbVhAxx+eGjn5JJLpBRDNERKXp60jnnrrYP/Vt3HMneu1BALRWicfz68/XbkjyU/X9xs/fpVHacV6VOstXT8eOgh2LhR9h/uPlwu8Tx06SJ18M44IzJjdSrRFlvDgSFa6xtKfr4S6KO1vq2a7V8Etmqt/1HF30YAIwDatGlz7Pr1NXoaE4YDRQe4Z9I9vLvw3RpdhiCFSvu368+4C8eRlhS9HF2v38t146/js2WfHVQstaoxndXlLF4+82Uatu8aE8tWTGjaNKQaVdXisIdhJAjoAA9NeYinpj9Fq+xW/HjVVAq3tmfVKrHqLF4s2Wler8TgtWwpiQidOkngcfPmkRvLZZfBuHGhuzXPO08yUlNTQ49J8/tFXNx9N4waVfO2dV1sgbhoP/kktFIbV10FL78s5yMSbX38fhEld955cE2tUqr7WLQWQbB6dWj7PvFEESpZWcEV4q2OvDyxtI0cWX1AfLROsdaSsPLEE/D993Jf5+aauxhdLgkXKC4W6+L998Oxx0ZnrE7DMWJLKXUFcBtwita6xpyKumLZ8vq9tHmuDVtzt5LmSatQ2b0yGs3Nx97MM4OeqXI7rSUOYffuql/bt8uDwO8XX3z5l9ZljUM9HpncPEmaRdlPMzfj77iUC6X444UqyZ5QMq58bz71U+uz7s511E+tH7kPyIEPkJCpS8eCLBKGfzyc6RumU+CVRUJWShafX/w5p7YPoklehNi0SR6Ipg2Eq6JFCxFs3bsHH+B94ICM4dxzpZZXbUT8FJu68CO9iKiBZcvkQRrqOencWUp+tG4dnmUoLw+2bJFg+PIxWpWp6ZyMHy+CPlQ3Wv36Yk077bTQy0IUF4tF6fLLJXmkJmIxhRQWSlD+55/DTz+JNbOoSARl6XSntZz/9HRpSH366XIe+vWLfqKN03CEG1EpdTrwX0Ro1RpcU1fEFsC4pePIK675DtZAanErmuYNYOVK8aH/9pvMmfv2ycoiP1+EUlKSiCWlZLXh84UZ3Nh6Bp6mq3G7ywIWdaAkONoHSR5ISYX0VDdL3r+cxo0j6EqsSwKlDh3Lmt1rOP2d08k5kEORvwiXcv3RkzPNk8aTA57kzj53omLsVv7LXySYN5y0e6XkYXDbbWKR8Purf9CXiojffpNmxB99ZG5Zi8Yp3lu4l/M+PI/Zm2eT783Hrdy4lIus5Cy+vvxr+rbqG/md1sLgwVJKINTjTUqSjNQbbhCXbzBB04GAnKO33hJrY21lEWoao9aSWLNggfn+q+Laa6VPp9bmosvrlbHPmwcXXWSmqXXT2MTPVmYv9dhEKwpJxUWAFIpozUayCaNYVqTjZ+NEtMWWBwmQHwBsRgLkL9NaLym3zTHAOMQCtsrkfeuS2KqM1rIinDNH6j/NmCErZaVkJeD1Vh1w27ChrOzbtZPvGzaUxW6zZpIZ1LChrK6yskSUKSUTV+nkFQjIS2v5mpcnQm73bti5U673rVth1y753ZYtMq6NG8smqUPBNRIydeRYJq2exPCPh5NbnPtHAkd5sQXiWj738HN545w3SPGYF9kNl337xAoSqSKIjRvDBReI6DrmGAniV0r289tvEpc1bpxYtIIl0qd46Y6lDHxnIDvzd/6R3OJW7j9q6aV50nh28LPc1OumyO64Fn77TdL4w7E4gpyDv/5VLCM+X81CJTdX5rgpU6Tkxk8/me2jtnMyc6bUoQq3Rlt2toimO+6QYPDS4ykvJAsLRWC53TB2rMQVzptnvg+NjZ91GlEVWyU7OAN4Din98IbW+gml1OPAXK31eKXU90APIKfkXzZorWusflIXxVZOjvjhX35Z6s3UlOHVpYtMPn37ykTWvr1MLgUF8n9JSSLMIhHrUJ5AQMRecbGs+JOS5LVli0wE558f2f3VFYECJPyxaK3518x/8ciPjxwUW+hSLpJcSRXKhqR50ujcqDMTL59I86wIBmfVwssvSxp8tLKmIkUkT/G4peO45vNrDoqvLC+2QETw8D8NZ9RZo2Iqgi+9VGK3IlEmpEEDCaC/4AKJ/2vaVD5LpWRBuHSpuLXGjq0+OaE6TM7JoEEi4iLV8qlTJylBcfTR8r3HA3v2yHy6aBFMmxZaCyArtpxH1MVWNKhLYmvbNvHBT58uQqm6FWBWlqTF33abWK8Cgfi0hIgpCS5QKpDAx1LoK+Sqz67i61VfH5ShmuZJ4+1z3+axqY+xcudKigNlTwaPy0N2SjYTLp9A75a9YzJWv19irlascNzHWIFIjM0f8PPX7//K/+b8j3xfxfOS6k6lflp9dubvrFCION2TTseGHZl4xURaZFVRlj8KbNki8VfRqNqvlCz4vN7wP1OT/1+zRooih2upizZWbDkP264njmzfLlV6p04Vk3F1N3DLltIC4plnZDWXnn4ICC2LI9i8fzPHjjqWr1Z+VaXQGjVsFMO7DefHq3+keVZzPK6ySqS+gI/dBbvpP6Y/YxaMicl43W5pqBuJrC8ns7tgN/3f6s//5h4stNI8abx7/rvMGzGPw9IPq5BQk+/LZ9nOZXR/uTvTN0yPyVhbtChrvRVptBbLT6yexR07ShZgPCqrB0XTyJcFiht16ViqwYqtKDNvnrg7ajOvDx4scVexbp5rObT5eePPdP9fd1bsXHGQ6zA9KZ0nBzzJFUddAUCj9EbMuG4GDdMaoiqtqgt8Bdz6za3c+vWtMWn3dOSRZa2F6iILty2k20vd/giEL0+aJ42XzniJC7peQPOs5sy4fgYNUhtUOCe+gI89hXsY9M4g/jv7v8TCg3HPPdLFoS6U4nvkEVkAO/pYtm4VBRqpVzBEcr9axyx7Np5YsRVl+vWTeKvaVuGffAILF0rgb7RbJjgK0xXNIbDyiTWvzXuNAW8PYG/h3oMalqcnpTOyz0hG9h1Z4fcts1sy/drp1Es5uN9KvjefMb+N4ZQ3T2F3QZDBNCFwzz1w1FFmLX8SifcXv8/xo49na97BXR5KM0GvPebaP37Xrn47pl83neyU7IPeq8BXwH3f38dln15GoS+6nZOTk6XFU12wOKakSFxYXTgWizOwMVsxoKBAekG9+ab8XF0mlcslNVr+/GcYOlTcjikpkg7tBLzeyHaJX7N7De8vfp83F7xJzoEc/NpPsb8YhUKjyUrOwhvwMrDDQK49+lqGdBoS1UKvYZMgMVtev5dbvrmFsYvGVtlBID0pnct7XM6rw16ttrTD/Jz5nPzmyeR6D87wSHYn0yitEZOunBTRVlNVsWWLxG/t2RPV3YREsKfYF/Bx97d3M3r+6GrPy70n3Msj/R+p8v8XbF3ASW+eRG7xweckzZNGu/rt+PaKb2ldr3VwAwuS554Tl6ITExiCPSd16VhqJUHmLydjA+QdgtcrDZZffFFSlVNT5XdVxXFlZEgqes+ekpXYvbsUjPN4JGVYaxFnHo8IsnCzEgMBiYvwesuycEozHrdskWDk6dPh8cfD28+6Pev4cMmHvDH/DTbu34jW+o8Mt1RPKlprMpIyKPIXVci8KhVeQzsN5Zqjr2Fwx8ExzbYyIgEmq+152znjvTNYsn0Jhf6DLR2p7lSGdBrC+8Pfr7EAL8C09dM46/2zqu2KkOZJ461z3+LCbhdGZOzVMXcunHJKdIKzwyGYU7wzfydnvX9WtS200pPSubHnjTw7+Nkaa5vN3DiTge8MrPI93MpNZnImX1zyBae0O8V8cEESCMj5mD07tCy7aBKKt2zQIJn7wqntFg2s2HIeVmw5kLw8cRvOny838uzZUp3X46m91lb5Olulr6ZNK9bbSkoSMeZ2lwmxQEmhUr9fvt+3T+prbd1aVl9rz56yivRbtlSssQWh3WMb9m3gw8Uf8saCN/h97+8AFVwaGUkZuJSL23rfxp197uSwjMOYvG4yj/30GL9u+ZVif3EFN1dWchb+gJ8zOp/BNUdfw8COA0l2O6BUscMnq4+WfMSl4y4lQM1+6tpEVnm01tU2Uy/lhNYn8NM1P1UIrI80X34prWOckkGWnm5uDXl/0ftcN/46inxFVX6WbuWmXf129GvdD5MEtJz9OUz5fcpBruHy73feEefx4fAPcQVTQTQIdu+WUgebNzsrLCKU2y43F3r1kixFX/TDEY2xYst5WLGVIGhdVkh05UrJTty4UQTRrl0ijvbvFxHmdpfV2VJK/tfnKytaWv5VOtmVtuJxucq+V6qiIPP7y+pspaZKOYrsbKl906QJfP212bHM2TyHF2a/wA/rfmBb7jaUUngDFbMEUtwppCWlcd4R53FK21OqFEyb9m/is+WfMXfLXLTW+HTF2S7VnUqAAD2b9+TsLmdzZ587SU+OUxqRgyerOyfeyQuzXojpPsvTKK0RC29eSIvs6JUiePFFKYoZbwtXerqEAvzrX7Vve9OXNzFqXi1NFqNEy6yWLLx5IQ3TG0bl/deskVY++/ZF5e1DItTbLidHkjJ27ozseEIlGDFvjIPnr0TBiq14Y9rTzBANHCCLPTRgNw3/eO2hAV6S8OM+6KVRVfxWXvXYR0N204A9f7xbffbirsoCUkNbhQVbF/D4T48zYdWEKl1U5VEokt3JuF1m/k+tNb6A7yDBVhXt6rXjpl43MbLvSFI9MYxwdWDvOl/AxwmjT2DOljkx2V9NuJWbSVdO4rT2p0VtH6+9Jo2I42XhSk+XlkKPPlrzsyu/OJ/jXjuOpTuXxmxsVZHkSmLK1VM4sc2JUXn/n3+WivDxFsAQvkBZvFgSng4ciK/WSE+XHo6vvRbhN7ZiK2ys2Io3js4fDoFy18y23G2MWzqO1+e/zsKtC41cVEmupLB66vkDfiPRleZJ48KuF3L10VdzctuTo+bGWrdnHV+u/JKxi8Yyf+t8UtwpHCiumAWRlZxFkb+Ins17cnmPyxnWZRjt6reLynhK2V+0nwFvDWBuTu33USTcsP6Av1rXVSlu5WbiFRM5vcPpYe+vOj77TIoDx/oBn5YmrWNuu63m7bbnbafzfzuzv2h/jdspFEnu8DJSTM4JwIcXfMhF3S8Ka1/VMXmy9KKMp+BKT5eM7yFDwnufZcvgpJNg797IVZgPhlKr6TPPROGxYsVW2FixFW8ibNmKK02bsmPNIsYtHcfo+aNZvH0xbuU+qOhiKWmeNJRSXHv0tdzb717a1GsTkWH4A36+XPklj/74KKt2r6LAW1BlvItLuchIkmJMw7sO5+qjrubENicaW9Sq2/eszbP4dNmnfLz0Y7bnbUeh/ggUT3Yn41IuujXphkazdMdSAjrwRxp/micNjaZpRlMu7HYh5x1xHn1a9glrTJVZuWslA98ZyNYDWytUfK9MvZR6/HTNT7St3zbsfWqtuX3C7Xy67NNqg+ZBjv+xUx/jL8f/JWqNrKdNg3PPFUtGbc2Jw8XjEaH19tuyz5qYu2UuQ98byp6CPTXGVDXJaMKdfe4kxR1+Esi3a75lyropNS5QUt2pPHrqo9x7wr1ROSezZkmgeTysQpmZMHGiWKUiwYYN8l7btkWmPZEp6enw8MPiKo8KVmyFjRVbdY1gJ8MIneP1e9dz0biLmLN5Tq1B0ZnJmaS4U7i3373cdOxN1EutBwEf+PPBlw++vJLv8yr9XPp9AVASgEYAdLnvAXCBkuCz2bs38/iyKUzethq/1nhrWcnXS8lmZN+RPNr/MeNj31+0n0lrJvHh4g+ZuGYiCkW+N/+gwH23y83Nx97Mzb1u/kPA/L73d16Z+wqvzn0Vv/ZXsHp5lOePchZDOg3h4m4XM7DjwCprJpkyYdUELhp3EXnFeTWep6zkLGZePzOi5RkCOsD5H57PpDWTahRc6UnpnNn5TN4+7+2ouXp37xYL19Sp0Uvdz8iQjg+ffCINsmvijflvcPs3t1e7MAFpf9SmXhvm3DiHhmmRiaXSWnP3t3czat6oKrMUS8lIyuD0Dqfz3vnvkZEc+WqxS5ZIaZt9+6IvgEFiWrOzxbJ25JGRfe8dO2D4cPj11+iXhXC7Rcy//rokgUQNK7bCxoqtukacxNau/F08P+v5Su8N+HLBux98+6F4H3j38iePZniWIsm7E4r2gO8AaD+4PKA84EqG5IaQ0rjka0NIrg+uFERIqRIx5an41ZUsOw0UiwAL+OQrfn7P2837m5dT4PeWCLsCefkKRMj5CykVbt2SNRdflQNpzao93p9+/4nnZz3PpDWTyPPm/VH/qyqSXEmkJaWR7E4+qLp62UelKfYXk+/Nr7bKeuk+MpIyGNRxEHf1vYuT2p5U26mR99eap6Y/xT+m/qNGoQMidr6/8nuOb3280XsHQ7G/mIFvD2T2ltk1FtJM86TRoUEHvr3iW1pmt4z4OEAu/TfflAKoxcWRc2WlpspD8G9/kxitmkqvFPuLueXrW3h/8fs1ih2XctE8szlzR8ylWWb112UoaK25bvx1fLTkoxrHkOpJpWVWSyZdOYkODTpEdAwg7rerr4bvv4+uWzE9XcpPvPeeJPdEA62lDteDD0YvRjA9Hbp0Edd4u3bR2ccfWLEVNlZs1TXiJLYAETE7psGOmbD1e9j9KwS84E4W4RUokhdAShNo3Aca9oZ6XSGzHaS1gKR64E4t2dZbIpYUKPcf1ippbqDKRFepAKtwXOUtXxrQJe+lRdiVWp2UW0SayyOWs+LdkL8ZmpxQ4e38AT+/bPqFT5d9yktzXvqj/ldNuJU75FgwX8BnFE+T4k7h9t63c96fqnc3FngLuOzTy5i0ZlKND1MQkfPpRZ8ypHOYASw1kFecR783+rFs57KDqqCXx+PykJWcxdeXfR0V4VdKfr48GJ96Si6VUK0R6eny/7feCg88UPuDfGvuVs547wyW71xeowBWKBqlN2LujXMj4tKtioAOMPyj4Xy7+tsarWulrvePL/yYwZ0GR3wcWsOYMXD77ZJBHUkrV3KyuHWffRZuvDE24bILF8L110s8V6SsXKVi/oEH4N57Y9QlwYqtsLFiq64RL7G1YyZMu6DEzVcIupqAhcP6Q9/RIqz8ReDJEKHjQPYX7efb1d/ywZIPmLRmUpXuwVI8Lg8KxeGND2f4n4bTu2XvsOOsSuO/xi0dx8pdK9HoKq1epe5GjWZIxyFc3P1iBnUcRHZKNhv2bWDQO4NYv299rS1Z0jxpjD5nNJd2vzSscZuwp2APx446lo37N9baLzHNk8YLQ1/ghp43RHVM+/fDuHEwahQsWCAP57y86mtBKSWuQp8POneWB/gll0gZlNr4ZdMvnDn2TPYX7a/1+Oul1GP2jbPp0qhL8AcVBF6/lzPGnsGMDTNqtX6medK4/6T7eeikh6ISx7Vjh2RtvvmmfL7hxD95PPK6+mrpmRnr7l5ai7Xujjtg0yYR96HUF8vIkP8zFfMRxYqtsIm62FJKDQGeB9zA61rrpyv9PQV4GzgW2AVcrLX+vab3tGKrBuIlthY+AkueKLMYVUef16HDtSUWqQjzaTMoDD/Z4Id8+OtO+LXYRUDXPCtmJWXgcbm5uceF3NztHNqk1xernL/EiucvLHFr+svFlZUc+x/vXXIOSi1w5X+HAhTr83fxyqppvLJqGn4d4EAtwsmlXPRq3JmtBXvYmLuD7OQMeS9V+p7lv4o765mBz3Br71tr/XwiRc6BHHqO6km+N79a9ypAkb+IQl8hhS82IGVnbHrvbKcJP3AqC+nBLPqyis4UkopGkUwRHVhLH2ZzNAs4iWm0YaPxe0/sBMOudOGv5doCSPdkMvqE6bRNPYrCQrH0lH4t/31pQc3S2nql9fXK3941icbSr15dyIsHTmWjfx5+ai/x3r/h5TxwxLukpIjFpfRraqqIg8xM+V2oemzjRnjySXj/fRl/bq7ZlKWU7BsklunBB2PgaqsFrWHGDBGQH38sY/T5qneZut3yGRYVQbduMGKExII1ahSb8Rb7i9lXuI/9Rfvp2KiT8f+t3/M72SnZZKdkRzSxJ9GJqthSSrmBlcBAYBMwB7hUa7203Da3AEdqrW9WSl0CnKe1rjHUz4qtGoiX2Ap44fexsPxZ2L9M4qt0SWxUhfG5oNW50OJMOOxkyGgrwkT7StyEyeJ2DEWMRUhsTc2Hj3KBrC4llroSa13AW05MKlR6CwZ1HMTQ9gPwpDYQF6gnU6x17rQS92RSSSxaUonL0iPu1vKiSsPBIktTlSjyaT9fr5rA5HU/lAjBkr/pAGXJAiXfB3ycO/UAKZ98UesxZxfBUXFIit2ZDssam2174q4MlBMb0QXJ5IatebDnkeQe3ov9+yE3Dwryoagk1NDlkleTJnB1n3Po0/YYsrMloDszUx7AaWnSCSIpqcxyA2VdIHR5/V7F18qUF1y5xbm8Ov9FCn2FfxQ6drlE7JS+SvdzjsrhxKLxqPLxigeJSA1d74N2l1Mm+KHqkvdVz18+H/w4NYl3xqYwa04S6353k+QBj0fLv2jw+RVeL7Rr66d3Lx9XXlbIaf29NbjZKn0YT78spq8Y4MPNHI5jET2YSy/mcwwHyMKPm2SKaMd6+vILR7KQ45hDC3KM31sD27auZp/bx/6i/ewrEsFUKpx+/P1Hxq8cX+tCsjw5z0Azg1tvawY0/3/Gb4vH5eGuPiNpW68N2anZ1EupT3ZqPeql1CM7JZtG6Y2on1rf/A0dTLTF1vHAo1rrwSU/3w+gtX6q3Dbflmzzs1LKA2wFmugadm7FVg3EM2arlIAX9i6GXb/Azl/gwBrI3wRF20tiuFJFeKABFyQ3howWkN5KguJTGoubMa25xHYlN4LkeiJelKtcnFbpy13OalQSx/WH6NAVBYgOwMdBZvMpj7yvLi+SgIEzDortQvsh4C+3XUm8WTArvAiJxqA4axVkGa5e41Sg9egXWrMhbzcHvIX4gnhQVIfpAwSCf4hURZLLQ3ZyOs1XjWTx6LKHuscdwO3R+Hzg95ddJ3/9Kzz9dMX38PtFeJTetqViyO2Wr9GktAtFeQuZZ+lDeJY/Ufs/H/82tL8yYmMJBOD332Hnz69RvPItkj3FNMrcRfsm63C5QpvTTtrTkek710RsjBA9kVITyWBgkzwYhcLj8pDkTiLJlYTH5cGlXLhdbvmq3LiVu0q3cUAH8Gu/fA34//jZ55di096At1Z3eXXoR+qGWzLaYms4MERrfUPJz1cCfbTWt5XbZnHJNptKfl5Tsk21zQ+s2KoBJ4itmvDmiugqLPcq2gHeA5K16N0n3/sOlJR+yCvLHPQXllmYKA16L3W9KRE0WldcWStxw/2RrehKgeI9UEuBVVKawMBpInoKt0HBNvDuLRtj8T7JtFSeEiuWW6xXriTZhzulLFPS5Sn5XRp40suJzdIA/0pfZ90g+4gUaS3lMymxAAjlPz+gzyhoeBwVRGL5z6/y70u/VxXe9GCWPgPL/x2BgwC1quz7JCDTVfJSkKzAB2S5FA1aDaFNdhvqp9X/w52RnZJNvZR6ZKVkkZWcFVJBUO+Kl9i/8nX2a9jv54+v+zTs9cEGH+wJQG5AxlOoIVfLz7kBGR9AEzdsPOZ0th/5HVu3im4t7T+amytZefv3y8vrFUtWaWP55GR5paTIz2532e9LXXdpaWXiq3L7rdKf3e6y9l3l23hV/l1RkWTTldYk83pFdJW+XmvQmCL3rgic3TLiIVAAzkiHaYWQoiC13CtFgQfY7pfzmtLwSLKSs8hMyiQ9OZ30pHQykzJJS0ojxZNCijuFZHfyH68kd1KVPUW11ngDXor9xfLaMonibT9RrCE/AHnlvuYF4EBArqkiIFtBkoIiLb8r1GXfK2BPR1dZUtFBVvQS0lvCWSsrhS+UoFTFX1V4rNRyzwMs+jss+YfJx147TU6BgT9G5r3iSMKILaXUCGAEQJs2bY5dv359WGOrswRTJDWGrWFigtaxSTGKBfZYnEkdOZZIHUYwz4hoFamtjUQY4x/UkeurzhxHBKlJbEUiRWwzUL6cX6uS31W1zaYSN2I9JFC+AlrrUcAoEMtWBMZWN6lL4ilY6tLNbY/FmdSRY4nUYcRdnBiQCGP8g0Qaa03UleOIEZGIAJgDdFZKtVdKJQOXAOMrbTMeuLrk++HAlJritSwWi8VisVjqCmFbtrTWPqXUbcC3SOmHN7TWS5RSjwNztdbjgdHAO0qp1cBuRJBZLBaLxWKx1HkiUmlSa/0N8E2l3z1c7vtC4MJI7MtisVgsFoslkYhyIrHFYrFYLBbLoY0VWxaLxWKxWCxRxIoti8VisVgslihixZbFYrFYLBZLFLFiy2KxWCwWiyWKWLFlsVgsFovFEkWs2LJYLBaLxWKJIlZsWSwWi8VisUQRK7YsFovFYrFYoogVWxaLxWKxWCxRxIoti8VisVgslihixZbFYrFYLBZLFLFiy2KxWCwWiyWKWLFlsVgsFovFEkXCEltKqYZKqe+UUqtKvjaoYpujlVI/K6WWKKUWKqUuDmefFovFYrFYLIlEuJat+4DJWuvOwOSSnyuTD1ylte4GDAGeU0rVD3O/FovFYrFYLAlBuGLrHOCtku/fAs6tvIHWeqXWelXJ91uA7UCTMPdrsVgsFovFkhCEK7aaaq1zSr7fCjStaWOlVG8gGVgT5n4tFovFYrFYEgJPbRsopb4HmlXxpwfL/6C11kopXcP7NAfeAa7WWgeq2WYEMKLkx1yl1IraxpcgNAZ2xnsQEcIei/OoK8cB9licSl05lrpyHGCPxYm0re4PSutq9VGtlIih/lrrnBIx9aPW+vAqtssGfgSe1FqPC3mHCYpSaq7Wule8xxEJ7LE4j7pyHGCPxanUlWOpK8cB9lgSjXDdiOOBq0u+vxr4ovIGSqlk4DPg7UNRaFksFovFYjm0CVdsPQ0MVEqtAk4v+RmlVC+l1Osl21wEnAxco5RaUPI6Osz9WiwWi8VisSQEtcZs1YTWehcwoIrfzwVuKPn+XeDdcPZTBxgV7wFEEHsszqOuHAfYY3EqdeVY6spxgD2WhCKsmC2LxWKxWCwWS83Ydj0Wi8VisVgsUcSKrSijlBqilFqhlFqtlKqqwn5CoJR6Qym1XSm1ON5jCQelVGul1A9KqaUlLaTujPeYQkUplaqUmq2U+q3kWB6L95jCQSnlVkrNV0p9Fe+xhINS6nel1KKS+NS58R5POCil6iulximlliullimljo/3mEJBKXV4uZjhBUqp/UqpkfEeVygope4qud8XK6XeV0qlxntMoaKUurPkOJYk6vkwxboRo4hSyg2sBAYCm4A5wKVa66VxHVgIKKVOBnKRrNLu8R5PqJSUKGmutZ6nlMoCfgXOTdBzooAMrXWuUioJmA7cqbX+Jc5DCwml1N1ALyBbaz0s3uMJFaXU70AvrXXC1w1SSr0FTNNav16SWZ6utd4b52GFRcm8vBnoo7VeH+/xBINSqiVyn3fVWhcopT4CvtFaj4nvyIJHKdUd+ADoDRQDE4Gbtdar4zqwKGEtW9GlN7Baa71Wa12MXFjnxHlMIaG1ngrsjvc4wkVrnaO1nlfy/QFgGdAyvqMKDS3klvyYVPJKyNWTUqoVcCbwem3bWmKDUqoekkk+GkBrXZzoQquEAcCaRBNa5fAAaUopD5AObInzeELlT8AsrXW+1toH/AScH+cxRQ0rtqJLS2BjuZ83kaAP9rqIUqodcAwwK85DCZkS19sCpOfod1rrRD2W54B7gSq7SyQYGpiklPq1pCtGotIe2AG8WeLefV0plRHvQUWAS4D34z2IUNBabwb+BWwAcoB9WutJ8R1VyCwGTlJKNVJKpQNnAK3jPKaoYcWW5ZBEKZUJfAKM1Frvj/d4QkVr7ddaHw20AnqXmOYTCqXUMGC71vrXeI8lQpyote4JDAVuLXHBJyIeoCfwP631MUAekLBxp/BHke2zgY/jPZZQUEo1QLwj7YEWQIZS6or4jio0tNbLgH8CkxAX4gLAH88xRRMrtqLLZioq9VYlv7PEkZL4pk+A97TWn8Z7PJGgxL3zAzAkzkMJhX7A2SWxTh8ApymlErY2X4n1Aa31dqR7Ru/4jihkNgGbyllLxyHiK5EZCszTWm+L90BC5HRgndZ6h9baC3wKnBDnMYWM1nq01vpYrfXJwB4kxrlOYsVWdJkDdFZKtS9ZUV2CtDiyxImSoPLRwDKt9X/iPZ5wUEo1UUrVL/k+DUnEWB7XQYWA1vp+rXUrrXU75B6ZorVOyNW6UiqjJPGCEpfbIMRdknBorbcCG5VSpf1uBwAJl0hSiUtJUBdiCRuAvkqp9JK5bAASd5qQKKUOK/naBonXGhvfEUWPsCrIW2pGa+1TSt0GfAu4gTe01kviPKyQUEq9D/QHGiulNgGPaK1Hx3dUIdEPuBJYVBLrBPCA1vqb+A0pZJoDb5VkV7mAj7TWCV02oQ7QFPhMnoN4gLFa64nxHVJY3A68V7JYXAtcG+fxhEyJ+B0I3BTvsYSK1nqWUmocMA/wAfNJ7OrrnyilGgFe4NY6koBRJbb0g8VisVgsFksUsW5Ei8VisVgslihixZbFYrFYLBZLFLFiy2KxWCwWiyWKWLFlsVgsFovFEkWs2LJYLBaLxWKJIlZsWSwWi8VisUQRK7YsFovFYrFYoogVWxaLxWKxWCxR5P8D4tfDZP5FG/4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_4\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_7\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_11\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_12\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uvVetB/Wt5ElJ6rovvwzPPhtdA9CinEXUun3HzG4o2hA9QTRhRQfIaxS1B2Dnmy2zanU/GybvhRF/g6SOYMtsWIfJZFa7cnMK9JwCp/0CR/+3xYUrn3++ZZulKVNUH8NhwyAtzb+iBWAyQWYmdO4M774Ljz4afFJbXGMvqqudZkD3M1XbHU3o5H7Z/P6P7UfDORuVyzAYRas+JpNSlMc9Dcd+BOYolOqIEW++qUq/tIRrr1UK1AsvwJAhauOVlXXoSEmB7Gz44x9h50544w1l8Y4Gy/KW+T1X46yhqCqM1VM1UUMrWxrF5mda9rphf4HjPlJ974KxhpgsalyvyXDaIqWcNQOHA/7xj+bFaplM6jVvv62ULFMzP/VpaXDrrao6dWaix4sXLfDhhmpEt4nBtd3RBGbz881rj5M1Ck79BZK7Bv47GWFNhx7nwCk/KrdwK8Pthr//vfmxWiaTslQ9+6xyGWYE8JSnpKhj6lS1Uesc4W5STreTPWV7/J5PtiTrptQJSliULSHERCHEZiHENiHEX3yc7y2EmCuEWCmEWCOECGMXqTggkYL3fMnqcTW//o/JBse+B8PvbVmhS0saZI2As9ZCu+C7t37/ffOtWh9+CDffrJSmlpKerhpOr1ihfk5YHAfU39sfwqyy3TShYy+Gol+CH5/UCU7+Xn2fRBiWZkuKips84qXQrxVnfPkllJc37zVpaTB7NlxzTfPXgtRUZQFbuza8zaYbs3n/ZpIt/pXsGleNbtuToIT8jRZCmIEXgDOBYcClQohhjYb9FfhISjkGuAR4MdR544qCgkNR1qEcwRDqHL6CIgt/bn7m+PGfQI/z1A66pZiTIKkznL4w6NYsr7+uYraC5fbbYeLE8ChIKSnQvbtyKyYsrmrjuDxzaqvunRdVdn+glNdgMNlUTTNrVngULS+WNOh7KQz6Q/iuGQe89FLzukZkZMDy5XDccS1fC2w26NgRFi1SG69IsKpgFdJgMXa4Hfy659fITK6JKOGwL08AtkkpdwAIIT4AzgPqR/JJwOuAaQfkhWFeTbjY8Ta4mqHBDLoJOp8UntYtJhOQCr/7EmaPBY//NibV1WpnGixHHQWPPBKaRasxKSlw8skqluOpp8J33ahhshgHn0knEMbgtE+7gj2U6GITTIv/UiQ+2fpi8OUejnwdMgfXBbqHGUsajHkCyjaqjVWCU1UF8+Y17zVvvQV9+oQed2UyKcXtiy+UpevAgdCu15ileUupdBhrkSsLWp5cpIkd4dhC9QDq5zXn1D1XnweAy4UQOcA3wK2+LiSEmC6EWCaEWFYUzhbqGv94XJDzOUGbtrJGwZjHQ7NoNcZkgfR+MPpxw2Fff20c1F6fTp3gq6/Cq2h5SU9X8SKR2t1GFHOqsbXFbQ9vnbWQFC2AFkZAx5ryzYEr9HvpMQl6To5sBqglFY77+FDAfAKHPnz7rbIyBcs118Bpp4U3wD09XbUKCzeL9jZtNm1qZOnMq8jD7or/otOahkQrQP5S4E0pZU/gLOAdIZrayqWUr0opx0spx3fq1ClKorVxCucHP9aSDid+FVrgrt9rp8HA61XZAT+89lrwLsRPPgkc/BoKqakwa1YCBsxnDiGg5Wq/bngbMtvfCE5pNSXBhFfCu3nxhyVVZQxDeEIfgiXMoQ9vvx38OjBkiKqXF+44y+RkOPpolTgTLqSUbCxuWJTWIixNlK0US4ouAZGAhEPZygV61Xvcs+65+lwHfAQgpVwEJAPNS0PTRIad7wRfA2jCq2DrEN6YkvpY0uCY93xmKJaVBe86OPNMGDu2ebvflpCSAn/9a2TnCDvZ4wxdtYCydCZAu6a4RXqUshVMGZXD/hS94rGWVBhyG6T1ic58EcBuV1nBwZCUpALpI1WyIT1dZTkffnh4rpdfmY+rUfJKijUFZ6Pvq0d6dJB8AhKOu+ZSYJAQop8QwoYKgJ/VaMwe4BQAIcRhKGVL+wljjccNOZ8SlKsmayT0PE9lOEUScxKMfLDJ0198EZwL0WyGF1+MTsZgaqrKcuzZM/JzhQ2TBTpMMB6T+1XLi9tqVA9ETxDKqrUdDLsnugVkTRYY9Wj05gszc+YEH0pw220qocUcwSomyckqHiwcrC5Yjc1HzF6ateHno8pZxZLcJeGZVBM1Qla2pJQu4BbgO2AjKutwvRDiISHEpLphfwJ+L4RYDbwPXC2lruEfc4p+Cd4dMPZp5fKINOZk6H81pDYsP/Dqq8FlH11yCXToEBnRfGGxqPithKLnJDAZbPcrtkDe14EtYBrf5H8fXBmVw+6KnJXYHyYb9Dw/unOGkXfeCc6FmJGhrM6RiNmsj8kEAweqjOdQWZG/gmpHw4SKGlcNfbKaWiIX5ywOfUJNVAnLN11K+Y2UcrCUcoCU8pG65+6TUs6q+3mDlPJYKeUoKeVoKeX34ZhXEyLBuhA7HAEdj4xeoUuTRfWDq6OiApYGEUYkBMycGdlYrcbYbHDxxdGbLyz0nBy4HP7KPxvX4wqW5AQKxA4X+d8Hjtcy2VQ/w3Bk9DaXBG0u7nSqJJlguPNOtRGKBunp8EwLa0LXZ8HeBbgalV1Js6YxrGPjSkqwZf8WtL0isdAV5NsqUsLe/xGUC3HkQ8aWkHBjskHviw4+XLo0uLiLKVNUq41o09yK9DEnYwD0ulD1sPRH1S7YMBOczShm5IspBTBN+j9aGx43lK4JPK77WbFz1bawTVasmTs3OAUqNVWVZkmNoh7brRucfnpo11hd0LQyfJ92fTi8y+GYG2UQCyHYXbY7tAk1USXRbhOacFG1Kzg3UcZg6HxCDNq3HLK8LF4cXLPZP/4xulYtL0lR8K6GndEzA/9N1z0MeV81v4lyW6ZsndosBGLQTaqHqCZoZs0KzoV47bXR72OakQEPNg01DZoqRxWF1U17aA7tOJRBHQaRam2oOVpMFp/KmSZ+0cpWW2X/0uCqWw++OTY74XrlJX74QbkQjOjYEcaPj7BMrYnUHjDwhsAWy0VXQulaVXleE5iiBYEr8NuyofPx0ZGnFfFLECGmQsC998ampdbIkS1/7brCdU0UKrMwM7LLSAa0H4BopD1WOapYkb+i5RNqoo5WttoqxYuCa5Db59KYuh2kVP0IA3HBBYEVMk0jRt4HtiwM6255nPDDiZD3beguxbZAMMHxfaaGJx6uDeF2w+bNgcdNmBBd92F9Qik1s6pgVZOyD6nWVAZlD2JA9gBqnA0/U27p5te9um1PIqGVrbZK4TwCVo3PHh+ZAqbNICcHamsDj5s+PcEbRMcCW3s4ZW7g0gOeWvj1Qlh1t3IpBlM/qq1SvDDwmMG3RKeIaStiy5bgSj5MmxY7ZSvYkhS+WJyzmGpnQ+uxEIKB2QPJSs4iydI0VmHtvrUtn1ATdbSy1RaRHtUnLRB9p8Vc2frtt8A7xu7dYVjThB1NMLQbCid8AeYg6qdtfRG+GgZ7PgFXjbbONKYmH5zlxmNs2ZA+IDrytCKWLQtu3CWXRC8LMZwsyWtaN6vGWcOAbPVZ6ZnRtJhfqb2UMntZxGXThAetbLVFyjcHl/7d97KYZy79+mvgoNiLLwaPrsHZcrqeDOOePdQ3z4jqPbDwMvh2NOR+oSrNO1u44Le2P1rxosDB8R2PCq4Gl6YBCxYErrM3ZkzsrFqh4JEeth/Y3uR5m9lGVnIWAIM7Dm5yPtWaypp9QWS+auICrWy1RQ4EsU1sPzY4a0eEmTs3cFDsxRcn5iIbVwy8Ho54KTiFC1Th018uhE/aw6KrlLXLUQpuh/rfWaF+lm51uB3K6uMoU27I2v2wJwKdfGNJ6frAmZudTlA9RjXNYsGCwGOmTo18i65IsP3Adiw+Nr+9Mg91wRvVZRSiUWxlrbtWt+1JIBLQ4KoJmaJfAwfH97k45i5Elws2bTIeIwSMGhUdeVo9/a+EjEEw7xz1+QgmNstth5wv1AFgzVTNrjMPg+TOdV0HpLpWTT6Ub1KW1WCSMxKNim0ErFvX7fSELSoaK1wuFbMViMsvT0xla1XBqibNpgEGdzhkzRqUPYg0axqV9ZJU7C47i3IWceuRYeyGrYkY+lvfFin8JfCYrqfG3IW4c6daPB0G9/xBg1qfNyqmdDoaztkMCy6B/YubX2PLWa7KiuwPouR/a6Nqh/F5YYF2OriwuWzaFHgd6N8f2rePnkzhZHn+ciodDTcfAsHhXQ51uB6YPRCzj7p4S/Pa4PcsQdFuxLaGxwWV2wIMEnFxU8jNDdxE9sgjtbIVdpI7wslz4Oh3IK1PdBslJzLVOcbn249SlkBNswgmOH78+MQt/bJg7wJko8zwNGtaA8vWwOyB2F1NPzu7S3c3KRmhiU+0stXWsBeACGCxyhwcF5lmubmqvo4RJ5wAmboQd/gRAnqdD+duhyNegeSusVG6Eqm3or3I+Hz2EdqF2AKWLAkcHH/00Ylb+mV94fomz5lN5oOZiAAdUzv6fG2SJYnNxUEUINPEHK1stTWq8wK7B7PHE7AGVxTIywtcY+uEE6IjS5vFZIZ+l8HkPTD2GWg/TmXcmSOgeFnS1bU7Hg3HvAuXuFRvxUTAVQOeAFar9H5xkXSSaGwLZIhHrQOBrODxyP7q/VQ5m7rq7S47A7MHHnwshKBHZo8m4wSC1ft0255EQCtbbQ17PgEVqU7HxkXG1M6dxq6BpCTo2zdq4rRtTFYYeB2cuQwm58L4Z6DTcSoA3prZAquXUJ8xa6a6RpdT4IgXYco+OH2hqvEW9X6cIVCTG1iRyhgEPgKhNcbk5hqfFwIOOyw6soSb1ftWk2zxnYjUKbVTg8eDsgc1GVPpqNRxWwlCWGzaQoiJwDOAGXhNSvkPH2MuBh5A3elXSymnhWNuTTOpyQ/cgLrT8XFxU9gRIN64b1+w2xMzAymhSe4IA65Th7OC3L3f8a/vpnP3iPPpXLsXyrdAbbEq+XAwO8+klKekzpA5FLLHQrsR6ues4YkfF1adE7jXaFqf6MjSyigK4J0dODBwuEG8sjJ/pc9YrO4Z3Zv0QxzZZSTfb/++QXyXRLJwTxBdCzQxJ2RlSwhhBl4ATgNygKVCiFlSyg31xgwC7gGOlVKWCCE6hzqvpoVU5xgXVRQmyGhaQC8W5ASIN+7RQwfHxxqnKZkzZj/A+qISPluxgA03b2i4U/e4AJFYVqqWUJ1Tp1wakNItOrK0IqSEkhLjMePHJ+46sGDvAhzupmmWgzo0tWIN7TCUFGtKk7Y+G4s3IqVsopxp4otwmC8mANuklDuklA7gA+C8RmN+D7wgpSwBkFIWhmFeTUuo3Gl8PqkzyNgHxwPs22d8vnv3xIzTaE3c8+M97CxRn6n8ynxu+/a2hgNMltavaEHgTQxAku8gZ41/ysrAFOAuNX584gbHr8xf6fP5+mUfvAzMHojVR7yt0+OkoDJBYhvbMOFQtnoAe+s9zql7rj6DgcFCiAVCiMV1bkdNLKjebXw+pVtgN2MUCGZH2707JMe27mqb5qedP/Hi0hepdqmdtt1l59217zJr86wYSxYDnBXGli1bdmDLl6YJ+fkqNtOIwYMDK2TxSK2rlpyKpub7VGsqQzoMafL8gOwBPq1gSeYkHSSfAETrI2oBBgEnApcC/xZCZDUeJISYLoRYJoRYVhTIUa9pGTX5xudTuhEPmYj79wduKNu/P1hjW3e1zVJcXcyFH11IjauhNafaWc0Vn11BbnmAqObWRqBq+8mdgqvIr2lAfn5gRapH0yS9hGBj8UZSLE2TKqwma4NMRC/dM7r7rKlV7az2ayHTxA/hULZygV71Hvese64+OcAsKaVTSrkT2IJSvhogpXxVSjleSjm+U6dOjU9rwkFtsfH5lO4xrxwPapENFPjer190ZNE0RErJJZ9c0qTqtZdqZzVTPpqC29OGLDmByj6YbIGbfGqakJ8fOPi9SwKVYqvPqoJVeGTTYDOH2+FT2TIJE13TuzZ53ulxsmBvEM0jNTElHMrWUmCQEKKfEMIGXAI09iN8jrJqIYToiHIrBsg104QdjwucAaoDpnSPeU9EUFmGgXa0PXtGRxZNQ5797VkW5SzC6cfd7PK4WF+4nkd/eTTKksWQQFYrYSUeLMaJRn5+4Fp72dnRkSXcLMld4rPGltPjpHtGd5+v6d++v8/ndUPq+CdkZUtK6QJuAb4DNgIfSSnXCyEeEkJMqhv2HbBfCLEBmAvcJaXcH+rcmmZSWwTmAAEQ6f0Dp7BHAVcQMfqJ2gstkVmzbw33/HhPg4yoZEsyR/c8mlRr6sHnqpxVzPx1Jr/l/BYLMaOPj1iaBujK8S1i927jWnupqYmbJLM4Z7HP57umd/XZmBpU+Qdf7Kva1yRLURNfhGUFkFJ+A3zT6Ln76v0sgTvqDk2scNuDqAXUOzqyBCCYPmeBYro04aXaWc2k9yc1idOymqx8fNHHjHxpZIMFv8ZVw+QPJrPplk20S24XbXGjS8C0+zBbtT7tCvYA6bqGmGBa/Lt59+41Pt+tm7KCJ1rsppSSzft9t9kZ0H6Az+cBhnUcRoolpcl3MNWayrrCdUzoMSGscmrCRwLmcGhaTDD9DpPiI1YuGMtWou5oE5Wbvr6JfVUNb/Bp1jTu+9199MjswcxTZ5JmbVictLS2lGu+uAbZ2uOVTAEsxh4nEMY6SCEpWnCo2Gx8Yw8QCte1a2IWNN1bvtev/j2i8wi/rxuQPQCbuWkwq8vj0q7EOEcrW20JWVdg0og4CI6H4CxbWtmKHv/b8D8+3vBxk2rXqdZUbp1wKwDXjbmO7JSGATR2l53vt3/PW6vfipqsMSGQez4OyqkkIo4A3tmOHYMwKsYhqwpWYTE3Nc0nW5IZ1mmY39cNzB7oNyOxzbjsExStbLUlpCvwyiTiwzeXiAtoa2Vv2V6u+eKaJjEhadY0/nXGv0iyKEXDYrLw3JnPNbFuVTmruOWbW9h2IIiOwolKIMuWuyYuWmAlGoEs3IkaSrAyfyVVjqbB8UnmJJ+ZiF56t+tNrct3xsBvuVrZimf0t78tEczuOk4CeYNZRBPRfZBouD1uzv/wfJ/Bt90zujNtZMMWp5OGTPJ5s6hx1TDp/Uk+izK2CgJl8NbkBW5UrWlCIAu3xZKYG7Nf9/yK20eRW5fHZRizZTFZ6JDawee5bQe2+SwloYkP4uPOqokSwcTNhHHlCiGI17rpOHB+CWT5HdPqlK0uXQL3KIpyUaEH5z3IxuKNTW4MadY0XjjrhSZZU0IIXjz7RU57+7SDleUBPNLD7rLd3DXnLp6Z+ExUZI8qSR2Vdcvjp06B264OU4L2lYkRwVi2ElHZWlu41ufzdpedPlnGDcv7t+/fJHYSlCK2o2SHoWVMEzu0ZastEYyLMJwtRUII4rWYA0fIBxNEn1AUFKjCl1Jy+7e3IR6A0S+NOvgcUqoxUWLh3oU8sfCJJlYtgeDwLodz2oDTfL7umF7HcFyf4zA3ynytdlbz7+X/Zs72ORGT2SfBKKihKrGpPQO7Emt1S9jmEqjBdCLGbZbXlnOg5oDPcx1TO2IJ4F0Y3nm4z+fNJjOrC3TbnnhFK1ttCWEJbNyKkybUVrMzoKwHfK9XCU9+RT7/Xv5vADYUbeCnnT9FXYZSeynnf3h+kxRzUEG8L5z1guHrn534rM+sqRpXDVM/mUphVRQVj3pK7IjnhyMegLPfPSu8Smxqz8BG4eo21sIoDAQKJ3C5Eq8w/9p9a0mx+nYp92sfuC3G8E7DSfKRkFFZW8ny/OUhy6eJDNqN2JYwWQiowQRTHiIKJFvteKTxXiAnB4b73uQlNPf/fP9Bt53T42TG7BmsvnE1Ikr+Eo/Hw/hXx1NU5bs/aYYtg7/88JeA18lKzsJeaUc2+syV2ksZ+8pY9szYgymKHYQX7V10MEj/p50/saNkh9+K3M0mtWfgmMjKHdD5+PDM10YIVD8rEZWtlQUrcbh8xy4O7xR4QRuYPZAkSxK17oYuaw8e3bYnjtHKVltCBKFsuZpmyMSCbln51LqM3TI7WmHDpz1le3hnzTsNAsl3lOzg223fctags6IiwzVfXMP2ku1+zxdWF/L9ju9bfH2JJLcil/M/PJ8vLv2ixddpLg/Oe5Daupgqt8fNP3/9J6+c+0p4Lp7UOXDLnoqtajMTJ0koiUAwlq1EY3HOYuzupgXEbGabYdkHLwPaD/Bbt25d4bqQ5dNEBu1GbEuYLBAoW6U6QMnmKJGdfgCPx/jjuXNncPW4Eol7f7q3SR2dKmcVM2bPiEph0G+3fsvba96O+DwAs7bM4sP1H0Zlru0HtjNv97yDj50eJ2+vedtv7EyzMZnBmmU8pmIruONjM5MoBFK2qqsTz7K1NG+pz+eTLckMyh4U8PX92vfz25qnsrYyfJ9pTVjRylZbwpYdePdduTM6sgRACGifVmI4Ji8vcIXpRGLbgW18suETn0UL8yvy+XTjpxGdv9xeznkfnBfRORpz2f8uo7Ay8vFb//j1H03eV4HgxaUvhm+SlABB9sULQTSNY9P4xxbg7SooSKxsRJfHxc4S32usR3qCyiRMtiSTlZzl81yKNUUHyccp2p7dlrBmqN6IRkHw1XvBXRu4InYU6NKugKKKzn7P5+a2rvIPd8+5G6fbt6mu0lnJHd/fweShkzGbIpOCdcwbx+CMcqVzt3Rz1OtHseP2yPmE91fv59217zZRtmpcNTy58EnuOuaug4VZQyK1N5Rt8H++Ogfc1WDR9baCJTvb+Hx+fmCFLJ6Yt2ue3+9YpaOSca+OC+o6jTs5eCmrLeM/K//DSf1OarGMmsigla22hq092A0yr2ryVD2gOFC2enXIYV3O4X7P5+UlZuq3LzYUbeCbbd/4LHTo5UD1AT5Y9wGXHX5Z2Of/Zss3bCre5Pe8SZgYlD2oRUH6Ukq2Hdjm93fbU7aHj9d/zEXDL2r2tYPh+SXPNwnS9+KSLt5b+x7XjLkm9IkyBkJ+gDHFi6HH2aHP1UboFyA5r6gosZStzzd9bnjeV/Zvc/lpV/SzlzWB0cpWWyO5SwBlK5/gip9Gnv6dja0du3ZBUux1wrBw5/d3NqmunmpJpcZVc1BRqHRWctecu7h4+MVYzeHrYVlQWcC0T6f5VYbSrGk8PfFprh97fYvn+Gj9R1z7xbVUOZvGLLmlm2tnXcuEHhMCFnRsLnaXnacWP+XXElDpqOSBeQ9w9eirQ8/2TB9oXNgUIP876HKytm4FSY8ekJICNX50EI8HKiogKyuqYrWYhTkLIz6Hr4KnmtgTlpgtIcREIcRmIcQ2IYTfnHAhxAVCCCmEGB+OeTUtILWn8Xl7vnI1xgF9O+0yTP12OGC7/6S5hGFl/kp+3vVzg1YbKZYUrhp9FWm2hn0Gy2vLw9rU2SM9XPjRhT6VIC8dUztyzejQLD8XDbuI3u16+z1f46xh8oeTfcarhcI7a94JeM0DNQeYvW126JNljwtc2LR4IcgwuGqTo9tJIFZ06xa4/ENxcXRkCQdb9m+J+Bwuj4tKR2XE59E0j5CVLSGEGXgBOBMYBlwqhGiSvyqEyABuB3S3zFiS1tf4fHVe4D5vUaJH+1ySA4gyb17gKtPxzh3f39HE8uKRHh488UEykzIbPF/lrOKeH+/x24y2uTy+4HFWFqz0q5CkWdN4/qznQ44T87bxSbWm+jzvlm627N/C/XPvD2me+nikh4fnPdxEkWyX1K7B40pHJQ/OezD0CTuMVw2njShZDaYw+L2mFMA06f9oJXTrFnhMfiDXbRwRLSXom63fRGUeTfCEw7I1AdgmpdwhpXQAHwC+UpoeBv4JtKL8sQQkvS8Ig62iuxocxlmA0aJ7+zwC1bz85ReoTOBN3OKcxSzJXdIkpuikfifRKa0TtxxxCymNXE41zhpeWR56fajlect5cN6DftPIAYZ0HMLZg8ITY3Ri3xM5ovsRTfopeql2VvPU4qf4ZfcvYZlv9rbZlNgbfpYzbBlMGzmtidK3tnAtK/NXhjahOVnFbRkhXZD3deLvEKJEt26Ba2nt2RMdWUJl3b7o1cD6YccPUZtLExzhULZ6APWLM+XUPXcQIcRYoJeU8uswzKcJheRugS1X+5dFR5YA9MzOCbjQ/vYbARWyeGbG7BlNlJ10WzozjpwBwDVjrmngXgRl3bp/7v2GSlIgKh2VTPpgkmFAboolhRfPejGsleufO/M5n61GvNS4apjy0RRKakJX+O+fe38TS4Lb4+ae4+7B7WkYn2Z32Xl4/sMhz0nXUwjYt2frK+BO4B1CFOnSJXB5l1WroDY8ht6I8uWWL6M216KcRVGbSxMcEb9NCSFMwL+APwUxdroQYpkQYllRke9WIZoQSekGfiwLBymcq8o/xJi+nXYFVLYSOWZr7s65rC1c2+T5JHMSp/Y/FYCu6V05rvdxTcY4PA6e++25Fs99/azrOVDtv/ihWZg5se+JHNnzyBbP4YuRXUYyacgkrCb/1tXy2nIu+/SykIq4Ls9bzobipmUYzh58Nr3a9WJU11ENnvdID99u+5a9ZSEW9e18oiqxYsS+H8Pb8L0VY7NBqm/P80GWLfMfQB9PzN89P2pz7TjQCttrJDjhULZygV71Hvese85LBjAC+FkIsQs4CpjlK0heSvmqlHK8lHJ8p06dwiCapgkp3QNXkd+/NHDsSRQwmzwMC9y9glWrIi5K2JFS8sfv/tjEOmUz25g+bnqDGKnbj7ydDFvDG3i1s5pHfnmEitqKZs/97tp3+XLLlz5bhnixmq08M/GZZl87GB4/7XEsBi1rHG4H83fP59Xlr7Z4jofmPUSNs+FnOMOWcTDQ//djf0+atWHygUd6eGzhYy2eE4BOxwTeqEgP7HgzcIFhDQAdOhifX7EisEIWD6wsCNFN3QyqXdV4tKs6rgiHsrUUGCSE6CeEsAGXALO8J6WUZVLKjlLKvlLKvsBiYJKUMj58VW2N1O6qjpYRB1aAJT5Wr5NPDuwm/OADqEqwLijfbf/uYFPk+piEienjpjd47sxBZ2L2kSHq8rh4ctGTzZp3d+lurp91vaEL0mqyMm3ENAZ1CNw6pCX0ateLG8bfYOhOrHJWccu3t7C5eHOzr7+7dDff7/i+SRycR3oOWgynHDalSVKAw+3gjZVvUGYva/acB0npBtbMwOO2vx64cbUGgK5djc+XlydGRmJxdXSFXJavb7HxRMjKlpTSBdwCfAdsBD6SUq4XQjwkhJgU6vU1YcaaCbZ2xmNcFWAPQwuVMKSnH3sspKcbj/nkk8QqbiqlZMbsGT7LLYzqMoq+WX0bPGcxWbhu7HXYzA2z2GpcNTyx8IlmxTel2dL43Q4Pp23H73HOeiczL3tD9UGJ0HH/5Kc5d02toRwnb3GRdmRTF2ogHlvwWJOYLIFg8tDJB+uTZadkc0T3I3y+PhSLGgAdjwk8pmw9VCVIZHeM6RmgWg0oV2I8c6D6gGHB4kjw1ZavojqfxpiwFDWVUn4DfNPoufv8jD0xHHNqQiB7nCquaMT+JYFrcgViikHxVC/vGQcTH3mkqqdlxL59sHq1GpsIfLbpM3LKc5o8n25LZ8ZRM3y+5oZxN/DC0heaPO+Wbh799VEeP+3xoObumNqR2W/E3n2VZYePPw5mZPOsAaX2Ut5c/WaTlijptnSuHn11g+euH3s9q/atahBEX+2s5h8L/sGMo2a0vHBst9OhYI7K7DVi1d1wzLuBY7zaOKNHwxdfGGcl/vwznH46AUvFxIqvtkZf8QlXVm+oZGWptmpms2osnpoKffvCsGFw2GHQvz8MGABDhyZ2slMgWvGvpvFL55MC1/rZ+z9wlkdHHgO6d1cVpAPx73+rStLxjtvj5o7v7vBdRFTC5KGTfb5uUIdBDOvYNIDN7rLzwpIXKKyKfDPnROClZS/5Daw/se+JDR6fN/S8JlX7QbkTP1r/UcuF6HFOcAHwuV+qXqQaQ444InBM1vLl8d2U/sedP0Z9Tl/JN+EiNxf+8x8osRl7LwroQlmZKs9TVgb798PevapkzyuvwN13w+WXq41yu3Zw7rnqurm5hpdNSLSy1RbpOCFw+YecL8NTfDEMjA+i38CnnwauNB0PfLj+Q5+xG2ZhZtrh00i2+P+7zDhqBum2pj5Vj/Tw0LyHwipnIuJwO3h8weNNylkIBBccdkGToPys5CyO6dXU5VfpqOSBnx9oeTZkWm/IGhnc2KU3gyvBAg6jzLhxgRWp5cvj16oF8FtO9Gt5N64xFyrl5fDkk8oqNWAA3HorZDsKEEi/RzeMvRu1teq6lZXq+Oordd0BA6BPH3jiCaWktQa0stUWaT8WAjU8dVVAcXzUajnllMDNZktKYPHi6MjTUlweF3fNucunVSvJksTNR9xs+PoLhl3gM8Oo1l3LGyvfILe8FW4Hm8EH6z5o4j4E5UK8avRVPl9z/Zjrm2R6AhRUFTB319yWCzP4FrAECDYEKPwZ9s0FHxY2jaJjR8gMkHNQVQULI992sMXsKt0V9Tk90kNeeV7I19m5E266SSUq3Hcf7N6tlKRIJSVVVanr79kD99+vvButAa1stUVs7SA5iNIa298AZ+x9c0cdFdyu9V//io0rMVj3xVOLn/Jb2yo7JZvi6mJ+2vmT32NxzmKO7308Jh9fW4fbwW3f3hacIF0SqK9ekLJKKXlw3oM+26GYhInjex/v83WThkzy6Ur0WrdaTK8pEGyfx6U36TIQARg7NvCYN9+Mz1ACl8dFbYzqFoZSSDUvD84/X8VWvfaaqmVW3fI6yi2iujr6c0aKsATIaxKQDhMg53PjMbmz4Mh/R0UcI8aPD06h+eorKCyEjCjHGwfjbSq3l3P3nLublCPwUlxdzJQPpwS8jlu68dDUuuWWbj7d9Cm/7P6F4/v4ViwOUhBE4kIggq0qH0Jh0ubww44ffMatmYSJqcOn+u3tmJGUwe/6/I7vd3zf5NyyvGWsK1zHiM4jmi+QNQO6n1n3HQvwHlTvhQWXwHEfxU3JlXjjxBNh7lxwGlTL+PJLFbsZb/y659eYzT1311xuGH9Ds14jJbzwAvzlLyo5yeg990dmpgp879NHxdtZreo6NTXKMrZ9u3IftiW0stVW6Xwi5M0Gj4EW4yyH4sXQ5cRoSeWTtDRVAmJuAK+OlHDPPfD669FTuGpr4Z13YPp043HXzrrWr6IFKtC9cTPqlnD5p5ez+4+7Q75OovHAvAd8WrVSralcOepKw9deN/Y6FuUsosLR0CzicDt4ZP4jvH/h+y0TatCNUPCDcskHIu9rWPd3GPF/wbkf2xjeIHmj+J3SUli6FI5rfrWQiPL99qaKfH1MwmRYc84It8eNw8AquiyveTUx9u2DqVNVKY1g3YQmk9oQT5yoLGGDBinlylvV31vxRcpDe6+UFKV8bdsGn30G336r5mzNdVi1stVW6XAEmG3GyhbAtldUqYgYp6f//vdqIQ3UdPqTT+Dvf4+esuV2qzgGI2XrQPUBPt34aVTk2VO+h++2fccZA8+IynzxwJp9a/w2kbaarBzd62jD15896GyfrkS3dPP55s/Jr8inW0a35gvW5RTwUYzWLxtmQvYY6H62tnA1YuzY4FryvPmmKhURqDZfNPFIDwLhd7N1ev/TOf+w81t07W0HtvH8kuf99jg12uA1ZtculRVYUhKcNatfP7jzTrjySqUkJSWpw0ugOFubDUaNUuUf7rhDKW3//S88/jjsaIXdhrSy1VZpPzpwkDzAnk9g3DMxV7bOPTe4BUBKuPFGmDUr8gtuVZXKztm3z3jcFZ9d0axFL1Su/vxq8u/Mj9p8seb2b2/3aRUUCEZ3Hc2bq94MeI0hHYawpnBNk+ddbpXU8N8p/22+YCYz9L8KtrwAMsj4rYVXwKk/Q7uR0KidUFsmO1vVayoMUOHkiy/g+eejIlITKip8b/J+y/nN7/c/zZrG1BFTm9SAC5aCygKeW+K/R2pOeQ5uj9uvG92LV9Hav19tII04/HB47DE44QSlICW1zCh3EJvtkGJ2zTVw1VWqNMSf/6zqJ7YWtLLVVrGkKoXrwFLjcdIF6x+Bwx8Ba+y2i+npcOqp8PXXgcfOnQsLFsDvfhfZdPDKSvjnP43HFFQW8M22b4wHhZmCqgL+t+F/XDDsgqjOGwuW5y3n590/+zwnkSzKWcSS3CUBr9O4dc/B56WLd9e+yxOnP0HX9AB9Y3wx6GZlHXYHqWx5amHOcTB6pnptSyxcrTSzcdw45W4yorgYfvgBzjwzfrpKrCtc5/ecxWRhYPbAFl+7S1oXPAa9bm1mG9sObGNIxyF+x3gVreJiYzdeUpJa737/e7WuRqIAqVdxO/VUlV36+utw113hnycW6GzEtsyAa8EcxO5522sgY9/H7frrg3cPXnqpiuGIVAxAVRWcfXbgTJlLP7k0MgIE4IavmhcUm6hM/9I4WM7uslPlrAp4BMoWu+nrm1omYOYg6HEeiGbsa6UbVv4ZfrkAHGXNU56clXBgefPlTACCrRD/wAPRL3BaUwMvvtj0+ZKaEspq/Qea1bprGdB+QIvnFULQPd1/bQSBYFXBKr/nnU6l2ARStHr1gnXr1Bqcmhr5Su8mk5rn2mth/frIzhUttLLVluk1JTj3hrsa1vxNLeQxZOJE45Yd9SkpgbPOisyiW1mpYgyWB7inldpLmb9nvuEYkzC1+DBif83+mBRSjCZ55XmsKFgRlbk+3/R5yxMYRs8EUwucCPmz4ethsPUFcJSohBVfd0S3XYUElKyGFTPghziLEA8TkycHN275ctiwIaKi+ORf/2r63Jp9a0i1+rdOuj3ulllM62FkGat0VBo2pH7iCZWcbKRo9e4NS5aoYqZpUfZsp6WpjMbWgHYjtmWSO0O7EVASxE5426sw/K8xdSUmJ8M556gg+GAqCqxcCbffDk89Fb74repqlWL+ahC9imf+MhOb2eb3Jn1S35P46aqfWizLfXPv47EFj/m0yggEd865k1+uiY/+aJHgD1//IWpzSST3/HAPT018qvkvTu8HvafCrveabyGuyYMVd8DKO6HLyTDg95A1HCyZaqPkKFUlWna8BVU7my9bAtG3r7KwbN0aeOydd6qQg2gEytvtqsWMr3iylQUrDZX0Hhk9EMGWUfHD4V0O54edP/g8J5Es3Ou72uuuXSqZyMg637Ur/PabKixriZG2EKt5w422bLV1BlwL5iDiQjxOWDgNXLGtMHfttc1bQF97DT76KHAWYzBUV6tqytdeG3hsUVURzy953u9Cm2HL4PYjbw9JnuvHXo/A90ItkazIXxHTGj+RxO6yh1SwsSW8uOxFnxX8g2L0TDCF0E9KelQZiQVT4esR8EVvmNUfZo+FtQ+0ekXLy9VXBxeQPX8+rFkTnVICHo9yXfpiUc4iQxf1gOyWuxC9DOk4xNB6tr7Qtx/u5ptV6Roj3nhDJSe0FoUnlmhlq63Ta0pwTXNBtRXZ9HRMe7mdckrzv/jXXQePPhpaJeLKSvjuO1XvJxjX5MPzH8Zt8L6ahImzBp3VcoGA3u16M7a7/9La1c5qZsye0fIef3HMn+f8OaoZnqDqbr207KWWvTilG4y8L7gYSY1fLroo+HihW2+NfOxWVZWynBcV+T4fqM7VyC5B9tA0YGD2QKwGirzdZW9S8Le6Gn780Tjz8NxzVcZhoBIOmuDQylZbJ6UrZB4W/Pi1f4OyDTHLeLJa4d57VfBkc5g5UxXcKy8PnNrcmOpqePhhmDIluFo/+RX5/HvFv/3uaK0mK9eOuRarOfTO2X886o8+e/t52VS8iZ92ttxVGY94PB5eWfZKTOb+29y/tfzFQ/4ItvbhE6YNMmhQ8N2mVqxQMUnhsGr7wuVSlm5/Vi2n28nesr1+X59qTeWwjs1Ye/0wMHugz56gXpItyawuaFhD4YcfAlsIH3ss+jFarZmwKFtCiIlCiM1CiG1CiL/4OH+HEGKDEGKNEOJHIUQrCXlrJQTrSgTlzph/ngqajxE33tiytO7vv4cxY1TD6poa412vy6UW6Z074bzz1MITLPfNvQ+3x79GZzFZuHH8jc2Q3D+ThkwyPF/lrGp11q35u+cbWg2tJivjuo1r0TG261ifvSe9lNeWs7l4c8sEN9vg6LfAnNKy12sAuOKK4K0tDz4IGze2rOVMIOx2mDTJf9LOpuJNJFv8p09aTdaQMhG99Mjo4bMor5caV02TjMSPPjLuI9mtm4qR04SPkD2xQggz8AJwGpADLBVCzJJS1s8HWQmMl1JWCyH+ADwGTA11bk2Y6HUBrGxGMZOafJg3CU78WilpAQrmhZu0NLj7bnjkkeAsTfXZsUO18+jTR9WLueEGFXjvXTBNJuWm/Ogj1R9sWfO6XbC7dDf/Xftfw53m4A6DGdxhcPMu7Aeb2caVo67kleWv+K0VtbN0J19v/ZpzBp8TljljzUPzH/KrbNnMNu465i7+fvLfW3z9yz+9nPfWvufTTSkQzPx1Jm9OfrNlF+96Mgy7Bzb8E9yxc8cnMhdfrIoJO4Iwrns8yqK9fj20axc+GaqqVBmEnQahcqsKVhm6up0eZ0g1tryYTWY6p3UmryLP53mH28GCvQu4i0Nr/IIFxklGZ54ZfOa3JjjCYdmaAGyTUu6QUjqAD4Dz6g+QUs6VUnpNIYuBnmGYVxMuUrtDx6Oa95qiX+C7I8BeAC3taB9CsP2tt4YWtLl7N/z1r8olMXq0qjUzcaKKyerQQVUybq6iBfB/P/4fLoMClunWdGYcNaPFcvviD+P/YBizUeWs4o/f/dGw+GGisKl4E4tzFvs9bzFZuGzkZSHNcfXoq0m3+c7CcEkXH67/kKIqP0E6wTDir9D5BDBFsOJuK2b4cFVNPlhyc+Hyy4Pv9ReI6mr4/HP48EPjcUvzlvrs1+nF4XbQMzM8t8L+7fsbnl9Z0LCdVSDXau/e2oUYbsKhbPUA6jumc+qe88d1gM86wEKI6UKIZUKIZUX+Ig41keHwh8HSzG9X+Wb4ZiSUrGhe0LzHrWoGzTN2gRmRmaniqEJdEDwe1YF++XKV4rxlS8uDarfu38qnmz7FZVC7zC3dXDTsohZK65vhnYfTL6uf4ZiCioKo9WeMJI/Mf8TQZdI5tTOHdQotDubEvicaD5Dw7G/PtnwCIeC4DyGlCzELm00OMvApDhFCKU/NCdz+6iuVfReqwlVZqUpKBJORvGjvIsPzndM6B2yjEywjOxsH2udV5DXIjA4UVWA2q/dZEz6i+k0XQlwOjAce93VeSvmqlHK8lHJ8p06doimaptNxkGZ8w/aJowTmnAAb/qFq/jgNAgG8xRfzvobZ42Dfjy0WF+APfwivayBU7v7hbpxu/+5DkzBx4bALSbOFf8s446gZpBn00qt0VnLHd3cYxpLFO/sq9/HJxk8MXYgt7TFXH4vJwgWHXeC3cKzdbeeZ356hxtlMH3Z9rBlw0pzmb3BCxZwCXU+DKQXRnTfM3Hxz86uYv/WWKnTckiQZONQL9eKLA7swpZRs2r/JcEz/LGNrVHMY1mmYYXxYiiWlQQmIQAlG+fmhZW9rmhIOZSsX6FXvcc+65xoghDgVuBeYJKVsod9JEzGEqLNutaAKoHTBur/Dp11g6U1QtEApXe5apWB5HFC+BbY8D18NVgH2FdtCFtlmg+eeiw9z9/rC9czeNtswcDvFksItE26JyPxTR0w1nBtU65D3170fkfmjwdOLnzYM9DcLM9NGTgvLXFeNvspQefVID2+tfiu0STIHwanzwJoZ2nWCxZwCHY+G382KznwRpHdv5fZvrsI1fz6MH6+UiWCtXA6HGnvddf4zDxuTV5HnN4bSy/DOw4O7WBAMaD+AJLP/9EKP9LB636GMxFGjjK/3/ffhkkzjJRylypYCg4QQ/VBK1iVAgxVPCDEGeAWYKKUM0LddEzN6ToKkTuBqYa60xwG7/qsOAGs71abEURZcW6AWcP75cPzxqvl0oAJ9keRP3/8pYH+9TmmdOKL7ERGZPzMpk/OGnMdH6z/yG5Rb6azkrjl3MXX41LCUnQiGhx9Werw38SAzE9q3V4USvUeXLoF32tXOap5f+rzhe9w9ozuDOgwKi9zH9z4es/Dv4qlyVvHw/IeZPm56wNZJhmSPgdMWwJzjwVWusn0jgTlVxYmd8IXKimwF3HefUgqaa4HZuhUOOwymT1eJNsnJqlByY8Wtqko999578I9/wLZm7A9X71tt2D0i2ZLM8E7hU7YGZg803GxVOav4Lfc3rh2j/J+XXgrz5vnPSNy2DQ4caH6JHY1/Qla2pJQuIcQtwHeAGXhDSrleCPEQsExKOQvlNkwHPq5rTbBHStnygB1NZBAmGP8sLLgkPIVLnf4bsIYLIeD992HoUNi3L+LT+abDZr7b/h0Wk8XvDdokTNw24baArTmkVG0/9uxRQfy5uSrj0ulUO2yHQymVUqo6OUlJysJntUJn861YxSw8uEDgs7Z8UVURH63/iMsOb14Q+f79sGiRyupavRqetXSho8v4DS+gC/ffr372KlxWq1K6TCb1O7jdKkYuM1Olmg8fDocfDiNGwNFHHwqE/s/K/xhatZLMSQdvJOHAbDJz8YiLeX3F635vYuW15Xy5+UvOG3qez/NBkzUCzlwOc8+C6j3gDsE96QtzCgy4DsY+GVoV+zhjzBgYOVLFWjaXykrVy/CZZ1TxzjPOgGOOUWEJDof6jM+dCx98oJSO5rIifwXVDv9aoM1sC0smopc+WX0CurXr90o988zAm9OHHlLvUTRaHrUFRLzW3xk/frxc1pJ0sEQmmIjESP+9pIRvx0Dp6sBjo8G04H7fpUvhd79rfimIsGBy8fSCFwzdBjazjWvHXEuaLY3qapXpuHOn6k+2aZPaSebkQHGxUka8wb+1tSoF2+Px/6f3KjIms8Q85m1ILcblVnkIqanQLktlWHbuDNnt4bErLqNPB+Pmt/v2qYr5c+bAzz8rBTA5WVkRIp0SbrOpuWpq1I3P7XHT66le5Ffm+31NsiWZDTdtoF/7FsQd+mHBngWc+e6ZVDj8xyGO7jqalTes9Hu+WbjtsPRm2P1BeOrYCSuYk+GYd6HnuaFfr8n1g4ygjuCaNX++UhziJb7I+6ue+d8zmb19tt9x6bZ0lk9fHrYSMACdH+9MUbX/xLJkSzLV/1d9cMN39NFKUTVaV5YvV5ufltQ1bIsIIZZLKcf7PKeVrTgiHpQtgMJfYO7EmBYuPUiQyhaothl//Wv0F96kJOMMxtpaVddmzhyVFbV5M6SkKKtOTU3g/m0mk9pxW62HDiGUtct7lJUFDvoVQilf/tK+S0tVfbGXX4YNG9Q8wVTfFgK6d1fVvb0VvpOS1O+YnKw+st4isrW1Kl5m61Z1FASI05YSPtv4GVd+fqVhGv3QjkPZePPGwMI2A4/00OnxThyo8W/aSLWm8tOVP3FkzyPDN3HBj7DkRrDnt9DCbFKuwl4XwpjHVKugSBAHyhbAsccqq2s83Mq8MnR/srvh5sAszFTfW40tjC7dI/99JEvylvg9n2pNZf1N6+mb1ReAlSvVe2e0QR06FJYsgQz/TSo09TBStnR7yXiiSxdjX1iwfSpCpfPx0ONcyP1C7bYThBkzVL+vn36KnoXLbAZfibNSKjfEU0+p1hg2m4oB8SpEjStad+yo3CK9e0O/fmqR698fevRQcU1ut1LKvBYuKZUSVj8eqrRUuR137lRVs3ftUq7INWuUgiOl76DgoiJVafv119Xv4x3jy82Qmqpqkh13nAqyHTxYKVoul7JCWa1KyfIXuOxVMF0upZAJoWTeskW5bubNaxp/d//P9xsqWsmWZK4bc53f8y3FJExMGzGNl5a95NeVWOOs4aH5D/H1tK/DN3HXU+DczbDzHdgwE6r2gMcFMkAZdEu6GtftdBj1iHJPtgGeeUb18IuJVdsHVY4qQwsTQHZKdlgVLVAZiUbKlsVkYVXBqoPK1pgxqoTFG2/4f+82bYKTTlJramaU8jhaK1rZihOW5y1n5rPH8fWWr5FInB4nHukh3ZaORVi47cjbuHnCzXSOlkBH/hu++gVqfFcljkeEgP/9D047TbkVI92E1mRSStKieuV0XC74z39U4+vi4kOWocayWCxw8slwwQUqXqRrV7XgWSxKoWmsrARTwLVjR3WMGqXaiHhjvZKTVczVnDnwab1SWzU1KoD9mWeUEuQvhiMlRfWFvP56OOooNS4jo6GMNltwwbRmc9MYkAED1HHGGaqch82m3EOvvw4L9y5ke8l2w2tKKZk6PDINKS4//HLeXP2mX2VPIvlp50/sKNkRsLCkF7tdKcK5uUpBLitT/5eUKMW3qAgOHDBRUnIVZWVX0T19E1PG/pdjB/7MkK4byEiuwOUxIwCTyU1+aQ/W5Y5m9rrz+G7DBWDNpF07lYjQsaPaDHTsqOLf2rVT/58bAa9irBg/XilbP/4YH1XP1xauJdWaSnltud8xXoUnnIzoPAKb2ea3Dl2Vo4oV+SuYPHTywef+8Q9VnNVIUV2+XClcP/6o1oJAPRXDicOhPBXNKWIbr2g3Ygxxup18uvFTHvnlEbaXbKfGWYNEHgyytpqtB7NZvDVUJg2exF+O+wtjuo2JvID7l8IPvwt/wG5zaIYb0UtNjVocVq+OnMJlMimL09Klh3qIbd2qlJKdO/2nlWdnqz6LU6cqBaex0hJJPB6VfdSunXLfnXaaKujqb6FNSlL1jO67Tymy0d7ZlpfDxbMm8t327wzHjeg8grV/WBsRGaSUdHmii6Glwmqycs3oa3jl3IbNsXNzYe1alVSwZIn6PydHKeApKUrx9CYJWCzKQtipk7qx1FeMOnVSR1qa+qwkmauwiGo80oLDk47LY8XlUgpbUZFSrOsrcaWlyrJZVKRebzarMSHH4cSJGxFg714YNixyTaeDRUp4ednL3PHdHdS4/K+bV426quUtn/zwxaYvuPLzKw2VvJP7nsyPVzWsb7hkCZxySuD3rnNnePddteGKRtB8ZaWKKZs2LYbJT81EuxHjjP3V+3lp2Us8tfgpHG5Hg11zqjWVE3qfwItnv8jG4o1M/3I6pfZSqpzq7v3Jxk/4autXDO4wmL8e/1fOG3oeFlOE/owdjoDh/wfrZ8YmfquFVa5TUtQu7JJLlEsqXG06DoqVrNx7c+YcUrR++00tWEYxWNOmwYsvKiUmOQadWryxX7t2wYQJ6ibsr0Hv0KHK/dmuXeyykYpc2/l518+GY1IsKVw/5voWXd/rjvW6Zz2eeskGJq+bVnD54Zfz3JLn/CZAOD1O3l7zNjNPnUnBzmw+/BDeeUcpODabUvjrF8Hs00cV15wwAcaOVZ+h1FS1g/d+dsxm5ZK12Xwp42l1h2+8Ll1vYgWoayUlKYVr8+bWF/Dcqxe89ppyi8U6WH5xzmJDRctqsjKic/hdvAOzBwZsOL+2sOmmZMIEtV4GUrgKC9UG7fzzVZZihw6RieWqqFCbgT/9qaElPtHRlq0osnbfWv654J/8b+P/EIgGX8g0axpd0rvw+qTXG7QLcbgdPPvbszzw8wM4Pc4GJuIMWwY2s40/Hf0nbhh/A9kp2eEX2uOGOcdCyUpVRytamFNhamhakpSqWfWjj4YvniMtTcUsvfvuoWKqVVUqdinPwON63XXw9NPxkUZ9+ukqBsNfQP2IEbBw4SFLSqyYvXU2kz6YZNjMN82awacnr6d2fzdyctQO+MABdZSUKOtOebm6iVRWqhuxV/nxLn1CHDrgkBJ28HyXdcjrjgJzbYN6GuLgP0BNB8b9upUNqzIOKjuNmTxZuW1691ZKUDwU4w2JOLJsebnoIpWEEukQAn9ICcNeGMbGYv/JGplJmbw1+a0G7rxwUOOsIWNmhmG9LavJSuFdhWQlZzU5t3SpCm0I1jp46qnwt78pZc3pDE3xqqhQG4KlS1Vow5w5Dc/HqZrSBJ2NGEMcLgf3/3w/zy55lmqncd0Vo2bCEonD7TAsL9A/qz9Pnv4kkw+bHIrITXGUwOzxKlA3QsVJG2BOgeM+hh5nh+Vy8+fD1VernVlLrVzJycrK8MQTKnap/n1m1izVq81fgUBQN/14CDBdtEgtkka7/99+g3Hj4sv64c3o/PFHWLFCuWxzc5XSkpx8KDszLU3Ff/Xrp6yP/fsr91xWlroZZGSoMamp6nXezM76SmX9JASHQ924q6vVTaiiQilvpaWqFtquXco1+MsvSrnzFzM0aJBKAmhVxKGyVV6u3uvCGJTOTkmBiko3aY+mGRbfzbBlsPC6hRGxbmX/M5sSe4nf8+2S2jHr0lmc0OcEn+fXrlXxfIWFwW9Qu3ZVa8r556v/bTb1fRXi0LrpxelU1/XWCXQ41Hf6s8+UguUvOzlO1ZQmaDdiDCi1l/K3uX/jhSUvGO7MBQIhBC6PK2B7B1AZUh4/VaZ3lO7g/I/OJ92azjvnv8OkoZNCq27txdYeTvsVvh0LtYWRq3INStGa8GrYFC1QwbNbtsALL6j4IymNFaP6pKaqm+/VV8PMmb4DNeOlYkcwrFplLEtKinJtxYui9e238Pe/qyDdpKSGGZ2g/h5Tp6pEg2OOUXLb7Up5arzQB0t9xcubsJDtx2jsLWcxb55yE3vdkY3Zs0eNGTdOydQSuTSBycxUSTKnnx7d7MTUVJXcsb1kOxaTxVDZqnHVBJ1M0Vx6t+ttqGzVumtZXbDar7I1cqTKZL7jDtVLMpj3sKAA/vtfdYD6rvTvr46+fQ8F1dfWquvt3q1iRXfsaFnB2ERFW7bCzObizTy24DHeX/c+Hunx+aWzmqz0zOzJ+UPPp1Na8xtuSylZX7Sezzd9jsPtwOlpGniTbksn3ZrOXcfexXVjrqNdchg6NlfuhDnHQW1xZFyK5hQY+xQMuiH8167D6VRuhqefhsWL1U3PaxWBQ9XNq6th4EC49VZ1EzXKhqmuVm7E3CYdQQ9x9dXw/POxdx298w7cdJN/V4HJpBTReGjT8fbbqqWKvyzJjAzluhUi9u8rqMK0//qXqlVWW6sUscaWrtGjVSzhRRepOCO7XSlnkY7j83gOFaRNTg7TXHFo2fLy9ttw443RUbhSUlQs02efwScbP+L6WdcbFsLNSs6i5G7/ClEoXPLJJXy4/kPDMVOHT+WDCz8IeK1ly+CKKw4ldcSSOFVTmqDdiBHGIz18t+07HvnlEZbnL8fpdvr0m6daU8lOyebVc17lzEFnhjyv3WXnsQWP8c8F/8TpdvpUulKtqUgpufzwy7nzmDtDr1hcewB+PhNK14UxaF6AJQ1O+Ay6nnro6a5dI5qG4sbEFgazmlGU0B4PJtKoYgTrGME6kgm+2eLS2fs56YJswwD5iy6CV145VOwzFpSWqgDtcv8JS/zf/6kj1grMDz+oEhZOp2/3XFKScosOHKiU5mimpBshpYo9mTVLKfQbNii3jDcD0auImc1K9sGDldtzyBDo2VNZBrKylJUmPV0pvjbbofegQTxZvXgzk0ldt6pKKcxlZcq1uX+/crtu364yZTdsCNPXKo6VLVAu//vvj2zAfFKSslbOnav+RnfPuZvHFz5u6M0Y1WUUq25cFRF5Hv3lUe6fez8ug3CPgdkD2Xrr1qCuJ6Vy8z38sPpM+/suRgqzWb2vsU56CBatbEWYc947h6+3qqKGGbaMJq47j0fi8ri5fsCDnJF1G5Xl1oNp2fv3qwwhb2aYy3WogKX3T9M4YLc+JhPU2vLYOnAG+7O/Qkhbg554QoDH5MAt1Bbv9x3e4qyeVzZILfdmlQS7duJxwtJbYNc7oZeFsKRBclc48WvIHNLwXISVrbDSpQs7FhZwwQXqxuYvNqx9e+WOvPRS9fdMT4+ey85b+uG99+DOO/0vYBaLqhU2eXLsA/p37lQK6hdfKLdD45ZBQqg6S2ecoWJNBgxQn+uaGvU98i7WVmv4gv3d7kN9KqVU71dysnEtNLtdyb9zpzq2bFGWsJIS9d1XQfySaq/cFjeYXHhwYjVbsFpMpCabsVrNmE3i4HfV67b0FouFQ+UkvN0FkpKU4uyNWWvfXrk0QybOlS2ABx6Axx+PzM06NVUlk/zww6Hg8OPeOI4FexcYvi5Yy1JL+Hj9x1w36zpDy5rVZKX63upmZ7Fv2gT//KfqF9m4SHM4MZvV57W2VlmB//IXlR2dCGhlK8JsKNrAso2FB1uQeHeO5RVQaweLFZJKRtK/WwcOP1y5D7p2VQG87duro127Q8G73oBfb1p6fWXLe9Tf0Xp3tZv3b6KwugCXSy3uNTXqqKxUi3lZGUw/fzj9OjffdemT4t9g0RVQkw+uZtqZTTYQFjj8QRgyAyJVviLKNwS3W7kwHnlExTJUV/u+tNmsYskuuED1duvRQ/2tzGa1iIeqgNWv1J6Soj6P33+vUqm//lo1mX3sMeOb0I03quw5iyX2Vi5Q8R2//KIUlTVr1Pdszx6lsHjLJAihlIvu3VVdoK5dlaXIq2R4rUbegPnk5ENlHszmhmUgvBX3vd+dkhIlQ2mpUlqLitTfeN8+9X+w8Sduj5vdZbvZWLSRjcUbWZ6/nLX71rLtwDYVduCygb0d1LYDexY40sFjBmkCaSbdmkm31F7079CPAdl96dO+F33a96RDantsNnFwE+VdUyKmzCeAsgXKdX7jjeFVuFJT1WbkP/9pGH8XKEDdLMw8eOKD3HvCveETph4r8ldw0lsnGdbaSrels/i6xQzvPLxFc9TWqmzlb76Bzz9X30FvL1N/pWSM8HadsNvVvXHyZDj7bBWDGS/W6mDRylYEsdtVxe49ew71kmv8ls6Yodq2VFUdiplItA+RXzwuXDv/i3nzU4iKrSDdBvFcQrUUESYYOB2G3A6pPSIrX4xuCFIqxeBf/1JKjtVq3MQ5O1t9jnr3VkGlQ4YoF1PPnqr6d/16UL7a9XgLVebkKAvKxo0qEHX3bpVhVD87y/urfvwxXHONUsz8pcqnpcHtt8Ntt6kbTFJS5IO7a2vVol1aqn7/BrhroSYXqusOez6e6kL255eSs9dDQaGF8korFVU2KqqSqLCnc6C6EyVVHSmp7kBZTTvKq9OotifjkQKPx4RHmvB4BG6PCZNJIoTEJCQm4cFk8mAxuUlLsZOZWklWSilZqfvpkFZEVsp+MlKqyEirJSPNybkPPEVS8iHzWY2zhq0HtvLYr4/xw84fKKou8pvcEk6sJis9MnpwzZhrmDZyGv2y+mE2RUDjShBlC1Qm6+TJag0OJY7LZFLfgb/9TVlc6r8FxdXF9PhXD78V3EF5Pl6b9BoXD7+45UIYUGYvo9PjnXyGlHhJt6Xz8tkvc9nhl6knpAR7IVTvUWEijhJwHABXBTjK1WNnKTjLVHKUrCtIhwnMyRSWd2XB+hFsz+3C+p3d2bSrC7sLOlB4IB2JwGzyHDQeuD0mBJLO7Svp0+0AQ/sWMLxfHgN67OPY4evonFlQ1yKubqETJnWcGMZ2WBFEK1sRpLZWVdTduNF3thSoLJVnn+WgxclmO7SjjmeklJTaS8kpzyG3Ipfc8lz2lO1hy/4tLMpZxO6y3Q3GD7PBOWkmJrVvxwhLLak4MQHSlIQ7vT+WLidj7noydJ8IBmUuwkoc3BCcThVb9MMPKjh//Xr19/d4Drm7jBBCWSgsFv+NqCsqAv8KJpNSmOpnYu7fr+IxXnlFyeOrPpRXhvHjVYX8Sy5RljivpSAlpeUKmMOh3gNvk+ydO5Wb87PPVAcAWZULOZ9D4TwoXqzaR5mTlVVUesBTC2l9oePR0OlYyBwMKT0gqYNS7BFqjMcFDeJoxKFfDHHoMRwaJ2W919R7rTCp+c3JamPhqqSyfBcf7l3N6n2rWZm/ki0HtlBY5bv+gEAgkQgENrPt4GE1WxEE/rx6pEclxrid1LprDW+soHri9cjowWEdD2N89/GM6TaGKYdNCThPQOLgu9UcKivhnntUGyiHo/kusPR0VaX+7bfVZqgxP+74kSkfTTG0KmUmZTL3qrmM7Ta2mdIHT8bMDMNeogLBy2POZXqmG8o3Q/VeQIC5zgIgPZCUDQNvgKzDVXhHchewpNb16HSpTXWDjYOp3udB/e+Rom59Uv+rtUsql75o9L2SEqh3PWECYVbfM5Ol0fXjl4grW0KIicAzgBl4TUr5j0bnk4C3gXHAfmCqlHKX0TUTRdnysnu3SqtfvVrdWHfuPNQ2Qwh1M+raVTJqFPTpI+nUSdCzB2S1h+xs0SQg9lAFa/V/Y3di/UrX0NDq4XA0DZItKZEUFsJNN6kPrNvjpqCygNyKXHLKc5i7cy4vLnsxKjvv+vTL6sd9v7uPnpk96ZHRgx6ZPchManlBKrvLzu7S3ewq3cXO0p1MO/1PZJYY+w+qstOZ/cub9M3qS7/2/Wif3B4Rri+2x6V2iM5KtWNz11BTaWfFKiu7dpvYtcfClm2pbN2Vyt78NAr3pwCQnOQGJE6nCZfbVGeFEUjZUC4hJCaTssSYzRKbVd1Bah1mPB5Bp2w7vbpVMaBvFUMH1tCnl5Mrbx/VRMy8PHjySdWU1u1Wnx9/gf6gPqMDB6qaRoMGKavcsGEq+L5dO3UNb+xhfeubyaS+E95A7dWrOeh+3769oYXtvHGf8/kd56ssVV+xgcICFxSp8x4HWCNQzjpI9lXu49I/9sIunX5Do21uOGavOo7Ig65hyvCSwPZsWNIDfukNa7qAx8/H1yShfwm8s7CL/6JGwZJgypaXtWuVe/zTTxs2XveFzabGjBypLFmTJ/v/tZ9Y+AT3/nSvoWXLarJSdFdReLLD/RCoqCrA0v4ZjDdXHNq00OjL3v2sQ9YkjwMQLd8gf9oV7KHE3ppgWgSCwyJARJUtIYQZ2AKcBuQAS4FLpZQb6o25CThcSnmjEOIS4HwppWHn2IRStgp+hJJVquhn1Q7l3nCUgKsa6bJT4epOafpkSl29KavtSml1R8pq2lFalUFpRTLFB5IoKbPicpvrDoHLLXC7BG6P96YlmsRrSQkmASazxGIGs1litoDZJLGY3VjMHpKTXHTu4KB9pp2sjGqmVwYfaWgSJlIsKaRYU0i1ppJiSSHJkkSyJdm4fpdULUzsLjt2l50aZw1VzirsLrth/ZnGvDflPS4deWmD52pdtSzYu4BPNnzClv1b2FW6i1J7KTXOGmrdtYbVk1OtqaRaU3F5XJTaSw3nTjKr3zPdlk7PzJ4MaD+A43ofx6UjL21afdldC5ufgbJNh/7+tYXgqlY7QJMVupwMPc+DlG6qbpm1nVIOzKlqR2mygrAihZWSEti9y82ePcotaLc3tGLV1h4qCugN/va2Y+neXSk8vfuY6NhRIKQLpEMlNbgdSmlJ6+P3juFyqeDpd99V2XRVVerawdYl82KxHMoQ9FrMmmtNOG7Ir/zy8DmAVIpqY/e0MMHJPymrlrtGvYeWEGpWhHpTuAkoa/nLo0oXA2XLa9GTnrr/vRY+2fCcLUjl1uHrTWn8+atfmr+etdHrrmrwXFMrh8vjYn/1fgqrCimqLqKwqpDCqkLyKvJYkb+CVQWrmva3rGkHGy+AnadgzTsBV2k3pNuMMEky2jkYOrKG3/1OculFSYwZGfhzNeXDKXy26TPDMem2dCruaeaXqZmc/8H5fL75c8Mx7W1p7J8+H1G5VVm3SlZB5Q5wlIKrXG0OzSnQ7jDIHAppvSGpIzK5y6G1y5IG5jT1nTOn1FmiTDT5u33RFxGSskWLeuTGgkgrW0cDD0gpz6h7fA+AlHJmvTHf1Y1ZJISwAAVAJ2kweUIpW+81XjhMNNgpHHYXjHms4RDp9UnXjQ92lxjiDeFvr4xjaMlyulaC1Y/lolOV2v0mRWAzIYGiNNjRHhw+wkgkUJ4EOZngPnMCmwZ0YWtVLruq95FXU0KlJ/Y5wMlmG11TO9AvoytD2/dlTEY2v9//+qEBdY3Eqa/4nbFE9Zr0IqVSgLx2kINm8yB8y1HcKe7de6hy+08/qdhEr4LndQGGg9TUQ9d0uVTc2mmnwQvP1kLxQtUUfd/Pqm1UbbF6r0xJdTsPE2QeBu1HQ3o/SB8Iqd0bKrWmpKbuxIPLT93/Xw5RSnIohHpTiIK1aNeCr+m7+xzjQcH8HsFkCxspdUGQX7aXzzZ8RFF1MYXVxeyrKiSvsoB9VYXsKN3V4uv6RdJUD2xEO1smfbN60z29K13SOtM5rROdUzvy/pr3Wb1/MyY/f0MpJRM6D+fHc19sOF9j/M4f3GfjubUf8JclL2AW/uL0JBZhYcPFH1DlrqWktpyS2nKKS3dxoLacv655nxq3AxOQZoJ0AemmuqP+zyblyjJRt+n39XPdYzPgBjxS3RkPHnWPZ3aETD9Ln7RlI6bsi1wSVRiJtLJ1ITBRSnl93eMrgCOllLfUG7OubkxO3ePtdWOK/V03oZQtV5WyatXkqcy8mjyo3V8vsLBc7QCsGSCsypJhTgJTsvoAmSzqZ4ufXYJ3N4eARVeqa7aQIX/axJYCHwEHddxwA7z8coCLRGGRBbjzGcEmh+/1CIDssQzqcTyju45meKfhHNbpMNJtLaxVsOYBWPcgAG4JO5yw3gHramFlLdj9CCEAm4Dnhkygx8kfQXWOsmzV5KpdoqMu4NRZBiJJZWGaLOrm7/0cCIuyypisytJlSQdLCkoJN9XbLdYtX0t+r67XYlpulvd4lLXN6/pbv14pYEVFKhuvrExZwbzFOuvXgzKZVHxXRoZyNXbooIL/+/ZVLkivS7J79wDxjFKq97U6V33faouhtkgpoN7MWFel+l66qsBVoxRbc937L5WL9uD7KUzqbyJMdX76uiBg6QY8qj+oMEH5RuN2VcldYEr8u+aem/UZH9+bfNA6ri53KG5NCMlr/86mdx9LXWNjdQjkwceikaIqhPDbBNmy63Use95okayvlMKNRcZj0muhVxn0qIDu5dCpWh0d6w5/m8rGSKAsCYpSoTgNilOhIA1yM2FvO/W82yDP4OH9E7klfzZ85X9MmiN4eVqKBMoC1PE79QpY3ozcJIvJgtVkPdhWzmwyq0OYsZgsTWINJRK3dOP2uHF5XLil+t/hdgRsO+fzd7pfW7bCqmwJIaYD0wF69+49bvfuhgHYmjq8vsQEIIFETSxhE0TWBBFTEQ/CRmEjE+yaH7a4RQOCkSUachwkHj4DQRKKqPHyGYgXOcJFpHsj5gK96j3uWfecrzE5dW7EdqhA+QZIKV8FXgVl2QqDbK2TBPngQUKJmljCJoisCSKmIh6EDTVoPQji6cYVT7IA8fEZCJJQRI2X9z1e5IgG4Sg+sBQYJIToJ4SwAZcAsxqNmQVcVffzhcBPRvFaGo1Go9FoNK2FkC1bUkqXEOIW4DtUHNwbUsr1QoiHgGVSylnA68A7QohtwAGUQqbRaDQajUbT6glLeL+U8hvgm0bP3VfvZztwUTjm0mg0Go1Go0kk4ryGuUaj0Wg0Gk1io5UtjUaj0Wg0mgiilS2NRqPRaDSaCKKVLY1Go9FoNJoIopUtjUaj0Wg0mgiilS2NRqPRaDSaCKKVLY1Go9FoNJoIopUtjUaj0Wg0mgiilS2NRqPRaDSaCKKVLY1Go9FoNJoIopUtjUaj0Wg0mgiilS2NRqPRaDSaCKKVLY1Go9FoNJoIopUtjUaj0Wg0mggSkrIlhMgWQswRQmyt+7+9jzGjhRCLhBDrhRBrhBBTQ5lTo9FoNBqNJpEI1bL1F+BHKeUg4Me6x42pBq6UUg4HJgJPCyGyQpxXo9FoNBqNJiEIVdk6D3ir7ue3gMmNB0gpt0gpt9b9nAcUAp1CnFej0Wg0Go0mIQhV2eoipcyv+7kA6GI0WAgxAbAB20OcV6PRaDQajSYhsAQaIIT4Aejq49S99R9IKaUQQhpcpxvwDnCVlNLjZ8x0YHrdw0ohxOZA8sUJHYHiWAsRJFrWyKBljQxa1sigZY0MWtbIkCiy9vF3QkjpVz8KSJ0ydKKUMr9OmfpZSjnEx7hM4GfgUSnlJy2eME4RQiyTUo6PtRzBoGWNDFrWyKBljQxa1sigZY0MiSSrP0J1I84Crqr7+Srgi8YDhBA24DPg7daoaGk0Go1Go9EYEaqy9Q/gNCHEVuDUuscIIcYLIV6rG3MxcAJwtRBiVd0xOsR5NRqNRqPRaBKCgDFbRkgp9wOn+Hh+GXB93c//Bf4byjwJwKuxFqAZaFkjg5Y1MmhZI4OWNTJoWSNDIsnqk5BitjQajUaj0Wg0xuh2PRqNRqPRaDQRRCtbISKEmCiE2CyE2CaE8FVBPy4QQrwhhCgUQqyLtSyBEEL0EkLMFUJsqGvzdHusZfKHECJZCLFECLG6TtYHYy2TEUIIsxBipRDiq1jLEgghxC4hxNq6OM9lsZbHH0KILCHEJ0KITUKIjUKIo2Mtky+EEEPqxc2uEkKUCyFmxFoufwgh/lj3nVonhHhfCJEca5n8IYS4vU7O9fH2nvpa+4NptRcL/Mh6Ud376hFCJGxGola2QkAIYQZeAM4EhgGXCiGGxVYqv7yJapeUCLiAP0kphwFHATfH8ftaC5wspRwFjAYmCiGOiq1IhtwObIy1EM3gJCnl6DhP+34GmC2lHAqMIk7fXynl5rr3cjQwDtVK7bPYSuUbIUQP4DZgvJRyBGAGLomtVL4RQowAfg9MQP39zxFCDIytVA14k6ZrfzCt9mLBmzSVdR0wBZgfdWnCiFa2QmMCsE1KuUNK6QA+QLUwijuklPOBA7GWIxiklPlSyhV1P1egbl49YiuVb6Sisu6hte6Iy0BIIURP4GzgtUBjNcEhhGiHyrZ+HUBK6ZBSlsZUqOA4Bdgupdwda0EMsAApQggLkArkxVgefxwG/CalrJZSuoB5KOUgLvCz9gdstRcLfMkqpdwopUyUAud+0cpWaPQA9tZ7nEOcKgWJihCiLzAG+C3GovilzjW3CtX3c46UMl5lfRr4M+Czg0McIoHvhRDL67pLxCP9gCLgP3Xu2deEEGmxFioILgHej7UQ/pBS5gJPAHuAfKBMSvl9bKXyyzrgeCFEByFEKnAW0CvGMgWiWa32NKGjlS1N3CKESAf+B8yQUpbHWh5/SCndda6ZnsCEOrdCXCGEOAcolFIuj7UszeA4KeVYlJv+ZiHECbEWyAcWYCzwkpRyDFBF/LhkfFJXaHoS8HGsZfFHXQzReShltjuQJoS4PLZS+UZKuRH4J/A9MBtYBbhjKVNzkKokQVxa41sTWtkKjVwa7mB61j2nCREhhBWlaL0rpfw01vIEQ537aC7xGRt3LDBJCLEL5e4+WQgR1/Xv6qwbSCkLUbFFE2IrkU9ygJx61sxPUMpXPHMmsEJKuS/WghhwKrBTSlkkpXQCnwLHxFgmv0gpX5dSjpNSngCUAFtiLVMA9tW12PP2LS6MsTytHq1shcZSYJAQol/dbvESVAsjTQgIIQQqBmajlPJfsZbHCCFEJyFEVt3PKcBpwKaYCuUDKeU9UsqeUsq+qM/pT1LKuLQUAAgh0oQQGd6fgdNR7pq4QkpZAOwVQnh7wp4CbIihSMFwKXHsQqxjD3CUECK1bj04hThNPAAQQnSu+783Kl7rvdhKFJCArfY04SWkCvJtHSmlSwhxC/AdKlvmDSnl+hiL5RMhxPvAiUBHIUQOcL+U8vXYSuWXY4ErgLV1sVAA/yel/CZ2IvmlG/BWXWaqCfhIShn3ZRUSgC7AZ+o+iwV4T0o5O7Yi+eVW4N26DdcO4JoYy+OXOsX1NOCGWMtihJTyNyHEJ8AKVHbySuK7ivj/hBAdACdwczwlSfha+1Gt9T4SQlwH7Ea11Ys5fmQ9ADwHdAK+FkKsklKeETspW4auIK/RaDQajUYTQbQbUaPRaDQajSaCaGVLo9FoNBqNJoJoZUuj0Wg0Go0mgmhlS6PRaDQajSaCaGVLo9FoNBqNJoJoZUuj0Wg0Go0mgmhlS6PRaDQajSaCaGVLo9FoNBqNJoL8P3uL9RmuB9+gAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_14\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_15\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_17\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_18\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_19\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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7FPiP1noScBPwl1jHFRLD7N2zcftbZjJNzJvI9IHTcbaqsr5ofwzTEUcXgc/CjZAz3UwjCkIn8Oab0ff0S0kxpQyGDOl44HdmJlxzDfz5zx17fzj8AT9bS1o2DXTanQzIGtBimS/gY1Xhqg6Ps2SJddX3rCy45JIO714QrEn2VnkRiIdnaxqwR2u9T2vtAd4AvtxqGw00ppN1Bw7HYVwhzmitWVnYssK0w+Zg+sDpTMybSLqz5S18ZUMlh6oOdWyw0lWARdrX8LvALrfGJyy2JI5QaHWy1hr++1/wR9n7/KmnzC5ibROTkQG33RY/YfJ56echXqx6bz1T+k1pucxXz8L8hR0eZ9Mm6+nW669vu/ZWQkn2i3EyHxtgPr+TuBVdVxGPb70/cLDZ68LgsuY8BnxNKVUIzAa+F25HSqm7lVLrlFLrSkpK4mCa0B52lO4IKXiY7kxnYt5EJuRNCCkH4bQ5Ox63VbnVev3A60wPw64i2U/YyX5C9PuT175WJ+vPP4++Nc8FF8Add8RviiwjwzRkzs6OfV+rCleFPX7PGXAOKfaWrqgVhSs6Ps4qa2H6P/8D3bp1ePexk+x9a5P52DiBxUyy01kS+2bgFa31AOBK4FWlQsuAa61f0FpP0VpPycnJ6STThEYW719MoFXLHI/fw4S8CfTN7Bty11ztqebjvR+3fyBfHTRYBMhkDIZmGVRdQrKfsOWEGDfefTc6r1ZWlinfEO9SBpmZ8OKLse9n4f6F1HpbupyG9xzOmJwxIRmKx+qPUVZX1qFxtrZxnzRhQod2KwgnNfEQW4eA5hXyBgSXNecO4D8AWuuVQCrQOw5jC3Fk9u7Z1PtaBq7YlZ2+mX1RSnF6r9ND3tOhXmuV28FuccXKHgcR+jIKQrx57bXoams9/XRiPDapqXDZZXD11bHtZ/mB0Kj1SX0nMSZnTEj7nlRHKmsOrWn3GG43FBdHXj9w4Ind6kgQEkU8xNZaYIRSaqhSyoUJgJ/VapsDwCUASqnRGLEl84RJhNaaZQeWhSwf1XsUShkv09kDzg5ZX1xbTHGtxdk3HJXbsYzX6j5eshCFTqGoCPbsaXu7IUPgxhsTVzQzMxP+8AdTELUjVDdUc7i6ZShsmiONM/POpF+3fmhaeltrPbUsP9hGSmEYPv/c+jMYN+7EbnUkCIkiZrGltfYB9wLzgB2YrMNtSqlfKKUa79V+CNyllNoEvA7cruPZDVWImT3H9rSoEN9Ic4E1td9UMlsVGE11pPJpwaftG8xbZe256nWmaQwtCAnmww+jC3R//PH4l2loTZ8+HfdurT28ljRnSxXksrsYnTMapRRDsoe0WOfXfubva79XuqTEOr577NjOrRgvCCcKcanvq7WejQl8b77sZ82ebwemx2MsITEsLVjaotI0QIYzg6n9ph5/PSFvwnEvVyPVDdV8vO9jrh9zffSDBRpAW3i2nN2j35cgxMCrr7ZdyDQvD77ylcQX6OzWzbT86QgrD66kztuyx6jb52ZMjqnCMz53PJ+Xft5i/eajmwnoALbQ8NmIuMP3tz5O795SyFQQwpHkOahCZzF7z+yQ4FqbsjEhrynadXTv0SExXRrNJ3s/ad9gAQ9oi4hkm0URH0GIE263yaxri3vvTbwtjQwe3LH3zc+fj69VkWCHzUFOukk0mtpvakidPJuysatsV7vGaWiwzv8Qr5YghEc6VwkALN0fWsKh3lfP6N6jj79OcaTQv1t/CioLWmxXWFVIeX05PdJ6RDeYzQnKFtm7ZdUvsT28kwfuox18sw1uibLwknBCsmWLCU63ijFyuYzY6qwGxx0ZR2vN+sPrQ5af1uO0457oMTljSHOm4W1oOX2/qnAVo3qPinqstkpERVurTBBONcSzJVBQUUCNN7SfWv9u/UlxtPQyTeo7KWS7NGda2OD6iNhSQFkEwFhVlm8PHRZaYBnAL5wUfPYZ+Kw6RgFXXNE5tjTSkbiwgsqCEK8WEOKVbr1Nrbe23V0gUlKsg/jr6iKvE4RTGRFbAksKluBQoU7OSXmhwmr6wOm47C2DMmo8NXyyrx1TiY4MsFk4VSs2m0bVgpBAVq5sWxzcdFMXF+iMglWFq8L2Pzyz75nHXw/OHhy2+fSygnbcJNF2e6KDB0VwCUI4RGwJzN0zN8Sz5bK7OHfguSHbTsidQJqj5VxHQAeYt2de9ANmnW7t2SrfBL5QT5sgxJO24rVsNrjyyuTvrrJk/xKqPdUtlqU6UhmbM/b4a5uyMaj7oJD3Hqw6SK0nek/y4MHW3sCtW6X0gyCEI8lPI0JnsCg/dCoh1ZHKxLyJIcsn5E3A7QtNScqvyKe6oTpkeVi6jwWfRdffyi0mpksQEoTfD3v3Wm9zzjmdY0usLClYErLM4/ccz0RsZFyfcSHbpTnTWHd4XdRjDR1qLaa2bu28+DZBOJGQK9opzuHqw5S7y0OWN/gaWsR8NNIno09I6w8wJ+2oiyS6ssFpMTdTvQdskj8uJI6dO038kRU33JD82XUNvgb2HAutyqrR9OvWr8WyKf2mhEw3un1uVhVGkZIZxG43BV4jUV4u04iCEA4RW6c4SwuWhsRggck87JPRJ+x7RueMDllW46lhQf6C6AfuHrqP42g/FLWznIQgtIONG9uu1v7Vr4IjyfO1NxZtDClmCjAke0hITbyxfcaS7mypHj1+T/viLYGJE63XL1woLXsEoTUitk5xPt77cUi8BxAyBdGccwecG1IANaADzNk9J/qBe52NZbPp3c+Dpyr6/QlCO1izBmoswgKHD4fuJ0Bt3VWFq0L6HoIpYtqaMTljQhrNA6w7vI72NPQ4+2zrwqUvvGD92QrCqYiIrVOccC07FIpzBkQOWJncbzKZrsyQ5bvKdoVUsY5IzgxwhO7jOEfmWWqxqEjNjXEHwsnK8uXWxTmnTj0xakYtyF8QEkPptDmZ0ndKyLbDegwLG2/p8XsorCqMeszx462zEhcssP5sBeFURMTWKUxJbQlHa0NrUWW4MpjSL/Rk3ciE3AkhjW3BxG1FHf/R7wrrKvLaB/v+Af42+oNYcV0R3KIjP4RTlh07rNdPnWqaQyc74Y63dGd6WM+0w+YIieNqXN6euK3zz7cWon4//OtfkpUoCM0RsXUK8+mBT0m1h79FDTcN0cjIXiNp8DWELK/z1LEwf2F0g9tTYOD1WP4Etzxm3bBaEDpAbW3bPf6mT0/+kg/FtcVUNYROtXsD3ohhAM3LQTRS46lh6YHQDhKRcLlMPJvV5/Ob37RdMFYQTiWS/HQiJJKP935MVZi4qAZfA6f3Oj3i+5x2Z9iaPT7tY/bu2WHeEYHhd4PDIt3Lcww2PgRhqtsLQkc5erTt4pxjIocsJg2rC1eHdHgA8Pq9DM4O32Rxar+pIY2nNZrF+YvbNfadd1pnah48CL//fdtNvgXhVEHE1ilMpAbSg7oPwml3hl3XyOS+k8Mu31ayLazXKyw508HeRm79nr+CuyhyH0VBaCdFRdZZhgMGJL9XC2DZgWXUNITeiAzqPihEUDVyRp8zyHBmhCzfdWxX2ED7SJxzTttlMZ58UspACEIjcTmlKKVmKqV2KqX2KKUeirDNV5VS25VS25RS/47HuELHKa8v52DVwbDrmrf5iMQ5A88hxR56V53qSGXt4bXRGaEUDL8LbBZuBu2HT68Dv0URVEFoB0ePWgdwT5p0YsQbLcxfSCBMD88z+pwR8T3hyraAae+z+ejmqMdWCu64wzorsb4evvWtzvVuSRakkKzEXEVGKWUHngUuBQqBtUqpWVrr7c22GQE8DEzXWpcrpcIXcBI6jTe3vRk2yF2h2Fa8jatfv9ry/aV1pfjD9C+sbqjmhfUvMGPQjOgMGf0A7HoGAhZBNBVbYMWtcO6/racdBSEKiorAaxEKOHEiZIQ6f5IKf8DP1pKtIcsdysHUflMjvm9EzxFhM4Z9AR+rC1dbJsa05rvfhT/9yXqbDz8029x3X+ITDurqYO5cU4xWEJKNeJTsmwbs0VrvA1BKvQF8GdjebJu7gGe11uUAWuviOIwrxMDz657HFwiNYNVotpduZ3vp9jDvahuN5p0d7/DPa/8Z3Rtc2TDpKVh/P/gtboEL34f134fJT5tG1oLQQYqKrAPkR4wAp/Usepezo3RHSDV4gHRXOmP7hAbBN5LiSCE3M5fD1YdbLK/31TM/fz73TLsnahsGDIAHHjCxWVbThY8+CsOGwdVXJ07E1tfDpk3wta+J2BKSk3hMI/YHms9HFQaXNWckMFIptVwptUopNTMO4woxsL2kY2IqGmq9tdR52hGsMewOSB/Q9nZ7XzQeLm81hBGKghAN+/dbTyMOiOKn2NWsLlwdthBpIBCwLEgMMLp3+KnEVQejL//QyEMPRSegbrsNZs1KzDRfTQ2sXw+XXQYNUYaLCkJn01lhoA5gBHAhcDPwN6VUduuNlFJ3K6XWKaXWlZSUdJJppx6Hqw7jTXBJhde3vh79xjY7nP0S2KPoYFv4PsyeADV7wSfRt0L7ORg+VPE4fft2jh2xsDB/IbXeUE+w2+9mWI9hlu+d0m9KSAcIgGPuY5TVlbXLjvR0ePbZtgWX3w+33AL332+8YPFq51NXB08/DRdcIPFaQnITD7F1CBjY7PWA4LLmFAKztNZerXU+sAsjvlqgtX5Baz1Faz0lJycnDqYJ4Xh548sJH+PtHW+37w0550L/q6yD5RupzTeCa/uT4K3qWGkIb2iLIuHUoKjIen2vXp1jRywsO7gs7PJ+3fqFnV5szrg+48hwhaqjVEcqqw+tbrctN9wAo0ZFl8H5t7+ZGmYHD0J1DIdgdTUcOwbXXmumKaUXo5DsxENsrQVGKKWGKqVcwE3ArFbbvIfxaqGU6o2ZVtwXh7GFDvD+zvcTPsbyg8vb/6azX4KMgaDsbW8baICt/wvv5MGmR6D+qPF0eavCl4kI+MFTaSrSV++Ftd9pv33CSUFpqfX67OxOMaPDVDdUc7jqcNh1w3sMp7i22PKRl5kX9r01DTUs3R99cdNGlILXX2+7FEQjGzeaGK5vfQt27zbCKZrWSIEAVFUZsfzDHxoP5Mcft9tcQegSVHsakEbciVJXAn8C7MBLWusnlFK/ANZprWcp037+98BMwA88obV+w2qfU6ZM0evWrYvZtqRFRdH4L0ENxlL/N5UGf+KDGxp+0oDLYZEbHo7aA8Zr5a1o/4Dpg0ztrtyLIHs8ONLMZ+irg7I1ULwYSleCO7RFUVhiaenThd9vVJzC9qWkRC7t0L27KQ2RElrVJGl4Zs0zfH/O98Ouc9gcbXq2gLA9EgGyUrKofKiyQ3bNm2c8TfXtrNJy6aXwxS+av8OHm/c3frVKQVqa8YQtWgRz5sD771uLs5h/tqfwsREXkt2+BKKUWq+1DpvSGxexlQhEbMFdd2r8flPtOiMDhgwx7vrTT4f+/aP7TbemrK6M3k/1bv8bO8C/r/s3N4+7uf1vPPYZzD8ffF0chCFiK/F2RCKB9tntkaedRo6EtWshK6tDu+4ULvnHJSzcH2VbrA7g/ak3KsEWjqefhkce6Xgx09RUkw2anm6mJevqYN++9k05ithC7OsirMRWPEo/CB3An5OLvSSyh6WIXP7+95bLUlOb7soDARg0CL70JbjxRpg2Lbrf+D82/SNGy6PnP9v+0zGx1XMSXDgHFs0Efx2EqQcmCB1Ba+v4np49kz/+Z/2R9Qnd/9zdc/nS6V/q0Hvvuw+Ki43o6kgxU7cbtmzp0NCCkNScAE0pTh4aT0KTJkFGVRGZGRpF+EdfQqN43W6orDRu9oYGE+/wzDPwhS9A797w7W/D8uXWNw3v7ng3gf/DliwpWNLxN/eZAZevhPT+0QXNC0IUBALWNyVWbXySgUAgQGVDx6b5ouW9ne/F9P7//V8jutKiSC4WhFMFEVudwLFjJqBzyBB4+GETINrQEJ82Fj6fSXk+dsxk+lx+ufFyRSLRd8XNKXeXh60yHzXZ4+CL26HvZWDv5EKmqbmdO57QKfh81llzyS62lh0In4UYTxbtXxTT+5WCJ56At9+Gbt3MtK0gnOqI2Eog1dXw85+b6b6//MV4pNobPNoeAgEj4CKFulW5q6j3dW6PwQ93fRjbDpzd4Pz3YOKvTB2uaDIVO4pygiPTjHddG/UBhBMSn+/E9mz9d8d/Ez7GgcoDcdnPFVeYKcHTT+8cL1dKyolRI004NRGxlSCWLIGhQ+Gpp4wAsmoP0lm8tuW1Th/z31vi0HNcKTj9+zBzPQy4xkwrqjj2U7GlGiF32rfgSztgwJfjt28hqWgrLrcjSSedycJ9iQuMb8QX8FFUE5+bjcGDYcMGuP12E3Nq1bi6o9jtRsxdcQVsT1xjDEGIiSS/jzvx0Boee8yIrPZ6sdLSTDbUqFGmDk16ujmRuN0m3uvzz82jraKMkSisKkShIjagvvmMm3HZ2382fH3r6xFLSRypPtLu/UWk+2g47y2o2W9qbBX8Oxjx3EEla08HFIy8F0b/EFKlkO7JjsNhLbh8Sd4Fak/5nk4Z582tb3Lf2ffFZV8pKcaz/8gj8KtfwSuvmO8g1htQp9OcH6+6Ch5/HEaH70IkCEmBiK04Ul8PX/0qLFwYvdAaN86852tfM+Uc6urM3XV6etOUhtZmfx6POXEFAqaY36uvmi730Y61cP/CsEILoFd6L167vmOer4LKgohxHpuLN6O1RsXTZZA5BM7+O0x4AvJfhSNzoGwtaD8BbNj8oSUjNAptz8SmvaaRdc650O9KGHILOJM4z1+IKw6HdbZhNMU1u4oqdxUef4QCYXFmzp45cRNbjQwYYETX44+b5tV//rM51/n90Z/DUlLMw+02/RYffdTEwgpCsiNiK074/aar/bJlbd+xKWUE1m9+Y1qDOJ1NRRS7dw+/fXp6ywrN114LF19s3PL/+IeJDSsujjymL+BjY9HGiOvH9RlnbbQF5w48lyUFSwiEqdzuDXjZfWw3I3uN7PD+IxFIyWXFsQdYueEBFizQlO3fy/h+K5h62jqy0irJTKkmoG3UNmRyrLYXK3eexc5j5zJo1AAuvhimd4NJDsJ0iRNOVuz2E9ez9d/tiY/XamTNoTUJ23dODvz61/DETw6xZc0Rli/zMW9RNis+60d1XSouhxebMueSgLbhC9hxOgKcNe4Ql194jBnnKc48JwdXjyHJP+8rCEFEbMWJ738fVqxoW2jNnGnu6Pr0MZk6sdAozG6/Hb7+dXPX+Nhj4bMcNxzZgNPmxE2ogTZsnDvw3A7bcWbfM8l0ZVLVUBWyTqFYWrA0rmKrpgZefhmefNI8b2gAj0cBw1m3czgvLfq65fs3fG6qXdts0K8f/OxnRvwmIp5ESC6UMt97JO9WdXXyXr9jTjZpB+XucgKBALZoGh5Gi7sUihdB4QdwZB52bxUT7SlMHOXjnhGmnl5pdS+q6rOoa0gnoG1kpNSSkVJLbvejxjtuT4c6B3ziBZsTci+BAVdD3iWQPiB+tgpCnBGxFQf+/GcTh2BVNdnlgmefhZtvNtXg40lqsAzVd78LN91kCp22Zsn+JRHjqjJcGZzZ98wOjz8hd0LEEg+13lpm757NnWfe2eH9N3LwoJl+aCz2GkvpjMZpi9274Tvfge99D+6/H+65xxS2FE5erMRWUVHyiu6VhSs7dbzVh1ZzzsBzYttJwAsH3jZN46s+B1sK+JqVg28Vb9m7Wxm9u5VF2JkGf61p+Abmb+E7UPSxGSetH4x+EEZK31Mh+ZBsxBhZvx5+/GNrodWrF6xaBbfcEn+h1Zz0dOOpWR6mB/SHuz6MGO/h134m5E7o8LhDewzFF4g8/7K0oP3NbZujNTz/vEkceO45I7LiUaOskZoaqKgwnrJhw+CDD+K3byH5yMyMvK6sLHnFVnGtRZxAAvjPtv90/M31R2Hzz+HtPrDmLqjYDAFPS6EVL3w1pjF9bT5sfDD++xeEOCBiK0a+9z3r4M7UVFiwAMaObRlzlShstlBB5w/4WXt4bcT3+AN+hvYY2vExlY3hPYdHXF/nrWN/xf4O7bukxBRqfeABI2gjNRCOB/X1pkL/TTclbgyh67HyXAYCRnwnG9uKt0VMbkkUC/IXtP9N/gbY8jjMGgrbf2sayndmj1NfHO/CBCGOyDRiDCxYAJs3Rw64VQreeMM0Vu3Ku+UtxVuw2yIXAx3eczg2FZvuntZ/GttKtoVdZ7fZWbJ/CUMmDmnXPj/+2Aif2trEiqzWdLSJrnBikJdnmhtHoqwsfKJKV7KjZAcOmyOsB9mu7EztP5XslOx273f5weVUe8J7m8rry9u3s9LVsOwGaDgG/ijTCx0ZkDMDskZD1ijoPgpS+4IteMLUPnAXQ/VOqNwB1bugeCl42mmbIHQxIrY6iNbGq2U1nXXXXXDJJZ3j0bJiyf4l+PyRp/mm9bfo7xMlZ/U/ize3vkmdL1Sp1HhqmLd3Ht+Y+I2o9/fii217DQWhI/Tvb73+6FEznZxMrDm8JmJcpFKKeV+bR1ZK+0uY3PH+Hby08aWw647WHsXj90RXe2/br2HrL6ITWWn9Ydg3YdD1kDXGNJu3p5pHOLoNN6Va/B6zf3sq1BZA4fuw70Wo2tn2mILQxcRlGlEpNVMptVMptUcp9ZDFdtcrpbRSako8xu1KNmyAAxZdLdLSTAyQVXxIZ/HR7o9w+8OnSaY50jir/1kxjzEhbwIOe2TtvjA/+srX8RBaLhdkZxsPhTOOxeaFE5+26jIdOtQpZrSLhfmRa+R1c3XrkNACk0mc6ggvclIdqWw+utl6B1rDxodh6y/bFlq9z4YLPoCrd8MZP4EeE8HuAld2ZKHVHLsLXN3BngJZI2HUfTBzA1y6DPpd0fb7BaELidmzpZSyA88ClwKFwFql1Cyt9fZW23UD7gNWxzpmMrBggXVNnvvuS45AW621ZRaT0+5kQl7Hg+MbGddnHHXeyPNvFe4KDlUdon+WtVvh/fc7JrTOOQcuuMB4EidPhqyspu/H4YDSUlizxnxvCxeanm3CqUn//qauXUP45Fzy803sVjyrHsSCP+CPOEUPxFRWZUzOGFLsKbh9oTdjvoCPVYWrmNIvwr2x1rD+Ptj7ovFORSI1D876G+ReBLa0+H2wNpd55EyH6W9C1Q5YaV32RRC6inj86qcBe7TW+7TWHuANIFxzuV8Cv4EwhZ5OQN5/P/LJGswUYjJ4tXaU7kBbVHGs89bFVNC0kQxXBr3Te0dc77K72sxKXL3aZGxGK7RcLvjGN2DPHlM367HH4AtfgB49TPHKxmrTdjvk5pq2Hk8+CStXmmbdX/5y8lxQhc4jN9f6RmjTpuQKkt9RauK1IhFL2ZbROaPDCi2Ael898/fNj/zmz//QttDqdyVctRPyLjPxWYk64JzdoMdk4+kShCQkHr/8/sDBZq8Lg8uOo5Q6Exiotf4oDuN1OV6vKfkQiZ49244L6SwW718ctrJ7I73Te5Phik89CqvyEdWeaj7e+3HE9bW1pgJ/tMHp111naiI98wycdpopENtYhd+KtDSTrTl5Mvzzn1BQANOnRzemcHKQl2d9zf/ss86zJRpWFa6KeAynO9OZmDexw/vOzci1TJ5ZXRhhIqJmH2z+mbXQGvRVmPFf0w6rAz1X243NDo4uDpAVhAgk/L5eKWUD/gD8MIpt71ZKrVNKrSspKUm0aR3m0KGmvoXhmDEjeQK7Z++eTb0vsjHjc8fHbaxzB56LQ0X+YObnR75LfvRRU727LTIy4F//MkKpR4/YqvBnZZl+bR9/bDxeVt+pcPKQm2vdH3HnzqZCwcnAovxFEafonTYnY3LGdHjfSimGZUfOBjjmPkZpXWnLhVrD8ltMbatIDLgGzn5ZxI8gBImH2DoEDGz2ekBwWSPdgDOAxUqp/cDZwKxwQfJa6xe01lO01lNycnLiYFpiqK21vjPOzU2OoGytNcsOLIu43q7snDug4216WjMxb6Kll6y4tjhsYcZNm+Cvf21boA4bZi6E110X3+Kw6ekmTmzdOiPghJObvDzrEAC/37o0RGez7GDkY7jeV8/o3qNj2r9VzGaqIzXUu3VkLlRuAx2ha3dKbzj7FRFagtCMeIittcAIpdRQpZQLuAmY1bhSa12pte6ttR6itR4CrAKu1lqvi8PYXYLfb90/zR7ZK9+p7C3fizfgjbg+w5XBpL6T4jbehNwJeP2Rx0t1pPJpwactlvn98LWvtd1TcuhQU4U/L89MBcabjAxToX7VKhFcJzvZ2dbNqMHEDyYDVQ1VHKk+EnF9ij2FXum9Yhpjct/JpNjDz8HXempZcXBFy4X5r1kXD536l+iyCwXhFCJmsaW19gH3AvOAHcB/tNbblFK/UEpdHev+k5GMDOtpiIoKIyK6miX7l6CIrAq9fm9MbXpaM6j7IMsq19UN1Xy8r2Xc1ssvm+wvq4tf797w6acmFi6RQjYlBQYPhk8+SdwYQtejlBHvVqxcGd+WUB1l3eF1pDsje4isOjdEy5icMRHLP/i1v2WQfMAPh2ZBpOPckQn9rzLlGQRBOE5colS01rOB2a2W/SzCthfGY8yuJDfXehpi1arkiP+Zs2cOtV7rK8ag7oPiNp5SipG9RrLp6Kaw6zWaT/a2VDK/+U3bF7X33jOCqzM8hikpMDq2WRnhBGDaNNi1K/L6DRuS44ZpxcEVlsfwpLzYPdNjcsZYeqQ3F28moAOmy0R5G9l+fWea4qPi2RKEFkjiewfIzIThFjeU+/cnx13xkv1LLNeP7DUSZTUf2gHOGmBdILWwqvB4G5ANG+DwYev93XILTJgQXaZhvOjqiv9C4jn3XOvp6A0bkqMsyPx98yM2eU91pMZU9qGRAVkD8EeKvwIcysHO0mCV9voisGrt1WsquDpWYFUQTmaSwP9yYnLVVebOONLd7zvvwO23d65IaE5BRQE1XutiQfGoHN+aaf2m8drm1yLejac50/j0wKdcffrVPPustYewRw949tnkqFcmnFxMmmSSWCIlZfj9MHcu3HBD59rVHK01G45E9iSl2FMYnRO7G1YpxeDswewqC+/q02hWFa4yYwU8RJxChPh5tN7JA/fRDr7ZBrckgVtSEJqRBPduJyaXXWbtAXn88a6dhlhasNSyDEO6Mz0uPRFbMyFvgmXdnhpPDfP3zae+3jTptvqMfvWr5ErBF04exo9vu6bb669DZWXn2BOO/RX7LT1Obp87prIPzRnfJ3IJmFpvLYv2LzIvHJlgEQdKfZGZRoyVDgstAIuAWkHoIkRsdZAZM6zLOxw5As8913X1tubsmWPp2bIre1za9LRmbM5Yy7Y9AR1g3p55zJplPU2Tlga33SZiS0gM6ekms9WKefO6zjMNppipXUW+cbHb7ORm5MZlrKn9p+K0RT6hHS8hkz0OIvRZBeDIPOv6W4JwiiJiq4OkpJjgbqt6T489BoWFpuJ8Z3P8TjQC9b56xuaMjfu4ac408jKsr2L5FfnMX+SxbIly/fXWGZ+CECtnthHuVFtr+ml2FUsLllLtiVzpd1j2sLjFXI7JGUOaM3IQW2FVITWeGkjvB5kWqZzlG8AvYksQWiNiKwZuv926JlNNDVx8MVRVdZpJABypPnI8CD0SuRm5lifXWGjLY5bmTGPRp9aFtf7f/4utOrwgtMV557XdLP5f/+q6PomLCxZbrh+fF7/uD6N7j8YfiDxlmeZMY93hYGnEIV8Dm4XL77P7oY14UUE41ZAA+RhwOOBPfzLNkCNlHxYWwqWXwoIFRjx0RkmIgtJivAFvxOa1WmsuGXZJ2zuqOwxlq6BmP9TsgapdUFsADSUQ8AYrSGtQdlBOSOkJ6YO4yuljDsqkioe5865yV+PeGzngLSMDxsXeG1sQLJk0yUxTeyxCjN57D55+utNMOk6Dr4G9x/ZGXJ9iT2Fy38lxG29I9hA8FrFW9d56VhWu4sIhF8KQW2HbE5F3lv8qnHYX9D4LbB3siZiaG2PcliAkFyK2YuS660zm4XvvRQ64/ewzmDjRxIAMHBjfVjPNCQRMjNjfn5hA9XPVaItKoWG9WnWFUDQfDs+F4iXgKTd3sAF3MAvJinrwVUHtfv4H+NpwO9jTQPtMhlLvc6DvZdDnIg4cG8GUp+xE2uO4ceazbMvrIAixMGlS250LSkrggw/Mcd4ZN0pVVaZn58aijaQ50/A2hI9BSHWkxjUMwG6zMyBrAPkV+WHXewNePtn3CQ/NeAgyh8DYR2Hbr8Af4S5z2Q1w6QpIH9CxAqfXFVmv/3d8S9YIQqKRacQYUcpUQR83zjqY9sABUy/queeMkLAqedARampg82Y46yx48UWTbZjhyoj4sDXWyvF74MDb8MkMmDUc1t0LB94Ed5EJdPVVRSG0QslQfjICNWRoNxm+CjKK5pCx+SdkzD+HXf98CIcj8sly4kQRWkLi6dmz7UryYGIz4328RsIXLKm1snAlDb7Ig3r93rhlIjZyRp8zLNevP7y+6QZuzI+MkIqUmeguhnnToGYf+LooS0gQkggRW3HA5YI5c0xleasMO48HHnzQFER99VUjumIpfurzQXW1ac78la+YO/Vt26J8s7sU1v8A3u4Nq74JJcuD4iqB1Vj9deCv51Chz3Lq5uyzpbCo0Dncemvbwn7DBnNcJTpho6YGfv1r83z+vvk0WASa+7WfAVkD4jr+1H5TLbMfPX4PByoPmBc2B5z3VrAURKQ3HIN5U83NWyLPK4JwAiBiK0706AFLlkD//m2nix85AnfdBaedBj/6Eaxda6YzKiqsS0X4fKbuT3W1md54/nm44grTQHnu3CgN9Xtg66/g/cGw+znwVZtHNKT0gh4Tof/VJiZj5D0w8l4Y/j8w8DpTPTo1F8s6PECDL8WyvlZbKfmCEC+uvTa66cEf/ajtulyxEgiYIr4Aaw5Zp0EOzh4c9+4PY/uMJcMZOcbBYXOw+lCzDt3ZZ8ClS8HZnYjHvK/W3MwtmgmVn5vAeYtAfEE4WZGYrTgyZIiZyrvqKhOn1ZbXqqgI/vIX8+jVy3imRo0y02ijR5taU3a78YgVFRlRtmMHbN/eDg9Wc8rWwbKvgLvEeJnaoudk6HMh9P+SEVLK3pTWbXPQpNUDEPAB2sR4KRuUb4TDs6F4MZQsA93kFnB7Ui29BFZtVAQhnowdazoUtCWkliwxHuSJExPTo7O21ni16urgaM1RqhqsU5itipB2lDE5YwhYFASt8dSwtGApXx371aaFPSbCZStNGIK3Mpg0E4aSZfDRaOh9Loz5sYnf9DeAs5t1+x9BOEkQsRVnsrNh8WJT/fzJJ43HyiJO/ThlZTB/vnkkhIPvwYpb2xZZqX1g6Dfg9O+bO1abs2ULjmjbcfQ+C3pMglH3myD5PX+FPX+D2v047L5wSYrH8YVvBScIcUcp05Lnr39tu+PDbbeZG554J7gEAsbb/fvfm9erD60mxZEScRrRaXMypd+U+BoBnNbjNNy+yBkDGh2+fl/30TBzPXx6PVTtjBw0D1C6ApZ+GVw9IPcicyOXd7k57/jrjVhTdpo8ZTq4zAb2dPBWwFHrGoKCkIyI2EoAdjv89Kdw5ZXwve/Bpk2Jn4Kw5PM/wqafmJNZJJzdYfwvzPQgGhxxCJqyu8wDjOg6/Qdw6H1SF27Fbo8sqrqqrpFwavKVr5gYyuo2ZtN37ICf/cy04opnv06322Q7NsYxLjuwjOqGyMakO9PjHhwP4LQ7ycvMo7CqMOI2u8t24/F7cNlbBbplDoHL18DOP8LmnwdLw1hUc/aUw8F3zAPMTV36IFMwNa2vKSWjlNmPuxhq803ZGavq9YKQxIj/NoFMngwrVsDs2SYTMVElH5qTmtrqQrDz/2DTo9ZCq98V8OV8I7QcafERWq2xp5p9D7iObuf81DJO5rPPxLsldB4zZkQf/P7HP8LWrfHrClFTA7/8JWzZ0rRsYf5CtEWzZ1/AlxCxBbS53zRHGpuKNoVfabPD6Afgqt0mhtOWGr0nPOCFmr2m9Ez+q7DvJdj7IuT/E47MDXrMRGgJJy5xEVtKqZlKqZ1KqT1KqYfCrL9fKbVdKbVZKbVAKTU4HuOeKFxwgREQb70FM2eaAPp4VkdPTTWP006Dn/zEFFIFoGIrbHzIeupwxD0w4y3j1nd0QrCU3cXY8SmWcS8bNsSWpSkI7cHhgMsvj25breGrX227Plc0+HwmDuy3v222UPnZWrzV8n0ev4ch2UNiNyAM0/pNw2ZxWfAEPKwqXGW9k/R+MOMNuKYARv8YnD3A0YntIFLj0y9SEOJJzNOISik78CxwKVAIrFVKzdJab2+22WfAFK11nVLqO8BvgRtjHftEQikjtGbONFOK8+aZqYu5c0EpjcMJDW7VZj0fm814yJQy+zn9dPj6103cybBhzTb0u01shNXd4KgHYPxjifFkWXDGGdbTqhs3dk4BSUFo5Oab4ZNP2p5KBDh40PTufPfdjnur3W4Tp3X11a28aull+LWfbq7w4iSgAwzrMQy7LQFR+piG1A67g5QIhUgb/A3sLY9c2b4FqX3M+eWMn0Dhe7D/DTi6IJhM44/dU2VzmYQc7YecGTD4RvNwdMIUgiC0E2VVZTyqHSh1DvCY1vry4OuHAbTWT0bYfhLwZ631dKv9TpkyRa9bty4m204ESmrKuODp29i108bXBzxB4PAEtm41J32Px5yIXS7zGDLETE2OHm2yFkeOtDjZ73vFFCiNVN+mx5lw6aedLrQaGTAADh2KvL6iArp37zRz4k80afkxHnsxIfa1wO2GPn2iE1uNTJliBFpmZvtuDurq4PPP4ZJLzO+8NQeG5VBfWRrx/Xk1kJXAIqt7e4DfYs5jYEof0g51sJWO1lC1A458Aoc/hOo9UH8E0E1Tjlqb1wCo4G9BmexF7TciLnMo9LsS8i41iTjxFJ9ybMRGstuXQJRS67XWYbNX4uE/6A8cbPa6EDjLYvs7gDnhViil7gbuBhg0aFAcTEtu1h9ezxWvXUGZrwx9muZN5yLuvuluVl32VMS+hlGz52/WhQSn/zv6eIoEMGmStdh65RX4znekkrzQOaSmmt/b009HXy1+3TojuD791FSjb6u+Hpjp8U8/NfW9Ik1FDtwXWWh1BqdZ97AHiqO7oLaHDCDHA70AJ+bKpABf8FFuhqUK4FDwsQx4JHRfR48a5SwISUSnBsgrpb4GTAGeCrdea/2C1nqK1npKTk5OZ5rW6byw/gXOf/l8SupKCBBAKUWdt44XNrzA9JemU1xb3PGdu0vgmIVXMGu0abXRhfVtZsywFlLPPSdB8kLn8v3vt19D7N0L48eb0hF1dZGnx6urjQZ48EH44hfbiPnKPQVjjmqB/cB6YBVGR30KrATWAnsICq0oOBU/PyHpicfV9hAwsNnrAcFlLVBKfQH4CXC11rqTOo0lH26fm6+98zV+MO8H1PnMmdmhmrxYdd46PjvyGWOeHdN2IGokyj8zTaAjMeBqUF0bFPXlL1sXh9y5E3bv7jx7BKF/f7jssvYLrtJSuO8+MzX++OOwdCkcO2YyDQ8fNk3q777b7P+556LIfCwqMtMsHX1EQ6L3LwhCC+IhttYCI5RSQ5VSLuAmYFbzDYJxWn/FCK0YXDYnNgUVBUz66yTe2fEOdd6g0LI56JfVj0dmPEK608RPeQNeyurLuPgfF/PnNX+m3XF13mqwSB0nrR9ECIDtLEaNghEjrLd58EHJShQ6l5//3EwpdoTycpNZeMEFpiNEt25GYF17LbzxRttFUwVBOHmJ2b2htfYppe4F5gF24CWt9Tal1C+AdVrrWZhpw0zgv8F+Xge01lfHOvaJxNzdc7nxrRup8dYQCLausSkbOek5rLxjJf269UMpxe9X/v64EKv31fPQ/IdYWrCUV6555bgYaxN7Kpb9Cb3VzSo1x8A7eeDuYKAsNn7wAz/f+17kIqaffGIeV1wRXTyMIMTKmWeaRuiLF4sTJyK5uWZONFmRaUQhCYk5GzFRnCzZiAEd4PHFj/PUiqeo9zUVFlUoeqf3Zu1daxmcbcqOaa154OMHeH7988cFF5hCgv2z+jP31rmc1vO0tget2g1zJkQuZNrnQrjgfXBmxfJfg3/HFiRb+2VNTo518+0+fWDPnvjWJesUkj0jR+yLyNq1cOGFXdf1Ieb/lny3sSH2xUay25dArLIRpYJ8AqlwV3Dpq5fyu5W/ayG0ALJTs1lxx4rjQgtAKcXvLvsdt42/rYUXq95Xz77yfUz860Q+2vVR2wNnjTAtLyJRsjRyw9hOJCPD1Aezit0qLoY77ujcC1+XtlYSupypU02JFZucHQVBiBPi2UoQb21/i9vevY0GX0NI6w2nzcmlwy6le2rkQlIrC1dysPIg/laiyK7snD/4fObeOheXwyKdb9uTsOVxCETIRRhyK0x9HpwxNHmL0bPFLZr9+2Hs2LYFzkMPwaOPJr7lUV2daTj89tsx7ijZ7+7EPkt27YKJE629rolCPFuIfVaIfUmLlWdLxFYCuHvW3fzts78ldIyslCzW37We4b2Gh9+gvgg+GAk+iyqN570DfWd2vE1PTDFbwC3mt/fkk/DEE20Hwz/+OPzwh4kTXLW18O1vw7/+JRc84JS373e/g8ce6/wkjXb/t/we8JRBQ6l55F3c9ntmTwK0idtUjuBfu8liTs01pWFScyCld6tHL9N6J5Y6W0nw3Voi9sVGstuXQERsdRINvgYmPD+BnWU7O2U8heKNG97gq2O/Gn6DA2/Dyq9H7o1oTzV9EXMvAEcMHq5ItOX5CootrxfGjDGxWW3x9a/Ds8+ajLF4tfRpaDAX1BtvhPnzzTIRW5zy9vn9pmjp5s3RN6qOBxH/W94qOLYeyjcH/26E2n3gqzfZxcppPre7qqDS4rPp4YK3LzZ19rTftM/RPrP/hlJoKDPnDJvLPJQd0MFtPfDlA5CW1/H/YBJ8t5aIfbGR7PYlkERXkBeAQ1WHuPLfV7L7WPjiUArF6b1OJzOlfaJGa82usl3UeGpCpiM1mtveuY3qhmruOPOO0DcPuh4Ovg0H34VAmCqKfjcsuQrO/AMMv6vLeoo5ncabdNFFbU/b/POfpgL3u++axtuZMWrEmhpYvhxuvRXKymLbl3ByYbfDf/5jphM7K46vxXVKayhdCbufg+IlxlvtSDfHbfPwgPRB0HMyZJ8BvaZBwVBw9TQJMPa0YJsbL+goFKPNYfoNgsla9lSAuwjKN0D5JqjYFJvQEoRTFPFsxYHF+xdzzRvXUOOpCYmxAkh3pPPeTe9x6WmXdmj/bp+bi/5xERuLNuL2hYqmdGc614++nheueoFUR6siQb56WHoNlCyL7OECyJkOZ71kAuudcUr9i9Kz1ci998LLL0d3YbPb4ZvfhJ/+FHr0aF+2ot9vRF1+vpkmeued0G3Es4XYF+Tpp+GRRzpHcKWnQ221H/b+Hbb/FhqOgq+OkLp5PSfDqB+a/oA2JwS8RojZnIkzzltrPGCuGJuWJtF3GxaxLzaS3b4EItOICUJrzW+W/4ZfLPlFSLZhI2mONN684U2uOv2qmMaq9dQy4+UZ7CjZQYM/NOg9zZHGkOwhzLl1TosMRwACflh3D+S/ai24UDDwWhh1P/Q807yOpX9iO8WWzwczZxpPk2U7k1Zcdhn86EdwVrAjZyBgPF6N2WR+v/FguVxmynDhQvjNb2DNmsj7FLGF2NdsmNtvh7feSqzgSkuD1/4V4Npe10LRAvCHCRazp8OFH0GvKWBLNZ6oE40k+m7DIvbFRrLbl0BEbCWA6oZqbnzrRpYULGlRE6s5aY40Xr7mZW4ce2Ncxqx0V3LOi+ewt3wvHr8nZL1d2clwZfDWV94K70Xb8QfY/KiZgmhrSiF9EAy5Bfp/yQivgNcst6WCPUIWZMBrantpbUTam20ItVtCf3u1tXDOOSYbLNqGwM0ZPhymTWuaYgwEjNDascOIq8LC6PYjYguxrxk+H1x8sfkNdeR32RYZGaZ6/YN3f25q5AVCj28Aht0OU5/r0ibyMZNk320IYl+H8PnMzUhW97btmzNbo5S5IW78a7OZWNz09NCHyxX/3ueJQMRWAhjz7Bh2lO4gLUImn1KKv1z5F74x8RtNC/0N4CkPPo5Bw7Fmz8vAW9EUrNr4Fx3MFnKAzcExr49pK97mcEMNpkp88KEAFN6AD1/Ax0e3fMSVI64MNaxqN6y63cRe+KJMs1J2yB4P3UZAxiDIHAEZA03wLMqILPcRqNoJdQehZp8J4I10wWgkjNgCqKyE884z/RE9bewiUYjYQuxrRXW1EVzbtsW3JER6uplC//WvQQXcMHs81BaEP37S+sPMtSYjMJayLTF2f+CWGOr0JeF32wKx7/guDh40N76HDpnn+fnmhrW42PQEra42MwdZWSaco08feGdFHtkNkX9btd1yefW3Rfj9RqB5veav38/xZZWVJoa2rMw0K2g+1p49ZpxkRMRWApi1cxZFNUWhKzxVUHeQ4Q4fF6e4oXIr1OSDt9IIKHtKUDwF57gCPlN6wZEJrh7mYXM2pWKjggVIA+avt5rSumLeKy0lEPAYD5WyYbKFgtvoAFffsIm8nHHhjdcajsyD9fdB3SETPN8VRU4jiC2AqirTpmfTpq7pj3hSiC0dMBlmxwV+8OGthOF3tv3+jY8Gf4e2pt9j64ctWDqg+e/3+CO74zFEyfD5hcHtNoV4Fy2Kz5RiWpqZ0v7e95ot9FTAuu/Dwf8CttCpf0emqZM35keQ2teIMkem+S6iJU5lWzpEkn63YOJjbX374Sotj7iNzs1FFYU593cWCfz8amrg7383CUiffWaEj8tl/rrdpufnF75gkplmzIC+fc0MQkODEU1aG/PsdvO+5p6rtswOBJoejQKscX8Oh2nZ5vGYm5NkRcRWIvHVmmy/vS9C+WcmU8ie2jJjKKUX9JhkvEM9JkD3saYZtKObEVoBn/EOaV90YyqbucDZU8x7fTXmIlq1y6SDV26F6f9uez9amyyjnX+GA2+ai6cvQqPCeJOaC9dZn7ACAfj97830itvdeedfh8Mc7DHRGRcUT6X5vss/g8rtULsf6g+Bu9R8j/4G000gY7D5vbl6grM7pOTA9IegtDLyvvv0hn0bMCK/+cMP+E0coLfa1Hdyl0BDiRFxnnKoPwJnv2jqMrWDxsQFtxt657T9+e3ZHX4qItzz1FQjbKy6FbTHzocfhj//ueMeLpfL2PTqq3B1pC6xngrI/yfs/D+oPWDOFX53S49XWn/ofRb0Ogt6ToHuo833qwPBcIHgTZSyBcs42ABb082e1pjvWIP2mO8VDW/1sP4PnCRiq9JdyYqDK1i4fyFz98xlV+kunHbn8dCQxgxwFew1m+5MxxvwMq7POK4YfgUXDb2IswecHX3f2uZobY6bukPNboQqwFtj4vU8lea1vyE42xG86T7vP23ve/39zT7DZn8bl2mf+S353eYaYrOBcvLFB37InFXj0Dp8+4SSEujdu0kECS0RsZUI6o/C+u/DoQ/Ci5TUPjD06zDiu+ZC5683adj2BHdU1gFjS3v7HvrqmspEFC81B7tyxC6+7OmYaU4b9D4HBl4Dg28yno8o2bQJrrnGuJMTXdE7Pd3U/Fq7NsYdJfKCcngOrPteswtwQ8tSACO+A8O+BdljzQla+wCb8TLZG+smxRmtjQ0BLw1uP7v2Z1NQAAUFsG+fmYYoLzcPtxuGDjWfdUaGuTPOzDQZpVlZ0L07XHdPHqkVkT0v3l65fL6oyAwb1ArN74wDAeN5qq01d+uVleZRU2OSKbJibAsKsG6dqc1WXBy5mXprlDKi7wtfMB6EnJwoB/PWmBuj0lXmUbEF6gpMbS2bk+N1sBovzI7M8AVJG+tmNXosj9fZ8hux3Fhnq+RTa3tOULG1rXgbr2x8hZWFK9lZupNj7mO47K7jnT6cNicBHWBw9mCm9ZvGuNxx+AN+Nh3dxJpDazhcfRibsuENeLEpGy67C4/fQ+/03ozpPYYZg2bwzUnfZFiPYeENqNhihFDtPiOyIHhNUOb/nPcFU4YnfYB5ODIBHbwZN7MWDBoJR4sj/ydz+8DB5kULdbM/rQUYTQLc5mDJpy7uuVexa5ciLc0cq81DOW65Be6+27SzAuN9stmM18npTFyLK4/HPHw+yM5OzBjxQMRWvPFWw6zh5k5Eh3GBZI2Gy1aak6AjiX2eVtQWQPGncHQRVH0OdYVm2kEHjOdONT+qgr8hfzDwPjXH3HF3GwG5F0Gf86DbyJhuherrjTfhhRfMbuKdFdaYvfjII/DAA3HwgCTygvLpV+DgW5hAvTD7uOYApA8030cs4r6DU03TH1vGit3TAfOZti4I2vq/7fc3eaASTSAQv3F8AR/5ZYW8/h8P/352GAcLzLR/69+mUub35fWazNmHH4azz27fWNXVJlbm4EEoKjIehiOHA9SXHcDuKcJFKWmqlAxnKT1Sj9K/RyG53YvomVFCVko5KY56bDY/NhU4/gCN1jYCwYfWNnwBJ9UN2eR1K8BpD+/e1am5qDa80pZ0WsyRpqCygKUFS/lg1we8tf2tmPfZHr458ZtcdtplnDfoPPpn9TcL978BK242z+0ZQY9VsxulKX+GkfeY5/76YGJS8EbJ5mx13rUgxpi8qi/5Wb8eVq82N7v798PhwyZ2yu024mrkSBgwAPr1M9OLPXtCbi706aPp1QuysxUOhzmXNj4aPc42W9MNUmOsVuPzQMCEkZSVmfGOHjW/9/JyOHIkDq3UEoiIrXhTs9+0wgkntAAGXg/nvmo8WbEQywHzNhCmflTMpAE9MeVwgyFl+AEfUAFE6wirru5QRdLKSvjb3+C3vzUCLFqPQiQyM4134ac/hZtvNlM7cSGRF5SAH47MgaL5ULIcKneYqaVGEWxzQdZY6DnB1E/LGAKpecbb6cjA3Cl7mu6UI/HB6dBgcQcdgXX7JjN38+V87nqCPXuMQKiqavJKXnQRXH65iffo3ducpLt3N56ulBR9/C65MQNJ66YHNH9uPmOlmj7uxueNrz0eE0/S+LeuzlwkokFrTWldKfvK95Ffkc++8n1sK97GZ0WfsaN0R+gbisfAwXPpdfQGVNkoHDqDbpk2pkwNMPPiDC67OI28KOqBer3w8cewbBksWGAC8j0e4xFrFK/DhplSJ2eeaTJvBwxoqjeXmtoUQ9PRyveNcTKuYOJxo3fw6NGmEivtpcJdwdpDazln6nVklkc+cN29s6nI30FeZvuLp3r8Hl7c8CJz9sxh+YHlHHMfw6ZsBFr9ztMcaQS0nym547ho4DQm9R5Jigom+9C4beMPrdnfxuVo6rxu1pXuYeGhjWw6tg+HslPfqiyPDUUATZ+0nlwwYApXDL2I28dcT0VRMRVFh3CXFUDdQRy+EpyBUpz6GE5VhyszC1dWHs60TOwpmShnJtrRDe3sYUIBXFng6IayOZo8U8fjK22oJVegPMfa/fk1Wm2VAOH1wv7D1Wzad5jPDxZzoLSU/JKj7C8pZl9xEQFPCnjToSETGrLA0w0CdtB20Ipurh5kubrTPbUbmSkZdEtNp1tKJmnOVDIzFVlZxvMcLisxPd0kTzkTWE4uFhIutpRSM4GnMZffv2utf91qfQrwT2AyUAbcqLXeb7XPpBZbAPtegbXfxQSwhongHnEPjH3EFAi1pwQz99qJBLFGxOeD99+HZ56B9evNRcVuNxeFSGbZbE3BnBkZxrtw//0mwyzu8QcJ/Py8XpOpefhw40NTXlyFzX0Yp+8IaaqIgbnldE8rp3vqUbJTi+nmKiHDcYxUeyUpTh+2lCxsKd1QriyUCgY3KTtKmSlfpRTKZkdrM0entd/8DTS+1thLF6Ii3HDolD6o61v+drWGQ+WlfLprE1sO7eHQsXJKKms4XFZBcUUNR8orzEnalwraBijsyoFN2VHajk3ZsWFDYQs+t5Od2p1uKd3IajxxuzLpl9WXwdkDyUrNahGz1XiyvvDCJhFR66klvyKf/PJ8nlnzDAvyF4RcmBOBy+bizsl3cs3p1zC0x1AGdR+Ey+6ivBxGjDDiqq7O3Ok3Z9w4E7hcV2f+X4luzN5RPH4Pm4o2sfrQahblL2LVoVWU1JZgt9lbFGYemDUQl93F3vK9x5e57C4UigxXBlP7TeXioRdzVv+zmNxvMpku6xs0b0MFlzw7GLffQ1PSUIDjcWn2VM4YfBm3nH4lFww4C6cj1Zyb7alB75EjODXraJnIhLI8puu99czf+wmvbX2NfeX5tBRo+vgUXmD7F1n/f/97/H1OpzlvaW2+61/9Ch58sOW+AwHwegIEvHVobw0KP4qAeahA03PM/1M1JlMFY/M05gFNzzXGm4k2tRSVMwO7MwWnSx3//g5UHiC/3NxkfLDzAz7a85HlZx9P+nfrz/3n3M+wHsMYmj2UoT2GkpUSh7n/BJNQsaWUsgO7gEuBQmAtcLPWenuzbb4LjNdaf1spdRNwrdbasvhU0ostMAGsB/4Lu/5sgtPtLhP71DzQvddZkHcJ5JxnAuRTc0xQovZjDmB78GBvpxjTfni9jYKGYcSW32/cwF5v06Mx/bb580lnti0WVq1s2n9zz0Lrv40XPIfDnFwcDhh6Th720hiEZDM0sJ8hrGEay5jBWqZSQwZu0lBoUqkniyqms4LpLGcaa+hLG9MgR4/Gll+cQLH14x8bz14kioqMOz/cUIkMam0cY93htby25bXjJ+uCygLK3ZGzuxJNdko3Bnfry9DuAxnYrR8Om4uFB1dSUHWIigaLJAHAaXOS5kgjw5VBmjONNEcaKfaURqdaWLTWuH1u6n311HnrqPXU4va5w3aXaESh6JPRh4EpZ3Dw1x9SV5163CPXnNGjYeVKcxz5fC2L93YVWmv2HNvDvbPvZfWh1VS28ZnGSrojndE5o3nmimeY2n8qjuaFXT0VYYP7AzgJqBTIvQjHxbMs968D3pYxUk1rjj9T234NO37Tbtv3lwzmT3Mf5HC3ezhwwEyLVVSYG8CGBnMDeOedJi6pRw/j8c3K0qSlmRsEl8t8904nOJxgCx7QWusQ7y80nn9DPcB+f8trQKPnd+G+xfx0w80cqTlKIFyIQhCnstHTlUa/1G70TcuiT0oGOSkZpDZOdTY+Qg4UhV8HKGuopaShhiPuSg7XVVDSUEOd3zorKduZyreHTuXJM74Q/F6UyaS3pxmPvSOz5d/0/iakpxMj+RMtts4BHtNaXx58/TCA1vrJZtvMC26zUinlAIqAHG0x+AkhtppTd9gEsJatN61xqnYYr5TNZe6YtD94AAdMrar0gSZIPKVn8G+OydBLzWkWxOqAxtIPjQ9vhQnObzgKW/83ojnq1sjfa16eOcgtycszgiMSubnmqt5BdF4eymr/XUkqMLNbUNFoQmKjnN3NgXx8eZiDOfeCtsc5uqRD5h056mDukm4cOGIj/0AK+wpSOVamKa+xU1Gj6NsnjfPPS6FntoOsbjZ69bTRu7cmI1OTngFpwcy8lBRISVFNmXsET8iNWXzK/O8CJryHQPPpPG1O0O4GhdsN7vqmgPR/7JvArNotLWx2AL1skGUDe7chDOs1hvG54xnZayQjeo5gaI+hpDjaH1/m8XvYX76f3cd2s6tsF5uPbmZv+V58AR/VnmrK6srwBlqexP+wpx+9Nx+OuE+XH0aVmkdKHCuiVKbA9hzY1wMCFuf/8y6/hQPn/YwVazJYsDSLz7akUVHpICUlgNNhLoG5eTamTbMxdqxi5EgYNIhgnEyTAGtoMBfV1hfgSLS+QXK5zI1RXZ2ZBi4vN4f8hHNKWHNoDcsPLmfR/kVsProZhaLW29LDr1DHg87TnemkO9PbFKqNeP1eI1S9tXj8nuP7ak6GIwOv9jKq1yguGnoR0wdO5/SMs5kwdCAAdpuP9JQ6MlNqyEipJSO1li9eVsMTv3CD32NipfwNJkGAZokCNKa42oNOKd3ss2tmQ4tDXwU/v2De4ob7TNmVVmgN1a7elFyyipK6EkpqSyipK2Hr0a38ceWfwJdmPLu+VPA2e+5LbVqnbcGHMgZoGzZlM95fm+24F1hrTQCfsR8/AR1A4wel6ZGezbWjrqF7Wha9MrvRp3sWud27M6zh91QVRRaR6vz3Gdp3GrkZucdFXMh/MESkNdsukvDRmhpPDTtLd9Lgb94+pKVHMTcjl9N6nhbRvq4m0WLrBmCm1vrO4OvbgLO01vc222ZrcJvC4Ou9wW1KI+33hBNb4Qj4g0U+94C7OFjEtMyIJfdRk/brOWbuxrzVzSq7B13ex2MHVNNdQmPNI0em2R/hrwY3Pvseu+u/bMwINJ10vV5zkX3llZbxLY1p8lq3rInicLTcLpznKnw8jfnr8zXFjTQuDwRgwrux3W0ceQryLOpvFWVA3wcjr7fizLKJrD97o8UGf4BRP7DeSTRi9eNvw9bH223fj0rgqYp2vEEDflfLE3jwJI1Wx6fswr5GgwqACv4N+1qDvQEcbnC4uTa7nnfOODP4Ww7+jhuncwIeEwCcezFNV6vmJ9TG1wDBOl7Ni/c2boKCz5+GXU+3+/MDG03HVhjamn5P4HcLQM4FcOniFot8PiN0mgfIHz1qgubLysz0eW2tedTVmWMsPd14QBqnIsMFKQcCTcdo4zmi0VNWV2fOF6mpZrqyMXN0zfl9w9cYDNLN1Y0vDPsCN4y5gUuHXUpORrQpl+Gp8dSwKH8R7+18jw93fkhxnUUcoc/Jj3QF9VXpVFWZz6Xxs6mrM7FAX/mKuVFwOpsyYtPSmjxGjd4jl8t8XuHOfW2d9/z+pgw6j6fJg7SuZDG/LLwoqv+3y+4iw5lhRKojBZfNhcvhwmV3YY+QUez1e/H4PTT4G8xfXwM13hrqvfWWntVGetjg2NffhOpdwRp9FSZL1VtlrlGubOMtsqcGw2NSTAkje0rT6+brml+3ADO1qWDP32Dv36L6HEIIc3wkCyeM2FJK3Q3cDTBo0KDJBQUFMdl2UpDkBU06al5AB47XrolE2DunONDWb15rsD9unY4YH9sieMXafJem6uFKy/iVRH120Pbnl+jxmxmS1MdGstvXUfO6+vvv6vGNDfLZxWhIUh8bHcVKbMWji+khYGCz1wOCy8JtUxicRuyOCZRvgdb6BeAFMJ6tONh24pPkP8iOmmeLNoU5AbR1MlEK9OOJD5I+UemUk3E0JIsdkUhy+zpqXld//109vrGho++Tzw5I+mMjEcTjircWGKGUGqqUcgE3Aa0jEGcB3wg+vwFYaBWvJQiCIAiCcLIQs2dLa+1TSt0LzMOUfnhJa71NKfULYJ3WehbwIvCqUmoPcAwjyARBEARBEE564jGNiNZ6NjC71bKfNXvuBr4Sj7EEQRAEQRBOJLq4QosgCIIgCMLJjYgtQRAEQRCEBCJiSxAEQRAEIYGI2BIEQRAEQUggIrYEQRAEQRASiIgtQRAEQRCEBCJiSxAEQRAEIYGI2BIEQRAEQUggIrYEQRAEQRASiIgtQRAEQRCEBCJiSxAEQRAEIYGI2BIEQRAEQUggIrYEQRAEQRASiIgtQRAEQRCEBBKT2FJK9VRKfaKU2h382yPMNhOVUiuVUtuUUpuVUjfGMqYgCIIgCMKJRKyerYeABVrrEcCC4OvW1AFf11qPBWYCf1JKZcc4riAIgiAIwglBrGLry8A/gs//AVzTegOt9S6t9e7g88NAMZAT47iCIAiCIAgnBLGKrVyt9ZHg8yIg12pjpdQ0wAXsjXFcQRAEQRCEEwJHWxsopeYDeWFW/aT5C621Vkppi/30BV4FvqG1DkTY5m7g7uDLGqXUzrbs60J6A6VdbYQFYl9siH2xkcz2JbNtIPbFitgXG2JfxxkcaYXSOqI+apOgGLpQa30kKKYWa61PD7NdFrAY+JXW+q0OD5hEKKXWaa2ndLUdkRD7YkPsi41kti+ZbQOxL1bEvtgQ+xJDrNOIs4BvBJ9/A3i/9QZKKRfwLvDPk0VoCYIgCIIgREusYuvXwKVKqd3AF4KvUUpNUUr9PbjNV4HzgduVUhuDj4kxjisIgiAIgnBC0GbMlhVa6zLgkjDL1wF3Bp//C/hXLOMkKS90tQFtIPbFhtgXG8lsXzLbBmJfrIh9sSH2JYCYYrYEQRAEQRAEa6RdjyAIgiAIQgIRsdUBlFIzlVI7lVJ7lFLhquZ3GUqpl5RSxUqprV1tSziUUgOVUouUUtuDLZzu62qbGlFKpSql1iilNgVte7yrbQqHUsqulPpMKfVhV9vSGqXUfqXUlmBs5rqutqc1SqlspdRbSqnPlVI7lFLndLVNjSilTm8W17pRKVWllPp/XW1Xc5RSPwgeG1uVUq8rpVK72qZGlFL3Be3aliyfW7jzcTRt7rrQtq8EP7+AUqpLM/4i2PdU8NjdrJR690TqRiNiq50opezAs8AVwBjgZqXUmK61qgWvYNoiJSs+4Ida6zHA2cA9SfT5NQAXa60nABOBmUqps7vWpLDcB+zoaiMsuEhrPTFJ07OfBuZqrUcBE0iiz1FrvTP4uU0EJmNanb3btVY1oZTqD3wfmKK1PgOwAzd1rVUGpdQZwF3ANMz3+iWl1PCutQoIfz6Ops1dZ/AKobZtBa4Dlna6NaG8Qqh9nwBnaK3HA7uAhzvbqI4iYqv9TAP2aK33aa09wBuYtkVJgdZ6KXCsq+2IhNb6iNZ6Q/B5NeZi179rrTJoQ03wpTP4SKqgRqXUAOCLwN/b2lZoiVKqOyYz+kUArbVHa13RpUZF5hJgr9a6oKsNaYUDSFNKOYB04HAX29PIaGC11rpOa+0DlmBEQ5cS4XzcZpu7ziCcbVrrHVrrpCgmHsG+j4PfL8AqYECnG9ZBRGy1n/7AwWavC0kSsXCioZQaAkwCVnexKccJTtFtxPTw/ERrnTS2BfkT8CMgbBeGJEADHyul1gc7QiQTQ4ES4OXgNOzflVIZXW1UBG4CXu9qI5qjtT4E/A44ABwBKrXWH3etVcfZCpynlOqllEoHrgQGdrFNkWhXmzshIt8C5nS1EdEiYkvoEpRSmcDbwP/TWld1tT2NaK39wWmcAcC04PREUqCU+hJQrLVe39W2WDBDa30mZpr9HqXU+V1tUDMcwJnAc1rrSUAtXTeFE5FgIeirgf92tS3NCcYWfRkjWvsBGUqpr3WtVQat9Q7gN8DHwFxgI+DvSpuiQZtyAEnlPT8RUEr9BBOS8lpX2xItIrbazyFa3jENCC4TokQp5cQIrde01u90tT3hCE4vLSK54t+mA1crpfZjpq8vVkolVQ27oPcDrXUxJt5oWtda1IJCoLCZt/ItjPhKNq4ANmitj3a1Ia34ApCvtS7RWnuBd4Bzu9im42itX9RaT9Zanw+UY2J6kpGjwfZ2jT2Di7vYnhMKpdTtwJeAW/UJVLtKxFb7WQuMUEoNDd6B3oRpWyREgVJKYWJmdmit/9DV9jRHKZXTmN2ilEoDLgU+71KjmqG1flhrPUBrPQTzu1uotU4KzwKAUipDKdWt8TlwGWZ6JynQWhcBB5VSjf1bLwG2d6FJkbiZJJtCDHIAOFsplR48ji8hiRIMlFJ9gn8HYeK1/t21FkWkzTZ3QniUUjMxYRRXa63rutqe9hBTBflTEa21Tyl1LzAPk43zktZ6WxebdRyl1OvAhUBvpVQh8HOt9Ytda1ULpgO3AVuCsVEAj2itZ3edScfpC/wjmHFqA/6jtU668gpJTC7wrrkO4wD+rbWe27UmhfA94LXgjdI+4JtdbE8LgiL1UuB/utqW1mitVyul3gI2YKZwPiO5qnm/rZTqBXiBe5Ih+SHc+RjT1u4/Sqk7gAJMS7tkse0Y8AyQA3yklNqotb48iex7GEgBPgmeZ1Zprb/dFfa1F6kgLwiCIAiCkEBkGlEQBEEQBCGBiNgSBEEQBEFIICK2BEEQBEEQEoiILUEQBEEQhAQiYksQBEEQBCGBiNgSBEEQBEFIICK2BEEQBEEQEoiILUEQBEEQhATy/wEo5HIT6je4FQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_20\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_21\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_22\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_23\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_24\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_25\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_26\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_27\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_28\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_29\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_30\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_31\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_32\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_33\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_34\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_35\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bYnWEuDwuar9Tm3SHlPtYjVZUVBwe+ZE3r9acPQ/tiSjUcO21cvPVCinYbBIeGjhQjI5IycoSzaqLLoKDrorV2fKmpNK2rdx0Sn71BoOE/gYPLtv5liQ3Vzv0GIgMR0ZhgjjI921QDOS4ZbKubq1O2pNpmAzhOeo3bYK+fbVvcooC774L99wjxnRZycqCZcvkswyVWJ03juPwZ1twHtd+vuFl0O8nMFjFgCoL7mzY/AZ0ez3kt6w9vJZzvj2HHJecp9lgxmK0FBbCWI1Wdj+0m4ZVItd3+ucfWTSFYuDEx4sH7Kqr5Hqx2yUna+tW0eCaNUvCkVqEMp3++KNcZ9EswImLCyH/7NQWmNkrNrl6gcLG7hyY0lS8n7EigmKMaFEZpB9OL+rGTqupVCrS5RHmee84vqPwRlCcvSf3ciI3WPemsjFt1zS8apHwa/H/A6Rlp7E6JXxD/bffYMYMbUOrShW5WQ4aVHbDIyEB2raVJHJSU2VGDucRjHDGSU3l118lkVdr2PfflxylaBpaEJ6hBTB+43iMSlHlnUEx4C4WnnJ73UzfNT2sMVVVvE6BwqbffAMjRkTH0AL5DM8OUwEhFucNwPI7A0s6NLsR+v0sOlllNbRARE07PhvWW95e8rbPvNKiegviTHGFf6uofLzy44gPKSsLbryxdEPLYpHw8bFj8PPPUpXcubN0R+jQQRZmX3whBSHffBNZlwiXS7ye0a50LjX/TFVh2a0i91GebH41+p600wTd2NKiIm+AHk+F3nzDYebumX6GDkCcMY65SWVM3g3C+8ve9wld9m7U20fkMdedy0crPwprTLcbHnhAewI2GqVVRps24RsKgTCbK76Fj8slIU2tif7qq+Guu6JnbESKqqq8v+z9Qq8GwIWtL6Rr3a6Ff2c6M3lv6XthjTt3roi0av187r1XbqQVaWTG6rzJ2AWpM0UjqSQ1ekLvL8us+u6HOfQP8njOcabsmFI4r8QZ47i1661c2+HawlCiw+NgzOoxONyRGQovvSQViME45xyRPnnmGfFoV62q3XaqShUpAhk6VApibrklvGP57rvYyasEJWk8ZG4HyrFbSfb+/BzBMDxpNXpCu0eh04sijltvsBRmnIboxpZOxPy+7ffCFahBMRTmlGQ6M5myY0pM9nkw/SArk4vU++JN8QztOJTrOlxXGE7xql5+3/p7Yc5HKEydGtjL8cILkptljXIrsGj3DAyXf/7RnuhNJvjkk4o3tABWJq/kSFZRmDXRksjQjkMZ3nk48cXadKxMWcn+U/tDHvd//9M2Mjt1gvfei76hFS6xOm+2vikl9yUxV4EBU6XPYQVSMg/NoBi4psM1DO04FLu56IL0ql4mbp0Y9vinTsHnnwcPsV16qXi4GzYM/TqIjxfD67PPJDczFBwOePbZCjC2vB5Y92T5e5jWPxea8KnBAh2egSsPwvnzoOsb0Pkl6PI6nD0RrjkKPT6CuArqERohurGlExEOt8PH6LEYLcSbi24Cf+/+m1jkA3619iufv1VULm1zKVe2u9LnJmRUjPyy+ZeQx33tNe1Jr1s3ePLJir/5xoJZs7QNjqFDK8/5jl4+ujB3B8SrMaTVEC5ve7nP67yqlzGrx4Q05pYt0jhci3feKaqirEhicd7kHRNFd60KwY7Pg7laGY647Hi8HkYvH+0TQqxlq0Wbmm04p+k5hTmhAFnOLN5Y/EbY+/jUX7rLh+uug19+iXyhYbeLsfXmm6W/dsyY2Ajnlkryn+WvqZa5Gw5NKr061d4cLlovFbC2RuIVNVql6tVoAUsVsFSX9kBlKMCoCHRjSycilh1a5pNH4SzRKiHHlcOuE7uiuk+P18OYVWMKE+EB6trr0rRaU3o37O07GbuyeH/5+yGNu22bNFbW4rXXou/RqixMn67d8/zaa6V6sKI5lXeKKTum+HyvZ9Q7gypxVWhRvQW17bULtzs9Tr5Y/UWhBEkw5s/XDh+2bSs6YhXtcYzVeXNkDigaeVgmO7S+r8Ib+v618y+f0KDZYGZ45+EAmAwmLmntK+K679Q+ViWvCnn8vDwxpgPlavXoIX0NbWV07tntokV3222BX6OqMGpUbLpSlMqW1yJvwxQpm18v3atVfwhcvAESW5fuYTXGQVxNGDgL2j0WveOMIbqxpRMR03dNJ9tZNFM0TGzoU5quqiqz98yO6j7/2fsPeZ6ipaBRMXJN+2vk/wYjg1v4lnrtO7WPzWmbSx130SLt7Y0awXnnVS6ZhmihqiLkqMU5sZVIC5lxG8b5KKfHm+ILb74A13W4zieB3KN6+GvnX6WOO2uWdsh4+PCKN7QgdudN2sL8Br8laDoMKlpcF3hj8Rs+uZgWo4XrO15f+PeNnW+kiqVK4d957jzeXhqqxgF8/33gThA2m6QSRLMg4pNPAj+/dGkF5Wqd2gzpW8N7T81e0O0t6PsdnPGeVKuGgycPDvwqjckDUa0rnPM7mBPDK8ww2aDLK9Dk+tJfW8H8C28jOuXB5O2TfUQHz2x8Jp3rdi78O9edy8Rt4edUBGP08tE+eVg2s42r2l9V+PcNHW8g0VLkknG6nXyyMsiMl8/8+dqr3aFD/70qILm5gc8t7FYfMaAgQbx4KE1F9QmjXd3+ap8+eZnOTN5d9m6pYy9dqr19yBB/kdLyJpbnzZG5aDb4rX9hWEnssWDb0W1sOrLJZ1u8OZ4z6p1R+PfgloN9POhe1ctfO//iaPbRkPYxenRgT9I770T/ug8Wjv788+jpaoXFrs9Dbxhdqy8MWih5U+2fhBa3QvvH4Kwf4bJd0Pjq0MZJ/it4Uru1Dpw3C4wRelZNduj7jejBVWJ0Y0snbI7lHGN/elFSbrwpngFNBzCw2UCfFfnyQ8tDC2+EQFp2GvP3zffZ5lE99G3Ut/DvIa2G+IQY3aqbHzb+oClPUZxly7S3Dxr07w0hGgyBC1krg/Te0oNLOZbjq8VTz16PZtWaFf7dp2Efv/etPbyWvSf3BhzX65VSfi06dYroUKNKrM4bVZW8GS1qnxnJoUaVd5e+62NIGRQD13W4zkcrz2qyMrD5QL/3fr7681LHP3JEqgW1aNgQbr+97OHDkpgCOGhyc6WVk1YIP6Z4XZA0LjRV/5Z3wnn/SE6Uye7bXNqcCImtoO/30CuEfMGdnwUJWyowYJrkC4bTwLokJjsM/LvC8w6DoRtbOmFT0KKnALPBTM8GPenTsA8JlqIVssUgrXuiwTfrvvEx5AAGtxjsI+hYzVqNjrU7+rxGURQmbZsUcFxVFQkALXpU7oVSmYiLCxwerQzthEomiJsUE9d1vM7nNUaDkYtaX+Szzat6+WzVZwHHzc0NfBOMlqxHWYjVeeN1SR9DLeLqRHy80SA9L52fN//s4ym3m+0M7TTU77U3d7nZx3ud587jgxUf4NaqsCzG338H9lo+/XT5erD//ju8cHW9evDVV3D0aL7NnAlTpojmV1gkTwvtdc1vkWq/kkZWScwJ0PxmeW0g8tLgWABXcsG+qrST5PeyYrRLuLOSohtbOmEzeftkn9yKXHcuXep2oWeDnj6r0xxXTmSiiyVQVRExzHUXJdokWhJ98jkKGNppqI8hmOXMYvSy0QHHDtaqo3rk7RUrPYoCXbpoPzc7uql2YXMi9wR/7fzLJ0HcarZydXv/sIVf6Njj5Ku1X/kVbBRgMlVej14szxvVFfjGGal46aR68JMS4iOwdfHt+m81uz30a+xfaXZx64t9vNcgHSWmbA8uNfPrr9o5UtWqwZ13lq8He/x4MZhC4d57pXfjzTdDrXylg4QEuOQS6eH6/vui2RcSSeNKT4xvdJV4q0KVADHZoeXt0DVA+eW+nwOHEI3x0GN09ELYpngx/iopurGlExaqqjJrzyyfbU2rNSXOFEfDKg19DB236mby9sll3ueiA4vIcPiqEDo8Di5sdaHfay9tcylGg++Pe/PRzew+oR1CcbsDe3gqQ7J0LLn0Um0vz88/V1Dybj7frv/Wz4tpUAz0btjb77UXtLzA7+arqmrAm2+wnKzjAbrXlBexPG8M1sDho0hVxPPCcYFqe9U8Xg+vLnzVx5sH0KlOJ37a9BPjN4z3eUzdMZWmVZv6vDbTmcmzcwKr1LvdMG+e9nM33li+RrbTKU3PQ+GhhySXLCHBP//LaJSw5z33iDBqqaiqVKMGw1IDzvwufK01UwK0fQiqd/d/Lum7wD0Q24wUTa1oEu3xokgU+jGAoigXAh8CRmCsqqpvlXi+CfA9UC3/Nc+oqlp2l4dOubP92HafikCAsxqfVfj/bvW6sWD/gsK/956S1j014iOXS/9oxUc+lY8AbWu21Ryzfa32JJgTfCZvj9fD56s/590L/JOIrdbAFUoOR+XQXIoVF14oAp4lz3/mTOmV2LWr9vtiidfrZdTiUT5eTICudboGrLhrXq05O44XaXdkOjN5ecHLfuE3EI9eixbiLSjJ8uXShLwiiPV5YzCCKVHbs5G5C6pVTMLaqCWjNFt7rUtdx/3T79d8j1Ye6K4Tu5i0bZKmF3Dt2sCh42HDyle8d86cwMdSnMGD4Y03Sj82m02u2aefFimJgJzaFDiMXED398EQqpusBAYrnDUOpncp2o/HGbjyUTFKG6dodyuIRoupGFHmI1MUxQh8CgwGDgGrFEWZqqpq8U/5f8CvqqqOURSlAzAdaFbWfeuUPzP3+LbosZvt9G/Sv/Dvgc0GsuTAksIeblaTlTl752jfAEJg38l9TNo2ySe0ApCalUqHTztovsdZohWJy+vig+Uf8Mq5r2Cz+K7ajEaZ0LSqlJKSRDn+30rfvtCqFWzY4P/ciBHiDYh20nBpfLrqU47m+FeXrTm8hpv/0A4RaIXOth7dyj97/2FQi0F+z513nraxNX26PFcRgq7lcd7U6A5pC/y3H5kLVTpUiMbJ20u0pRtKK2rR4snZT2oaWzt3anuv4uOhp2bL4Njxxx+lhxDbtpUG16EagQkJ8OKLItb7VyAFkMOztDsHFFCzDzS5LvKKQIMBbE2g5QjYnV+wcHK9GGFaraHqDea/FliLxtn2BnarqrpXVVUnMAG4osRrVKBAIKUqkBKF/epUAJO2TfKZCA2KgZ4Nimas3g17Y7cUzRIZjgym7pwa8f6enP2kn6EFcDTnKNuObdN8nMo75fd6j+rhlYWvaO4jUBVaIP2tfwuKAh99pG1QrVwpvSLLW3QxkCp4jjuHDEeG5iPQjfnJWU9qbh84UFu09aefIj7sMlMe503dQaBoeC4O/hE41BND5u+bT7ojPWrj7T25l32n9vlt37VLW2Zh4MDyV3CfM6f0sOVPP4W/yLHZ4IcfghhohyaBN8jJ9v1GlNrLgjkRzngbzFXl72NLwRsgRN36vgqXGylvomFsNQQOFvv7UP624rwM3KQoyiHEqzVSayBFUUYoirJaUZTVR4+Gpp2iU36UbNEDsgLtULvIw9SzQU+/m0CkrXu8Xi+Td0yO6Fi1CNTWJJBw6YwZpTesPd3p3x/69NHOZfruO3j11egbXIHywXYc20FqVnjN0IOx/sh6MvL8v8BBg7RDx5mZ0s4lUI/MWFFe502ds7VvqGnzITc5avsPlYdnPBz1MUdO97+1bNyoLbPQs2f5hhAzM+HgweCvueACaXgfSb6oyQSPPKLxhCcPTqwJ/MY6A8QrVRbphQIUg8hGABz+W9vYMleB+oOjs7/TiPI622HAd6qqNgIuBsYriv8nrarql6qq9lRVtWft2rX9BtGpWEq26AFoXbM1ZmPRarm2vbZPlRRE3rrnh00/lFrSHQ4ZjgxWHFrht71vX+3Q0dSpwasV/y389hvUrq1d/j5qFNxwA6Snl90IcTrFw/Dyy9rPPzYz+m03np/7vN+2mjWlukuriuu116TEvjw1kMrrvKl9jiQza7HhOXCFWRVhrRv+geVzLOcYG9MCNKgsA9N3T/ebMwK14urSJbT8qWixfHnp8iIffBB5GNtul9ytqlVLPHF0SXCvVcdno9eA3GSHDk+J++6Y/1wLQMPLQxdW/RcRDWMrGWhc7O9G+duKcyfwK4CqqssAK3B6tezWYdquaT4K7gBnNznb73Xd6/tWpWhVMIbCy/NfDvs9pfHkbP8Qy3nnaXs6PB54990K6l9WjtSsCXPnBp7kp02D5s3hs8/EWApX+drhkPdMmSIh2/fe83+N1+vl7z1/h3/wpfDNum80tz/3nLb3ICtLqjTLy7tVrudtMEKn57WTkg9OgsMzwR3GiV+dCsNV/0cIvLtUW/HeoBiwm+0hPRSNFkNe1cv4DeN9Ty2AN6lt25AONWosXBj8t3PFFdIirCwYjWJw+ZAyHVwBJjFrPfFsRdPLZIyHnAOBQ4j1B4t36z9GND7hVUBrRVGaK4piAYYCJZN0DgDnAyiK0h4xtvQ44WnGlO1TfJLjEywJmlo45zY7F3OxqpZcdy4Tt4bXuic1K5WkUwEkn8vA4gOLcbp93VUJCdI0VsvT8eGH5e/pqAjatJGwaWKidkj15El44gmp5Hv2WVi/XoyoU6fEQCn4fFRVbiinTkk+zL594h074wy4/vrAKt6frvrU59oqjkExhPTQIsedw5y9/iXvTZvK8WjpK23aBNddF30jW2u88j5vWtxOwGl/+W3gOBpzr4Oqqvy65Ve/7fGmeN447w023785pMdDfR7ymWcK+Hb9t8X2FThs3aRJ1E4pJGbODFz5DCLzUNYG8DabSEZUKW7LHJ4NBOhL2OLW0qsUw8WUAOnbA8sw1Dk3uvs7TSizE1VVVbeiKA8CMxFZh29UVd2iKMorwGpVVacCjwNfKYryKJIsf5saSRKPToVxNPuoT4seAFTo1aCX32t7N+yNzWzzSX5dkbwCp8eJJUSl4A+Wf6C53agYiTeHVjGT7cz2S65XUflp00/cdsZtPtsffxy++QZcJe4zeXlw1VWwZEn5V+aVN/36wZo1kjdy5Ii2d+fIEUmq/+gjCVe0ayePJk1EJsPphLQ02LYNtm8PXY1+xu4ZftsMioH+TftzQYsLQhrjly2/sOGIb2mlQTEwadskzm9xvt/rP/sMFiyQDgIlZ6MZM+D888WrZ7OVTV3e5ZLPZcQI+PHHEvsp7/M22aDrG7D+GfCUsP7cWTDrTLhgqYQIy5owHYBFBxaRlp3mt92rermz+53UsoUW9Li7+918tfYrXCWMw1Upq9h1fBeta7Yu1NHTWiyV5+/Z7ZbcsUCccQbUrx+dfXm9slgA8q3NAG2aAFrdG70QYgGKAXIPaRvt5mriTfsPEpWIdb5m1vQS214s9v+tgL8LROe0YU6StOgpXm7u8rpoU7ON32t7NOjhJ1JoMUrrnv5N+/u9XgstbSGrycoz/Z5heOfhIY3x7tJ3+WbdN4UyFAVM2DLBz9hq0UKqk/75x9/gWr8eHnwQPv44ugm1WVkVIzMQjNatpYT8wQdF3NTlknCqFunpsGKFPMpCWnYac5Pm+m23mW28O/hdejQIrW9SpzqduOmPm3wEcL2ql3Ebx/HekPewmnyNB7td5B569dIO76xYIdIYb70lOV4WS3g5Pl6vjLtsmYhPJiX5GlsVdd60eQBS/oIj8/1DPbkpMOMMOOcPaewbg4qxUYtHka0R1upYu2PIhhZAh9odqBpX1W+u8Xg9jF4+ms8u+QynM7CxVZ75Wps2yfXjCBBZGzEieir2iYny+wUg93Dg8sf4hhAfI8Mn+yB4NFZrtfrK9mi05znN+G+VA+hEzJTtU3xa9IAIiJZUawfpUVjTVtNnW44zhxm7/FfxWmxO26zdVFeF28+4ndY1W4f0uP2M2zW9YPP3zddcWX/9dWAPxrffwn33RS+0lJUFY8dGZ6xoY7OJl2/9ehE+jY+Pbu+4kgbr12u/1gyHmQwmv/y/YJzf4nwcbu272e9bf9fc3qFD8O/91ClpmdKrl3wmmZliZAa6abrdRcUEf/4Jl10mnkKt8GmFnbeiQL+fA+fNOE/CnHNh+e3gOA6uEHvLhMChjEOaBma8KZ5but4S1liKojC001BMiq/V5PK6+H7D92Q5s4Jet+WZGrBkif8irgCDAYYPj67xV5iPlr4FjHHaL6o3KHYh48ydoCHZQ7XOkWt5neboxpZOqaiqqtnjsEX1Fizcv1Dz0aJaC5/XulU3v2z5JaT9fbryU03BxnqJ9WhSNfREC60QJ0iI5dt13/ptr19f5A4ChRfGjxfZgKSkyNvZ5OXJDfzOO+HRRyMbo7xo21ZEEhcvFs9OYmLgnK7SSEyUlfvFF0v1YwFe1ctHKz7yU04HuLzt5Zo98wJhM9vo26iv3/YsZ1bAhGyAoUNh3LjgYaWtW8U7VbOmhGheeAEmTZLQ0PbtsHmz5OS8/rrk/9WtC1deCfPna49X4edtqQ4D/xZV+UAcnAiTG8Oq++H4SpEQcJ7yb+/jdcl2dw5kaSySivHJyk/warTuUVE1BUlL44aON2A1+7uEFBTGbxiPxRLYMxtoeyyYNSuwptfZ/jVGZabQmZW+Rb43LRpdKdpYsSArQHJmYqv/pFcLohRG1Pl3M23nNDKc/ro9f+/5mzlJ2v22SvZtA0g6lcSeE3toWaNlwH3lufMYv3E8HtV3JjQqRq7tcG1Yx200GBnScggTt/km5+e6c/lo5Uc81e8pv5vaVVdJVdDkydo5S8uXixHy7LOS5wUlklEDkJUllUI//yyJ5idPhnUqFUr37vD99+LZWbwYJvzkYvlSF3v3mcjLM2CNc6DgBRRkNavgcMWBotCssZMzuhu5flgcQ4b4e5DmJs0lS0NyoEpcFa7v4N9ovDSGdx7O6pTVfmGqHcd3sPXoVh9NuOJcey00bCjGYE5OYMkPl0sadZe1WXelOO8a3WHwQvhnoLTxUTWsD08u7PtBHnG1oHo3qNYFqrSXxr8eB2TvE7XwUxshe7//GPk43A7GrB6juZBqUqUJjas21nhXcHo17IVJo0VLtiubUUtGcU+Pe0GjahFEQ69mTc2nos7mzYGfGz48+vljheMdXxWgKlCBeudFd6fFCaTblhB47v+3oxtbOqUydp12vKtkrkQofLrqU94f8n7A5ydtm6S5qreZbVzV7qqw93d9x+uZuWemXwg0w5HBogOLNHPIvv0WUlPFsNIyuFwueOUVePNNMczuuUcSXBMSfF9vsUioYutWGfOnn05jkdTcIxiTpzLA+T0DBq6CQVZQVU6mm9mV2pKsvAScbgtmo4t4Sy4t6iRRt2YmisEkN2xza0i6BRpfLavbfN5b9p6fnAjIjfm85uHfDC5tcykPzXjIb7vL6+LjFR8z5lJtYVuAM88UTaZHHglsbEeLSnPe1bvBxethznmQmxpcSd5xDFL/kUcE/LrlV83KS4vRwk1dbopoTINi4Or2V/Pd+u/8xj6ee5wF++dTrdpAzcXNvn3lY2ypKiQH0Yy96qoY5o+dXK+9vVoXAhmhUcFxTHu7LXyD+t+CUlmLAnv27KmuXr26og8jdIK5/WP9Gcd431XfrKrp2YqEZlWbkfRIYEmHXl/1YnWK//duN9tJfyZdM0csGOl56dR5t47falpB4er2VzPxem1Jirw8CQMtWhS6rlStWqJHZbWKZyQlpXTF6Ii+nvK81k5uhHVPSk89gwncZUhaM1jl2BNawSUbSc1KpdkHzTS9oOc1P485t2h7TUuj7Sdt2Xl8p992u9nO0SePhlTNunIl3H67VCrm5JQ9v6cgjJqWRuU8b48DtrwJ296WsKAaRKMgEqx16XC4BtuObfN7yma2sWbEGtrViqwR6Zy9c7j6l6s156jBLQZz9P1ZrF/v/77x4+GmyGy8sEhJgZYttcOIDRrA7t1lq3YNiKrCLzbtNj2t74cz3ol+JWLBfn8OkGtw7SmwlFRd/fegKMoaVVU1O27qOVs6QcnIy4iaoQWwP30/3gB3rj0n9rA5TdvfPrjl4LANLYCq1qp0quPf/FBFZdrOaZzM1Y7nWa1S9n/rraFPhMeOwapVYqCtWFG6oVWpydgB8y+FWX3Fk+F1lM3QApn0PbmQvgmAsWvHanox7WY7wzoNi3g3QzsO1ZQYURSF37b+pvEOf3r3lgqyadMkpys+XgymcAoF7HaRwxg0SMKwBddDpTxvYxx0eRku2SIil8Z47T6KYWEQzaXENqzsM9VfOiaf6tbqERtaAP2b9verOC5g0YFFNGmpfd1u3Fg+HSJ275brQIt+/WJ4DLnJgS/YGj1iY2hBfjg6wH5jtc/TAN3Y0glKoBBipKiozNqrrSY/ZvUYPF7/vJFESyI3dLwh4n0O6zTMv/wdMBgM/LjpR413CEajaDHNni06UtHOq9DqR1jhqCps/0DK/w//nV++HYJbx1wNrHXAUgOU0o1ir+rl45UfazZTdnldXNrm0rAPvYAr212pKXZZWqJ8SQwG6R35449w/JjKN58c5OHbt3NmlwNUT8jEbHRhs+Rgj8vGFpeDxeTAbs2lW5tk7r5hBx+9vpfkgx5mz5ZQUVzcaXDeCc3h/H/gwjXQdqR8r6ZEUEKNcynyemM8NBsGA2fCpdt5Z8k75Lr847ImxcSwzpEbmABmo5mLW1+s+ZxX9XKiygLNMN3SpeXTiHrXrsBipoGaokeFrL2BhUVrhCYrEhFeV2BF+hDmhn8res6WTlB+3vRz1Mf8eu3XXNjqQp9tLo+LsWvH+gkUgiTbD2k5JOL9XdrmUl6a/5Lf9hxXDqOXj+aBXg8Erf7q1w927pSmzO+/L0ZYpNWIIF4SrxeuDr/4Kra4c2DJMEido62RU4C5KjS8FBpfI6XcBXkYqkcmWcUIeamQsR0OTYGDk/0SZmfvmR0w569l9ZbUS4hc/6dbvW7EmeI0tZz2nBTvqZa30w/VC8eWw/5fiD/wC9fGZXHtEAOcnw14ychJJDMv0SdXrUbCCbmWjDb5LOYBjS6HpsOh3vnMTppf+c8boGp76P4e3i6j2L1kHpn7lmHLWUZt0waqxafh8Rrxeg0YFBWjwU22sypHHB3JjDuTuAa9aTPgIiz5qxNVVVl6aCkWo8VP6sLj9TC8UwDdPFWFU5sk7yh9E5xYC5m75DpV3XKdGeIgoRl32msyRTFiMpoo7kPwqB4OmeZhs13sly+5fHlkDZ/DZdu2wJIx558fWXVvSOQdRVN+ASAxhonqqhv5DjQKLv5jzaeLoxtbOkEpqUwdDWbv9S/lmr5resC2Je1rtad6fPWI99e2ZlsSLYmaN7kjWUdYc3gNPRtohtkLiYuTJsXPPgu//ipG1549MlGGor0VHy8Te2IiPPyw5APVqRPpGQXGZpP9xMVJS5oePaSasGtXCY0FvLl4nDDnfDi1PnCpeNWO0O1tqHe+hBWD9TezNZZHzbPgjHchfSusewqOSD5SoARxi9HC0E5DwzvpEiiKwhVtr+C79d/5dRBwuB18uOJDvrrsq8ADqF44NBnWPi6Jvp5czUq9KrZMqti0NKhUX3X2fT/BoT9BMfJeXs/Ke975uN0iWfHzz/DHHyZcrsGo6uDC69xicmCz5GAxOXF5zOQ648lzSaw9Pl7aXrndMHiw5ERddJHC1vu3av7+4kxx1IivUbRBVaWCbv/P8rm5syUU5s4moOGQd5gLgeSW8bgxyusaDJHFQO2zOZRUk4E/+b/N44F586QXZiwJpBxvt0OzZjHcseOYto6WtW5sPUxK/negheqNzOCaVA/yQmxHgQGGl6OuR4joxpZOQLYd3abpaSor6Y50sp3Z2C1F6pbvL3/fr2IQIM4YF5Wb0JXtruSrtV/5GXR57jw+WvER464aF9JYdrsYSrffLpVrd3zyJdlJnVEPnsmuXb5hCZNJDJ7evcU71qsX9OwZuUDozp3www/woKEudbz+E08qdQsr6LKy4PhxWLtWboAmkxiGw4ZJHlqfPsWOQ1Vh6U1Suq9laFlqQO8vocFFEpYwmAILJZbEnP8d1+gO/SdDxjbUJTeyfc92FBRNj+J1Ha7z2xYut3W7jXEbxvkZHV7Vy4bUIAuIw7Ng1QPimXOXwX3pgwruDInQHovgvN05cGQunNwgj4wtkHNIvqsCb6LBAtZ63KbWZpwCKgrF82ZKPW/E+Pj5Z3jqKbl+srO1CwOc7jicbu3vPze3qIpzyhRpcA6QkVGVqtYgidGqKo2w1zwkqueevLCT9Gsrxbyxh/+Ao7PBYKHeNcewWLQXRT//DAMGxDCUh4QRtejdWz6rmKUTOI5q/56rdpSCiFiJixrM2jIiELmxFbKhBSGlPVQAurGlE5DvN3yvud2oGEmwhNbGI8OR4XfDA/h166/c3u12QMIMBzcuRtFwlCh5Dq6+8lk4/mzoB67BLY3hm9tALfE796oeds+aACEaW8Wp0uAwS2vdg6WuhROTTmC32FFVuWkZjdFTXd+0Ce6/X/oWejzwqjc1rPcXlzD46isx2OrUEc+c7OAVSJmuXfZfowecO0PED8vaK8+cANW7o1y8jr2NW+A9qlJyBWxQnZhe0NbCCof+QK4BVI3vwFh7P9xdYqPHAatHwr4fg8sfFCe+IdgaiqGjuiEvTTSnAnhoFQX2PrxX04NrUAy+elHOU3Dwd0j6AY4tE+O2IHxWEtUjHoysPfRnD7ktFVSTXbyPie2g2XBoci3GhOYBT2XKFBg5Ek6ciG4D7sxQxOePrYRV90LGTv9+jRGjinYY8rlfeqlUH5Zk4kRpwxUrgsk+dO0aOHE+KuQcQtPDVKVt4FyuaKCYtPcLUiRjqGQ9ysoJ3djSCYhWyxwDBq7veD23dr01pDHeXvI2c/f5tucwYGD70e2FfyuKwt7RsV2NnHUQnK8GetYFgeWXAvLWkrcAcHqcfLHmCx478zEUJXqaOamp8OST8Pvv0h4mGu1FPB7xWhTmnGXvh22jtHO0qnWF8+dHtz+ewQgGO6ZU/3ZJ0cYc6PMque/cVJh7gTTsDZarVr0bNL4WmlwD9mZiVHmLlZIp+R6/3MOQMgP2T4Cji3xW+VoCnD44TsDWt2Hnx4BSZHxoClNqY1ZU8OR/wRmbYMv/yeMG/3PLyYG77hJjKxSJE6NR+og2bCgeGbdb5Cx27Qrcwiggqhc2/R9seyf4514cxSSFGIY4MTwDeW9KcM01co4l87acThg1Cl58Mbp9Tws4fFjmA61WPZ06Ra8foiY5Aaw8a72YNRkHxLo1mLVDmHlHK19D2HJCN7Z0NFFVlX/2+osXJsQlMLzzcIa0Ci1hfX/6fpYnL/fJ1/DiZcqOKYwaPCpqx1venMw9ydg1RZWary96nQd7P6hZeh8Js2eLqnleXozL09c+oT0pxjeA8+f8+0u1nadg1pniBQgUtmp0BZzxnuS6GCwl2o1ofD72JtDyTmg6VAytjS/Cnq98DbOSeF3iYdz+PuANyYAImQBjHTwoshQHDgSuylMU6NsXrr9eKiobNJDXFm91YzBIruCJE9Ky6OefYc6cUq5bdzYsugbSFgU3tCzV5fNvcgNU6yTfgdeZ7z1UxLh1nhKpkoOTpMVQjr/myvnnBz7HMWOk/VIs2LdPcti0BHI7hVirEDF5ATzgtkaxT1S31IK8w/7bcw5Jxet/EN3Y0tFk27Ftmm01HG5HqcnkxenVoBdGjWTMpFNJHM857tew+nRh9PLRePGioKCi4nA7GLdhHHd1v6vMY3/0ETzzTOkK5nXrSnikVy9JhG/YUCZ2j0c0v9atE92vGTMC5I1k7ISUadpGRs9PJQH+31w95HHCvAvFE6X1GVRpC2dPBHvT8HvIGUxF4o3d3oLOL0qlpxaZu2HBZZB9oPQQprmaHFeVNhLGLAxhHpXmvxk7At9ki3H8OJx1lnhetHoEGgxiYL39NlStWpT4DkX/lqROHbjxRrj8cjHSXnsNPvlE4zr2umDOIKky1BLcBKjSTgor6p0vry/++ZeUt7DWlkeN7tDtdUjfJsUNafMLX5KQIL+RZcv8d5WZCc8/L30to+l0ycsTAzQQzWNtcziOa2+PbxjjHQPx9bWNray9UOec2O+/EhIVY0tRlAuBDwEjMFZV1bc0XnM98DISzN2gqmqAel+dysDM3TM1Na/sZntY5emd6nTS1BSKM8YxJ2kO13cMvw9cRZPtzOaD5R+Q585DyU9CznZl89L8l7i92+0Ria8W8Prr8MYbwQ2ta6+V/opdu0oYR+sGUbcudOwo4ZM335S8kY8/hi++KOZxSJ6mrThf/Qyof4H/Te3fxsp7pChAK0TX6Ao460dRvS/D9wlIGNacAAP+9H8udQ4svFLysQIl9tqbS95VsxshoYV4ghRjvvCoMT+cmSdGiTFObrL7f4P9P8KJNX7D5eWJRystTdvQ6txZmmzXrRt+4rjBIMYZwEsvyaLh5puLvUBVYfmd+Z+7hqFlSoRen0lbp8JijBBDXgVe2Brd5bM+uR6WFUnEP/us9CHUkm35+GM5zq5dAxuT4ZKVJT1QtcL/ZjPUqOG/Pao4AzRgtdaO8Y6RMPvJtf7bM3eB1y3f63+MMi9bFUUxAp8CFwEdgGGKonQo8ZrWwLNAP1VVOwKPlHW/OsWoWze87SEwadsk8jTCD2fUPyOsccxGs2bj6UxnJlN3TI34+CqSL9Z84dcoG6QYYNK2SRGPO22aGFuBcmf69JEy8m++kf9braWvxOPjJcTTurUYcfv3iwEGiLyB1g2vxR2SExNLynBtRmXfJzfCgV+0Q1j1h8BZP4HJXnZDqzglQ7LHVsKCy/OrHjXuyImtoN8voure6X+ifWWMA0s18fQYTPn5MUY5Vks1McBsjaDtgzBoAQxeArXO8hn2jjukklYrzHfDDeL9adGi7BV6drsYFL/+Wmzj9vcl8V/Lg1elLVy6VSQbTLay3ZDNCVCrL1y8qXDTJZcENnBUVdpzBarADJecHAm7njih/Tk3bx7b3ptA4GrauFox3jFQpbX29oztZe9EcZoSjRhBb2C3qqp7VVV1AhOAK0q85m7gU1VVTwKoqhr77NjKxOHZcGS+TK6BVhtlITWV5PRDKC+D+RUTWY5MmT1Sw6taK8DhdrAqZZXfdpPBxLnNzg17vH6N+2lu/3v331TW3pyBcHqcvLHojcIcNLPBXJinleXM4vm5z0d0Trt2SVuYQBPwc89JGX3HjpHfBBMSoF49+O47aYrN8RXaL2x4SXSNDC1SU+UaDecRjHDGSU2F1Q8GKIvvIKHDWOeqZeyEeRcECBsq0OFZuGgDNLoKTPHhJzQbzGKA1eoL582CM6WyeO3awE22L7tMDHm7PbpCm4WJ57lHYOML2udsbw4XLAdrfTnfaGAw+YQfDQZpIB9ogZKcDOeeK0n0ZTG4srNFX2zxYgnXahlbrVtrexWjhqoGzkG0VIvhjvOxN9WWlji2tETO43+HaPykGgLFMxIP5W8rThugjaIoSxRFWZ4fdvx3USeAa7aqAouvk1DBvMEwqS5MrAWzB8CGFyS/IAp8tuozANxeNz9uDNyCJhSWHlxKnMnfs2E32+ndsHfY453d5GxNqYhcd65m49zKzPiN433CogbFUBhKBEjJTGHWHu12RIFQVfE2BfJoffWVhEBstujcBBMS4IrLnNqJ8VCkCP9v5cTa/PCahgHXe6yov8cSrwfmXwwujZ6jihEGTIVOz4nBZyxjTEsxiNHV+FoAHnhAO1G8SxcxwKPdksqHTS9r6y+Zq8CgudJHMcZG/rBhwXWtNmyAM8+U8J9WBWEwvF4xtK69Fv74Q7Yd1khbAhEzjansQwD5ESB2+lrFsTXWTkPIPQyuaOnXnV6UV+DUBLQGzgUaAQsVRemsquqp4i9SFGUEMAKgSZMm5XRoZcDjhOQpsOMj+DADFKvGalkFV7rvJudxOLpQ9HO2vyergDYPSV5GBB3RXR4Xn63+rPDvt5e+zYgeI4K2oAnGtF3TNFWuc925YSXHF9CrQS/N7aqqMmvPLNrWaiuhnSPhCNdFkRBDWh6vhxfnvVjYDkVB4Yp2V3Aw/SBLDy0FJHfrubnPhVytCTBrFuzdq72afuQR8XhFu1raZnXLjVhrUo40Kf50UXlOnq5taNYdKO2HYtY/JZ+9X+cnsWsYe32/l+MwRVmHwGRj1SrRbNNyEn79dYwNrZxkSPpeuyKz2yiRIyiHPB6LBV5+WRYvgfTEtm+X3K0vvhBPVyiSEFlZkJQkgsHr1hVtDzSl1awZa2OroGWOltEVJQHAYCS0CCxsemyZtLD6jxGNWSUZKL4UbpS/rTiHgKmqqrpUVU0CdiLGlw+qqn6pqmpPVVV71q5dDkl8ZeHwbJjaApbfAUcXS5JtuOXaqktyRjK2w7onYXJkHoUpO6bg8XoKPSxHso6w7JBG2U2o422foim8WM1azbe1Roi0q9UOl8f/5pbrzmXitonyR0WHlUJg0rZJZDiKvBEJlgTu7n439/a818dzt/3YdpYeXBrSmKoqWlpaE3+fPtGvkCrEaA28+tXyuITC6aLynDxZfnslaX5L7MOHrixY97R23kqbB6HxFdE3tPKZNk07pHXBBdCuXYxtzJTpaN7kbU2g+a2x1X0qwf33Q8uWwUWHk5Ol0veKK2DLFvEGlhRozc4Wb3RysgjCdu3qa2iBVAVrUb9+jD/vgs4CWpRHhXGVdoHnl8Mzw8/bslZgjmeUiManvgporShKc0VRLMBQoGTm82TEq4WiKLWQsKK/YubpQE4yzL9MwoK5ydFr6eHJLlQ8DpdRS0b5tLrJceXw3tL3IhrraPZRDmQc0HwuEq8WgNFgpG3NtprPrTi0QlNiorKhqirPz33ex+NnNBg5t9m5XNnuSh9jMteVy/Nznw9p3FWrxKulxSefxNDboBhk9alF2qIY7bQSoKrS8kaLehfE/kaUMi1AKK0adH1DQmkxYsoU7dDYjTeWg85k8p/auVqt7optnz4NjEaYMEGKR0pjzhzRw2raVEKwn34qHRi++AKefloqNxs1kjxIrfXfyQApujGvD1E9BPRglYexpRikqlmLQ5PD/86vToXhqv/jNKLMfltVVd2KojwIzESkH75RVXWLoiivAKtVVZ2a/9wFiqJsRVqBP6mqagARkErM0SUw/xKxysPs2xUrth3dxpa0LT7bVFSm7ZrG0eyj1LaH5yH8Z+8/xBnj/Awgi9HCuU3Pjfg4z2l6DpvSNvm17okzxbHs4DIGNBsQ8djlwey9s0nJTCn826gYuanzTRgNRhLjEhnccjB/7fwLkM9/xaEVbDyykS51uwQdd+5c7Rvg2WeLtyGmNLxUQuAlvUwHfoG654avLXU64HUQsJVIfDmsng9N1V5Utbo7v81JbFBV2LxZ+7kLLojZbotIW6i9veGlFZIw3b69NJN/7LHQlPPT0qTdj1bLn2CcOqW9vVasCwIVAwGv80gJN02g80vSVLykFzk3BU6ug1pnRvf4KjlR+XWrqjodmF5i24vF/q8Cj+U/Tk8OTYElw0sXHVSMUPts0XqpdSYkts5vLeES9dyjS0X/5ci80HuwBeGDFR9ohugMioGxa8fy7Dnh9RScsmOKZkPoeFM8vRpq516FwlmNz2LchnF+Y+c4c5ixe0alN7aenfNsYa4WgNVk5bZutxX+PaL7CBbuW0iGU0JwDo+DF+e9yOShk4OOO326dmhn6NAY59CANJbe87X/zX//BPGy/BuNraDh5whzWcK6CQWgybXRq8LTIC8v8KnHPGPD6w4cmq5S9j6YkTJihCx2/vorNIMrEgL1hoy5xlaw/oSqJzJvYrhpArXPlmtaazW581NpiG3WaIj7L+W/pywWCQcnw9LhwdtK2JtD25HQ4nYK20iUzEOo1gXqDRYBQ4NZDLgdo8X6j4BsZzY/bPwBd76XLc4Yh6Io5LpzyXXn8sGKD3iq31Mhi2wWJKxrkePKoXv97hEdJ0iSvFYemFt1M3n7ZN4a5KeDW2lYdnAZ249t99lWzVrN5/MY0moI3mIeIq/qZeaemew5sUdTZ6yANf6ak4B4G2Kdp03d8yGupr+xpXpE8POcieHlD1nrlt3oiDXBcoMcx6TvXrhE45yrdS37GJUVryNffFUjGmCMMEs8CsUYiiIVmDfdBFOnxsbgCiTlUq1a9PflQ4HYrRaqt3xCtzV7gzvAB3DwD+j9ZeyPoRLxL+7FESWy9ooKcSBDy2gVL8Alm6H1A6JhYqkaeFI3mOV5kw2aXA/nzxPhwggm+R83/egjO6Aoio/YZo4rh5l7ZoY8XqAWPQB17HWoEhf5KqRljZaaxhYUte6prDw/93mf3o4Wo4U7z7jTp9rTYrRwbftrMRTLh3B73by6MGD3a1yuwJNxs2ZlPuzSMRih5yfaBtXhv2Hz6+GVaZ8OeRWKIlpaWqT69wItN8pidPyklPqwTlJQtIoCCJzEHTUM1sCVaZ5wu1fnE6ViDKMRfvwxNp5kqzWwXleVWDt0FCMBPVthNDUvE+YEqbTXwpMDe78Fje4i/1Z0YysYHqckwwcytKq0hct2iWyDyRZ+7kGB8nOjK2SceoNDfquqqryz9B2f0NaFrS6kc53OhX9nObN4e8nbIY85c/dMTWV0gD4N+4Q8jhYGxUDH2h01nyto3VMZ2XRkE8sPLffZZlAM3NL1Fr/X3tn9Tmzmohnb7XXzy5ZfOJypLbbjdMpkr0W0WoaUSoOLoWonUDR2uPVN2P35v0/xueGV2vlR+34EV2RFKmXGHaFbJUSjQ1GgU+NNms/Nnh3ZrkPGYJSG0lqkB0gkK0cMBhg7VsKK0TK47HZp4B2ImMo+QH6pZYDbuzNde3ssaHq9tF3SYvMrVGhVcjmjG1vB2PAcZO/TdsfW6AFDVorisbmMpdrGOIld958c8ltWJq/0uYknWhIZ3nk4N3W5ifhiuR8rDq1g36l9IY35+7bfNfsYWk1W+jftH/KxBWJAswE+np8CMp2ZTNk+pczjx4IX5r2Ao8Tqu0X1FpqhwbMan4XV5OvR9Kpe3lqsHSK1WqW3oRaOclp8oijQfwrE1UAzZ2ndk7D8djFCAoUETjcaXKTtSUqZDllJwQUhY8WJ1THfxRU9pmga8ePHa/cLjCp1AuRkHvozcu9WFFEUGD1amra3aBGatpYWVitUry7Vin//HTgVIOYpAhC4stVZjlGEFrcHrn7MS4Nt7/77FnMB0I2tQOQdg12faSexxzeE8+aIgRTL3mlBeHfpuz6hLYfHwZCWQ7iy3ZU+FX9evHyy8pNSx1NVlfWp60mwJFAlrorPA4hIOb4kZzY6kzhjnN/4NrONxQcXl3n8aDN/33ym7PDXHNt+bDumV0x+D8urFr9wqNPj5KOVH5F0MslvfKMxcO7Gtug0FgiN+Lpw/vzAk/OB30RTbvcX4oE53SfHWn0hUVuKhBV3Bs/N1CJsDSANo3b/LzH/XC/uNkNTPX3WLBHyjEZPwIA0vASMGhbMnrEVY9wGoH9/6Rv52mticIXaGishQQytu++W/qM33iifZyAtr3IxtgK15ck7Wg47zyexFSQE6JMI4j13pleqayBW6Anygdj5EZoxb8UonoBYt/QIwvGc4/y5808fo6pXg14kxiWSGJdI/YT6JJ2Sm7vT4+TLNV/y+nmva7bgKUBRFOZMiCPFdcLvuQQn9P3fWRrvCo8rFfitFTiN/jezFoasStee/K6pd2luD5R7FoxbJ9/Kwtv9y9/PPFNW0yWZMUP6IJZbOLFqO+mhN+9C0Y4rGU52HIO1j8KWN6RyrvmtUOMMEfJVVclRLFCk9zrlX1O8eIoqG4oCvT6DOef5L6ZOrBah4r7fhF4ccHUAUdyfAtxpTQn+RQl7v4Uur8RM0BSgV4tVdOoEK1f6VybedRcsWRK5R6dUGlwMjPTfnpssBlfLu8KrxoxhMYbJJJ0b7rwTnv3qHzbPb8OmJU1wOOS5Arxe6W84cCBcdx1ceKGvfpbHU8HGVlwNyNHQTMwN0EMoVrR5ANY+LlqSJfHkwfwL4YJlMb32KwO6saWFOxe2f6CtCN/qXsnVKmvPsjIwdu1Yn3CczWzj5i43F/49rNMw3ln6Dq78liQqKr9v+53hnYcHHbfPJn9DK5oYVbhkV6BnY7vvcNmQuoE9J/dEbbxFBxaRlpVGnQTfQoiLL4Z58/z71Y0bBw8/XI7GFojH5+INoiWXlaTt1XUchV1j5GG0irRJlXbi7TVYpOIsLw0yd0iz5ZKtqioLtfpA/SGQMgO8JT78A7+KQdTz49goypts/saWJ1eaY/cZG7MKUEURUc5zzvEvzNiwAYYPh59/jpHkSHx9aHkn7P7KP0F7w3MS2rU3C71lT7gGbgSolgy+yLqI+DPjOf7HCZL2mDh2TJTj4+OhalXR6zIFOGRVDa5SH3MCeVxzDmpvjxVNr4c1Dwd+/tQmWHojnPVT7Ds4VCB6GFGLI3O1fyWKETq/IFUWFYRX9TJ6+Whyi+XPeLweLm9b1Gvqmg7X+HixspxZjFo8qlyP83Tn1sm3Rn3Mu/+822/b4MHal9r27fDPP4FzumKGvSlctA7aPSINaw1BMnk9eTJRHvgNdnwA296G7e/Dvh9EzqSyGloFnPWDqOhrFQfs/Qb+GSA3pmgnzff8WNug2j9BUhdiWAHao4e0oNFST586VTxc2dnRDSkWtqLq9JK25IA7S7yMrgxp0l1J+GD5B7hVN5nOTH7YOJ42beCss+Q3e/bZoh4fyNACSRMIpG0W05BtAfENtLfnpYbfWq4sWKpD3fOCv+bQFNj40umfohAE3djS4ugi7Qmv4eXl0zE9CLP2zPKpQARJ2K6fWL/w7zPqnUFciQTg3Sd3syE1QJsSHR+STiax4Uj0P6s/d/7p1+C7bVuZuLUMrkcfDSwNEVMMZuj6OlyxT5TNjfFSvh9NKkOvM5MNzp+bXxygMRWeWA1TWsCaR8Rb50yPjjHQ+FpIbKNdEbnuKUgaH9ObzrffynWnlb/1889iUCQlBRbkDJWsLGlXM3Ro/gZrLTjjbe0UjJyDMLOXhL0ircyMIhmODN5Z+k7h38/OeVZTPDoYRmNgo6q01q5RwRag127m7vI1tgA6/a/01Jvt78Kiq8F5Ckr7rFXvaWeY6caWFqlz0CxJbXhZhSvevr3kbZ8btsVo8QsPKorC1e2v9gk1OtwORi8fXW7HeTrz7tJ3NbcbFANWkzWkh6KRBK2i8uVqfyG/UaMkubYke/fKjUqrSXW5YK0jXpjL90CX/xOJCEOcdqJzMAxWMdhsjaHD03DJ1sBhoPImvi5csBTsjbW9eKpbvFx/NIRF10huVd4R8LrE+HKeKvbvKZGLcWWIEHIgFAXOnSaCslrJ8qvvhxV3xawC1GoVr2nt2trSIxs3Qps2cM890mQ5IyN0D6vXC+np8p5XX4WGDUWhvZA2D0Dzm7RvvFl7YVon8Yy6c0q/4caQ95e9j8frKfwdZzuzGbdhXFhjVLhny1pHe5GUvkUWVOVJ7bOkgr+0Lg2HZ8G0jpA6SwzCkk4Pd648TqyF2WWvkC9PFLVcTOzw6dmzp7p6dezLoTWZEO+fxwFw2W5IDKwGHmv2n9pPu0/akVdsVWIz21h992ra127v89p/9v7DNb9cU9g+BqTlTuoTqYHFSSs0wYDwl3vBjjfC6zrHlUO9d+v5tRVKtCQy4doJXNz64pDG+WrNVzw681E/L2TDxIbsf2S/n6r/RRdJ6xCt1j333w9vvx3d5OWsrAibD+emQvJfcHwlnNoAmXvAdUrCQ4pBPnfVLTfThOZioNXsJQnSVdqU7aBj8H0X4sqApTfLQksrkbckRpvkq9nyc9W8Lslny9hRevi0IMyXsRNm9g78+ria0P4pMVBUT3gLvUC5S8VCjAcPwqBBcOCAf85gAQaD6EXdcANceSXUry+v9Xh8XxMfL16s2bNFlX3OHF/5Ep+vx+uGuYPkGgpU+ZnQArqNkt6JXmdo5+7Ohl8DXNRhiOum56XT8P2GZLuyUVAKC5Hq2Otw6NFDmEPM1/V6A+vo5eZqL7Ciyv5fYMXdGr04FbghJ3g3BS3CzYcr+ZmfWAOzzwm92tdcTfK96l8klZXuLDi2TDy/peWdVZCYsqIoa1RV7an1nJ4gXxKvK7DCrr1J+R5LCT5Z9YlPSxiAqnFVaVfLv2PxgKYDCtv4FGBQDIzbMI4Hez8Y0+M8nflizRcBhV0HtwhddPbaDtcycoZ/9VW6I50/tv/BtR2u9dn+449SfZiq4fD57DMJ60yYIKGfskzSHo/cBF9+Gd79oR4ciUJFlwmweORfD+ACnJnAxvzHT8Cjvu85cgTqRNAaJ1YU6Nzt+1mqLj3ZwcMUnhwxNk+VIdxcpQ0Mmg9zB4MzA9QSlrbjOKx/WipAG10OTYdLg/CCik/FiHgK1KJ+d0arNPkNgcaNYd06ydOaMkW7XY3XC0uXyuPhhyVHqXlzaNRIrkWXS5o0794d2GDzw2CCc2dIC7TDs7WN26y9sPg6KVRocDE0vUHandka559vvq6CYpIFQMY2ODgpxAMIzrtL39WcA7Kd2Xy3/jvu7uGfe6lFsIrDrKxyMLbiagfQuFIhe78UepUnNXpIKs6hP+T6LQ3XKdj9pTz+BeierZK4smBide0+XhXYeiTLmUXNt2v6tdPpUqdLQMHRabumFUpAFFA1rionnjqBQWsmqBelm28k1K2rbWkEI8qeDqfHSYP3GnA811cry6AYuL3b7Yy9fGxY4w0eP5h/9vq3gGldozU7Htzh0+4HJHxz5pmBe7TVrSshx+uukyrFcCoVvV4Zd/Vq8ZRt2wZqpI2Xo0Ul8GRq4nFKuHDD/2Th5ecZKAPWuv4h1LyjYlgcXx2CV02RxOcqbYpVgLpEmiNjh9xEA7XGgYBz2OTJ8NBD4p2KhcCp5tejqrDtHdj0cn4OUSjfoSIJ1wXn7TwV/HwLCHHuPpV3ikbvNyr0SFuNVlAoFHuubavNoccOYQmxW0iBQVqSPXtEPDWmZO6G6d20r6l+E8SADYdwG65rfea5qTC1pXalczTRPVunAca4wD9eryuyWHcUmqY+M/sZzb6FG9M2sjFtY8iHku5I59NVnzKyj4bmTbjGDpTfDbAcGL9xvKaCvs1s484z7gx7vHt63MOKQyv8QpKHMw8za88shrQa4rO9SxdR877pJu3E+CNH4Lbb4K23xGC68UbxNBgMUq5f0n7OyZGJ3mSSSrMxY2DRorBP4/TAlSGeHUNc6PIBgTBaoPU90OI2ySHZ9yOkTAMMsiLXSjEoiWIW3SiPA+r0h+Y3y6reUtX/tdba0iN1z9ew/hkZP6BXTRVtqtzkMpygP1deCZddJgnyTz0lBlc0qhKDioIqCnR4CupfILIXJ9aHYGyq4IydTExJr1a8Od6n8jvHlcN3679jRI8RIY2XkCAGbEm0tkUde/PAHqTjq6DRleH15IyG3EZ8PejxMawZGXuDq5IRFc+WoigXAh8CRmCsqqqa/UkURbkGmAj0UlU1qNuqYnO2rNqhxMv3Sh5KuJQ11g3Evx6vaQhEQh17HY48ESUPVkUaW1Hct8froekHTUnO9L+J1bHVIfWJVD9PVGnkufOoMaqGz2RdQI/6PVg9Qvv6njtXyvNzcoLf7BRFSvm7dIGuXaFdO+m55nJJHs6aNbBlCyxbpp0Ldlp5tjxOSRg6qnGjrQp8bs7PF/NI6Cm+gfxWq3WGOudCnXPAHKIcuBZeNxxbykd/30Uni5fzqtWA7KR8UVe3hLMMZglzVe0kgq9VOkjYLxztII9DvGobX5Sx3VmE5vEJAS2vmgZuN8yfL4bXpEnyt6qGVqgRHy8eV5cLLrhAFg4XXRRivuGRBbDmIckD9DrFcxUNQjzvkl4ts8HMI30fYf+p/fy29bfC3K1wvFvNmomifElmzpTPJ+ZMbSkh2ZLUPgcG/Klt/IdLCPmBfqy8H5K+j53B9W/0bCmKYgQ+BQYDh4BViqJMVVV1a4nXJQIPAyvKus+YU7UjnFzrvz1tYWTGVhn5fat2z8JISctOY+ORjXSp2yVqY57u/LH9D9Id/onKZoOZ27rdFrahBdJT8vK2l/Prll991P4Bth3bxtKDSzmrsb8y/3nnSS7NRRfB4cOBb3KqKmHBilqTxBx3NiT9KMbHybXwiRU8Zu2bsLfYNnemiKpm7oDDM6XNkCdX2oY0HSpaeeFiMLEwFx5O2oWCwtYH/tLMlSwzxjhofa8IgKbOFYHVQ5OLPBTubEo1vow28e553VB/MDQdJnlPIRqbJpMkzg8aBF99BWvXwo2fvg1HO9Ig+xJ27xbPa4HX1GKBpk3F4O/SRXIPzzpLW1oiKHUHwEXrpTn1/gmQ9IOERxVT/nkHCxcqRdplBjM0uR6aDYda/UJuqfb2krd9vFpGg5G7u99NSmYK03dPL6wCz3Hl8PW6r7mv532ljlmtmraxlZYW0iGVnWpdtI2t48vFg1tR9PxIvufjKwPnSEdKZZCV0SAaYcTewG5VVfcCKIoyAbgC2Frida8Co4Ano7DP2FJvEJxcj5/8Q8o0aHxVucs/PDPnmaiP+fDfDzPv1nlRH/d0RFVVnpvznJ8GFoDJYOK2brdFPPZd3e9i+q7pfqHEXFcuz899PuB30KoVbN0Kn38O//ufeKZCTkAOAZMJqFm3YnP0ApGxE7a/JzdbRSkKqYWSVOuHKiFGgIytsPWNiIwtj9dT2L5JReXev+5l/m3zwz+cSPMimwKNgQb5/68HWJBYghdwA2nAASA5B5KBPYBnCpDf5D2CogSDAaxNNrOz2dMYmhuY/+ghH02/qKMoUK0zTltnlp58nVVzd5Gbsp5E71Za11xD2/rbscVlYza6cXtNOFxx7DnSkh1p3Tnh7YSpVic69uvKwPaGkPsaApzMPcmHKz70WdS2qdmG1jVb07JGS+xme+H8kO3K5oW5L3BHtzuCtkADqFVLe/vh8uqYU7O3VA+XzEH2uuDYCvG8VgQGEwyYCtO7QE4ymlJLkWCyVx5ZmRJEw9hqCBSvwzwE9Cn+AkVRugONVVWdpihK5Te2ap8Nuz4Hd4bv9oN/QM9PoRwlSlIyUth9YnfUx12wbwEOt6PUyeK/wOy9s0nJTNF8rmFiQz9ZjXAY2GwgRg3VbBWVFYdWsOnIJjrX7az5XrMZRo6EW26R6sEvv5RS8rKITdrtEha65BLg90qWo+fKgHXPQNJ3cjPQKlIpjq2xhAtNNtHx8jrFMHOeEINNywMUoZjjN+u+ISUzpVAKYHXKambsmsFFrS8Kb6BIjdv9+Y+yULduRN/RU7OfAqR7xf8t+D8+v/TzMh6INi4XTJwo1/ny5eIZy85ujccTpJFxCRQFEr+QhUmHDnDrrdLnsDTeWvwW3mIxe7vZzsjektdqUAyM6DGCt5e8jcMjXpg8dx5j143lgV4PBB23dm3t7ceOyfnGvB1X1U7y+3Bl+D938HcxxiqqRY6lWlE1bu7h8BvAF0cxSfrAgCnROrqoE3NRU0VRDMD7wOMhvHaEoiirFUVZffRoOXYmL0m989G0tFU3bHkdXCEkLxSnDG7N0Su0hUijIbD5/YbvIz6ufxPPzXnOTw8LIM4YF1oirKpC3jHxhh5dKuHmo0vg+GqMeUe4sfNwTYMrz53Hi/NfLHX4qlVh9Gg4flx6Jl5yieRmJSaqJCSoQe0fux2qVJEbV58+8PHHUgfx+++ln1a5krYIpraCpG9l0tUytOLrS2jorB/h6iNw6XYY+DecM0m2nf0bnDsdhqyC607BgL+g1T2ih1UGMhwZPDn7SZ9rJNuVzT1/3RO2qvjpxsYjG5m/b37hHPL9hu9Jzohucv6pU1JlW78+jBgh+WJ5eSKM6gkWOdRAVeV9TiesXw/PP1/6e07knuCTVZ/46Bd6VA83dCyq1itZIJPtyubFeS/icAcPgdWrp709NTW6nuqAVOso4WTNg5hN1PIBIyWhBVy8GRpfU7rCfCCMNqjRHS7dJsUolZRoeLaSEQd3AY3ytxWQCHQC5ufnvdQDpiqKcnnJJHlVVb8EvgRJkI/CsUWGyQZtHpRm1CUrj3Z+Ci3ugKodYt40VVVVJm6d6LfdbrLz+vmvl9pYuoD3lr7H+8vfL2xMXcD3G74Puarm30ybmm00Kz2rxFXhlq63+G70ukXH6Mg8SJkOmbtEzBJDfmVP8e9UBa+Lh/McrLAacBAvfTVNCTJBKAYaVwnQUkMDq1Wqxq68ErJy3DR7/kKsJ3twX523WLtWKbzJmM1SndipE5xxhuTQdOwoRlel5NAUWDI8cLJs/Quhx2hpVOwncBlkgm54CdQZUBSK3PC85H+FyQvzXij0aBTnRO4JPl75MY+d+VjYY54uPD37aZ/Qmlf18srCV/ji0i/KPLaqwvffi36XyxVaaypFkSR8m0304kqrmAwko1Kctxa/hadYGyYFhUtaX0JiXFEcsmm1pnSp24VVKasKtzncDsauHcsDvQN7t+rWFW90SaNx167wDcmIsDcLXGiQsUOU+sNpfB4LTPFw1njYewGsuk+KXEKq+M0vSmn7EHR5LeTcvIqizNWIiqKYgJ3A+YiRtQoYrqrqlgCvnw88UamrEUF6oU1uqv2l25vCxZvKVt0EpVZxLD6wmAt/uNDP62IxWkh+LJlatgAJASXYkraF3mN7k+PynXmsJiub7ttEqxqtwj/2Av4l1YhBUVVIWwDbR4sUgMGcLwEQQWKnYhJj3pMnIn8XLI3okEYtHlWYy/fT1T8xrPOwiMYJm2h/5kk/wsq7tUMICS3gzPGS5BuN5u+uLEm4TmgW8lt2n9hNlzFdCitKLUYLJoOp8LeUYEkg6eGkkH+Lp1OXhg2pGzjz6zPJdef6KKlbTVZ2j9xNwyoNIz6MY8dg2DCplA1W5di6NQwcCJdeCuecIwsGt1sMFaNRFha5uVIkMmWKeMXWr/c1wIKd8vGc4zT5oInP3JhgSWDq0KkMbD7Q57U/bPyB+6bd55PbWSO+BsmPJWM1aSuUfvGFdo/T2rWlYjjmwqYAU9tA1i7t5854TxwLZUmWj6QaMRA5yZA0TtJ4HMfl/usjxaTIYlUxQotboeXd4r2rJASrRixzGFFVVTfwIDAT2Ab8qqrqFkVRXlEU5fKyjl9hWOtAy7u0XZvZ+2HeEOldpsauydU7S97xM5AA2tdqH/rkDnSo3YFEi79h6PF6+GjFR2U6xn81Hids/xAmN4IFl0Hyn/kaSJmRV9Cobsmf8Dql9UQE7Dmxh/9b8H+Ff9877V6O5RyL7HgqkoxdgQ2t2mdLZVrN3tExtEDGCcPQArjvr/t8vFqtqrciwVJ0PC6Pi6dnPx2d46tkPDn7yUKvlqHYrcKrenl5/ssRj5uaKpIlCxdqG1pWq6ja798vVbnvvy8aYNWqScK+xSLeLYtFbFebDfr3h9dfh3nzJNz+4otQs2bpx/Lm4jd9vFogbc0GNBvg99qr21/t91qH28GXawIrnNeokV+MUoKjR8tRhrDO2QTsSbjny9JzIyPhfvIV/sN82BtBp+fgqgPwdDZM8cAypBHFCmCWCq9kwg2noOeHUL2T/xjlVuoZHrqCfDA8DqmWyNyNZg5X1U5w3j/i4YokyTDIiiAtO42mo5v65BGAKBq/MvAVnuwXXp3ByOkjGbN6jF8bigRLAmlPpBFvjg9rvEL+rZ6tw7Ng+W1iGAVr26KYJF+gTn+wNS26FjwOeW/eEcnfOrZMO0wW5upPVVXO+vosVqasRFVVVFQsRguXt7mc367/LbxzjIRofeZeD8zsCac2+i9YaveDgTMrPLzxz95/uGLCFT5erC8v/ZJDmYd4ef7LhdvjTfGsuGtFwEIHH06TLg3rU9dz1tdnBfToWU1Wdj64k8ZVQw+Dg+y+Tx+pxtNSVr/2WvEGmc2lCKKWQk6OXI6vvQZvvqn9mh3HdtD+0/Z+siw142tS266d2Z6SmUKGwzfZ3KSYOPrUUapZq/m9fs4cuOYaac5dkl27pOo45hycDMtu9S/4KuCidVC9W+Tja93Hbox8uKhQQXaNriAfKcY4EX6bcYb2jTJ9M/zZUuLFrUZIC4myqlfn88XqLzQXI4qicFX7q8Ie77qO1zFu4zi/iQLg1y2/cmu3WyM5zErLxx/LijI+XtpitGsnrvtSozheD6y6F/b9FDiHqFpXaHgxNLwMqp8hXi5DnHZjV68rPy8iXirkUqbD4b8l5ysCvln3DZvSNuFVvYVJy06Pk+m7p/P37r+5sNWFEY1b7hz+WxYxJQ0tkx3Onljhhpbb6+buqXf7eJZNBhPXdri2MDm6gDx3HiP+HMHSO5eWrsd2mnRpeHLWkz65Wu1rtedI9pHCz8Pj9fDS/Jf45opvQh7T4xHtrpQUCQUWx2SS7gmXXRadhuu2/LVvsAT54b8P9zO0AI7nHvdr2RUMt+rm9sm388fQP/yeq1MncE7Zjh3lZGzVHRg8B2rz69D363KXNPqvEfNqxNOeKm2g77dSWq6FO1sa1/7dQ9ptuDLBmR5YE8jrkaazQcpcC8J7WkKmte21I8qxOqvxWXg1Qp5ZzixGLRkV9ngVzdGjkFNFu8ozlbo8+SQ8/rhIJ1x+OTRpIq0zunaVdjd79mi80Z0LCy4JbGhV7QTnzYYLlkCnl6DWmWJgmatqG1og+V2WqmKIV+sE7R6D/lPgki2i5xYGqVmpPDLzER+F64JckRxXDrf8cYumVlilJGlcvjp6CdqMrBST/pjVYziaU1QRbTVZeaTPI5iNZqpZqzGs0zBMiiysVFQ2pW1i6o6pFXW4UWXd4XUsObik0BBJsCTwcJ+Hua/nfYXXm8vr4ufNP3Mw/WCwoXz45BPYt0/b0Jo0KXqGVnESAkSgtx3dxtpUDeHqCJmyYwoncvy7G7RsGTjxf8OGsrdDCglL1eAVuYf+iFgSRSd0dGMrFJpeH9zgAsjYLh6R32vBkqGw8f8geZpsz9orXo0j82HbKFhxJ0wKLAw4Y/cMzeonk8HkU44cDiaDiYtbX6z53P70/axJWRPRuOWJ1wu//QY9e0LjxlDHk4qC6veoTyoOh0xyWVniwnc4JLSwcSP83/9JpV6rViKpAIiHZd4QkW0oaWgpBjjjXRiyAuoMFK9LOD3FimMwSaixagc4x38lHIw7p9zpU2reqEoj4k1F12SWM4snZj0R2XGVJ6oqvw0tWtxWcbo/+ZzMPcnzc573K0y5v9f9hf9/8qwnMRuLRJKyXdncO+3eUqUATgeemP2Ez0LP7XVzXcfruL3b7T5KAV6v18fDF4y0NPEyaeVovfaadE2ItqEVjOG/h1bJHSoqKrdPud1vu80WOBy6bVtsmn5r0ugq6depheqRKkDXabJQO03Rja1QaXqD6PaYEiVPJxBep4RItr4BCy6Fv9pLf6q/2sKcgVJ+fnAiuDSC+PmMWjzKT3EcZHV9bYdrIz6FYZ2GUSXO32vgcDt4f9n7EY8ba1QVZsyQUOAdd0jPv4Ky70jIy5PHnj3wQoGY+PYP4MRaf4+jIQ76TxW9JpMtuuXFYSR+/7HtD+bvn18o35FgSeCtQW/xQv8XsJnFOMl15zJuwziWHYws8b7c8DoCe3YTyyOuEpxn5zzrIwViUAxc3vZynzye9rXbc0a9M3zel+HI4P3llfd3FAprUtaw/NDyQq+WgsLlbS8nwZJA46qN6VavW+FrnV4nE7ZMYP+p0tVWJ03Sjni2bSve5/I0tLakbWH9kfVRH3fqzqma3q2mTbVfv2KFf/P4mNHgwuDOgoOTpCVWIE2ucIlCy8WICdadogLRja1wqHceXLxRtHuMsZkd9rpg9WHtwgCTwUTPBpq5dyFxQcsLNFfeHtXDpO2TOJlbHq3ow+PwYTj7bLjuOkkojfZKMDsbyNoHG/8HHg3rrc+XkvMQrYq4CDiVd4o7p97pkz9UzVqNazpcwz0978FULE8w153LsN+HVXIPSwxykCbVk0TdkB6BDeZtR7cxbsM4H8+y1WTlmX7+LbOe7/+8T2VijiuH1xa+xpGsCkqAjwJPzH6CXFeRIWy32Lm/Z5FHb2SfkT7n7PF6QvJu/fSTtubVQw+JQG958vaStzW3x5viaZDQIKSH2eDvJVJQ+Hjlx37bO3TQPo5du8opjAhS1auW0u5q+R0RtsTS4DPyG8OH8QhGOONEkhdZDujGVrgkNIPz/4F+P0NcHdH8iCIfZ8b7lReD/JCvbHslBiXyr8xmtmk2PgZZvVc2RfmFC6F9e1i1KrgXS1Gge3e4+WbJx5o0CaZNg8mTJVH+7rvFYAvYGDdpnLaER71BomxcwWGtkTNG+ukAvX7e6xgUAzazjafOeqrQuwVwNOcoryx4pSIONTSMcYGN14xtkY2ZF46BE/gOd9Okm/xyJRskNiDHlcOSA0t8HomWROJKhJOdHid3TvVVGz9dWJ2ymhWHVvgkjdvNdvo3LVLlvqrdVT5tbVxeF79u/TWod0tVpf2OFlddJXpZ5cWRrCP8ttW/ajfBksCsm2eR/HhySI/xV433k9NRURm9fLSfXE/XroHb8qxapb096hjMUEt77i8ka09+hxQ9nBgL9GrESGl0GdTbJyHBHR/Bqc2IYngYHgXFKGEqa11o8yBqs5sZ/3F7TIYcP5E8t9fNDZ2C5Gu5s0XNPHOP/GjSt+brObkkT8hogyptualBK5YfXIrJaKa4h8HldfHlmi95pO8j4XwKMWPCBAkZBlOV7t4d7rtPSquNxnyZFru/az47W8rMLRaYORO++krCkoXs/V77e2v3WIVXxc3fN5/ft/7u42mxmW0+3QNG9hnJm4uL6ttzXDmMXj6a4Z2H07FO5RH886HRVWLklqwG2/M1dH29Qj73/5v/f5pJ0ymZKVzy0yWa7ymZW+n2upm2axo/bfop5A4PlYUnZj1RKPUA0q7qnh73+FRYxpvjua7jdYzfOL6w4Mbj9fDCvBcYd9U4zXHz8gJ7cMo74vPawtf85G9AjK1+jfuFPM4V7a7gjql3+G13eV18uupTnjyrSJqndWupitaSuvj7b+jXr5zETZvdCMdXahemFLDlDXEitLozNEeCOwSJfh1A19mKHln7IGk8qXt+orojhThvrrZHxJMnlWlV2kGdc6D5zT4aJ4sPLNas8LFb7Fza5tIiz5aqikGVMg32/wrpm8CQ/4v15AQQqjPgMtqYmuWRnBRLLajVR3SiElrRplY7ejToEd55x6AsfckSGDw4sKHVrp0ktp9zjkxS4a6MMzNFMHHkSFi00I17vAXNHmE35AauMiwHcl25tPyoJYezDhdus5vtvHvBu9zb816f1z435zlGLx9d6JVRUOhUpxPr7lmHMZp5ZtH6vlP/gYVX+U/8BgtcugPsTaQwIVRKaX3lRwl9sxxnDolvJWpW7EZCvCmerGezMEQjKaccpB9WJq9k4HcDySl287SarGx/YDtNq/kmHS0/tJxB4wb5FBBYTVa23r+V5tWb+43tdIqxoWVwuVzaop+xIDUrlRYftvAxKEGMyufOeY4XB4SW7F/AHVPuYNyGcX7GW9W4qiQ/lozdIguGTZvEoNJqIN+3rxhcVcsjx8mVCb/XCa0VTs9PocUtwQ0udy6cXAOzz9F+PlwF+YrUbIwSus5WeZDQjOMt76fRH/+H1WTl2CMHsGbvlQvc6xQ3rjEeqrSF+ADdSYGzm5wdfD9eNxz4DTa+CLkp+X2k8lfXpcbbvZg9WVxTmCd5BDKnQ848ucl1qXgV8r17pdFyIEPrscfglVckzyPSSToxUaoR//wTJv7ilJu6xmoXQ4QtLCbVCyOsZYDh2k3Snp3zrF8encVo4daut/oZBY/2fZQPln1Q+LeKyp4Te/hwxYeVs3df3fNFo+zYMt+FgdcJi66CQQvy23KEaKxY64YZSvTlhok3RM3QAsmde3zW44y+ULuRfGVj5PSRPoYWQOsarclz57Hj2A6f7dXiqpFgSfAxtlweF4/PepxJN0zyG9tikd+clrDntm3QOQQt2Gjw6oJXNb1aCopUWobJfT3v49ctv/pVrbq8Lj5Z+QlPny2dBVq2DNyjce3acvJqgVRBN7pc7h+lNaBe/QCc2gRdX5XoS/HWdO4cef+Oj2FzJU5XqGTonq0ocu9f9/LFGmnQ+sb5b/Ds2c9Gb3DVC3vGwob/SSVXMFdwpETSyyqKqxFVlYl32zbtVfB330nIMJB2TiRkZ6nY/7JqG6pXpUB8YImOgJTRywLw46YfuWnSTeHvW4NFty8q3YgPlWiuPnMOwV/ttBX6q3WG8+flN+0uQwZ1CH3bth/dTvvP2ke+jwAoKBx78hg1bDXKOFBsV/xfr/2au/68y2+71WTVTAQHCZ9qNW+fd+s8zm12rt/2YcMkNaAkTz8trXVsMU6LPJx5mBYftdDULuzbsC/L7gq/gldVVZp90IwDGQf8nqsSV4WUx1IKvVs1asDJAPVHK1dCr15h7z4yjiyQKvlQ7x8Gi8ixNLsZrLVEQ/LwTOkT6zoV/L26Z8sHPUE+Suw6vovvN3xfqOr9+sLXo9evLisJ/u4Nax4Dx9HgPxRzVajRU6Qq2j4C7Z+U3KPmt0gbFGvlLIsFSWjfv1/b0HrttegbWgD2BEW0s7RI+kHa7pQzua5cbp8c/ko7EJf8dIlPUnOlwdYIzv5Nu//oqU0im5IyQ1bSMTz+F+a9oLndbrbTrFqzkB7WAOHmt5dqV75VJh6d+ajm9jx3HpnOTM2HlqEFcOtk7U4UN9yg/dv97DPJ6Yo1ryx4RdNzmWBJ4KE+D0U0pqIoPND7AR+tuwLcXrdP39nm/tHVQr77LnIZm0AE/LnU6Q9xoffVxeuE3V/CP+fI73FWX9j0UumGlo4fumcrSlz4w4X8s/cfvKoXFZU4Yxy3dr2VLy77omwDp8yAxdeLN0sr1AVQtSM0Gw6NrwV7M3mtooj7V1FkVeB1yvuNVnCehEOTYf8vkLagaJwK9Gx5PNJW54D/IpFLLoFffomhFs/+X0VotqQRa60j+UOWauGNV0bP1qBxg5iTNCe8MUrh7u538+VlgRvmhkwsVp9pC2H+JYEXETV6Qvf3oGYfyXk02UNri+X1woQA+Wr5n/nGIxvpO7avXx5PvCmelXevpFOdTiGdwqw9s7jm12v8FPzjTfHsGrmLhlUahjSOJjFc8X+55kvu+eueMo1Rkjm3zOG85uf5bHO5oE0bUZAvyeWXizRErH7fwbxaNrONY08ei7g3bGpWKs0+aKYpQp1oSSTl8RQSLAk88ohURmsZQfXrS/pENMOJ6elB8sB2fQnrHgve8zUa6J4tH3TPVhRYfGAxiw4s8skHcHgcjNs4jp3Hd0Y+cMpMWHSN3IS0DK3aZ0tuy5CV0P4JaS1ktEh7BnMVCb8YLPml9oliNBitEhprOUL6Pl66A5pcT1Dto3Jg7lxtN7vBAJ9+GmPRw0ZX5ocLS3wGeWmw9MbwK27K4D2cmzQ36oYWwFdrvyrbtRhL6vSHwUsgoYW2l+vEavhnAEysLr+H7e9LcYjHWdQey3lK/nVlijcyez/s+SroblVV5a6pd2nehDvV6RSyoQUwuMVgzUbEbq+bh/9+OORxypvHZz0e9TFvm3yb3zazWXofxmvYNFOnwsMPR9+7U8DLC17W9GoZFAPXdrg2YkMLoF5CPc5sdKbmcx7VU+jdOvfcwF75w4cldSKaBC0aan4TFT3f/xfRE+TLiKqq3PPXPYXaKkaDEVVV8ageXB4XI2eMZOZNM8Mf+MgCWHS1ttK2KQF6fynJjsb48Kq2CjAYwZAoRlifr6F99CfdcJg1S3uyve46yXeIKUYLnDMJZvb2/7xTpsv3cPZE+axDqey7OoCoXikeL6/q5YmZ/u12DIqB9rXa+6h3B2PJgSXsT9/vo5dkwMATs55g6rBK2r+vehe4ZBtsexu2vAl4/fu1eXLhyBx5rH9aPFxxdaTq12ST17tzwHlCvLel8Mf2P9h6dKtfM+IESwL/6/+/sA5fURSePftZnpr9lG/iuNfF9F3TWX5oOX0b9Q1rzFjz3frvAvbSNCqhVbBqJZwfzDjI4gOL/fIEzz5btPB++ME/Yfzrr+HECQmpxcVFR+jU45FOE3/v/hvAT07H6/UysvdI/zd63RLGPr4cMnZIw/Ts/bL48jpk4asYpP1NXC2esMezWFEwKcb87TIfuzwupuyYwnPnPMdZZwUPl37yCXzwQeDWPuHgdkt+3F3+aXiCySbpJVvf1hZy1okJehixjEzYPIG7pt5VOMFajBYMiqFwtWwz2/j7xr85p2mA8lgtXBkwuRm4NG4YcbWlR5+1HmjkCkSM1yUVk+ESJddv+/awfbv/9okTJVerXNg7Tvpbahm4iW3gzO+lGXWkavKlJGt/vvpznpj1hF91k81s4+CjB6kRH5rVuefEHjqN6eTnsbGZbfx67a9c0kZbMyokysPVn3sEdn8h+nVeJ7g1aubLgrUujsv30/SDphzJ9q9grGOvQ8pjKWFLZmQ5s6j7bl0/UUuAjrU7svG+jZGJEsfoM+/zVR9Wpqz02WZSTAxsPtAvDBiISdsmsTplta9hrxi4ut3V/Ha9v3io1ysG1+TJ2hV6devChx9KaFFRIgutud0iN7F+PdxzDyxdnekXJgaZqwu9kbmpsO9H2D9BDC2DRYwqrYb0GpzwgFuxyMLNnQcJzaHBhdi7vordKvG8Bg3Ei6VFlSoifK7l+QuXrCw4/3xpBxQQj0PayOUml32HgdDDiL7PRcPYUhTlQuBDwAiMVVX1rRLPPwbcBbiBo8AdqqoGbah1OhhbDreDJqObkJaTBsgkc0PHGziQfoAlB5cUvq59rfZsvn9z6BPtqgdg7zf+K3tLdRiyCuyNI5cliDZR+IGoqsg4aOUzHD0KtcLI5ywzydNh8XWBJ9l6g+CMd6BK+/z8oYTQvF2qF34OnD+UkplCm4/b+BlaccY47ut5X9gSAtf8cg1Tdkzx8zzUjK/J3of3avbIDInynBC9btGR2zsOjswtqhgN8QYISN5iwY2zVl/JbWx5J68vfJ03Fr/hZxjZTDZeHviyjyhlOIycPpIv1nxR2MOyALvZzueXfs5NXSKoMI3BZ774wGKG/DDE7/zjjHHseWhPyDlma1LWMOC7AX7XrdVkZeO9G2lds7Xfe7xeuPVW+OOPwKHDBg3g0Uclsb52bTGeEhIC9xLMypKPyeGA6dNh1CjYvFmeC/gRqV44NEXC0sdXiUcqUM/OSDBYYGhRLtcNN8CvvwZ++VdfiSFaVq/epk3QpUsIl0bqP7DgivB+T+GgG1u+z5XV2FIUxQjsBAYDh4BVwDBVVbcWe81AYIWqqjmKotwHnKuqahA59NPD2Bq1ZBSvLHilcMKymW0sun0RyRnJ3DjpxsJm0naznbGXj2Vop6GlD5q5B6Z31v7R9/sFGl1RtlL4aBPkB5J+SsVslskjWA5Bbq64zz0aaWkeTzk2ay3gxFrJDcpLCzwRmatKzly9QdDgIrA3zw8xeBENG0Umb4MFHMcgdTYs164wVId5GfLDEOYlzcNdQozWarKy7+F91E0ILw9s69Gt9Pyyp9+K3mqyMrzzcL6+/OuwxiukXj04oqFnVbduTHuSedwqW1bs4vD6+aintlDNsI06tiRqJ6RiNrkwGjx4vQbcXhMnsmuRlt2UE662eBI6UrP9OXQ6pxvxNrkIU7NSaflRS00PlNVk5fDjhzXzr0Ih6WQSHT7roJkHViO+BgceOVAoBxAyMbgJ9R3blxXJ/q6Pfo37sfiOxSGPo6oqLT5qwb5T+3y2mwwmrmh7BROvnxjgfRI6fPhhMaTcQfof160LAwbAkCHQrJnMFTabhOWysuSymz0b5s2TRHOtffmROhdW3gN5qcGruxWTiE7XOlPyOs1VJJ3A65QIhOOYGGrHV2rPFcUMjrFj4ZFHAhuY9evD7t1lk8HIyoIrrpAc2JAujQVXSBGWqiFvXxasdQOnUwTiX25sRSNnqzewW1XVvfk7mwBcARQaW6qqziv2+uVAdASEKpDjOcd5dcGrPhN2s6rN6F6/O13rdiXOFFdobGW7snloxkNc2e5Kv7wBPw5O0u7TV6svNLy0UhhaqgpJSZLUeW5CXewajXdTqUvTOrKK9Xjkd2Q2y4q1Y0fRlenQQR6NGwffV7lTo7sUDmx/Dza/Dqj+E6krXbwuKdNg7aPi4YqrJfkQRpsYXu5syR0qJX9o4taJLD241M/QMhvM3Nzl5rANLYAOtTswoOkAZu2d5ZMcnOfO4+dNP3N7t9sj0t5ypxyi/Sft2X1yNyAivAtvW+jT0iVa5OWJJ+Dbb2HFCgWTqQ0ORxucYfTKNRqluCIvT4Rshw2Dtc0f05QuMCkmhncaHrGhBdC8enP6Ne6nWeRQ0Kj6zUFvaryz/Fi0fxGb0zb7bU+0JGrnMAVBURQe6PUAL8570cewL2hbtOPYDtrWaqvxPskpGjIERoyA+fPF4NIyuo4ckesgmFcoZDwOabh8aHLghVTdgVD/Qqh/Qb4H2yEpFlppGx6nLIxN8dJF5Mg8SJ0Fh6b6dfHoV0o3oMOHxSC7++7IwoleL2zdKoZWyPT+HP5sDe4oGltGW/iG1n+AaHi2rgUuVFX1rvy/bwb6qKr6YIDXfwKkqqr6msZzI4ARAE2aNOmxf3/QSGOFcu9f9/Ld+u8KS34TLAl8evGn3NL1FkB0tl5f9HrhBGQz23ih/ws8c/YzwQee0VNaIJSk73fS2qcMjajLQnq69BX8/Xf51+US4yk3l7BufgUU3AS9XhnL49GeaFNTy79/mg/Ok5A0XkINjmPSoiJII+NwOWGqTYu9TtId/vLaVpOVnQ/upHHVINZoENYdXke/b/pp5qs0qtKIXSN3lW78l+CjFR/x3JznyHHloKJiN9v54eofuLLdlREdoxb79kmy8Nf5zresKOr32mzQ7WYLW6r731xsLpj/HbQ5XrZ9LG8Elw0Dl4Y39/zkOH4fF6awVBBDdvcuFaNRvMe1awdueFyc3l/1ZlWKfwdku9nOsaeOhX1NpGWn0WR0Ez/5A6Ni5PK2l2uqypdkxw5pIj9hgswN0ahMLMj3KuxGkZsK84ZID1mtyEGjK6DbKIhvIAZDJG2uVK94yjwO2PQy9Pq08CmvV3Kzgp1bzZqiNRhJ9XV2NgwaVNT0O+Rb+46PYP2z0QknGuOh4RVw9s/hv/df7tkqV2NLUZSbgAeBAaqqBlWLrMxhxF3Hd9H1864+N7EESwJHnzxaOFEdzT5Kkw+a+IQT7GY7+x7ZRy1bgCQkVYUJZm2Zh6vTwFo7qucRCuvWwRtvwF9/yUSu1d8LZIJs00a8BzVryuRvsYgBlZcn79u6VR5aVTldusDGjf7bf/wRhleGfr6qCseWw4Ff4fDf0uy7wINVMrdOC4NFZDc8eVJBV28QNL6a4St+5retv+H2+lqaRsXI0E5D+eHqH0of250r4RBPnqymFaPsz1qHAT9exqL9i/wq7ixGC4/0eYRRg0eF/BEczT5Ki49akOXMQkEpHLOOrQ5JjyRhM5dNBjw7W9TEx4yR60arcW9x6tQRT6nNJp4Ah0OSro8flxtWINSKLnsPcc71eCT/pk3/etgy/b3HR6hLq4RUkdHzyvknJEikt3lzOO888ab06FFkeCzYt4BLfrrEL8fKoBi4rettfH1FZOHlQNpw8aZ41t6zlna12oU0zrFjMGWKLOoWLJB5RVXl2ijtY7PZJPczL0/O+dpr4corRb8PVxZM65jf4qzEqi6uFvSfDNW6Rl78ooUry2+8AQNg4cLgb3vhBXjqqfAEnPPyJIx68cVF20K+tasqrLovX8S5DFauwQo1zoDz50bWU/ZfbmxFI4yYDBRfejfK31byIAYBzxOCoVXZeWjGQz5hCIvRwt3d7/ZZEda21+biVhczecfkwjCO2+vmf3P/x+eXfq49sCeXgPoncTWjdfghkZICd9wBixbJD9nr9TWSataE66+XCf2MMyQU6HDI68xmybMyGOQ34vHIw+uVCTEtTQyrhQtFrDQpCS67TAyxkt6tCRPkuWiURAckUB5SMKxA63SoC9QBGgC1AAuiXqcCLuAkcBhIdcJRJ+wCMg4B3wHfkf3dEBokNPAb3max8crAEn3HnOlwYhUcXSYJ41lJ0lHA65CJTjEg148qD08eo1G4wWLAiTFfdy3/oRg5kXcirFN++O+HC6/74sZWliuLVxe8Wqbw2IoVcNVVcOpU4L6YNWqIXtFFF0n4qXZtueYK5mFVLQpX5+VJOGXaNPn3oH9v90qJyyWNiceMkeO2WMDjSSWgz6GE1y8jQx47d8rN12qVz7NbN2kL8/isx/0MLRCjqGRz83B4sPeDrExeWZg6UYDT4+TZOc/yx8PLQvqN1QLuzH94MLCW7myjPbtpyWY6s5uW5GLDhRkTbuJw0IT9dGIzbXJ20Zpd9GEF8cvyYBnwOOIe33k/ONL8Da3E1jBoUb4GYZRTNDQMt8svl+8hmAzEa6/BWWeJYRZKONHtllMcGkJKsCaKAr0+k/kleWpkHi6DRT7LgbMiM7T+A0TDs2VCEuTPR4ysVcBwVVW3FHvNGcBExAO2K5RxK6tna8mBJVzwwwU+uVpWo5VtD26jWbVmPq9dfmg5g8YN8pncglXp4HHCL/FohqmGOiOTZoigKfL48XDffRIeLOld6N4dXn1VjCyPp2xio3l5coPculVWrpdcoh0y2rpVpCFiRgzyjcIi2G8wL00U7nd/CRnbJTfEnRthQqtBbgAeh6jjX6kh1x+AFYdWMPD7gYXeXLPBjMlgKvw73hTPpvs20bJGy7CPasYM8UIEatZ77rkSVmzXTq6ZxMTQiyYyM8Xbcfw4PPOMKJV71cr3faelweuvS34aBPYgl4V5SfO59KdLNY2tJlWbsO/hfRHn3rk8Lmq+XdPP2AKZ83L/Vw49eQLREXjB7u+1Mdrgki1gaxxZyDACkpIkZzXQgqIAu12iCs2alR4aTk+XBW9Sku/2sG/tXrcUBqXOCc/DZYwHe1O4YKlUzEeK7tkKjqqqbkVRHgRmItIP36iqukVRlFeA1aqqTgXeARKA3/J/zAdUVb28rPsub7xeL8N/H+5XxdSiegtWJq9kZbKvZo2qqiRaEn0mN4fbwfBJw1l1t3/OBEaLCDVq6QplbJfmvOESsqEF4OXrr+Ghh/xvfHFx4mUaPFhWy0EVikOkILTRo4f8lho10tbauuceWe3HulltpeLYStjwLBxdImHBgtVmabG1oHilggogJ3RXj8fr4dbJt/qEzfs17kdyZjK7TsjayelxcvefdzP31nCyc0VH7ZZbtG8+jRvD999LMUVBSCXcsvgCj2ijRuItevFFwD9fu8LIyxOZgrffLhLh1MJkgq5dJSw4aBA0bChej7g4uSRyckQUdO5cWLIEVq3y/w0/PlPbqxVnjOPBXg+WqcjBbDRzS9db+Hz1535yIw53BQcyBqHtrWn/hFTNlZOhBRLebdAA9uwJ/rrsbNHKWrlS8ry05j6nUx6XXeZvaEWEwSTh1O3vw8YXilVXB8Fog6ZDJTcthh6tGTPkN2CxyCM+XgzRatVitsuoo4uahsHDMx7mo5Uf+W2PN8VjCtCrzeV1aZaBf3LRJzzQ+wH/Nyy6Dg5qlEt3/B90fB7CTF4Np0/f8l19OP/t5X6TtM0mCu9nnBFbg2f2bAklaSWQPvSQ5I7FpG1PZfJsZe2T/Im0hflhZY3fp2ISw7tWX6h7vvxrqSaufMUoE6TXKdWQJ9eJ7MSx5XBijW9icIg6OGNWjeHJ2U8W3qjtZjt/Df+L5Ixk7p12b6EKud1sZ8K1E7i0zaUhjbt3L3TurO3R6tNHCjFsttCSvkPF6wVDgwjCxtGimETGgQOyeDl0SPszqFEDHnhAfhMdOoghZjYHDy05HGK42mxSaDBzpngF9+6FEdfFsynRfy6q4oAf/oBaZcyP3lsd7roc8jRsl6XflG3sMvEdoHUNXXkIbGXoWRkh//d/MpeFUlhkt0vhwJ13ijGekCAGuscjaRh33w3JAXRJy3Jrzz66krjNL2NKmydzSvE+isZ4wABV2ormYL3QBHBLHtuePVIYsXMnbNgA7/5Yj1pujdxEpS5tq6T6vT8vTwyvpk3FW9itG7RtK8ZnNOeMcIi5qGksqGzGVkZeBtVGVfNLNI4Ui9FCznM5/irV+3+BFXf5a79YasAVSaLzEg5hGFvnvf4P87ed7/cj/fDDyMuRw0FVJVdh9WrtysQ334SRI6NrcGVlQUJiJTG2UmbA4htkFa5VJFF/CHR6QRozex0yCZpC+DA8eRI6NNmk9cjWUbD/JwlNl8LxnOM0/7C5T3iodY3W7HhwB26vm3rv1eNEblHuVz17PZIeSSq1os3rhb59Ye1af321Pn3gn3/CSxCOOVEOcSxbJsnMmZn+51+lityQR4yQ3Zbld+d0yvjTp8M11/4H++FZgLFIzKUkwzwVUt29fbukY5QWSixOo0aSq9i8uYScZ82S9IpgRHpr339qP20/aYvNbGPPnXOpnr5OckTz0qS9W53+Io8RZqTF65Uc4F9+gd9+kwVGQX6llkfXYpE5wGKR1xV0BsjJ0f7sTCZZZKT7F3aXG7qxFQUu/vFiZuyeEdUxR/YayUcXl/CUuXNgSjNJfC5Jizug50eh3WALCDFny+0xEnerA6/qPytlZcW4EXQxUlOlMvGoxumDqDCPHUuhWGqkFIRs3n0XXvysEng6toyCzf+nXZJe9zzo/YWEPMxRqBRwZYrnK4Sii1sn38qEzRMKE+MTLAmMuWRMoRL6m4ve5LVFr/kI+z5x5hP838D/CzrujBlSYFEyRy8+XoQdG/jXDFQsUTS29u2Ta1wrL6tnT/ls7PboLm5cLjBb/oPGlgKMQ4pWSnJdZmTVhxHkwZakXTvx6sSSSG7tx3OO0/2L7hzIkHzOLnW7sOzOZWWqNM7NFe/q++8XVQwXX2AYjRIe79JFPN1dukDLllC9uhhYHk9R8YvBIHN/VpaETjdvFq/Y1q1SvZqTU7HpXbqxVUZ2H99N6080EtrLiILCiadP+IsoHpwES2/RTlLs+ha0eaDsJcolPF5Ot5n423I1jS2ns3zdshs3iocrkB5NvXqSpH/jjeH3T3O75Qe/dKm0A9myJcIfZzQ9HYemwJLh/nkligG6vA5tR4ZnYEeJ1Smr6f9tf59crRrxNUh9PBWzUS6IU3mnaPBeA5/XxJvi2frAVr+CkeLceCP8/LP/R/XUU/DSS5UwPy9K37fTKV6N7dv9PVoXXACTJsVwYVOZwuWhEK3f2IwecHKt//Yzx0HT4eHnbIURLQA0w/UffywFG4GKQqJBuB93riuXM78+k61Ht+L2ulFRsZqsnN3kbGbcOCNgqkwwfv4ZHnxQvFclz/XMMyU95NJL5bdQkIsVLg6HjB8XB4sXS05jRRHM2KoYhczTjJcXvKy53Wq0Ui+hXkgPc4BKwveXve+/sdFVULOXlOiXZMMzksDoDmCJRIjF5KJtfe2l1p9/Bm+nEW26dJFGtYFuOqmpEtbs0EFyH/bskdWTlmyA0ynbs7LEWzZmDPTvLze2LVu0Ri9ncg7B0pu0E3h7flphhpZX9XLb5Nv8jKjHz3y80NACqGatxi1db/G5vl1eF/f8dU/Q8adO1b4Z3HZbJTS0osjEieLZKmloNWokCukx9SBXpDpwRe67zQPav6HNr4K3Yqokhw3Tbk9WUbi9bq6YcAU7ju/A5XVhwICCQp47j6UHl3LnlDsJxzGjqrJwuusuKdwobmg1aCCpIrNmwXXXSaiwatXIPblxcfJ+q7ViDa3S0D1bpbD7xG66jOnip8JtM9tYeNtCejToEdI4U3dM5aZJN/mVRtvNdvY/sp+athIhHVcmzDpL1I69GgHt2v3grB+l1DbcPC7QXJ1NWnUVt3w1yc+j1KiR6CDVqiWrj/Ji40bRUzp5MnCVVgEFbYDatZO8hvh4MbSSk6Wt0LZtEp7SokI9Wxv+B9veKWqyXEDDy6DfzxViaAGMXTuWR/5+xKd6Ld4UT8rjKX6e2L0n99Lxs44+hSA2s41J109iSKshfmM7HGJQaTUed7ujU+kadaL0fZ99tlQLluTzz0XXrqISewNSkeX40dq3OwemdYDsA/gVnDS5TrpzmMKw8MMKIxKwEOWii6SAIVYfY6jjqqrK7VNu57etvxWmAyRaEvF4PeS45W+72c6jfR/l1fNeDWnMb74Rr1XJe0nXrqL/lpBQCa/1KKCHEcvAxT9ezKw9s/zKmdvXas/WB0rJUCyGx+uh3nv1OJZzzGd7nDGO27vdzphLx/i/yXkSZvaRSULL4FIM0hqhyyuQ0BLwSI++Ug8mL1/PyxdVhfsWqYwf7+/yrVdPXLR16sRYYLQE6elSiTN9epEuV7SpUGPr9zra+XkXrIBavcM/riiw7+Q+Wn/S2kfVXkGhbkJd2tRoo/meTWmbOJnn2wPSarJy5PEjVLH6Lgb+q8aW11sgUur/3KlTsjqvdPwbjC2A9K3wdy9tD3K9wXDORBEGNpZhNRkovBjA2Fq/XtIlwkmUD4dQP6IX573Ie8ve88m7/Onqn0jOTOap2U8VLrhsZhujh4xmRI8Rpe63Vi3xaJVk40bJy/q3oocRI2TpwaUs2L/Az9BKsCTwdL+nwxrLaDDySJ9HiC/RzNThcfDdhu/YdVxD69VSHYashHrng1HDw6F64dAfML0z/NUW1j4OKdMha68YVF5nUSWa1yUhqyMLYNNLmseoKBJmu/12/1BOaqoIiz76qDRMzciIjhu8QPE6EFWrSuhl8WJJoox2iKlCK948DnAEaMRXMzSPaSy48McL/doHqaikZqWy8MBCzUdJQwuk6fVVv1zltz0uLnC4TEtn7d9CXl5g+6E8FzD/Sap2gHN+z/cUl/gSUmfDX+3g0CQpUCnpZY4R3bqJOHRFLi6+XPOlj6FlMVq4uPXFXNHuCu7reR9d6nbBqMgB5rhyeOTvR/hr519Bx3Q4ZPGgRceO0Tz60wvdsxUAVVXpMqYLm49u9nuuZB/EUNHqlwjSB29wi8HMuClAtaOqQtI4WD1SlMND6cUH4uUyWMTQ8mSXLlAHhauwBQskifnUKX9XsKJI3tOIEWIANWggqzNFkZtoIHXvrCwx0Gw2UfRet04qC//6q/QwYQELF0pVy8yZ4iUIZqgFIiFBQoy9e0vy5g03hD9GVFbdXhdMiENTS2uoQ767cCljpdQvW35h6MRI+35os/zO5fRp1Mdn2803S9/Lkh/VY4/BK6+UX/VryETh+1bVon6hJTlyRLzGlY5/i2ergPStMGcQOE9oRwvszaDD09DgYqn+9eTmz6NBksO9XhGinlhN+/kgenbbt4t+YbD2PZFS2kf0146/uH7i9T4pMtWt1dnz0B6qx4sS/P5T++n4WUefdAKb2cacW+bQt1HfgGM3aaLdImvBArlnVErvdRTQw4gR8OuWX7ljyh1+istmg5kHej3A6AtHRzTuNb9cwx/b//DT67KZbcy6aRb9mvQL/GbHcenQvn004I16kjzWunB1kXicwwHjx0uvruPHxaDS8maZzZIr1aWLTBz16vmrW588KW7zjRslMb2kARfSZaiq4DoFuUfISz/K7H8Ufvq9Dqs21uRgahVMRg9mk8dnPEUBj9dAntNCvVqZdG5zkmFXHuayS1xUq1tTzjmSBt/RuhFMaQnZe/23nzsdGlwU/nGVoVLK6/Vif9OuKcJbFmrbapP2ZJrPtlmz4Jpr/KUfrFYROWzcmMpFlL7viy6SbggleestePjh8Cpry4V/m7EF4DwF65+FpO+kRU3JfokFmKuJYHCd/hJqtNYRlfSCBazXIWOlLYAj87XFqKFU8eAbbpAq1GgXIQX7iFYcWsF5487z6YZiM9v45ZpfuLStryjxF6u/8OupWSWuCivvWknbWtrtGP76S86rZDpK06aSs1Vwj/i3oRtbYeJwO2j6QVOOZPt7CKwmK1vv30rz6s0jGnvZwWUMHj9Ys21Gx9od2XTfptLbZrhzYM83sHuMiFQa4rRb/JSG0SbeLmttaHYzdHtd82WqCsuXSzhv0iQJI8bFicFUllCiooiXyePRkHnwuuHkeji2FI4uFvXz7AOSp2aIk39VL2AAS1W8cfU4lNGBnSnNyXbYcLrjMBlcxMfl0qLuIZrX2IzZfVjy4Lyuovd7nXBDtgiEhnvwgQjnN7VtNGx83l9fq2ZvOH9eeIm7UCZj69WFr/LivBf9XmJQDCRaSo9zqahkOjI1hX9/vfZXrut4XdFrVVnhrlzpfw316AHz5/87RU3//FMq0Upe79WqSRFHvXqRHV7M+DcaWwVk7ob1z0nzZYMp+ovXAkoxtpKSpLI6mt4tu127zyzAzN0zufini/GWiHQkWBKol6BxAapwKPOQ3yLMZDCx+u7VdK3XVXM/Y8dKknzJnDS7Hb7+WppyQwz05Cow8V43tsLk7SVv838L/s+vByLAgKYDmH/b/IjHVlWVNp+0YfcJ/9I4u9nO15d/zQ2dAse1TpyQH+fx4+Ityjl5jOp5s2hq/IN6cRuobj2EQfHgdFtRizfcVcBidGBQPJzMa8AxR1v2uq/imPkizNWaUqOGrLqD4jgB2Ukk78ti2QoTW7ebWbm+Olt3V+dQWjXMJhcmgxdFKRGuVEFFwasayHXGUbd6Ju1anKBn55N07phHj+4e2g/oL689ugw2vSztagzm/BVknrSoqdEdap8jK83q3WSlqZjk+cIcIyV/klbyd6wWHYRizO/fpcq5pG+Fo4ugs3YOW1CidSNwnoI/22gnybd5CLq9ERMR20LybwTpeek0Gd2EDKdvXDbeFM+L/V9kUMvQaqp/2vgTn63+DIfHN0TTpGoTdo3chaVYAvL+/ZLDoaWn1r07zJkjIedoVsB6vaE3sfYhSt+31ystehYv9m/X0q1bUaWWqcxda6PEv9nYKsCZDgd/h52fwKnN+c3ecwJ7vMKhRLQgEHfcIWH1UFr4lIaiiPG22T8Dho1HNtLt825R64RiNpjZ+/BeGlVppPn80qWSbrJvn//vvEEDKX66+WapeHc4xLMbjnfX6RRjzmiU8X/9VbqMVBS6sRUmjUc35njOcZ8bA0gy+7cXTqKVehEHD0o+U3q6/HvsmDxOnJC/XS6ZWAvUb0EuCKMRTjb7hoOdH8SABUO+baAo4MFBE2tnxp2zkqZNpS3DihVywa5eLTcnt7vogmzaVGQOqleXlXHt2rIyblDrBNXMSZiVHIw48ahm3GocWd4mpJyox5E0hdRUMdZOnZLY+r598jAakQM+tRGOLYOjS+HEasjeL5OP0SpVO/amkNAczNUhrhoeUx0OnmpBlqsOeS4beU4TTpcRs8lDnNmN1eIg3nCcJjWTiPOmgOOkhARzDko/wEHzYclQMYCK9wQ02aHtw9D+yXyvliW6DU+9LjHqwiWaN4ITa2D2Odrq8U2ug77fgBIHxjIs2UqplLp/2v18u+5b8krkA9a21Sbl8ZSQBQ0zHZnUe6+e30LFbrbzQv8XePps38KSKVPE06NVkVW/vqyA+/eX1W9ERlLxY8uUfoQRJelG8fs+cULC7seP+1dkNmki+YiNGkXXs5eTE2FxyX/B2CqOKwOOrZBF2OHZkL1P0jcUpcijXnQQ4hn3uqSYKb4h1D0X6gyAWmdBfOjaYidPynWZmlr2U7PZ5L7RqZPv9uSMZJp/2ByXtyzN7P1JsCRw+PHDJFi0L1hVFd3E114TpXdF8f+9N2woVYpt2kg6SqdO0KKFeMFMJrkveTxFnT/275d0lPXrJe1g2zb5t2B/FYVubIXJ7hO7SctOIzkZFi6CjRtEODMt2YYhrVthx/FWreSG0LAhtG5d1IXcbJYLpOAiMRrzcyjzWw+4XCprUleTme3iyBFpRHvkiEy+rzzemPlTGzNmjPytqkVx7549JaF74EDZb26uPF/QDT0c92nBRet2yxjx8WAxZMOOj2HXmPwqObWoVLpGT2jzoPTEiq8vhoGqivvdYAnPYPF6JN/B65Z9GK3wZyvITfFP4r9gufTgCjecFmuifSM49CcsGZb/eZd4v70ZnPEeNLwIMEZWnh7E2NqctpleX/XyCxPYzDZeHfgqj535WFi7euTvRxizekxhi5/i4+0auYsGib69eObMgSuuCNwx4KyzYPRo8fzk5ooREmqCbUaG/DaSk0Wxe+LEStAxAJlPzj9fbq4li0OMRlnxv/KK3DjLUqmYnS1zz4cfwv/+F8EA/wZjq14U2nHZgRpIQ2sD4AU8wCkgA80aFyCsyofNm6UvaFlU5W02+O47EQstjtPjpN/X/VhzeI2fVyvBkkAdewjHqEJKVorfPKGgcFmby/hj6B8YSuk1efCgGF7jx0uBlNUqi6i8vLJ59YzGosVEJEVT0UI3tsJk5ky4776iVUZBLL1HD3FRXnON/O12y0RecMGUhbw8eZx9Nuzd62/5//OPNO6Ni4tRiEFVRUIia6+/h+W8fyRR1BAXvConUrwu+MUulZYluS4jsn6AUehfFpSYVEpth3lDJKSo5eWyN4W2j0LDi8UAc+eAMS64p8/rklyUidW1D3WYl15f9WLt4bWaRRupj6eSGBfe538w/SBtPm7j5yUzG8xc0uYS/rjhD7/3rFkDV17przZdnCpV4Jxz4MILJeRdEHoo7h1SFPlNpqfD7NmizzZvnu+9tjIYWyA3hauvlnxILUPTZJK55rLLxLtXp06Rsak1B3i94r2zWOQzXLpUzv+HHyR/p7Kcd7nvOxrGVqQU9D4NkV9/FemdSAwum03uW+++67vdq3q57tfrmLF7hp84dy1bLfY8tIcqcaEJYydnJNPu03ZkOX0TwuxmO3eccQcfXfRRgHf643RKSszOnbBlm4s5y46xaZNKWrId1WnHZFIxGg2YDIbCPGavt8hxERcnnuAOHaQwq23bIs9YRRFzY0tRlAuBD5He6mNVVX2rxPNxSDvQHsBx4AZVVfcFG7OijC23O7CHKDs7OoZVsH1XqaIt3pmWJpNszCo4vG74rUq+rESJnV+dJuXPpjB3Hm7+kDEeVI+vzk3N3nDWD2CtJwn9ofYxi5LKc0BidRNyZ8OWN6Ulk6oGbidijJcctpp9od4gCVkY48Ug9rrEWHOegrT5UmRweJbmMD91/pERf47wK9iwGC3c2/NePrzww4hO46oJVzF151S/JFyb2cb04dMZ0GyA33tycyXUMHq0TKilyYFUry73MptNHg6H/EZPnAh+f6tMRoeqFuWZ5OQE9u6BnO+ZZ8qCrHFjmQ8KzjszU9pRLVkCy5aJtzwqh/lvMLZOs32/9JIYTOEYXDYbnHuutMEq6fV9eMbDjF031i+0H2+KZ8rQKQxuOTis4/t+/fc8MP0BvznDZrbxf+f+H0+c9USpY+S4clh2cBn/7P2HabumsfXoVl89S2c8uGzgsWBS7bSr0YVBrQZwTvM+9G7SjaoJcZVSmy6mxpaiKEZgJzAYOASsAoapqrq12GvuB7qoqnqvoihDgatUVQ2qblSRnq0XXoB33inqLl7APffAs8+KOq7XG1xTKlK2bJF+UuvWye+9wKtWu7ZsHzlSQpWRJBMGoiDJsKq6BVbcBSfzd17gmYirDS3vkj59lmoixmm0lp47Fa7Bc+VB2PwGJH0LigXcWYi/Hqh7PjS6AupfILlinjwJOSpGMTBCCa153TAhgCVdWYytAvKOwrb3YM9YMT7d2RR+FlEi0wtNk6trCpJajVZ2PbQrYOJraaxJWUP/7/prFpk0r9acnSN3BswDO3QIPvlEWtgUeGuiRbAqraDE+Pt2OiU/7YMPJNTidkveZ7QwGCKsHP6PGjwVum/k2n/ssdDU5W02STL/9FN/Q+u9pe/x4vwX/X6HVqOV6ztdz/dXfh/2samqyqDxg1i4f6Gf+LHNZOPrK75maCdfrb4sRxbPz32eGbtnkHQqCbfXjYLi4003KSasZitWkxVVVXG4HeS6c32MsIL3mA1m2tRsw1Xtr+J/5/yPOJNGH+EKINbG1pnAy6qqDsn/+1kAVVXfLPaamfmvWaYoiglIBWqrQXZe0TpbJ0+KVsiPP0oS3vHj4lVSFElKL0jia9tWXJn160PN/PaGBa5OVS36tyAJ3mAoeiiKhDtSUyVxd/fuokqKQ4dgwgQJhaxfL6t1m03GqlKlaN+dOkk4pVYtWflWrVoUZijYR8FxeL2yck5Pl/M7flz2vWkT7NoFfxREd3IOwb4Joqx8cr0IABptgCp9GKt1giptoWonsDWCuFr5PRqrgjlBKgQVA0UVgV55uLPBlQ5TW2h/6AUGjzNd5B5S/5FjyNwt4UvFKAaT1wVV2si+rXWk4ie+EdibSEK9YpLXqx55vSdPzinnIGx9M/i+Q6W8JmPVC0eXiNRHyjRJ4DXGgauYIRohD5+I58t0VTMH4/K2lzN56OQyjd/jyx6sPbzWb7vdbOeVga+UmgvmdMo1+d13Ur0HYoSEUyJvNsvv1uGQNIBhwyTvMWzK8ea7dav0lps4EVJS5PhzcsLTYVIUyfVyOGROGDIExo2L4GD+qwZPBRtbINf8HXeI1I7WAsFmk8X2xx/LdV3ykCdsnsAdU+7wCx2CFL7seWhP2CkCBRzOPEzbT9r+f3v3HqRVXcdx/P1h100hDPAWAiYVktE04C0scxSqQXM0U4smCyvHpsnS7DKak2lNjpdu0zRTU17GjGgML5FjgXmradJCA11EbuEmCKyQcVFEYb/98fttLsuzu2zPnt/C+nnNnOGcZw/P55x9nufs9znn9/udXe71C+mM2exzZtPY0Mi9/7yXe5bdw9L1S9neF707a2hqaGLCQRM47YjTmDJ2CpNHT+71gON9pepi62xgWkScn5c/AbwrIi7ssE5zXmdVXl6R11lf6zmh/4utzrZuTcXQypWv9txrbX11CIaNG1MbjLa29I20oWHnRvLtPSm2b09T++NDhqSD4fDhMGJEKrBq2bIl5S9dmrahtTU1Q2htTb0gn38+nQFoH6W9vcirVegNHpwKtmHDUmZ7L8YfdXW5/ZUtsGU5bFoKL6yEra3pjNW2Vti2Po1d9crmdCYqduRG7pGnPAyDBqWpYXAqGDoXCt11kY422LwCNj0JW9fCS2vTmFsvPpOWt61PPRtjey7sIj9/zmVQKtT2GZoKx063X9rd7tk76a+D8dZ1sOGRVIBtWpx+Dy+tzeOHbefV/W5Ixe9+h8DgMTB0XOohdeDk1N5L4m0/fhtLNizZJaJpUBMPn/8wk0ZOqmtT562YxykzT9nlUiLAyYefzP0z7t/t52prSw2IH3wwDYy7ZEnqkfTcc6koax/SoaEhfekZMya135gwIbV3OvbYOoeQ6KfXe+PG1LPsL39J+9/SkgqwDRvScaStLW1aQ0P6PI8cmXopjx+fxjE7/vg6x+56rRY8fZXdy/sldrZjR7rMPHNmGn39hRfS+/joo2H69FSMdXW3BX32GDj00V0ebxzUyNxz5zJl7JTd3YuaZj0xi3PvPLfm53vU0FGs3rx61/8UgjWTYMGn4MmziM0je5XZ8p8Wblt0GzcvuJmn1j9VcwiLy064jKunXt2r5+0re02xJekC4AKAww477OiWlpa6tm1P0V7wvNayB/SOd9XotpcNYvtcL/a7u89+jwPr7vbmVJ+Rcip+q+2hr/eA3u8BkH3VSeKbD/XdZrV/fd0dO756CYOu/37Nn/XVW6arT3fbFd9g0JVX1c7ey44tveHLiGZmZmYV6q7Y6ovm3X8HxkkaK6kJmA7M6bTOHGBGnj8buL+7QsvMzMxsoKh70KSI2C7pQmAuaeiHmyJikaRvAfMjYg5wI3CrpOXAv0kFmZmZmdmA1ycjVEbEPcA9nR67osP8S8A5nf+fmZmZ2UBX0fCcZmZmZgYutszMzMwq5WLLzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwqVFexJWmEpHslLcv/Dq+xzkRJf5W0SNLjkj5aT6aZmZnZ3qTeM1uXAvdFxDjgvrzc2YvAJyNiAjAN+KGkYXXmmpmZme0V6i22zgBuyfO3AB/qvEJELI2IZXn+WaAVOKjOXDMzM7O9Qr3F1iERsSbPrwUO6W5lSccBTcCKOnPNzMzM9gqNPa0g6Y/AG2v86PKOCxERkqKb5xkJ3ArMiIi2Lta5ALggL26RtKSn7avIgcB6Zzvb2c52trOd7ezd9KaufqCILuujHuVi6KSIWJOLqQcjYnyN9fYHHgSujojZ/3dgIZLmR8Qxzna2s53tbGc729n1qvcy4hxgRp6fAfy28wqSmoA7gV/sDYWWmZmZWV+qt9i6Bni/pGXA+/Iyko6RdENe5yPAicB5khbkaWKduWZmZmZ7hR7bbHUnIjYAU2s8Ph84P8//EvhlPTn94GfOdrazne1sZzvb2X2hrjZbZmZmZtY9367HzMzMrEIutjqRNE3SEknLJdUaEb+q3JsktUpqLpXZIXuMpAckPZlvq3RRwex9Jf1N0sKcfVWp7A7b0CDpH5LuLpz7tKQncjvG+YWzh0maLekpSYslHV8od3yHtpsLJG2SdHGJ7Jz/pfw+a5Y0S9K+BbMvyrmLqt7nWseT3bm9WoXZ5+T9bpNUWU+xLrKvz+/zxyXdWdUdTLrI/nbOXSBpnqRDS2V3+NmXJYWkA0tlS7pS0uoOn/NTS2Xnx7+QX/NFkq6rIrvXIsJTnoAG0oCrbyYNvroQeHuh7BOBo4DmftjvkcBReX4osLTgfgt4fZ7fB3gEmFx4/y8BfgXcXTj3aeDA0q93zr4FOD/PNwHD+mEbGkiDIb+pUN4oYCWwX16+DTivUPY7gGZgMKmt7B+Bt1aYt8vxBLgOuDTPXwpcWzD7SGA8aQigYwrv9weAxjx/beH93r/D/BeBn5bKzo+PAeYCLVUda7rY7yuBr1T1OveQfXL+fL0uLx9c9XbszuQzWzs7DlgeEf+MiJeBX5NuSVS5iPgT8O8SWTWy10TEY3l+M7CY9IepRHZExJa8uE+eijUklDQa+CBwQ0/rDhSS3kA6SN0IEBEvR8R/+mFTpgIrIqKlYGYjsJ+kRlLh82yh3COBRyLixYjYDjwEfLiqsC6OJz3eXq2q7IhYHBGVD1LdRfa8/DsHeBgYXTB7U4fFIVR0bOvm78cPgK9VldtDduW6yP4ccE1EbMvrtBbfsBpcbO1sFPBMh+VVFCo69hSSDgcmkc4wlcpskLSAdN/MeyOiWDbwQ9LBqOZdDSoWwDxJjyrdPaGUscBzwM358ukNkoYUzG83HZhVKiwiVgPfBf4FrAE2RsS8QvHNwHslHSBpMHAq6axDSb26vdoA9Wng9yUDJX1H0jPAx4ErCuaeAayOiIWlMju5MF9CvamqS9ZdOIL0WXtE0kOSji2Y3SUXW/Y/kl4P3A5c3OkbWaUiYkdETCR94zxO0jtK5Eo6DWiNiEdL5NVwQkQcBZwCfF7SiYVyG0mn3n8SEZOAF0iXlYpRGuz4dOA3BTOHk87ujAUOBYZIOrdEdkQsJl3Cmgf8AVgA7CiR3cX2BAXPIO8JJF0ObAdmlsyNiMsjYkzOvbBEZi7ov07B4q6TnwBvASaSvth8r2B2IzACmAx8FbhNkgrm1+Ria2er2fnb5uj82IAnaR9SoTUzIu7oj23Il7IeAKYVinwPcLqkp0mXjKdIKjYmXD7T0n6a+07SZewSVgGrOpxBnE0qvko6BXgsItYVzHwfsDIinouIV4A7gHeXCo+IGyPi6Ig4EXie1DaypHVKt1Vrv1ftHnF5pQRJ5wGnAR/PhWZ/mAmcVSjrLaQvFQvz8W008JikWvc57nMRsS5/iW4Dfk65Yxuk49sduYnK30hXLSrpHNAbLrZ29ndgnKSx+Zv3dNItiQa0XPXfCCyOiO8Xzj6ovXeQpP2A9wNPlciOiMsiYnREHE56re+PiCJnOiQNkTS0fZ7UiLdIT9SIWAs8I6n9PqZTgSdLZHfwMQpeQsz+BUyWNDi/56eS2icWIeng/O9hpPZavyqVnfV4e7WBSNI0UlOB0yPixcLZ4zosnkG5Y9sTEXFwRByej2+rSJ2g1pbIby/qszMpdGzL7iI1kkfSEaQOQP11Y+pX9XcL/T1tIrWlWErqlXh5wdxZpNOtr5A+GJ8pmH0C6ZLC46TLGwuAUwtlvxP4R85uBq7op9f9JAr2RiT1eF2Yp0Ul32s5fyIwP//e7wKGF8weAmwA3tAPr/NVpD94zcCt5B5LhbL/TCpqFwJTK87a5XgCHADcBywj9dYaUTD7zDy/DVgHzC2YvZzUFrf92FZVj8Ba2bfn99rjwO+AUaWyO/38aarrjVhrv28Fnsj7PQcYWTC7iXTXmmbgMWBKFdm9nTyCvJmZmVmFfBnRzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwq5GLLzMzMrEIutszMzMwq9F9WPLu+xBso4gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_36\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_37\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_38\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_39\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_40\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_41\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_42\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_43\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_44\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ftYd5uyCawtjZsDPlff/wB+2UfbpYVr5M+jtlIzDUSotQMIVFiIeoalULZV9E7k0d6Y6QlwyzOPjhB2Cnjsvs9QKPPJLYKPvLXxJHtaIIh+lYyVDtqQYA1PkSKQglwN5P9c93ABkt6+6Xnj77LFWwKu+7wkLg738n+kdjI6VFp08HpmgHpeNiZ/1O4f+U0bwD2PF6cqMboLVq7d+TbpbWOG9YlzzaqoV4KcCG9UDZ55TqTYZwAFh5h/Z7PAwsu15fEAUAQuI4aE8cmsZW0ANsfT55+lAJ717VS5zHSMd6oSdNvKuBSJyljaVJtozzAZv+pX/AAfpCwb8WbH0e8CncbbMTOOZtYMoPQJ8rAGdXKqnPGwwM/TNw5jag/zXxj7nqLl2h+0TwemliTyVSkwgeD/D886lHyfaqh3m7IerpV3uqEQjF4TZpYN484McfKQqTaWyu2QwGkiBYt39dajtvfUHfgqhEvH02PZGa4eaNP1/8+c/6x1YwCLz8svZ7Gzcmj2pFEY1uJUO1l4ythtYGfSeoxIZHUpvDeJgiIACqq4F771Vfm/POA3bsAO65Bxg+nCRXunShFP8XXxBBPj9f38dFOYmG5nOAojkpzS/JFxnD45xzYOl1Bsd5nAG48QkyfHR9fhDY97X2e1VziWetF+E2YIeOCocM4NA0tna8YYwYpzHpLViQOqm4WcdYiC5CUe5WSqhbAfg6Rv1Bh0M4SBOz/KY32YEp3wO9LgAsbhJoksNsJ42Xcc8CQzQ8qsZNQMW3ycPWSfDKK/GjULm5FMl57DEi5BYWJj/eGwaHeaaMvWRoamuCN0AOgc1skxwMPbj/fv2ctlRQ662FL+gDYwxhHsaKihQYD0EfEXxTSa1I0PihfBXEQ0oFcRbA0lJg82b9h/F6qdJQC+++m1rkVXk7aSGaPmz2G0hn1a6gyHKqiOhdPf+8+r676ioquigoABwO8T2TCcjJAU49FZg/n+7NZNjbSB6MIQ4uD0fWrBQ8iyQGelrjvHY5Rbb0RFv1IBygqFY8gr0WWBztri3Pp+acHEAcesZWOASsf9jghKjGiy+2z+IU5WwZIoxufgYIG+EiHQIo+0LtQR37HtBpLGBxJt7X4qaUYs9zxde3vUQTRhoIhciQ0hpLN99MvJd//YvSN48/TuXp//hH4uPpJTofLGyr3QaX1QUAsJgs2FabRFIjgp07SRZDC2438Kc/kaDw3LlE+LemoNe4rmodHJbY6rq8fLn+nctm0MKYKez+IHXxRa5trX/xBWBOUVtyVxzb94MPUoso6hmDUWPL6/dGNRf1Y+PjBqMsPvj9lJmQR38nTQJeeonGUiLY7cCgQcBXXyX/qGhVeX1rPcKpjpHqBamn65IgrXG++WmDXNc4qJyNlE0RrayNv5EqNVM1AlOJhKWBQ8/YqlmUsYHS0gLMmNE+1aPRsHpjW2Nqk4+/ESj7LLOT/q8JGx4Vb67upwHdTwYsLn37W9zA0bKwczgI7HorQVSLAa7egDOxuvMPP2hHah57jB4uV2zyd7kApxO466740YdFi1JLbR8MbK3dKo3tQCggVSYmw8sva6ewjjiCIjj330/6Y8cfT5y13buBfjqbJazdvxZtwZgxLj/HpNj6XOJUlqMEcPfVb0Bteyljc5Xeik05tIyznTv1Ed5TRbRKL8iD8ARS8BD8DaSinkpURIZPPxWjdHY7GZMundOBwwGMHZt4m2A4iKY2IvUysNSLALa/mvFojeFxHmiOFIppXG9rAXD4E8AFdcCFzcAJX1O3hGTY8QYQ1El6ToS9H8aPeHUAHHrG1u73U+MyJcCnnwKWdmh4xDmXxP04eGqk0fIvAXagujD9j8EFoFHGTWAWYPx/AEuKbSBMslDJ/jnxre3i44AzNgNnbCLO18lLqQ2UBt5/X52OPu88imrF87Bzcqhy8frr1e+9/377pNkyiS21W+Dx0yLiD/uxdv/apPtwTgamkhh/+OHATz9RW6wc2c+Zm0s8m6VLgT59kp/TsvJlaJNFPkM8pE/rrrUqfsovdxAw5Ufg7N3A6euBs3YB3U9NfLzGjYAvTU2mCOrrFYK1OqE1fmbMaJ/WTuVNscrcKFleF/Z9C5hS7Jsjg/K+u+MOcfzoQbLt97fsh81M52i32KXKRF0ItQKln8GoMRkPhsd52QzApLG+uPsBZ20HBt0E2DoR7aLbScApK9WZADlCrfH5V6liy3MZy1i1Bw4tY4tzYO/HcUPtcPUERt4HHP4kUHRs0sN99FH7iEDW+mphiQxou9meGm+r9JNDi+yeCo7NB0wyAkaPM4z1lZNHwXa9o329e54LTP6WCPYWF6UoOx0BnLSIKh5lCIeBmTNFm61LF+JcJUtl5OQATz4pRm44Bz7+OD7/q2dP4L77aL9jkw/zdsPqytUIyxaRqO5PIqxdq05h2e10/fLi/JQWCxlhH3+c/Jyi5fAMZFHYzXZd54WyGdpOTv5wWnC6HA+YHRQZdfcGJn4MDLwu/vH2fJh2ajqKb76ha5QJvPNO+0RM5XNcNKqvC3veN5wGCocZ5s6NPWeM0vSpGlvJUNZUJhlbDCw1Y6vyx3Zxng2P8+2vqec7ezFw4gLAmi/OjSYLPT/2HaDLJO3j7fsOYBnoy9hWBzTri4wfLBxaxlbj+vgViP1+A5y+CRj+d2DonVSVNv6VuAM9FIJwo2YSFc0V0s1pYiaJv5UU4VBq7Qz+V1BSEr/7nhbibfvguWK4uudZgDmNmZVzWmSVHIFOY2mCsSgsJZOZXpv0DeCOhVlWrlQbRg8+qCbmxoPDQXyuKNavj1+B+JvfUPuav/+dNJd++IGI+aoIbUmJvg9PhgTH2VwjMrb1iCt+953a2LrnnuSN3i0WYOhQ+v7xEOZhqTQ/xENgYPAFffoqtXa+pfaq7cXA1J8As1sdDbC4gbH/pqa4WiibmRohOgHee09fUU4y1NRQJWJ7IBrNSinNFg4ClT/Ff794IjB1DnDycoq4KLBm72hhChk9Wh+ZP1WUNZWBR+aIQDiQmrFV8V3GnWfD4zwcAGqXql8/8kXAXqQd8QJorE/8iJwNJcpnZiaFuP9HKnTqwDi0jK1932qXz5ZMAY58gUKf0RSRxQX0vQwY85jmoX75JfGNabUCgwcDRUWpn2ZFSwVMEV5HmIf1R7bqf0Hcn9RkBY54FriohfSjhv0VQDvkA+TIhIHEeeZIItWKtijdTk1vdm3aosGNY8CE99WGlhyWHDLkI/jsM9E4Kiigaii90QiLBTjllNjzb7/VrhabMoXEKnNyYqRxlwu47DLihQmorMzM7xfnt+OcSxVaUTS2NcIXSBw2+eQTUd+pqIhSP8kigAB970T9Sfc07IHZRJyPHFsOXFYX/CE/lpQtSXzgcFA7hTj2X+TtxxtjFhdw7PtiWhqgqsamzFg1nJNMRiYwZ07mImRK1LfWg4GBMaY/jVi3Mj5HZ9RDwORZQMkkoPM4msenzQPMscjLip3jBO7fSSelVkyhF2VNZWgN0g3uD/lTU2zf9w0ynUI0PM5rV6gNms7jifdqTpLKtbiAoX9Rv56p4EDZl4kjnLZOxB87iAbZoWVslX6qVuC1F1FIX2txtLiBQdcDXaep3pozJ76g4tChpM+yYkWMsJsKz6GiuQLBiFHoD/n1R7b2z9GuWjG7gFNWAQOuoe9kzQOG/40mo1Srnf6XIefAuPsZSyHKUb0AqqhWr/MBZ/fE+5ksQPEEmqhADXvlxpHelk5yyMfXp5+qBSeLiiiNpmWUuN3E+5qmHubthlpfrVSVZYlEj50WZ8KG1B4PpRHluEkdsEiIRBV566rWSen7bjndpL9/qfwl8UEb1qkNptxBNBbMSSZ3Sw7QXyFEWrNIOwoQhas3OYJdT0x8bFDHikxh7tz2oU1wztHU1gQGBs65/shWxSztKsQeZwFDbhdTWtYcoHAcOZwRbK0cJPDSzjtPfzQ5Fexs2Al/KDYvb6vTV3WLtrrE/S6t+UDvC4mykGi8KGB4nFf+qM4MHfGcvs+2uIFhdxGJPorWaqA1Qw3vExltg28Fzi0HTl4GnLMXKDwiM5+ZIg6dlZbziDaIAiPvTxKFcANHqUu+vv1WW0H58MOBZcuAHj2InOtwUCl6skbBcuxr3id5QiEe0u8Jlc/Ubudw+JNATn/xe1pziDs0+Fb9J/a/DH+DyIHpdiLS9hgrZimKLVgkmqFDeMfkBMY+Dc7V+ke3364vUiNHdJHgnBoEK3H//YmP6XbHr2xsD2yt3Qq7hQwRl40WRcZYworExYvFxdBkImNLb+UYkFgTaU3lGomwP6RoiCSyurdxL0KJuk1UL1BXo455XG2AacGaA4x+WCR5V80HgnGqG7qdTCT7I18GjvsMOP4LEuSNgyVLMkdon5OgFiQdtPgjFhyjgqBo94ykKP9SnWq1F1MHCE3n2Qn0vYSI2wA2lg2X3nI4gDFjDJy8DiibrO9p0Nmyp2ZxfEMmbwhwxkbgqNfp+56yEnB203VYw+O8/EvRmc8bChSMSM1hH/Db2N/VC0UOrRIlU4Bxz1NgwJzgJvdVAv44RWRHPAuMeZjuEWsu4OgCTJsbn0PWjjh0jK2WHVB9XWsB0P/q5N6nrVCoHgqHyaBSwmQiwT+XS8wc5OQAZ5xBYWo92NO4ByEZiV9Xyx7OKayuROejgP6/1ZY2sOYAox+icvRfOzx7xIkr97DUqxCVqJ4vPi+eSOFqPTCZgMKx2LNHXAz79CFyvFHs2KHOWhUUAFdfnTwFVFhIQo0HAttqt0mRrWJXMQDAF/AlNLbWrxcdnPHjM5vWWlK+BCEegpmZcWT3I+GPLCx2s121YArY960o0RCdL/QYWwBFOrufHnteMUtbSqTLJOC4T2nRsObS/dt1GkWo4+CnnzITjWprowbW7YFqbzXssjm4rFkHp4lzqthUYvhfExqfsLiBcdT4cWdVf+nl7t3bp/UTAFW6XDctpGoeEND48Zw9qLLZ3jU2FnIGUfYiUeAgAkPjPBwAGlaLrw34XWrkfYtb5M5VL4jPRxt8C3DCl8Dgm4AR9wKnLCfpFC1UL9BewzsfCQy4Vj3PW9zAse8e8JTioWNs1a5QW+CDdeYgrLnAyFin8e1xxuP11wO9e2tTNNxu4L//1RemVkay5GXRceHZA00O1uFPJdaQMtkpugdkjhSd6WNlAsEW8fe36eyzEQ++CnWfr4G/T+yBqcCwerVITj/88PRa0KxYoR5/elNtubnAAw8k3y4T2FSzSfKu+xT0gd1sRyAcwOrK1XH3WbpUNLZOOy2zaZ+o9ITL6sKgzoMkI9BkMsWv1OKcIhBy9L4wtdYq1jxg0A2R44WBhjXqbZgZOPoN9WJqcQGdDo9bXr8wfl9qjBxJNIdAAPj++8QRwnXrEl/r++6jKsXmZuCaBF2ttFDlqZJSWQD00SY0WqfB7AQG/D658+woAYonwtMWu5Zud+qpe71QSirU+er0aVpVzoZm9H3csxQRkt/oZiutUyMSKB1HYGicN29XS2z0uzI5V0sJefRt/0/Q/H49zwXGPBpbt8wOIHcgFTtorXFV80n/Sw5mAo55J35k0FZAEbMDiEPH2KpZrLaiB9+kX8wyfziQMwAAedhK7ofJBDz0UOKy4dxc4NJLk39UVOAvCl0choa16moQd7/k+WmThRYHS05iUnSGiNEHDcoyenOKeTol6laJkzozAz3PoYpDvTA7sGqVGHkYOzb1FKIcixerIxmppNqGDwcGDDD++XqxpnINODgcFgf6F/SXUorRxtRaUGpFnXceYDMusSSgLdgmLYqMMfQr6Ic+BVQx2uJvwZpKDQMIIM6JkjfU/xqKOqWCLsfR/83btNMyg26gFJkWrDnA+JdUC4vfH7/P5dix1Gqme3cy9idOJCJ9vPlr1ar4UiJPPUUCuw4H7f/ss6QPpxfVnmqpWg/QKf1Q94s6qtL3cuhSDze7gWF/RTAc29/pbJ8UaZiHUe+rBxCTWTAzM2q8NYl35Bxo1GgOXXQspZK1jByLGxh8M837cWB4nDdtgmAu5A7Sv3YKiBhL4ZB2ZNJkpWI1VSW3jaSZ+mosoLXLoPrdu59O3Nl4KU6LGxj6p8RR0Azj0DG2quZB+EEKRgMWHdwaCYyMEpCXp2xBcdJJyQVOc3OBP/5Rx6kqjKv61vrkO9WvVvM8Bt2gj7DBw0S4/TVDOTHrEbb9rCvwPtN+zD1TbPtTOA5GOGBbtoge9XHHpSeUO2+euGiMHq2vd1sUjFGj3fbGltotAKgn4uDOg6XXoz1BlQiHxfYxbje1SskUNtdshjPSrqkt2Ib+nfpjSGdSvw7zMBaXxekP1LxVNLrNLqBwTOonEB1LjRs1KuwYRZ8TGXBmF9D7IuGlDRu0o1EmE1XA5ssKJZ1OYNgwasishYULtUVOjz+eIvpyB8HtppZS/furt9dCladKKggCoE/EuW6FWlV9yO06+ZImoOsUmM2xG6W1tX3EWqs8VbCaKZ0crQDUJWzqLdU2FEY9mDhVaLICwzSq/iIwPM4bN4lzZslkY60RowZa8zbtNPugG+Ovy9ZckktRpv+at6i3HXxL8rHAw0Cv85Kfc4aQEWOLMXYKY2wLY2w7Y+xujfd7M8bmMMZ+YYytZYydlonP1Q0ejljmMvQ8Wz+nAiByZR+yqpcuVXt5N9+sTwxv8ODEiwTnXPKEogjzMJrbkgjl1CxW8DxYJK2lI89izQEOuy35dv/LsLggzA6BxuT7JKyU4WIxQvFEQxyAckWGeOTIlA8hIRwmDS05zj47tXJ2p1Nf9DUdcM6F6O3ILiMlkq434JVam8hRWipGkw87LLPimuuq1gnRlSJXEYZ3GQ5rZI7YUKURZQBo0ZCnDDuPJ+mGVBHllTRuVDtNXU5IPldZc2OpyAhWrdJOjV14oXYjc6eToqBa7y3VkFcCKKrl1AgOWK3AI48kPuUoqjxVQuuYpram5Gm26gUAZJOwsxsVAelFOACnKzagPJ7209iKaibKexEmNbYaN6nTdvbOVMWcCCYrRX/icKkMj/O6FeL60uNMwJpGCL55i7YxOfxvSZwKJ9D7gthzf4M6Y+XoEosUJ4I1FzjswBWIpT28GGNmAC8AOBXAMACXMsaGKTa7B8BHnPPDAVwC4MV0PzcleMvV3mLvC5Pn9pXIOwwApRHlKCgApk7Vd7OazcDvfhf//WZ/s3QzmCPn7LA4kpMqlZWWhUekViWSo7N53P8q3H3EdE/LzvgVX0bQ/dSUyq+jkGdbCwvTU68uL1enty+8MHUS+WGHGT8HPdjXvE8a2/6QH6NKRkml8S6rS7Mh9b59otE4ZEhmIxGrKlZJVXHdcrqBMYb+nfrDaSVLosZXI3HMBDRuEsVMuxxnLDURTT8rFzUg4jTpWNg6jRE4MStXqiPwJhPwxBPxo50mE6moy8E59URU4oQTSOZGa96zWsnQ15OS3te8TygIApC8P6Jyvut+WmqK+9Zc9O0Xu1kqKtpHQ0xuVOXa6KIHQjqETZs2qdPTvS/SxwXkiFRbq2F8nCuMsM5HJT+PRGjaonZKio5Ofu9Yc4mYL53XRvU+fS7X3xs4f4QgMN2eyIQtPx7Ads75Ts65H8B0AGcrtuEAoqJG+QAy0/RLLzy7Rc/Q7JAMp5QQaoXfTzemHCeeqL+SxW4HLrgg/vsVzRWSBxT1NszMnJg0GvSpozDdT09cVqtEhlqDdFjYCiEM93jkU6OIaGalijpZxmT06PSiNbt3iwaJw2HMcIqnPp8pbKvbJnn7wXAQXXO7osBRAICiXlo6RNUKGs+wYelx25RYWh4L3fTt1BcA0K8g5oC4rC5tPln9avF5t1NTJw3L0aDw5Ez2SBReBxeQhyO8JYKWBMgJJ5BzGA9OJ3DjjaLRXlGhndq+887EXECLRV9xhtLwsJvtiXmq/kY1IbrnOfpSiDKMGBMrkvF620cdv6ypTHIkilxFklp7vHS5hLpVahmfQTfo4wJac4CBf9B8y/A4l+t9mez6q67joW6VWraj71X6HJWio2N6XY3r1QZo30tT4JOFyVA/AMiEsdUDQKnseVnkNTnuB3AFY6wMwDcAbtE6EGPsOsbYCsbYimrl7JoOPLvFfogFo4xFNSwubNum5kGcdVZqEYkeyqsjQ0VLRSyiZaUPSqoi37RJPbh6np3apJ/iRPU/B8bESpimzSLnKh04uxnqNs850CTLmBUVpRet2b1bTG+PGmWsGXUqulVGsK12m5Q27OToBBMzoSSHqlc9AQ+21Kg5GNXVYpXmEUckFihNFZuqY/nX4cWkv9S/U39J0T4UDmlXajUrDMP8ocZPgofVVXZdjtNf2WhxAX0ukZ5qRaMuvji5kRoKARNk2aodO9SFCDZb8mi+1UqflwzRuS1KILeYLIlV5JUyLgD1nkwRgwaFhBTo559rayemg90NuyXNxG453SRHOqmwqbIi1VYI5A7W3lYJZooreGtonIfaRMMvp58+zmsiNCqcCmYiDbR4LX/kCAeAXpHq27pV4rmYnbS+64XFTeLDBwAHiiB/KYA3Oec9AZwG4B3G1DkuzvkrnPNxnPNxxcVxKm+MQJkyKhyXGl8rCpMVGzaoF8Rp01LL9yeKHFQ0V0j6Q3k2Cga2hdoSR7ZadonPTXYgX5nJzUIVfar4PvH28XRdlMjpr63cnwSBgEhmdzjS443s3CkaV+PGGWs/0h4tS+TYUL0B3sj92DWnKwCgd15vAORYaMk/VFeLC+HQNGwaJep99VJqxWFxYEgREYbzHfkSudkT8GDlPg0du1ZZHtjsTE+7zVumNtq7nqRLO0lC/ggAZDBVaQSHzj8/uZHqdgMXybj227er2z9Nnqwvmp+fTynfRFBWH3IkUZH37hUnYWcPQ82aDx/DhYjdrFmZN7aiRpXdbEf33O5StiKpUHWLwlIumaStlh8PGjpthsd5W63IR80dpD9NFw+e3eLzwiP0O6zW3FgEV6ktWXR0atcpus8BQCaMrXIAvWTPe0Zek+NaAB8BAOd8MQAHAANdAw2icSOElFGXEwyWrZKwn3xB69o1cVheC4mqwypaKtAWibgUuYpgggn+kB+lTaXxd/KWigOs06jUB9yhgJ5ni5Uu5TOBQIImqOdVApfx2CMecvobimwFg6JxZbenZ2xt3CgSok84of2jVEawZn/Ma++TT3yJgZ0HSq8pG1QDQFmZGLXr3Dlz57O+ar3EWbGZbejfKUa07pEbC0MvK1coGYdaxaiTu296Hr8WKbrH6fq8femcKEJRXq6ORh1+uD5eksUiVqRu3aqOkF5yib5ovtlMEh2JEC0IijqZoXAosfyDZ6/o3BQebogGMe5ol2BErliR2WgpEFOLt5lt6JHXAyxiJCZ0nltr1M5b15NTyz4oxxHSGOf+WoDJPLDcQelJJrTVqn+v4hM0zzkuiiKcMWWgwcjanq7hqBOZMLaWAxjEGOvHGLOBCPAzFdvsBTAVABhjQ0HGVgbzhEmgDPV3PtLwoXbsECf9Y49N3RtKtKDubdwr5fi7uLtI+kO76nfF36lll3hzFowytPj/6tF1mhgOL/vCUERKhZwBKYqZEkIh0UG32dJLIyoVvo80PszbFXICfNTI6l/QX+Jx7W1Si0PVKGSJMhl9W1e1TkprhnlY4LDIF6SoXIUEf71YZJPTP72Ju3mL6CSZ7CTmmAoi43D7dvU1Oucc/SRwl4s01wDqR6ksDjz7bH2GicORuLo12hcxCgaGtmBb4shWy06FcznWkPMc1ReLIhSiRu1GUu/xEE2RmpgJffL7SAZljbcmfsVl8xZ1mrTr5NQKnjSKdQyP87YacWIqGJl6cZkcTVvUxlqP01MrMOKc7rVAg/h6t5MNZK0OTIIv7U/hnAcB3AxgFoBNoKrDDYyxBxljZ0U2+xOA3zPG1gD4AMBvuS4J3QxB2cxTZw8pLexS2DyjR2eWqLurgT7AZrahi7uLpKycMLLVrGhxUnhk6qKKhwIcxUCObPEK+YAVt2i3xEgF+SNSEzONwGwWFzG/Pz1hxTLFMO9mfJi3G0LhEPZ7qJjDbrZLE37PvJ5wRCbbUDiEWm+tsJ9SVT8dLTIllpUvgy9SGeUL+NC3oK/03oguI6S//SG/aAS01Ynpq5z+hipSJXh2i85A4djUZSQiBuuOHeprdvLJ+kVgGYsZIkojvm/f1MRkByegGjX7m2GKGBFWkxU2sw1BHlQJOwtoUkQ+uxxnjBYCitDJ5++HHkq9QCReOyTOuaQZxsExoNMABCIRHcZYfP1Eb5k4EZhdGamYMz7Oa8XzyU1T4K5pi5qHmHJzaEaNrJW/e16SnLUW0pGwSAEZMek4599wzgdzzgdwzh+JvPYPzvnMyN8bOecTOOejOedjOOdJyDIZhl82qK35SOdrKxe08eMzO/FHW/PYzDZ0y+0mTUTRBUoTSlKtwcq4QwLD7xZ5NXumU7l9OmnXPGOTj8Uipv1aW9NrGVIvG+ZywcqOhL2Ne6UIlt1iR8+8ngDI2IqmWOwWu6pHotxwMJky+93m74n1uLRb7Phk4yd4Z807eGfNO6jz1Unna2ImLC9fHtvRXydGG/KGpGdsNSt4Op3GGDYiNm8WIzQmExVM6IXbTQR4QD3nHXNMfDV5LSSqsK32VEvRe7vFLnGHErYo8yg83vzh2tvpwCWXiEGblhbg2mvVkhnx4PdrFyIAFL2KFjsFQgEM7DxQ0hNzWBzx5R98+0Sju2CEMe02BQyP87ZakQOWzhgHqMBBnm53900tagdQMMFXLqYebZ0MZRgOFDJoJnRQhFohiFm6egHhVsPl2UrS6XDj97kmokaViZnQM7enpLmVUFXZp2iNk6ezauVQRO+LKJolx7xzgJMWRdKBBsLjzp6GTsVqFSf6tjbjxlZrq+h89upFr2WqnU2msK1um0QSBiAYW1GvP8zD2Fa3Dcf0OkbaTv7dGKPnmdDZCofD2NkQWy19AR9u/OZG6XkoHJLS+r6gD6+uehWnD440jfbXQZhbjHjVcvgUi2/BGMPcUqXsw5AhapJ7MkyYQNIkSsPq+ONTq75OlLqs8lRJBkmOLQfeAC3CCTlNPsV7duP0X5cLuO464PnnY4T/GTMownXvvYmzFoEAyWKceCKwX8MXLmsqg91iR1uoDW2hNnTN6QqX1QVPwAMGhrKmMowq0bCAvaUivSFvaOrGiAJpjfOwX6zmT7eBs2c3hPumcFxqvUSj8CoM8vzhZMSlI73SjuiAvm+G4W8QrV93L8O8ikBADBnbbESQzySiRhUDQ6/8XgiFaZC3BdukEmIB4ZCohm4rzPK1EsFsB4bcIXpAgUbgh4lA9ULtlGI4lFgqxKCnxxiQlxd7XlNjPI3Y0CAaVr16tV9j3XSwtXarVAASCAUkY6trTldpfHv8HqFEHRCjx6lEVZJBrjsEULqnxd8iPXyKiMIvFb/EnrTViYtQuvIpSqep8HDDh9q+XXw+dmzqxygqonSkUupmwoTUIouJGlhXe2N9EXPtucLrmuBcLGqxdUqb4Hz77Wr+2eOPk7irx6NtpLa0UK/OY47RrvoERP0wm9kGm9mGfAdpewXCCYRNlaTvghFp93JNa5zzEATjKBUiuxaUmZj8ocacCl+5SLTPH244Enwg8Os3tgKNIq/C1cvwD1JZKU4cgwZltmVIa7BVCjOHeAh9C/pKC5PD4lB1j6ed9ouD39k9c/pRv1YM+3MknSyDvx74aSqw+Epg/xy6hpzT67vfBb5LwCkwUHYehbyqbvVq7dYnetDYKBokvXq1v4SDEayvWi8ZVf6QHyVuktewmq3Is5PlycGxev9qYT/ld0k1ShMPs7bPSml7Qe8u5CVDPIp0PX6/InqdKjlehlqR8oajj06dW+r1knK/HIxltielvC9iZ2fn5JF8Zbrf1TPtyuuePbUFWl94gTIXr79O0iOck8O9aBHwhz8QhUQpcC1HWVOZREiPivZ2dtIN7w14sbcxTpdwr4KfWzgu7bx5WuOch0QvMF2DRhmZ7DTW2DE9yir8sanJpBxg/PrTiP4GMQTr7guYjK1o5eXipN+3b2ajB5UtlXBYHPAEPPCH/OiV10uqWLGYLKhorhBIjQCoUsRklUq+4eoOYx1CDyGYHcDED4E5J8euWxRlM+gBgDrU67iWBsjxUZSUxKIQ9fVAc7OxtiENDeJ83LevccOtPbF2/1rp7wJHgdScFwBK3CVoaG0AAJWwqTwCCJCxlYkU6aKyRSltHwgHEAwHqXBF5fGnsQgFvWLqyJpneOEIh9Wk7aOOSn29ttkoaiM3bPv3z5yhC4h9Ebu4u0iyIM3+ZnDOJR6fhEBTZL6LGLnOHsjEfHfPPcD06XQvyu2KPXuo0fb118fS13qxu2G3FDEqdFLDyRJ3CdaBcrxabakAaNBC0u+fldY4B4t8+cibei7CZ12T9JaVwagmZMt2CL99fjv3GUsTh0ZkS468wwx7Cfv2ieOsa9fMRg8qmiuk6sNAKIACZwFybESO4ODaKvKBJgBygb/uaUVaDhl0OQ4YcV8SQqWemZWllbbtqaB7abVY0YNGxTA/zPgwb1fsqN8h/R1VjY+id35v6e/y5nKhNL5XLzWROROQG396MXf3XPojk+kVX4VYDp872DApuq5ObYj2M9D61OUiY0tenTdwYGaNLXlfxO653aVIEAOTBDgFqDIVxgRNlbDZgK+/Js3EeFzAVFP82+tiudwu7i4A6DtGEa08V0Ee4TRZAXv6At9pjfOowRUF1yGXo9fQAgB37+TbaMGjiAw6OmD5tQy//lXZ3yDm9NOQfaivF/ki3bol5iOkioqWCimM7rK6YGIm5Dvy0djWiEAooE0aVYpyOrunXy1yqGD4XyiytelJ42KUzEzjKxGBNYGXd5j/AZjN/5DG1fz5wHHHpV7h2tAgRlk7ouyDL+AT2rB4/B7cPut26bk8TR4Oh7GrYZek/9OlC91r0bT9tm30Wrqo8dYk30iB73d8j6n9p9LvLjD3dRjdej1+l/GITVUVOYFRI8lmo+pUIygtFcfVgAGZLbqIStowMHTL6SYZXjazDVWeKoHHBYDmO/l1dvUCLJkJ4Q4aBCxYQNqJTU36jat4wsG7G3dLf3fLoRsyylEEyNBUQRnhdJRQZWIqwrYaSGucMzMEYyuTNBVHV+NEVWXaPY1CiQOBDuj7ZhiBRkUlhfGZorlZ9Or69cus4nBFc4VUERLlr0Rz/b6gT1t7JtgsGpM5/dO+MQ8pjLofGPM4RbiSechmF+DqDRTL+rDxYPJKoQSL69i+KwUuzapV+svO5WhsFB2BjlaFCAA/7/5ZciYAYE/jHjyz5BnpIe/JFuRBzNwS00YuLhajyKtWpX8+Xr9XWtxTgUQ2ZiYxDKLRIkUFvR6/o8TwXFVVJUY1+/Uzzi1Vyj4MHZrZrgRRA9tusaPQWQhnxHCymCzaJHllpiJ3YEYLgoYNI07W4MHJOW4mE6Xq775b+325cxw1skpySmCPVDxrGkCtlaKz7CgxpI4vR9rj3JIjrimZEIKOIneguuG2XgTlEyWj1HsHxq/f2Aq1icZIGryK5maxH1hvg9HPeChrKpPIw1Ejq9gVCyHvrNcQdAk0icaku29mT+pQwGE3A2dsArpOBTfZwOUtfWCiFj9mNzDkduDMrWoPykjZcgRj+qwWNKR++cVYalopG9ERyfGLSxentP2qfTGLqrhYtGvWrTNmlMrx/Q5jcn/rqyJNdJUefyYXIUc3wxHqqipxLAwcaLyCU0kAHzFCezujiEY6LSYLChwFcNtiFo6minygEaKUT4YnYZDBtX49cN99xBXMzRUjzTk55MwceywVtdx7r/oYnHPJmLKZbVJkq9hVLOlZhXlYUM8HoI7cOdIvd097nNs7iw6lMqKUDhwlEO6hVCCvELcX6nN2DiIOgRBIGMLNyYyvQvX1YsQz06kaeQ4/WrUSrdYC4qjIB5pEz8fVQ71NFsnh7o19497AsGd6YMlpf8UQNAEhD2DvQm1+iifEvDtl5JCHYPRW6l20VzAi9u4lzZ5UOTZhRUarIxpbv1T+knwjGdZVxyJdJSWiwbB5s1ohPVU0tjXCYrJI1XBdXF1i2kIyVHuqMWvHLEkHTOKSqYytNE9IDndvw9pKVVWiUzhggHG6g7JN0oABxo4TD9GCCBMzocBRgDx7Hqo8VQiEA0LKWYLSuUyXMtG1q6ZIlgXAXQDugAnLMB6zcBIq0Q0O+HB4y2qcjq9RtKAWiHKy9+8X8tr1rfUSud9mtqHYTU5zsbtYEqp2WBwobSzF8C4yscZAM4Qx5ShJm5OW9ji3F4nnVL8WKDkxM6RQRxfjARB5cZOjGwVWOjCF5tdvbGWwYqhOYdBngjMiR2ljzJiKEiq75cYsOm3OViPAZZN8BsiUhyq+3vo1GsPA2/V+/HPa0/E3tBWIz3kQgLGyf8ZIcFKeFnv6aeDRR1MTjlSKmnZEY2tjzcaUtt/dsFv6u39/kai9eXP6fMmN1RslHTsAOPOwM/HaWa+ptqv2VKPXv3tJz5v8TWgNtsJhyRWjEJmMbLl6Jd8mDiorxX6tw4YZv1byrgSMZVZXUN4XkYGhwFGAfDuRy9qCbdppxKA3Y5kKANpqpDKYEcYxWIJjsCTxcUpKhBuwrKkMDosD/pAfZmaWMhTFrmIplW5iJpQ1lYnGVlBRFOAoSduASHuc2zqLBm7TZuK4mjLQEs7o9+NcTD86u6KjV+H/+tOIKhKrcfuyoUF8XlBg+FCakFcbRo0seX/EWl+teqc2hQXYgS37jo4PN3wIAPhs82eJN1RWvSQSPNWBk08WjaN33kmdC6iMbGWyhVSmoKkTlwDNbc3S30oB4epqtfOTKpaULZEWPqvJiqFFQzW3K3KJaWOnxUmiq/ZCUfbD35DeCQkfYtyq2aso0jIajQqFxFRtYWFmBWWb2pqkKE+Yh1HgKJAkEkI8pC36yUOZNbbaCWVNZWCRaBAHFyJb0YpLTWHTYIv4/dy90+bgpj/OO4uORPO2zKXsXAYjuCGvOt3awcW8f/3GVlQjRIIOYazPugLvM9WjcescaZP8/MwrdEeNKQaGrjk02XZ2dpYIlS3+FikULEHe9xHo8AOuo6It2IaFpQsBUMm2ZgojCntnUcBSKUKYIs47T9TWamwE3nxTjE4kQiBAkR75MO+I6vG+QGos7RAPwR+MTfIjR4rvf/VVeou/PNLmtDgxoFDbKmEsdj8CFJFZV7WOujXI0yuN642fjBJpRKjLFXU0xQYPpZSQKCoS05Ppotob64sYCodQ4ChAZ1dM5VezPyLPHC2kPVHWVCal44LhoBTZKnIVSULVvoBPTQ0JKIytDHDS0h/nBSIvtWV7+iryURilvQSaRSPU2S19UeF2xq/f2FIaH3rIzHEqhpp8sWqH/Pz0OSNyBMNBSVfGbrFLnK3Ors6S8KPdbFeTRpU8kTR7aB2qmLdnntAg+bvt38Xf2FYoetRNW+JvqwNjx6ojUf/4h/4KMr8fmDlTfC2TWkiZQK23VqhE1Itl+5ZJfx91lBjxmznTuN5Wc1szGltjlW0cHP0K4hPl+nWKvecJeLCqYhWNA/nC2LQ17SinBGWqOgUoeVbyLgWpoLZWjLgWFWXWiJf3RfSH/ShwFAgFQZq6gsqqug5aeb2nYY/U57Et2CZFtmxmm+Q8c3C1sGmwWYwapSFVBGRonDOTOB59FcmdekdJ4vel7QxGcAPNYpbK1qnD9kSM4te/MjMTMlUx1NIay1FbrcblQbRQ5amSbkKrySp5eIXOQikcbTVb1bwt5eSTjWwZwmebPpOMXW/AK6UUNWFX9J9sXJ9WRaLJBJx9thiZqqkBfvOb5BV3LS3ArbcSt0a+fyYjEJnAz7t/NrTf/D3zpb9HjRLL8efMMc5NW1+1Hk5rTJ/JF/QJC40Sw4tjvBoOTmXx9kKRL+nZlRneltmZ1n2sHDNGNbY8HpEDXVSUmebfUVR7Yn0RwzwMl9WFLu4u0nyn3R+RKyZeHUtYnEwF3s/gl1FgW13MiLKYLHBYYvSOaH9EQEPYNNgiOtCO9Di4GRnngLp1lGd34g8+rxK4jNMjEewGPYFgixhY6OBRLSBDxhZj7BTG2BbG2HbGmKbqCGPsIsbYRsbYBsbY+5n4XH0nZxZ/FG48HNUWjFnOFktmja2K5gpYzbRymJgpFtlydkY44j0zMLW3lzW2MoIZW2ZI1xkA5uyeo07ZRmErFJ+37Ew7onHZZWpC/MyZRJaPZ3C1tADvvUe92wBxYcxk1DUTWFKWhGAcB/IKxqOPFlOrXi/wySepRfGi0cJ1VesQDMV2dJgdkradFoYUDREWzM01m8mbDsmMq5admbn/lByZFCGPiDJmXBdLOe6KijLLBaz2Vkv3mMvqAmNEko+mFut99eqdVNpmOvLIibTNDBqiKpSIkRx5cYdyXEV5aYBGqtTfCIHqkqZ2VEbGOQB0UjRFr/hO7AtqFEYjuOE2CEGUTKU12xFpG1uMMTOAFwCcCmAYgEsZY8MU2wwC8FcAEzjnwwHclu7n6oZVWTFkfBXiPHa5Mm5syYwoDi5Ftjq7OksTUiCsoSIvT2NE1cyzSAnbardJoXZrJD1oYRYsKo3TT8xeJE7yLbugiwuYANOmaVcf/uMfwJ/+RAtfNGXm8dCC+s9/Us+2KOQpto5mbO33qBc8pvFPCTmpvls3tdzKk08ai+Kt2LcCXpmB3D2ve4Ktgf6d+ktpZoCEIuvamgGzzKP27BFb7hgFs6Z1H8urNgsKjI8Fr8J/KCrKrFju/pb9kq6g20ohywJHgVQQ1NTWJLRsIigyFekStV8ETeRaDy3E27ZSLP6Qq8PLjSsgVmkOaETvlBpWaRrvGRnnvjqg02hxbFd8r66cTBUmK2AyWNClvD86aKGEHJmIbI0HsJ1zvpNz7gcwHcDZim1+D+AFznk9AHDONdTq2gnWfDGypWxvkwJC4djAz3S1V0VzhWRUBcNB6QYtdBZKhMrWQKtGiwf5pKAjLJ4opP7+oRkV+2rrV1I6Y2DhQJiZGZ6ABzM2z9Dewd1b1Hhp2Zm4ClQHf8FkAv76V23V6v/8hxznW28FHnwQuPNOoHt3kodQHiOKJuPDvF3QJmvxYTVZ8cS0JxC+L6x6vHvuu1I/UABCyToAnHOO+D3Xr6d0oh6Dy+sFXn2V/l5evlx4b2CngRp7xNCvoJ8Q+XRanVi3fx21x4oi7FcrnCuhh8tisiCdMna5sZUOqd3jUesKZlJSpLy5XLqm8o4ZUR6XiZnQ7G8Wd1JxcDuYVxGBXB1eblwBQPec2JgJhANiD8hAku+bIjI2zvOGitGjqvnpOxa2zsbV41XG1iEQ2QLQA4C8pKIs8pocgwEMZowtZIwtYYydkoHP1QdbAQQjpHlHvC2TgvPYcTLN2drXvE+q1vKH/FIa0Wa2SdGWMMJCeBqAeDOm2Tom3ejM/yqmr5+O1mAr3FY3Tux/Itw2N0I8hE83faq9g8kqqsi3VStaRygg5y8k4DD87nfxjXiPB3jjDVK1fvlltQwJIGZXdhgf5u2CjdWxiiiHxYFe+do6Uj3zekqLLSA2rgaAM85QRwCvu06fQeHxAH/7G1VZba3bKrwnaB1poG9BX6Gasi3URpVauYPFDevXJD4JPVwWll7YXH4t0iG1e73ivj0yrJcslz2I8piinTMAKlRRFQSZMkcLaS/sa9onOBdLypag8PFC6fHZppi0TCgcwi8VMrHfDNJCMjrO84YAIZkVH2wGGpKM9WQwO/SlgTWhGNQdnBwPHDiCvAXAIACTAFwK4FXGWIFyI8bYdYyxFYyxFdXVCUrvU4G1AIKX2LJd5FmkgLAijZhJ7GrYJVRruawxooXc09/TuEfcUXkzGh68hyaa25qxev9qAFT6fNHwi6TURpWnCnsa9mjvmKMon65JrRWNFpxO4LHHkvdkiwf5+rx9e8ciycuvo4mZhIa8cvTM6yn0cavx1ki6RAAwcaL6e+3bB/zxj4mLCbxe4sV5PJSyl3vvLosLgzsPjr8zyMPPtcXaOLUGW7GsfBnQaQwEZ67iW3FRMoI0KuxCIZHDVpRGb16PR5TWKNFZYKYX8hRx1Lns5OgkzYNmZlZLsFhyMkYLaS8s3ydGkwLhAOpb66VHq2x8cHAsLpPNHcqITRrV5Rkd586uRMmRY9uL6khcKkinklR1nQ6NNGI5ALmb2jPymhxlAGZyzgOc810AtoKMLwGc81c45+M45+OKjYrDKGHLF4l83lIgbGwyZCy2mmVS3A8Qjagca47U6gEQvT0VZ0t5M2Y5Wylh9s7ZUhVoMBzEMb2OkVK4JmbC19u+1t6xQCH6VPFdRsr+//AH4PDDU0/XMCaOydJSMZ10MNHY2igZsABd53jGVo+8HoJn7bQ6hYotux0480x1Vdx//0tpWI9HjMZEhTkvvRSYPZteW7d/ncBLsZqt6N+pf9Lv0TNfPOeVFSvJ47fIrOPqBdQ2JB0w42lEr1ct12DUMfR4Mme4aUGeaosKahY4CiQ6BQdXc5pUtJA0Fvt2gtRTUCe21sqiTxmMbGV0nANAyRRxw70fpyc1lMY4V1Fm9AQZDjKFJhPG1nIAgxhj/RhjNgCXAFCo/mAGKKoFxlgRKK2o0VW5HWAtUJRn7zVskJhYbL9M6xjJjahE1SuqyUdJDMxGtlLCxxs/lnghI7uMhImZcGzPYwGQBMQH6z/Q3jF/hMjTqlqQkWvPGDB9uihyqgcOh0iE3ru34wibbqvbpio/756rTdR1WBzCtiZmEhcjALfcoh39e+454JRTgMWLYxGeb7+lhsFyHbK1+9eqjL9E2kNRDCoU/cMddTvAcweKC2LdyvS7OKTh8Xu9YqFEOqR2r1ec5zp1MnxamqhvjVUbRnWoChwF8EcyD8FwUJ1GtOZDWGhbFNIJHQBy2Qc92FUv+w5CwZMpLec5o+Occ6D76RRZlA7oAba9ZNzJZGbj6XKlkaenevcgU2jSToZxzoOMsZsBzAJgBvAG53wDY+xBACs45zMj753EGNsIIATgLs65Ru+ZdoA1Vww1e0sNVy4oja2Mas7IjKhOTnFWkwv9NbQ2gHMei3zZFDNg1tjSDc45vtn2DQBa1Kf1nwYAmNp/Kr7d/i18QR+Wly+HN+AV0roAgLzBpO0STQk0rM1Yb7wePYDPPycyeDKdLYDSj2++CVxySey10tKO0x9xW+02oaosx5YjeNxKFLuKJdJwa7BVJfw4cSL1JdUSNF2wgN5nLP48vqR8ibSgRz8jHodMjhFdRmDG5hmxNJfJjFLuRG95sUQ4AFQvBLpOiXMUHdATzfisq+bi4anqBwtfA4BSPvn5xo2tpibxGhpNb2uBcy61YzLBhCInRbZcVpeU+moLtqnTiNY8iLSQHXTNO1A1mmA86YDIw5Wr45uRNPITZxwAwJJ9gF+2HKQzzvc07kHfksnq6s91DwIDfgfAgL5IOg22VcZWx0snK5ERzhbn/BvO+WDO+QDO+SOR1/4RMbTACXdwzodxzkdyzqdn4nN1gZkAi2wg+CoMC6CZTDFjKxDInLHFOUeDr0F6nm/PR0Nrg/SQpxHNzEyluFEoReHSTWEcQvil8heJH5Rjy8HE3hMBAMf0PEYqP7db7Jiza45658JxYkUiOLBnesZu+mnTgJ9+osXSFOcuZYwWwK++Ai66SNRTqqhIPTrWXthcsxkef8xqVFZnKSFPMfpDfqzZLxJxGQPuvTfx4p/IYV5duVp43tnZWfq9E2Fg4UDB6LaYLFhTXxbhhcqw8420qp7T6XLhaXUL81Q6Ug3KIox449AIGtsapUIIm8UmOZiMMbgi87Vmf0Rbvhjt8ZWnz5HLMFS82iSQp1MFIyIcTG54J4jWrFYsBemM83X71wHuXpHIogzBZmDVHdRmKFWkU3Wrqkrt+Over19BHhBbAvCQ4fJssynmJmQyjbizfifCsjDmgtIFKHmqRHrI1cz9Yb/QwgTWPJEc2JahwoL/YTz8MPDzz8kj1DO3zERbMCKrEWzFUT2OAgCMLBkpVRM1tzVrVyXaC9XjZMdrGYtuAcD48cCqVcBFF4bhsIfgdvphtQThcvrhsIdw5hlhLF0KTIkEUeSNmkMh6rHYEbB6/2phfPfKS+xdK3klWhyYyy9Xa27pAgtjb+NemJkZZmaGiZnQO19f/7l+Bf0Q4iFpX4/fQ+fW5Xhxw9LP0/Pa09CO8gWcYLIFLB1jSxk5TLU5eiJUe6phs9DJWUwWwaF022JWdFmzwtiy5CkyFWUdLppf600taSPXwRIjNtxwGjHMgb1B6BvnDRuAjU8Av/wF2PAo+pn82uMcAHqeozZ0dv4X2PWmtu5WomK0tCJbiqjf/0Bkq2M2lso03P0o3BxF0xag+Nj4258nE6iTtXRw22ODKRjMnKen9LTDPCykOVTbV6zGqQNPpSfWPAqhRyu2vOVA3mGZObH/AYSKS2Cujnl3lSjBffdRaq1rV+Dxx4Hzz9fe96MNH0nNYgudhZKQrMVkwdCioVizfw04OL7c+qWYuo2iZDKw6+3Y8/rVxCEpGJGZLxf0oX/DP/DBeS9h/5Su+HrlJNS35CPf1YxTjpiPnp32AC1XAoEnAWse+vUTJR+2bCG+0sGGpEIdQTKSblTrLBp1VLU0AaVI330XmDxZfw9JAAA34e1ZLuxFLPI0ddcK4A/Jw9RHm4CnjgBaZAbMpW8/DXz3T2DfN0AosmiGvMDuD4B+V4iip3oRDkKXZp7WrmGTsGs6VdPKIqBMRraqvdVSZMvMzIKxlWfPk0Rw97coIje2fIWxVd7humb4gqk1XBc6Vaiqy8OGSOgmBrzdFdg7/BHptan9p4obVc4GVt4eScUGidvMzDia2fBU1xy0dD2FuKkALh15Ke3T/xpg97tqqZsVf6TfZeD19B1MZoo41q1M+dwFJEiTCsigk9teODSMrfxhwP7ZsedVPwOdx6dMRM1zxipffL7MTT5KAnAyCIuPJU+8QZP1rPoVYft24DhzJWosikhjmLhOO3YAV10FrF0L3H+/mPbdXL0Zm2o2Sc+rPFVwPBwjNssnwBpvDb7d9i1OG3yaeAJdTwRKPxM9upW3Asd/AVg15OBTQdV8YMHFQKABCPlQkrMD15ygEM8KAdj9NlD6CXDsuxg27FSp4g6g6N748ZmXKUkFnHOUNsZk+CzMgoGFiYUVe+f3hsPigCdAE3pTWxN8AZ9AnAeoMfUllwDvvy+28UkElwu4dKGxFJ81DNy0XPlqDVA8Uc0pWHcv0PdSQ59DenkGjS1uEhz+dHh7ygKLTBpbVZ4qQepGbmzJeweqdbasYqcMX3n6BQkZRshApK3OW4dCV2FGq8svzQUw8S/qNzinSNb6BxRUCAA8BCv34SanD2j5FOjeDRjzWOy8Oh9JaXOVriCndOKW/wN6X0y/yf6fqDo3HvREcPUYWgDNwTzU4QxvOQ6NNGLuILEtQM3SxCKUcZDvjOVlqqszx4nZWZ9aYebehr2xJ9Y88QZt2ZmZnlUdHGvXAkceCVRVJU7per3Av/4ltrUBgKeXPC08D/Mw2kJt0kM5YT679Fn1wYsnqlMY+39KuzE1Kn4A5pwCtFaoJ0MlQq3U4mP++Rg0iKoSo1i6VB/Bvj1R460RtH4cVkfSNGLPvJ4Ct8RpcWJ73XbNbZ95Bhg0SN+96HQCxxyj67RTQ94Q9Wu+CmD7f4AUoxwA0kojhrkY2UrH2GrXyJYn1hcxzMOCsVXoiFVfN7Q2qHeWFwUFPclTSHpU+zMElTSPTkg9QFVK6O0wly/9PbD+IR1ziw/Y+gIw79zYa4wBQ24HzHEI8Z49wKYngPUPJja0gLQiuCr4Kjscd0+JQ8PYyukrKszWrTAk71/gbpD+Doe1q6GMYG/j3uQbybdvUhhbcvh0LND/4/B6gdNPJwKvHnkDj4dSTnLEbcUTB3P3zlW/6O4jaixFsfCy+H3DkhnC++cC886JpaT0IuRD374iR2fFCmOcnUyNa4DK4OWNbc3MHFdjKwqlsCljLG45fV4eVSAOGpT4uzqdlFL95pvUzl8XGAOKJqhfX3svVT+HUuSThIMwOjWHw+J+HTWytd+zH60BWhyD4aBgbEXT+UCc/oguxfhJFv3Qo9qfIcgbp6cCqQhEWWyRad3EHf8F9nwAhHR6YSEvUPmj+Fr/qzNzLun2tZSjtaLD87YODWPL3VdkS/v2GRI27eQWu9DXZki8orSpNPlGMghaW9ZcCHkD377MDuIDhZolpIGmA3fckfq1lzfV9fq9ar2yJGgNtqpLuhmjkDkUoWvPLuDn09Ved6iNqnfiwbcfmHd26oZWBH37isN8374DJ2y6dClFGZXYWrtVSMmGeCipsdUjt4egD+QNeLGlZkvc7fPzyeA64wzA4eCwWGIXwW4Pw+HguPJKMrQy2UhZQL+rRA0igH7/H6dQKlge4QqHEmsThXyG0yFcESlIJ4UsH0tmc2Z128qbyqWiiUA4IBhb8mpVs8mMpjZF2lfZvaFuVeZOLE2kKmgaxdaaCJVEKeWTSS5Swzpgxc0GHDmFYWYvBIbeFT+6pRdppMtV8FUm3+Yg49AwtnL6qUOM+75L+TCFObWQGzYVxiLGKqh4CUkQ1acBQNIP8pSVbx8yFpptLyj7fuQzSpt9dRjw5WHApqfjLkZz5wJvv50iKVqBV1e9ami/Z5Y8o36x72WARaMha80i4JtRREINB8no2vsx8JVGyimKpddqp50KRgJHvQGcXw1c4gcuqAOOfY94hzL066c2rr5LfZgL4qhKeL2AJ1f8/fazEpx8MtC7N+lfXX45sCbiqG+q3gRvIPZb+gI+9MhL3GTPbXMLOlzBcFAl/yAgHEJ+46f49PqJWP/YCFx9wn9x5ICVOGbwUtw09TnsfGYw/nPxybDV/pDZhqZy9DhDe2H0ldO43vkmGds8RGNj1pHxj9VWY7jXm7x/K5BeNEp+qUymzF668uZYkxHOOZyye6jYVQxTZGmymW1qxyjvMAhzXPWCDpNCSpV/G4XEw7XmilV6bXXaO6QKHgbmX5S5rMewv4iSSkaQ0TRiRYfSWtPCoUGQt7gBR3HEEImg9FOg+2lU3aITec5mWM0BBEI0EZaVJdlBJ4Su7zogVCo6uokaI759iSugHCX6SYftAX89MP0IoGqujDfHY3IczVuBtfcAW58Fjp8JdBol7co5qYenY2gBEHuRAci15aqEZAEyauUq18qeZwCAoqPJw9NKG7bsBH4+DTShJFmp9s+hB5cv2AwY8zgw+CbShjNFoh22TkCfS6gMe/e75K2GA3C7geJiimhF8emnwGmnUfRHD8JhigBdfrn4OufAs88C99wDcF4JwdflACI/X3U18OGHJMo6bhzgvG61QIR2Wp1CWjEeil3Fgl6RvJG1gH3fAkuuoesfbMGAYuCVa65Vb1e5nYwcRwkZ+/szdA9EHQdbPlWnVsxSb+OvB1bcSA89Y4FHIl/KXnQ6IBdeBhIbzkmPJTPUMll9DVDfviicVqeqPZnNYkNrsBVmZkaVp0osqsjpC5idsQhN/WoytjoCUV7x08abW5ramgQ+mj9qqFtzI9XlEQe6dT+QO0C1f8rY+wngK1WfIECc3y6TKT3bup8cxGTZEYsTOOI5YOnv9KckTTb63aJzPQ8hY8ZWW1XH+P0T4NAwtgCg8Eig/IvY84rvUi7LznU0wyIztnbupMUp3UkokcyDFjh4TIrAZCbelj9iFLTVJM7zn6cIt75/AKNgLbuBWUfRzZZIhC7ko5Ti98dQBKfXOQCoum5nnFoCp5MMhCOPJC7Xu+8C69Zpb7uodJH0d549D2+d8xbOGXKOarslZUtw8rsnS2mM9VXrEQqHYDbJUjzMBAy8Dtj0ZILvpCMksPZ+RXifAce8BfQ6T9uDjIr19r0ccPYgnhfo+38hG+bffZdaIUdzM/Dee6KxVVsLXHwxsGSJPsJ9KEQG8aJFQO/zRa6VvBtCIvTI7SEYWyqhyHAAWHlbJGKkMy0SbCFCWmWS30MrtaEnrDP4ZqB6UeJUsV4RR3+dIWNL3r8VSE8PUD6vcZ7Zjhn7mmMegcVkwScbP5Gey6ND/pAf+5r2CfvC3SdikESe169OP6WVIQRktAEzM+Nvx/0Nd0+8W7XdW6vfwk3f3CRV3Eq8NGWjbZ+yzbAB8DCw+i/aRWF9rwSO+DdF0+TtgdbeC2x9LvFx+1wM7P2I1tJkETNmBuxdAGd3oC6iExnypddbUY5wgLIC6VaBtyMOjTQiAJRMEpXjgx4i/qVARMhzNsEiEzbdt09/yXk8hMNhwfPXC/lkJYi2AkBzar25Dgj8jcCPkwB/jU61X06L6KLYqv/II9qL/VlnUaDi6aeB664jTteSJRShcSnm4N0Nu4W0RFuwTRIzVeLwrocLTZGBONGtgdeltxI1bQHqFMcdcW/E0ErSI8XiprF9xP8BACZNEo0rjwf4MYVhzhhtH0VdHclHzJ+vvvZ2OzByJHD88bRNd0W7w1CIqxTAk/G1oujXSezh5gv4YtydcBCYezaJKaoMLUbVgcUTgaJjgdzEMhMZRbdTyHPPBFpToxZEoYxsZcrYAtTVielAXrXX2NqIa7+4Vnq8uupVibPnC/rw5dYvxZ1dvcUq4EAD4D8w3d+SQV5ZbrfY43ZLKHYXCxW30n1iyYGwLHtSU6PXRMUscsKVOOL/gPEvERXFlk/GvS2fHqP/CRzzbmJjiDFgwnQKZJgSRZUYEf9PWkB8ryjaao3p0MVDBxkD8XDoGFuF49Q/7IaHgbB+smDX/EphMquoAPxp8hf3texLvpEG1lXJwjZuhTJw7TJ0KIRDlE7zVaZeXRNZTMvKgIUL1W9ffTXwwQdAbi49ACIFu1xkeCxZIqbQvt76NZgsdJ1ry0W3XG0pcrvFLqQvfEEfvtj8hXpDdy+g328Mt4HC5qdFIn3uwAgnQmczOosb6P8bAJS6U0ayHn5YLBCIB68X+L//i43pQAA48US69vJx3rkzcN99ZOAuWECRtFmzSPdszhzghBOi32Ofqj1IMkHTKAYVDpJ4OwClmqQeictvoDS03Js2O4ABvwfO2QucvBw44Utg0tfAqauB0zcBvS9qfw0ekwU4/En9v1si+IzNCyZTWAieZSqNCGSOIK90MDk4mvxN0kMpCio4lgDNd1oc3ExX7hmAXPHeYrLEjeQWu4qFayDxdq25ouPm25d+C7btr6ppDoP/CAy4Jv5YteZQRmHkA4mPbbYBk7+J0Ck0jmVxUzBg2lyKSMqr53kws1y7ZPSYAygBooVDx9jqNEZNPq5ZAjRr6/dooUdhOUI8NmGXl6dPGjVavSJUZ+UovPfa5YZ0xNoN214G6tek1b9q5kx18GjUKOD559XRqyicTpIEeO212GvTN0wXJvOjempHtaKY3HeyZJwFw0F8vPFj7Q1HPWgsJO4oAUpniByJcS+lLk0SiaiMGaPmtC1ZQoZQMjBGvKwonnwS2LxZNLTuuouaXP/5z2TE5uUBBQX0cDopyvXVV6SD1qV7KwLhAGxmG2xmG8zMLEYRW6sjOj7nATMHAB/lANOdwMf5GFP+JgAOm8kCm8mKFn8LpWgqZwO73xcjWt1PA86tBMb+i3gn1hzAVkAPixvIHwIc9SpwTjmJMrYn+l4O2IvSP47OylwlGNonjQhkztgSpGt0QGVsmR3qaH75l0AgUfr2wEDZOLvYHcfYchcjJJOB8Qa81DpMWdHqq0yv71/QB1R8K77WaQww5lH1ZylhcQND7gC6TEq+3bQ5wAkzI4rzjOZCexEw5gngnD1AwXDaVtlb0d+g/7skQ7IooFwC5ADIgChx6HC2rDmAsyvp3six+m7guE91eaM9OpWj1R8Ll27aFH+h14tEJe2JsKNepiaeO4AW5yjJsmFteqKamYS/HlhztzaJsvORwMAbgG4nU1SgYS2w7T9UvKBYNN59VzQirFbgs89EEU8tOBzAqacC555LE9ry8li6zm62Y1r/aQn3P77P8Xhn7Tto9tNEXtZUhn3N+9A9V5Ezc3Qhb3Hr/+n31ixu4MSFwDcjY6+5+1ErqRS7G0QNvZwcalNUqhjmd99NZPl4zZu9XuDNN4GaSLZh715K28ojYg88APzpT2RUxYPJROcwZAiwes4A5HVuEDSzcm25lDZdcSt1cmBmdSow3IqzWRMaBgDc7ADAwQZci9yuo4EvB4nb9zwbOPb95JVR1jx6TPkp8XbpwmQGxj4DLL5Cv8NjsgGF44GG1bEIhLeU7ucUjW6bxS/cOelE3pX3VqaMrVTnvFqfRnqo8HCgXJairvz+oBOkfQGfIFkSCocSRrbaZBErh8WBfc370M+SA2Hua92fXsSu4rtI71zZnHTkf5Kk/WSwuMhR6dpVf1EJA2DmQLAGwE2RRwQXAzhLtm1bDeBKXJ2cEPJ1r3YF0ONsw5W87Y1DJ7IFACVToap+qJgF7PlQW2pAMbu4HV5YzbG4fGNj+grdgtGUAgQhVFdPMYXVsPagTzwS1j+sNvxMVmDs08DUOUC/KwFXdzJWuk4Djn4DOHGB4Lk2NQErFS22fvtbKgTTU5zgdgMvvADM2TVHkBSwW+w4pmdiSfGjex4t6ERZTBZ8u+1b7Y1H3ENkdT3pKpMdKBgDVP4AYUx2OyntcOnUqeoo4KxZVCWolU4Mh2kevfPO2GsPPywu1BdeSIZWPGNNCasVKCoiGYc8ex49rE6wX/4EfDsG2P8DeewJyO25JiCPtyCPe5C761Vg74fEz4miYCQVUKRSgn4gCLQ9zwZKpujkbzHAVggMul4cN637E6eP4qRE3HYPOI/dFOlEtvIUesmZMrZS7ZgRdXQEFE8UDdGgh6pNDyLKmsqEllL+kD9uZMttcwt0BovJQnqLtkKRUtC6P3HEPFlqbNe7YsFG56OAgmGpVXU5Uqze5QDijbtWAPJx5EtTP0m+7jWu79CC3hkxthhjpzDGtjDGtjPG1KUXse3OZ4xxxti4THxuyuh1LmDRqPBZfiM1EA7ILKewHwipy/k754pe1lZjsioSlI1WrSYr8u35qodDYTwJ+7n7iatryEfRg4ONUCu1LFEKyE6YTqRyi1sdwbHmAp3HAaesoIkHwPffq73sf/yDIih64XYDn276VJi4fQEfxnQdk3C/3vm9BQPNE/Bg+vrp2htbc4CpP1G/ykRgJjImJ31FPRDlBkev8wBrepyfc8+N8dfkuPFGYNcu0UHw+6lA74wzxMjh++/HFmqbDXj9df2GVhSCenmgBZg9iSKXodaYt87MRCw/9j3gpMXEsZo2j9IP+cNi+4d8wJZnxWjRUW9kjpCeSTAGTPiQxDdZEu0fi5ucjvwhUEc0EjDS46REXHYvQuGY0eb1GjeSlHIh6fC/5NjdsDul7TWrtQvHqn/7TU8CAWN9LzOBsqYyqbk2QG2Icm3xK0rz7HnCtmVNZeR0yo3s1v2Jo5vJUmNKA3TEPYApxXvGQFVsXLRCNMR8aeonyQ3Rxg2pZwQOINI2thhjZgAvADgVwDAAlzLGhmlslwvgVgBL0/1MwyiZoq0cH24DvhtLTTRDbRSJqZyjKUDZvUDkDyxblp7HV5IjeiaXjrgUX1/2tepx05E3CYt+kUvGC8kfpo7Mlc8EUpSUyDjKvoBqiI18kKI3idK2Jhvl+yd/DzAz5syh6FYUJ5ygXzcqitxcjplbZgqvDSwcCLslMamdMYZx3UXfYEHpAuJXaMHdG5gyi/gQWpOk2UmVOdN+Ik5Rs8woZiagWKPtS4qYMkVbOb6tDRg7lkjwbW1kTM2ZQym/jQoZK7MsyHLWWWkG23gYmHsGKX1HDUuzAzj8KeD8WjK++1xCJNtOo4EuxwGH3UpE9zO2ErkdABpjjcOR0x/IH5650vFMw+IEpswmfT8tWQJmodeP/5wMLUc3MaLh2W2I0O+2ewRjq7bWeMV0To44Dhob42+bCuSCpnoQ1KJEFIxRRzH2fRe/TdYBQFlTmZAyz7PnCfphShQ6Y5V5vqCPjC2TRYy+alURpgK/TBTVWkDZA1Pq4ypjUEa2PHvTS5PKx4Zn76++EfV4ANs55zs5534A0wGcrbHdQwAeh5A8PsCw5sQnyIb9wNq/Ax86gOk24OdTNEOcvYtEcufq1emlEuXpQJvZhrHdxmJC7wmqx/ie42GXVVPKRQFhcalJufu+NtSSKKPY8qwYws4fBgz9U3JiJkCVo3mHAYNvxmJRgxS33556lGV3w27U+mqRY8tBji0HdrMdk/tOFrZpaSHZg4ceAi66CDj7bOCqq4DcqhNhMzmkfYPhIFZVJGgR0vlIrB//OfbkHRUxrvKIGGp2AgP/AJy1k4wFQCRCFx6ROJqhEzk5pLelBb8f+PvfKVJoswGnnKLdCUHeH/GWW9QppZSw7iHiU0THo7MbcOoaYNANsVJzpdFkttG4zhtEqeVBN4nGa/9rMyv81B5wlqBmymKsKzyJDCtrPkU9zS6gx+nAGZto8QPUEY3mHYaoAC6bF4FQLJpWXW08IuVyie1+MtWezEiz5rDSo3UUacwjHNjwWPsWB82/CFj/CFCzTNXntLSpVJCKkbcg0oKczxUMB7GjLkIpsStSjwaLJQCIY6jzkQdfaV9pbLVWpXdOYbnBzUnLsYMiEzG3HgDkdNwyAEKJF2NsLIBenPOvGWN3ZeAzjaP/1UDd6iSqt/Hd+IEl28FYzNNfriG7lArkOkQ2s01owipHobMQJtmCtN+jyKEXjAQqZYKlB1v+obWGGn7Lcfi/UpNHsOaAj3gAmzeLL594YupCsn0L+uLziz8XWsecOvBUAEQGf+IJ4I03KPXl84kLlDPvFrDBvWFxcpx7LjD5BDPG9xiv/AgBl357OzZVb0LjTSvg9u4mj7VgNMlERMHDpDUThbuf6jhGcfXVyR2BRNGq6NpWUEAaWobhqwA2PRabUO2dgZOXAfYSwKyzvYbFTakM+SI64OqOw0tMgDfWT8dfFs1A7S0bUOjbTVH0vMPEFCkQi2hE02A8CLRWAq5eqmMmgt3aJrTsqakxHnl3u8nYikbGtHpfGoHgKOpEaVMp+hT0EV/sNBbYP1t8bfvL1HEhZ2B6EZymbUAnG1Avyw7kAyj9mMSxNz5O3NMxj9OaYjJje912IbIlZB800DVXrKiU+LvObkCLjMvbuB7IG2zse8gzHkVHpd9iJ100QlxeW6sSN5BO1vFE2b2jej5dqw4Y8W73BCdjzATgaQC/1bHtdQCuA4DevXsn2dogepwNLL8p+XZx0L/LTrhcsUVs/Xox1K4HHk8sMiM3mszMjM5ObWOrs7OzoMtS51P0zCo6Fqj8CZKsMg9Th/dBNxyc6oyahVTxEq0UKRgFdDk+5Qlwz16LEMDo08dYSosxplKJ5xx46SUihgeDFPXRagXka3IBKy5FG4CPVgHfFwBXJqAa7KjbIUl6vLF9Pm456hbtDcN+is5Ev4/FjUzVrJx9NnCT8WEuoVs3WmyTVX3GxcYnxB9s+N8pCqvX0IqieQdikuHIjLzCAcA3274BAHxXvhqXjbws8cb2YpFz1LgpZWOLMcBm9aMtQD9YTY3xAKDbLTo1meoFW+tNPUS2tXar2tjqdQ7NM/J0YjgAzD+PDHqTIvwd0JFi5BxYex+w+SnghZC23x32x+a1VbcDGx8DztquIv53zemqsXMMPXNFgd/SxkjMwt2H+j1GUbuCem+mKgcDiJIyJVONHSOTaIQ4xbVWImFXhWQdT5zdRBHvytlAn4vUEhMdAJmY2csByGeEnpHXosgFMALAz4yx3QCOBjBTiyTPOX+Fcz6Ocz6uuFhfW4+U4SgCepxpOLc7vOcGwbjinHhbqUDuaSqNJnkeX47Ors6CLktbsE0oM0anMWoe1LbnM5KWMoTKn0SPo//VhkQ/12x0C6mMMWPSq7CKwu8HzjmHdKO8Xv0l8h4P6aslwpOLngQAMDA8uuBRhONxEsIBCLeg2ZHcI/usK004mo/YwCwqAs48M3VHQImcnDT5Wrvfj+kEWQsojWokIiVPiTOT2KxXCzqvU3siGA5iSdkSAMBHGz5KvoNTIa5bu8LQ/Wu3xNKRNTViKjAVuFyioVZWlpmKxBY9Ro8C2+s0hOK6ngjN3nqNG4F55xJfKdRK6b6gl6JRiRAOAAsvAzb/iww4Pdc+2AJ4qIm0sltCj9zEkgbdc7vDIhvHkuOd0x/C92rapN2gPlV0Ojz9Y6SLBgByP6t5e3oR6oLR4vOqecbFpdsZmTC2lgMYxBjrxxizAbgEgMRE5pw3cs6LOOd9Oed9ASwBcBbnfIX24Q4ARv7DsIU/vOcGVfn8Z5/pU+iOItr2ojXYKhCtQzwUN43Y2dlZpctS2SKz+juNUk8OzdtJBuJgoPJ7CMn5bicbCuuXloqG0BFHpM7XUiIcBs4/H/jhB3WarXt34P77gY8+onY/b79NKTm9/QUbWxvx9pq3AQAmZkKzv1mKbqhgskK4RvIqvXhIqJIs7vuPfxAvK1XIF9iWljSoUb7KWNNZgAxuo5DrAvFw8ka5KVyn9sKi0kUSuXv2ztmCs6QJtyJy07jeEP/IYY0ZprW1qfXGFE7HLf721dXpd8wAEL+4JAEEqZsocgfFT4tV/gB8ORhY83dg/f3A98dSx5B44BxYeAmlCPX22pT2pfEkqcADMMGEHnmJja1id7FQoNPsb0YgFCD5GHmlZePG9Infrp6R+eYgQ2kztlWnR5AvOkpcy1srkyvJHySkbWxxzoMAbgYwC8AmAB9xzjcwxh5kjJ2VeO+DhIKRREY2gPxCp6qsftYs/d5/OEyLPABUtlTCYYktIv6QP24a0WV1xZqVgnRZBKKpu6/2AvTLXQdeTZ6HRWV+s9Nw5/qKCrGaauJE4556FA88APz0k5gyHDYMmD6dlNb/8hfSlTr1VODKK6l6r7ISuPde4jAlwqurXgUDgwkmmJgJLf4WPDT3Ie2NlQZ/0INMGgIjR5JxmirkC2xFhfHFGi0Kknf+MOOckfyhAOQ6VNVxN+0omLF5BsI8TOOBmaQoV1woIxp1y2FkinbYYgO7tdV4T0OlU1NTkxljK2QgWlfaVKp+kTGg57mIe43aaqkV1vqHgYY1iT9gy7PAvlnqCkdnD+oOcepq4Lwq4JxSYOJHpKUm/6hgm8AHdVgdKHEn1sAqdhULTe0dFgfx2ZzdRAmD5m3pc5AKRsdSn0ZQksFWNz6F95ZO/8f84WoJkLKZHUfUW4aMEEQ4599wzgdzzgdwzh+JvPYPzvlMjW0nHdSoVhQjH0ith5nJDhx2G3BeJQ47THxr61YKsetBSwvw6qv0d0VzhXCzBUNB5Du0c82MMeTYYtU3HFwkmjKTdpi4en5EtFUjDG0gnK8LrVXiZNH5KMNh8N27RUN25Mi4m+rCtm3UhkYeiTz3XEoFn38+qaMr+Um5uWRk3X03cfR6xaHRhMIhPLHwCXiDXjitTkmqY13VOqzdrxFhZCZJSwyAlI7IJB54ILVIoN1OBlqUq9PQACw1KtaiXFT1cAfjpf/WPwiBs7XzzYNfWZUEn276VOJZ+gI+zNgyI/EO7n6iTETzdiTks8SByyrea3LZlJSO4xLvvXTI9lGoqgp1Qojiy9H7Qn3VzYnQshNY8zdF0RQjmZoztwJD/0ySJI5iihD1vhA45h3gtHVU7ABqKSR3nC3MElfQNIpid7EgbGo1WSkV6e6r8N45tTtLB9bc9Ay2yko6J62HFuJtyznQY6i4beMG4+eVP1xNsN/53/RaHLUTOh5l/0ChZDJQNEF/fteSQ4Jw0K7O+r//E8vl48HvJ3kBIFKVIxurTqtTqDhUQm6I+UN+dQl1z7O006MrbyPxOHllSqCl/VKM3lLxPLqcYFh8Um7EFhRoi3Wmgt/9ToyUTZtGrYCilVeJ4HKRgxfP+PhiyxdS30WHxYGLhl8EEzPBH/Lj0fmPau8kbyJetzLjOjGTJwMTJuiPTuXkAH/8oygY+9xzBhdsR4lCB6c0ceURoD8FsOO1xOHkg9x0dk/DHiGtFORBfLLxk8Q75Q9Vp9oNVBXnu0RBrIaGlA8BgOQ+5FGxdMj2UexpNBbFiCsX0eUEGDFIBaz+mxj1MVmB42cAQ26nSKxZ4+ax5gJ5Q4mIX3QsSptKhabrjLG4rXqkU3d3ETTEwjxMJPm8weoIW8V36ekmmp3oMMu9kptYtyr5vBAPrl5qp67+lw6ZSuwgV/8ggDFgwgf61HHNThIftFOKb8wYdbTggw+Sk5Hb2iiqFXXuKpor4Jfd5HJFYS10cnSS/m4NtqoImSiZom08BpuBb0YBez+iQR1qA7Y+D8w+PvEJG4W3VJz/SiYZroiUd4no1Cm9NMbOnaIIbffuwCefpNbf0mIBCrVrGPDQ3IfQ4m+BiZlw6YhLceWoK+G2uhHiIczYMkNYfCXkysKkPEyK8hkEYzQ29RipTifw+eeUQpUHIL780uAimztIfL77ncyF9z27iNMUL1JykJvOfrX1K8lxikY4K1sqYxVnWsgbqo4Al8/UbiWWAF0LRMMk2u8yVRQXi45JTU36BRebazYn30gDNd44X8JsAwb9Ib0qu7IvFI3gXwS6Tk3e2slkJg29yd+irKlMKIQJ8VDyyJarWFDHl+Z0s0NaayRUzk6vFY2e4psDBZdCaaBpc8pjXAJjMc1COZTdJjoAOsjVP0iwF5IHkyjqYnYBQ+4kVesIRoxQTzoNDdToV0u5O4pQCHj55djzsqYyoaIwmQieUrdlV4Mi7ZRIFDPUCiy5GphuJ+HWNX9tv0rF1v0Al3kqtgLDh5LzqhyO9CrjXntNXJtvuskYH0lrn5X7VmJrHfVucllduGzkZTiuT2zMgAPPL3tevWOX48XUUdnniScJAxGbwkJgxozEDaRdLpLAOO44+n6XXRaL9Pn9wDXXpC7e6w+YKIIcTZU0bQGq5qrEIAUk+37y1P+SqxWihh0H0zdMhzfgRY4tB0OKqBOFiZniF0sAJO6q5LRV/oRUeXy9CkUnzKhkg1L6oaYmDQmQCPY2iUR3BiaJBcsfTos4WD2BBIPvsFuhWZWoF/K5quuJQN9LU6OYmF0obSyVotpApC9ikshWji1HkPMJhAMx+Qi5EwYQfy8dCR8eQtII4IGq4M3pC8H0qFueHnlfiz6z653Ei0UoQ72nUsChbWwB1B5l4seArRO4LCrEwcgIG/InYOR9wi4jR2rrMd1+e3y1Zo+HuEJ7ZXON0liKR46PQkm4VHnJJgtFkRLiAHj5Ib9oyKXRv04u8+BwpMcZ+eijWGSMMeD3v09/8Yjin/P/KalH28w2HNXzKFhMFpw79FwwMLSGWvHs0mfVlVjdToTwm1QoGlMrYTBiM2EC8PHHFB2UG4uMkRH2pz8B98mG+T33iJWMn3xC41evweX3RyIqI+8Tf//VdyfubJDs+1lj0V00bgAWXmrcK24neANeLC8nteMwD+OMwWfAbrbDG/DG76sZRe5A8XnjeqCtPqXP79m5TEiJb9hgnCQvb4vl86XXLQMAipxFgiHVv1N//OeM/6gef5nwF2G7AntB/IO6elIkypDBxcS56ug3UjO0AMBkwc76nUJKMBgOJnWeGWOq3onb6yOFRYVHQPg+4QCw71vjlXshX0YrndOCu6/oVHjL0iPvdzlO3RLLXwes/kt8XjI/8K3sDmljq6ICeOcd4LxbTkffP+7Au/MuRHVTERq9eVi89Sgc8fdlmHjTg3j6GTM2bIgZyg4HMFhD0LemhqIDSu5WMAjU1QH//Kf4urKcuUtOl4Tn2y1XzHVrKjH3vih9wmi64EHRq9AT4o/jVYU9se9oRMYgikAA2COji0yZkkaVnQL7mvfhm+3fgIPDxEy4aNhFUgrpylFXItdOE2qIh9SLbU5/sTm6ZxdQvbBdqmnMh32H3LvG4LwLQigqIk7OUUdRavXBB8Voba9eZHDJU6wPPEAGmdcbX+okHKbxv2kTMG4cqKl4l+Nj6e2GNaSBFPQYs5yPeVOcWMu/BBZcAPgbgEBzvL0OKObsmiOV9JuYCacPOl16vrhssaiPp4SWl779lZQKTLrkVQlje9u21KRp5Ois8P9KE2RB9UCZDhzceTAuG3mZ6nHR8IsEDlRSba5hf02t0tVkB/r9VhxLBaMMR+El9fcIcmw5CfsiRtHJ2Ul4LjnQBSPVBsT2V4yPcT2yMgcKeYepU5q1abRiKZ6ozXPY/jL1GFVy3YIeYEMcDm074pAztjinCEf//kC/fsCNNxJPZU9FJ1z10jvockM1Cn7fiAkPLMaqHSOwcCH1kjvqKJGrc8op2i1jXnkFePhhmtxCIfIEt20jyQIl30hZYdMtR0EcVKDEXSJMQLU+DSXmnmcd/LJXZhFvJj2VIXG8KrNJJuSaRoHJ1q1iFOukk0QSeDq48OMLpQU0zMN4eeXLYA8wsAcYTnznRDS1Ebu8xd+C6766Tl2R1es8UaRzxY3peXpx8Oj8R7HXvwan3vU+qqupsfDixZQW18Kdd5IkhtzI/de/gJ49gUcfpdR5UxP939BAkY+ffwZOO414jVL6asL74iJW+QMw6yjAs5M8z3jjVSti1XUq0O8KcSHa9y0Z6ytvoz5ygRYyvvwNdIzGjerjtCM+3fQpmttoURzbdSzGdhsrRT3tFjt+3v1z/J07jVFHgne/m9Lnd8mrEiJbO3YYj2x1Ufh/ytZZqaLKUyUYm/Fa2hQ4CoRIUYu/RZC+UaF4AlB0DMD0toByRVomye7FXufr318BZZZBzq9NBGWqUVoTtIolKhWtiVKBRp/fg4a8wzSaiH9tXLg1f5h2woaHgZ+mkoMXaKHnQQ+w5TlgY9bYalfs3EkNeq+5Bti1ixbvlhaqMLviCuqN9+WXwLffAh9+CNx6Ky02ra1kNMmreiZPjr9YP/44MHUqRQLuugsYO1ZMH0YhN5YYWNL2DoXOQqEZdYu/RZiQAAC2TtTa4WD+tGa7aGyloX5sNse+X2tr6j0Ro6ivFyM3XboYP5Ycdd46LCpdpHt7f8iPZ5c9K7445HaFrs52agESTOLNp4A9DXswfy+R7x+c92DihSsCq5U05Hr3Fg2u+npyKEpKKD151lnUr3LAABr385Ucf1snYMpsSgFGqy0bNwBfDgJ+nEyVhf4GmgzDAUrrNO8A1t0HTYx7gbhgcqMk3AbsfAP4oi8waxww9wzg51OpMOTr4bqvU7rgnGPmlpng4LCYLJjWfxrsFjsGFJLOXHNbMz7b9Fn8A+QNVUeCPbspnagzMtElTyzE2L7deBS3e3fx+bp16XVwKG0qFXhKXdza0fwCRwECsgo1s8mMhtaG+AdmDDj2PcCig7JgdgHHvk+SD/JFv+/l2pWHOqDsVRtPnFqJkhyRGtLY1khzulaxBA8Bu94yJnlSt+rg90WMwlYgihQDpPyeTKg4HpgJ6ByngWtrFQnarroNWPcAMPfMCF/5wEf5Dhlja/Vq4PDDgV9+ifEOzj+fok67dwMvvkhK4WecQVGriy6itN/SpaTCfP/9YmTkhBMSk+GXLAEeeoh672ltFwwH0eKPLaYOiyMpZ6uzq7Ogy2U327Ur3Ibcrm/SUSJT5fLO7iLhsc1gORQAty0W3WhtNV56rgwmpSuMGsUfv/tjyvs8OPdB8YW8wUChYrJY/zCw9xMgETE4Bfxr8b/AIv8qmiuwYO+C5DuBorlLl9J4V1bg+v2kOzZ/PrBihZqILVzjghHAqauAnAEiJ6ZuBbD8BuCTTsB0K/BxAfCBBfhyILDpKe2TMlmoOnjA7zT4gJyI+NULgZolYkPfKNpRFmJD9QYpcuOyujCh9wQAwOS+k8HAwMHxxeYv4hu7BSO0q85W3Bx/kVWUzXfJqxIMoqoq446FskVtOilJAChvjvW6MjNz3MiWw+IQrpHNbEO1N4mQraMLcPSb6vSbHCYHUS26nwLUr469bi8GXIkV3+MhEAqg2S+m97q6EzvOUSj7IzrMDuxv2U/FElaN6vT1DxrLXIS8xI3qKMjpJz6vX52eNlav8+Jzg3kQ2PE6Xbv9c4x/Rpo4JIyt8nJaLJqaaNHNzwe++w54801g4EAyorRK410uil4VFlJKZevW2Hs5ORT1MooqT5UQpbKYLEm9oc7OzqIIntmqrT9TdAxNHqlC2fTTKJTNcytnGxaglJex19QY99Dz8kSDqzoDAuScc5V2ksvqQp49T/WQo761HhuqFUJ+ox5QLBIcWHIN9bcMetUVfOGwbmJ4i78Fr//yOsKRlIk34MVD8+Ko2mugsJAiXI8+SvdJMpFUs5lI9xMmKN7I6QucvgEY/Sjx1JTcQh5O3CpFbiSZrMC4Z4HjPgMc3fTxFC05JEeRqXGuga+2fiVFm30BH47sfiQA4Pg+x0uixC2BFmyp3aJ9AEcX6iGpRO0yoPwrba0lhbFVnFetSrnv25fS1wBAEaxu3cSo5o4d6RWpyKkTNrMtLomcMQaXNXY/mJlZ27FUote5wNA/aS+8ZjdFQI6MVAXLU2v2IsOp+4qWCkHQFAC653WPs7WIHnk9BG1Fq9kak/TRita01QJr79EmfifjtVUv1HVOBwT5Su4CpwpCo3pb3eL0yexAOCSMrWuvFb2xDz8EJk1KjbPjdgM9FI7PeecZJ21XNFfAao5Ff0zMpCuypWxqrEmSZwwYckdiD0+JJItVvCrL8eMpdXTXXcCcORFelasXaXlFUT1ffJ4C5GXsLS0UZTSCww4TK0gXLjSurB3FDzt/EDh0bqsbC69eiAVXL1A9ThpwkrSdhVnw3NLnxIOVTCKdNCYfUJyq92YdRarIrVW02LbVAnveB36cpOs83/jljdhnmyzg4Ji/d36szFwHGANuuQXYX8nx1INlGH1YFWyWAHKdHuS7mpHvaobNEkBJYSMuPasUyxa24OefNQ5ksgCH3UIGz/hXSZTS4qaIgzUPsOYDljzisNm7AP1+A5y4ALg0rG0kdT+FWqgc8w7qc0YgwKw07q359DA76bjdTgGO/wI4I46RkwTjxwN/+APw/vui9psS09dPl3qYds/tLhVHHNPzGMkIC/Mwvtr6VfyDFCut1AhW/hForRAdl6AHWP1XYTOX3aeSptlogLbm8VC6Xe7gbNmSmi6dEnKCvMVkSVixp+yYUe3R5yFt73kVXnScBO4oobFlyaMxMewvwLQ5sciq3Liy5hhOLTW2NiIQCiDfno98ez7sZjv6F2hoP2mgZ15P2Mw2aV9f0BdLl3Y7SZ1uA4CtLwD1K0WJmGALabIpIV8DquYmjpQfSCHgwsPV/Lhd7xheJ5A7uOOkSeMgQ8mUjouyMiLtRsPqZ5+dmqK2HMpQ/OWXqysM9UJpJHFwFDrjqGVGUOgsVJUXx1VWHnAtsOGfOpuqmii9o0AoRIvLm28CixYBu1kJSnhspalECZZHikjmzqXigLY2YNy4zph/E4/5GXUrDHd271O0Bwxh8IhfsGYNedupwukkjlF5JIvx5Zfpc7YenvewpP/DwHDR8IswptsYzW1vPepWLClbgqa2JgR5EG+veRuPT3tcbM909OvUPDeg8LAb1wPLfk+PFBHmYTy24DF4A17k2nIxodcEfL/ze4TCITy16Cm8ePqL+g5UORvY+hKc+3/E9T1DuP7+AAL+ELZUHIYmXx6s5gD6FO1Bl/wailptaQX2HQZcW5XYOokiH0DnVmp/6AdQCaCtCsBbkQfoOErGNkBE4l7nYNxnf8KuhgBqb1iITqYQAAY4u5KIoo788+7dwCOPAA+hBF2hHufLl9P9EAxSi6f77oPQuqvOW4eN1TGrZkSXEVi3fx0AioKaI3y11mAr3l7zNu489k7tE+l2YkQxXJFObN0PfDMamPgJ0HUyVaYtvooqMhXIz6f0YRTz5lG3hFTmPbNZzW1sbCTOntFWeXLelYmZEhpbufZcaZ4MhALJ04gRPDT3Iby99guM+u08TMzvRBHgTqPVfCw5rzTkR9LIyGddNQt5RgJY9C7Q2hpT7h9//9+A0N+SnusVDBjQE+CMDGhzGDj6+auAyv1A0bEUwVVKpfAg8ONU4Ij/AwZeR1yu9f/UJn3LO1LULoPQ8koJpTPzfjtGinIPI6qL3Iuv/4Ua1ycTk9UCY+SYbXlW1E3rQPjVR7ZmzyaibxR//WvmqtD69weGG+TeVjRXqAynZGnEQmeh5DUDlKbY1xwnP2BxURWYnuiW2U5q+jIsWQIMHQrccAM1bW5tBbrySlgtHMcew3HxRRx3/6YSF19MQphmM0WK2tqAhQsZWL6s/1U4kLwiLI5X1a1ThdBYd8EC4yryp50WI8n7/bGF0wg2VW/Cin2xFp85thxcOerKuNtP7TcVwVDswxgYXln1iriRowtwwszUIpJJ8PXWryU+SZiH8adj/gS31Y1AOIC31rwlVUrGRctuYPYUYO7ZQNlnNBkGW4BwG6ydh2DEsUNx7MmDcOSUQegy5HCKkAabKGrQuE6foQUAjQB2AtgGYA8ALQc3wQq/dv9a7GzYCQ7ghc2zgKKjgaKjAHefpIYW51QcM3w48NZbQDdUgoFLj26ILUItLXQvfPQRcUDleHj+wwKp+8ddP2Lifydi4n8n4rg3jxNEL9dVrVNJv0goPi6+2negEZhzIvCBlXhuGoYWoI7C//KLtjZgIpjNQJ8+6krG1atTO0407RjmYYGnCiQWcpa/1xZs05VGbPG34MMNHwIAHpn/T+LAFY2P33InimBzcoX1BDpU47YCE/fGHjad1Z8mDkwoje13TBnA9ke+Z+Hh8VObPEQ8vul24ENn/Oo6OQ+qcYNxEnqmkTdEW9x43f3G5S0GXicWGnUw/OqNrfp6cXFW6sakC2UfOb0oby6XysEBwB/0J00j2sw2WGXEcw6uVpGXo2QylTMn6v8YDa/LjKPnnycdqm3bKJXAGCmKz55NBtW331IU67nn6P8vvySPd948StmazaD0DmReVcW3ifPxcjFLGfoU7YHNHNtv1SrjBN3rrxc9+//7P2NyEqEQ8MTCJ4RWGwBExXgF7BY7Th10qvTcG/TiyYVPqqtJuxwPnPCFMYNLw2B9aN5D0gJ35uAzMbnfZOH911e9Hv94++cA34wEqufFIqQlk4GJHwEX1AMnLQaOeg0Y9zww/mVgwofA+dXAqauB4X8T9cPaGY8teAwAGbFPL34aAZ0K0eEw8Jvf0H3s9cZPlysRCqmNl+giH0VrsBVNbU3SI6To2PB/S/5P++D5w5F8ak5cTTpU0et35crEHQS0sGkTGVvK7/nzz6ndN1HdwYbWBmH+CvNwQmOr0BGL9IcRRmlTcpGvN1e/iVBkEf95z8/Y3bA7/sZ5Q2J/e0vTa2eTn3wT3Yg6FSZrRNw0EZJUFctbZvEQsPnfHUMEOHcANIVSd74Rv6AqGSctb7BaFFgvDkAK9VdvbPXuLS6wO3em37lejvPPT13DxuEg9Xh5CTQYBEJoPEQ5IFEkbew67jlSWNZK45ldJOQ3PBbufukl4M9/jk2wgwZRuffLLxM3y+mkFEV+PpGl8/Lob4eDIlzPPENGGrpMAqwyJvWO1w1V0YzpsxptwdgPuHq1cZL84YeL1VUbN5Jyeqqq2GW1dZi+Ybq0eDIwnDPkHIG/pYWrRl+FPFuMLO8L+jBziwbPous0YPIsqupMZnSZHYCtMzDpO1UaYO3+tVhftR4A9d28avRVMJvMOH/o+TAxE7wBLx5b+Ji0OAmoWgD8fAZFsXiI5BtO+Ioib70vpPJta06kaiqXeFG2AoogdBoNDL8HOGeP8XyTFuIcq8pThc83fw6AUlOBcAAfb/xY1yFvuIHabCnHQJ8+ZIwvXkzjZOlSugcGDdI+jj/ojx9ljoOPNn6k/QZjZHSngZEjxWrQurrU+I6hEDlXNpu6F+iiRalFyaLnUeWpkvpEAslV1pWViuVN5XG2JIR5GI8ueBRBHgQDQygcwr8W/Sv+Dp3GxtJs4UCke4NBvAgKkcZ7KJFo20rZfdzz7MTOcjL0OFPkRm3VSRtobzCTBkkexJtbfoO6ZVmolXooJsNht6Uu6m2yt2vRjPQx7f4JBxknnihGtu68M7FkQ6pwu4kon0qDVs7V6vFuq1uX4nC+XXSh4nK2orDlo3HCGtTZpiLA3QhzE0LcgiB3osp9BTzHzpNkGnbsIOMjOpH260cl/UOG6GtkDFCUr08fAMXHikTelp1A2YyUq026FVTAKotsVVTojz4owRjw+uuih/+f/wCPPabP4AqFKLJ33K2vCpETh8WBq0ZflXT/kwacJKSZWvwteGT+I9obd5kInLkdGHwTTR6WXJnwqTlG+u33G+DsXUD3k1WH+Of8f0pp52A4iKn9pwIArhh1BdwRQ9gb8OLLrYpUVNALLLgwFs0yO4BTfyFBUb0TmcVJBlplZfssQjI8v+x5iRNlYia0+Fvw0NyHkmqJvf028N57YqR0+HDqI7lpExHijz6aokTjx5M+35o1wPffR9Tx5cda+7a+6yJDWVOZOrIZRa/zUm8bI8PAgWoi+wJ9ah8AKBoV1Uvr21d8b8WK1Ejy0fm32lMtVN75Q/6ExpZSg0spAq3Et9u+FdLigXAAb6x+QxKYVaFgJFUoRrH7XSCQIK1+IAnkUXQ7WRQ8ThV9LxNlePx1wNbnO0Z0q+sUaJogFbOApdfFqrCDHpKG+Gla8mP2vjD1nr9pOjZ6kRFjizF2CmNsC2NsO2Psbo3372CMbWSMrWWM/cgY65OJz9WDvDxSiY8usOvWAS+8YKzHVzxv7v779VclRpv8Ko0kgSidAEoSfTzSaGsrLSZHHQV06e5Gv99+iWmP/oiHZ9yL+z65H8c+sAyDLv8PCousmDYN+Owz4N57RcP07bdpUk3FkAQihFprXqQJsQyr707Z2GIMGNZD5HvNmGGca3XssWQcyzXTHn6YRG03b6ZFRhn5bGuj3/7LL2nRLf3pNNy+yo47FwJ3LgTu/c6HSYNOpJNN8HBYnXjnPZ+0350LgQU3/xJ/H6sLGPskcEkLcG8z8EEQmAHgwxDwQBNwuRc46j+ALY+2lzGid9XvwqebPpWqVw8rPAyvrXoNLy5/ERtrNkop0BZ/C+6YdYf4hXe9JS46ox6k0niDRQ7tibZgG/WcDLXBZrZJFWylTaVYXLY47n51dVRdKZ8HJk8mruIZZ9B8oYygWq30+tSplEo755zYe6+tes3Q+b+39j3tN3qdl1YniAHqehd88YX+ClyHg2gBgFrixutNrbpxdkT4vNpbLRjAjDGVZIIcRa4iqagAULf6USKaMs+x5WBYceyk5dW4ArpOEzlNFd8lbi1msC9pWigYBdgTF04lRP4I9f7rHgBaKxM3hD8QKJ4Ynwy/531g9gnA+vuBVXcAs48nvmIyWHOBwbfo78drdgJjDoyafNpsMsaYGcALAE4EUAZgOWNsJudcfjv+AmAc59zLGLsBwBMALk73s/Xi4YeBr7+maiO/n9JkVVWk8G61igT6ePB4SGvovPPU7w0cCNxxB/DvfyfnEzkcwNNPAwNeEY0k3e0d3KJ+VmNrIzjnUlSMczIKrruOzjnKl/D7GeZtOArzNhylOuaPP9IiYzLFUqJTpgCjR6cp/jn4FqB6EZFPAcC7F1h1OzD26ZS89gmDF2LZzqOlQMizz9LvkApXLhyOVVW98gqlI7dtixmXM2bQ44QTSMZi0CBaWBsbaWF96il5b8WRePILY57h+ZvoEYOOSZsD2Bp5JEJJiRQtuvLzK4WoyYbqDfjT93+SnssjbLsaduH9de/jspGX0Qu734tFtexFwOCb02om3p74YP0HUjq30FmI84aehxeXvwhvwIuH5z2Mby7/RnO/v/1N5B2NGUP3TTINMYDGkdtNUbEo5O1aLCYLJvWdpBKsBIAFexdIzYYZmJTmVcFWQHIgFbOSn5AGBgxQO4fffKM/Bb9yZcwwGzmS5kh5RPmdd6g/bLIIV2MjFaJccAGlEeXjLhltosBRAKvZilCQft/61vq4267bvw5r968FQJWLtx19G+6YdQea/c14bOFjuHn8zYIgNABa6IuOJkkEgCIou94G+l7RcWQEGCPi9/pHEjdwT7R/v98Am56Mke1DXuDn04CTlwPMnR5XLR0UT9AW8I2ibgU9UsXI+yNK+0ly3SYH/dZJeXGZQSau8ngA2znnOznnfgDTAZwt34BzPodzHl2dlgBQz0LtCJeLuBfy0PpTT5HH9sYbJAfg89Hk4vXS3y0t9Ly5mSbh008nflY83HMPcZcSwe0mUnl+Pkdjq2ilx1NSVkLZ0sdiskhtfwIB4KqrgEsvpSIweUNsm41SHyeeSJ75mDFiSbfHI07OAwdmoJ1Nt5PF0mOAmqlufy052VGGKcN/EtKYK1emLtIoj2C4XOS19++vJg3PnUuRjcMOI37XyJEUAZE3se7oaGptwsJSUcDQH/ajNdgqPZR6bX/+4c+xJy27Y3+7emsLaXYAcM7x8LyH0eJvgdVkxRWjrsDlIy+H0+oEB8ec3XOwp0H9w/l8ZCxEjS2Hg0SOUyWQR+eSfc37hNZbJmbChxd8iP+e81/V494T7pWibxxcm7MXxcDrjRUZOEqQl6c2rBoaKA2aDC0tdH2iGDBAfW1mzNB3KnY7pV0BdV/EnCQl/gWOAoFQ3+JvUY3bKB5b8JgUrR3XfRwuHn6xEL2Nq2s28DoxNb7qjsSpxIOBfskpCpqIpj0H/A6qpb5pC/DDRKC1Wt0WKNRK6cb2hq0T4GwHU8DiBI7+rz6+6+FPZv7z4yATxlYPAPIykbLIa/FwLYBvtd5gjF3HGFvBGFtRnQmJbxkKC6mS7aabaOIwm2kBvf56aqwblTm4806KfP3xj5RWyM+n3m9z5yY+vsMBvPtufE/P6QSOOILSVbW+WhWZusStjw+gbFZtt9hJRiIInHoqkX3l0bVjjiGDsraWwvkffQR88gl9n5oaIgGPHKn+nIYG481rJZgswNC71IN+1W2UUgx6xBs9HNDsBzhl+E+q6qd77xWNyWRQKhAUFlI5/JVXpsY/sVojEbVMEr8zDFVaUAfKm8uxpjKyEssXn0Bj8nLqz7qSJo/mI8UcdAqYu2euxOOxmW24dMSlGN9jvNSZIczD+NdiNUF6xgzRkTj3XLo/jToXX2/9WoiaFLmK4mrmHd3zaCGVtqdxT3wuUvdTU+efmF0S2bdXL/Xbb7+dnEJhsQAzZTbggAFqet3OnfpUPZYsiX1eaaPYFzHPodGORoZOzk6iurrJqtkfscpThc82f4YQD8FtdePqMVcj156Lib0nAiBjK27HhN4XkfhtFEEPRX2ijYs7Aty9hWpxfWAx0ndOX6DH6WruV8Na4OthwMZ/UiufUBvgqwQ2Pw18lUZ7lFTQXsrv3U8lZz+ewWV2USW1LZNlpIlxQOOHjLErAIwDoGlOcs5f4ZyP45yPKy420G4mCex24IkngA0biOzarRsZSW43GV7vv0/VeM8/D/z3v2ScRRdXt5uiW4kwZQpFwXJyRJ6T202G0PffU1S3orlCFdLulqtPqbPYVawqn65oqcBf/0rRu2h0KjeXUqfff08GRU4OGY4FBfTIywM6dSJjc/Fi4NVXqUl3FN9+m17DWQlDbtPm+mx7AfhqCPWrqllCjVK3vwrMnqza1GlrxcSJ4msff0wcKz3n6PWSJIUSDgcR5L/6irhcTqfI5ZIjJ4fe/+1vafwkJH5rIZVt0wDnHDO3xlZKE0zondcbw4uHqx7yYgsGFuMd9TwrNjG37CB9nkT8oQT6Q5rl3RmCXFQ2z56HUSWjwBjDxcMvhpmZ4Q/58fovr6sI0p9+Khrqt9xC94NRfLjhQ3gDMQ/n2J7Hxt12UKFYzmg1W/HtNk3fkyo7e1+ojg4ngsw4kwuuRvH558mNys2bxchx//7afNU330xMm2hupoKUKOR9EYHEGlta79vMNk0V+ReWvSBl44PhIM4Zcg4A4OoxV0tRxE01m7C6crX6Q0wW4PAnRFpD/S8U9fHXqaM+mULDeqBpG+Ap1cfNG3SjSOZPBiVNY8xj2k6Tv476sM7oBXzoAD7vBqz5e5J7OoPocXbq1YN6MfEjBHpchrApdi04TAibXAgd/R7QO0Gqqh2QCWOrHIDch+oZeU0AY2wagL8DOItznkbHyfTRrx/xdvbto6qjc26dC/Pg7zBsRAADB9L7w4cDEycC//gHcXaammhRRteuCUnQU6YyrG4ZgGtCr6A/duAoLMF/PJfjk88Y7A7apmjgKPTe34beDZAeJ974r6QEazCGCeffhh61AWm/LpUtqJnyb7z4Ymzi69SJ1K4nTyYjIRHvymYjY/CyyyiiF43yNDeTgWKkkECAxQUc+ZL2ROEtAzY+Bnx/DPDdEcCKm+Lm6C+/XORocQ5ceGFyvR+vlyJ78+Yh7m83eQrDwkUMy33D8VjrrTgDX2IgtqEvdmE41uMavIYXWq5Cqa8zXnmVoXcfkYyeFjIsjTB3z1xh4bdZbNh882asv3G96jH9gumSFAUHx1tr3iLttwG/FyuYFl2RuG/cQajS2lq7VWqmbWZmnHnYmShtKsXexr2Y2n+qRLzmnOM/K/4j7LtqVezvnj0ppW4UbcE2IWXrsrgwrX/8qinGGMZ0jX2gXIRTEyPuSUzalsNkA/pcIj09/HD1vb9vHzkq8YSBm5uBuxUlTm63tjH67LOJZXQ8HmD69Njz/R5xAU/WMaPAUSCkDU3MpCoIagu24d9L/o3WSOXzsKJh8AQ82Nu4F6O7jkZbkCaI1kArHvj5Ae0P6n0xkDdMjPw0rAFmDgK2v0zcH39j7BFqBRrWJTx3hPxA+TfA2vuBn04CCmRLbT4Dvp8AfDeWHM6PXPRZCy4BNv+fdhFR38soPaYXLkUH8dyBQH+txu0HGV2nIVMOmd9PvMT77wdOOgno1sMC1+RXcfnz72DhtuOwq7ofpi+5DEfesxzOQedg0CDgkksou9MOfq8KLFl5dNIDMGYBUXengoys5QAu45xvkG1zOIBPAJzCOd+m57jjxo3jK1YYIMelCI/fg55P90RDWwMuGX4JPrjgg8Q76JBnONA4DvOwADFBzZ9+okhNqnpUnJPK+s8/x+Qx7rgDeOghOpaeqsRwmLxggWjMOUkJ7PsmOWkxDmpO4ujVSy3bcfLJlBbVIst7PFS+fs45ZJTxTIer4907WmPE6H2W4rGmvT0NP+76UXp+xuAz8OWl2irj/pAfnR7vJBlnbqsbz5zyDH439nfAugeBjY/HiPLdTwUmTCdNGi01bjmUbT4SVW4pv5/O6zT8xeFCaxynxSmknDyyHnAmZkLbPW2wmCwIh2ksRyOiF14IvPaa8cjWDzt+wAUfXyBJDuTacjH/6vkY3XV03H0e+PkBPDz/YamAwWFxoPHuRkGDSsDK24Ht/0l+71jcwFm7AAdlBb7+mpwoZQVinz7kZGpx1NavF2kF0Z9j8mRo9rp8+GHg9tvVqfjmZqJivPlm7Dh9/t0He5tikjdXjb4Kb53zVtyvU9lSif7/119S3s+z5+G/Z/8X5w2NVSn94cs/CJ0YlMLPvqBPMNjWXL8Go0pGqT/MWwZ8PRIINKjfYxag85GRnq+t1PamVSP1exmn/qUbHiVdQTAg5AHASVy0YDTJTdiLyDAO+QDPHkrn1a+hY5rswCVxiPB7PgKWXqPWoFLC7ASm/kTkfzlCbcC3Y4HmLamnp6PfTwvpzneLrgB2vw9dxUIa51RVBTz6KEVRGaN5n3Mqcho9msZzUREFFnw+ymKtXUv8xcpKmg8yJQfFGFvJOR+n9V7akS3OeRDAzQBmAdgE4CPO+QbG2IOMsbMimz0JIAfAx4yx1YyxBKzQAwu5wvYXW77AsvJliXfIVCQibfY5YTsGYCViv+2YMST3YET4kzFKsYwdG5uIn36a0ovRNjmNjWpvNhym19vaSINn0iSNAx/7HpA3VL+XLoejBEVFZAgqL9usWUT437uXJvi2thjZ/803iexuRCX+fxHb67YLUZZcWy5+O/q3cbe3mW04fVAsN+4JePDwvIeJUzTiXiLmRiOS+74FvhwE7P04QqBt1E5/GG21kQL2Nu4VDC2AFlVPwCM95AjzsBTVUE6qBQWpS5vI8dmmz4QWNP6QH8O7JO7hdWyvY4VKPJvZJkXpNDHqgeTClhY3MPJBydAC6D7WimDt2aPN3fJ4yEDSQrQllxJPPUX3mpzf6fdT4Pfdd8VtG9oahOdKHS0lChwFQpeGQCggpBHD4TD+u/q/wj7+kF8YB0pC/Q1f3aD9Ya6ewORvIwUJSgcgCNQsBvZ+RM2etQwtANj1LvBFX2DbS1SB7exKvKALm6nyb/yrwPC/AoOuBwZcQxp6ox4iJ+bsncA55US7iIfeFwK5Q9TnJwezAt1PVxtaADlJk77OWHeKjKH/1cZSiY4SvPsu6cC99BLN/127kvPU3EzZnVdfpRZ9119P1KGbbqLgwfTpxDssLwduuy3TX0gbGVnxOeffcM4Hc84HcM4fibz2D875zMjf0zjnJZzzMZHHWYmPeGCwq34Xnl36LIKRflG+oA/XfHFN3IoXAJnj6oRCGRF7nPf6dpjcMRf1gQeMK6wD5KH++CNw9dVkcDFGmjqTJgHFxVTt+NprwJw5pKr98880cV97LXHgjjqKDC4VzHZgyvfU8FrPzc6spNV14kKJ6Pm3v2lzqpYtoxvuvPNomzvuIO/95pszQPL/H8KTi8T2P/6QX2gRpIXfjP4N8uyxsE6tr5YiY4wBR74IjPkn/V7MSl774iuBj/OBeedQ64/K2cS5q5oH7P0EWHlre309CTd+fWPK+zy95GnN19NNH8zYMkOYL4YVD0vaSWB8j/FCqy6P3yMp4GvCmgcc8Ux8zg6zAPYuwGG3CC936xZ/LrjxRiLBt7TQPeL1Eh9xzhzt7ceN05bFaGigIpy6OjKyWluB0lKKrMv5lMq+iGZmRpEzcQW2w+IQhJ5bg61CKvKF5S8IUhJ6sKhskSbJHgAZKCcvBezFqTuFllxg2XUUrQq3USr+1F9iMhLW3IgensJiNdsjnRecgKs7OTnxwBhw9GuJU4FmOzWojoecvmRUWvN1cAEZGUGTvmlfhfUuJ6SeMbK48WZTJa67joz9tjbg97+noqcrrqB1LNrhROkk2O3kZDmdQPfuVGx1IPCrV5BPhBu+vgH+kB9Oi1Oq8tvdsBtvr0ldDfpgYcEC0UM98cT0PHWADJoXXqBKookTKfyal0fewsyZpKw9ZQqpa0+eTIbZp59SH8rc3AQCr/bO1DdvwLWRBTzOiVpygMKxwOmbSIk+giOOIG89XtR69myKxL3yCpDhYtYOj8bWRryz5h3B2JrSb0pSLaNp/acJavhR9XUAdKEP+yNw5rZYj01rHokhVv0MrP4z8NOJxLmbfQKlineKkYZMe8TBcBA/7BDbqlhNVtjMNtVDDm/Ai+Xly1XGekOD8UKQrbVbBQkXBoap/aYm3S/fkS9IuIR4CJ9vSmBsAUD/39BCrFxoTTaqVjtxgcizi0Cr0higaPTll5PR9cADNG988kn8j48XJQNIr27sWFq0/vY3Ev5VUhrrffVCes9mtiUlyAOiFhcHR1lTmfT8/5aKRoWZmTXHAVNEgp5f9nz8D8wfCpy+HuhzeeRaJ1giLW7iRk34kCoXo2ne7qeRcWxxpd4YOZn+YKcxwHGfaDisjAyoqT+R0ZYIxROA09YAOf3jR5QsbmoXdvJyohC0J0wWqgqF/oWr2ePAzTfHCjdOO41axblcqWtD6tHWywQOWWNr9s7ZmL93viSI+OjUR5Fjy4En4MFt390mtH3oyJD3OzOZ4lfTGcGoUUQsr6qi6NWVvwnAml8DZgrDbqeBHeVyFRWRR/HGG9RSJy7MNmDcs1gw+BF81laAsCWHxOXMTlo4up0MHPMWRbQ0Jo3nnkvjOx6APn0HCyv2rUBrsBVuqxtuqxtWkxXXHXFd0v3sFjvOHnI27GY73FY3nBYnVlSsEJtsu7oDEz8ALmwEjp8BDLkNVSwfYTD6zcwuqjhlFpqku58OHPU6cPbehB7xzp3q14qKiLQ+ZAiF/T//nFLUUXyx+QtYzbFFu9BZiKo7qzQfV4y6QlpoTTDh6cVPw2SKtJOKYMEC45HgrbVb4Q/5pWtuMVlw4oATde178oCTpWvusrpQ460RChu0cPPuUnyadyYtgiYHGb89zgJOWRl3gZ02Lf7iwznpaT30EPU7TISePRPrkJWVUaX3v/9NUS4lqr3VggFsMVl0GVtKLa5oRePO+p2C4WUz2bDppk2a4+C5U5+DW2bEvPHLG4lbOTmKgWPeAE79BXNsw1Fv7kSR3eg4N7uBricB414EztoR+T1kF7n3Be0iihoIEAf1r8+diivenIPNlSPQFrTBH7RhxZ5jcMGry/DwS0di+XIdEX13H+C0dcD4/wCdjqB71+yk75k/AjjiWeDMLUD+kCQHyhCG/hkwq50FTZhsWNN6mzCuL7ggNQmfg4G0FeT/FxEIBfC7mb+DN+CFhVlw2cjLcMWoK3DXD3ehxd+CtlAb7vnpHjx76rMH+1STQj4BhsN0Q+ptHaQX+fnAGWeG8GLzGQj0/R6mkBMzLvwZIzqNh9NJnoHdrj8SvLF6I6Z9fTfaQm04ousYLLh8JhxmC2ArTEq+HjOGqiafespApWSc3noSDJK1OwKm9JuC3e90JvE0AJYw0P0f5+ra920TUJELAERucwYA27O91dfLbAdKJmOjqQTDv/wX7MyE3TevQVdHLhldFndkwo4/ECorqVpoxgwibu9CCUpAqaFKlAjOw9atFDFtbSWy69KlxLGMcrLMzIzLRl6GAmeB5mddM+YafLH5CzT7mxFGGDO2zECVpwrjxnXBjh2x81m6lDoHpIrTB52OnW93UlxzdY9KLbxoAu5VXHPX8/3jjtGN1RvxwooX8DIzo/S2PehmNVGUMUkkZMIEuj/lBqtRjB9PHEkjqPJUCQUMJmbSZWzl2nMBmUxHVJPsqUVPCenb4V2GY1DnQcrdAQCXjrxU6J5Q7a3GnN1zMKXflISfvbEthCkb1sHMzCi7ZRO62l0UPbR1BuTSPTn9iXweRd1KitQk+m0+65pAXsEEXBazlr7/ngjgS5bQ3O71AsHgeLz33ToU5VYjGLKgwdsJAPDlXDJ6w2GgOacrmB4xNACwA3AFgVYAvvUgOUyZXs7+/UCXxBy7tJA3iDiiO98SWyhpgZnR/6Tr0CaTE1y5ErjoogMXpTKCQzKy9dyy56Q+WxazBX+e8GeYTWb8ecKf4bK60BpsxWurXsPmGh1dxg8yjj5ajPRs2BB/W6PgnOOGr2/Agr0LYIIJYbMPF399IrzOrejcmT5fr6FV0VyBSW9Okhokb6jZjAu/vBFhR0nyKrcI7rmHOFqp1Bh0dK8nXTDG0HtHDXo3Ar0bge4p8NStYUj79W4Eir2Iq1jJOcc1X1wDAGjjYfzxx38Arh4UDbC44g6EUAh4/HESyHzjDTq8zwd0RSX69eW45mqOZ/5SiXvvpWboZ59NKemmJkpfbdgAbGpcgW11sWJmp9WJy0deHvd7HdfnONVrzy19DhdcIDZWf+45/T0D5TgY1zzEQ7h11u2As5uullfjxydvIaYX06YZjwJWe6oFQVMguc6W1jY13ho0tTXhzdVvSnwtp8WJq8dcHfcYhc5CHN7tcOl5QpHTCJTX/I+z/x4Z511EQwugqGL/38ZSe1tfBEpnpNQlQwQZkaWlVPxz7rmxCvHoOJ04kYoZ/vDHYtx4WyfcdBPRLIJBont4PNBvaAFk89cDiFfweiAi+aMeSc4jMzuBIXeg+4Au+O1vY/P6iy+SA5eK0HUUieRLMolDztjaVrsNf539V8k77pHbA19v/Rr/Xvxv+EN+ib/SGmzF2dPPRvhA/RIGMWGCmCa491662TKJxxc+jvfWvQdvwItCZyFcVhea25ox6c1JqPLo15tqbmvGpLcmob61Hk6LE73yeiEQCuCnXT/h1m/1E6stFtJT6d5dX19Ll4v00n71OABp0s83f471Veul9NxXW7/C4tL4DZ8BIq+efDKlq7xeir526UK8wH37yJB69ll6//77gX/+E3jrLUpfr11LvCKzGfjn/H8KxHKb2Yajeqh7fUZhMVlw3tDzpHNtDbbi2WXP4qRT24Sg5ZdfZqhjQjvByDWPIi+PUoCZwHHHGY+aV3mqhNR0mId1GVtKLa6G1ga8uupVgTgf5mGcPyyxQOW1h18LtzVmnC4pW4JttfFViFK+5ke+QFpYZjcADiy5inQDPXvI6FIaXvKm1peoSYOrVgEjRhCNI2osT5lCxUtRzcfHHye+3YMPUjTrp58ogjlzJnHo/udoE44iYPjfEhdQmR3AMBKCe+EFkjZxuykJcdVVVG24Zw8ZXYkMr1CIrpXPpy1p0h5IW2ervdBeOlv9numH3Y27pecWk0UIbwdDQYRlImt/n/h3PDz1YX0Hb099pTjHCYcpWrB7d2y3bdtImDUT6hLT10/HNV9cA1/QB6fFibm/nYu//fg3zN0zFwAwuPNgLP3dUrhtib3sYDiIKW9NwbLyZfCH/BjRZQQ+vehTjHl5DLxBL1xWFx6Z8ghuO/o23edWXU1Vkjt3xtdJcbnopvztb3UcMFNpxIOos9Wex/EFfOj7TF9UeatgM9lgMVvgDXgxpPMQbLhpg3AfyT/i5JOJGxUls152GSn3W636IiUtLcCaXeWY+sUAKSJqYib84Yg/4MXTX0y47+yds3Heh+eh2U8eSI4tB8+d+hxWv/lb/Oc/sXEzZAhVtcojXsng8QDuAV319a3Rg5ISVRrRyDVX4u67qXBE3khaL+RDJBSiNldGooD3z3kA98+9X3rusDiw+9bdKMlJvIhf9flVeGdtrFEjA0Oxu1hw8kaVjMKa69ckPE6NtwY9nu4hGXxWkxW/Gf0bvHrWq6ptDV9zzoHKH4Bf7gKat5MQMA+SvlbncUDhePrfGqlKDAcAXwVQswhY8zfpMJv3HYbxD26WnObcXHI+TjxRW09QiXCYxnXSaH4HmlskhAPA/AvpOoYUIVlLLlW0y2QtOAd++AG46y5g+3aKggeDpK81bhxFdseNi1UlBgLEKV60iAzahQvpeabMoEQ6W4cUZ+udNe8IhhYAoXpLC48ufBR3TrgzuRcWirPaL/sDeTvObkDBKKDTKMDRVWNhDwMtOwFvuXYuf/HVxBcw2ajCy1kCOEpgcpTgkfuG4rqb8yUxt/PPp96Hubn6Da5gUE2k/XHnj7h6xtVoDbXCYXbgmjHXYHDnwXj+tOdxxCtHwBPwYHvddpzx/hmYfdVsVQsi6atxjss/vRzLy5ejLdQGh9mB1896HV3cXfD34/+OR+Y9Am/Ai7/O/iu65XTDxSMu1nXOxcXEt/n93Zvw1TsDYYIVwSB952CQon4PP0yp1gOFUEi7pub3vycPrHNn8lhHj049FXqw8eiCRyWjZUSXEdjv2Q9vwIvSplK8/svr+P3Y36v2eestmtiihtYVVwAvv5watyInB5hd+wZCPCQpwzOwhCnEKCb1nQQODofZATBSHH968dNY8OBv8fbbMWNr82ZKk/3wA/EgE0VMg0Ha79xzgR8S8QAzsAgZueZKXHABORxGjC05zGbqE/vee6kvTqVNpcJzf8iPfEfyvnRKLS6LyYIab43QISBRCjGKIlcRRpeMxpr9a2BiJoR5GO+sfQcvnP6CqnLV8DVnDOh2Ej0a1hP/qHo+wg2bwBs2I7jtE3AeAkMYJOBpAocJJhODlZF6VjjMcOkLn0hRGcZIjmPYMP2N0k2m/2HahMkKHPcpsPR3wN7pAExkmJrtwJTZQCdRLJgxUos/6SQS5H3rLSoi2LSJ7ulPPqE5ORymMWsy0YMxihgWFwOnnHJgvtohE9ninKPn0z2xryXW9MtqsmoaCP6QXyBf3nH0HfjXyeqGtqj7hQTsKr4jD+WGENAou575AKKOt8lG+eZQG1WunL2bmvxufx3Y9zXQtJmIl0VHA3lDgBNfAGoirNYunYFtC8lTCrX+f3tnHiZVcTXut3qZFYZ9F4gLoAiIShTcUARFQQmoEZdEo6BoFBXNJ2q+CPpT+YL7EpCgRkVZElwgbCIqGJR9G2RHBhh2EZiNmemlfn+c28vMdPd03+4BJPU+Tz9zu+f26XOrT9c9VXXqHNlifGSD1Kw7shZ/14+5ql8Tvv02tD377LNh9my5uVf3Iy0ulpmxs8NyMc7YNIO+E/tWOC+8U/L7/cH8ZCAzXOvvX48jgvdw+XuXM3/H/OBzp3JWaHePz1MhnuPv1/1dspjHwbwf59Hzw544/Zm80mYtmaWnkZEhuX9OPz0uESFszmzl5UnusSlT5Hinp2kw6Bsk8LsZclN2OsXR8Pnkpp2fLzvw4tYpAb1SKWfHkR2c+eaZHPUepVZaLT4a8BE7C3by+NzHKfYUk5Oew/aHt1cZlJxyiiQOBLHFH3+0l619++HtzLihY3CNvN5RuPkHcMRxCQtbwupQpgU6eOpx2cqfmTpVlh7CY5pOPVWW4gcOlOYJv2mVlspr06bJ8s369dU04XFq80gf2aCBpGZJBIej6tLqzJlwyy2Jz271/vAaZm+dHXzucrjw/G/13t+o/4ziz1/9ObhrvJa7Fs9/qXEWSRiIyw+3rYHsOBzJzfVhblif0LgYbjxYcTYxFW1eWirxQ2PHwqpV8rxlo320bbSGeln7yHCX4nJ6KfOkU+rJYHfB6Wzc24GDR7KoU0eW3QPO1r33ymageGa0EuZ49S1N45wNbgKcBRQAq4HKy/yNGsXM8bOPxqyhE/toQikZeHGRThkZlHI6W+nAWrICQWop2gAQa2brv8bZ+mD1B9w/4/4KhWv3PbYvOEIKZ37efPp83Cd4bpY7i00PbKJFTgs5Yf+3UkKjYL3snNA+yV3S5Er5GyzJ4Jb/+UqhcKuUefjpO9jxCbT+LeR9KFm4MxpDlzclP4vvqDhl8QSLe4pEflodDh2SnDr79oXyBtWuLfEuQ4bIDd3lkmUbra3yNRq2bZPgwvHjQ5nWtx3axumvn14loLU6+rbpy/RbK5aGGTpzKG8sfSMhOQCLBi2KGY8DkLsvl4veuYgiKx6ifmZ9Vt27ipZ1WsZ8X1QSdLY2bZLp6y++CLXpmWdK1vqLL5adk82aySyJ1jKzsHOn7Jz5/nvpkLdvT1CnOPSqCTl9P+7L7C2z8WkfTbObkj8sn1JvKU1ebEKxp1hmPs+9i7f6vBV8z9atkj4k4Mw8/bRkc7addDeVpbKsa//Tn6hQVzRAw4by27nwQqk1WlAgCRPfeKNikeaadLbstHk07r5bKiokEoKak1N1F2NpqbRHouVNzh17Hiv3rgzJTs/hyPDqt0iOXTaWYXOGBUv21Emvw/6XnKT9FCG/hB0qLd0m0+ZlZVJn77nn5GsuLJQ+uF8/SfLatSuccYYMfl0u6Q8OHZKYxYULZQbrq6/EyQ18TwcPytJtjXCiO1vVEd5QyRJhCd8O//XOVmFZIa1ebRXMHJzuTGdYt2E8f+XzEc/XWtPmjTZsPST7w90ON33b9uWTmz+BLe/C8gdD68mn/EYS2KXVl+C9CIkFK+AphCNr4csrQltcu7wJbe6DOOIvYrFnj8Qw7dhRtTPs1El2ttSrJ6PVn3+GWbNknTt03bJTp8u4Lmw6uAmNxqmcdGzckStPq5qssaCsgPdWvhec4Up3pvNev/e4peMtAHy59Uv6fNyHcquIsVM5ua3jbTTKblRF1uL8xSzKXxSU1Sy7GavvWx3xXID8gnw6j+3MwaMHSXOk4XA48Pg8tKzTkpX3rowr+LYKCThb330nsUiBUiVdushSTYcOMntVnUNRUiIfV+3SwAngbM3aPIsBUwZQ6i0lw5XBsK7DuLPznQA89+1zfJz7MR6/h3RnOt/f/X1w59eCBbLsFLhhf/AB/O53iaseJFWddFjHqrWU6xg/3t6uvZpytuy2eTTmzpXwgkQ2z2RlRU6vcs01MmseL04nNB/dqsJSYvPazdk1bFe17520dhL3TL8nuKyXk57Du9e/Gz0g/ji1eUmJLGWtXCnH2dmy0WPQIBn8Vjeb6/XK+37721B6DaUIhkXUCCdA33JMZR0DX+e/3tl6ZM4jjF02llKveCAZzgy2PbytQhbnyry/6n0emPVAsMREliuLpX1G0n7j0yFH6/RBVqbgBJN7+Mrgm74yy+UrkZqBF30kuUa0AncEeWtGwNqRkeU5s+Fm0bOkRLI4jxsnI614HX+3G0pKvfT8oCeL8hcFA5Gb1WrGlqFbomYiH7tsLI998VhwFjDTlcms22ZRP7M+3d7pVmF2cFjXYTzbI/KWa5/fxzljz2HdgXVoNG6Hm3YN27F40OIqn11QVsB5b59H3uE83E43t3a4lfyCfL7O+xqHctC5aWfm3zmfdFeCUyhx/jh//FGc18CNqFu3UKxPyjvG49whlnpLqf1C7QqxjbXSQmsaWusKtQhrpdXiyONHcDgcrFsnAaqBdnrhBUnrEM8O0oRJsp0+/RTuuEOW4eOppZmWJtcRc6v5cWjzaJSXy0ArXodSKYlJmzq16v8++khmy+PdZl+rFqgnc4IOE8BZDc9i3R/XxXiXMGfLHG7+180cKROPPdOVyctXv8yQLkOiK16ZY9DmV18tg4vSUkmF8803EpeZaALmIUOk7w6oXFCQ2KaNhDDOVsqp0ULUJzqbD27mjcVvBB0theK6dtfFdLQABnYYiDMs50eJtwTv6j+HHK3M5pJlN1FHC2SJ8Mq5srOi5U1QdgC+6ArzesLqJ9Cbx8HBpVC8E47uhbKf4cxH4IYD0H83XF8p9bYv1AlkZUnZglWrZNdXVhbUzvHjclc1tPR0GXHl5MgIbPC0wSzcuTDoaGW6Mpl04yRxdnzlos/BZVIPb/ds2DWDe5u1pmO9VjitWbmj3qP0ntCbS9+7tELn1KxWM/63+/+itcx0bNokO9TmzpUZtjmznTzYbDJpDumdPH4PG37aQP/J/Stkey73ldN7Qm/yC/LxaR85aTm8ds1r/OM3/yDdlU6Zr4w1+9Zw69RbY2eJToLXX69YumT6dBnJnrDB7tHaYddM2D0H9n0tAb2lB2SjRhi3Tb2tyiaSovKi4KNy0eei8iKe/Ep2Vp11VsWbzZgx0Uu+HG/695ddvA8P9VEr209ObS8uV8W2cDrl9axMP/cM8rJ+fc3okkybRyMtDe65J/4l3MxMSR4ciQEDEiuJ0qRpxbqIUCl/VpS+hd2zqVu8CW9YwtBSbyn7iqLlI4v8+TNnymzR119LEPWBA1UHocm0+ZYtEpQdWE0YPlwGY3YqXfTrVzE+a/RoG8mb4yDa73D2bCl7tmSJhDgkulxsiM5JP7O188hObv2f09FeiaB0++Dtf0Pbg9W8EZjQCcaG+aifdoNGgUmWuh2h13dQqZxEBarJEuwf6GPZMhkFLf/uJ/TPa8hJ28WZrfdx9qm7OKVeHpnuQlwOLw6HD5/fhdfn4khZY86u8zmZbvkV6vQmqBuqrjd7fB5e+XYMT703F+/us2hZeBMtXOeQ7kqjaVOJRzn3XEmQ53LBsDnDWLJriTiU5Ufok5PJEzklUoA4szHU7SxlHpxWqRBHGmgP2wv3M3jlvynxFIO3GI+vDF+dTqS5alFQCIWHXTRY8jo7l3WitFRuwh06SNBuero8HA6ZUVjsHcca9QFHj1qJ+X5uw+4x79GwoQTlt3uzHVsPbUWjUSha120dHIEePnqYXYW7gv/r1rIbC+9aWP0XHSDOkdBNN4XqyCklHVei9bhs6xRDL0BmTQ8shH1fwf75svHCVwpDSuFQ2M2kQS34ZrgsZfvKwFsEBRvh0CrQHrh+Kwd9ikajG1WI3audVrvK1nef3xeMmwMJfi4YXkCmO5MXX5TcWYEbxmOPSexWygN+7YyICzZKO+39SrJ+W3ZenH4hC7d05/vcM1iU25ri0nQy08vp0n4nF3fcwiXtviXHswhK8uG3hdETMdrQ6WDJwaTbPBoHDkiposDO0GgoJX3CggXRz3nlFdlIUJ0jUKsW/OMDH5/4f8/2I9uDfcsfGtbj7oyfq+1bjpQe4bZln3G4rAC8xeAvZ+T1U7jk1BtYuFBinObPl51npaWQV9qUBt5Qn1tUqwmvDd9LWVko8HzjRhmMejwSV6iykmvzVcsyufrq0BLtuHGy+9gOHg+0ayextCCzpxs2QMuW9meEN26UdvrqK4kX3b9fYsGX725KvbKKbfXSY9JWgYLiq1fLJp68vMjx41pryhrVJ+Pg4eBr3kYNcO3/KWE9j5QeQTVrTs7h0PSrbtIYtTfx0IElu5ZwWvuLaVggfV5Zw3qkH0hRnF8M/uuXEVMW63EzcA3gRjrYXv+RHCquKB3cx9EDeuesuYoHps5h7175gQ0YICPJ9u1Dy39ut8Q7OJ2hnUGBh8cjTorPV/XGpbVm5uaZDJkxhL1Fe/H6vcHkfJnuTJ669Cke7fZo1WW2Q2tg+VAJ5EdJTpiOI6BBV8AvOVAcbqmjpawtudovuWS0X3ZLKhc+7ebd99MZPlxiDnw+CTa+915o1SoUrxR+fYH4hMD1eb0yui4sDO3W6za+G4t2LUrsK2t/M5NumhTfyXE6W599JikMAjeaN9+UPF41Uioi3hu23wc/vgurhlvfhw/a3A9n3CuFin0lskTtcMv3pqxG1155r/bJsTMTvIWQ3pC7Pr+LCWsmBDN1X9D8Al686sWIav7+098H06pkuDJ4/OLHGXH5CLxeyWy9YYM4pUrBU0+FAuXjKZpeUiIOQoMGKWgnSJmd43DH3shiw9lKRZvHYtgw2RAQa6k0K0tmgS64IPo5R49KUuHDh2N+HGecIbPY6vCJ27ck2+ZPXjSCZs1CNSHPP18cQLv9weLFksA0sOTbvLnsgG3XLv5BSlGRhDsMHSqzVEpJXOmIERKoHyjt5nbLQNHhkLby+6V9/P7QIDJaPryVe1YyePpgcvfnUu4rx6Vc+LSPdFc6t3e6nVFXjqJBVqwfreD1exm3fBxPzHuCovKiYCYAp3JSL7Mer/d+nYEdBlZIZBuN/IJ8Hpr1EDM2z6DMV4bL4cLn95HmTOPSVpfytz5/i1rWKRXUuLOllOoNvIakGBqvtR5V6f/pwAfA+cBB4GatdV4smTWV1LQKiXSIniKY1wMO54K/VCqmXzHbcriyqga4R5nZ2rSnDec8sZpSjzhpzzwju6JSUUR62sZpDJszjJ0FOyn3lZPmSOOWjrfQuk5rXlv8GoXlhbgcLjKcGQy9cCgjLx8pcQd+H/yzjrVMqqWY8GWfyAgzQSZNkt1Pgc5i3jyJa4o3T0wkxi0fx/0z7g9uAXcoB81rN8dRaSXcq73sKdwTHKU6lZPPB35On7Z9qv+QOJ0treX6Jk8Ode7jxsmyrdsd/wi0rCyOZZ147TNvEiy+O7TM3WMeNOwWfSBQDav2ruKidy4K7gLLdmcz+/bZXNLqkojnT147mcHTBwfjcrJcWWx4YAMt67Tk8GGZKfnxx9CsSvv28NJLsqGjrExu8OHtVlIiHX5ZmeTOGTmymvp+iTilKbLzaknQ2Uplm0ejutktpSRT/Pz5kf8fzquviuMcLQ6sVi2YMAH6XXfi9i2pavNtq1ty7bWhAdgVV0gcoNOZ2CxuYAfj7NmyoSFwnUrJgO6pp6BFC3GEwoPu/X55b0aGLIc/+aTMZJWUiMn16QOffJKaurm5+3IZPH0wK/aswOP34Ha46f6r7lx12lW8tfQtdhXI6oLT4eTms29mTJ8xUZNev/z9yzz/7fMUlhfi8XnIcGXw4AUPUuwpZvyK8ZT5ykhzptEoqxHP9XiOOzrfEVHOTyU/MWjaIGZunonX78XpcNK2QVvuPvduJv8wmZV7VuL1e3E5XHRv3Z3x14+ndd3WyTdGJWrU2VJKOYFNQC8gH1gK3KK1Xhd2zv1AJ631EKXUQKC/1jpm5soT0tkC8JbAd7dLbi1/mYy6GneHdo9A0yukA/GVWiM0S7blIPDPOgAcKGhIm0c3U+avS2mpJB18/30ZXdh1uA4UH6D7P7qz/qfEA0mW37Oc85qeCzPaQ8lOma6v2xF6LpDRZrS4tCjO5MKNF9Fr1Fw8/iy8XtmV89BD8kNPdLnN74c5W2dxw5Qbgh1iljuLKTdOiepAjVs+jmFzhlUIzp9/53y6NI/4GwiRQECl1tKhvfpqaBTYvr2MJPv3h7p1paMLzEoGrsXvF8di/374+GNxshPSKZpeBxbCV71khkB74Zznod1DYo+OKI0e5fvTGs7fCSvDZj/aNWjH+j+ujzq69Pq9NH2xKQePyvq8y+Hi2jOu5fNbPgekLR5+WG6+gXxVIA5Wt26SULRFC7H/wkJYt05SamzYEPuyg8TbTlqnzM6FikWDbemEzEafP+78CukRkm3zaIweLTMckZyk7GwpeNyhQ0wRgDhsnTvLklflhKkZGfDrX4vTpkhdm6eyb/H5NL8en7o2X7RIHKSCAplZys4WB+m++6SQemDFIjDbprXMIgVWKBYulP5k1iz5rFWrZHfi7t0Vl2sbN5bfS9eusunB75cUEf/5jzhYgdnGM8+UpcDiYkkLtGCB/N7szriVeksZPG0wE3InJPzeqTdNpf9Z/YPtuvGnjXR+u3MwljpeGmc3ZtngZcEBhV/7GbN0DA/MeiBhnZ685ElGXjESV7T+0QY17Wx1A0Zora+2nj8BoLV+IeycOdY53yulXMBeoJGO8eEnrLMV4PBa+GEU5FsjNF+ZzHZlNJaZrqwWEnugfbIsc1gSkAbfXlyH5zYd5u235eOaNpUfau/eEtNUv37ox6m1PJQKZcB1u2UtffGycna0eIVn5z9Lma8Mr9+L2+GmbYO2/OHcP5BZaWZDa82X275kxqYZFQq59mvXj5d7jaLZ3unww/8DTwG468Ip/aHFNZL9PrOZVd3eUmp6WyiLXBsx70BrHv8uj88/l86kTRtxKnv0gLZtxeEI5PqqfH2BTnPbNpi8YCUv/nwJJR65M6Q50+jXrh9TbpoS9avRWnPxuxezZNeS4ExY3Yy6rLhnBafWOzX6d2pj98ru3VIK5Z13QpmKi4tl1NmxoyRWzcgQUUePypLK2rVynJ0dx1b8ROyzKA9WPQ75n4vt5bSBljdCkx6Q01ZqjvnLAB3z+5tYAIP3Q7H1MbXSajGmzxhu73R7TFVf+PYFnl3wbAWneNZts7is9WXBc3JzZUfiggViF8XFsWsSBnZ4pqWFlmkikkg7+cphy7iU2Dkg9e2S1Gli7kQGTx8cHCCkss0j8fjjsvwd7nBlZUkg+SWRJ3UicuCAOFW7doXy+6Wni+3Pnx+WFDaFbZ6qvuW1eRP5qDC1be71yoz36NESK5WWFnKU2rYVJzYnR/QoL5cMJKtXSz+Sni59RfjSrM8nfcuoUXJOtTtgkZmxsjLpW8aNkyoaBQUyAOzfX1J3dOokOQAj3WMCbZWRIdE3ubmaI6dM4cFZD1JUXsRR71HcDjf1M+tz/6/vp2FW1azMa/ev5d2V71LuK0ejyXZn06lJJ/7a869MyJ3A+6vfp8xbFtx5fnnry7n+zOurxMl5fB4mrp0YnEVzKidpzjQe7vowPU/tyYOzHyTvUB4l3hIcykGGK4Mh5w/h9PpVM1rvKtjF35b+jaLyIrzaS5Yri0bZjXi779tcfcbVsRs1Tmra2boR6K21HmQ9/x1wodb6gbBz1lrn5FvPt1rnRI2iO+GdrQDeYgmuPbQGfvpejkt2hGYYcMjMgjsHyo9I8DFIyZ0Be9FaRvHLlsnjxx/lR1VUJD9IpeQHG6jr5PGEgrIbNQJH25l8d1pohsflcDG612geuvChmGvc3+/8nt9M+g37S0Id2qDzBvH366xaYcXbJb7i4DIo3CTBwGWHwekGNCg3ONPkOv3lgIKCTQTT/FrX5/FIDaoVK+T6duyQfGDl5aHOLxAzELi+QHxakybQqHdrPvHsCOrYJLsJ6/64rkqB2srkHc6j05hOFbabP9fjOZ689ElYMgS2vB06OXCzTGKrsNcrOXbWrIGlSyXuYssW6cwCNyGXS24E7drJqLRLlzjyTtmxT78Hfl4Bh1bI91e8A0r3WN+TZY+B2Bi/Rx5FW4IzsLfugYlhHXrLnJZsGbqlSlmTyhw6eojTXj8tmM8OYPjFw3mh5wtVzi0rk8Sus2ZJDNyuXfKazyc3lIwM2To/YIBsqz/zzMhNEcTu7zhJO1e3hT7jo49kKdmOTi2G3sruBhODz5Npcx0ektLoUuhVNdJda6lPOWKEOExdusgu2wvD8wjv+jfMvy70PIpTuXevbHyYMkVs/O67ZcYpPGVBeFO0arid7cuPf9+yvcut7KiTmjaPZOc7dohuK1dKXr7cXHGkPB5xcAL6tWghcV7duonNX3FF5M/dt092Cn72mWyqKioK7SpMTxdnqlcvyW3Xo4fMegXYvl1it5YtkwFffr7oEki47HbLfcbjCcVWlpeLzAat9vPFOeE1LBV3n3sXb137Vsz0OtsPb+e6ideRuz83+Fraujsob/9+8HlOWg6TbpzENW2uidHiMGHNBAZPH1xhJqxOep1gShD5wEth8lR0ceTcjCA5N3/36e/4fGNo9jfdmU7BEwXVfu/x8ItxtpRS9wD3ALRq1er87dWm104BlYPnq8kkq0ZWH6QXD3se3VNt+olwAqOOqq9rGDkCRj4TfC1eDat880//BTViZNw6RVXKBlGvzx+oIxYiZqBk7khY+0xQZgUaXYq6KsYWq3BbSFFG4YAe0VSuzp72jIamYUsIe7OhWYylR/10Ar/nKIpF6hPiCU5N9r0xVKqeBH/HCRFDqVh57FTzpqgwnXSTJujdkXXy+zXqmRE4nk3+d/xMd3g6jpireBiZIln+Pz2KY3SEkmc20AWFqNrxB0LF7DsrcSLY+YjLFSPibHNNdDspOriHWvWTv8eALLmGY7ed/H6NUqrC59hvc43DEXpvYu1v73qqwywjGgwGg8FgMNQgNZ3UdCnQRil1qlIqDRgITKt0zjQgsI3gRuCrWI6WwWAwGAwGw8lC0mH4WmuvUuoBYA6S+uFdrfUPSqlngGVa62nAO8CHSqktwM+IQ2YwGAwGg8Fw0pOSPY9a65nAzEqv/SXsuBS4KRWfZTAYDAaDwfBL4kSt5mYwGAwGg8FwUmCcLYPBYDAYDIYaxDhbBoPBYDAYDDWIcbYMBoPBYDAYahDjbBkMBoPBYDDUIMbZMhgMBoPBYKhBjLNlMBgMBoPBUIMYZ8tgMBgMBoOhBjHOlsFgMBgMBkMNYpwtg8FgMBgMhhrEOFsGg8FgMBgMNYhxtgwGg8FgMBhqEONsGQwGg8FgMNQgxtkyGAwGg8FgqEGScraUUvWVUnOVUputv/UinNNZKfW9UuoHpdQapdTNyXymwWAwGAwGwy+JZGe2hgPztNZtgHnW88qUAL/XWp8N9AZeVUrVTfJzDQaDwWAwGH4RJOts9QPet47fB35T+QSt9Sat9WbreDewH2iU5OcaDAaDwWAw/CJI1tlqorXeYx3vBZrEOlkpdQGQBmxN8nMNBoPBYDAYfhG4qjtBKfUl0DTCv54Kf6K11kopHUNOM+BD4A6ttT/KOfcA91hPi5RSG6vTL04aAj+dYLKMTsdeltHp2MsyOh17WUanYy/L6HTsZZ2IOrWO+h+tte0HsBFoZh03AzZGOS8HWAHcmMznJaHnshNNltHJXN+JqtPJfn0nok4n+/WdiDqd7Nd3Iur033B90R7JLiNOA+6wju8APq98glIqDfgU+EBr/a8kP89gMBgMBoPhF0WyztYooJdSajPQ03qOUqqLUmq8dc5vgcuAO5VSq6xH5yQ/12AwGAwGg+EXQbUxW7HQWh8Erozw+jJgkHU8AZiQzOekgHEnoCyj07GXZXQ69rKMTsdeltHp2MsyOh17WSeiTlFR1nqlwWAwGAwGg6EGMOV6DAaDwWAwGGqQk97ZUkr1VkptVEptUUpFynAfr5x3lVL7lVJrk9SnpVLqa6XUOquE0UM25WQopZYopVZbckYmo5cl06mUWqmU+neScvKUUrlWfN6yJOTUVUr9Sym1QSm1XinVzaacdmHxgquUUgVKqYdtynrEau+1SqmJSqkMO3IsWQ9Zcn5IVJ9I9hhP+aw45dxk6eRXSnVJUqfR1ve3Rin1abzVI6LIetaSs0op9YVSqrkdOWH/e1QppZVSDZPQaYRSaleYbV1rVyel1INWW/2glPprEjpNDtMnTym1KglZnZVSiwK/ZStXoh055ygp25arlJqulMqJQ07EvtKmnUeTlbCtx5CVkK3HkGPHzmPeV+K19Rg62bHzqDolausx9ErI1mPISdjOE6amtzsezwfgRBKonoYkU10NtLcp6zLgPGBtkjo1A86zjmsDm+zoBCiglnXsBhYDXZPUbRjwMfDvJOXkAQ1T8P29DwyyjtOAuimyib1AaxvvbQFsAzKt51OAO23q0QFYC2QhsZNfAmck8P4q9gj8FRhuHQ8H/s+mnLOAdsA3QJckdboKcFnH/xePTjFk5YQdDwXG2pFjvd4SmANsj9dWo+g0Angswe8+kpwrLBtIt543tiur0v9fAv6ShF5fANdYx9cC39iUsxTobh3fBTwbh5yIfaVNO48mK2FbjyErIVuPIceOnUe9ryRi6zF0smPn0WQlbOuxri8RW4+hU8J2nujjZJ/ZugDYorX+UWtdDkxCSgwljNZ6AfBzsgpprfdorVdYx4XAeuQmnqgcrbUusp66rYftADyl1ClAH2B8deceC5RSdZBO+x0ArXW51vpwCkRfCWzVWm+3+X4XkKmUciGO0m6bcs4CFmutS7TWXmA+MCDeN0exx2rLZ8UjR2u9XmudcELhKLK+sK4PYBFwShKyCsKeZhOHvcf43b4C/E88MuKQlRBR5NwHjNJal1nn7E9WJ6WUQnaDT0xClkbyJALUIQ57jyKnLbDAOp4L3BCHnGh9pR07jyjLjq3HkJWQrceQY8fOY91X4rb1VN2fqpGVsK1Xp1e8th5DTsJ2nignu7PVAtgZ9jwfm4ZTEyilfgWci8xK2Xm/05o23Q/M1VrbkmPxKvKDjJjdP0E08IVSarmSqgB2OBU4ALynZGlzvFIqOwW6DSTOm09ltNa7gBeBHcAe4IjW+gubeqwFLlVKNVBKZSGjqZY2ZQVIqHzWceAuYFYyApRSzymldgK3AX+xKaMfsEtrvToZXcJ4wFr2eTeeJa0otEXsYbFSar5S6tcp0OtSYJ+2atPa5GFgtNXmLwJP2JTzA6GB7k0kaOuV+sqk7DzZfjdOWQnZemU5ydh5uKxkbD3Ctdm280qykrL1KG2esK1XkvMwqbHzqJzsztYJi1KqFjAVeLjSSCZutNY+rXVnZAR1gVKqg01d+gL7tdbL7bw/Apdorc8DrgH+qJS6zIYMF7IUMUZrfS5QjCwZ2EZJgt3rgX/afH895IZxKtAcyFZK3W5HltZ6PbLU8AUwG1gF+OzIiiJfk8RMZ6pRSj0FeIGPkpGjtX5Ka93SkvOADT2ygCex6ahFYAxwOtAZccBfsinHBdQHugJ/AqZYo/VkuAWbA4sw7gMesdr8EayZZhvcBdyvlFqOLN+Ux/vGWH1lonaein63OlmJ2nokOXbtPFyWpYMtW4+gk207jyDLtq3H+P4SsvUIclJl51E52Z2tXVQcQZ1ivXZcUUq5kS/6I631J8nKs5bXvgZ62xRxMXC9UioPWWrtoZSynRvNmgEKTA9/iiznJko+kB82W/cvxPlKhmuAFVrrfTbf3xPYprU+oLX2AJ8AF9lVRmv9jtb6fK31ZcAhJH4gGfYpqUEaqEUa11JUTaOUuhPoC9xm3RxTwUfEsRQVgdMRZ3m1Ze+nACuUUpHqv1aL1nqfNejxA3/Hnq2D2PsnVnjAEmSGOa7A/UhYy9wDgMl2ZVjcgdg5yCDF1vVprTdora/SWp+P3BS3xvO+KH2lLTtPZb8bTVaith6HTnHbeQRZtmw9kk527TzK9dmy9RhtnpCtR5GTEjuPxcnubC0F2iilTrVmNQYiJYaOG5YH/w6wXmv9chJyGilrt4tSKhPoBWywI0tr/YTW+hSt9a+QNvpKa21rxkYpla2Uqh04RoJGE97BqbXeC+xUSrWzXroSWGdHpzCSHenvALoqpbKs7/FKZM3fFkqpxtbfVkhn8XESukEc5bOONUqp3sjy9PVa65IkZbUJe9oPG/autc7VWjfWWv/Ksvd8JGB2r02dmoU97Y8NW7f4DAkcRinVFtkQkkxh3J7ABq11fhIyQGJXulvHPQBbS5Jhtu4A/gyMjeM90frKhO08Vf1uLFmJ2noMOQnbeSRZdmw9hk4J23mMNv+MBG29mu8vbluPIScldh4TneKI+xPtgcTCbEJGUk8lIWciMn3qQYz2bptyLkGmvdcgS0ergGttyOkErLTkrCXOHUdxyL2cJHYjIjs/V1uPH5Js887AMusaPwPqJSErGzgI1EmyfUYind9a4EOsHTU2ZX2LOJCrgSuTtUegATAP6Si+BOrblNPfOi4D9gFzktBpCxI3GbD1andWxZA11Wr3NcB0JJg4YTmV/p9H/LsRI+n0IZBr6TQNaGZTThpSaWMtsALoYVcn6/V/AENSYFOXAMstG10MnG9TzkNIP7wJKeum4pATsa+0aefRZCVs6zFkJWTrMeTYsfNq7yvx2HoMnezYeTRZCdt6rOsjAVuPoVPCdp7ow2SQNxgMBoPBYKhBTvZlRIPBYDAYDIbjinG2DAaDwWAwGGoQ42wZDAaDwWAw1CDG2TIYDAaDwWCoQYyzZTAYDAaDwVCDGGfLYDAYDAaDoQYxzpbBYDAYDAZDDWKcLYPBYDAYDIYa5P8D0PfUodWvFWIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0_45\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_4\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_7\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_11\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_12\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_14\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_15\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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dyhn+4ELY9ABUF7c9njYye9fsqDUVEWp9tSwsWqjFVjwom6kaL4fqEx2J9fhOKguLNBVaVVVw++1aaDVm+mfTY17EAVT5qpi9a3bcxNa2bapXpBmhlZMD8+ap6bxECa2m8YwfrzJz06YlOprEocVWsrL5IfC3sVo1oyuMeQrO+YYSVw53bHEVDYcTPB3V772vVe1Adr8Bnz6mVkUmAClliwXwARlg1q5ZPHvls3GKqp3iq4A1X0/POi1QFhZ2rDzsPB66TYJOY6DjCOWNJYPK2uX4JqjYBoeW2J5NfuAB1fzZDrp0geHDVc2Ox6Omv8rKVHPpZM1u+II+1h9o2UdrYfFCpJRxaQv2ne+YE8MdOsCaNcorKzOxDhVnkJEBPXqo4vn2ihZbycjxjVD6z9ZnC4QDRjwMo396umdhW3G41W3QXTDwDvjsN7DtF3G3oSg+UUytv+WTe+GxQiobKumQ0SEOUbVTPnkIAhanRdwdod8t0Ocm6DRaNTaPfH4DNVC9G05+CnvfDWfUbKrNK/9QdWiwavudx6nvzaC7VANp4QBXlGVgfa6HQK26KCpfCSWvQtlsywXt+vXwxhuqWbFVXHCBmra67TZliFlXp+p3hFDrfEIhVURdWwtvvw2vv66EQbKwpmwNGa6MZjNbALX+WopPFDO4y2Bb41m3TjVtNmoCKgS8+y4MGqT+n5MNp1OJwfaKFlvJRiigmva2durCmQlT34MeU9V0odVETnzDfwzdp8EHN8TVd2hJyRJDV5SZ7kw+2PMB1w+9Pg5RtUNOfAKl/7Ju+rDvLWrausdU1ffTnXv2GFc2dBqlbn0+ry4k9s+Bwr+qukKrkCFY8zVrpg8ze8OkV1StpMMNjhYqlB3u09nkXldDt8mQH259VDa97fGgRM9dd1kzfSgE/M//wJNPqqJorxfcbvVcRowkuterpsa++lUl9n75S/jTnxLvLD6/cD41PmPHssXFi20XWz/5ibn36MknYfLk5BRaERzteEleO37pScru/0BdWev+NqM7XL0Welxij9BqjDtbTYVcswEye9m7r0bMKphlKLNV7atmfmF0ewiNBaz/fxC0QGhl9YerVsHk16DXVUrMRxNaTXF3VOKr35fh0rkw5S1wd2p7PAC734T6w23ciFDi8foCyLtMxdqS0IqGuwNkdIGLXofLFijx1kbeew/272/zZjj3XFi7Fl54AfLyVDYrIrRawu1W47t3V6vVNm1SBpuJZGbBTIIGMvW1/lqm77RG+MZiwwZ1MypAP/95+PGP7bF00FiDJWJLCHGNEKJACFEkhHgkyvP9hRDLhRCbhRBbhBDXWbHftCMUhE8fbV2mKHcoXPcp5A6LPj1hB65MyD4Hrv0UOo6yfXchGeLDvR8aHjuvKHEGhGlN+Ucqs9XW1YeDvw2f3w5dLzAmsKLhcKgLi743wY3F0LuNh5agTy0EaUu2tsMwdREy9tfgzmmdyGqKK0eJtusLYMj9rd5MIND2Wi2HQ2VdtmyBcePafoLPyVGtYjZtgsceUyvp4s3J+pMUHze++GfVvlXNWkS0lYcfNp7VyspSU8JtMSfV2E+bxZYQwgn8BbgWGAncLoQY2WTY48A7UspxwG3AX9u637Rk79vgP9sVvUU6DIer10FGD3DGwUilMU4PeLrC5z6GTufZuquth7dGfdwhHAjOnlo8UHWgWT8uTSvZ9EDbaoiy+sFVq2HcC2ExYjAd0hxOr8oAXfwOTHlbZYRaQ+H/tU1odZkAV6+HTuer12YlDo/a5rjn4YK/t2oT//pX28xLO3eGzZuVN1dWlnXCyOlU23vkEdU2qHt3a7ZrlBW7V+B1nT3/5nF6cEf5fDqFk40HN9oSS1ERfPyx8azW/fcnRqBqzGFFZmsiUCSlLJFS+oC3gJuajJFA5OjXEThgwX7Ti1AQPnnE/AokT2e4fAm4ctXqwUTgcKhpkssXq6lMm1hSsgR/0H/W4x6nJ2pz2AxnBstLo7tBa1rJ4eXKLb61ZPVVWZ+u+daLEVCfw743wlUfqd/NEGyArU+0vsl0p/PhimUqS2fnd9GVA4PuhAl/NPVnPp/KmLQ2q5WbCx9+qKb77JquysmBc86BVauUsIsXswtmU+mLfqGb4Tq7+Kwh2MCi4kW2xLJihfHapsxMlQ1MBosHTfNYIbb6APsa3S8LP9aYJ4GvCiHKgHnA96JtSAhxnxBigxBiw5EjibEWSBj73jPfDkQ44JJZyuIh0ZWHwqHqaC6dD8KeE82MnTOirhSKJsBAeeLMKZxjSyzk5dmz3WQh1uvb/FDrxUhGN1Wf5elsTTYrFk4v5AyGyxaZm8Lb+9/Wr67tOAquXGF/rWQEVzac83WVHTTIu++2vig+KwuWL1d1WrEK363C41G99laujF8NUqz2XwIRtRuFL+jj/c/etyWWBQuMNwP/9reN18lpEku8ztC3A/+UUvYFrgP+JYQ4a99SyhellPlSyvzu8c4jJxIp4ZOHzWe1Rj4KncZaY+1gBc4MVa8y5mnLN+0P+tlwcEPU57pndyckQ1Gfs+vqk0OH1Ptm9c0sdsQgpXp9TanYCRXbW/f/5e4AV34I3p72Cq0IrkzoPBamzTQu/nc81zpvqw7D4KoP1Ws8+7BmH+4cVYR/3i8MDf/1r1uX1fJ4YOFCGDkyfivdvF4YPBiWLbPfL2pvxV5O1Ee/0B3dYzQ9sntEfW57+XbDqxfN8IGJhbX336+zWqmCFUeG/UC/Rvf7hh9rzL3AOwBSyo8BL9DNgn2nB8c3QINJp78u+TDqp/ZMxbQFdw4M/6Hlm123f13U2gmA/N75DO86POpzlQ2V7D652/J42iWFf1HWJGZxeuHypZAzML41ha4sZSVx0b8gSk3fGRzfCNWl5vfh6axEpKtjfIVWBFc2DP8RDL6v2WFbt6pGxGZxOGD2bOUAHm+TzMxMGDUK5s+3tyZpSckSnFEEuUAwdcBULuhzQdS/87q9hhfsGGX/fuXsb4QOHXSj51TCiqPDemCIEGKQEMKDKoCf1WTMXuAKACHECJTYamfzhM2w5x2Ty+gFXPRG/FYdmsWGuBYVL6IuioGmx+lhav+pTOk/JerfOYWTpSVLLY+n3RGsh5J/gow+ZdssF7+rptkSkYF1Zas2Vec/0/y4Hb9pnWfY5NfCNVoJnMZ3ZcP430HOuTGH/PGPrWs0/fDDcNFFiVvplpUF+fnKQ8ouZuycQY3/7AxVjieHSX0nccmAS8iI0nmj2lfNvEJrVzx/+KHxacGLLjLXxkeTWNp8hJBSBoDvAguBz1CrDrcLIZ4WQtwYHvYg8C0hxKfAm8DXpEy0hV0SsedNkCYyBv1uiau3VTIws2Bm1Aaxma5MJvSewKS+k8jxnJ3lq/HXMLNgZjxCTG/2tbI+ZcDtYd+3BPYOceXAsB+olYLRaDgGZTOUmakZBt4BPS5Ljml8RwZMfT/qlGl1tbIGCJosRxs9Gh5/PPHeTdnZ8KMfqeya1YRkiBW7V0R9LiiD5PfOJ793ftQi+ZAMMXvXbEvj2bDBeGbrsssS/95ojGNJclZKOQ9V+N74sSca/b4DiJ56aO9UfGauMF44VFPp1voSpSA1vhp2HIm+Aq7WX8u4nuPomdMz5t+v3LMybr3M0pbPfmO+nsmbBxP/Lzmmup1euPi/MCfKdHPxy8oK3QwZXeGCvyXHawPV+zTnHBj2/bOeeuMN84k3p1MV1CeLG7nXq8xYhw61drtbD29FNuMXN6jTIHpk96DOH31lwYGqAxyuPkxejjULZk6YOBVMnKgtH1IJ7SCfaPa+Z24FVP+vqFVd7YhV+1ZFtXYA6OTtROfMzgzrOgxfMPo8STAUjCnWNAao2AmVO83/3cS/q4xLMiAc4O0Box47+7mCP5lvzTP+D9aYlVqJO+esYnkplcO70dVtEe6/H/r0Sfwi5wgOB3TrpgxZrWRh8cKYq5lHdhuJEIIcTw552dHFVIYzg6Wl1pUpGM1qgc5qpRpJ8lVqx+x+3XizW+EMG0G2n6wWwIKiBVQ3RM+qjOs1DgCnwxmzV1lIhiw9ILY7Cv9mvjC++1ToeaVaoZosuLJhxINnPlazF3wmF6d0vRD6fSE5pg+bIs4s+CkuhgMmXQ27d4df/Sr5TuY5OfCzn1m7zek7p0e1kxEIpg2cdup+rCL5Kl8Vswqalii3ngYTfc89Sab1Nc2jxVYiqdkLNftaHheh/5eVl1U7Y86uOYQ4u57G5XAxrf/pA+KUftFnqusCdbb3MktryqabK4wXDpj0snlT0XjQdEXrocXmfeHGPpecQgvOWu25dKn5GdLnn0/e6SkrPaXqA/VsOrgp6nM5nhwm9Zl06n6sInmAxSWLsaoE2YyNgxWNxDXxQ4utRLJvurkj4bnfandZreN1x9lTsSfqc9nubPJ755+6P7nvZLLd0Y9Wa8rWRC2w17RA3WGoN9nyqP+XlZ9WMtJ06m/fdHPteXIHq8xWImweWsH06eZWrHXpArfdljy1Wk2xMpvz8b6Po7boAVUcP6H36QUVE3pPiFokD0q07TrWCl+NKOSaOLwfP27JLjVxIjWOGOnKnreM14o4s6D7RfbGk4Ss2L0i5hVlXaDu1DQiwPhe43HEOAm6HW42HrCnl1lac3i5+dqkkY+kxkWBDEH5SnN/M/zHiWuLZZJQCD76yNzffPObrfPWTUXmFc2j2hd70cegToNO/T6259iYRfJSSpaULLEkpp491eIEIyxdCvWtcCvRJAYtthJJZYHxsb2vUb3b2hlzd82lyhe9ajTbnX2Gu/PI7iOpD0Q/+jQEG1hcstiWGNOaA3MhYKJqt9MYyB1iXzxWcuITc+PdHWDQXclXGB+DTz4xV+DucMCDDybOUyvezCqYFbPzxKjuo85YvZzjyYm54rAuUGdZ654pU4xPJX74obkaL01i0WIrUfirzC2lH3gneNpfvdaC4gUxnzu/5/ln3Hc73QzsNDDqWF/Qx4ydMyyMrJ1wyKRAHf5AyogRDi6GkIlatHO/kVJpnyVLzBmZXn998k4fWs3xuuMxO0sIBNMGTDvr8Ym9J8bc3uqy1ZaUKUyebLwWa9Mm+/tUaqxDi61EUbXLuNGjww29rrE3niRkf+V+jtUei/qcUzjPKI6PMKnvpCijFVvLt8acCtBEoWYP+CuNj3d3UNYk8eh9aAVl75tYCeyAEQ9DjJrAZGT6dHOZj0ceUS1g2gPLS5fHLE+IOMc35ZKBl8Ss8XI73Kzfv77NceXmwsCBxsYGAkpwaVIDLbYSRWWB8avk7lPNXYGnCUtLl+J2Rj9xZ3uyoy7Hvrj/xWTFaBfkdXlZvW+1pTGmNYeXm1upN+hu8y7siSJQZ24asfu05G2PFYWGBti82fj44cPh/PNbHpcuzN41O2Z5QlAGmdDr7G4DE3pNwBOjt2d9oJ6FxQstie2qq4yvm3rmGXPeXJrEocVWoqj4zPgqqM7nJ5dfUZyYvWt2zAJWX9DH+F5n9+8Y32s8Lmf0des1vhoWFS+yNMa0pmy2uanuET9JHkf1ljjykTn7ht7XqUUqKcKaNeammL761eS1e7CDBUWxyxOAqOUIY3uOpdYffWmnP+S3rEzhiiuMr0qcN081r9YkP1psJYoTmyCKd1RUOo1NXl8fm5BSsqxkWcznPU4PvXLO7g85usfomAfEoAwya5d1BoRpjZRQvtz4+A7DVAubVOHQEvCbEJJ9b1ItcVKEBQvMWT7cdFP7McksPVFKRUNFzOdHdx8dtbVXtic76jEnwmdHP2t2daNRLr0U/CYmMh56SGe3UgEtthJFxWfGx3YaY18cSUrh8ULqg7HXNY/uEf2A6HV56dehX8y/KzlRQkV97AOtJkxVobnVr3mX2xeLHZzcguGLHU8XyB5gazhWs3ChqukxQm4uDEmRBaRWsLhkcUyLmFjF8REm9oldJO91eflgzwdtjq9LF7j3XuOZydmzYc8e4++3JjFosZUIZAjqyoyPzxnU8pg0Y0nJkpiuzALB1P5TY/5trNYaoA6IK/eY9FZqjxxdY864s8+NyekYH4vqYuNje16RcrYre/caHzttWvvya5qxc0bM7HeOJ4cL+14Y82+nDZgWs0i+2lfN3F1zLYnxscfM2XbccIP5/pea+KLFViKoOwjC4JSEu0NqncQsYmbBTOoC0VcO5mbkcmGf2AfEqf2nxjwgVjVUMa9wniUxpjU1pRAwOg8loMfFtoZjOXUmGgb2uTF1atFQU1AnTxoff911ydcH0S5CMtRs9ikQCpzRlaIpzRXJh2SIuYXWiK2ePeFrXzM+tbt7N3zpS+amjjXxxRKxJYS4RghRIIQoEkI8EmPMV4QQO4QQ24UQ/7FivymLv8q42OowzMRJLz0IyRCr9q6K+bw/6I9aHB+huQOiRDK/cH6bY0x7KnZgvKbwvJTyn8JfCUETBlS9rk6Z9jwA+/aZ88u67jrjruWpzieHPolafhDBIRwM6Bh7yri5InmAQ9WHOFh1sE0xRvjZz8xlt5YsgV/8AqrbXjZmG+25n2ObjyBCCCfwF+BaYCRwuxBiZJMxQ4BHgSlSylHAD9u635Qm5DO+ttebB6TQicwCjBwQ+3fsH/P58/LOa9ZP63DNYQ5VH2pTjGlPVaHxsT0vN37xkAxUl4LToMddVt+UyyyXlhpfWZiXp7Io7YWFRQvxBWIL7abO8U1pqUje7XSztHRpm2KM0KsX3H23uVWlzz0Hr76anFOKNTWqq0F7xYrLtYlAkZSyRErpA94Cbmoy5lvAX6SUJwCklCY726YZoQbAoNhyeIyPTRMWFy/GH4y9HGdk95EtHhBjtdYAtZJxWWnslY4aoNZETWHfm40b9CYD1aXGv1I5g9XFUQqxe7fx1WwXXdS+Wr5M3zkdX4z3UyCYNjB2cXyE5orkq33VzNw5s9XxNeXXv4YePYxfmwN8//vw858n15RidbVatHHJJYmOJHFYIbb6APsa3S8LP9aYocBQIcQqIcQaIUT7s0NvjJmDt8Nj7puWBswqmEVDjIJkgeDi/i3XB+X3il13UeWrsqyQNS0JBcF33Pj4LrGndJOSmlJoZqXrGeQMSq2sHVBUZHy65pxz2k+Lnjp/HZ8e/jTm87meXCb1id2BIsIlA2I7yQMsKY29uMcsHTvCokXm+1X+9rdw881QUWHORsIOamvh6afhi19MfCyJJF5HERcwBLgU6At8IIQYI6U82XiQEOI+4D6A/v1jTxOlPGZ6aAkXtme23u8J9Yct3qgD7gia/itf0MfGgxtjPh+rlUZTpg2Yxvyi+TFFm25K3Qx1B8CRAUEDl8buDqnTCzFCxWfGL3hyBqeUczzAjh3GS+hGjGg//fVW7VuF1+XFF6Nezx/yM6H32c7xTZnQW9WExmp67w/62Xl0JyO6j2hTvBGGD4d//xvuvNNctmrxYhg9Gv7yF2WUmplprgasrVRVKUuKb34T1q6N336TFSv+6/cDjY2N+oYfa0wZMEtK6ZdSlgK7UOLrDKSUL0op86WU+d27d7cgtCTFzMkp5LO/+NhyoQWGi6ubsLZsbczidlDF880Vx0cY32t8s1ef1b5qSk6UtCrGtKdmt/H+htkDVOubVKLSjMfdqJQqjgcoNuFqMWyYfXEkG/ML51PdELt6vKXi+AgtFckHZdDyThU33wzf+x5kmywfLCtThrVXXAFbt8aneL66Gg4fhm99C8aM0UIrghVHkfXAECHEICGEB7gNaGrTPQOV1UII0Q01rdh+z3ROD4aL3kM+42PTgIXFC5stbg/KIOd0PqfF7YzrNa7ZA6JDOFhaYk0ha9pRsxukwaxk9kBS7vNZs9v42JzBtoVhF2batwxILa/WNjGrYBahZi4CYxklNyXLnUXv3N4xn68P1DN95/RWxdgcv/oVTJzYumnftWth7FhlJ/Hpp2qa2cqaLr9fiaz9+9WU4YAB8Pbb1m0/HWiz2JJSBoDvAguBz4B3pJTbhRBPCyFuDA9bCBwTQuwAlgM/kVIea+u+UxZHhvHzU7CelDuZtYFZBbMIyNjTrMO7Do/p/tyYDhkd6JoVu31Mjb+GmQXWFbKmFdUmPLayB6ZeK6kGE4eerL72xWED9fXmWrfkxV5HklYcrT3K3srYTq8tOcc3ZWLv2EXyAGv3r212kU9rcDhgzhy1qMFsDVeE995Tomv4cHjySSgsVKsEKyogaKLqIxRSn7PKSjh6FP78Z2WO27cvvPBC+1p0YRRLaraklPOAeU0ee6LR7xJ4IHzTODIwPM1WsxtE+zDBqfZVs/PozmbHTOk/xfD2xvca36yB6Qd7PiAkQ4bEW7vCjMdW7tDUE1vNiPkzcGamnO3DwYMq82Fk6X+PHuZOsKnMstJlZDgzYtZr5WbkGqoFjTBtwDTmFM6JWbflcXpYt3+dqeOVEbKy1Kq+r38dpk9vvcXD3r1KFL3wApx7LkyYoOq7Jk6EUaOUHYiUpz8fDoeyEzl+HHbuhPXrYcsWddu8ObVs9hJFai2zSRe83YwX6FYXp97JrJV8uOdDMt2Z+BuiXxEaLY6PMK3/NGUjEYq+PYlkW/k2zss7r1Xxpi1mptk6DrctDNsIGVQYnk7KpiWFGlA3NBgvgh40SI3PTCHXjtYyu2A2Vb7YKT9/0M+EXi0Xx0fI753fbJF8nb+OBUULLBdboETP668rT61f/KLtRqHFxer2zjunH3M41MIJr1cthm9oUDfdf7H16Ev6RODpbLxIPuSHhqP2xpMkzC+aT7Wv+QpOMwfECb0nkOWOnW8PhAIsKVlieHvthqCJo3d2ivXtlBLDWTvhTrlLdr/fuFNMt272xpIsSClZWLyw2TEtGSU35fye5zdbE+oP+ZlRMMPw9swiBDz6qJpW7NjRuImtUUIhJeJOnFDZrJoaLbTaihZbicLMScqMm3cKM7dwLiEZ+0ToC/oY2nWo4e2N6zkuZn9FUIWsM3bOMBNi+yBGJjAqGV3si8MOQn4MH/bMLGRJEnwmmlO43e3Dwq/kREmLF3Fj8sYYKo6P0FKRPMCuY7uobKg0vM3WcPnlsH27arnU2jouTXzQYitRdBptfOzxzdCMCEkHjtUeo6yyedfyIV2G4HQYr1/rmtWVDhkdmh2z/sB6ywtZUx4zYivFDD9VqyyDhz3hJtXElhnTSKNNjlOdRcWLmhVSAsG0/saL4yNc2OfCZp/3urys3L3S9HbN0qcPzJwJy5bBeeeZt4eIJ+1ZEKbYkTKN6DIe9r4L0sDRsWKbWh3mzrE/rgSxfPfyZgtYAS7qd5Hp7Y7NG8uS0thThW6Hmw0HNjC532TT205bjHwmI6Ta4g1TiyHicIFjsaGw77Op4J8FdGpxbHvJbL372bvNTvlle7IZ3m04h6vNvQ9je45ldsFs6mN0I6hsqGRmwUxuGHaDqe22lgsvVL0H33sPvvtdZcWQLD0SMzLUVOdPf5roSBKHFluJIneY6idn5FL06Mdpf1Scs2tOswWs2e5sJvc1L4imDZjGij0rCMRw7a8P1LO4ZLEWW41J58yWw2PcQyzkx/buDbYYChsjzQ8pAPgCvhb7oFb7qrl/3v0Ik+91SIZidqiI8Nqnr/HSjS+Z2m5bEAK+9CW44Qb4059U256qKmUJEu+Vp0JATo4qe7zrLnjqKUhnr/KWSLEjZRrRYZjxqcGK7eCvTLll6GZ4e3vzDnh1/jqe++g5/rD2D6a2W9VQ1WwdmD/k58/r/swTlzwRc0y7w0xReDzO2La0kzJAyEQBVJLgdhkXyr44NKdING9ue9PQuFirCttKIBRgw4EN5PeO3avVDjIy4Mc/hgcfhE2b4LXX4I031LV9TY0qgLeL3FxVTP/5z8O99yr3erfBhhTpjBZbiSJnkPFmuAC734Jh3zXeRiWFWFu2tsWDXYgQu47vsmX/R2qPcLT2KN2y2snyrJYwY3Ugg4DNn8lEZX9CPiC1pkk9Lp/hMrP20BT4X5/+K9Eh8PcNfyf/xviKrQhCKA+tCRPgD3+AVavg1Vfh/fdVtsvrVZYO9a3Umm63sg4JBFTm7LLLVJuea69tH5YiZtBiK1E43ODtoZr+GmHfOzD43rQUW//Y9I9Eh8BLm17ikYsfSXQYyYGZqUGjU3KpSMPRlPu+uZ1+pMHpML8//TNbq8tWJzoE5hbOTXQIgPLOmjpV3V55BcrL1UrGHTtg40aVASsqUp+LSD1fxLMtFDptchoKqXY8558P+fnKDHXUKOjfP76NrlMNLbYSSdeJUDbD2Nija9P2xNacy3u8+O+O/2qxFcGMwIhRC5cWyJASXJk9Ex2JYTwuH1IaE1vHj9scTII5Wnu0WeuXeHGw+iChUAiHUSXSs6fq5GwzPcK3yxo9JoETdKbGl00DGdTjRSLIoIEMGvBST3eO4CiUUAi824odHz6s2he0M7QOTSQD7wRXrsHBEvbNMO5+bQZv4hqkhUIhDlYfTNj+I2w9vDXRISQPpsSWPbUuSUNt7H56yYgZsVVamt72D3/f8PdEh3AKU31Y4yC0YiGALpygH2UMppjRbGcM2xhKIQPYSx7lONpqh9JeGnI2QWe2Ekmva4y37QEo/AsM+Ao4LC6U/8Ih42P/Y23B8Pyi+ZZur7X4Q35KT5QyqHOKOaLbgRmxVVuWULFuO5UFKgOdIvTseIh6v7H2XocOpXfh8n93/DfRIZzi1U9e5ZYRtyQ6DE0C0ZmtROLOgW4mLAeOb1Q2EHZktxLEPz/5Z6JDOMXfNybPlXBC8XQ1PraqyL44koGK7easMBJMVkYdWRmxPaUaIyUcOWJzQAlk+5HtiQ7hFCv32G9uqklutNhKNOfcAy4TZqWbHlTNcdOEpaVLEx3CKXTrnjAdTDSXrtiRmuLfadDKurpUGQqnEL07GVx0A+zZY2MgCWR7+faY3nqJoLKhssWWQZr0xhKxJYS4RghRIIQoEkLErDIWQnxRCCGFEIlZB5uM9LnB3FTiyS1weHlaFCb7Aj5O1J9IdBinKDqe5lkao3QYCo4MY2NrdkMwtcQIYHyqtKaUVGvZc25eseGxu+xxU0k4yZilfnXzq4kOQZNA2iy2hBBO4C/AtcBI4HYhxMgo43KBHwBr27rPtCKjK3QcY+5vPnnInEBLUt7a/laiQziDoAyy6eCmRIeReLIHgtOE2ErFVbLS4MVKZQE4U8swaESfHYbH7tiRnn5bM3eaKEiPE0YNVtO+gDzdX18MrMhsTQSKpJQlUkof8BZwU5RxvwCeB9J8+VIrOOcecwf0ih1wYJ45U9Qk5I0tbyQ6hLP4x8bEe34lnOyBxpM51bvNmaAmC0a/O/5KqC6xNxaLGdKzyHDD39JSqE3BxGRzhEIh9lXuS3QYZ7Hx4EZjAw8dUgV1Vt/MYkcMUqrX1w6xQmz1ARp/ssvCj51CCDEe6CelTA53t2Sj3y3mvwzr7gN/atcArNq3KtEhnMWcwjmJDiHxZA+AoEF/orr94DC2+i2pcHcyPrZsZkoVyQ/sttvwKsOPP04/+4fFJYuRSTj16wv62HMyTYvkNC1ie4G8EMIB/A540MDY+4QQG4QQG46k8zKZpmT1hbxLMfV2+E7AR19KueLdCJX1ldT4k6QlfSP2V+4nZGfjsFTA09F4TZMMGu+CkExk9TM+9uD8lPqeDepRarjpcFkZnEiesklLeHnzy4kOISb/t+H/Eh2CJkFYIbb2A42PXH3Dj0XIBUYDK4QQu4FJwKxoRfJSyhellPlSyvzu7a09+NhnjdfJRChfCTueg0DqZbhe+eSVRIcQFYlMqhWSCcOMa/rBhfZ2trWD3CHGxx792Px3M4H077qXOhPG6QsWpN7b1xxLSpYkOoSYvPfZe4kOQZMgrBBb64EhQohBQggPcBswK/KklLJCStlNSjlQSjkQWAPcKKXcYMG+04fOY6HbFDDY1+wU234Bh5ZCErSlMMPb295OdAgxeWVzcgrBuJJtwtz1wHwIVNkXix10u9D49GfID8fW2xuPhWS4fXToYHz83LlQlWJvXywaAg1JtcK5KcUnjK8U1aQXba5slVIGhBDfBRYCTuAVKeV2IcTTwAYp5azmt6A5xbjnYPFU4/UyEVbdDpcvVoLNZbG7vE1sPrQ50SHEZHHJ4kSHkHg6DIPDBjN85SvBmWJ1Wz2vgq1PgtGMzr73oMsEcBmsPDeKNw/qrW/P0rev8enB5cvBm2JvXyw2HdyEx+HB18JqbbfDzTfGfQNh9uI2BoFQgJc3v9xirZgDB/sq9tGvo4lpbE1aYMkyIinlPGBek8eeiDH2Uiv2mZZ0mQBdJ0H5Ckx5+wTrYOllMOk16HuDOZPUBHCy7iQNQWPGrB0yOlh2QAzJEFW+li/hj9cdJyRDOEQ79vzNDXttGTHQ9R2H2n2QO9j+uKyi0xgw8/4emAvn/8r6OGxqlXXllbB9u7HpwRMnlLnp0KHGQ0lWFhUvMlQcP7HPRP7vemvrp5aWLqX0ZGmzYzJcGSwpWcLXx33d0n1rkp8UXLOd5ox7HpZcYj67FfLD6jtgzNMw4oGkznCt2reKbHd2iwXynb2d+e+XretvFpIhPv+fz+NvYWVZpjuTLYe3MLbnWMv2nXJ0GK7qlIx2KzgwF4Z8FxxOe+OyCiEg73Iom2FsfFURnNgM3afYGpZVXHstvPwyVFYaGz9rFvzwh+BK8TPC9J3TW/x+O3AwbcA0y/d9YZ8LWxRbNf4apu+crsVWOyTFv1ppSNcLoO/NsO/91rXl2foEVBfBBX9VmYkk9ECaXzSfWn/Lq7su6ncRV5xzhaX7HtFtBFvKtzQ7JhgKsqRkSfsWW90vMif4Dy6EQV9TKxmtxqapNvreDIeWGF9gsv0ZmPIWuE0URCWIKVOg3oQN33/+A9/5TmqLrWpfNTuOtGzompORw4V9LrR8/9MGTGNmwUzqWqifXbF7hc6ct0P0u52M5P+5bTUwpa/DwomqliZQAzK5lhrNLZzbYqrf7XDbcvU5pX/LmYmGYIPuk+jOVVOJRin/0L4Ve184BHdIYzcz9LzSnH/WgQXgT41K8qwsGDXK+PjNm6E4xWu3P9zzIZnuls2hfUEf+b2t7xg3ofcE3M6WLVMkkq2Ht1q+f01yo8VWMpLRJSy42jAVWLEDll0Jy6+Bk9vafpLwV6lttpHymnIOVLXsy5TpzmRCrwlt3l9TJvWdRI6n5Zq2DQc24AumfkukNtHnBhAGUx2Batg/N7WaUmf1gQwzFjMSdjyfMlYrt9yCYXNTgGefNT7tmIzMLZxLVUPLxzmP00Pv3N6W7/+8vPMMZez9Qb9ehNMO0WIrWRl4J3TNB9FGe+cjH8H88+Hju+HwCjU15K+AlgrUgz7wVajx5R/C2nthrolL5RgsL11OhoEMSL2/nnG9xrV5f00Z32u8oYL7DFcG6/avs3z/KUWvz5lbfbfzN+ZrDRNNn89jym6l5FVz4xPIVVeZW2X43nsYNkNNRubsmmOoOH5MjzEIYf176HV56d+hf4vjGoINvP/Z+5bvX5PcaLGVrAgBF/8X3BatLCyboVYsvpMLy66Gbb+EYxug4ahq+xP0qZ8NR+H4Ztj+K1hxHbzTAZZMg73WFKrPKZxjaEVgB28HumR2sWSfjRnebbihlZB1/joWFS+yfP8pRbfJ5vpvHl0D9SnW96zP9WrK1CiBaiW4DK6mTST5+RAw2G8bVEPqv/wFU4aoycKh6kMcqm75s+cQDi4deKltcVzY11gt2MaDG2kIJP9nSGMdWmwlM97uMG2muSbVLSGDcGwtbP8lLLwA3usO/82FtzPUz/e6w4LxsO0pOLoapImjtQGMChi7itNdDhfndj63xXH+kJ+ZBTNtiSFlcHqVRYIZdjyXMnVNAPS4xHxD961PQQs+TsmAywUXmqwD/8tf7InFbpaWLDVUL5XjyWFin4m2xTG1/1Sy3C1ng70uLx+XfWxbHJrkQ4utZKfHxTD2OXBabKaYAHaf3E1lQ8tFIS7h4pIBl9gWx+R+kw2N++zIZ9T4kq9/Y1zpcxM4TExl737TnH9VonHnKn87MzQchc0PpUQj+FtugUwT12qHDsG8eSrLlYzEytTNKJhBta/l98MX9NlSCxohv3c+LgN1jjW+GuYXzrctDk3ykUJHxXbMsO/DkG+nvOBaVroMp2jZhynLk2XLaqEIF/W9iGx3y4sPMt2ZfLT3I9viSAl6Xmm8rQ1AsBZKXkuJzM8pRjwELhNTiQDFL0LN7qRfEHDlleAweZT/0Y+SV2z5onyspJQsLTHW7cCu4vgIY/LGUGugaXlQBnXmvJ2hxVaqMO430P8rKS24ZhbMbNHIFFR/s3E9rS+OjzC+13hDHjfVvmoWFC2wLY6UoGu+eeG087cQsnb62Vb6XG8uewfKTuWjL7fOCy+OjBgBnTqZ+5t9++C556A6yRJ3NTXwpz+d/fjOozsNd6Q4L+88W4rjIxgtkgcoPVnKibrk7eOosRYttlIFIWDSy9D7mpQUXFJKVu5eaWhsljuLvJw822IZ1WMU9YGW63RCMsScwjm2xZESONzKaNcM1SVQ9CIYuMJPChwuGPYD8952lTvh08eSejpRCHjwQeW7ZYbnnzfeWzFeVFfDU0+d/fjiksWEDHgJOoTD1vKECJP6TTI0LsOZwfLdy22ORpMsaLGVSgiHWqF47jdSTnDtOLKDgMFsx5g8k0XZJvE4PQzoNMDQ2L0Vezled9zWeJKewd8y329zyxNJn/U5gyH/Y6od6SkK/heOrkpqYfm1rxnrkdgYnw++8hWoTZKXVVMDt94afaXk+5+9b+jiKcdjj3N8U6b2n0qWAcuUKl8Vswtm2x6PJjnQYivVEA7I/5NqimvlKkWr8Z6ZmVpSsoSgbLm+xSEcTOtvvXN8Uyb1MXb16XV6WV7azq8++30J00okUAUbvpfUWZ8z8PaA3tdi3kNLwsob4dj6pBVcnTvDzTebr91aswb++lcldBJJbS28/jqsjJIY9wf9rN2/1tB2fAEfE3rbVxwfYUKvCTgN9ght92UK7QgttlKV4T+Ai98Fd0cQJmyi7US4VKHx1OmqxUojZhTMMHz1aefS7AhT+k8xtES70lfJ3MK5tseT1LgyYeBdxt3kI+x+Q9mMpEqx/IifmDNxjRDywYpr4MQn0EJfvETxwAPmDE4jPPYY7NiROO+t+nrVRujBB6M/v/7AetwOY8e/DFeGrcXxEc7LO8/QsQ7gZMNJdp/cbW9AmqRAi61Ups91cH0B9JjWttY+VuDMhm6T4Pqd0O/mM54KhAKsKVtjaDO+oI/xvcbbEOCZjO81HpfBJt366hO1ItbgSe0MPr4rJQxAAfX5zT6ndX8brFftsSq2JaXguuAC6NvX/N/5fHD55UrwNMT5bfT5YO9emDYttthbULSgxcbPEc7LO8/C6GKT4cqgf0djRfIO4WBJyRKbI9IkA5aILSHENUKIAiFEkRDikSjPPyCE2CGE2CKEWCqEMFYwo2mZzDy4fDGM/73KKsV7atHpVfU8Y5+FKz+ArLOvHDce2Gj46tPtcMfl6nNMjzGG+pgBnKg7QVllmc0RJTkdR0DuMPN/V3cQVt2atFNsZyCEmqJvbT1ksA6WXq4EVxJOn/7kJ5Ddimuy6molePbujW69YAd+Pxw4AFOmwMmTscfN2DnDUC1ovIrjI0zqa6xModZfq5vetxPaLLaEEE7gL8C1wEjgdiHEyCbDNgP5UsrzgHeBX7d1v5pGCAFDvgW3lMHQ7yvBZXYpu+l9utV+Bn8Hbt4Hw76n4ojCkpIlhpdmj+4x2tal2REy3Zn0ye1jaKzL4WJZ6TKbI0oBRjxovlAe4MB8WPvN1BBceZeETU5b+RkMVMOiSbDlcQjUQDB5DKvuuEO5yreGEyfg4ouV6andHlyBABw9qoTW0aOxx1U1VLHz6E5D24xXeUIEo0XyACv3rDS0mlKT2liR2ZoIFEkpS6SUPuAt4KbGA6SUy6WUkSPtGqAVCW1Ni7g7wLjn4MZiGPpddd+sWWNLuHLVbci34YZdMOF34OnU7J/MKJiBL9jyJbFAMLX/VIsCbRmjB99qfzWzCmbZHE0K0L8VhfIR9rwJm3+sBEiyk/9H8zYQjZEhtUpxzgi1UrGtWa5APfhOtm0bKPuHp55qXXYLoLwcLroIDh60r2i+tlbtZ/JkldlqjpV7VpLpMpbJj1dxfIQJvScYLlMA+PTQpzZGo0kGrBBbfYB9je6XhR+Lxb1A1D4FQoj7hBAbhBAbjhw5YkFo7ZTMXjD+t/CFIzDpVeg+VWW63B3MFzkLp/o7h0c1Jr7wH/DFI+qElNWyZq4P1LPl8BZDu8rx5Bhu5GoFU/tPxesydlJdVroMKVspNNIFpxeG3N96IVL4N9U7MdkFV+ex0OtaEG3MDtfuU83f131TTaf6q8wtFvBVqlqwfe/CrJb7eRrh2982176nKfv3w7Bh8I9/KGFk1lKiOWpr4bXXYMgQ2LOn5fFzdhlrag/xK46PMKbHGMO1ZP6gXze9bwe0MqncOoQQXwXygaiT51LKF4EXAfLz89v5mc0CnB62eYfx0yOdKKsYzJ8v+CqTXdWIIyuhejfUl4PDGRZgApCqUXXIr5bCZw9Qxfe9rlZCqxUn2dX7VuN1eQ1ltoIyGJfi+Ajje43H4/QYWjnUEGxg17FdDOvWirqldGLkI7CrDd2Kt/1SZUaH/j9wJXhRR3Nc8Fc4tAgCFhQp7Xlb3TqPgwG3w6C7wNNZ1XgJh7qgkVI1fRdOdWFTvgKKX4EDcy0VpxkZyh3+Bz9ofXaqvl619HnjDXjrLejZs/XZMlBxHDsGt90GH5vozTyvcB7SYKb1/LzzWxld64gUyRefKG5xbEOwgfd3vs/DFz9sTzCBGnWsN8uMAep8AOpz2fjmCP90ZqpzRWZfdfHt7Q4ZPcI/u6vn3B1jlpi0J6wQW/uBfo3u9w0/dgZCiCuBx4BLpJQpsjwpNQnJEHN3zeWXH/6SrYe3UheoQyC4evGvyPXk8vCUh/nGtG+Q68mB+sPqyxjyq9VmrizlkWVRM+EFRQtMNXMe0DF+ayfO73m+4SJ5KSVLS5dqsZXRBYb/CHb+TomF1vDJw3BwIUz5T3ha2iKDXiuL0jPzVHZ44wMQtEjsnNisbp88BBld1YKDnHPUCUsGoaFcudJXl54+ydnAPffA00+3fSpwwwbVDujxx5U1QyAAubnG/bwqKsDthj//GX7+c3OrHfdX7qe8xpiAcAonlwyMX3F8hMn9JhsSWwCfHPqE+kC94Uz7GTQcg5Nb1e3YeqguhoYj0HAc/JWABEcGdBRQYTCH0RGo3WsyEIe6IHeEL95lUGVyZVDVeno6Q0Y3uHyR+r2dYYXYWg8MEUIMQoms24A7Gg8QQowD/g5cI6VshcTWGKGyoZJXNr/C86uep9pXTbXv9MnHIRynHnts2WP8dNlPuef8e/jxRT/mnM7WTFFEY/au2YbMTAFGdhsZl+L4CDmeHPKy89hfdda1wVnUBeqYsXMG919wfxwiS3JG/ETVJLWFw8tg1mAY/wcYeFvbslyhoHKr3/F822JqyrnfUk21j61TWScraTgGDavh6Gprt2sAlwv++U+4/vq2O8T7/UooPfMMfO5z8PWvw7XXqselVJk0pxOCQSWmhFACa/FieOUVWLCgdZYSS0uX4na6DS28yfZkx7U4PsLF/S7m/c/eN3RB53V5WbV3FVecc0XzA30n4MgqOLIaDi+Hys/URY8zU1mshGJk6YMB+Gv4d2eWyjx5e6jsUyQDldkLPF3URbdwKdEk3OpNC/nVdyAUAOlXFzZ1+1XGrOEI1B9RFwv1R8Bfcfb+/RXqVrO7XQotsEBsSSkDQojvAgsBJ/CKlHK7EOJpYIOUchbwApAD/Dd8Mt0rpbyxrfvWKIqPF/Ob1b/htU9fQwhxxpfb7XDjdDjJdGXiC/qo8decagb90qaXePWTV5nSbwqPTX2MSwdeaqnYqaivoOREieHxF/e/2LJ9G2VC7wnsL2hZbAGs2reKYCho2B06bfF0hJEPw/ZnIdiGs3WgWtUz7XlD1RZ6uoSvjA36eQXrVTF6ZYHy86rY3vpYoiEETHkL5o5SjvhpxGWXwXXXwaxZ1tg5+HwwZ466ud0weDAMHw79+yvB5fOpBtc7d0JhYdv3ObNg5hkXk83REGhgQq/4FcdHmNB7Ak5h7FhR7atmXtG86GKrshD2z4Ld/1G2Ik5vOIvbqGCuaS2gM0vVHnYaA13yVUP5zD7qu4tQwixyASGcKvPlNFmjGAqoi5yQv9F2POpnoFoJsROfKGPjk1tVVrehmeWlaY5I1qLf/Px8uWHDhkSHkbRIKVm+eznPfPAMq8tWEwwF8Uc+9GEyXZncMOwG/vea/6VLZhf+sOYPPL3yafwh/1k1VDmeHLpndeexqY9xx5g7yHS33a9rVsEs7pp+F5UNlS2OzfXk8uINL3Lb6NvavF8z/O7j3/Ho0kcN1ZTleHJYcc8Ke1c1mRW7ifr+Buth1jmq8NsqOo+Hc+6BgV9VgkuG1BRjRHwFG9RVvMOtpkhKXlYu9VVF5vd1h4n/twML4cNbWj9tmggMvL5jx+Dcc9V0XioRCkk6P9+ZigZjgXfyduLEw/Hvqt0QaCD32dyzjsuxGNJlCLu+t0vdkVJZpnzyCFQXqfuxslYACFVb2/dGyLsCcgYqqxWHO/G1kcF6dXNmqoxuFC/GdEEIsVFKmR/tubgWyGvaTp2/jje2vsEzHz7DkZojp7JUjcl2ZzOo8yBeufEVLuhzwanHH5ryEPecfw8PLnrwVPPWSIFpZIrxhwt/yI8W/ojv5H+HH0z6QZtW8MwrnEdVg7GMQEiGEnL1Ob7XeMMF/P6gnyUlS+K6hDxpcXrhwlfgwy+2LbvVmBObYOMm2PhD6DAMOgxXP90dlfDyHVfTJhWfqemIeNH7ahj5qJqmtKp+Kwno2lVN5d11V/I0nDaCmab2ED/n+KZkuDIY0HEARSeMXQzsqdjDsdpjdA2egBXXq2m6QAvZO2cmDLobRj+uVo07c04XzXls9lo0itN7enFVGgutltBiK0XYengrv1/ze97e/jZSyqjLijOcGWQ4M3jk4ke4YdgNOISDHUd2nDXup1N/yk3DbuJny3/G3oq9Z2wrkpr//Zrf8/s1v2dq/6l878LvcfPwm03HPL9ovuHVQoFQgHO72Fc7FouxPcdS5zeWsWgINjCjYIZ9q4ZSjd7XQK/PwYF5Fvc/lKpQvNKYYWVcGP24qq86vKKFDENq8YUvwMKF8O9/p47gWlS8iGDIWB2oUzjj6hzflEn9JhkWWxnODNbufJPrSp+EhhOcMU0YjczecPVacHcCdyvMhjVxRYutJOfVza/y6NJHOVxzuMWxMvzv2Y+e5dmPnjW0/VhiKJL6XrZ7Gct2LyPDmcH/TPgfnr3iWbI8La8eO1x9mEPVh1ocF2F4t+E4LFoBaYZO3k50yexi6P8XYPPBzfiCPjxm6xvSlYkvwuwhqdNsurUIAVPfhcXTVG1YKH0WVP/pT7BmjWo4HbB4HYAdTN85nfqgMcGb7cnmwj7x8+5rytT+Uw0XyVf5qsgu/AMEj2PIPHjSq+DtGV79p0l29LuUhPiDfp5e+TQvrH7BcJsbUE2cjUyHtYaGYAN/XPdH/rTuT1x97tX8+wv/pmtW15jjl5Uuw+P0GI7non4XWRWqacb1HMeCYmPNpjNcGawpW8O0AdNsjipF8HZXReRWTicmK65suHIFLL5YZd2SVWB680wN93hg3jwYNSoF6recPtbtX2d4eEOgIaHT/hN6GS+SByiq2M8luU5jq18DVeFxNp7G3++p7IEsxQF32GdtkqxosZVEHKs9xt82/I3fr/k9Nb6aZoWWUzhxO92c1+O8ZkWPGer8dWw/sp1jdcdi9uqSSJaULKHv7/vy5ZFf5uEpDzOqx6izxs3ZNcfwaqEcTw6T+05uU+xtYdqAaSwtXWqokLXWV8ui4kVabDWm9zWqPdSuP6e/4HLnqobriyYrP6NkE1zOTPiC8YxyhD59YO5cZd+Q1NOJfdbicrgMX4RmujPpmdOzbfsMBZQXoQxGvyEBR1Tjz9GdBxoyTY7wxNEgd3fvhbv+kLJYaI4198IVKyD3XPW5tAPLhRa0OD2apmixlQRsK9/G86ue590d7yIQzbZ5yHBm4Ha6eebyZ7j/gvtN9d8yyordK7h31r0crj4ctQA/IAMEAgHe3PYm7+54l/PyzuPxaY9z3ZDrTk0FLi5ZbGqf8XSOb8qE3hPIcmcZWt0UkAFmFszkl5f/Mg6RpRDn/wrKV8LxTS2fJFIdT0f43GpYeT0c35wkAlOozNvF77R6C1OmwIwZcNNNUJesCy9DbrI92WS5jRnh3jrq1jMfaDgOtWXKr8p3XN0ajitLgrpD0HBY/e47GfaGqlZTxsIVNnoW4RXD4ZuA0903pPrZ6PcMGeKmrAAr68LjTm3DEf7dccZ2pcPDvon/4ZyCp+HIR2rfsZpU+ytgwXh1sTP6Z9DpfLWttvT1bIo3zybB1f7Q1g8JIiRDzCucxy8++AVbD2/FF/Q1a/4pEHhdXm4fczu/vvLXlmWzYhEIBXhx44s8suQRfEFfi1eSOZ6cU+70lw26jEkvTTLcG8zj9FD709qE+VcdrT1Kn9/1MTzl6XF6OPbQMXI8NhSlpor1QzTqDsP885WxYbJfvZqxfohFKAAbfwAl/0ys4HJkhJ25l0DH4W3e3KxZqnVOMgquzEwDmbdQEKoKoWKr8mA7vlmtYq3dG+6U4VXfMxlpT9bQzLSdUKthPZ1V94RIf9kzsliiSbYrpGxK/CeVkPMdb942xJERNhINH/++fFL9PLkVPn0cDi5QMQdasNDpOFLZP/S6BrpeqLYZ8inPrXjUmP7HxLHLiu9fEtKc9YMWWwli/N/Hs/nQZgSCHE9Os8XhgVCAUT1G8dINLzEmb8ypx6VUB57KyjNvFRVn3j96VHnqVFaqAthQSDk6B4Pq98jN4Th9czrVz5DnBAX9H2F/13/hxI1wCHVN1+gCTwgQSOqDtQRkgAxnBiEZMnT1GZRBBnUaxJbvGGtWbRd5L+RRF6gzVKRf469h+T3L7TFhTWWxBVBVDAsuAH/8fY1MYeXBvuhl2Pj98Ek7zrUormzoPAEumWGpM/d778HddyfXlGJmpmqAfeedTZ4I1sPRNXBoKeyfA5XblYBBKK8pYrwn3jzIHQodhiqbkdxh4O2mzHXdHdXUnNMbNu/0teK9Fap43ZGh7gZqVPuciAirPwgnt0HVLqjcpQRisPbsz6a/UrW32vO2El6IsKBr4c3J6qtMTTuOUb1tcwapNlHuTuCKOM77ARnOtLnDpqSRDFwUpFT7loFGWbfw63zHxMWnFlvJQ7qLrY/2fsTJ+pMxn5dSiaOy/VB9pDOeQxdRWCgoLlZOzMeOqYawDodybHa5TturSKmEVCCgnJqlVAeqjh2hQwfVvywipiI/HY4zhVdEjFVVqThOOgupzy4AwOUGZ/hvItqg8T5Dws+M7G/grIz9+hpz3mHon+DC3F1d1c0IzhBcXdUDxyEb0uupLrZAXZEvmpK8ruvevFbVNTVLdQmsvgtOfmpp4+iYODzqJJ7/J+WzZEObq3Xr4Jpr1Pc/mMB6ZodDHbPmzlVTnYD63JevgM9+C4cWK1EUqI2doRIO6HGpyvr0mKqyQA6PEmrCpcxzE7AampA/LAhRMdSXQ1afZsYHoXIHHF0bnrbfqD57wqkET3P/B40RTiW8Grfr8XYPN42OZOzc4YxbuF1PpGWPDKr9NBw5s11PnbFOHIAWW8lEuoutphw4oA4mixbB9u2wZ486wHm96mdd3dkHvJ49YehQGDBAtcUYPlz93qmTOjhlZ0NWltpGKKT6lUUyW0ZxOJSQc7vV7/X1KpbqaiXEKipUrDt3wt696vfCQtgf7AmH03iuPy8PDll8wob0EFugMg3LrgoLjySJUbhU1uKLNn0upYTS12HD98KZBxtEV6QlSq+rYeLf1UnSRsrK4PLL1c9ETCtmZqqv2vLlMHBg+MGKz2DF59VJvqXPV1ZfGPp9OPcbSjg4syHd2m3JENTsCTej3qIyZLUHVK2V75iqPwvWgTMDRDhzJUPIkB9C9Vgi0x0e44tF7LjYSRK02EpStm2D//wH3nlHHcxcLqiJcXx2ueDii+Hmm9XV5oAB6tje0KCyU5mZakyiCAROC0KPR4k8W2jNFXySfsajki5iC6BiByy5TBUjJ7po3umFnHPh8qWQac4awTSBGij6B2x7RhmgtuQCbgQRrunp83kY8yR0Gt32bRqkthYeeQReeim+giszU01l/uY3kBOZoTq5Ta0ENSLiO4+FK5bHr2YpmQkFCNWXs+vAKj7ds5Tt+5ZTVVlCFxGgnxvyPF56ZfegZ1ZXuno74haE69kCgAw3pXY1alAd/unMVILW2/N0Y2tv99M/rSzWTwG02EoyQiF46CH461+VSPE3cx5yOOBnP1Pj/X6VrUqkqEo4WmydSbK/trrDsOxKZZOQqN6CzizocYkyJXXZdRUQhZBf1dkUvQjH1qmrfzNTq5FCbk9XGHArDPseZA+wL94WWL8ebr0VystjXxRaQVaWaiX01ltwUVP7vcK/waYH1PRfS4x9Hkb82N7pwST2oZJSUnS8iCUlS5hZMJOP9n6EEAJ/0H9qwZNA4HK48If8CAS5GbnU+esY2Gkg1w+9nmsGX8PF/S82vPqzvaPFVpKxaRNMmtS8yIpw443w7rtqGi+dkFJNQx47BidOqOnIxreaGpW1q6098/bW2+bFVv4EqcoOQuF+ruGfkRuo7GDk5nZDRoa6svZ61c/MTHUSyMo6/XiHDqoOrvGtSxd1osjIaOV/TLqJLYBAHaz/Duz9b5xX7Ql1ZT3iIRjzRGJqciL4q+HQItg/V9V1VZWojFekIBl5ejVbVj9VsN3jUuh7M3QYkri4m+DzqSL1p546XU5gFTk5Kiv++OPwne+o79hZVO+GhRPD2dIWapOyB8FVH9jbzsbMCjwztLKm6UDVAZaWLGX2rtksLV1Knb8OIcQZDva5HtUc++pzr+YLw7/Avsp9vLT5JcpryvEH/af8Bh3CQY4nh3p/PSO6j+CmYTfxuXM/x8Q+E3E70+yEZBFabCUZVVVwwQWq0L2l1T7dusHixXDuuYmfKjTLvn2qBciuXbB1K2zZAgcPwsmTSkw5HOrgGinS798fhgyBXr2UaMnLU3VpnTqdLuwfPLUnziPGryRDPfKoLT5dH3BqFWWT3yOLC5xOpV8itWk1Nafr0yor4fhxVV93330qHstJR7EV4dASWHWHyu4YbLfSapzZanrj4negc5NGxE8+qdRCovEC2Si3wxDgAyppe4lbVVWjeTd78PvhzTfhiSfgyBFVPtDQig5GHo86pnXurN6Wu+820D+57jCsuh2OfgyEmq8VcuXAgNtg6P9Tqw1lCNzZ5gONhS2ZLQyLrZLjJby+5XXWlK1hw4ENVDZU4nQ4zzJSjawQH9l9JNcNuY5JfSbhdZ+pZnef3M2SkiUsLl5MfbAeX8BHqJGFi1M48Tg9BEIBhnYdyuR+k7nynCv54ogv2uL3mIrYLraEENcA/ws4gZeklM81eT4DeB2YABwDbpVS7m5um+kstkAdrH77W3j5ZSVKmqvXcjhg2jS45RbVOLZbNyUG3G4lwBwJvGCPxtq1arrh8GGV4WloUPFGIz8fpk+HHj3U/4nfr16X16uET7sjncUWgL8KNv9YFZLLFk6UrcGZqTJYIx+GkY+oouhWEFmUUlursjkNDWf+jPXYV+8y9/69+Hd5xlsYuQBofCEAZ37HI89FFq94POp71tLPjAyVmfV4QEi/mtYN1Kn3INSgfgYbot8PNqi6OymB0KnPnZSws6gD7y8awL9mDKZ4bweyMgNICTV1LkIhR6PXECI7M4AQUFvnYmDfKr56UxFfvHoPo4ZWnH69QqDMPoWqVXNmhFdfetTvPcLdG6pLYddfofQ1lSUUzubr43LOgbwroPtk6DpJ3ZdB9focbmWjYScW+FDV+mv587o/8/a2t9lavtVQ1wuBwCEcCCEQBsvhJZKQDMXsJNKUblnduGrQVXzvwu8xqe8khA2rY1MBW8WWEMIJ7AKuAsqA9cDtUsodjcbcD5wnpfy2EOI24BYp5a1RNxgm3cVWYw4cUL3J3nxTLbf2+U6vQqytPft82qMHDBumbiNHqlWIAweqjFCHDqdXHQYCp/82YvMQOTgb+S5IeVoANd6WEKen29xudSF98KBaiVhQAEuWwOzZKvPj98cWWgBXXqkydz6feq1eb4zpg/ZCuoutCLUHYNsvofSf6n5b67lc2YBD1egM+4FyeY/BvHmq8XJZmfrcHj582ocuIpxCIfVdufFGlYXu3ft0djUn5/RKX4/n9PfA5YKcwT0R5cYyHTIvD9+eM7OujX9Gfm/8uMNx+rvd+Bb5ntbXwzm8gqu2UDml1x1Uy/N9x5QQCfrCAlcqgXHuvSrjk9Fd+Uq5c8GVq2rbnJlhkeNuVBQdLtQXLk65pcOpz2FNjaSwEHbuFOzcqUoE6ushwwudOgmGD5MMHy4ZOhRycpq+4LBxX8ReIBRQAi8UCFsP+NTnpMPws78n1buVFcKBecp5vb5cxQ/hYvpookFAZm/ls5U7BLIHqpVy3jxlEuvprD5Hrlwl8iL2B+oFn/G6z3odwhEW+g61/0AVzOjX3MfhTMJiyx/0s27/OhYVL2JmwUx2HNlBSIaaNcAGcDvc5HhyyHJntdksuj5QT42v5tRUpIyRehUI3A43uRm5XHHOFdww9AYuH3Q5vXN7t2n/qYTdYmsy8KSU8urw/UcBpJTPNhqzMDzmYyGECzgEdJfN7Lw9ia2mHD+uREtBgbKB2LEDdu9WJ4aKijOnEyOeWJGDrRDqZBDx1Gp8a/yYy3VmnZLLpQ7aEX+tiGdWS4ap1dUqhsgJJzIlWF4OH36oYo9MIRYXq4NvZPow8hqkVNOFgwcrK4vIVGKPHqr+KSfntIVFRIT6/adNWa3SG5FjZWNRGgyeOaVYVaWmTa64wpp9xgzCKKkqtiLUl8PuN1V2omKHOkEZWr0n1BRRyAfdJimvqf5fMVSb06WL+hxGiFyIRD5PjYn139vYQqVxpimeWeZI/WHj74AQ4P5vk8+QcAKOcI1TkxcULYMiQ+H6sUYbPaPFjAkSVUAeqFWu8ZW7VNPwE5+on7X7wi7yGWELiHC93Cnj0mZ8utwdm/hQNeMgH/KpujKTnmshCVvowpJBjzJz50w2HNygOmz4awmEosfmdrhxO930yO7BvePu5c4xdzKo8yBT+zWCL+hjYdFCXtr8EouKF+F2uKnyxV7wkePJwR/00y2rG9cOvpbrhlzHpQMvpXOmdea7yYbdYutLwDVSym+G798FXCil/G6jMdvCY8rC94vDY47G2m57FlvN4ffD/v0qG9ZUAFVUqCv0SNH5yZPq8aoqlTWqq4teIB65NZ3CiJxAhFAiJztbCZ8OHVQdVefO6sTVvfvZ4u7aE9FFg5RQXZ/DsequHK3qxtHAeZwc/sqpwvgTJ5SYqaxU8dbXn741NKhbRKx5vacFUWTbkRNe49gjArCxY37TE1Xj7EF9vcpu+P1n7isjQ/0c1XkOv7vxBmNv2NjnYeRDxt/gJBBb11+vPN+McOKE+ixYQn05HJgPJzYrs8bKXarVSeQq3uGCjDyV2egyAbpOUH5T7g6mdhMKqQuao0fVZ+3IEfX78eNKUFdUqO9MVZW6HxH5LtdpzzmP5/QFQ0ScO52nx0YWVTT9LEZ+Nv2ONf4eNv5+RgRd5LPq86nvReS7EbnoiFwo+f3wv78PqP5+DUdPG042HD198vdXqlugSmWKXLnKf8nhDN/C7WMcHk71BIxktBxelTFyecOrJZv2+osIs/DPtd9UjukWIu409pk/91woKmryoJTq/6Bpb8TI/fojytm98f+ZvzKceY28MaHTv5/KmDUWpJFpUIf6P3PlhFv+dFEO9d48ZZWQ0VU9ltElnEXrwsdHSrjojesN/1/kZedxz/n3cPf5dzOqxyjDf9dWanw1zN41m39s/Acr96xsMdMW4cpzrmTxXeb65qYSKSO2hBD3AfcB9O/ff8KePXvaFFtSkyxFumEkWGNuF6GywvYiXcNsfQq59WnLTLalBOcu4+MdJtIdZS+E6GnwYvhQNvT9iYMnuoR4wsJWmVLCUy/Ckx9Yt81kQjyZ6AiaYPGX7+fL4cmV1m0v2Wj4yYNkvPDbRIdhCxKQFZWIXOPHzkTXR5nVEImO1070NKJGo9FoNBqNjTQntqyoMFgPDBFCDBJCeIDbgFlNxswC7gn//iVgWXNCS6PRaDQajSZdaLM5hpQyIIT4LrAQZf3wipRyuxDiaWCDlHIW8DLwLyFEEXAcJcg0Go1Go9Fo0h5LnMiklPOAeU0ee6LR7/XAl63Yl0aj0Wg0Gk0qkWR2mBqNRqPRaDTphRZbGo1Go9FoNDaixZZGo9FoNBqNjWixpdFoNBqNRmMjWmxpNBqNRqPR2IgWWxqNRqPRaDQ2osWWRqPRaDQajY1osaXRaDQajUZjI1psaTQajUaj0diIFlsajUaj0Wg0NqLFlkaj0Wg0Go2NaLGl0Wg0Go1GYyNabGk0Go1Go9HYiBZbGo1Go9FoNDbSJrElhOgihFgshCgM/+wcZcxYIcTHQojtQogtQohb27JPjUaj0Wg0mlSirZmtR4ClUsohwNLw/abUAndLKUcB1wB/EEJ0auN+NRqNRqPRaFKCtoqtm4DXwr+/BtzcdICUcpeUsjD8+wGgHOjexv1qNBqNRqPRpARtFVt5UsqD4d8PAXnNDRZCTAQ8QHEb96vRaDQajUaTErhaGiCEWAL0jPLUY43vSCmlEEI2s51ewL+Ae6SUoRhj7gPuC9+tFkIUtBRfCtMNOJroIGxEv77UJp1fXzq/NtCvL9XRry91GRDrCSFlTH3UImExdKmU8mBYTK2QUg6LMq4DsAL4lZTy3VbvMI0QQmyQUuYnOg670K8vtUnn15fOrw3060t19OtLT9o6jTgLuCf8+z3AzKYDhBAeYDrwuhZaGo1Go9Fo2httFVvPAVcJIQqBK8P3EULkCyFeCo/5CjAN+JoQ4pPwbWwb96vRaDQajUaTErRYs9UcUspjwBVRHt8AfDP8+7+Bf7dlP2nKi4kOwGb060tt0vn1pfNrA/36Uh39+tKQNtVsaTQajUaj0WiaR7fr0Wg0Go1Go7ERLbYSgBDiGiFEgRCiSAgRzXU/ZRFCvCKEKBdCbEt0LFYjhOgnhFguhNgRbj/1g0THZCVCCK8QYp0Q4tPw63sq0THZgRDCKYTYLISYk+hYrEYIsVsIsTVcG7sh0fFYjRCikxDiXSHETiHEZ0KIyYmOyQqEEMMa1TR/IoSoFEL8MNFxWYkQ4kfh48o2IcSbQghvomOKJ3oaMc4IIZzALuAqoAxYD9wupdyR0MAsQggxDahGrT4dneh4rCRsb9JLSrlJCJELbARuTqP3TgDZUspqIYQb+Aj4gZRyTYJDsxQhxANAPtBBSnl9ouOxEiHEbiBfSpmWPkZCiNeAD6WUL4VXumdJKU8mOCxLCZ8j9gMXSin3JDoeKxBC9EEdT0ZKKeuEEO8A86SU/0xsZPFDZ7biz0SgSEpZIqX0AW+h2h6lBVLKD4DjiY7DDqSUB6WUm8K/VwGfAX0SG5V1SEV1+K47fEurqzEhRF/g88BLLY3VJBdCiI6ole0vA0gpfekmtMJcARSni9BqhAvIFEK4gCzgQILjiStabMWfPsC+RvfLSKMTdntBCDEQGAesTXAolhKeYvsE1cN0sZQyrV4f8AfgISBqF4s0QAKLhBAbwx050olBwBHg1fA08EtCiOxEB2UDtwFvJjoIK5FS7gd+A+wFDgIVUspFiY0qvmixpdGYRAiRA7wH/FBKWZnoeKxEShmUUo4F+gIThRBpMxUshLgeKJdSbkx0LDZysZRyPHAt8P/C0/rpggsYD/xNSjkOqAHSrebVA9wI/DfRsViJEKIzagZnENAbyBZCfDWxUcUXLbbiz36gX6P7fcOPaVKAcC3Te8AbUsr3Ex2PXYSnZ5YD1yQ4FCuZAtwYrmt6C7hcCJFWHoDhDAJSynJU546JiY3IUsqAskbZ1ndR4iuduBbYJKU8nOhALOZKoFRKeURK6QfeBy5KcExxRYut+LMeGCKEGBS+irkN1fZIk+SEC8hfBj6TUv4u0fFYjRCiuxCiU/j3TNQijp0JDcpCpJSPSin7SikHor53y6SUaXN1LYTIDi/cIDy99jkgbVYFSykPAfuEEJH+u1cAabE4pRG3k2ZTiGH2ApOEEFnh4+gVqJrXdkObHOQ15pFSBoQQ3wUWAk7gFSnl9gSHZRlCiDeBS4FuQogy4OdSypcTG5VlTAHuAraG65oAfiqlnJe4kCylF/BaeDWUA3hHSpl29ghpTB4wXZ3LcAH/kVIuSGxIlvM94I3whWoJ8PUEx2MZYYF8FfA/iY7FaqSUa4UQ7wKbgACwmXbmJK+tHzQajUaj0WhsRE8jajQajUaj0diIFlsajUaj0Wg0NqLFlkaj0Wg0Go2NaLGl0Wg0Go1GYyNabGk0Go1Go9HYiBZbGo1Go9FoNDaixZZGo9FoNBqNjWixpdFoNBqNRmMj/x+VfyUYxzLaogAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_17\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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wr3+pDsOnnBKbcTqpqdCkCTz+uErW79TJ2u0fLj7MxpzwY3kCMsDrG163dsdGlB6EpT9NzjvndX8IVgAmXw5LUrP612puZLJRvAc2Pph4b5xZdj0POYuivwlqNhBOuQcmfg3TNsGFe2D6NpiyCk5/EtpPUiOGLEZKuO66+FQdxpv3tr6H06BfWnFlMU+veDpOFiUnOmcrxqxZA3/5i7kPYXq66scyZowaSBprMjNh4EBYu1YNNrWKNze9icNg1EiFv4IX177IfefcZ92OjVh6XXJ6KI6tCLZ4SELbk5mjS2Df3OTzgkoJ312XHB4tUDcRq+5quDDM6K7GYXW6AIRbzWwMNQC++SDo8RM1TDtvJWx6RM2DtIBXXoHvvgNvkmUmmOHpFU/X6JVYH5uObmL/8f10bdY1DlYlH9qzFUMqK+Gii8wJLY9HjXEYO1aF++KF2w0tW1rbl2XmmpmU+Yz/6MPFh9lydIt1Ow7HgY8g9/vElpE3BBkIeuO00Ior/koV1krG933vq1CwNn5946JBSlhyVcMqPF0ZMOTvyoPV7WpIaQHuzNBCC8DhgpRm4ExVcxzHzIWJi6Fp/6j+hOPH1XzZxpanBXCk+AgrD9ad5NI+sz0Z7poXKiEEr6x7JV6mJR1abMWQefNUiwcjhIBPPoHBg5V3K944HNbkhAFkHc9iW+62Os93bdaVJilNajznl35e3fCqNTsOR8ALy//PmvBh80Ew5DE473uYthku2KFCFGfNU3fWjpTo91Gd3S9DqQUNRF2Z0P0aOOcjOH8NXLATpm2ESd/CqX+EjB7R76MxsePZ5Gxv4SuDVb+MyWy/mLD/TShYH/lNUNfL4aL90Pd2NUTa4Y583+5MaH2m+vwOf1qN6WkA991nXS8tpxO6dlWV5MOGqVm58bz5rs1rG16rE6VwCReXnXJZnerEcl85z616DruOAEw0OowYI6RU4cNiE22c7r5bfbASIbSs5o1Nb9SZk+VyuLi0/6U8vbJmTL/SX8lLa1/ir+P/GtvZWjtmRFeRld5RzXrreaO6e3akgLOWqGoxBNqfp+6es9+HHc+oDtrR4C+HNVEOxe10AfT+P2h/rgoruUOo6hbDlOAq2Qs7n4fds1Rl2MmKtxg23J88+U7V2faf5CmikBLW3Rv58X3qH+HUe5VnK1ocTnB4oOdPodUI+HJCRMd+djY8/3z0bR6GDYMbb1Qzcl0uld8rpRJfbreKPDz7LHz8cXybpD6z8pk6UQpPiocrB17Je9veI7swu8ZreWV5rDm8hmEdhsXPyCRBe7ZixNdfq4nsRgwdCg88EJtE+EQwa82sGrOzADxuD5eccgmtPK3qrM8vz2dDzobYGeQthvV/bLhg6fIjmL4VBvwOMrqou+HaQgtAOFSIwpUBXa+E8Z/CiBkqP6ShbHui4WGs1FZqiPGZr0KnacqOUEILVNjFlQ7NToHBf1PeujZjGm53srP9v9bkOzUfrDwmUzfClWVwjYSrfXBxFpz9njq2hIX3u74SlYcUrQfX6YFeN8H4hXDpEbjar2y/olh5gQY/qvKkouXgfCjPiex3hj1undCqjssDzU+Fycsh1XxfnD/8oWHNqau47jo10/Drr+HWW1Vbn4wM9bV5c1XIlJam2v68+KJqYv3cc/Hxdm05uqWOmAJ1kzyy00im95mOoOZNcrmvnFmrE9jWx8ZosRUjHnzQOIbvdquE+LQorsd2Yk/+HvYW7K3zfLmvnJGdRzK5V90O2JX+SuasnxM7ozb/vWEXTleGEiqjZ4O7SWSiyeFUv9/9WhVqbN6AFv6V+bDxbw27cLY7F6Zvh7bnKNsjweWBtLbqQjvoIZVsXO9+2kVumx0IZ7evFDY/Gl2uVtfL4fx1cN4S6H2LuohXHT/CCZ7O0PkiGDULLjsCp/0F3M0avr8qtj8dXZ5WeicY/j+4LAeG/Rs6TFLHQlUYyZUBLYdB/1+p4/rcr6Dd+IbtS0pYe6/5CQ7CAaNfhl63WC+0qnCmqXD6lFXg6WK4fOdOePPNhiXFZ2SoNJOnn4YuXdTNttsgEtq0qfq9666DLVvUbN1Y8sLaF/CFmGs5qvMo3E430/tOp0lq3dSQORvmhPy9kx0ttmLAjh2qW7wRV1yhemjZaZ5hNLy+8fWQ8foh7YeQ4kzh/N7n18nb8ga8vLzu5djE+f2VDfMONemrvFmdL47uxO7ygKebyu/qdVNkv7vrhYZdOIc8Cue8D6ktVSJwQ3F5oN8v1YUntXXoNYcPq4umFQ+zWLGvw4fr3/6O5xo+ONmZDme9DSNfgBaD1LFTT2NfQHkaU1rCgHtg+mZodmrD9gsqV2vTQw33arWboBLNq8RMOJHuTFWe0Hbj4JwPYehjJwSZWXK+VsO8zTLk79DlUuVZjiXOFEjvoBLnneFLwn//e3PtfGozaJASS9OnNyyikZ6u2vUsXQo//3nkv2+GgAzw4poX8QZqKsl0VzqXnXIZAOd0P6dOFANASsnnuz+PjWFJjCWXeSHEFCHENiHETiHE70O83lUI8ZUQYo0QYr0QYqoV+7UrC0yOibr/fuUmbiy8sOYFKvw1EwrcDjcX9r0QgHHdx9V5HVSPltWHVltv0IEPIv+dtLaqR09aRyU4osXhUNs5/UnlzTCDlLDtv5GLxAG/hz63W3fn786ApqfAhM+jC4cmC/4K2PTXhgmWjO5w/lroOCVyQeBMh9T2MHmZCi02hF2zGi4ST7lHiaaUZqFD5OFwZUDv2+DcL8Hd3Pzvrb3XfE5cp+nQ5+ex82jVxuFS54HRL9e7JD8fPvoocrE1dapqEdGpU3Q5ug6HqmB/7DHVdsJqvtn3TchztRCC83qdB0BmSiYD2gyos6aosojnVj5nvVFJTtRiSwjhBJ4CzgcGAFcLIWr/B/4EzJNSDgWuAhp197MvvjBOmDz7bOjYMT72xIPtx7ZzsOhgnefTXGlM7DkRgDYZbeiQ2aHOmgpfBS+vr//E1mC2/Qd89Y+YqIMzHSZ8qbxCVrsbXR4Vlmx1hvHaY8uhMjey7Xf5EQy8z/o7f2cKNOkDY+YBMSxisANZb6vK1UhpN0EJrcyeDRfoDocSE6Nnw9B/ENF7LSVsfiTyhH5nGox9B067P7obC3cGtBoJ0zeZ887lLlcViGZIawuj58RPaFXh8kDHydD9xyFfnju35jxaMwwcCG+8oUKBVp1eMjPh4ovhj3+0ZntVPL/6eUoq6x5PHreHPi37/PDzZadcRkoIgf7prk8pqojg3HsSYMW/fASwU0q5W0pZCcwFat/CS6AqO7cZUPeq3Ij4/nvjNXfcEZ/GpfHi1fWv4g8R9vIGvJze8fQffp7Se0qdpEqf9PHq+letDSWWZMGxuv1h6kU44Ox31QXT6vYNVbgyVNK6UXLx9qci82q1Hg2jX7LGExcKl0cJimH/is327cLO583nEFXRfDCc84HyCoULGZrFlaG8OKf9xfzv5K9pWLXtma9BhymqLUi0ONOUd27i15BmkMu37T/mj+/RLyfOq+rKhDNC+wUinX/Ypg18/nlszvmZmXDvvUp0WUG5r5x3t7yLDDE2aXKvyTUqxyf3mkxqiHQFl8PFu1vftcagRoIVYqsTkFXt5+zgc9V5APixECIbmA/cGWpDQohbhRArhRArjx49aoFp8ScvTz2MGDOm8eRqAcxeN5tKf91E9OEdhtcYSD2l95Q6SZWgOsp/n21CpZpl1wuRrR/2b2g9pv6GiFbhagITF9V/gfOVQNZbqpmpGTK6wbhPYie0qnBnqPYXvW6J7X4SRUUe5JpItKyOuzmM/6TB/ZnqxZUBp/wGOtQtKAnJ7pdUCDQS+v5Sja6x8nh3OFSu1zkf1V9lKQPBru0mbqw6TlOfyWhyD6MlxL63bYPdEaSbpaTAwoWxzc/NyIA5c9RM3Wj5aPtHIcfzNElpwoX9Lqzx3LAOw0LeZBdXFvPMimeiN6YREa/L/dXAbCllZ2Aq8IoQdTMqpZQzpJTDpZTD27QxX35rJ5YvN47Ft2gBrep2QUhaNuVsIre0btgr1ZnKRf1rOjnP6XYOZd66d7Vl3jJeXmdRKFEG1Hgbs12pW42EXjfHPvkWlPcjta1KZA/F/rfCVwDWZlQMPVq1cWXA6f9WVWuNjex3I2zDIGDsW6rvWqTJ4WZweVSj3Ixu4dfJgOoYH0lT0NajYcjDsTneHSmqhciwx0O/nrfK/I3EkEfj85kMRwgv96xZkbV7mDUL+vZVoiuWpKefEHXR8PSKpymqrBsCrPBXcG6PmnPdnA4nZ3c7O+R21hxeEzK15GTFirPEAaB6nWzn4HPVuQmYByClXAqkAfWUOCU3mzYZj+cZMaJxDSyds34OXn/dXBe3082EHhNqPNcivQXdmte9gPiln7mb5hIweyIOx5FF5pOchRPOfCW+oQpXOvS8QTVCrc3W/5gPZXW5DFqe3rDu2Q3F4YaRz8dvf/Fix3OR5TwNvE+J9FgeN06P8lqGG5x89LvIRt2ktVXJ8LEU6K4M6HVj6GT/7PfMeeHajrOml5fFBALwwgtqFJsZxo6FSy6JT8qIw6EKrv7xj4ZvI7c0l++yvgv5Wvdm3UP2Sry0/6V43HX/QIdwxLatT5JhhdhaAfQRQvQQQqSgEuBrl4HtB84FEEKcghJbyRknNKC42PiD2KcPpCbQM24lUkpeXv9ynRJhUOXDg9sNrvP81D5TQw6q9gf8fLs/yq7roJpSmm1i2udnkNYhNt6JcDjSYOSLNZ8r3A5FdUcdhcSZBmc8G/87f0cKtBmrenk1FsoOm0/YBjXiZcBvY//eO1zKs3X6E/Wv2T07surJs95SoexY48qAUS/W7Ve17w2QJooQhjwSP49tBHz1lXmh5XSqRqTxHLeTlgZXX93wcOIbG98IGUJ0CmedKEUVk3pNCnmTXOYr47lVuiqxiqivMFJKH3AHsADYgqo63CSEeFAIURXg/Q1wixBiHfA6cL1spAOUzIznSU+PvJLFrqw9vJbj5aGTc0d1HhXygzu512QyU+peqEq8JcxeOzs6g3wlcPATTOWEuDJV485EhCocDmjSu+Zzu2ebL9/v96vEJQ67M+GMpxKz71iwf14EoVsBI2fErzrO5YEe10HTfnVfC/iU7Wa9wR3PhxZDI2/v0FCcKSoXsorSA1BatyN5HVqNUI2A430DZIJnnjF3jge4+ebE9P1NS1MjhBrC0yufptRbV7xnpGQwtU/ojk3dm3enRVro2OXh4sNsOBLDCSFJhCVHs5RyvpSyr5Syl5TyoeBz90spPwh+v1lKOUZKOVhKOURKudCK/doRM7F8h0MNn24MvLL+FSp8dcMCaa60H/pr1WZs17Eh87YCMsBbm9+Krvtw7lLzCbUDfmdNBVlDqS3yDnxo7q7f3VzNh0tkPkt6I+pbsvN5896hbleoRrXxxJECp/+v7vNHvjK/DeFUo4Piecw4UoICLzgn7+DH5vLi+v1SeX5tRlmZmk1oxk2QlgYPP5yYMWwOB/TvH/nv7czbye780Jn/Fb4KRnceXe/vTu0ztU6VOYDX7+XFtS+G+I2TD/vdOiQ5Zj5c5eXRzdOyC1JK5qyfgy9Ecq7L4eLcnqFDTU1Sm9C7Ze+QrwEs2ruo4UYdWaRGrhjhcEO/X8S/f099+MuhaLu5tT1vSLxaj3QMkF0p2QfFO82tFU4Y9p8EhG5d0Ga0Smyvzq4XzOf39bhOzcuMN440GP5f9f3euSby4oSqQgzhEU80K1aYT3K//fbYJ8SHoyEib/ba2fXmzJ7R6QxSXfXfxF7Q94KQVebegJfZa2fjDzSCC16UaLFlMR6PcYjw4MHop8TbgRUHV9SZCF+FQHBqm/obHE7vOx1niNBNcWVxdKHEg5+Yq8zqdEHD9xELji03Hxa0k0hMdva9YSriDEC3qxP3vjvSa1awBrzBCQkmjBeOYLg8AQLZ4YAWwbzNgnXG61uebsvwIcC335o7bwsBv/tdYrxaDUVKyczVM0O270lzpf0woqc+xvcYH3J0D4Av4IvuBrqRYM+jOonp0cO49cPy5cZDR5OBV9a9EjIcCDCm65gaze9qM6nnJDJS6l64JJL3tr4XsrrREH8lHN9obm2/X6rZdHbhyNdqvp0Rrc6of1ahJnIOfAwBM1dQBwx+OHEePYdDCZEqCtabb1XR9YrEeiId6erYNtN4tfNF4asvE8inn5pLjh83LvkKoL7P/p4Sb2ivo1M4fxjRUx9NU5vSt1XfkK8VVxYzY/WMqG1MdrTYspiRI41DhPv2NWyAqZ0IyACvbXwtZEO7dFd6vflaVYzpOqZeoeYQjoYNMs1bZS7XI72TSsK1Ewfnm8vX6nP7yTGnMB5Iqbqvm6HDZNUlPpFUP7Zzl5k7XgAG3p9gseVQQ6cNBjsDShjGK4E/AgIBWLXK3Nqf/Sy5vFqgxvOESowHSHGmcErrUwy3cWn/S3GHaEMjkXy47cN6t3+yoMWWxXTrpkp+jTD7wbUr32V9F9LlDGpYae3+WrXxuD2c0ib0B7iosogX1kTYAR4gZ7E5L0Wvm8xlucaLgA/y1xqvc3qg648Sm9TfmCjZa74ZaK+brBlrEw3V85iOfGlu5E1mL+PGqPGgaIdxnmFqa8joGh97ImTrVnNpkhkZcMEFyTUdpNJfybxN8+rN15rUc1LYKEUVU3pPIb2eiQQuh4v3t74flZ3Jjj5rW4wQMGwYfP11+HUvvgjDh6smdMnIS+teCnsnVJ9LuToX9r2QzUc3h6w+nL9jPuW+ctJcEXhxDs6HgAk/f5+fx34sTyQUrFMJ+0ZCsetl5sv8NcbkLjMXinOmQ4ep9solMjtaqPs19rC7aIdxmLxpf1UoYkPP7ZIl5u7PLrss+aIWH2yt3RbzBB63h1GdR7Erb5fhdlqmt6Sinoa1RZVFPLHsCa4+7eoG25nsaLEVA849F777DrxhvPxz58ITYXoV2hl/wM+bm96s907onG7nmLoTmthzIk8uf5LCisI6rzkdThbsXFBvI706yADkrTBe17R/4j0UtTnytUp4NqLrlY2nCtAOHP3GXDVfx6nB/49NBHplPlSY7And6yZ7iJeCDcZhzya9IxyZFD8WLoRSE1GwO+5Ivhvo2+bfVm++Vqm3lPu+uo/7F91valvh2vYsO7CMjTkbGdh2YIPsTHZscMvT+Bg/3jhB0uuFxx839wG2G4v3La5XaGW4M7igr7lKv1GdR9VbwRJxKLFgo7nGlG3GQoh+MAnl4Cfmwp9txsTelpOJI4swVc3X+xZIsVExxbEV5oZfNxton2KK45uM1zTtby6vKwF8843xmlatYNCg2NtiJXsL9nK0NLxwL/GWUFxZbOoRKoe3Ovd9eZ+V5icV9ryNSHLGjIG2bY07DT/9tCoRTjbu/OTOkINKQX0w7/zkTn7x6S9MbSvcndCH2z8ktzSX1h4TF4zjGzElojqeD26btU0wU0GZ2Su+MxAbO/5KKN5hvM7dFNqeE3t7IuHoUnO95Hr8BIRNjpmSPcZrmg+2ZbLTkSNQUGC87swzVWuIZKpE/NOXf4rr/j7e8XFc92cn7HdkNwKEgPvvN65IyctTlStmxz/YgTJvGZuOhr9LLfOVUeotNfUIN3haInnkm0fMGVaaZS5huO1Yc9uLF1JCRa7xurZjdb6WlRSsN1e52vkicyHeeHLkC3OJ/T1/Yp/KPm/om7MaNDHO80wEK1eqjvBGnHNO8lUhvr3l7bjuzxvw8smOT+K6T7ugxVaMuOoqc/MPX31V5QOUmdAJduDf3//beJGFvLTuJXMLC7cZX4A8XewXpqjINRf+7DBZ52tZybFl5gRL54vt9b6bbVeR2dNeuYkG4SUA0trE3o4GsG+fuf5a551nrhLdLny7/9t60zhiyQOLHoj7Pu2AFlsxIjUVfv1r4wanADfcAMePJ8cIn2dXPhvX/R0rO8bBwoPGC4uMq2WUd8hmpUKlWeZmOdotlJXsHDbZOqH1qNjbEgkle8x5OFuNNCdw4oHZUS02DZPv22d8M5ySAv1CzAq3M/d9lZj8qRUHV0Q3/zZJ0WIrhtx+u7kUhMJC5YIuKEic4DKTqF9cWUxWYVbsjanFQ98+ZLyodL/xmrbj7dU1HqDEhN2prSGlZextOZkw4x1KaQEpCZgnGI789eb6rLUdax/PVqDSnPfWzJoEsN3EyNLTT0+e6ARAIBBg8b7FCdm3RMb9pt0O6AT5GNKqFTzzDPz851BiMH91+3YYPVpVvbRoEd8hpqWl8MtfwvPPh1/32JLH4mNQLV7b8BpPTX0q/KLyHOMNNTPughx3SrOgnt40P5DZQ3lhzHjANOYw0zqh1Qj7ve9lB1RyvxFtx9mjvxYEPWxmKoBjUCX8TnsoPxLVJvauWQ0MDbvmrLOSKzF+zoY5YfNlY83jSx/njhF3JGz/icCST6MQYooQYpsQYqcQ4vf1rLlCCLFZCLFJCPGaFftNBq67DiZPNvdB3LEDBg+GbdvikzRfWan2c9llMHOm8fqZq00sigEF5QXsK9hX/wJvsblmpp4u1hllFcW7IGAgtjxdsV27imTGXwF+E67c1qPtN/C7ZL9xmxBHiqpetQuOFMCEyz4WYc8ohRbAofwOhmvOPNNcEr1dePTbR40XxZA9BXsoKC9IqA3xJmqxJYRwAk8B5wMDgKuFEANqrekD3AuMkVKeCvwq2v0mEy+8AM2bm1t75AgMHQoPPKC8YeEao0ZDSYkarNqnj/pqREF5AYeKD8XGGBP89eu/1v9iaZa5vkNpba0zyCoKtxmvyehqj8aUjYWyg2o4shHtzrVfHlHxTuM1LQaby0eLFw63uTwzu+VTouoRjhUbh5K7d4+9LVZR4atgS+6WRJthvtK8kWBFGHEEsFNKuRtACDEXuAjYXG3NLcBTUsp8ACmliZhP46FZM3jnHZg40Vxc3++Hf/0L3nhDeZzOOkuFFd0WnPcLC5XQuv56VQVplkR/MOZtnsfMi+rxrJVmGYdM3E2xZYqimZytzF72CmUlO2UHVd6TkSOlxZB4WBMZZo6XViPtJRKFUPlYRp4rX7HtPIm5Ra1xOvz4AuHfzw7Gzi/b8MWeL0h1ptY7WsftcDO1z1QGtWt4h9ZAIMCjSx4N2+R0abbJkVONBCvEViegetZ0NjCy1pq+AEKIJYATeEBKacKf0ng480x46CH405/Md43PzoYpU+C001Te149/rO60mkaY411Wps53330HTz4JH38c+fyuF9e+GNkvWExRZRE7ju2gT6s+dV8s2W/cC8nTNZh/Y5O+Q1WUmfAW2rT/UNJSesDY0+LpbJ+cp+qYOV5anwkum7U4caQah26L90Bau/jYY5KsY11IdVdQ4avfsywEtEyi+pVnVz5br9ACcAgHz0x7hg5NolOQ32Z9y9f76h8SvPzAco6VHqOVx2ZFKDEiXmcTF9AHGAdcDTwvhGhee5EQ4lYhxEohxMqjR03O/koifv1rePBB8ER4HtywAW67DVq3hptugtmzYcsWqKiAoiJVxVj9cfy4Cj8eOADz58N990HPnmpm4/vvRy60iiqKDEc6uB1umqY2bfAjw0RX939+98/QL1TmGedsZXTB1GiWeGNmNl9G19jbcTJRdtBEnlxnc3mA8abymPGazB6xtyNS0jsarykwMUkhzmTndcbovNG6dfIMny6sKGThrvAhjfaZ7aMWWgCXnXIZ6a76w/Uuh4s3Nr0R9X6SBSs8WweA6pnHnYPPVScbWCal9AJ7hBDbUeKrxuRgKeUMYAbA8OHDbXhljJ7f/EYly//2t5GXCldWwltvqQeothK9e0OXLmqbLpfaZkEBbNpk3dzF+Tvm43F5KK1nREi6K507R9zJqM4N70kUIMC1b18b9o5r1aFV9fyy19hTkd7ZXqGVKkw1e7TX3X7SU7LPWEild8R2RQm+EjDTnyjdhjGtpn2N882ObwR/ua3yE7PzOuP1h/eGd+mizs1meiommrc3v43b4a73POsQDtOzbY04r9d5/P6LkPVygBrt9syKZ7jtjNss2Z/dsUJsrQD6CCF6oETWVcA1tda8h/JovSiEaI0KK+62YN9JyR13QLducPXVxi0hwhEIqJYRZvrARMOsNbPqFVoAfunnt2N+G7U7eEb3GWHvujbmbORI8RHaZdYSHwEvYCC2XOmoCLaNkBJDuyFYzaWxDDNJ5umd7JcnV3pACREjb6hdhk9Xp/lgNXA9nJeoaIftxFZReRO8vvA3aZ07x8kYC3hm5TMUe+s/fjJTMpnWd5ol++rbqi9prjRKvfVfO3bm72RX3i56tbRR9WyMiDqMKKX0AXcAC4AtwDwp5SYhxINCiAuDyxYAx4QQm4GvgHuklCb84Y2XCy5QPbV694YMe+WE1qCwotCw+V2nJp0sibtf2PfCsG5np8PJW5vfqvuCUUgI1EBeu+XgSB+mvCc2bfaYtJRkG6/xdLHVRR9Q4U+jY8GZpvKj7EbT/sbJ70U7QNir9aPX58Yvw583OnQwN5ot0WQXZrP+yPqwa8p95Zzd7WxL9ieEYEqvKWHXBGSA2WtnW7I/u2PJ1UdKOV9K2VdK2UtK+VDwufullB8Ev5dSyruklAOklKdJKedasd9kZ+hQlXt1//0qj8uOH9j3t76PO0z4TSCY2meqJfua0GMCjjCCqNRbysw1ISoSjZqCgqo+s5vYCnhNdta2md3JTrmZooTesbcjUswk9qd3tFfbhyqa9DY+1ot2YsrTG0e8fjdShrc7NTU5ZiK+su4VhMHN3WltT8Pjtq644sJ+F9Ikpf7ZopX+SmaumYmUjTJrqAY2vLyfXLhcKn/rqqtU8vvSpdGFFhtCenr9FY4zV88M63ZuktqEyb0mW2JH/9b9cRmMItlydAsHCg/QqWmnE0+aaobowPIcHAu6U5vDnp21wQHX2GT+XiRUFhiv8diwKKH8iLEXN72jfWYiVqdJHxUiDIuEg/Oh6xW2ucEo9xp7Cd1uc2PZEs2M1TMoD/M/SHGmcNkpl1m6z3N7nhs2DxfUGLhlB5ZFlfObDCTBIXJy0LUrfPYZzJ0Lw4erbsSxHNkjBGRmQtu2cO+9sDtEBl1+WT7LDiwLu51yr7VuZ6NtOYSDeZvm1XrSxBslfVh+1xwXoQUxudu3xHZ7eSFMY0aMpLePvR2REqg0Hurs6YQtT+uprc0JqH3zwFcUe3tMYpQcD+qG2e5ia93hdeSUhG9vmeJMYXJva26cq2jtaU23Zt3CrinzlvH8aoNZcY0Amx8iJx/Tp8OKFaqa8K67VP+WJvV7YSMmI0MJuYsuUm0gDh1SrSFCtaN4Z8s7hp6m7i260yytmWX2XdD3grBtIMp8ZXVDiWYSmc1ULNoVO3oqkhkz76fbumPaMswUgqS2tmfVrRCQEf6iC8Dhz8Bhn1w5v984PuhwqD/PzsxaM4sKn3G6xZD2Qyzf90X9LsIZJoTsl37mbZpHpZmZn0mMFls2pWdPeOQRyMmBefPgmmtUMr3brTxSTZoYf8DT0lR40O2Gdu1UB/u//x0OHoR334UJE8Lfkc1cM5MSb/0xTQcOpvWxpnKlivE9xhsOSN2dv7vmrERTnq0kFltG3gyNeWQAU/3W7FiUIL0Y2i5smJtYReeLVaFKOHzFkL/Gun1G2TYlxWUsAHw+VRluV/wBP6+sfyVsN3eAcd3Hhc2ZbShT+0wlIyV8cYRA8OnOxt3nXOdsxYL27dWQQwtwAlOCDwAfTrZ5+7GWIaxgOJsZQDnplJFGACdpwZ/acZjR5d8zpHwtg1hP0yNFcAT4HFU7GoojR1RcEThacpQ1h8Kf9DJTMy3L16qiV4tepLvTKfOFT/Kdu3Euvzvrd+oHhxv1ToU5mVTkBj0DSdAMpzbe4+DOTLQVjYOAD3WPaXB1tKPYMlUI4sZ2/cGq6HwxbH8KfAbTHnY+D81OBbcFLv1LDxuvea3+9yvVbfye211sfbnnS/wGN2wZ7gwu6X9JTPZ/ZpczDb1qRZVFPLvyWS7sd2HYdcmMFluxwCKhFQoXfk5lM6eymWt5zdqNt2sX7P0Eb295G6fDGVa/lHnLGNN1jKUmCCGY0H0Cb20J0eIhSLmvnFlrZp0QW850VW0Y7oRSkkXS5hiVHQjm4miiRvqU58fIy2lH75CZjvZ29my1OsPcur1zYOg/YmuLSZRnK0C4IJDX+8Np05bMWDWDosrweXD+gJ9JPSfFZP+prlSGdxzOkqwlYdd9uedLCsoLaJ7WPCZ2JBottjQheX7182Gb0YFqWpeZYr3HZVrfaXy661OKK+uvgswqzGJn3k56t+ytRqs4UsNXapXut2cuixmKdkOrEYm2onFgNpQciySceFSvCrtW3QYrVzueD/vnhV8aqIRNf4NBD4IrsR5dt9OL0xHAH6hfbBUUKMGVZp9Usx8oqSzhox0fGa5r5WlFl2ZdDNc1lEtPuZRVh1ZR7qu/GtLlcPHW5re4edjNMbMjkdj0FkiTSA4VHWJTzqawa5zCadlYh9qM7z4en8FYEiklr20IevY8XYyvL2WH7Nns0QxF23SSvFU4XJjK2YpFfl88qlcDdq26DdrU9QpwmQgP7njOFjmWKa5KHCL8Zy87G/w2/Xi+u/VdwyIngWB63+kxtWNyr8lhk+RBje95esXTMbUjkWixpanDm5vfNEyUzEjJYFKv2LiduzXvRtPUehp/BanwV/Di2heDxnQN5mOFQfpV7lMyUrIPDHLYNCYRLnMX8WQVt9JnC5FSLx0mmZv44C+FLf+CMGPC4kG7ZkcM87aysuzb1PTZlc+GjRCA6pUYa7E1oM0A3E7jyMKW3C01i58aEVpsxYJ2STo4OGj3zNUzDRPUy33ljO48OmamTOwx0XBNTkkOW45uURVHZvJZyg5aYFksMPgYluwP9gnTRI1wYs6zlaRiK2DzBCJ3U2gxxNzarf9Ww7cTSOeW2ThFePF64IA9h1AfLj7MyoMrDdeVecsY131cTG0RQjCxp/E5HeDldS/H1JZEocVWLDh8WJ3wrHiYxYp9HT5M1vEsth8znmx9aptTSXfH7gwzre+0sGMeAHwBH69ueFXlqaS0NN5oiU3vmFwG4zFKs+xZHZeMCIGp054Z8W5H/GWErWqxA71uMZeL5SuC765NqODq0ioLXyB8GK68HMps6Hh+bcNrplo5DGgzICa5t7W5uN/Fhvsp95UzY/WMRjm+R4stTQ3mbpqLMEgOdjlcMcvXqmJ89/GGTe4q/ZXMXjtbfTDTOxpv9OgSc+Xz8cZISBXvSt7kfjtiRriWH429HbGg7KBxSD3R9LjOXG88UE1O985NmODq1OIAZZXGN5VHbXi4PLPyGcMIhcvhilnLh9pM7DnRVOPSnJIcVh9aHQeL4ouuRtTUYNbqWWErRgA8bk/M8rWq6NCkA609rTlQdCDsuvzyfNYfWc/gzB5QsC78RnMWq/lsZjrOxxOjEKH0Q/46aD0yPvY0dpxpxr2eyg5A077xscdKyg5i2z5bVThT4dQ/wfo/qdwsI1beBm3OVPMVDZK9rSYtpQJPagnF5eFzSLOyVCNquyClZMCq/TgMahHSfD6uuewByHsg5ja1A664BJYbdLFxyEpSJ06GzbkxtymeaLGl+YHd+bvZd9w41Fbhq2BEp9i3Ijiv13knkuDrwev3MmfDHAa37oe6yIRxP+etVBdau2E4oBc4+Am0GArOGA7MPFlIawPFBvP3ivdAu/HxscdKSg8kxzHS51bY8GdzawOVsOh8mLwcUlrE3cvbrmmOodjatAnGjrXPjEQhBO+/ZL9Q+Cvvml15LJZmJARLDg0hxBQhxDYhxE4hxO/DrLtMCCGFEMOt2K/GWl7f8LqpWPng9oNJicMJ/fze5xvmbXkDXl5Z9woys7uxkApUwvHN1hloFa7woywAyFkUzMfRRI2ZkHPx7mAbhSSjItd4JI4dcGXAKfeohsRmKNkHn56uPHdxTgXo0irLcM3XX0Nx+KI/zUlO1GJLCOEEngLOBwYAVwshBoRY1wT4JbAs2n1qYsMLa1+gwuBE5na4uajfRXGxZ1z3cYb2ABSUF7CjwmfujvfQJ/bLaUnvbLzm2DJw2bDkKRnJ6GG8puyAOY+j7ZDgLUi0EebodycRhTxLs+HT4UoIx/F/06vtLsM1S5aoGbQaTX1Y4dkaAeyUUu6WUlYCc4FQV+O/An8HkvEM1uiRUtJvbTa98gj76HvYy+UX/1FVdcX40SazLZeurTS0qUtOBZXX3WfO83NkkXXJtlEOuf2B9hMwvOj4y6HQuErUNFbZnow06YXh+112MHnbbZTnJNoCc6Q0hz63RdZsuCIXFoyEY8vBGx9XUp8OO3AZJNwcOAAlie1SobE5VuRsdQKq+1mzgRqZvEKIYUAXKeXHQoh7LNinxmKEEMx/0X4x/tffNruyANJ7QMme8Mtyl1qXtxXlkNsfaHcu7HkZvIXh1x2cD036WpOTY5XtyUh6JxW+CpecXXrAvjMGjSjNhmZ1ggv25NTfw67nzTU6rcJXBJ+fA50vgRHPqDYSZkLxtQl4TXm5O7fMJi3NOEz43XdwYeOdo6yJkpifTYQQDuBx4Dcm1t4qhFgphFh51I61tBp7034ihh4LXzEc/sJeXbbbnGUuLLJ7NkibhUCTEU8n45BzyV57FlOYoWBD+KHsdiK1FYx6AZwGveZCkf0uvNcNNj+qOs1XHjf3d3uL1Oft8Bfw8UDD5f07bjU1KnPBAu3d0tSPFWLrAFB9gmXn4HNVNAEGAouEEHuBUcAHoZLkpZQzpJTDpZTD27RpY4FpmpOK9hPNNUvc/qQSXXYhrTWktjVeV7hFVclpoiO9k3HDYF8JlNp14oABud/Z6/g2osulaoyP2d5b1QlUwMa/wdutYMkVsPNZKMlSYspbCJUFSoR5i9V7kv0hrLwTPuipKhyNPOHA4K7rqDTh9F+yxL4zEjWJx4ow4gqgjxCiB0pkXQVcU/WilPI40LrqZyHEIuBuKaXxHAGNJhLajjXX+fvw5/brEN5+ggolGrHtCRj2OLhNDPPVhCa9o7mwVe4SyOwec3MsJ3dZw4RLIhk5Cz7qr3KyGoK/HA4tVI+Vd0BaWzVVwpWhqhd9xVC6v0EebZfTz8CBsGpV+HUbNth7UpImsUTt2ZJS+oA7gAXAFmCelHKTEOJBIYSOYGviR3oH1YfHCBmAHTPsVW3W/jxzXrl9b6hhypqGk9rK3OzDI1/FLQk7Iow64JcdSL42Iamt4JwPzbeCMKI8Bwq3Qt4qOL5RhYWjSB2YPNl42HQgAK+8Al4d6deEwJKcLSnlfCllXyllLynlQ8Hn7pdSfhBi7Tjt1bIpyTpAG07Y3vYcc+t3PY+pgcTxou1Yc9VvviI48H7y5OTYESFUJZwRucsAG+X2VeEwkUuWl4Sn2NajYPDD4GxAsnuMOftsyDBh1owZmAo5xoXGcD5vRCRpuY0mJiTrAO3gEG0AOk4x5yEq2Qt5q+2TKJ/R1ZzdoEKJgSTzXNiNjG7Gawo32zMc5zAx2/Hw5/acA2pEv1+q2YkNSZiPIaNGmRs2vWEDHDoUe3tM0RjO540ILbY0jYs2Y82FiABW/8Ze4ZY2Y82ty/1eCUXt3Wo4bcdhePqTAWsnDljV28xguDCgmuBadWzHsyebEHDG09D/LlsJrmbNoJPBTL8qnnpKVyVq6qKTPzSNi8yeqrKv1HjGI8eWQc7XKl8qzgNuQ9L9GuWR8BnM7QNVUXXeUnDY54KUVLQZAzufB59Bb7PDn0Pz06yZx2dVbzNPJ+WZDUfeKuvyn+Ldk00IGPxXlX+5/j5zw6pjSVBsjh8PL4Yf1QrAq6/CI4/E2CZN0qE9W5rGhRDQ7xfm74pX/dI+lYmdIqgnKVgP2e/ZK8k/mWg10tz/ff88+4Xj2pxlvMZXojygycwpdykvl1WiMVKcHug49QexOXEiNDFRBHz0qJqVqNtAaKqjxZam8dHjJ+ZDiUU7Yevj1o3wiQZnirLdbLXhql/YRygmG56O4DIhyPNWmfM0xpN248wlke+aYTyVwO70/CmMmauEj1EVppU4PdDlMjj7RI3XmDHgMznB6Xe/g4oEavTSBDsDNXXRYsvumKnKaISVG1GR1hraTTC/fuPfoCJP1W4nmj4/Nx+yqjgGq36VXA0s7UTLM8yt2z3bXt6tVqMw1dI8+wN7JvhHSucLYdoGaHt27CsVnekqDWHMXDjz5RrFCN26Qfv25jazbh188UXi2kDY4VSmqYkWW3bn8GEOFR6k/3/74XzAgeMBgedv6czb+EajrtyImn6/AJfJxp+BClg0Bfw28G41PxUyephfv/tFyHrHHp65ZKP9uebEyJ5X7DWUuml/CJhpExIcTdUYyOwJ534JY15XYsjq0KJwqm32vRMu2gOdLwi57LbbIN3kru+6KzFiq7hY54zZES22bM6GIxsY9OwgduXvIkAAh3BQ5ivj+veu5+FvHkbqlsWhaR/h+I/jm2HxRWrGWqLp/6vIBut+f1Mw3GWjyspkoPUoc/MPC7dA+ZHY22MWh1OJcjPsmqnG1TQWOl+gxNDA+yCltfkbqvpwpqu+ZV2vgPPXwtC/hw0vX3edea/Rzp3w9NPxr0w8fhz++c/47lNjjBZbNmbBzgWMnjWa3NJcfLXuZMt8ZTz0zUNc/971dV7ToC5IvW+NTHAd+QpW/DzxXqJuV0bW1kH6YNE0KM2GgG5fbZqWp5sXqLtm2UvMtpuAqdP3wU+sqaS0Ey4PnHovXHoIRr+k3gtnGribmsh3FEqgOVKUh3DgfXBJNox5DZr2Ndx1u3aq55ZZ/vxnc/25rKKkBG680UaNVTU/oMWWTXlu1XNc8sYllHhPXPhdtdoTlHpLeWvLW0x4aQKFFUmeCBsLet8MIsJDfM/LsOXxxOZBuZtCpwuACMrpfcXwxXg1eNevz7SmcHkgo4u5tWbmVsaTNmPAbaIJbqBCVVQmupDiNlSemZUPpxu6XgoTv4Rry+H+QnjdB0uAPUA+cBwoAA4Ba4D3Jfy7CG6phAu2wsA/qBzPcPvJyanxp9xxh7mqRFCJ6hddFJ+E9ZISeOEFWLgw9vvSRI4WWzYjIAPcvfBu7lpwF2XV7qSdwkmXpl24+8y78bhPuLlLvaUsP7CcYc8NI+t4ViJMti+ZPaHFsMh/b8P9sOJ2NRcvUV7D/r+OPC+l7ADMH6T6h0U7089OCeGxxGwj2dJsyH4X/DbxHLYaaf5/tOHBxDfAjXUk0w9sBT4Engb+hJrYextwO3A38E9gHvA9EMnHo1YB0gUXRJaA/t13KtcrluHEsjJYtgx+9avY7UMTHVps2YhyXzkXz72YZ1Y+Q6m35q1Q09SmfH391zxy7iNc0v+SGoKrwl/B3oK9DHluCGsOrYm32fZm2L8blky752X4ZLDK12lIWNHvBW8ULQPajIZWZxCRdwug/DB8fg5seEDlnzVkHJG3WDV7PRno+iNwNTW3dsOf7ZMon95ejXgyQ8keOPihDjFbRGoqXHGF8WDq6rz0Ejz3nEpetxqfT40IuvhiXYVoZ7TYsgm5pbmMnjWaz3Z/VkdoZbgz+OInX9ClWReEELx40YsMaT+EVGfqD2v80k9eWR5nvXgW83fMj7f59qX1CDWapSE9eop3wyfDYOsTSnCZSTT2lahGo7lL4OMBke+zOqc/aS6Buw4Stv4LFp4JBRuV6DJqfhrwq55Mlfmw5h74anKDTI45pQeUl6nsMJTnRl/Q0P48lFvEBEU74cCH9gnT9rrF/PGx7k9abFnI//2f+arEKu6+W3m5rMzhCgSUgJswAYps1g5OUxNh12q24cOHy5Urk3ByfQPYcWwH42aP42jpUby1TojprnTevPxNpvWdVuP54+XHGTZjGPsK9uGv1cAz3ZXO3yf9nTtH3Blz2+vFTB8giGzIaUMp3AGfDIqu27ojBTpMhl43Q4fzgiGcarY7UqF4J+ycAfvfjKx67Zow78G3V6rWDtF4VDJ7QrdroNdNkNa2Zv6OcAAOyHpb9ZM6ujgyb1g4280S8EKHDpBzLPy6ZsBzVR5dqR4BnxLSKc0htQ2kdwBPV1Wt12IYtBgCKc3Cb/e7H8Pe16jx/6wPTxeYvtVcQ9SGYGbsTdV7XpIFH/ZReVlmGDEDul8HroYIeAOM7L7W+l3GlVrnKSmha1fIzo5sMx4PzJ4N558PmSbnztdHWRnk5sLUqbBxY93XLT212ul8bmOEEKuklMNDvqbFVmL5dv+3THt1GkWVRchaJ3uP28NjEx/j9hG3h/zdrONZDHluCHlleXVe87g93DDkBp48/0kckSaJW4HdPpzLblH9ksxemMLh9EDTfuAOVjV5jyuPS9nBhm0vnGApyYKP+ls3Hy6jhxJc7qZqULG3UIVKG+r1aIjYKs+Fgx+ruYO5y9ScP2caIK0rTHCkqm36StWMvRZDVef1TtOh+cCaaw9/CYsvNt8pfshj0Pf22AiuSMQWwPwhULDO3LZTW6u2Ca4or/KhOMnEFijRdMcdDcvFuv56+N//IC0tsnBkFcXF8MkncMMN9e9fi634E3OxJYSYAjwBOIGZUspHa71+F3Az4AOOAjdKKcNOCj4ZxNazK5/l15/+mvIQHpc0VxrXnnYtD45/MOw2NuZs5OK5F9dIpq++jVGdR/H+Ve/TNNVkXopV2O3DWZkP73e35/gSI8Gy4S+w+bHED+QNhVmxVbwbst6FPXOgcLMSqfGu+HSkgHDDlbX2G/DD262UaDaDKwOmbYL0zjU6jFtCpGJr+1Ow5rfmj41eN8HpT0TWx80MRnbfRuyT5GNFu3YhG0f7/dC9e+TerSpOOQU++ghat4amJk/PpaUqdHjnnUrshUOLrfgTU7ElhHAC24FJQDawArhaSrm52prxwDIpZakQ4ufAOCnlleG225jFViAQYMLLE/h6X/1JyA7hIM2ku98f8FMRpjIp1ZnKyltWMrDdwHrXWI4dP5x7XoXlt9pPtBgJFn8FfHSK8gCZCXXFk3C2+ysh6y3Y+JBK0gblTUs0oWxe/jPVANTsTM2m/WHKCuu9RJGKrfIceK9rZB7b0a9Al0ut9cxFanc02Ojc8tZbykvV0ErDlBSYPh1+9jMYO1Ylu9cOL1ZWqk70hw7Bs8/Ca6+p743QYiv+xFpsjQYekFJODv58L4CUMuTAACHEUOB/Usox4bbbWMVWaWUpA54ewL7jYR17liMQfHDVB0zvNz1OO7Tph3PxxXDwU2vCiVZh5iKUvx4WjrKHWKlOKNv9FUq4rP+zep8j8WA5UqH5aSrs17SvCnt6OkFaOyVshFMdW9Kv9lNxFMoOQcl+KN4F+Wshfw1U5EZm89Gl8NV5kdna5Ucwera1XqKGiJYFo+HY9+b34UhV3dKb9IZavfsazEkqtgIB5aHavj36bbVoAT/6EZx9NrRqBW435OXBjh1KYG3ebLyN6mixFX/CiS0rPmmdgOoNnrKBkWHW3wR8EuoFIcStwK0AXbuaLGtOIvYV7GPgMwMprox/w0yJ5IK5F/Cv8/7FXaPvivv+bcOo2SqpuMJGYssMLQbBoAdh/QP2mOFYH0eXwDc/UvlPZlpmCKfqddX1cug4VQkrX6nqBO42IWI8nVQSPCjx5S9TrT58Jarf2N5X4cDH4C0Iv52q0T2RiK2st6DdOdDzBuvDcpHQ5//g+EbztgcqYNH5MHUdOOKcXtDIcDhUS4dp06JvXJqfD88/rx6axkdcM6eFED8GhgP/CPW6lHKGlHK4lHJ4mzZt4mlazNlXsI9RM0cZCq0MdwZNUpo0+OE0aHHw289+y4trX7TyT0suUprD2HesH2QbD/rfBa2GK8+E3fBXwup74MtJqteXkdBqPhjOfBUuL4Cz31OjlTK7q9EyKc3MCa3aOFPV/9eZCqktoeP5cMbTcOlhmLxMzb+rb5yLENDjJybGvdRi1a+VJy2RI546Xxx5gUPJXvj28sSPpmoEjBunHu5GNhVJYy1WeLYOANVnXnQOPlcDIcRE4I/AOVLKJHMrRMfKgyuZ9MqksCN1PC4PC69byJiuYaOrhpT7yhk1cxSbj26u00YCVD+uO+bfwY5jO3howkMIs+7hWFGRp0JCwqEudMKpQhvCGfw5Bva1HQvDn4KVd9gvfyscwgHnfKB6f5XsNZ9fFGvKc+HzsSqUZxTmbDlcNZptOVSJRqvCWPXhDnpuWo2Akc/D8P/Wv7bXTbDjGfBH0GZD+uDz8Wq7Pa6LzsPV0J5hKc2h/UQ1B5EI2nYcWqga4I77RL1PThuK+CRh1izo10/lVmk0obDiTLcC6COE6IESWVcB11RfEMzTeg6YIqXMqbuJxsv7W9/nmneuqdOotDoet4dZF86KWmiBqkD8/CefM/iZwRwuOUwgRM+kUm8pTyx7gh15O5hzyRxSXVGcZCuPqzEx5Tkqf6b8qPq+ZTrkGVx4mwHvdkR1SQ/2TZKBml+d6epCkNJKtSxI7wiezqqDdmob9UhrAy0GR9a4tNcNqknltv8kVnCltTNeUx13U5i4COYPhsq6LT/iSlo7JRC+GA9Fu0CGudI43DDoIeh7GzjSVfwl3ribnhBfoWg2AFqPgSNfEFEhgvSpAea5S5UnrSGCy1sMhxZE/ntVDPorHPky8py+vFXw8akw7iNodmrktgd82jsGtG+vwn833hjbsTya5MWq1g9Tgf+gWj+8IKV8SAjxILBSSvmBEOJz4DTUOFCA/VLKC8NtszEkyD++9HH+9OWfQrZlqMLj9vCHsX/gj2P/aOm+d+bt5PQZp4f1pqW70jm17aks/PFCWqS3CL9BGYDCberknLdGJeQWblUjaZxpyusipbrg+ssJebFyNw0KpFbq4lvlyRJOtX3pVxcu6Vdl+OVHofJY6CabjpRgKb9T7evyBtSVSwnf36CakMZbcAk3pLeDixs4zzJ/PXx+drCVRZyTUp0eGHgfnHI3fDVF5WkFwjSMTW0FE79R42USmdtkhmMr1fva0EKEFsPgnPfA3Sy8sKvCW6K8t+vvg62Pm9tHfYnmn48PjllqwPEgXMrj2Psm9esugzB7wK/eo+Jd8PWFULrfeB+NMEG+NhdeCAsWqArCRKMT5OOPbmoaZ/wBP7fNv4056+cYerSuGHAFL1z0QkzCed9lfceklydRGiY8keJMoV1GOxZdv4ieLXrWfLHsCBycr7pr5y454TnylVDnhO5IgaanBCvJhqhKp7QOqpFiagtwNVGiKVBhMvwl1Dadqcp74i2AimPKa1ayHwrWqFE0BRuUh6ehJ3IpYc3dsOPZ+AkuRypk9oCJi5VXrqEUboPPxqq/P14hRWc6DPsP9LkVdjwHq+8K/7450+G871UT2GQJU302Fo5+G8UGhJo2MOB3KvHeX65EpsOtPgPeYnVz4i2Azf+APbMj6/9W37Ge+z18cW50x3Fae+jzc+h7RzDE6wB3sBfBDwUIaXBgPmx5TBUhRGt3pNj4wp+XB6edplozJPrSqsVW/NFiK46UVJZw8dyL+TbrW8p99d/tpzpTGdl5JF/85AtcDc1bkVJ5ggI+5VGSMpjv5A5+dTBv0zyuf+/6sN41h3CQ6c5kwXULGNVppLo7XvcH5cUK13yy7TjoconKF2nSG3zl6kPpzIh9mMhXpoSbM101LE1vH932Nv0dNv4l9q0VnB4lSCcsNOf5MKI0Gz47S40HimYckSFCvdejX1LDm6VUTWKNPBqjXoSuVxp7SuzE0e9Uor8V4jutLbQ8HZoPAndz5QEs2qG8w4VbaZAXKpxoWXimEl3RejuF60QLjozuSihWHFWtNfJWmW8AW52TQGwBbN0KZ5wRm6HTkaDFVvzRYiuOLMtexoSXJ4TMlapO/9b9WXz9YpqkNqn5QsCnxr6UZp94FO9WJ+jSLFXp5S0KhtYCgDjRewgRDMdVf83BP/Md3J/rQyJOrKv6+sP3cP2Ay3gmY686odbbXkCoROCB96sLidNjfRftRHH4c/j2CuW5C8QgDuBMh36/Ui0crEwM9xarZq3Z78fGO+dMV/lZ53ygLsAAh78IjrgJc0VJbaXCpMlY+fn5hGBILoKE83gRTrQcW6GS3u3Wjw1OGrEF8NlncNFF1g6djoT09OhbUdQgCd5zOxDrPluaaozsPJKSP5jIkJQSSvbBwQ/URf7Y91B2GHyF4Eg7EXIIVBhc+IPerZDHuATp5+7mfu5uHvwZAIfyNAiX2oe/TO3PswPy1ofPvznjWehxTWzmqyWa9hNh+nY1mPjot9b1s3JmqFDMWW9B27Os2WZ13Jkw5jWVe/b9TdXy5qLFoUJ/3a+F05+s6Z3KXWpcPdf5EpXbk4xafPiTsGCEPUVLOFqdAe3OhUOfRje8XBMVkybBjBlw663xF1weD0yYEN99aozRYiuelB9VJ8GstyFnsbogClfdAbj+UqiRgiNUbo+ni6rE83QGTzf1XFWiuMMNONSFNlCpHt5i1SKgNFt5xUqzldcsUFm3gsjvU3fFRuGHnj+1PvfmnfYqDBYVDrjGgryltNYw4VM48hWs/IV6/xo6w8+Zrv4vA/+sBhfHOmep6+XQfhJsegS2/1flcTXIQxcMGbYeqfKzWgyqu6QyH0OvT2qr5D1Wmg+EnjfC7heST3CNeAY+7B9ZCwuN5fz4x8rDdN118RNcHo9qsPraa/HZn8Y8WmzFGhlQnqutj8ORReriG+7indZWlZ+3m6D6QaV3hJQWJwQUBBPHgxWARvgrg16OgBJ2znTlsSk/opLLDy1QXpzjW6DjtGD5eBiPxaa/qcRfp8fc/s0Q9cUTLA/3tBsPU9er3kXb/6vElyOlrjCujSNNudwzukGvW6H3LScSjONBSnMY+nfVAHX7/2D3i0oYSZ+B8BLKWyn90Pki6PcLldxdH6mtglWkYURL2SF17Dks7PYYz2NlyKPBStUkE1uezjDkEVh3r27LkGAuu0wJoMsvV4IrEMOotMcDV1+tPGqJ6KyiCY8WW7HkwEew7BYlrqoEVqiZfC2GqaTjblcqceWvUBe+6rlQDhfQgMGxzhT1qI4j2G+oSR9oPxl18Qn2tFp6PWS/ozpSh7qQbvybKvUfeL9qFImIPvk5rZ1FF1Fr+TZrCbd+eDf7j+/nobP/xM87diUlfzUcWw6F25UolQElwjxdVKPOViOUaG3SK2F2F1YU8o+l/+OfS//JkPaDefHsf9G/ci/kLYf8dSrvL+BTYsndBJoOgNYjlMjvcJ45b1S7ieB4OHyoNesd1XcqWXFnqtmH3/wouZrfgvKk7npeVezabXj5Scb558Pq1crjdOCA9V4uh0N50J59VnnTNPZEJ8jHgsoCWPpT5dEKd5LO7KlyoNqMBhFCFCWKwh2w4QEluhwpKhwZyhvQpI8aidL+POUFcWUG879SwNUAYRiOOA66XXlwJb/69FesObyGUm8pDhyku9NJdaXytwl/4+ahN+N22m82R1Wz2oe/eRh/wP9DNWyaK40JPSbwz/P+Sf/W/a3b4UcDoHBL+DVD/wl9fmZdfy0zx4EZIjlWvr0KDnxgHw+XWduPb4ZPz7CPUDyJEuRDUV4Od94Jr76qRrNa4eXKyICuXeG996Bv3+i3Vy9J+p7HG12NGE+kVGXjR78JH7bpe6cKUzjc1oZZrMRfCUcXw/634eDHKoHf5VHeuVAJ2CktVa5Ls4GqXNzTBdI7qNBoakvV6FG4g969QLUPZtXXatWRVTlo3kIVBvuwt7G9UZ7MN+Zs5DcLfsM3+7+p0SrDIRw/VJdmuDPITMnksUmPce1p1+K0QSVmha+CZ1c9ywNfPUBloPKH3m4CgQy+t07hxO10c2G/C3n03Efp0aJH9Dve+zosvyV8qEo4YfxCaD3amvYPiRBbvlKYPwiK92CL6sRIbN8zB5b/nz0E10kutqpYvx7uuEN5u0pLG2ZmRoYKG/773yp0GPOwYZK/5/FCi614smcOLP9Z+PBKqxFw7lfWe39ijbdQ9Qcq2AB5K1S/naJdKh/ImXpiiK/0qVBoqGooR6rKQavqIO+o1UE+UNVBvtA4P6o6ae3UwOEGsDNvJ/d8dg8Ldi6gwl/xg7BKcaaQ6c6kd8vebDq6iRLvif9pZkomLdJa8Pjkx7n0lEtxWJW/FgG+gI/Za2fzhy/+QKm3tIZ9HreH4R2Hs+bQGsp8ZfgC6n/hEi5cThdXnXoVf5vwNzo17dRwA6SEpT9R4cKwjU09cM6H6riPNn/NkgR5Ir/wF+2ET4Y2vFjCKhpynC+7Bfa+mljPXBSfzzo0kgv/4sXw5JPw8cdqiHVxsUTK+v+2tDT1tV8/uP12lXhf9VzMaSTveazRYiuerPmd6qwcjlPuVnPirAwbxrGiz+eDgwchKwv275MU5pXgL83BV3KUQOlRHN6jpASOkuk6SKu0bFp6DtMy4yjN0/NIcVXiEAH1cAR++F4iCAQc+KWTgHQQCDgISCeFZU3JL21FXml7cks7U1DZiQra4neruYjuzDY4PG342S9aIRyReT32H9/PH774A29veRuv34u/Wo6ax+3hvF7n8cKFL9A8rTmz183mzvl3Uu4rr7EuMyWTDpkd+PfkfzO1z9S4DPYOyABzN87lns/u4Xj58Roiy+1w43F7ePnil7mw/4UcLDrIVW9dxepDq2usS3Gm4BRObhp6E/efcz9tMhrYyT7gg6/Og6NLw7cMAdWZfOg/gt7cGIbMYxVyPvCR6sOWCNHi9KhcwLHzIv9dfyUsHA3HN4XOGY01rgy4wkKRatcLf+Vx1eS3JEtVfXuPq0fFMfWozD/xnK9YeYR9ZZSUuvhozTS+2z6aZbtGsu1QP8oq0wlIB26Xly4tsxjWfRWj+3zPBcM+pnvbbJVf6/Ko1A13ExU1SGmhoguprVWhzIDfmX+vjLDre24ztNiKJ/vfhKU3hPdstRgCk5ZY69mKQXilqAiWLFEdkdeuhQ0bYO9eKChQd1Rut8o78HrVo21blT/QpQu0agVNm0Lz5ur71q3V982aQZMm4HQq17cQ6quUaltSgt+vkkgLC9W+jh1Tj7w89dzx45CdDfv3q4TTQEDlQJjlcPFh/vzVn3l5/cv4Ar4fvD6gPD/p7nSev+B5rhx4ZY3f21ewj8vfvJzNRzfXEC6gwos9W/TkP1P+w4QesWlyI6Xkva3vcdfCuzhacrSODR63h7O6nsWcS+bUEE9SSp5Z+Qz3fHYP5b7yGg13U52pOIWTO0bcwb1j76V5WvPIDfOVwNcXQO5y495kGd3U0OSuPzI3g68hxDK/b88c1UA2noLLma6mNZzzfsNTDiqPq+7yxTtj07C3PpwZMP5Ta/vLJfLCL6XKhctfrSq484OTAMoOK498VZV4wKseDreaCerpEqwsD87MTGmlKnpTWiph5G6qfhdHsMo7mE4hq9Itgl5/X5H6X1bmQUUeVOQGBVyh6vBfsl8Jvsp8uNrC1h9abJlCi614IiUsvkS1VAh3p9/jOjjjmWA7Bgt6EVkktvaeKXnnHZg7F9atU6KqoqKumDntNFVdM3489O8PHToowVVZqT6XLhekpChBZhV+v9q+16sEltOpqnAKCpSYM2LnsZ387OOfsXjfYgIyUMNDBZDiSKFLsy7cOPRGmqU2C7kNieSLPV/w8faP8Qa8dV5PcaTQoUkH7jv7Pm4adlND/sw6BAIB/rn0nzy+9HHyy/KprHWxFAjcDjeXn3o5ozqPQhD6WMgpyWHm6pnklubW2YbL4cKBg/P7nM+z05+lfWaE44+kVPMl19x9otVIONLaQ5/b1OcgrS2WVLVWEetiip2zYNWd8RFcznRofSaMmx+9J7wyHxaMUrlnsu6xazlV4eP2Ft98xPvCH/Cp83nW25D9QbA/oqg7I9bVRFXzdpoGzYeo+aeuDHWcyEBw0odb/R+tSjuQgWDqRaX6igheT4S1Pe602DKFFlvxxlcCK+6A/fPC57KktYfTn4BO04n6YmNBGPGR93/HXz98FKi/PPmSS1SJscejBFXccgaiILswm6veuoolWUvCrnM73KYT3qWUVPorf0hAD0Wz1GY8N/25Oh6ySHhq+VPc89k9YWdbCgQpzhTTIcza3rxQTOszjZcvfpmWnpYR2Uvhdvj2cjViymx+U2ZP6HyxejQ/Td3h+8tVfp8z1Xw/uUA5IOBNEzMno03W3v0SrPh5bAWXM0NNNThrbtDrYQHlubBoivLKxCppXjjAmQnnvKf61Vm+/The+It3w1dTVVjQV0zINhopLdQM0A5T1HHrbmKdmLILJt/zgF8SCJyIWMQhq8JWaLGVKHK+gWU3QelBCATvbkKR2Us1kux+rarm85Up4WV1XkuYO36f34n7J8Zu59JS5U1KBkq9pdz7+b08ufzJsOsEgjRXWoPyrbx+b0gPV3V6t+jNB1d/wCltTjG93RUHVjD9tenklOaEXed2uBvUhiIgA1T4KsKKRYHg4XMf5q7Rd5ESiVdFSlW9uua3KqThKyWiXk9p7dQA5MyeKvSS2V2FYVKaK+8AjmARRhmUHgiGTrKgLFv1ESvcarwPKyrjjq2ARedDZaH1niJnupo8MOC31l+xAl5YeYcKiVotuBxpqqnqhIXKs2MhARlg//H9tO8zlLTcgrBrvW1a4TuQRbo7ypPV+vth40OE9dT2vROGPW7tvNMY5OBWVqo826NHVVpGbi7k56vUjPx8laZRUKAeRUUqkiCEiiAs3NCeVt7w9hxPb8dvrzuM06kiD36/OhVUpYhUpYlIqWwpKTnx8HpP7C8zEz78EFpGeJ9nB2IutoQQU4AnUFPQZkopH631eirwMnA6cAy4Ukq5N9w2G4XYAnVk5a2EbU+oqi2HWw2Sru/i48pQVVttxkK7ceDpGhz4nB50R/uDd47pxvkbVXMPA15AwFvNwy5/fP6v+NM7/0aI+oeYTpsGzz2n8q+EUB4uu1Hhq+CZlc/wwKIHqPRVUlaP98HtcDOl9xSm952OUzS8hcOu/F08veJpSrwlIT1GDhykulKZ3nc6j058lJ4teta7rY05G7lrwV18u//ber1ZKc4Umqc15/YzbqdTk4ZXE3oDXuZtmseSrCVU+kPn8XhcHjJTM3ls4mP8eNCPI2t1IaVqgLvpYTWZwEwH/nhhVRuC8hz49kolvKyYpen0qHPAmNeUVyuW7H5Jia6A15rEeWc6dL4URs6IKh9VSklOSQ4bczayIWcDyw8sZ83hNezJ34NE4vV7a9wkeFweSmvN6XQFhU8bTxtObXMqozqPYlC7QZzW7jR6t+z9w+uGlOyHRdOhZM+JJsa1cTeFETPUDbO/QoUTo+3FYEFayJq9Q3gldw3ffw+7dimBlZ6uxBOoQqcePeCss2DQIBg2DDp1Unm1Hs+JfFy/Xz2qEOKE/q/9fdXXKllR39fqPcYcDmWTy6XSTioqkueGvjYxFVtCCCewHZgEZAMrgKullJurrbkNGCSl/JkQ4irgEill2NhKoxFb1fGVwOEvIfs91fC0/BA40oPuaYMcF0cqeDpVm43YRSVXOlKD43tS1F2/Pzi4OlCh9leafeJxfKOhiQfOkXzwgcrZWrYMUlPVB652WLFPHyW8xo2DU05RifFCqMZ9UqoPj9utHs6G65gfqJ6IX3XH5HYr+3JyoGNHFR57ce2L/OGLP1DmLauTQF5FuiudthlteefKdxjWYVj0xgEllSXcMf8O5m2e90Ofq9pUtV248tQr+duEv9G5aecfXttxbAf3fHYPC3ctpNxXXq/HKd2Vzu0jbuehCQ9F5m0Kw6K9i7jizSsorCikwh/6opvpzqRFegv+dd6/uGzAZZG3uvAWqbyXva/CoYXBY7UsMcOSrWxDUMWBj1XPMW9hw0bkOIKf3/6/gVP/EJvCgVBUHFMeyH2vq/NGuPFL9eHKVIUPI19QkwgioLCikJmrZ/Lu1nfZlruNY6XHCMSpl5nH7aFn854Mbj+YB8c9SI8WPUJ7twN+yFkEWe9C9rsq983hVv/n6u+XM12NWet4PrQcpiIWKa2qHecO9T92mqjGtcCzJa41vrYfOaIKm6Q8+UJ+sSDWYms08ICUcnLw53sBpJSPVFuzILhmqRDCBRwG2sgwO2+UYqs2lfmqZD5nkRpMXXZA5VRIn/rgCodKevRHGIZpKLUuQmVlSnBt3aoqEdeuPXGHVJUAD+oOyedTjfa6dFHCq6oasWlT5Q5u3Vp9bd5cVSO6XCfi+lVu5+qP0lJVdZifr9zdx46pn6uqEbOyTrjEHc4AM79/nXs+u4fCisJ6RRYosXLDkBv41+R/keayPuHskx2fcO0711LiLanXW1SVG3bjkBu5ceiNPL70cd7Z+k6d9hPVSXOl0SKtBW9f8Taju4y23O7j5ce59aNb+Wj7R/WKRVCtLtpntuffk//NtD7TGtbqIuBVIfacb1Tz34J1SqQ404M3CwYtJIyomgEq/YCEpv1VknmbM6HDZFUFFgv8FbD3Ndj2HyjaYWIQuEN5sRxu6HWLGrGT0SU2thlRsAm2P6nEMI7685OqcKYrL0+rEdD/18qrE0aAl/vK2Zq7lY05G1lzeA3LspexNXcrxyuO4w/4w4azq6iv8CMcZrYL6rwgkfRs0ZOh7YcyotMITmt7GgPbDqzbFqVoZ7AKcRvkrVbViWUH1XnamX5iZmiVlzu9nYpQeDopL5i7qWrVkNo6WJHYAtzNg5WMgh8qEoVDbUcGgg/fiSbPFXmw7Mawf9P6/afxyrH1LFsGO3eqc2VKyolzr9+vztVjxsDAgcqz1bmzOkdnZqo1lZUnPFtVV+tQnqxQX6tChlXf//A/kTV/djiUTVWP0lK1/2Qk1mLrR8AUKeXNwZ+vA0ZKKe+otmZjcE128OddwTW59W33pBBb9eEtVLkopVlBr1SWOnkX71E/ewuCH0J/zQ8jkh/unkTVV6c6AaR3UHefTfoGc2CCHrL0zpDWJqKETinVBzcrS7VfqN6SoapNQ37+iXyA4mIVly8trRnHr/6AEwmVVQ+HQ3muMjLUh69pU+XibtHihIBr3hwOeDfwpG+Qod1tPG1440dvML5HDJJ2q3Gs9Bg3vH8DH27/0HjxtunQ7yODRYKfDL6Op6Y+RWZKbM9C7255l+vfv57CisLwCws7wOMHDbdn+vRSkQf5a1UvqNJsKNmnvpYfVt4XXwlq6kCVGA1ejBwpajpBaluV35XRTZXaZ/ZSsyozetS5ZTejD8vKoiz+KNwGBz5UgjJ/dbA1QHAepStTTVloExw4335izTmo9XDwoArzGBHVKd1frrx0OYshd4n6O6py7hzu4AzQM1Qrh84XKwFhAnHhzXD6LMN1nZp0YmzXsUzqNYkxXcbQt1Vfy3rX5ZflszR7KV/v/ZqFuxeyKWeTYb6lQzjw3uet48kNZVKau4zOLbPp0iqLji0OMueFwmriKFe1a6gsUA9fUbDXVqmKQkiJOn/LaudyqDFVA3HivO70qO1G4AX0+Z1k53Umt6g1x4pbkVvUmry+sykqcVNQoM7j+fknbmrLy0/kbKWlqfNw1aP2ebr216owYajzfVWrnqprQkmJulmviog0aQJff236z7IVSSO2hBC3ArcCdO3a9fR9+/ZFZdtJR5L6gqM1OxCQ8JcHEA8+aLg2nu+OmU9W4OyzcSxebLjObnZLwHm/sVWxafIqieYduf+rAA8Yv+W2QwJ/OZuktL3sWC4pzYwznuPRFLgKs9e+v57r4IEkvPgX5x4gs2WELVwsQK7/C2KT8bk4LJflxs4DHUN0GFGj0Wg0Go0mhoQTW1Y0A1kB9BFC9BBCpABXAR/UWvMB8NPg9z8CvgwntDQajUaj0WgaC1E3BpFS+oQQdwALUK0fXpBSbhJCPAislFJ+AMwCXhFC7ATyUIJMo9FoNBqNptFjSRc2KeV8YH6t5+6v9n05cLkV+9JoNBqNRqNJJhrZTAGNRqPRaDQae6HFlkaj0Wg0Gk0M0WJLo9FoNBqNJoZosaXRaDQajUYTQ7TY0mg0Go1Go4khWmxpNBqNRqPRxBAttjQajUaj0WhiiBZbGo1Go9FoNDFEiy2NRqPRaDSaGKLFlkaj0Wg0Gk0M0WJLo9FoNBqNJoZosaXRaDQajUYTQ7TY0mg0Go1Go4khWmxpNBqNRqPRxJCoxJYQoqUQ4jMhxI7g1xYh1gwRQiwVQmwSQqwXQlwZzT41Go1Go9FokoloPVu/B76QUvYBvgj+XJtS4CdSylOBKcB/hBDNo9yvRqPRaDQaTVIQrdi6CHgp+P1LwMW1F0gpt0spdwS/PwjkAG2i3K9Go9FoNBpNUhCt2GonpTwU/P4w0C7cYiHECCAF2BXlfjUajUaj0WiSApfRAiHE50D7EC/9sfoPUkophJBhttMBeAX4qZQyUM+aW4Fbgz8WCyG2GdlnU1oDuYk2ogEkq92QvLYnq92QvLYnq92QvLYnq92QvLYnq92QvLZ3q+8FIWW9+siQoBgaJ6U8FBRTi6SU/UKsawosAh6WUr7V4B0mCUKIlVLK4Ym2I1KS1W5IXtuT1W5IXtuT1W5IXtuT1W5IXtuT1W5IbtvrI9ow4gfAT4Pf/xR4v/YCIUQK8C7w8skgtDQajUaj0WiqE63YehSYJITYAUwM/owQYrgQYmZwzRXA2cD1Qoi1wceQKPer0Wg0Go1GkxQY5myFQ0p5DDg3xPMrgZuD388B5kSznyRkRqINaCDJajckr+3Jajckr+3Jajckr+3Jajckr+3Jajckt+0hiSpnS6PRaDQajUYTHj2uR6PRaDQajSaGaLFlIUKIKUKIbUKInUKIUN30bYkQ4gUhRI4QYmOibYkUIUQXIcRXQojNwZFQv0y0TWYQQqQJIZYLIdYF7f5Lom2KBCGEUwixRgjxUaJtiQQhxF4hxIZg7ujKRNsTCUKI5kKIt4QQW4UQW4QQoxNtkxFCiH7VcnXXCiEKhRC/SrRdZhBC/Dr42dwohHhdCJGWaJvMIoT4ZdDuTXZ/v0Ndf8yMAkw2tNiyCCGEE3gKOB8YAFwthBiQWKtMMxs1SikZ8QG/kVIOAEYBtyfJ+14BTJBSDgaGAFOEEKMSa1JE/BLYkmgjGsh4KeWQJCwtfwL4VErZHxhMErz/Usptwfd6CHA6anzbu4m1yhghRCfgF8BwKeVAwAlclVirzCGEGAjcAoxAHSfThRC9E2tVWGZT9/pjZhRgUqHFlnWMAHZKKXdLKSuBuahxRrZHSrkYyEu0HQ1BSnlISrk6+H0R6gLUKbFWGSMVxcEf3cFHUiRQCiE6A9OAmUZrNdYghGiGquqeBSClrJRSFiTUqMg5F9glpdyXaENM4gLShRAuwAMcTLA9ZjkFWCalLJVS+oCvgUsTbFO91HP9MRwFmGxosWUdnYCsaj9nkwQX/caEEKI7MBRYlmBTTBEMxa1FzQv9TEqZFHYD/wF+C4ScBGFzJLBQCLEqOLEiWegBHAVeDIZvZwohMhJtVIRcBbyeaCPMIKU8APwT2A8cAo5LKRcm1irTbATGCiFaCSE8wFSgS4JtipSIRgEmA1psaRoFQohM4G3gV1LKwkTbYwYppT8YXukMjAi6/22NEGI6kCOlXJVoWxrIWVLKYahw/+1CiLMTbZBJXMAw4Bkp5VCghCQKrQSbW18IvJloW8wQzBG6CCVyOwIZQogfJ9Yqc0gptwB/BxYCnwJrAX8ibYoGqVomJIXXPxxabFnHAWrePXQOPqeJMUIIN0povSqlfCfR9kRKMBz0FcmRNzcGuFAIsRcVKp8ghEiaPnpBjwVSyhxU7tCIxFpkmmwgu5r38y2U+EoWzgdWSymPJNoQk0wE9kgpj0opvcA7wJkJtsk0UspZUsrTpZRnA/nA9kTbFCFHgiMAq+Yq5yTYnqjRYss6VgB9hBA9gndxV6HGGWliiBBCoPJYtkgpH0+0PWYRQrQRQjQPfp8OTAK2JtQoE0gp75VSdpZSdkcd419KKZPijl8IkSGEaFL1PXAeKuRie6SUh4EsIUTV7Nlzgc0JNClSriZJQohB9gOjhBCe4DnmXJKgIKEKIUTb4NeuqHyt1xJrUcQYjgJMNqLqIK85gZTSJ4S4A1iAqlx5QUq5KcFmmUII8TowDmgthMgG/iylnJVYq0wzBrgO2BDMfwL4g5RyfuJMMkUH4KVgFasDmCelTKo2CklIO+Bdde3EBbwmpfw0sSZFxJ3Aq8Gbud3ADQm2xxRBYTsJ+L9E22IWKeUyIcRbwGpUxfMakqur+dtCiFaAF7jdzsUUoa4/qNF/84QQNwH7UGP/khrdQV6j0Wg0Go0mhugwokaj0Wg0Gk0M0WJLo9FoNBqNJoZosaXRaDQajUYTQ7TY0mg0Go1Go4khWmxpNBqNRqPRxBAttjQajUaj0WhiiBZbGo1Go9FoNDFEiy2NRqPRaDSaGPL/GhbJIDpjWZcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1_18\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for key in ffms:\n",
+    "    print(key)\n",
+    "    _plot_weights(ffms[key].transpose())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/african_caqtls_asb.ipynb b/src/evaluation/figure_notebooks/figure_6/african_caqtls_asb.ipynb
new file mode 100644
index 00000000..8da008f6
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/african_caqtls_asb.ipynb
@@ -0,0 +1,1303 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "asb_sig = pd.read_csv(\"/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/tables/sig.asb.scores.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atac_model=pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds_asb/atac/ATAC/summary.mean.variant_scores_new_2.tsv\", sep='\\t', header=0)\n",
+    "atac_model_50m=pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds_asb/atac_50M/ATAC_50M/summary.mean.variant_scores_new_2.tsv\", sep='\\t', header=0)\n",
+    "dnase_model=pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds_asb/dnase_predss/DNASE/summary.mean.variant_scores_new_2.tsv\", sep='\\t', header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atac_model[\"jsd_sign.mean\"] = atac_model[\"jsd.mean\"]*np.sign(atac_model[\"logfc.mean\"])\n",
+    "atac_model_50m[\"jsd_sign.mean\"] = atac_model_50m[\"jsd.mean\"]*np.sign(atac_model_50m[\"logfc.mean\"])\n",
+    "dnase_model[\"jsd_sign.mean\"] = dnase_model[\"jsd.mean\"]*np.sign(dnase_model[\"logfc.mean\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7559, 25)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atac_model.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7559, 72)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>Esan.mean.logfc</th>\n",
+       "      <th>Esan.mean.abs_logfc</th>\n",
+       "      <th>Esan.mean.jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>...</th>\n",
+       "      <th>afr.mean.jsd</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.abs_logfc</th>\n",
+       "      <th>eur.mean.jsd</th>\n",
+       "      <th>afr.mean.abs_logfc.pval</th>\n",
+       "      <th>afr.mean.jsd.pval</th>\n",
+       "      <th>eur.mean.abs_logfc.pval</th>\n",
+       "      <th>eur.mean.jsd.pval</th>\n",
+       "      <th>variantID</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>0.148617</td>\n",
+       "      <td>0.186169</td>\n",
+       "      <td>0.029973</td>\n",
+       "      <td>0.005416</td>\n",
+       "      <td>0.003405</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.036430</td>\n",
+       "      <td>-0.027498</td>\n",
+       "      <td>0.218047</td>\n",
+       "      <td>0.052142</td>\n",
+       "      <td>0.048619</td>\n",
+       "      <td>0.019607</td>\n",
+       "      <td>0.048772</td>\n",
+       "      <td>0.015193</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1000079</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>0.012582</td>\n",
+       "      <td>0.017991</td>\n",
+       "      <td>0.000220</td>\n",
+       "      <td>0.000198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.021034</td>\n",
+       "      <td>0.004492</td>\n",
+       "      <td>0.028228</td>\n",
+       "      <td>0.019831</td>\n",
+       "      <td>0.457881</td>\n",
+       "      <td>0.074831</td>\n",
+       "      <td>0.783580</td>\n",
+       "      <td>0.160148</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>-2.087074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>0.013834</td>\n",
+       "      <td>0.078046</td>\n",
+       "      <td>0.014067</td>\n",
+       "      <td>0.001314</td>\n",
+       "      <td>0.000522</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.014347</td>\n",
+       "      <td>-0.002473</td>\n",
+       "      <td>0.040055</td>\n",
+       "      <td>0.015031</td>\n",
+       "      <td>0.230560</td>\n",
+       "      <td>0.212956</td>\n",
+       "      <td>0.603360</td>\n",
+       "      <td>0.309351</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>3.172063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>0.139453</td>\n",
+       "      <td>0.139453</td>\n",
+       "      <td>0.021787</td>\n",
+       "      <td>0.003160</td>\n",
+       "      <td>0.001386</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.020315</td>\n",
+       "      <td>-0.208648</td>\n",
+       "      <td>0.208648</td>\n",
+       "      <td>0.021750</td>\n",
+       "      <td>0.071184</td>\n",
+       "      <td>0.081884</td>\n",
+       "      <td>0.053131</td>\n",
+       "      <td>0.128314</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>-0.354500</td>\n",
+       "      <td>0.354500</td>\n",
+       "      <td>0.029879</td>\n",
+       "      <td>0.010773</td>\n",
+       "      <td>0.006039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.032463</td>\n",
+       "      <td>0.552679</td>\n",
+       "      <td>0.552679</td>\n",
+       "      <td>0.034941</td>\n",
+       "      <td>0.006371</td>\n",
+       "      <td>0.025877</td>\n",
+       "      <td>0.006414</td>\n",
+       "      <td>0.041549</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr      pos allele1 allele2           rsid  Esan.mean.logfc  \\\n",
+       "0  chr1   976215       A       G   1_976215_A_G         0.148617   \n",
+       "1  chr1  1000079       A       G  1_1000079_A_G         0.001207   \n",
+       "2  chr1  1038800       T       G  1_1038800_G_T         0.013834   \n",
+       "3  chr1  1038819       T       C  1_1038819_C_T         0.139453   \n",
+       "4  chr1  1038845       G       A  1_1038845_A_G        -0.354500   \n",
+       "\n",
+       "   Esan.mean.abs_logfc  Esan.mean.jsd  Esan.mean.abs_logfc_x_jsd  \\\n",
+       "0             0.186169       0.029973                   0.005416   \n",
+       "1             0.012582       0.017991                   0.000220   \n",
+       "2             0.078046       0.014067                   0.001314   \n",
+       "3             0.139453       0.021787                   0.003160   \n",
+       "4             0.354500       0.029879                   0.010773   \n",
+       "\n",
+       "   Esan.mean.abs_logfc_x_jsd_x_max_percentile  ...  afr.mean.jsd  \\\n",
+       "0                                    0.003405  ...      0.036430   \n",
+       "1                                    0.000198  ...      0.021034   \n",
+       "2                                    0.000522  ...      0.014347   \n",
+       "3                                    0.001386  ...      0.020315   \n",
+       "4                                    0.006039  ...      0.032463   \n",
+       "\n",
+       "   eur.mean.logfc  eur.mean.abs_logfc  eur.mean.jsd  afr.mean.abs_logfc.pval  \\\n",
+       "0       -0.027498            0.218047      0.052142                 0.048619   \n",
+       "1        0.004492            0.028228      0.019831                 0.457881   \n",
+       "2       -0.002473            0.040055      0.015031                 0.230560   \n",
+       "3       -0.208648            0.208648      0.021750                 0.071184   \n",
+       "4        0.552679            0.552679      0.034941                 0.006371   \n",
+       "\n",
+       "   afr.mean.jsd.pval  eur.mean.abs_logfc.pval  eur.mean.jsd.pval  \\\n",
+       "0           0.019607                 0.048772           0.015193   \n",
+       "1           0.074831                 0.783580           0.160148   \n",
+       "2           0.212956                 0.603360           0.309351   \n",
+       "3           0.081884                 0.053131           0.128314   \n",
+       "4           0.025877                 0.006414           0.041549   \n",
+       "\n",
+       "       variantID  meanLog2FC  \n",
+       "0   1_976215_A_G   -1.852605  \n",
+       "1  1_1000079_A_G   -2.087074  \n",
+       "2  1_1038800_G_T    3.172063  \n",
+       "3  1_1038819_C_T    2.390945  \n",
+       "4  1_1038845_A_G    2.163432  \n",
+       "\n",
+       "[5 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7559, 72)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'abs_logfc.mean', 'abs_logfc.mean.pval', 'jsd.mean', 'jsd.mean.pval',\n",
+       "       'logfc_x_jsd.mean', 'log_prob_log2s.mean', 'abs_logfc_x_jsd.mean',\n",
+       "       'abs_logfc_x_jsd.mean.pval', 'max_percentile.mean',\n",
+       "       'max_percentile.mean.pval', 'logfc_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_max_percentile.mean.pval', 'jsd_x_max_percentile.mean',\n",
+       "       'jsd_x_max_percentile.mean.pval',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'logfc_x_jsd_x_max_percentile.mean', 'jsd_sign.mean'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atac_model.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.38643911409950277, 9.28356026162717e-268)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "asb_sig[\"sign_jsd\"] = asb_sig[\"GM12878.ENCSR637XSC.mean.jsd\"] * np.sign( asb_sig[\"GM12878.ENCSR637XSC.mean.logfc\"])\n",
+    "\n",
+    "atac_model.loc[asb_sig[\"allele1\"] != asb_sig[\"rsid\"].str.split('_').str[2], \"jsd_sign.mean\"] = -1* atac_model[asb_sig[\"allele1\"] != asb_sig[\"rsid\"].str.split('_').str[2]][\"jsd_sign.mean\"]\n",
+    "\n",
+    "scipy.stats.pearsonr(asb_sig[\"meanLog2FC\"], asb_sig[\"sign_jsd\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dnase_model.loc[asb_sig[\"allele1\"] != asb_sig[\"rsid\"].str.split('_').str[2], \"jsd_sign.mean\"] = -1* dnase_model[asb_sig[\"allele1\"] != asb_sig[\"rsid\"].str.split('_').str[2]][\"jsd_sign.mean\"]\n",
+    "atac_model_50m.loc[asb_sig[\"allele1\"] != asb_sig[\"rsid\"].str.split('_').str[2], \"jsd_sign.mean\"] = -1* atac_model_50m[asb_sig[\"allele1\"] != asb_sig[\"rsid\"].str.split('_').str[2]][\"jsd_sign.mean\"]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ATAC (572M) (0.6409080506614266, 0.0)\n",
+      "DNase (0.6443754935588125, 0.0)\n",
+      "ATAC (50M) (0.6325856097326924, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ATAC (572M)\", scipy.stats.pearsonr(asb_sig[\"meanLog2FC\"], atac_model[\"jsd_sign.mean\"]))\n",
+    "print(\"DNase\", scipy.stats.pearsonr(asb_sig[\"meanLog2FC\"], dnase_model[\"jsd_sign.mean\"]))\n",
+    "print(\"ATAC (50M)\", scipy.stats.pearsonr(asb_sig[\"meanLog2FC\"], atac_model_50m[\"jsd_sign.mean\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7fe27d8bb550>"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 576x360 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "data = pd.DataFrame()\n",
+    "data[\"sd_sign.mean\"] = atac_model[\"jsd_sign.mean\"]\n",
+    "data[\"Beta\"] = asb_sig[\"meanLog2FC\"]\n",
+    "data[\"max_percentile.mean\"] = abs(atac_model[\"max_percentile.mean\"])\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"sd_sign.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"sd_sign.mean\", hue=\"max_percentile.mean\", data=data, s=15, palette=\"PuRd\")\n",
+    "sns.regplot(x=\"Beta\", y=\"sd_sign.mean\", data=data, scatter=False, ci=None, color=\"red\")\n",
+    "#sns.jointplot(x=data[\"Beta\"], y=data[\"sd_sign.mean\"],s=0,ax=ax)\n",
+    "\n",
+    "norm = plt.Normalize(data['max_percentile.mean'].min(), data['max_percentile.mean'].max())\n",
+    "sm = plt.cm.ScalarMappable(cmap=\"PuRd\", norm=norm)\n",
+    "sm.set_array([])\n",
+    "\n",
+    "# Remove the legend and add a colorbar\n",
+    "ax.get_legend().remove()\n",
+    "ax.figure.colorbar(sm)\n",
+    "#plt.savefig(\"subfigs_new/microglia_caqtls_scatter_plot.pdf\", transparent=True, dpi=300)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allca = pd.read_csv(\"/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/tables/all_caqtls.scores.tsv\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1 = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1[\"rsid\"] = data1[\"chr\"] + \"_\" + data1[\"pos\"].astype(str) + \"_\" + data1[\"allele1\"] + \"_\" + data1[\"allele2\"]\n",
+    "allca[\"rsid1\"] = allca[\"chr\"] + \"_\" + allca[\"pos\"].astype(str) + \"_\" + allca[\"allele1\"] + \"_\" + allca[\"allele2\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35547, 6)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allca = allca[allca[\"rsid1\"].isin(data1[\"rsid\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "score_dir = '/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/score_outputs'\n",
+    "\n",
+    "ancestries = [i for i in os.listdir(score_dir)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>Esan.mean.logfc</th>\n",
+       "      <th>Esan.mean.abs_logfc</th>\n",
+       "      <th>Esan.mean.jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>...</th>\n",
+       "      <th>abs_beta</th>\n",
+       "      <th>rsid1</th>\n",
+       "      <th>GM12878.ENCSR637XSC.mean.sign.jsd</th>\n",
+       "      <th>Maasai.mean.sign.jsd</th>\n",
+       "      <th>Yoruba.mean.sign.jsd</th>\n",
+       "      <th>Luhya.mean.sign.jsd</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "      <th>Esan.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>Mende.mean.sign.jsd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>-0.009280</td>\n",
+       "      <td>0.022847</td>\n",
+       "      <td>0.008537</td>\n",
+       "      <td>0.000175</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>chr1_866300_A_C</td>\n",
+       "      <td>0.012423</td>\n",
+       "      <td>0.007669</td>\n",
+       "      <td>-0.008802</td>\n",
+       "      <td>-0.006376</td>\n",
+       "      <td>0.009833</td>\n",
+       "      <td>-0.008537</td>\n",
+       "      <td>-0.008332</td>\n",
+       "      <td>0.007980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>959339</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_959339_C_T</td>\n",
+       "      <td>-0.001519</td>\n",
+       "      <td>0.042955</td>\n",
+       "      <td>0.016538</td>\n",
+       "      <td>0.000735</td>\n",
+       "      <td>0.000690</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.071807</td>\n",
+       "      <td>chr1_959339_C_T</td>\n",
+       "      <td>-0.021913</td>\n",
+       "      <td>-0.015979</td>\n",
+       "      <td>0.018475</td>\n",
+       "      <td>0.018084</td>\n",
+       "      <td>-0.019140</td>\n",
+       "      <td>-0.016538</td>\n",
+       "      <td>0.021064</td>\n",
+       "      <td>-0.018353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>960509</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_960509_A_T</td>\n",
+       "      <td>0.019035</td>\n",
+       "      <td>0.043295</td>\n",
+       "      <td>0.017585</td>\n",
+       "      <td>0.000681</td>\n",
+       "      <td>0.000560</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.024701</td>\n",
+       "      <td>chr1_960509_A_T</td>\n",
+       "      <td>0.021601</td>\n",
+       "      <td>0.016399</td>\n",
+       "      <td>0.018414</td>\n",
+       "      <td>0.015839</td>\n",
+       "      <td>0.014366</td>\n",
+       "      <td>0.017585</td>\n",
+       "      <td>0.018586</td>\n",
+       "      <td>0.013021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976038</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_976038_A_C</td>\n",
+       "      <td>-0.002968</td>\n",
+       "      <td>0.031418</td>\n",
+       "      <td>0.012498</td>\n",
+       "      <td>0.000420</td>\n",
+       "      <td>0.000173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.112788</td>\n",
+       "      <td>chr1_976038_A_C</td>\n",
+       "      <td>0.011073</td>\n",
+       "      <td>0.011493</td>\n",
+       "      <td>0.012195</td>\n",
+       "      <td>0.011414</td>\n",
+       "      <td>0.011089</td>\n",
+       "      <td>-0.012498</td>\n",
+       "      <td>0.014719</td>\n",
+       "      <td>0.012412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976536</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_976536_C_T</td>\n",
+       "      <td>0.036712</td>\n",
+       "      <td>0.041889</td>\n",
+       "      <td>0.012956</td>\n",
+       "      <td>0.000660</td>\n",
+       "      <td>0.000418</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.124874</td>\n",
+       "      <td>chr1_976536_C_T</td>\n",
+       "      <td>0.012069</td>\n",
+       "      <td>0.012636</td>\n",
+       "      <td>0.010555</td>\n",
+       "      <td>0.011849</td>\n",
+       "      <td>0.008071</td>\n",
+       "      <td>0.012956</td>\n",
+       "      <td>0.013633</td>\n",
+       "      <td>0.011389</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 86 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     chr     pos allele1 allele2          rsid  Esan.mean.logfc  \\\n",
+       "1   chr1  866300       A       C  1_866300_A_C        -0.009280   \n",
+       "14  chr1  959339       C       T  1_959339_C_T        -0.001519   \n",
+       "16  chr1  960509       A       T  1_960509_A_T         0.019035   \n",
+       "25  chr1  976038       A       C  1_976038_A_C        -0.002968   \n",
+       "27  chr1  976536       C       T  1_976536_C_T         0.036712   \n",
+       "\n",
+       "    Esan.mean.abs_logfc  Esan.mean.jsd  Esan.mean.abs_logfc_x_jsd  \\\n",
+       "1              0.022847       0.008537                   0.000175   \n",
+       "14             0.042955       0.016538                   0.000735   \n",
+       "16             0.043295       0.017585                   0.000681   \n",
+       "25             0.031418       0.012498                   0.000420   \n",
+       "27             0.041889       0.012956                   0.000660   \n",
+       "\n",
+       "    Esan.mean.abs_logfc_x_jsd_x_max_percentile  ...  abs_beta  \\\n",
+       "1                                     0.000082  ...  0.006546   \n",
+       "14                                    0.000690  ...  0.071807   \n",
+       "16                                    0.000560  ...  0.024701   \n",
+       "25                                    0.000173  ...  0.112788   \n",
+       "27                                    0.000418  ...  0.124874   \n",
+       "\n",
+       "              rsid1  GM12878.ENCSR637XSC.mean.sign.jsd  Maasai.mean.sign.jsd  \\\n",
+       "1   chr1_866300_A_C                           0.012423              0.007669   \n",
+       "14  chr1_959339_C_T                          -0.021913             -0.015979   \n",
+       "16  chr1_960509_A_T                           0.021601              0.016399   \n",
+       "25  chr1_976038_A_C                           0.011073              0.011493   \n",
+       "27  chr1_976536_C_T                           0.012069              0.012636   \n",
+       "\n",
+       "    Yoruba.mean.sign.jsd  Luhya.mean.sign.jsd  Gambian.mean.sign.jsd  \\\n",
+       "1              -0.008802            -0.006376               0.009833   \n",
+       "14              0.018475             0.018084              -0.019140   \n",
+       "16              0.018414             0.015839               0.014366   \n",
+       "25              0.012195             0.011414               0.011089   \n",
+       "27              0.010555             0.011849               0.008071   \n",
+       "\n",
+       "    Esan.mean.sign.jsd  GM12878.ENCSR095QNB.mean.sign.jsd  Mende.mean.sign.jsd  \n",
+       "1            -0.008537                          -0.008332             0.007980  \n",
+       "14           -0.016538                           0.021064            -0.018353  \n",
+       "16            0.017585                           0.018586             0.013021  \n",
+       "25           -0.012498                           0.014719             0.012412  \n",
+       "27            0.012956                           0.013633             0.011389  \n",
+       "\n",
+       "[5 rows x 86 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allca.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35547, 86)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allca.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for outer_ancestry in ancestries:\n",
+    "    print(outer_ancestry)\n",
+    "    allca[outer_ancestry+'.mean.sign.jsd'] = allca[outer_ancestry+'.mean.jsd']*np.sign(allca[outer_ancestry+'.mean.logfc'])\n",
+    "    allca.loc[allca[\"allele1\"] != allca[\"rsid\"].str.split('_').str[2],outer_ancestry+'.mean.sign.jsd'] = -1*allca[allca[\"allele1\"] != allca[\"rsid\"].str.split('_').str[2]][outer_ancestry+'.mean.sign.jsd']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GM12878.ENCSR637XSC</th>\n",
+       "      <th>Maasai</th>\n",
+       "      <th>Yoruba</th>\n",
+       "      <th>Luhya</th>\n",
+       "      <th>Gambian</th>\n",
+       "      <th>Esan</th>\n",
+       "      <th>GM12878.ENCSR095QNB</th>\n",
+       "      <th>Mende</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ancestry</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GM12878.ENCSR637XSC</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.911271</td>\n",
+       "      <td>0.920471</td>\n",
+       "      <td>0.925185</td>\n",
+       "      <td>0.921426</td>\n",
+       "      <td>0.920947</td>\n",
+       "      <td>0.941677</td>\n",
+       "      <td>0.914851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maasai</th>\n",
+       "      <td>0.911271</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.954493</td>\n",
+       "      <td>0.957497</td>\n",
+       "      <td>0.954255</td>\n",
+       "      <td>0.948734</td>\n",
+       "      <td>0.913282</td>\n",
+       "      <td>0.951579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yoruba</th>\n",
+       "      <td>0.920471</td>\n",
+       "      <td>0.954493</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.960244</td>\n",
+       "      <td>0.957104</td>\n",
+       "      <td>0.957161</td>\n",
+       "      <td>0.921366</td>\n",
+       "      <td>0.956367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Luhya</th>\n",
+       "      <td>0.925185</td>\n",
+       "      <td>0.957497</td>\n",
+       "      <td>0.960244</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.956365</td>\n",
+       "      <td>0.955541</td>\n",
+       "      <td>0.922344</td>\n",
+       "      <td>0.954838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gambian</th>\n",
+       "      <td>0.921426</td>\n",
+       "      <td>0.954255</td>\n",
+       "      <td>0.957104</td>\n",
+       "      <td>0.956365</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.950060</td>\n",
+       "      <td>0.928452</td>\n",
+       "      <td>0.957745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Esan</th>\n",
+       "      <td>0.920947</td>\n",
+       "      <td>0.948734</td>\n",
+       "      <td>0.957161</td>\n",
+       "      <td>0.955541</td>\n",
+       "      <td>0.950060</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.920357</td>\n",
+       "      <td>0.954099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GM12878.ENCSR095QNB</th>\n",
+       "      <td>0.941677</td>\n",
+       "      <td>0.913282</td>\n",
+       "      <td>0.921366</td>\n",
+       "      <td>0.922344</td>\n",
+       "      <td>0.928452</td>\n",
+       "      <td>0.920357</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.921769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mende</th>\n",
+       "      <td>0.914851</td>\n",
+       "      <td>0.951579</td>\n",
+       "      <td>0.956367</td>\n",
+       "      <td>0.954838</td>\n",
+       "      <td>0.957745</td>\n",
+       "      <td>0.954099</td>\n",
+       "      <td>0.921769</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     GM12878.ENCSR637XSC    Maasai    Yoruba     Luhya  \\\n",
+       "ancestry                                                                 \n",
+       "GM12878.ENCSR637XSC             1.000000  0.911271  0.920471  0.925185   \n",
+       "Maasai                          0.911271  1.000000  0.954493  0.957497   \n",
+       "Yoruba                          0.920471  0.954493  1.000000  0.960244   \n",
+       "Luhya                           0.925185  0.957497  0.960244  1.000000   \n",
+       "Gambian                         0.921426  0.954255  0.957104  0.956365   \n",
+       "Esan                            0.920947  0.948734  0.957161  0.955541   \n",
+       "GM12878.ENCSR095QNB             0.941677  0.913282  0.921366  0.922344   \n",
+       "Mende                           0.914851  0.951579  0.956367  0.954838   \n",
+       "\n",
+       "                      Gambian      Esan  GM12878.ENCSR095QNB     Mende  \n",
+       "ancestry                                                                \n",
+       "GM12878.ENCSR637XSC  0.921426  0.920947             0.941677  0.914851  \n",
+       "Maasai               0.954255  0.948734             0.913282  0.951579  \n",
+       "Yoruba               0.957104  0.957161             0.921366  0.956367  \n",
+       "Luhya                0.956365  0.955541             0.922344  0.954838  \n",
+       "Gambian              1.000000  0.950060             0.928452  0.957745  \n",
+       "Esan                 0.950060  1.000000             0.920357  0.954099  \n",
+       "GM12878.ENCSR095QNB  0.928452  0.920357             1.000000  0.921769  \n",
+       "Mende                0.957745  0.954099             0.921769  1.000000  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs_logfc_dict = {x: [] for x in ancestries}\n",
+    "abs_logfc_dict['ancestry'] = []\n",
+    "\n",
+    "for outer_ancestry in ancestries:\n",
+    "    abs_logfc_dict['ancestry'].append(outer_ancestry)\n",
+    "    for ancestry in ancestries:\n",
+    "        abs_logfc_dict[ancestry].append(scipy.stats.pearsonr(abs(allca[outer_ancestry + '.mean.sign.jsd']),\n",
+    "                                                 abs(allca[ancestry + '.mean.sign.jsd']))[0])\n",
+    "        \n",
+    "abs_logfc_df = pd.DataFrame(abs_logfc_dict)\n",
+    "abs_logfc_df.set_index('ancestry', inplace=True)\n",
+    "\n",
+    "abs_logfc_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Ancestry vs. Ancestry ChromBPNet Absolute LogFC Pearson Correlation')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.clustermap(abs_logfc_df, annot=True, cmap=\"crest\")\n",
+    "g.fig.set_figwidth(12)\n",
+    "g.fig.set_figheight(12)\n",
+    "g.fig.suptitle('Ancestry vs. Ancestry ChromBPNet Absolute LogFC Pearson Correlation', fontsize=18, y=1.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GM12878.ENCSR637XSC</th>\n",
+       "      <th>Maasai</th>\n",
+       "      <th>Yoruba</th>\n",
+       "      <th>Luhya</th>\n",
+       "      <th>Gambian</th>\n",
+       "      <th>Esan</th>\n",
+       "      <th>GM12878.ENCSR095QNB</th>\n",
+       "      <th>Mende</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ancestry</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GM12878.ENCSR637XSC</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.725571</td>\n",
+       "      <td>0.730734</td>\n",
+       "      <td>0.735344</td>\n",
+       "      <td>0.747349</td>\n",
+       "      <td>0.740282</td>\n",
+       "      <td>0.752435</td>\n",
+       "      <td>0.733996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maasai</th>\n",
+       "      <td>0.725571</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.796325</td>\n",
+       "      <td>0.809070</td>\n",
+       "      <td>0.816647</td>\n",
+       "      <td>0.806274</td>\n",
+       "      <td>0.735885</td>\n",
+       "      <td>0.813943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yoruba</th>\n",
+       "      <td>0.730734</td>\n",
+       "      <td>0.796325</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.806265</td>\n",
+       "      <td>0.808546</td>\n",
+       "      <td>0.800769</td>\n",
+       "      <td>0.740764</td>\n",
+       "      <td>0.806431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Luhya</th>\n",
+       "      <td>0.735344</td>\n",
+       "      <td>0.809070</td>\n",
+       "      <td>0.806265</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.814617</td>\n",
+       "      <td>0.808537</td>\n",
+       "      <td>0.744509</td>\n",
+       "      <td>0.811847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gambian</th>\n",
+       "      <td>0.747349</td>\n",
+       "      <td>0.816647</td>\n",
+       "      <td>0.808546</td>\n",
+       "      <td>0.814617</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.818919</td>\n",
+       "      <td>0.755806</td>\n",
+       "      <td>0.825278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Esan</th>\n",
+       "      <td>0.740282</td>\n",
+       "      <td>0.806274</td>\n",
+       "      <td>0.800769</td>\n",
+       "      <td>0.808537</td>\n",
+       "      <td>0.818919</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.742561</td>\n",
+       "      <td>0.815894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GM12878.ENCSR095QNB</th>\n",
+       "      <td>0.752435</td>\n",
+       "      <td>0.735885</td>\n",
+       "      <td>0.740764</td>\n",
+       "      <td>0.744509</td>\n",
+       "      <td>0.755806</td>\n",
+       "      <td>0.742561</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.751026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mende</th>\n",
+       "      <td>0.733996</td>\n",
+       "      <td>0.813943</td>\n",
+       "      <td>0.806431</td>\n",
+       "      <td>0.811847</td>\n",
+       "      <td>0.825278</td>\n",
+       "      <td>0.815894</td>\n",
+       "      <td>0.751026</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     GM12878.ENCSR637XSC    Maasai    Yoruba     Luhya  \\\n",
+       "ancestry                                                                 \n",
+       "GM12878.ENCSR637XSC             1.000000  0.725571  0.730734  0.735344   \n",
+       "Maasai                          0.725571  1.000000  0.796325  0.809070   \n",
+       "Yoruba                          0.730734  0.796325  1.000000  0.806265   \n",
+       "Luhya                           0.735344  0.809070  0.806265  1.000000   \n",
+       "Gambian                         0.747349  0.816647  0.808546  0.814617   \n",
+       "Esan                            0.740282  0.806274  0.800769  0.808537   \n",
+       "GM12878.ENCSR095QNB             0.752435  0.735885  0.740764  0.744509   \n",
+       "Mende                           0.733996  0.813943  0.806431  0.811847   \n",
+       "\n",
+       "                      Gambian      Esan  GM12878.ENCSR095QNB     Mende  \n",
+       "ancestry                                                                \n",
+       "GM12878.ENCSR637XSC  0.747349  0.740282             0.752435  0.733996  \n",
+       "Maasai               0.816647  0.806274             0.735885  0.813943  \n",
+       "Yoruba               0.808546  0.800769             0.740764  0.806431  \n",
+       "Luhya                0.814617  0.808537             0.744509  0.811847  \n",
+       "Gambian              1.000000  0.818919             0.755806  0.825278  \n",
+       "Esan                 0.818919  1.000000             0.742561  0.815894  \n",
+       "GM12878.ENCSR095QNB  0.755806  0.742561             1.000000  0.751026  \n",
+       "Mende                0.825278  0.815894             0.751026  1.000000  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs_logfc_dict = {x: [] for x in ancestries}\n",
+    "abs_logfc_dict['ancestry'] = []\n",
+    "\n",
+    "for outer_ancestry in ancestries:\n",
+    "    abs_logfc_dict['ancestry'].append(outer_ancestry)\n",
+    "    for ancestry in ancestries:\n",
+    "        abs_logfc_dict[ancestry].append(scipy.stats.pearsonr(allca[outer_ancestry + '.mean.sign.jsd'],\n",
+    "                                                 allca[ancestry + '.mean.sign.jsd'])[0])\n",
+    "        \n",
+    "abs_logfc_df = pd.DataFrame(abs_logfc_dict)\n",
+    "abs_logfc_df.set_index('ancestry', inplace=True)\n",
+    "\n",
+    "abs_logfc_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Ancestry vs. Ancestry ChromBPNet Effect Size Pearson Correlation')"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.clustermap(abs_logfc_df, annot=True, cmap=\"crest\")\n",
+    "g.fig.set_figwidth(12)\n",
+    "g.fig.set_figheight(12)\n",
+    "g.fig.suptitle('Ancestry vs. Ancestry ChromBPNet Effect Size Pearson Correlation', fontsize=18, y=1.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/african_caqtls_variant_scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/african_caqtls_variant_scoring.ipynb
new file mode 100644
index 00000000..4b5c6f8a
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/african_caqtls_variant_scoring.ipynb
@@ -0,0 +1,2799 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>Esan.mean.logfc</th>\n",
+       "      <th>Esan.mean.abs_logfc</th>\n",
+       "      <th>Esan.mean.jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>...</th>\n",
+       "      <th>afr.mean.jsd.pval</th>\n",
+       "      <th>eur.mean.abs_logfc.pval</th>\n",
+       "      <th>eur.mean.jsd.pval</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_866281_C_T</td>\n",
+       "      <td>-0.017276</td>\n",
+       "      <td>0.035986</td>\n",
+       "      <td>0.010862</td>\n",
+       "      <td>0.000482</td>\n",
+       "      <td>0.000195</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.649093</td>\n",
+       "      <td>0.519612</td>\n",
+       "      <td>0.686427</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>0.202449</td>\n",
+       "      <td>0.006546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1_866300_A_C</td>\n",
+       "      <td>-0.009280</td>\n",
+       "      <td>0.022847</td>\n",
+       "      <td>0.008537</td>\n",
+       "      <td>0.000175</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.800424</td>\n",
+       "      <td>0.441420</td>\n",
+       "      <td>0.636851</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>0.202449</td>\n",
+       "      <td>0.006546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_870176_T_A</td>\n",
+       "      <td>0.007269</td>\n",
+       "      <td>0.019045</td>\n",
+       "      <td>0.013279</td>\n",
+       "      <td>0.000265</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.331473</td>\n",
+       "      <td>0.251585</td>\n",
+       "      <td>0.294618</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>0.137133</td>\n",
+       "      <td>0.250142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_904947_G_A</td>\n",
+       "      <td>-0.027669</td>\n",
+       "      <td>0.027669</td>\n",
+       "      <td>0.010101</td>\n",
+       "      <td>0.000265</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500773</td>\n",
+       "      <td>0.799295</td>\n",
+       "      <td>0.513734</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>0.174397</td>\n",
+       "      <td>0.164112</td>\n",
+       "      <td>0.174397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_905373_T_C</td>\n",
+       "      <td>-0.132262</td>\n",
+       "      <td>0.132262</td>\n",
+       "      <td>0.014543</td>\n",
+       "      <td>0.001979</td>\n",
+       "      <td>0.001549</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.187633</td>\n",
+       "      <td>0.100221</td>\n",
+       "      <td>0.095542</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>0.098145</td>\n",
+       "      <td>0.130176</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 77 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos allele1 allele2             rsid  Esan.mean.logfc  \\\n",
+       "0  chr1  866281       C       T  chr1_866281_C_T        -0.017276   \n",
+       "1  chr1  866300       A       C  chr1_866300_A_C        -0.009280   \n",
+       "2  chr1  870176       T       A  chr1_870176_T_A         0.007269   \n",
+       "3  chr1  904947       G       A  chr1_904947_G_A        -0.027669   \n",
+       "4  chr1  905373       C       T  chr1_905373_T_C        -0.132262   \n",
+       "\n",
+       "   Esan.mean.abs_logfc  Esan.mean.jsd  Esan.mean.abs_logfc_x_jsd  \\\n",
+       "0             0.035986       0.010862                   0.000482   \n",
+       "1             0.022847       0.008537                   0.000175   \n",
+       "2             0.019045       0.013279                   0.000265   \n",
+       "3             0.027669       0.010101                   0.000265   \n",
+       "4             0.132262       0.014543                   0.001979   \n",
+       "\n",
+       "   Esan.mean.abs_logfc_x_jsd_x_max_percentile  ...  afr.mean.jsd.pval  \\\n",
+       "0                                    0.000195  ...           0.649093   \n",
+       "1                                    0.000082  ...           0.800424   \n",
+       "2                                    0.000193  ...           0.331473   \n",
+       "3                                    0.000234  ...           0.500773   \n",
+       "4                                    0.001549  ...           0.187633   \n",
+       "\n",
+       "   eur.mean.abs_logfc.pval  eur.mean.jsd.pval           region           snp  \\\n",
+       "0                 0.519612           0.686427  1:866099:866351  1_866281_C_T   \n",
+       "1                 0.441420           0.636851  1:866099:866351  1_866300_A_C   \n",
+       "2                 0.251585           0.294618  1:869800:870282  1_870176_T_A   \n",
+       "3                 0.799295           0.513734  1:904938:905544  1_904947_G_A   \n",
+       "4                 0.100221           0.095542  1:904938:905544  1_905373_T_C   \n",
+       "\n",
+       "   distance      pval      beta        se  abs_beta  \n",
+       "0       181  0.974280  0.006546  0.202449  0.006546  \n",
+       "1       200  0.974280  0.006546  0.202449  0.006546  \n",
+       "2       375  0.071532 -0.250142  0.137133  0.250142  \n",
+       "3         8  0.290840  0.174397  0.164112  0.174397  \n",
+       "4       434  0.188152 -0.130176  0.098145  0.130176  \n",
+       "\n",
+       "[5 rows x 77 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/tables/all_caqtls.scores.tsv\", sep=\"\\t\")\n",
+    "data[\"rsid\"] = \"chr\"+data[\"rsid\"] \n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data[\"pval\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 77)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "219382"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(data[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1 = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1[\"rsid\"] = data1[\"chr\"] + \"_\" + data1[\"pos\"].astype(str) + \"_\" + data1[\"allele1\"] + \"_\" + data1[\"allele2\"]\n",
+    "data[\"rsid1\"] = data[\"chr\"] + \"_\" + data[\"pos\"].astype(str) + \"_\" + data[\"allele1\"] + \"_\" + data[\"allele2\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>label</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>30783058</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_30783058_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>36023355</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_36023355_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>46420268</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_46420268_A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>58156036</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_58156036_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>66413586</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_66413586_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35542</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>176509430</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr2_176509430_T_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35543</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>219988687</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr1_219988687_G_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35544</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>142840715</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr4_142840715_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35545</th>\n",
+       "      <td>chr13</td>\n",
+       "      <td>92422559</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr13_92422559_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35546</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>22173739</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr7_22173739_G_T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35547 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         chr        pos allele1 allele2  label                rsid\n",
+       "0       chr1   30783058       C       T      1   chr1_30783058_C_T\n",
+       "1       chr1   36023355       C       T      1   chr1_36023355_C_T\n",
+       "2       chr1   46420268       A       C      1   chr1_46420268_A_C\n",
+       "3       chr1   58156036       C       A      1   chr1_58156036_C_A\n",
+       "4       chr1   66413586       T       C      1   chr1_66413586_T_C\n",
+       "...      ...        ...     ...     ...    ...                 ...\n",
+       "35542   chr2  176509430       T       A      0  chr2_176509430_T_A\n",
+       "35543   chr1  219988687       G       T      0  chr1_219988687_G_T\n",
+       "35544   chr4  142840715       C       T      0  chr4_142840715_C_T\n",
+       "35545  chr13   92422559       C       A      0  chr13_92422559_C_A\n",
+       "35546   chr7   22173739       G       T      0   chr7_22173739_G_T\n",
+       "\n",
+       "[35547 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35547"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(data1[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35547, 6)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 78)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data[\"pval\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(data[\"pval\"]<1e-20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sum(data[\"pval\"]>1e-1)\n",
+    "\n",
+    "# sig_data = data[data[\"pval\"]<1e-20]\n",
+    "# sig_data[\"label\"] = 1\n",
+    "# nonsig_data = data[data[\"pval\"]>1e-1].sample(sig_data.shape[0]*50, random_state=1).reset_index(drop=True)\n",
+    "# nonsig_data[\"label\"] = 0\n",
+    "\n",
+    "# combine = pd.concat([sig_data, nonsig_data])\n",
+    "\n",
+    "# print(combine.shape)\n",
+    "\n",
+    "#combine[['chr', 'pos', 'allele1', 'allele2', \"label\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combine = data1.merge(data, right_on=[\"rsid1\"], left_on=[\"rsid\"], how=\"inner\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_x</th>\n",
+       "      <th>pos_x</th>\n",
+       "      <th>allele1_x</th>\n",
+       "      <th>allele2_x</th>\n",
+       "      <th>label</th>\n",
+       "      <th>rsid_x</th>\n",
+       "      <th>chr_y</th>\n",
+       "      <th>pos_y</th>\n",
+       "      <th>allele1_y</th>\n",
+       "      <th>allele2_y</th>\n",
+       "      <th>...</th>\n",
+       "      <th>eur.mean.abs_logfc.pval</th>\n",
+       "      <th>eur.mean.jsd.pval</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "      <th>rsid1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>30783058</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_30783058_C_T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>30783058</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003459</td>\n",
+       "      <td>0.000589</td>\n",
+       "      <td>1:30782947:30783426</td>\n",
+       "      <td>1_30783058_T_C</td>\n",
+       "      <td>110</td>\n",
+       "      <td>2.348260e-26</td>\n",
+       "      <td>0.892182</td>\n",
+       "      <td>0.058667</td>\n",
+       "      <td>0.892182</td>\n",
+       "      <td>chr1_30783058_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>36023355</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_36023355_C_T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>36023355</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.357734</td>\n",
+       "      <td>0.539651</td>\n",
+       "      <td>1:36023015:36023420</td>\n",
+       "      <td>1_36023355_C_T</td>\n",
+       "      <td>339</td>\n",
+       "      <td>2.749310e-21</td>\n",
+       "      <td>1.014200</td>\n",
+       "      <td>0.080886</td>\n",
+       "      <td>1.014200</td>\n",
+       "      <td>chr1_36023355_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>46420268</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_46420268_A_C</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>46420268</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.001954</td>\n",
+       "      <td>1:46420152:46420415</td>\n",
+       "      <td>1_46420268_A_C</td>\n",
+       "      <td>115</td>\n",
+       "      <td>1.440130e-21</td>\n",
+       "      <td>-0.955278</td>\n",
+       "      <td>0.075327</td>\n",
+       "      <td>0.955278</td>\n",
+       "      <td>chr1_46420268_A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>58156036</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_58156036_C_A</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>58156036</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001123</td>\n",
+       "      <td>0.001793</td>\n",
+       "      <td>1:58155425:58156125</td>\n",
+       "      <td>1_58156036_C_A</td>\n",
+       "      <td>610</td>\n",
+       "      <td>1.676900e-28</td>\n",
+       "      <td>0.930381</td>\n",
+       "      <td>0.056719</td>\n",
+       "      <td>0.930381</td>\n",
+       "      <td>chr1_58156036_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>66413586</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chr1_66413586_T_C</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>66413586</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003826</td>\n",
+       "      <td>0.006414</td>\n",
+       "      <td>1:66413221:66413866</td>\n",
+       "      <td>1_66413586_T_C</td>\n",
+       "      <td>364</td>\n",
+       "      <td>1.458150e-21</td>\n",
+       "      <td>0.706032</td>\n",
+       "      <td>0.055685</td>\n",
+       "      <td>0.706032</td>\n",
+       "      <td>chr1_66413586_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35542</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>176509430</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr2_176509430_T_A</td>\n",
+       "      <td>chr2</td>\n",
+       "      <td>176509430</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.612724</td>\n",
+       "      <td>0.554915</td>\n",
+       "      <td>2:176509024:176509619</td>\n",
+       "      <td>2_176509430_T_A</td>\n",
+       "      <td>405</td>\n",
+       "      <td>7.423710e-01</td>\n",
+       "      <td>0.050625</td>\n",
+       "      <td>0.153524</td>\n",
+       "      <td>0.050625</td>\n",
+       "      <td>chr2_176509430_T_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35543</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>219988687</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr1_219988687_G_T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>219988687</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.206795</td>\n",
+       "      <td>0.396391</td>\n",
+       "      <td>1:219988230:219988744</td>\n",
+       "      <td>1_219988687_G_T</td>\n",
+       "      <td>456</td>\n",
+       "      <td>8.490300e-01</td>\n",
+       "      <td>-0.028801</td>\n",
+       "      <td>0.150855</td>\n",
+       "      <td>0.028801</td>\n",
+       "      <td>chr1_219988687_G_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35544</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>142840715</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr4_142840715_C_T</td>\n",
+       "      <td>chr4</td>\n",
+       "      <td>142840715</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.409176</td>\n",
+       "      <td>0.037545</td>\n",
+       "      <td>4:142840426:142840840</td>\n",
+       "      <td>4_142840715_C_T</td>\n",
+       "      <td>288</td>\n",
+       "      <td>6.779140e-01</td>\n",
+       "      <td>-0.053251</td>\n",
+       "      <td>0.127793</td>\n",
+       "      <td>0.053251</td>\n",
+       "      <td>chr4_142840715_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35545</th>\n",
+       "      <td>chr13</td>\n",
+       "      <td>92422559</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr13_92422559_C_A</td>\n",
+       "      <td>chr13</td>\n",
+       "      <td>92422559</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.086384</td>\n",
+       "      <td>0.110136</td>\n",
+       "      <td>13:92422540:92422740</td>\n",
+       "      <td>13_92422559_C_A</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1.419690e-01</td>\n",
+       "      <td>-0.161272</td>\n",
+       "      <td>0.108836</td>\n",
+       "      <td>0.161272</td>\n",
+       "      <td>chr13_92422559_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35546</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>22173739</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0</td>\n",
+       "      <td>chr7_22173739_G_T</td>\n",
+       "      <td>chr7</td>\n",
+       "      <td>22173739</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.625153</td>\n",
+       "      <td>0.873778</td>\n",
+       "      <td>7:22173635:22174556</td>\n",
+       "      <td>7_22173739_G_T</td>\n",
+       "      <td>103</td>\n",
+       "      <td>1.549780e-01</td>\n",
+       "      <td>-0.249679</td>\n",
+       "      <td>0.174055</td>\n",
+       "      <td>0.249679</td>\n",
+       "      <td>chr7_22173739_G_T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35547 rows × 84 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr_x      pos_x allele1_x allele2_x  label              rsid_x  chr_y  \\\n",
+       "0       chr1   30783058         C         T      1   chr1_30783058_C_T   chr1   \n",
+       "1       chr1   36023355         C         T      1   chr1_36023355_C_T   chr1   \n",
+       "2       chr1   46420268         A         C      1   chr1_46420268_A_C   chr1   \n",
+       "3       chr1   58156036         C         A      1   chr1_58156036_C_A   chr1   \n",
+       "4       chr1   66413586         T         C      1   chr1_66413586_T_C   chr1   \n",
+       "...      ...        ...       ...       ...    ...                 ...    ...   \n",
+       "35542   chr2  176509430         T         A      0  chr2_176509430_T_A   chr2   \n",
+       "35543   chr1  219988687         G         T      0  chr1_219988687_G_T   chr1   \n",
+       "35544   chr4  142840715         C         T      0  chr4_142840715_C_T   chr4   \n",
+       "35545  chr13   92422559         C         A      0  chr13_92422559_C_A  chr13   \n",
+       "35546   chr7   22173739         G         T      0   chr7_22173739_G_T   chr7   \n",
+       "\n",
+       "           pos_y allele1_y allele2_y  ... eur.mean.abs_logfc.pval  \\\n",
+       "0       30783058         C         T  ...                0.003459   \n",
+       "1       36023355         C         T  ...                0.357734   \n",
+       "2       46420268         A         C  ...                0.000100   \n",
+       "3       58156036         C         A  ...                0.001123   \n",
+       "4       66413586         T         C  ...                0.003826   \n",
+       "...          ...       ...       ...  ...                     ...   \n",
+       "35542  176509430         T         A  ...                0.612724   \n",
+       "35543  219988687         G         T  ...                0.206795   \n",
+       "35544  142840715         C         T  ...                0.409176   \n",
+       "35545   92422559         C         A  ...                0.086384   \n",
+       "35546   22173739         G         T  ...                0.625153   \n",
+       "\n",
+       "       eur.mean.jsd.pval                 region              snp  distance  \\\n",
+       "0               0.000589    1:30782947:30783426   1_30783058_T_C       110   \n",
+       "1               0.539651    1:36023015:36023420   1_36023355_C_T       339   \n",
+       "2               0.001954    1:46420152:46420415   1_46420268_A_C       115   \n",
+       "3               0.001793    1:58155425:58156125   1_58156036_C_A       610   \n",
+       "4               0.006414    1:66413221:66413866   1_66413586_T_C       364   \n",
+       "...                  ...                    ...              ...       ...   \n",
+       "35542           0.554915  2:176509024:176509619  2_176509430_T_A       405   \n",
+       "35543           0.396391  1:219988230:219988744  1_219988687_G_T       456   \n",
+       "35544           0.037545  4:142840426:142840840  4_142840715_C_T       288   \n",
+       "35545           0.110136   13:92422540:92422740  13_92422559_C_A        18   \n",
+       "35546           0.873778    7:22173635:22174556   7_22173739_G_T       103   \n",
+       "\n",
+       "               pval      beta        se  abs_beta               rsid1  \n",
+       "0      2.348260e-26  0.892182  0.058667  0.892182   chr1_30783058_C_T  \n",
+       "1      2.749310e-21  1.014200  0.080886  1.014200   chr1_36023355_C_T  \n",
+       "2      1.440130e-21 -0.955278  0.075327  0.955278   chr1_46420268_A_C  \n",
+       "3      1.676900e-28  0.930381  0.056719  0.930381   chr1_58156036_C_A  \n",
+       "4      1.458150e-21  0.706032  0.055685  0.706032   chr1_66413586_T_C  \n",
+       "...             ...       ...       ...       ...                 ...  \n",
+       "35542  7.423710e-01  0.050625  0.153524  0.050625  chr2_176509430_T_A  \n",
+       "35543  8.490300e-01 -0.028801  0.150855  0.028801  chr1_219988687_G_T  \n",
+       "35544  6.779140e-01 -0.053251  0.127793  0.053251  chr4_142840715_C_T  \n",
+       "35545  1.419690e-01 -0.161272  0.108836  0.161272  chr13_92422559_C_A  \n",
+       "35546  1.549780e-01 -0.249679  0.174055  0.249679   chr7_22173739_G_T  \n",
+       "\n",
+       "[35547 rows x 84 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35547, 84)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35547"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(data1[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1_x', 'allele2_x', 'label', 'rsid_x', 'chr_y',\n",
+       "       'pos_y', 'allele1_y', 'allele2_y', 'rsid_y', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.abs_logfc', 'Esan.mean.jsd', 'Esan.mean.abs_logfc_x_jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'Esan.mean.max_percentile', 'Esan.mean.abs_logfc.pval',\n",
+       "       'Esan.mean.jsd.pval', 'Esan.mean.abs_logfc_x_jsd.pval',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile.pval', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.abs_logfc', 'Mende.mean.jsd', 'Mende.mean.abs_logfc.pval',\n",
+       "       'Mende.mean.jsd.pval', 'Yoruba.mean.logfc', 'Yoruba.mean.abs_logfc',\n",
+       "       'Yoruba.mean.jsd', 'Yoruba.mean.abs_logfc.pval', 'Yoruba.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Luhya.mean.logfc', 'Luhya.mean.abs_logfc', 'Luhya.mean.jsd',\n",
+       "       'Luhya.mean.abs_logfc.pval', 'Luhya.mean.jsd.pval',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.abs_logfc', 'Gambian.mean.jsd',\n",
+       "       'Gambian.mean.abs_logfc.pval', 'Gambian.mean.jsd.pval',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.abs_logfc', 'Maasai.mean.jsd',\n",
+       "       'Maasai.mean.abs_logfc.pval', 'Maasai.mean.jsd.pval', 'afr.mean.logfc',\n",
+       "       'afr.mean.abs_logfc', 'afr.mean.jsd', 'eur.mean.logfc',\n",
+       "       'eur.mean.abs_logfc', 'eur.mean.jsd', 'afr.mean.abs_logfc.pval',\n",
+       "       'afr.mean.jsd.pval', 'eur.mean.abs_logfc.pval', 'eur.mean.jsd.pval',\n",
+       "       'region', 'snp', 'distance', 'pval', 'beta', 'se', 'abs_beta', 'rsid1'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rsid_map = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/convert_hg38_to_hg19/hg38_rsids.bed\", sep=\"\\t\", header=None)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rsid_map[\"rsid\"] = rsid_map[0] + \"_\" + rsid_map[2].astype(str) + \"_\" + rsid_map[3] + \"_\" + rsid_map[4] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rsid_map_dict = {}\n",
+    "for i,r in rsid_map.iterrows():\n",
+    "    rsid_map_dict[r[9]] = r[\"rsid\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35578, 7)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/enformer_predictions.tsv\", sep=\"\\t\", header=0).drop_duplicates()\n",
+    "enformer_predictions.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_predictions[\"rsid1\"] = enformer_predictions[\"rsid\"].map(rsid_map_dict)\n",
+    "#enformer_predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35578, 8)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "enformer_predictions.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>rsid1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>801680</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs12134490</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>chr1_866300_A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>894719</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs187243360</td>\n",
+       "      <td>0.000328</td>\n",
+       "      <td>0.000412</td>\n",
+       "      <td>chr1_959339_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>895889</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs72891151</td>\n",
+       "      <td>0.000195</td>\n",
+       "      <td>0.000245</td>\n",
+       "      <td>chr1_960509_A_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>911418</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs113068688</td>\n",
+       "      <td>0.000127</td>\n",
+       "      <td>0.000157</td>\n",
+       "      <td>chr1_976038_A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>911916</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs74045046</td>\n",
+       "      <td>0.000141</td>\n",
+       "      <td>0.000175</td>\n",
+       "      <td>chr1_976536_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35573</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51001432</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs140517</td>\n",
+       "      <td>-0.002317</td>\n",
+       "      <td>-0.002354</td>\n",
+       "      <td>chr22_50563003_G_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35574</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059118</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3091400</td>\n",
+       "      <td>0.001514</td>\n",
+       "      <td>0.001934</td>\n",
+       "      <td>chr22_50620690_T_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35575</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51060049</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs11912237</td>\n",
+       "      <td>0.000067</td>\n",
+       "      <td>0.000085</td>\n",
+       "      <td>chr22_50621621_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35576</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>-0.000557</td>\n",
+       "      <td>-0.000713</td>\n",
+       "      <td>chr22_50628124_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35577</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066921</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs131715</td>\n",
+       "      <td>-0.000046</td>\n",
+       "      <td>-0.000059</td>\n",
+       "      <td>chr22_50628493_G_A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35578 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         chr       pos ref alt         rsid  DNASE_SAD  DNASE_SAR  \\\n",
+       "0       chr1    801680   A   C   rs12134490   0.000039   0.000052   \n",
+       "1       chr1    894719   C   T  rs187243360   0.000328   0.000412   \n",
+       "2       chr1    895889   A   T   rs72891151   0.000195   0.000245   \n",
+       "3       chr1    911418   A   C  rs113068688   0.000127   0.000157   \n",
+       "4       chr1    911916   C   T   rs74045046   0.000141   0.000175   \n",
+       "...      ...       ...  ..  ..          ...        ...        ...   \n",
+       "35573  chr22  51001432   C   G     rs140517  -0.002317  -0.002354   \n",
+       "35574  chr22  51059118   T   G    rs3091400   0.001514   0.001934   \n",
+       "35575  chr22  51060049   G   A   rs11912237   0.000067   0.000085   \n",
+       "35576  chr22  51066552   C   G    rs6151406  -0.000557  -0.000713   \n",
+       "35577  chr22  51066921   G   A     rs131715  -0.000046  -0.000059   \n",
+       "\n",
+       "                    rsid1  \n",
+       "0         chr1_866300_A_C  \n",
+       "1         chr1_959339_C_T  \n",
+       "2         chr1_960509_A_T  \n",
+       "3         chr1_976038_A_C  \n",
+       "4         chr1_976536_C_T  \n",
+       "...                   ...  \n",
+       "35573  chr22_50563003_G_C  \n",
+       "35574  chr22_50620690_T_G  \n",
+       "35575  chr22_50621621_G_A  \n",
+       "35576  chr22_50628124_C_G  \n",
+       "35577  chr22_50628493_G_A  \n",
+       "\n",
+       "[35578 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35547, 84)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(enformer_predictions[\"rsid1\"].isin(combine[\"rsid_x\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_predictions[\"RA1\"] = enformer_predictions[\"ref\"]+\"_\"+enformer_predictions[\"alt\"]\n",
+    "enformer_predictions[\"RA3\"] = enformer_predictions[\"alt\"]+\"_\"+enformer_predictions[\"ref\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_predictions[\"RA2\"] = enformer_predictions[\"rsid1\"].str.split(\"_\").str[2:4].apply('_'.join)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>rsid1</th>\n",
+       "      <th>RA1</th>\n",
+       "      <th>RA3</th>\n",
+       "      <th>RA2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>801680</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs12134490</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>chr1_866300_A_C</td>\n",
+       "      <td>A_C</td>\n",
+       "      <td>C_A</td>\n",
+       "      <td>A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>894719</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs187243360</td>\n",
+       "      <td>0.000328</td>\n",
+       "      <td>0.000412</td>\n",
+       "      <td>chr1_959339_C_T</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>T_C</td>\n",
+       "      <td>C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>895889</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs72891151</td>\n",
+       "      <td>0.000195</td>\n",
+       "      <td>0.000245</td>\n",
+       "      <td>chr1_960509_A_T</td>\n",
+       "      <td>A_T</td>\n",
+       "      <td>T_A</td>\n",
+       "      <td>A_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>911418</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs113068688</td>\n",
+       "      <td>0.000127</td>\n",
+       "      <td>0.000157</td>\n",
+       "      <td>chr1_976038_A_C</td>\n",
+       "      <td>A_C</td>\n",
+       "      <td>C_A</td>\n",
+       "      <td>A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>911916</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs74045046</td>\n",
+       "      <td>0.000141</td>\n",
+       "      <td>0.000175</td>\n",
+       "      <td>chr1_976536_C_T</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>T_C</td>\n",
+       "      <td>C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35573</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51001432</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs140517</td>\n",
+       "      <td>-0.002317</td>\n",
+       "      <td>-0.002354</td>\n",
+       "      <td>chr22_50563003_G_C</td>\n",
+       "      <td>C_G</td>\n",
+       "      <td>G_C</td>\n",
+       "      <td>G_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35574</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059118</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3091400</td>\n",
+       "      <td>0.001514</td>\n",
+       "      <td>0.001934</td>\n",
+       "      <td>chr22_50620690_T_G</td>\n",
+       "      <td>T_G</td>\n",
+       "      <td>G_T</td>\n",
+       "      <td>T_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35575</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51060049</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs11912237</td>\n",
+       "      <td>0.000067</td>\n",
+       "      <td>0.000085</td>\n",
+       "      <td>chr22_50621621_G_A</td>\n",
+       "      <td>G_A</td>\n",
+       "      <td>A_G</td>\n",
+       "      <td>G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35576</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>-0.000557</td>\n",
+       "      <td>-0.000713</td>\n",
+       "      <td>chr22_50628124_C_G</td>\n",
+       "      <td>C_G</td>\n",
+       "      <td>G_C</td>\n",
+       "      <td>C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35577</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066921</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs131715</td>\n",
+       "      <td>-0.000046</td>\n",
+       "      <td>-0.000059</td>\n",
+       "      <td>chr22_50628493_G_A</td>\n",
+       "      <td>G_A</td>\n",
+       "      <td>A_G</td>\n",
+       "      <td>G_A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35578 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         chr       pos ref alt         rsid  DNASE_SAD  DNASE_SAR  \\\n",
+       "0       chr1    801680   A   C   rs12134490   0.000039   0.000052   \n",
+       "1       chr1    894719   C   T  rs187243360   0.000328   0.000412   \n",
+       "2       chr1    895889   A   T   rs72891151   0.000195   0.000245   \n",
+       "3       chr1    911418   A   C  rs113068688   0.000127   0.000157   \n",
+       "4       chr1    911916   C   T   rs74045046   0.000141   0.000175   \n",
+       "...      ...       ...  ..  ..          ...        ...        ...   \n",
+       "35573  chr22  51001432   C   G     rs140517  -0.002317  -0.002354   \n",
+       "35574  chr22  51059118   T   G    rs3091400   0.001514   0.001934   \n",
+       "35575  chr22  51060049   G   A   rs11912237   0.000067   0.000085   \n",
+       "35576  chr22  51066552   C   G    rs6151406  -0.000557  -0.000713   \n",
+       "35577  chr22  51066921   G   A     rs131715  -0.000046  -0.000059   \n",
+       "\n",
+       "                    rsid1  RA1  RA3  RA2  \n",
+       "0         chr1_866300_A_C  A_C  C_A  A_C  \n",
+       "1         chr1_959339_C_T  C_T  T_C  C_T  \n",
+       "2         chr1_960509_A_T  A_T  T_A  A_T  \n",
+       "3         chr1_976038_A_C  A_C  C_A  A_C  \n",
+       "4         chr1_976536_C_T  C_T  T_C  C_T  \n",
+       "...                   ...  ...  ...  ...  \n",
+       "35573  chr22_50563003_G_C  C_G  G_C  G_C  \n",
+       "35574  chr22_50620690_T_G  T_G  G_T  T_G  \n",
+       "35575  chr22_50621621_G_A  G_A  A_G  G_A  \n",
+       "35576  chr22_50628124_C_G  C_G  G_C  C_G  \n",
+       "35577  chr22_50628493_G_A  G_A  A_G  G_A  \n",
+       "\n",
+       "[35578 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "enformer_predictions1 = enformer_predictions[enformer_predictions[\"RA1\"] == enformer_predictions[\"RA2\"]]\n",
+    "enformer_predictions2 = enformer_predictions[enformer_predictions[\"RA3\"] == enformer_predictions[\"RA2\"]]\n",
+    "enformer_predictions2[\"DNASE_SAD\"] = -1*enformer_predictions2[\"DNASE_SAD\"] \n",
+    "enformer_predictions2[\"DNASE_SAR\"] = -1*enformer_predictions2[\"DNASE_SAR\"] \n",
+    "\n",
+    "enformer_predictions_new = pd.concat([enformer_predictions2, enformer_predictions1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35506, 12)"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35547, 84)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#len(set(enformer_predictions[\"rsid1\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "enformer_predictions_new2 = enformer_predictions_new.merge(combine, right_on=\"rsid_x\", left_on=\"rsid1\", how=\"inner\").drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6052\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(35547, 84)"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# if row['allele1'] != row['rsid'].split('_')[2]:\n",
+    "#         if row['allele2'] == row['rsid'].split('_')[2]:\n",
+    "#             afr_corrected_logfc_asb.append(-1 * row['afr.mean.logfc'])\n",
+    "#             eur_corrected_logfc_asb.append(-1 * row['eur.mean.logfc'])\n",
+    "            \n",
+    "print(sum(combine[\"allele1_x\"] != combine[\"rsid_y\"].str.split('_').str[2]))\n",
+    "combine.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        C\n",
+       "1        C\n",
+       "2        A\n",
+       "3        C\n",
+       "4        T\n",
+       "        ..\n",
+       "35542    T\n",
+       "35543    G\n",
+       "35544    C\n",
+       "35545    C\n",
+       "35546    G\n",
+       "Name: allele1_x, Length: 35547, dtype: object"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine[\"allele1_x\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         chr1_30783058_C_T\n",
+       "1         chr1_36023355_C_T\n",
+       "2         chr1_46420268_A_C\n",
+       "3         chr1_58156036_C_A\n",
+       "4         chr1_66413586_T_C\n",
+       "                ...        \n",
+       "35542    chr2_176509430_T_A\n",
+       "35543    chr1_219988687_G_T\n",
+       "35544    chr4_142840715_C_T\n",
+       "35545    chr13_92422559_C_A\n",
+       "35546     chr7_22173739_G_T\n",
+       "Name: rsid_x, Length: 35547, dtype: object"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine[\"rsid_x\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dnase = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/dnase_preds/DNASE/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)\n",
+    "data_dnase[\"rsid\"] = \"chr\"+data_dnase[\"rsid\"]\n",
+    "data_dnase[['Chrx', 'Posx', 'REF', 'ALT']] = data_dnase['rsid'].str.split(\"_\", expand=True)\n",
+    "data_dnase[\"jsd_sign.mean\"] = data_dnase[\"jsd.mean\"]*np.sign(data_dnase[\"logfc.mean\"])\n",
+    "\n",
+    "data_atac = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/atac/ATAC/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac[\"rsid\"] = \"chr\"+data_atac[\"rsid\"]\n",
+    "\n",
+    "data_atac[['Chrx', 'Posx', 'REF', 'ALT']] = data_atac['rsid'].str.split(\"_\", expand=True)\n",
+    "data_atac[\"jsd_sign.mean\"] = data_atac[\"jsd.mean\"]*np.sign(data_atac[\"logfc.mean\"])\n",
+    "\n",
+    "data_atac_50m = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/atac_50M/ATAC_50M/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac_50m[\"rsid\"] = \"chr\"+data_atac_50m[\"rsid\"]\n",
+    "data_atac_50m[['Chrx', 'Posx', 'REF', 'ALT']] = data_atac_50m['rsid'].str.split(\"_\", expand=True)\n",
+    "data_atac_50m[\"jsd_sign.mean\"] = data_atac_50m[\"jsd.mean\"]*np.sign(data_atac_50m[\"logfc.mean\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35506, 96)"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions_new2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'ref', 'alt', 'rsid', 'DNASE_SAD', 'DNASE_SAR', 'rsid1_x',\n",
+       "       'RA1', 'RA3', 'RA2', 'c', 'chr_x', 'pos_x', 'allele1_x', 'allele2_x',\n",
+       "       'label', 'rsid_x', 'chr_y', 'pos_y', 'allele1_y', 'allele2_y', 'rsid_y',\n",
+       "       'Esan.mean.logfc', 'Esan.mean.abs_logfc', 'Esan.mean.jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'Esan.mean.max_percentile', 'Esan.mean.abs_logfc.pval',\n",
+       "       'Esan.mean.jsd.pval', 'Esan.mean.abs_logfc_x_jsd.pval',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile.pval', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.abs_logfc', 'Mende.mean.jsd', 'Mende.mean.abs_logfc.pval',\n",
+       "       'Mende.mean.jsd.pval', 'Yoruba.mean.logfc', 'Yoruba.mean.abs_logfc',\n",
+       "       'Yoruba.mean.jsd', 'Yoruba.mean.abs_logfc.pval', 'Yoruba.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Luhya.mean.logfc', 'Luhya.mean.abs_logfc', 'Luhya.mean.jsd',\n",
+       "       'Luhya.mean.abs_logfc.pval', 'Luhya.mean.jsd.pval',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.abs_logfc', 'Gambian.mean.jsd',\n",
+       "       'Gambian.mean.abs_logfc.pval', 'Gambian.mean.jsd.pval',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.abs_logfc', 'Maasai.mean.jsd',\n",
+       "       'Maasai.mean.abs_logfc.pval', 'Maasai.mean.jsd.pval', 'afr.mean.logfc',\n",
+       "       'afr.mean.abs_logfc', 'afr.mean.jsd', 'eur.mean.logfc',\n",
+       "       'eur.mean.abs_logfc', 'eur.mean.jsd', 'afr.mean.abs_logfc.pval',\n",
+       "       'afr.mean.jsd.pval', 'eur.mean.abs_logfc.pval', 'eur.mean.jsd.pval',\n",
+       "       'region', 'snp', 'distance', 'pval', 'beta', 'se', 'abs_beta',\n",
+       "       'rsid1_y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions_new2.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dnase = data_dnase[data_dnase[\"rsid\"].isin(enformer_predictions_new2[\"rsid_y\"])]\n",
+    "data_atac = data_atac[data_atac[\"rsid\"].isin(enformer_predictions_new2[\"rsid_y\"])]\n",
+    "data_atac_50m = data_atac_50m[data_atac_50m[\"rsid\"].isin(enformer_predictions_new2[\"rsid_y\"])]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35506, 96)"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions_new2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          chr1_1079456_A_G\n",
+       "1          chr1_1201373_T_C\n",
+       "2          chr1_1274256_A_G\n",
+       "3          chr1_1307603_G_A\n",
+       "4          chr1_2556714_A_G\n",
+       "                ...        \n",
+       "35501    chr22_50562787_G_C\n",
+       "35502    chr22_50620690_G_T\n",
+       "35503    chr22_50621621_G_A\n",
+       "35504    chr22_50628124_C_G\n",
+       "35505    chr22_50628493_G_A\n",
+       "Name: rsid_y, Length: 35506, dtype: object"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions_new2[\"rsid_y\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data_atac = data_atac[data_atac[\"rsid\"],isin(enformer_predictions_new2[\"rsid_x\"])]\n",
+    "#print(data_atac.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data_atac_50m = data_atac_50m[data_atac_50m[\"rsid\"],isin(enformer_predictions_new2[\"rsid_x\"])]\n",
+    "#print(data_atac_50m.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(data_atac_50m.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:9203: FutureWarning: Passing 'suffixes' which cause duplicate columns {'pos_y', 'chr_y', 'rsid_y'} in the result is deprecated and will raise a MergeError in a future version.\n",
+      "  validate=validate,\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dnase1 = data_dnase.merge(enformer_predictions_new2, right_on=[\"rsid_y\"], left_on=[\"rsid\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_atac1 = data_atac.merge(enformer_predictions_new2, right_on=[\"rsid_y\"], left_on=[\"rsid\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_atac_50m1 = data_atac_50m.merge(enformer_predictions_new2, right_on=[\"rsid_y\"], left_on=[\"rsid\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'logfc.mean',\n",
+       "       'abs_logfc.mean', 'abs_logfc.mean.pval', 'jsd.mean', 'jsd.mean.pval',\n",
+       "       ...\n",
+       "       'eur.mean.abs_logfc.pval', 'eur.mean.jsd.pval', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'rsid1_y'],\n",
+       "      dtype='object', length=125)"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dnase1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5056273175153359\n",
+      "0.4286308116836259\n",
+      "0.3900412324153197\n",
+      "0.217104025050456\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff59b357dd0>"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "#keyd=\"GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile\"\n",
+    "keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(data_atac1[\"label\"].astype(int), abs(data_atac1[keyd]))\n",
+    "roc_auc = metrics.average_precision_score(data_atac1[\"label\"], abs(data_atac1[keyd]))\n",
+    "plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=\"ChromBPnet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "print(roc_auc)\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(data_atac_50m1[\"label\"].astype(int), abs(data_atac_50m1[keyd]))\n",
+    "roc_auc = metrics.average_precision_score(data_atac_50m1[\"label\"], abs(data_atac_50m1[keyd]))\n",
+    "plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=\"ChromBPnet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "print(roc_auc)\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(data_dnase1[\"label\"].astype(int), abs(data_dnase1[keyd]))\n",
+    "roc_auc = metrics.average_precision_score(data_dnase1[\"label\"], abs(data_dnase1[keyd]))\n",
+    "plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='cyan', linestyle='dashed', label=\"ChromBPnet w/ DNASE, AP=\"+str(round(roc_auc,2)))\n",
+    "print(roc_auc)\n",
+    "\n",
+    "keyd=\"DNASE_SAD\"\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(enformer_predictions_new2[\"label\"].astype(int), abs(enformer_predictions_new2[keyd]))\n",
+    "roc_auc = metrics.average_precision_score(enformer_predictions_new2[\"label\"].astype(int), abs(enformer_predictions_new2[keyd]))\n",
+    "plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='orange', label=\"Enformer SAD, AP=\"+str(round(roc_auc,2)))\n",
+    "print(roc_auc)\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.02"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.round(sum(enformer_predictions_new2[\"label\"].astype(int)==1)/enformer_predictions_new2.shape[0],2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_atac1.loc[data_atac1[\"allele1_x\"] != data_atac1[\"rsid_y\"].iloc[:,1].str.split('_').str[2], \"jsd_sign.mean\"] = -1* data_atac1[data_atac1[\"allele1_x\"] != data_atac1[\"rsid_y\"].iloc[:,1].str.split('_').str[2]][\"jsd_sign.mean\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dnase1.loc[data_dnase1[\"allele1_x\"] != data_dnase1[\"rsid_y\"].iloc[:,1].str.split('_').str[2], \"jsd_sign.mean\"] = -1* data_dnase1[data_dnase1[\"allele1_x\"] != data_dnase1[\"rsid_y\"].iloc[:,1].str.split('_').str[2]][\"jsd_sign.mean\"]\n",
+    "\n",
+    "data_atac_50m1.loc[data_atac_50m1[\"allele1_x\"] != data_atac_50m1[\"rsid_y\"].iloc[:,1].str.split('_').str[2], \"jsd_sign.mean\"] = -1* data_atac_50m1[data_atac_50m1[\"allele1_x\"] != data_atac_50m1[\"rsid_y\"].iloc[:,1].str.split('_').str[2]][\"jsd_sign.mean\"]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_p = pd.DataFrame()\n",
+    "combine1 = enformer_predictions_new2\n",
+    "data_p[\"sd_sign.mean\"] = combine1[\"DNASE_SAR\"]\n",
+    "data_p.loc[combine1[\"allele1_x\"] != combine1[\"rsid_y\"].str.split('_').str[2], \"sd_sign.mean\"] = -1* data_p[combine1[\"allele1_x\"] != combine1[\"rsid_y\"].str.split('_').str[2]][\"sd_sign.mean\"]\n",
+    "data_p[\"beta\"] = combine1[\"beta\"]\n",
+    "data_p[\"label\"] = combine1[\"label\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.68\n",
+      "ChromBPNet ATAC:  0.67\n",
+      "ChromBPNet DNase:  0.69\n",
+      "Enformer:  0.53\n",
+      "ChromBPNet ATAC:  2.006209098139025e-94\n",
+      "ChromBPNet ATAC:  3.766850718777501e-92\n",
+      "ChromBPNet DNase:  2.0428541794203464e-99\n",
+      "Enformer:  1.1430928828877437e-50\n"
+     ]
+    }
+   ],
+   "source": [
+    "keyd=\"jsd_sign.mean\"\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(data_atac1[keyd][data_atac1[\"label\"]==1], data_atac1[\"beta\"][data_atac1[\"label\"]==1])[0],2))\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(data_atac_50m1[keyd][data_atac_50m1[\"label\"]==1], data_atac_50m1[\"beta\"][data_atac_50m1[\"label\"]==1])[0],2))\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(data_dnase1[keyd][data_dnase1[\"label\"]==1], data_dnase1[\"beta\"][data_dnase1[\"label\"]==1])[0],2))\n",
+    "print(\"Enformer: \",np.round(scipy.stats.pearsonr(data_p[\"sd_sign.mean\"][data_p[\"label\"]==1], data_p[\"beta\"][data_p[\"label\"]==1])[0],2))\n",
+    "\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(data_atac1[keyd][data_atac1[\"label\"]==1], data_atac1[\"beta\"][data_atac1[\"label\"]==1])[1])\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(data_atac_50m1[keyd][data_atac_50m1[\"label\"]==1], data_atac_50m1[\"beta\"][data_atac_50m1[\"label\"]==1])[1])\n",
+    "print(\"ChromBPNet DNase: \",scipy.stats.pearsonr(data_dnase1[keyd][data_dnase1[\"label\"]==1], data_dnase1[\"beta\"][data_dnase1[\"label\"]==1])[1])\n",
+    "print(\"Enformer: \",scipy.stats.pearsonr(data_p[\"sd_sign.mean\"][data_p[\"label\"]==1], data_p[\"beta\"][data_p[\"label\"]==1])[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "num nan\n",
+      "0\n",
+      "(0.6765237637222654, 2.006209098139025e-94)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7ff59b45df10>"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "\n",
+    "data = pd.DataFrame()\n",
+    "data[\"sd_sign.mean\"] = data_atac1[data_atac1[\"label\"]==1][\"jsd_sign.mean\"]\n",
+    "data[\"Beta\"] = data_atac1[data_atac1[\"label\"]==1][\"beta\"]\n",
+    "data[\"max_percentile.mean\"] = abs(data_atac1[data_atac1[\"label\"]==1][\"max_percentile.mean\"])\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "print(\"num nan\")\n",
+    "print(sum(np.isnan(data[\"Beta\"])))\n",
+    "print(scipy.stats.pearsonr(data[\"sd_sign.mean\"], data[\"Beta\"]))\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"sd_sign.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"sd_sign.mean\", hue=\"max_percentile.mean\", data=data, s=15, palette=\"Oranges\")\n",
+    "sns.regplot(x=\"Beta\", y=\"sd_sign.mean\", data=data, scatter=False, ci=None, color=\"orange\")\n",
+    "#sns.jointplot(x=data[\"Beta\"], y=data[\"sd_sign.mean\"],s=0,ax=ax)\n",
+    "\n",
+    "norm = plt.Normalize(data['max_percentile.mean'].min(), data['max_percentile.mean'].max())\n",
+    "sm = plt.cm.ScalarMappable(cmap=\"Oranges\", norm=norm)\n",
+    "sm.set_array([])\n",
+    "\n",
+    "# Remove the legend and add a colorbar\n",
+    "ax.get_legend().remove()\n",
+    "ax.figure.colorbar(sm)\n",
+    "#plt.savefig(\"subfigs_new/microglia_caqtls_scatter_plot.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Beta', ylabel='sd_sign.mean'>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "data = pd.DataFrame()\n",
+    "combine1 = combine[combine[\"label\"]==1]\n",
+    "data[\"sd_sign.mean\"] = combine1[\"GM12878.ENCSR637XSC.mean.jsd\"]*np.sign(combine1[\"GM12878.ENCSR637XSC.mean.logfc\"])\n",
+    "data.loc[combine1[\"allele1_x\"] != combine1[\"rsid_y\"].str.split('_').str[2], \"sd_sign.mean\"] = -1* data[combine1[\"allele1_x\"] != combine1[\"rsid_y\"].str.split('_').str[2]][\"sd_sign.mean\"]\n",
+    "data[\"Beta\"] = combine1[\"beta\"]\n",
+    "data[\"logfc_x_max_percentile.mean\"] = abs(combine1[\"GM12878.ENCSR637XSC.mean.max_percentile\"])\n",
+    "\n",
+    "sns.scatterplot(data=data, y=\"sd_sign.mean\", x=\"Beta\", hue=\"logfc_x_max_percentile.mean\", s=10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6722146578666769, 8.20108309064487e-93)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "scipy.stats.pearsonr(data[\"sd_sign.mean\"], data[\"Beta\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Beta', ylabel='sd_sign.mean'>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "data = pd.DataFrame()\n",
+    "combine1 = enformer_predictions_new2[enformer_predictions_new2[\"label\"]==1]\n",
+    "data[\"sd_sign.mean\"] = combine1[\"DNASE_SAR\"]\n",
+    "data.loc[combine1[\"allele1_x\"] != combine1[\"rsid_y\"].str.split('_').str[2], \"sd_sign.mean\"] = -1* data[combine1[\"allele1_x\"] != combine1[\"rsid_y\"].str.split('_').str[2]][\"sd_sign.mean\"]\n",
+    "data[\"Beta\"] = combine1[\"beta\"]\n",
+    "\n",
+    "sns.scatterplot(data=data, y=\"sd_sign.mean\", x=\"Beta\", s=10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5251740484869941, 1.1430928828877437e-50)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "scipy.stats.pearsonr(data[\"sd_sign.mean\"], data[\"Beta\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'ref', 'alt', 'rsid', 'DNASE_SAD', 'DNASE_SAR', 'rsid1',\n",
+       "       'RA1', 'RA3', 'RA2', 'c'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions_new.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0       True\n",
+       " 1      False\n",
+       " 2      False\n",
+       " 3      False\n",
+       " 4      False\n",
+       "        ...  \n",
+       " 692     True\n",
+       " 693    False\n",
+       " 694    False\n",
+       " 695     True\n",
+       " 696    False\n",
+       " Length: 697, dtype: bool]"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[combine1[\"allele1_x\"] != combine1[\"rsid_y\"].str.split('_').str[2]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6722146578666769, 8.20108309064487e-93)"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#scipy.stats.pearsonr(abs(data[\"sd_sign.mean\"]), abs(data[\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1_x', 'allele2_x', 'label', 'rsid_x', 'chr_y',\n",
+       "       'pos_y', 'allele1_y', 'allele2_y', 'rsid_y', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.abs_logfc', 'Esan.mean.jsd', 'Esan.mean.abs_logfc_x_jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'Esan.mean.max_percentile', 'Esan.mean.abs_logfc.pval',\n",
+       "       'Esan.mean.jsd.pval', 'Esan.mean.abs_logfc_x_jsd.pval',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile.pval', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.abs_logfc', 'Mende.mean.jsd', 'Mende.mean.abs_logfc.pval',\n",
+       "       'Mende.mean.jsd.pval', 'Yoruba.mean.logfc', 'Yoruba.mean.abs_logfc',\n",
+       "       'Yoruba.mean.jsd', 'Yoruba.mean.abs_logfc.pval', 'Yoruba.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Luhya.mean.logfc', 'Luhya.mean.abs_logfc', 'Luhya.mean.jsd',\n",
+       "       'Luhya.mean.abs_logfc.pval', 'Luhya.mean.jsd.pval',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.abs_logfc', 'Gambian.mean.jsd',\n",
+       "       'Gambian.mean.abs_logfc.pval', 'Gambian.mean.jsd.pval',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.abs_logfc', 'Maasai.mean.jsd',\n",
+       "       'Maasai.mean.abs_logfc.pval', 'Maasai.mean.jsd.pval', 'afr.mean.logfc',\n",
+       "       'afr.mean.abs_logfc', 'afr.mean.jsd', 'eur.mean.logfc',\n",
+       "       'eur.mean.abs_logfc', 'eur.mean.jsd', 'afr.mean.abs_logfc.pval',\n",
+       "       'afr.mean.jsd.pval', 'eur.mean.abs_logfc.pval', 'eur.mean.jsd.pval',\n",
+       "       'region', 'snp', 'distance', 'pval', 'beta', 'se', 'abs_beta', 'rsid1'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combine.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'ref', 'alt', 'rsid', 'DNASE_SAD', 'DNASE_SAR', 'rsid1_x',\n",
+       "       'RA1', 'RA3', 'RA2', 'c', 'chr_x', 'pos_x', 'allele1_x', 'allele2_x',\n",
+       "       'label', 'rsid_x', 'chr_y', 'pos_y', 'allele1_y', 'allele2_y', 'rsid_y',\n",
+       "       'Esan.mean.logfc', 'Esan.mean.abs_logfc', 'Esan.mean.jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'Esan.mean.max_percentile', 'Esan.mean.abs_logfc.pval',\n",
+       "       'Esan.mean.jsd.pval', 'Esan.mean.abs_logfc_x_jsd.pval',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile.pval', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.abs_logfc', 'Mende.mean.jsd', 'Mende.mean.abs_logfc.pval',\n",
+       "       'Mende.mean.jsd.pval', 'Yoruba.mean.logfc', 'Yoruba.mean.abs_logfc',\n",
+       "       'Yoruba.mean.jsd', 'Yoruba.mean.abs_logfc.pval', 'Yoruba.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Luhya.mean.logfc', 'Luhya.mean.abs_logfc', 'Luhya.mean.jsd',\n",
+       "       'Luhya.mean.abs_logfc.pval', 'Luhya.mean.jsd.pval',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.abs_logfc', 'Gambian.mean.jsd',\n",
+       "       'Gambian.mean.abs_logfc.pval', 'Gambian.mean.jsd.pval',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.abs_logfc', 'Maasai.mean.jsd',\n",
+       "       'Maasai.mean.abs_logfc.pval', 'Maasai.mean.jsd.pval', 'afr.mean.logfc',\n",
+       "       'afr.mean.abs_logfc', 'afr.mean.jsd', 'eur.mean.logfc',\n",
+       "       'eur.mean.abs_logfc', 'eur.mean.jsd', 'afr.mean.abs_logfc.pval',\n",
+       "       'afr.mean.jsd.pval', 'eur.mean.abs_logfc.pval', 'eur.mean.jsd.pval',\n",
+       "       'region', 'snp', 'distance', 'pval', 'beta', 'se', 'abs_beta',\n",
+       "       'rsid1_y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "enformer_predictions_new2.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "697"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(enformer_predictions_new2[\"label\"]==1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/bqtl_aupr_curves.ipynb b/src/evaluation/figure_notebooks/figure_6/bqtl_aupr_curves.ipynb
new file mode 100644
index 00000000..a329739c
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/bqtl_aupr_curves.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "#snps=pd.read_csv(\"/mnt/lab_data/kundaje/users/anusri/QTLs/bQTLs/src/pu1.txt\",header=0,sep='\\t')\n",
+    "dnase_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/DNASE/summary.mean.variant_scores_new.tsv\"\n",
+    "atac_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/ATAC/summary.mean.variant_scores_new.tsv\"\n",
+    "atac_preds_50m=\"/mnt/lab_data2/anusri/enformer/bqtls/chrombpne_preds/ATAC_50M/summary.mean.variant_scores_new_2.tsv\"\n",
+    "\n",
+    "#atac_preds=\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/GM12878/subsampling/GM12878_250M/bqtls_pu1_preds/snp_scores.tsv\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/bqtls/source.tsv\",header=0,sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps_selected\n",
+    "snps.head()\n",
+    "snps['rsid'] = snps[\"Chr\"].astype(str) + \"_\" + snps[\"position\"].astype(str) + \"_\" + snps[\"POSTallele\"].astype(str) + \"_\" + snps[\"ALTallele\"].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(32283, 31)\n",
+      "(32283, 31)\n",
+      "     chr        pos allele1 allele2  rsid  logfc.mean  abs_logfc.mean  \\\n",
+      "0  chr14   36491929       A       G     0   -0.003573        0.005533   \n",
+      "1   chr1  212970898       C       T     0   -0.028217        0.028832   \n",
+      "2   chr1  247241664       G       A     0    0.005677        0.007244   \n",
+      "3  chr10  131934894       T       C     0   -0.007499        0.011212   \n",
+      "4  chr12  113859396       A       G     0    0.008961        0.012476   \n",
+      "\n",
+      "   abs_logfc.mean.pval  jsd.mean  jsd.mean.pval  ...  \\\n",
+      "0             0.836895  0.004586       0.844625  ...   \n",
+      "1             0.452418  0.009260       0.436804  ...   \n",
+      "2             0.797832  0.005027       0.810293  ...   \n",
+      "3             0.700605  0.009278       0.433587  ...   \n",
+      "4             0.680960  0.005033       0.804005  ...   \n",
+      "\n",
+      "   max_percentile.mean.pval  logfc_x_max_percentile.mean  \\\n",
+      "0                  0.310667                    -0.000256   \n",
+      "1                  0.748764                    -0.000256   \n",
+      "2                  0.008445                     0.005185   \n",
+      "3                  0.005655                    -0.007059   \n",
+      "4                  0.501710                     0.000249   \n",
+      "\n",
+      "   abs_logfc_x_max_percentile.mean  abs_logfc_x_max_percentile.mean.pval  \\\n",
+      "0                         0.000756                              0.543318   \n",
+      "1                         0.000269                              0.705848   \n",
+      "2                         0.006616                              0.245502   \n",
+      "3                         0.010595                              0.168818   \n",
+      "4                         0.000483                              0.606662   \n",
+      "\n",
+      "   jsd_x_max_percentile.mean  jsd_x_max_percentile.mean.pval  \\\n",
+      "0                   0.000700                        0.434209   \n",
+      "1                   0.000091                        0.739142   \n",
+      "2                   0.004652                        0.127204   \n",
+      "3                   0.008748                        0.059325   \n",
+      "4                   0.000216                        0.607714   \n",
+      "\n",
+      "   abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+      "0                               0.000004   \n",
+      "1                               0.000003   \n",
+      "2                               0.000033   \n",
+      "3                               0.000098   \n",
+      "4                               0.000002   \n",
+      "\n",
+      "   abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+      "0                                    0.611521   \n",
+      "1                                    0.672452   \n",
+      "2                                    0.340609   \n",
+      "3                                    0.202098   \n",
+      "4                                    0.661193   \n",
+      "\n",
+      "   logfc_x_jsd_x_max_percentile.mean                rsid1  \n",
+      "0                      -8.197982e-07   chr14_36491929_G_A  \n",
+      "1                      -2.921052e-06   chr1_212970898_T_C  \n",
+      "2                       2.570196e-05   chr1_247241664_A_G  \n",
+      "3                      -6.947830e-05  chr10_131934894_C_T  \n",
+      "4                       1.409264e-06  chr12_113859396_G_A  \n",
+      "\n",
+      "[5 rows x 25 columns]\n",
+      "(32283, 34)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bqtls_dnase = pd.read_csv(dnase_preds,sep=\"\\t\", header=0, index_col=False)\n",
+    "bqtls_atac = pd.read_csv(atac_preds,sep=\"\\t\", header=0, index_col=False)\n",
+    "bqtls_atac_50m = pd.read_csv(atac_preds_50m,sep=\"\\t\", header=0, index_col=False)\n",
+    "bqtls_atac_50m['rsid1'] = bqtls_atac_50m[\"chr\"].astype(str) + \"_\" + bqtls_atac_50m[\"pos\"].astype(str) + \"_\" + bqtls_atac_50m[\"allele2\"].astype(str) + \"_\" + bqtls_atac_50m[\"allele1\"].astype(str)\n",
+    "\n",
+    "bqtls_dnase = bqtls_dnase.merge(snps, on=[\"rsid\"])\n",
+    "bqtls_dnase['logratio']=np.log2((bqtls_dnase['prechipfreq'].astype(float)+.01)/(bqtls_dnase['POSTfreq'].astype(float)+0.01))\n",
+    "print(bqtls_dnase.shape)\n",
+    "\n",
+    "bqtls_atac = bqtls_atac.merge(snps, on=[\"rsid\"])\n",
+    "bqtls_atac['logratio']=np.log2((bqtls_atac['prechipfreq'].astype(float)+.01)/(bqtls_atac['POSTfreq'].astype(float)+0.01))\n",
+    "print(bqtls_atac.shape)\n",
+    "\n",
+    "print(bqtls_atac_50m.head())\n",
+    "bqtls_atac_50m = bqtls_atac_50m.merge(snps, right_on=[\"rsid\"], left_on=[\"rsid1\"])\n",
+    "bqtls_atac_50m['logratio']=np.log2((bqtls_atac_50m['prechipfreq'].astype(float)+.01)/(bqtls_atac_50m['POSTfreq'].astype(float)+0.01))\n",
+    "print(bqtls_atac_50m.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_enf = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/bqtls/enformer_predictions.tsv\", sep=\"\\t\", header=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr14</td>\n",
+       "      <td>20579247</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs56163433</td>\n",
+       "      <td>-0.000005</td>\n",
+       "      <td>-0.000006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr14</td>\n",
+       "      <td>20763006</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs1000608</td>\n",
+       "      <td>-0.000121</td>\n",
+       "      <td>-0.000154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr14</td>\n",
+       "      <td>20891188</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs1713454</td>\n",
+       "      <td>-0.000005</td>\n",
+       "      <td>-0.000005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr14</td>\n",
+       "      <td>20924449</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs3136818</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>0.000084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr14</td>\n",
+       "      <td>20944397</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs111949975</td>\n",
+       "      <td>0.000069</td>\n",
+       "      <td>0.000078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32056</th>\n",
+       "      <td>chr21</td>\n",
+       "      <td>47711903</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs61202414</td>\n",
+       "      <td>0.000026</td>\n",
+       "      <td>0.000032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32057</th>\n",
+       "      <td>chr21</td>\n",
+       "      <td>47894705</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs7277132</td>\n",
+       "      <td>-0.000008</td>\n",
+       "      <td>-0.000010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32058</th>\n",
+       "      <td>chr21</td>\n",
+       "      <td>47920197</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2839296</td>\n",
+       "      <td>-0.000081</td>\n",
+       "      <td>-0.000105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32059</th>\n",
+       "      <td>chr21</td>\n",
+       "      <td>47955095</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs146076255</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32060</th>\n",
+       "      <td>chr21</td>\n",
+       "      <td>48027177</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs13047696</td>\n",
+       "      <td>0.000134</td>\n",
+       "      <td>0.000173</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>32061 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         chr       pos ref alt         rsid  DNASE_SAD  DNASE_SAR\n",
+       "0      chr14  20579247   G   T   rs56163433  -0.000005  -0.000006\n",
+       "1      chr14  20763006   A   C    rs1000608  -0.000121  -0.000154\n",
+       "2      chr14  20891188   C   G    rs1713454  -0.000005  -0.000005\n",
+       "3      chr14  20924449   T   A    rs3136818   0.000076   0.000084\n",
+       "4      chr14  20944397   C   T  rs111949975   0.000069   0.000078\n",
+       "...      ...       ...  ..  ..          ...        ...        ...\n",
+       "32056  chr21  47711903   C   G   rs61202414   0.000026   0.000032\n",
+       "32057  chr21  47894705   G   A    rs7277132  -0.000008  -0.000010\n",
+       "32058  chr21  47920197   C   A    rs2839296  -0.000081  -0.000105\n",
+       "32059  chr21  47955095   T   C  rs146076255   0.000001   0.000002\n",
+       "32060  chr21  48027177   A   G   rs13047696   0.000134   0.000173\n",
+       "\n",
+       "[32061 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_enf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_enf['rsid'] = data_enf[\"chr\"].astype(str) + \"_\" + data_enf[\"pos\"].astype(str) + \"_\" + data_enf[\"ref\"].astype(str) + \"_\" + data_enf[\"alt\"].astype(str)\n",
+    "data_enf['rsid1'] = data_enf[\"chr\"].astype(str) + \"_\" + data_enf[\"pos\"].astype(str) + \"_\" + data_enf[\"alt\"].astype(str) + \"_\" + data_enf[\"ref\"].astype(str)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bqtls_atac1 = bqtls_atac.merge(data_enf, right_on=[\"rsid\"], left_on=[\"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "bqtls_atac2 = bqtls_atac.merge(data_enf, right_on=[\"rsid1\"], left_on=[\"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "bqtls_atac2[\"DNASE_SAD\"] = -1*bqtls_atac2[\"DNASE_SAD\"]\n",
+    "bqtls_atac2[\"DNASE_SAR\"] = -1*bqtls_atac2[\"DNASE_SAR\"]\n",
+    "\n",
+    "bqtls_atac_full = pd.concat([bqtls_atac1, bqtls_atac2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bqtls_atac1_50m = bqtls_atac_50m.merge(data_enf, right_on=[\"rsid\"], left_on=[\"rsid1\"], how=\"inner\").drop_duplicates()\n",
+    "bqtls_atac2_50m = bqtls_atac_50m.merge(data_enf, right_on=[\"rsid1\"], left_on=[\"rsid1\"], how=\"inner\").drop_duplicates()\n",
+    "bqtls_atac2_50m[\"DNASE_SAD\"] = -1*bqtls_atac2_50m[\"DNASE_SAD\"]\n",
+    "bqtls_atac2_50m[\"DNASE_SAR\"] = -1*bqtls_atac2_50m[\"DNASE_SAR\"]\n",
+    "\n",
+    "bqtls_atac_full_50m = pd.concat([bqtls_atac1_50m, bqtls_atac2_50m])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(31872, 40)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_atac_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(31872, 43)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_atac_full_50m.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32283, 31)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_atac.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bqtls_dn1 = bqtls_dnase.merge(data_enf, right_on=[\"rsid\"], left_on=[\"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "bqtls_dn2 = bqtls_dnase.merge(data_enf, right_on=[\"rsid1\"], left_on=[\"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "bqtls_dn2[\"DNASE_SAD\"] = -1*bqtls_dn2[\"DNASE_SAD\"]\n",
+    "bqtls_dn2[\"DNASE_SAR\"] = -1*bqtls_dn2[\"DNASE_SAR\"]\n",
+    "\n",
+    "bqtls_dn_full = pd.concat([bqtls_dn1, bqtls_dn2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(31872, 40)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_dn_full.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32283, 31)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_dnase.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import scipy.stats\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42\n",
+    "\n",
+    "\n",
+    "def  get_auprc_curves(data_atac, data_dnase, data_atac_50m):\n",
+    "    \n",
+    "    keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "    #print(sum(np.isnan( abs(merged_atac[keyd]))))\n",
+    "    #print(merged_dnase.shape)\n",
+    "    #print(merged_atac[np.isnan( abs(merged_atac[keyd]))])\n",
+    "   \n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(data_atac[\"label\"].astype(int), abs(data_atac[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(data_atac[\"label\"], abs(data_atac[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=\"ChromBPNet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(data_atac_50m[\"label\"].astype(int), abs(data_atac_50m[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(data_atac_50m[\"label\"], abs(data_atac_50m[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=\"ChromBPNet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "\n",
+    "    \n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(data_dnase[\"label\"].astype(int), abs(data_dnase[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(data_dnase[\"label\"], abs(data_dnase[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='cyan', label=\"ChromBPNet w/ DNase, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "    \n",
+    "    #enformer\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(data_atac[\"label\"].astype(int), abs(data_atac[\"DNASE_SAR\"]))\n",
+    "    roc_auc = metrics.average_precision_score(data_atac[\"label\"], abs(data_atac[\"DNASE_SAR\"]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='orange', label=\"Enformer SAD, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "    \n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "    #plt.savefig(\"subfigs_new/dsqtls_final.pdf\", transparent=True, dpi=300)\n",
+    "    \n",
+    "#     plt.Figure()\n",
+    "#     plt.scatter(merged_enf[\"DNASE_SAD\"][merged_enf[\"label\"]==1], merged_enf[\"Beta\"][merged_enf[\"label\"]==1])\n",
+    "#     plt.show()\n",
+    "    \n",
+    "    keyd=\"jsd_sign.mean\"\n",
+    "\n",
+    "    data_atac[\"jsd_sign.mean\"] = data_atac['jsd.mean']*np.sign(data_atac['logfc.mean'])\n",
+    "    data_atac_50m[\"jsd_sign.mean\"] = data_atac_50m['jsd.mean']*np.sign(data_atac_50m['logfc.mean'])\n",
+    "    data_dnase[\"jsd_sign.mean\"] = data_dnase['jsd.mean']*np.sign(data_dnase['logfc.mean'])\n",
+    "\n",
+    "    print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(data_atac[keyd][data_atac[\"label\"]==1], data_atac[\"logratio\"][data_atac[\"label\"]==1])[0],2))\n",
+    "    print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(data_atac_50m[keyd][data_atac_50m[\"label\"]==1], data_atac_50m[\"logratio\"][data_atac_50m[\"label\"]==1])[0],2))\n",
+    "    print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(data_dnase[keyd][data_dnase[\"label\"]==1], data_dnase[\"logratio\"][data_dnase[\"label\"]==1])[0],2))\n",
+    "\n",
+    "#     import seaborn as sns\n",
+    "\n",
+    "#     plt.figure(figsize=(8,5))\n",
+    "#     data = pd.DataFrame()\n",
+    "#     data[\"sd_sign.mean\"] = merged_atac[merged_atac[\"label\"]==1][\"jsd_sign.mean\"]\n",
+    "#     data[\"Beta\"] = merged_atac[merged_atac[\"label\"]==1][\"Beta\"]\n",
+    "#     data[\"logfc_x_max_percentile.mean\"] = abs(merged_atac[merged_atac[\"label\"]==1][\"max_percentile.mean\"])\n",
+    "\n",
+    "#     sns.scatterplot(data=data, y=\"sd_sign.mean\", x=\"Beta\", hue=\"logfc_x_max_percentile.mean\", s=10)\n",
+    "    #plt.show()\n",
+    "    #plt.savefig(\"subfigs_new/dsqtls_final.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.6\n",
+      "ChromBPNet ATAC:  -0.58\n",
+      "ChromBPNet DNase:  0.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_auprc_curves(bqtls_atac_full, bqtls_dn_full, bqtls_atac_full_50m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.04"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.round(sum(bqtls_atac[\"label\"])/bqtls_atac.shape[0],2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32283, 32)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_atac.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'abs_logfc.mean', 'abs_logfc.mean.pval', 'jsd.mean', 'jsd.mean.pval',\n",
+       "       'logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean.pval',\n",
+       "       'max_percentile.mean', 'max_percentile.mean.pval',\n",
+       "       'logfc_x_max_percentile.mean', 'abs_logfc_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_max_percentile.mean.pval', 'jsd_x_max_percentile.mean',\n",
+       "       'jsd_x_max_percentile.mean.pval',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'logfc_x_jsd_x_max_percentile.mean', 'Chr', 'position', 'ALTallele',\n",
+       "       'POSTallele', 'label', 'POSTfreq', 'prechipfreq', 'logratio', 'chr_y',\n",
+       "       'pos_y', 'ref', 'alt', 'DNASE_SAD', 'DNASE_SAR', 'rsid1', 'rsid_x',\n",
+       "       'rsid_y', 'jsd_sign.mean'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_atac_full.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/bqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/bqtls.ipynb
new file mode 100644
index 00000000..8726b3e7
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/bqtls.ipynb
@@ -0,0 +1,475 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "snps=pd.read_csv(\"/mnt/lab_data/kundaje/users/anusri/QTLs/bQTLs/src/pu1.txt\",header=0,sep='\\t')\n",
+    "dnase_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/DNASE/summary.mean.variant_scores_new.tsv\"\n",
+    "atac_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/ATAC/summary.mean.variant_scores_new.tsv\"\n",
+    "#atac_preds=\"/mnt/lab_data2/anusri/chrombpnet/results/chrombpnet/ATAC_PE/GM12878/subsampling/GM12878_250M/bqtls_pu1_preds/snp_scores.tsv\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps.head()\n",
+    "snps['rsid'] = snps[\"Chr\"].astype(str) + \"_\" + snps[\"position\"].astype(str) + \"_\" + snps[\"POSTallele\"].astype(str) + \"_\" + snps[\"ALTallele\"].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bqtls_dnase = pd.read_csv(dnase_preds,sep=\"\\t\", header=0, index_col=False)\n",
+    "bqtls_atac = pd.read_csv(atac_preds,sep=\"\\t\", header=0, index_col=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 34)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_dnase = bqtls_dnase.merge(snps, on=[\"rsid\"])\n",
+    "bqtls_dnase['logratio']=np.log2((bqtls_dnase['prechipfreq'].astype(float)+.01)/(bqtls_dnase['POSTfreq'].astype(float)+0.01))\n",
+    "bqtls_dnase.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 34)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_atac = bqtls_atac.merge(snps, on=[\"rsid\"])\n",
+    "bqtls_atac['logratio']=np.log2((bqtls_atac['prechipfreq'].astype(float)+.01)/(bqtls_atac['POSTfreq'].astype(float)+0.01))\n",
+    "bqtls_atac.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bqtls_dnase['pvalue'] = bqtls_dnase['pvalue'].astype(float)\n",
+    "bqtls_atac['pvalue'] = bqtls_atac['pvalue'].astype(float)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1041"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bqtls_atac['pvalue']<1e-7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "905400"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bqtls_atac['pvalue']>0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'bqtls_atac' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-71f30283ca30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbqtls_atac\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'bqtls_atac' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "bqtls_atac.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42\n",
+    "\n",
+    "def get_auprc(bqtls_df, bqtls_df_dnase):\n",
+    "    bqtls_df_sig = bqtls_df[bqtls_df['pvalue']<1e-9].reset_index()\n",
+    "    bqtls_df_sig[\"label\"]=1\n",
+    "    bqtls_df_nsig = bqtls_df[bqtls_df['pvalue']>0.1].reset_index()\n",
+    "    bqtls_df_nsig[\"label\"]=0\n",
+    "    bqtls_df_nsig = bqtls_df_nsig.sample(bqtls_df_sig.shape[0]*50)\n",
+    "    \n",
+    "    alldf = pd.concat([bqtls_df_sig,bqtls_df_nsig])\n",
+    "    keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "    #keyd=\"Beta\"\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(alldf[\"label\"].astype(int), abs(alldf[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(alldf[\"label\"], abs(alldf[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=\"ChromBPNet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "    print(roc_auc)\n",
+    "    \n",
+    "    bqtls_df_sig = bqtls_df_dnase[bqtls_df_dnase['pvalue']<1e-9].reset_index()\n",
+    "    bqtls_df_sig[\"label\"]=1\n",
+    "    bqtls_df_nsig = bqtls_df_dnase[bqtls_df_dnase['pvalue']>0.1].reset_index()\n",
+    "    bqtls_df_nsig[\"label\"]=0\n",
+    "    bqtls_df_nsig = bqtls_df_nsig.sample(bqtls_df_sig.shape[0]*50)\n",
+    "    \n",
+    "    alldf = pd.concat([bqtls_df_sig,bqtls_df_nsig])\n",
+    "    keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "    #keyd=\"Beta\"\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(alldf[\"label\"].astype(int), abs(alldf[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(alldf[\"label\"], abs(alldf[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='red', linestyle='dashed', label=\"ChromBPNet w/ DNase, AP=\"+str(round(roc_auc,2)))\n",
+    "    print(roc_auc)\n",
+    "    \n",
+    "    alldf = pd.concat([bqtls_df_sig,bqtls_df_nsig])\n",
+    "    index = np.arange(len(alldf[\"label\"]))\n",
+    "    np.random.shuffle(index)\n",
+    "\n",
+    "    keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "    #keyd=\"Beta\"\n",
+    "    \n",
+    "    shuff = abs(alldf[keyd].to_numpy())[np.array(index)]\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(alldf[\"label\"].astype(int), shuff)\n",
+    "    roc_auc = metrics.average_precision_score(alldf[\"label\"], shuff)\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='green', linestyle='dashed', label=\"Random AP=\"+str(round(roc_auc,2)))\n",
+    "    print(roc_auc)\n",
+    "    \n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.44686949839204126\n",
+      "0.43470330000193674\n",
+      "0.019821494216998248\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "get_auprc(bqtls_atac, bqtls_dnase)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'abs_logfc.mean', 'abs_logfc.mean.pval', 'jsd.mean', 'jsd.mean.pval',\n",
+       "       'logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean.pval',\n",
+       "       'max_percentile.mean', 'max_percentile.mean.pval',\n",
+       "       'logfc_x_max_percentile.mean', 'abs_logfc_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_max_percentile.mean.pval', 'jsd_x_max_percentile.mean',\n",
+       "       'jsd_x_max_percentile.mean.pval',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'logfc_x_jsd_x_max_percentile.mean', 'Chr', 'position', 'Depth',\n",
+       "       'ALTdepth', 'REFDepth', 'ALTallele', 'POSTallele', 'POSTfreq',\n",
+       "       'prechipfreq', 'pvalue', 'logratio'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls_atac.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "snps_atac = bqtls_atac[bqtls_atac['pvalue']<1e-9].drop_duplicates()\n",
+    "snps_atac[\"output_sign_result_1\"] = np.sign(snps_atac[\"logfc.mean\"])*snps_atac[\"jsd.mean\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f63c95d7310>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "plt.figure(figsize=(8,8))\n",
+    "\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=snps_atac,\n",
+    "    x=\"logratio\",\n",
+    "    y=\"output_sign_result_1\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "ax = sns.scatterplot(x=\"logratio\", y=\"output_sign_result_1\", hue=\"max_percentile.mean\", data=snps_atac, s=15, palette=\"Greens\")\n",
+    "sns.regplot(x=\"logratio\", y=\"output_sign_result_1\", data=snps_atac, scatter=False, ci=None, color=\"#90EE90\")\n",
+    "#sns.jointplot(x=data[\"Beta\"], y=data[\"sd_sign.mean\"],s=0,ax=ax)\n",
+    "\n",
+    "norm = plt.Normalize(snps_atac['max_percentile.mean'].min(), snps_atac['max_percentile.mean'].max())\n",
+    "sm = plt.cm.ScalarMappable(cmap=\"Greens\", norm=norm)\n",
+    "sm.set_array([])\n",
+    "\n",
+    "# Remove the legend and add a colorbar\n",
+    "ax.get_legend().remove()\n",
+    "ax.figure.colorbar(sm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(633, 35)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps_atac.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "437"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bqtls_atac['pvalue']<1e-11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.575064669218782, 5.298554840966146e-57)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.pearsonr(snps_atac[\"logratio\"], snps_atac[\"output_sign_result_1\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/caqtks_latest_filter.ipynb b/src/evaluation/figure_notebooks/figure_6/caqtks_latest_filter.ipynb
new file mode 100644
index 00000000..44e890da
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/caqtks_latest_filter.ipynb
@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import scipy.stats\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42\n",
+    "\n",
+    "data_dnase = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/DNASE/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "data_dnase[['Chrx', 'Posx', 'REF', 'ALT']] = data_dnase['rsid'].str.split(\"_\", expand=True)\n",
+    "data_dnase[\"jsd_sign.mean\"] = data_dnase[\"jsd.mean\"]*np.sign(data_dnase[\"logfc.mean\"])\n",
+    "\n",
+    "data_atac = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/ATAC/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac[['Chrx', 'Posx', 'REF', 'ALT']] = data_atac['rsid'].str.split(\"_\", expand=True)\n",
+    "data_atac[\"jsd_sign.mean\"] = data_atac[\"jsd.mean\"]*np.sign(data_atac[\"logfc.mean\"])\n",
+    "\n",
+    "data_atac_50m = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/chrombpnet_preds/atac_50M_model/ATAC_50M/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac_50m[['Chrx', 'Posx', 'REF', 'ALT']] = data_atac_50m['rsid'].str.split(\"_\", expand=True)\n",
+    "data_atac_50m[\"jsd_sign.mean\"] = data_atac_50m[\"jsd.mean\"]*np.sign(data_atac_50m[\"logfc.mean\"])\n",
+    "\n",
+    "#data_enf = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/enformer_predictions.tsv\", sep=\"\\t\", header=0)\n",
+    "\n",
+    "def  get_auprc_curves(data_atac, data_dnase, data_atac_50m):\n",
+    "\n",
+    "    qvals = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/source1.tsv\", sep=\"\\t\", header=0)\n",
+    "    print(\"source:\",qvals.shape)\n",
+    "    \n",
+    "    data_enf = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/enformer_predictions1.tsv\", sep=\"\\t\", header=0).drop_duplicates()\n",
+    "    qvals[\"rsidn\"] = qvals[\"chr\"] + \"_\" + qvals[\"Pos\"].astype(\"str\") + \"_\"  + qvals[\"Ref\"] + \"_\"  + qvals[\"Alt\"]\n",
+    "    data_enf[\"rsid1\"] = data_enf[\"chr\"] + \"_\" + data_enf[\"pos\"].astype(int).astype(\"str\") + \"_\"  + data_enf[\"ref\"] + \"_\"  + data_enf[\"alt\"]\n",
+    "    data_enf[\"rsid2\"] = data_enf[\"chr\"] + \"_\" + data_enf[\"pos\"].astype(int).astype(\"str\") + \"_\"  + data_enf[\"alt\"] + \"_\"  + data_enf[\"ref\"]\n",
+    "\n",
+    "    merged_enf1 = data_enf.merge(qvals, how=\"right\", left_on=[\"rsid1\"], right_on=[\"rsidn\"]).drop_duplicates()\n",
+    "    merged_enf1 = merged_enf1[~np.isnan(merged_enf1[\"DNASE_SAD\"])]\n",
+    "    merged_enf2 = data_enf.merge(qvals, how=\"right\", left_on=[\"rsid2\"], right_on=[\"rsidn\"]).drop_duplicates()\n",
+    "    merged_enf2 = merged_enf2[~np.isnan(merged_enf2[\"DNASE_SAD\"])]\n",
+    "    merged_enf2[\"DNASE_SAD\"] = merged_enf2[\"DNASE_SAD\"]*-1\n",
+    "    merged_enf2[\"DNASE_SAR\"] = merged_enf2[\"DNASE_SAR\"]*-1\n",
+    " \n",
+    "    merged_enf = pd.concat([merged_enf1,merged_enf2])\n",
+    "    print(\"Enf:\",merged_enf.shape)\n",
+    "    #print(merged_enf.head())\n",
+    "    \n",
+    "    # source \n",
+    "    qvals = qvals[qvals[\"rsidn\"].isin(merged_enf[\"rsidn\"])]\n",
+    "    \n",
+    "    #print(data_atac.shape)\n",
+    "    merged_atac = data_atac.merge(qvals, how=\"right\", left_on=[\"chr\", \"allele1\", \"allele2\", \"pos\"], right_on=[\"chr\", \"Ref\", \"Alt\", \"Pos\"]).drop_duplicates()\n",
+    "    merged_atac_50m = data_atac_50m.merge(qvals, how=\"right\", left_on=[\"chr\", \"allele1\", \"allele2\", \"pos\"], right_on=[\"chr\", \"Ref\", \"Alt\", \"Pos\"]).drop_duplicates()\n",
+    "    merged_dnase = data_dnase.merge(qvals, how=\"right\", left_on=[\"chr\", \"allele1\", \"allele2\", \"pos\"], right_on=[\"chr\", \"Ref\", \"Alt\", \"Pos\"]).drop_duplicates()\n",
+    "    print(\"ATAC shape\",merged_atac.shape)\n",
+    "    \n",
+    "    keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(merged_atac[\"label\"].astype(int), abs(merged_atac[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(merged_atac[\"label\"], abs(merged_atac[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=\"ChromBPNet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(merged_atac_50m[\"label\"].astype(int), abs(merged_atac_50m[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(merged_atac_50m[\"label\"], abs(merged_atac_50m[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=\"ChromBPNet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(merged_dnase[\"label\"].astype(int), abs(merged_dnase[keyd]))\n",
+    "    roc_auc = metrics.average_precision_score(merged_dnase[\"label\"], abs(merged_dnase[keyd]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='cyan', label=\"ChromBPNet w/ DNase, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "    \n",
+    "    #enformer\n",
+    "    \n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(merged_enf[\"label\"].astype(int), abs(merged_enf[\"DNASE_SAR\"]))\n",
+    "    roc_auc = metrics.average_precision_score(merged_enf[\"label\"], abs(merged_enf[\"DNASE_SAR\"]))\n",
+    "    plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='orange', label=\"Enformer SAD, AP=\"+str(round(roc_auc,2)))\n",
+    "    #print(roc_auc)\n",
+    "    \n",
+    "    plt.legend()\n",
+    "    #plt.show()\n",
+    "    #plt.savefig(\"subfigs_new/dsqtls_final.pdf\", transparent=True, dpi=300)\n",
+    "    \n",
+    "#     plt.Figure()\n",
+    "#     plt.scatter(merged_enf[\"DNASE_SAD\"][merged_enf[\"label\"]==1], merged_enf[\"Beta\"][merged_enf[\"label\"]==1])\n",
+    "#     plt.show()\n",
+    "    \n",
+    "    print(\"Random: \", np.round(sum(merged_atac[\"label\"]==1)/merged_atac.shape[0],2))\n",
+    "    keyd=\"jsd_sign.mean\"\n",
+    "\n",
+    "    print(sum(merged_atac[\"label\"]==1))\n",
+    "    print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(merged_atac[keyd][merged_atac[\"label\"]==1], merged_atac[\"Beta\"][merged_atac[\"label\"]==1])[0],2))\n",
+    "    print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(merged_atac_50m[keyd][merged_atac_50m[\"label\"]==1], merged_atac_50m[\"Beta\"][merged_atac_50m[\"label\"]==1])[0],2))\n",
+    "    print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(merged_dnase[keyd][merged_dnase[\"label\"]==1], merged_dnase[\"Beta\"][merged_dnase[\"label\"]==1])[0],2))\n",
+    "    print(\"Enformer: \",np.round(scipy.stats.pearsonr(merged_enf[\"DNASE_SAD\"][merged_enf[\"label\"]==1], merged_enf[\"Beta\"][merged_enf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "    print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(merged_atac[keyd][merged_atac[\"label\"]==1], merged_atac[\"Beta\"][merged_atac[\"label\"]==1])[1])\n",
+    "    print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(merged_atac_50m[keyd][merged_atac_50m[\"label\"]==1], merged_atac_50m[\"Beta\"][merged_atac_50m[\"label\"]==1])[1])\n",
+    "    print(\"ChromBPNet DNase: \",scipy.stats.pearsonr(merged_dnase[keyd][merged_dnase[\"label\"]==1], merged_dnase[\"Beta\"][merged_dnase[\"label\"]==1])[1])\n",
+    "    print(\"Enformer: \",scipy.stats.pearsonr(merged_enf[\"DNASE_SAD\"][merged_enf[\"label\"]==1], merged_enf[\"Beta\"][merged_enf[\"label\"]==1])[1])\n",
+    "    \n",
+    "    import seaborn as sns\n",
+    "\n",
+    "    plt.figure(figsize=(8,5))\n",
+    "    data = pd.DataFrame()\n",
+    "    data[\"sd_sign.mean\"] = merged_atac[merged_atac[\"label\"]==1][\"jsd_sign.mean\"]\n",
+    "    data[\"Beta\"] = merged_atac[merged_atac[\"label\"]==1][\"Beta\"]\n",
+    "    data[\"logfc_x_max_percentile.mean\"] = abs(merged_atac[merged_atac[\"label\"]==1][\"max_percentile.mean\"])\n",
+    "\n",
+    "    sns.scatterplot(data=data, y=\"sd_sign.mean\", x=\"Beta\", hue=\"logfc_x_max_percentile.mean\", s=10)\n",
+    "    #plt.show()\n",
+    "    #plt.savefig(\"subfigs_new/dsqtls_final.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "source: (45390, 7)\n",
+      "Enf: (45390, 17)\n",
+      "ATAC shape (45390, 35)\n",
+      "Random:  0.02\n",
+      "890\n",
+      "ChromBPNet ATAC:  0.59\n",
+      "ChromBPNet ATAC:  0.6\n",
+      "ChromBPNet DNase:  0.61\n",
+      "Enformer:  0.44\n",
+      "ChromBPNet ATAC:  2.6143639485677044e-86\n",
+      "ChromBPNet ATAC:  1.1267082836016674e-86\n",
+      "ChromBPNet DNase:  1.6706179667313613e-91\n",
+      "Enformer:  1.193930091984343e-43\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ufve73zFt2rRRaqnsSlcwexzW9AR0FgOWlDObuNewrYpVj72GpJC46v/dSnpZzgi3UiYbGXKgv4K076yiZuVmAnY3WpuJgiVTSa0oOuvjnUmZWoBHH32Ub37zm/zqV786p/bLZEf4nR7cPU7iMpNQqk//NaZQKkguGl4mPr/DC8Ry8/vd3rNqp0x2MZC77q8Q7TurOPjqWgL2WHKRgN3NwVfX0r6z6qyPeSZlardv305TUxPXXHPNWZ9HJjtW0Bdg7eNv8u5Pn2P/21tG7Dx504qpvG0u0z6xiKzyghE7j0w20uQ7+itEzcrNREPhQY9FQ2FqVm4+p7v6U4lGo3z1q1/lqaeeGpHjy65MQY+f7upWALobOkbsPFqTnvHXyMNNskufHOivEEfu5M/08TNxujK1LpeLvXv3Mn/+fADa29tZsWIFr7/+OpWVJ6RjlsnOiCnRyvT7rqKrupW86XItAZnsdORAf4XQ2kxDBnWtzXTWxzxdmVqr1Up3d/fA7/Pnz+fXv/61HORl5yx/Zhn5M8sAaNy0H3t9OxlTSkjITx/llslkFx95jP4KUbBkKorjJi0p1CoKlkw962OeSZlamWwkubvs7PnnKpo2HaB+7a7Rbo5MdlGS7+ivEEfG4c/nrHuA5cuXs3z58kGP/ehHPxpy29WrV5/TuWSy42mMeuJy0+ira8OalTzazZHJLkpyoL+CpFYUjdjEO5lsNGgMWibftwx/nxtzWsJoN0cmuyjJgV4mk13StCYDWpNhtJshk1205DF6mUwmk8kuY3Kgl8lkMpnsMiYHeplMJpPJLmNyoJfJZDKZ7DImB3rZOTmTMrX//Oc/KS0tpaysjLvuuusCt1B2IQV9ARq2VdHb1DXaTZHJZP3kWfeys3YmZWqrqqr42c9+xvr164mLi6Ozs3MUWywbaXve3sq2l9ehsxq58Yf3YE6yjnaTZLIr3qgHekmSlgG/A5TAn4UQPz/u+a8CnwXCQBfwaSFEwwVv6GXg0Ed72fDcKlw9TswJFmbeuYDiOePO+nhnUqb2f//3f3nooYeIi4vVAE9OlpOaXM78zlg514DLS8gfHOXWyC51rrZuXO29JBRkorXISyjP1qgGekmSlMAfgCVAM7BFkqTXhRD7j9lsB1AphPBKkvR54JfA7Re+tZe2Qx/t5YM//ZtwMFbBztXt5IM//RvgrIP9UGVqN23aNGibw4cPAzBr1iwikQg/+MEPWLZs2VmdT3bxK1s2GZ1FT3xWEvFZSaPdHNklLODyseOvb+O3u8mcXkrpTfNGu0mXrNG+o58KVAshagEkSXoeuB4YCPRCiFXHbL8R+MQFbeFlYsNzqwaC/BHhYJgNz606p7v60wmHw1RVVbF69Wqam5uZO3cue/bswWazjdg5ZaPHlhrP5Jtmj3YzZJcBEY0S8vkBiPhDo9yaS9toB/oMoOmY35uBUxWA/gzw9oi26DLl6nEO6/EzcboytRC7y582bRpqtZq8vDzGjBlDVVUVU6ZMOevzymSXo2AwiFqtRpKk0W7KRUFnNTLxU8txNnWSVJo72s25pF0ys+4lSfoEUAn86iTPf06SpK2SJG3t6pJn/B7PnGAZ1uNn4tgytcFgkOeff54VK1YM2uaGG24YKGbT3d3N4cOHB8b0ZTJZzI6Ne3jg+v/Hz77+Oxx9Z3/xfbmJL8ggd/5EjMlxo92US9poB/oWIOuY3zP7HxtEkqTFwHeBFUKIwFAHEkI8KYSoFEJUJiXJY4PHm3nnAlSawR04Ko2KmXcuOOtjnkmZ2qVLl5KQkEBpaSkLFizgV7/6FQkJcvERmexYW9buoKOli3XvbaL+cONoN0d2mZGEEKN3cklSAYeBRcQC/BbgLiHEvmO2mQi8BCwTQlSdyXErKyvF1q1bBz124MABxo4de76afkk637PuZacmf+ZkZ2r7x7v53X/8iaKyAr74/c9iizv7njbZlUuSpG1CiMrjHx/VMXohRFiSpC8C7xJbXvd/Qoh9kiT9CNgqhHidWFe9CXixf+yqUQix4qQHlZ1U8ZxxcmCXyS5Ck2ZM4MnXfoNKo0apHO2OVtnlZrQn4yGEeAt467jHvn/Mz4sveKNkMpnsAtPqtaPdBNllSr50lMlkMpnsMjbqd/QymUx2pkRUcHjdXlxdDornjMOcbBvtJslkFz050MtksktGR3ULa/4UG+kT4QhTbr80sqWFQ2FaatuIT4nDbDONdnNkVxi5614mk10ytEYdGkNsLFt/CQXMlS+s4ief/gV//Pb/4nF5Rrs5siuMHOhl5+R0ZWobGxtZsGABEydOZMKECbz11ltDHEUmOzNxGYmsePRuln/zNsYuLB/t5pyxhoOxOlw1e2px2+VAL7uw5K572Vk7kzK1P/nJT7jtttv4/Oc/z/79+1m+fDn19fWj12jZJS8+O4n47EsrKdaiW+ejNxkoKMslOfPSarvs0icH+ivIhnc28dITr9HT0UtCSjy3PHg9M5edqrTAqZ1JmVpJknA6Yyk9HQ4H6enp5/YiZLJLUFF5IUXlhaPdDNkVSg70V4gN72zirz9/lmB/jfCe9l7++vNnAc462J9Jmdof/OAHXHXVVTz22GN4PB7ef//9s3wFsiuNq9dF0OMnQS53K5OdE3mM/grx0hOvDQT5I4L+IC898dqInve5557jU5/6FM3Nzbz11lvcc889RKPRET2n7NIVdLrp2LaPll1VvPC9p/j7157k0Ib9p9/xItC+v4EdL6ymfX/DaDdFJhtEDvRXiJ6O3mE9fibOpEztX/7yF2677TYAZsyYgd/vp7u7+6zPKbu8de06RPvHO2nZdghXpwMRFXTVd5z18dwuD22NHYx0TY9wMMyWp1dy+MMdbHl6JeHguddP72np5t0/v83Hr6wnHAqfh1bKrlRyoL9CJKTED+vxM3EmZWqzs7P54IMPgFiRF7/fj1xdUHYySo0aAIMqyrSbZzF+yURKZp9dfQan3cVvvvYHvnHb91nz+vrz2cwTKFVKEvJSAYjPS0WpOvdR0Z0f7OTjf63n3f99m5qth8/5eLIrlzxGf4W45cHrB43RA2h0Gm558PqzPuaxZWojkQif/vSnB8rUVlZWsmLFCv7rv/6L+++/n9/+9rdIksRTTz1Ff3EimewEiRXF6OKtaKxmDMlnfxEK0NvRx+Fd1QBU7a1h/vWzz0cThyQpJCbfvYiC+eXYMhKRFOf+GbclWQHQm/T4O8++500mG9UytSNFLlM7tPM96152avJnbnSFw2He/sf71B1oYOntCymuKBrtJg2Lz+5my3MfIPwBUgoyKFkxa7SbJLvIXZRlamUX1sxl0+TALrtiqFQqrrt32Wg346zpbSbKr5uJp6OXhKKs0+8gk52EHOhlMtklxef28f7T79PT3M2iexeTVTI4CAY8fqrW7AYhKJo3Aa1JP0otPXfWzCSscoId2TmSA71MJrukNB9qYtPrHwOQlp96QqBv2lHNjpc+AkBrNlA0d/wFb6NMdjG5ombdX47zEWQXJ/mzNnISs5JIK0xHpVaRMy73hOcNNhOSQkKSJAxxl07hG5lspFwxd/Q6nY6enh4SEhLkWd+yESWEoKenB51ON9pNuSxJfT0sXVSEPnMe6ZOKT3g+fVwuy757JwhIzE876/N4Wjtx1DZhykzFkptx+h1ksovUFRPoMzMzaW5upqura7SbIrsC6HQ6MjMzR7sZlyVXXROS34u/uppIWSEqw4lj8Il5Zx/gj2hdvw1/Vx99B2oovmcFKvnCTXaJumICvVqtJi8vb7SbIZNdUdqbO/F5fOQV55y3Y9rGFtK37zDmvCyU+vMbfBvrWvjrH/6BzqDn+jmToKsPfXICiuMS4HRWt7Dz5Y+wZSRSftNstAb5IkB28bpiAr1MJruw6qua+O79P8Xj9PD1X3yRWUsGL+0Mef107jgEComUicWodNozOq45JwtzzpkvNwsHwzh7nFgSLajUp/7KW79qE++8tgqA8RXFzF2xEG289YRA37ytiu7qVrqrW8meXETq2PN3ISOTnW9X1GQ8mUx24XS2duGyu4lGBU11rSc833eogeaPttO8Zht9VU1DHOHcCSH495/f4mf3/Jx3//ruabcvGVeIyWwkKSWBotICTJmpqIcYGkguyUKpUZFUmIElLWEkmi6TnTfyHb1MJjtrIhrF39VJNBJGl5iMUqMZeC4zwcZNdyzBH4ow7+qZJ+yrthhAkpAk0JiNI9K+oD/I9ve3A7Bt5Tau+tRVqPvz6Q9l4tQJ/OOtx1GqVMQn2k66XWZ5Adf/7LMotSrUWs1Jt7vYtO+qxtXWQ9rEIkznUOdCdmmRA71MJjtrQacdd31N/28SxvTY7HQhBM0fbiHfF0ShVpGYYD1h37iCLMruvQYkCVNq4oi0T6vXct3nr2P7yu1ULq08ZZA/IukM26KzGM61eReUu72Hvc+/DwLCvgBjb5w72k2SXSByoJfJZGdNUqpAkkCIQePYkiSRUJJLy/rdJI7NQXmSu15T2shnfatcMpnKJZNH/DwXO5VOi85qwm93o7WMTA/KEaFg6IwuqmQXxhVT1EYmk42MoMsJkQhqq21QjopoKIy314nOZkJ1EXRvO7odRIJhdGY9PrefhLQrr+va02XH1+ciLjd1oCTw+fbSU2/w6t/f5vbPXM91dy0dkXPIhiYXtZHJZCNCY7YM+bhCrTqrcWAhxCmTWoXcXtxt3RiS49BazWd0zI66dp7/j78R8AQonF3Ghrc2cec37mDykklDbh8JR/A4PJjjzZdVgi1jkg1jkm3Eju9xe3nhf1/F4/Lyz/97jWW3LkJ9mpUOspEnz7qXyWSjwm930r37EN6O7oHH7HWtbH/iFQ6//hEhf2DI/Ro+2EzNq6toWLWVlm2H8fa6TnuunuYuvA5vLID3usidlM+2Dbvoaj0xgVYkHOFff3iV7936A1b9c/VZv74rkcGo5+ZPXovJbOTGe5bLQf4iIf8VZDLZqGjfsANXfQsqo56i25ej0mnpPlCHt7MXb2cvKeWFWHMGZ7gTQhB0uFGbDbQdbsOxai/JY3OY9sB1J9x5b39vG+v/tY4p10ylfH45M26ejd/jR20z8sHvXwQgMS2Bmz63YtB+HqeXj15bTzQSZeM7m1l4+4Lz9poDvgC9TZ3EpSegM106k/mEEHTVd6A36zEnnjix8ghJkrjjczey4q5lGC7hqoGXGznQy2Sys9Ze386q51ZhsplYePdCjMOY5CVJEhGtluo+H+6N+5gyfxJxBZl07qrGnJ2C/pjlba0tHbz2wtskJMaxZP40fO3ddL4dm4fjd3oQUYGkPBroI+EIH/z9fZzdDt5/6j0qFkxk/r1LAGiubcVg1uN1+UhIPXENvDnOxA0PrmDj25tYcvfis3xnhrb+7x+w74Md5E8Zw7Iv34RSpYy1aWcNzbtqyKwoILO84Lye83zYv3o37zz2GtYUG7f8xyewpZ56SEYO8heXUQ/0kiQtA34HKIE/CyF+ftzzc4H/BiYAdwghXrrgjZTJZEPau24vOz7cAUDe+DzGzR53xvumzpzE1n+8x3N/X4n0zPv8+M/foqRiDFO+fDtKtQq3w0vN7nrSC9N4618r+esfnwMgOz+T6bMno01MoKe6maTiLBTKwaOQSpWSqddM5cO/f8DUa6ehMRyzvj8/ne89+XVcdjdF42NBNeQPcuCNDfTWtVN2wywW3jafhbfNP8d3ZzARFbQebASgeW8DIX8IpUlJOBhi099X4rN7aNldS+rPslFdZDPWu5s6AXB02HF1O08b6GUXl1EN9JIkKYE/AEuAZmCLJEmvCyH2H7NZI/Ap4P9d+BbKZLJTyRiTgUqtwpxgJjk7eVj7aq0mNOZY97UkgaSIBWu1Xkc0GuXNx17l8ObD5FXkkz0jVqfCZDaRmBQLMgn5aSScojrdnNvmMXlZJWq1GhGJQv/xhRCk5w7ez9naTd1HewBo3VlN0pgTU+z6PX4ObDqANclK/vj8M3qN0WiUTW9spGZ7NVOvm87cT11FzaZDZFfkozPF8uMrVSpSx+ZQ9/F+0kpzUF6E49ql88oJ+YNYkuNIK5aLNV1qRvsTNRWoFkLUAkiS9DxwPTAQ6IUQ9f3PRUejgTKZ7OS6O/pILEhFrVOh6O+GHooQgoDHj9aoGzSWvuC6OSTp9ShRkFd0NLhGI1F62+wA9LX3cceiu/jfF36DyhVE6vURDoRQaU991+to7WH3ax8TjURRaxRMuH4m9t0HCTo9pM2dhDElCRGNIikUmJJtJJfm0FPVQlLx0Hn0P37jY97+y9so1Uoefuxh0gvST/v+2DvsvPXEm7HXFI1w74/vI3vC4IsESSEx5e6FFC+swJIWf1HO8k/KSWbx564Z7WbIztJoB/oM4Ngk183AtJNsK5PJLjIdDZ1U7YplxrP3OEhMHzzmHbTbiUYibFm5h3UvfsSc2+cy+7Z5A887Gjqofis21i7UStpauxkzeQxjp5Sw4pHrqdleTV5FAVqDjkSNgZVPvQVA+FNXUTy//JRta9hymMZtVQBklGXjbmzHURPrOnfXt+HpcnPozQ2kTixCoVKSVVnMxLsWnzTjXTgYBmIXIZFw5IzeH2OckbEzSjnw8X4KJ4056XYavZbEU/ROyGTnYrQD/XkjSdLngM8BZGdnj3JrZLIrw8xrphGNREjKSCTvuApuQbudrs2bQaNn42sfE/IH2fjqx0y7fibq/rtxhTrWC6C3mfjwtfW01bax6e3NPPrsd8kuzSG79OgxhRCY0uIRWjVepwdHTSOW/CwkScLr8nJwyyESMhLIKY79+0/MT0WhUqK3GDCnxmPOTsHbkkjQ7sKQkUzd2j34ep24ux04u10o1Ao0Zh06y9CV6KZfNx2j1Ygt2UbWSe76j6fVabn567fi7nMTlxo37PdXJjsfRjvQtwDH/ovJ7H9s2IQQTwJPQiwz3rk3TSaTnU56Xhq3PXLzkM9Fg0EQAkJ+Zt04naqt1YyfXUbIFxgI9Kkl2Sz8yk2EQxE8/95IW20bGYUZqDQnfjV1t/fSF4pQu62K9o4+NH0dZGs0mLNSWfPyWt59eiU6o46vPfEVkjIS+wvP3IdSpURvNQFgvG4h0WgUlVZD0lgnXQcaCAQjNO+pByCjoogjMw38bj+NBxtJzk7ClhyHOc7MzOtPLM5zOlq9Fq3+zErwHhEJR/D2uTHGm0+YaCiTDddoB/otQJEkSXnEAvwdwF2j2ySZbDAhBNs37iESCjN5VjlK5cnHoi9HPp+ff/7tNXp77dxy93Vk5Qw9Nn18RjttYiLW0lKioRDTMjJQ+kIcfnMT9sMtzH34BlRaNZIkkT4uNtHuprxUZl87FVMkgLu6HmtRHopjJqY52uy4HW4AXH1uJKVqIAgGfEEgVq0uHAwdbYNeg6+9AykUQJeYgEKtGsgSljZxDIkl2TRuraZm/X6UGhW2zKO59ze88hGtB5tQ6rXc+JWb0Rl1Q75uV68Ld4+TpOxkunYexNfdR/KksQN5/LuqW3G09ZA+IQ9D/wXHqUSjUdb//X32vLuNyptnM+1WufiM7NyMaqAXQoQlSfoi8C6x5XX/J4TYJ0nSj4CtQojXJUmaArwCxAHXSZL0QyFE2Sg2W3aF2bJuB9/7/M8A+O6vv8K8ZcO/q7uUbd+4iz/8+v8AMJtNfPbhuwc9H/T4aHh/M94eB3lLpmHJSgFis+hNWUc77BwtPQD0NnYQCYdPmExnspkIaaBnZ2xcXW02YMrKGHg+syQDSQmdTd1k5iaRMDYTY3rs/nvuTXOwJlhIzkoiLe/oWLfzcDWumjoklZL0xQtQGwePv6v1OnKmlWC06VEbdcT3rxwI+oM0bavG3tJDSlEGwUBwyEDv7nXx4g+eoaepi3mfWIiyoRYApVaDKS0Jd7eDD3/7MiF/kJIlk6i84/TJd4LeAAdX7wbg0NrdVN40i32rdtN6sImyBRPIKss97TFksmMNK9D3L4dLOXY/IUTjuTRACPEW8NZxj33/mJ+3EOvSl8lGhc/jP+Zn3yi2ZHSkZiRji7PgsLvIzj/xn6K7rZuuvbEJeb1VTQOB/ngVt86lYeshwjo1tVVNjK04cXLakUAsKRUoNFqq1+wi7A+SN7OMgmljyZlYSMvOWtR6Ncn9PQF97X3sX70Td5eDQ5sOkpCWQGpu6uADHzOYJ4RAhCMo1Cocbb3UrtmBv64RlV6L/u6rMSTFEY1ECLpjf3cJsMQPnc/fY3fT0xRLo9vb3ktOSgLejh6M/aVuRVQQFdET2nAqOpOeWZ9czOGP9lG2eCKOTgfv/vENELElfnKglw3XGQd6SZIeBv4D6ACOLHUTxBLZyGSXrRkLKvnqjz5POBxh3tWzRrs5F1zBmDz+7+Xf4XZ5KSkrPOF5Y3Ic5qwUvN12rLknnzmeXJTBc0+/yfuvr8VkMfI/L/+chOTBiVcCqFCkZqO1GOnrcLDj+VUAKFRKxiyahEqjJmdq8aB9tr62gZ3vbkOhVGDNTaZ2Z81AoLeMKUBtMaOxmFEbDUTDEVo+2oa9qoHMeVPoaHcScnlACMJePwGHC0NSHIpolClLJ9DT0kfhzNKTv6bcVJY8eA1d9R2MX1yBzaYl4g9gSIud35xsY+EjN2Fv7SGz/MzW3gOULZxI2cKJQGyuQObYbJr3N5JWlHGaPWWyEw3njv4RoFgI0TNSjZHJLkYarYZlNy0c7WaMqszsk68Z11pMlN65FBGJoNKdfNKZ3+XD7/QC/Qlyji1pG41yaNVu2g82otYoad9RzfT7r0FSKBDR6EmXvAEojTqkTCsanQZbejw5x9zxqg0G1PlHfw863XTvOgRA3+F6UivH03m4EWt2Opa0hIGhAH+vg2hzC3GA0u896bklhUTF0lit+6DDTt/uXYBAUkkYUmIXPSnFWQilkqptVeSVF2BNtp30eEPRmXRc/81bcXU7SRxmUiKZDIYX6JsAx0g1RCaTXbqUahWcJqObiEYpsMVhXTqLnMIs4pOOLjfrru9g/VPvAZBQnIG5PI+E/FQW/L9b8Tq9JBWeeCe7f9VOWvY3oow3snPTPgAe/snnyOi/6w2HwgQDoUF51zUWE4nlxdgPNxA3JgdbWgKz7rua7oMN7H7+A7pr2phwx2IMqQkkTSzB3+vEVjj0crvjRcMRjvTPi3B44HGP3c2LP/0HOqOOjtp2lj4w/MQzBosRwzDqCJwLEY0iolEUqtGeqy07X4bzl6wFVkuS9G9goH6kEOI3571VMpnssqM16iiekEdiUxepWUmDnjNYDBgTzHh6XKhtBt79eAu2sRlYDSYe/+6fSc1N5YEffZq4ZBshjxev288Hf/o3CMgcn4vRYmBcxRgaNh2m+1Ar026cwcuPv87BbYe599t3MXFuLLmOQqUkc/4U0mdPRKmOTQZUqpT01rYSCYToqWrG3dmLEOCPqEibNRlTypmtf9fGxWEuKkZEwuiTBs9TyC7JwtnQiaelG0+vk0AkyrOPvYij18knvnQbuf0pd8OBIEq1aiAd8IUWcHqofnMd/l4nhSvmYM1OPf1OsovecAJ9Y/9/mv7/ZDLZcY5fYnax2vLxTnZs3c3chTMoKSs6o31qDzWwb8dBJkwpJafgzBLGBH0B3D1OrKnxRAIhIi4naZlWgn32Qe+VKdHK4q/exO51e/jd75/C6XCjUCjIx8LEaWVs+HAbLbWtRDs7aV27FUtJPplluTTvrSezLJeffHop+z7cxboX1gCQmJ3IzrWxmev7Nx9k4txyXHY3CoWE0WIcCPJHpIzLp7emFWtmEhqzkTW/foFIIISrvZe4iYUYzAZSsk7dbS4pFBhST5yjYLSZiE+Jo31XHV67h+6GLlq6e1nz7w0AlEwcQ+6YLHoP1lP/3gZsBVnkLJ6O8jQpfkeCp70He00zAPaaZjnQXybOONALIX44kg2RyS51a9/+mBeffI0FK+ZwwyevRjFKd2WnY+918NUHH8XpcLFt0y7+9Mx/nXafgD/IT7/x39RXNTJ+8lh+87cfnfT1ObodfPTaetRqFWpviOqP9lJ+3TQyJxdgS9IifF4s2aknXBAlZacw43oLH67fzPoPN5OZnIz7YDfx2UnMunYGeaU5dK3dAoDzYC2L71+G3x8mPjMRpUpJzrhcNr2+EZPNSNbYHO747LV4Ou1kVxZRu7eOJ7/7F9RaNQ/852fJPG4oID4/nZlfvhVJkvC7vKj1WiKBEF3eAH9+8L8xWo189bEvknaKyYanUji9hJa9DdjS40nKS0EdbyQzP52+Ljsl5bEJjvbqRiL+ID37akidUoYh+cJXiDOmJmAryMTf48A2xAoL2aVpOLPuk4BvAGXAwIJSIcSVPUtJJuv35rPv0tbYwXN/eJn5184cNAZ9MVGpVaSkJeF0uAYqwZ1PO9bsYuUz7wMwbU5sUU7z7jo67Q7GpcSSDUVDwSH3NRj1fO9XX6WzrYuD7+2ivi/MlOtmkFeRj0KpJDquiGg4jDknA1NyHOZjLjbyKgr4whMPo1Kp8Npd7O+vV69SKdFmJeK2x5LtdFW3Eul1kVqag/aY8fsjFx46s4HpD1yHs62b7f258j0OD/Zu56BAHw6HWfPGBvo6+5i3YjZJaSfWtg8FQ3hcHtJKsrn1559GoVQgSRIGm4n//Ot3CYciWOLMAMQV5+Bq6sBamInWZj6Ld/7caS1GSu9YQjQSvSir6MnOznD+ks8CLwDXAg8CnwS6RqJRMtmlaMG1s2msbmHJTfOxjNIX9ZkwmY385vEfcWh/NROnjD+jfbQ6Dd/5xSPs23mI8imlp+ytSEyLR1JIqFQqiqaW0GM1oVEqaGjrZatbTVaaFbVfQlHXRlpeGn63n10fbEer01A2bzx6g46cgixyPp9F0Bdk3fOr2fXuFiYtqyR30hgseSe/0zTHW/DY3TjaekjKS6arrpP4dBs5U8dSMW8CGdkp1L+3lcMePyVLp1B+0+yhD6RT09ZpJ9lqZN6KmSTmJFM4oWDQJod2VvPXnz8DxFYR3Py56wc97/f7+eOP/8pH727k89+9j6tunD/oeYNp8EqCuKIcrPmZKEY586KkUKC8SHujZGdnOIE+QQjxF0mSHhFCrAHWSJK0ZaQaJpNdaq6+YzGzlk7FaDaiPEXJ1otBdl4m2acImEe4+lxU760jsyCdgpJcCkpyaahroqmhhayco93foUAQv9OHKdHCuJnjeOSxL+Jx+Skcn0do2liqVu8gq7KAw3trWLexjsM73mPBLfO488u3sOGVdXz47IcolAqCbh99QT8JChVppbloDFrCbhfFaTo823biSjRhPsVSP4C9K7ez5V/rmHPnbKZeX0lycTY6Wxyf/dGncXX08c6OWHKfcPDEXoVoNEpvRx/P/PYFdqzdTeXMceSpNcx+8DrUGhXRaJS2g01oDToscWb0Rh2hYJi0Icayezv6WP3v9QBsXrP9hEA/lOEE+a6OHmoP1FM0roD4RNsZ7ye78gwn0B9JIN0mSdI1QCtw4QeRZLKLmCVu6Axqlxqnw4VWp+WlJ17jozc2kJGfxtwbZ6Myqvn6l36ISqnkiWd+TcXkcQR8AT78n9dp3FHD7PuuQkuE7sYO1m49iDktgQe+90km3TofEY2iVERZ8852AJL6S9pGI7H8W0IIvC4vj//uGa5dPgejWhBub2fMmCzcdbEJYt7O3oFAHw2HAOmEZWAqjQoRFax7YQO3/fQ+dLajQyjmlDhmf2EFjtYeMieemPznjaff4bk//ouK6ePQm/R0dPQy5/q5qPWx+cfV6/ax+k9voVSrWPH9u/iPv3yTTSu38fwf/oXH5WXJrUdT3CanJ3HnAzfx8aqtLL5+3gnnGi5HlwOtTk3fgTrCQvCHJ19h95b9zFhUyaO//dpJ9wv4gwS8ASzxF28vk2xkDSfQ/0SSJCvwNeAxwAJ8ZURaJZPJLoj2ti6sVjN6w9E87utXb+bRr/ycsvJiSnNiwbC7rZd1b21Cm6QlGAgSBOprGqmYPA5vr4vG/rvkpt21KLu6EVHBuMJM/vX2xwQDdyJFY2PjSYV5/L8/PIK924HNbOTDx15Fb9Ix+6aZEI7S0+ciNSOJ+JR4NBoFYSDQbcdckI2EwNo/QSzocuE4tB8AW0kpatPRIDbuqknEpcWhcLpw1TTSvH4P5tR4cmaPQ63VkFaWS9oQaWSFEHzw6lpEVLB36wFuf+AGsvPTKZ0yduBO29MXG+ePhML4XD6SCtN475+r8Lq8vP7U28y/fjZqTWy2fMDhZmJWKrO/9glMVi3duw9iLcpFrR+6OA6Av89J0O3BlJaM4pheoY3/3shLv/0XN39qMaKhGVW8heb6VgDamjqIhCND9iK57W7+8uOnObjtMJ/5j3upXDDplJ8H2eVpOLPu3+z/0QGcvjKDTCa7qL3xr/f4j6//kulzJvOz//4e1v55BVvWb8ft8rBp3XZW3LyMpJR42lq6+XjlZpbdvYh7778NpVLJnIUzALC7PBRcW0m4zU7RzDKa3t9M0O0jLjGOB755D13VbTRuOUTtun1Y0uJZ9u07SM5IYus/19DYP9nNMCaD9Su3UjFzHPd++gZ2vb6J0JgM9CmJKHUawmHQZWeii7cBEHY5ifpjuehDbtegQK/V64iLM1C9ehNRSxzt+xtJm5BP9aodZEwspGjxRKRoFI3NOmjmvyRJ3HL/Ct585l0WXD+Xq+9YdMJ7NmbOOKLRKHqzgcS8VBr2NnDzp6/hxT+/wdLbFw0EeYD23TXYmzqJBIK49nVBNNZzkTihZMi/R8Dhpuql9wi5vWTMrSRl8tHUu4e2HkYIQUdrD8mSRLjXycNfv4dD1c1Uzi4/6VBRR3MXezfGLogObjkkB/or1HBm3Y8BHgdShBDjJEmaAKwQQvxkxFonk8nOu0gkwjtvfMjqleuJRqNsWLOF1ua2gUA/d8lMtm7cRXFZIdPmTMJiM1N/uImKWeMYN3UscceMB9cfbuL7n/k5wUCQ+79zL+5OO15Jia04lwObqylbZGTrxg0oVEoSS3Nw1LURDobRAkkF6SiUCowJFnJLcwj7glhNBsKuAEFPgNUvrOfqe+fi63XSvKuGyOo9dMyeQOmiSWhsNjS2OJAkNFbbCa9RazOjMmhRGHVICgUBjx+/w0PN6l1YrEqEo5fEKRMxZQ/OBzD3mpnMvebk1QmN8WYm3xird/Dh399nzXOrsCTZ+OnT3yMpI3HQtp4eF50Hm+g81ETxnLFE+npQaE6+Nj7s9xNyx9LtBp3uQc9NWTYFZ48Tc1YKZbNiFwCWrBSmLTt1zoaMgnSW3LGQ6t01VC6Sg/yVajhd9/8LfB34E4AQYrckSf8A5EAvu2yEQ2G2vbmJvrZeJl49hZS8s0sYEg2H6di6F09LBylTJ2DOOrv11yNhy8c7+c6Xf8rs+dMonzyO6bMmkV+UO/D8pKkT+Pvrfxx0t5s7Jmsge1skHGHD6xvobOoivSSTYCA2qc3e4yDY5aGzuo3OmjYK5k4gPj2RlgNNHN58GCSY99mlGPvHinMmF5F3zRR69jWy/eX1jF1YzoRlk9m5YT/mMelk5qWhSk5G+AUhbywZZ+v+enyRKD11HZjNetKykjAXqIiGIzRsOkDIHyRn2lgMSfGU3nMt4WCI3EWTsTd24GztJn1CPnhjQTR0XDCNhiPYaxqQJAW2gmwk5alnnrv7XLH/9ziRBCfkBTAkxOZrKFRKEsYVodWXYspMRQjBujc2cGhHNTOunkrZ1LGx7ZMTyL5qJv5eBwmlg2f4l04bS+m0saf5yw4WDoXRaNTc9vBNw9pPdvkZTqA3CCE2H/dhDp9sY5nsUtR2uIU1f4utAVdrNaR85uwCfaDPSff2WJepvarhogr0er0OpVLJutWb+OEvv8ENty0/YZtTZfdrPtzM6398A4B5+nnc/517sfc4WLBiNn1VrUgKCa1Rj1qnJhQKou7vVralxLH6zQ2Y0uMwWYxk52fg9QVwtPYSjURxtPfh6HPyyh9fRwiBQqWge/Mh4rKTSKsspq/Tzp7DzeSoNexfsweASbNLSSzMIOj1cfC9LegsRiSFRNGCiWitZo6U2LGmJ5JeUYSkkPA0NRNyuzHmDL6b76tqoGllLFudtHwutsJsAAIuH5FQEEO8ddD25YsmEolEEQqJloY2ko5L65s7azzGBCs6q5G4Y8rmdrf18MyvXkAIgavPSVpOCvEp8UiSROIQ1QHPRtuhZlY+/gamBAtLHryWgC/Azre3YEmyMvGaaahHIeuebPQMJ9B3S5JUQH/VBkmSbgHaRqRVMtkosSRZic9IoLelh+S8s68UpraYMOWk425qw5R5caURLZ9cxp+f/y0Ou5OZc6YMe39LooWkrCS0Wg2KqCAvN52Cm2KzypVR2PjMByiUCjqMSlzvOyjPTKds6hhUcUZc1Y38+Vd/x2ozc+eDtzDr2hkcSowj0NVHohn8NbWk5afRWtNKek4y3lQL+6ub0CvS0KYns6CyBG9HH/vX7CEuJQ69UY8hzkTIHyAchbbDrVgLM/jTN55k/OxxzLhuxsBFi1qnoXXjbto27iZt2ng0ZtOg1zXo2qb/F0+3g21/eRNfn4uKT1xFyrhYqdmuhk42vbKBNR9sIRqJkrmrmorZgyt2q7Rq0soH35kD6Awaxk4ew/6thzBbTDz2wO+598efJG983rD/FifTuLuOvpYe+lp6aK9ppa2qhV3vbgMgpSCdnGGUzJVd+oYT6B8CngRKJElqAeqAT4xIq2SyUWJNtnH7jz+Jz+klKefsA71KqyHnqtlEgkHUxpOXWB0tk6ZMOP1Gx/C5ffS295KSk0xcchwP/PJzrHr2Aza/vpHtb2/l848/TEJ6Ara0eKbcPo+g08nuFz6koaoZrVbLksWVhJQKxljV9LzTx8HNh3ld92+unj8ZlS9ARlk6QoSxt7lZcds8fFGBWq3g+9/4PdFIFFu8FbNCS5LHhy3NxpgpxRjMerQ2I3qriYMf78HR2gtAZ1UL1durqdlZw7hZ4wg5vUTDYeJyUujYup9oMEzH1v2kTC7Fbfew6u8fEA6FmX/3AnKWzUZSSFjzYjkCvN12vN2xop2Ops6BQO/o7KOrupUx4/M5uLOaifMrzvi9bNmwn7FxJibcuZCGph4CvgAd9e2nDPQBX4CPX1pHX1sP02+cddq69NkT8jj40R7MCRZSCtIJB8IgxS5kLcMskyu79A1n1n0tsFiSJCOgEEK4Rq5ZMtnoMcebMcebCXl9eNu70MVZ0cZZT7/jcRRqFYrLII1owBvgjf95jX1r9zBpaSVjFpTx7muryDLFJp9p9BpU6qOzvuNTbez7cDPzp4ylNj+N3IxkerfsBwEV9y3n0J5aoiqBQqukaethfD1O3PY8glEJR5cDU5wJpU6NPs7ImLF5HNxbQ1piEt5OO/adVTh3K8iaUkJcehxpxVlISgXC7yN3cgHuXg/mjFgX+qRFE/F1O1j32CuIqGD6/deQPmMCbZv2kjq1jKbqVjrq29n94U4AskqzmbZixsDrEEJgy00lb8Ek/A43qeVHu9WzxuUy8eopBAMhrn/gOrKKz6zID0DA5SVkdxN2eiiZMZ7ErGRKTjP+3lbVwoYX1wJgTrScNtCnFWdy96/vR6FUolAqsMwbT2JuMjqjDnPi8D/LskvbcGbd24B7gVxAdaQ7TAjxpZFomEw22rp37MVxuA6VyUjudYtR6bSn3+ky0FbfzqqX1mBNtLLo9vk4e5zU764F4ODG/extaWDlG6spKMzmsw/eQU5pLtYk28D++jgLKoMWXZ+DubMm0N3cRRgw5aVxeMdhPnjrI0Q0yq23L0NjsaFQSGiMBva+u42JV03EEPUTVUBHt5Nrl89m8bWzcXW60CglQsEwUUAKhQgeOkxrazM5V8/FkpaAJRJEqxA499dw+31LGHfDbOpW7UREYzXiA04PmfPKSaooYfuanTz55SfQ6NRMmzORjoPNpPcHz2gkgv3gIXztHcSVllK8fPoJ75FWr6V8cQVBjx9bZjKS4swrFubNLUdjNmDNSCRtwold+0e4nG7WvreR+EQbpWWFJOem0NXYRcaYMys2ozpuhn9STspJthxawBfg1affpqmmhes+sZTiCedn/oDswhvO7cZbwEZgDxAdmebIZMMT6OvD39mJJi4OffLZd7UPJRKMJYOMBoIDa6CvBJvf38ra12KpW3NKsiidMpZJy6ZweNNBxs+fQJO9GwCFRsXY2WXYjpukZkqNZ9LnbiQSDGJOS6S7rQdXVx/bVu/iwFtbGF9RjNlgIHCgkwPhNkoWlOPqslM0oxSDViLQ1AdAWnE+IaWGXat20F7fzvT5E4lPtKDVa0nNT8Sxp4eg3YW/z0nRzIkEnIV0/9/bRAIh/O09/PuFlSR4guRMKyEaFahNetydfSjUSpoPNyOEIOALMnZ2GTc9chNGmxGAsNuDqzp2YeNpbcGQdmKA9HTZ2fT4qwScXspunU/2tNITtjkZc2o8JVdPA2JFb/Zt2IfOqKdkSvGg7d566QOe/PXTSJLEb5/+MXf95FMEvH7iUs9/QlJXfTM9ew5gzssivrQYSSFRe6iBF598DQBbolUO9Jew4QR6nRDiqyPWEpnsLLhqawna+/C0NKOeNRuV9vzddSdVlKFLiEOfFI/KoD/9DpeJzIIMJIWELclGYloiCqWCxfcuYfG9SwBorGsiLsnG+IqSE4L8EUeWlgEkpiWgVChw9nnoau5iwpzxVEwfy+anVwGgNWiZcu9i7M1dRN0u2to6kPQ6et0ByheUEVIqeOG/X8bh9TPnjrnoTHoioSjC7UZl1GNMjXXVay1mxt40h679dfT6/Tz306dZePUMxvijtB9sIugNsP1v76G1GIjLSGTG/ApSCtIZP7MMrf7o50Zl0GPISMfX1o4+eei7YL/TS8AZW/Pu7bKf9Xu96a2NvPT7V5EkiQd/8VmKK48m0zlSOEiSJBRKBQaLAYPl6HyPUCBE/cFG4lPiSDjD4O/pcRIOhLCmD66013ewikCvnUCvHXNuFhqTkZSMJArK8qg72EDxeDnIX8qGE+j/LknS/cCbQODIg0KI3vPeKpnsDKnNJoL2PtRmy3mv+qU9y7H5S93kBROxJdtoPNxEwB8Y9Fxvr50v3/8o9TVN3Pf5O1gxuRxJpSR1Simak1wM9XT2EfAFOLyzmvzx+Rh0WlpWbmPWvQvpaOkmrTyXt379EvW76ph49WR21HZj73FiMBnxdrrITjby1Z98ClOcHvu2bUhKJSmzZ+LPTEdv0A0aUlHhJz5ZSTxGFi6fTkZhFnFaI+0Hm5AkiM9JQmfUoTbriXN4mTJz3ECQ3//x/tiFyNwJJE6qIBoKoTzJhWNcbgpjb5iNr8dJ5tSj4+s+rx+dXnvK5YnHCrg8QGw+QOC4df3Lb1mELd5CXIKVsoriE/b98OU1vPrEG6RkJ/OV330Ra8KpP6uOli7W/f4VQr4AUz+znPRjVgSYc7LwdXZjKchF1Z+iNz4pju//z9dwu7ykZCSd7LCyS8BwAn0Q+BXwXfqX2PX/X16nIbtgXA43CIG5P4ubOb8AXVISSoPxhOImsrO34e1NrPrXWqwJFv7jr9/G1j+By+v20twQy7HeWt9GB2pEVNDY0k3OtHEIIcguPDqGvG3Dbn72ld+SkBzPQ//vbqJ9brqr23CFIhw4UMuefTWozVrqd9cB0F7TTnF5ITs+3k96SgIhjx9nnR1HTRMl18cmyolIhLqDtTx43w/RG3T899M/YUx/ghmFqn9cWqVi6a2LiU+MY/9728mfX44tNR5fdT3e9k7UiiQm37uExKJYW9tq23j6B08TjUZx9bm49nPXnjTIQ6zKXO5xy+k+fPUjnn3sRRbdOJfbH7zxjCoYVi6sQPj96A1axkwsGvSc0WRgyYqTF8Ppao4NoXQ2deFxeE4b6D3dToKeWNpgV3svHBPobSUFmHMyUGq1gxIFGS1GjBbjaV+H7OI2nG/GrwGFQojukWqMTHYqtQcb+NlX/5tIKMy3fvtlxowrQKFSoY27eIooiqjA7/SgNekHFSU5E66mNto378GUnkTKlPGjeuESDMTmJwT9QaLHzE/IzE7nZ489yv7dB5kzbSLBTXtBreTAvjrWbTzAns37+fJPPseelTtRKCREgpaAP0hrYzs1m/aSHo1QMGUc2qsqWfv+VqwOQcP2OqZeN5XOhi6KJheQW5FP2YQC+rpcWI1qOtbvJLVyLKbsLCSVEpVOx9urd8aq3Xl8dLZ1DwR6XUoqSoORkNOFtasLb2uI3W9vBWDyLbMR/Vn8IqEwmZPGICkkPnxlLVW7a8idVEDt1ip0hsFFZ0LBEG89s5LDu6u55hNXUVo5dK76lS+vxu3w8Prf3mHZ7YuIT4obcrtjWTNSmX/v1bEa8OrhJbGZd+NstHoNWYWZpOefunQvQOKYTEqWTyXoDZAxafBFhSRJV9Tw1JVmON8k1YB3pBoik51OzYE6utpi15lVe2sZM+7kM5aHK+AN4Ox1kpCWgOK41KddLd2EQ2HSjslu5mrvpXN/PbbsZBKOuYOtWb2Tva9+RM7MMspvmT+sYG+vasDb1oW3rQtrfjaGlITT7zRCrvvkMrIKM8gZk0VYRNi1bR/FZYW47W6mzZzEomVzCHs9HAxH2LB6F0GFksbqZqKRKPW766jbXYukkFjyqUVMnVWONc5Mql4DHh9ak4643DTMai2RUJi2g80Uj8tF6fZgTTDR+M5aNPHxaEMCQ1oWxdfPwNPYhL+3F1vxGADmLtHR09WHxWpm8ozygXYrlCoklZq+PfsA0CYnY7AZiYQjZJTlEUg0E/H6SJ1QiEKlpLW+jb/+/FkApi6axN3fu5sxk8cMei9a69v515OvA5CQGk/hmDQkpQq1cfCd7lW3LeC5/3mZRTfMxTqMcsUtzV10tfUwfnLJoLkCp5NVlElW0ZnNwAfQ6LWMHWIFwaUkGgrjrGtGUquw5Gac8RDJlW44gd4D7JQkaRWDx+jl5XVXEG9bJ76uTgxpaeiTLmwgSk1PYtzksUQjEVIzY2OG0UiUmj21GC2GM7qrGUrAG+DZ//wHBzYeYMXnr2POzXMGnqvf38BjX/sj4WCYOx65meyCDDLKcqh6dzPtu2vQxVuY9dD1KPUa9m84SNfG2Hrxhg37KLl6Gnqr6RRnHsyUkULvwVrMmalohrHfcEWj0YGJXieTnJnEVbcvxN7n4OFPfYdD+6r50tc+y2t/fZuM3HS+89+PYFZHSbVEuPGmidgDegiEiUSipGUlU59gpnBCPpG9tcxJTsCUlYLSasCaYCEkFLz6jf8la3oJ6cUZZBVnYTWpMFTk4OnqQ1IocHU68LT30nOwgbzZYwh7fTgOVWPKzsLV2k33pgMsmz6R7BnjTrgwU2m1aBMTCXR3o09K5NaffRohoG1nFfteWYdKpyF1Uuyu3BJnoXB8Pj2tPVROKWXMhHz0psF3tgkpcZROKeHA1kMsuqqCnm1bkZRK4idORmM+WjVvwXWzmX3V8NLLtja189TvnqP2QAMr7lrKzfddN+R2zk47rs4+kgszUOs0Z3z8y429qp7WtVsAyLlmHuass/s3f6UZTqB/tf8/2RUqEgzRuWkL0WAIX3sXGUvmn9MVtYhGcTfWE+zrw5STizb+1BcOLdWtxJssuN0e+trtAGx+byvP/+oFtHotX/rdQ2QUDp1IJBqNIqJiYNzU1daDz+4moTADl93NgY0HUOvU9DR38/dHn2LqtdMonjaWrtZuAv0FVQ59vJ89L3zEwgeXYc1KxBMRJBYk4+toARFFr4kSNuoxpyeQM70M3TDHNuOK8zBmpqDUaFCOUKKd7Zt284sf/J6ikgK+/uiDqAMeRDSCLikVpebEu0mn3cXh/TVIkkRHYyfhUJiGqkbam7uIH5+LymQGIcguzGGO3Y+nz8W4eeWMmVRE/Y5q/NVBwi4veoseW4qKDr+PYK2dyusr0YgABWOnojVr8dTWgRoiFgPdLiuWBA2e9l7MWSnokxMJu5yY83ORJInmzQdo+jh2x27LTsGWPXhmvEKtJnFSBZFAgJDHh6+xCUN6CgFXrEMy7A8S9sX+piarka/86gvUr91F7fvbWX+ggcpPX40t9ehn0WQ18cgvHsTt8KANOHE7YvMEIgE/HBPoAVRqJd2HGgCJhKKs066vf/OVlbz2xjtMmjIBj3voDlOfw8P7//USzo4+JqyYwaSbZp/6jyyTHWc4mfH+NpINkV38JIUCjc2Kv7MbjcV8zt1mYa8XT2MDAL7OjlMG+uaaFl598k2ikSjTl02lfOY4ABz9S5sCvgAuu2fIfbtaunn2V8/jdXm59XPLMYR81G6pI+D0kD+vgty55dzw8A3YO/rY8trH/eurAxRPG0vp1BKW3bMEZ7cT0e0iIATC78YWp8CdZiUpxUTY40SEQyQlJ/DMu9tY9sByChdUDPv9EJEIhIMIpQQjFOjXfrCBqoN1VB2s44HP3YgxGEtwqVBr0ScfHZoIBkOEQ2Gy8zL5j199nZ72Htr3tTG2YgwTysfQtW4/a9btxzYul0A0wpQCHeXLKgE4sOUg7/3jA7ILMjBo9SQXZJI+IZ3nX1rLS8++w+Jr53LX7BLCHi8Rlwtl8dHxYmdbD2a9Cty9ZM4qo88eJK60GGth/kCJV1P/UjJjkhWtZXB6YRGN4m1pJuiwo0/LoH3dZsIeH+7GFnJnT0GlUWNIsGA7JnmMJc5M0BELsu72Xta88hHLP3MNao0ae30rruYu4sdkkZiWQNhnQEQiKNRqdEPMDenaX8+eZ98DYMInlpJUdur89RvWbAZgx9Y9PPK1TxIKBFFrB9+xB/0BXP2fc0+P85THO1M9NS301rWRPDYX63HldS9mtqJcFColklqNKfPiKRR1sTunbxNJkn4ghPjBeWrLFSHodOLv6kBtsaJPOr8JXkaaQqUkaeokQk4XmjjbOR9PqdWiTUgg0NNLVGtk8yvr0VsMlM0vP6E7tq/TgUan5s4Hr2ZMSSqm/vlSkxeWEw4EsCZaiYu30LC7jqyynEH71+6r4+C2wwAc2LyPgtx0Dh1sQ2/Rk+YJoFAomHX9TEKBEL4+N3vW7KZkRiwBitFi5NrPXoPf5aNm437CAT9xcUoQUWwJRhqretj13k4KJ+dRsSSHBx97iKTs2N81Eo7QWNVMXJJtYNb6sepqGjl8oIZJUyaQlJKAt70Vf0crkkqFZUwZKp3uhH3O1bTZk3n9pfcoLivAkhBPpMMDQqDUHe2u7uro4Wff+h0NtU189+dfZvkNi/C6ffzXw7/D2dRH4gwDHVti72dcVjzbNu5HoVAw/apYgZyNb2/m0LYqDm2r4gvfvZ2URD0i5GfPrioA1n+4mTuWTQWPF2GyUvVxFYnpZhIK0tn/xjYKi+II+QMEmpvJnjEFSZJQHhP8sqaVYs1KRmM2oLMYcTe1IgBTRioRvw9PYz0Q+3wpdTrCHh8qgx5jvJXiZVOHfF/y506gr8eBKxRm5SsfseiuJYhgiP3Pv08kEMTZ0knZ7UtQ6fVYCk6+pjwSPFrQMxIKnfbv8ckH7uD5v77MjOkV7PzbR9S+s5dFD6/AFH90jN+aEs+8z19HT30HedOGngg4HEGvny3/9zZBt4+uQ03MfOjGcz7mhaJQq7CNOX/Ff64U53rbsO28tOIK4mlsINDdBVIzmqlmlPrRnekaCfgJe9woDUZUutO3Jex1Ew358LU60SamoDGf+aSj4ynUamwlZUTCYbb9eytr//4BAOYEK7kVR1dtdrZ08cSjf6Zy4SRKxiQggl68rY2orTZUnh4mF5tQaNV8+NTbNO5u5Jbv3ka8VYlCoUCp15Nbkk1eWS4+l4+iCQW07GvG0eXA0eUgvHjiwHnUWjUrHrmRhfcuRunupWvLVsz5+egS4tGZ9ZQtmczbf/43Kq0FERUEgh466rvQx5k4+PFhJiyfQVqGbeB4H768ln/+/mUyC9L58m+/iPWYJDJOh4vvfOWnHNpXw7U3LeFHv/pmrCsYEOEwYZ93RAL9jLlTeG3N02g1GhTREF63BSQBHO2dqTlYx/aNu9Fo1Wxav52Na7cytiCfT37rbpx9LhLjLWyp70QQJSnTypylFQSPubAaM2kMWz/YQdn0scRZNXia27AV53DXHQtYvzGdtMQEVEkphNQ6iAh0eh+97R6y52cy/d5EfK2t+No6UOr1KCSBp7UJSZLQJaYgohFEJIwlIwlJknA1NtPyQay0bMaCmRgzU1HHxRPq6+2/64vHkGLDkHbqCoLxuamkzRpH84fb+PR3PoHJaiTk9aM26IgEgqjPYJKcu9eFymam5MZ5CBHFlnf0jlOIKGGPG0mtQaU9+nddcNUspk8qYsfrW9jraMPn8NK+rx6DIow5Jx1zf+XD3CnF5PZnznP1OFn/whpC/iAzb59PQsbw5spIkgKtSU/Q7UNtOP+fMdnF55wCvRDijXNtgCRJy4DfAUrgz0KInx/3vBZ4GpgM9AC3CyHqz/W8o0Vl0BMAlHoD0nlePiUiEQI9PUgqFdr4M8yU1dpI2OlAqTNgKSxB6k86E7D3EujqQG2LR5eYjCRJRIIBQo5ewu4j9YwkoqEIuvjTLyMKB/yIcAiVwTSoy19SKpEiEY6sLFKoFAT77Hg7ezEkx15DNBIlHI7g6HXS2RcgyaxA6MxICiXRUAS11UbY70chxYJNvFVBxNVLRJKABEwGA1/9/cMIIVBr1HR2uLGk2FDrNQSPa2eguwel34OrugYApU6LLuHoe5lakMGLf3ydSbPGQUSwa/VuRDTK5KWTCXr8ODv7sCTH3o/W2th68+aaVqIeB/bORrTxCeiSU2k72MzSMRNZXF5JtaOz/7WrUMcnIKICaSBVxfln7i/P6qhrxdfSQzQSIRpSoLHELkSKxxUyb+kMFColT/0pVjd9yaLZ+Hu9XP25awCY9+BivD0Odq/ch7fPQ+nCCbjbuzGlJjJu2liuvXMROqMOEQVdShJo9Ywdk0dxfhbalCQsaSn0HKxh97MfIqJR0iaPwddYhz49jfbWPhRRBRFPEEmKEOjqAkCgwN/dQ+/BFiSVkswF04iGIwOvKxIOo1CpsBaPRYRCeNpaIBxAIQEiMug9CHm8dO08hKRSklRejEqnZdzUsYw7JvmN2qCj9M4leDp6seacupvY2WXn1Z88h72tl4X3LyNeF6B3+w6sY4owZqTh62iL9dYoVZjzx6AyHJ2/oUtIIm96GR11PVjT4gi1ttLV1YO9ppGSu64btHJDRKP0NbbRvLee3tZeUvLTSMiYOagt0XCYSMCPUqsbcommWq+h8tNX42juJiFf7v6+EgynqE0ScD/9RW2OPC6E+PTZnlySJCXwB2AJ0AxskSTpdSHE/mM2+wzQJ4QolCTpDuAXwO1ne86R1Hu4kbbth4gvzCStcuhqVMbsXDS2+NjdyjDXzR7P296Jv7sHQ1oKuoR4fJ0duKpjXaq28eVobacOwEIIiMS+AKORMAIxcF/n72gj4vcS9rrRWONQajSx7SUFSq2OSMBPJBCi/d1VZCycgyHl5JmzwgE/ruqDiEgYQ3o2usTBQxbe1mbS4iIsu28OCoUGx9Y9RFrbKLlrOZIkkZqdwiO/+jy7Nu7lNz9+nvKpxZROHcf8wiJ0SYn4O1oxpqZQukBPekkOCqVEJPYCQSkhKRSojynwkVqYzuo3P0Z43MzPTMBZU41So0GTmEzI2YNEFLXFQsjpRBsXT9DlwtPYiFKvp3zueNJzU3n6q0+SNSEv9p4Aap2K137wd5RqFcu/dTsphenMvX42CpWS/NJccNsJe1yE3U40cfHUfHyI3rpYALvnmzcDIClVhPt6UOoMKIaYGHe+hV0+XPUtAKhMR2f5xyXY+OF/f5PqQ3WsWrkOr8eHTqMZNNtbBNy4er20H2gGoONgI1GHk5JbFrF/3V62vLEJAMtnl5BXlkbE7aPvYAMRf4C4KJhTkgj09Qx0JEgSRLwu3NVOdBYd1St3orUasRWkoui/MAz7Avj73Hj6l1i6mzuIL8lDzKoEAZbc2FIzhVIJSiUqnQHMNlAqCPoDHDs10l7dSMeWvQBoLSbixw6d98uYFIfxDNbDu3tc2NtiSUK76jtIKDIT9vsJOp0YM9KIBHyx9y0SJhzwDQr0kiSRVprLDT/KASGoe3stwS7QJ8QNSl4D4KqrQ9lZz5Jbyvnw9b2kFQ2edS6EwNPSQMjRh8YWjzErb8i5NOaUeMwpF0/+CdnIGs4t5WvAR8D7QOQ0256pqUB1fwlcJEl6HrgeODbQXw/8oP/nl4D/kSRJEke+YS8ijR/txNnYQc/BeuKLstAOsURKMYy77VOJBAJ0b9kWu4vv7SVt7iyEOKbwijh9ERZJkjCk5xByO1AZzSiURz8Omrh4fG1eNHGJg+8KlAqUJgtKsw37gTqICsJe3ynPI8IhRCQ2dhkJBobYAKRohKQ4iVBIiVcIFBrNoC+o0iklpOelEQ5HiESjjJ8aG0MPueyAIOjoZezMCSg1mtidHCAplEg63QnFboonjeHhXz2IQqlA5enF0xgLVha9ASGUEPGjSbISVz4BjcmE4/BhvC2xY2qsVuLTEyhbUE7VxgNc99ByfH0OdFotdaEIkVAER1sPKYXp5JXmkFeaA4C3vQWvx4UmIQmFSk3W+BwOrttD3sRC8ktjQSYaDBANhYiGHIzgDf0AbZwZpU5LNBxGazOf8HxhcR5P/ONX1B2sJz0xmTGTjq4vV+kMWJOjpJRk4Ol2YTBqBybGJWTE8uOrtGoSspMJO9yIKPi7Y8VqQk4PkkKBJSOFoqsm4WrtJSEvCfx2AOJykkgqzSMSDOLt9aJWhEACU4IWU0Yajpp2UCowpMY+m7YxQwfpoMdDqK8HALVt8IQzjcUMUuwi8HwsZUwpTGf2PYvpa+thzLQiQvWxC27RXxhJbYlDhCMo1BrUxhPfa4j9e0SSyFowDd/4vligPy5I+/t7N1QhH7d+705MicddhESjAz1uYY8rdrErrzW/4klnGi8lSdophKg4ryeXpFuAZUKIz/b/fg8wTQjxxWO22du/TXP/7zX925w0Q19lZaXYunXr+WzqGWlcu4P6D7aSWJpL8Y0LUGpGLrNZNBymY8Mmgr19GHOySJxYTjQcwt/dhUKpQpuYdG5L34QgGgqiUKmRjllz7etoJeRxozJZ8Hf2gaTAWpSHUnPytb1CCAK93USCfrRxSSeMPUeCQfzdXah0eqJCiaetG1NmMrohgs/x/H09sVz3RiO6pFQkSSLocuCuqUKh1WHOL0SpPfk4pK+zA+ehA0gqFXETKohGJQL2PrQ2K9r+pVPetjb69uxBqdeTOHkyKoOBSCiC1+Gma/t+LBlxRBVKDm+qRaXVUn7NFPTHLa0TQhANBlCo1UiKWFess8uBzqxD05+rPdDXi7uhFo3FijE7b8Qz40UCATytzYhoFGNaBiqD4fQ79Qv09eDvbEdltSGUBoJ9LsxZKQNj2R0NHajUShLSYwHWUV1Dz746wh4f6XOnYOofe46GQkSjAkQUf2c7RKPo0zIIeoM4G9sxZSRDJIRSrRwYIopVFRSn/MwB+Hr78La2xrK+2eKxZAwep/d29iIpFegTbGf8us9EJBCgb88+gi4X8ePHoUuMjaFHw2EkhWLQv6fh8nV14W1pQZeQgDEra8htgk47QacdjcWGxmI763PJLj2SJG0TQlSe8PgwAv1PgA1CiLfOY6POW6CXJOlzwOcAsrOzJzc0NJyvZp4xEYni63OiMRtQaUc+qUXI5yPsdKOJs572S+90opEwIacDhUqNun+CXSTgJ+RxodIbUeljQUBEIkSCAZRa3Tl9YZ0tV6edzuoWEvPTsKbG425swFVTjTYhEcuYElQ6LUJECTr6kJRqNGYL0VAI++E6opEotuK8EyrcBVxOwi4nkYAftdE8ZMWyoNuNQqU64SJFRCL47d1IAjS2+KO51o88L8SwLrhEJAIKxQXJ+BUNh/F0tEE0giE1HaV6eJ+h4by2oNdDyOFEZTShtZz6Ai4aCRN2uVBotKgMBnx9TiRAd5JscyIaBSk2yWzQ45EIAXsvklKJxnri3fFIE9EoToeHpoYWcnJS0RBFbTKhGOb7PJKikQj+rnZE5EguhYunbZeqoDO2BPLInJcL6WSBfji3DI8A35EkKQCEiI2uCSHEubyaFuDYy9LM/seG2qZZkiQVYCU2KW8QIcSTwJMQu6M/hzadNUmpwJBoG7HjR4JBIl43Sr0RpVaLWq9HfZ5m7Qd6uvG1NwMSlsJiVAYT3tYmQk47Cq0WS2Ep0UgIb0szRCMo9EZMGUPfURwrHAzRursWjV5Lalnu6bf3+2PL92wnXryE/QHW/+Vt2g81k5ifxrJv3Y6vs2Og/Z7mFqyF+fi7u6jfeAhnp5P8OePRKCXaP94BxJbnxI8dvDxKAgLd7UBsmEFttSGEhL2+BZUUwZCcgDYudjcphCDkcoIkoTFbCNh78Xe0QTRKVAgC3W6CDie2MflISnDWVKEyGGM9C6f5Eg25XPi7OlAajBhSY5Ok+nrsVB2so6gkj7jzfOfpbGsl2hebCNgXCLPqowPMXjyFtKxTz1A/sOMwCn8QTUSQPDYHY8KJXwFhv4+Qy42kVhPs6yPgcBDyRhBCkFI54YQL4UgwiLuuDhGJoLZa8He0IClVKMzJHHzxQ1AqKL1jKZbMZHxdPURDIQypyYR9XjxNtShUGoxZuYN6bwJ93XhbGgGQ8segMQ9e4mjvc/L6i++g1+u47par0OnP7wz0YCjMj7/1X6z/cDM33XYVn75lFtqEREzZF8/ysEBvN/6O1oHfjRnZJ902GolyaPNBolFByfQSlOe5WuT5FAn4ifj9qAzGc54LNRz+nh56d8S+a+InTkSXMHpprI81nIQ5p+9HHb4tQJEkSXnEAvodwF3HbfM68EngY+AW4MOLcXx+JEVDIYIuD8G+LkKOXlQmc2yG/Pm8ox4Y0xcceXePjPmLSBQQBLq7jo55SopT3tHt23UIjUaNosvDjuc/BAnmPXIzSWNOnps7Go7Qs30nwb4+9CkpJFZOGvS8p6OdgDe2BC3o9RHos6NQa1CZzKiMRgL9V9LOTie734yt/JTUaornjYuNUwoxqKTpEQq1GkmpjCVCUalACKpX78KsDRHwufA2NZIyexYqnZZAXw/umtj4a8iUzKrV2+jp6GHJwgmk6T10bNw+cNyIXoXC7SbsdqNNSibgiSAQxGWnUrutisMb9pM3qZDiWWUA+Hs6iYaDCFeYgE6HymzhPx/9HWve28C8q2by88e+d16/XIPBMCpJASJKMBTi/37zLJ0tXTz0vZPPr9320S7eevptKmw2Iv4g2dPGMukTSwgFYuPoao0aEY1i37+fYF8f2vgEgk4nkimRlvW7Yn8DvQFlRir1BxvIL80jPslGoKebaDi2BiLsjSWvEZEwruau2Mz6cARXUwdqrZLWDz8CIUicPAGFCqKBANFAgJDHfdwwzanv4Fe+uYr/+eVfAEhIimfhsvObcc7v9bN1wy4Adu86jOKOuYjw6dfWX0jHfoec7vvkwMYDPPujZwC44zt3MmHehFNuP1oi4RC+jlYiAT8qowlj+skvXs77uf1+jnyBRvz+C3be0xnOrPtJQzzsABqEEOEhnjstIURYkqQvAu8SW173f0KIfZIk/QjYKoR4HfgL8HdJkqqBXmIXA1cMIQTdO/bgbmjCmJmGWq8kGggMfJjOF21CEqhUKFVq1MbY5CRDejYhlzN2VaxSH02RqlCc0BUa9vsR4TBqk4k1Kzfw7Yd+ikqt5Ec/+nL/C4ndERwrtgwoiNoYGxaIhoJEhYRCpyfkdp+Ykz0UZtysfPo6kkkZm4NKo8Lf3oGkVKIymLAW5OO3u1BIAmt6PI7WXkxJNp7++cuMLc9h3KwyzLknXmiodHp0KelEPF5QqujqstNW04K5NLaSQOLofKZjv6h3bdzPU394KfZemfTcfV8pKpORsNuDxmph6+5DlGRa8UQUBNr62P+3lQgBUx64jjVPraSvtYfDHx8gpzwfnUmPEFEivlh2P41IJhyOcHhfNQCH91UTDkfOKdAHA0H+9uQ/2bl1L5984Hbys1NQaWITQ+1tsYlyemMsUK55Yz37tx1i9vLpJBgNHP5gJyljs3AF/Qy6zpagtaaVF37xPEqVktu/eQdJmYlEfEcnaCr0ekIu18BnNhQM88QP/szuTfupnFfBt37zCJJCIhqIBXi1xYLamIFSryd4uIW4wljxEqVaIhoKDRwn6HSh1KmRVJr+C7XB/ya0cQlEgrEld2rTib0OtngbAEqlAlvc+b+PscZZ+I9ffY3N63cwd+EUtInJaKznNhE37PMStNtRmUznlMPiCG18YuzvGY2iOU0K6shxSxnPlL25i57qFhIK0rFljXySMBEOE+y/ISF6+knJ55MuKYloQQEIgS7p5CuRLrThdN3/EZgE7On/fTywF7BKkvR5IcR7Z9OA/jH/t4577PvH/OwHbj2bY18ORDiMty3Wrezv6cNcOQ6V0Tyw3v1UQh43rrpaFCoV5rz8U05KU6jU6BMG/yNU6fSDkuhoE5NR9K/NPXZ5UNjrpXfvLkQwiGVMCe2tXbEu7mCYoFpi0l2L0Bi1JBcf7eqPhkJ0bNyGp6mVpCkTsRbl4Whop3HdQfSJFrIXTMRdWwuAMTsbpUZDNBBCr1Ohz7WhM0ho4+JImDIFEQyiS0pCUippXL0VhRRm4tVjCYYgorfQVttGW20bqcVZmJobURmM6BISqd1dS2tVCyXTxyIp1az9907qDzVTPK2Ajdv3YkmeQUpKAgFfEL/Dg8oQoXZDDUG3B0tOIhFPiLhEG/YeB/mlRegS4sm+eh4Rrx9dYjyeHYf57ePvcnhfLQ9+5W4UUYFCq6a+tpmc6UWo9mpJLUgbSHmq1GgJE8stoDYYUWo1fPdnX2XDmi3MnDcF7TnO+6iraeTx3/wVgMTkeL7x1bsIdHaBJJGRFMe3fvklJk4fT3d7L0/+9G+IqMDe62BmcT7Nu2po3lXD0kc/QU9jJwaNGlNUkFKcRf2eOjrqY0MojQcaSMlJwVZahr+3j2BUiUGvwnGolqRxeQgB1oIs2ppiQwbtTZ2xC5hjJgKqTUa0cbFJfFqbg3BPD0gSusklGFKTSZoykWgohEKjINBjR2XSIyJBgvYutDbbQFetp62T9o82oVCryVg4G21/YD9i0dVziEv4JVqdhvEVQy+HPVdTyosptlqw5KZhSDv3L35PYwPBvh4ktZr4CZPOuVtakiT0iWcWfEtnlHLrN25DRKOMmz3+jPaJRiLsfO4DHE1dWNITmPu120asjsMRSo0WtcVGyGlHfYEnIyo1Gsz5Q68COdaRXAcqnf6MvsvP1XDe8VbgM0KIfQCSJJUCPwK+AfwLOKtALzs1hVpN4uQKvK3tGDPSBsZuz0Sgt5eQPXanpo1PQJ98NNA7e50EvAGSMs/8y0eSJCSlAmdnN7V7dmNJjmPMtBIigQAieKTb1cPi5XNx2l3oDTpmL5yOwXTiPIKw14+nKTY26G3vwFqUh6u5M5arvNMO4TDupnoAlHodxoxM9GlJhL0eQKBPiX056Y5bqqg2GmhctYWk8XmkTi7F449QOKkIn9tHnFnCXVcLSBiKy/j7958m4AvQXtdO5dJyisemYdQpSc1ModvhoK2lC/+B/kmdWj1Kg57a1TsBMLp9tHs8/O7Zn+J2uMnNz2Tz6x/TuK+BiqsmU5icSFlFCX/62dMAHDpczw3Lp7PxQBU//dLPyC/MJj0uiY27DlA8bzzZRVmIKEjq2N/oyF3z1JkTmTpzIudDanoK02ZPZvP67UyaOgG10UJAagch0NpszLs6VvY34AtQPr2MnRv2UlJeRGpuBk3bq0mfkIc1xca8+RXsefY9vIB65jjyJ+STVZKFSqUip38ehjYujoYd9Wx9bhXWjATmPbCMYG8vuqREDElxPPzD+9m9aS8VM8ajVqvwO8LoUjNRGfRorEeXjMUX56O1xuoqGFJiwd9SEDtHNBRCbbEQ8bgI2j1EwiHCXg9BTwCNxUjI4QQhiAaDhNzuEwK9QqGgcnqsxK3P60N/nuux167fS8v2KkxWHV17axh/34qTli12Oz189PoGFAqJudfPHuhZOd5AkZxRWDKn0qiYuGi4n0UJZf/FiFKtuiATIiWFAlNOfv/F4MjnoxguIaJ4mhsIOfvQxCViysod8XMOJ9CPORLkAYQQ+yVJKhFC1Mo1gUeWKTMdU+bwyzFqLBa8KlVs7e4xCVE6mzp54htP0tdp51P/cS/lc89srC0aDuOur2HP5mbWv7IFJIlP/uIzpBelY8zJIxoIoE9OwWwwcv8jnzjlsdRmI/HlZXhbO7Dkx9aaJ4zNw9/rRJ9oQxtnw9scW1d8ZMa/Lj4BpUaNgJOuRU6ZWIwuwYLGaCDocOJvaGbMlBw+fHsrkVAEJYAk4be7iU+Lo622HUkhseYPb2NNtTH1uvEorEl88VM34HX4kbROJKWCA+v2k1tZiMaoIxwMkVeUhrFXj0mjofHDHTgPNvPO/70PQuDqcZE9Jp2cgkzuuP8Gdm7ay4LlsyiYWc6L768DoLa6kbwFWTiqWmhr6CC7KIuozxe7wwZM2bnA+Z0cZrWZ+fXjP8DhcJLev9TMUlQGIoLKcPTzodVr+cKPPktPey8ZeWkolUrSxuWisxrQ6LQkFOcy4d6rQQjii7KQFAoe/O3nkZAG1Riwt8S6Tx0tPQSDYMnLJdrf5Vs2uZiyybGUrkGHg67NW0AILMVFaG2DL96MqUNfjCrUavSJSYR0WqLhICq9AVdjG93b96K2mkmbOw2Lz4dSo0GfevK71peffpN/PPEyN3/qWu68/6bzEoy8fW62PP0+QgjicpLIKUo5ZSW7bat28K/HXwPAEm9hxkly8huyclBb41AZL+wks7OlUCqYeNdCemrbiM9LO+mFzvkmKZQotRfnZEERiRL2xOYThT3OYa/MORvDCfT7JEl6HHi+//fbgf39KWovrhkmMgA0VhsJk6ciSdKgNdldLd30tMeyeDVXNZ9xoJckCYVaPTBOrFBIsUpSSiWmrBMnvETDYbydPWjMJjTmwevKJYWC+LJi4suKBx4zZyRTctuSgd9VU6cBDFyktO6t4+D7O0guTCVvYjb6lFTcLh///r+3cdvdLL9vGRn56cTlZxIJBrHv2UXU4yEvJQlJIVF/qAeTDnyeEEk91dz62UW09QUJOz0cqGmno7qdnpYCbOoAktGIvbaLuJwUHJ0OQkoV0WCQktn5IEmolSHSE804mzrw9TgIhyUyx2TQfKiZzIIU2muacEYEt9y3guycDDa+swW9Ts/yGxYScHlJS0tCKWkpHldI2ZQSdm/bT8DlojAtA53Nito0EnNfwWgyYDQd7SZXnWTVhslixHRMLgBLytG7bEkhkTBm8N97qLkDhXPGgQS2jASMFh3N760m4g+QMnvq4AtXIY7OOYkOf+6J2mQZGIO3V28EIOR0o1SrSa6sOOW+4VCYl556A5fTzT//8hor7lqGyTS88sJD0Ri0pI3LpXVPHSlFmeQumXTKyW62BGusx0whDaqJcLzjh9MuBabkOEzJp88ueKVQqFQYMnMJuRxoLBdm2edwAv2ngC8AX+7/fT3w/4gF+QXntVWy80Y5xFV/wfh8rvrEEhw9DiYuOPOuOEmpxJhdQLklEWtWKpYEG2mFJ+9p6Nl9iK7tezFmpRNXOgZL1onr00/l2F4IgEMf7qJldx0tu+uIT1Cj0Gio2dfK+tdjRU0yizLIyE8n7Pfh7+wi7IlNbFNKgm/+/hFqd9bgaOgkzqpA8jgRwTgmLq5k878/pi3oZ8zUQqqbOnjtZ8+y6Oa52MISjp11NOyuA8CYaMaQH4fBoCEaiWBMSkKXmIA5M4lOIDsnmfKZJRi1Ef77v/7BwV1VXHPbEra8tYVI/0TEL/zosxSV5BF0+7GkJSApJFoa2/j5d35PcmoiEypL+fTDxy88uTTF56YyNTfWc+CsbyLi618x0eeAYwK9xmYjsXIyYb8fQ+qpl/ad9pxlxagMBnSJ8WjMp894p1KruP0zN/Dck//ipnuvOS9BHkClVTPtM1fj7XViSYk77bh0+ezxfPPxr6BQKsgrzT0vbZBdvLTWOLTWC3fxM5zldT7gv/r/O577vLVINuJ0Rh3XfObqs9pXpdNhTtUxPvXUQbuvqRN3nxdNUiId+5to2VbNmBvmkVh6+okqR/j6XNR/sAUUErkLKkkrzaZ5Zw2pJVkEgwKFWk1aXhpJmUk4e5wDX5CB3h78nS2Y87KIRgSqpDScXU7ee+p9Wrq7WbZ8MnEmI7bEWJfw9rV7aDjUTMOhZpbfsxCAtvoOvO4wqcWZSAoJlVZFwZQSzCYNu/7y71iq0lkTKCrKY+J91/KX3z3Ly8/8m7Gledz/lXuo3hebSFhX1UhOcRa1+xvIGxtrn95qQj8o7apEemYqWzfsRG/UE4mc2+z6i1HI3osxKw0EGIaoI35kzsW5UurUxI3NO+nQzlBuuPtqlt+yCM15TnKlNWjRGs58DkzB+DP/tyGTDcdpA70kSf8UQtwmSdIehsjALYS4OBdTykZNT20ba3/3MtFwhHHXTiXcv/bd22Uf1nH6qpro3B1bXmbNTsWUl8qUTy2ie+M+at/bgTk1FYNJz6e+ejPG1AQSM2JfqiqtDoRAhP24whY+/N4zxGUmkJgWj9qmI+QM4osoMMTFEqgUjMthz6YDjJtSjNlgYO7SqZgtRtIS42k73My8exbS2+ciszQbEQxhy08n7A/g6+5j259eIf+qaVy1YApal484k4G+PXVMmz+ZxNR4gsEgfZ0OJi4sZ861M4Z8ncmpCTTWxfLt791+AL8vMKh7faSIaJRAX3dsadXxNQ3O53mEiCUw8ThQ6PVoztNd8/GCTjvOwwdACMxFJSeM9Z/K+Q7yMtnF5Ez+ZT/S//9rR7IhssuH3+kZKB8qqdVkzZtEyOMjaVzBsI5jSk9EbdSBJNHQbueprz6O2WZi+YJykCAaFex8diX6JBu2budAoFebLehSM3A222nYdohIKEx3XQdzH7yGqN9Py/tbcAPtO6ooWDKFA9urueEzy2nfWceu1zaRVpKJc18zxcsSkYwSrm178ZuthPxB7D1ONh1sZvz4PPwNbfjMJuz9k/FKy/Nw1zSiyE2hMC+FNet2sn/3YYrLCvjWf34JU/88BY/Lw5t/fRd7t53l91xFVlEmX/ru/ax972NmLphyQYI8QMhpx9u/sgEkdEnDG1o5E+6mFvr2HcRckIM+ORltXNyITSKLBoMDY/3RwPEFiC9Onq4++qpbsGQlY8kc+TXmsivTaQO9EKKt/8duwCeEiEqSNAYoAd4eycbJhhYKhgaVXb3YpIzNYcItcwn5g2RNGYveenZ3cJbMZCofugWQeOeFDxFRgbPXhaEwk+T5E/FKAke8hb21rVi7nKROKsSUYMPX2Y6vo4umtXsxJcSTOiaD+Oxk8iYWEnR76Px4LyGvH2NyHJFIhISUeA7vqkHrjlXWk1RKiq+ZjDXZRt+eWI9C8bQSkCRaa1o4tL0awhGKx+fzzj8+BGD2wokotFoqPnsNzr17yChJZeUHQUrLx2CxmknPPtpdXbO7lvdfiO2XkpVMVlEmEyaXUlpeTPwIplA+wTHDAyO1ltdZXUfI5aZ35z6yli8ZSI40EjS2OPT9aZnPR4XIC6Hm7Y+x17SgsRqZ/PmbB4oCyWTn03D66tYCcyRJiiO2Zn4LsZn3d49Ew2RD+/cr7/P4r//KiluX8ZmH77qoxnIjAT8RnxelwUjRMCb5HdGx4xCduw6TOrmEpPFFAGj6726nLanE4/RijjNhzUrih1/+HcFAkAnTS9m0ejuz5ldSvW4rBRMKUBn0iKCf+MIMequaqVxWSfq0WIIPrUnHlIduJGh3oLfoUABp2cm0HWxm3NJK4hItZI/Pw5YaT9gfRGvQojLo6AmE+ertj2IyGZh27TRKxuXQ2djDnCWVSHoNOrOekFaD3+2NZeMKBrj+5kX8+X/+iTveS3tLJzkFsax8qTkppGQn09PWS15pLg3VzfzwS7/C5XDz6H9/jQlTSs/PH+Q0NGYrpoISEFHUx+WBP1+M2Rn4e3ox52ajHCL98PmkUKkxpp++/sLF5MhyM6VKNSpFomRXhuEEekkI4ZUk6TPAH4UQv5QkaecIteui4XV5qd1TS0p2yrCSy4yUl555g66OHv7vD//gxjuuJik18fQ7XQAiGsXdWEvE60FtsdEZULPq3+soHl/InKumn3b/sD9I46qtRAJBGldvJ6E0H8UxFzEp2Snc+ZVYgsQ1/96AxxVLl+r3xbpolWolNqMKf3c3xrHjsBQXYylRkr14BhqzAXdHH36Hm7i8NNQqgberEVcXRMN5zL99AZOvqsRkM6E8Zp2vSqfBmpuOUqtmw7/W0Ndpp6/Tzpw55YhwGE9TDx2HW9AYtPgSDVQ5DrL5XQU3f24JXfVtHN5Thcftw+P2UVfVgFajJhQIkVWYybee+CoBX5CE1HhWv7We9uZYprhDe6ovWKAHzksa1VOxFuRhzEhHqVFfsEAWCUfY8+9NdFS1MGH5VNJKcy7Iec9GwdUzSBybizk9CZX24u2lk13ahhXoJUmaQewO/jP9j108t5Mj5MN/fMjal9aSkJ7AQ797CJPt9Et2RtL1ty6jsbaZ62+/mrjEC7c8o6u+g13vbsWSZGPStdNQaQZ/dISIxvKQE8tZ//ZLa/n3CytRKBUUjs0j7TRL65RaNcnlRbRt3kdy+ZhBQf54FTPGcf0nryYYCJFXkk1JRREzZo7FGPLR1OwivP1dNHotRdfMwhhvwtPjZPMTrxF0eym+diYZFbkDx2pv6ETZ5KZ+0wGyK8eQN6MUZ4ed7vp29BoFVa+tQRdvIW98IWPG5aHTaLAKQXJmMk07m4BYV3/QH0SNhFKtQpOSwvYXN2LVmZg2dyLWeCsJCXF8796fEgwE+covvsDEORMw9d9ET5haxlU3zCcqohSW5hHwB9CO8N3vhTRUIaGRZG/pZvvLscREBqvxog70+jgL+pOU35XJzpfhlqn9NvBKf+GZfGDVyDTr4uHqdQHg6HLEKnSNshvuuJol187DYNRf0PraB9bsYc/KWPnFuMwEtry7FZ/bxzUPXEtqfhoKpQpTdh5hjxu12YLZuheAzLz0IVPgHk+SJLIXVJI2tQy18dTbW+Mt3POl2054fO1La4k4PCjDEGjtwl7bjDHJRtjrJ+ju7wFwuFEYTBxqcCMJgd3vIri/CV+fu7/OfSobnv6Ahh01TF1WjogKfN0ODG3d3DCnAkdjB3pJQVJuOgs+nUjN9ip8wRDx2UkEOp24O+2E3H4W3r8UR5+Tz0wciyRJbFy5BX//6oO2pg6OHdiIT7TxpR/cz3/94HG+ct+j3HzPdTz8nc+c8PpkZ8aUYCGtLIe2/Q2kFJ+8WqJMdqUYzjr6tcTG6Y/8Xgt86cjvkiQ9JoR4+Pw2b/QtuGMBiZmJZBVnEZdycWR3ulCzso+VmJsMElhT4gj5Q1RvqyIUCFG1vYrU/NhEs2MzlN32meuZNGMCyemJWI+7Y3G29RAOhIjLSRl0sSIpFCdk0BuO5PRE3vjHakRUMHVpBZbMWC+CJTOJ8XcswtfnJLW8iLr9DTQ3ONAZdGzdtJuZk8fi63OTWZ5P+4drKSi00ttkweOPkloxBpVRR/eBOlR6LcXXzSK+IAOd1YTOCpOuiWXv87t9PPfEE4S8Qbq6+3jyn//C3uvk+7/8GlffuIjx08u48f5r8XsCZGak0HywicySLDav3ErN3jqmLJ7MmndjiX/WrvyYB//fvRf1hMuLmdakZ9EjNxBw+TEnjczcA5nsUnI+F87OOo/Humik5qWSmndu2bouB6XzJpCUm0Lj9mo2/vU9pi+exJ6th8gZols0Gomy+l8fseWDbVzzyWWkpB+d22Bv6mDDY68QCYaZdM9VZEweM6x2hP0B/F3dqC1mtNbBFxBqjQrRn0LVmJaEuX+5nSRJON1B9n10gCKhYNOHO6jbFptNv/DmOdibusheOpk4sxZnZw9mv4+K5ZORAkHs1U0o1EqyZpVjSI7H6w2x7plVmBPNlC6sGEgNq1KrSOyf1CcMSuy9sVzWh3Yd5uobF2E0G7j5syvY8uYmXv3liyhVSm77/t089dNnEFGBu8/FV77/AB+9v4kFV8+Sg/w50ui0aC6j4Q+Z7FyMbL1A2WXFHG9m9xsbiYYj2Bs6+cIfHkY/RDd7b5edl/74KgDvv7iKyfMrBp7zO7xEgrHCJt4+17Db0LfvIM7DtaiMBjKWzEOljxV+aT3YRMPOGmbcOgedSc+4heUD+4SCIdb9/X28Dg89TV2oc2ITGK1JVvrqO7A3dpE1Lod3//I+CJh793zGza+g7r3NhDyxuuphfxBvNMq+VTupWr8PSSFhspkYd/UUIJbydOFDK+ht7kZnM1Bd1YDb6yNdMzhDm98dO14kHEEIQU5JNvX7G0jLS2PRNXNZdM3cYb8nMplMdipyoJedMa1JT+Wtcznw4U4qrps+ZJAHsFgNLLxlLqv+9RFTFkwa9FzimExKb5hNyOMns7J4yP1PJdKfCCXi9yNEdODx3Su3cWhtbF7AbT/9FHrz0eENERVklGRStekQ2ePzMBSnYE4yU1yaz95XN2KwGvEFggN5HyWtlkg4gqTVYExLBEmirdPOM4/+jYyCdDLT45EE6KxGemtb6dhTQ3xhJilleRjjzYjo/2/vvsOjvK9Ej3/PVPVR7x2EhACBQFRTDLhhO05sgkuK7cRpN9ncTTZ3b5z13Xs3z5PivUn23uzNbnadsrE3zS2O7di4A8YYML33ItSQhHqZGUkzv/vHjDFFQhJIGmk4n+fh0bzzFh29gM687/v7nWNYvnQBB9/ZTdmSjz5wAJTfOger3UZMYgxFsyfzle8/QtXJWnbt2M8Lv3uF21ffFFYD8ZRSoScf9r2+5gOJ7DLGjEzj7GtUUVFhtm/fHuowwtaV2ip21dTQsu8AkenpWLNzcCXFDzposLu9i/rT9aTmpRE9QHEdb1sHFocdf08PXVW1RCQlEJXx0Uj+nX/ZwsYn3yIhM4mU/FRWfvlOHFGBhOnt9rD2+78nIjYKe4STld+4G4Bzp2tp3rkXvyOKrh4/rW1ekjOSyJ+RS9PeQ/T6BJ/Pgt1hYfMHx9i2bjcAj/z9g7iinXjqmvD19VH9wSEsdhvL/u6zOC8YePjheRqsdv2zT77Ivzz+HwB872ePsnjl4NMRlVLqUiKywxhTcen7I3lF/9MRPJYax66UuLtr6/H39NJ1por0SYWDJnnj9/PKv7zMofcOULKolE8+et9FPc0BWo9XUvn6JuxxMfiysymaV0pE9MW92metmkd7fSs1Byo5te0o3s94zid6T7ubivtX0FpVT+b0gvP7RMdEUOWGzc9tQkRY8sVVTL9xJvVbd9Hb0IDYbBjjpKWmgYI0F2b5LDLyU5k0JY2dT71Jb4ebxKIsMssn4+l0n59yWHu0mveeeZekrCSSC9JZ+8tXmX3TbFZ+9qZ+E35i8kfP+V3xOnhMKTWyhtLU5mX6aWbzIWPMXcGvvxm5sNREFZWVQU9zM5GZGdiiBp9W5+vzUXusFoCao9X4+nyXJfrus01gDL1tHZyqPURPpxenMaRPLyA5OOLfYrVQfMM0RGDu6sXnR1tX7zvN6z95DpvTzq3fvIdzh05yevdhJs0rxZWeDDExGL/BYLAFP5Q4g81uLA47EVGxdNU2EhUfy83lefTW1dK8cxe5C0o4d7gGa5+XnroWClctxhocQHdw436Obj0MQOnyMlobWnnn9+8wd9VcEtIuL826fNViEpMTiIyOpGT65Ks57UopNaChXNH/OPj1HiAd+G1w+QGgfjSCUhNXdGYGUWmpQ66dbnPY+djX7+LoB0cpqijC3k91sPgpeXQ1tdLW4aGl9iyycS/edje1+09x86OfQiyBBJ1RkkNGSaAE6oe3zZurGvD1+vD1+mg6Vctb67bx+mvvs+oTN/JX3/0SxcvL6e0zWCwWCuYEyu66igpwJibQWVVNV1UNsdMLeeEX7/DxhxcTQ2AaYPqsKUQlxnPmzc0AuBtbzsebNTUHy1+spOalkTctn90b9nDrQzezfe02UvPSmLl81kU/n8VioTxYolcppUbaUJrabAAQkZ9ccu//ZRHRB+HqMsNtkFJYPpnC8oGvZCNTEqk6XI9bhKysJKzdHrztbmKSXOeT/IV62ttoP34Mq9NJ7qwC2hvasDttpE7N4YMf/QaA99Zt566PLSVjaj7zVi+5OH4RIpMTEAuca3ezf38VyUUZbHrjACvWLCS7bDIRiQnYY6LwtrbT09lFQslHjwSmLppG5hPZOCIdRMZEkleQwrvPb2LXu/sREVJyUsicnDWsc6SUUldrOM/oo0WkMFgoh2BlvNFpLK3GlbbTNdRvP0hcXiZpc6aOefMNEaFk1Tyqtx8hfXoBSZOyaK1uJDG///oG3uZm+jo76OvswJWazuKHbw687/Yyf+EsKitryMvPonLDHkynm5ajp3EVZBFbmM3+V7YSm5pAzvwS2hvb+PPzm9j9/n4Skl088tf3k1iYS3ebB4fLT1d3D3VWCy5HFFt+/iKTV84hf0kZEJi6B9DV1E7t9sNERgTuVETGRuKMiug3bqWUGg3DSfTfANaLyMngcj7wpZEOSI0/jXuO0l5ZS3tlLa5J2UQmXjxgrKumls7Tp4nKyiI2f3TqimeWTSKz7KN+9tFJA9cHd8TH011nx4+V+lON5KcECuc4I52UTJ9MzeEa0mNi6Glup6u+ib4eH/W7jtDS3EXd/tPUcZrG2iZamjqQ4OgUZ6STybMK2fHEq3Q1tTF99VJeWr+FUwdPcXthIX2eHo6/vYPU4nSwReBt78KVk4a7pYPGM03kTk4l8cEVZJdPISkzaVTOkVJK9Wc4iT4OmA4UAHcBiwj0qFdhLi4vg9bjZ4iflI2jn7nz7ceO0dfZFWj9mpGOzXn188D7evvY+qdN1B6pZv7di8idUTD4Pj2BHgS24GA4e5yLA3ta6O720nJ8O+nTCs+P0r/lgZXceM9SOqsb8LR20t3h5vC2E8RnJpJVlMWxDfuITU8gPtVFjLeLyTPLmLdiNgVTcjDeXrqa2gDobGjhbHU9J46fwSyaje1sK1kz82k+cpwjG47jbmpn2j1LyVs4jew5xXQ0tlC8ZCbxOaHvgKiUur4MJ9H/vTHmWRGJBVYQGKT3c2D+qESmxo2UmcW4CrKwRUacH1l+IYlLgM4u+pyxdLV140q9+kTfdKaBTX9YD0BMQuygib6lqoHNv1yLWISFX7gdH9DW3M72/Sc4uOMIdzxwE45Ix0X7OJx2EicFnpFv/8M7uFu7cLd2Mf2Ohdz1w0ew2q2ceXc37RG5bHptJ7NuLMOzdQfOKfmUrbmRjrNNWNLiuXHuHBaWl5E2K5+ClBg81WfwWRy4mwLlbzsbWrDYrBTfPOeqz4dSSl2r4SR6X/DrHcAvjDGviMj3RiEmNc6ICE5X7IDrjxxooKW6hZqjB7kzLQtX6tU3/4lNiSd3RgFVB06TXZoz6PbnTtTRXtcMQN2RKtb++g3is5M4uOMIADVV9VguGVNg/P7z4wyyygqp2nWcpMIM4rOTccZE0rD/BGe37sdis5I3KYMtL3/AXZ8sp/XQCWLmz+P4gZM07ThG/YFAm9oZC6bjys8jMikBqzOC6TjprG8hd8HY9ZVXSqmBDCfR14jIvwM3A/8oIk5gbEdlqXGpeFEp219uZ9Ztc8mc0v9ocl+fj5ZzrbgS467YsCUqLoq7v3Mfnk43rtT4Qb93anE2KZMzQQRrVATdbV30enu4/VM3cba6kRX3XFw7/syBSra9vAWL1cKSB5aRMS2fu77/eSw2K92NLfS5PfS0dWFx2nHlZRDd5yenNAenxYtj8kye/9/PYk2Pwxn8Gax2G5GxkVjsdiISA8/e82/QqXJKqfFjyCVwRSQKuA3YZ4w5JiIZwAxjzBujGeDV0BK444u/z8ez//w8bzy3gcW3L+Ch73z6sqI4V9Ld0sn2Zzfg7fJQsWYZCdnJF63/sGOdMX52vbaDjpYO8kpySMhKIj7j4oFvb/36dd5//j0Alty3jOUP3gRA66kaDvz+DUAouXcFnXVNnH5rGwAZFVOZctcS2s+185tv/hsRcVHEFKaQnZtOblEWvuZ2HDGRZM0pvujn6u3pZeNL79N6ro0Zi0uJS3Rd1MlPKaVG0jWXwDXGdAN/umC5DqgbmfBUOOtu7WLn+4GGM1vf2sGa/3oPMXFXnpnZeqKK7oYWEovzqDtay/H3DgCQVpR9WaL/cC69YGXOHfM4vG43G372Is6YCFY99qmLkn1KXioWmxW/z0/SBcdpqqrH+AJNcno73aSXT6Fh73G6G1qIDraijUuO45P/41M0VtaTX5xO2949tB85xanNgccEzrhoUktyzx/z+J4TPPPT5ykoL+Q3v3oWm83C9/7tMUpnDq817/sbtvH7/3ieeTfM4dOfv+eKdfOVUupS2r1OXcTv83P6cCVxCXEkD2EaWEd9C77ePuKzB75SjXRFsXzVAra/v485S2YOmuQ9Le2c3LAL4/Phbe8kafoU4tIS8HR0kzIpY9CYOs8FRsZ7Oz142rsx6YkceGMHx947SNkdc3nwh5/D7zPYe3vY/su/0JkYw0vPvMGdy+aQlZdB4pRcHNGRlD14O96ObmIv+KCQWZxNZnE2bcdOYHx+rLbAFbxYLbQ1d3D86fUUzi4ivSgLV7KLqLgoPD1ePG4PAKePnRl2ov/z06/y/oZtvL9hG0tXLCB/0uBjF5RS6kMhS/Qikgg8TWA+/mngXmNMSz/bvQYsAN4zxtw5ljFej957+X3+8E/PkpCWwN/89OtXTPYtlfVs+tkL+Hr6mPfIKjIumOd+Iavdxi0P38by+5Zf1N1tIJXHa/nTCx9gd9hY/cXbKMhK5mP/8Fm6m1oxPnPRYLr+TFkaKFoTk+widXIW7rYuNv/2HYzfsOvFLXzyh5+jz9vLhh88RW+3l9MO4dCBkxw6cJLv/PivKQ5OIXTGReMc4ENJVEYavR2dxNhtxBflY4uO5Lnv/QF3ezeVu09y/w8+R2ZBBt/++TdpqD1HUm4yVouF+Utn93u8K5m3qJy31r7L0pULSU69vFa+UkpdSSiv6B8F3jbGPC4ijwaXv93Pdj8CooAvj2Vw16uG6kYAWupbaGtqu2Kid7d04PMG5rB3NbZd8bgWq2VISR6g4UwjPZ4eejw9uIPjPXu73Oz41av0dLqZtnoZeYumD7h/XFoCFWuWnV+OiImkZPlMDr2zm6IbAiPhrQ4bGeVTOLNpH7NnFWOiI4iKjaJ84ZUH0rVVN9LX10tHYzu93ZBbMQlndAS93h5caQm427uJTXGd79qXlptGWm4aMxZMG9LP3p/Vn/4YC5fNJSExnqh+6hgopdSVhDLRfxy4Mfj6SWA9/SR6Y8zbInLjpe+r0XHDHQvwG0Nadgr5U69c5S6lJIeSOxbQ5+0hs7xoxGKYunAqZyvPYrfbKZ5bAoC3001PpxsAd3PHsI5nsVlZ9OBNzP7EIqLiY4DAlMHUmZM5d6aR2q1HuW31UgoWXnk6XNPJWnb+9k3qI+xsWb+LGxbPBGMounEWdqeDO755Nw2n6weceXC1RISsnMEfWSilVH9CmejTggP6AM4CaSGM5bpl/Ia246fp7erCNSmPjIIM7v36PUPa1x7hpPjWuSMeU3JmMmv+Zs1F7yXkpTNt9TK6m9vJnj91SMfxdnvpausiMSMRq81KdOLFtQB6Oj3UHagMvO5yc+DPG4nLSCJ73tTzV+QX8rR1EZGbyju/foVeby/bth1i8W3zzq93pSXgSrv6GgJKKTUaRjXRi8hbBFrbXuqxCxeMMUZEhjbPb+Dv9SWCtfdzc3MH2Vp9yH2uifrNgamIxucntaIsxBH1z2K1XPF2/aW6O7v53fd+x/Gdx1n9jdXMu33eZdukleQw51Mr8Xa68TS1UbXlIABx2Sm4si4fXJg2NQ9PRxcLV1awfeMeFt86j6yZ/Y9LUEqp8WJUE70x5qaB1olIvYhkGGPqgnPyG67xez0BPAGBefTXcqzridXpwOJw4O/pwR5Gz3/bGts4tuMYACf3new30VvtNiYvC3ywObFuFwBRya5+6/kD2CIcTFo6i9wF0/jkf/k4CddQAVAppcZKKG/dvwQ8BDwe/PpiCGO5bjldceSuWk6f20NUavLgO0wQSRmJPPDf1rDrvX3Mvqkcb0sztqhorAM03ClYUkZ8XhqR8THY7BbObt4JBpLKSrDHRF20rd1h1ySvlJowQpnoHweeEZFHgErgXgARqQC+Yoz5QnB5I1ACxIhINfCIMeb1EMUclpzxcTjjB277OtF0t3Zyeu179Fad5a77l+Fw+mnZtwePJYZzLT6yS/NIv2Q+vsVmJakwE4DmwydoORi4G+CIjyWxtAift4ee9k4c8XFY7UP/b3Pgg0Ps2riXGQunMXMYjx6UUmqkhCzRG2OagJX9vL8d+MIFy0vGMi418R1ctwtfZWCcZ+upGhJyYhG7nY0v7KL2aC2JWUk89JMv44hwXLav8RvczZ2IzQoIzsR4jN9P9YZttBw+RXLZFLJvvPwxQH/6+nz89sd/pL66kW1v7+DxZ79LZBg9HlFKTQzalEYNytfno6W+hb7evn7Xt1fVc+SF9VRv3oc/WEY2VIwxHNp2DGtaKo6keOKL8ojNLyAiLQObPdiIxmajn0H1APS6PRx97QPcfQ78UfFEpSXj6+2j/VQNAG0nqzE+P8YY9q3fw3vPbKB9gBoCNpuVSTMKASiaOQmH8/IPFkopNdq0BK4a1JtPvcm7T29g8eolrPriKnzeHpoOncYW6SCxOJ+zu47SsPc47D2OKy+d2BA2bhERblizjCNbDjK5opTk4kAtAIcrnlu/Gk/1wUpiE2I4/f5B0qfnE5sSf9H+9sgICpbPpnrrQRJKJ7HuuXdxRjoovnEuXZW1uCblIlYLVYfO8MKPngOgx93Diodu7jeeB76xhqV33UBmXjpWm9aoV0qNPU306op6e3rZ+cYOAHa8sYOVn11Jy4ETnHn7AwBs9zmJzUymftcRYjKSBywZezU8bi/NdU0kZyb3e5t9IIXlkygsv3zaW2JWEpExEbz23afo6fKQv2Aq8z9320XbiEXIXz6HnMUz2fjSZp79f4E+Tl/+/heYdcsN57dzRjmxO+30ensxHi++3r7Lnt37+/rw1jaQFhtBtGvkzotSSg2HJnp1RXaHnVVfvJ3ta7cx+5Y5OCOdyAWtWC1WC+lzSojLTccRE4E9amSeQfv9fp796fO8/+pWlt29hPu/+ckROa7A+VaycklL2fWvbMLd7Wb+wpmk5KURFRv4WcQiREZHXHSc1Lw0bl69kLaGNmyNTfR0dBOZePGAxqZDpzi9dhMAU+69BVd+5oj8DEopNRya6NWgyleWU76y/PxyyvTJ2CIjsEU4iM0J1EOKHuHpZj2eHnZv3AfA3k37WP21T2B3DO+fa1dDM71dHlx56eeb4ES4olnytY/TUn2OjNKPSvzu2LSHf/6HXwBQf+cZblg0izm3VhDtisIR4aSon8I4ebMmcfKdnaQtLiMiWFr3QqbPf8Fr37BiV0qpkaKJXg2bxW4jqSR/VL9HRFQED3xrDfs27ad82axhJ/nO+mb2/uolfD29FH1sMelzPiqbG50Uh8/tRvgoEUdGRSAiGGNw2m3U7jtJ6R3zmTZ/4Pr3KdMKSZlWOOD6pNICsAhWhw1X4cjWv1dKqaHSRK/GrYoVs6lYMfy2rhDodufrCXTW87Z3XbTu1BtbOXfgJNEZScx46HZsDgcz50/n+7/4OxpP1RHZ2UNesJnOtbA6HaQOs/e8UkqNNE30Q9B6toVtL23GEeFg7icWERUXNfhOKqRceRlMuuMGejq6SZ1VfNE6b0cg8XtbO+GC6YAz5pbC3I+u4A9t3E/j6bNMXTqDlDztuaSUmpg00Q/B0S2H2PXqNgBS8tMoXXrlnuXqcl6PlzefeovKg5Xc+vCtTJ41us1gLFYLmXMvvu3ubu3EHumk8JYFtByvIi43DVvkxYPsanYfp+qDwyRNyWbtz1/B1+PD3eHh1q/eOarxKqXUaNFEPwQpealYbVbskQ4Ss8KnHvxYqjtRx7qn1wOwe/2eUU/0l6rZfph9z6wjeUoOZZ+6mdyl5ZdtY/yGAy9uoquxjYZDZ5g0u4ijWw4Tmxw+5YGVUtcfTfRDUFA+mc/981ex2a3EpbhCHc6ElJyVzORZkzm1/xSTZw08gG24/L19tJ08g1ituApzzo+uv1TTiRowhnNHzuBpaccRdXFRn/aWDl57+m26HRaSE2PJnJJD/spyZt5aQWZJ9ojFq5RSY00T/RAlZiaGOoRR1+vtxe60X/NxPO1deJo7iM1KPl9EJiY+hs9//2G8XV7ikkbuCrn1eCW167cCYFm1lLj8/pNy5uwpeFo6SSjMICbt8r/L7e/u5vlfvgzA5751P2Wrl2Fz2EnMSBqxWJVSKhQ00SsAdr2xg3eefJPZt1Ww7FMrzheVGa6eLg97nnyNjppGCm+ZS+GKOefXOSOcOCP6bxN7tcRyYdH6AQrYA8lFOSQX5Qy4PjUrGZvdhjGGrElZ2BzX/oFHKaXGA030CoBtf9lKV2sXG59+l4rb5xF7lVfdvd1eOmrPAdB1tnkkQ+yXa1IeYrEgViuxuRmD7zCA6RVTefy3/xNjDLmTr3yr3tvtpbmyHldGIlH9FMpRSqnxRBO9AqDijnm8/ZvAFX10/NXXZY9OcTFtzXLaqurJKB/9OeQWm5X4ovwROVbOpKEVtdn53LsceWc3qVOyuflbq7FrVzql1DimiV4BMPvWCqYvLcMRee1JK2P2FDJmh2+hmJYzDQA0nT5Ln7dPE71SalzTRK/OG4kkP5Y83R6qT9SSlpNK7BjeQp+9ZimV24+RUZpLpBZPUkqNc5ro1YT13L++yLo/vcuMhdP42g++OCIzBoYivTiH9OKBB/YppdR4ooleTVjH950A4MiuY3g93jFL9FfD19vH1j9vovpQFfM/sYi8spGrJaCUUleiiV5NWPd9fTU71u1i6twSYlxjd+ve7/PhaWjA4nQSkTi0+grnqhrZ+Lt1AETHR2uiV0qNGU30asKaWlHM1IriwTccYV1nqmg9eAhESFu0AEd8/KD7xKW6yJtZSOXeU+SU5o1+kEopFaSJXqlh8vf5Ai+Mwe/zDWmfyJgo7n70Pjwd3bhSE0YxOqWUupgmeqWGKSY3G7FZsDkjcA7x1j2AM9KJM3JkKwMqpdRgNNErNUxWp5O4goJQh6GUUkNydQXNlVJKKTUhaKJXSimlwpgmenVdathzlN1PPE/d9oOhDkUppUaVJnp13TF+P9Xv7cbT1E7Vhh30ebyhDkkppUaNJnp13RGLhbTZxYhFSJ8zFesATWl6u7rprKqlt9s9xhEqpdTI0VH36rqUuaCMlLIp2COdiMhl643PT917H+A+20h0TiZZyxf1u51SSo13IbuiF5FEEXlTRI4Fv15WRUREZonIZhE5ICJ7ReS+UMSqwo+I4IiORCz9/xcwfh897Z0A9LZ3gjFjGZ5SSo2YUN66fxR42xhTBLwdXL5UN/CgMWYacBvwf0UkfuxCVNcri91OxpL5JM6YStrCOQN+IFBKqfEulLfuPw7cGHz9JLAe+PaFGxhjjl7wulZEGoAUoHVMIlTXtej0FKLTU0IdhlJKXZNQXqakGWPqgq/PAmlX2lhE5gEO4MRoB6aUUkqFi1G9oheRt4D0flY9duGCMcaIyIAPQUUkA/hP4CFjjH+Abb4EfAkgNzf3qmNWSimlwsmoJnpjzE0DrRORehHJMMbUBRN5wwDbxQGvAI8ZY7Zc4Xs9ATwBUFFRoSOnlFJKKUJ76/4l4KHg64eAFy/dQEQcwAvAU8aY58YwNqWUUioshDLRPw7cLCLHgJuCy4hIhYj8MrjNvcBS4GER2R38Mysk0SqllFITkJgwnB9cUVFhtm/fHuowrksNZxqoPV5DwYxCXCmuUIejlFLXDRHZYYypuPR9rYynRoy7081v/+E/aaxuZObyWdz/nftDHZJSSl33tAqIGjF+n5/u9m4A3J3dIY5mdLWcbebEzmN4u7UhjlJqfNMrejViol3RfOZ/fYbKg5UUzysJdTijpqutiz9897c0V5/jhjVLWP7gzaEOSSmlBqSJXo2o/BkF5M8oCHUYI84YQ/uJSnraOiAxkda6ZgBa61tDG5hSSg1CE71SQ+BpbqX+/cAAz/ipk7nn2/dSe7SG0sXTQhyZUkpdmSZ6pYbA5nRgi4qkr9uNLTqKktIplCwsDXVYSik1KE30Sg2BPSaa7FuW0tvlJjI1KdThKKXUkGmiVyOuz9tD9Xt7cTe3kr1oJrFZ4dEBzhEXiyMuNtRhKKXUsGiiVyOuo6aRqo27AHDGxYRNoldKqYlI59GrEReZGEdUagJYhNjs1FCHo5RS1zW9olcjLiI+lrKH76TP00NkYlyow1FKqeuaJno1KuxREdijIkIdhlJKXff01r1SV8Hv82F8vlCHoZRSg9IreqWGydvaRuO2nYgIKfNm44jTxxNKqfFLr+jVuNRZ20j7mbOMxzbKnnNN9La109Pahudcc6jDUUqpK9IrejXudFSd5cjTr2P8hkl3LSOxZHzVzo9IScaREI+IhYhkLZ6jlBrfNNGrcae324PxB67ke7s8IY7mck5XHJnLlwAgFr0pppQa3zTRq3HHVZhN7k3z8fX2kTh1fF3Nf0gTvFJqotBEr8Ydq91G2uypoQ5DKaXCgl6WKKWUUmFME71SSikVxjTRK6WUUmFMn9GrsNBY18Rrf3wLR4SDufOm0XCshrxZk8koyQl1aEopFVJ6Ra/Cwrb1u+iub8Xu6WX9L9ay48+bWf+rtfh9/lCHppRSIaWJXoWFrPQkrLVtuE82EpfiAiA+IwmLVf+JK6Wub3rrXoWFtKwUjDF0t3Qy4/a5VNy9mNRJGVd9POM3eLs9RMREjmCUSik19jTRq7CQPiWbm79xD+62TgrnleC8hgTt9/nZ9swGDr61i3n3LWPaLXNGMFKllBpbmuhV2MifUzQix/F0dLPv1W0YYzi0bo8meqXUhKYPMJW6RGRcNHPWLCEmOY6yVXNDHY5SSl0TvaJX6hJiEcrvWsjMO+dj0Zr2SqkJLmS/xUQkUUTeFJFjwa8J/WyTJyI7RWS3iBwQka+EIlY1cXk6uqg/VImnrWvY+2qSV0qFg1D+JnsUeNsYUwS8HVy+VB2w0BgzC5gPPCoimWMXoprIjN+w+4/r2PyvL7L7j29j/DqnXil1/Qllov848GTw9ZPAJy7dwBjTY4zxBhed6JgCNQx+n4/22iYA2mrO4evzhTgipZQae6F8Rp9mjKkLvj4LpPW3kYjkAK8Ak4G/NcbUjlF8aoKz2m2UP7CC+oOVpJXmYXPYh7W/p6Ob2v2niUtPJLkgfZSiVEqp0TWqiV5E3gL6+w352IULxhgjIqa/YxhjqoCy4C37P4vIc8aY+n6+15eALwHk5uZec+wqPKRMySFlyuX17k9uOUTdwUoK5peQOS2/330PvradA2u34Yh0csd3P0tMsmuUo1VKqZE3qoneGHPTQOtEpF5EMowxdSKSATQMcqxaEdkPLAGe62f9E8ATABUVFf1+aFDXlz63F6vDjlxSBre7rYuNT7yK3+enpebcgIm+r6cv8LW3T2vmK6UmrFDeun8JeAh4PPj1xUs3EJFsoMkY4w6Oyl8M/J8xjVJNSM2HT1Kz/gNck3PJWjoX6wW37R0RDjJKc6nZd5q0KdkDHqP0tgpiU1zEZyURl3bZpBCllJoQQpnoHweeEZFHgErgXgARqQC+Yoz5AjAV+Enwtr4APzbG7AtVwGriaD9djfH5aD1yitTZ07AmfnTb3ea0s/Qrd9BR30Z8dtKAx4hJimOqVsVTSk1wIUv0xpgmYGU/728HvhB8/SZQNsahqTCQMKUQT3MbroJsHK6Yy9ZHxEQRERMVgsiUUmpsaWU8FZZchdnEFWQhIqEORSmlQkrnpauwpUleKaU00SullFJhTRO9UkopFcY00SullFJhTBO9UkopFcY00SullFJhTBO9UkopFcY00SullFJhTBO9UkopFcY00SullFJhTIwJv46uItJIoFHOeJQMnAt1EBOInq/h0fM1fHrOhkfP1/CN1TnLM8akXPpmWCb68UxEthtjKkIdx0Sh52t49HwNn56z4dHzNXyhPmd6614ppZQKY5rolVJKqTCmiX7sPRHqACYYPV/Do+dr+PScDY+er+EL6TnTZ/RKKaVUGNMreqWUUiqMaaIfYyLyIxE5LCJ7ReQFEYkPdUzjnYisEZEDIuIXER3tOwARuU1EjojIcRF5NNTxjHci8msRaRCR/aGOZSIQkRwRWSciB4P/H/861DGNZyISISIfiMie4Pn6bqhi0UQ/9t4EphtjyoCjwHdCHM9EsB+4B3g31IGMVyJiBf4FWAWUAg+ISGlooxr3fgPcFuogJpA+4FvGmFJgAfA1/Td2RV5ghTFmJjALuE1EFoQiEE30Y8wY84Yxpi+4uAXIDmU8E4Ex5pAx5kio4xjn5gHHjTEnjTE9wB+Bj4c4pnHNGPMu0BzqOCYKY0ydMWZn8HUHcAjICm1U45cJ6Awu2oN/QjIoThN9aH0eWBvqIFRYyAKqLliuRn8Jq1EiIvlAObA1xKGMayJiFZHdQAPwpjEmJOfLFopvGu5E5C0gvZ9VjxljXgxu8xiBW2G/G8vYxquhnDOlVOiJSAzwPPANY0x7qOMZz4wxPmBWcCzWCyIy3Rgz5mNCNNGPAmPMTVdaLyIPA3cCK43ObwQGP2dqUDVAzgXL2cH3lBoxImInkOR/Z4z5U6jjmSiMMa0iso7AmJAxT/R6636MichtwH8H7jLGdIc6HhU2tgFFIlIgIg7gfuClEMekwoiICPAr4JAx5p9CHc94JyIpH86qEpFI4GbgcChi0UQ/9n4GxAJvishuEfm3UAc03onI3SJSDSwEXhGR10Md03gTHOD5V8DrBAZJPWOMORDaqMY3EfkDsBkoFpFqEXkk1DGNczcAnwVWBH937RaR20Md1DiWAawTkb0EPoi/aYz5SygC0cp4SimlVBjTK3qllFIqjGmiV0oppcKYJnqllFIqjGmiV0oppcKYJnqllFIqjGmiV0oNSkR8welUe0Rkp4gsGmT7eBH56ljFp5QamE6vU0oNSkQ6jTExwde3An9njFl2he3zgb8YY6aPUYhKqQHoFb1SarjigJYPF0Tkb0Vkm4jsvaDn9uPApOBdgB+JSIyIvB28G7BPRLSznlJjRGvdK6WGIjLYhSuCQMWvFQAicgtQRKBNrgAvichS4FFgujFmVnA7G3C3MaZdRJKBLSLykvZ6UGr0aaJXSg2F+4KkvRB4SkSmA7cE/+wKbhdDIPGfuWR/AX4Q/BDgJ9BCNw04O/qhK3V900SvlBoWY8zm4FV5CoEE/kNjzL9fuE3wGf2FPh3cfo4xpldEThO4O6CUGmX6jF4pNSwiUgJYgSYCTXQ+H+xRjohkiUgq0EGgedOHXEBDMMkvB/LGOGylrlt6Ra+UGooPn9FD4Cr+IWOMD3hDRKYCmwNdTOkEPmOMOSEim0RkP7AW+EfgZRHZB2wnRO06lboe6fQ6pZRSKozprXullFIqjGmiV0oppcKYJnqllFIqjGmiV0oppcKYJnqllFIqjGmiV0oppcKYJnqllFIqjGmiV0oppcLY/wcJ5KZW9nlxtwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "get_auprc_curves(data_atac, data_dnase,data_atac_50m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'merged_atac' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-bda1e882a0c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmerged_atac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"label\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'merged_atac' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "merged_atac[\"label\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/caqtls_latest.ipynb b/src/evaluation/figure_notebooks/figure_6/caqtls_latest.ipynb
new file mode 100644
index 00000000..73298ef3
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/caqtls_latest.ipynb
@@ -0,0 +1,2465 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/lead_caQTL_variants.tsv.gz\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals[\"chr\"] = \"chr\"+qvals[\"Chr\"].astype(str) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>chr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10352</td>\n",
+       "      <td>rs555500075</td>\n",
+       "      <td>T</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>0.38240</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.565592</td>\n",
+       "      <td>0.091926</td>\n",
+       "      <td>0.055039</td>\n",
+       "      <td>-1.165323</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10177</td>\n",
+       "      <td>rs367896724</td>\n",
+       "      <td>A</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>0.32395</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.021318</td>\n",
+       "      <td>-0.141834</td>\n",
+       "      <td>0.062422</td>\n",
+       "      <td>-0.618151</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1142714</td>\n",
+       "      <td>esv3584990</td>\n",
+       "      <td>T</td>\n",
+       "      <td>&lt;CN0&gt;</td>\n",
+       "      <td>0.13485</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.202811</td>\n",
+       "      <td>-0.089320</td>\n",
+       "      <td>0.072935</td>\n",
+       "      <td>-1.315789</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297303</th>\n",
+       "      <td>277126</td>\n",
+       "      <td>22</td>\n",
+       "      <td>50451910</td>\n",
+       "      <td>esv3648079;esv3648080;esv3648081</td>\n",
+       "      <td>C,&lt;CN3&gt;</td>\n",
+       "      <td>&lt;CN2&gt;,&lt;CN4&gt;</td>\n",
+       "      <td>0.08385</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.052782</td>\n",
+       "      <td>-0.006902</td>\n",
+       "      <td>0.093478</td>\n",
+       "      <td>-1.528304</td>\n",
+       "      <td>chr22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297304</th>\n",
+       "      <td>277126</td>\n",
+       "      <td>22</td>\n",
+       "      <td>50451910</td>\n",
+       "      <td>esv3648079;esv3648080;esv3648081</td>\n",
+       "      <td>C,&lt;CN4&gt;</td>\n",
+       "      <td>&lt;CN2&gt;,&lt;CN3&gt;</td>\n",
+       "      <td>0.08385</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.052782</td>\n",
+       "      <td>-0.006902</td>\n",
+       "      <td>0.093478</td>\n",
+       "      <td>-1.528304</td>\n",
+       "      <td>chr22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297305</th>\n",
+       "      <td>277126</td>\n",
+       "      <td>22</td>\n",
+       "      <td>50451910</td>\n",
+       "      <td>esv3648079;esv3648080;esv3648081</td>\n",
+       "      <td>C</td>\n",
+       "      <td>&lt;CN2&gt;,&lt;CN3&gt;,&lt;CN4&gt;</td>\n",
+       "      <td>0.08385</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.052782</td>\n",
+       "      <td>-0.006902</td>\n",
+       "      <td>0.093478</td>\n",
+       "      <td>-1.528304</td>\n",
+       "      <td>chr22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297306</th>\n",
+       "      <td>277127</td>\n",
+       "      <td>22</td>\n",
+       "      <td>51194960</td>\n",
+       "      <td>rs376693911</td>\n",
+       "      <td>AAGTTTTGTCTCAGAGGAGTACCCGGCC</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.18075</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.344576</td>\n",
+       "      <td>0.159435</td>\n",
+       "      <td>0.048414</td>\n",
+       "      <td>0.406650</td>\n",
+       "      <td>chr22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297307</th>\n",
+       "      <td>277128</td>\n",
+       "      <td>22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>rs2879915</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.26020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.726414</td>\n",
+       "      <td>-0.027808</td>\n",
+       "      <td>0.038060</td>\n",
+       "      <td>-1.804563</td>\n",
+       "      <td>chr22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>297308 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Peak  Chr       Pos                              RsID  \\\n",
+       "0            1    1     10352                       rs555500075   \n",
+       "1            2    1     10177                       rs367896724   \n",
+       "2            3    1    875770                         rs4970379   \n",
+       "3            4    1   1142714                        esv3584990   \n",
+       "4            5    1    930533                         rs3128110   \n",
+       "...        ...  ...       ...                               ...   \n",
+       "297303  277126   22  50451910  esv3648079;esv3648080;esv3648081   \n",
+       "297304  277126   22  50451910  esv3648079;esv3648080;esv3648081   \n",
+       "297305  277126   22  50451910  esv3648079;esv3648080;esv3648081   \n",
+       "297306  277127   22  51194960                       rs376693911   \n",
+       "297307  277128   22  51222052                         rs2879915   \n",
+       "\n",
+       "                                 Ref                Alt       AF  Inside_Peak  \\\n",
+       "0                                  T                 TA  0.38240            1   \n",
+       "1                                  A                 AC  0.32395            2   \n",
+       "2                                  A                  G  0.62500            2   \n",
+       "3                                  T              <CN0>  0.13485            0   \n",
+       "4                                  C                  G  0.35435            0   \n",
+       "...                              ...                ...      ...          ...   \n",
+       "297303                       C,<CN3>        <CN2>,<CN4>  0.08385            0   \n",
+       "297304                       C,<CN4>        <CN2>,<CN3>  0.08385            0   \n",
+       "297305                             C  <CN2>,<CN3>,<CN4>  0.08385            0   \n",
+       "297306  AAGTTTTGTCTCAGAGGAGTACCCGGCC                  A  0.18075            1   \n",
+       "297307                             G                  A  0.26020            1   \n",
+       "\n",
+       "          P_Lead      Beta        SE  Log10_BF    chr  \n",
+       "0       0.565592  0.091926  0.055039 -1.165323   chr1  \n",
+       "1       0.021318 -0.141834  0.062422 -0.618151   chr1  \n",
+       "2       0.044994  0.155446  0.057836 -0.232537   chr1  \n",
+       "3       0.202811 -0.089320  0.072935 -1.315789   chr1  \n",
+       "4       0.099392  0.174020  0.040562  1.751829   chr1  \n",
+       "...          ...       ...       ...       ...    ...  \n",
+       "297303  0.052782 -0.006902  0.093478 -1.528304  chr22  \n",
+       "297304  0.052782 -0.006902  0.093478 -1.528304  chr22  \n",
+       "297305  0.052782 -0.006902  0.093478 -1.528304  chr22  \n",
+       "297306  0.344576  0.159435  0.048414  0.406650  chr22  \n",
+       "297307  0.726414 -0.027808  0.038060 -1.804563  chr22  \n",
+       "\n",
+       "[297308 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAATGElEQVR4nO3db4xd9X3n8fdn7dCiZFMgZEcIs2tWsbZy4y1JLPAqfTAbtGBIVVOJRiAaTMrGlQJSInm1dfKEbihS8oDQRUrRuouFqbIhiKTFKmSpRRilfQCBJGzMn42YpUbYIqBgAnGiJpr0uw/uz8mdYX6e8Xi4d2DeL+nqnvs9v3PO734Hz2fuuedeUlVIkjSffzHuCUiSVi5DQpLUZUhIkroMCUlSlyEhSepaO+4JLLczzzyz1q9fP+5pAPCTn/yEt7/97eOexophP17PnsxmP2YbZT++/e1v/7Cq3j23/pYLifXr1/PYY4+NexoATE1NMTk5Oe5prBj24/XsyWz2Y7ZR9iPJc/PVPd0kSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqest94vpkrN9137Lub+emGa5Z5D4Pfu7Dy3psSVoOvpKQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1LRgSSc5J8lCSp5I8meSTrf6nSQ4nebzdLh3a5tNJppN8P8nFQ/WtrTadZNdQ/dwkj7T6V5Kc0uq/1h5Pt/Xrl/XZS5KOazGvJGaAnVW1EdgCXJdkY1t3S1Wd1273A7R1VwC/BWwF/iLJmiRrgC8ClwAbgSuH9vP5tq/3AK8A17b6tcArrX5LGydJGpEFQ6KqXqiq77TlHwNPA2cfZ5NtwF1V9bOq+kdgGji/3aar6tmq+jlwF7AtSYAPAfe07fcClw3ta29bvge4sI2XJI3ACX3BXzvd8z7gEeCDwPVJrgYeY/Bq4xUGAfLw0GaH+FWoPD+nfgHwLuBHVTUzz/izj21TVTNJXm3jfzhnXjuAHQATExNMTU2dyNP6pZ2bZhYedAImTl38Ppc65zeTo0eProrneSLsyWz2Y7aV0I9Fh0SSdwBfBT5VVa8luQ24Eah2fzPwR2/ILBdQVbuB3QCbN2+uycnJJe1nsd/Yulg7N81w84HFtfjgVZPLeuyVaGpqiqX+bN6q7Mls9mO2ldCPRV3dlORtDALiS1X1NYCqerGqflFV/wz8JYPTSQCHgXOGNl/Xar36y8BpSdbOqc/aV1v/G228JGkEFnN1U4Dbgaer6gtD9bOGhv0+8ERb3gdc0a5MOhfYAHwLeBTY0K5kOoXBm9v7qqqAh4DL2/bbgXuH9rW9LV8OfKONlySNwGLOhXwQ+ChwIMnjrfYZBlcnncfgdNNB4I8BqurJJHcDTzG4Muq6qvoFQJLrgQeANcCeqnqy7e9PgLuS/BnwXQahRLv/qyTTwBEGwSJJGpEFQ6Kq/gGY74qi+4+zzU3ATfPU759vu6p6ll+drhqu/xPwBwvNUZL0xvAT15KkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldC4ZEknOSPJTkqSRPJvlkq5+RZH+SZ9r96a2eJLcmmU7yvSTvH9rX9jb+mSTbh+ofSHKgbXNrkhzvGJKk0VjMK4kZYGdVbQS2ANcl2QjsAh6sqg3Ag+0xwCXAhnbbAdwGg1/4wA3ABcD5wA1Dv/RvAz4+tN3WVu8dQ5I0AguGRFW9UFXfacs/Bp4Gzga2AXvbsL3AZW15G3BnDTwMnJbkLOBiYH9VHamqV4D9wNa27p1V9XBVFXDnnH3NdwxJ0gisPZHBSdYD7wMeASaq6oW26gfARFs+G3h+aLNDrXa8+qF56hznGHPntYPBqxYmJiaYmpo6kaf1Szs3zSxpu56JUxe/z6XO+c3k6NGjq+J5ngh7Mpv9mG0l9GPRIZHkHcBXgU9V1WvtbQMAqqqS1Bswv0Udo6p2A7sBNm/eXJOTk0s6xjW77lvy/Oazc9MMNx9YXIsPXjW5rMdeiaampljqz+atyp7MZj9mWwn9WNTVTUnexiAgvlRVX2vlF9upItr9S61+GDhnaPN1rXa8+rp56sc7hiRpBBZzdVOA24Gnq+oLQ6v2AceuUNoO3DtUv7pd5bQFeLWdMnoAuCjJ6e0N64uAB9q615Jsace6es6+5juGJGkEFnMu5IPAR4EDSR5vtc8AnwPuTnIt8BzwkbbufuBSYBr4KfAxgKo6kuRG4NE27rNVdaQtfwK4AzgV+Hq7cZxjSJJGYMGQqKp/ANJZfeE84wu4rrOvPcCeeeqPAe+dp/7yfMeQJI2Gn7iWJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVLXgiGRZE+Sl5I8MVT70ySHkzzebpcOrft0kukk309y8VB9a6tNJ9k1VD83ySOt/pUkp7T6r7XH0239+mV71pKkRVnMK4k7gK3z1G+pqvPa7X6AJBuBK4Dfatv8RZI1SdYAXwQuATYCV7axAJ9v+3oP8ApwbatfC7zS6re0cZKkEVowJKrqm8CRRe5vG3BXVf2sqv4RmAbOb7fpqnq2qn4O3AVsSxLgQ8A9bfu9wGVD+9rblu8BLmzjJUkjcjLvSVyf5HvtdNTprXY28PzQmEOt1qu/C/hRVc3Mqc/aV1v/ahsvSRqRtUvc7jbgRqDa/c3AHy3XpE5Ukh3ADoCJiQmmpqaWtJ+dm2YWHnQCJk5d/D6XOuc3k6NHj66K53ki7Mls9mO2ldCPJYVEVb14bDnJXwJ/2x4eBs4ZGrqu1ejUXwZOS7K2vVoYHn9sX4eSrAV+o42fbz67gd0AmzdvrsnJyaU8La7Zdd+StuvZuWmGmw8srsUHr5pc1mOvRFNTUyz1Z/NWZU9msx+zrYR+LOl0U5Kzhh7+PnDsyqd9wBXtyqRzgQ3At4BHgQ3tSqZTGLy5va+qCngIuLxtvx24d2hf29vy5cA32nhJ0ogs+Gduki8Dk8CZSQ4BNwCTSc5jcLrpIPDHAFX1ZJK7gaeAGeC6qvpF28/1wAPAGmBPVT3ZDvEnwF1J/gz4LnB7q98O/FWSaQZvnF9xsk9WknRiFgyJqrpynvLt89SOjb8JuGme+v3A/fPUn2Vw9dPc+j8Bf7DQ/CRJbxw/cS1J6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUtWBIJNmT5KUkTwzVzkiyP8kz7f70Vk+SW5NMJ/lekvcPbbO9jX8myfah+geSHGjb3JokxzuGJGl0FvNK4g5g65zaLuDBqtoAPNgeA1wCbGi3HcBtMPiFD9wAXACcD9ww9Ev/NuDjQ9ttXeAYkqQRWTAkquqbwJE55W3A3ra8F7hsqH5nDTwMnJbkLOBiYH9VHamqV4D9wNa27p1V9XBVFXDnnH3NdwxJ0oisXeJ2E1X1Qlv+ATDRls8Gnh8ad6jVjlc/NE/9eMd4nSQ7GLxyYWJigqmpqRN8OgM7N80sabueiVMXv8+lzvnN5OjRo6vieZ4IezKb/ZhtJfRjqSHxS1VVSWo5JrPUY1TVbmA3wObNm2tycnJJx7lm131L2q5n56YZbj6wuBYfvGpyWY+9Ek1NTbHUn81blT2ZzX7MthL6sdSrm15sp4po9y+1+mHgnKFx61rtePV189SPdwxJ0ogsNST2AceuUNoO3DtUv7pd5bQFeLWdMnoAuCjJ6e0N64uAB9q615JsaVc1XT1nX/MdQ5I0IgueC0nyZWASODPJIQZXKX0OuDvJtcBzwEfa8PuBS4Fp4KfAxwCq6kiSG4FH27jPVtWxN8M/weAKqlOBr7cbxzmGJGlEFgyJqrqys+rCecYWcF1nP3uAPfPUHwPeO0/95fmOIUkaHT9xLUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktR1UiGR5GCSA0keT/JYq52RZH+SZ9r96a2eJLcmmU7yvSTvH9rP9jb+mSTbh+ofaPufbtvmZOYrSToxy/FK4j9W1XlVtbk93gU8WFUbgAfbY4BLgA3ttgO4DQahAtwAXACcD9xwLFjamI8Pbbd1GeYrSVqkN+J00zZgb1veC1w2VL+zBh4GTktyFnAxsL+qjlTVK8B+YGtb986qeriqCrhzaF+SpBFYe5LbF/B3SQr4H1W1G5ioqhfa+h8AE235bOD5oW0Ptdrx6ofmqb9Okh0MXp0wMTHB1NTUkp7Mzk0zS9quZ+LUxe9zqXN+Mzl69OiqeJ4nwp7MZj9mWwn9ONmQ+J2qOpzkXwH7k/zf4ZVVVS1A3lAtnHYDbN68uSYnJ5e0n2t23beMsxoExM0HFtfig1dNLuuxV6KpqSmW+rN5q7Ins9mP2VZCP07qdFNVHW73LwF/zeA9hRfbqSLa/Utt+GHgnKHN17Xa8err5qlLkkZkySGR5O1J/uWxZeAi4AlgH3DsCqXtwL1teR9wdbvKaQvwajst9QBwUZLT2xvWFwEPtHWvJdnSrmq6emhfkqQROJnTTRPAX7erUtcC/6uq/neSR4G7k1wLPAd8pI2/H7gUmAZ+CnwMoKqOJLkReLSN+2xVHWnLnwDuAE4Fvt5ukqQRWXJIVNWzwG/PU38ZuHCeegHXdfa1B9gzT/0x4L1LnaMk6eT4iWtJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkrpO5v9Mp2W0ftd9Yznuwc99eCzHlfTm4CsJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLL/hb5Ub5xYI7N81wTTueXywovTms+FcSSbYm+X6S6SS7xj0fSVpNVvQriSRrgC8C/wk4BDyaZF9VPTXemelkjeur0cFXMdKJWNEhAZwPTFfVswBJ7gK2AYaElmycATXX8Cm4N5LBqKVKVY17Dl1JLge2VtV/bo8/ClxQVdfPGbcD2NEe/jvg+yOdaN+ZwA/HPYkVxH68nj2ZzX7MNsp+/Juqevfc4kp/JbEoVbUb2D3uecyV5LGq2jzueawU9uP17Mls9mO2ldCPlf7G9WHgnKHH61pNkjQCKz0kHgU2JDk3ySnAFcC+Mc9JklaNFX26qapmklwPPACsAfZU1ZNjntaJWHGnwMbMfryePZnNfsw29n6s6DeuJUnjtdJPN0mSxsiQkCR1GRJvAL9KBJLsSfJSkieGamck2Z/kmXZ/+jjnOEpJzknyUJKnkjyZ5JOtvip7kuTXk3wryf9p/fhvrX5ukkfav52vtAtWVpUka5J8N8nftsdj7YkhscyGvkrkEmAjcGWSjeOd1VjcAWydU9sFPFhVG4AH2+PVYgbYWVUbgS3Ade2/i9Xak58BH6qq3wbOA7Ym2QJ8Hrilqt4DvAJcO74pjs0ngaeHHo+1J4bE8vvlV4lU1c+BY18lsqpU1TeBI3PK24C9bXkvcNko5zROVfVCVX2nLf+YwS+Bs1mlPamBo+3h29qtgA8B97T6qunHMUnWAR8G/md7HMbcE0Ni+Z0NPD/0+FCrCSaq6oW2/ANgYpyTGZck64H3AY+winvSTqs8DrwE7Af+H/CjqpppQ1bjv50/B/4r8M/t8bsYc08MCY1FDa69XnXXXyd5B/BV4FNV9drwutXWk6r6RVWdx+CbFM4HfnO8MxqvJL8LvFRV3x73XIat6A/TvUn5VSJ9LyY5q6peSHIWg78gV40kb2MQEF+qqq+18qruCUBV/SjJQ8B/AE5Lsrb95bza/u18EPi9JJcCvw68E/jvjLknvpJYfn6VSN8+YHtb3g7cO8a5jFQ7t3w78HRVfWFo1arsSZJ3JzmtLZ/K4P8Z8zTwEHB5G7Zq+gFQVZ+uqnVVtZ7B741vVNVVjLknfuL6DdD+EvhzfvVVIjeNd0ajl+TLwCSDrzp+EbgB+BvgbuBfA88BH6mquW9uvyUl+R3g74ED/Op882cYvC+x6nqS5N8zeBN2DYM/Vu+uqs8m+bcMLvY4A/gu8IdV9bPxzXQ8kkwC/6WqfnfcPTEkJEldnm6SJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEld/x8oZVPBImEl1wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qvals[\"Log10_BF\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals = qvals.sort_values('Log10_BF').drop_duplicates([\"chr\", \"Ref\", \"Alt\", \"Pos\"], keep='last')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dnase = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/DNASE/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "data_dnase[['Chrx', 'Posx', 'REF', 'ALT']] = data_dnase['rsid'].str.split(\"_\", expand=True)\n",
+    "\n",
+    "data_atac = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/ATAC/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac[['Chrx', 'Posx', 'REF', 'ALT']] = data_atac['rsid'].str.split(\"_\", expand=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_atac = data_atac.merge(qvals, how=\"left\", left_on=[\"chr\", \"allele1\", \"allele2\", \"pos\"], right_on=[\"chr\", \"Ref\", \"Alt\", \"Pos\"]).drop_duplicates()\n",
+    "merged_dnase = data_dnase.merge(qvals, how=\"left\", left_on=[\"chr\", \"allele1\", \"allele2\", \"pos\"], right_on=[\"chr\", \"Ref\", \"Alt\", \"Pos\"]).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 27)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_atac.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(177813, 27)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_atac.drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>abs_logfc.mean</th>\n",
+       "      <th>abs_logfc.mean.pval</th>\n",
+       "      <th>jsd.mean</th>\n",
+       "      <th>jsd.mean.pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_875770_A_G</td>\n",
+       "      <td>-0.007064</td>\n",
+       "      <td>0.019171</td>\n",
+       "      <td>0.638875</td>\n",
+       "      <td>0.010017</td>\n",
+       "      <td>0.345494</td>\n",
+       "      <td>...</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_930533_C_G</td>\n",
+       "      <td>0.067004</td>\n",
+       "      <td>0.067004</td>\n",
+       "      <td>0.221500</td>\n",
+       "      <td>0.013255</td>\n",
+       "      <td>0.217156</td>\n",
+       "      <td>...</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_565286_C_T</td>\n",
+       "      <td>0.025990</td>\n",
+       "      <td>0.033121</td>\n",
+       "      <td>0.444937</td>\n",
+       "      <td>0.009459</td>\n",
+       "      <td>0.394183</td>\n",
+       "      <td>...</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>0.188153</td>\n",
+       "      <td>3.085214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1_569204_T_C</td>\n",
+       "      <td>-0.040934</td>\n",
+       "      <td>0.051658</td>\n",
+       "      <td>0.293225</td>\n",
+       "      <td>0.018886</td>\n",
+       "      <td>0.119670</td>\n",
+       "      <td>...</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>0.140264</td>\n",
+       "      <td>-0.671608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_748141_G_A</td>\n",
+       "      <td>-0.034312</td>\n",
+       "      <td>0.068099</td>\n",
+       "      <td>0.223811</td>\n",
+       "      <td>0.008248</td>\n",
+       "      <td>0.466251</td>\n",
+       "      <td>...</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.05005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988758</td>\n",
+       "      <td>0.347965</td>\n",
+       "      <td>0.059578</td>\n",
+       "      <td>4.597219</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos allele1 allele2             rsid  logfc.mean  abs_logfc.mean  \\\n",
+       "0  chr1  875770       A       G  chr1_875770_A_G   -0.007064        0.019171   \n",
+       "1  chr1  930533       C       G  chr1_930533_C_G    0.067004        0.067004   \n",
+       "2  chr1  565286       C       T  chr1_565286_C_T    0.025990        0.033121   \n",
+       "3  chr1  569204       T       C  chr1_569204_T_C   -0.040934        0.051658   \n",
+       "4  chr1  748141       G       A  chr1_748141_G_A   -0.034312        0.068099   \n",
+       "\n",
+       "   abs_logfc.mean.pval  jsd.mean  jsd.mean.pval  ...     Pos         RsID  \\\n",
+       "0             0.638875  0.010017       0.345494  ...  875770    rs4970379   \n",
+       "1             0.221500  0.013255       0.217156  ...  930533    rs3128110   \n",
+       "2             0.444937  0.009459       0.394183  ...  565286    rs1578391   \n",
+       "3             0.293225  0.018886       0.119670  ...  569204  rs112660509   \n",
+       "4             0.223811  0.008248       0.466251  ...  748141    rs2427893   \n",
+       "\n",
+       "   Ref  Alt       AF  Inside_Peak    P_Lead      Beta        SE  Log10_BF  \n",
+       "0    A    G  0.62500            2  0.044994  0.155446  0.057836 -0.232537  \n",
+       "1    C    G  0.35435            0  0.099392  0.174020  0.040562  1.751829  \n",
+       "2    C    T  0.59780            1  0.998561  0.886884  0.188153  3.085214  \n",
+       "3    T    C  0.40125            1  0.477613  0.251458  0.140264 -0.671608  \n",
+       "4    G    A  0.05005            0  0.988758  0.347965  0.059578  4.597219  \n",
+       "\n",
+       "[5 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_atac.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_atac[\"pos1\"] = merged_atac[\"pos\"] + 2\n",
+    "merged_atac_new = merged_atac[merged_atac.columns[0:2].tolist()+[\"pos1\"]+merged_atac.columns[2:39].tolist()]\n",
+    "pathe=\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/peak_list/\"\n",
+    "merged_atac_new[merged_atac_new[\"Log10_BF\"]<2].to_csv(pathe+\"nonsig.bed\",sep=\"\\t\", header=False, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_atac_new[merged_atac_new[\"Log10_BF\"]>2].to_csv(pathe+\"sig.bed\",sep=\"\\t\", header=False, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "890"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(merged_atac[\"Log10_BF\"]>20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>pos1</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>abs_logfc.mean</th>\n",
+       "      <th>abs_logfc.mean.pval</th>\n",
+       "      <th>jsd.mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>875772</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_875770_A_G</td>\n",
+       "      <td>-0.007064</td>\n",
+       "      <td>0.019171</td>\n",
+       "      <td>0.638875</td>\n",
+       "      <td>0.010017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>930535</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_930533_C_G</td>\n",
+       "      <td>0.067004</td>\n",
+       "      <td>0.067004</td>\n",
+       "      <td>0.221500</td>\n",
+       "      <td>0.013255</td>\n",
+       "      <td>...</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>565288</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_565286_C_T</td>\n",
+       "      <td>0.025990</td>\n",
+       "      <td>0.033121</td>\n",
+       "      <td>0.444937</td>\n",
+       "      <td>0.009459</td>\n",
+       "      <td>...</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>0.188153</td>\n",
+       "      <td>3.085214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>569206</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1_569204_T_C</td>\n",
+       "      <td>-0.040934</td>\n",
+       "      <td>0.051658</td>\n",
+       "      <td>0.293225</td>\n",
+       "      <td>0.018886</td>\n",
+       "      <td>...</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>0.140264</td>\n",
+       "      <td>-0.671608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>748143</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_748141_G_A</td>\n",
+       "      <td>-0.034312</td>\n",
+       "      <td>0.068099</td>\n",
+       "      <td>0.223811</td>\n",
+       "      <td>0.008248</td>\n",
+       "      <td>...</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.05005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988758</td>\n",
+       "      <td>0.347965</td>\n",
+       "      <td>0.059578</td>\n",
+       "      <td>4.597219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226341</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>51021273</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr22_51021271_T_C</td>\n",
+       "      <td>-0.018318</td>\n",
+       "      <td>0.041597</td>\n",
+       "      <td>0.376340</td>\n",
+       "      <td>0.012623</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>rs540889063</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.05500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.362698</td>\n",
+       "      <td>0.010717</td>\n",
+       "      <td>0.018974</td>\n",
+       "      <td>-2.153040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226342</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>51059485</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_51059483_G_A</td>\n",
+       "      <td>-0.044313</td>\n",
+       "      <td>0.054137</td>\n",
+       "      <td>0.313944</td>\n",
+       "      <td>0.012971</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.553273</td>\n",
+       "      <td>-0.061982</td>\n",
+       "      <td>0.032636</td>\n",
+       "      <td>-1.221145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226343</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>51066554</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr22_51066552_C_G</td>\n",
+       "      <td>-0.085015</td>\n",
+       "      <td>0.086177</td>\n",
+       "      <td>0.171138</td>\n",
+       "      <td>0.019822</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.921861</td>\n",
+       "      <td>0.087364</td>\n",
+       "      <td>0.030622</td>\n",
+       "      <td>-0.324830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226344</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>51082409</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_51082407_T_A</td>\n",
+       "      <td>-0.030337</td>\n",
+       "      <td>0.030337</td>\n",
+       "      <td>0.479451</td>\n",
+       "      <td>0.004881</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>rs62241515</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.42505</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.315251</td>\n",
+       "      <td>-0.016347</td>\n",
+       "      <td>0.055462</td>\n",
+       "      <td>-1.737317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226345</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>51222054</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_51222052_G_A</td>\n",
+       "      <td>-0.002690</td>\n",
+       "      <td>0.078680</td>\n",
+       "      <td>0.179327</td>\n",
+       "      <td>0.019944</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>rs2879915</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.26020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.726414</td>\n",
+       "      <td>-0.027808</td>\n",
+       "      <td>0.038060</td>\n",
+       "      <td>-1.804563</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>177813 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       pos      pos1 allele1 allele2                rsid  \\\n",
+       "0        chr1    875770    875772       A       G     chr1_875770_A_G   \n",
+       "1        chr1    930533    930535       C       G     chr1_930533_C_G   \n",
+       "2        chr1    565286    565288       C       T     chr1_565286_C_T   \n",
+       "3        chr1    569204    569206       T       C     chr1_569204_T_C   \n",
+       "4        chr1    748141    748143       G       A     chr1_748141_G_A   \n",
+       "...       ...       ...       ...     ...     ...                 ...   \n",
+       "226341  chr22  51021271  51021273       T       C  chr22_51021271_T_C   \n",
+       "226342  chr22  51059483  51059485       G       A  chr22_51059483_G_A   \n",
+       "226343  chr22  51066552  51066554       C       G  chr22_51066552_C_G   \n",
+       "226344  chr22  51082407  51082409       T       A  chr22_51082407_T_A   \n",
+       "226345  chr22  51222052  51222054       G       A  chr22_51222052_G_A   \n",
+       "\n",
+       "        logfc.mean  abs_logfc.mean  abs_logfc.mean.pval  jsd.mean  ...  \\\n",
+       "0        -0.007064        0.019171             0.638875  0.010017  ...   \n",
+       "1         0.067004        0.067004             0.221500  0.013255  ...   \n",
+       "2         0.025990        0.033121             0.444937  0.009459  ...   \n",
+       "3        -0.040934        0.051658             0.293225  0.018886  ...   \n",
+       "4        -0.034312        0.068099             0.223811  0.008248  ...   \n",
+       "...            ...             ...                  ...       ...  ...   \n",
+       "226341   -0.018318        0.041597             0.376340  0.012623  ...   \n",
+       "226342   -0.044313        0.054137             0.313944  0.012971  ...   \n",
+       "226343   -0.085015        0.086177             0.171138  0.019822  ...   \n",
+       "226344   -0.030337        0.030337             0.479451  0.004881  ...   \n",
+       "226345   -0.002690        0.078680             0.179327  0.019944  ...   \n",
+       "\n",
+       "             Pos         RsID  Ref  Alt       AF  Inside_Peak    P_Lead  \\\n",
+       "0         875770    rs4970379    A    G  0.62500            2  0.044994   \n",
+       "1         930533    rs3128110    C    G  0.35435            0  0.099392   \n",
+       "2         565286    rs1578391    C    T  0.59780            1  0.998561   \n",
+       "3         569204  rs112660509    T    C  0.40125            1  0.477613   \n",
+       "4         748141    rs2427893    G    A  0.05005            0  0.988758   \n",
+       "...          ...          ...  ...  ...      ...          ...       ...   \n",
+       "226341  51021271  rs540889063    T    C  0.05500            1  0.362698   \n",
+       "226342  51059483   rs76733141    G    A  0.06000            1  0.553273   \n",
+       "226343  51066552    rs6151406    C    G  0.06000            1  0.921861   \n",
+       "226344  51082407   rs62241515    T    A  0.42505            1  0.315251   \n",
+       "226345  51222052    rs2879915    G    A  0.26020            1  0.726414   \n",
+       "\n",
+       "            Beta        SE  Log10_BF  \n",
+       "0       0.155446  0.057836 -0.232537  \n",
+       "1       0.174020  0.040562  1.751829  \n",
+       "2       0.886884  0.188153  3.085214  \n",
+       "3       0.251458  0.140264 -0.671608  \n",
+       "4       0.347965  0.059578  4.597219  \n",
+       "...          ...       ...       ...  \n",
+       "226341  0.010717  0.018974 -2.153040  \n",
+       "226342 -0.061982  0.032636 -1.221145  \n",
+       "226343  0.087364  0.030622 -0.324830  \n",
+       "226344 -0.016347  0.055462 -1.737317  \n",
+       "226345 -0.027808  0.038060 -1.804563  \n",
+       "\n",
+       "[177813 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_non_sig = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/peak_list/nonsig_peak_annot.bed\", sep=\"\\t\", header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_non_sig_no_peak = full_non_sig[full_non_sig[51]==0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_peaks_all = full_non_sig_no_peak[np.arange(41)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_peaks = full_non_sig[(full_non_sig[51]>0) & (full_non_sig[50]==\"0\")].drop_duplicates(np.arange(40))[np.arange(41)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_peaks_all_sub = nonsig_peaks_all.sample(36500).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8110, 41)"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nonsig_peaks.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_all = pd.concat([nonsig_peaks_all_sub, nonsig_peaks])[np.arange(41)[0:2].tolist()+np.arange(41)[3:].tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(merged_atac.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_all[\"label\"] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>32</th>\n",
+       "      <th>33</th>\n",
+       "      <th>34</th>\n",
+       "      <th>35</th>\n",
+       "      <th>36</th>\n",
+       "      <th>37</th>\n",
+       "      <th>38</th>\n",
+       "      <th>39</th>\n",
+       "      <th>40</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>160677613</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_160677613_C_T</td>\n",
+       "      <td>-0.006140</td>\n",
+       "      <td>0.033261</td>\n",
+       "      <td>0.434114</td>\n",
+       "      <td>0.008840</td>\n",
+       "      <td>0.418240</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.1450</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.599084</td>\n",
+       "      <td>0.014915</td>\n",
+       "      <td>0.017421</td>\n",
+       "      <td>-2.101169</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>230508244</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1_230508244_T_C</td>\n",
+       "      <td>0.022412</td>\n",
+       "      <td>0.034922</td>\n",
+       "      <td>0.435949</td>\n",
+       "      <td>0.007122</td>\n",
+       "      <td>0.572663</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.7000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.368925</td>\n",
+       "      <td>0.117345</td>\n",
+       "      <td>0.056146</td>\n",
+       "      <td>-0.827758</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>168994011</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr4_168994011_C_A</td>\n",
+       "      <td>-0.000400</td>\n",
+       "      <td>0.016360</td>\n",
+       "      <td>0.665052</td>\n",
+       "      <td>0.004222</td>\n",
+       "      <td>0.889222</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.2950</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.574949</td>\n",
+       "      <td>0.060176</td>\n",
+       "      <td>0.037263</td>\n",
+       "      <td>-1.372773</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>1615372</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9_1615372_G_A</td>\n",
+       "      <td>0.095218</td>\n",
+       "      <td>0.095218</td>\n",
+       "      <td>0.175024</td>\n",
+       "      <td>0.012624</td>\n",
+       "      <td>0.294793</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.0800</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.011157</td>\n",
+       "      <td>-0.290187</td>\n",
+       "      <td>0.080269</td>\n",
+       "      <td>1.053361</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>34127122</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr11_34127122_C_G</td>\n",
+       "      <td>-0.042363</td>\n",
+       "      <td>0.099692</td>\n",
+       "      <td>0.127116</td>\n",
+       "      <td>0.013583</td>\n",
+       "      <td>0.205307</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.0800</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.917202</td>\n",
+       "      <td>0.049130</td>\n",
+       "      <td>0.017981</td>\n",
+       "      <td>-0.690789</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168069</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>44455473</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr22_44455473_A_G</td>\n",
+       "      <td>-0.068482</td>\n",
+       "      <td>0.068482</td>\n",
+       "      <td>0.211127</td>\n",
+       "      <td>0.029070</td>\n",
+       "      <td>0.047526</td>\n",
+       "      <td>...</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.5500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.943300</td>\n",
+       "      <td>-0.137981</td>\n",
+       "      <td>0.046070</td>\n",
+       "      <td>0.017363</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168109</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>45684098</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr22_45684098_T_C</td>\n",
+       "      <td>0.332763</td>\n",
+       "      <td>0.332763</td>\n",
+       "      <td>0.015969</td>\n",
+       "      <td>0.017665</td>\n",
+       "      <td>0.132350</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.7749</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.901112</td>\n",
+       "      <td>0.127909</td>\n",
+       "      <td>0.044940</td>\n",
+       "      <td>-0.166127</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168189</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50541416</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr22_50541416_A_G</td>\n",
+       "      <td>0.011979</td>\n",
+       "      <td>0.063533</td>\n",
+       "      <td>0.257233</td>\n",
+       "      <td>0.007904</td>\n",
+       "      <td>0.491813</td>\n",
+       "      <td>...</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.1296</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302305</td>\n",
+       "      <td>0.030018</td>\n",
+       "      <td>0.061512</td>\n",
+       "      <td>-1.659754</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168209</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50700083</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr22_50700083_C_T</td>\n",
+       "      <td>0.009713</td>\n",
+       "      <td>0.017407</td>\n",
+       "      <td>0.649649</td>\n",
+       "      <td>0.008549</td>\n",
+       "      <td>0.432890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.3350</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.224398</td>\n",
+       "      <td>0.019085</td>\n",
+       "      <td>0.020382</td>\n",
+       "      <td>-2.002190</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168215</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50732510</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_50732510_G_A</td>\n",
+       "      <td>-0.105004</td>\n",
+       "      <td>0.105004</td>\n",
+       "      <td>0.122717</td>\n",
+       "      <td>0.013126</td>\n",
+       "      <td>0.216583</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.2300</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.847751</td>\n",
+       "      <td>-0.031705</td>\n",
+       "      <td>0.041454</td>\n",
+       "      <td>-1.756493</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>44610 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0          1  3  4                   5         6         7  \\\n",
+       "0        chr1  160677613  C  T  chr1_160677613_C_T -0.006140  0.033261   \n",
+       "1        chr1  230508244  T  C  chr1_230508244_T_C  0.022412  0.034922   \n",
+       "2        chr4  168994011  C  A  chr4_168994011_C_A -0.000400  0.016360   \n",
+       "3        chr9    1615372  G  A    chr9_1615372_G_A  0.095218  0.095218   \n",
+       "4       chr11   34127122  C  G  chr11_34127122_C_G -0.042363  0.099692   \n",
+       "...       ...        ... .. ..                 ...       ...       ...   \n",
+       "168069  chr22   44455473  A  G  chr22_44455473_A_G -0.068482  0.068482   \n",
+       "168109  chr22   45684098  T  C  chr22_45684098_T_C  0.332763  0.332763   \n",
+       "168189  chr22   50541416  A  G  chr22_50541416_A_G  0.011979  0.063533   \n",
+       "168209  chr22   50700083  C  T  chr22_50700083_C_T  0.009713  0.017407   \n",
+       "168215  chr22   50732510  G  A  chr22_50732510_G_A -0.105004  0.105004   \n",
+       "\n",
+       "               8         9        10  ...  32  33      34  35        36  \\\n",
+       "0       0.434114  0.008840  0.418240  ...   C   T  0.1450   1  0.599084   \n",
+       "1       0.435949  0.007122  0.572663  ...   T   C  0.7000   1  0.368925   \n",
+       "2       0.665052  0.004222  0.889222  ...   C   A  0.2950   1  0.574949   \n",
+       "3       0.175024  0.012624  0.294793  ...   G   A  0.0800   0  0.011157   \n",
+       "4       0.127116  0.013583  0.205307  ...   C   G  0.0800   1  0.917202   \n",
+       "...          ...       ...       ...  ...  ..  ..     ...  ..       ...   \n",
+       "168069  0.211127  0.029070  0.047526  ...   A   G  0.5500   1  0.943300   \n",
+       "168109  0.015969  0.017665  0.132350  ...   T   C  0.7749   1  0.901112   \n",
+       "168189  0.257233  0.007904  0.491813  ...   A   G  0.1296   1  0.302305   \n",
+       "168209  0.649649  0.008549  0.432890  ...   C   T  0.3350   1  0.224398   \n",
+       "168215  0.122717  0.013126  0.216583  ...   G   A  0.2300   1  0.847751   \n",
+       "\n",
+       "              37        38        39     40  label  \n",
+       "0       0.014915  0.017421 -2.101169      .      0  \n",
+       "1       0.117345  0.056146 -0.827758      .      0  \n",
+       "2       0.060176  0.037263 -1.372773      .      0  \n",
+       "3      -0.290187  0.080269  1.053361      .      0  \n",
+       "4       0.049130  0.017981 -0.690789      .      0  \n",
+       "...          ...       ...       ...    ...    ...  \n",
+       "168069 -0.137981  0.046070  0.017363  chr22      0  \n",
+       "168109  0.127909  0.044940 -0.166127  chr22      0  \n",
+       "168189  0.030018  0.061512 -1.659754  chr22      0  \n",
+       "168209  0.019085  0.020382 -2.002190  chr22      0  \n",
+       "168215 -0.031705  0.041454 -1.756493  chr22      0  \n",
+       "\n",
+       "[44610 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nonsig_all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigall = merged_atac[merged_atac[\"Log10_BF\"]>20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "sigall[\"label\"] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>abs_logfc.mean</th>\n",
+       "      <th>abs_logfc.mean.pval</th>\n",
+       "      <th>jsd.mean</th>\n",
+       "      <th>jsd.mean.pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>pos1</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1620860</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_1620860_G_A</td>\n",
+       "      <td>-0.138112</td>\n",
+       "      <td>0.138112</td>\n",
+       "      <td>0.083123</td>\n",
+       "      <td>0.017476</td>\n",
+       "      <td>0.146502</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.450</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.264973</td>\n",
+       "      <td>0.612583</td>\n",
+       "      <td>0.043073</td>\n",
+       "      <td>21.847344</td>\n",
+       "      <td>1620862</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1620885</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1_1620885_T_C</td>\n",
+       "      <td>0.017374</td>\n",
+       "      <td>0.072250</td>\n",
+       "      <td>0.203682</td>\n",
+       "      <td>0.022865</td>\n",
+       "      <td>0.080495</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.450</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.264973</td>\n",
+       "      <td>0.612583</td>\n",
+       "      <td>0.043073</td>\n",
+       "      <td>21.847344</td>\n",
+       "      <td>1620887</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>4327635</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_4327635_C_T</td>\n",
+       "      <td>-0.913667</td>\n",
+       "      <td>0.913667</td>\n",
+       "      <td>0.000829</td>\n",
+       "      <td>0.044784</td>\n",
+       "      <td>0.017817</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.550</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999782</td>\n",
+       "      <td>-0.666789</td>\n",
+       "      <td>0.041505</td>\n",
+       "      <td>25.445000</td>\n",
+       "      <td>4327637</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>16821365</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_16821365_C_T</td>\n",
+       "      <td>-0.079640</td>\n",
+       "      <td>0.079640</td>\n",
+       "      <td>0.194069</td>\n",
+       "      <td>0.013253</td>\n",
+       "      <td>0.217739</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.398977</td>\n",
+       "      <td>-1.042415</td>\n",
+       "      <td>0.070059</td>\n",
+       "      <td>23.359123</td>\n",
+       "      <td>16821367</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1028</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>22345647</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_22345647_C_T</td>\n",
+       "      <td>-0.327098</td>\n",
+       "      <td>0.327098</td>\n",
+       "      <td>0.018307</td>\n",
+       "      <td>0.028709</td>\n",
+       "      <td>0.050619</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.767072</td>\n",
+       "      <td>0.038644</td>\n",
+       "      <td>32.266929</td>\n",
+       "      <td>22345649</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226225</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>48314463</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_48314463_G_A</td>\n",
+       "      <td>0.706527</td>\n",
+       "      <td>0.706527</td>\n",
+       "      <td>0.002365</td>\n",
+       "      <td>0.030788</td>\n",
+       "      <td>0.042415</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.410</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.627204</td>\n",
+       "      <td>0.098564</td>\n",
+       "      <td>26.641349</td>\n",
+       "      <td>48314465</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226241</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50050468</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_50050468_G_A</td>\n",
+       "      <td>0.016586</td>\n",
+       "      <td>0.016586</td>\n",
+       "      <td>0.677033</td>\n",
+       "      <td>0.005834</td>\n",
+       "      <td>0.698898</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.138840</td>\n",
+       "      <td>-1.033692</td>\n",
+       "      <td>0.078351</td>\n",
+       "      <td>20.007726</td>\n",
+       "      <td>50050470</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226242</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50052921</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_50052921_G_A</td>\n",
+       "      <td>-0.016846</td>\n",
+       "      <td>0.016846</td>\n",
+       "      <td>0.644669</td>\n",
+       "      <td>0.008453</td>\n",
+       "      <td>0.447029</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.138840</td>\n",
+       "      <td>-1.033692</td>\n",
+       "      <td>0.078351</td>\n",
+       "      <td>20.007726</td>\n",
+       "      <td>50052923</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226243</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50053871</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_50053871_G_A</td>\n",
+       "      <td>-0.049552</td>\n",
+       "      <td>0.049552</td>\n",
+       "      <td>0.310879</td>\n",
+       "      <td>0.007635</td>\n",
+       "      <td>0.515667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.138840</td>\n",
+       "      <td>-1.033692</td>\n",
+       "      <td>0.078351</td>\n",
+       "      <td>20.007726</td>\n",
+       "      <td>50053873</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226270</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50339008</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr22_50339008_C_T</td>\n",
+       "      <td>0.106206</td>\n",
+       "      <td>0.118501</td>\n",
+       "      <td>0.115676</td>\n",
+       "      <td>0.017535</td>\n",
+       "      <td>0.149492</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.140</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.430608</td>\n",
+       "      <td>1.429469</td>\n",
+       "      <td>0.063316</td>\n",
+       "      <td>36.948644</td>\n",
+       "      <td>50339010</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>890 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       pos allele1 allele2                rsid  logfc.mean  \\\n",
+       "88       chr1   1620860       G       A    chr1_1620860_G_A   -0.138112   \n",
+       "89       chr1   1620885       T       C    chr1_1620885_T_C    0.017374   \n",
+       "224      chr1   4327635       C       T    chr1_4327635_C_T   -0.913667   \n",
+       "810      chr1  16821365       C       T   chr1_16821365_C_T   -0.079640   \n",
+       "1028     chr1  22345647       C       T   chr1_22345647_C_T   -0.327098   \n",
+       "...       ...       ...     ...     ...                 ...         ...   \n",
+       "226225  chr22  48314463       G       A  chr22_48314463_G_A    0.706527   \n",
+       "226241  chr22  50050468       G       A  chr22_50050468_G_A    0.016586   \n",
+       "226242  chr22  50052921       G       A  chr22_50052921_G_A   -0.016846   \n",
+       "226243  chr22  50053871       G       A  chr22_50053871_G_A   -0.049552   \n",
+       "226270  chr22  50339008       C       T  chr22_50339008_C_T    0.106206   \n",
+       "\n",
+       "        abs_logfc.mean  abs_logfc.mean.pval  jsd.mean  jsd.mean.pval  ...  \\\n",
+       "88            0.138112             0.083123  0.017476       0.146502  ...   \n",
+       "89            0.072250             0.203682  0.022865       0.080495  ...   \n",
+       "224           0.913667             0.000829  0.044784       0.017817  ...   \n",
+       "810           0.079640             0.194069  0.013253       0.217739  ...   \n",
+       "1028          0.327098             0.018307  0.028709       0.050619  ...   \n",
+       "...                ...                  ...       ...            ...  ...   \n",
+       "226225        0.706527             0.002365  0.030788       0.042415  ...   \n",
+       "226241        0.016586             0.677033  0.005834       0.698898  ...   \n",
+       "226242        0.016846             0.644669  0.008453       0.447029  ...   \n",
+       "226243        0.049552             0.310879  0.007635       0.515667  ...   \n",
+       "226270        0.118501             0.115676  0.017535       0.149492  ...   \n",
+       "\n",
+       "        Ref  Alt     AF  Inside_Peak    P_Lead      Beta        SE   Log10_BF  \\\n",
+       "88        G    A  0.450            0  0.264973  0.612583  0.043073  21.847344   \n",
+       "89        T    C  0.450            0  0.264973  0.612583  0.043073  21.847344   \n",
+       "224       C    T  0.550            1  0.999782 -0.666789  0.041505  25.445000   \n",
+       "810       C    T  0.625            0  0.398977 -1.042415  0.070059  23.359123   \n",
+       "1028      C    T  0.670            1  1.000000 -0.767072  0.038644  32.266929   \n",
+       "...     ...  ...    ...          ...       ...       ...       ...        ...   \n",
+       "226225    G    A  0.410            1  1.000000  1.627204  0.098564  26.641349   \n",
+       "226241    G    A  0.670            0  0.138840 -1.033692  0.078351  20.007726   \n",
+       "226242    G    A  0.670            0  0.138840 -1.033692  0.078351  20.007726   \n",
+       "226243    G    A  0.670            0  0.138840 -1.033692  0.078351  20.007726   \n",
+       "226270    C    T  0.140            1  0.430608  1.429469  0.063316  36.948644   \n",
+       "\n",
+       "            pos1  label  \n",
+       "88       1620862      1  \n",
+       "89       1620887      1  \n",
+       "224      4327637      1  \n",
+       "810     16821367      1  \n",
+       "1028    22345649      1  \n",
+       "...          ...    ...  \n",
+       "226225  48314465      1  \n",
+       "226241  50050470      1  \n",
+       "226242  50052923      1  \n",
+       "226243  50053873      1  \n",
+       "226270  50339010      1  \n",
+       "\n",
+       "[890 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sigall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_all.columns = sigall.columns\n",
+    "alldf = pd.concat([nonsig_all,sigall])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'abs_logfc.mean', 'abs_logfc.mean.pval', 'jsd.mean', 'jsd.mean.pval',\n",
+       "       'logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean.pval',\n",
+       "       'max_percentile.mean', 'max_percentile.mean.pval',\n",
+       "       'logfc_x_max_percentile.mean', 'abs_logfc_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_max_percentile.mean.pval', 'jsd_x_max_percentile.mean',\n",
+       "       'jsd_x_max_percentile.mean.pval',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'logfc_x_jsd_x_max_percentile.mean', 'Chrx', 'Posx', 'REF', 'ALT',\n",
+       "       'Peak', 'Chr', 'Pos', 'RsID', 'Ref', 'Alt', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'pos1', 'label'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alldf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>abs_logfc.mean</th>\n",
+       "      <th>abs_logfc.mean.pval</th>\n",
+       "      <th>jsd.mean</th>\n",
+       "      <th>jsd.mean.pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>pos1</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>160677613</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_160677613_C_T</td>\n",
+       "      <td>-0.006140</td>\n",
+       "      <td>0.033261</td>\n",
+       "      <td>0.434114</td>\n",
+       "      <td>0.008840</td>\n",
+       "      <td>0.418240</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.145</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.599084</td>\n",
+       "      <td>0.014915</td>\n",
+       "      <td>0.017421</td>\n",
+       "      <td>-2.101169</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>230508244</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1_230508244_T_C</td>\n",
+       "      <td>0.022412</td>\n",
+       "      <td>0.034922</td>\n",
+       "      <td>0.435949</td>\n",
+       "      <td>0.007122</td>\n",
+       "      <td>0.572663</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.368925</td>\n",
+       "      <td>0.117345</td>\n",
+       "      <td>0.056146</td>\n",
+       "      <td>-0.827758</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>168994011</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr4_168994011_C_A</td>\n",
+       "      <td>-0.000400</td>\n",
+       "      <td>0.016360</td>\n",
+       "      <td>0.665052</td>\n",
+       "      <td>0.004222</td>\n",
+       "      <td>0.889222</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.574949</td>\n",
+       "      <td>0.060176</td>\n",
+       "      <td>0.037263</td>\n",
+       "      <td>-1.372773</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>1615372</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9_1615372_G_A</td>\n",
+       "      <td>0.095218</td>\n",
+       "      <td>0.095218</td>\n",
+       "      <td>0.175024</td>\n",
+       "      <td>0.012624</td>\n",
+       "      <td>0.294793</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.011157</td>\n",
+       "      <td>-0.290187</td>\n",
+       "      <td>0.080269</td>\n",
+       "      <td>1.053361</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>34127122</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr11_34127122_C_G</td>\n",
+       "      <td>-0.042363</td>\n",
+       "      <td>0.099692</td>\n",
+       "      <td>0.127116</td>\n",
+       "      <td>0.013583</td>\n",
+       "      <td>0.205307</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.917202</td>\n",
+       "      <td>0.049130</td>\n",
+       "      <td>0.017981</td>\n",
+       "      <td>-0.690789</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226225</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>48314463</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_48314463_G_A</td>\n",
+       "      <td>0.706527</td>\n",
+       "      <td>0.706527</td>\n",
+       "      <td>0.002365</td>\n",
+       "      <td>0.030788</td>\n",
+       "      <td>0.042415</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.410</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.627204</td>\n",
+       "      <td>0.098564</td>\n",
+       "      <td>26.641349</td>\n",
+       "      <td>48314465</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226241</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50050468</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_50050468_G_A</td>\n",
+       "      <td>0.016586</td>\n",
+       "      <td>0.016586</td>\n",
+       "      <td>0.677033</td>\n",
+       "      <td>0.005834</td>\n",
+       "      <td>0.698898</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.138840</td>\n",
+       "      <td>-1.033692</td>\n",
+       "      <td>0.078351</td>\n",
+       "      <td>20.007726</td>\n",
+       "      <td>50050470</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226242</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50052921</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_50052921_G_A</td>\n",
+       "      <td>-0.016846</td>\n",
+       "      <td>0.016846</td>\n",
+       "      <td>0.644669</td>\n",
+       "      <td>0.008453</td>\n",
+       "      <td>0.447029</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.138840</td>\n",
+       "      <td>-1.033692</td>\n",
+       "      <td>0.078351</td>\n",
+       "      <td>20.007726</td>\n",
+       "      <td>50052923</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226243</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50053871</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_50053871_G_A</td>\n",
+       "      <td>-0.049552</td>\n",
+       "      <td>0.049552</td>\n",
+       "      <td>0.310879</td>\n",
+       "      <td>0.007635</td>\n",
+       "      <td>0.515667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.138840</td>\n",
+       "      <td>-1.033692</td>\n",
+       "      <td>0.078351</td>\n",
+       "      <td>20.007726</td>\n",
+       "      <td>50053873</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226270</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50339008</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr22_50339008_C_T</td>\n",
+       "      <td>0.106206</td>\n",
+       "      <td>0.118501</td>\n",
+       "      <td>0.115676</td>\n",
+       "      <td>0.017535</td>\n",
+       "      <td>0.149492</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.140</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.430608</td>\n",
+       "      <td>1.429469</td>\n",
+       "      <td>0.063316</td>\n",
+       "      <td>36.948644</td>\n",
+       "      <td>50339010</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>45500 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr        pos allele1 allele2                rsid  logfc.mean  \\\n",
+       "0        chr1  160677613       C       T  chr1_160677613_C_T   -0.006140   \n",
+       "1        chr1  230508244       T       C  chr1_230508244_T_C    0.022412   \n",
+       "2        chr4  168994011       C       A  chr4_168994011_C_A   -0.000400   \n",
+       "3        chr9    1615372       G       A    chr9_1615372_G_A    0.095218   \n",
+       "4       chr11   34127122       C       G  chr11_34127122_C_G   -0.042363   \n",
+       "...       ...        ...     ...     ...                 ...         ...   \n",
+       "226225  chr22   48314463       G       A  chr22_48314463_G_A    0.706527   \n",
+       "226241  chr22   50050468       G       A  chr22_50050468_G_A    0.016586   \n",
+       "226242  chr22   50052921       G       A  chr22_50052921_G_A   -0.016846   \n",
+       "226243  chr22   50053871       G       A  chr22_50053871_G_A   -0.049552   \n",
+       "226270  chr22   50339008       C       T  chr22_50339008_C_T    0.106206   \n",
+       "\n",
+       "        abs_logfc.mean  abs_logfc.mean.pval  jsd.mean  jsd.mean.pval  ...  \\\n",
+       "0             0.033261             0.434114  0.008840       0.418240  ...   \n",
+       "1             0.034922             0.435949  0.007122       0.572663  ...   \n",
+       "2             0.016360             0.665052  0.004222       0.889222  ...   \n",
+       "3             0.095218             0.175024  0.012624       0.294793  ...   \n",
+       "4             0.099692             0.127116  0.013583       0.205307  ...   \n",
+       "...                ...                  ...       ...            ...  ...   \n",
+       "226225        0.706527             0.002365  0.030788       0.042415  ...   \n",
+       "226241        0.016586             0.677033  0.005834       0.698898  ...   \n",
+       "226242        0.016846             0.644669  0.008453       0.447029  ...   \n",
+       "226243        0.049552             0.310879  0.007635       0.515667  ...   \n",
+       "226270        0.118501             0.115676  0.017535       0.149492  ...   \n",
+       "\n",
+       "        Ref  Alt     AF  Inside_Peak    P_Lead      Beta        SE   Log10_BF  \\\n",
+       "0         C    T  0.145            1  0.599084  0.014915  0.017421  -2.101169   \n",
+       "1         T    C  0.700            1  0.368925  0.117345  0.056146  -0.827758   \n",
+       "2         C    A  0.295            1  0.574949  0.060176  0.037263  -1.372773   \n",
+       "3         G    A  0.080            0  0.011157 -0.290187  0.080269   1.053361   \n",
+       "4         C    G  0.080            1  0.917202  0.049130  0.017981  -0.690789   \n",
+       "...     ...  ...    ...          ...       ...       ...       ...        ...   \n",
+       "226225    G    A  0.410            1  1.000000  1.627204  0.098564  26.641349   \n",
+       "226241    G    A  0.670            0  0.138840 -1.033692  0.078351  20.007726   \n",
+       "226242    G    A  0.670            0  0.138840 -1.033692  0.078351  20.007726   \n",
+       "226243    G    A  0.670            0  0.138840 -1.033692  0.078351  20.007726   \n",
+       "226270    C    T  0.140            1  0.430608  1.429469  0.063316  36.948644   \n",
+       "\n",
+       "            pos1  label  \n",
+       "0              .      0  \n",
+       "1              .      0  \n",
+       "2              .      0  \n",
+       "3              .      0  \n",
+       "4              .      0  \n",
+       "...          ...    ...  \n",
+       "226225  48314465      1  \n",
+       "226241  50050470      1  \n",
+       "226242  50052923      1  \n",
+       "226243  50053873      1  \n",
+       "226270  50339010      1  \n",
+       "\n",
+       "[45500 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alldf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4243927401202084\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "#keyd=\"Beta\"\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(alldf[\"label\"].astype(int), abs(alldf[keyd]))\n",
+    "roc_auc = metrics.average_precision_score(alldf[\"label\"], abs(alldf[keyd]))\n",
+    "plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=\"ChromBPnet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "print(roc_auc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/caqtls_latest_lcl_gaffney.ipynb b/src/evaluation/figure_notebooks/figure_6/caqtls_latest_lcl_gaffney.ipynb
index 45201799..8db958c6 100644
--- a/src/evaluation/figure_notebooks/figure_6/caqtls_latest_lcl_gaffney.ipynb
+++ b/src/evaluation/figure_notebooks/figure_6/caqtls_latest_lcl_gaffney.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,286 +21,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qvals[\"chr\"] = \"chr\"+qvals[\"Chr\"].astype(str) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Peak</th>\n",
-       "      <th>Chr</th>\n",
-       "      <th>Pos</th>\n",
-       "      <th>RsID</th>\n",
-       "      <th>Ref</th>\n",
-       "      <th>Alt</th>\n",
-       "      <th>AF</th>\n",
-       "      <th>Inside_Peak</th>\n",
-       "      <th>P_Lead</th>\n",
-       "      <th>Beta</th>\n",
-       "      <th>SE</th>\n",
-       "      <th>Log10_BF</th>\n",
-       "      <th>chr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10352</td>\n",
-       "      <td>rs555500075</td>\n",
-       "      <td>T</td>\n",
-       "      <td>TA</td>\n",
-       "      <td>0.38240</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.565592</td>\n",
-       "      <td>0.091926</td>\n",
-       "      <td>0.055039</td>\n",
-       "      <td>-1.165323</td>\n",
-       "      <td>chr1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10177</td>\n",
-       "      <td>rs367896724</td>\n",
-       "      <td>A</td>\n",
-       "      <td>AC</td>\n",
-       "      <td>0.32395</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.021318</td>\n",
-       "      <td>-0.141834</td>\n",
-       "      <td>0.062422</td>\n",
-       "      <td>-0.618151</td>\n",
-       "      <td>chr1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>875770</td>\n",
-       "      <td>rs4970379</td>\n",
-       "      <td>A</td>\n",
-       "      <td>G</td>\n",
-       "      <td>0.62500</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.044994</td>\n",
-       "      <td>0.155446</td>\n",
-       "      <td>0.057836</td>\n",
-       "      <td>-0.232537</td>\n",
-       "      <td>chr1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1142714</td>\n",
-       "      <td>esv3584990</td>\n",
-       "      <td>T</td>\n",
-       "      <td>&lt;CN0&gt;</td>\n",
-       "      <td>0.13485</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.202811</td>\n",
-       "      <td>-0.089320</td>\n",
-       "      <td>0.072935</td>\n",
-       "      <td>-1.315789</td>\n",
-       "      <td>chr1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>930533</td>\n",
-       "      <td>rs3128110</td>\n",
-       "      <td>C</td>\n",
-       "      <td>G</td>\n",
-       "      <td>0.35435</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.099392</td>\n",
-       "      <td>0.174020</td>\n",
-       "      <td>0.040562</td>\n",
-       "      <td>1.751829</td>\n",
-       "      <td>chr1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297303</th>\n",
-       "      <td>277126</td>\n",
-       "      <td>22</td>\n",
-       "      <td>50451910</td>\n",
-       "      <td>esv3648079;esv3648080;esv3648081</td>\n",
-       "      <td>C,&lt;CN3&gt;</td>\n",
-       "      <td>&lt;CN2&gt;,&lt;CN4&gt;</td>\n",
-       "      <td>0.08385</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.052782</td>\n",
-       "      <td>-0.006902</td>\n",
-       "      <td>0.093478</td>\n",
-       "      <td>-1.528304</td>\n",
-       "      <td>chr22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297304</th>\n",
-       "      <td>277126</td>\n",
-       "      <td>22</td>\n",
-       "      <td>50451910</td>\n",
-       "      <td>esv3648079;esv3648080;esv3648081</td>\n",
-       "      <td>C,&lt;CN4&gt;</td>\n",
-       "      <td>&lt;CN2&gt;,&lt;CN3&gt;</td>\n",
-       "      <td>0.08385</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.052782</td>\n",
-       "      <td>-0.006902</td>\n",
-       "      <td>0.093478</td>\n",
-       "      <td>-1.528304</td>\n",
-       "      <td>chr22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297305</th>\n",
-       "      <td>277126</td>\n",
-       "      <td>22</td>\n",
-       "      <td>50451910</td>\n",
-       "      <td>esv3648079;esv3648080;esv3648081</td>\n",
-       "      <td>C</td>\n",
-       "      <td>&lt;CN2&gt;,&lt;CN3&gt;,&lt;CN4&gt;</td>\n",
-       "      <td>0.08385</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.052782</td>\n",
-       "      <td>-0.006902</td>\n",
-       "      <td>0.093478</td>\n",
-       "      <td>-1.528304</td>\n",
-       "      <td>chr22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297306</th>\n",
-       "      <td>277127</td>\n",
-       "      <td>22</td>\n",
-       "      <td>51194960</td>\n",
-       "      <td>rs376693911</td>\n",
-       "      <td>AAGTTTTGTCTCAGAGGAGTACCCGGCC</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0.18075</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.344576</td>\n",
-       "      <td>0.159435</td>\n",
-       "      <td>0.048414</td>\n",
-       "      <td>0.406650</td>\n",
-       "      <td>chr22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297307</th>\n",
-       "      <td>277128</td>\n",
-       "      <td>22</td>\n",
-       "      <td>51222052</td>\n",
-       "      <td>rs2879915</td>\n",
-       "      <td>G</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0.26020</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.726414</td>\n",
-       "      <td>-0.027808</td>\n",
-       "      <td>0.038060</td>\n",
-       "      <td>-1.804563</td>\n",
-       "      <td>chr22</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>297308 rows × 13 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "          Peak  Chr       Pos                              RsID  \\\n",
-       "0            1    1     10352                       rs555500075   \n",
-       "1            2    1     10177                       rs367896724   \n",
-       "2            3    1    875770                         rs4970379   \n",
-       "3            4    1   1142714                        esv3584990   \n",
-       "4            5    1    930533                         rs3128110   \n",
-       "...        ...  ...       ...                               ...   \n",
-       "297303  277126   22  50451910  esv3648079;esv3648080;esv3648081   \n",
-       "297304  277126   22  50451910  esv3648079;esv3648080;esv3648081   \n",
-       "297305  277126   22  50451910  esv3648079;esv3648080;esv3648081   \n",
-       "297306  277127   22  51194960                       rs376693911   \n",
-       "297307  277128   22  51222052                         rs2879915   \n",
-       "\n",
-       "                                 Ref                Alt       AF  Inside_Peak  \\\n",
-       "0                                  T                 TA  0.38240            1   \n",
-       "1                                  A                 AC  0.32395            2   \n",
-       "2                                  A                  G  0.62500            2   \n",
-       "3                                  T              <CN0>  0.13485            0   \n",
-       "4                                  C                  G  0.35435            0   \n",
-       "...                              ...                ...      ...          ...   \n",
-       "297303                       C,<CN3>        <CN2>,<CN4>  0.08385            0   \n",
-       "297304                       C,<CN4>        <CN2>,<CN3>  0.08385            0   \n",
-       "297305                             C  <CN2>,<CN3>,<CN4>  0.08385            0   \n",
-       "297306  AAGTTTTGTCTCAGAGGAGTACCCGGCC                  A  0.18075            1   \n",
-       "297307                             G                  A  0.26020            1   \n",
-       "\n",
-       "          P_Lead      Beta        SE  Log10_BF    chr  \n",
-       "0       0.565592  0.091926  0.055039 -1.165323   chr1  \n",
-       "1       0.021318 -0.141834  0.062422 -0.618151   chr1  \n",
-       "2       0.044994  0.155446  0.057836 -0.232537   chr1  \n",
-       "3       0.202811 -0.089320  0.072935 -1.315789   chr1  \n",
-       "4       0.099392  0.174020  0.040562  1.751829   chr1  \n",
-       "...          ...       ...       ...       ...    ...  \n",
-       "297303  0.052782 -0.006902  0.093478 -1.528304  chr22  \n",
-       "297304  0.052782 -0.006902  0.093478 -1.528304  chr22  \n",
-       "297305  0.052782 -0.006902  0.093478 -1.528304  chr22  \n",
-       "297306  0.344576  0.159435  0.048414  0.406650  chr22  \n",
-       "297307  0.726414 -0.027808  0.038060 -1.804563  chr22  \n",
-       "\n",
-       "[297308 rows x 13 columns]"
+       "(297308, 12)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "qvals"
+    "qvals.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals[\"chr\"] = \"chr\"+qvals[\"Chr\"].astype(str) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " "
    ]
   },
   {
@@ -347,32 +101,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f71b6693cd0>"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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oQTKT/ZqdiDsjq8tKFUesVrs3pzgWrqjegx1opyEr1q9fjCid0nDVjIgY/FIISZUDhL0kGOWE0s/BTdwnUl0w/dBQDgUrn8iXHb9t7WU7tjRfou7n4zbLOJWQKB0Y/FIoSWRLw2Yyw55QehHcxBm0xH0i1QWit2t1vLx3eyLBlu49mGxX0tKwsFMlzUH5oDtQvohj566jISXyQuD5J9fi8MRW48cnedWMiMwx+KXUCJvJDHtC6XZwEyRoMTnJxn0i9QqmkyoNUb0H1ev3QlovUac1KB90B8oX8erZa61/N6Rs/ds0AO6HRbREWcDgl0JJ4rJr2Exm2BNKt4Mb06DF9CQb94m0F1kp53vQLVzsVVYsrZeo0xqUD7pj565rbw+S/e3XNQZEg4TBLwWW1GXXKMFXmBNKkOAmjmDfNGgJcpKN80Taq6yU8z2kqZY1rZeo0xqUD7qGpjOS7nYiSi8GvxRYUpddux18PbV5DY6eveabaYwr2DcNWnp5ku11VqrXr++U1kvUaQ3KB11eCOXvYF6IHmwNEUXB4JcCS/Kya7eCn/J8BW+er7QFvgLAM090vn5cwb5J0OI1Npgn2e5LUzBuS2tQPuief3JtWzmS83Yi6i8Mfikwkwxmmi5fq+j6y56+fKPjvlGDfefiNQHgkeE8Fu83OvaLnWHWycpJNu3HThqkMSgPw+uzTttxYJccRen2QETpwOCXAvPLYPZDK6YgAW2UGkv34jUJ4M79Bl7csa7jpKkKyG2q+w+ifjh2qFOYFmBenzWAVB4Hhye2ZuL3kGjQ5Xq9AdSfHhp6cOisGrHaRup6lQmkhS5wVd0+uWsTCla+7TYrL3Dn3hI2TJ3A+PQpbbmC1+I1N11ALmDeSqnf9cOxQ+3sL3h2PazdneRAWX8VA/D+rHkcEFGSGPxSIHa2plp7MO72bn257T790IpJFdDa2evyfAXj06dagS0AHHl6K0rFAgSawT4kUK3VIfEgK6UKgIMsXgsSkA+qfjh2gObvweg3vof1UyewfuoEth/6nme99iAL8gXPyeuz7pfjgIj6E4NfCsQkI9MPQdzEaKktoC0VCzjydDO7+tJbF1Gp1toCWwA4M7UTb0/vxsjwEOrL7cGrLiulW6Smut0rIHdzB+iDEnj1w7FTnq9g8o0LuLX44AtgtVbH5OsXBuZzCCJsdxKvz7ofjgMi6l8MfikQk4xMkCCulyZGS62A9szUTkyMloyC+yBZKd0iNdXtuoDcXeNoZ9/dAfogBF79cOzMnLyCeqMzsKsvy0xelg/yBc/J67Puh+OAiPoXF7xRICaLv/q5FZNJYBtkAZzpCnH3yvaX927X7q9BHm+blmPHq9OA16X3Qbosb9ptIWwLMJPPutfHARENJga/FIhpg/1+bcVUHLHaLmc7b7cFHTLgt0I8aIeDQa+H7PWx4/d56L78AINzWT7IMRmlBZjXZ93r44CIBpdv8CuEeBjAvwbw0Mr935BSfl0IsQHAnwL4VQDnAfznUsr7SW4sxS9oL81uZ+bC9voM+zhdmaLz9rj3QdBMLsfbJsvv85jctQmTb1zoKH2wcsLosnza+teqBD0m2QKMiPqJSeb3HoCdUsoPhBAWgP9bCPEXAL4C4GUp5Z8KIf43AP8QwP+a4LZSzML2VO3mFLYw2+f3OK/g43atM+uruj3OfRA0k8vxtsny+zzsz/3Q8UutqwTFgoWDe7b4HhP90sd40K8uEFG2+Qa/UkoJ4IOVf1or/5MAdgL4vZXb/xjAQTD47Stprx0Nu31+i9bCXNJOMqsa9DXTUhc7qEzr2sPs77T/ztl4dYGIBplRtwchRF4IsQDgfQDfB3AVQFVKubRyl58BSM9fbjKS9uxO2O3zepxfYNyLVeaTuzbByrevjLfy6kvodouz/bMLAICX925vdaqgeCR5DKT9d87GbgtENMiMFrxJKRsAtgshigD+DMBm0xcQQnwZwJcBYN26dSE2kZKStuyOe0TqyHAed+53jvv12z6v92V6SdskqxqkdtP3vu5aY0Xtcb9cMu+FOOtok8ysp+13zqbaf0ee3sqrC0Q0kIT0aUTe8QAhvgagBuAPAPyGlHJJCPFpAAellLu8Hjs2Nibn5uZCbyzFyx1MAc3sjqq3bNLsEalu+ZxAwzFQwmT7vN7XzMkryuCjVCzgzNRO4+0Nsu/87js+fcpom0zvlzVpOo79+G1rLxbD9dP+IyIyJYQ4L6UcU/3Mt+xBCLFmJeMLIUQBwGcB/CWA0wC+uHK33wfwr2LZWuoa06EK3aAbhbq8LANvn9f7Ul3OBYA795YCDYkwGYbhd999swtYP3VC2zbLnaVO4pL5IEyKC/JZ9JrXsdmr4SX9tP+IiOJgUvbwUQB/LITIoxksvyal/HMhxL8H8KdCiMMA5gH88wS3kxKSll6aulGoEjDOappkzVQr9YHmeNogJQRBAtGwwan7Unjcl8zjLqN44ds/xJmrN1v/Ht+4Gke/9OlQ2xZEv9TR2nS/c71aDNdv+4+IKCrfzK+U8sdSylEp5SellH9bSvmNldv/Skr5KSnlb0kpn5VS3kt+c2lQhR2RaguSNZsYLWFkuPN7X5Bsly7gVN0eNjh9avOatn/HvQgpzoyfO/AFgDNXb+KFb/8w1LYFEeSzSLNeBaGDsv+IiEwZdXsgSppuFOrwkFAGsO7L9YeOXwoUyEUNNIIEorpSCz+nL99o+3fcZSpxBlvuwNfv9jgNSmeCXgWhg7L/iIhMcbwxpYI9HepPzl2DY30bavXljkvxqsv1OrpAzrSEQFdKEaQjgPO+Xttqsu2mZSomJSBp7TwQ1KD0Pe7V8JJB2X9ERKYY/FJqHJ7YitOXb3QEZO66R9Xleh1dIGcSaPjVxAapl7bvW56vKEfjBtl2P6a1vIM0KS4ttetOaR8d7n7ttO0/IqKkMPilVDG5FG96Wd4rkDMJNJJYgGQ/bt/KkIow2+7HdLvjDLbGN65WljiMb1wd+LnSKGggm/bR4UREWcbgl1LF5FK87j7FgoVHHhoKlGlT9eS1gxxdbjbqAqSJ0ZJnCUQpYsYvSC2vOwC2a6SD9pw9+qVPG3d76EUv2yjCBLL9MsbYRL99XkREfhj8Uk+5T6xPbV6DN89XPC/F6y7XH9yzxfek7HUiVzX7V4mjJlb3HtwL2MIEHkFqeXWB3dy7N9s+B5OA79mxdXjnF7XWtj471jnRsR+n1IUJZAelfVgcnxeDZyJKG3Z7oJ5RtSd783wFzzxR8uxoELbrgV87NJNa4rhqYidGS3jmiVKrlVteCDzzRKkj8A0z9CDI6n1dYHfs3PVA3TNMt1X3eoeOX/J8T3ELMtwjTCA7KO3DorbD69XgDiIiL8z8UiRhaiHt++eE6BhuUas3cPryDd/BFn61kart8gq8JkZLnsGMAGLNWpXnK3jzfKX1/htS4s3zFYw9vrptEEeYS+dBanl171k3dER3f9PsqO7xtxbrKM9XupIRDJrNDNMVo18WEx4oX8Sxc9fRkBJ5IfD8k2tbnVeA6BnsQSr/IKLBweCXQjMJIpxB6KMFC3fuL7U6HXgFWFEulaq2y6vDwq3FOg6UL2qDnFKxYDxlzpRfUFCer7RNoHMyCTxMF07p3nNe8cXEvn+QbXLfrns9AF0LiIIGZGEC2X5oH3agfBGvnr3W+ndDyta/7QA4aju8QSn/IKLBwuCXQjMJ4JxBQ7WmDubciiNWoMycO1BevL/UsV1+rcWOnr2GF3as8603jotfUOB1WTnopXOvLxK6wO6ZJ0qB9oVpkDS5a5O200W3AqKgAVnYQDbtnRuOnbuuvd0OfqNmsAellzQRDRbW/FJoumCh4sjcmvbjtRWsPKSEcZ2hqqZQlzH1ItGcqOZVSxykTtSPX02oVyAYJBj3q7nU1U8fntgaqK7atM54YrSEYsFSPke3AqIw9bgToyWcmdqJt6d348zUzlQHtaZ0V16ct0edKsjpcUSURkJq/gAmYWxsTM7NzXXt9chcmDKD8elT2kvYBStvHPjmhcCylK3X3T+7oGwzJgC8Pb3beBuCUj2/TdcJoliwjLpMmDyfnXFVDfpwvt7C1z9n/Dq6/aMr5YhabnLo+KXWlw/dvtG99yijmk23z24xJ4C2Y8z+d9Q2c2G2p1dlERtf+q4yAM4LgatHPh/b6/T6fRJRNgkhzkspx1Q/Y9lDBvm1FzMtM1AFEbZavaGtG3USaGaanEGHrgeuKjMX56Vyr8yfLotdrdVDtepSXUpXtXlzEiuvNz59yjiACHKJP462Vnfry63/1u2bbtTDuhdy7fjNVfjRtdut9ybxIOB1HsPdar2WhpZvzz+5tq3m13l7nNJe/kGUFfwi+gAzvxmjyrrpAlhVdtC0F64tSAbYzv4BMM4MmmR+hQCGhEB9WX+s+2UeN0yd0A69AOJZFOf1XtyfkWmmNEjmN2iWOMprJcm9kMuL7gta0tucpn3l1e2BiAZDr6649ZJX5pc1vxmjymAGmWQWtI5XQGKlla0v52I50zpDVU1hBwnMPLut7fle3LEuUB2jXz1qHBlor+dwf0amvVaD1FxGXZmflpX9uoVcKkFbusUlLfvq8MRWXD3yebwzvRtXj3yegS/RgIras3vQsOwhI5ylCqbiKDNYdFwGN1Gp1lqX9b0yYO4Wal4B+WPFQuRLr6pV7+7XiMqrDZiKacszwKzEIOrK/LSs7PcrtXEK2tItLmnZV0SUDWn5wp0WzPxmgHPFvylddlB3ci4WLJRiOnH7TYFydzDwaqEm8KA7QpRuDXY2etVIZ6eCuFav67K0UbsjmHYqiLoyPy0r+/OGlxoKVh7PP7m2J9ucln1FRNkwKFMn48LgNwOClyoAf2fdo5g5eaUjUNSdtA/u2YIzUzthWOHgy+tyjOn7EQBe2LGureews+XXvtkFbD/0PeMgeGK0hPmvfQ6v7N0euvWT3/Oryj0O7tkSe6Ck+iJgMnI5zPZ3q57Mfk+6zO/4xtWRW7rFpdf7ioiyhV+423HBWwb4LdYy4SyM91oxGqT1WE4AjxYsbV9eXesxk/eTFwLffG6b0Xb1Q9F/nAuTVAsf8jmBhmJBYD/sG8B7Iabd7eGdX9S4yhnBV3xzURzRYMhatwevBW8MfvtUkIM4rl64JivRg3SDsHICM89u09Yi617P9P2UioXW/vG7f7dX2QcR9yrdoMdDmveNzat7gm5KWT8E9XELeizpOme8uGMdA2AiSjV2exgwqkv4+2cXcKB8UXl/1eUOKydg5YMVKZgusHJezl01YmlrVuvLEjMnrwS+HGPS4UEAbfvH752mueg/7lW6Qd9rmveNzWsxB1c5PxB0X3iNQCYi6lfs9tCHdO3Kjp69hrHHV3dkcHQr/gG0TeTyU1Qs9lJRdVbQlSq8V60FHnrgvL/XtC4n9zADtzQX/ce9SjdoV4k07xubV/cErnJ+IOi+MBmBTETUbxj89iHdiUoCrT65brpWX84aXr+AKMr5zq+1U9BWZM77u0tAdO9Dt/lhiv67WTsVd1ssv7ZtTn77Ji01ZLrShsldmwJNDAzDax+kZf/Ygh5LulZwph01qCltxwFR1jH47UNeAV6YbJYdSPrV6972aCnmxys4AYItqnnh2z/Emas3W/8e37i6rSbVaOobmsFwKcSJSDWadvKNCzj4nUu4XasHOrmZnBT99l1Q7kz7yHAed+53fuaPDOfxT/4zfV1sGkb02vyuHkTZf37BrW4fuF+3l/vHFvRY6tYI5EGWpt8TImrigrc+VJ6vYP/sgvFI4qDP/dXXLsQ28tU9jEIIoLrYDBCf2rwGpy/f0AaqqkU17sDXNr5xNY5+6dMoz1dw8DuXPHv/Rnk/gFlwbbKgKsjio7gyR7rnCbOiX7cfVo1YmP/a5wJvW5zc79M+1oLuP7/PyGuhHYBUjDB2Y7eH7krLKGuirGG3hwF0oHwRR89eawuA41rBHld3Aa/nATqzcW55IXD1yOfbbls/dUJ7/1f2bje+nA/oW6n5MW0d53dyG/3G95T11kmdFOPuGuG1H17Zu71nWS3V+7R7PgcN2vwCF90+sIsCdD8Lc9xRf/I6RngcECXHK/hl2cOA8LtEHUTQBWg6fivL/YLUoItqDh2/FGiYh13nGDQTZrpgzKsEpTxf0S40dD8urqyv1+cR5vm89oPXczrfT3HEgpQIXC7iJeiCUC9+C8T8amg5wpg4ypoofdjqrA/ZvTedoeGd+w3MvdtZDhCW6UhcL16Bg0ltctBFNaZdK4AHdY6qtnFeo5UBs1ZrgPfJzavNlrOrRpjt04m764FXzazqOcvzFWw/9D3sm11ovZ9bi3VUa3Xf9xZkNLXfgtAg/EaCerXp40QlAjhZiyiNGPz2oTT23lQFJ16Bg0nWw7moxn7+uNiX+lXZYr8esKpexlauPVD3O7l5BZwf3F1qBXdx9qiNe7b7xGhJ28PZ/Zx2EO9Xi616b0G/AHi9n6CBvl/g4jWmWPczAMaBPPU/jrImSh+WPfSIqnb1HcP6r2713jS93K5bzfzMEyW8eb7SFrxZeYE795Y8g6CcAH7vyQf1mUGmxpkoFQut7ha6bHGlWsP49Cnte3a3ZouzdMIe/jExWoo1Wxt31wgAOLhni9FzqoJ4Hfd7C1quMblrk3ZBaNBA36QEyKtNn+o4CbPyn62y+lvQVo5ElCwGvz2gW7S1fuqEUQCcZO9NZ89f51AIr5O0Lnt6+vINHHl6a1t95wd3vQNfAPjoo4W2hUlBAiebAFqvV19+sK/s4HvD1AnkfPZXkoHJ5K5N+OrrF9BYVn9hMa0pDSKuWm7dc1aqNeSFaMve2j8PEqy731vQLwAToyXMvXtTuSA0TKAfZ+ASpu6arbKIiOLFsoc+pOuxGbX3pvPyMtC5Ul13Sdpr4ZazdnhkeKgtENVxBzVhspwFK4evf2ELZp7d1rrcKADUG7JVY2qSKfcrMQhbkzv37k1t4AuY1ZSGEUctt+o57e2096l7P5gG66r3FqZc4/DEVry8d3vqLjUHDeQPlC9i3+wCxzMTEcWIwW8fOjyxFS/uWNfK9OaFUPbEDcokw6q6JK1jmsHTPc6u8w1TzLFYX8bkGxcAAGemduLXPzwc6nmA5nbrFlyFrcn1qs82rSl1C7IoLG5++0G3SPCR4TyKBcvzvYX9ApBEoO8UZn8HCeTtha06WRzPTEQUB5Y99KnDE1tjbzRvcjINEtCqMngmwyGcXRii1PnWGw9qZ//6l/dDP09xxOq47Lx/dgFz797Uvn+/mmGvrLM7APS67O5VpmJvY5zHia7Ewy+jGaXkIolyjah0pQhz7970HKYRpO7abwErW2WRE+vCicwx+E2Z8nylZ3+w/IJT3SVp1WOKBctoTK+VE/jQw0OtqW/2H+zx6VNGga+u/tlm0o/XS8HKQ8rOnsQSwKtnr0E4I07Fa+tqM73qtk0/f3cA5n62sL1tTV/P+f5MapNNgvgwi8p6QZfpdtYZqz7/IIG813EdtPSFgdFgY104UTAMfnvgnend2kVvYQcOxEEVnNqxXUlzwtRlsg7u2dLx/EFO/KaXdP3qdoMuAnxl7/aOAQxeC/T8yoZ1i5mef3Kt8pK2Sd22M9vrx+5tG8cx5VXaECSj6dx++0uA6eLKtPDqJeyk+vxNA3mvL3ZB6pcZGA2+uAfYEA06Br89oksY9rKOL8zl5aCPMT3xm05RKxULeP9vaqgvq39uBw8f+fCwUemDvX1xtlerVGvYMHUCjxYs1BvLuHO/+Zw5AM7NHt+42rdEIcx2RTmmnBlDXZxvL2wE/I8D9/bbn49J0JgmpscnEH7/674gvbhjXaD9wsBo8MU9wIZo0DH47ZG0jrz0C051l0/9TqJBL7uqMokq63/VOwgprezPlz7/CeybXfB8Lns7J0ZLodqreZHozCC74/UfXbvtW/YSZruKIxbGp08FvuRtGmjbx6zJcRCl32+aeF0lcQv7O21/ETp27joaUiIvBJ5/cm3gGm4GRoMvrecTorRi8NsjSQwcAJKt7TtQvuhb06jbpqCXXd2ZxJzmEvCZq/qRzgIPFt2ZtoXyGy6RJJNsXNDtsvICH9xdarWjC3LJ2zRQvXNvybhWPUq/3zRRZbqf2rymY6hL1N/pOBa2pjUwYh1yfJI6nxANKga/PZLECvYka/vK85WOoQFAM2A7dPxSx/sA4FmXahLoOTOJGzQ10l5ecFweNg26/IZLeLHyAo2G7MjoBuG3nUG2a5WmZtn0krfpPqvW6sbHmen298OJW5XpHnt8dU8COq9AMo2BEeuQ45XGjihEacbgt4fiXsGeZG3fzMkr2prPW4v1tszi5OsXANFsNeZFFVzpTuJBg9FiwWrLmJk+3jlcwvSyNtAsr/Ab22zCLxsXZLtGhociXfLW7TPVQizT48yknEW3uLIXDpQvBio76EVXCr9AMo2B0aDUIacpe522jihEacYhFwMkydq+IM9RX5a+gS/QGeh5TUvTDUlQUXWbMHm833AJ3TsSaA7SuB0x8DUd3GC6XfYJWcXkkrduuISuA4HJMWJvv469L9NwEreHTNjvtyElXj17DQfKF3u8Ze1MBq0kPfAjqEGoQw473ZGIeo+Z3wGi7bkbcsGTyXOH5Q70yvMVfPW1C9qM4pmpnQCgvI+TO2vozMw8WrDwsJVr9RR+avMaz4EE7kzK+PQpz9pJ3T4SQt0SzcoBH3rY6uhx7Md0u4ojFu7cW+q4vWDl8dTmNb7HhC5jqCtnMa0htRcUmjxHrzJr5fmKdrrasXPXYx8wE0U/BpJprUMOYlCy10RZxOB3gCiHSERY8OT33GHlhWjrU2pnUPwyivb9ddvhDKjtgNBZElCt1VGw8nh57/ZA7103Rc39mrraSjvTmVQQ5/W515fb9+mqEQu7P/nRtoVZXseE7lJq1BpS3b5yBuWPFizcub/UuorQrbpQ+3jUaUiZqsvdJoFkmrYXSGcdclD9+KWDiJqE9OvSH6OxsTE5NzfXtdfLIvdJTleHWioWWtlU3WPdJ0hddlYlnxNoLHfez8oLzHxxm1HmsvVcQuCbz21rC5Z127FqxMLd+rJvTan7veuo2n15Df7oZabS5HPPC4GHrVyr17BT0P0S5n26M/FCoC0T7+6WoBJkO8PwOx5zAnhoKK/8ktOLY0F1jDq3x+/nvZK2gDwo3XGS9PFJRGaEEOellGPKnzH4HWwbpk4oa0IFgLend7f+bXqC1D2f83lf2LEOf37h58rgq1iwsPD1zxlto5N7W0we47WNzvfuJe4T3Ge/9QP85P07rX/n0Ayk4z75h9k/QfaLH1VgA6gzxvbn6hd06rYz7iDKb98VrBxqiqkqzmOi2wGn1z5gkJaMtH6pIKImr+CXZQ8DzrS2zrR+zav215kJPaqpl1QtCjOpJ3ZvS5Qa5JHhvLLeVRVA6F4jzGu7A1/gwaCLuC/ph9k/cdVb6roPPGzlPI8x08vF7sv5cbfM8tp3L+5Ypz22ndvf7XpQr5X+vDyfjDR20SAiM+z2MOB0K/bdtXWmJ0jd872yd3vbKvIgXQZMOzlUqjWMT5/ChqkTuHNvCVZe+D5G5c79RtsK7ck3LuBv/eO/wL7ZhY7bvQRd1e0OfN3cK/SjCNIdw/mYOOgCP7vu3M3ZW9mP+9g16XQQlNcxfnhiq9GxnaaAM0rHD/KWti4aRGSGwe+AU7XGshdg2YHk+PQpFEcs5ePdJ0jd87n/6JsG3arnzAt9UGsHp9VaHZDNGt9wIfAD9YZUXsb2a9cWV6Dq5BcclecrbZ+bLgC396nXvnQqFqzYTtxBAzxnb2X3MWPlROszVh1rSQSZfse4ybGdpoAzyO8iEVEWsOwhA9yXRFWXiq2cgJUXbQGfV7DqFyi5LwkWV6aN7Z9dwMzJK55txcrzFeyfXfCtWa0vS4wMD2H+a5/Dxpe+a7QQL05JZPHcwdFvvXQCS5q35XeJ3687hk3VFzkKbcu9goV7S8sdNZLO3spAsMvIQVpmBakN9jrGTbYzTd0Mun15PuhgEOqtfl94SBQGg98+EecfKNWl4vqyRLFg4ZGHhmL7I2gHEEHrMidGS9g3u2D0GnYA+vyTa7V9WZMSNIuXAzxHH7uDI6/A1+ZXR6oKfPz6G0elC/zsANvrOA4ypao8X9H2MXYHmXHXBvtt58RoCXPv3mwLAp95oncTuKJM/wryt8ceDGKzB4MAYACcQhwzTVnFbg99wHRVse4k5b5dt5gnztX+TqPf+J6y3tNeba7abt0QBBV7oZ1JwFywclgynEBn5QUg0dErt/k8D/r3Hjp+qfX+igULB/dsUZ443IGB6j04Py/T95/U5xZF0tkk1e8E8GCgiD2CueRzPCXR8eBA+SKOnr3WceWiHzsBBO1ooLsCkxcCV498PtFtpeDYCYQGGbs99DmTleO6b/Bz797sGGigk0Q9Ynm+4rnQSbXddsmDe6CEjv1eV41Y2tey1erLrTrS6mIdxRFLOwzi6194kKmsVGsdAZUq2K7W6tg3u4B9swsdAe2b5zvrc8c3rsbRL3269W9dUOcljQuXomQaTah+J4AHk/TsAKxSrXl+KQpSumIS0Ht9wenH6V9Bu1boSo+6XZJEZrqxMJNlMJRGDH77gMkfKN1Jyv6j48erHjFoFs95f69XfqxYwKHjlzq2W7r+P+AfCNfqDdw1DBidtcLu7dVdindbP3XC93WclxB1wdq/uXoT5flKW3lCkMA3qwuX4jo5m35xML08fOzcdc/nMd3utAxL0X1Z1r0P+wui6nZKn6THTLMMhtKKwW8K+J3oTP5A6U5GJoGvneVUnVyD1oQFyVw+tXmNcZ2uSd4oSG7JuT+DZimDtDizs2S6z0cCbVk00+BIIP7BGG5pWAij2oa5d2+GHnDiFOSLg2kG1O/3zSSo6FUdpup1dV86de9DV3v//JNrY9xSikvSCzN1XwaPnbvO4Jd6isFvj5mc6Ez+QOkCZF0mxmlkeEh7Ug162TNI5vL05RtG90uCANoyrkEEbXEWJIvmN5xiSAA/PbK7FRTqumdE5a5bjSsAc9Yzu8tI3PXrB79zqW1KYKVaw1dmFzwXDZoI88XB9PKw1++baVDR7QEZXq+rKj/yeh92QMPL3P0h6U4gLIOhtGLw22O6E92h45daf4CitFZ65olSW82vile20fSkH3SRlt/rJs2dcQ2S5QzTx3Zy1yZt+zZnFm1y1yZMvnGhY0GeAPBowcLtWh3bD30Pd+4vte5jGpiavsfyfEW5YCtqAOb+ouesy3Vuv9fVg8iB78qCuEq1hkPHL7Ve04/p5WFd5vOR4Tz+yX9mttitVwMyvK5OlIoF4+Do8MRWBrt9JMn6fJbBUFox+O0x3Qnn1mK9LTNp0loJUAfIY4+vbutI4FYcsZTjfgGzk36YRVq/8lAeHy4Mhx5RHAd7YtxTm9d0LAr0CiaDjA62s2R26yt3YGTlBdb/aqG1Sr55Uug8WbQGezj+v5NzqpkqsxrkPc6cvKItK4gSgHldFXAG1kHrnoNwnodvLdZbU/z8Tv6ml4fjyHwmXYcZ9HWzsPI/DSU+g4hlMJRWnPDWY14ntKCX13WjNidGS5j/2ufw4o51HdPQrLzAB3eX2sb6vvTWxVZdq8l0qDDBSn25WfMbZASvve2lGIOASrWGo2evBRqR6zU6+JW927WTwcYeXw0r1/4J1BsSZ67ebGVHGlLCoAub9r3YI5rt5wrzHr0C3CgBmF/gbP+8m1cE6g1p9HtmOtkQaAbAV498Hu9M78bVI58PnAXt1US2rE6Cs7+86/4GUniHJ7bixR3rWpnevBB4ccc6XhmgnmPmt8e8+tPqSgvCZicOT2zF2OOr257jzr2ljkyiMwunG5Jg15v6ZUF1l71q9QZOX76BI09vbT33yHAed+7rg2hn/WmcgmY5/cpQvOqnVT2DuyHIe9R9pgKIFAj5HSt2YB0ksx4H02A76fZtztcBujeRrdev22u9qrHOCpbBUBox+O2xidFSx8Iem1dpQdAFSO7A+eW92zExWsIGTcsuZ0DgPOn/1ksn2i5jea0IF/Be2PBetdY2Bc50qlu3eGU5wwRCvaxx1lG9R9UlfuBBnTQQbtGb7nmB9gyj1/2snMCnNqzC2b+61Sor2PGbq/Cja7e1Vx8EgIetHGp1dcVwFvskp+11e6lXNdZE1DsMflPg4J4tvvWEQbIT7kDXq97Tr77QpGevakW4yYAKZ9BhLz7qlSAr2sPqdkbTzfQ9OjOA7i83dmnFvtmFwPWs7udVdXuwjzddIFtflnjnF7WOaWHO47Q4YkFK4Hat3speAsDk6xc6Mu9WXgz8ZX3y1qsaayLqHQa/KWByuTFI1wV3hthr5b7XQp4gC9kk0DZhzS/wdQdefpPZvJQiBpV2V4wTP/55azseGgpeDu9XluKV0XQb37ga7/yihveqNeR82tUVC5byyoGT/R5PX75hdEnbzgDqxp8C4RrWe2UWTY831e+CacbSeZVF1d+aC5+yJ+let0SUPgx+YxL1pOl38i5qRvcWR6y2f+t6darYZQf249zbPj59yngh2yPDedzVXFZ2U/V1jcKr9CKfE2h41Nk6xxg7xw9Xa/XAZSV+ZSnufe31BcEOfB8tWLi/1MBiXX3vYsFSXjkAgJwAluWD/Q0E761scunXr2G96e+G6cLJsBk5v9+xXg2XoN7Kaq0zUZYx+I1BN06ausSf+/YgdWp2EKELCoI8l9dCNTdV2ySv7KVd53nm6k3tczpLL5yX0/3egx2wR1n0Up6v4KuvXejIztbqDexzDaFw7muvEcl2ttUro1uw8ji4Z4vRyTvsMWpSquGVlQ7yuibHW5IZOS58yq4s1joTZRmD3xh046R5WxMEuW83rSu18gJPbV6j7e8b5LmCWj91oqNp/sE9W5Q1mcCDOs8Xd6xr9U9VsZvxO4NrrwAT8B8/7BeQ2cGdVwCoC/g+8uFh/PUv73s+v447e+538g57jJqUang1rA/yun7HW9xXDNy48ImIKBvY5zcG3Thp6i715oRo60fp1YPWqd5o1mt69bY0fa4wnK87+cYFzL17E488pP8uZtcu/8ajD+OVvds7+hXbVONm/dhBuIrfJXbTS/X21L7yfAXj06ewYeoEhvJ5/MpDwfevwIPsuf1c49OnPPuS6o5Fvy83zv62Ol4N64P8bkzu2qT9XO0vNUlm58IeA0RE1F8Y/MagGydNXSDakBL7ZxdwoPwgs/jMEyVtEOHFPfRA1djf2bA8LnYg7rdoyxmku2udbapxs37sjhgqutttQb7g3FqsY/L1C22B/y/vNQPnIPv0sWIhcGN+3bEoAN9m/vbwlHemdwduWK97XdXnNzFawguKQSzdWnyU1SEPRERZw+A3Bt04adqBqCpIkgCOnr2GA+WLGJ8+hVcV3R1MuYM559S4yV2bMPvv9GUH3VKrNyAlYtnn9mNO/Pjnyp/rbrcF/YLjLuuw/2W6T+3t9SonUNFlVZ29e00EnV42uWsTrHznK39wd0kZdB+e2IqX927HKkdwHKbzhhdn9t2ZMQ8yxY2IiPqXkF0MZMbGxuTc3FzXXi8pqtXrQLTVwqYr4jdMndAGtia9df24a2adtnzt/wi0sC1pL+5Y12rdpertOvfuTeVc+UeG81i832jbz161we9M79b+TNWey8oL1MPOKHYRaGZJq4v1tu3VHQcCwNua7dW9R6/HxGH7oe8ps/q6Y021TwtWHn9n3aNtwy2C9Bj2e24Gue38/h71oiUc29ARURBCiPNSyjHVz7jgLSDd6vUjT2/VBo1hnnP/yiAB9yIfr0VBUcMtr8xpeb6SqsAXAF49ew0jVg4SQHWx3jaIYfKNC9oA9M79RmyLp3SdFnRT+4LwCsp0re+8MtG6fshJ17TqFmvqSkZ0WW1nt48wPYa9npsdHR7w69DRi5ZwqtfcN7uAg9+51NbxhIjIBIPfgJI4eXr15nWfWJ7avEaZzYzKLxgMcmm8mxZXWpW5w1y/zKv7C8aIlWs9l5NAM9vul+FX7bf9swuBv5DYbdryQrSVMbizbh/cXep4rGpamTNb9mjB6shKu7/wJJFdCzpBK0gdtd1j2HS7B6mjQ5jPyuQxfn/jevEFQrewNGg/biIigMFvYEmcPP0e6wyCnIMYnKKUPHhd1rf1Y3Dgx/kFw+Q+k29cAOSDul2/jFfQz8OewqYaRf363LXWJX+dR4aHPHv7Vmt1WDmBVYoyCtX9dVcgggZdunZpd+41636DtjxzakgZKBPpFYh387K63zhmkyA2aPbV9DF+f+N68QXC67lV/bSJiLxwwVtASXR2MHnse9WaNvuRFwIv7FjXsQDMygnlYiMnrxZWQbdRZdWIhYLVP4eZvbdUCwvrDdmxYE21yMwOMoJ4ZLhZ4nD68g3tJX+/RXHu8gLV8VJflhgZHsLb07tbrcMOlC9i40vfxb7ZBd8rEAfKFwN1mQAeLCRb5erwYGft3I/1annmlhcCh45fMl78p1uc+tTmNcbvS7dgzpS7U8etxTqqtbrx/gS8s7NRH+P3N64XLeFMntt03xER9U9UkhJJdHYw6af7mMe0smUpcXhia8dK9b2fWotHhr2T+5VqDRtf+m6rVVqUbVSRElhqmI09TgN7UMZygIWg7s/Fq/evLqgrjgxjYrQUKXvmDhBMMnQHyhfx6tlrRt0mavUGjp27bhx0OYPEQ8fVNdCqx06Mloyz5jt+c5Wy9hlQv39dRwfdlw7dF5sgwb+bX29ovyBW9968bg/yGL+/cb1oCWf698dk3xERsewhoCTmwNuPVY3IBZoBk93eyqt20ll7qlrVnhPN4M79EiaLh+zn3Te7EOSteS76iqM7RRA5ASgGyHWwP1fTS+/DQ7m2SXkmZRSq1wTCT9VTBR8mtbbHzl0P9Dq6INkdQLmPP12AqnosoF+cZ7O7PZy+fEN7H122UFWjvV9zXJt8sQla72ryBcfvPkHrqIM8xu9vXBJ/A/3Yz33o+CXPYwkYzBItIooXg98QkpgDbz+fO2AVAF7YsU77c13GRXWS9gv87MVDXtuoC8CDMg1E42T6ehLNelRT95aWW/ukUq1pg3q7xMQrAJnctcmzU4XKqhELX/9C54p3Va2tfYnfDtbDLsjTbb/NdPKd6rFe2+7ufrHBo0VdkEykaWAYR72ryRccv8v8uv3j9Z6DPMbvb1wSfwP92K9p10vr9iEn8hGRHwa/KWKScZl79yaOnbve6gjwzBPqk1CY7Ie9eMi5DY8WLAiB1gKppzavaVuQFVa3A9+gorQpU701ZycGrwBkYrQUuE3aXUWXCvu5gPbjKcjn5w7iVQvy3NtvMz3+vIIv97arsou6QLJYsAIFZ6aBYZiMq8lrOZmUEITJvvYiY5sEZxAc9AsAERHAIRep5l59rgpcdL1gR7/xPd/LgypWXrR1NHCzAyB7uEROkwmMqliwcG9pOXKQnRZWTmDm2W2e3RL8Mlpe8kJgWUrfgGZ8+pTR849vXI1L7/2yFYQ7s8teXRGCvIe8EPjmc9siBV9xDq0w6fYQ1+tF7fZATRx8QUQ6XkMuGPym1IHyRRx1jSn2q5G16yDHHl+NydcvaAPYqJyTubwmzoWVE8C3ntsOwKzGr194Tc9TBVVuupIDN69gzO/zyguBHb+5Cj+6drsjwHN+6dFN/fJ7DybbGFQ3AiB3v2Tn1RAGXERE6cMJb10Q5wm4PF/pCHwB/8Vh9sK1P/tRJbHAF2jWrI5Pn8Lkrk2hF2h5cbYZ+5uaf+1twcp3LUNs5YBlGS7b7VUKoGrX5RTkPXotwNJ9Xnbm+DcefRj//ue/VC7qch6Tqh6xXnW+qxLMbrrrT+0uE3EFw6p+yQUrj5f3bjd6XmYniYjSha3OYhBH+yOnmZNXImVTvcYQu3uthmW/x6c2r/HtJRxUfVniD/+s2U/WL8jMC9FqXdUNS8vA1SOfx4s71inblq0asVAsqPexri60PF/xzG7b7biCvEf7C4qqh66qZVRDytaxq9sW9yfhbiulC+4FgPmvfQ4LX/9cW3/hJMT9uwiE66mb5PYQEVE0DH5jEOXkaHP2RPXKpEYJM1eNWNrFUWHU6g2cvnwj0jbp3Lnf8M10WvlmzSgQrDtDFPYksDfPVzpKUl7csQ7zX/scDu7ZEqgPqtdxsmrEapVKBH2PqkDL3edWNcwjCGfA24vhB25x/C66RenwkMT2EBFRNCx7iEHU9kemtZJ22zO77jJIdrhg5SElYi8PiLvkIQg7bDOtM43DnXtLyhIFCbR6zjrLACrVGvJCtAU87qyn13Fya7GOF779w44aXKAZGO/+5Ec9uzeoSiCcZQJercKcdPXmzsA2aPutA+WLbZ1Lnn9yrWerPRNJjN6N0uGhF6OAeyWJz5OIKAnM/MZAdxLMCWE0AtW0J+oLO9bh8MRWnJnaibend+PFHeuMt/HI01sjte+KUy6mVPH9hlSO5E1StVY3mig2MVpqlRjYpRu6S95+QdSZqzeV73FkeKhtsp+OV6Cle+1iwWqbgqYan+0ObHXT01QlDu7Jcna9ut+kQT9Rs8+q0cVRJpqlIRveDUl9nkRESWDwGwOTOsp9swvaANg0C+TOohye2IoXd6zzvXRdKhYwMVqKfIk7DvmcSH2P37BMBj2oLnmHHR1tHzcToyWcmdqpDYC9Ai1dYHdwz5bWl6wzUzuV47OjdGvQTZYLOnHOLUqgqqvPBRD6vfdiFHAvJPV5EhElgWUPIblXcDvbQOliu32zC4FW4DutGrGUK9gPT2xtBcV+Td+T6McbVGNAI98ggx7ct/uNt9ZxB7Vhpn4FGXzgN9XLffypOkLYdO8z6jEaZZCD15eVsIv0BmWwhJ+kPk8ioiQw+A1BdZJ/83yllQ1ab1hHafOb+GTlBT64u9S63K4LKvxOtKUE2pL1I6+2YaZjl4sFC488NBRq+pgqE2s/dv/sglEtt3NinPs5ggZa7rGx+2cXMHPySuAgzS/T7dwur/1cnq+EHhhhv2ddP2UvSdXn9mIUcLfpelCn4WoTEZEbg98QvE7ycWSH3E3079xb6qjX1QUVXid+vyA7K7zev0nga5cF6D5r55Qz1YhgXSbWHl+t6vHcQXOHsIFWkKytji5ItJ/L+dxe9VZBXjeO7bbFMbo4q55/ci1ePXtNeTsRUdpwwlsIXlOy/KawvTO9O/DreWWS3VlMe3IWoM4AegVm5K/kk01VlZ7Y+9nvsc7nMBkR7DUxLijd2OMgr6F7Dl1WsGDlUNO03jN9Xb9xzab7HDAbXcyBFXpZ7fbAY4IonTjhLWZeNbp+wWSYP5ReY21VGehDxy/hbn1Zmw1znsid44PTFAyvGrEijzV+caUtnGmpR8HK46GhnLIrRrFgYeHrn/N9DtVVATvwNQ0i7c/Ir3zG5HK86fEWxyV/Xc2xvg3bsvaYM31dv/sFyQT7lY3EmWUeRM71B1nBY4KoPzH4DSFs+cAre7cb/aF0ByxBF42ogkZdWYZz6IVEM1gRkFhUZORGrJzy9iREDXxXjVitE7FXpt55/69/YQuAzr7BAsA/2PZRAO1ZWftLiTO7GFfdqMkEsJwQnvWxqhPz5BsXcPA7lzrGDMdxyV8XPO6bXdA+JurrmiwWDVKS5FU2Ene5E/U/HhNE/YnBbwjuk7xXCcTbjjKH8elTvn8oVQFLXBnZSrXWyiaOb1yNd35RU27PiJVTllMMD3Uv+I3K/r5Qnq8g55E5V12enXv3Zlv9ogQw+++aLZucAyXc/XsBfTBW1HTr0DGZANaQ0jPLpDox1xuyldl2bneYThEqquDRK/iN+rqmX0TDLFpzfwnVBdmDOLCCzGRpiAnRIGHwG5LzJK+rO3Rnr0z+UOoumwcJgIsFy3egxZmrN7U/W6wvt5UM2BPK+mmhXLVWx/qpE777rSEl3jxfwdjjqwFAW2tbb0jlgh5brd7Qtioz7dbhZHry9MoymTyHs5UX4H3JP2xdo1cngKitwEzbxAVdtBbkS2i/LIhjbWr8uEiSqD8x+I0g6Kp+kz+UuoDFrhv1y0IBwO0YJrmdvnwDk7s2YfKNC6g30lIJHJzJlqvqpMPQBV9WTnRkzP0ujZpczrfpjpmiYd20c1gG8CAQdWafo9Q1+nUCiNoKzH6sVwY4aCbO9EtovwysYG1qMuK6YkJE3cXgNyT3ycR5YtStMDf5Q6kLelaNWG0LprwWQ8URqr5XreHQ8Ut9HfgGEbXG2IuuVMQrIAvyxUOVZSrPV/DB3SWj7bMfrwuQHrZykeoaxx5fjT85ew3OvZBbuT0K1aAZXXbeay+qMqKmX0LTnj316hzC2tTosjLEhGjQMPgNKcyqfpM/lLqg54O7S22Lm0xKG6IIknmkcCSaJTPak6Vhz2FVlmnm5BXUDZoWOx+vW7yjy6aaHh8zJ6/AHf4vr9weNkjQDZqJ43leeuuiNmseZ3u5pKlat7mxNjW6LAwxIRo0DH5DCrvQwe8P5cRoCQe/c6kjsK0vS+xzTN46uGeL50KiqO7cM8sa9hsrJwCBti8X3Wjxpmv5ZXdgANovP5sErwLAM0+ojye/41AAHV++ggZCptO7klgUpAvU43qeu4rn6rfL2ar35sbaVCLKIq9BS+RBd9KI42TiVbPrrNWLIodmFksnyaxyr+SFwN5PrcXMF7e13ns3Al8B4MjTW/HIcF7583pD4tDxS223mQSGEs3abBW/4/Dt6d04M7WzLXAOeuyatuCL83elPF/xHWyhMqSJ073KG5xWjVhtwy76gd8x1G/BPBFRXBj8hjS5axMKVnswE9fJxC8osBdomWbeVJahfg+DzO7sAABnpnaiVCwYB7450WwPZ+WD73OJZvu0O/f1Wbhbi/VWYLdh6gRyEbOqXseh7rhRHQ9eW+H15cnvecP8rtiX8f0murkD3SEB/PSIerKiaQA+MjxkNJnP/vzGp08Z9WpOktd7KxULfRfMExHFhWUPISW50MGkd2kcC7TsbXVOeTORE4BBOWkqORf5BLns/mjBwju/qKHekBDiQR9hE6ViAcfOXfe9n7OMJWpWFQCsHKBaa2d3WXBzHtN+/aWDBK9x/a6YXMZ/avOaQFPG4ugTXJ6vdJQqxdVNIUp7Mt0CWwa9ROmS1dHgvSRkwOlhUYyNjcm5ubmuvV4/81qlTdGVigUs3l8K/SXCa2yv+35Hnt4auT77keE87i8tt9UB6wIZ3UKnnAB+78l1Rn9UvUoLdN1MkmYyqS/MgjRngKkbiKJ7Xr9FZVEWyKmeO2jwyt6+ROl2oHxR2aXmxR1mf6tJTwhxXko5pvqZb+ZXCLEWwL8E8BE0E0F/JKX8n4QQqwHMAlgP4B0Az0kpb8W10fRAN+pSs6ZSrcHKCVh5Eaqdm0ngKwQgILE/hoWJd+43UCxYEAKoLtY9AxldhvSjjxaM/5jqMp0C6Fm3A5MOJGEW0TkXoeoCTl2W2y8bncSiviBdMuLoRMAAmig5uquCx85dZ/CbIJOa3yUAX5VSfgLADgD/tRDiEwCmAPyfUsqPA/g/V/5NMSjPVzD5xoXWid4Zmq0asbSPc9dnhq8IbrJyUZ8h3erLEo8MDyGptylls8dvXF9cqrU67taX8fLe7R2L1Zx0AaI93nr91Am88O0fer5Wkgs6wzKpUY+6fROjJRx5eitKxQIE/Gtj/YLbKNuThtG5zjpriQflHL2uZyYaFLoSN9PSNwrHN/iVUv5cSvmjlf/+JYC/BFAC8DsA/njlbn8MYCKhbcyU8nwF+19bUGYjV41YGBlWJ+sFgBd2rGs7ab+wY12kBW0zz26LtKiuH1Rr9Z7VLxesXODA28786RZXlecrRl96zly9qQyAnd0U3M/T6+4AzsBUJa7tmxgt4czUTmVHDDev4Dbq9qThC4hX9pmIotOdYwf93NtrgRa8CSHWAxgFcA7AR6SUP1/50X9AsyyCIrCzLLovfLcW69oaVen67/9w+y5ePXsNxYKFnIBnpwGd1+euYZnfPhNT00x+81Op1tpqiJ2Lq2ZOXjHONJ+5erPtknbByrVNozOZWthtulHGXj2Pk6RbMLdqxMLXv7Al0vakYXRuGrLPRIPMb/w7JcM4+BVCfAjAmwD2SSn/Rji+lUgppRBCec4VQnwZwJcBYN26ddG2tk951cyZLLZx8qr/df4C2c9TrdVRsPIYcQU2Js5cvRno/lkXZxeMvMGx4GRn44IukHQGV6rjQzW10H08P7V5DU5fvtG1mlDddEVdz+MkJdn1JQ2jc3V11hyOQRQPu66X3R66y6jbgxDCAvDnAE5KKb+1ctsVAJ+RUv5cCPFRAD+QUnqmJLLY7cFrxTbQmcEyEWYBXLFg4d7ScqgpWGQmrn1sl7CMPb460PEhAKMvUGG25+3pZp9ck5G5YdppBVlUpev64NxOikccHSeIiHrBq9uDb82vaKZ4/zmAv7QD3xXfAfD7K//9+wD+VdQNHUReNXMmfUtVwoQ21VodzzxRYh1Rgqq1Oh4aynkuSlQZsXJt9bUSaA3jcC6+8vNYsZDIIgmJZjsewKzXbq3ewB/+2UVsfOm7WD91Ahtf+m7r8SpBF1WloRY2K4IuACQi6ge+mV8hxH8C4P8CcBHNwWAA8I/QrPt9DcA6AO+i2erM8zp5FjO/XlkqIFwgmxcCv1IYimXQBcWvYOXxsJVTfj7urH3ByuOhoZxynHReCHzzuW2YGC2hPF/BV1+7oA1u7Wxckr2hX9yxDkfPXgvdvULXt1LXU1jVI1c1UAJgNpKIiNpFyvxKKf9vKaWQUn5SSrl95X/flVL+Qkr5d6WUH5dS/j2/wDervLJUup/lhYBAc9GMqt1YQ0p8cHcp1KhdSl6t3oCUUHbaeNhqZoadWbTbisAXaH7OL711EQfKF/HSWxc9s7oPDTV/lZMcWf0n565Fyq7q+lmaLqqyM8TuwHfViMXAl4iIjJn0+aUIVMGIvWJb9TOB5irPt6d3Y/5rn8PeT6lXfNaXJaycaJUxCAH0Yyxs5QU+8uHhXm9G7G7X6jjy9NaOEohafbmjV69XQFmrN/Dq2Wu+pQbVWr3V8cHZDizOMpdl2RwfHDa41gXvpmUMupKLkeEhBr5ERGSMwW/CvGrmJkZLeOaJkrLeszzf/J9d+6myWF9uBRRSAiEGlfVcvSHx17+83+vNCDwQxK+uVwLaXqjuPqlxZWudz3vn3hKA+Buln758o+N4ftHVX1q3L3WBuNcXRCe23SIiojgE6vNL4XiNGD19+UZHDaUdxCzeX2J3hi4oWHk880QJb56vGO/vqkG9tVftrTNgs48Nr5peU/ZisaSOm/eqNeXx7OzW8LCVU/Yw1vWtNG3plYa2Wxz1S0TU/xj89pgua5XUoiVqcg9vAIA3z//M+PFR86nOgM0OqBpSGrex090vJ+Ab+JaKBbxXraE4YqG6WA/0XlSBprsdVq2+3LyktNL32KRvpdcXRFuvhz6436dzuAgDYCKi/sHgN6S4MkC6bFbQIQdkzj19qzxfweTrF1Dv0pxjZ8DmDqicU9V0x0BONBe4qbKrJm/B2UFh/dQJz+00CTRVtbjLAPIQEJD4jUcfxtjjqwFE+73p9dAHr7aF/RT8MntNRFnH4DeEODNAumwWyx2SNz59yniyXlR2IOseE6ybVgYAH354CHfuL6HuKOa28gKQ4UcjFwvmPYjt1ml+QZLu6oW9T+3fj7l3b7aVloT5vfEruUgymBuEmmNmr4mIGPyGEmcGSJfNSrJfq4qVF9j7H63F63M/w72lcIFVv7i1WMe+2YXWv7uRYbdLGirVGg4dv4SD37mE2zXvkoNqrQ4rJ7BqpTzhsWIBd+4tKXsCm7ByAgf3bDG+v0kpAqC/euFkd61Q3R4lc9rNYC5szXGaMq2Dkr0mIoqC3R5CCJsBKs9XMD59ChumTmB8+pR2ghVg3gGgVCzgkeHonQLqDYnTl2+g3hjswLeX7ED31mIdVZ/A12aXYrw9vRtnpnZqewL7KRULmHl2W0eA8/Fff0T7GK+pbE5Ru1VEyZx6BXNxM+1K4RR0el3SvP52Bfn7RETUzxj8hhBmvKruJGgPMHDevn92AXPv3sSRp7dCMeOiTaVaw5378ZRIVKo1o5pRP37bTMHcWqy3ApEwnQ0KVg7vVWuYOXmlI6D5/lc+ow2AXz17zSgAdrfzC9pbOEq3hm6WIoQZ9dvN4NyEbl8XR6xUBelEREli2YMh56XLRwsW8jmBxnJ7LaY7A+R8jKqutFZv4Ni56x23SzQDD9Vl4n7QpXVjmeLu3xuEXR+sKwn4/lc+g40vfVdZ/nHs3HXPLg02Z4mEuxTBS9RuDd1uf2ZaCmLzC867XRKhW2MgZWeXEJZDENGgYubXgDtrW63V2wJfAB19p9yP0dWVsqPDYEkq6W0HrmHrfW26rGOcx6dpJjgvROSxxGFKEbrJ6ypRL0oidNlrXTlNPy3mIyIyxcyvAd1YVaf6smzLkpg8BmiWCCSRKRUAXtixrm+zx/0qqa8yeSFi6wBi13c6M4664zDseGS/THDBykcOfO3XAXrX/syPV2/iXi0+U2WvdQtsuzlAhIioWxj8GjDNfjjvZ5wxSSjw/Y83rsafX/h5/E9OXScQ7xUCu77T2SFBdwlIN5UtiKAB6me/9QP85P07rX9//Ncfwfe/8pm2+6Spg4IXr/e+39FxxKkX2dZeDxAhIuomBr8GTFo52fcL+phlNBck3a0vxxIHPzSUQ04AZ67ejOHZ+t+IlcNiyJ64aWFPoouj9Z2uvtM+Du8vSTSkNJrKFoRpraw78AWAn7x/B5/91g9aAXC/9arVvfc0jGu2pT2DTkQUJwa/BlRZETd3lsTkMbawAwtU+r1H74sxlmoUrDyGh/o/+AWaC92svGgbeBGUXWOryzjerS/j7endoZ8/Du7AV3W7SbnAgfLF1mLSuAP5uKQt2xp0MR8RUb/igjcDqkUiL+5Y59nySPWYEYu7289b538Wy/P4LeTpN9VaHZDN0cxAuIV1y1JiYrQUqlVfHOLqI+vXQeFA+SJePXutVSrSkNK4bVs3hWmdRkRE0THzayhMVsS96Gfy9QtJbNpAiStLe2ZqZzO4EkhuFVqX1Zcl/qa2hFf2bgegX6SkYwe3vcg4xlmq4FcucOzcdeXjTNu2dROzrURE3cfgt0tmTl5pTeui5B0oX8Sb5ytIeye5oLF5Q0rsn11o1QGvGrFwa9E/u+0Mbrtd31mer+Crr11Q9rl2dzb4+K8/oix9cA7i8Ave+6GtYL8s2Atr0N8fEfU3Br9dwn6Z3aUaHpI2poGrm/2uKtUaLJ9xegJQBh/dyjjaGV/dZ+H+vfj+Vz7j2+3BL3jPKwbK2LenQb8t2Atq0N8fEfU/Br9dortUO0BX5VPFJPBNqseyqbv15dABsM3rakKpWMCZqZ2tf/ciG+fX71pVZ+xua6biFbw//+Ra5aLJONq2xaFX/X27ZdDfHxH1Pwa/CXKPRFat1mfgmwxd9s/JJPDNASgM53HnvjqAM3kdnVq9gYeGcrByIvaSGHcNb1zZuKABtNcVj6TqjO263rR2e/BbsNfvBv39EVH/Y/AbMzs4qFRrbVndaq0OKyfwiEcg5adYsHDn3hJrhw08/+RaHPu31zvHUAe0DKA4Mow799Un7oaUKFj50NPXbtfqeHnvdvyjt34c22K/kiIojSMbFyaA1l3xiGO0sZfDE1tTE+y6pam/bxIG/f0RUf9j760Y2cGB/YffHXbVlyXuRghwqrU6A18DL+5Yh8MTW7Ec0756r1pDSXPitttT6X7uxw4IZKjmZZ0Emp0u3EFlHNk4rwBaZ3LXJhSsfNttBSuPbz63LbOXwHX7ZFCmqQ36+yOi/sfMb4z86huBdK04H1RHz16LbVAG0BwHfPPOPeXPntq8JvTz2gGByXFjSgIYnz7VkfmNIxvnF0B7lURw5f8Dg75PBv39EVH/E7KLwdjY2Jicm5vr2ut124apE741vL1eZEXBedXkrhqxcLe+bBS85gA8OmKhulhvCwhMjhshgCFhXhtcsPJtZQXukgXVffyMT59SBtB2mUXU59fph2ltRESULkKI81LKMdXPWPYQI5Ms2kNDuY5LgpRuXgHnrcW6cdbWLnh5e3p3W2mC33Fj5QVefm47Zp7dZtyuy12OEMc0Ma/L2WFKIkz0y7Q2IiLqHyx7iJEq++V2t76Ml/dux6HjlyK1uKL+dGuxjvJ8pS0je+fekveDVmJv+zF+x5jNXaYQtbev1+Xs/bMLRtvgRVU20U/T2uLEIRFERMlh8BujidES5t69iaNnr2kvY9tZvigL36i//eGfXcTEaElZiqBSX5atrgzOANRvtLF9rMUZSOkC6Kg1xbpOEv0wrS1ucQ6JYBBNRNSJZQ8xKc9XMD59Cq96BL5JLHDqB+mYq5Ued+43WkGJ6XHgzKBOjJZwZmonXtyxTrtv7WPN2YFE4kEgVZ6vRH8jDlFX+OvKJnTSMq0tCXGVkHTrsyci6jcMfmPgbnGmY9dYZqnZe16IzAzyCBKO2dk4U+4Manm+gjfPV5T7VgB45olmhjapWly3qDXFQX8n0jKtLQlxDYno1mdPRNRvWPYQA5MMXqlYaFvg5BcoD4plKVHKwPu1RwmvnzphdP8g+0OVQfU65iSA05dvAOjutK0oNcW634lSsYCnNq/JVLeHuIZEcNIaEZEag98AdPVzfoGMO3gxWRg3KEaG89oeuYMinxN4r1ozDnyDUE1rA/wDGPvnXoFUmupBda3S7G0a5GDXzWtfBMFJa0REagx+DekWocy9e7NtjLFbXoi2S42qBXGrRizs/uRHcfTcNQzaOp6wo5zTys5Enr58A+9Va8jnBJYSatxsZ5NV/K4e2AGOLpB6avOaQIuqkg6UORjhgbj2RVxBNBHRoOGQC0O6Bv95IbQrz3N40NvVSz4n8M1nt2H/7EJm6mP7jWpgwwvf/iHOXL3p+7hnnii1gmXTz9f9OHcA5NUpQjXgwh1I6VrtqQLuOAZkUG+kKbtPRNRNXkMuGPwaMpnCFUVpJVM36LWx/cRewPaYK9tr/zvICOUXd6zD4YmtGP3G93z7O9tXAt48X1EGnMCDVmf2ly/7/+vKJJzK8xXs0/TlBYB3pne3/dtrspsuMx0EAzQiIoqbV/DLsgdDusvMXpnfIN6r1vDy3u3G2V/TrDKF92jBwsE9WwAAk29cQL3R/GQq1VqgwBdA6/4mh8rd+jJO/PjnypX6h45fahun3JASYuX/mwS+ADxX+wugYwiH7guZs+44bAAbZ09bIiIiE8z8GtJd+n3miVLgQCgqO8hhmUTyClYeORFP7XJeCCxLmehnZlKO4HcVY9WIhZHhIVSqNc96djvzG6UsIumsMhERZZNX5pd9fg3p+pgentiKgtW93WgHBROjJQa+XVCrN2JbtNeQEsURK5bn0jHp4+q32v/WYr0VkPoNbAGi9ZPVda2oVGvYMHUC49OnOJSBiIhixbKHAHR9TI88/UlMvn4BdceqfysnsPdTa3Hixz/3rfE0ZeUF7txbwoapExgZzvs/gFLng7tLsPKiVUKhUyxYuLe0HKodnl8btDha7TmzulH6yXp1rXBOJQNYBkFERPFg5jcGE6MlzDy7rS0rPPPsNhye2Ir5r30Or+zd3lrQFkW9IVGt1SGRzhZiucGdOBub+rLEI8NDvsfDlsc+3HalIYicEJ7ZUvsqxqqQWWjnwBZAn0k26SerGovsxqlkREQUJ2Z+AyjPV9paRBVXFkTZGWFdZsq+3WuF/SBIqN1t3xEAhnJAXbMi8XatjoN7tuCrr13QLpY8c/UmNqz5UKvuNcgAjYaUvtlS+3h1L1S7c28J1Zr+SoWqT2yUfrLunra6Q4hTyYiIKC5c8GaoPF9pW/Fvs3ICM89u8xwM4LdwiEjnlb3bMTFawie//n/gb+4Fz/abdoCwqRav2ceu13PF0a6sPF/RfiHgAjgiIgqCrc5iMHPyirJOs74sMXPySseJ3h1EMPANpliwPDOQWWFncH986LdDBcDOmlnAbGrYQ0O51nG7asTC17+wxTeQ9bryYcL+fVEFvpxKRkREcWLm18OB8kUcO3fdqI+vOyuma+FEZsY3rsaPrt2OtChrUDiznmGPq1UjVlt/YEA9CU7Xsuz1uWtt0+zGN67G0S992vj1/TLDXhMUv/mc+spK0NcgIqLsYKuzEA6UL+LVs9eMB1jYGTZ7oRFrFKM5c/UmPrbq4V5vRio4jyWTBWIqtxbrvu3IdC3L/uDNH3eMcT5z9SZe+PYPjV7bDqorKzW97t8VQP/7sixloGEZXq9BREQEsOxB69i564EfYwcTE6MlzxZOZOYn79/p9SakgrNrgnOBWBzHlzPo1AWg95bUK/fcAbHNnYFdvL+kDbzt96P7fTHpGAF49xpm9peIiJyY+dUIO7LYDiDCZugoWwSal/Z1VPWuE6OlQIu/ClYexYK6rZkzuDQNNL2oMrC6Ptd+Ge0gtb5Reg0TEVG2MPjV0AUkeSHwzvRubZ9WO4BwToQjUikVC3h573Z887ltyi9Kq0asjhHB5fkKxqdPYYNh6zP7OQ7u2eIbXOoC0CBUGVgdd0ZbNUHRNGsbpdcwERFlC4NfjeefXKu9vTxfwZ17Sx0/cwcTQTN0NLhWjVh4cce6toEVzk4M7sDvlb3bMf+1z3UEvs6sqomR4aFWJwa/4FJ3H930btXtpplWr4z229O7WyO8TUXNHBMRUXaw24MHZ7eHvBB4/sm1GHt8tXI07CPDeVj5HG7X6h0rzTe+9N3QZRQ0GEasHGr1ZWXQatrDNkynBwHg7endgR7jtmHqhHK7Vc+t28ZiwcIjDw0l2omB3R6IiMjm1e2BwW9AupO7e4hFwcrjmSdKOH35Bhe+ka9SseAbtOmCUC+rRiyMDEcLOnXHvCpo92qXxkCUiIi6ha3OYqS7rOsOSmr1Bl49e42BLxkxadGlq1+169PdVepWXuCDu0uR239N7toEK9/+7FZeKEsKotbuEhERJY2tzjTK8xUc/M6l1pQxe9IVW5hRGCNWDot1dcswN12Lrqc2r8HRs9c6rjDYwaX7sv+de0sdU/JCt/9yf7vzSEFHnfZGRESUJGZ+FcrzFUy+fqEtcLi1WMfkGxfw1OY1bGE2YASAgm5VV0yGh/IdmVkv7isM5fkK3jxfaYs5BYBnnngQaLoXjN3WjIcO2v5r5uQV1Jfbo117rDcREVG/YeZXQXWyB4B6Q+L05Rt45olSRwaO+pM9lhoA9s8uJPaZujOwfiSA7Ye+h3+w7aPaunEJ4PTlG9rnKI5Yyh67Qdp/lecr2isd/dBDl4vgiIjIjcGvgldZw3vVGk5fvuEZJOWFYHeHlFN1Kph79yZePXvN+DmsnMCHHh5CdfFBh499swva+wc9Lqq1uu/22AGoO8h7avMafHC3sx2frlZXxV68phOmh243g1H34jtnazkGwERE2cWyBwWviVuPrazK11k1YuGbz23rWCBE6aIK3MYeX90xCc3uz2sPK7GPjVKxgL2fWouRYfPvj0l8IXqsWFBOVTt69pry6sUjK31/bc6hGePTp9oWw3kNrAjTQ1e1nWEW4JnyGnlMRETZxeBXwStImdy1yTPjZV9mbigCD0oHVeBmB2bO8oSClccnPvphHDt3HZVqrdXr+Z3p3ZjctQlvnq90BHJeX3lWjahHDEdx594SDh2/1BHk6Y4+Zx3wk//k+9g3u6ANRr2ugITp4NDtYJQjj4mISIVlDwolTUeHYsFqnfC9Lm/PnLwCxr7pZHftcAduusDszNWbrX83pGyVIZy+fEN5fy+qMoSogtYS21/cPvutH+Cvf3m/4+fObhC6Mo28EKHKBrodjOo6s3DkMRFRtjHzq6AblXpwzxYAzXpBXXeAYsFiZinFdn/yo5g5eaXjMn+Q9nXHzl0P9RmryhCSpMpC37m3hPJ8BT95/472cfZ7010BCVu+oQs6owSjXmUbHHlMRFnh9beQOjH4VTBp1P/MEx/reJyVEzi4ZwszSylmDx5xXuZ/4ds/DPQcDSn74jN+Yce6jlKLaq3uuYgNeBCMljTvUXe7n7iDUb8aYg7cIKIs6PZ6ikHAsgcNr0b9ds9VJwFg76fWth7z1dcvsO63D7hLG0zdubcEKy9Qbzz4jN0jrpMQpGPE4YmtOH35Rke7M7/yDDsYndy1STmqOGywav9uxNXtwauG2Nn7mMEuEQ0yk7+F1I6Z3xBUB5qz5+rEaAkffojfKwZZtVYHZLOG2M4qvrBjnWeXj6j9PwSAbz63LdCQlaDlGR/58HDbtLhavdHqcLFqxMJDQznsn10IfVnNPYgjyh9mLmgjIuLfwjAY/IagO6Aq1VorKNBN16LBUV+WGBkeagVyY4+v1qZ+7eA4ynTAx4qFjkv5Jo/Rbc/Hf/2Rtts+/uuP4NwffrY54fCNC606aDvTfGuxjmqtnprLaknUEBMR9Rv+LQyO6UkfquEBOY9Lz5VqDZNvXOjONXDqOecXoUPHLykXtQmg7fJ+mOmAzuEUzkv566dOeD7Oq3RBl3U9dPxSWzmHTq8vq8VdlkFE1I/4tzA4Br8eVBOiTCaAmQQONBgeKxZwoHzRM6CVQCtI9JsOqKV50PjG1cqa5fGNqwGEq7NVjUTWCXNZLa4pb3HXEBMR9SP+LQxOyC6O4R0bG5Nzc3Nde72oxqdPBWqBRdkSJLlvj1PeMHUi9AWBUrGAM1M7O25/4ds/bAuAxzeuxtEvfTrkq/hnk022Scf9hRJoZijYhYGIiOIkhDgvpRxT/YyZXw8sFicvQYLY4aFmeb1u8IIJ3fEYJdBVKRYso+EZYS6rcVUyERH1Ghe8eWCxOMXl3tIyAH2v2xd3rGstYrO7K7h163g8uGcLrJz3crpVI1aobC1XJRMRUa8x8+tBVUROFNVDQ7nWMaUat6wrDejW4gV3/VhxxIKUwO1aPXItGUcOExFRrzH49aAKAu7WG6jVl3u8ZZS0JJp1qILau4pjKQ2LF5IaDsFVyURE1Gtc8GZIFbjQYCsVC3ivWvNsbWdqfONqvPOLmjLrGXTRWL+Lq9sDERGRDhe8xUC1UIcGlzMgVX3xsXICH3p4CNXF9lKA8nwFk68vwJnQtbsvbNB0UahUayjPV3wDQGfQ+GjBghDoeP1+wJHDRETUS8z8GorSoor6T7FgtdW4AuoyBHdAeuf+Uluf54KVxzNPNPv7enV58Gv35Xflge3CiIiIHvDK/DL4NcSev9ll5QVmvritI7A0LYUxrR/OC4FlKZWZXJPjb9WIhZHhIZYTEBFR5nkFv2x1ZkjVooqyod6QOHT8UsftpqUwpl8vG1JColkG8dJbF1Ger7R+ZtIK7NZiHZVqTfsc3VCer2B8+hQ2TJ3A+PSprr8+ERGRH9b8GrIzaIeOXwo0/pUGw63FOsanT7VlVZPsTese/BBmOEa3hkfYpR+Vaq0ty20H4ACYgSYiotRg5jcgVWsq6j+rRqzAj3FnVYsGz6EbFeE9QuLB69lZ1Dv3lmDlTR7VLunhEXbphx2Yu7PcdgBORESUFgx+A2DHh8ExMjxkFIDq1OoNSAnPUphVIxZe2LFOeZ+HrRxWjVi+22AH3NVaHZBoPaZYsFr/XSoWUCyoA/Gkh0eY/E7Y3SyyhiUgRETpxLKHADiCdXBUqjXkI/bvvV2r4+W92/GV1xawrHmawxNbMfb46o5ymeagFIGX927HvtkFo9erL0uMDA9h/muf6/iZavGdwIPssWrxW9B+u6o2bqayVv7g/jxYAkJElB4Mfj04g4PiiJXM2C/qGdPAV/exSwAHv3NJG/jawe7EaAkzJ6901IrbJQGlAPW87i9g7lZrD1s53Fqs+9beBg3OyvMV4yBdpVv1x2mhyohnbR8QEaUVyx40nLWMEs1Apotd4SglClZeW7oArJQjeLAve+uC2/eqtUCdRJxlDOX5CibfuNBWGvHBvSWsGrF8a2+9gjOVOOp2s3TlRPdes7QPiIjSisGvBut7KS8Ejjy9FYcntuLI01uRF8GrhO3AVOexYgEToyUceXorSsWCbw2wPXADaHYecQ7UAJpt2XTdSJyBV9DgzC9os7e9l/XHaaJ7r1naB0REacXgV4MZGmpI2bpEPTFawnLMqf+ClW8FsxOjJZyZ2om3p3cbPz5oyz1n4KULwnQdLPyCNnvbz0ztxME9Wzoy2c73mgWqbH7W9gERUVox+NVghqb/FKx4D2d3ptf0mCgZ3u+ZJ0rK+k+vDLNp+YFf4DW5a5OyddoHd5daXQmc3Qru3FvSvtb4xtVt/3ZnskvFQuZGL6dhH7DbBBGRGhe8KZTnK54ne0qnWsw9mO0Fcc4hDn4EmllQk3HEs//2OsYeX90RED3/5Fq8evaa8jHOKxLFgqWsOS4WLBzcs0XZycG5QE6lvixbAbZzQVy1VoeVE2gsSzj38vjG1Tj6pU93PM/EqDqwz5Je7gN2myAi0hOyi6u4xsbG5NzcXNdeLwxVyyjqb1ZOYGlZamtvC1ZOGzgXCxbu3F/qqK3VKRULODO10/g4yguBZSk7Wo39rX/8F8ptsp8fsFuPXUDd0W7CygnMPLtN27HBZJsE9BPlnK9P6aX78sXPj4iyQghxXko5pvoZyx5cuNBtsJSKBcw8u81z0dlDQ/pOC9Va3TjwBYCnNq8B8OCyt98kuYaUbVPj7EvTR57+pG/pwsRoCTPPbmu7tK4LfAHzY/uxYoHdCvocPz8iIj0Gvy48OQyWyV2bcPA7lzzv49euLIjTl2+0/ntitIT5r31O2/3AzdlqzKRmNOiQCpOyDTvAZreC/sbPj4hIjzW/LrrLvdSful3CovrydHDPFky+ccEog+x8vFfNaJiaTq+JdnapgzOAdu87divoH5O7NvHzIyLSYPDrojppUH8SQNc/R1VmzQ4m3SOOTR+vEmaCmNdEO3eLNfs5gmSWKT34+RER6TH4dXGeNJgB7m9xLeW0cgIfenjIqK+uLrNmZ3E3TJ3wWHjnn5nz6zyhK9spz1e0mV9da7YsdmwIWkqSZln8/IiITDD4VXhw2ffHsbfPou4oFiwIEXwQhFvJEQCZtC/zoyursafJeQUrJt0aVJlj+3GqwDdtl8K7FXyqXgcA24MREWUAF7wp2C2kwgS+eSHw4o51CWwVBXFvqRE58LW9PncNG1/6rlHg6zeEQjf565vP6bs0OJ/bK/DVBbK6x5kE3F7iHqJgB+n2SGh3B4y46F7n0PFL2lISIiIaHMz8Khw6fqmtd2oQzz+5Fm+e5ySlXosrY1+p1gJle/26hZjWYqoyk17PXfLIkuoe5xzfHFQSQxTC1DHH+Tq6LxbsAENENFgY/LqU5yuRMoZ/9qMKF8tlmMmCNb9aTF1gWRyxQh2bulILsfJaYQLLJALVbvWmDfp8bA9GRDRYGPy6RL3Eeec+A9+silo/67WYrVZv4KGhHApWXvnlyivzOrlrE/bPLnQstJNA6GA1iUBVF6THHXzqXqdYsHBvabln7cEOlC/i2LnraEiJvBB4/sm1ODyxtSuvnQWDtJiRiKJhza+L38l71YjFml5Silo/a9eh6tyu1VuDL1R09akToyVth4mwwWoSQxR09dBxB5+61zm4Z4vvYJGkHChfxKtnr7UWJTakxKtnr+FA+WLir50F3aonJ6L+wMyvi/YSsQBefm5760T46tlr3d40gveghl4qFQuRgiST0cOPrbyGV8s0XTBbijmrmsQQhW71pvV7nV5kA4+du669ndnf6LpVT05E/YHBr4vupB4kA2TlAHZIS8bVI5/Hb750AiHXIybCyovI2Um/DKw7sAxaImASrAa5LJxUoNqt3rRp64Gr+0KXxi96/ahb9eRE1B8Y/Lp4ndSdwYHXwAAOx0hGXgiU5yuxBr6xfFFZ2Z4oNYVeY7VVnRyCZl79gtUw3RvSFkD2M93fk7wQPdiawdOtenIi6g9CdjGzMDY2Jufm5rr2enEyGTAAAAUrx8EYCeqnLxdBrhiojq+ClcczT5Rw+vINbcAaV+ZVN8CjVCzgzNTOUM9J5uyaX7cXd6xj2UMMdL9f3arpJqLuE0Kcl1KOqX7mm/kVQvwLAP8AwPtSyr+9cttqALMA1gN4B8BzUspbcW1wGrgDi8X7S0YtzBj4JqtfAl+gWVN46Pglo5OrKjP71OY1ePN8RZuNjTPzysvCvWUHuOz2kIxu1ZMTUX/wzfwKIf5TAB8A+JeO4Pd/BHBTSjkthJgCsEpK+Qd+L9YvmV/TLC+RiVf2bg91kg2ajY2SCWbml4iIBkmkzK+U8l8LIda7bv4dAJ9Z+e8/BvADAL7Bb5o5A4dcSjsKUHfkhcCylNr2YEHpVpT7BatBsrFRJ66F6d5gEmyztyoREaVN2D6/H5FS/nzlv/8DgI/EtD094e4BycA3u6y8wDef24aX926PbbFRpVrD+PSptp6iJn1Hg/TS9WrlZGJitBSox63J9rO3KhERpVHkbg9SSimE0EaLQogvA/gyAKxbl87hECY9VikbHhlu/kq89NbFWL8EuTOxumB13+wCZk5eweSuTYGysXHU7AapITbpm8reqkRElEZhM79/LYT4KACs/P/3dXeUUv6RlHJMSjm2Zs2akC+XLC7qIdvtWj2xL0POTKzXMecMlE2zsUlMXPNiEmzr7tNPixaJiGjwhA1+vwPg91f++/cB/Kt4Nqc32OsxO4Z8KhmKI1aiX4bs5/Y75pwZ0jNTO/H29G6cmdqpzZh2azSwzSTY9nqPL3z7h7FvExERkQnf4FcIcQzADwFsEkL8TAjxDwFMA/isEOInAP7eyr/7lipwoMG05FPJIKU+aIujArg4YrU6K/g9X6Va86yPLc9XMD59ChumTmDm5BU880TJuGY3KpNg2yvwPnP1ZiLbRURE5Mek28Pzmh/93Zi3pWfcPSC53C27qrU6Du7ZYjxwYubkFePL+FZe4IO7S7i1WAfQHAwnAM/jTdexQdXd4c3zla417TfpmzoxWsK+2YXEt4WIiCgIjjdW0I0apcFnZ2OPPL3VuEWXSYBXKhZw594SqrV62+0SQLFg4d7SsrLOWLdALKnFZEFak3G8sT+2eiPqD/xdzZawNb8Dha3OyCYBHDp+yfj+E6MlFAuW9ucFK49X9m7HmamduO0KfG23a3UceVo/yUtVg5zERLYkWpONb1wd6PZBwlZvRP2Bv6vZw+AXbHU2yPJCQKz8f1O3FuvYN7vQ9odw8o0L2j+EB/dsUdaMrxqx2soQvBaJTYw263V1P3crjqgDbt3tJryyyc76YnfPYi9Hv/TpjkB3fONqHP3Sp0NvZ7+I2nuZiLqDv6vZw7IHsNXZIHv+ybU4PLEV5fkK9s8uhK7nrjck9r+2gP2zC61LYsCDmtdHCxYetnKoLta1l8z8+vYG6euruzgR5aKFV2uyyTcuoN6QrX8HmR6XhUBXJYnsPBHFj7+r2cPgF83MGnuPDqaj565h7PFm5jFqMYsdWNrBICRQX27eWK3VUbDyeHnvds8aWUC/SMz98+KIBSmB/Y7BF/Z9vEoowvL6PbADXxuHVfjT7U+2ViRKF/6uZg/LHgA8tTmdwzeok5UXGLHMD1spga/MLjSD1RjVG7IV+NpMLpP59e21f/7y3u24W19GtVZX1qAlMdQiaMu/QcuKhC3t0Ol272UiCoe/q9mT+eC3PF/Bm+dZ1N4vBDqzkH6WQzwmLF1AGDSw8qtBS+KP9cRoyXPhndsgZUWSWPBi789u9V4monD4u5o9mS974GK3/nK/S0FsWDkhUJ6vtP3RVPXk9auZ9atBM+mzG8bEaMmod7Eq0O7nVkFJtY5jOzii/sDf1WzJfPA7aJduKZyClUOtvhz5eRpSdgS2YQIrkxq0sH+s/YLUyV2b2ha4AUBOAI8WLO2CvjABfppwwQsRUXZkPvjlYjcCgLv1ZRQLVscQijDcga1XF4X1Uyda//74rz+C73/lMwCCdX4IwjhIdSXY80Lg61/Yog1kk8qcdgsXvBARZUfma35Z0E5AM8jR9esNwxnwmgZQP3n/Dj77rR8ASK4GzaSf5czJKx2L+erL0nMxX79nTrnghYgoOzKf+Z0YLeHgdy7FkvGjdBnOC+Ma4ff/phmkPfNECcfOXY885c8Z8KqyuDo/ef9O67+TqEEzCVLDBLL9njlNqoaaiIjSJ/PBL9Cc0PXV1y+gsZzuxVQUTJDFcfVlYPL1C4CIZ7z15K5NbbW17iEYUUttwi4uMwlSwwSySZVpdBMXvBARZUPmyx4AYO7dmwx8CfVlGUtLtMJKH2Jn66xqrY7qYrNn7517S5GeX9WWa9/sAtZPncDGl76LA+WL2sfqelo7bw9TAsBWQURE1C8yn/ktz1dw9Oy1Xm8GDQgrJ3Dk6U8qa2vtsNqrxCYHKFulObO8i/eXtCUUDSnx6srxfHiis2fv6cs3lI9z3h62BICZUyIi6geZD35nTl6JPPaW0ksg+lhjnVKxgKc2r8Hpyzc6gsT9swuhnnMZaOu+oOrOYOLYuevK4Ne0nrefAtl+7i9MRETdl/ngt19Wo1M4//HG1fg3f3UTMZTxtlh5gZkvbvMMsKLU9TpbhIUdwqKrW+73hWlu/d5fmIiIui/zNb/9etInM//+57+MNfAFgEeGh1pZWd3IYlXdbBD2l7KwX85yAsptG7SWXiat24iIiJwyH/xGDVIo3W4txt/C7natrlx09tJbF1tBpnMBGNAsv3CycgKrRizta9hfynRfzooFq/XcShLKbRu0hWn93l+YiIi6L/NlD87FPZz0RiYeKxaMJpo562btutRKtYa8EKgvS4wMD+ETH/0wzly92fEadvcFXQuxg3seTFs7UL7Y6k2cFwLDQ6JjVLNz2/qpntfPoJVxEBFR8jKf+QWaQcqZqZ3emTQiPCgRCJpxnBgtta4y2PW4lWpNGfgCD7ovmGRqD09sxdUjn8c707tx9cjncdcV+PptWz8btDIOIiJKXuYzv06DGBxQfASaE+DshWhBM45BFq85j8WgmdosZUM5mY2IiIJi8LuiPF9BTohYpnvRYJJ4kJENM9EsyJerKIHqIExbC2KQyjiIiCh5DH4BvPDtH2ovPxM52QFsmIyjafuzqIEqs6FERER6mQ9+D5QvMvAlY86MbNCM4+SuTZh844LnCOW8ELF0X2A2lIiISC3zC96Onbve602gGBULFl7Zuz2RxYtRMrJ2twevwLdg5fHN57yHZxAREVE0mc/8ssZ3sFRrdcycvILJXZvw1dcuxPb5RsnIuqeQ6djPf6B8EX9y7hqWVzbdHtFcYvkCERFRZJkPfvNc5DZwKtUaJt+IL/AFECkja9Llwc5U/61//BcdPXrtd5Hm0b12Zps1xkRElHaZL3t4/sm1vd4ESoBXeUG3+XV5KFh5rP/VAvbPLnQEvm61egOHjl+Kc/Mi85t2F8fz68ZIExERBZX54PfwxFa8uGNdrzeDUm7m5JXQj/VqW1YqFvDMEyX8m6s3YRqu31qspyoA9Jp2F1XSgTUREWVP5oNfoBkAFwtWrzeDUkyXvS3PVzD6je9h/dQJrJ86ge2HvtcRmOmmkL2ydzvOTO3E6cs3jANfWxyBZVyCTrsLIsnAmoiIsinzNb+2aq3e602gFFNlb8vzlY7WZdVaHZOvXwDwoC7Xr+9umCAxTdMIk5wol2RgTURE2cTgdwUXvvW/kuEQiTDu3FtCeb7StohL17qsviwxc/JK2329+u6aDr9wKo6k50pFkhPlujGqmYv1iIiyhWUPKxj4kpdqrd5Ra+qVfQySmVSVRQgAL+5Ypy3HSdPhOjFawpGnt6JULECg+SUkjkEdgL5kJK5RzawpJiLKHmZ+V6wasXBrkaUP/SyprK/NrjW1gzqvjG2QzKRXWcTRs9eUj7kdsUzHK9sZJhOa1ES5pEc1e9UUM/tLRDSYGPyuSFMmjaKxcoBPx7DQKtUaxqdPNUcVa8YVWzkRODOpCx6TuOzvHrrh7B8MQPuzXgWDSY5qZk0xEVH2sOwBzWCAC94GR30ZeGXv9sSe3xkQznxxG1Y56m+LBQszz8Y3ojiJy/66bOeh45fw1dcuZKq7gu5LRJw1xURElC6Zz/yW5yut1fk0OF566yJyAq0RwXGzA8IzUztjCXR1pQZJXPbXZTW9yn4GNROa5GI9IiJKp8wHvzMnr6CeVIREPVOrN2Dlkgt+gWYGeMPUiVABqTPYfbRg4c79pVb5hLvUIO7L/mG6SwxqJjTpmmIiIkqfzAe/g5rRouTqfp3sDgGTb7T39rWpMrpAe12tquQmyUVXqmynl0HPhCZZU0xEROmT+eC3yC4PFIN6Q+LQ8UttQZRuYdnDVs4o8Ezqi5kq23nn3pIyCM8LEVvbMiIiojTIfPB7zzD7RekigMAjgZ2PHcqJ2Mtd3F+idAvLTDOuSZYauLOd7kAdaGZ8GfgSEdGgyXS3h/J8BYvduDZOsYsStkoA639tpK1Lg5dfeSjfNsDBVJTMbbdLDZIcVEFERJQmmc78Dmr7JvL3k/fv4MUd6zD2+GrMnLyiXQAmAPz40G+33bb90PeUJQLuaWy6hWXFgoV7S8ttWVYrJ/Chh4dQXaz3bNFVFmpfOcqYsuhA+SKOnbuOhpTIC4Hnn1yLwxNbe71ZRD2T6eCXi92y7di56zg80cxujk+fMh4mcXDPFky+fqGtbMLKCRzcs6Xtfro2Wvb9ehGEZTn48xrukZV9QNlzoHwRrzomRTakbP2bATBlVaaD3zAtn2hwNBxj/YL0ezVtj+V3v24HXFkP/jjKmLLo2Lnr2tsZ/FJWZTr4DdryiQZLTjz4b2egan8hqtUb2De7gH96+if4/lc+0/ZY0xKBNJUSZD344yhjyiLnl3yT24myINML3uxFPo8M5/3vTAPnoaH2w39itISR4c5fiZ+8fwef/dYPurRVycl68MdRxpRFeSEC3U6UBZkOfm0c8JZNdxWdPn7y/h3lfXW395OsB3+TuzahYLV/0R30AR5Ezz+5NtDtRFmQ6bIHQH0pmLLhsWKhYwFYUP20gCxIXfMg4ihjyiK7rpfdHogeELKLdT9jY2Nybm6ua6/n54Vv/xBnrt7s9WZQDxSsPJ55ooQ3z1eMv/zkhUBDSpQ0Y4rt501zf9x+CtaJiIjCEkKcl1KOKX+W1eCXgW922cGrV39fPwUrj5wA7tzvDJxLxQLOTO2MupnUQ1n+kpDl905Eg8Mr+M1s2QMD32xaNWK1AtP9swuhn8crW5yVBWSDKsst4bL83okoO7jgjTLl1mIdB8rNk3lSC70kgPHpUyjPVxJ5fkqWV0u4QZfl905E2cHglzLn6NlrKM9XlKv/41Kp1jD5xgUGwH0oyy3hsvzeiSg7Mhn8MiDJNgm0Bjs880QJ7m6XJt0vTe5Tb0gcOn4p+AZST2W5JVyW3zsRZUcmg19ewiM7k3X68g24l3xKAMWCpc0Kl4oFvLBjnVHW+NZiPeKWUrdluR9wlt87EWVHJhe88RLeYBIr/8ekgYmdydIdC7drdby8d7vnqvexx1e3fs45KYMjy/2As/zeiSg7Mhn8PlYshG5xRen18t7t+Edv/RiLisltTs5Mlu5YeKxYwMRoyfOk7/z59kPfQ7XWmeUtFiyjbWd7qXTx++wHWZbfOxFlQybLHpJc6ES9IQSwb3bBN/AtFQttQyjiusx7cM8WWLn2SmArJ3Bwzxbfx9rtpSorGWS7vRRr04mIiOKXycyvHfjsi9DnldLFdFbLU5vXtGW14rrMG+V5vNpLmW4HM8dERERmMjvhDQDWT53o9SZQl+WFwNUjn+/1ZrTZMHVCWTMsALw9vdv38e7BBED6xywTERElyWvCWybLHii7GiG+7JXnKxifPoUNUycSGV4Rtb0UBxMQERGZY/BLmZIXJh16H+hGPW7UumMOJiAiIjKXyZpf6m8CCN1a7Pkn1wa6fxz1uH6i1h17dazoNtYeDy5+tkQ0KBj8Ul/JCeD3nlyH05dvtE7CJm3r7Mcdntga6PWCZFWjBAdR2ktN7tqkrPnt9mACd+2xnSUHwCCpz/GzpSj4xYnSJrPB74HyxV5vAoWwLIE3z/8MR57+ZOuP5/j0KW0ALAC8sONB0Bv0j7BpVtUrOACSHRqQlsEE3ciSU2/ws6Ww+MWJ0iiTwW95voKjZ6/1ejMopFp9ue2PpyrzaZMATvz45zh9+QYq1VpbyYTJH+GnNq/Bq4pj5anNa9r+rQsODh2/hLv15cT/8KdhMAFrjwcXP1sKi1+cKI0yueBt5uQVjqPtc7V6A1997QLK8xVMjJZw5Gl9OcOtxXore+v+3P26Ipy+fMPodl0QcGuxnplODFG7VlB68bOlsPjFidIok5lf/tINhoaUbaUFeSFCtTLzOh68/nA7SyhyAV+7Uq1hfPpUT8oUkqq/S0vtMcWPny2FlaYFuUS2TAa/poukKP2cpQVhAl/A+4+w7lgpjlhtwYDqtQtWHg8N5VCt1Tt+JoDW83azBi7J+ru01B5T/PjZUlj84kRplMkJb+X5CkcbU8sre7drT+K66Wm6oDYvBJalbAUHADoer2vVVioWcGZqZ6T34ke3OLAbr01E2cRuD9QLXhPeMpn5nRgtMfilFq8/wrqM137N8bMspXIksfPxuqsO3SjHYf0dEXVbGhbkEjllMvgFog1KoPTwysKaKBYs3/uo/nDPnLxiXMfmfrwu+9qNGjjW3xERUdZlsttDeb7CwHcA5IXAkae34uCeLR3jgU1YOYGDe7YEflx5voI795Y6bjetY4s6zjiKXr42ERFRGmQy8zuIbaayaFnKVkZ17t2bOHbuOhpSQgAYGc7jzv1GR4bf/ncpZN2ZqgYYAFaNWPj6F7YYPV8vFw9x4RIREWVdJoNf1jcOBvtSfXm+gjfPV1odFySak+BWjVi4tdheDiHRHHUcNuBTNWwHgJHhoUDP18saONbfERFRlmUy+C0qgiLqP/aUNd0EIVWQCjQD48k3LrT+HSQLGnbBGFc7ExERpUMmg19VvSb1H3vKWphMfr0hQ40eDrNgjLPtiYiI0iNzC94OlC/ifoPL3QaBHfSG7VQQZvSwbsHYU5vXYHz6FDZMncD49CmU5yutn3vNticiIqLuylzw+yfnrvV6EygmxZFmmzJVQBqFVyZ5YrSEI09vRalYgEBz4dwzT5Tw5vkKKtUaJJqZ3f2zCzhQvuj5fKw9JyIi6r7MlT0sM+k7MG7X6ijPV1qlA1997UKgEccjVg6L9eWO2/0yyaq+ve7MrgRw9Ow1jD2+mr11iYiIUiRzmV8aHMsS2De7gPHpUwCAbz63DfmcMH58vSFh5dvvH6bnrS6DK9EseWBvXSIiovTIXPBbsDL3lgeecwHZhx8yv5hRX5Z4ZHiorYThyNNbAy9C88rgvletKUslwrwOERERRZe5sodnnvgYXj3Lut9BYy8gux1wzPHtWh0LX/+c8f1VLcsmd23C/tkF5dRAOzBmb10iIqJ0yFQatDxfYeA7wOyANIgg97dbljkXttkZ5xd2rIO74IKlDUREROmTqeCXraX6jx1QFgsWRnxKVoojVqDOD0GDU6+WZYcntuLlvdtZ2kBERJRymSp7YGup/lLSTELbfuh7qCrKG6R8MDRi3+yC9nkF0CpZeH3uWtt9xzeuxtEvfVr5OL+WZSxtICIiSr9MZX7ZWqq/LN5XT+LT1fXat/sFoG9P78aZqZ14fe4azly92fazM1dv4oVv/1D5ON3xw+OKiIiof2Qq+GX9ZX+5tVjH5BsX2qalAcDIsLqswR56ATTLJJT3cdzuDnz9bk9Dy7LyfEU7SY6IiIj8ZarsYWK05Hk5nNKn3pD46msXsH92AY8VC3hq8xrcud9Q3lfKB90YVGURVk7g4J4tobfFzii7uz10q9TBXnBn1x07F9yx3IKIiMhMpoJf6k/21LZKtYajHt06qrV6W3AINOt7JfT1w0H1sq7Xa8Edg18iIiIzmQp+eYm4/3kNL84LoRwzXCoWcGZqZ6tk4L1qDcURCzkAncONm4ve0shvwR0RERH5y1TNL1udDS6BBxlit/eqtY4evbcW69rAV9ftode44I6IiCi6TAW/zJANBvcwCYHmkImSR3CoKhlwKxULqQ18AeCpzWsC3U5ERESdMhX8MkPW/wpWvhXo2sMkXt67HYcntnp2YzD54pP2L0enL98IdDsRERF1ylTN71Ob13C8cR/LC+E5Nc2rG8PMySuo+AS3af9yxJpfIiKi6DIV/DJD1r8KVh7PPNEMYu22Z6ruDbpuDJO7NnV0gnA/f9r7QD9WLCgD+LQH7URERGmSqbIHZsjSoViwtPW5TnkhWqUNzzxRwpvnK60Fa3aPW9MOHhOjJRx5emurXGLViIViwWo9v1dGOS3SMGSDiIio32Uq81scsXBrUT0al7rDyjcHTUyMlrDxpe9qOzQUrHxbQDo+fSpyj9te9uiNQ6+HbBAREQ2CTAW/mjiLumjmi9tawZou8AXQkYllvWtTvwfwREREvZapsgfVyFvqnrwQ2D+7gPHpUyjPV5AX7qZlD+7nDvCKI5byvqx3JSIioiAiBb9CiN8WQlwRQvxUCDEV10Yl4YVv/7DXm5AZBSvXUZsKNDO9dr3u5BsXkBPqzO/zT65t+3d5voIP7i513M/KC9a7EhERUSChg18hRB7APwXw9wF8AsDzQohPxLVhcTtz9WavNyEz7taX2xaXqTK89YZE3TViLSeAF3esw+GJrW23z5y8gvpyZ6D8yPAQSwCIiIgokCiZ308B+KmU8q+klPcB/CmA34lns6ifPVYsYGK0hMldm/BYseBZ2+v00UcLHYEvoK/rvc0yFiIiIgooSvBbAnDd8e+frdxGA0xdpfuAXYpQnq/gpbcu+g6WcNIFubq6Xtb7EhERUVCJL3gTQnxZCDEnhJi7caN3QyasTC3tS87Le7drF6oBD0oRZk5e0Q6U0NEFs+xvS0RERHGJEhJWADhXJn1s5bY2Uso/klKOSSnH1qxZE+Hlolla9r8P+ds/u+BZxmCXIni1IFs1YsHKtQfQXsGse0BFvwylICIiovSJ0uf33wH4uBBiA5pB7+8C+L1YtioButGwFIxf9a7dkky3v0vFAs5M7UR5vhJoWAP72xIREVEcQge/UsolIcR/A+AkgDyAfyGlvBTblsXsF7+82+tNyIQP7i6hPF/B5K5NeOmti22lD87sLoNZIiIi6oVIE96klN8F8N2YtiUx5fkK7jY43i2oUohseX1ZYubkFZyZ2gmAo3iJiIgoXTIx3njm5JVeb0JfsUsTAGB8+lTgANiu92V2l4iIiNImEz0QWOsbjHOxmqrTgr1UTdf1gS3IiIiIKK0yEfx6teaiTs7gVdVp4eW92/HO9G5887ltbEFGREREfSUTZQ+mE8aoyR286soX7NtY10tERET9IhPBb5iFW1lVLFiBglfW9RIREVE/yUTZQ9YuwwsAr+zdjlf2bm8rVxjfuNpzPHHByuPgni1d2koiIiKi7stE5nditIR9swu93oyueWHHulY21p2VdQ6XeLRgQQigulhnyQIRERFlwkAHv+X5Cv7wzy7izv2G/5370IiVgwRQqzdnN68asfD1L2zhpDQiIiIijYENfsvzlb7P9o5vXI1L7/0S1Vq9dZtJgEtEREREagMb/P7Bmz/u9SaEVrByOPL0JxngEhEREcVsYIPfe0vLvd4ET8zgEhEREXXfwAa/afHijnU4PLG115tBRERERGDwG7t3pnf3ehOIiIiISIPBb0QMdomIiIj6B4PfgBjsEhEREfUvBr8GGPASERERDQYGvx4Y9BIRERENFga/Cgx6iYiIiAYTg18HBr1EREREgy3X6w1ICwa+RERERIOPwS8Y+BIRERFlReaDXwa+RERERNmR+eCXiIiIiLIj08Evs75ERERE2ZLp4JeIiIiIsoXBLxERERFlRmaDX5Y8EBEREWVPZoNfIiIiIsqeTAa/H/nwcK83gYiIiIh6YGCD31f2blfe/isP5XHuDz/b3Y0hIiIiolQY6vUGJGVitAQAmDl5Be9Va3isWMDkrk2t24mIiIgoewY2+AWaATCDXSIiIiKyDWzZAxERERGRG4NfIiIiIsoMBr9ERERElBkMfomIiIgoMxj8EhEREVFmMPglIiIiosxg8EtEREREmcHgl4iIiIgyg8EvEREREWUGg18iIiIiygwGv0RERESUGQx+iYiIiCgzGPwSERERUWYw+CUiIiKizGDwS0RERESZweCXiIiIiDKDwS8RERERZQaDXyIiIiLKDAa/RERERJQZDH6JiIiIKDOElLJ7LybEDQDvdu0FH/g1AP9PD16XuO97hfu9N7jfe4P7vXe473uD+93f41LKNaofdDX47RUhxJyUcqzX25FF3Pe9wf3eG9zvvcH93jvc973B/R4Nyx6IiIiIKDMY/BIRERFRZmQl+P2jXm9AhnHf9wb3e29wv/cG93vvcN/3Bvd7BJmo+SUiIiIiArKT+SUiIiIiGqzgVwjx20KIK0KInwohphQ/f0gIMbvy83NCiPU92MyBY7Df/wshxA0hxMLK//7LXmznoBFC/AshxPtCiP+/5udCCPE/r3wuPxZC/J1ub+MgMtjvnxFC3HYc71/r9jYOIiHEWiHEaSHEvxdCXBJC/HeK+/CYj5nhfucxnwAhxMNCiH8rhLiwsu8PKe7DuCaEgQl+hRB5AP8UwN8H8AkAzwshPuG62z8EcEtK+VsAXgbwP3R3KweP4X4HgFkp5faV//2zrm7k4PrfAfy2x8//PoCPr/zvywD+1y5sUxb87/De7wDwfzmO9290YZuyYAnAV6WUnwCwA8B/rfhbw2M+fib7HeAxn4R7AHZKKbcB2A7gt4UQO1z3YVwTwsAEvwA+BeCnUsq/klLeB/CnAH7HdZ/fAfDHK//9BoC/K4QQXdzGQWSy3ykBUsp/DeCmx11+B8C/lE1nARSFEB/tztYNLoP9TgmQUv5cSvmjlf/+JYC/BFBy3Y3HfMwM9zslYOU4/mDln9bK/9wLtRjXhDBIwW8JwHXHv3+Gzl/Q1n2klEsAbgP41a5s3eAy2e8A8MzKZcg3hBBru7NpmWf62VD8Pr1yqfIvhBBber0xg2bl0u4ogHOuH/GYT5DHfgd4zCdCCJEXQiwAeB/A96WU2mOecY25QQp+Kb2OA1gvpfwkgO/jwbdUokH0IzTHam4D8L8AKPd2cwaLEOJDAN4EsE9K+Te93p6s8NnvPOYTIqVsSCm3A/gYgE8JIf52jzdpIAxS8FsB4MwofmzlNuV9hBBDAB4F8IuubN3g8t3vUspfSCnvrfzznwF4okvblnUmvxMUMynl39iXKqWU3wVgCSF+rcebNRCEEBaaAdhRKeVbirvwmE+A337nMZ88KWUVwGl0rjdgXBPCIAW//w7Ax4UQG4QQwwB+F8B3XPf5DoDfX/nvLwI4JdnoOCrf/e6quduDZs0YJe87AP4/KyvgdwC4LaX8ea83atAJIX7DrrkTQnwKzb+zPBlFtLJP/zmAv5RSfktzNx7zMTPZ7zzmkyGEWCOEKK78dwHAZwFcdt2NcU0IQ73egLhIKZeEEP8NgJMA8gD+hZTykhDiGwDmpJTfQfMX+P8nhPgpmgtWfrd3WzwYDPf7fyuE2IPmquGbAP6Lnm3wABFCHAPwGQC/JoT4GYCvo7kgAlLK/w3AdwF8HsBPASwC+P/2ZksHi8F+/yKA/0oIsQSgBuB3eTKKxTiA/xzAxZUaSAD4RwDWATzmE2Sy33nMJ+OjAP54patSDsBrUso/Z1wTHSe8EREREVFmDFLZAxERERGRJwa/RERERJQZDH6JiIiIKDMY/BIRERFRZjD4JSIiIqLMYPBLRERERJnB4JeIiIiIMoPBLxERERFlxv8L7IU9rsRp8agAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 864x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#plt.scatter(np.abs(qvals[\"Beta\"]), qvals[\"Log10_BF\"])"
    ]
@@ -386,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -405,7 +136,7 @@
        "(177813, 39)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -416,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +156,7 @@
        "(226346, 27)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -436,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -809,7 +540,7 @@
        "[177813 rows x 39 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/src/evaluation/figure_notebooks/figure_6/caqtls_lcl_gaffney-Copy1.ipynb b/src/evaluation/figure_notebooks/figure_6/caqtls_lcl_gaffney-Copy1.ipynb
new file mode 100644
index 00000000..32642219
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/caqtls_lcl_gaffney-Copy1.ipynb
@@ -0,0 +1,1168 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gt = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcls_2016/RASQUAL/rasqual_atac_1M.gz\", sep='\\t', header=None)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>15</th>\n",
+       "      <th>16</th>\n",
+       "      <th>17</th>\n",
+       "      <th>18</th>\n",
+       "      <th>19</th>\n",
+       "      <th>20</th>\n",
+       "      <th>21</th>\n",
+       "      <th>22</th>\n",
+       "      <th>23</th>\n",
+       "      <th>24</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Peak1</td>\n",
+       "      <td>rs10454458</td>\n",
+       "      <td>1</td>\n",
+       "      <td>737085</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.198347</td>\n",
+       "      <td>0.476176</td>\n",
+       "      <td>0.8868</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.606369</td>\n",
+       "      <td>0</td>\n",
+       "      <td>624</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11</td>\n",
+       "      <td>737085</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.669344</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0           1   2       3  4  5         6         7         8       9   \\\n",
+       "0  Peak1  rs10454458   1  737085  C  T  0.083333  0.198347  0.476176  0.8868   \n",
+       "\n",
+       "   ...        15  16   17  18  19      20   21  22  23        24  \n",
+       "0  ...  3.606369   0  624   7  11  737085  0.0   0 NaN  0.669344  \n",
+       "\n",
+       "[1 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/Q.val.txt\", sep=\"\\t\", header=None, names=[\"peakid\", \"qval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_peaks = open(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/pid.fdr10.txt\").readlines()\n",
+    "sig_peaks_ids = [line.strip() for line in sig_peaks]\n",
+    "sig_peaks_ids = qvals[qvals[\"qval\"]<0.1][\"peakid\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_atac = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/ATAC/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac[['Chrx', 'Posx', 'REF', 'ALT', 'Peak_id', 'obs_scores']] = data_atac['rsid'].str.split(\"_\", expand=True)\n",
+    "data_atac[\"obs_scores\"] = data_atac[\"obs_scores\"].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dnase = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/DNASE/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "data_dnase[['Chrx', 'Posx', 'REF', 'ALT', 'Peak_id', 'obs_scores']] = data_dnase['rsid'].str.split(\"_\", expand=True)\n",
+    "data_dnase[\"obs_scores\"] = data_dnase[\"obs_scores\"].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_data = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcls_2016/RASQUAL/peaks.bed.gz\", sep='\\t', header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9913</td>\n",
+       "      <td>10650</td>\n",
+       "      <td>Peak1</td>\n",
+       "      <td>9913,</td>\n",
+       "      <td>10650,</td>\n",
+       "      <td>738</td>\n",
+       "      <td>49.864499</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0     1      2      3      4       5    6          7\n",
+       "0  1  9913  10650  Peak1  9913,  10650,  738  49.864499"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks_data.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_atac=data_atac.merge(peaks_data, right_on=[3], left_on=[\"Peak_id\"], how=\"left\" )\n",
+    "data_dnase=data_dnase.merge(peaks_data, right_on=[3], left_on=[\"Peak_id\"], how=\"left\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "117\n",
+      "41\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dnase[\"center\"] = (data_dnase[1]+data_dnase[6])\n",
+    "data_atac[\"center\"] = (data_dnase[1]+data_dnase[6])\n",
+    "data_dnase[\"dist\"] = abs(data_dnase[\"pos\"]-data_dnase[\"center\"])\n",
+    "data_atac[\"dist\"] = abs(data_atac[\"pos\"]-data_atac[\"center\"])\n",
+    "print(sum(data_dnase[\"dist\"]<100))\n",
+    "print(sum(data_dnase[data_dnase[\"dist\"]<100][\"Peak_id\"].isin(sig_peaks_ids)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_atac=data_atac.merge(qvals, right_on=[\"peakid\"], left_on=[\"Peak_id\"], how=\"left\" )\n",
+    "data_dnase=data_dnase.merge(qvals, right_on=[\"peakid\"], left_on=[\"Peak_id\"], how=\"left\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "data_dnase1 = data_dnase[data_dnase[\"dist\"]<100].reset_index()\n",
+    "data_atac1 = data_atac[data_atac[\"dist\"]<100].reset_index()\n",
+    "# data_dnase1[\"log10qval\"] = np.log10(data_dnase1[\"qval\"])*-1\n",
+    "# data_atac1[\"log10qval\"] = np.log10(data_atac1[\"qval\"])*-1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "data_dnase2 = data_dnase[(data_dnase[\"pos\"] >= data_dnase[1]) & (data_dnase[\"pos\"] < data_dnase[2]) ]\n",
+    "data_atac2 = data_atac[(data_atac[\"pos\"] >= data_atac[1]) & (data_atac[\"pos\"] < data_atac[2]) ]\n",
+    "# data_dnase2[\"log10qval\"] = np.log10(data_dnase2[\"qval\"])*-1\n",
+    "# data_atac2[\"log10qval\"] = np.log10(data_atac2[\"qval\"])*-1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1934, 38)\n",
+      "841\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data_dnase2.shape)\n",
+    "print(sum(data_dnase2[\"Peak_id\"].isin(sig_peaks_ids)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_atac1[data_atac1[\"qval\"]==0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1934, 38)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dnase2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  # Remove the CWD from sys.path while we load stuff.\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  if sys.path[0] == '':\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "146\n",
+      "1125\n",
+      "0.13\n",
+      "321\n",
+      "1300\n",
+      "0.247\n",
+      "377\n",
+      "1356\n",
+      "0.278\n",
+      "479\n",
+      "1458\n",
+      "0.329\n",
+      "508\n",
+      "1487\n",
+      "0.342\n",
+      "549\n",
+      "1528\n",
+      "0.359\n",
+      "581\n",
+      "1560\n",
+      "0.372\n",
+      "612\n",
+      "1591\n",
+      "0.385\n",
+      "637\n",
+      "1616\n",
+      "0.394\n",
+      "667\n",
+      "1646\n",
+      "0.405\n",
+      "681\n",
+      "1660\n",
+      "0.41\n",
+      "701\n",
+      "1680\n",
+      "0.417\n",
+      "726\n",
+      "1705\n",
+      "0.426\n",
+      "750\n",
+      "1729\n",
+      "0.434\n",
+      "762\n",
+      "1741\n",
+      "0.438\n",
+      "769\n",
+      "1748\n",
+      "0.44\n",
+      "786\n",
+      "1765\n",
+      "0.445\n",
+      "796\n",
+      "1775\n",
+      "0.448\n",
+      "812\n",
+      "1791\n",
+      "0.453\n",
+      "829\n",
+      "1808\n",
+      "0.459\n",
+      "841\n",
+      "1820\n",
+      "0.462\n",
+      "146\n",
+      "1125\n",
+      "0.13\n",
+      "321\n",
+      "1300\n",
+      "0.247\n",
+      "377\n",
+      "1356\n",
+      "0.278\n",
+      "479\n",
+      "1458\n",
+      "0.329\n",
+      "508\n",
+      "1487\n",
+      "0.342\n",
+      "549\n",
+      "1528\n",
+      "0.359\n",
+      "581\n",
+      "1560\n",
+      "0.372\n",
+      "612\n",
+      "1591\n",
+      "0.385\n",
+      "637\n",
+      "1616\n",
+      "0.394\n",
+      "667\n",
+      "1646\n",
+      "0.405\n",
+      "681\n",
+      "1660\n",
+      "0.41\n",
+      "701\n",
+      "1680\n",
+      "0.417\n",
+      "726\n",
+      "1705\n",
+      "0.426\n",
+      "750\n",
+      "1729\n",
+      "0.434\n",
+      "762\n",
+      "1741\n",
+      "0.438\n",
+      "769\n",
+      "1748\n",
+      "0.44\n",
+      "786\n",
+      "1765\n",
+      "0.445\n",
+      "796\n",
+      "1775\n",
+      "0.448\n",
+      "812\n",
+      "1791\n",
+      "0.453\n",
+      "829\n",
+      "1808\n",
+      "0.459\n",
+      "841\n",
+      "1820\n",
+      "0.462\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "from scipy.stats import pearsonr\n",
+    "chrombpnet = {}\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "random_bc = []\n",
+    "\n",
+    "pearsonr_atac = []\n",
+    "pearsonr_dnase = []\n",
+    "for qth in np.arange(0,0.1+0.005,0.005):\n",
+    "    #print(qth)\n",
+    "    data1 = data_dnase2[data_dnase2[\"qval\"]<=qth]\n",
+    "    data1[\"label\"]=1\n",
+    "    \n",
+    "pearsonr(data1[\"logfc.mean\"], data1)\n",
+    "    data2 = data_dnase2[data_dnase2[\"qval\"]>0.15]\n",
+    "    data2[\"label\"]=0\n",
+    "    \n",
+    "    dataf = pd.concat([data1, data2])\n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(sigs)\n",
+    "    print(total)\n",
+    "    print(np.round(sigs/total,3))\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "for qth in np.arange(0,0.1+0.005,0.005):\n",
+    "    data1 = data_atac2[data_atac2[\"qval\"]<=qth]\n",
+    "    data1[\"label\"]=1\n",
+    "    data2 = data_atac2[data_atac2[\"qval\"]>0.15]\n",
+    "    data2[\"label\"]=0\n",
+    "\n",
+    "\n",
+    "    dataf = pd.concat([data1, data2])\n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(sigs)\n",
+    "    print(total)\n",
+    "    print(np.round(sigs/total,3))\n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,5))\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'increasing signficance (decreasing qval)')"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.scatter(np.flip(np.arange(0,0.1+0.005,0.005)).astype(str), np.flip(chrombpnet[\"ChromBPNet ATAC (572M)\"]), label=\"ATAC (572M) ChromBPNet\")\n",
+    "plt.scatter(np.flip(np.arange(0,0.1+0.005,0.005)).astype(str), np.flip(chrombpnet[\"ChromBPNet DNase (68M)\"]), label=\"DNase (68M) ChromBPNet\")\n",
+    "plt.scatter(np.flip(np.arange(0,0.1+0.005,0.005)).astype(str), np.flip(random_bc), label=\"random\")\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.legend()\n",
+    "plt.ylabel(\"AP\")\n",
+    "plt.xlabel(\"increasing signficance (decreasing qval)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([                                       'chr',\n",
+       "                                              'pos',\n",
+       "                                          'allele1',\n",
+       "                                          'allele2',\n",
+       "                                             'rsid',\n",
+       "                                       'logfc.mean',\n",
+       "                                   'abs_logfc.mean',\n",
+       "                              'abs_logfc.mean.pval',\n",
+       "                                         'jsd.mean',\n",
+       "                                    'jsd.mean.pval',\n",
+       "                                 'logfc_x_jsd.mean',\n",
+       "                             'abs_logfc_x_jsd.mean',\n",
+       "                        'abs_logfc_x_jsd.mean.pval',\n",
+       "                      'logfc_x_max_percentile.mean',\n",
+       "                  'abs_logfc_x_max_percentile.mean',\n",
+       "             'abs_logfc_x_max_percentile.mean.pval',\n",
+       "                        'jsd_x_max_percentile.mean',\n",
+       "                   'jsd_x_max_percentile.mean.pval',\n",
+       "            'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "                                             'Chrx',\n",
+       "                                             'Posx',\n",
+       "                                              'REF',\n",
+       "                                              'ALT',\n",
+       "                                          'Peak_id',\n",
+       "                                       'obs_scores',\n",
+       "                                                  0,\n",
+       "                                                  1,\n",
+       "                                                  2,\n",
+       "                                                  3,\n",
+       "                                                  4,\n",
+       "                                                  5,\n",
+       "                                                  6,\n",
+       "                                                  7,\n",
+       "                                           'center',\n",
+       "                                             'dist',\n",
+       "                                           'peakid',\n",
+       "                                             'qval',\n",
+       "                                            'label'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "data_atac_sig[\"jsd_sign.mean\"] = data_atac_sig[\"jsd.mean\"]*np.sign(data_atac_sig[\"logfc.mean\"])\n",
+    "data_dnase_sig[\"jsd_sign.mean\"] = data_dnase_sig[\"jsd.mean\"]*np.sign(data_dnase_sig[\"logfc.mean\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6744185283131905, 1.2080274872385788e-112)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yS86tm+VCEu0k5snRCkmSMF72uTTboB7EnByd5x/8q7cXNOhqX9Vcmq5xZaZBpRlxbbbJxakaT714kW+fuEwpYzNeDXBtFZLybJPxSkApY3N2or6ufijt9Q8WPI4f7OeJY0M8dniQMFHhqjulr8rtYD0e+iiwp+v33a3buvkd4EkAKeUJIYQHDACTm7FIjWazaXunRc8mTlJm6ymNMMFA9W8BSFJIUmVETAFGq1pUoLTpe/uyPLC7xKuX5pZIET+2v4dn3hrjx+dnMA3BSI/HPQN5zk7UOLIzvyBEEyUp4xWfA4N5pqo+Z8er+FFMlEhKGZtdvRnSVDJR9UkSiSHAMgS1MOkkFs9O1PnconNbKyTRNoz/8XdeY7Ye4dkGAzmH6VoIQE9mYdVp+6rm9FgFzzY78sjxasCx4SIvX5zh8aND1PyYvKu09+3Wv91hobUM73rWrwuOlmc9HvorwL1CiANCCAf4LeDpRcdcAT4LIIQ4CnjA1GYuVKPZTNre6c6i6ntecE1S1bZlWRIJAuWdm0KQcSw+vKvEQN5juOjSiGKefus6p8YqfGhnjn/92ihvj1awDEEjiHlvosZrl2fpyVrs7c8teOz+nMNsPWKq6vPT8zM0IxXiAaiHCRcma1ydbbSuHlTYJ04lD+/t4dCOHGNlvyNhPD1W7pzbxakaJ85P88zbY7x0fmaBF9/m6HCJHcUMIz0ZDgzkacZpZxzfTD3kZxdnuTBV49snLi+4qmlLDF3L6IREBKoTZd6zCFq7YhCnFD17QzHuO6VV7VZkTQ9dShkLIf4e8H2UJPFPpZTvCCF+H3hVSvk08A+A/1EI8VVUgvTvyNs1rFSjWQfdErtGlFBuxghDMFHxSaK0Y1C7MYRBf86mHippY3/e5eJUjfPTDT62r5e9/TmqfsxfvDFGM4ypBbEq+8+7BHFKPUypB8mSQcHDJY9KM+ad6xUaofqbZpggZUuTjfK8drQaYJmG6jXz9tUyUWuhbQnjUy9e5PGjgwRhzE8uzCq5ZNZGSoN/8exZ/re3xujLOQuqL0sZi4lyk7l6yHwzRKAKqbKuRd41CaKEH5+b4RcP9VP1Y4qejR+pZPFkxSeR8OLZKfb3t1onFFzOTtQIYtVbfn9/dkMx7pXkj8CC3MMHKSbaroVJeki05q6nW13xV2enVJhF/Y/ub0dPxuZLn9zPZLXJW1cr7Or1eOd6BUNAT8ZRhlBKrs02VPimFZsxhMA2BUmS4toWGcckTSUZ26CUdRjIu/z6wyP80V9e4NpcHdcyqfkRQSIXbCwF16TgWri2SdGzuDbf7CRiH97bw2DB4+JUjbOTqn8MUuLHqjnYzqKHH8VMVENsQ7CvP8uhHXkMwyAIY94eLRMlkoofESUpAujNORwYyONHCQD3jRRpRilpmvLW1Xlm6hGmIRjM26RS0Juz2VFwuTTTpBnFZB2LkR6vo3LZrOlFH3SY9Z0+GFsPidZoVqD7y61K0GdXbL7ViGKmqj6uZfHQnhKNKCVrWzgWjM41kUB/XpX3J63Ou4ahwiNp2uqtbiSUTIt6nDJVC6g0IyYqPjuLLg/tKWGbgvGyj2EYZAxJkkrVxhdIJfTlXR7YXeKd6xXiVFL0rAUSxvGKT9Sqrsy7JtP1ENs0mG+GVJqqajXnWkzXQhJZ48hQnitzTVzLpD9vMZB3eG+yCqh8Qbvq9KG9JcJEdjznn4+WyTomWcekN+/Rn7XVLNZE8tmjOzbVSJ4eK/NPnj7VKZo6NJhbtlf7etnOhUnaoGvuarq/3FNVXylH/OWPDWPJd18fZbjk8aGdBYZKGVzb4OJ0jTRV6pjrczEZ2yRMYlLeT1JFLW17mkp6sg4FT3JtroltGewouJy8XqEv55CkKbP1gDhViVghVLdHIQRxmlIP1DSjg4N57hsp4ljmgvDNbD1S/WOEIGgZY9cSzDVUmwPHFJiGQZioWPlY2acaxBw/2MeF6UYrEesQJylRKvFsk/t3FbFNkx0Fu2Pwnjs1gWMZlDIOh3bkeG+yvqRUv/36boZnvp5e7etlOxcmaYOuuavp/nK/da1MKgUZS9CMl4YiBRDFKdO1gBfO+gyXXPwoJYxSJBDGKCNupJ3H6I5oyhRigTLajQjHVAlOzzapBjEF1+LiVEzBs1sJQeWVF10T0xD4caoqVuOk03JgsR7bMgTDJY+ca/H6lXlMAX6UkkqJIQSOZZKkEsdScfiZeshQ0cOzLY4f7AdUq96XL8yS9yw+fqBvgc67bWBtU4BU5fqvXZ7vFAItV6r/QdhIr/b1stUmIW0mujmX5q6mrRV/b6LCuYmqSgoKoeLfXVhGy2M2wLVMQDJWDpTOvOVJC6GMfhhLMrZJf9amP+eoGLohyHsWjmlwbc6nEbT+Drg802CqGvDq5TniVPIrH97Jnr4stmXgWoI4lUgEO4sef+PwIAMFb8EAiG499lc+ew+GYeBYJh/ZU6In6+DHql3BQM4mRSVaezM2FT/GNg0++6EBXjo/wzNvj3Hi/DQ1P2Zvf5b7R4pLdN5tA3vfSJGgpQt3TEE9SKgFCYd2vK+k2Qwj2VbWHNrx/tDs7l7tN6J82c4qGu2ha+5qnrx/iD/8/lleuzyHMEAgiJKUxVoBiZIu2i3DnUoI47QjJTSEQAIZW1APVLiiP+9Q85VqxRDQCBMytkGcJCRSadwlYBkGu3s9rsw2EQLeulpubSgSmUoSQ7CnL8OHd5U6YYbFKo3f+dT+BaGNb524wkTFZ6jo8esPj3Blpslfn5thMG+RppLrZZ8klTywq8hLF+Y4sjPPWNlnph5S8WO+8pl7+NwDu5a8Xu0rGkPYPLy3h3NTdarNCNNQvc9fujBLf85RxxjGB67e3Giv9vWwnSchaZWL5q5jsTE8O17hpYuzpGlKM0oA0alKbCN4X/Fitgp7DEMQtMItPVkb2xBErUrSgmdxcDDPq5dmiROViMw5hpIuhjFIpWDJOhY7Sy6mYXB5pk6UpHi2iWuZzNZVsdLeviyfe2AEUL1gwjjpNARbrNK4MFXjmz88T5xK+nI2O4sepml0QjTfPnGZH7eaYB0bKXDqepWaH/Powb5OorEdjvjqE4eXvHbfeO7sknDFpekaZ8ZrHB7KM17xma1HWIbgK59dflPY6Ht1JytSbgarqVx0yEVzV7FcL+13xqqM9HjsH8jjWSZRlzG3DLDEQvli0hp40Z9zsC2BYwmslueeSElP1ub4wX7++EsfY3dvFs82SaTETySDBZfdvVmO7erhF+4d4OBgjkSCZ5sM5N1OcU6cpGQdEylhvOwzWW12QgMCOonc7l7h3z5xmW/+6Dx+FNMIY96brPGT8zPM1vxOcnKw1efl00d2sKOQIWz1He9uXbBa7Hu5cMWZCWXMDwzmOX7PAJ97YJhP3NPP2Yn6so+xEXSZ/8bQIRfNXUV71ufpsQoVP8IUgpofMd8ISWVKPUg71aISSNPlHycBKs2IvqxD1Y+Ya8b05WyG8g5pCtfLPs+8Pcp4xafiR4hWaKbaOm5PX4Zjw6UF3u4PTo3jWAYSSQpkHJOejMVcM+atqxWeODbEx/b38Ed/eYFGEBGlEtcyGCh4HBzI8ta1MjU/ohmlWIbq3hjGkpPXq9iWSiYuVngUPVsVQXVJNVeLfS8Xrtjdm2HfwMIq1M1Ujegy//WjDbpm29MdYnnt8mxHOhiEMaPloNWvZWkxkQEYxvu9XRZTCRJ+4dAA56ZqVJox/Xm309jrymyDf/r0KeotY56kECcpQqSA3Sm2aatU/Chmtqbi171Zm6GiUqr4UcJAwWNvf44n7x/iD549w3i5SS2IMQ2VbLVNg1cuBqStMI4ALFNdfDuWoBbEHW39YoXHoR05TpyfpeBZS8bircRiA7tcGGa7qEbuNLRB12xb2sOf24MTjg4XqAcxfpRiGYKxSoAhJKovqCBYJFVMWdlDb3Nhuk7VjxnIO0zVfGbrIUjwbBUvbydNHRMQBnEima6FHB7K8e0Tl3nz6hzT1aC1JtUvxo8Sxso+fTkb0zDY16dG4n37xGXem6hRCyLCBEgkgYCxuQYDxQx51yJO2zH2pDNMwzZEZ7D14kEatmlyYCDHUNFlrOzfUIJwreEci9+T7Vhyv1XQSVHNtqQdK78wVUNK2RltVmmGNMKUKEmIU4kphPLQBUsM+npwTOjPuQgBY+Wg04lRCOXZWwIcy8A0lPxQXQZIhooZZlpVnFU/AiDrWmRsg9l6hGMaFDM2j+zr7SQ1/5P/7+tMVwLqUYJAKW0k6kril+7bQSLhvYkq42VfVae2TqfkWRy/p597h4qMzjdxTIGABf1cNqOacy1DrROcm4Mu/dfcNbQNyw9OjeOYqnXrYN5FCBUZF0IwmHe4MtdUvVdagy1u1K1JUnBMg8maMuamIWg7Saq3C1gSeloNrSrNiETCRDUgY5sEsZIwOqaq7Exa1ZlJqtQzBwbzHeM4XQ2ohknnuc12sL+1mXz6yA6afszovI8hBK6lwjFBnPKzCzOMzvsEcYJrqQTs13556WzTG2U9ce7tXHK/VdAqF822oVvBohxhSaUZM99QHrDbqo5MpGq0taPgfiBjDkrxcmWuqdrFCqXFVglQA89WXy/TEDTCmHqYIATYpoqzN8KYuNVfPYglfizxo7RTnl/y7I4xf+btUepdxrz93KlUX+K5hiqMmWlG7O/PMlBwsU2j1QsmZbaplC/tSs6L03W+c+LyBzjzjbPewRuaG0d76JptQ7cHWMiovh8DeTWwIeMoK1rwVBl5EMa8cnnuAxnzNoahvPEkBdMUqme6obSOpmj1DA8ijFYCM+uYrRi3II7TTuhEIan4MRnH4vBQvuO9/vcvXMBAxfW7kUDetdjb6/FPnj7FuckatiHozzutNruqilMI1efFtUxyroWUkp+en1mxHe3NiHVv55L7rYL20DXbhm4P8NBgjnIjZL4REsRqvNp4NeC+kSK//vAIg6UMgwVvSYn/jZCmdCpF45aGPU0lEsm+vgx7+rJkHNXnxDYNcq6FaRgdVUobo9UKQIWFbPYN5Dre65W5Jhl7+a/rSE+G96bqVJoRpYxFlKSMzvlEyfsevW0KLAPmGiH1IObabINrc01eODOJbdLppX56rLysVr99Xzenx9Rs1K/9+VsLRtWtxHYuud8qaIOu2TYsmeEpBEmqPOSdJY/7R0r89vF9nJ2oU8rYjPRmsD6gRRe0xtO1Z5GiYsMHBvMMlzIYholrm/TnHATK6E9WA3oyNlnbBAGWKXAM1T/GMg2KnpqetNB7lQSJxLMMTN6frGQbagPozTqUMjY7ixmyro0E6kGCRJJ1TWzDQEqoBTHX5pvUgqSz+b1xpUyUqHF2z56cWHCl01249OzJic55r9fod6OLhG4+OuSi2TZ0y+fOTdY6cy8/uq+HgbwyOu0wwnDJ49Bgjtcvzy4o618Ny6C1AajOh7T+TrW4FRRci6xr8ru/+iG++aPzTFR88q5FGCfM1EMMwDKhmaSMl31sU3BwIMfu3gyXZxrMNSIcS/Uhn61HXJ6p8w+fPALAQM7hStBEGOA5BmmrWnVvX4ZqELMj7wCQcy329Ga4lCT4sWT/QJ6BnM2pMVXiH7YadTm2wUiP9373wsk6Hz/Q17kiWKu97I0mOO/EIqE7SWqpDbpm07jdH/zuKsaJasBQweXeoTwDeWWcCp7FO9fLVPyYN6/M05d38CwDP0pZj2IxTpUaJeuYarxa63YJLQMv2dOX4Zs/PA8CCp5NPYiph0oBYxiCIFEJ02LGojfrMNKT4dJ0Hdc22d1rMlUNCFPJUMFlpOR1Xr+DO/LUw5j5RkgzVlcErmUwWHBJEa1wi7rgzrkWgwWPih+zs+AyXvE7jcCyjsnBwRzNKMFoKX/ag5y7rwjWinVv557i3XRLLbuvRLbqlYU26JpNYat88Ls9wMVG6fJ0nWtzTY4M5Sk3QirNCATrMuZtGlGKH6WYhkqCSuiqzjQZK/skiWSkxyPrmEzXgo4SJUpUf3TbVG0DmqHqUz5Z9SllHDzbYN9AnkODOSSSN66W+dqfv8Wungw78i4fGirw8+tVMlKNr3Ntk4lKwEjJ48psg6xtMlR0SKTSvH/hIzv56fk5oiRluOQxXPI4M1FjZ9Ej76l+6aDUQI5pcHW2QVh0mawGndepPSd1caHQzUxw3m7HoJs7TWqpY+iaTWE9cddbyXIJuLMTauTa/oE8j+zvpZSxlx0GvRYpyluXKEOeb41iS6XENiBIEt6brDE61yBjm5hCEMv3Db9KhkrqQcx42ccyVBGRY5kM5Gx+fq3M//rmGDO1AMtQG9P1ss/l2SaDBZd72yPnJIRJSi1M+MVD/ZiG4MJ0g9maMvInr6vz/ZsPjHD8ngH2D+Q5MpTn7ESt0y8dYL4Zs6vHI5USxzI5Olzk8I48Z8ZrvDteWTbWfbMSnDcSm7+Z3GlSS23QNZvCVvvgHx0u8fjRQU6NVXj6reucGqvQk7PZ26+aSA3kPT5xsB/7AyZFJeBHCWGS4ocJYxVViRonUmnLowSr61vW3gxac5cJ4gTbUmsIooSXLswyVvZVcjRj8ebVMmGcsK8/hxBQ9CyqQYxnqylGUZxyaabOfDPmo/t6GCllGO7J8PGD/czWQt4drzJde3+m3t7+HHv6M5QyNnGqBj//4qF+Ls00ma2HhLEKxRwYzPOJe/o5Nlziq08sLUC6WQnOreYYLEm0s7WlljrkotkUtprG+PRYmedPT3FsuMijrTFqL52f4efX5mlGKRU/Up0Go2TtB1sFx4Awhbar306wtvXisZTIVLUIiBIWxN0BwkQZ8gP9qltilEhcW7CrxyPfqi49N1Xn0QN9uLbJsZESpYzNdM3n9FgF2xTkHNXE6+WLNfpzdmeuZ1/eodKMODdZ7+QRqn7cMdLdYbJUpiCNzrzOwYK35oZ8MxKcWy02v5E+NVsB7aFrNoWtpjFeztPbWXR4/fI85WZE3jEpNyOSNZpvrUW46O8lXXpylEZdoNoDiK7b28cKVMXnxZkGhhAcHS5wz2AeyzSoBzGTVZ8z41VePDvF/r5s53V9b6KGY6qq176cg2ebREnKtbkmU9WAly7MMJCzQdKK4y99T7pfo1JGDZZ2LaPTG/3KTJ0rs41168w3g63mEd9pUktt0DWbwlb74C8XAmpEKcWMTTFjM1MPqfpqUPNmk0olZTSF+oJlbAPDMJaN11uGwGm1um1GiZJT7shRbkaMzjWJ4hTXUkMvwkTy+NFBShlbqXhKLj2ehWmonu5BnBLGKTuLaqDyhekGO4sO/Xl32fdkQSFWa2anlJJqM+LSdI3Xr8yzs+De0lj2VnMMQH22v/rEYf7wNx5cNvy0ldAhF82msZU0xsuFgGbqISM9HvfuyFFpRhQ8m56MxTtjtU1//lTSiY07lppYZAjVw6Vt1x1T4FlGq5jIoidrYxgGtmmSd0ylG09T9pYyPLinhG2anJ2o89UnDjNV9XnnegXDEEzVAupBjGcZYJutkn/VlGu8GvKN33xw2fel+zVqz+w8OVoBIRmrBBwcyDJeDTg3VSfvWewsuKuqOzZDnbKd533eCrRB12xLlot92qbBzqLH21fLzNVDEqmGMK+3sGijCCQFzyFOJHGi+p1HiTLkedfqtAv45fuGsE2TUkY143r25ATVMOHgYI5DbUULSh3THhA9UQmo+jF516LgQrUZ0ZdzuG+kyHQ9oubHFD2LYsZe0Rgu1xv94GCeLz92gP/qB2e5NtvEtQ3yrkkQJZydqNFYIeewmbLVreQY3Glog67ZdrQ9xbFyk5cvzpCmkr39Of72Q8P87OI8F6frWCaEiepueCPGfK1NwDEh59iM9GSo+RGXZ2OS9P2CoDiVeJYg1zKk7UTbajr6diz52ZMT7OnLsrPkcm6yTsWPyDiqKvbeoSL3to5f/PeLWc0brvgxCDqVpJ6tiqkqi+LbbVbSa3/nxGUGCt6W0JTfDWiDrtlWtD3FNE2Zq4eqXayEnQWXd8frOKbAMQW1MOk0rAo3KHRp929ZqSDJswT3DOa5NNOgEUTMN2NsU/VqUVOFJJ861M/5yTozjZCXL87w0J6eBY+xkrqiPVM0lSmljMOhHTkG8h6T1SY/OTdLuRltSI2xkjdsIJmq+kxUfDK2Sc41W8nl5U3GcuqUII75yblZPvOhHXdEleV2QBt0zbai7SmeHqt0ern4UcJ4NeDYcJF3xysM92YYnfNxLYN6GBMmG7Porc64wPKeepRKDu3II4Tg+nyTehirqUmo6UieZXB+sk4jSvjkwX72DahqzD949gwjJa8zSejxo4Ocnah3vNuP7e/h+dNT2KYAaeBHCa9dnuej+3pwLYtfPNRPKWN/4Njz6bEy882YYqsFcTNKCOKUD48UOLbC4y2Xszh1vUpv9s6pstwOaIOu2Va0PcWKH1Fw1cfbtQxqfkzBsxAIgihlV4/HXDO6oW6LiVwoPVyMkFALEn7twZ38Nz88T86x8GyDWqCMY96zmGmEfPJgPwcG8wCEccKVmQbX5hr0Zh3evDLP9w3BVz57D1994jCghjGXMjb3jRR5/co8rqWuNk6OVjqx780wlM+enODIUJ4zEzWKnt3q9RIzXgn5z1ZQmyx3RTHXiPjkPX0LjtvKVZbbAW3QNduKtqdYbBXltGO/+ZaReWhPidcuz5NI2N2TIYhTmuMVwnh9k4vaHvlqMXQBTFR8/u2b42Rs1bY2TCSuZWCbgkaYYBqCnGd2/ubcVB1DSCaqIVnHoi9nM1UN+Mf/9hR/8cZ1hgouPzk/Tca2KGRsDvRnmWlEVJsRCLmpYYzR+SZ7+3PkPasToy96FqXsygnW5eLxnzrUj2OZC47bylWW2wFt0DXbiranuLPo8u54VY2Gk7CvVZTz5ccO8MlD/XzzR+eZqYcgJVJKjFazrbXorvQ0hfLWuzFQsfX5ZkjetejL2kxUQ3pbG4ohIElTBvIeL1+Y4/g9goG8R82PKfsxGcvAs03qQUzFj4mSlPFykwtTqrzfNgRBZHBxJuLhvT04llLHbGYIo70pDuS9ToXpWglWWBqPb+cz4M6ostwO6MIizbai7SnuH8iztz9LMWOzuy/Dga6QxOce2MU3fvNBPnGgj+m68ohztoljrv34bURXpMYUaiKQbYhOyX8qoZRxyLo2Q0WXmXqoNgHDYG9fjo/t70MAJ0crqqmXKWiEKYMFF4DZRghA3jUZrwQUPIuhgstsQ6lMXFPwzvXKDRXdrDVpaLOKe7ZasdndgPbQNduO9eiYjw6XGCh4DORd+nMOzSjh3GQNkbw/uGI1uqs+JaoFbTeeZXBoR47XLs+rjouGYE9vhrA1lOLcVJ2Utq68wn0jRSrNSHVxlJJmmGAIKHgOM/WwM+A6SlI826TcDDGEsWEDuR69+GYW92hN+a1FG3TNXcvofJP+nEMQp2Qdi96cw0w1IEX1Ll8NoxVET1GXuW17bghwLMFgwVtQfWmbBkIIDg5kuDDdwLUMCq6aM1rwbH7hUD9z9ZCfXJhhoiKwBOQzNkIIdhY9FToCBgqqS2Q7BLJRY7ne/t7aEN+Z6JCL5q5lV0+G4ZIyln6U0Ju1iRO5pjEXqMk/nmOocIul+pkPFT36MhZpCqPzDU6cn6bmxxwczPN7nz/GwcE8o/M+bqt/TJhI7t9VJE1TvvnD8wyVMvzq/TvZ25fFbA0oPbwjz4d3F6n6sXqsgewH6m+y1docazaXdRl0IcSTQogzQohzQoivr3DMvy+EOCWEeEcI8f/b3GVqNJvPk/cPYRgGR4bySprXjFhP80VJq3sirc6KQpCxVXl8xU/IOwZpCmNlnzPjNR4/OsjBwTxZ2+DSTINr802SVPLRfT0A/OzSbCf0YhqC+3cVGSp6SCTj1YDpWsjxg308erCPOOUDxaK3WjdDzeayZshFCGECfwQ8AVwDXhFCPC2lPNV1zL3A/w34BSnlnBBix81asEazWXTHim3LBCGYrgbkPYtyU3U3XA5ToGZ0SlpJTFUFWg1iBgoOu3uzBHFKEKccGcrzk3MzPH96ilLG5shQnoofE6eSuXrIhekG9SCh4KkCqJ+em8GPEhIpCWJVDRo7Jl88vm9TQiB3Wn9vzcZYj4f+ceCclPKClDIE/gz4wqJj/kPgj6SUcwBSysnNXaZGc3Pobo26ty9LT84hlap/esZe+PVoC1sSCWU/xhAC1zL59JFBRnqzZFoyGSEEnm3iWgZjZZ83rs534taHduQ7j/fG1XkAbNMg51oq2dkImW1EpKkK6wBcmWnw7ROXN+18tfJk+7KepOgu4GrX79eARxcdcxhACPETwAT+qZTy2cUPJIT4MvBlgL17997IejWam8aungxHhgq8fmUe21SqkmakgjB2S3Pe7bOHcYofxQgBNT8m55id40FVqCqFitmJWw8WPB7e28O5yRpX5xrs6c1yoD/LxZkGfpTQiJJOi93+nKuaY0nZMf6bgU54bl82S+ViAfcCnwZ2Ay8KIT4spZzvPkhK+RTwFMAjjzxyMzqWajQ3zJP3D3FltsHDe3s4M1FlqqJizQbKmC+JrwsIYsm5SdUvvBFEnTmi7XJ52zR4aE+Jqh93FCWDBU9VUArBseEipYxNb85RUkaphl4MlzxyrdYFqjJ18wdxaLYf6zHoo8Cert93t27r5hrwspQyAi4KIc6iDPwrm7JKjeYW0B1Tt0yDn4WzJIkkiJIlnRXVeDlJI0woN0MO7ygwNt/kwyMFGlHKbF31ifnKZ+/h4GCep168yGwtYLzid+772w8P8+64GvfWn3dxLJOpik+YpJiGQLbi6LUg4fjBvqUL1mgWsZ4Y+ivAvUKIA0IIB/gt4OlFx/wblHeOEGIAFYK5sHnL1Gg2l5WqJdsx9ftGShzoz5F3LWxraSzdMsBqjY4zhMGBwTxf/MQeGrFkohrQl3f4ymfv4XMP7OLocEl1TpysMVMP6cvZHNmZ593xemekXDue/bVfPsyHdhYBqPgRAAcGcnzx+L5b9+Jo7ljW9NCllLEQ4u8B30fFx/9USvmOEOL3gVellE+37vslIcQpIAH+UynlzM1cuEZzo6xWLQmqyObfvDlK3jE7ipbuZlyy9X+JlOwqefyf/3cH+cm5GX58boberM0n7+nDtSyePz3FwcE8R4dLnJ2o84mD/Qv6oZSbUWekXDcHB/MfeJSb5u5kXTF0KeX3gO8tuu0fd/0sgb/f+qfRbGlWm67TiFJKGZuhgkvFj3FsE9cSC4qNBKoBV84U/AeP7uH501OcHJ0niGKuzEZcm2vy6IFedhQznQrM5QZArFTQo5OWmhtFl/5r7jpWMq7PvD1JwbNVDFsIglb7XdcyEcJAStUCt612+eSBPhqhJElSJqshriXI2AZhLHn54hxPHLOpBSqxutwACF3Qo9lstEHX3HUsNq7TNZ9XLs5xebqBaxs4loFrmRhCknNNmnHK/r4shiFIUsh7FgcHssSp2hzGKz6uJRAIhBA4lqAZJZweq/LpI6rGThf0aG4F2qBr7jq6jWsQx7x8YY7Jqo9pCBKpZIfqZwjilPtGSh15YZvuDeHNK/MM5l3GKwEAEoltCOYa7/db2cwOhhrNSmiDrrnr6Dauz52apeBZzNYFjmfRjNR80SBO8SyDiUrA//Xxe3n+9BSwvHf9/ZPjJBJ2llymqyF+lDJc9Hhkf6/uYKi5pWiDrrkraRvXdjz9X/6siQHkXWXUoyQla5sM5G0+98CuJcqTbu/6K5+9h2/+6Dx+lLKvP8twycMwDC011NxytEHX3NW04+k7ix7X5pq4lkHWMRFY5FyrU9Czmne9nMHXUkPN7UAbdM1dTTuefnAgRy1QPceDOGGklGF3X3bdXrYOp2i2Atqga+5quuPpjSih4seUMhbHhkvay9bccWiDrrnr0d61ZrugR9BpNBrNNkEbdI1Go9kmaIOu0Wg02wRt0DUajWaboA26RqPRbBO0QddoNJptgjboGo1Gs03QBl2j0Wi2CdqgazQazTZBG3SNRqPZJmiDrtFoNNsEbdA1Go1mm6ANukaj0WwTtEHXaDSabYI26BqNRrNN0P3QNZouTo+V9Sg5zR2LNuiabcmNGOZn3h7lmz86T5Sk9OccojjhqRcbfPmxA9qoa+4IdMhFs+04PVbmqRcvUm5GDJc8ys2Ip168yOmx8qp/880fngegP+cQxClnJmqkacqzJydu1dI1mg+E9tA1245nT05QytiUMjZA57/PnpxY0dN+9uQEcSrpy9kIIfBsE4Cxso9tmRt6fh220dwutEHXbDtG55sMl7wFtxU8i9H55rLHnx4r89ypCeYaAbM1H8sySCU4poEh4Pg9A53j1jLU7auDUsZecHWgwzaaW4E26Jptx66eDOVm1PHMAap+zK6ezJJjn3l7lG/+8DzXy00aQUws1e0GYAj182uXZ/gP/+dXuDrT5PBQnn0DuRUN9Y1cHWg0m4WOoWu2DafHynzjubO8c73MSxdmuDRdI5WScjOi3Ix48v6hJcd/80fnCeKEJEk6xhwgBWKp/v30/CynR8sg4OxkjZla0DHai+Pro/NNCt5CP2m1qwONZjPRHrpmW9Ad6jg6XCRrm5wZr9GIEo4Nl/jNj+1e4iE/e3KCKEkJ4hSEgSESUrn0saWE0XLAQdvAMk3+6uwUxYxNwbUoZe0Fx27k6kCj2Wy0h67ZFnSHOgwhODCY5xP39HNsuMRXnzi8bLhjdL5Jf86hGSWkUsIyxhxACBDA9fmAqWpAI0wouBYVP+bqTHOBeubJ+4c6VwSrXR1oNDcD7aFrthSLE4+Hh3KcnaivqRhZLhEaxDHPnZpd8W939WSI4oQz41WiJCVdZV2uJQiSFFeaZB1TefXA4aH8gvj40eESX37swIJzWO7qQKO5GWiDrtkyLFaIXJqu8d3Xr/HQnp5VE5GwNNQxXfN5+cIcBc9aVm1yeqzMVNXn5YuzBHFCvII1F61/jmWSpAmmIXAtA9c2uW+kSH/eXRIfPzpc0gZcc1tYV8hFCPGkEOKMEOKcEOLrqxz37wkhpBDikc1bouZu4TsnLnNhqsbLF2f42cVZLkzVybkW49UAQ4gVE5GwNNRxcrSCAO4bKS752/bG4VgmWccgSdUXQaywLssUNMIE2xL05Ww+eaif4wf7GSx4Oj6u2VKs6aELIUzgj4AngGvAK0KIp6WUpxYdVwD+L8DLN2Ohmu3N6bEyz787iZSSVMJsLaTqRxwYyFLz485xyylG2mGaqh8xOt+klLEIk5SPHehFCHjpwgxTVZ9mlACCExdm2FlwKWVs5hoxQoBrG5iGwBSCSuv5cq5JkkqSVPLArgL37izyysU5Tpyf5dGDvbiWRbkZ8Zsf230rXyqNZkXWE3L5OHBOSnkBQAjxZ8AXgFOLjvsvgH8B/KebukLNTeFmVjPeyGN/+8RlgjjFEgLHEsqQSrg253NkuNg5ru0Rt5/j1Fi5ow8fLDhcnqnz7lgFQwgaQYwAXNuk3IxIUjANwfW5BpVGRN6z8KMEQ6jbk1QiDLBNgWkIfudTB3npwgzlZoRpWQwVMzx6UPDO9QpvXa3wxLEhHR/XbCnWE3LZBVzt+v1a67YOQoiHgT1Symc2cW2am8SN9Dq52Y/9xtV5BvMOElqG18CzDOphws6Cu0Axcngo13mOciMCAT8fLfPCu5OMl32iJKHqR1yebXJlrsmFqRo1P6YZJUgpCeIEBJybquNYRus5JYaAMEkRAhxLfTUqfkTBNTtXCYMFj8cOD3JspLiiekajuV184KSoEMIA/l/A31nHsV8Gvgywd+/eD/rUmhtko9WMG/G4b7RSUiDI2CbDJZPZRkgYp7iWQd6zODCYX6AY6X6OahBT9Cwu1wPmGhFJKpHQ0ZOnUv0TSEqe0YmHN8OYOJHs7slwcbpOmKQdLz2VkLHhuVMTVJoRzSBmR+n9OLmOm2u2Kuvx0EeBPV2/727d1qYA3A+8IIS4BHwCeHq5xKiU8ikp5SNSykcGBwdvfNWaD8RGqhk36nHfaKXkQ3tK1AKlItndk2FXT4a8Z/Ppw4N89YnD/OFvPNjxiLufo+jZBHFKzY8IExWmWbY4CPBjiUCQdUw826Qv7zDSm6GUscjYJlnbImubICFKJMgUxzKYrAVkLKF15Zotz3o89FeAe4UQB1CG/LeA/0P7TillGRho/y6EeAH4mpTy1c1dqmaz2Eg140Y97hutlPzi8X2MVwKmawEVP8K1TA4M5Pji8X2rPsehHTl+cm6GZrRCVVAXfpySSyV9WZtmlPJff/4YR4dL/O533+bk9QphklJuRvTmHPwoYbIWcs9gnn19WRpRyljZX6Ar110VNVuNNQ26lDIWQvw94PuACfyplPIdIcTvA69KKZ++2YvUbC5P3j/EUy9eBJT3XPXjFdUaG+1cuJHHbtM2jEptYrKj6HJsuLTAQHYbT8cUTFQC6MuSSkmlGa1U5NnBEMpzL2Yssq7Fx0eKnccOEsljhwcxhOAHp8YpuOprUQsSPnGwn1RKxso+v/Op/XznxGX+wb96m0YYAYIHd5fY27+6Rl6juVWsK4Yupfwe8L1Ft/3jFY799AdfluZmspFqxo163ButlFzcg6W9ASw25k+9eJE0VV7yTD0kSVKCOObKrI+U0J+zqAcxzXjpc5gtY+6YKkQzkHf55KH+TiOvU2MVTAGljMNMNWC87OOYgh1FtZH9/Oocp8aqPPP2GKmU7MjbhImkGaa8da1M3rMYyKtjNyMPodHcKELKtS9VbwaPPPKIfPVVHZXZ6nQb3G6P+0Y90cWGbarq41jmgg2jvYF89YnDAHzjubNcmq5xZqKmqjQtg6maMrz9OZcUiSUEU7VAlfCvEEc3W3rz4aLL3v48Bdfi7EQNP4qZqgWAIE1TEimRUpCxDXb3elyabWIbBmGcqC6MrVi9IcAUgr19Gf7mg7s6nvwf/saDN/U11NzdCCFek1IuW7ypS/81q7KZvUm6DZtlwAtnJjk3WWP/QJYHd/cwWFBe7uKQzuh8k7Gyj2sZeLZJI4wpNyLiVJLKlJxrMVFR1aSuZRBEKcJQBjxM1GOYBvRmLMIELk43Ga+ElLI2Occk79mMV5qEieqsqPYCSRQknBqvM5i3kQjCJIWWPh5UZWmSSq7MNnlvosKOYmbBlUt78/rBqXEc0+D+XUUMYa9b+aPRbBRt0DVrslm9SdoJ1jBOePNqmTSVSCl5b6LGeDng4/t7ObyzuCSks6snwxtX5ujPOQDM1sOW8RY0o5Q9fS6gxsUZwsBzBLt6MvRkHV65NIuUkKYwU1dVoSlQDxOaoVLVOK0JRct59QBTtQhLqBYAUbLwIMNQm8Vrl+f55CGrkyvo3ryQIKXktcvzfHRfDwN5T/dI19wUtEHX3DJG55vYJrx4dppKU3nYjimIW+X1P7s0h20amKaxIIn65P1DfP+dcaZqAUGUMF0LMA2DUsYiSsCPEnoyNs1QFQVFScpMLeDidH2JkV4cYYxSSRImq3ZaBDXoIo1l5zgDFW4RBriWgR8nC0Io3eqgQsYmiBJcS3Buss5AXveA0dwctEHX3DQWK1PevDLHeNknTiUCCULgx5Kca5GxDap+wng14PdacsLux7AEXJhrYgpllKNEMluPODZcwLVNpmsBI70ZPvuhAf67Fy5QC5bJjq7AWsZ8ueMMA0pZm50ljyiRFDP2gquYbnXQocEcr1+ZxzUF5WbY0bLrHjCazUYb9G3EeocY32y1xemxMt85cZm/PjdDb9ZmZ9Hl3GSdiYqvhkUI8COJa6kKUcsQDBY8dvca7O3LLlG3lDI2WdeiP2czU48wDRX6SCWcmajx8f297O3LMlLy+LdvjmMIgWebRElKvFIchfUb8m4KroltCnYUMxRck2qQUA9ivvKZexYc160OGix4PLy3h3euVzCEQSlj6x4wmpuCnli0TVhPRefN7OGy+DlOXq/Qk1H+wquX5zEMsFsKlbxrtkIjkrxrEqeSIE7ZWfQWhCG6wxa1IEEIg4xtIlrtcD3bJE5S3rhaph7E2JZJKlNSKTENwXoUXCu1zF2JwYLLJw72YxqCyVpIMWPz9V85wuceWNDeaEk7X8cyOTiY57/69x/QPWA0Nw3tod9BrOZdr6ei81ZMpO8kPpOUgmshhCCME8bKMUmqwiX3DinJ4Gg5IEolOcfi8I48pmksKKnvDlvkPYuxcpM4TUlSSZSkhEmKBBphwqWZOvcO5SllHObqEX6UIKVgxblyLSTKqK9HvCuAjG2yo5jBta1VZYd6cpHmdqAN+h3C4mk+iysT11PRudGqz/WGZ7qPe+d6mQd3lyh6Nn6kwhGNMEECjilIJVydbbKj4LC3L4MpDPb0ZzgwmF92RFynxH8wx/nJGkGcIqXqihinEgNIpGS6GvLd10cpeBZhnFIP4nUba9dSQ0P9NdoHWAY0omTdG6GeXKS51WiDfoewlne9norOjVR9rrWBrHTcexNVXrk4x5GdeU6P+Vyba5K2tN1hLHFMJeGbqIZ87sM7+e3j+1Y0et1tBPrzLvePFPjrczMYqOIeKaGd+pRAM0oJopCMYxB32ebFRt0QMJh3mGuEhIkK/RhCdMIvK5l1CZ1Zou2NUFeAarYSOoZ+h7BWF8P1TJvfyET67g1ktfFv7eOiJOFnF2ep+BETFTWrc7qmQioScAzVYzxF6b4/tr+X//LXH1jTw/3yYwcoZWzGyj4f2dvHsZ0Fcp6FZHnDqzTmacc4u5YgY6vfDKG87B7Poh4mIN839qmUKz5m57FTqDYjfnBqnBfPTtHwo5uek9BoNoL20O8Q1vKu1xOzXc8xbY/z37w5ylDB5dCO/IoVnACnxsqMzalBEhnbJOcaCKEMX4qq1jSFoJS1cCylPIliyX0jS7sVHh7KcXaivsTbXWz0v/fz60RJSjNMV/WmDQFpKolaM0NTCZYQ9OZsrpcDEILdPS6TtUB56ayufElRlaGOIaj5MaeaFR7c3XNTcxIazUbQBv0OYT1dDD9ozLY7fDJUcKn4Ma9fmefhvT3LDkR+5u1RXr88Ty2IMQ1oBikz9RQhlBceJSl5z6Yeqji6bZrEiSSVsjN1qB2quThV47uvX+PhvT1kXZPv/fw6/5+fXGQg73D8YD9fbIVmnrx/iL94Y5SBnMOV0F/xXAQqZh8nqhjIACyhXrupeoSgPaHo/S1hLRmjIcAwBK5jcd+uEj+7OMdY2Wf/QL5zjK4A1dxOtEG/Q9gM1cRKcfHHjw5ydqLOD06NkyQS1zKohzHlppoGdG6yhmOZCzaQ02Nlvvmj8xQ9i4ofkibgq1YnGEhMy6CZQMWPsQwVp1Yj4OAXDvVzdqK+ICcwXg3IuRbnp+rU/Ih5P8YxlSd84sIs45WAr/2ykvv94qF+njs9sWrCs50QbRtp2xQYhsBzTAZci6mKT9RWy8Syc7wpIJHvP67R9Vh51+JDw0WOH+wHoC9nM1MPFzyvrgDV3E60Qb+D+KAe+HKJ1bl6wDd/eJ5P3NOPHyXM1UIViuj1SFLJWLnJeMUHIfjS8b0LJJBRkjJYcKkFMfONCKQKWxhC9Ri3TGVyZSuiPZB32NuX4yufvZc/+fGlBYqb6apPM0yYbYRYhioMSlLJbDNiqhZycbrOzy7O8GsPjvALh/r563MzFLyEip+seL4p75f6h4mkYKurhvGyT5ik5D2LOJaYhoqPp7AgmQqAAMcQOKaBIQSHBnOdu3YWPSqtK6X19n7XaG4m2qBvIW62YmI52eJY2acexpwaqzBe9pFIsrbFZCUgkeDZFlnH5NhwkedPT3FwMN+RSfbnHCYqPvUgJmpVZJqt5leGlJQyFkEssU2DjGPQm3M7XnZ3TmCq6lNuxq1BzYIkVS1040UxkGqQ8BdvXOOH705ScC2a0cpBEgNwTIMoSTCBrGuRSlVdapsGQgiG8i5TtRDprxxuSaXanKI0Yadn4lgmqZRU/RjTNPjbDw3zw3enmaj4DBW9BZueRnOr0SqXLcKtqOLc1ZOh2ppeP13zeenCDCevl5muBczVAxzLQKZQD2Pmm6EKQRiqJe1ilcuungypTBmdb5JIpR4BFa5wTIOSZxHEqmqzmLEYKWUwhPLsv/bnbzFd9bk8U6fcjDg3WaPgKkOZdUwMwRJj3qYZSaIoVYY/WVmTYpmQdSxsA3KehWUI4iQliBLqYUwQJ4yWfQYKDhnHxFyjZFRK1XXxvfEKY2WfUsbm8aODvDte59hwkc8/ONLZ9LTKRXO70B76FuFWVHG2E6tz9YB3x6sIIZBSqVBm6xGOaWA60IxTwkTiWAY516Q3p9rTLpZJfuely2RsZYiTVIVaPMvAMCCfsQlqISMlj56szbW5BtPVkAuTNSzTwDSE6iU+U2e8GlDybB7YVWS+GXNxurbqecz7EYYpcCxo7U8IFsbMkxRm6gFJCnEzxjBQVauJqjRFKsXKpekGgvd7nK+E0epB8+5Ejf/811TzsG88d/amv2cazUbQHvoWYS2d+WbQTqyOVZShK2Zs+rI2rm2oBKOUpK0hEXnXpJixMQ2jEzdeLJOMkgQ/SjqhD9dUnmwzTJmrR+Rdk9l6wJtX57heDohTSdmPCOOUckN1HZxpROzvy1LM2EzXIx7YXeILH9mFvcons70JOaaJADxLYBqiY8wFtCYKKaOfAFEKliFaPdhVt0QpJUGcECZyTQ9djbAThHHauUq5Fe+ZRrMRtIe+Rdjo7M4b5ehwib19WR490IchBC8B842Qqh9RD1P29GaVVjxJQcLhoTz9eXdJy9dn3h7FbxlygQqRxClkrJSBvEuUJFSaEWGrKyIo7zlKJEma0owSUglxmhLEKXGSUg0SfnBqgl09GUotA78SO4outcAkiJuYQhDK92M0krbuXGIiMIUy2I0oxUCNoTMNJWm0DBDCIE7TVWPyEtT5ipT/6aeXmK761P2IFyeqqsmYZ3FoMIdjmVrlorltaIO+RViPznw1VkuoLr7PNQVVPyZKVK+VsbKPYxrs6c1wz2COMxM1Dg7m2VFwEajE6WKZ5LdOXKGUsZmphQsSis0YjpRcxsoBzSilN+tQ8SOEkJ2Wt9Ug7ox6MwWMlZv0ZGxMQw2rALAtk76sZLaxtK95KWOpiUWmyWDeZqIaddQsbSmjAbiWCajQUcYxmayGOJZq15tKNYHItQySVLK3P8/p65VVteiJhJxt4JqCF85M4ccpnm3Qn3MIwpiXL8yytz/LP3zyyLreM41ms9EGfYvwQXTmq/VdAZbcd72slCnTtZC8azJUVGqP6/NN6kHCh3eV2DeQW3WY8UTFb8kLDRqLPNurcz6O2eq3gvKO066QhlSzLUCCZZo0w5jpekhPxmFfX477RopcenuMsr/UmFsG9OUcXNtkX1+WV69EDBYcJqsqjNQOhceAjFRTsKJnUm8NvFD9WBKSVCV8wyhFCMlAToWe1lLOmKbBjqLHVDVAAH1Zh4xjUfEj8p7FSEtF9I3nzur+LppbjjboW4jFOvPTY+V1GYbVEqrt37vvK3oWPx+dJ0mg4gt2Fj1+9f6dvHO9wlw95LUrc/zVe1PkXYt7B3PLJvmGih7vjlUwDNGJd0tU4lHJAw0KrolAYFsmSZSQsSTNWMWjbQGGKWiGCUIIkBDGKdO1gOdPTSxrzEHN7/z4gT4826LcjIjilCRNsQwD2Qq7tBOciaS1NoM4jRjK28w1IhKpHscQ4CeqYvSlCzPEyerdGdsFRjnXYrzik8qUWEo+0So0SqXk3fHKupqaaTQ3A50UvU20jfXX/vwtvvHc2SVSt43IGEfnmwRxzEsXZvjBqXFeujBDEMeMzjeXJO6maz7vjlcJIsmhHTl29WQ6U32mqj7Xy02CKCVrmwRRyutX5nn5wvSS5/zS8b2AMsKmIUikKs7JWAa2IRAChnsyBEmKlBLbECTyfTc9RbTK7lXsxTQFu3ozBHG6pPoSlCFVxlnw0oUZTo1VqAURzSghjJVRBpUI7SZJVZXo8YMDfHR/HxnHwjREa+Np95qBMFHJ01XFLkJ1dKwHMaYQGMKg6C3MeZSb8bqammk0NwNt0G8D6zHW6+12COCagpcvzOFHCQXXwo8SXr4wh2sKdvVkuDxd58SFGZ47NcELZ6YIooRixiJMJJ5t4loG56bqTFdD4hTmGiEz9ZBEphiGwZW5paqNg4N5HthVJEwkfiw7MfFGlJBxTB7e28OOosdg3gHAj1MsQ1B0DSxDYLQSlUIIDINWDF10kqWLkSilStKaJbqz4PKhnUVyjkkQp4Rxit0aON0mayvZZX/eJWMbnLxepZix6c85yJZsMZV02vuuRSpBCMlkK9xkmYKdRXdB58qiZ2nli+a2oQ36bWA9xnojkrh2KKCbdujg8FCON67OU2lG5ByDqh8zW4840J8liFP8KMExBZdn6oSpKtIXQsW/5xoxVT+i0oyWHWXXl3MZzDud51LOsWC6FlBpROzrz/GrHx7hyM4iR4eLFDybWKrzsE0TiZIbZh2L3pyLa5skLQnhSjLCWEIQJbzw3hR/+e4kOwoegwVHFRotMsxhovT0aSp5+eIcQZQojz5JW2PhDAzBmhr0boJIUvZjHtxT4v/+N4+yfyDfKTT68mMHuG+k1CneaqP7u2huFTqGfhtYz+SgjcgYw0TysQO9XJhuUPNj8p7F0eECU7WAb524gkRS8SPCOCHvWbimQZjCR/f1cG6yznQtoB7EuJYgiuWCKs1EKr323/326/ziIdX1sL0hnRqrYAg1OLkettQpJliGwc/HquzuzxLGCeenVKFQPUiIU0lPxsCzTRphTCol9TDm2lyD/f05Cp5JI1pdQihaBUxT1YCMY+KYBkMlj0ozopwoY6pa54IgZbyikr0HB7I4lsm5qRppCrFMN2TMQfV039eXxbUtDg7ml8wSBT6QWkmj+SBoD/020F2C32axsd7IMIpdPRk82+L4wX6eODbE8YP9NMOEqzNNZmshw0WPwbyLY5l8ZHcJxzK4PtfkvYkaM7UQ2zBwTEHGFMvK9ixT0JOxOHldJfzeuV6m4FnU/JhmV2GREJB3bWxTeb6vX57n9SvzmKL9QZMkqcSPVLWmbapkqBCq6KcRxiAMkHLJFUc3UcvrTqSklLFxLJPhngyJlFhCFQBZQhUPpRKqfkLWNsi6NnlPDZe2TEEk6cTe10ucpNitVgjLhb8WD+Voe+46Iaq5FWiDfhtYj7HeiGFY7vHOTNQ4PJSnL+8siJXP1COGiy7NKGGmHtKXszmyM48QBvPB8l5xI0zxbJMwSSllbCp+TNWPVVfFVr+WNhVfed0jJY+JiupXPlhwCROJJQSuKagHcUc6mEiJZ5mUMjaNUMX2Xdsk56qeLovtbdsAV/xY3Sclfpzye58/xp6+LAcGc3i2iTAMHMtQmwZQyNhMVXwuTdcJY7WhWAb052w2glRPuWpc/Ohwia8+cZg//I0H+eoTh7Ux19wydMjlNrBezfl62+Uu93i7ezPsG8iR9yxevzIPKM91uhZgGwafOtS/YDDD6HxjRXVJnEguzzRIpOTU9TKGISg3I4I46XQ0pNXFUPV1kQwWXEbnm0xVfYQQDOQdPNuk5kfMNSN6cw7j82reqCooSqkFcafJV3/OIYh8YrkwJiIk5DyTIJaESQpC8KlD/RwdLvHQnh5evjCrGnG1WuJKBD0ZE0CdX0sAn7Skk0XPZqq2ckXqYtLWFYWOi2u2Itqg32RWquC8Ec35M2+P8q0TVxa0am3HcBc/3jeeO0u5GTFY8Hh4bw/npurM1kL68y6ljMXe/tyCxzaECpMsVpi0E65hnNKXs7kwVSdMUvoyNkkq6c85WEZELUiQSDK2KqP/2aU5PMugL2tDq/lXKSMpZR0+eWiA3z6+jy/+8c9aoRcoN0OaUYollM67FiTkXBM/SvBbTcqt1sSgjG2xu8cmTJTa5reP7+P0WBkBnelIjinIZCwyjsnuXo+XL8wihKA/axMlkjhWm9elmcaG3k8BRHGq4+KaLYk26DeRZ94e5Zs/Ok+UpPTnHKI44akXG0tCJ6tVeraPe+btUf75vztDzrXYkXeoNCP++b87A7BsYq67lUB/K37ervp89uTEkoTrTD1kV8ljvBKozoTITifDjC1a03kiEDBUcBgt+zimyZFdefYP5Jmq+rx1rcyVmToS2FXyCJOUC9MNVcgjoB5E9OdSJioB3zlxmUxLdVPxU6VsMVQ/GD9O6c2a+JFoTUBS8fDerINjGezuzTJdC+jPuzx+dJDvnLjMX5+boTdr85E9Jd64UqYexAwWXB7cU+LcZB3HMhFCySqbQUyK8uA3imWAZRo6Lq7ZkugY+k3i9FiZb/7wPNAKH8Qpb10rc2q0zN//VwuLidYjY/zWiSvkXEsdY6ikXM61+NaJK8s+/2ox+OVi7rZp8NDeXh490IttGiRSkHUMejyT+3f1MFOPiJIEKSX1MKXcDDk8lOfMRI1yM6I/72IZgsGCx1DRw7UN5huRClGg4s5BrAqJco7JG1fLjJQymEL1VpGoAc5G69iZetgJ6ZQyyu+YqYVMVgLmGyF7+7J86fhenj89xcnrFXpax1yaafLw3h6Gih6mIejLuUzXAjzbpDdrK/15VzJ0IyIXx4RjIyWeOKZL+TVbE+2h3ySePTlBnEr6cjbNKGG87DPfjLAM2FnKLPDC1yNjnKj47GgV6XSOcc1O4nE5VorBLxdz/8pn7uH501McGiry0L6+jtwuYxvU/JgzEzVyrhoUEcYpQQxZ12R3r+qMODrfJEokjx7s5cJUg0vTdeKWrt1oDckAiFPJeEVNRhouebw7XiXvmqRSTSkyTYODA1muzjXxo4Scqyo7DQSpSkky1wjZ3ZvlJ+dmKGVswiSl4FqqhQAw04j42IFe3rpWZqzs05932d3jcfJ6lShJkZIFrXbXY9RtQ1DyLAby7rJKI41mK6AN+k1idL5JX86m3IyYroX4UaIqGRNJpak6Hba98PVozoeKSmddyrx/UVUNEoaKCzeC9dAd13dNwVTVZ3S+SdY2mCg3efliA4HgoT0lPnmon2/+8Lwq54dOA6yBvMPpsSqfPrKDrz5xGHg/bn9oR47TY5XWSDkASSoledciTlWl58cP9NKMUnYWPeYbYasjo2B3j4dtmbi2iZRqAwjiFMsSlBzlYffnXfb0ZXn54gwf2VOi3IwYnWt0Svlty2S46PJLx3by1ScOq9DXD8+32gSkC6SZ6zHmAhVqyWdshoruul9b3ZhLc6vRIZebxK6eDDuLHtO1sGUIU9JUtsIANucm6x0vfD0yxi8d30s9UF5zmqqkXD2IOz1VYO3+MO1j2m0HbBNOXJjl5QuzVJshr16e46/emyZJJHv7PE5er/Avnj1LLYgoZd6f4TlccnEtg7mGWmP7ed+5XualCzPU/Jg9vRklKBEqvJGxVQxboEIsv318H19+7AAf29+L51g4pknBNZhtRFyba9KTsci6JrZpkHMtelrhKNMQFD2bgmcRtFoc2IbAj1KiRNKMVG/116/Mc3gox+mxMs+fnuLIzjw9GRu5DgtuCtWKYKTkMVx0ODiQ4/DOIn/zgREcy1yxp86tGCOo0azGugy6EOJJIcQZIcQ5IcTXl7n/7wshTgkh3hZC/FAIsW/zl3pn8eT9Q5imQcY2cCxl2SSCkZLX6RHe9sLXozn/3AO7+PqvHKGYsZmshRQzNl//lSOdhOh6jUl3vP7CVIOCZ2EY8OrleeYbIZ5lMFMP+On5WRphTG/GIkokEtGSOuZIpOrBcv9Ige+cuMzf/fbrvHBmkoG8w+Eded66Nk+5GbVG0pkMFT1VOBQkFDyLr3z2nk446J/9+gP8Z08exrEMwgQytsFA3lEDM+KURqjaD8w2Qhqh6hPTn7N58ewUM/WQyarPZCUA1LCMMJE0whjPNvjpuRmePTlBkqSMVQJcy1i13zmoL4QQsLs3w2DBxTDU9KYwSdfsqbOR/jsazc1gzZCLEMIE/gh4ArgGvCKEeFpKearrsDeAR6SUDSHEfwz8AfCbN2PBdwptIz06r6o1e3Mu9SAm79mt/inGAunbejTnn3tg17KKFlj/TNLueH3Fjyi4FlPVROnHpepvPteIyDkWjTChN+uQddSot9F5n8cOD1L1Y67ONmhGKaNdCck3r5Y50J/FFAauI+jJ2FyebTBZCRjIOzx27wC9OYe/PDPN2Yl6JxxxdqLOpw71c2aihmsZuK2y/iBphWSaEc0gASHZ1ePy7ngNgWq+VQuSVuMvQIJjCDKOCRL+t7fHsC0DKSHrGCTp6jHz9n1JCucmVc7As0Srp/r74bCViorWkwvRaG4m64mhfxw4J6W8ACCE+DPgC0DHoEsp/7Lr+JeAL27mIu9Ujg6X+L3PH+tIEv0o5vRYlflm3OmLst746lqx2fUak+54fbG1uTSjBM82MITojJWzTUEQq/Fwg0WPgwPZTpJxV0+GsKikkOMVf0FC8q3RMnnHZKzic2Agx9HhIhU/ptwIOD1exTQM+nI205Um339nnN29GS5M1cjaJmGcUPEjXMvAjxP6ci5/6yG14U1Vfd65XuHMRJ1dPRnuGynywtkpXEtp54M4wbNNUqk6L07VQizToBnGCARVPyLfMtDNeHmTbrVkkxLV2MtJUmqBerxfun9n57iViopu1RhBjWYl1hNy2QVc7fr9Wuu2lfgd4N8td4cQ4stCiFeFEK9OTU2tf5V3MN3hlDiFTx/Zwf/w2w/zz379gQ0Z87XCKevpDwML2wQcHMxS9dU4uLxrkXctoiQl0yrzN4Uy6ocGc3i2xS8d29kpZw8TScGzKHo2Qaubl2sZ1IOE+UZExjZVCb4QmAJmairE1E4Uv361jB/GjJWbzNYjLs82yTomg3kX1zJVr5ZUdvq7CwGPHR4k51o8dniQwYKHZ6nKVDVGTg2YaLfFRcDOoosQgkRKhFBGOuNYmK3XorutQFsuqRQ1qsmYbQo8S01kqjbjNXvqbKT/jkZzM9hUlYsQ4ovAI8DfWO5+KeVTwFMAjzzyyAb73N25rLeEfyXWE05Zbibp1dkGYdHla3/+1gKv/n3JYszxg33M1kPeGavSm7X48K4Cp8dqzDciSp7F/v5MpyipuzKy7Y0e2pHjtcvz6kYp8SyDehCzbyDbOXa6FqqeW0iEENSCGMc0CBNJrRIwWHCYqoVMVQP29eeo+BFzjYiRktfp7/7a5XmODOUZKnpUfTVEYqDgYRkqdKVa4QoMQ/VE31XysEyDYsam5sckqRp8rZp5CWbrIVFrQpFAGW8181R9LB3TZLiUIeuYjJWbjFcDHNtcdTTgBxkjqNFsBusx6KPAnq7fd7duW4AQ4nHgd4G/IaUMNmd5GlhfOGWxMXFN0er5bdKft5ZUny42MqfHyp2Kyz19WT5WdBmvBJwaq9Gfc5ckadsbSCljc6A/w2tX5qkHCUNFF8toyRulkhz6cULWNrEMdUEYtIZRNKME0xD0Zh3SNOV6OeDMRJUkTcm7Jp5jEcQprmUQxClnJmodvTzAwYEsr1QDcq7Fw3tLnJmoIwDPNkgkJHHKR3aXePniXCtJaZJ1TEbnQmzLpCdr0pOxuThdJ1jUR7caJLw3WWVfX47hUoa9fVn+8DceXPKardTWQaO5Hawn5PIKcK8Q4oAQwgF+C3i6+wAhxEPA/wB8Xko5ufnLvLtZbzilu8vfQMFjX39uQ4qL96bqOKaBa6spP58+soPPfGgHAwVv2cZhX37sAFGc8M5YjeFShl97cJjHDu9Qo+eihNm6iocPFTyyrkXWMTsJ4WakVCNDRRVCmqnHFD2bI0MFDGEghODgQBbXNqkFCUXPYndvhs89sGtBCOvRg30cP9hHznM4frCPw0N56mHMWNlHppJS1ubDIwVSKTEM1ZPdsQxsw2Co6NGbU2EZwdIvQxBL3pusUfPDJa+1lihqtiJreuhSylgI8feA7wMm8KdSyneEEL8PvCqlfBr4fwJ54M9bybErUsrP38R131UsF05ZqznURhQXbeM0Uwvoy9oEUcLrV+Z5eG8P/Xl31TaxAwWPz3xox4JE4P27egjjhMGCx+h8k0M7BOcmVRjn2lxDFfdIyYO7exnuyfDsOxOkqWQg77S8dzVgeqYecbw1gLk72bjaFYYKHTkcGSowXgn4yblZfvFQP7/xsT0889YYPzwzRZSkuK0B1VnHUrH31uMsp4J5b7LBFx5a2Mh3vaoijeZWsq4YupTye8D3Ft32j7t+fnyT16Xp4kZisxtRXLSN00DexY+UWgTg3JRqarWaSmOljWOsHHcqSE+PlfmDZ88QxCk7DA+nJU3c1ZslTCQ9GYucayGlwLVNPr6/l4vTDaZrAamUa25g7Q3pwlStI6G8PKt6ujitXusAP79eoeBaNMKYREquzKpOi21TvVxSp33f//jXl2iEshNW0RJFzVZEl/7fIWw0NrucV395ps5IyVuSJG0bp+4Ep2MKZmvhmob0ymyDN6/M05d3ODSYY7DgLdk4nj05wb7+HA/s7unc1t5svvrE4U7LgO7NxzYNxqtBRya52gbW3pAW93Q5N1Xn0QN9jM43OXFhptPc7MpsgzCMSSVcnK6vOrVIAoaQhHGyIA+hJYqarYgu/d+mLK4+DeMEQwhsy8Qy4IUzk/zdb7/O7373bVxTUPVjBvIeH93Xg2ebzDYi+vLOim1i217xcNHFNKDSjHj98jwXp2pLpHprDbxeTu5XDWLuHVzYs30l2o+/WEJZa01W2tWTYaLiU3BNcq7FYN7BFAIhlYY941g4hhpbtxypFLgtT7+dh9ASRc1WRHvoG2ArN15aS3HxjefO4liqeOfNq2Vcy+jMCe3LORhCQF+WvpzL0eH3e6ev5RWXMjZ5z+oMmx6vBvze548t+Lu1vNnFISXHFJ3Np28Zhc5iVpJQ2qboXGGcuDDTaW7mxyk9WZVUdW3VijhOUuYbEXP1kMV1R0kq8Wzl+7Q3Ii1R1GxF7iiDfjsN6nqGUNwu1rO2dljl5YsVXMvAs02klFSDmH39OcI46bTBdU1Bxjb4kx9fWvF17o4hD+Q9BvIeqZSMlf0lx64nqbvc5rOehOPpsTLTVb8z4GJ/f4axcsBcM+ZTh/r57VY17peO7+0MBAkilZSNU8mjB3qZqUc0wxi7YDJY8Kg0A8bKgRrMYUDWMSk3Y6ZrPrZpLtiIbvd7r9F0c8cY9NttULeyqmE9a2t7sTU/Ju+qpGcQp53Ohe0kZvfrvJx+vc1GYsgb9WZXSzh2b+qOKZioBOzpy/ILh/o4db3KqbHaAkPept0D51snrjBWbuJaBp842Me9Q0V6az4nzs9S8Cxcy2CyIil4NgN5m7mGGkZtCDg5WuHgYF6PntNsWe4Yg367DepWVjWsZ21tL9k2BUGUQKus//5dxQWGeL2v80allBvxZlfaLFxTLNjUXzw7RdWP2Vly2VHIsOPI+3+33HO1m5t1b1qplNimyYGBHENFl8lqQJRKhgoOPVkH0ZqHmvMswiTdEldkGs1K3DFJ0bUSazeb9Rb33A7Ws7a2l3zfSJGJWsBYuYkfxfzs4ix/dWaSU109zdfzOq+n5e+N8uT9Q1ydbfDCmUm+/84YL5yZ5OpsAwkL2tNGiSTvmpybrK+61sUst/Zff3gEAVyf9wHJRCXgvYkaU9UAISRRklLo6rio0WxF7hgP/XbLxG6kuOdWsd61HR0u8dvH9zFW9pmth5QbIVO1kJxjMpB3KDfVcImsbXJgMN/5u9VCKSspYD5orqPdU0W0lOCplEzVAj60s9g5Ju9ZBGFMxY/WXOtya+rWyf/h989ycbpO3jUpehbXyz62IXBMgxhBJYq5Z0BsmbyJRrMcd4yHfrtlYjfTI72Va2trwj99ZAc7Shn29WUZyLtcmGpQytgcGcpztjX4+UZe580oie9e4y/dt5NPH9nBvv4c5Wa84Erk0GCOWqBaCay21rXW9OzJCaZrAQXPIuNYJBLyjkWSSsJEknNNhkouzVjqgRWaLc0d46FvBZnY7VA1rOXtLr7/dz61f9U1dsfbuxOkbS93b3+ORvS+4mWjr/Nm5DpWygkUPZWkbf/uWCZ7+7OMlLxVC5DWWtPofJMgTjpDLMI4VX1n4pSca7K7N4uUkpofb5m8iUazHHeMQYe7Tya2lrKnXVI/Ww8J45T3Jqq8fW2ef/jkkRVfp+7QVb41lxPen8hT9WOOtZp83QibkTxeKbx230iJJ+8fWrCBrXau613Trp4M701UCeIUzzbVOLxITUHq7hCZb4WztkLeRKNZjjsm5HI3staMym+fuMyVGdWPpJ3IvDLT4NsnLq/4mAsGXAxkO9WUfVmLF85M8qN3J5mq+jfcNXAzkserhde6O0p+9YnD69rgd/VkuDJT56ULM52BGVdm6p01PXn/EAN5l6of0wxjco6JHyfYptGaMlVRuQVL6GpQzZZGG/QtzFrKnjeuzqu+4a3JQJ5tkndN3rg6v+JjLp6g9OjBPj60M887YzUAPnlP36qT7ddiM3Idm52vODyU4/UranB13lFVsK9fmefwUK7zfF/75cMcP9hHnIJlGjy4q0Rv1qGYcci7FhnbZLwS8vjRwbvqKlFzZ3FHhVzuNtZS9gjEkg6BkveVISuxOHT1jefOsqOYWfA8cGMa/83KdWxmeO3sRJ2H9vQwXg2o+THFjM3hHXnOTtT5XNfz/bNff6DzN//ou29TCxMljfQsDg3mcCxzwd9oNFsNbdC3MGvJER/aU+LEhVmEEJ2pPrUg4fjBvg09z2YXTW21XMfofJN9A7kFUsxUyhXP7/RYmR+fm6E3Y5F3zU5/+I/sKVEL4mX/RqPZCuiQyxZmrdDDF4/v48CAChu0VSoHBnJ88fi+DT3PVi6a2gw2en7PnpygN2uDEJ1QlmsZnB6rbpvXRLM90R76BrnVDcJW83bbsd8Pup6tXDS1GWz0/EbnmxwbKfDGFZVDiNOUqUpAPUy4f6TIM2+PcnaiviW7bmruboSUy81pufk88sgj8tVXX70tz32jdMsIuw3DVikw+iA88/Yo3zpxhYmKz1DR40vH93YaWm0HNrIRtwduTFaavHJpjvlGhGnC7p4Mx4ZLvHFVjefb25/bVp8BzZ2BEOI1KeUjy92nPfQNcLsbhN0sTo+Vef70FMeGizx6oI+qH/P86SkODubv6PPqZiNx/SfvH+q0AhBAKWMRS4gTycWZOjnXYrwSsH8gv20+A5rtgY6hb4Db3SDsZrGW3v1u4+hwiaGiS8GzCBKJY5ns7slQyjqMldXko+7+MdvhM6DZHmgPfQPc7gZhN4ut3Br4dhEmkscOD+JenCVoDc5uhyerQbLtPgOa7YH20DfA7W4QdrPY7iqXG6H9mhwazBHEKX6U4EcJvVmbehCzs+huq8+AZnugDfoG2ModFz8I23Wj+iC0XxPHMvnIHvX+zjdjPr6/j6//yhH2D+S31WdAsz3QKhcNsLUHYN8u9Gui2YpolYtmTbZadeetZiXjfTe/Jpo7Dx1y0dz1bMZQDo1mK6ANuuauR8s2NdsFbdA1dz3btb5Ac/ehDbrmrkfLNjXbBW3QNXc9Wrap2S5og66569mu9QWauw8tW9Ro0LJNzfZAe+gajUazTdAGXaPRaLYJ2qBrNBrNNkEbdI1Go9kmaIOu0Wg024Tb1m1RCDEF1IHp27KAW8MA+vzuZLbz+W3nc4PtfX77pJSDy91x2ww6gBDi1ZXaQG4H9Pnd2Wzn89vO5wbb//xWQodcNBqNZpugDbpGo9FsE263QX/qNj//zUaf353Ndj6/7XxusP3Pb1luawxdo9FoNJvH7fbQNRqNRrNJaIOu0Wg024RbatCFEL8hhHhHCJEKIVaUFAkhLgkhfi6EeFMI8eqtXOMHYQPn96QQ4owQ4pwQ4uu3co0fBCFEnxDiOSHEe63/9q5wXNJ6794UQjx9q9e5EdZ6L4QQrhDif2nd/7IQYv9tWOYNs47z+ztCiKmu9+v/dDvWeaMIIf5UCDEphDi5wv1CCPHfts7/bSHEw7d6jbcUKeUt+wccBY4ALwCPrHLcJWDgVq7tVp0fYALngYOAA7wFHLvda1/n+f0B8PXWz18H/sUKx9Vu91rXeT5rvhfAfwL8962ffwv4X273ujf5/P4O8P++3Wv9AOf4GPAwcHKF+38V+HeAAD4BvHy713wz/91SD11KeVpKeeZWPuetZJ3n93HgnJTygpQyBP4M+MLNX92m8AXgW62fvwX8rdu3lE1hPe9F9zn/a+CzQghxC9f4QbiTP2vrQkr5IjC7yiFfAP5nqXgJ6BFCDN+a1d16tmoMXQI/EEK8JoT48u1ezCazC7ja9fu11m13AkNSyrHWz+PASjPaPCHEq0KIl4QQf+vWLO2GWM970TlGShkDZaD/lqzug7Pez9q/1wpH/GshxJ5bs7Rbxp38fdswmz6xSAjxPLBzmbt+V0r5b9f5MJ+SUo4KIXYAzwkh3m3txLedTTq/Lctq59f9i5RSCiFW0rzua71/B4EfCSF+LqU8v9lr1WwK/yvwL6WUgRDi76KuRj5zm9ekuUE23aBLKR/fhMcYbf13UgjxF6hLxy1h0Dfh/EaBbi9od+u2LcFq5yeEmBBCDEspx1qXrZMrPEb7/bsghHgBeAgVy91qrOe9aB9zTQhhASVg5tYs7wOz5vlJKbvP5Y9ReZLtxJb+vm02Wy7kIoTICSEK7Z+BXwKWzWDfobwC3CuEOCCEcFCJti2tBOniaeBLrZ+/BCy5IhFC9Aoh3NbPA8AvAKdu2Qo3xnrei+5z/t8DP5KtbNsdwJrntyie/Hng9C1c363gaeD/2FK7fAIod4UNtx+3OCP9t1ExrACYAL7fun0E+F7r54OobPxbwDuoUMZtzx5v1vm1fv9V4CzKa72Tzq8f+CHwHvA80Ne6/RHgj1s/fxL4eev9+znwO7d73Wuc05L3Avh94POtnz3gz4FzwM+Ag7d7zZt8fv+P1vfsLeAvgQ/d7jVv8Pz+JTAGRK3v3u8A/xHwH7XuF8Aftc7/56yirtsO/3Tpv0aj0WwTtlzIRaPRaDQ3hjboGo1Gs03QBl2j0Wi2CdqgazQazTZBG3SNRqPZJmiDrtFoNNsEbdA1Go1mm/D/BwJvmr/K0UV2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data_dnase_sig[\"logfc.mean\"], data_dnase_sig[\"obs_scores\"], alpha=0.5)\n",
+    "print(scipy.stats.pearsonr(data_dnase_sig['obs_scores'],data_dnase_sig['logfc.mean']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7ff236fe3250>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data_dnase_sig[\"jsd_sign.mean\"], data_dnase_sig[\"obs_scores\"], alpha=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(-0.03014559908424015, 0.11686422647093589)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "print(scipy.stats.pearsonr(data_atac_sig['obs_scores'],data_atac_sig['abs_logfc_x_jsd_x_max_percentile.mean']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.2811877545943154, 2.257738745972026e-50)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(scipy.stats.pearsonr(data_atac_sig['obs_scores'],data_atac_sig['logfc_x_jsd.mean']))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.3730024279092575, 4.126979535936469e-90)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "print(scipy.stats.pearsonr(data_atac_sig['obs_scores'],data_atac_sig['jsd_sign.mean']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(scipy.stats.pearsonr(data_atac_sig[data_atac_sig[\"peak_overlap\"]]['obs_scores'],data_atac_sig[data_atac_sig[\"peak_overlap\"]]['logfc_x_jsd_x_max_percentile.mean']))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print(scipy.stats.pearsonr(data_atac_sig[data_atac_sig[\"peak_overlap\"]]['obs_scores'],data_atac_sig[data_atac_sig[\"peak_overlap\"]]['jsd_sign.mean']))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.scatter(data_atac_sig[data_atac_sig[\"peak_overlap\"]][\"logfc_x_jsd_x_max_percentile.mean\"], data_atac_sig[data_atac_sig[\"peak_overlap\"]][\"obs_scores\"]-0.5, alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.scatter(data_atac_sig[data_atac_sig[\"peak_overlap\"]][\"jsd_sign.mean\"], data_atac_sig[data_atac_sig[\"peak_overlap\"]][\"obs_scores\"]-0.5, alpha=0.5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "156"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_atac_sig = data_atac[data_atac[\"Peak_id\"].isin(sig_peaks_ids)].drop_duplicates()\n",
+    "data_atac_nonsig = data_atac[~data_atac[\"Peak_id\"].isin(sig_peaks_ids)].drop_duplicates()\n",
+    "data_atac_sig[\"obs_scores\"] = data_atac_sig[\"obs_scores\"].astype(float)\n",
+    "data_atac_nonsig[\"obs_scores\"] = data_atac_nonsig[\"obs_scores\"].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4763933590690797\n",
+      "0.4718914237544454\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.mean(data_atac_sig['obs_scores']))\n",
+    "print(np.mean(data_atac_nonsig['obs_scores']))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f5510a95e50>"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data_atac_sig['obs_scores'], density=True, alpha=0.5, label=\"sig\")\n",
+    "plt.hist(data_atac_nonsig['obs_scores'],density=True, alpha=0.5, label=\"non-sig\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/dsqtls_variant_scorer_final.ipynb b/src/evaluation/figure_notebooks/figure_6/dsqtls_variant_scorer_final.ipynb
index d1f27548..362a9357 100644
--- a/src/evaluation/figure_notebooks/figure_6/dsqtls_variant_scorer_final.ipynb
+++ b/src/evaluation/figure_notebooks/figure_6/dsqtls_variant_scorer_final.ipynb
@@ -1226,6 +1226,7 @@
     "\n",
     "\n",
     "#abs_logfc_x_jsd_x_max_percentile.mean\n",
+    "\n",
     "keyd=\"abs_logfc_x_max_percentile.mean\"\n",
     "key_short=\"logfc_x_max_percentile\"\n",
     "plt.figure(figsize=(10,6))\n",
@@ -1300,6 +1301,7 @@
     "\n",
     "\n",
     "#abs_logfc_x_jsd_x_max_percentile.mean\n",
+    "\n",
     "keyd=\"jsd.mean\"\n",
     "key_short=\"jsd\"\n",
     "plt.figure(figsize=(10,6))\n",
@@ -1454,6 +1456,7 @@
     "\n",
     "\n",
     "#max_percentile.mean\n",
+    "\n",
     "keyd=\"max_percentile.mean\"\n",
     "key_short=\"max_percentile\"\n",
     "plt.figure(figsize=(10,6))\n",
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/Untitled.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/Untitled.ipynb
new file mode 100644
index 00000000..6ab11fab
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/Untitled.ipynb
@@ -0,0 +1,5436 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from scipy import stats\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Locus</th>\n",
+       "      <th>deltaSVM</th>\n",
+       "      <th>Enf.CAGE.Agnostic</th>\n",
+       "      <th>Enf.CAGE</th>\n",
+       "      <th>Enf.DNase.Agnostic</th>\n",
+       "      <th>Enf.DNase</th>\n",
+       "      <th>Enformer</th>\n",
+       "      <th>G3</th>\n",
+       "      <th>G7</th>\n",
+       "      <th>dSVM LASSO</th>\n",
+       "      <th>dSVM RF</th>\n",
+       "      <th>dSVM (1 DHS)</th>\n",
+       "      <th>G5</th>\n",
+       "      <th>CADD</th>\n",
+       "      <th>Inter-replicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F9</td>\n",
+       "      <td>0.483518</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>0.573</td>\n",
+       "      <td>0.291</td>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.624</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.557</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.489</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>-0.086</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GP1BB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>0.555</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.563</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.261</td>\n",
+       "      <td>0.371</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>-0.083</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HBB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.550</td>\n",
+       "      <td>0.578</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.446</td>\n",
+       "      <td>0.416</td>\n",
+       "      <td>0.445</td>\n",
+       "      <td>0.328</td>\n",
+       "      <td>0.393</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HBG1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.178</td>\n",
+       "      <td>0.302</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.189</td>\n",
+       "      <td>0.474</td>\n",
+       "      <td>0.571</td>\n",
+       "      <td>0.591</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>0.342</td>\n",
+       "      <td>0.479</td>\n",
+       "      <td>0.174</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HNF4A</td>\n",
+       "      <td>0.091578</td>\n",
+       "      <td>0.173</td>\n",
+       "      <td>0.156</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>0.288</td>\n",
+       "      <td>0.335</td>\n",
+       "      <td>0.339</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.225</td>\n",
+       "      <td>0.151</td>\n",
+       "      <td>0.134</td>\n",
+       "      <td>0.191</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.89</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Locus  deltaSVM  Enf.CAGE.Agnostic  Enf.CAGE  Enf.DNase.Agnostic  \\\n",
+       "0     F9  0.483518              0.245     0.252               0.573   \n",
+       "1  GP1BB       NaN              0.370     0.469               0.555   \n",
+       "2    HBB       NaN              0.491     0.596               0.550   \n",
+       "3   HBG1       NaN              0.178     0.302               0.104   \n",
+       "4  HNF4A  0.091578              0.173     0.156               0.170   \n",
+       "\n",
+       "   Enf.DNase  Enformer     G3     G7  dSVM LASSO  dSVM RF  dSVM (1 DHS)  \\\n",
+       "0      0.291     0.572  0.624  0.391       0.557    0.566         0.489   \n",
+       "1      0.564     0.563  0.556  0.566       0.261    0.371         0.421   \n",
+       "2      0.578     0.600  0.446  0.416       0.445    0.328         0.393   \n",
+       "3      0.189     0.474  0.571  0.591       0.397    0.439         0.342   \n",
+       "4      0.288     0.335  0.339  0.397       0.225    0.151         0.134   \n",
+       "\n",
+       "      G5   CADD  Inter-replicate  \n",
+       "0  0.564 -0.086             0.88  \n",
+       "1  0.348 -0.083             0.88  \n",
+       "2  0.392  0.049             0.77  \n",
+       "3  0.479  0.174             0.92  \n",
+       "4  0.191  0.111             0.89  "
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/CAGI/output/\"\n",
+    "dirs = os.listdir(odir)\n",
+    "\n",
+    "enformer = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer/allGroupsBenchmark.txt\", sep=\"\\t\", header=0)\n",
+    "enformer.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cagi_df_list = []\n",
+    "\n",
+    "for idir in dirs: \n",
+    "    idir=str(idir)\n",
+    "    ifile=odir+idir+\"/summary.mean.variant_scores_new_2.tsv\"\n",
+    "    if os.path.isfile(ifile):\n",
+    "        if not ifile.endswith(\"tsv\"):\n",
+    "            continue\n",
+    "        data = pd.read_csv(ifile, sep=\"\\t\", header=0)\n",
+    "        #print(data.head())\n",
+    "        data[[\"chr_temp\", \"pos_temp\", \"ref_temp\", \"alt_temp\", \"label\", \"Confidence\", \"challenge\"]] = data[\"rsid\"].str.split(\",\", expand=True)\n",
+    "        #print(data.columns)\n",
+    "        data[\"sign_jsd.mean\"] = data[\"jsd.mean\"] * np.sign(data[\"logfc.mean\"])\n",
+    "        data[\"model_used\"] = idir\n",
+    "        data = data[['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean', \"sign_jsd.mean\", 'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'abs_logfc_x_jsd_x_max_percentile.mean.pval', \"label\", \"Confidence\", \"challenge\", \"model_used\"]]\n",
+    "        cagi_df_list.append(data)\n",
+    "\n",
+    "cagi_df = pd.concat(cagi_df_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>challenge</th>\n",
+       "      <th>model_used</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.143521</td>\n",
+       "      <td>-0.024822</td>\n",
+       "      <td>0.001164</td>\n",
+       "      <td>0.043634</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.131439</td>\n",
+       "      <td>-0.023676</td>\n",
+       "      <td>0.000977</td>\n",
+       "      <td>0.047589</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.086450</td>\n",
+       "      <td>-0.023025</td>\n",
+       "      <td>0.000608</td>\n",
+       "      <td>0.067393</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.013275</td>\n",
+       "      <td>-0.027508</td>\n",
+       "      <td>0.000358</td>\n",
+       "      <td>0.125360</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.033312</td>\n",
+       "      <td>0.020898</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.150673</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr       pos allele1 allele2  \\\n",
+       "0  chr6  37775290       G       A   \n",
+       "1  chr6  37775290       G       C   \n",
+       "2  chr6  37775290       G       T   \n",
+       "3  chr6  37775291       T       A   \n",
+       "4  chr6  37775291       T       C   \n",
+       "\n",
+       "                                             rsid  logfc.mean  sign_jsd.mean  \\\n",
+       "0   6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.143521      -0.024822   \n",
+       "1   6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv   -0.131439      -0.023676   \n",
+       "2   6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv   -0.086450      -0.023025   \n",
+       "3  6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv   -0.013275      -0.027508   \n",
+       "4   6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv    0.033312       0.020898   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                               0.001164   \n",
+       "1                               0.000977   \n",
+       "2                               0.000608   \n",
+       "3                               0.000358   \n",
+       "4                               0.000256   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean.pval  label Confidence  \\\n",
+       "0                                    0.043634   0.03       0.01   \n",
+       "1                                    0.047589   0.03       0.01   \n",
+       "2                                    0.067393   0.07       0.01   \n",
+       "3                                    0.125360  -0.07       0.01   \n",
+       "4                                    0.150673   0.03       0.01   \n",
+       "\n",
+       "              challenge            model_used  \n",
+       "0  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "1  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "2  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "3  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "4  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "odir = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/data/\"\n",
+    "dirs = os.listdir(odir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_HBG1.tsv\n",
+      "challenge_PKLR.tsv\n",
+      "challenge_MYCrs6983267.tsv\n",
+      "challenge_TERT-GBM.tsv\n",
+      "challenge_MSMB.tsv\n",
+      "challenge_F9.tsv\n",
+      "challenge_IRF6.tsv\n",
+      "challenge_HNF4A.tsv\n",
+      "challenge_IRF4.tsv\n",
+      "challenge_SORT1.tsv\n",
+      "challenge_ZFAND3.tsv\n",
+      "challenge_HBB.tsv\n",
+      "challenge_LDLR.tsv\n",
+      "challenge_TERT-HEK293T.tsv\n",
+      "challenge_GP1BB.tsv\n"
+     ]
+    }
+   ],
+   "source": [
+    "enf_dfs= []\n",
+    "for idir in dirs:\n",
+    "    idir=str(idir)\n",
+    "    ifile=odir+idir\n",
+    "    if os.path.isfile(ifile):\n",
+    "            #print(ifile)\n",
+    "#             if idir in [\"challenge_MSMB.tsv\"]: # MSMB ref and alt also dont match\n",
+    "#                 continue\n",
+    "            if not ifile.endswith(\"tsv\"):\n",
+    "                continue\n",
+    "            if not idir.startswith(\"challenge\"):\n",
+    "                continue\n",
+    "            print(idir)\n",
+    "            orig = pd.read_csv(ifile, sep=\"\\t\", header=None, comment=\"#\")\n",
+    "            orig[0] = \"chr\"+orig[0].astype(str)\n",
+    "            orig.columns = [\"chr_hg19\", \"pos_hg19\", \"ref\", \"alt\", \"label\", \"Confidence\"]\n",
+    "            ifile1 = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/CAGI5_train_test_hg19_to_hg38_liftover/\"\n",
+    "            liftoverd = ifile1 + \"withChr.\"+idir\n",
+    "            lft_data = pd.read_csv(liftoverd, sep=\"\\t\", header=None)\n",
+    "            lft_data.columns = [\"chr_hg38\", \"pos_hg38\", \"ref_hg38\", \"alt_hg38\", \"label_hg38\", \"Confidence_hg38\"]\n",
+    "            assert(lft_data.shape[0]==orig.shape[0])\n",
+    "            \n",
+    "            combined = pd.concat([orig, lft_data],axis=1)\n",
+    "            combined[\"challenge\"] = idir\n",
+    "            #print(combined.head())\n",
+    "            \n",
+    "#             assert((combined[\"ref\"]==combined[\"ref_hg38\"]).all())\n",
+    "#             assert((combined[\"alt\"]==combined[\"alt_hg38\"]).all())\n",
+    "            assert((combined[\"label\"]==combined[\"label_hg38\"]).all())\n",
+    "            assert((combined[\"Confidence\"]==combined[\"Confidence_hg38\"]).all())\n",
+    "            \n",
+    "            enf_dfs.append(combined)\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>ref_hg38</th>\n",
+       "      <th>alt_hg38</th>\n",
+       "      <th>label_hg38</th>\n",
+       "      <th>Confidence_hg38</th>\n",
+       "      <th>challenge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271035</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249805</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>challenge_HBG1.tsv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chr_hg19  pos_hg19 ref alt  label  Confidence chr_hg38  pos_hg38 ref_hg38  \\\n",
+       "0    chr11   5271035   G   A   0.19        0.06    chr11   5249805        G   \n",
+       "\n",
+       "  alt_hg38  label_hg38  Confidence_hg38           challenge  \n",
+       "0        A        0.19             0.06  challenge_HBG1.tsv  "
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf_df = pd.concat(enf_dfs)\n",
+    "enf_df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>challenge</th>\n",
+       "      <th>model_used</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.143521</td>\n",
+       "      <td>-0.024822</td>\n",
+       "      <td>0.001164</td>\n",
+       "      <td>0.043634</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.131439</td>\n",
+       "      <td>-0.023676</td>\n",
+       "      <td>0.000977</td>\n",
+       "      <td>0.047589</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.086450</td>\n",
+       "      <td>-0.023025</td>\n",
+       "      <td>0.000608</td>\n",
+       "      <td>0.067393</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.013275</td>\n",
+       "      <td>-0.027508</td>\n",
+       "      <td>0.000358</td>\n",
+       "      <td>0.125360</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.033312</td>\n",
+       "      <td>0.020898</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.150673</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1242</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775848</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775848,G,C,0.19,0.03,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.012688</td>\n",
+       "      <td>0.011428</td>\n",
+       "      <td>0.000087</td>\n",
+       "      <td>0.362118</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1243</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775848</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775848,G,T,0.02,0.0,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.014364</td>\n",
+       "      <td>-0.010755</td>\n",
+       "      <td>0.000096</td>\n",
+       "      <td>0.424178</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1244</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775849,C,A,0.0,0.0,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.009251</td>\n",
+       "      <td>-0.012372</td>\n",
+       "      <td>0.000121</td>\n",
+       "      <td>0.369296</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1245</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>6,37775849,C,G,-0.02,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.007366</td>\n",
+       "      <td>0.012662</td>\n",
+       "      <td>0.000069</td>\n",
+       "      <td>0.434610</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1246</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775849,C,T,0.03,0.02,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.011113</td>\n",
+       "      <td>-0.013638</td>\n",
+       "      <td>0.000189</td>\n",
+       "      <td>0.363735</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>28422 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr       pos allele1 allele2  \\\n",
+       "0     chr6  37775290       G       A   \n",
+       "1     chr6  37775290       G       C   \n",
+       "2     chr6  37775290       G       T   \n",
+       "3     chr6  37775291       T       A   \n",
+       "4     chr6  37775291       T       C   \n",
+       "...    ...       ...     ...     ...   \n",
+       "1242  chr6  37775848       G       C   \n",
+       "1243  chr6  37775848       G       T   \n",
+       "1244  chr6  37775849       C       A   \n",
+       "1245  chr6  37775849       C       G   \n",
+       "1246  chr6  37775849       C       T   \n",
+       "\n",
+       "                                                rsid  logfc.mean  \\\n",
+       "0      6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.143521   \n",
+       "1      6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv   -0.131439   \n",
+       "2      6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv   -0.086450   \n",
+       "3     6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv   -0.013275   \n",
+       "4      6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv    0.033312   \n",
+       "...                                              ...         ...   \n",
+       "1242   6,37775848,G,C,0.19,0.03,challenge_ZFAND3.tsv    0.012688   \n",
+       "1243    6,37775848,G,T,0.02,0.0,challenge_ZFAND3.tsv   -0.014364   \n",
+       "1244     6,37775849,C,A,0.0,0.0,challenge_ZFAND3.tsv   -0.009251   \n",
+       "1245  6,37775849,C,G,-0.02,0.01,challenge_ZFAND3.tsv    0.007366   \n",
+       "1246   6,37775849,C,T,0.03,0.02,challenge_ZFAND3.tsv   -0.011113   \n",
+       "\n",
+       "      sign_jsd.mean  abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0         -0.024822                               0.001164   \n",
+       "1         -0.023676                               0.000977   \n",
+       "2         -0.023025                               0.000608   \n",
+       "3         -0.027508                               0.000358   \n",
+       "4          0.020898                               0.000256   \n",
+       "...             ...                                    ...   \n",
+       "1242       0.011428                               0.000087   \n",
+       "1243      -0.010755                               0.000096   \n",
+       "1244      -0.012372                               0.000121   \n",
+       "1245       0.012662                               0.000069   \n",
+       "1246      -0.013638                               0.000189   \n",
+       "\n",
+       "      abs_logfc_x_jsd_x_max_percentile.mean.pval  label Confidence  \\\n",
+       "0                                       0.043634   0.03       0.01   \n",
+       "1                                       0.047589   0.03       0.01   \n",
+       "2                                       0.067393   0.07       0.01   \n",
+       "3                                       0.125360  -0.07       0.01   \n",
+       "4                                       0.150673   0.03       0.01   \n",
+       "...                                          ...    ...        ...   \n",
+       "1242                                    0.362118   0.19       0.03   \n",
+       "1243                                    0.424178   0.02        0.0   \n",
+       "1244                                    0.369296    0.0        0.0   \n",
+       "1245                                    0.434610  -0.02       0.01   \n",
+       "1246                                    0.363735   0.03       0.02   \n",
+       "\n",
+       "                 challenge            model_used  \n",
+       "0     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "1     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "2     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "3     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "4     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "...                    ...                   ...  \n",
+       "1242  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1243  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1244  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1245  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1246  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "\n",
+       "[28422 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enf_df[\"label\"] = enf_df[\"label\"].astype(float)\n",
+    "enf_df[\"Confidence\"] = enf_df[\"Confidence\"].astype(float)\n",
+    "cagi_df[\"label\"] = cagi_df[\"label\"].astype(float)\n",
+    "cagi_df[\"Confidence\"] = cagi_df[\"Confidence\"].astype(float)\n",
+    "enf_df[\"pos_hg19\"] = enf_df[\"pos_hg19\"].astype(int)\n",
+    "cagi_df[\"pos\"] = cagi_df[\"pos\"].astype(int)\n",
+    "\n",
+    "cagi_df_new = cagi_df.merge(enf_df, left_on=[\"chr\", \"pos\", \"allele1\", \"allele2\", \"label\", \"Confidence\", \"challenge\"], right_on=[\"chr_hg19\", \"pos_hg19\", \"ref\", \"alt\", \"label\", \"Confidence\", \"challenge\"], how=\"left\", validate=\"many_to_one\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28422, 23)"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28422, 13)"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28422, 13)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>ref_hg38</th>\n",
+       "      <th>alt_hg38</th>\n",
+       "      <th>label_hg38</th>\n",
+       "      <th>Confidence_hg38</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.143521</td>\n",
+       "      <td>-0.024822</td>\n",
+       "      <td>0.001164</td>\n",
+       "      <td>0.043634</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807514</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr       pos allele1 allele2  \\\n",
+       "0  chr6  37775290       G       A   \n",
+       "\n",
+       "                                            rsid  logfc.mean  sign_jsd.mean  \\\n",
+       "0  6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.143521      -0.024822   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                               0.001164   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean.pval  label  ...  chr_hg19  pos_hg19  \\\n",
+       "0                                    0.043634   0.03  ...      chr6  37775290   \n",
+       "\n",
+       "  ref alt  chr_hg38  pos_hg38 ref_hg38 alt_hg38  label_hg38 Confidence_hg38  \n",
+       "0   G   A      chr6  37807514        G        A        0.03            0.01  \n",
+       "\n",
+       "[1 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df_new.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.isnan(cagi_df_new[\"pos_hg38\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ENCSR056PFI_pancreas',\n",
+       " 'ENCSR595HZQ_pancreas',\n",
+       " 'ENCSR724CND_keratinocyte',\n",
+       " 'ENCSR000EPQ_keratinocyte',\n",
+       " 'ENCSR000EJR_HEK293',\n",
+       " 'ENCSR035RVH_keratinocyte',\n",
+       " 'ENCSR236KPK_pancreas',\n",
+       " 'K562',\n",
+       " 'ENCSR464TKV_pancreas',\n",
+       " 'HEPG2',\n",
+       " 'ENCSR999VDH_pancreas',\n",
+       " 'ENCSR000FEK_SK-MEL',\n",
+       " 'ENCSR539WBA_pancreas',\n",
+       " 'ENCSR338NEI_pancreas',\n",
+       " 'ENCSR242RPQ_pancreas',\n",
+       " 'ENCSR828FVZ_pancreas',\n",
+       " 'ENCSR405YLV_pancreas']"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(set(cagi_df_new[\"model_used\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cagi_df_new[\"model_used\"] = cagi_df_new[\"model_used\"].str.replace(\"K562\",'ENCSR000EOT_K562')\n",
+    "cagi_df_new[\"model_used\"] = cagi_df_new[\"model_used\"].str.replace(\"HEPG2\",'ENCSR149XIL_HEPG2')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ENCSR056PFI_pancreas',\n",
+       " 'ENCSR595HZQ_pancreas',\n",
+       " 'ENCSR724CND_keratinocyte',\n",
+       " 'ENCSR000EPQ_keratinocyte',\n",
+       " 'ENCSR000EJR_HEK293',\n",
+       " 'ENCSR035RVH_keratinocyte',\n",
+       " 'ENCSR236KPK_pancreas',\n",
+       " 'ENCSR000EOT_K562',\n",
+       " 'ENCSR464TKV_pancreas',\n",
+       " 'ENCSR999VDH_pancreas',\n",
+       " 'ENCSR405YLV_pancreas',\n",
+       " 'ENCSR000FEK_SK-MEL',\n",
+       " 'ENCSR539WBA_pancreas',\n",
+       " 'ENCSR338NEI_pancreas',\n",
+       " 'ENCSR242RPQ_pancreas',\n",
+       " 'ENCSR828FVZ_pancreas',\n",
+       " 'ENCSR149XIL_HEPG2']"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(set(cagi_df_new[\"model_used\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_ids = [\"ENCSR178JBL_pancreas\", \"ENCSR464TKV_pancreas\", \"ENCSR828FVZ_pancreas\", \n",
+    " \"ENCSR539WBA_pancreas\", \"ENCSR405YLV_pancreas\", \"ENCSR595HZQ_pancreas\",\n",
+    " \"ENCSR000ELH_keratinocyte\", \"ENCSR000EPQ_keratinocyte\", \"ENCSR035RVH_keratinocyte\",\n",
+    " \"ENCSR724CND_keratinocyte\", \"ENCSR000FEK_SK-MEL\", \"ENCSR000EJV_HEPG2\", \"ENCSR149XIL_HEPG2\",\n",
+    " \"ENCSR000EKN_K562\", \"ENCSR000EKP_K562\", \"ENCSR000EKQ_k562\", \"ENCSR000EOT_K562\",\n",
+    " \"ENCSR000EJR_HEK293\"]\n",
+    "\n",
+    "convert_dict = {\"ENCSR464TKV\":\"ENCSR228IKB\",\n",
+    "                \"ENCSR828FVZ\":\"ENCSR421KNA\",\n",
+    "                \"ENCSR539WBA\":\"ENCSR522WLH\",\n",
+    "                \"ENCSR405YLV\":\"ENCSR524OCB\"}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import deepdish as dd\n",
+    "datat = dd.io.load(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/sad.h5\")\n",
+    "enformer_preds = pd.DataFrame()\n",
+    "\n",
+    "enformer_preds[\"chr\"] = datat[\"chr\"].astype(str)\n",
+    "enformer_preds[\"pos\"] = datat[\"pos\"]\n",
+    "enformer_preds[\"ref\"] = datat[\"ref_allele\"].astype(str)\n",
+    "enformer_preds[\"alt\"] = datat[\"alt_allele\"].astype(str)\n",
+    "\n",
+    "enformer_preds[\"rsid\"] = enformer_preds[\"chr\"].astype(str) + '_' +  enformer_preds[\"pos\"].astype(str) + \"_\" + enformer_preds[\"ref\"] + '_' +  enformer_preds[\"alt\"]\n",
+    "\n",
+    "\n",
+    "enformer_labesls = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/target_humans.txt\", sep=\"\\t\", header=0)\n",
+    "enformer_labesls[\"encode_id\"] = enformer_labesls[\"file\"].str.split(\"/\", expand=True)[8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>genome</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>file</th>\n",
+       "      <th>clip</th>\n",
+       "      <th>scale</th>\n",
+       "      <th>sum_stat</th>\n",
+       "      <th>description</th>\n",
+       "      <th>encode_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENCFF833POA</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2</td>\n",
+       "      <td>mean</td>\n",
+       "      <td>DNASE:cerebellum male adult (27 years) and mal...</td>\n",
+       "      <td>ENCSR000EIJ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENCFF110QGM</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2</td>\n",
+       "      <td>mean</td>\n",
+       "      <td>DNASE:frontal cortex male adult (27 years) and...</td>\n",
+       "      <td>ENCSR000EIK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENCFF880MKD</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2</td>\n",
+       "      <td>mean</td>\n",
+       "      <td>DNASE:chorion</td>\n",
+       "      <td>ENCSR000EIL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENCFF463ZLQ</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2</td>\n",
+       "      <td>mean</td>\n",
+       "      <td>DNASE:Ishikawa treated with 0.02% dimethyl sul...</td>\n",
+       "      <td>ENCSR000EIP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ENCFF890OGQ</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/dnase/encode/...</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2</td>\n",
+       "      <td>mean</td>\n",
+       "      <td>DNASE:GM03348</td>\n",
+       "      <td>ENCSR000EIS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5308</th>\n",
+       "      <td>5308</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CNhs14239</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/cage/fantom/C...</td>\n",
+       "      <td>384</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sum</td>\n",
+       "      <td>CAGE:epithelioid sarcoma cell line:HS-ES-2R</td>\n",
+       "      <td>CNhs14239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5309</th>\n",
+       "      <td>5309</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CNhs14240</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/cage/fantom/C...</td>\n",
+       "      <td>384</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sum</td>\n",
+       "      <td>CAGE:squamous cell lung carcinoma cell line:RE...</td>\n",
+       "      <td>CNhs14240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5310</th>\n",
+       "      <td>5310</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CNhs14241</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/cage/fantom/C...</td>\n",
+       "      <td>384</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sum</td>\n",
+       "      <td>CAGE:gastric cancer cell line:GSS</td>\n",
+       "      <td>CNhs14241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5311</th>\n",
+       "      <td>5311</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CNhs14244</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/cage/fantom/C...</td>\n",
+       "      <td>384</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sum</td>\n",
+       "      <td>CAGE:carcinoid cell line:NCI-H727</td>\n",
+       "      <td>CNhs14244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5312</th>\n",
+       "      <td>5312</td>\n",
+       "      <td>0</td>\n",
+       "      <td>CNhs14245</td>\n",
+       "      <td>/home/drk/tillage/datasets/human/cage/fantom/C...</td>\n",
+       "      <td>384</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sum</td>\n",
+       "      <td>CAGE:lung adenocarcinoma, papillary cell line:...</td>\n",
+       "      <td>CNhs14245</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5313 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      index  genome   identifier  \\\n",
+       "0         0       0  ENCFF833POA   \n",
+       "1         1       0  ENCFF110QGM   \n",
+       "2         2       0  ENCFF880MKD   \n",
+       "3         3       0  ENCFF463ZLQ   \n",
+       "4         4       0  ENCFF890OGQ   \n",
+       "...     ...     ...          ...   \n",
+       "5308   5308       0    CNhs14239   \n",
+       "5309   5309       0    CNhs14240   \n",
+       "5310   5310       0    CNhs14241   \n",
+       "5311   5311       0    CNhs14244   \n",
+       "5312   5312       0    CNhs14245   \n",
+       "\n",
+       "                                                   file  clip  scale sum_stat  \\\n",
+       "0     /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
+       "1     /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
+       "2     /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
+       "3     /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
+       "4     /home/drk/tillage/datasets/human/dnase/encode/...    32      2     mean   \n",
+       "...                                                 ...   ...    ...      ...   \n",
+       "5308  /home/drk/tillage/datasets/human/cage/fantom/C...   384      1      sum   \n",
+       "5309  /home/drk/tillage/datasets/human/cage/fantom/C...   384      1      sum   \n",
+       "5310  /home/drk/tillage/datasets/human/cage/fantom/C...   384      1      sum   \n",
+       "5311  /home/drk/tillage/datasets/human/cage/fantom/C...   384      1      sum   \n",
+       "5312  /home/drk/tillage/datasets/human/cage/fantom/C...   384      1      sum   \n",
+       "\n",
+       "                                            description    encode_id  \n",
+       "0     DNASE:cerebellum male adult (27 years) and mal...  ENCSR000EIJ  \n",
+       "1     DNASE:frontal cortex male adult (27 years) and...  ENCSR000EIK  \n",
+       "2                                         DNASE:chorion  ENCSR000EIL  \n",
+       "3     DNASE:Ishikawa treated with 0.02% dimethyl sul...  ENCSR000EIP  \n",
+       "4                                         DNASE:GM03348  ENCSR000EIS  \n",
+       "...                                                 ...          ...  \n",
+       "5308        CAGE:epithelioid sarcoma cell line:HS-ES-2R    CNhs14239  \n",
+       "5309  CAGE:squamous cell lung carcinoma cell line:RE...    CNhs14240  \n",
+       "5310                  CAGE:gastric cancer cell line:GSS    CNhs14241  \n",
+       "5311                  CAGE:carcinoid cell line:NCI-H727    CNhs14244  \n",
+       "5312  CAGE:lung adenocarcinoma, papillary cell line:...    CNhs14245  \n",
+       "\n",
+       "[5313 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_labesls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "chr1_209816391_G_A\n",
+      "chr1_209816391_G_C\n",
+      "chr1_209816391_G_T\n",
+      "chrX_139530462_C_A\n",
+      "chrX_139530462_C_G\n",
+      "chrX_139530462_C_T\n",
+      "gtr than 1 chr19_11089246_A_C\n",
+      "gtr than 1 chr19_11089246_A_G\n",
+      "gtr than 1 chr19_11089246_A_T\n",
+      "gtr than 1 chr19_11089247_C_A\n",
+      "gtr than 1 chr19_11089247_C_G\n",
+      "gtr than 1 chr19_11089247_C_T\n",
+      "gtr than 1 chr19_11089248_C_A\n",
+      "gtr than 1 chr19_11089248_C_G\n",
+      "gtr than 1 chr19_11089248_C_T\n",
+      "gtr than 1 chr19_11089249_C_A\n",
+      "gtr than 1 chr19_11089249_C_G\n",
+      "gtr than 1 chr19_11089249_C_T\n",
+      "gtr than 1 chr19_11089250_A_C\n",
+      "gtr than 1 chr19_11089250_A_G\n",
+      "gtr than 1 chr19_11089250_A_T\n",
+      "gtr than 1 chr19_11089251_A_C\n",
+      "gtr than 1 chr19_11089251_A_G\n",
+      "gtr than 1 chr19_11089251_A_T\n",
+      "gtr than 1 chr19_11089252_A_C\n",
+      "gtr than 1 chr19_11089252_A_G\n",
+      "gtr than 1 chr19_11089252_A_T\n",
+      "gtr than 1 chr19_11089253_T_A\n",
+      "gtr than 1 chr19_11089253_T_C\n",
+      "gtr than 1 chr19_11089253_T_G\n",
+      "gtr than 1 chr19_11089254_A_C\n",
+      "gtr than 1 chr19_11089254_A_G\n",
+      "gtr than 1 chr19_11089254_A_T\n",
+      "gtr than 1 chr19_11089255_C_A\n",
+      "gtr than 1 chr19_11089255_C_G\n",
+      "gtr than 1 chr19_11089255_C_T\n",
+      "gtr than 1 chr19_11089256_A_C\n",
+      "gtr than 1 chr19_11089256_A_G\n",
+      "gtr than 1 chr19_11089256_A_T\n",
+      "gtr than 1 chr19_11089257_A_C\n",
+      "gtr than 1 chr19_11089257_A_G\n",
+      "gtr than 1 chr19_11089257_A_T\n",
+      "gtr than 1 chr19_11089258_C_A\n",
+      "gtr than 1 chr19_11089258_C_G\n",
+      "gtr than 1 chr19_11089258_C_T\n",
+      "gtr than 1 chr19_11089259_A_C\n",
+      "gtr than 1 chr19_11089259_A_G\n",
+      "gtr than 1 chr19_11089259_A_T\n",
+      "gtr than 1 chr19_11089260_A_C\n",
+      "gtr than 1 chr19_11089260_A_G\n",
+      "gtr than 1 chr19_11089260_A_T\n",
+      "gtr than 1 chr19_11089261_A_C\n",
+      "gtr than 1 chr19_11089261_A_G\n",
+      "gtr than 1 chr19_11089261_A_T\n",
+      "gtr than 1 chr19_11089262_T_A\n",
+      "gtr than 1 chr19_11089262_T_C\n",
+      "gtr than 1 chr19_11089262_T_G\n",
+      "gtr than 1 chr19_11089263_C_A\n",
+      "gtr than 1 chr19_11089263_C_G\n",
+      "gtr than 1 chr19_11089263_C_T\n",
+      "gtr than 1 chr19_11089264_A_C\n",
+      "gtr than 1 chr19_11089264_A_G\n",
+      "gtr than 1 chr19_11089264_A_T\n",
+      "gtr than 1 chr19_11089265_A_C\n",
+      "gtr than 1 chr19_11089265_A_G\n",
+      "gtr than 1 chr19_11089265_A_T\n",
+      "gtr than 1 chr19_11089266_G_A\n",
+      "gtr than 1 chr19_11089266_G_C\n",
+      "gtr than 1 chr19_11089266_G_T\n",
+      "gtr than 1 chr19_11089267_T_A\n",
+      "gtr than 1 chr19_11089267_T_C\n",
+      "gtr than 1 chr19_11089267_T_G\n",
+      "gtr than 1 chr19_11089268_C_A\n",
+      "gtr than 1 chr19_11089268_C_G\n",
+      "gtr than 1 chr19_11089268_C_T\n",
+      "gtr than 1 chr19_11089269_G_A\n",
+      "gtr than 1 chr19_11089269_G_C\n",
+      "gtr than 1 chr19_11089269_G_T\n",
+      "gtr than 1 chr19_11089270_C_A\n",
+      "gtr than 1 chr19_11089270_C_G\n",
+      "gtr than 1 chr19_11089270_C_T\n",
+      "gtr than 1 chr19_11089271_C_A\n",
+      "gtr than 1 chr19_11089271_C_G\n",
+      "gtr than 1 chr19_11089271_C_T\n",
+      "gtr than 1 chr19_11089272_T_A\n",
+      "gtr than 1 chr19_11089272_T_C\n",
+      "gtr than 1 chr19_11089272_T_G\n",
+      "gtr than 1 chr19_11089273_G_A\n",
+      "gtr than 1 chr19_11089273_G_C\n",
+      "gtr than 1 chr19_11089273_G_T\n",
+      "gtr than 1 chr19_11089274_C_A\n",
+      "gtr than 1 chr19_11089274_C_G\n",
+      "gtr than 1 chr19_11089274_C_T\n",
+      "gtr than 1 chr19_11089275_C_A\n",
+      "gtr than 1 chr19_11089275_C_G\n",
+      "gtr than 1 chr19_11089275_C_T\n",
+      "gtr than 1 chr19_11089276_C_A\n",
+      "gtr than 1 chr19_11089276_C_G\n",
+      "gtr than 1 chr19_11089276_C_T\n",
+      "gtr than 1 chr19_11089277_T_A\n",
+      "gtr than 1 chr19_11089277_T_C\n",
+      "gtr than 1 chr19_11089277_T_G\n",
+      "gtr than 1 chr19_11089278_G_A\n",
+      "gtr than 1 chr19_11089278_G_C\n",
+      "gtr than 1 chr19_11089278_G_T\n",
+      "gtr than 1 chr19_11089279_G_A\n",
+      "gtr than 1 chr19_11089279_G_C\n",
+      "gtr than 1 chr19_11089279_G_T\n",
+      "gtr than 1 chr19_11089280_C_A\n",
+      "gtr than 1 chr19_11089280_C_G\n",
+      "gtr than 1 chr19_11089280_C_T\n",
+      "gtr than 1 chr19_11089281_G_A\n",
+      "gtr than 1 chr19_11089281_G_C\n",
+      "gtr than 1 chr19_11089281_G_T\n",
+      "gtr than 1 chr19_11089282_A_C\n",
+      "gtr than 1 chr19_11089282_A_G\n",
+      "gtr than 1 chr19_11089282_A_T\n",
+      "gtr than 1 chr19_11089283_C_A\n",
+      "gtr than 1 chr19_11089283_C_G\n",
+      "gtr than 1 chr19_11089283_C_T\n",
+      "gtr than 1 chr19_11089284_A_C\n",
+      "gtr than 1 chr19_11089284_A_G\n",
+      "gtr than 1 chr19_11089284_A_T\n",
+      "gtr than 1 chr19_11089285_C_A\n",
+      "gtr than 1 chr19_11089285_C_G\n",
+      "gtr than 1 chr19_11089285_C_T\n",
+      "gtr than 1 chr19_11089286_T_A\n",
+      "gtr than 1 chr19_11089286_T_C\n",
+      "gtr than 1 chr19_11089286_T_G\n",
+      "gtr than 1 chr19_11089287_T_A\n",
+      "gtr than 1 chr19_11089287_T_C\n",
+      "gtr than 1 chr19_11089287_T_G\n",
+      "gtr than 1 chr19_11089288_T_A\n",
+      "gtr than 1 chr19_11089288_T_C\n",
+      "gtr than 1 chr19_11089288_T_G\n",
+      "gtr than 1 chr19_11089289_C_A\n",
+      "gtr than 1 chr19_11089289_C_G\n",
+      "gtr than 1 chr19_11089289_C_T\n",
+      "gtr than 1 chr19_11089290_G_A\n",
+      "gtr than 1 chr19_11089290_G_C\n",
+      "gtr than 1 chr19_11089290_G_T\n",
+      "gtr than 1 chr19_11089291_A_C\n",
+      "gtr than 1 chr19_11089291_A_G\n",
+      "gtr than 1 chr19_11089291_A_T\n",
+      "gtr than 1 chr19_11089292_A_C\n",
+      "gtr than 1 chr19_11089292_A_G\n",
+      "gtr than 1 chr19_11089292_A_T\n",
+      "gtr than 1 chr19_11089293_G_A\n",
+      "gtr than 1 chr19_11089293_G_C\n",
+      "gtr than 1 chr19_11089293_G_T\n",
+      "gtr than 1 chr19_11089310_A_C\n",
+      "gtr than 1 chr19_11089310_A_G\n",
+      "gtr than 1 chr19_11089310_A_T\n",
+      "gtr than 1 chr19_11089311_G_A\n",
+      "gtr than 1 chr19_11089311_G_C\n",
+      "gtr than 1 chr19_11089311_G_T\n",
+      "gtr than 1 chr19_11089312_A_C\n",
+      "gtr than 1 chr19_11089312_A_G\n",
+      "gtr than 1 chr19_11089312_A_T\n",
+      "gtr than 1 chr19_11089313_G_A\n",
+      "gtr than 1 chr19_11089313_G_C\n",
+      "gtr than 1 chr19_11089313_G_T\n",
+      "gtr than 1 chr19_11089314_C_A\n",
+      "gtr than 1 chr19_11089314_C_G\n",
+      "gtr than 1 chr19_11089314_C_T\n",
+      "gtr than 1 chr19_11089315_T_A\n",
+      "gtr than 1 chr19_11089315_T_C\n",
+      "gtr than 1 chr19_11089315_T_G\n",
+      "gtr than 1 chr19_11089316_T_A\n",
+      "gtr than 1 chr19_11089316_T_C\n",
+      "gtr than 1 chr19_11089316_T_G\n",
+      "gtr than 1 chr19_11089317_C_A\n",
+      "gtr than 1 chr19_11089317_C_G\n",
+      "gtr than 1 chr19_11089317_C_T\n",
+      "gtr than 1 chr19_11089318_A_C\n",
+      "gtr than 1 chr19_11089318_A_G\n",
+      "gtr than 1 chr19_11089318_A_T\n",
+      "gtr than 1 chr19_11089319_C_A\n",
+      "gtr than 1 chr19_11089319_C_G\n",
+      "gtr than 1 chr19_11089319_C_T\n",
+      "gtr than 1 chr19_11089320_G_A\n",
+      "gtr than 1 chr19_11089320_G_C\n",
+      "gtr than 1 chr19_11089320_G_T\n",
+      "gtr than 1 chr19_11089321_G_A\n",
+      "gtr than 1 chr19_11089321_G_C\n",
+      "gtr than 1 chr19_11089321_G_T\n",
+      "gtr than 1 chr19_11089322_G_A\n",
+      "gtr than 1 chr19_11089322_G_C\n",
+      "gtr than 1 chr19_11089322_G_T\n",
+      "gtr than 1 chr19_11089323_T_A\n",
+      "gtr than 1 chr19_11089323_T_C\n",
+      "gtr than 1 chr19_11089323_T_G\n",
+      "gtr than 1 chr19_11089324_T_A\n",
+      "gtr than 1 chr19_11089324_T_C\n",
+      "gtr than 1 chr19_11089324_T_G\n",
+      "gtr than 1 chr19_11089325_A_C\n",
+      "gtr than 1 chr19_11089325_A_G\n",
+      "gtr than 1 chr19_11089325_A_T\n",
+      "gtr than 1 chr19_11089342_T_A\n",
+      "gtr than 1 chr19_11089342_T_C\n",
+      "gtr than 1 chr19_11089342_T_G\n",
+      "gtr than 1 chr19_11089343_C_A\n",
+      "gtr than 1 chr19_11089343_C_G\n",
+      "gtr than 1 chr19_11089343_C_T\n",
+      "gtr than 1 chr19_11089344_G_A\n",
+      "gtr than 1 chr19_11089344_G_C\n",
+      "gtr than 1 chr19_11089344_G_T\n",
+      "gtr than 1 chr19_11089345_G_A\n",
+      "gtr than 1 chr19_11089345_G_C\n",
+      "gtr than 1 chr19_11089345_G_T\n",
+      "gtr than 1 chr19_11089346_C_A\n",
+      "gtr than 1 chr19_11089346_C_G\n",
+      "gtr than 1 chr19_11089346_C_T\n",
+      "gtr than 1 chr19_11089347_C_A\n",
+      "gtr than 1 chr19_11089347_C_G\n",
+      "gtr than 1 chr19_11089347_C_T\n",
+      "gtr than 1 chr19_11089348_G_A\n",
+      "gtr than 1 chr19_11089348_G_C\n",
+      "gtr than 1 chr19_11089348_G_T\n",
+      "gtr than 1 chr19_11089349_T_A\n",
+      "gtr than 1 chr19_11089349_T_C\n",
+      "gtr than 1 chr19_11089349_T_G\n",
+      "gtr than 1 chr19_11089350_T_A\n",
+      "gtr than 1 chr19_11089350_T_C\n",
+      "gtr than 1 chr19_11089350_T_G\n",
+      "gtr than 1 chr19_11089351_C_A\n",
+      "gtr than 1 chr19_11089351_C_G\n",
+      "gtr than 1 chr19_11089351_C_T\n",
+      "gtr than 1 chr19_11089352_G_A\n",
+      "gtr than 1 chr19_11089352_G_C\n",
+      "gtr than 1 chr19_11089352_G_T\n",
+      "gtr than 1 chr19_11089353_A_C\n",
+      "gtr than 1 chr19_11089353_A_G\n",
+      "gtr than 1 chr19_11089353_A_T\n",
+      "gtr than 1 chr19_11089354_A_C\n",
+      "gtr than 1 chr19_11089354_A_G\n",
+      "gtr than 1 chr19_11089354_A_T\n",
+      "gtr than 1 chr19_11089355_A_C\n",
+      "gtr than 1 chr19_11089355_A_G\n",
+      "gtr than 1 chr19_11089355_A_T\n",
+      "gtr than 1 chr19_11089356_C_A\n",
+      "gtr than 1 chr19_11089356_C_G\n",
+      "gtr than 1 chr19_11089356_C_T\n",
+      "gtr than 1 chr19_11089357_T_A\n",
+      "gtr than 1 chr19_11089357_T_C\n",
+      "gtr than 1 chr19_11089357_T_G\n",
+      "gtr than 1 chr19_11089358_C_A\n",
+      "gtr than 1 chr19_11089358_C_G\n",
+      "gtr than 1 chr19_11089358_C_T\n",
+      "gtr than 1 chr19_11089359_C_A\n",
+      "gtr than 1 chr19_11089359_C_G\n",
+      "gtr than 1 chr19_11089359_C_T\n",
+      "gtr than 1 chr19_11089360_T_A\n",
+      "gtr than 1 chr19_11089360_T_C\n",
+      "gtr than 1 chr19_11089360_T_G\n",
+      "gtr than 1 chr19_11089361_C_A\n",
+      "gtr than 1 chr19_11089361_C_G\n",
+      "gtr than 1 chr19_11089361_C_T\n",
+      "gtr than 1 chr19_11089362_C_A\n",
+      "gtr than 1 chr19_11089362_C_G\n",
+      "gtr than 1 chr19_11089362_C_T\n",
+      "gtr than 1 chr19_11089363_T_A\n",
+      "gtr than 1 chr19_11089363_T_C\n",
+      "gtr than 1 chr19_11089363_T_G\n",
+      "gtr than 1 chr19_11089364_C_A\n",
+      "gtr than 1 chr19_11089364_C_G\n",
+      "gtr than 1 chr19_11089364_C_T\n",
+      "gtr than 1 chr19_11089365_T_A\n",
+      "gtr than 1 chr19_11089365_T_C\n",
+      "gtr than 1 chr19_11089365_T_G\n",
+      "gtr than 1 chr19_11089366_T_A\n",
+      "gtr than 1 chr19_11089366_T_C\n",
+      "gtr than 1 chr19_11089366_T_G\n",
+      "gtr than 1 chr19_11089367_G_A\n",
+      "gtr than 1 chr19_11089367_G_C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr19_11089367_G_T\n",
+      "gtr than 1 chr19_11089368_C_A\n",
+      "gtr than 1 chr19_11089368_C_G\n",
+      "gtr than 1 chr19_11089368_C_T\n",
+      "gtr than 1 chr19_11089369_A_C\n",
+      "gtr than 1 chr19_11089369_A_G\n",
+      "gtr than 1 chr19_11089369_A_T\n",
+      "gtr than 1 chr19_11089370_G_A\n",
+      "gtr than 1 chr19_11089370_G_C\n",
+      "gtr than 1 chr19_11089370_G_T\n",
+      "gtr than 1 chr19_11089371_T_A\n",
+      "gtr than 1 chr19_11089371_T_C\n",
+      "gtr than 1 chr19_11089371_T_G\n",
+      "gtr than 1 chr19_11089372_G_A\n",
+      "gtr than 1 chr19_11089372_G_C\n",
+      "gtr than 1 chr19_11089372_G_T\n",
+      "gtr than 1 chr19_11089373_A_C\n",
+      "gtr than 1 chr19_11089373_A_G\n",
+      "gtr than 1 chr19_11089373_A_T\n",
+      "gtr than 1 chr19_11089390_A_C\n",
+      "gtr than 1 chr19_11089390_A_G\n",
+      "gtr than 1 chr19_11089390_A_T\n",
+      "gtr than 1 chr19_11089391_A_C\n",
+      "gtr than 1 chr19_11089391_A_G\n",
+      "gtr than 1 chr19_11089391_A_T\n",
+      "gtr than 1 chr19_11089392_T_A\n",
+      "gtr than 1 chr19_11089392_T_C\n",
+      "gtr than 1 chr19_11089392_T_G\n",
+      "gtr than 1 chr19_11089393_C_A\n",
+      "gtr than 1 chr19_11089393_C_G\n",
+      "gtr than 1 chr19_11089393_C_T\n",
+      "gtr than 1 chr19_11089394_A_C\n",
+      "gtr than 1 chr19_11089394_A_G\n",
+      "gtr than 1 chr19_11089394_A_T\n",
+      "gtr than 1 chr19_11089395_C_A\n",
+      "gtr than 1 chr19_11089395_C_G\n",
+      "gtr than 1 chr19_11089395_C_T\n",
+      "gtr than 1 chr19_11089396_C_A\n",
+      "gtr than 1 chr19_11089396_C_G\n",
+      "gtr than 1 chr19_11089396_C_T\n",
+      "gtr than 1 chr19_11089397_C_A\n",
+      "gtr than 1 chr19_11089397_C_G\n",
+      "gtr than 1 chr19_11089397_C_T\n",
+      "gtr than 1 chr19_11089398_C_A\n",
+      "gtr than 1 chr19_11089398_C_G\n",
+      "gtr than 1 chr19_11089398_C_T\n",
+      "gtr than 1 chr19_11089399_A_C\n",
+      "gtr than 1 chr19_11089399_A_G\n",
+      "gtr than 1 chr19_11089399_A_T\n",
+      "gtr than 1 chr19_11089400_C_A\n",
+      "gtr than 1 chr19_11089400_C_G\n",
+      "gtr than 1 chr19_11089400_C_T\n",
+      "gtr than 1 chr19_11089401_T_A\n",
+      "gtr than 1 chr19_11089401_T_C\n",
+      "gtr than 1 chr19_11089401_T_G\n",
+      "gtr than 1 chr19_11089402_G_A\n",
+      "gtr than 1 chr19_11089402_G_C\n",
+      "gtr than 1 chr19_11089402_G_T\n",
+      "gtr than 1 chr19_11089403_C_A\n",
+      "gtr than 1 chr19_11089403_C_G\n",
+      "gtr than 1 chr19_11089403_C_T\n",
+      "gtr than 1 chr19_11089404_A_C\n",
+      "gtr than 1 chr19_11089404_A_G\n",
+      "gtr than 1 chr19_11089404_A_T\n",
+      "gtr than 1 chr19_11089405_A_C\n",
+      "gtr than 1 chr19_11089405_A_G\n",
+      "gtr than 1 chr19_11089405_A_T\n",
+      "gtr than 1 chr19_11089406_A_C\n",
+      "gtr than 1 chr19_11089406_A_G\n",
+      "gtr than 1 chr19_11089406_A_T\n",
+      "gtr than 1 chr19_11089407_C_A\n",
+      "gtr than 1 chr19_11089407_C_G\n",
+      "gtr than 1 chr19_11089407_C_T\n",
+      "gtr than 1 chr19_11089408_T_A\n",
+      "gtr than 1 chr19_11089408_T_C\n",
+      "gtr than 1 chr19_11089408_T_G\n",
+      "gtr than 1 chr19_11089409_C_A\n",
+      "gtr than 1 chr19_11089409_C_G\n",
+      "gtr than 1 chr19_11089409_C_T\n",
+      "gtr than 1 chr19_11089410_C_A\n",
+      "gtr than 1 chr19_11089410_C_G\n",
+      "gtr than 1 chr19_11089410_C_T\n",
+      "gtr than 1 chr19_11089411_T_A\n",
+      "gtr than 1 chr19_11089411_T_C\n",
+      "gtr than 1 chr19_11089411_T_G\n",
+      "gtr than 1 chr19_11089412_C_A\n",
+      "gtr than 1 chr19_11089412_C_G\n",
+      "gtr than 1 chr19_11089412_C_T\n",
+      "gtr than 1 chr19_11089413_C_A\n",
+      "gtr than 1 chr19_11089413_C_G\n",
+      "gtr than 1 chr19_11089413_C_T\n",
+      "gtr than 1 chr19_11089414_C_A\n",
+      "gtr than 1 chr19_11089414_C_G\n",
+      "gtr than 1 chr19_11089414_C_T\n",
+      "gtr than 1 chr19_11089415_C_A\n",
+      "gtr than 1 chr19_11089415_C_G\n",
+      "gtr than 1 chr19_11089415_C_T\n",
+      "gtr than 1 chr19_11089416_C_A\n",
+      "gtr than 1 chr19_11089416_C_G\n",
+      "gtr than 1 chr19_11089416_C_T\n",
+      "gtr than 1 chr19_11089417_T_A\n",
+      "gtr than 1 chr19_11089417_T_C\n",
+      "gtr than 1 chr19_11089417_T_G\n",
+      "gtr than 1 chr19_11089418_G_A\n",
+      "gtr than 1 chr19_11089418_G_C\n",
+      "gtr than 1 chr19_11089418_G_T\n",
+      "gtr than 1 chr19_11089419_C_A\n",
+      "gtr than 1 chr19_11089419_C_G\n",
+      "gtr than 1 chr19_11089419_C_T\n",
+      "gtr than 1 chr19_11089420_T_A\n",
+      "gtr than 1 chr19_11089420_T_C\n",
+      "gtr than 1 chr19_11089420_T_G\n",
+      "gtr than 1 chr19_11089421_A_C\n",
+      "gtr than 1 chr19_11089421_A_G\n",
+      "gtr than 1 chr19_11089421_A_T\n",
+      "gtr than 1 chr19_11089422_G_A\n",
+      "gtr than 1 chr19_11089422_G_C\n",
+      "gtr than 1 chr19_11089422_G_T\n",
+      "gtr than 1 chr19_11089423_A_C\n",
+      "gtr than 1 chr19_11089423_A_G\n",
+      "gtr than 1 chr19_11089423_A_T\n",
+      "gtr than 1 chr19_11089424_A_C\n",
+      "gtr than 1 chr19_11089424_A_G\n",
+      "gtr than 1 chr19_11089424_A_T\n",
+      "gtr than 1 chr19_11089425_A_C\n",
+      "gtr than 1 chr19_11089425_A_G\n",
+      "gtr than 1 chr19_11089425_A_T\n",
+      "gtr than 1 chr19_11089426_C_A\n",
+      "gtr than 1 chr19_11089426_C_G\n",
+      "gtr than 1 chr19_11089426_C_T\n",
+      "gtr than 1 chr19_11089427_C_A\n",
+      "gtr than 1 chr19_11089427_C_G\n",
+      "gtr than 1 chr19_11089427_C_T\n",
+      "gtr than 1 chr19_11089428_T_A\n",
+      "gtr than 1 chr19_11089428_T_C\n",
+      "gtr than 1 chr19_11089428_T_G\n",
+      "gtr than 1 chr19_11089429_C_A\n",
+      "gtr than 1 chr19_11089429_C_G\n",
+      "gtr than 1 chr19_11089429_C_T\n",
+      "gtr than 1 chr19_11089430_A_C\n",
+      "gtr than 1 chr19_11089430_A_G\n",
+      "gtr than 1 chr19_11089430_A_T\n",
+      "gtr than 1 chr19_11089431_C_A\n",
+      "gtr than 1 chr19_11089431_C_G\n",
+      "gtr than 1 chr19_11089431_C_T\n",
+      "gtr than 1 chr19_11089432_A_C\n",
+      "gtr than 1 chr19_11089432_A_G\n",
+      "gtr than 1 chr19_11089432_A_T\n",
+      "gtr than 1 chr19_11089433_T_A\n",
+      "gtr than 1 chr19_11089433_T_C\n",
+      "gtr than 1 chr19_11089433_T_G\n",
+      "gtr than 1 chr19_11089434_T_A\n",
+      "gtr than 1 chr19_11089434_T_C\n",
+      "gtr than 1 chr19_11089434_T_G\n",
+      "gtr than 1 chr19_11089435_G_A\n",
+      "gtr than 1 chr19_11089435_G_C\n",
+      "gtr than 1 chr19_11089435_G_T\n",
+      "gtr than 1 chr19_11089436_A_C\n",
+      "gtr than 1 chr19_11089436_A_G\n",
+      "gtr than 1 chr19_11089436_A_T\n",
+      "gtr than 1 chr19_11089437_A_C\n",
+      "gtr than 1 chr19_11089437_A_G\n",
+      "gtr than 1 chr19_11089437_A_T\n",
+      "gtr than 1 chr19_11089438_A_C\n",
+      "gtr than 1 chr19_11089438_A_G\n",
+      "gtr than 1 chr19_11089438_A_T\n",
+      "gtr than 1 chr19_11089439_T_A\n",
+      "gtr than 1 chr19_11089439_T_C\n",
+      "gtr than 1 chr19_11089439_T_G\n",
+      "gtr than 1 chr19_11089440_G_A\n",
+      "gtr than 1 chr19_11089440_G_C\n",
+      "gtr than 1 chr19_11089440_G_T\n",
+      "gtr than 1 chr19_11089441_C_A\n",
+      "gtr than 1 chr19_11089441_C_G\n",
+      "gtr than 1 chr19_11089441_C_T\n",
+      "gtr than 1 chr19_11089442_T_A\n",
+      "gtr than 1 chr19_11089442_T_C\n",
+      "gtr than 1 chr19_11089442_T_G\n",
+      "gtr than 1 chr19_11089443_G_A\n",
+      "gtr than 1 chr19_11089443_G_C\n",
+      "gtr than 1 chr19_11089443_G_T\n",
+      "gtr than 1 chr19_11089444_T_A\n",
+      "gtr than 1 chr19_11089444_T_C\n",
+      "gtr than 1 chr19_11089444_T_G\n",
+      "gtr than 1 chr19_11089445_A_C\n",
+      "gtr than 1 chr19_11089445_A_G\n",
+      "gtr than 1 chr19_11089445_A_T\n",
+      "gtr than 1 chr19_11089446_A_C\n",
+      "gtr than 1 chr19_11089446_A_G\n",
+      "gtr than 1 chr19_11089446_A_T\n",
+      "gtr than 1 chr19_11089447_A_C\n",
+      "gtr than 1 chr19_11089447_A_G\n",
+      "gtr than 1 chr19_11089447_A_T\n",
+      "gtr than 1 chr19_11089448_T_A\n",
+      "gtr than 1 chr19_11089448_T_C\n",
+      "gtr than 1 chr19_11089448_T_G\n",
+      "gtr than 1 chr19_11089449_G_A\n",
+      "gtr than 1 chr19_11089449_G_C\n",
+      "gtr than 1 chr19_11089449_G_T\n",
+      "gtr than 1 chr19_11089450_A_C\n",
+      "gtr than 1 chr19_11089450_A_G\n",
+      "gtr than 1 chr19_11089450_A_T\n",
+      "gtr than 1 chr19_11089451_C_A\n",
+      "gtr than 1 chr19_11089451_C_G\n",
+      "gtr than 1 chr19_11089451_C_T\n",
+      "gtr than 1 chr19_11089452_G_A\n",
+      "gtr than 1 chr19_11089452_G_C\n",
+      "gtr than 1 chr19_11089452_G_T\n",
+      "gtr than 1 chr19_11089453_T_A\n",
+      "gtr than 1 chr19_11089453_T_C\n",
+      "gtr than 1 chr19_11089453_T_G\n",
+      "gtr than 1 chr19_11089454_G_A\n",
+      "gtr than 1 chr19_11089454_G_C\n",
+      "gtr than 1 chr19_11089454_G_T\n",
+      "gtr than 1 chr19_11089455_G_A\n",
+      "gtr than 1 chr19_11089455_G_C\n",
+      "gtr than 1 chr19_11089455_G_T\n",
+      "gtr than 1 chr19_11089456_G_A\n",
+      "gtr than 1 chr19_11089456_G_C\n",
+      "gtr than 1 chr19_11089456_G_T\n",
+      "gtr than 1 chr19_11089457_C_A\n",
+      "gtr than 1 chr19_11089457_C_G\n",
+      "gtr than 1 chr19_11089457_C_T\n",
+      "gtr than 1 chr19_11089458_C_A\n",
+      "gtr than 1 chr19_11089458_C_G\n",
+      "gtr than 1 chr19_11089458_C_T\n",
+      "gtr than 1 chr19_11089459_C_A\n",
+      "gtr than 1 chr19_11089459_C_G\n",
+      "gtr than 1 chr19_11089459_C_T\n",
+      "gtr than 1 chr19_11089460_C_A\n",
+      "gtr than 1 chr19_11089460_C_G\n",
+      "gtr than 1 chr19_11089460_C_T\n",
+      "gtr than 1 chr19_11089461_G_A\n",
+      "gtr than 1 chr19_11089461_G_C\n",
+      "gtr than 1 chr19_11089461_G_T\n",
+      "gtr than 1 chr19_11089462_A_C\n",
+      "gtr than 1 chr19_11089462_A_G\n",
+      "gtr than 1 chr19_11089462_A_T\n",
+      "gtr than 1 chr19_11089463_G_A\n",
+      "gtr than 1 chr19_11089463_G_C\n",
+      "gtr than 1 chr19_11089463_G_T\n",
+      "gtr than 1 chr19_11089464_T_A\n",
+      "gtr than 1 chr19_11089464_T_C\n",
+      "gtr than 1 chr19_11089464_T_G\n",
+      "gtr than 1 chr19_11089465_G_A\n",
+      "gtr than 1 chr19_11089465_G_C\n",
+      "gtr than 1 chr19_11089465_G_T\n",
+      "gtr than 1 chr19_11089466_C_A\n",
+      "gtr than 1 chr19_11089466_C_G\n",
+      "gtr than 1 chr19_11089466_C_T\n",
+      "gtr than 1 chr19_11089467_A_C\n",
+      "gtr than 1 chr19_11089467_A_G\n",
+      "gtr than 1 chr19_11089467_A_T\n",
+      "gtr than 1 chr19_11089468_A_C\n",
+      "gtr than 1 chr19_11089468_A_G\n",
+      "gtr than 1 chr19_11089468_A_T\n",
+      "gtr than 1 chr19_11089469_T_A\n",
+      "gtr than 1 chr19_11089469_T_C\n",
+      "gtr than 1 chr19_11089469_T_G\n",
+      "gtr than 1 chr19_11089470_C_A\n",
+      "gtr than 1 chr19_11089470_C_G\n",
+      "gtr than 1 chr19_11089470_C_T\n",
+      "gtr than 1 chr19_11089471_G_A\n",
+      "gtr than 1 chr19_11089471_G_C\n",
+      "gtr than 1 chr19_11089471_G_T\n",
+      "gtr than 1 chr19_11089472_C_A\n",
+      "gtr than 1 chr19_11089472_C_G\n",
+      "gtr than 1 chr19_11089472_C_T\n",
+      "gtr than 1 chr19_11089473_G_A\n",
+      "gtr than 1 chr19_11089473_G_C\n",
+      "gtr than 1 chr19_11089473_G_T\n",
+      "gtr than 1 chr19_11089474_G_A\n",
+      "gtr than 1 chr19_11089474_G_C\n",
+      "gtr than 1 chr19_11089474_G_T\n",
+      "gtr than 1 chr19_11089475_G_A\n",
+      "gtr than 1 chr19_11089475_G_C\n",
+      "gtr than 1 chr19_11089475_G_T\n",
+      "gtr than 1 chr19_11089476_A_C\n",
+      "gtr than 1 chr19_11089476_A_G\n",
+      "gtr than 1 chr19_11089476_A_T\n",
+      "gtr than 1 chr19_11089477_A_C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr19_11089477_A_G\n",
+      "gtr than 1 chr19_11089477_A_T\n",
+      "gtr than 1 chr19_11089478_G_A\n",
+      "gtr than 1 chr19_11089478_G_C\n",
+      "gtr than 1 chr19_11089478_G_T\n",
+      "gtr than 1 chr19_11089479_C_A\n",
+      "gtr than 1 chr19_11089479_C_G\n",
+      "gtr than 1 chr19_11089479_C_T\n",
+      "gtr than 1 chr19_11089480_C_A\n",
+      "gtr than 1 chr19_11089480_C_G\n",
+      "gtr than 1 chr19_11089480_C_T\n",
+      "gtr than 1 chr19_11089481_A_C\n",
+      "gtr than 1 chr19_11089481_A_G\n",
+      "gtr than 1 chr19_11089481_A_T\n",
+      "gtr than 1 chr19_11089482_G_A\n",
+      "gtr than 1 chr19_11089482_G_C\n",
+      "gtr than 1 chr19_11089482_G_T\n",
+      "gtr than 1 chr19_11089483_G_A\n",
+      "gtr than 1 chr19_11089483_G_C\n",
+      "gtr than 1 chr19_11089483_G_T\n",
+      "gtr than 1 chr19_11089484_G_A\n",
+      "gtr than 1 chr19_11089484_G_C\n",
+      "gtr than 1 chr19_11089484_G_T\n",
+      "gtr than 1 chr19_11089485_T_A\n",
+      "gtr than 1 chr19_11089485_T_C\n",
+      "gtr than 1 chr19_11089485_T_G\n",
+      "gtr than 1 chr19_11089502_G_A\n",
+      "gtr than 1 chr19_11089502_G_C\n",
+      "gtr than 1 chr19_11089502_G_T\n",
+      "gtr than 1 chr19_11089503_C_A\n",
+      "gtr than 1 chr19_11089503_C_G\n",
+      "gtr than 1 chr19_11089503_C_T\n",
+      "gtr than 1 chr19_11089504_A_C\n",
+      "gtr than 1 chr19_11089504_A_G\n",
+      "gtr than 1 chr19_11089504_A_T\n",
+      "gtr than 1 chr19_11089505_G_A\n",
+      "gtr than 1 chr19_11089505_G_C\n",
+      "gtr than 1 chr19_11089505_G_T\n",
+      "gtr than 1 chr19_11089506_G_A\n",
+      "gtr than 1 chr19_11089506_G_C\n",
+      "gtr than 1 chr19_11089506_G_T\n",
+      "gtr than 1 chr19_11089507_T_A\n",
+      "gtr than 1 chr19_11089507_T_C\n",
+      "gtr than 1 chr19_11089507_T_G\n",
+      "gtr than 1 chr19_11089508_C_A\n",
+      "gtr than 1 chr19_11089508_C_G\n",
+      "gtr than 1 chr19_11089508_C_T\n",
+      "gtr than 1 chr19_11089509_G_A\n",
+      "gtr than 1 chr19_11089509_G_C\n",
+      "gtr than 1 chr19_11089509_G_T\n",
+      "gtr than 1 chr19_11089510_T_A\n",
+      "gtr than 1 chr19_11089510_T_C\n",
+      "gtr than 1 chr19_11089510_T_G\n",
+      "gtr than 1 chr19_11089511_G_A\n",
+      "gtr than 1 chr19_11089511_G_C\n",
+      "gtr than 1 chr19_11089511_G_T\n",
+      "gtr than 1 chr19_11089512_A_C\n",
+      "gtr than 1 chr19_11089512_A_G\n",
+      "gtr than 1 chr19_11089512_A_T\n",
+      "gtr than 1 chr19_11089513_T_A\n",
+      "gtr than 1 chr19_11089513_T_C\n",
+      "gtr than 1 chr19_11089513_T_G\n",
+      "gtr than 1 chr19_11089514_C_A\n",
+      "gtr than 1 chr19_11089514_C_G\n",
+      "gtr than 1 chr19_11089514_C_T\n",
+      "gtr than 1 chr19_11089515_C_A\n",
+      "gtr than 1 chr19_11089515_C_G\n",
+      "gtr than 1 chr19_11089515_C_T\n",
+      "gtr than 1 chr19_11089516_G_A\n",
+      "gtr than 1 chr19_11089516_G_C\n",
+      "gtr than 1 chr19_11089516_G_T\n",
+      "gtr than 1 chr19_11089517_G_A\n",
+      "gtr than 1 chr19_11089517_G_C\n",
+      "gtr than 1 chr19_11089517_G_T\n",
+      "gtr than 1 chr19_11089518_G_A\n",
+      "gtr than 1 chr19_11089518_G_C\n",
+      "gtr than 1 chr19_11089518_G_T\n",
+      "gtr than 1 chr19_11089519_T_A\n",
+      "gtr than 1 chr19_11089519_T_C\n",
+      "gtr than 1 chr19_11089519_T_G\n",
+      "gtr than 1 chr19_11089520_C_A\n",
+      "gtr than 1 chr19_11089520_C_G\n",
+      "gtr than 1 chr19_11089520_C_T\n",
+      "gtr than 1 chr19_11089521_G_A\n",
+      "gtr than 1 chr19_11089521_G_C\n",
+      "gtr than 1 chr19_11089521_G_T\n",
+      "gtr than 1 chr19_11089522_G_A\n",
+      "gtr than 1 chr19_11089522_G_C\n",
+      "gtr than 1 chr19_11089522_G_T\n",
+      "gtr than 1 chr19_11089523_G_A\n",
+      "gtr than 1 chr19_11089523_G_C\n",
+      "gtr than 1 chr19_11089523_G_T\n",
+      "gtr than 1 chr19_11089524_A_C\n",
+      "gtr than 1 chr19_11089524_A_G\n",
+      "gtr than 1 chr19_11089524_A_T\n",
+      "gtr than 1 chr19_11089525_C_A\n",
+      "gtr than 1 chr19_11089525_C_G\n",
+      "gtr than 1 chr19_11089525_C_T\n",
+      "gtr than 1 chr19_11089526_A_C\n",
+      "gtr than 1 chr19_11089526_A_G\n",
+      "gtr than 1 chr19_11089526_A_T\n",
+      "gtr than 1 chr19_11089527_C_A\n",
+      "gtr than 1 chr19_11089527_C_G\n",
+      "gtr than 1 chr19_11089527_C_T\n",
+      "gtr than 1 chr19_11089528_T_A\n",
+      "gtr than 1 chr19_11089528_T_C\n",
+      "gtr than 1 chr19_11089528_T_G\n",
+      "gtr than 1 chr19_11089529_G_A\n",
+      "gtr than 1 chr19_11089529_G_C\n",
+      "gtr than 1 chr19_11089529_G_T\n",
+      "gtr than 1 chr19_11089530_C_A\n",
+      "gtr than 1 chr19_11089530_C_G\n",
+      "gtr than 1 chr19_11089530_C_T\n",
+      "gtr than 1 chr19_11089531_C_A\n",
+      "gtr than 1 chr19_11089531_C_G\n",
+      "gtr than 1 chr19_11089531_C_T\n",
+      "gtr than 1 chr19_11089532_T_A\n",
+      "gtr than 1 chr19_11089532_T_C\n",
+      "gtr than 1 chr19_11089532_T_G\n",
+      "gtr than 1 chr19_11089533_G_A\n",
+      "gtr than 1 chr19_11089533_G_C\n",
+      "gtr than 1 chr19_11089533_G_T\n",
+      "gtr than 1 chr19_11089534_G_A\n",
+      "gtr than 1 chr19_11089534_G_C\n",
+      "gtr than 1 chr19_11089534_G_T\n",
+      "gtr than 1 chr19_11089535_C_A\n",
+      "gtr than 1 chr19_11089535_C_G\n",
+      "gtr than 1 chr19_11089535_C_T\n",
+      "gtr than 1 chr19_11089536_A_C\n",
+      "gtr than 1 chr19_11089536_A_G\n",
+      "gtr than 1 chr19_11089536_A_T\n",
+      "gtr than 1 chr19_11089537_G_A\n",
+      "gtr than 1 chr19_11089537_G_C\n",
+      "gtr than 1 chr19_11089537_G_T\n",
+      "gtr than 1 chr19_11089538_A_C\n",
+      "gtr than 1 chr19_11089538_A_G\n",
+      "gtr than 1 chr19_11089538_A_T\n",
+      "gtr than 1 chr19_11089539_G_A\n",
+      "gtr than 1 chr19_11089539_G_C\n",
+      "gtr than 1 chr19_11089539_G_T\n",
+      "gtr than 1 chr19_11089540_G_A\n",
+      "gtr than 1 chr19_11089540_G_C\n",
+      "gtr than 1 chr19_11089540_G_T\n",
+      "gtr than 1 chr19_11089541_C_A\n",
+      "gtr than 1 chr19_11089541_C_G\n",
+      "gtr than 1 chr19_11089541_C_T\n",
+      "gtr than 1 chr19_11089542_T_A\n",
+      "gtr than 1 chr19_11089542_T_C\n",
+      "gtr than 1 chr19_11089542_T_G\n",
+      "gtr than 1 chr19_11089543_G_A\n",
+      "gtr than 1 chr19_11089543_G_C\n",
+      "gtr than 1 chr19_11089543_G_T\n",
+      "gtr than 1 chr19_11089544_C_A\n",
+      "gtr than 1 chr19_11089544_C_G\n",
+      "gtr than 1 chr19_11089544_C_T\n",
+      "gtr than 1 chr19_11089545_G_A\n",
+      "gtr than 1 chr19_11089545_G_C\n",
+      "gtr than 1 chr19_11089545_G_T\n",
+      "gtr than 1 chr19_11089546_A_C\n",
+      "gtr than 1 chr19_11089546_A_G\n",
+      "gtr than 1 chr19_11089546_A_T\n",
+      "gtr than 1 chr19_11089547_G_A\n",
+      "gtr than 1 chr19_11089547_G_C\n",
+      "gtr than 1 chr19_11089547_G_T\n",
+      "gtr than 1 chr19_11089548_C_A\n",
+      "gtr than 1 chr19_11089548_C_G\n",
+      "gtr than 1 chr19_11089548_C_T\n",
+      "chr1_109274651_C_A\n",
+      "chr1_109274651_C_G\n",
+      "gtr than 1 chr1_109274652_G_A\n",
+      "gtr than 1 chr1_109274652_G_C\n",
+      "gtr than 1 chr1_109274652_G_T\n",
+      "gtr than 1 chr1_109274653_A_C\n",
+      "gtr than 1 chr1_109274653_A_G\n",
+      "gtr than 1 chr1_109274653_A_T\n",
+      "gtr than 1 chr1_109274654_A_C\n",
+      "gtr than 1 chr1_109274654_A_G\n",
+      "gtr than 1 chr1_109274654_A_T\n",
+      "gtr than 1 chr1_109274655_C_A\n",
+      "gtr than 1 chr1_109274655_C_G\n",
+      "gtr than 1 chr1_109274655_C_T\n",
+      "gtr than 1 chr1_109274656_T_A\n",
+      "gtr than 1 chr1_109274656_T_C\n",
+      "gtr than 1 chr1_109274656_T_G\n",
+      "gtr than 1 chr1_109274657_G_A\n",
+      "gtr than 1 chr1_109274657_G_C\n",
+      "gtr than 1 chr1_109274657_G_T\n",
+      "gtr than 1 chr1_109274658_G_A\n",
+      "gtr than 1 chr1_109274658_G_C\n",
+      "gtr than 1 chr1_109274658_G_T\n",
+      "gtr than 1 chr1_109274659_A_C\n",
+      "gtr than 1 chr1_109274659_A_G\n",
+      "gtr than 1 chr1_109274659_A_T\n",
+      "gtr than 1 chr1_109274660_A_C\n",
+      "gtr than 1 chr1_109274660_A_G\n",
+      "gtr than 1 chr1_109274660_A_T\n",
+      "gtr than 1 chr1_109274661_A_C\n",
+      "gtr than 1 chr1_109274661_A_G\n",
+      "gtr than 1 chr1_109274661_A_T\n",
+      "gtr than 1 chr1_109274662_A_C\n",
+      "gtr than 1 chr1_109274662_A_G\n",
+      "gtr than 1 chr1_109274662_A_T\n",
+      "gtr than 1 chr1_109274663_G_A\n",
+      "gtr than 1 chr1_109274663_G_C\n",
+      "gtr than 1 chr1_109274663_G_T\n",
+      "gtr than 1 chr1_109274664_C_A\n",
+      "gtr than 1 chr1_109274664_C_G\n",
+      "gtr than 1 chr1_109274664_C_T\n",
+      "gtr than 1 chr1_109274665_C_A\n",
+      "gtr than 1 chr1_109274665_C_G\n",
+      "gtr than 1 chr1_109274665_C_T\n",
+      "gtr than 1 chr1_109274666_C_A\n",
+      "gtr than 1 chr1_109274666_C_G\n",
+      "gtr than 1 chr1_109274666_C_T\n",
+      "gtr than 1 chr1_109274667_T_A\n",
+      "gtr than 1 chr1_109274667_T_C\n",
+      "gtr than 1 chr1_109274667_T_G\n",
+      "gtr than 1 chr1_109274668_G_A\n",
+      "gtr than 1 chr1_109274668_G_C\n",
+      "gtr than 1 chr1_109274668_G_T\n",
+      "gtr than 1 chr1_109274669_T_A\n",
+      "gtr than 1 chr1_109274669_T_C\n",
+      "gtr than 1 chr1_109274670_C_A\n",
+      "gtr than 1 chr1_109274670_C_G\n",
+      "gtr than 1 chr1_109274670_C_T\n",
+      "gtr than 1 chr1_109274671_C_A\n",
+      "gtr than 1 chr1_109274671_C_G\n",
+      "gtr than 1 chr1_109274671_C_T\n",
+      "gtr than 1 chr1_109274672_G_A\n",
+      "gtr than 1 chr1_109274672_G_C\n",
+      "gtr than 1 chr1_109274672_G_T\n",
+      "gtr than 1 chr1_109274673_G_A\n",
+      "gtr than 1 chr1_109274673_G_C\n",
+      "gtr than 1 chr1_109274673_G_T\n",
+      "gtr than 1 chr1_109274674_T_A\n",
+      "gtr than 1 chr1_109274674_T_C\n",
+      "gtr than 1 chr1_109274674_T_G\n",
+      "gtr than 1 chr1_109274675_G_A\n",
+      "gtr than 1 chr1_109274675_G_T\n",
+      "gtr than 1 chr1_109274676_A_C\n",
+      "gtr than 1 chr1_109274676_A_G\n",
+      "gtr than 1 chr1_109274676_A_T\n",
+      "gtr than 1 chr1_109274677_G_A\n",
+      "gtr than 1 chr1_109274677_G_C\n",
+      "gtr than 1 chr1_109274677_G_T\n",
+      "gtr than 1 chr1_109274678_G_A\n",
+      "gtr than 1 chr1_109274678_G_C\n",
+      "gtr than 1 chr1_109274678_G_T\n",
+      "gtr than 1 chr1_109274679_G_A\n",
+      "gtr than 1 chr1_109274679_G_C\n",
+      "gtr than 1 chr1_109274679_G_T\n",
+      "gtr than 1 chr1_109274680_G_A\n",
+      "gtr than 1 chr1_109274680_G_C\n",
+      "gtr than 1 chr1_109274680_G_T\n",
+      "gtr than 1 chr1_109274681_G_A\n",
+      "gtr than 1 chr1_109274681_G_C\n",
+      "gtr than 1 chr1_109274681_G_T\n",
+      "gtr than 1 chr1_109274682_C_A\n",
+      "gtr than 1 chr1_109274682_C_G\n",
+      "gtr than 1 chr1_109274682_C_T\n",
+      "gtr than 1 chr1_109274683_A_C\n",
+      "gtr than 1 chr1_109274683_A_G\n",
+      "gtr than 1 chr1_109274683_A_T\n",
+      "gtr than 1 chr1_109274684_G_A\n",
+      "gtr than 1 chr1_109274684_G_C\n",
+      "gtr than 1 chr1_109274684_G_T\n",
+      "gtr than 1 chr1_109274685_A_C\n",
+      "gtr than 1 chr1_109274685_A_G\n",
+      "gtr than 1 chr1_109274685_A_T\n",
+      "gtr than 1 chr1_109274686_A_C\n",
+      "gtr than 1 chr1_109274686_A_G\n",
+      "gtr than 1 chr1_109274686_A_T\n",
+      "gtr than 1 chr1_109274687_G_A\n",
+      "gtr than 1 chr1_109274687_G_C\n",
+      "gtr than 1 chr1_109274687_G_T\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_109274688_G_A\n",
+      "gtr than 1 chr1_109274688_G_C\n",
+      "gtr than 1 chr1_109274688_G_T\n",
+      "gtr than 1 chr1_109274689_A_C\n",
+      "gtr than 1 chr1_109274689_A_G\n",
+      "gtr than 1 chr1_109274689_A_T\n",
+      "gtr than 1 chr1_109274690_C_A\n",
+      "gtr than 1 chr1_109274690_C_G\n",
+      "gtr than 1 chr1_109274690_C_T\n",
+      "gtr than 1 chr1_109274691_T_A\n",
+      "gtr than 1 chr1_109274691_T_C\n",
+      "gtr than 1 chr1_109274691_T_G\n",
+      "gtr than 1 chr1_109274692_C_A\n",
+      "gtr than 1 chr1_109274692_C_G\n",
+      "gtr than 1 chr1_109274692_C_T\n",
+      "gtr than 1 chr1_109274693_A_C\n",
+      "gtr than 1 chr1_109274693_A_G\n",
+      "gtr than 1 chr1_109274693_A_T\n",
+      "gtr than 1 chr1_109274694_G_A\n",
+      "gtr than 1 chr1_109274694_G_C\n",
+      "gtr than 1 chr1_109274694_G_T\n",
+      "gtr than 1 chr1_109274695_C_A\n",
+      "gtr than 1 chr1_109274695_C_G\n",
+      "gtr than 1 chr1_109274695_C_T\n",
+      "gtr than 1 chr1_109274696_G_A\n",
+      "gtr than 1 chr1_109274696_G_C\n",
+      "gtr than 1 chr1_109274696_G_T\n",
+      "gtr than 1 chr1_109274697_C_A\n",
+      "gtr than 1 chr1_109274697_C_G\n",
+      "gtr than 1 chr1_109274697_C_T\n",
+      "gtr than 1 chr1_109274698_C_A\n",
+      "gtr than 1 chr1_109274698_C_G\n",
+      "gtr than 1 chr1_109274698_C_T\n",
+      "gtr than 1 chr1_109274699_C_A\n",
+      "gtr than 1 chr1_109274699_C_G\n",
+      "gtr than 1 chr1_109274699_C_T\n",
+      "gtr than 1 chr1_109274700_C_A\n",
+      "gtr than 1 chr1_109274700_C_G\n",
+      "gtr than 1 chr1_109274700_C_T\n",
+      "gtr than 1 chr1_109274701_T_A\n",
+      "gtr than 1 chr1_109274701_T_C\n",
+      "gtr than 1 chr1_109274701_T_G\n",
+      "gtr than 1 chr1_109274702_G_A\n",
+      "gtr than 1 chr1_109274702_G_C\n",
+      "gtr than 1 chr1_109274702_G_T\n",
+      "gtr than 1 chr1_109274703_G_A\n",
+      "gtr than 1 chr1_109274703_G_C\n",
+      "gtr than 1 chr1_109274703_G_T\n",
+      "gtr than 1 chr1_109274704_A_C\n",
+      "gtr than 1 chr1_109274704_A_G\n",
+      "gtr than 1 chr1_109274704_A_T\n",
+      "gtr than 1 chr1_109274705_C_A\n",
+      "gtr than 1 chr1_109274705_C_G\n",
+      "gtr than 1 chr1_109274705_C_T\n",
+      "gtr than 1 chr1_109274706_C_A\n",
+      "gtr than 1 chr1_109274706_C_G\n",
+      "gtr than 1 chr1_109274706_C_T\n",
+      "gtr than 1 chr1_109274707_C_A\n",
+      "gtr than 1 chr1_109274707_C_G\n",
+      "gtr than 1 chr1_109274707_C_T\n",
+      "gtr than 1 chr1_109274708_C_A\n",
+      "gtr than 1 chr1_109274708_C_G\n",
+      "gtr than 1 chr1_109274708_C_T\n",
+      "gtr than 1 chr1_109274709_C_A\n",
+      "gtr than 1 chr1_109274709_C_G\n",
+      "gtr than 1 chr1_109274709_C_T\n",
+      "gtr than 1 chr1_109274710_A_C\n",
+      "gtr than 1 chr1_109274710_A_G\n",
+      "gtr than 1 chr1_109274710_A_T\n",
+      "gtr than 1 chr1_109274711_A_C\n",
+      "gtr than 1 chr1_109274711_A_G\n",
+      "gtr than 1 chr1_109274711_A_T\n",
+      "gtr than 1 chr1_109274712_A_C\n",
+      "gtr than 1 chr1_109274712_A_G\n",
+      "gtr than 1 chr1_109274712_A_T\n",
+      "gtr than 1 chr1_109274713_T_A\n",
+      "gtr than 1 chr1_109274713_T_C\n",
+      "gtr than 1 chr1_109274713_T_G\n",
+      "gtr than 1 chr1_109274714_G_A\n",
+      "gtr than 1 chr1_109274714_G_C\n",
+      "gtr than 1 chr1_109274714_G_T\n",
+      "gtr than 1 chr1_109274731_T_A\n",
+      "gtr than 1 chr1_109274731_T_C\n",
+      "gtr than 1 chr1_109274731_T_G\n",
+      "gtr than 1 chr1_109274732_C_A\n",
+      "gtr than 1 chr1_109274732_C_G\n",
+      "gtr than 1 chr1_109274732_C_T\n",
+      "gtr than 1 chr1_109274733_A_C\n",
+      "gtr than 1 chr1_109274733_A_G\n",
+      "gtr than 1 chr1_109274733_A_T\n",
+      "gtr than 1 chr1_109274734_G_A\n",
+      "gtr than 1 chr1_109274734_G_C\n",
+      "gtr than 1 chr1_109274734_G_T\n",
+      "gtr than 1 chr1_109274735_G_A\n",
+      "gtr than 1 chr1_109274735_G_C\n",
+      "gtr than 1 chr1_109274735_G_T\n",
+      "gtr than 1 chr1_109274736_G_A\n",
+      "gtr than 1 chr1_109274736_G_C\n",
+      "gtr than 1 chr1_109274736_G_T\n",
+      "gtr than 1 chr1_109274737_G_A\n",
+      "gtr than 1 chr1_109274737_G_C\n",
+      "gtr than 1 chr1_109274737_G_T\n",
+      "gtr than 1 chr1_109274738_A_C\n",
+      "gtr than 1 chr1_109274738_A_G\n",
+      "gtr than 1 chr1_109274738_A_T\n",
+      "gtr than 1 chr1_109274739_G_A\n",
+      "gtr than 1 chr1_109274739_G_C\n",
+      "gtr than 1 chr1_109274739_G_T\n",
+      "gtr than 1 chr1_109274740_C_A\n",
+      "gtr than 1 chr1_109274740_C_G\n",
+      "gtr than 1 chr1_109274740_C_T\n",
+      "gtr than 1 chr1_109274741_C_A\n",
+      "gtr than 1 chr1_109274741_C_G\n",
+      "gtr than 1 chr1_109274741_C_T\n",
+      "gtr than 1 chr1_109274742_T_A\n",
+      "gtr than 1 chr1_109274742_T_C\n",
+      "gtr than 1 chr1_109274742_T_G\n",
+      "gtr than 1 chr1_109274743_G_A\n",
+      "gtr than 1 chr1_109274743_G_C\n",
+      "gtr than 1 chr1_109274743_G_T\n",
+      "gtr than 1 chr1_109274744_T_A\n",
+      "gtr than 1 chr1_109274744_T_C\n",
+      "gtr than 1 chr1_109274744_T_G\n",
+      "gtr than 1 chr1_109274745_G_A\n",
+      "gtr than 1 chr1_109274745_G_C\n",
+      "gtr than 1 chr1_109274745_G_T\n",
+      "gtr than 1 chr1_109274746_C_A\n",
+      "gtr than 1 chr1_109274746_C_G\n",
+      "gtr than 1 chr1_109274746_C_T\n",
+      "gtr than 1 chr1_109274747_C_A\n",
+      "gtr than 1 chr1_109274747_C_G\n",
+      "gtr than 1 chr1_109274747_C_T\n",
+      "gtr than 1 chr1_109274748_C_A\n",
+      "gtr than 1 chr1_109274748_C_G\n",
+      "gtr than 1 chr1_109274748_C_T\n",
+      "gtr than 1 chr1_109274749_C_A\n",
+      "gtr than 1 chr1_109274749_C_G\n",
+      "gtr than 1 chr1_109274749_C_T\n",
+      "gtr than 1 chr1_109274750_C_A\n",
+      "gtr than 1 chr1_109274750_C_G\n",
+      "gtr than 1 chr1_109274750_C_T\n",
+      "gtr than 1 chr1_109274751_A_C\n",
+      "gtr than 1 chr1_109274751_A_G\n",
+      "gtr than 1 chr1_109274751_A_T\n",
+      "gtr than 1 chr1_109274752_G_A\n",
+      "gtr than 1 chr1_109274752_G_C\n",
+      "gtr than 1 chr1_109274752_G_T\n",
+      "gtr than 1 chr1_109274753_G_A\n",
+      "gtr than 1 chr1_109274753_G_C\n",
+      "gtr than 1 chr1_109274753_G_T\n",
+      "gtr than 1 chr1_109274754_C_A\n",
+      "gtr than 1 chr1_109274754_C_G\n",
+      "gtr than 1 chr1_109274754_C_T\n",
+      "gtr than 1 chr1_109274755_G_A\n",
+      "gtr than 1 chr1_109274755_G_C\n",
+      "gtr than 1 chr1_109274755_G_T\n",
+      "gtr than 1 chr1_109274756_G_A\n",
+      "gtr than 1 chr1_109274756_G_C\n",
+      "gtr than 1 chr1_109274756_G_T\n",
+      "gtr than 1 chr1_109274757_G_A\n",
+      "gtr than 1 chr1_109274757_G_C\n",
+      "gtr than 1 chr1_109274757_G_T\n",
+      "gtr than 1 chr1_109274758_G_A\n",
+      "gtr than 1 chr1_109274758_G_C\n",
+      "gtr than 1 chr1_109274758_G_T\n",
+      "gtr than 1 chr1_109274759_G_A\n",
+      "gtr than 1 chr1_109274759_G_C\n",
+      "gtr than 1 chr1_109274759_G_T\n",
+      "gtr than 1 chr1_109274760_T_A\n",
+      "gtr than 1 chr1_109274760_T_C\n",
+      "gtr than 1 chr1_109274760_T_G\n",
+      "gtr than 1 chr1_109274761_C_A\n",
+      "gtr than 1 chr1_109274761_C_G\n",
+      "gtr than 1 chr1_109274761_C_T\n",
+      "gtr than 1 chr1_109274762_G_A\n",
+      "gtr than 1 chr1_109274762_G_C\n",
+      "gtr than 1 chr1_109274762_G_T\n",
+      "gtr than 1 chr1_109274763_G_A\n",
+      "gtr than 1 chr1_109274763_G_C\n",
+      "gtr than 1 chr1_109274763_G_T\n",
+      "gtr than 1 chr1_109274764_G_A\n",
+      "gtr than 1 chr1_109274764_G_C\n",
+      "gtr than 1 chr1_109274764_G_T\n",
+      "gtr than 1 chr1_109274765_C_A\n",
+      "gtr than 1 chr1_109274765_C_G\n",
+      "gtr than 1 chr1_109274765_C_T\n",
+      "gtr than 1 chr1_109274766_A_C\n",
+      "gtr than 1 chr1_109274766_A_G\n",
+      "gtr than 1 chr1_109274766_A_T\n",
+      "gtr than 1 chr1_109274767_G_A\n",
+      "gtr than 1 chr1_109274767_G_C\n",
+      "gtr than 1 chr1_109274767_G_T\n",
+      "gtr than 1 chr1_109274768_C_A\n",
+      "gtr than 1 chr1_109274768_C_G\n",
+      "gtr than 1 chr1_109274768_C_T\n",
+      "gtr than 1 chr1_109274769_C_A\n",
+      "gtr than 1 chr1_109274769_C_G\n",
+      "gtr than 1 chr1_109274769_C_T\n",
+      "gtr than 1 chr1_109274770_C_A\n",
+      "gtr than 1 chr1_109274770_C_G\n",
+      "gtr than 1 chr1_109274770_C_T\n",
+      "gtr than 1 chr1_109274771_C_A\n",
+      "gtr than 1 chr1_109274771_C_G\n",
+      "gtr than 1 chr1_109274771_C_T\n",
+      "gtr than 1 chr1_109274772_A_C\n",
+      "gtr than 1 chr1_109274772_A_G\n",
+      "gtr than 1 chr1_109274772_A_T\n",
+      "gtr than 1 chr1_109274773_G_A\n",
+      "gtr than 1 chr1_109274773_G_C\n",
+      "gtr than 1 chr1_109274773_G_T\n",
+      "gtr than 1 chr1_109274774_C_A\n",
+      "gtr than 1 chr1_109274774_C_G\n",
+      "gtr than 1 chr1_109274774_C_T\n",
+      "gtr than 1 chr1_109274775_C_A\n",
+      "gtr than 1 chr1_109274775_C_G\n",
+      "gtr than 1 chr1_109274775_C_T\n",
+      "gtr than 1 chr1_109274776_C_A\n",
+      "gtr than 1 chr1_109274776_C_G\n",
+      "gtr than 1 chr1_109274776_C_T\n",
+      "gtr than 1 chr1_109274777_C_A\n",
+      "gtr than 1 chr1_109274777_C_G\n",
+      "gtr than 1 chr1_109274777_C_T\n",
+      "gtr than 1 chr1_109274778_T_A\n",
+      "gtr than 1 chr1_109274778_T_C\n",
+      "gtr than 1 chr1_109274778_T_G\n",
+      "gtr than 1 chr1_109274779_C_A\n",
+      "gtr than 1 chr1_109274779_C_G\n",
+      "gtr than 1 chr1_109274779_C_T\n",
+      "gtr than 1 chr1_109274780_T_A\n",
+      "gtr than 1 chr1_109274780_T_C\n",
+      "gtr than 1 chr1_109274780_T_G\n",
+      "gtr than 1 chr1_109274781_C_A\n",
+      "gtr than 1 chr1_109274781_C_T\n",
+      "gtr than 1 chr1_109274782_C_A\n",
+      "gtr than 1 chr1_109274782_C_G\n",
+      "gtr than 1 chr1_109274782_C_T\n",
+      "gtr than 1 chr1_109274783_T_A\n",
+      "gtr than 1 chr1_109274783_T_C\n",
+      "gtr than 1 chr1_109274783_T_G\n",
+      "gtr than 1 chr1_109274784_T_A\n",
+      "gtr than 1 chr1_109274784_T_C\n",
+      "gtr than 1 chr1_109274784_T_G\n",
+      "gtr than 1 chr1_109274785_T_A\n",
+      "gtr than 1 chr1_109274785_T_C\n",
+      "gtr than 1 chr1_109274785_T_G\n",
+      "gtr than 1 chr1_109274786_T_A\n",
+      "gtr than 1 chr1_109274786_T_C\n",
+      "gtr than 1 chr1_109274786_T_G\n",
+      "gtr than 1 chr1_109274787_C_A\n",
+      "gtr than 1 chr1_109274787_C_G\n",
+      "gtr than 1 chr1_109274787_C_T\n",
+      "gtr than 1 chr1_109274788_C_A\n",
+      "gtr than 1 chr1_109274788_C_G\n",
+      "gtr than 1 chr1_109274788_C_T\n",
+      "gtr than 1 chr1_109274789_T_A\n",
+      "gtr than 1 chr1_109274789_T_C\n",
+      "gtr than 1 chr1_109274789_T_G\n",
+      "gtr than 1 chr1_109274790_G_A\n",
+      "gtr than 1 chr1_109274790_G_C\n",
+      "gtr than 1 chr1_109274790_G_T\n",
+      "gtr than 1 chr1_109274791_G_A\n",
+      "gtr than 1 chr1_109274791_G_C\n",
+      "gtr than 1 chr1_109274791_G_T\n",
+      "gtr than 1 chr1_109274792_A_C\n",
+      "gtr than 1 chr1_109274792_A_G\n",
+      "gtr than 1 chr1_109274792_A_T\n",
+      "gtr than 1 chr1_109274793_C_A\n",
+      "gtr than 1 chr1_109274793_C_G\n",
+      "gtr than 1 chr1_109274793_C_T\n",
+      "gtr than 1 chr1_109274794_T_A\n",
+      "gtr than 1 chr1_109274794_T_C\n",
+      "gtr than 1 chr1_109274794_T_G\n",
+      "gtr than 1 chr1_109274811_G_A\n",
+      "gtr than 1 chr1_109274811_G_C\n",
+      "gtr than 1 chr1_109274811_G_T\n",
+      "gtr than 1 chr1_109274812_C_A\n",
+      "gtr than 1 chr1_109274812_C_G\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_109274812_C_T\n",
+      "gtr than 1 chr1_109274813_C_A\n",
+      "gtr than 1 chr1_109274813_C_G\n",
+      "gtr than 1 chr1_109274813_C_T\n",
+      "gtr than 1 chr1_109274814_C_A\n",
+      "gtr than 1 chr1_109274814_C_G\n",
+      "gtr than 1 chr1_109274814_C_T\n",
+      "gtr than 1 chr1_109274815_A_C\n",
+      "gtr than 1 chr1_109274815_A_G\n",
+      "gtr than 1 chr1_109274815_A_T\n",
+      "gtr than 1 chr1_109274816_G_A\n",
+      "gtr than 1 chr1_109274816_G_C\n",
+      "gtr than 1 chr1_109274816_G_T\n",
+      "gtr than 1 chr1_109274817_G_A\n",
+      "gtr than 1 chr1_109274817_G_C\n",
+      "gtr than 1 chr1_109274817_G_T\n",
+      "gtr than 1 chr1_109274818_T_A\n",
+      "gtr than 1 chr1_109274818_T_C\n",
+      "gtr than 1 chr1_109274818_T_G\n",
+      "gtr than 1 chr1_109274819_G_A\n",
+      "gtr than 1 chr1_109274819_G_C\n",
+      "gtr than 1 chr1_109274819_G_T\n",
+      "gtr than 1 chr1_109274820_T_A\n",
+      "gtr than 1 chr1_109274820_T_C\n",
+      "gtr than 1 chr1_109274820_T_G\n",
+      "gtr than 1 chr1_109274821_T_A\n",
+      "gtr than 1 chr1_109274821_T_C\n",
+      "gtr than 1 chr1_109274821_T_G\n",
+      "gtr than 1 chr1_109274822_T_A\n",
+      "gtr than 1 chr1_109274822_T_C\n",
+      "gtr than 1 chr1_109274822_T_G\n",
+      "gtr than 1 chr1_109274823_G_A\n",
+      "gtr than 1 chr1_109274823_G_C\n",
+      "gtr than 1 chr1_109274823_G_T\n",
+      "gtr than 1 chr1_109274824_C_A\n",
+      "gtr than 1 chr1_109274824_C_G\n",
+      "gtr than 1 chr1_109274824_C_T\n",
+      "gtr than 1 chr1_109274825_T_A\n",
+      "gtr than 1 chr1_109274825_T_C\n",
+      "gtr than 1 chr1_109274825_T_G\n",
+      "gtr than 1 chr1_109274826_C_A\n",
+      "gtr than 1 chr1_109274826_C_G\n",
+      "gtr than 1 chr1_109274826_C_T\n",
+      "gtr than 1 chr1_109274843_G_A\n",
+      "gtr than 1 chr1_109274843_G_C\n",
+      "gtr than 1 chr1_109274843_G_T\n",
+      "gtr than 1 chr1_109274844_T_A\n",
+      "gtr than 1 chr1_109274844_T_C\n",
+      "gtr than 1 chr1_109274844_T_G\n",
+      "gtr than 1 chr1_109274845_G_A\n",
+      "gtr than 1 chr1_109274845_G_C\n",
+      "gtr than 1 chr1_109274845_G_T\n",
+      "gtr than 1 chr1_109274846_C_A\n",
+      "gtr than 1 chr1_109274846_C_G\n",
+      "gtr than 1 chr1_109274846_C_T\n",
+      "gtr than 1 chr1_109274847_T_A\n",
+      "gtr than 1 chr1_109274847_T_C\n",
+      "gtr than 1 chr1_109274847_T_G\n",
+      "gtr than 1 chr1_109274848_T_A\n",
+      "gtr than 1 chr1_109274848_T_C\n",
+      "gtr than 1 chr1_109274848_T_G\n",
+      "gtr than 1 chr1_109274849_C_A\n",
+      "gtr than 1 chr1_109274849_C_G\n",
+      "gtr than 1 chr1_109274849_C_T\n",
+      "gtr than 1 chr1_109274850_A_C\n",
+      "gtr than 1 chr1_109274850_A_G\n",
+      "gtr than 1 chr1_109274850_A_T\n",
+      "gtr than 1 chr1_109274851_T_A\n",
+      "gtr than 1 chr1_109274851_T_C\n",
+      "gtr than 1 chr1_109274851_T_G\n",
+      "gtr than 1 chr1_109274852_T_A\n",
+      "gtr than 1 chr1_109274852_T_C\n",
+      "gtr than 1 chr1_109274852_T_G\n",
+      "gtr than 1 chr1_109274853_T_A\n",
+      "gtr than 1 chr1_109274853_T_C\n",
+      "gtr than 1 chr1_109274853_T_G\n",
+      "gtr than 1 chr1_109274854_T_A\n",
+      "gtr than 1 chr1_109274854_T_C\n",
+      "gtr than 1 chr1_109274854_T_G\n",
+      "gtr than 1 chr1_109274855_T_A\n",
+      "gtr than 1 chr1_109274855_T_C\n",
+      "gtr than 1 chr1_109274855_T_G\n",
+      "gtr than 1 chr1_109274856_C_A\n",
+      "gtr than 1 chr1_109274856_C_G\n",
+      "gtr than 1 chr1_109274856_C_T\n",
+      "gtr than 1 chr1_109274857_G_A\n",
+      "gtr than 1 chr1_109274857_G_C\n",
+      "gtr than 1 chr1_109274857_G_T\n",
+      "gtr than 1 chr1_109274858_T_A\n",
+      "gtr than 1 chr1_109274858_T_C\n",
+      "gtr than 1 chr1_109274858_T_G\n",
+      "gtr than 1 chr1_109274875_G_A\n",
+      "gtr than 1 chr1_109274875_G_C\n",
+      "gtr than 1 chr1_109274875_G_T\n",
+      "gtr than 1 chr1_109274876_G_A\n",
+      "gtr than 1 chr1_109274876_G_C\n",
+      "gtr than 1 chr1_109274876_G_T\n",
+      "gtr than 1 chr1_109274877_G_A\n",
+      "gtr than 1 chr1_109274877_G_C\n",
+      "gtr than 1 chr1_109274877_G_T\n",
+      "gtr than 1 chr1_109274878_C_A\n",
+      "gtr than 1 chr1_109274878_C_G\n",
+      "gtr than 1 chr1_109274878_C_T\n",
+      "gtr than 1 chr1_109274879_A_C\n",
+      "gtr than 1 chr1_109274879_A_G\n",
+      "gtr than 1 chr1_109274879_A_T\n",
+      "gtr than 1 chr1_109274880_G_A\n",
+      "gtr than 1 chr1_109274880_G_C\n",
+      "gtr than 1 chr1_109274880_G_T\n",
+      "gtr than 1 chr1_109274881_G_A\n",
+      "gtr than 1 chr1_109274881_G_C\n",
+      "gtr than 1 chr1_109274881_G_T\n",
+      "gtr than 1 chr1_109274882_C_A\n",
+      "gtr than 1 chr1_109274882_C_G\n",
+      "gtr than 1 chr1_109274882_C_T\n",
+      "gtr than 1 chr1_109274883_C_A\n",
+      "gtr than 1 chr1_109274883_C_G\n",
+      "gtr than 1 chr1_109274883_C_T\n",
+      "gtr than 1 chr1_109274884_A_C\n",
+      "gtr than 1 chr1_109274884_A_G\n",
+      "gtr than 1 chr1_109274884_A_T\n",
+      "gtr than 1 chr1_109274885_G_A\n",
+      "gtr than 1 chr1_109274885_G_C\n",
+      "gtr than 1 chr1_109274885_G_T\n",
+      "gtr than 1 chr1_109274886_T_A\n",
+      "gtr than 1 chr1_109274886_T_C\n",
+      "gtr than 1 chr1_109274886_T_G\n",
+      "gtr than 1 chr1_109274887_C_A\n",
+      "gtr than 1 chr1_109274887_C_G\n",
+      "gtr than 1 chr1_109274887_C_T\n",
+      "gtr than 1 chr1_109274888_A_C\n",
+      "gtr than 1 chr1_109274888_A_G\n",
+      "gtr than 1 chr1_109274888_A_T\n",
+      "gtr than 1 chr1_109274889_T_A\n",
+      "gtr than 1 chr1_109274889_T_C\n",
+      "gtr than 1 chr1_109274889_T_G\n",
+      "gtr than 1 chr1_109274890_G_A\n",
+      "gtr than 1 chr1_109274890_G_C\n",
+      "gtr than 1 chr1_109274890_G_T\n",
+      "gtr than 1 chr1_109274907_T_A\n",
+      "gtr than 1 chr1_109274907_T_C\n",
+      "gtr than 1 chr1_109274907_T_G\n",
+      "gtr than 1 chr1_109274908_T_A\n",
+      "gtr than 1 chr1_109274908_T_C\n",
+      "gtr than 1 chr1_109274908_T_G\n",
+      "gtr than 1 chr1_109274909_T_A\n",
+      "gtr than 1 chr1_109274909_T_C\n",
+      "gtr than 1 chr1_109274909_T_G\n",
+      "gtr than 1 chr1_109274910_G_A\n",
+      "gtr than 1 chr1_109274910_G_C\n",
+      "gtr than 1 chr1_109274910_G_T\n",
+      "gtr than 1 chr1_109274911_C_A\n",
+      "gtr than 1 chr1_109274911_C_G\n",
+      "gtr than 1 chr1_109274911_C_T\n",
+      "gtr than 1 chr1_109274912_T_A\n",
+      "gtr than 1 chr1_109274912_T_C\n",
+      "gtr than 1 chr1_109274912_T_G\n",
+      "gtr than 1 chr1_109274913_G_A\n",
+      "gtr than 1 chr1_109274913_G_C\n",
+      "gtr than 1 chr1_109274913_G_T\n",
+      "gtr than 1 chr1_109274914_T_A\n",
+      "gtr than 1 chr1_109274914_T_C\n",
+      "gtr than 1 chr1_109274914_T_G\n",
+      "gtr than 1 chr1_109274915_G_A\n",
+      "gtr than 1 chr1_109274915_G_C\n",
+      "gtr than 1 chr1_109274915_G_T\n",
+      "gtr than 1 chr1_109274916_A_C\n",
+      "gtr than 1 chr1_109274916_A_G\n",
+      "gtr than 1 chr1_109274916_A_T\n",
+      "gtr than 1 chr1_109274917_T_A\n",
+      "gtr than 1 chr1_109274917_T_C\n",
+      "gtr than 1 chr1_109274917_T_G\n",
+      "gtr than 1 chr1_109274918_G_A\n",
+      "gtr than 1 chr1_109274918_G_C\n",
+      "gtr than 1 chr1_109274918_G_T\n",
+      "gtr than 1 chr1_109274919_T_A\n",
+      "gtr than 1 chr1_109274919_T_C\n",
+      "gtr than 1 chr1_109274919_T_G\n",
+      "gtr than 1 chr1_109274920_G_A\n",
+      "gtr than 1 chr1_109274920_G_C\n",
+      "gtr than 1 chr1_109274920_G_T\n",
+      "gtr than 1 chr1_109274921_G_A\n",
+      "gtr than 1 chr1_109274921_G_C\n",
+      "gtr than 1 chr1_109274921_G_T\n",
+      "gtr than 1 chr1_109274922_G_A\n",
+      "gtr than 1 chr1_109274922_G_C\n",
+      "gtr than 1 chr1_109274922_G_T\n",
+      "gtr than 1 chr1_109274939_A_C\n",
+      "gtr than 1 chr1_109274939_A_G\n",
+      "gtr than 1 chr1_109274939_A_T\n",
+      "gtr than 1 chr1_109274940_A_C\n",
+      "gtr than 1 chr1_109274940_A_G\n",
+      "gtr than 1 chr1_109274940_A_T\n",
+      "gtr than 1 chr1_109274941_C_A\n",
+      "gtr than 1 chr1_109274941_C_G\n",
+      "gtr than 1 chr1_109274941_C_T\n",
+      "gtr than 1 chr1_109274942_A_C\n",
+      "gtr than 1 chr1_109274942_A_G\n",
+      "gtr than 1 chr1_109274942_A_T\n",
+      "gtr than 1 chr1_109274943_C_A\n",
+      "gtr than 1 chr1_109274943_C_G\n",
+      "gtr than 1 chr1_109274943_C_T\n",
+      "gtr than 1 chr1_109274944_A_C\n",
+      "gtr than 1 chr1_109274944_A_G\n",
+      "gtr than 1 chr1_109274944_A_T\n",
+      "gtr than 1 chr1_109274945_G_A\n",
+      "gtr than 1 chr1_109274945_G_C\n",
+      "gtr than 1 chr1_109274945_G_T\n",
+      "gtr than 1 chr1_109274946_T_A\n",
+      "gtr than 1 chr1_109274946_T_C\n",
+      "gtr than 1 chr1_109274946_T_G\n",
+      "gtr than 1 chr1_109274947_G_A\n",
+      "gtr than 1 chr1_109274947_G_C\n",
+      "gtr than 1 chr1_109274947_G_T\n",
+      "gtr than 1 chr1_109274948_C_A\n",
+      "gtr than 1 chr1_109274948_C_G\n",
+      "gtr than 1 chr1_109274948_C_T\n",
+      "gtr than 1 chr1_109274949_T_A\n",
+      "gtr than 1 chr1_109274949_T_C\n",
+      "gtr than 1 chr1_109274949_T_G\n",
+      "gtr than 1 chr1_109274950_G_A\n",
+      "gtr than 1 chr1_109274950_G_C\n",
+      "gtr than 1 chr1_109274950_G_T\n",
+      "gtr than 1 chr1_109274951_G_A\n",
+      "gtr than 1 chr1_109274951_G_C\n",
+      "gtr than 1 chr1_109274951_G_T\n",
+      "gtr than 1 chr1_109274952_C_A\n",
+      "gtr than 1 chr1_109274952_C_G\n",
+      "gtr than 1 chr1_109274952_C_T\n",
+      "gtr than 1 chr1_109274953_T_A\n",
+      "gtr than 1 chr1_109274953_T_C\n",
+      "gtr than 1 chr1_109274953_T_G\n",
+      "gtr than 1 chr1_109274954_C_A\n",
+      "gtr than 1 chr1_109274954_C_G\n",
+      "gtr than 1 chr1_109274954_C_T\n",
+      "gtr than 1 chr1_109274971_C_A\n",
+      "gtr than 1 chr1_109274971_C_G\n",
+      "gtr than 1 chr1_109274971_C_T\n",
+      "gtr than 1 chr1_109274972_T_A\n",
+      "gtr than 1 chr1_109274972_T_C\n",
+      "gtr than 1 chr1_109274972_T_G\n",
+      "gtr than 1 chr1_109274973_C_A\n",
+      "gtr than 1 chr1_109274973_C_G\n",
+      "gtr than 1 chr1_109274973_C_T\n",
+      "gtr than 1 chr1_109274974_A_C\n",
+      "gtr than 1 chr1_109274974_A_G\n",
+      "gtr than 1 chr1_109274974_A_T\n",
+      "gtr than 1 chr1_109274975_A_C\n",
+      "gtr than 1 chr1_109274975_A_G\n",
+      "gtr than 1 chr1_109274975_A_T\n",
+      "gtr than 1 chr1_109274976_T_A\n",
+      "gtr than 1 chr1_109274976_T_C\n",
+      "gtr than 1 chr1_109274976_T_G\n",
+      "gtr than 1 chr1_109274977_C_A\n",
+      "gtr than 1 chr1_109274977_C_G\n",
+      "gtr than 1 chr1_109274977_C_T\n",
+      "gtr than 1 chr1_109274978_A_C\n",
+      "gtr than 1 chr1_109274978_A_G\n",
+      "gtr than 1 chr1_109274978_A_T\n",
+      "gtr than 1 chr1_109274979_A_C\n",
+      "gtr than 1 chr1_109274979_A_G\n",
+      "gtr than 1 chr1_109274979_A_T\n",
+      "gtr than 1 chr1_109274980_G_A\n",
+      "gtr than 1 chr1_109274980_G_C\n",
+      "gtr than 1 chr1_109274980_G_T\n",
+      "gtr than 1 chr1_109274981_C_A\n",
+      "gtr than 1 chr1_109274981_C_G\n",
+      "gtr than 1 chr1_109274981_C_T\n",
+      "gtr than 1 chr1_109274982_A_C\n",
+      "gtr than 1 chr1_109274982_A_G\n",
+      "gtr than 1 chr1_109274982_A_T\n",
+      "gtr than 1 chr1_109274983_C_A\n",
+      "gtr than 1 chr1_109274983_C_G\n",
+      "gtr than 1 chr1_109274983_C_T\n",
+      "gtr than 1 chr1_109274984_A_C\n",
+      "gtr than 1 chr1_109274984_A_G\n",
+      "gtr than 1 chr1_109274984_A_T\n",
+      "gtr than 1 chr1_109274985_G_A\n",
+      "gtr than 1 chr1_109274985_G_C\n",
+      "gtr than 1 chr1_109274985_G_T\n",
+      "gtr than 1 chr1_109274986_G_A\n",
+      "gtr than 1 chr1_109274986_G_C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_109274986_G_T\n",
+      "gtr than 1 chr1_109274987_T_A\n",
+      "gtr than 1 chr1_109274987_T_C\n",
+      "gtr than 1 chr1_109274987_T_G\n",
+      "gtr than 1 chr1_109274988_T_A\n",
+      "gtr than 1 chr1_109274988_T_C\n",
+      "gtr than 1 chr1_109274988_T_G\n",
+      "gtr than 1 chr1_109274989_T_A\n",
+      "gtr than 1 chr1_109274989_T_C\n",
+      "gtr than 1 chr1_109274989_T_G\n",
+      "gtr than 1 chr1_109274990_C_A\n",
+      "gtr than 1 chr1_109274990_C_G\n",
+      "gtr than 1 chr1_109274990_C_T\n",
+      "gtr than 1 chr1_109274991_A_C\n",
+      "gtr than 1 chr1_109274991_A_G\n",
+      "gtr than 1 chr1_109274991_A_T\n",
+      "gtr than 1 chr1_109274992_A_C\n",
+      "gtr than 1 chr1_109274992_A_G\n",
+      "gtr than 1 chr1_109274992_A_T\n",
+      "gtr than 1 chr1_109274993_G_A\n",
+      "gtr than 1 chr1_109274993_G_C\n",
+      "gtr than 1 chr1_109274993_G_T\n",
+      "gtr than 1 chr1_109274994_T_A\n",
+      "gtr than 1 chr1_109274994_T_C\n",
+      "gtr than 1 chr1_109274994_T_G\n",
+      "gtr than 1 chr1_109274995_C_A\n",
+      "gtr than 1 chr1_109274995_C_G\n",
+      "gtr than 1 chr1_109274995_C_T\n",
+      "gtr than 1 chr1_109274996_T_A\n",
+      "gtr than 1 chr1_109274996_T_C\n",
+      "gtr than 1 chr1_109274996_T_G\n",
+      "gtr than 1 chr1_109274997_G_A\n",
+      "gtr than 1 chr1_109274997_G_C\n",
+      "gtr than 1 chr1_109274997_G_T\n",
+      "gtr than 1 chr1_109274998_G_A\n",
+      "gtr than 1 chr1_109274998_G_C\n",
+      "gtr than 1 chr1_109274998_G_T\n",
+      "gtr than 1 chr1_109274999_G_A\n",
+      "gtr than 1 chr1_109274999_G_C\n",
+      "gtr than 1 chr1_109274999_G_T\n",
+      "gtr than 1 chr1_109275000_T_A\n",
+      "gtr than 1 chr1_109275000_T_C\n",
+      "gtr than 1 chr1_109275000_T_G\n",
+      "gtr than 1 chr1_109275001_T_A\n",
+      "gtr than 1 chr1_109275001_T_C\n",
+      "gtr than 1 chr1_109275001_T_G\n",
+      "gtr than 1 chr1_109275002_C_A\n",
+      "gtr than 1 chr1_109275002_C_G\n",
+      "gtr than 1 chr1_109275002_C_T\n",
+      "gtr than 1 chr1_109275003_T_A\n",
+      "gtr than 1 chr1_109275003_T_C\n",
+      "gtr than 1 chr1_109275003_T_G\n",
+      "gtr than 1 chr1_109275004_G_A\n",
+      "gtr than 1 chr1_109275004_G_C\n",
+      "gtr than 1 chr1_109275004_G_T\n",
+      "gtr than 1 chr1_109275005_G_A\n",
+      "gtr than 1 chr1_109275005_G_C\n",
+      "gtr than 1 chr1_109275005_G_T\n",
+      "gtr than 1 chr1_109275006_T_A\n",
+      "gtr than 1 chr1_109275006_T_C\n",
+      "gtr than 1 chr1_109275006_T_G\n",
+      "gtr than 1 chr1_109275007_G_A\n",
+      "gtr than 1 chr1_109275007_G_C\n",
+      "gtr than 1 chr1_109275007_G_T\n",
+      "gtr than 1 chr1_109275008_T_A\n",
+      "gtr than 1 chr1_109275008_T_C\n",
+      "gtr than 1 chr1_109275008_T_G\n",
+      "gtr than 1 chr1_109275009_C_A\n",
+      "gtr than 1 chr1_109275009_C_G\n",
+      "gtr than 1 chr1_109275009_C_T\n",
+      "gtr than 1 chr1_109275010_C_A\n",
+      "gtr than 1 chr1_109275010_C_G\n",
+      "gtr than 1 chr1_109275010_C_T\n",
+      "gtr than 1 chr1_109275011_A_C\n",
+      "gtr than 1 chr1_109275011_A_G\n",
+      "gtr than 1 chr1_109275011_A_T\n",
+      "gtr than 1 chr1_109275012_C_A\n",
+      "gtr than 1 chr1_109275012_C_G\n",
+      "gtr than 1 chr1_109275012_C_T\n",
+      "gtr than 1 chr1_109275013_T_A\n",
+      "gtr than 1 chr1_109275013_T_C\n",
+      "gtr than 1 chr1_109275013_T_G\n",
+      "gtr than 1 chr1_109275014_C_A\n",
+      "gtr than 1 chr1_109275014_C_G\n",
+      "gtr than 1 chr1_109275014_C_T\n",
+      "gtr than 1 chr1_109275015_A_C\n",
+      "gtr than 1 chr1_109275015_A_G\n",
+      "gtr than 1 chr1_109275015_A_T\n",
+      "gtr than 1 chr1_109275016_C_A\n",
+      "gtr than 1 chr1_109275016_C_G\n",
+      "gtr than 1 chr1_109275016_C_T\n",
+      "gtr than 1 chr1_109275017_C_A\n",
+      "gtr than 1 chr1_109275017_C_G\n",
+      "gtr than 1 chr1_109275017_C_T\n",
+      "gtr than 1 chr1_109275018_C_A\n",
+      "gtr than 1 chr1_109275018_C_G\n",
+      "gtr than 1 chr1_109275018_C_T\n",
+      "gtr than 1 chr1_109275035_T_A\n",
+      "gtr than 1 chr1_109275035_T_C\n",
+      "gtr than 1 chr1_109275035_T_G\n",
+      "gtr than 1 chr1_109275036_C_A\n",
+      "gtr than 1 chr1_109275036_C_G\n",
+      "gtr than 1 chr1_109275036_C_T\n",
+      "gtr than 1 chr1_109275037_A_C\n",
+      "gtr than 1 chr1_109275037_A_G\n",
+      "gtr than 1 chr1_109275037_A_T\n",
+      "gtr than 1 chr1_109275038_G_A\n",
+      "gtr than 1 chr1_109275038_G_C\n",
+      "gtr than 1 chr1_109275038_G_T\n",
+      "gtr than 1 chr1_109275039_A_C\n",
+      "gtr than 1 chr1_109275039_A_G\n",
+      "gtr than 1 chr1_109275039_A_T\n",
+      "gtr than 1 chr1_109275040_C_A\n",
+      "gtr than 1 chr1_109275040_C_G\n",
+      "gtr than 1 chr1_109275040_C_T\n",
+      "gtr than 1 chr1_109275041_A_C\n",
+      "gtr than 1 chr1_109275041_A_G\n",
+      "gtr than 1 chr1_109275041_A_T\n",
+      "gtr than 1 chr1_109275042_A_C\n",
+      "gtr than 1 chr1_109275042_A_G\n",
+      "gtr than 1 chr1_109275042_A_T\n",
+      "gtr than 1 chr1_109275043_A_C\n",
+      "gtr than 1 chr1_109275043_A_G\n",
+      "gtr than 1 chr1_109275043_A_T\n",
+      "gtr than 1 chr1_109275044_T_A\n",
+      "gtr than 1 chr1_109275044_T_C\n",
+      "gtr than 1 chr1_109275044_T_G\n",
+      "gtr than 1 chr1_109275045_G_A\n",
+      "gtr than 1 chr1_109275045_G_C\n",
+      "gtr than 1 chr1_109275045_G_T\n",
+      "gtr than 1 chr1_109275046_C_A\n",
+      "gtr than 1 chr1_109275046_C_G\n",
+      "gtr than 1 chr1_109275046_C_T\n",
+      "gtr than 1 chr1_109275047_T_A\n",
+      "gtr than 1 chr1_109275047_T_C\n",
+      "gtr than 1 chr1_109275047_T_G\n",
+      "gtr than 1 chr1_109275048_A_C\n",
+      "gtr than 1 chr1_109275048_A_G\n",
+      "gtr than 1 chr1_109275048_A_T\n",
+      "gtr than 1 chr1_109275049_C_A\n",
+      "gtr than 1 chr1_109275049_C_G\n",
+      "gtr than 1 chr1_109275049_C_T\n",
+      "gtr than 1 chr1_109275050_T_A\n",
+      "gtr than 1 chr1_109275050_T_C\n",
+      "gtr than 1 chr1_109275050_T_G\n",
+      "gtr than 1 chr1_109275051_T_A\n",
+      "gtr than 1 chr1_109275051_T_C\n",
+      "gtr than 1 chr1_109275051_T_G\n",
+      "gtr than 1 chr1_109275052_T_A\n",
+      "gtr than 1 chr1_109275052_T_C\n",
+      "gtr than 1 chr1_109275052_T_G\n",
+      "gtr than 1 chr1_109275053_G_A\n",
+      "gtr than 1 chr1_109275053_G_C\n",
+      "gtr than 1 chr1_109275053_G_T\n",
+      "gtr than 1 chr1_109275054_T_A\n",
+      "gtr than 1 chr1_109275054_T_C\n",
+      "gtr than 1 chr1_109275054_T_G\n",
+      "gtr than 1 chr1_109275055_C_A\n",
+      "gtr than 1 chr1_109275055_C_G\n",
+      "gtr than 1 chr1_109275055_C_T\n",
+      "gtr than 1 chr1_109275056_T_A\n",
+      "gtr than 1 chr1_109275056_T_C\n",
+      "gtr than 1 chr1_109275056_T_G\n",
+      "gtr than 1 chr1_109275057_A_C\n",
+      "gtr than 1 chr1_109275057_A_G\n",
+      "gtr than 1 chr1_109275057_A_T\n",
+      "gtr than 1 chr1_109275058_A_C\n",
+      "gtr than 1 chr1_109275058_A_G\n",
+      "gtr than 1 chr1_109275058_A_T\n",
+      "gtr than 1 chr1_109275059_C_A\n",
+      "gtr than 1 chr1_109275059_C_G\n",
+      "gtr than 1 chr1_109275059_C_T\n",
+      "gtr than 1 chr1_109275060_C_A\n",
+      "gtr than 1 chr1_109275060_C_G\n",
+      "gtr than 1 chr1_109275060_C_T\n",
+      "gtr than 1 chr1_109275061_T_A\n",
+      "gtr than 1 chr1_109275061_T_C\n",
+      "gtr than 1 chr1_109275061_T_G\n",
+      "gtr than 1 chr1_109275062_G_A\n",
+      "gtr than 1 chr1_109275062_G_C\n",
+      "gtr than 1 chr1_109275062_G_T\n",
+      "gtr than 1 chr1_109275063_C_A\n",
+      "gtr than 1 chr1_109275063_C_G\n",
+      "gtr than 1 chr1_109275063_C_T\n",
+      "gtr than 1 chr1_109275064_T_A\n",
+      "gtr than 1 chr1_109275064_T_C\n",
+      "gtr than 1 chr1_109275064_T_G\n",
+      "gtr than 1 chr1_109275065_G_A\n",
+      "gtr than 1 chr1_109275065_G_C\n",
+      "gtr than 1 chr1_109275065_G_T\n",
+      "gtr than 1 chr1_109275066_T_A\n",
+      "gtr than 1 chr1_109275066_T_C\n",
+      "gtr than 1 chr1_109275066_T_G\n",
+      "gtr than 1 chr1_109275067_G_A\n",
+      "gtr than 1 chr1_109275067_G_T\n",
+      "gtr than 1 chr1_109275068_G_A\n",
+      "gtr than 1 chr1_109275068_G_C\n",
+      "gtr than 1 chr1_109275068_G_T\n",
+      "gtr than 1 chr1_109275069_C_A\n",
+      "gtr than 1 chr1_109275069_C_G\n",
+      "gtr than 1 chr1_109275069_C_T\n",
+      "gtr than 1 chr1_109275070_C_A\n",
+      "gtr than 1 chr1_109275070_C_G\n",
+      "gtr than 1 chr1_109275070_C_T\n",
+      "gtr than 1 chr1_109275071_T_A\n",
+      "gtr than 1 chr1_109275071_T_C\n",
+      "gtr than 1 chr1_109275071_T_G\n",
+      "gtr than 1 chr1_109275072_C_A\n",
+      "gtr than 1 chr1_109275072_C_G\n",
+      "gtr than 1 chr1_109275072_C_T\n",
+      "gtr than 1 chr1_109275073_T_A\n",
+      "gtr than 1 chr1_109275073_T_C\n",
+      "gtr than 1 chr1_109275073_T_G\n",
+      "gtr than 1 chr1_109275074_G_A\n",
+      "gtr than 1 chr1_109275074_G_C\n",
+      "gtr than 1 chr1_109275074_G_T\n",
+      "gtr than 1 chr1_109275075_A_C\n",
+      "gtr than 1 chr1_109275075_A_G\n",
+      "gtr than 1 chr1_109275075_A_T\n",
+      "gtr than 1 chr1_109275076_G_A\n",
+      "gtr than 1 chr1_109275076_G_C\n",
+      "gtr than 1 chr1_109275076_G_T\n",
+      "gtr than 1 chr1_109275077_A_C\n",
+      "gtr than 1 chr1_109275077_A_G\n",
+      "gtr than 1 chr1_109275077_A_T\n",
+      "gtr than 1 chr1_109275078_C_A\n",
+      "gtr than 1 chr1_109275078_C_G\n",
+      "gtr than 1 chr1_109275078_C_T\n",
+      "gtr than 1 chr1_109275079_A_C\n",
+      "gtr than 1 chr1_109275079_A_G\n",
+      "gtr than 1 chr1_109275079_A_T\n",
+      "gtr than 1 chr1_109275080_T_A\n",
+      "gtr than 1 chr1_109275080_T_C\n",
+      "gtr than 1 chr1_109275080_T_G\n",
+      "gtr than 1 chr1_109275081_G_A\n",
+      "gtr than 1 chr1_109275081_G_C\n",
+      "gtr than 1 chr1_109275081_G_T\n",
+      "gtr than 1 chr1_109275082_T_A\n",
+      "gtr than 1 chr1_109275082_T_C\n",
+      "gtr than 1 chr1_109275082_T_G\n",
+      "gtr than 1 chr1_109275083_T_A\n",
+      "gtr than 1 chr1_109275083_T_C\n",
+      "gtr than 1 chr1_109275083_T_G\n",
+      "gtr than 1 chr1_109275084_C_A\n",
+      "gtr than 1 chr1_109275084_C_G\n",
+      "gtr than 1 chr1_109275084_C_T\n",
+      "gtr than 1 chr1_109275085_T_A\n",
+      "gtr than 1 chr1_109275085_T_C\n",
+      "gtr than 1 chr1_109275085_T_G\n",
+      "gtr than 1 chr1_109275086_A_G\n",
+      "gtr than 1 chr1_109275086_A_T\n",
+      "gtr than 1 chr1_109275087_T_A\n",
+      "gtr than 1 chr1_109275087_T_C\n",
+      "gtr than 1 chr1_109275087_T_G\n",
+      "gtr than 1 chr1_109275088_T_A\n",
+      "gtr than 1 chr1_109275088_T_C\n",
+      "gtr than 1 chr1_109275088_T_G\n",
+      "gtr than 1 chr1_109275089_T_A\n",
+      "gtr than 1 chr1_109275089_T_C\n",
+      "gtr than 1 chr1_109275089_T_G\n",
+      "gtr than 1 chr1_109275090_T_A\n",
+      "gtr than 1 chr1_109275090_T_C\n",
+      "gtr than 1 chr1_109275090_T_G\n",
+      "gtr than 1 chr1_109275091_T_A\n",
+      "gtr than 1 chr1_109275091_T_C\n",
+      "gtr than 1 chr1_109275091_T_G\n",
+      "gtr than 1 chr1_109275092_A_C\n",
+      "gtr than 1 chr1_109275092_A_G\n",
+      "gtr than 1 chr1_109275092_A_T\n",
+      "gtr than 1 chr1_109275093_A_C\n",
+      "gtr than 1 chr1_109275093_A_G\n",
+      "gtr than 1 chr1_109275093_A_T\n",
+      "gtr than 1 chr1_109275094_C_A\n",
+      "gtr than 1 chr1_109275094_C_G\n",
+      "gtr than 1 chr1_109275094_C_T\n",
+      "gtr than 1 chr1_109275095_C_A\n",
+      "gtr than 1 chr1_109275095_C_G\n",
+      "gtr than 1 chr1_109275095_C_T\n",
+      "gtr than 1 chr1_109275096_C_A\n",
+      "gtr than 1 chr1_109275096_C_G\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_109275096_C_T\n",
+      "gtr than 1 chr1_109275097_C_A\n",
+      "gtr than 1 chr1_109275097_C_G\n",
+      "gtr than 1 chr1_109275097_C_T\n",
+      "gtr than 1 chr1_109275098_T_A\n",
+      "gtr than 1 chr1_109275098_T_C\n",
+      "gtr than 1 chr1_109275098_T_G\n",
+      "gtr than 1 chr1_109275115_C_A\n",
+      "gtr than 1 chr1_109275115_C_G\n",
+      "gtr than 1 chr1_109275115_C_T\n",
+      "gtr than 1 chr1_109275116_T_A\n",
+      "gtr than 1 chr1_109275116_T_C\n",
+      "gtr than 1 chr1_109275116_T_G\n",
+      "gtr than 1 chr1_109275117_T_A\n",
+      "gtr than 1 chr1_109275117_T_C\n",
+      "gtr than 1 chr1_109275117_T_G\n",
+      "gtr than 1 chr1_109275118_C_A\n",
+      "gtr than 1 chr1_109275118_C_G\n",
+      "gtr than 1 chr1_109275118_C_T\n",
+      "gtr than 1 chr1_109275119_T_A\n",
+      "gtr than 1 chr1_109275119_T_C\n",
+      "gtr than 1 chr1_109275119_T_G\n",
+      "gtr than 1 chr1_109275120_T_A\n",
+      "gtr than 1 chr1_109275120_T_C\n",
+      "gtr than 1 chr1_109275120_T_G\n",
+      "gtr than 1 chr1_109275121_C_A\n",
+      "gtr than 1 chr1_109275121_C_G\n",
+      "gtr than 1 chr1_109275121_C_T\n",
+      "gtr than 1 chr1_109275122_A_C\n",
+      "gtr than 1 chr1_109275122_A_G\n",
+      "gtr than 1 chr1_109275122_A_T\n",
+      "gtr than 1 chr1_109275123_A_C\n",
+      "gtr than 1 chr1_109275123_A_G\n",
+      "gtr than 1 chr1_109275123_A_T\n",
+      "gtr than 1 chr1_109275124_A_C\n",
+      "gtr than 1 chr1_109275124_A_G\n",
+      "gtr than 1 chr1_109275124_A_T\n",
+      "gtr than 1 chr1_109275125_G_A\n",
+      "gtr than 1 chr1_109275125_G_C\n",
+      "gtr than 1 chr1_109275125_G_T\n",
+      "gtr than 1 chr1_109275126_G_A\n",
+      "gtr than 1 chr1_109275126_G_C\n",
+      "gtr than 1 chr1_109275126_G_T\n",
+      "gtr than 1 chr1_109275127_A_C\n",
+      "gtr than 1 chr1_109275127_A_G\n",
+      "gtr than 1 chr1_109275127_A_T\n",
+      "gtr than 1 chr1_109275128_C_A\n",
+      "gtr than 1 chr1_109275128_C_G\n",
+      "gtr than 1 chr1_109275128_C_T\n",
+      "gtr than 1 chr1_109275129_C_A\n",
+      "gtr than 1 chr1_109275129_C_G\n",
+      "gtr than 1 chr1_109275129_C_T\n",
+      "gtr than 1 chr1_109275130_A_C\n",
+      "gtr than 1 chr1_109275130_A_G\n",
+      "gtr than 1 chr1_109275130_A_T\n",
+      "gtr than 1 chr1_109275131_G_A\n",
+      "gtr than 1 chr1_109275131_G_C\n",
+      "gtr than 1 chr1_109275131_G_T\n",
+      "gtr than 1 chr1_109275132_G_A\n",
+      "gtr than 1 chr1_109275132_G_C\n",
+      "gtr than 1 chr1_109275132_G_T\n",
+      "gtr than 1 chr1_109275133_T_A\n",
+      "gtr than 1 chr1_109275133_T_C\n",
+      "gtr than 1 chr1_109275133_T_G\n",
+      "gtr than 1 chr1_109275134_C_A\n",
+      "gtr than 1 chr1_109275134_C_G\n",
+      "gtr than 1 chr1_109275134_C_T\n",
+      "gtr than 1 chr1_109275135_C_A\n",
+      "gtr than 1 chr1_109275135_C_G\n",
+      "gtr than 1 chr1_109275135_C_T\n",
+      "gtr than 1 chr1_109275136_T_A\n",
+      "gtr than 1 chr1_109275136_T_C\n",
+      "gtr than 1 chr1_109275136_T_G\n",
+      "gtr than 1 chr1_109275137_G_A\n",
+      "gtr than 1 chr1_109275137_G_C\n",
+      "gtr than 1 chr1_109275137_G_T\n",
+      "gtr than 1 chr1_109275138_T_A\n",
+      "gtr than 1 chr1_109275138_T_C\n",
+      "gtr than 1 chr1_109275138_T_G\n",
+      "gtr than 1 chr1_109275139_T_A\n",
+      "gtr than 1 chr1_109275139_T_C\n",
+      "gtr than 1 chr1_109275139_T_G\n",
+      "gtr than 1 chr1_109275140_C_A\n",
+      "gtr than 1 chr1_109275140_C_G\n",
+      "gtr than 1 chr1_109275140_C_T\n",
+      "gtr than 1 chr1_109275141_C_A\n",
+      "gtr than 1 chr1_109275141_C_G\n",
+      "gtr than 1 chr1_109275141_C_T\n",
+      "gtr than 1 chr1_109275142_T_A\n",
+      "gtr than 1 chr1_109275142_T_C\n",
+      "gtr than 1 chr1_109275142_T_G\n",
+      "gtr than 1 chr1_109275143_C_A\n",
+      "gtr than 1 chr1_109275143_C_G\n",
+      "gtr than 1 chr1_109275143_C_T\n",
+      "gtr than 1 chr1_109275144_T_A\n",
+      "gtr than 1 chr1_109275144_T_C\n",
+      "gtr than 1 chr1_109275144_T_G\n",
+      "gtr than 1 chr1_109275145_T_A\n",
+      "gtr than 1 chr1_109275145_T_C\n",
+      "gtr than 1 chr1_109275145_T_G\n",
+      "gtr than 1 chr1_109275146_T_A\n",
+      "gtr than 1 chr1_109275146_T_C\n",
+      "gtr than 1 chr1_109275146_T_G\n",
+      "gtr than 1 chr1_109275147_C_A\n",
+      "gtr than 1 chr1_109275147_C_G\n",
+      "gtr than 1 chr1_109275147_C_T\n",
+      "gtr than 1 chr1_109275148_T_A\n",
+      "gtr than 1 chr1_109275148_T_C\n",
+      "gtr than 1 chr1_109275148_T_G\n",
+      "gtr than 1 chr1_109275149_C_A\n",
+      "gtr than 1 chr1_109275149_C_G\n",
+      "gtr than 1 chr1_109275149_C_T\n",
+      "gtr than 1 chr1_109275150_C_A\n",
+      "gtr than 1 chr1_109275150_C_G\n",
+      "gtr than 1 chr1_109275150_C_T\n",
+      "gtr than 1 chr1_109275151_C_A\n",
+      "gtr than 1 chr1_109275151_C_G\n",
+      "gtr than 1 chr1_109275151_C_T\n",
+      "gtr than 1 chr1_109275152_C_A\n",
+      "gtr than 1 chr1_109275152_C_G\n",
+      "gtr than 1 chr1_109275152_C_T\n",
+      "gtr than 1 chr1_109275153_G_A\n",
+      "gtr than 1 chr1_109275153_G_C\n",
+      "gtr than 1 chr1_109275153_G_T\n",
+      "gtr than 1 chr1_109275154_A_C\n",
+      "gtr than 1 chr1_109275154_A_G\n",
+      "gtr than 1 chr1_109275154_A_T\n",
+      "gtr than 1 chr1_109275155_C_A\n",
+      "gtr than 1 chr1_109275155_C_G\n",
+      "gtr than 1 chr1_109275155_C_T\n",
+      "gtr than 1 chr1_109275156_T_A\n",
+      "gtr than 1 chr1_109275156_T_C\n",
+      "gtr than 1 chr1_109275156_T_G\n",
+      "gtr than 1 chr1_109275157_C_A\n",
+      "gtr than 1 chr1_109275157_C_G\n",
+      "gtr than 1 chr1_109275157_C_T\n",
+      "gtr than 1 chr1_109275158_C_A\n",
+      "gtr than 1 chr1_109275158_C_G\n",
+      "gtr than 1 chr1_109275158_C_T\n",
+      "gtr than 1 chr1_109275159_A_C\n",
+      "gtr than 1 chr1_109275159_A_G\n",
+      "gtr than 1 chr1_109275159_A_T\n",
+      "gtr than 1 chr1_109275160_C_A\n",
+      "gtr than 1 chr1_109275160_C_G\n",
+      "gtr than 1 chr1_109275160_C_T\n",
+      "gtr than 1 chr1_109275161_C_A\n",
+      "gtr than 1 chr1_109275161_C_G\n",
+      "gtr than 1 chr1_109275161_C_T\n",
+      "gtr than 1 chr1_109275162_C_A\n",
+      "gtr than 1 chr1_109275162_C_G\n",
+      "gtr than 1 chr1_109275162_C_T\n",
+      "gtr than 1 chr1_109275163_C_A\n",
+      "gtr than 1 chr1_109275163_C_G\n",
+      "gtr than 1 chr1_109275163_C_T\n",
+      "gtr than 1 chr1_109275164_A_C\n",
+      "gtr than 1 chr1_109275164_A_G\n",
+      "gtr than 1 chr1_109275164_A_T\n",
+      "gtr than 1 chr1_109275165_G_A\n",
+      "gtr than 1 chr1_109275165_G_C\n",
+      "gtr than 1 chr1_109275165_G_T\n",
+      "gtr than 1 chr1_109275166_C_A\n",
+      "gtr than 1 chr1_109275166_C_G\n",
+      "gtr than 1 chr1_109275166_C_T\n",
+      "gtr than 1 chr1_109275167_T_A\n",
+      "gtr than 1 chr1_109275167_T_C\n",
+      "gtr than 1 chr1_109275167_T_G\n",
+      "gtr than 1 chr1_109275168_C_A\n",
+      "gtr than 1 chr1_109275168_C_G\n",
+      "gtr than 1 chr1_109275168_C_T\n",
+      "gtr than 1 chr1_109275169_C_A\n",
+      "gtr than 1 chr1_109275169_C_G\n",
+      "gtr than 1 chr1_109275169_C_T\n",
+      "gtr than 1 chr1_109275170_C_A\n",
+      "gtr than 1 chr1_109275170_C_G\n",
+      "gtr than 1 chr1_109275170_C_T\n",
+      "gtr than 1 chr1_109275171_T_A\n",
+      "gtr than 1 chr1_109275171_T_C\n",
+      "gtr than 1 chr1_109275171_T_G\n",
+      "gtr than 1 chr1_109275172_G_A\n",
+      "gtr than 1 chr1_109275172_G_C\n",
+      "gtr than 1 chr1_109275172_G_T\n",
+      "gtr than 1 chr1_109275173_T_A\n",
+      "gtr than 1 chr1_109275173_T_C\n",
+      "gtr than 1 chr1_109275173_T_G\n",
+      "gtr than 1 chr1_109275174_G_A\n",
+      "gtr than 1 chr1_109275174_G_C\n",
+      "gtr than 1 chr1_109275174_G_T\n",
+      "gtr than 1 chr1_109275175_A_C\n",
+      "gtr than 1 chr1_109275175_A_G\n",
+      "gtr than 1 chr1_109275175_A_T\n",
+      "gtr than 1 chr1_109275176_A_C\n",
+      "gtr than 1 chr1_109275176_A_G\n",
+      "gtr than 1 chr1_109275176_A_T\n",
+      "gtr than 1 chr1_109275177_G_A\n",
+      "gtr than 1 chr1_109275177_G_C\n",
+      "gtr than 1 chr1_109275177_G_T\n",
+      "gtr than 1 chr1_109275178_A_C\n",
+      "gtr than 1 chr1_109275178_A_G\n",
+      "gtr than 1 chr1_109275178_A_T\n",
+      "gtr than 1 chr1_109275179_G_A\n",
+      "gtr than 1 chr1_109275179_G_C\n",
+      "gtr than 1 chr1_109275179_G_T\n",
+      "gtr than 1 chr1_109275180_A_C\n",
+      "gtr than 1 chr1_109275180_A_G\n",
+      "gtr than 1 chr1_109275180_A_T\n",
+      "gtr than 1 chr1_109275181_G_A\n",
+      "gtr than 1 chr1_109275181_G_C\n",
+      "gtr than 1 chr1_109275181_G_T\n",
+      "gtr than 1 chr1_109275182_A_C\n",
+      "gtr than 1 chr1_109275182_A_G\n",
+      "gtr than 1 chr1_109275182_A_T\n",
+      "gtr than 1 chr1_109275183_G_A\n",
+      "gtr than 1 chr1_109275183_G_C\n",
+      "gtr than 1 chr1_109275183_G_T\n",
+      "gtr than 1 chr1_109275184_T_A\n",
+      "gtr than 1 chr1_109275184_T_C\n",
+      "gtr than 1 chr1_109275184_T_G\n",
+      "gtr than 1 chr1_109275185_T_A\n",
+      "gtr than 1 chr1_109275185_T_C\n",
+      "gtr than 1 chr1_109275186_A_G\n",
+      "gtr than 1 chr1_109275186_A_T\n",
+      "gtr than 1 chr1_109275187_A_C\n",
+      "gtr than 1 chr1_109275187_A_G\n",
+      "gtr than 1 chr1_109275187_A_T\n",
+      "gtr than 1 chr1_109275188_T_A\n",
+      "gtr than 1 chr1_109275188_T_C\n",
+      "gtr than 1 chr1_109275188_T_G\n",
+      "gtr than 1 chr1_109275189_A_C\n",
+      "gtr than 1 chr1_109275189_A_G\n",
+      "gtr than 1 chr1_109275189_A_T\n",
+      "gtr than 1 chr1_109275190_T_A\n",
+      "gtr than 1 chr1_109275190_T_C\n",
+      "gtr than 1 chr1_109275190_T_G\n",
+      "gtr than 1 chr1_109275191_A_C\n",
+      "gtr than 1 chr1_109275191_A_G\n",
+      "gtr than 1 chr1_109275191_A_T\n",
+      "gtr than 1 chr1_109275192_T_A\n",
+      "gtr than 1 chr1_109275192_T_C\n",
+      "gtr than 1 chr1_109275192_T_G\n",
+      "gtr than 1 chr1_109275193_T_A\n",
+      "gtr than 1 chr1_109275193_T_C\n",
+      "gtr than 1 chr1_109275193_T_G\n",
+      "gtr than 1 chr1_109275194_T_A\n",
+      "gtr than 1 chr1_109275194_T_C\n",
+      "gtr than 1 chr1_109275194_T_G\n",
+      "gtr than 1 chr1_109275195_G_A\n",
+      "gtr than 1 chr1_109275195_G_C\n",
+      "gtr than 1 chr1_109275196_T_A\n",
+      "gtr than 1 chr1_109275196_T_C\n",
+      "gtr than 1 chr1_109275196_T_G\n",
+      "gtr than 1 chr1_109275197_T_A\n",
+      "gtr than 1 chr1_109275197_T_C\n",
+      "gtr than 1 chr1_109275197_T_G\n",
+      "gtr than 1 chr1_109275198_T_A\n",
+      "gtr than 1 chr1_109275198_T_C\n",
+      "gtr than 1 chr1_109275198_T_G\n",
+      "gtr than 1 chr1_109275199_T_A\n",
+      "gtr than 1 chr1_109275199_T_C\n",
+      "gtr than 1 chr1_109275199_T_G\n",
+      "gtr than 1 chr1_109275200_A_C\n",
+      "gtr than 1 chr1_109275200_A_G\n",
+      "gtr than 1 chr1_109275200_A_T\n",
+      "gtr than 1 chr1_109275201_T_A\n",
+      "gtr than 1 chr1_109275201_T_C\n",
+      "gtr than 1 chr1_109275201_T_G\n",
+      "gtr than 1 chr1_109275202_T_A\n",
+      "gtr than 1 chr1_109275202_T_C\n",
+      "gtr than 1 chr1_109275202_T_G\n",
+      "gtr than 1 chr1_109275203_T_A\n",
+      "gtr than 1 chr1_109275203_T_C\n",
+      "gtr than 1 chr1_109275204_A_C\n",
+      "gtr than 1 chr1_109275204_A_G\n",
+      "gtr than 1 chr1_109275204_A_T\n",
+      "gtr than 1 chr1_109275205_T_A\n",
+      "gtr than 1 chr1_109275205_T_C\n",
+      "gtr than 1 chr1_109275205_T_G\n",
+      "gtr than 1 chr1_109275206_T_A\n",
+      "gtr than 1 chr1_109275206_T_C\n",
+      "gtr than 1 chr1_109275206_T_G\n",
+      "gtr than 1 chr1_109275207_T_A\n",
+      "gtr than 1 chr1_109275207_T_C\n",
+      "gtr than 1 chr1_109275207_T_G\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_109275208_G_A\n",
+      "gtr than 1 chr1_109275208_G_C\n",
+      "gtr than 1 chr1_109275208_G_T\n",
+      "gtr than 1 chr1_109275209_C_A\n",
+      "gtr than 1 chr1_109275209_C_G\n",
+      "gtr than 1 chr1_109275209_C_T\n",
+      "gtr than 1 chr1_109275210_T_A\n",
+      "gtr than 1 chr1_109275210_T_C\n",
+      "gtr than 1 chr1_109275210_T_G\n",
+      "gtr than 1 chr1_109275211_T_A\n",
+      "gtr than 1 chr1_109275211_T_C\n",
+      "gtr than 1 chr1_109275211_T_G\n",
+      "gtr than 1 chr1_109275212_T_A\n",
+      "gtr than 1 chr1_109275212_T_C\n",
+      "gtr than 1 chr1_109275212_T_G\n",
+      "gtr than 1 chr1_109275213_T_A\n",
+      "gtr than 1 chr1_109275213_T_C\n",
+      "gtr than 1 chr1_109275213_T_G\n",
+      "gtr than 1 chr1_109275214_T_A\n",
+      "gtr than 1 chr1_109275214_T_C\n",
+      "gtr than 1 chr1_109275214_T_G\n",
+      "gtr than 1 chr1_109275215_G_A\n",
+      "gtr than 1 chr1_109275215_G_C\n",
+      "gtr than 1 chr1_109275215_G_T\n",
+      "gtr than 1 chr1_109275216_T_A\n",
+      "gtr than 1 chr1_109275216_T_C\n",
+      "gtr than 1 chr1_109275216_T_G\n",
+      "gtr than 1 chr1_109275217_G_A\n",
+      "gtr than 1 chr1_109275217_G_C\n",
+      "gtr than 1 chr1_109275217_G_T\n",
+      "gtr than 1 chr1_109275218_T_A\n",
+      "gtr than 1 chr1_109275218_T_C\n",
+      "gtr than 1 chr1_109275218_T_G\n",
+      "gtr than 1 chr1_109275219_T_A\n",
+      "gtr than 1 chr1_109275219_T_C\n",
+      "gtr than 1 chr1_109275219_T_G\n",
+      "gtr than 1 chr1_109275220_G_A\n",
+      "gtr than 1 chr1_109275220_G_C\n",
+      "gtr than 1 chr1_109275220_G_T\n",
+      "gtr than 1 chr1_109275221_G_A\n",
+      "gtr than 1 chr1_109275221_G_C\n",
+      "gtr than 1 chr1_109275221_G_T\n",
+      "gtr than 1 chr1_109275222_G_A\n",
+      "gtr than 1 chr1_109275222_G_C\n",
+      "gtr than 1 chr1_109275222_G_T\n",
+      "gtr than 1 chr1_109275223_A_C\n",
+      "gtr than 1 chr1_109275223_A_G\n",
+      "gtr than 1 chr1_109275223_A_T\n",
+      "gtr than 1 chr1_109275224_T_A\n",
+      "gtr than 1 chr1_109275224_T_C\n",
+      "gtr than 1 chr1_109275224_T_G\n",
+      "gtr than 1 chr1_109275225_G_A\n",
+      "gtr than 1 chr1_109275225_G_C\n",
+      "gtr than 1 chr1_109275225_G_T\n",
+      "gtr than 1 chr1_109275226_G_A\n",
+      "gtr than 1 chr1_109275226_G_C\n",
+      "gtr than 1 chr1_109275226_G_T\n",
+      "gtr than 1 chr1_109275227_G_A\n",
+      "gtr than 1 chr1_109275227_G_C\n",
+      "gtr than 1 chr1_109275227_G_T\n",
+      "gtr than 1 chr1_109275228_T_A\n",
+      "gtr than 1 chr1_109275228_T_C\n",
+      "gtr than 1 chr1_109275228_T_G\n",
+      "gtr than 1 chr1_109275229_T_A\n",
+      "gtr than 1 chr1_109275229_T_C\n",
+      "gtr than 1 chr1_109275229_T_G\n",
+      "gtr than 1 chr1_109275230_C_A\n",
+      "gtr than 1 chr1_109275230_C_G\n",
+      "gtr than 1 chr1_109275230_C_T\n",
+      "gtr than 1 chr1_109275231_G_A\n",
+      "gtr than 1 chr1_109275231_G_C\n",
+      "gtr than 1 chr1_109275231_G_T\n",
+      "gtr than 1 chr1_109275232_T_A\n",
+      "gtr than 1 chr1_109275232_T_C\n",
+      "gtr than 1 chr1_109275232_T_G\n",
+      "gtr than 1 chr1_109275233_G_A\n",
+      "gtr than 1 chr1_109275233_G_C\n",
+      "gtr than 1 chr1_109275233_G_T\n",
+      "gtr than 1 chr1_109275234_T_A\n",
+      "gtr than 1 chr1_109275234_T_C\n",
+      "gtr than 1 chr1_109275234_T_G\n",
+      "gtr than 1 chr1_109275235_C_A\n",
+      "gtr than 1 chr1_109275235_C_G\n",
+      "gtr than 1 chr1_109275235_C_T\n",
+      "gtr than 1 chr1_109275236_C_A\n",
+      "gtr than 1 chr1_109275236_C_G\n",
+      "gtr than 1 chr1_109275236_C_T\n",
+      "gtr than 1 chr1_109275237_A_C\n",
+      "gtr than 1 chr1_109275237_A_G\n",
+      "gtr than 1 chr1_109275237_A_T\n",
+      "gtr than 1 chr1_109275238_G_A\n",
+      "gtr than 1 chr1_109275238_G_C\n",
+      "gtr than 1 chr1_109275238_G_T\n",
+      "gtr than 1 chr1_109275239_T_A\n",
+      "gtr than 1 chr1_109275239_T_C\n",
+      "gtr than 1 chr1_109275239_T_G\n",
+      "gtr than 1 chr1_109275240_C_A\n",
+      "gtr than 1 chr1_109275240_C_G\n",
+      "gtr than 1 chr1_109275240_C_T\n",
+      "gtr than 1 chr1_109275241_C_A\n",
+      "gtr than 1 chr1_109275241_C_G\n",
+      "gtr than 1 chr1_109275241_C_T\n",
+      "gtr than 1 chr1_109275242_C_A\n",
+      "gtr than 1 chr1_109275242_C_G\n",
+      "gtr than 1 chr1_109275242_C_T\n",
+      "gtr than 1 chr1_109275243_G_A\n",
+      "gtr than 1 chr1_109275243_G_C\n",
+      "gtr than 1 chr1_109275243_G_T\n",
+      "gtr than 1 chr1_109275244_G_A\n",
+      "gtr than 1 chr1_109275244_G_C\n",
+      "gtr than 1 chr1_109275244_G_T\n",
+      "gtr than 1 chr1_109275245_G_A\n",
+      "gtr than 1 chr1_109275245_G_C\n",
+      "gtr than 1 chr1_109275245_G_T\n",
+      "gtr than 1 chr1_109275246_G_A\n",
+      "gtr than 1 chr1_109275246_G_C\n",
+      "gtr than 1 chr1_109275246_G_T\n",
+      "gtr than 1 chr1_109275247_G_A\n",
+      "gtr than 1 chr1_109275247_G_C\n",
+      "gtr than 1 chr1_109275247_G_T\n",
+      "gtr than 1 chr1_109275248_T_A\n",
+      "gtr than 1 chr1_109275248_T_C\n",
+      "gtr than 1 chr1_109275248_T_G\n",
+      "gtr than 1 chr1_109275249_C_A\n",
+      "gtr than 1 chr1_109275249_C_G\n",
+      "gtr than 1 chr1_109275249_C_T\n",
+      "gtr than 1 chr1_109275250_T_A\n",
+      "gtr than 1 chr1_109275250_T_C\n",
+      "gtr than 1 chr1_109275250_T_G\n",
+      "gtr than 1 chr1_109275251_G_C\n",
+      "gtr than 1 chr1_109275251_G_T\n",
+      "chr1_209816391_G_A\n",
+      "chr1_209816391_G_C\n",
+      "chr1_209816391_G_T\n",
+      "chr20_44355519_C_A\n",
+      "chr20_44355519_C_G\n",
+      "chr20_44355519_C_T\n",
+      "chr10_46046835_G_T\n",
+      "chr10_46046835_G_C\n",
+      "chr10_46046835_G_A\n",
+      "gtr than 1 chr5_1295005_G_A\n",
+      "gtr than 1 chr5_1295005_G_C\n",
+      "gtr than 1 chr5_1295005_G_T\n",
+      "gtr than 1 chr5_1295006_G_A\n",
+      "gtr than 1 chr5_1295006_G_C\n",
+      "gtr than 1 chr5_1295006_G_T\n",
+      "gtr than 1 chr5_1295007_C_A\n",
+      "gtr than 1 chr5_1295007_C_G\n",
+      "gtr than 1 chr5_1295007_C_T\n",
+      "gtr than 1 chr5_1295008_C_A\n",
+      "gtr than 1 chr5_1295008_C_G\n",
+      "gtr than 1 chr5_1295008_C_T\n",
+      "gtr than 1 chr5_1295009_A_C\n",
+      "gtr than 1 chr5_1295009_A_G\n",
+      "gtr than 1 chr5_1295009_A_T\n",
+      "gtr than 1 chr5_1295010_G_A\n",
+      "gtr than 1 chr5_1295010_G_C\n",
+      "gtr than 1 chr5_1295010_G_T\n",
+      "gtr than 1 chr5_1295011_G_A\n",
+      "gtr than 1 chr5_1295011_G_C\n",
+      "gtr than 1 chr5_1295011_G_T\n",
+      "gtr than 1 chr5_1295012_G_A\n",
+      "gtr than 1 chr5_1295012_G_C\n",
+      "gtr than 1 chr5_1295012_G_T\n",
+      "gtr than 1 chr5_1295013_C_A\n",
+      "gtr than 1 chr5_1295013_C_G\n",
+      "gtr than 1 chr5_1295013_C_T\n",
+      "gtr than 1 chr5_1295014_T_A\n",
+      "gtr than 1 chr5_1295014_T_C\n",
+      "gtr than 1 chr5_1295014_T_G\n",
+      "gtr than 1 chr5_1295015_T_A\n",
+      "gtr than 1 chr5_1295015_T_C\n",
+      "gtr than 1 chr5_1295015_T_G\n",
+      "gtr than 1 chr5_1295016_C_A\n",
+      "gtr than 1 chr5_1295016_C_G\n",
+      "gtr than 1 chr5_1295016_C_T\n",
+      "gtr than 1 chr5_1295017_C_A\n",
+      "gtr than 1 chr5_1295017_C_G\n",
+      "gtr than 1 chr5_1295017_C_T\n",
+      "gtr than 1 chr5_1295018_C_A\n",
+      "gtr than 1 chr5_1295018_C_G\n",
+      "gtr than 1 chr5_1295018_C_T\n",
+      "gtr than 1 chr5_1295019_A_C\n",
+      "gtr than 1 chr5_1295019_A_G\n",
+      "gtr than 1 chr5_1295019_A_T\n",
+      "gtr than 1 chr5_1295020_C_A\n",
+      "gtr than 1 chr5_1295020_C_G\n",
+      "gtr than 1 chr5_1295020_C_T\n",
+      "gtr than 1 chr5_1295021_G_A\n",
+      "gtr than 1 chr5_1295021_G_C\n",
+      "gtr than 1 chr5_1295021_G_T\n",
+      "gtr than 1 chr5_1295022_T_A\n",
+      "gtr than 1 chr5_1295022_T_C\n",
+      "gtr than 1 chr5_1295022_T_G\n",
+      "gtr than 1 chr5_1295023_G_A\n",
+      "gtr than 1 chr5_1295023_G_C\n",
+      "gtr than 1 chr5_1295023_G_T\n",
+      "gtr than 1 chr5_1295024_C_A\n",
+      "gtr than 1 chr5_1295024_C_G\n",
+      "gtr than 1 chr5_1295024_C_T\n",
+      "gtr than 1 chr5_1295025_G_A\n",
+      "gtr than 1 chr5_1295025_G_C\n",
+      "gtr than 1 chr5_1295025_G_T\n",
+      "gtr than 1 chr5_1295026_C_A\n",
+      "gtr than 1 chr5_1295026_C_G\n",
+      "gtr than 1 chr5_1295026_C_T\n",
+      "gtr than 1 chr5_1295027_A_C\n",
+      "gtr than 1 chr5_1295027_A_G\n",
+      "gtr than 1 chr5_1295027_A_T\n",
+      "gtr than 1 chr5_1295028_G_A\n",
+      "gtr than 1 chr5_1295028_G_C\n",
+      "gtr than 1 chr5_1295028_G_T\n",
+      "gtr than 1 chr5_1295029_C_A\n",
+      "gtr than 1 chr5_1295029_C_G\n",
+      "gtr than 1 chr5_1295029_C_T\n",
+      "gtr than 1 chr5_1295030_A_C\n",
+      "gtr than 1 chr5_1295030_A_G\n",
+      "gtr than 1 chr5_1295030_A_T\n",
+      "gtr than 1 chr5_1295031_G_A\n",
+      "gtr than 1 chr5_1295031_G_C\n",
+      "gtr than 1 chr5_1295031_G_T\n",
+      "gtr than 1 chr5_1295032_G_A\n",
+      "gtr than 1 chr5_1295032_G_C\n",
+      "gtr than 1 chr5_1295032_G_T\n",
+      "gtr than 1 chr5_1295033_A_C\n",
+      "gtr than 1 chr5_1295033_A_G\n",
+      "gtr than 1 chr5_1295033_A_T\n",
+      "gtr than 1 chr5_1295034_C_A\n",
+      "gtr than 1 chr5_1295034_C_G\n",
+      "gtr than 1 chr5_1295034_C_T\n",
+      "gtr than 1 chr5_1295035_G_A\n",
+      "gtr than 1 chr5_1295035_G_C\n",
+      "gtr than 1 chr5_1295035_G_T\n",
+      "gtr than 1 chr5_1295036_C_A\n",
+      "gtr than 1 chr5_1295036_C_G\n",
+      "gtr than 1 chr5_1295036_C_T\n",
+      "gtr than 1 chr5_1295053_C_A\n",
+      "gtr than 1 chr5_1295053_C_G\n",
+      "gtr than 1 chr5_1295053_C_T\n",
+      "gtr than 1 chr5_1295054_G_A\n",
+      "gtr than 1 chr5_1295054_G_C\n",
+      "gtr than 1 chr5_1295054_G_T\n",
+      "gtr than 1 chr5_1295055_C_A\n",
+      "gtr than 1 chr5_1295055_C_G\n",
+      "gtr than 1 chr5_1295055_C_T\n",
+      "gtr than 1 chr5_1295056_G_A\n",
+      "gtr than 1 chr5_1295056_G_C\n",
+      "gtr than 1 chr5_1295056_G_T\n",
+      "gtr than 1 chr5_1295057_C_A\n",
+      "gtr than 1 chr5_1295057_C_G\n",
+      "gtr than 1 chr5_1295057_C_T\n",
+      "gtr than 1 chr5_1295058_C_A\n",
+      "gtr than 1 chr5_1295058_C_G\n",
+      "gtr than 1 chr5_1295058_C_T\n",
+      "gtr than 1 chr5_1295059_G_A\n",
+      "gtr than 1 chr5_1295059_G_C\n",
+      "gtr than 1 chr5_1295059_G_T\n",
+      "gtr than 1 chr5_1295060_C_A\n",
+      "gtr than 1 chr5_1295060_C_G\n",
+      "gtr than 1 chr5_1295060_C_T\n",
+      "gtr than 1 chr5_1295061_G_A\n",
+      "gtr than 1 chr5_1295061_G_C\n",
+      "gtr than 1 chr5_1295061_G_T\n",
+      "gtr than 1 chr5_1295062_A_C\n",
+      "gtr than 1 chr5_1295062_A_G\n",
+      "gtr than 1 chr5_1295062_A_T\n",
+      "gtr than 1 chr5_1295063_G_A\n",
+      "gtr than 1 chr5_1295063_G_C\n",
+      "gtr than 1 chr5_1295063_G_T\n",
+      "gtr than 1 chr5_1295064_G_A\n",
+      "gtr than 1 chr5_1295064_G_C\n",
+      "gtr than 1 chr5_1295064_G_T\n",
+      "gtr than 1 chr5_1295065_A_C\n",
+      "gtr than 1 chr5_1295065_A_G\n",
+      "gtr than 1 chr5_1295065_A_T\n",
+      "gtr than 1 chr5_1295066_G_A\n",
+      "gtr than 1 chr5_1295066_G_C\n",
+      "gtr than 1 chr5_1295066_G_T\n",
+      "gtr than 1 chr5_1295067_A_C\n",
+      "gtr than 1 chr5_1295067_A_G\n",
+      "gtr than 1 chr5_1295067_A_T\n",
+      "gtr than 1 chr5_1295068_G_A\n",
+      "gtr than 1 chr5_1295068_G_C\n",
+      "gtr than 1 chr5_1295068_G_T\n",
+      "gtr than 1 chr5_1295069_G_A\n",
+      "gtr than 1 chr5_1295069_G_C\n",
+      "gtr than 1 chr5_1295069_G_T\n",
+      "gtr than 1 chr5_1295070_G_A\n",
+      "gtr than 1 chr5_1295070_G_C\n",
+      "gtr than 1 chr5_1295070_G_T\n",
+      "gtr than 1 chr5_1295071_C_A\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr5_1295071_C_G\n",
+      "gtr than 1 chr5_1295071_C_T\n",
+      "gtr than 1 chr5_1295072_G_A\n",
+      "gtr than 1 chr5_1295072_G_C\n",
+      "gtr than 1 chr5_1295072_G_T\n",
+      "gtr than 1 chr5_1295073_G_A\n",
+      "gtr than 1 chr5_1295073_G_C\n",
+      "gtr than 1 chr5_1295073_G_T\n",
+      "gtr than 1 chr5_1295074_G_A\n",
+      "gtr than 1 chr5_1295074_G_C\n",
+      "gtr than 1 chr5_1295074_G_T\n",
+      "gtr than 1 chr5_1295075_G_A\n",
+      "gtr than 1 chr5_1295075_G_C\n",
+      "gtr than 1 chr5_1295075_G_T\n",
+      "gtr than 1 chr5_1295076_C_A\n",
+      "gtr than 1 chr5_1295076_C_G\n",
+      "gtr than 1 chr5_1295076_C_T\n",
+      "gtr than 1 chr5_1295077_C_A\n",
+      "gtr than 1 chr5_1295077_C_G\n",
+      "gtr than 1 chr5_1295077_C_T\n",
+      "gtr than 1 chr5_1295078_G_A\n",
+      "gtr than 1 chr5_1295078_G_C\n",
+      "gtr than 1 chr5_1295078_G_T\n",
+      "gtr than 1 chr5_1295079_C_A\n",
+      "gtr than 1 chr5_1295079_C_G\n",
+      "gtr than 1 chr5_1295079_C_T\n",
+      "gtr than 1 chr5_1295080_G_A\n",
+      "gtr than 1 chr5_1295080_G_C\n",
+      "gtr than 1 chr5_1295080_G_T\n",
+      "gtr than 1 chr5_1295081_G_A\n",
+      "gtr than 1 chr5_1295081_G_C\n",
+      "gtr than 1 chr5_1295081_G_T\n",
+      "gtr than 1 chr5_1295082_A_C\n",
+      "gtr than 1 chr5_1295082_A_G\n",
+      "gtr than 1 chr5_1295082_A_T\n",
+      "gtr than 1 chr5_1295083_A_C\n",
+      "gtr than 1 chr5_1295083_A_G\n",
+      "gtr than 1 chr5_1295083_A_T\n",
+      "gtr than 1 chr5_1295084_A_C\n",
+      "gtr than 1 chr5_1295084_A_G\n",
+      "gtr than 1 chr5_1295084_A_T\n",
+      "gtr than 1 chr5_1295085_G_A\n",
+      "gtr than 1 chr5_1295085_G_C\n",
+      "gtr than 1 chr5_1295085_G_T\n",
+      "gtr than 1 chr5_1295086_G_A\n",
+      "gtr than 1 chr5_1295086_G_C\n",
+      "gtr than 1 chr5_1295086_G_T\n",
+      "gtr than 1 chr5_1295087_A_C\n",
+      "gtr than 1 chr5_1295087_A_G\n",
+      "gtr than 1 chr5_1295087_A_T\n",
+      "gtr than 1 chr5_1295088_A_C\n",
+      "gtr than 1 chr5_1295088_A_G\n",
+      "gtr than 1 chr5_1295088_A_T\n",
+      "gtr than 1 chr5_1295089_G_A\n",
+      "gtr than 1 chr5_1295089_G_C\n",
+      "gtr than 1 chr5_1295089_G_T\n",
+      "gtr than 1 chr5_1295090_G_A\n",
+      "gtr than 1 chr5_1295090_G_C\n",
+      "gtr than 1 chr5_1295090_G_T\n",
+      "gtr than 1 chr5_1295091_G_A\n",
+      "gtr than 1 chr5_1295091_G_C\n",
+      "gtr than 1 chr5_1295091_G_T\n",
+      "gtr than 1 chr5_1295092_G_A\n",
+      "gtr than 1 chr5_1295092_G_C\n",
+      "gtr than 1 chr5_1295092_G_T\n",
+      "gtr than 1 chr5_1295093_A_C\n",
+      "gtr than 1 chr5_1295093_A_G\n",
+      "gtr than 1 chr5_1295093_A_T\n",
+      "gtr than 1 chr5_1295094_G_A\n",
+      "gtr than 1 chr5_1295094_G_C\n",
+      "gtr than 1 chr5_1295094_G_T\n",
+      "gtr than 1 chr5_1295095_G_A\n",
+      "gtr than 1 chr5_1295095_G_C\n",
+      "gtr than 1 chr5_1295095_G_T\n",
+      "gtr than 1 chr5_1295096_G_A\n",
+      "gtr than 1 chr5_1295096_G_C\n",
+      "gtr than 1 chr5_1295096_G_T\n",
+      "gtr than 1 chr5_1295097_G_A\n",
+      "gtr than 1 chr5_1295097_G_C\n",
+      "gtr than 1 chr5_1295097_G_T\n",
+      "gtr than 1 chr5_1295098_C_A\n",
+      "gtr than 1 chr5_1295098_C_G\n",
+      "gtr than 1 chr5_1295098_C_T\n",
+      "gtr than 1 chr5_1295099_T_A\n",
+      "gtr than 1 chr5_1295099_T_C\n",
+      "gtr than 1 chr5_1295099_T_G\n",
+      "gtr than 1 chr5_1295100_G_A\n",
+      "gtr than 1 chr5_1295100_G_C\n",
+      "gtr than 1 chr5_1295100_G_T\n",
+      "gtr than 1 chr5_1295101_G_A\n",
+      "gtr than 1 chr5_1295101_G_C\n",
+      "gtr than 1 chr5_1295101_G_T\n",
+      "gtr than 1 chr5_1295102_G_A\n",
+      "gtr than 1 chr5_1295102_G_C\n",
+      "gtr than 1 chr5_1295102_G_T\n",
+      "gtr than 1 chr5_1295103_A_C\n",
+      "gtr than 1 chr5_1295103_A_G\n",
+      "gtr than 1 chr5_1295103_A_T\n",
+      "gtr than 1 chr5_1295104_G_A\n",
+      "gtr than 1 chr5_1295104_G_C\n",
+      "gtr than 1 chr5_1295104_G_T\n",
+      "gtr than 1 chr5_1295105_G_A\n",
+      "gtr than 1 chr5_1295105_G_C\n",
+      "gtr than 1 chr5_1295105_G_T\n",
+      "gtr than 1 chr5_1295106_G_A\n",
+      "gtr than 1 chr5_1295106_G_C\n",
+      "gtr than 1 chr5_1295106_G_T\n",
+      "gtr than 1 chr5_1295107_C_A\n",
+      "gtr than 1 chr5_1295107_C_G\n",
+      "gtr than 1 chr5_1295107_C_T\n",
+      "gtr than 1 chr5_1295108_C_A\n",
+      "gtr than 1 chr5_1295108_C_G\n",
+      "gtr than 1 chr5_1295108_C_T\n",
+      "gtr than 1 chr5_1295109_C_A\n",
+      "gtr than 1 chr5_1295109_C_G\n",
+      "gtr than 1 chr5_1295109_C_T\n",
+      "gtr than 1 chr5_1295110_G_A\n",
+      "gtr than 1 chr5_1295110_G_C\n",
+      "gtr than 1 chr5_1295110_G_T\n",
+      "gtr than 1 chr5_1295111_G_A\n",
+      "gtr than 1 chr5_1295111_G_C\n",
+      "gtr than 1 chr5_1295111_G_T\n",
+      "gtr than 1 chr5_1295112_A_C\n",
+      "gtr than 1 chr5_1295112_A_G\n",
+      "gtr than 1 chr5_1295112_A_T\n",
+      "gtr than 1 chr5_1295113_G_A\n",
+      "gtr than 1 chr5_1295113_G_C\n",
+      "gtr than 1 chr5_1295113_G_T\n",
+      "gtr than 1 chr5_1295114_G_A\n",
+      "gtr than 1 chr5_1295114_G_C\n",
+      "gtr than 1 chr5_1295114_G_T\n",
+      "gtr than 1 chr5_1295115_G_A\n",
+      "gtr than 1 chr5_1295115_G_C\n",
+      "gtr than 1 chr5_1295115_G_T\n",
+      "gtr than 1 chr5_1295116_G_A\n",
+      "gtr than 1 chr5_1295116_G_C\n",
+      "gtr than 1 chr5_1295116_G_T\n",
+      "gtr than 1 chr5_1295117_G_A\n",
+      "gtr than 1 chr5_1295117_G_C\n",
+      "gtr than 1 chr5_1295117_G_T\n",
+      "gtr than 1 chr5_1295118_C_A\n",
+      "gtr than 1 chr5_1295118_C_G\n",
+      "gtr than 1 chr5_1295118_C_T\n",
+      "gtr than 1 chr5_1295119_T_A\n",
+      "gtr than 1 chr5_1295119_T_C\n",
+      "gtr than 1 chr5_1295119_T_G\n",
+      "gtr than 1 chr5_1295120_G_A\n",
+      "gtr than 1 chr5_1295120_G_C\n",
+      "gtr than 1 chr5_1295120_G_T\n",
+      "gtr than 1 chr5_1295121_G_A\n",
+      "gtr than 1 chr5_1295121_G_C\n",
+      "gtr than 1 chr5_1295121_G_T\n",
+      "gtr than 1 chr5_1295122_G_A\n",
+      "gtr than 1 chr5_1295122_G_C\n",
+      "gtr than 1 chr5_1295122_G_T\n",
+      "gtr than 1 chr5_1295123_C_A\n",
+      "gtr than 1 chr5_1295123_C_G\n",
+      "gtr than 1 chr5_1295123_C_T\n",
+      "gtr than 1 chr5_1295124_C_A\n",
+      "gtr than 1 chr5_1295124_C_G\n",
+      "gtr than 1 chr5_1295124_C_T\n",
+      "gtr than 1 chr5_1295125_G_A\n",
+      "gtr than 1 chr5_1295125_G_C\n",
+      "gtr than 1 chr5_1295125_G_T\n",
+      "gtr than 1 chr5_1295126_G_A\n",
+      "gtr than 1 chr5_1295126_G_C\n",
+      "gtr than 1 chr5_1295126_G_T\n",
+      "gtr than 1 chr5_1295127_G_A\n",
+      "gtr than 1 chr5_1295127_G_C\n",
+      "gtr than 1 chr5_1295127_G_T\n",
+      "gtr than 1 chr5_1295128_G_A\n",
+      "gtr than 1 chr5_1295128_G_C\n",
+      "gtr than 1 chr5_1295128_G_T\n",
+      "gtr than 1 chr5_1295129_A_C\n",
+      "gtr than 1 chr5_1295129_A_G\n",
+      "gtr than 1 chr5_1295129_A_T\n",
+      "gtr than 1 chr5_1295130_C_A\n",
+      "gtr than 1 chr5_1295130_C_G\n",
+      "gtr than 1 chr5_1295130_C_T\n",
+      "gtr than 1 chr5_1295131_C_A\n",
+      "gtr than 1 chr5_1295131_C_G\n",
+      "gtr than 1 chr5_1295131_C_T\n",
+      "gtr than 1 chr5_1295132_C_A\n",
+      "gtr than 1 chr5_1295132_C_G\n",
+      "gtr than 1 chr5_1295132_C_T\n",
+      "gtr than 1 chr5_1295133_G_A\n",
+      "gtr than 1 chr5_1295133_G_C\n",
+      "gtr than 1 chr5_1295133_G_T\n",
+      "gtr than 1 chr5_1295134_G_A\n",
+      "gtr than 1 chr5_1295134_G_C\n",
+      "gtr than 1 chr5_1295134_G_T\n",
+      "gtr than 1 chr5_1295135_G_A\n",
+      "gtr than 1 chr5_1295135_G_C\n",
+      "gtr than 1 chr5_1295135_G_T\n",
+      "gtr than 1 chr5_1295136_A_C\n",
+      "gtr than 1 chr5_1295136_A_G\n",
+      "gtr than 1 chr5_1295136_A_T\n",
+      "gtr than 1 chr5_1295137_G_A\n",
+      "gtr than 1 chr5_1295137_G_C\n",
+      "gtr than 1 chr5_1295137_G_T\n",
+      "gtr than 1 chr5_1295138_G_A\n",
+      "gtr than 1 chr5_1295138_G_C\n",
+      "gtr than 1 chr5_1295138_G_T\n",
+      "gtr than 1 chr5_1295139_G_A\n",
+      "gtr than 1 chr5_1295139_G_C\n",
+      "gtr than 1 chr5_1295139_G_T\n",
+      "gtr than 1 chr5_1295140_G_A\n",
+      "gtr than 1 chr5_1295140_G_C\n",
+      "gtr than 1 chr5_1295140_G_T\n",
+      "gtr than 1 chr5_1295141_T_A\n",
+      "gtr than 1 chr5_1295141_T_C\n",
+      "gtr than 1 chr5_1295141_T_G\n",
+      "gtr than 1 chr5_1295142_C_A\n",
+      "gtr than 1 chr5_1295142_C_G\n",
+      "gtr than 1 chr5_1295142_C_T\n",
+      "gtr than 1 chr5_1295143_G_A\n",
+      "gtr than 1 chr5_1295143_G_C\n",
+      "gtr than 1 chr5_1295143_G_T\n",
+      "gtr than 1 chr5_1295144_G_A\n",
+      "gtr than 1 chr5_1295144_G_C\n",
+      "gtr than 1 chr5_1295144_G_T\n",
+      "gtr than 1 chr5_1295145_G_A\n",
+      "gtr than 1 chr5_1295145_G_C\n",
+      "gtr than 1 chr5_1295145_G_T\n",
+      "gtr than 1 chr5_1295146_A_C\n",
+      "gtr than 1 chr5_1295146_A_G\n",
+      "gtr than 1 chr5_1295146_A_T\n",
+      "gtr than 1 chr5_1295147_C_A\n",
+      "gtr than 1 chr5_1295147_C_G\n",
+      "gtr than 1 chr5_1295147_C_T\n",
+      "gtr than 1 chr5_1295148_G_A\n",
+      "gtr than 1 chr5_1295148_G_C\n",
+      "gtr than 1 chr5_1295148_G_T\n",
+      "gtr than 1 chr5_1295165_G_A\n",
+      "gtr than 1 chr5_1295165_G_C\n",
+      "gtr than 1 chr5_1295165_G_T\n",
+      "gtr than 1 chr5_1295166_A_C\n",
+      "gtr than 1 chr5_1295166_A_G\n",
+      "gtr than 1 chr5_1295166_A_T\n",
+      "gtr than 1 chr5_1295167_G_A\n",
+      "gtr than 1 chr5_1295167_G_C\n",
+      "gtr than 1 chr5_1295167_G_T\n",
+      "gtr than 1 chr5_1295168_G_A\n",
+      "gtr than 1 chr5_1295168_G_C\n",
+      "gtr than 1 chr5_1295168_G_T\n",
+      "gtr than 1 chr5_1295169_A_C\n",
+      "gtr than 1 chr5_1295169_A_G\n",
+      "gtr than 1 chr5_1295169_A_T\n",
+      "gtr than 1 chr5_1295170_G_A\n",
+      "gtr than 1 chr5_1295170_G_C\n",
+      "gtr than 1 chr5_1295170_G_T\n",
+      "gtr than 1 chr5_1295171_G_A\n",
+      "gtr than 1 chr5_1295171_G_C\n",
+      "gtr than 1 chr5_1295171_G_T\n",
+      "gtr than 1 chr5_1295172_C_A\n",
+      "gtr than 1 chr5_1295172_C_G\n",
+      "gtr than 1 chr5_1295172_C_T\n",
+      "gtr than 1 chr5_1295173_G_A\n",
+      "gtr than 1 chr5_1295173_G_C\n",
+      "gtr than 1 chr5_1295173_G_T\n",
+      "gtr than 1 chr5_1295174_G_A\n",
+      "gtr than 1 chr5_1295174_G_C\n",
+      "gtr than 1 chr5_1295174_G_T\n",
+      "gtr than 1 chr5_1295175_A_C\n",
+      "gtr than 1 chr5_1295175_A_G\n",
+      "gtr than 1 chr5_1295175_A_T\n",
+      "gtr than 1 chr5_1295176_G_A\n",
+      "gtr than 1 chr5_1295176_G_C\n",
+      "gtr than 1 chr5_1295176_G_T\n",
+      "gtr than 1 chr5_1295177_C_A\n",
+      "gtr than 1 chr5_1295177_C_G\n",
+      "gtr than 1 chr5_1295177_C_T\n",
+      "gtr than 1 chr5_1295178_T_A\n",
+      "gtr than 1 chr5_1295178_T_C\n",
+      "gtr than 1 chr5_1295178_T_G\n",
+      "gtr than 1 chr5_1295179_G_A\n",
+      "gtr than 1 chr5_1295179_G_C\n",
+      "gtr than 1 chr5_1295179_G_T\n",
+      "gtr than 1 chr5_1295180_G_A\n",
+      "gtr than 1 chr5_1295180_G_C\n",
+      "gtr than 1 chr5_1295180_G_T\n",
+      "gtr than 1 chr5_1295181_A_C\n",
+      "gtr than 1 chr5_1295181_A_G\n",
+      "gtr than 1 chr5_1295181_A_T\n",
+      "gtr than 1 chr5_1295182_A_C\n",
+      "gtr than 1 chr5_1295182_A_G\n",
+      "gtr than 1 chr5_1295182_A_T\n",
+      "gtr than 1 chr5_1295183_G_A\n",
+      "gtr than 1 chr5_1295183_G_C\n",
+      "gtr than 1 chr5_1295183_G_T\n",
+      "gtr than 1 chr5_1295184_G_A\n",
+      "gtr than 1 chr5_1295184_G_C\n",
+      "gtr than 1 chr5_1295184_G_T\n",
+      "gtr than 1 chr5_1295185_T_A\n",
+      "gtr than 1 chr5_1295185_T_C\n",
+      "gtr than 1 chr5_1295185_T_G\n",
+      "gtr than 1 chr5_1295186_G_A\n",
+      "gtr than 1 chr5_1295186_G_C\n",
+      "gtr than 1 chr5_1295186_G_T\n",
+      "gtr than 1 chr5_1295187_A_C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr5_1295187_A_G\n",
+      "gtr than 1 chr5_1295187_A_T\n",
+      "gtr than 1 chr5_1295188_A_C\n",
+      "gtr than 1 chr5_1295188_A_G\n",
+      "gtr than 1 chr5_1295188_A_T\n",
+      "gtr than 1 chr5_1295189_G_A\n",
+      "gtr than 1 chr5_1295189_G_C\n",
+      "gtr than 1 chr5_1295189_G_T\n",
+      "gtr than 1 chr5_1295190_G_A\n",
+      "gtr than 1 chr5_1295190_G_C\n",
+      "gtr than 1 chr5_1295190_G_T\n",
+      "gtr than 1 chr5_1295191_G_A\n",
+      "gtr than 1 chr5_1295191_G_C\n",
+      "gtr than 1 chr5_1295191_G_T\n",
+      "gtr than 1 chr5_1295192_G_A\n",
+      "gtr than 1 chr5_1295192_G_C\n",
+      "gtr than 1 chr5_1295192_G_T\n",
+      "gtr than 1 chr5_1295193_C_A\n",
+      "gtr than 1 chr5_1295193_C_G\n",
+      "gtr than 1 chr5_1295193_C_T\n",
+      "gtr than 1 chr5_1295194_A_C\n",
+      "gtr than 1 chr5_1295194_A_G\n",
+      "gtr than 1 chr5_1295194_A_T\n",
+      "gtr than 1 chr5_1295195_G_A\n",
+      "gtr than 1 chr5_1295195_G_C\n",
+      "gtr than 1 chr5_1295195_G_T\n",
+      "gtr than 1 chr5_1295196_G_A\n",
+      "gtr than 1 chr5_1295196_G_C\n",
+      "gtr than 1 chr5_1295196_G_T\n",
+      "gtr than 1 chr5_1295197_A_C\n",
+      "gtr than 1 chr5_1295197_A_G\n",
+      "gtr than 1 chr5_1295197_A_T\n",
+      "gtr than 1 chr5_1295198_C_A\n",
+      "gtr than 1 chr5_1295198_C_G\n",
+      "gtr than 1 chr5_1295198_C_T\n",
+      "gtr than 1 chr5_1295199_G_A\n",
+      "gtr than 1 chr5_1295199_G_C\n",
+      "gtr than 1 chr5_1295199_G_T\n",
+      "gtr than 1 chr5_1295200_G_A\n",
+      "gtr than 1 chr5_1295200_G_C\n",
+      "gtr than 1 chr5_1295200_G_T\n",
+      "gtr than 1 chr5_1295201_G_A\n",
+      "gtr than 1 chr5_1295201_G_C\n",
+      "gtr than 1 chr5_1295201_G_T\n",
+      "gtr than 1 chr5_1295202_T_A\n",
+      "gtr than 1 chr5_1295202_T_C\n",
+      "gtr than 1 chr5_1295202_T_G\n",
+      "gtr than 1 chr5_1295203_G_A\n",
+      "gtr than 1 chr5_1295203_G_C\n",
+      "gtr than 1 chr5_1295203_G_T\n",
+      "gtr than 1 chr5_1295204_C_A\n",
+      "gtr than 1 chr5_1295204_C_G\n",
+      "gtr than 1 chr5_1295204_C_T\n",
+      "gtr than 1 chr5_1295205_C_A\n",
+      "gtr than 1 chr5_1295205_C_G\n",
+      "gtr than 1 chr5_1295205_C_T\n",
+      "gtr than 1 chr5_1295206_C_A\n",
+      "gtr than 1 chr5_1295206_C_G\n",
+      "gtr than 1 chr5_1295206_C_T\n",
+      "gtr than 1 chr5_1295207_G_A\n",
+      "gtr than 1 chr5_1295207_G_C\n",
+      "gtr than 1 chr5_1295207_G_T\n",
+      "gtr than 1 chr5_1295208_G_A\n",
+      "gtr than 1 chr5_1295208_G_C\n",
+      "gtr than 1 chr5_1295208_G_T\n",
+      "gtr than 1 chr5_1295209_G_A\n",
+      "gtr than 1 chr5_1295209_G_C\n",
+      "gtr than 1 chr5_1295209_G_T\n",
+      "gtr than 1 chr5_1295210_T_A\n",
+      "gtr than 1 chr5_1295210_T_C\n",
+      "gtr than 1 chr5_1295210_T_G\n",
+      "gtr than 1 chr5_1295211_C_A\n",
+      "gtr than 1 chr5_1295211_C_G\n",
+      "gtr than 1 chr5_1295211_C_T\n",
+      "gtr than 1 chr5_1295212_C_A\n",
+      "gtr than 1 chr5_1295212_C_G\n",
+      "gtr than 1 chr5_1295212_C_T\n",
+      "gtr than 1 chr5_1295229_G_A\n",
+      "gtr than 1 chr5_1295229_G_C\n",
+      "gtr than 1 chr5_1295229_G_T\n",
+      "gtr than 1 chr5_1295230_T_A\n",
+      "gtr than 1 chr5_1295230_T_C\n",
+      "gtr than 1 chr5_1295230_T_G\n",
+      "gtr than 1 chr5_1295231_G_A\n",
+      "gtr than 1 chr5_1295231_G_C\n",
+      "gtr than 1 chr5_1295231_G_T\n",
+      "gtr than 1 chr5_1295232_G_A\n",
+      "gtr than 1 chr5_1295232_G_C\n",
+      "gtr than 1 chr5_1295232_G_T\n",
+      "gtr than 1 chr5_1295233_G_A\n",
+      "gtr than 1 chr5_1295233_G_T\n",
+      "gtr than 1 chr5_1295234_A_C\n",
+      "gtr than 1 chr5_1295234_A_G\n",
+      "gtr than 1 chr5_1295234_A_T\n",
+      "gtr than 1 chr5_1295235_A_C\n",
+      "gtr than 1 chr5_1295235_A_G\n",
+      "gtr than 1 chr5_1295235_A_T\n",
+      "gtr than 1 chr5_1295236_G_A\n",
+      "gtr than 1 chr5_1295236_G_C\n",
+      "gtr than 1 chr5_1295236_G_T\n",
+      "gtr than 1 chr5_1295237_C_A\n",
+      "gtr than 1 chr5_1295237_C_G\n",
+      "gtr than 1 chr5_1295237_C_T\n",
+      "gtr than 1 chr5_1295238_G_A\n",
+      "gtr than 1 chr5_1295238_G_C\n",
+      "gtr than 1 chr5_1295238_G_T\n",
+      "gtr than 1 chr5_1295239_C_A\n",
+      "gtr than 1 chr5_1295239_C_G\n",
+      "gtr than 1 chr5_1295239_C_T\n",
+      "gtr than 1 chr5_1295240_G_A\n",
+      "gtr than 1 chr5_1295240_G_C\n",
+      "gtr than 1 chr5_1295240_G_T\n",
+      "gtr than 1 chr5_1295241_G_A\n",
+      "gtr than 1 chr5_1295241_G_C\n",
+      "gtr than 1 chr5_1295241_G_T\n",
+      "gtr than 1 chr5_1295242_T_A\n",
+      "gtr than 1 chr5_1295242_T_C\n",
+      "gtr than 1 chr5_1295242_T_G\n",
+      "gtr than 1 chr5_1295243_C_A\n",
+      "gtr than 1 chr5_1295243_C_G\n",
+      "gtr than 1 chr5_1295243_C_T\n",
+      "gtr than 1 chr5_1295244_C_A\n",
+      "gtr than 1 chr5_1295244_C_G\n",
+      "gtr than 1 chr5_1295244_C_T\n",
+      "chr1_209816391_G_A\n",
+      "chr1_209816391_G_C\n",
+      "chr1_209816391_G_T\n",
+      "chr22_19723265_C_A\n",
+      "chr22_19723265_C_G\n",
+      "chr22_19723265_C_T\n",
+      "chr11_5250079_G_A\n",
+      "chr11_5250079_G_C\n",
+      "chr11_5250079_G_T\n",
+      "gtr than 1 chr1_155301395_T_A\n",
+      "gtr than 1 chr1_155301395_T_C\n",
+      "gtr than 1 chr1_155301395_T_G\n",
+      "gtr than 1 chr1_155301396_G_A\n",
+      "gtr than 1 chr1_155301396_G_C\n",
+      "gtr than 1 chr1_155301396_G_T\n",
+      "gtr than 1 chr1_155301397_C_A\n",
+      "gtr than 1 chr1_155301397_C_G\n",
+      "gtr than 1 chr1_155301397_C_T\n",
+      "gtr than 1 chr1_155301398_T_A\n",
+      "gtr than 1 chr1_155301398_T_C\n",
+      "gtr than 1 chr1_155301398_T_G\n",
+      "gtr than 1 chr1_155301399_T_A\n",
+      "gtr than 1 chr1_155301399_T_C\n",
+      "gtr than 1 chr1_155301399_T_G\n",
+      "gtr than 1 chr1_155301400_T_A\n",
+      "gtr than 1 chr1_155301400_T_C\n",
+      "gtr than 1 chr1_155301400_T_G\n",
+      "gtr than 1 chr1_155301401_C_A\n",
+      "gtr than 1 chr1_155301401_C_G\n",
+      "gtr than 1 chr1_155301401_C_T\n",
+      "gtr than 1 chr1_155301402_A_C\n",
+      "gtr than 1 chr1_155301402_A_G\n",
+      "gtr than 1 chr1_155301402_A_T\n",
+      "gtr than 1 chr1_155301403_G_A\n",
+      "gtr than 1 chr1_155301403_G_C\n",
+      "gtr than 1 chr1_155301403_G_T\n",
+      "gtr than 1 chr1_155301404_T_A\n",
+      "gtr than 1 chr1_155301404_T_C\n",
+      "gtr than 1 chr1_155301404_T_G\n",
+      "gtr than 1 chr1_155301405_G_A\n",
+      "gtr than 1 chr1_155301405_G_C\n",
+      "gtr than 1 chr1_155301405_G_T\n",
+      "gtr than 1 chr1_155301406_T_A\n",
+      "gtr than 1 chr1_155301406_T_C\n",
+      "gtr than 1 chr1_155301406_T_G\n",
+      "gtr than 1 chr1_155301407_G_A\n",
+      "gtr than 1 chr1_155301407_G_C\n",
+      "gtr than 1 chr1_155301407_G_T\n",
+      "gtr than 1 chr1_155301408_G_A\n",
+      "gtr than 1 chr1_155301408_G_C\n",
+      "gtr than 1 chr1_155301408_G_T\n",
+      "gtr than 1 chr1_155301409_G_A\n",
+      "gtr than 1 chr1_155301409_G_C\n",
+      "gtr than 1 chr1_155301409_G_T\n",
+      "gtr than 1 chr1_155301410_C_A\n",
+      "gtr than 1 chr1_155301410_C_G\n",
+      "gtr than 1 chr1_155301410_C_T\n",
+      "gtr than 1 chr1_155301411_C_A\n",
+      "gtr than 1 chr1_155301411_C_G\n",
+      "gtr than 1 chr1_155301411_C_T\n",
+      "gtr than 1 chr1_155301412_T_A\n",
+      "gtr than 1 chr1_155301412_T_C\n",
+      "gtr than 1 chr1_155301412_T_G\n",
+      "gtr than 1 chr1_155301413_G_A\n",
+      "gtr than 1 chr1_155301413_G_C\n",
+      "gtr than 1 chr1_155301413_G_T\n",
+      "gtr than 1 chr1_155301414_G_A\n",
+      "gtr than 1 chr1_155301414_G_C\n",
+      "gtr than 1 chr1_155301414_G_T\n",
+      "gtr than 1 chr1_155301415_G_A\n",
+      "gtr than 1 chr1_155301415_G_C\n",
+      "gtr than 1 chr1_155301415_G_T\n",
+      "gtr than 1 chr1_155301416_G_A\n",
+      "gtr than 1 chr1_155301416_G_C\n",
+      "gtr than 1 chr1_155301416_G_T\n",
+      "gtr than 1 chr1_155301417_C_A\n",
+      "gtr than 1 chr1_155301417_C_G\n",
+      "gtr than 1 chr1_155301417_C_T\n",
+      "gtr than 1 chr1_155301418_T_A\n",
+      "gtr than 1 chr1_155301418_T_C\n",
+      "gtr than 1 chr1_155301418_T_G\n",
+      "gtr than 1 chr1_155301419_G_A\n",
+      "gtr than 1 chr1_155301419_G_C\n",
+      "gtr than 1 chr1_155301419_G_T\n",
+      "gtr than 1 chr1_155301420_C_A\n",
+      "gtr than 1 chr1_155301420_C_G\n",
+      "gtr than 1 chr1_155301420_C_T\n",
+      "gtr than 1 chr1_155301421_G_A\n",
+      "gtr than 1 chr1_155301421_G_C\n",
+      "gtr than 1 chr1_155301421_G_T\n",
+      "gtr than 1 chr1_155301422_G_A\n",
+      "gtr than 1 chr1_155301422_G_C\n",
+      "gtr than 1 chr1_155301422_G_T\n",
+      "gtr than 1 chr1_155301423_G_A\n",
+      "gtr than 1 chr1_155301423_G_C\n",
+      "gtr than 1 chr1_155301423_G_T\n",
+      "gtr than 1 chr1_155301424_A_C\n",
+      "gtr than 1 chr1_155301424_A_G\n",
+      "gtr than 1 chr1_155301424_A_T\n",
+      "gtr than 1 chr1_155301425_C_A\n",
+      "gtr than 1 chr1_155301425_C_G\n",
+      "gtr than 1 chr1_155301425_C_T\n",
+      "gtr than 1 chr1_155301426_C_A\n",
+      "gtr than 1 chr1_155301426_C_G\n",
+      "gtr than 1 chr1_155301426_C_T\n",
+      "gtr than 1 chr1_155301427_A_C\n",
+      "gtr than 1 chr1_155301427_A_G\n",
+      "gtr than 1 chr1_155301427_A_T\n",
+      "gtr than 1 chr1_155301428_T_A\n",
+      "gtr than 1 chr1_155301428_T_C\n",
+      "gtr than 1 chr1_155301428_T_G\n",
+      "gtr than 1 chr1_155301429_G_A\n",
+      "gtr than 1 chr1_155301429_G_C\n",
+      "gtr than 1 chr1_155301429_G_T\n",
+      "gtr than 1 chr1_155301430_G_A\n",
+      "gtr than 1 chr1_155301430_G_C\n",
+      "gtr than 1 chr1_155301430_G_T\n",
+      "gtr than 1 chr1_155301431_A_C\n",
+      "gtr than 1 chr1_155301431_A_G\n",
+      "gtr than 1 chr1_155301431_A_T\n",
+      "gtr than 1 chr1_155301432_A_C\n",
+      "gtr than 1 chr1_155301432_A_G\n",
+      "gtr than 1 chr1_155301432_A_T\n",
+      "gtr than 1 chr1_155301433_T_A\n",
+      "gtr than 1 chr1_155301433_T_C\n",
+      "gtr than 1 chr1_155301433_T_G\n",
+      "gtr than 1 chr1_155301434_G_A\n",
+      "gtr than 1 chr1_155301434_G_C\n",
+      "gtr than 1 chr1_155301434_G_T\n",
+      "gtr than 1 chr1_155301435_A_C\n",
+      "gtr than 1 chr1_155301435_A_G\n",
+      "gtr than 1 chr1_155301435_A_T\n",
+      "gtr than 1 chr1_155301436_G_A\n",
+      "gtr than 1 chr1_155301436_G_C\n",
+      "gtr than 1 chr1_155301436_G_T\n",
+      "gtr than 1 chr1_155301437_A_C\n",
+      "gtr than 1 chr1_155301437_A_G\n",
+      "gtr than 1 chr1_155301437_A_T\n",
+      "gtr than 1 chr1_155301438_G_A\n",
+      "gtr than 1 chr1_155301438_G_C\n",
+      "gtr than 1 chr1_155301438_G_T\n",
+      "gtr than 1 chr1_155301439_G_A\n",
+      "gtr than 1 chr1_155301439_G_C\n",
+      "gtr than 1 chr1_155301439_G_T\n",
+      "gtr than 1 chr1_155301440_G_A\n",
+      "gtr than 1 chr1_155301440_G_C\n",
+      "gtr than 1 chr1_155301440_G_T\n",
+      "gtr than 1 chr1_155301441_A_C\n",
+      "gtr than 1 chr1_155301441_A_G\n",
+      "gtr than 1 chr1_155301441_A_T\n",
+      "gtr than 1 chr1_155301442_G_A\n",
+      "gtr than 1 chr1_155301442_G_C\n",
+      "gtr than 1 chr1_155301442_G_T\n",
+      "gtr than 1 chr1_155301459_T_A\n",
+      "gtr than 1 chr1_155301459_T_C\n",
+      "gtr than 1 chr1_155301459_T_G\n",
+      "gtr than 1 chr1_155301460_G_A\n",
+      "gtr than 1 chr1_155301460_G_C\n",
+      "gtr than 1 chr1_155301460_G_T\n",
+      "gtr than 1 chr1_155301461_G_A\n",
+      "gtr than 1 chr1_155301461_G_C\n",
+      "gtr than 1 chr1_155301461_G_T\n",
+      "gtr than 1 chr1_155301462_T_A\n",
+      "gtr than 1 chr1_155301462_T_C\n",
+      "gtr than 1 chr1_155301462_T_G\n",
+      "gtr than 1 chr1_155301463_C_A\n",
+      "gtr than 1 chr1_155301463_C_G\n",
+      "gtr than 1 chr1_155301463_C_T\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_155301464_T_A\n",
+      "gtr than 1 chr1_155301464_T_C\n",
+      "gtr than 1 chr1_155301464_T_G\n",
+      "gtr than 1 chr1_155301465_T_A\n",
+      "gtr than 1 chr1_155301465_T_C\n",
+      "gtr than 1 chr1_155301465_T_G\n",
+      "gtr than 1 chr1_155301466_A_G\n",
+      "gtr than 1 chr1_155301466_A_T\n",
+      "gtr than 1 chr1_155301467_T_A\n",
+      "gtr than 1 chr1_155301467_T_C\n",
+      "gtr than 1 chr1_155301467_T_G\n",
+      "gtr than 1 chr1_155301468_C_A\n",
+      "gtr than 1 chr1_155301468_C_G\n",
+      "gtr than 1 chr1_155301468_C_T\n",
+      "gtr than 1 chr1_155301469_T_A\n",
+      "gtr than 1 chr1_155301469_T_C\n",
+      "gtr than 1 chr1_155301469_T_G\n",
+      "gtr than 1 chr1_155301470_A_C\n",
+      "gtr than 1 chr1_155301470_A_G\n",
+      "gtr than 1 chr1_155301470_A_T\n",
+      "gtr than 1 chr1_155301471_A_C\n",
+      "gtr than 1 chr1_155301471_A_G\n",
+      "gtr than 1 chr1_155301471_A_T\n",
+      "gtr than 1 chr1_155301472_G_A\n",
+      "gtr than 1 chr1_155301472_G_C\n",
+      "gtr than 1 chr1_155301472_G_T\n",
+      "gtr than 1 chr1_155301473_G_A\n",
+      "gtr than 1 chr1_155301473_G_C\n",
+      "gtr than 1 chr1_155301473_G_T\n",
+      "gtr than 1 chr1_155301474_G_A\n",
+      "gtr than 1 chr1_155301474_G_C\n",
+      "gtr than 1 chr1_155301474_G_T\n",
+      "gtr than 1 chr1_155301475_A_C\n",
+      "gtr than 1 chr1_155301475_A_G\n",
+      "gtr than 1 chr1_155301475_A_T\n",
+      "gtr than 1 chr1_155301476_G_A\n",
+      "gtr than 1 chr1_155301476_G_C\n",
+      "gtr than 1 chr1_155301476_G_T\n",
+      "gtr than 1 chr1_155301477_A_C\n",
+      "gtr than 1 chr1_155301477_A_G\n",
+      "gtr than 1 chr1_155301477_A_T\n",
+      "gtr than 1 chr1_155301478_C_A\n",
+      "gtr than 1 chr1_155301478_C_G\n",
+      "gtr than 1 chr1_155301478_C_T\n",
+      "gtr than 1 chr1_155301479_A_C\n",
+      "gtr than 1 chr1_155301479_A_G\n",
+      "gtr than 1 chr1_155301479_A_T\n",
+      "gtr than 1 chr1_155301480_G_A\n",
+      "gtr than 1 chr1_155301480_G_C\n",
+      "gtr than 1 chr1_155301480_G_T\n",
+      "gtr than 1 chr1_155301481_A_C\n",
+      "gtr than 1 chr1_155301481_A_G\n",
+      "gtr than 1 chr1_155301481_A_T\n",
+      "gtr than 1 chr1_155301482_G_A\n",
+      "gtr than 1 chr1_155301482_G_C\n",
+      "gtr than 1 chr1_155301482_G_T\n",
+      "gtr than 1 chr1_155301483_A_C\n",
+      "gtr than 1 chr1_155301483_A_G\n",
+      "gtr than 1 chr1_155301483_A_T\n",
+      "gtr than 1 chr1_155301484_A_C\n",
+      "gtr than 1 chr1_155301484_A_G\n",
+      "gtr than 1 chr1_155301484_A_T\n",
+      "gtr than 1 chr1_155301485_G_A\n",
+      "gtr than 1 chr1_155301485_G_C\n",
+      "gtr than 1 chr1_155301485_G_T\n",
+      "gtr than 1 chr1_155301486_A_C\n",
+      "gtr than 1 chr1_155301486_A_G\n",
+      "gtr than 1 chr1_155301486_A_T\n",
+      "gtr than 1 chr1_155301487_G_A\n",
+      "gtr than 1 chr1_155301487_G_C\n",
+      "gtr than 1 chr1_155301487_G_T\n",
+      "gtr than 1 chr1_155301488_A_C\n",
+      "gtr than 1 chr1_155301488_A_G\n",
+      "gtr than 1 chr1_155301488_A_T\n",
+      "gtr than 1 chr1_155301489_A_C\n",
+      "gtr than 1 chr1_155301489_A_G\n",
+      "gtr than 1 chr1_155301489_A_T\n",
+      "gtr than 1 chr1_155301490_A_C\n",
+      "gtr than 1 chr1_155301490_A_G\n",
+      "gtr than 1 chr1_155301490_A_T\n",
+      "gtr than 1 chr1_155301507_G_A\n",
+      "gtr than 1 chr1_155301507_G_C\n",
+      "gtr than 1 chr1_155301507_G_T\n",
+      "gtr than 1 chr1_155301508_G_A\n",
+      "gtr than 1 chr1_155301508_G_C\n",
+      "gtr than 1 chr1_155301508_G_T\n",
+      "gtr than 1 chr1_155301509_C_A\n",
+      "gtr than 1 chr1_155301509_C_G\n",
+      "gtr than 1 chr1_155301509_C_T\n",
+      "gtr than 1 chr1_155301510_C_A\n",
+      "gtr than 1 chr1_155301510_C_G\n",
+      "gtr than 1 chr1_155301510_C_T\n",
+      "gtr than 1 chr1_155301511_A_C\n",
+      "gtr than 1 chr1_155301511_A_G\n",
+      "gtr than 1 chr1_155301511_A_T\n",
+      "gtr than 1 chr1_155301512_C_A\n",
+      "gtr than 1 chr1_155301512_C_G\n",
+      "gtr than 1 chr1_155301512_C_T\n",
+      "gtr than 1 chr1_155301513_C_A\n",
+      "gtr than 1 chr1_155301513_C_G\n",
+      "gtr than 1 chr1_155301513_C_T\n",
+      "gtr than 1 chr1_155301514_C_A\n",
+      "gtr than 1 chr1_155301514_C_G\n",
+      "gtr than 1 chr1_155301514_C_T\n",
+      "gtr than 1 chr1_155301515_T_A\n",
+      "gtr than 1 chr1_155301515_T_C\n",
+      "gtr than 1 chr1_155301515_T_G\n",
+      "gtr than 1 chr1_155301516_G_A\n",
+      "gtr than 1 chr1_155301516_G_C\n",
+      "gtr than 1 chr1_155301516_G_T\n",
+      "gtr than 1 chr1_155301517_T_A\n",
+      "gtr than 1 chr1_155301517_T_C\n",
+      "gtr than 1 chr1_155301517_T_G\n",
+      "gtr than 1 chr1_155301518_C_A\n",
+      "gtr than 1 chr1_155301518_C_G\n",
+      "gtr than 1 chr1_155301518_C_T\n",
+      "gtr than 1 chr1_155301519_C_A\n",
+      "gtr than 1 chr1_155301519_C_G\n",
+      "gtr than 1 chr1_155301519_C_T\n",
+      "gtr than 1 chr1_155301520_C_A\n",
+      "gtr than 1 chr1_155301520_C_G\n",
+      "gtr than 1 chr1_155301520_C_T\n",
+      "gtr than 1 chr1_155301521_C_A\n",
+      "gtr than 1 chr1_155301521_C_G\n",
+      "gtr than 1 chr1_155301521_C_T\n",
+      "gtr than 1 chr1_155301522_A_C\n",
+      "gtr than 1 chr1_155301522_A_G\n",
+      "gtr than 1 chr1_155301522_A_T\n",
+      "gtr than 1 chr1_155301523_C_A\n",
+      "gtr than 1 chr1_155301523_C_G\n",
+      "gtr than 1 chr1_155301523_C_T\n",
+      "gtr than 1 chr1_155301524_A_C\n",
+      "gtr than 1 chr1_155301524_A_G\n",
+      "gtr than 1 chr1_155301524_A_T\n",
+      "gtr than 1 chr1_155301525_G_A\n",
+      "gtr than 1 chr1_155301525_G_C\n",
+      "gtr than 1 chr1_155301525_G_T\n",
+      "gtr than 1 chr1_155301526_A_C\n",
+      "gtr than 1 chr1_155301526_A_G\n",
+      "gtr than 1 chr1_155301526_A_T\n",
+      "gtr than 1 chr1_155301527_A_C\n",
+      "gtr than 1 chr1_155301527_A_G\n",
+      "gtr than 1 chr1_155301527_A_T\n",
+      "gtr than 1 chr1_155301528_T_A\n",
+      "gtr than 1 chr1_155301528_T_C\n",
+      "gtr than 1 chr1_155301528_T_G\n",
+      "gtr than 1 chr1_155301529_C_A\n",
+      "gtr than 1 chr1_155301529_C_G\n",
+      "gtr than 1 chr1_155301529_C_T\n",
+      "gtr than 1 chr1_155301530_C_A\n",
+      "gtr than 1 chr1_155301530_C_G\n",
+      "gtr than 1 chr1_155301530_C_T\n",
+      "gtr than 1 chr1_155301531_C_A\n",
+      "gtr than 1 chr1_155301531_C_G\n",
+      "gtr than 1 chr1_155301531_C_T\n",
+      "gtr than 1 chr1_155301532_T_A\n",
+      "gtr than 1 chr1_155301532_T_C\n",
+      "gtr than 1 chr1_155301532_T_G\n",
+      "gtr than 1 chr1_155301533_C_A\n",
+      "gtr than 1 chr1_155301533_C_G\n",
+      "gtr than 1 chr1_155301533_C_T\n",
+      "gtr than 1 chr1_155301534_C_A\n",
+      "gtr than 1 chr1_155301534_C_G\n",
+      "gtr than 1 chr1_155301534_C_T\n",
+      "gtr than 1 chr1_155301535_C_A\n",
+      "gtr than 1 chr1_155301535_C_G\n",
+      "gtr than 1 chr1_155301535_C_T\n",
+      "gtr than 1 chr1_155301536_C_A\n",
+      "gtr than 1 chr1_155301536_C_G\n",
+      "gtr than 1 chr1_155301536_C_T\n",
+      "gtr than 1 chr1_155301537_C_A\n",
+      "gtr than 1 chr1_155301537_C_G\n",
+      "gtr than 1 chr1_155301537_C_T\n",
+      "gtr than 1 chr1_155301538_A_C\n",
+      "gtr than 1 chr1_155301538_A_G\n",
+      "gtr than 1 chr1_155301538_A_T\n",
+      "gtr than 1 chr1_155301539_G_A\n",
+      "gtr than 1 chr1_155301539_G_C\n",
+      "gtr than 1 chr1_155301539_G_T\n",
+      "gtr than 1 chr1_155301540_A_C\n",
+      "gtr than 1 chr1_155301540_A_G\n",
+      "gtr than 1 chr1_155301540_A_T\n",
+      "gtr than 1 chr1_155301541_A_C\n",
+      "gtr than 1 chr1_155301541_A_G\n",
+      "gtr than 1 chr1_155301541_A_T\n",
+      "gtr than 1 chr1_155301542_C_A\n",
+      "gtr than 1 chr1_155301542_C_G\n",
+      "gtr than 1 chr1_155301542_C_T\n",
+      "gtr than 1 chr1_155301543_G_A\n",
+      "gtr than 1 chr1_155301543_G_C\n",
+      "gtr than 1 chr1_155301543_G_T\n",
+      "gtr than 1 chr1_155301544_G_A\n",
+      "gtr than 1 chr1_155301544_G_C\n",
+      "gtr than 1 chr1_155301544_G_T\n",
+      "gtr than 1 chr1_155301545_C_A\n",
+      "gtr than 1 chr1_155301545_C_G\n",
+      "gtr than 1 chr1_155301545_C_T\n",
+      "gtr than 1 chr1_155301546_C_A\n",
+      "gtr than 1 chr1_155301546_C_G\n",
+      "gtr than 1 chr1_155301546_C_T\n",
+      "gtr than 1 chr1_155301547_T_A\n",
+      "gtr than 1 chr1_155301547_T_C\n",
+      "gtr than 1 chr1_155301547_T_G\n",
+      "gtr than 1 chr1_155301548_G_A\n",
+      "gtr than 1 chr1_155301548_G_C\n",
+      "gtr than 1 chr1_155301548_G_T\n",
+      "gtr than 1 chr1_155301549_C_A\n",
+      "gtr than 1 chr1_155301549_C_G\n",
+      "gtr than 1 chr1_155301549_C_T\n",
+      "gtr than 1 chr1_155301550_T_A\n",
+      "gtr than 1 chr1_155301550_T_C\n",
+      "gtr than 1 chr1_155301550_T_G\n",
+      "gtr than 1 chr1_155301551_C_A\n",
+      "gtr than 1 chr1_155301551_C_G\n",
+      "gtr than 1 chr1_155301551_C_T\n",
+      "gtr than 1 chr1_155301552_T_A\n",
+      "gtr than 1 chr1_155301552_T_C\n",
+      "gtr than 1 chr1_155301552_T_G\n",
+      "gtr than 1 chr1_155301553_C_A\n",
+      "gtr than 1 chr1_155301553_C_G\n",
+      "gtr than 1 chr1_155301553_C_T\n",
+      "gtr than 1 chr1_155301554_T_A\n",
+      "gtr than 1 chr1_155301554_T_C\n",
+      "gtr than 1 chr1_155301554_T_G\n",
+      "gtr than 1 chr1_155301571_A_C\n",
+      "gtr than 1 chr1_155301571_A_G\n",
+      "gtr than 1 chr1_155301571_A_T\n",
+      "gtr than 1 chr1_155301572_T_A\n",
+      "gtr than 1 chr1_155301572_T_C\n",
+      "gtr than 1 chr1_155301572_T_G\n",
+      "gtr than 1 chr1_155301573_T_A\n",
+      "gtr than 1 chr1_155301573_T_C\n",
+      "gtr than 1 chr1_155301573_T_G\n",
+      "gtr than 1 chr1_155301574_T_A\n",
+      "gtr than 1 chr1_155301574_T_C\n",
+      "gtr than 1 chr1_155301574_T_G\n",
+      "gtr than 1 chr1_155301575_C_A\n",
+      "gtr than 1 chr1_155301575_C_T\n",
+      "gtr than 1 chr1_155301576_C_A\n",
+      "gtr than 1 chr1_155301576_C_G\n",
+      "gtr than 1 chr1_155301576_C_T\n",
+      "gtr than 1 chr1_155301577_T_A\n",
+      "gtr than 1 chr1_155301577_T_C\n",
+      "gtr than 1 chr1_155301577_T_G\n",
+      "gtr than 1 chr1_155301578_C_A\n",
+      "gtr than 1 chr1_155301578_C_G\n",
+      "gtr than 1 chr1_155301578_C_T\n",
+      "gtr than 1 chr1_155301579_T_A\n",
+      "gtr than 1 chr1_155301579_T_C\n",
+      "gtr than 1 chr1_155301579_T_G\n",
+      "gtr than 1 chr1_155301580_C_A\n",
+      "gtr than 1 chr1_155301580_C_G\n",
+      "gtr than 1 chr1_155301580_C_T\n",
+      "gtr than 1 chr1_155301581_A_C\n",
+      "gtr than 1 chr1_155301581_A_G\n",
+      "gtr than 1 chr1_155301581_A_T\n",
+      "gtr than 1 chr1_155301582_T_A\n",
+      "gtr than 1 chr1_155301582_T_C\n",
+      "gtr than 1 chr1_155301582_T_G\n",
+      "gtr than 1 chr1_155301583_C_A\n",
+      "gtr than 1 chr1_155301583_C_G\n",
+      "gtr than 1 chr1_155301583_C_T\n",
+      "gtr than 1 chr1_155301584_C_A\n",
+      "gtr than 1 chr1_155301584_C_G\n",
+      "gtr than 1 chr1_155301584_C_T\n",
+      "gtr than 1 chr1_155301585_T_A\n",
+      "gtr than 1 chr1_155301585_T_C\n",
+      "gtr than 1 chr1_155301585_T_G\n",
+      "gtr than 1 chr1_155301586_T_A\n",
+      "gtr than 1 chr1_155301586_T_C\n",
+      "gtr than 1 chr1_155301586_T_G\n",
+      "gtr than 1 chr1_155301587_T_A\n",
+      "gtr than 1 chr1_155301587_T_C\n",
+      "gtr than 1 chr1_155301587_T_G\n",
+      "gtr than 1 chr1_155301588_T_A\n",
+      "gtr than 1 chr1_155301588_T_C\n",
+      "gtr than 1 chr1_155301588_T_G\n",
+      "gtr than 1 chr1_155301589_T_A\n",
+      "gtr than 1 chr1_155301589_T_C\n",
+      "gtr than 1 chr1_155301589_T_G\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_155301590_T_A\n",
+      "gtr than 1 chr1_155301590_T_C\n",
+      "gtr than 1 chr1_155301590_T_G\n",
+      "gtr than 1 chr1_155301591_C_A\n",
+      "gtr than 1 chr1_155301591_C_G\n",
+      "gtr than 1 chr1_155301591_C_T\n",
+      "gtr than 1 chr1_155301592_C_A\n",
+      "gtr than 1 chr1_155301592_C_G\n",
+      "gtr than 1 chr1_155301592_C_T\n",
+      "gtr than 1 chr1_155301593_T_A\n",
+      "gtr than 1 chr1_155301593_T_C\n",
+      "gtr than 1 chr1_155301593_T_G\n",
+      "gtr than 1 chr1_155301594_G_A\n",
+      "gtr than 1 chr1_155301594_G_C\n",
+      "gtr than 1 chr1_155301594_G_T\n",
+      "gtr than 1 chr1_155301595_A_C\n",
+      "gtr than 1 chr1_155301595_A_G\n",
+      "gtr than 1 chr1_155301595_A_T\n",
+      "gtr than 1 chr1_155301596_T_A\n",
+      "gtr than 1 chr1_155301596_T_C\n",
+      "gtr than 1 chr1_155301596_T_G\n",
+      "gtr than 1 chr1_155301597_A_C\n",
+      "gtr than 1 chr1_155301597_A_G\n",
+      "gtr than 1 chr1_155301597_A_T\n",
+      "gtr than 1 chr1_155301598_A_C\n",
+      "gtr than 1 chr1_155301598_A_G\n",
+      "gtr than 1 chr1_155301598_A_T\n",
+      "gtr than 1 chr1_155301599_A_C\n",
+      "gtr than 1 chr1_155301599_A_G\n",
+      "gtr than 1 chr1_155301599_A_T\n",
+      "gtr than 1 chr1_155301600_T_A\n",
+      "gtr than 1 chr1_155301600_T_C\n",
+      "gtr than 1 chr1_155301600_T_G\n",
+      "gtr than 1 chr1_155301601_T_A\n",
+      "gtr than 1 chr1_155301601_T_C\n",
+      "gtr than 1 chr1_155301601_T_G\n",
+      "gtr than 1 chr1_155301602_T_A\n",
+      "gtr than 1 chr1_155301602_T_C\n",
+      "gtr than 1 chr1_155301602_T_G\n",
+      "gtr than 1 chr1_155301619_A_C\n",
+      "gtr than 1 chr1_155301619_A_G\n",
+      "gtr than 1 chr1_155301619_A_T\n",
+      "gtr than 1 chr1_155301620_T_A\n",
+      "gtr than 1 chr1_155301620_T_C\n",
+      "gtr than 1 chr1_155301620_T_G\n",
+      "gtr than 1 chr1_155301621_C_A\n",
+      "gtr than 1 chr1_155301621_C_G\n",
+      "gtr than 1 chr1_155301621_C_T\n",
+      "gtr than 1 chr1_155301622_T_A\n",
+      "gtr than 1 chr1_155301622_T_C\n",
+      "gtr than 1 chr1_155301622_T_G\n",
+      "gtr than 1 chr1_155301623_G_A\n",
+      "gtr than 1 chr1_155301623_G_C\n",
+      "gtr than 1 chr1_155301623_G_T\n",
+      "gtr than 1 chr1_155301624_G_A\n",
+      "gtr than 1 chr1_155301624_G_C\n",
+      "gtr than 1 chr1_155301624_G_T\n",
+      "gtr than 1 chr1_155301625_T_A\n",
+      "gtr than 1 chr1_155301625_T_C\n",
+      "gtr than 1 chr1_155301625_T_G\n",
+      "gtr than 1 chr1_155301626_C_A\n",
+      "gtr than 1 chr1_155301626_C_G\n",
+      "gtr than 1 chr1_155301626_C_T\n",
+      "gtr than 1 chr1_155301627_A_C\n",
+      "gtr than 1 chr1_155301627_A_G\n",
+      "gtr than 1 chr1_155301627_A_T\n",
+      "gtr than 1 chr1_155301628_T_A\n",
+      "gtr than 1 chr1_155301628_T_C\n",
+      "gtr than 1 chr1_155301628_T_G\n",
+      "gtr than 1 chr1_155301629_C_A\n",
+      "gtr than 1 chr1_155301629_C_G\n",
+      "gtr than 1 chr1_155301629_C_T\n",
+      "gtr than 1 chr1_155301630_C_A\n",
+      "gtr than 1 chr1_155301630_C_G\n",
+      "gtr than 1 chr1_155301630_C_T\n",
+      "gtr than 1 chr1_155301631_A_C\n",
+      "gtr than 1 chr1_155301631_A_G\n",
+      "gtr than 1 chr1_155301631_A_T\n",
+      "gtr than 1 chr1_155301632_C_A\n",
+      "gtr than 1 chr1_155301632_C_G\n",
+      "gtr than 1 chr1_155301632_C_T\n",
+      "gtr than 1 chr1_155301633_G_A\n",
+      "gtr than 1 chr1_155301633_G_C\n",
+      "gtr than 1 chr1_155301633_G_T\n",
+      "gtr than 1 chr1_155301634_T_A\n",
+      "gtr than 1 chr1_155301634_T_C\n",
+      "gtr than 1 chr1_155301634_T_G\n",
+      "gtr than 1 chr1_155301635_G_A\n",
+      "gtr than 1 chr1_155301635_G_C\n",
+      "gtr than 1 chr1_155301635_G_T\n",
+      "gtr than 1 chr1_155301636_A_C\n",
+      "gtr than 1 chr1_155301636_A_G\n",
+      "gtr than 1 chr1_155301636_A_T\n",
+      "gtr than 1 chr1_155301637_A_C\n",
+      "gtr than 1 chr1_155301637_A_G\n",
+      "gtr than 1 chr1_155301637_A_T\n",
+      "gtr than 1 chr1_155301638_T_A\n",
+      "gtr than 1 chr1_155301638_T_C\n",
+      "gtr than 1 chr1_155301638_T_G\n",
+      "gtr than 1 chr1_155301639_A_C\n",
+      "gtr than 1 chr1_155301639_A_G\n",
+      "gtr than 1 chr1_155301639_A_T\n",
+      "gtr than 1 chr1_155301640_G_A\n",
+      "gtr than 1 chr1_155301640_G_C\n",
+      "gtr than 1 chr1_155301640_G_T\n",
+      "gtr than 1 chr1_155301641_A_C\n",
+      "gtr than 1 chr1_155301641_A_G\n",
+      "gtr than 1 chr1_155301641_A_T\n",
+      "gtr than 1 chr1_155301642_T_A\n",
+      "gtr than 1 chr1_155301642_T_C\n",
+      "gtr than 1 chr1_155301642_T_G\n",
+      "gtr than 1 chr1_155301643_A_C\n",
+      "gtr than 1 chr1_155301643_A_G\n",
+      "gtr than 1 chr1_155301643_A_T\n",
+      "gtr than 1 chr1_155301644_T_A\n",
+      "gtr than 1 chr1_155301644_T_C\n",
+      "gtr than 1 chr1_155301644_T_G\n",
+      "gtr than 1 chr1_155301645_T_A\n",
+      "gtr than 1 chr1_155301645_T_C\n",
+      "gtr than 1 chr1_155301645_T_G\n",
+      "gtr than 1 chr1_155301646_T_A\n",
+      "gtr than 1 chr1_155301646_T_C\n",
+      "gtr than 1 chr1_155301646_T_G\n",
+      "gtr than 1 chr1_155301647_T_A\n",
+      "gtr than 1 chr1_155301647_T_C\n",
+      "gtr than 1 chr1_155301647_T_G\n",
+      "gtr than 1 chr1_155301648_T_A\n",
+      "gtr than 1 chr1_155301648_T_C\n",
+      "gtr than 1 chr1_155301648_T_G\n",
+      "gtr than 1 chr1_155301649_T_A\n",
+      "gtr than 1 chr1_155301649_T_C\n",
+      "gtr than 1 chr1_155301649_T_G\n",
+      "gtr than 1 chr1_155301650_T_A\n",
+      "gtr than 1 chr1_155301650_T_C\n",
+      "gtr than 1 chr1_155301650_T_G\n",
+      "gtr than 1 chr1_155301667_G_A\n",
+      "gtr than 1 chr1_155301667_G_C\n",
+      "gtr than 1 chr1_155301667_G_T\n",
+      "gtr than 1 chr1_155301668_C_A\n",
+      "gtr than 1 chr1_155301668_C_G\n",
+      "gtr than 1 chr1_155301668_C_T\n",
+      "gtr than 1 chr1_155301669_C_A\n",
+      "gtr than 1 chr1_155301669_C_G\n",
+      "gtr than 1 chr1_155301669_C_T\n",
+      "gtr than 1 chr1_155301670_C_A\n",
+      "gtr than 1 chr1_155301670_C_G\n",
+      "gtr than 1 chr1_155301670_C_T\n",
+      "gtr than 1 chr1_155301671_C_A\n",
+      "gtr than 1 chr1_155301671_C_G\n",
+      "gtr than 1 chr1_155301671_C_T\n",
+      "gtr than 1 chr1_155301672_A_C\n",
+      "gtr than 1 chr1_155301672_A_G\n",
+      "gtr than 1 chr1_155301672_A_T\n",
+      "gtr than 1 chr1_155301673_T_A\n",
+      "gtr than 1 chr1_155301673_T_C\n",
+      "gtr than 1 chr1_155301673_T_G\n",
+      "gtr than 1 chr1_155301674_T_A\n",
+      "gtr than 1 chr1_155301674_T_C\n",
+      "gtr than 1 chr1_155301674_T_G\n",
+      "gtr than 1 chr1_155301675_T_A\n",
+      "gtr than 1 chr1_155301675_T_C\n",
+      "gtr than 1 chr1_155301675_T_G\n",
+      "gtr than 1 chr1_155301676_T_A\n",
+      "gtr than 1 chr1_155301676_T_C\n",
+      "gtr than 1 chr1_155301676_T_G\n",
+      "gtr than 1 chr1_155301677_C_A\n",
+      "gtr than 1 chr1_155301677_C_G\n",
+      "gtr than 1 chr1_155301677_C_T\n",
+      "gtr than 1 chr1_155301678_T_A\n",
+      "gtr than 1 chr1_155301678_T_C\n",
+      "gtr than 1 chr1_155301678_T_G\n",
+      "gtr than 1 chr1_155301679_T_A\n",
+      "gtr than 1 chr1_155301679_T_C\n",
+      "gtr than 1 chr1_155301679_T_G\n",
+      "gtr than 1 chr1_155301680_T_A\n",
+      "gtr than 1 chr1_155301680_T_C\n",
+      "gtr than 1 chr1_155301680_T_G\n",
+      "gtr than 1 chr1_155301681_G_A\n",
+      "gtr than 1 chr1_155301681_G_C\n",
+      "gtr than 1 chr1_155301681_G_T\n",
+      "gtr than 1 chr1_155301682_T_A\n",
+      "gtr than 1 chr1_155301682_T_C\n",
+      "gtr than 1 chr1_155301682_T_G\n",
+      "gtr than 1 chr1_155301683_A_C\n",
+      "gtr than 1 chr1_155301683_A_G\n",
+      "gtr than 1 chr1_155301683_A_T\n",
+      "gtr than 1 chr1_155301684_C_A\n",
+      "gtr than 1 chr1_155301684_C_G\n",
+      "gtr than 1 chr1_155301684_C_T\n",
+      "gtr than 1 chr1_155301685_T_A\n",
+      "gtr than 1 chr1_155301685_T_C\n",
+      "gtr than 1 chr1_155301685_T_G\n",
+      "gtr than 1 chr1_155301686_T_A\n",
+      "gtr than 1 chr1_155301686_T_C\n",
+      "gtr than 1 chr1_155301686_T_G\n",
+      "gtr than 1 chr1_155301687_T_A\n",
+      "gtr than 1 chr1_155301687_T_C\n",
+      "gtr than 1 chr1_155301687_T_G\n",
+      "gtr than 1 chr1_155301688_A_C\n",
+      "gtr than 1 chr1_155301688_A_G\n",
+      "gtr than 1 chr1_155301688_A_T\n",
+      "gtr than 1 chr1_155301689_C_A\n",
+      "gtr than 1 chr1_155301689_C_G\n",
+      "gtr than 1 chr1_155301689_C_T\n",
+      "gtr than 1 chr1_155301690_T_A\n",
+      "gtr than 1 chr1_155301690_T_C\n",
+      "gtr than 1 chr1_155301690_T_G\n",
+      "gtr than 1 chr1_155301691_G_A\n",
+      "gtr than 1 chr1_155301691_G_C\n",
+      "gtr than 1 chr1_155301691_G_T\n",
+      "gtr than 1 chr1_155301692_A_C\n",
+      "gtr than 1 chr1_155301692_A_G\n",
+      "gtr than 1 chr1_155301692_A_T\n",
+      "gtr than 1 chr1_155301693_A_C\n",
+      "gtr than 1 chr1_155301693_A_G\n",
+      "gtr than 1 chr1_155301693_A_T\n",
+      "gtr than 1 chr1_155301694_G_A\n",
+      "gtr than 1 chr1_155301694_G_C\n",
+      "gtr than 1 chr1_155301694_G_T\n",
+      "gtr than 1 chr1_155301695_T_A\n",
+      "gtr than 1 chr1_155301695_T_C\n",
+      "gtr than 1 chr1_155301695_T_G\n",
+      "gtr than 1 chr1_155301696_T_A\n",
+      "gtr than 1 chr1_155301696_T_C\n",
+      "gtr than 1 chr1_155301696_T_G\n",
+      "gtr than 1 chr1_155301697_A_C\n",
+      "gtr than 1 chr1_155301697_A_G\n",
+      "gtr than 1 chr1_155301697_A_T\n",
+      "gtr than 1 chr1_155301698_G_A\n",
+      "gtr than 1 chr1_155301698_G_C\n",
+      "gtr than 1 chr1_155301698_G_T\n",
+      "gtr than 1 chr1_155301699_C_A\n",
+      "gtr than 1 chr1_155301699_C_G\n",
+      "gtr than 1 chr1_155301699_C_T\n",
+      "gtr than 1 chr1_155301700_T_A\n",
+      "gtr than 1 chr1_155301700_T_C\n",
+      "gtr than 1 chr1_155301700_T_G\n",
+      "gtr than 1 chr1_155301701_C_A\n",
+      "gtr than 1 chr1_155301701_C_G\n",
+      "gtr than 1 chr1_155301701_C_T\n",
+      "gtr than 1 chr1_155301702_T_A\n",
+      "gtr than 1 chr1_155301702_T_C\n",
+      "gtr than 1 chr1_155301702_T_G\n",
+      "gtr than 1 chr1_155301703_A_C\n",
+      "gtr than 1 chr1_155301703_A_G\n",
+      "gtr than 1 chr1_155301703_A_T\n",
+      "gtr than 1 chr1_155301704_G_A\n",
+      "gtr than 1 chr1_155301704_G_C\n",
+      "gtr than 1 chr1_155301704_G_T\n",
+      "gtr than 1 chr1_155301705_T_A\n",
+      "gtr than 1 chr1_155301705_T_C\n",
+      "gtr than 1 chr1_155301705_T_G\n",
+      "gtr than 1 chr1_155301706_G_A\n",
+      "gtr than 1 chr1_155301706_G_C\n",
+      "gtr than 1 chr1_155301706_G_T\n",
+      "gtr than 1 chr1_155301707_A_C\n",
+      "gtr than 1 chr1_155301707_A_G\n",
+      "gtr than 1 chr1_155301707_A_T\n",
+      "gtr than 1 chr1_155301708_A_C\n",
+      "gtr than 1 chr1_155301708_A_G\n",
+      "gtr than 1 chr1_155301708_A_T\n",
+      "gtr than 1 chr1_155301709_T_A\n",
+      "gtr than 1 chr1_155301709_T_C\n",
+      "gtr than 1 chr1_155301709_T_G\n",
+      "gtr than 1 chr1_155301710_C_A\n",
+      "gtr than 1 chr1_155301710_C_G\n",
+      "gtr than 1 chr1_155301710_C_T\n",
+      "gtr than 1 chr1_155301711_C_A\n",
+      "gtr than 1 chr1_155301711_C_G\n",
+      "gtr than 1 chr1_155301711_C_T\n",
+      "gtr than 1 chr1_155301712_A_C\n",
+      "gtr than 1 chr1_155301712_A_G\n",
+      "gtr than 1 chr1_155301712_A_T\n",
+      "gtr than 1 chr1_155301713_G_A\n",
+      "gtr than 1 chr1_155301713_G_C\n",
+      "gtr than 1 chr1_155301713_G_T\n",
+      "gtr than 1 chr1_155301714_G_A\n",
+      "gtr than 1 chr1_155301714_G_C\n",
+      "gtr than 1 chr1_155301714_G_T\n",
+      "gtr than 1 chr1_155301715_G_A\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_155301715_G_C\n",
+      "gtr than 1 chr1_155301715_G_T\n",
+      "gtr than 1 chr1_155301716_A_C\n",
+      "gtr than 1 chr1_155301716_A_G\n",
+      "gtr than 1 chr1_155301716_A_T\n",
+      "gtr than 1 chr1_155301717_G_A\n",
+      "gtr than 1 chr1_155301717_G_C\n",
+      "gtr than 1 chr1_155301717_G_T\n",
+      "gtr than 1 chr1_155301718_C_A\n",
+      "gtr than 1 chr1_155301718_C_G\n",
+      "gtr than 1 chr1_155301718_C_T\n",
+      "gtr than 1 chr1_155301719_A_C\n",
+      "gtr than 1 chr1_155301719_A_G\n",
+      "gtr than 1 chr1_155301719_A_T\n",
+      "gtr than 1 chr1_155301720_G_A\n",
+      "gtr than 1 chr1_155301720_G_C\n",
+      "gtr than 1 chr1_155301720_G_T\n",
+      "gtr than 1 chr1_155301721_G_A\n",
+      "gtr than 1 chr1_155301721_G_C\n",
+      "gtr than 1 chr1_155301721_G_T\n",
+      "gtr than 1 chr1_155301722_G_A\n",
+      "gtr than 1 chr1_155301722_G_C\n",
+      "gtr than 1 chr1_155301722_G_T\n",
+      "gtr than 1 chr1_155301723_G_A\n",
+      "gtr than 1 chr1_155301723_G_C\n",
+      "gtr than 1 chr1_155301723_G_T\n",
+      "gtr than 1 chr1_155301724_C_A\n",
+      "gtr than 1 chr1_155301724_C_G\n",
+      "gtr than 1 chr1_155301724_C_T\n",
+      "gtr than 1 chr1_155301725_T_A\n",
+      "gtr than 1 chr1_155301725_T_C\n",
+      "gtr than 1 chr1_155301725_T_G\n",
+      "gtr than 1 chr1_155301726_G_A\n",
+      "gtr than 1 chr1_155301726_G_C\n",
+      "gtr than 1 chr1_155301726_G_T\n",
+      "gtr than 1 chr1_155301727_T_A\n",
+      "gtr than 1 chr1_155301727_T_C\n",
+      "gtr than 1 chr1_155301727_T_G\n",
+      "gtr than 1 chr1_155301728_A_C\n",
+      "gtr than 1 chr1_155301728_A_G\n",
+      "gtr than 1 chr1_155301728_A_T\n",
+      "gtr than 1 chr1_155301729_G_A\n",
+      "gtr than 1 chr1_155301729_G_C\n",
+      "gtr than 1 chr1_155301729_G_T\n",
+      "gtr than 1 chr1_155301730_G_A\n",
+      "gtr than 1 chr1_155301730_G_C\n",
+      "gtr than 1 chr1_155301730_G_T\n",
+      "gtr than 1 chr1_155301731_G_A\n",
+      "gtr than 1 chr1_155301731_G_C\n",
+      "gtr than 1 chr1_155301731_G_T\n",
+      "gtr than 1 chr1_155301732_T_A\n",
+      "gtr than 1 chr1_155301732_T_C\n",
+      "gtr than 1 chr1_155301732_T_G\n",
+      "gtr than 1 chr1_155301733_G_A\n",
+      "gtr than 1 chr1_155301733_G_C\n",
+      "gtr than 1 chr1_155301733_G_T\n",
+      "gtr than 1 chr1_155301734_G_A\n",
+      "gtr than 1 chr1_155301734_G_C\n",
+      "gtr than 1 chr1_155301734_G_T\n",
+      "gtr than 1 chr1_155301735_G_A\n",
+      "gtr than 1 chr1_155301735_G_C\n",
+      "gtr than 1 chr1_155301735_G_T\n",
+      "gtr than 1 chr1_155301736_G_A\n",
+      "gtr than 1 chr1_155301736_G_C\n",
+      "gtr than 1 chr1_155301736_G_T\n",
+      "gtr than 1 chr1_155301737_C_A\n",
+      "gtr than 1 chr1_155301737_C_G\n",
+      "gtr than 1 chr1_155301737_C_T\n",
+      "gtr than 1 chr1_155301738_T_A\n",
+      "gtr than 1 chr1_155301738_T_C\n",
+      "gtr than 1 chr1_155301738_T_G\n",
+      "gtr than 1 chr1_155301739_G_A\n",
+      "gtr than 1 chr1_155301739_G_C\n",
+      "gtr than 1 chr1_155301739_G_T\n",
+      "gtr than 1 chr1_155301740_G_A\n",
+      "gtr than 1 chr1_155301740_G_C\n",
+      "gtr than 1 chr1_155301740_G_T\n",
+      "gtr than 1 chr1_155301741_A_C\n",
+      "gtr than 1 chr1_155301741_A_G\n",
+      "gtr than 1 chr1_155301741_A_T\n",
+      "gtr than 1 chr1_155301742_G_A\n",
+      "gtr than 1 chr1_155301742_G_C\n",
+      "gtr than 1 chr1_155301742_G_T\n",
+      "gtr than 1 chr1_155301743_C_A\n",
+      "gtr than 1 chr1_155301743_C_G\n",
+      "gtr than 1 chr1_155301743_C_T\n",
+      "gtr than 1 chr1_155301744_C_A\n",
+      "gtr than 1 chr1_155301744_C_G\n",
+      "gtr than 1 chr1_155301744_C_T\n",
+      "gtr than 1 chr1_155301745_T_A\n",
+      "gtr than 1 chr1_155301745_T_C\n",
+      "gtr than 1 chr1_155301745_T_G\n",
+      "gtr than 1 chr1_155301746_G_A\n",
+      "gtr than 1 chr1_155301746_G_C\n",
+      "gtr than 1 chr1_155301746_G_T\n",
+      "gtr than 1 chr1_155301763_A_C\n",
+      "gtr than 1 chr1_155301763_A_G\n",
+      "gtr than 1 chr1_155301763_A_T\n",
+      "gtr than 1 chr1_155301764_T_A\n",
+      "gtr than 1 chr1_155301764_T_C\n",
+      "gtr than 1 chr1_155301764_T_G\n",
+      "gtr than 1 chr1_155301765_C_A\n",
+      "gtr than 1 chr1_155301765_C_G\n",
+      "gtr than 1 chr1_155301765_C_T\n",
+      "gtr than 1 chr1_155301766_A_C\n",
+      "gtr than 1 chr1_155301766_A_G\n",
+      "gtr than 1 chr1_155301766_A_T\n",
+      "gtr than 1 chr1_155301767_C_A\n",
+      "gtr than 1 chr1_155301767_C_G\n",
+      "gtr than 1 chr1_155301767_C_T\n",
+      "gtr than 1 chr1_155301768_T_A\n",
+      "gtr than 1 chr1_155301768_T_C\n",
+      "gtr than 1 chr1_155301768_T_G\n",
+      "gtr than 1 chr1_155301769_G_A\n",
+      "gtr than 1 chr1_155301769_G_C\n",
+      "gtr than 1 chr1_155301769_G_T\n",
+      "gtr than 1 chr1_155301770_T_A\n",
+      "gtr than 1 chr1_155301770_T_C\n",
+      "gtr than 1 chr1_155301770_T_G\n",
+      "gtr than 1 chr1_155301771_G_A\n",
+      "gtr than 1 chr1_155301771_G_C\n",
+      "gtr than 1 chr1_155301771_G_T\n",
+      "gtr than 1 chr1_155301772_A_C\n",
+      "gtr than 1 chr1_155301772_A_G\n",
+      "gtr than 1 chr1_155301772_A_T\n",
+      "gtr than 1 chr1_155301773_T_A\n",
+      "gtr than 1 chr1_155301773_T_C\n",
+      "gtr than 1 chr1_155301773_T_G\n",
+      "gtr than 1 chr1_155301774_A_C\n",
+      "gtr than 1 chr1_155301774_A_G\n",
+      "gtr than 1 chr1_155301774_A_T\n",
+      "gtr than 1 chr1_155301775_A_C\n",
+      "gtr than 1 chr1_155301775_A_G\n",
+      "gtr than 1 chr1_155301775_A_T\n",
+      "gtr than 1 chr1_155301776_T_A\n",
+      "gtr than 1 chr1_155301776_T_C\n",
+      "gtr than 1 chr1_155301776_T_G\n",
+      "gtr than 1 chr1_155301777_A_C\n",
+      "gtr than 1 chr1_155301777_A_G\n",
+      "gtr than 1 chr1_155301777_A_T\n",
+      "gtr than 1 chr1_155301778_T_A\n",
+      "gtr than 1 chr1_155301778_T_C\n",
+      "gtr than 1 chr1_155301778_T_G\n",
+      "gtr than 1 chr1_155301795_C_A\n",
+      "gtr than 1 chr1_155301795_C_G\n",
+      "gtr than 1 chr1_155301795_C_T\n",
+      "gtr than 1 chr1_155301796_C_A\n",
+      "gtr than 1 chr1_155301796_C_G\n",
+      "gtr than 1 chr1_155301796_C_T\n",
+      "gtr than 1 chr1_155301797_C_A\n",
+      "gtr than 1 chr1_155301797_C_G\n",
+      "gtr than 1 chr1_155301797_C_T\n",
+      "gtr than 1 chr1_155301798_A_C\n",
+      "gtr than 1 chr1_155301798_A_G\n",
+      "gtr than 1 chr1_155301798_A_T\n",
+      "gtr than 1 chr1_155301799_G_A\n",
+      "gtr than 1 chr1_155301799_G_C\n",
+      "gtr than 1 chr1_155301799_G_T\n",
+      "gtr than 1 chr1_155301800_T_A\n",
+      "gtr than 1 chr1_155301800_T_C\n",
+      "gtr than 1 chr1_155301800_T_G\n",
+      "gtr than 1 chr1_155301801_T_A\n",
+      "gtr than 1 chr1_155301801_T_C\n",
+      "gtr than 1 chr1_155301801_T_G\n",
+      "gtr than 1 chr1_155301802_C_A\n",
+      "gtr than 1 chr1_155301802_C_G\n",
+      "gtr than 1 chr1_155301802_C_T\n",
+      "gtr than 1 chr1_155301803_T_A\n",
+      "gtr than 1 chr1_155301803_T_C\n",
+      "gtr than 1 chr1_155301803_T_G\n",
+      "gtr than 1 chr1_155301804_G_A\n",
+      "gtr than 1 chr1_155301804_G_C\n",
+      "gtr than 1 chr1_155301804_G_T\n",
+      "gtr than 1 chr1_155301805_A_C\n",
+      "gtr than 1 chr1_155301805_A_G\n",
+      "gtr than 1 chr1_155301805_A_T\n",
+      "gtr than 1 chr1_155301806_C_A\n",
+      "gtr than 1 chr1_155301806_C_G\n",
+      "gtr than 1 chr1_155301806_C_T\n",
+      "gtr than 1 chr1_155301807_C_A\n",
+      "gtr than 1 chr1_155301807_C_G\n",
+      "gtr than 1 chr1_155301807_C_T\n",
+      "gtr than 1 chr1_155301808_A_C\n",
+      "gtr than 1 chr1_155301808_A_G\n",
+      "gtr than 1 chr1_155301808_A_T\n",
+      "gtr than 1 chr1_155301809_C_A\n",
+      "gtr than 1 chr1_155301809_C_G\n",
+      "gtr than 1 chr1_155301809_C_T\n",
+      "gtr than 1 chr1_155301810_T_A\n",
+      "gtr than 1 chr1_155301810_T_C\n",
+      "gtr than 1 chr1_155301810_T_G\n",
+      "gtr than 1 chr1_155301811_T_A\n",
+      "gtr than 1 chr1_155301811_T_C\n",
+      "gtr than 1 chr1_155301811_T_G\n",
+      "gtr than 1 chr1_155301812_T_A\n",
+      "gtr than 1 chr1_155301812_T_C\n",
+      "gtr than 1 chr1_155301812_T_G\n",
+      "gtr than 1 chr1_155301813_T_A\n",
+      "gtr than 1 chr1_155301813_T_C\n",
+      "gtr than 1 chr1_155301813_T_G\n",
+      "gtr than 1 chr1_155301814_T_A\n",
+      "gtr than 1 chr1_155301814_T_C\n",
+      "gtr than 1 chr1_155301814_T_G\n",
+      "gtr than 1 chr1_155301815_T_A\n",
+      "gtr than 1 chr1_155301815_T_C\n",
+      "gtr than 1 chr1_155301815_T_G\n",
+      "gtr than 1 chr1_155301816_T_A\n",
+      "gtr than 1 chr1_155301816_T_C\n",
+      "gtr than 1 chr1_155301816_T_G\n",
+      "gtr than 1 chr1_155301817_C_A\n",
+      "gtr than 1 chr1_155301817_C_G\n",
+      "gtr than 1 chr1_155301817_C_T\n",
+      "gtr than 1 chr1_155301818_T_A\n",
+      "gtr than 1 chr1_155301818_T_C\n",
+      "gtr than 1 chr1_155301818_T_G\n",
+      "gtr than 1 chr1_155301819_C_A\n",
+      "gtr than 1 chr1_155301819_C_G\n",
+      "gtr than 1 chr1_155301819_C_T\n",
+      "gtr than 1 chr1_155301820_T_A\n",
+      "gtr than 1 chr1_155301820_T_C\n",
+      "gtr than 1 chr1_155301820_T_G\n",
+      "gtr than 1 chr1_155301821_G_A\n",
+      "gtr than 1 chr1_155301821_G_C\n",
+      "gtr than 1 chr1_155301821_G_T\n",
+      "gtr than 1 chr1_155301822_T_A\n",
+      "gtr than 1 chr1_155301822_T_C\n",
+      "gtr than 1 chr1_155301822_T_G\n",
+      "gtr than 1 chr1_155301823_C_A\n",
+      "gtr than 1 chr1_155301823_C_G\n",
+      "gtr than 1 chr1_155301823_C_T\n",
+      "gtr than 1 chr1_155301824_T_A\n",
+      "gtr than 1 chr1_155301824_T_C\n",
+      "gtr than 1 chr1_155301824_T_G\n",
+      "gtr than 1 chr1_155301825_C_A\n",
+      "gtr than 1 chr1_155301825_C_G\n",
+      "gtr than 1 chr1_155301825_C_T\n",
+      "gtr than 1 chr1_155301826_A_C\n",
+      "gtr than 1 chr1_155301826_A_G\n",
+      "gtr than 1 chr1_155301826_A_T\n",
+      "gtr than 1 chr1_155301827_A_C\n",
+      "gtr than 1 chr1_155301827_A_G\n",
+      "gtr than 1 chr1_155301827_A_T\n",
+      "gtr than 1 chr1_155301828_C_A\n",
+      "gtr than 1 chr1_155301828_C_G\n",
+      "gtr than 1 chr1_155301828_C_T\n",
+      "gtr than 1 chr1_155301829_C_A\n",
+      "gtr than 1 chr1_155301829_C_G\n",
+      "gtr than 1 chr1_155301829_C_T\n",
+      "gtr than 1 chr1_155301830_A_C\n",
+      "gtr than 1 chr1_155301830_A_G\n",
+      "gtr than 1 chr1_155301830_A_T\n",
+      "gtr than 1 chr1_155301831_A_C\n",
+      "gtr than 1 chr1_155301831_A_G\n",
+      "gtr than 1 chr1_155301831_A_T\n",
+      "gtr than 1 chr1_155301832_G_A\n",
+      "gtr than 1 chr1_155301832_G_C\n",
+      "gtr than 1 chr1_155301832_G_T\n",
+      "gtr than 1 chr1_155301833_A_C\n",
+      "gtr than 1 chr1_155301833_A_G\n",
+      "gtr than 1 chr1_155301833_A_T\n",
+      "gtr than 1 chr1_155301834_A_C\n",
+      "gtr than 1 chr1_155301834_A_G\n",
+      "gtr than 1 chr1_155301834_A_T\n",
+      "gtr than 1 chr1_155301835_A_C\n",
+      "gtr than 1 chr1_155301835_A_G\n",
+      "gtr than 1 chr1_155301835_A_T\n",
+      "gtr than 1 chr1_155301836_T_A\n",
+      "gtr than 1 chr1_155301836_T_C\n",
+      "gtr than 1 chr1_155301836_T_G\n",
+      "gtr than 1 chr1_155301837_G_A\n",
+      "gtr than 1 chr1_155301837_G_C\n",
+      "gtr than 1 chr1_155301837_G_T\n",
+      "gtr than 1 chr1_155301838_C_A\n",
+      "gtr than 1 chr1_155301838_C_G\n",
+      "gtr than 1 chr1_155301838_C_T\n",
+      "gtr than 1 chr1_155301839_A_C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gtr than 1 chr1_155301839_A_G\n",
+      "gtr than 1 chr1_155301839_A_T\n",
+      "gtr than 1 chr1_155301840_G_A\n",
+      "gtr than 1 chr1_155301840_G_C\n",
+      "gtr than 1 chr1_155301840_G_T\n",
+      "gtr than 1 chr1_155301841_A_C\n",
+      "gtr than 1 chr1_155301841_A_G\n",
+      "gtr than 1 chr1_155301841_A_T\n",
+      "gtr than 1 chr1_155301842_G_A\n",
+      "gtr than 1 chr1_155301842_G_C\n",
+      "gtr than 1 chr1_155301842_G_T\n",
+      "gtr than 1 chr1_155301859_T_A\n",
+      "gtr than 1 chr1_155301859_T_C\n",
+      "gtr than 1 chr1_155301859_T_G\n",
+      "gtr than 1 chr1_155301860_A_C\n",
+      "gtr than 1 chr1_155301860_A_G\n",
+      "gtr than 1 chr1_155301860_A_T\n",
+      "gtr than 1 chr1_155301861_A_G\n",
+      "gtr than 1 chr1_155301861_A_T\n",
+      "gtr than 1 chr1_155301862_C_A\n",
+      "gtr than 1 chr1_155301862_C_G\n",
+      "gtr than 1 chr1_155301862_C_T\n",
+      "gtr than 1 chr1_155301863_C_A\n",
+      "gtr than 1 chr1_155301863_C_G\n",
+      "gtr than 1 chr1_155301863_C_T\n",
+      "gtr than 1 chr1_155301864_T_A\n",
+      "gtr than 1 chr1_155301864_T_C\n",
+      "chr1_155301865_G_A\n",
+      "chr1_155301865_G_C\n",
+      "chr1_155301865_G_T\n"
+     ]
+    }
+   ],
+   "source": [
+    "sad = datat[\"SAD\"]\n",
+    "sar = datat[\"SADR\"]\n",
+    "\n",
+    "enformer_sad = []\n",
+    "enformer_sar = []\n",
+    "\n",
+    "cagi_df_new_drop = cagi_df_new[cagi_df_new[\"model_used\"].isin(valid_ids)]\n",
+    "num_enf_missing=0\n",
+    "for i,r in cagi_df_new_drop.iterrows():\n",
+    "    #print(r)\n",
+    "    rsid = r[\"chr_hg38\"] + \"_\" + str(r[\"pos_hg38\"]) + \"_\" + r[\"ref_hg38\"] + \"_\" + r[\"alt_hg38\"]\n",
+    "    #print(rsid)\n",
+    "    #assert(len(enformer_preds[enformer_preds[\"rsid\"].isin([rsid])].index.values) <= 1)\n",
+    "    if len(enformer_preds[enformer_preds[\"rsid\"].isin([rsid])].index.values) > 1:\n",
+    "        print(\"gtr than 1\", rsid)\n",
+    "        index = enformer_preds[enformer_preds[\"rsid\"].isin([rsid])].index.values[0]\n",
+    "        encid = r[\"model_used\"].split(\"_\")[0]\n",
+    "        if encid in convert_dict:\n",
+    "            encid = convert_dict[encid]\n",
+    "\n",
+    "        assert(len(enformer_labesls[enformer_labesls[\"encode_id\"]==encid][\"index\"].values)==1)\n",
+    "\n",
+    "        #print(sad.shape)\n",
+    "        enformer_sad.append(sad[index,enformer_labesls[enformer_labesls[\"encode_id\"]==encid][\"index\"].values[0]])\n",
+    "        enformer_sar.append(sar[index,enformer_labesls[enformer_labesls[\"encode_id\"]==encid][\"index\"].values[0]])\n",
+    "        \n",
+    "    elif len(enformer_preds[enformer_preds[\"rsid\"].isin([rsid])].index.values) == 1:\n",
+    "        index = enformer_preds[enformer_preds[\"rsid\"].isin([rsid])].index.values[0]\n",
+    "        encid = r[\"model_used\"].split(\"_\")[0]\n",
+    "        if encid in convert_dict:\n",
+    "            encid = convert_dict[encid]\n",
+    "\n",
+    "        assert(len(enformer_labesls[enformer_labesls[\"encode_id\"]==encid][\"index\"].values)==1)\n",
+    "\n",
+    "        #print(sad.shape)\n",
+    "        enformer_sad.append(sad[index,enformer_labesls[enformer_labesls[\"encode_id\"]==encid][\"index\"].values[0]])\n",
+    "        enformer_sar.append(sar[index,enformer_labesls[enformer_labesls[\"encode_id\"]==encid][\"index\"].values[0]])\n",
+    "    else:\n",
+    "        print(rsid)\n",
+    "        num_enf_missing+=1\n",
+    "        enformer_sad.append(None)\n",
+    "        enformer_sar.append(None)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#datat.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "cagi_df_new_drop[\"SAD\"] = enformer_sad\n",
+    "cagi_df_new_drop[\"SAR\"] = enformer_sar\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.isnan(cagi_df_new_drop[\"SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cagi_df_new_drop.to_csv(\"../final_figures_new/cagi.mpra.per_model.scores.tsv\", sep=\"\\t\", index=False, header=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19161</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>155301817</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_155301817_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20568</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>155301817</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_155301817_C_G</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        chr        pos ref alt                rsid\n",
+       "19161  chr1  155301817   C   G  chr1_155301817_C_G\n",
+       "20568  chr1  155301817   C   G  chr1_155301817_C_G"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_preds[enformer_preds[\"rsid\"].isin([\"chr1_155301817_C_G\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.005596"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sad[19161,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.005596"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sad[20568,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/afr_susie_agreement.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/afr_susie_agreement.ipynb
new file mode 100644
index 00000000..9d34e1a9
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/afr_susie_agreement.ipynb
@@ -0,0 +1,858 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "data_sus = pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/AFGR_wgs/sumstats/QTD100010.all.tsv.gz\", header=0, sep='\\t')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>position</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>ma_samples</th>\n",
+       "      <th>maf</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>type</th>\n",
+       "      <th>ac</th>\n",
+       "      <th>an</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_13380_C_G</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.036145</td>\n",
+       "      <td>0.158910</td>\n",
+       "      <td>0.537757</td>\n",
+       "      <td>0.377599</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>6</td>\n",
+       "      <td>166</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs571093408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_13380_C_G</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.036145</td>\n",
+       "      <td>0.354570</td>\n",
+       "      <td>0.356531</td>\n",
+       "      <td>0.382530</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>6</td>\n",
+       "      <td>166</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs571093408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_13380_C_G</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.036145</td>\n",
+       "      <td>0.640431</td>\n",
+       "      <td>-0.194644</td>\n",
+       "      <td>0.414872</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>6</td>\n",
+       "      <td>166</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs571093408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_13380_C_G</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.036145</td>\n",
+       "      <td>0.284761</td>\n",
+       "      <td>0.432374</td>\n",
+       "      <td>0.401066</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>6</td>\n",
+       "      <td>166</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs571093408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13380</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_13380_C_G</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.036145</td>\n",
+       "      <td>0.929664</td>\n",
+       "      <td>0.032113</td>\n",
+       "      <td>0.362486</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>6</td>\n",
+       "      <td>166</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs571093408</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  molecular_trait_id  chromosome  position ref alt         variant  \\\n",
+       "0         Interval_1           1     13380   C   G  chr1_13380_C_G   \n",
+       "1         Interval_2           1     13380   C   G  chr1_13380_C_G   \n",
+       "2         Interval_3           1     13380   C   G  chr1_13380_C_G   \n",
+       "3         Interval_4           1     13380   C   G  chr1_13380_C_G   \n",
+       "4         Interval_5           1     13380   C   G  chr1_13380_C_G   \n",
+       "\n",
+       "   ma_samples       maf    pvalue      beta        se type  ac   an  r2  \\\n",
+       "0           6  0.036145  0.158910  0.537757  0.377599  SNP   6  166 NaN   \n",
+       "1           6  0.036145  0.354570  0.356531  0.382530  SNP   6  166 NaN   \n",
+       "2           6  0.036145  0.640431 -0.194644  0.414872  SNP   6  166 NaN   \n",
+       "3           6  0.036145  0.284761  0.432374  0.401066  SNP   6  166 NaN   \n",
+       "4           6  0.036145  0.929664  0.032113  0.362486  SNP   6  166 NaN   \n",
+       "\n",
+       "  molecular_trait_object_id     gene_id  median_tpm         rsid  \n",
+       "0                Interval_1  Interval_1         NaN  rs571093408  \n",
+       "1                Interval_2  Interval_2         NaN  rs571093408  \n",
+       "2                Interval_3  Interval_3         NaN  rs571093408  \n",
+       "3                Interval_4  Interval_4         NaN  rs571093408  \n",
+       "4                Interval_5  Interval_5         NaN  rs571093408  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_sus[\"position0\"] = data_sus[\"position\"] - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['molecular_trait_id', 'chromosome', 'position', 'ref', 'alt', 'variant',\n",
+       "       'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac', 'an', 'r2',\n",
+       "       'molecular_trait_object_id', 'gene_id', 'median_tpm', 'rsid',\n",
+       "       'position0'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_sus = data_sus[['chromosome', 'position0', 'position', 'ref', 'alt', 'variant',\n",
+    "       'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac', 'an', 'r2',\n",
+    "       'molecular_trait_object_id', 'gene_id', 'median_tpm', 'rsid',\n",
+    "       'molecular_trait_id']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  phenotype_id    quant_id    group_id     gene_id chromosome  gene_start  \\\n",
+      "0   Interval_1  Interval_1  Interval_1  Interval_1          1       15924   \n",
+      "1   Interval_2  Interval_2  Interval_2  Interval_2          1       28812   \n",
+      "2   Interval_3  Interval_3  Interval_3  Interval_3          1       86875   \n",
+      "3   Interval_4  Interval_4  Interval_4  Interval_4          1       96446   \n",
+      "4   Interval_5  Interval_5  Interval_5  Interval_5          1      180773   \n",
+      "\n",
+      "   gene_end  strand   gene_name       gene_type  phenotype_pos  \\\n",
+      "0     17561       1  Interval_1  open_chromatin          16742   \n",
+      "1     29760       1  Interval_2  open_chromatin          29286   \n",
+      "2     87570       1  Interval_3  open_chromatin          87222   \n",
+      "3     96770       1  Interval_4  open_chromatin          96608   \n",
+      "4    182080       1  Interval_5  open_chromatin         181426   \n",
+      "\n",
+      "   phenotype_length  \n",
+      "0              1638  \n",
+      "1               949  \n",
+      "2               696  \n",
+      "3               325  \n",
+      "4              1308  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+     ]
+    }
+   ],
+   "source": [
+    "peaks_data=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/peak_metadata/QTD100010_peak_metadata.tsv.gz\", sep=\"\\t\", header=0)\n",
+    "print(peaks_data.head())\n",
+    "peaks_data = peaks_data[[\"chromosome\", \"gene_start\", \"gene_end\", \"phenotype_pos\", \"phenotype_id\"]]\n",
+    "peaks_data[\"chromosome\"] = \"chr\"+peaks_data[\"chromosome\"] .astype(str)\n",
+    "peaks_data[\"center\"]  =  ((peaks_data[\"gene_start\"] + peaks_data[\"gene_end\"])/2).astype(int)\n",
+    "# peaks_data[\"start\"] = peaks_data[\"center\"] - 100\n",
+    "# peaks_data[\"end\"] = peaks_data[\"center\"] + 100\n",
+    "peaks_data = peaks_data[['chromosome', 'gene_start', 'gene_end', \"phenotype_id\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe = data_sus.merge(peaks_data, right_on=['phenotype_id'], left_on=[\"molecular_trait_id\"], how='left')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n = merged_dataframe[((merged_dataframe[\"position\"].astype(int) >= merged_dataframe[\"gene_start\"].astype(int)) & (merged_dataframe[\"position\"].astype(int) <= merged_dataframe[\"gene_end\"].astype(int)))]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "merged_dataframe_n.to_csv(\"af_qtls.inpeaks.effects.tsv\", sep=\"\\t\", index=False, header=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n1 = merged_dataframe_n[merged_dataframe_n[\"ref\"].str.len()==1]\n",
+    "merged_dataframe_n1 = merged_dataframe_n1[merged_dataframe_n1[\"alt\"].str.len()==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(847696, 24)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n1.to_csv(\"local.af.effects.tsv\",sep=\"\\t\", index=False, header=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_dataframe_n2 = merged_dataframe_n1[['chromosome_x', 'position0', 'position', 'ref', 'alt', 'molecular_trait_id',\n",
+    "       'variant', 'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac',\n",
+    "       'an', 'r2', 'molecular_trait_object_id', 'gene_id', 'median_tpm',\n",
+    "       'rsid',  'gene_start', 'gene_end',\n",
+    "       'phenotype_id']]\n",
+    "merged_dataframe_n2['chromosome_x'] = \"chr\"+merged_dataframe_n2['chromosome_x'].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0      1      2\n",
+      "0  chr1  13543  13743\n",
+      "1  chr1  16145  16463\n",
+      "2  chr1  16894  17094\n",
+      "3  chr1  29014  29454\n",
+      "4  chr1  86964  87164\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import pybedtools\n",
+    "peaks_data_merged=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/merged.peaks.af.new.bed\", sep=\"\\t\", header=None)\n",
+    "print(peaks_data_merged.head())\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(merged_dataframe_n2)\n",
+    "peak_bed = pybedtools.BedTool.from_dataframe(peaks_data_merged) # this is a merged peaks file\n",
+    "vars_in_bed = beds_var.intersect(peak_bed, c=True)\n",
+    "\n",
+    "vars_in_bedn = vars_in_bed.to_dataframe(names=merged_dataframe_n2.columns.tolist() + [\"count\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn2 = vars_in_bedn[vars_in_bedn[\"count\"]>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chromosome_x</th>\n",
+       "      <th>position0</th>\n",
+       "      <th>position</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>ma_samples</th>\n",
+       "      <th>maf</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>...</th>\n",
+       "      <th>an</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>16279</td>\n",
+       "      <td>16280</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>chr1_16280_T_C</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.066265</td>\n",
+       "      <td>0.098929</td>\n",
+       "      <td>...</td>\n",
+       "      <td>166</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs866639523</td>\n",
+       "      <td>15924</td>\n",
+       "      <td>17561</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chromosome_x  position0  position ref alt molecular_trait_id  \\\n",
+       "2         chr1      16279     16280   T   C         Interval_1   \n",
+       "\n",
+       "          variant  ma_samples       maf    pvalue  ...   an r2  \\\n",
+       "2  chr1_16280_T_C          11  0.066265  0.098929  ...  166  .   \n",
+       "\n",
+       "  molecular_trait_object_id     gene_id  median_tpm         rsid gene_start  \\\n",
+       "2                Interval_1  Interval_1           .  rs866639523      15924   \n",
+       "\n",
+       "  gene_end phenotype_id count  \n",
+       "2    17561   Interval_1     1  \n",
+       "\n",
+       "[1 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn2.to_csv(\"afr.inpeaks.kaur.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(109646, 51)\n",
+      "(109646, 51)\n",
+      "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+      "       'IsUsed', 'pval', 'beta_afr', 'enformer.encsr000emt.variantscore.sad',\n",
+      "       'enformer.encsr000emt.variantscore.sar', 'enformer_recomputed',\n",
+      "       'chrombpnet.gambian.variantscore.logfc',\n",
+      "       'chrombpnet.gambian.variantscore.jsd',\n",
+      "       'chrombpnet.esan.variantscore.logfc',\n",
+      "       'chrombpnet.esan.variantscore.jsd',\n",
+      "       'chrombpnet.mende.variantscore.logfc',\n",
+      "       'chrombpnet.mende.variantscore.jsd',\n",
+      "       'chrombpnet.yoruba.variantscore.logfc',\n",
+      "       'chrombpnet.yoruba.variantscore.jsd',\n",
+      "       'chrombpnet.maasai.variantscore.logfc',\n",
+      "       'chrombpnet.maasai.variantscore.jsd',\n",
+      "       'chrombpnet.luhya.variantscore.logfc',\n",
+      "       'chrombpnet.luhya.variantscore.jsd',\n",
+      "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+      "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+      "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+      "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+      "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+      "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+      "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+      "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+      "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+      "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+      "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+      "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+      "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+      "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+      "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+      "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+      "       'snp', 'distance', 'se', 'abs_beta', 'id_hg19', 'chr_hg19', 'pos_hg19',\n",
+      "       'ref_hg19', 'alt_hg19', 'labeln'],\n",
+      "      dtype='object')\n",
+      "(109646, 52)\n",
+      "(83148, 76)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "afrs = pd.read_csv(\"../final_figures_new/Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)\n",
+    "afrs = afrs[afrs[\"IsUsed\"]]\n",
+    "afrs[[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\"]] = afrs[\"id_hg19\"].str.split(\"_\", expand=True)\n",
+    "threshold = 1 / (10**(6))\n",
+    "print(afrs.shape)\n",
+    "#print(afrs.head())\n",
+    "#afrs = afrs[(afrs[\"label\"]==1) | (afrs[\"pval\"]>1e-3)]\n",
+    "print(afrs.shape)\n",
+    "afrs[\"labeln\"] = 1\n",
+    "afrs.loc[afrs[\"label\"]==1,\"labeln\"]=1\n",
+    "afrs.loc[afrs[\"pval\"]>1e-3,\"labeln\"]=-1\n",
+    "\n",
+    "afrs = afrs.rename({\"beta\": \"beta_afr\"},axis='columns')\n",
+    "print(afrs.columns)\n",
+    "#print(afrs.head(1))\n",
+    "print(afrs.shape)\n",
+    "datan1 = afrs.merge(vars_in_bedn2, left_on=[\"allele1\", \"allele2\", \"dbsnp_rsid\"], right_on=[\"ref\", \"alt\", \"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "datan2 = afrs.merge(vars_in_bedn2, left_on=[\"allele2\", \"allele1\", \"dbsnp_rsid\"], right_on=[\"ref\", \"alt\", \"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "datan = pd.concat([datan1,datan2])\n",
+    "print(datan.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8702\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datan = datan1\n",
+    "\n",
+    "data1 = datan\n",
+    "data1[\"log10p\"] = -1*np.log(data1[\"pvalue\"])\n",
+    "\n",
+    "from scipy.stats import rankdata\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "rank_kumasaka = rankdata(abs(data1[\"beta_afr\"]))\n",
+    "rank_kaur = rankdata(abs(data1[\"beta\"]))\n",
+    "colors = np.array(['grey']*data1.shape[0])\n",
+    "threshold=3\n",
+    "#colors[data1[\"Log10_BF\"]>threshold] = 'r'\n",
+    "#colors[data1[\"RsID\"].isin(dbsnp)] = 'blue'\n",
+    "th=5\n",
+    "colors[data1[\"log10p\"]>th] = 'r'\n",
+    "\n",
+    "# plt.scatter(rank_kumasaka[data1[\"Log10_BF\"]<-1], rank_kaur[data1[\"Log10_BF\"]<-1], alpha=0.5, s=1, color=colors[data1[\"Log10_BF\"]<-1])\n",
+    "# plt.scatter(rank_kumasaka[data1[\"Log10_BF\"]>threshold], rank_kaur[data1[\"Log10_BF\"]>threshold], s=0.5, color=colors[data1[\"Log10_BF\"]>threshold])\n",
+    "\n",
+    "plt.scatter(rank_kumasaka[data1[\"log10p\"]<2], rank_kaur[data1[\"log10p\"]<2], alpha=0.1, s=1, color=colors[data1[\"log10p\"]<2])\n",
+    "plt.scatter(rank_kumasaka[data1[\"log10p\"]>th], rank_kaur[data1[\"log10p\"]>th], s=0.5, color=colors[data1[\"log10p\"]>th])\n",
+    "\n",
+    "plt.xlabel(\"Afr beta\")\n",
+    "plt.ylabel(\"linear model kaur beta\")\n",
+    "\n",
+    "print(sum(data1[\"log10p\"]>th))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6829\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'linear model kaur beta')"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "data1[\"log10p\"] = -1*np.log(data1[\"pvalue\"])\n",
+    "data1[\"log10afpval\"] = -1*np.log(data1[\"pval\"])\n",
+    "\n",
+    "from scipy.stats import rankdata\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "rank_kumasaka = rankdata(abs(data1[\"beta_afr\"]))\n",
+    "rank_kaur = rankdata(abs(data1[\"beta\"]))\n",
+    "colors = np.array(['grey']*data1.shape[0])\n",
+    "threshold=6\n",
+    "colors[data1[\"log10afpval\"]>threshold] = 'r'\n",
+    "\n",
+    "plt.scatter(rank_kumasaka[data1[\"log10afpval\"]<3], rank_kaur[data1[\"log10afpval\"]<3], alpha=0.1, s=1, color=colors[data1[\"log10afpval\"]<3])\n",
+    "plt.scatter(rank_kumasaka[data1[\"log10afpval\"]>threshold], rank_kaur[data1[\"log10afpval\"]>threshold], s=0.5, color=colors[data1[\"log10afpval\"]>threshold])\n",
+    "\n",
+    "print(sum(data1[\"log10afpval\"]>threshold))\n",
+    "plt.xlabel(\"Afr beta\")\n",
+    "plt.ylabel(\"linear model kaur beta\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8151857259560876, 0.0)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "plt.scatter(abs(data1[data1[\"log10p\"]>th][\"beta_afr\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]), s=1, c='r')\n",
+    "plt.xlabel(\"AFR beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"log10p\"]>th][\"beta_afr\"]), abs(data1[data1[\"log10p\"]>th][\"beta_afr\"]), c='black')\n",
+    "plt.xlim((0,1.75))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"log10p\"]>th][\"beta_afr\"]),abs(data1[data1[\"log10p\"]>th][\"beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.819631660058969, 0.0)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "plt.scatter(abs(data1[data1[\"log10afpval\"]>threshold][\"beta_afr\"]), abs(data1[data1[\"log10afpval\"]>threshold][\"beta\"]), s=1, c='r')\n",
+    "plt.xlabel(\"AFR beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"log10afpval\"]>threshold][\"beta_afr\"]), abs(data1[data1[\"log10afpval\"]>threshold][\"beta_afr\"]), c='black')\n",
+    "plt.xlim((0,1.75))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"log10afpval\"]>threshold][\"beta_afr\"]),abs(data1[data1[\"log10afpval\"]>threshold][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/final_kumsaka-Copy1.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/final_kumsaka-Copy1.ipynb
new file mode 100644
index 00000000..617baf1a
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/final_kumsaka-Copy1.ipynb
@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: DtypeWarning: Columns (11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "kaur_eu=pd.read_csv(\"../final_figures/afr.inpeaks.kaur.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "eus = pd.read_csv(\"../final_figures_new/Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)\n",
+    "eus = eus.rename({\"beta\": \"beta_afr\"},axis='columns')\n",
+    "\n",
+    "datan = eus.merge(kaur_eu, left_on=[\"allele1\", \"allele2\", \"dbsnp_rsid\"], right_on=[\"ref\", \"alt\", \"rsid\"], how=\"inner\").drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8036724482849446, 0.0)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "data1 = datan\n",
+    "data1[\"log10afpval\"] = -1*np.log(data1[\"pval\"])\n",
+    "\n",
+    "data1[\"log10p\"] = -1*np.log(data1[\"pvalue\"])\n",
+    "\n",
+    "th=5\n",
+    "plt.scatter(abs(data1[data1[\"log10p\"]>th][\"beta_afr\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "plt.xlabel(\"Afr beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"log10p\"]>th][\"beta\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"log10p\"]>th][\"beta\"]),abs(data1[data1[\"log10p\"]>th][\"beta_afr\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.4222479536219453, 0.0)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "threshold=-1\n",
+    "plt.scatter(abs(data1[(data1[\"log10afpval\"]>-1)&(data1[\"log10afpval\"]<3)][\"beta_afr\"]), abs(data1[(data1[\"log10afpval\"]>-1)&(data1[\"log10afpval\"]<3)][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Afr beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "#plt.plot(abs(data1[data1[\"Log10_BF\"]<3][\"beta\"]), abs(data1[data1[\"Log10_BF\"]<3][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[(data1[\"log10afpval\"]>-1)&(data1[\"log10afpval\"]<3)][\"beta\"]),abs(data1[(data1[\"log10afpval\"]>-1)&(data1[\"log10afpval\"]<3)][\"beta_afr\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7971568735906679, 0.0)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "threshold=6\n",
+    "plt.scatter(abs(data1[data1[\"log10afpval\"]>threshold][\"beta_afr\"]), abs(data1[data1[\"log10afpval\"]>threshold][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Afr beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"log10afpval\"]>threshold][\"beta\"]), abs(data1[data1[\"log10afpval\"]>threshold][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"log10afpval\"]>threshold][\"beta\"]),abs(data1[data1[\"log10afpval\"]>threshold][\"beta_afr\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = np.arange(2, 70, 1)\n",
+    "x_values=[]\n",
+    "y_values=[]\n",
+    "for i in range(len(steps)):\n",
+    "    if pearsonr(abs(data1[(data1[\"log10afpval\"]<steps[i]) ][\"beta_afr\"]), abs(data1[(data1[\"log10afpval\"]<steps[i])][\"beta\"]))[1] < 1e-3:\n",
+    "        x_values.append(steps[i].astype(str))\n",
+    "        y_values.append(pearsonr(abs(data1[(data1[\"log10afpval\"]<steps[i]) ][\"beta_afr\"]), abs(data1[(data1[\"log10afpval\"]<steps[i]) ][\"beta\"]))[0])\n",
+    "    else:\n",
+    "        x_values.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "        y_values.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD7CAYAAAB68m/qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUsElEQVR4nO3de4xcZ3nH8e/jzQY2QLNATIrtGPuPxBAIJbAKRVElys2hVAkKLSSoUqlK00qEm5Bbu61CmqrFyG0Rf0SVDOUiWki5uMYIlw0igNSUix0ccO1kUxMu8boJ5rKhkC1ZO0//mNmwnszsntmdnTPnzPcjrbLznuOZJ9bsb18/73vORGYiSaq+NWUXIEnqDQNdkmrCQJekmjDQJakmDHRJqgkDXZJq4qyyXvi8887LTZs2lfXyklRJd9xxxw8zc227Y6UF+qZNmzh48GBZLy9JlRQR3+t0zJaLJNWEgS5JNWGgS1JNGOiSVBMGuiTVRGm7XCRpNe09NM2uySlOzMyybnyMbVu3AJwx9pvPXMsX7z654nNW8udefen6nv0/R1m3z52YmEi3LUr11hqqvQrQpc45d2yUnz98irnTv8y30TUBwRljrZZ7znL/3NjoCO+6+pKuQj0i7sjMibbHDHRJC/VyZvupO6aZnTvd8bVWM2SrYv34GLdvf0nh8w10aUh1O0Pu5cw2gOrFa/8F8J2dryp+/iKBbg9dqoDlzJpbZ8jTM7P881e//+hzTs/Msu0T3zwjiGdm5x7z2nOPLB3L7c4xzItZNz7Ws+dyhi6VbKmwXu6s2RnymYahh+4MXVpFS7U82s2iezVrHoQwX+qXymqH7BMffxYzD825y+WMkyKuAN4LjADvz8ydLcc3Ah8GxpvnbM/M/Ys9pzN0VV23Yd1OFWbRK5l9vuYF6x/zd9KPXS6rEZaDYkWLohExAtwDvBw4DhwArs3MowvO2Q0cysx/jIiLgf2ZuWmx5zXQNcjqEtbLmSH3amZb10At20pbLpcBxzLz3uaT3QJcBRxdcE4Cv9L8/lzgxPLLlfqrXXgvtZj4L1/9/pJhvZphXmTWvNwZcqcgLhLOBni5igT6euC+BY+PAy9sOedG4NaIeDPwBOBl7Z4oIq4DrgPYuHFjt7VKXet2wbGMsG6dRfdy1lw0YA3ieujVoui1wIcy8+8j4kXARyLiOZn5yMKTMnM3sBsaLZcevbb0qIUB3i6siyw4rmZYt2o3i+71rFnDo0igTwMXLHi8oTm20B8CVwBk5lci4vHAecAPelGk1M5SrZLl7g4pajlh3c0s2rBWt4oE+gHgwojYTCPIrwFe33LO94GXAh+KiGcBjwdO9rJQDbcife4irZKiVjuspdWwZKBn5qmIuB6YpLEl8QOZeSQibgIOZuY+4B3A+yLi7TR+Dt6QZV2xpFpYqnXSLrx79YYzrFVVhXrozT3l+1vGbljw/VHg8t6WpmGxnNbJcsO76IKjYa0q8kpR9d1yZt9FtdsxstTuEANcdWGga1Wt5uy7Nbw77Rhxd4iGhYGunlrN2fdC3YS3NCwMdPXM3kPT7NhzeFVm3/a5paUZ6Fq21nbKQw+fWvTeJkU5+5aWx0BXYUu1U4py9i2tDgNdhRRppxTh7FtaPQa62upVO8XZt9Q/Broeo3U23k07ZXxslCc87ixn31IJDHQBZ87I10Rwehl3bhgbHeHGK59tgEslMdD1mBl50TC3nSINFgN9CC23P247RRpsBvqQWW5/3HaKNPgM9CGw3P74SASPZDoblyrCQK+55fbHx0ZHeNfVlxjiUoUY6DVjf1waXgZ6jdgfl4abgV4juyanCl/NaX9cqh8DvcJa2yvdzMjtj0v1Y6BXVLv2SqdPqrc/Lg0HA71Cltp+mLT/WDb749JwMNArouj2wwTWj485G5eGkIFeEUUXPNePj3H79pf0oSJJg8ZAH1DLWfAcGx1h29YtfahO0iAy0AdQNwuebj+UNM9AH0Dt2iudFjzdfihpnoE+IBa2WDrdbcUFT0mLMdAHQGuLpRMXPCUtZk3ZBajYDhYXPCUtxRn6ADixyA6WANsrkgox0EvQuiVx/JxRfvLQ3GPOs8UiqRsGep+125I4uiYYHQnmTv9yOdQWi6RuFeqhR8QVETEVEcciYnub4++JiDubX/dExEzPK62Jdv3yuUeSJ5x9FuvHxwgaM3O3I0rq1pIz9IgYAW4GXg4cBw5ExL7MPDp/Tma+fcH5bwYuXYVaa6FTv/zB2TnufOcr+lyNpDopMkO/DDiWmfdm5sPALcBVi5x/LfCxXhRXR+vGx7oal6SiivTQ1wP3LXh8HHhhuxMj4hnAZuC2DsevA64D2LhxY1eFVtnCRdBzx0btl0taFb3eh34N8MnMbLupOjN3Z+ZEZk6sXbu2xy89mOYXQaebV4DOzM5BwpPPGbVfLqmniszQp4ELFjze0Bxr5xrgTSstqk46LYKec/ZZHLrBnrmk3ikyQz8AXBgRmyPibBqhva/1pIh4JvBk4Cu9LbHaOi2CLnYxkSQtx5Iz9Mw8FRHXA5PACPCBzDwSETcBBzNzPtyvAW7J7PBROkOi6EVDLoJK6rVCFxZl5n5gf8vYDS2Pb+xdWdXkRUOSyuTNuXrIi4YklclL/3vIi4YklckZeg950ZCkMhnoPbRt6xbGRkfOGLNfLqlfbLmsUOuulte8YD1fvPukHxMnqe8M9BVot6vlU3dMu+gpqRS2XFag3a6W2bnT7JqcKqkiScPMQF8BrwKVNEgM9BVwV4ukQWKgr4C7WiQNEgO9C3sPTXP5ztvYvP2zXL6zccv3d119iVeBShoI7nIpqN2Olh17DvOuqy/h9u0vKbk6SXKGXpg7WiQNOgO9IHe0SBp0BnpB7miRNOgM9ILc0SJp0LkoWtD8zpWF923xPi2SBomB3oVXX7reAJc0sAz0RbTeSdEZuaRBZqB30GnfOWCoSxpILop24L5zSVVjoHfgvnNJVWOgd+C+c0lVY6B34L5zSVXjomgH7juXVDUG+iLcdy6pSmy5SFJNGOiSVBO2XJq8KlRS1RnoeFWopHqw5YJXhUqqBwMdrwqVVA+FAj0iroiIqYg4FhHbO5zz2og4GhFHIuKjvS1zdXlVqKQ6WDLQI2IEuBl4JXAxcG1EXNxyzoXADuDyzHw28Lbel7p6vCpUUh0UWRS9DDiWmfcCRMQtwFXA0QXn/BFwc2b+BCAzf9DrQleTV4VKqoMigb4euG/B4+PAC1vOuQggIm4HRoAbM/NzrU8UEdcB1wFs3LhxOfWuGq8KlVR1vVoUPQu4EHgxcC3wvogYbz0pM3dn5kRmTqxdu7ZHLy1JgmKBPg1csODxhubYQseBfZk5l5nfAe6hEfCSpD4pEugHgAsjYnNEnA1cA+xrOWcvjdk5EXEejRbMvb0rU5K0lCUDPTNPAdcDk8BdwMcz80hE3BQRVzZPmwR+FBFHgS8C2zLzR6tVtCTpsSIzS3nhiYmJPHjwYCmvLUlVFRF3ZOZEu2NDeS8Xb8QlqY6GLtC9EZekuhq6e7l4Iy5JdTV0ge6NuCTV1dAFujfiklRXQxfo3ohLUl0N3aKoN+KSVFdDF+jgjbgk1dPQtVwkqa4MdEmqCQNdkmrCQJekmjDQJakmDHRJqomh2Lbo3RUlDYPaB7p3V5Q0LGrfcvHuipKGRe0D3bsrShoWtQ90764oaVjUPtC9u6KkYVH7RVHvrihpWNQ+0MG7K0oaDrVvuUjSsDDQJakmDHRJqgkDXZJqwkCXpJow0CWpJgx0SaoJA12SasJAl6SaMNAlqSYKBXpEXBERUxFxLCK2tzn+hog4GRF3Nr/e2PtSJUmLWfJeLhExAtwMvBw4DhyIiH2ZebTl1H/NzOtXoUZJUgFFZuiXAccy897MfBi4BbhqdcuSJHWrSKCvB+5b8Ph4c6zVayLiWxHxyYi4oN0TRcR1EXEwIg6ePHlyGeVKkjrp1aLoZ4BNmflc4PPAh9udlJm7M3MiMyfWrl3bo5c+095D01y+8zY2b/8sl++8jb2HplfldSRp0BQJ9Glg4Yx7Q3PsUZn5o8z8RfPh+4EX9Ka87uw9NM2OPYeZnpklgemZWXbsOWyoSxoKRQL9AHBhRGyOiLOBa4B9C0+IiKcveHglcFfvSixu1+QUs3OnzxibnTvNrsmpMsqRpL5acpdLZp6KiOuBSWAE+EBmHomIm4CDmbkPeEtEXAmcAn4MvGEVa+7oxMxsV+OSVCeFPoIuM/cD+1vGbljw/Q5gR29L69668TGm24T3uvGxEqqRpP6q1ZWi27ZuYWx05IyxsdERtm3dUlJFktQ/tfqQ6PkPgt41OcWJmVnWjY+xbesWPyBa0lCoVaBDI9QNcEnDqFYtF0kaZga6JNWEgS5JNWGgS1JNGOiSVBMGuiTVhIEuSTVhoEtSTRjoklQTBrok1YSBLkk1YaBLUk0Y6JJUEwa6JNWEgS5JNWGgS1JNGOiSVBMGuiTVhIEuSTVhoEtSTVT+Q6L3Hppm1+QUJ2ZmWTc+xratW/yQaElDqdKBvvfQNDv2HGZ27jQA0zOz7NhzGMBQlzR0Kt1y2TU59WiYz5udO82uyamSKpKk8lQ60E/MzHY1Lkl1VulAXzc+1tW4JNVZpQN929YtjI2OnDE2NjrCtq1bSqpIkspT6UXR+YVPd7lIUsUDHRqhboBLUsVbLpKkXyoU6BFxRURMRcSxiNi+yHmviYiMiInelShJKmLJQI+IEeBm4JXAxcC1EXFxm/OeBLwV+Fqvi5QkLa3IDP0y4Fhm3puZDwO3AFe1Oe+vgXcD/9fD+iRJBRUJ9PXAfQseH2+OPSoing9ckJmfXeyJIuK6iDgYEQdPnjzZdbGSpM5WvCgaEWuAfwDesdS5mbk7Mycyc2Lt2rUrfWlJ0gJFAn0auGDB4w3NsXlPAp4DfCkivgv8OrDPhVFJ6q8igX4AuDAiNkfE2cA1wL75g5n5YGael5mbMnMT8FXgysw8uCoVS5LaWjLQM/MUcD0wCdwFfDwzj0TETRFx5WoXKEkqptCVopm5H9jfMnZDh3NfvPKyJEnd8kpRSaoJA12SasJAl6SaMNAlqSYMdEmqCQNdkmrCQJekmjDQJakmDHRJqgkDXZJqwkCXpJow0CWpJgx0SaoJA12SasJAl6SaMNAlqSYMdEmqiUKfWDQo9h6aZtfkFCdmZlk3Psa2rVt49aXryy5LkgZCZQJ976Fpduw5zOzcaQCmZ2bZsecwgKEuSVSo5bJrcurRMJ83O3eaXZNTJVUkSYOlMoF+Yma2q3FJGjaVCfR142NdjUvSsKlMoG/buoWx0ZEzxsZGR9i2dUtJFUnSYKnMouj8wqe7XCSpvcoEOjRC3QCXpPYq03KRJC3OQJekmjDQJakmDHRJqgkDXZJqIjKznBeOOAl8r82h84Af9rmcXqhi3dbcP1Wsu4o1QzXr7qbmZ2Tm2nYHSgv0TiLiYGZOlF1Ht6pYtzX3TxXrrmLNUM26e1WzLRdJqgkDXZJqYhADfXfZBSxTFeu25v6pYt1VrBmqWXdPah64HrokaXkGcYYuSVoGA12SasJAl6Sa6OvtcyPimcBVwPw9cKeBfZl5Vz/rkKQ66tsMPSL+DLgFCODrza8APhYR2/tVxzCIiHMjYmdE3B0RP46IH0XEXc2x8bLraycizoqIP46Iz0XEt5pf/x4RfxIRo2XX104Va4Zqvj+gmnX3u+a+7XKJiHuAZ2fmXMv42cCRzLywL4V0KSLOBXYArwaeBiTwA+DTwM7MnCmtuA4iYhK4DfhwZt7fHPtV4PeBl2bmK8qsr52I+BgwA3wYON4c3kCj5qdk5utKKq2jKtYM1Xx/QDXr7nfN/Qz0u4Gtmfm9lvFnALdm5kB+OGhF30RTnf4+FztWpoi4JzMv6vZYmapYM1Tz/QHVrLvfNfdzUfRtwBea/yTd3fz6HPAF4K19rKNbmzLz3fNhDpCZ92fmu4FnlFjXYr4XEX8aEefPD0TE+c22130l1rWYH0fE70bEo+/JiFgTEa8DflJiXYupYs1QzfcHVLPuvtbct0DPzM8BFwF/BUw2v24EtjSPDaoqvoleBzwV+HJE/CQifgx8CXgK8NoyC1vENcDvAPdHxD3NFt39wNXNY4NovuYHmjX/N4NfM1Tz/QHVrLuvNXul6BIi4snAdhq7c57WHH4A2Eejhz6QM7Fo7CjaAHw1M3+2YPyKQf0FGhEvpLFG8W3gmcCLgKOZub/UwgqIiKc2v31vZv5eqcV0KSJ+A7gMOJyZt5ZdTyfN98fdmflgRJxD4+fy+cAR4G8z88FSC2wjIt4C/Ftm9mXyZ6CvQET8QWZ+sOw6WjXfRG8C7gKeB7w1Mz/dPPaNzHx+ieW1FRHvBF5JYyvt52kEzJeAlwOTmfk35VXXXkTsazP8EhprLmTmlf2tqJiI+HpmXtb8/o003it7gVcAn8nMnSWW11FEHAF+LTNPRcRu4OfAp4CXNsevLrXANiLiQRp1fhv4KPCJzFy1e7Ub6CsQEd/PzI1l19EqIg4DL8rMn0XEJuCTwEcy870RcSgzLy23wsdq1vw84HE02hYbMvOnETEGfC0zn1tmfe1ExDeAo8D7afzLIoCP0Wy3ZOaXy6uus4XvgYg4APxWZp6MiCfQ+BfdJeVW2F5E3JWZz2p+f8bEJCLuzMznlVZcBxFxCHgB8DIa7ZcrgTtovE/2ZOb/9vL1+nphURVFxLc6HQLO73CsbGvm2yyZ+d2IeDHwyeaOoiizsEWcyszTwEMR8e3M/ClAZs5GxCMl19bJBI0F/b8AtmXmnRExO6hBvsCaZitxDY1J3UmAzPx5RJwqt7RF/deCfxV/MyImMvNgRFwEzC31h0uSmfkIcCtwazSuT3glcC3wd0DbTx5aLgN9aecDW3nsroUA/rP/5RTyQEQ8LzPvBGjO1H8b+AAwkLMv4OGIOCczH6IxowEevQ5gIAO9+YP6noj4RPO/D1CNn6lzacwSA8iIeHpm/k9EPJHB/YUP8EbgvRHxlzQ+ru0rEXEfjc0Jbyy1ss7O+PtsXoezD9jXXAfo7YvZcllcRPwT8MHM/I82xz6ama8voaxFRcQGGjPe+9scuzwzby+hrEVFxOMy8xdtxs8Dnp6Zh0soqysR8Srg8sz887JrWY5mwJyfmd8pu5bFRMSvAJtp/PI8npkPlFxSRxFxUWbe07fXM9AlqR6826Ik1YSBLkk1YaBLUk0Y6JJUEwa6JNXE/wMxjKmHtUQZLQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(steps,y_values)\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(steps,y_values,c=\"blue\", s=5, label=\"full\")\n",
+    "plt.ylim((0.5,1.0))\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Log10BF increasing\")\n",
+    "plt.ylabel(\"pearsonr\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = np.arange(0, 60, 1)\n",
+    "x_values=[]\n",
+    "y_values=[]\n",
+    "for i in range(len(steps)):\n",
+    "    if pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) ][\"beta_afr\"]), abs(data1[(data1[\"log10p\"]>steps[i])][\"beta\"]))[1] < 1e-3:\n",
+    "        x_values.append(steps[i].astype(str))\n",
+    "        y_values.append(pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) ][\"beta_afr\"]), abs(data1[(data1[\"log10p\"]>steps[i]) ][\"beta\"]))[0])\n",
+    "    else:\n",
+    "        x_values.append(steps[i].astype(str))\n",
+    "        y_values.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(steps,y_values,c=\"blue\", s=1, label=\"full\")\n",
+    "plt.ylim((0.4,1.0))\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Log10 pvalue increasing\")\n",
+    "plt.ylabel(\"pearsonr\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/final_kumsaka.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/final_kumsaka.ipynb
new file mode 100644
index 00000000..b29d7bd2
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/final_kumsaka.ipynb
@@ -0,0 +1,793 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "kaur_eu=pd.read_csv(\"../final_figures/eu.inpeaks.kaur.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "eus = pd.read_csv(\"../final_figures_new/Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n",
+    "datan = eus.merge(kaur_eu, left_on=[\"Ref\", \"Alt\", \"RsID\"], right_on=[\"ref\", \"alt\", \"rsid\"], how=\"inner\").drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.7525696609368799, 0.0)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "\n",
+    "distal_rsids = list(set(datan[(datan[\"Inside_Peak\"]==2) & (datan[\"Log10_BF\"]>3)][\"RsID\"].values))\n",
+    "data1 = datan[datan[\"Inside_Peak\"]==1]\n",
+    "data1[\"log10p\"] = -1*np.log(data1[\"pvalue\"])\n",
+    "\n",
+    "th=5\n",
+    "plt.scatter(abs(data1[data1[\"log10p\"]>th][\"Beta\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"log10p\"]>th][\"beta\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"log10p\"]>th][\"beta\"]),abs(data1[data1[\"log10p\"]>th][\"Beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only local\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.7479207429626905, 0.0)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"only local\")\n",
+    "pearsonr(abs(data1[(data1[\"log10p\"]>th) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"log10p\"]>th) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "distal too\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.6765798015696909, 4.0138740501326115e-77)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"distal too\")\n",
+    "pearsonr(abs(data1[(data1[\"log10p\"]>th) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"log10p\"]>th) &  (data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.800480231379623e-05"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1[\"log10p\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5466470291205056, 0.0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "threshold=-1\n",
+    "plt.scatter(abs(data1[(data1[\"Log10_BF\"]>-1)&(data1[\"Log10_BF\"]<3)][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>-1)&(data1[\"Log10_BF\"]<3)][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "#plt.plot(abs(data1[data1[\"Log10_BF\"]<3][\"beta\"]), abs(data1[data1[\"Log10_BF\"]<3][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>-1)&(data1[\"Log10_BF\"]<3)][\"beta\"]),abs(data1[(data1[\"Log10_BF\"]>-1)&(data1[\"Log10_BF\"]<3)][\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.415398813306908, 0.0)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "threshold=-1\n",
+    "plt.scatter(abs(data1[(data1[\"Log10_BF\"]>-5)&(data1[\"Log10_BF\"]<-1)][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>-5)&(data1[\"Log10_BF\"]<-1)][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "#plt.plot(abs(data1[data1[\"Log10_BF\"]<3][\"beta\"]), abs(data1[data1[\"Log10_BF\"]<3][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>-5)&(data1[\"Log10_BF\"]<-1)][\"beta\"]),abs(data1[(data1[\"Log10_BF\"]>-5)&(data1[\"Log10_BF\"]<-1)][\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7452742929842012, 0.0)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "threshold=-1\n",
+    "plt.scatter(abs(data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]),abs(data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only local\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.7403681278936277, 0.0)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"only local\")\n",
+    "\n",
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>threshold) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>threshold) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "distal too\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.6885152817255216, 1.1347623759772453e-83)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"distal too\")\n",
+    "\n",
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1[\"Log10_BF\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2.74001910180254"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1[\"Log10_BF\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = np.arange(-2.0, 30, 0.2)\n",
+    "x_values=[]\n",
+    "y_values=[]\n",
+    "for i in range(len(steps)):\n",
+    "    if pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) ][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i])][\"beta\"]))[1] < 1e-3:\n",
+    "        x_values.append(steps[i].astype(str))\n",
+    "        y_values.append(pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) ][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) ][\"beta\"]))[0])\n",
+    "    else:\n",
+    "        x_values.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "        y_values.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD7CAYAAABkO19ZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAeNElEQVR4nO3df5Bd9Xnf8fdHywJL3LAiKIm1/JDqCjAMQRtvsRM6rROPkWzXsCUFhJMx7g/TZoJdKKNWmnENIW0tR0Oh09KOIXXrSW2QShhFLo4VWqB/EMvWKhIQiQjLEECLf2xA20yirVlJT/+454qju/fHuT/Pved+XjM77J5z7u5Xy97nfu/zfb7PUURgZmbFtSzvAZiZWXc50JuZFZwDvZlZwTnQm5kVnAO9mVnBOdCbmRXcGXkPoNL5558fq1atynsYZmYDZe/evX8eESuqneu7QL9q1SpmZmbyHoaZ2UCR9Gqtc07dmJkVnAO9mVnBOdCbmRWcA72ZWcH13WJsq3bsm2XrrkO8Mb/AyvExNq67lOnJibyHZWaWu0IE+h37Ztn8+AssLJ4AYHZ+gc2PvwDgYG9mQy9T6kbSekmHJB2WtKnK+YskPS1pn6TnJX00dW5z8rhDktZ1cvBlW3cdOhXkyxYWT7B116Fu/Dgzs4HScEYvaQR4EPgwcATYI2lnRBxMXfY5YHtE/GdJlwPfAFYln28ArgBWAv9L0iURcXpUbtMb8wtNHTczGyZZZvRXA4cj4uWIeBt4FLi+4poAfjL5/FzgjeTz64FHI+LHEfEKcDj5fh21cnysqeNmZsMkS6CfAF5PfX0kOZZ2D/Brko5Qms1/ponHtm3juksZGx057djY6Agb113a6R9lZjZwOlVeeQvw3yLiAuCjwO9Kyvy9Jd0maUbSzNzcXNM/fHpygi/ccCUT42MImBgf4ws3XOmFWDMzslXdzAIXpr6+IDmW9o+A9QAR8S1JZwPnZ3wsEfEQ8BDA1NRUSzexnZ6ccGA3M6siy6x7D7BG0mpJZ1JaXN1Zcc1rwIcAJL0XOBuYS67bIOksSauBNcB3OjV4MzNrrOGMPiKOS7od2AWMAF+OiAOS7gVmImIncBfwsKQ7KS3MfioiAjggaTtwEDgO/EanK24qeeOUmdnpVIrH/WNqaipabVNcuXEKSouyztebWdFJ2hsRU9XOFarXjTdOmZktVahA741TZmZLFSrQe+OUmdlShQr03jhlZrZUIbpXlpUXXF11Y2b2jkIFevDGKTOzSoVK3ZiZ2VIO9GZmBedAb2ZWcA70ZmYF50BvZlZwhau6ATc2MzNLK1ygr2xsNju/wObHXwBwsDezoVS41I0bm5mZna5wgd6NzczMTle4QO/GZmZmpytcoHdjMzOz0xVuMdaNzczMTle4QA9ubGZmlla41I2ZmZ3Ogd7MrOAKmbop8w5ZM7MCB3rvkDUzKyls6sY7ZM3MSgob6L1D1syspLCB3jtkzcxKChvovUPWzKyksIux3iFrZlaiiMh7DKeZmpqKmZmZjn5Pl1maWdFJ2hsRU9XOZUrdSFov6ZCkw5I2VTl/v6T9ycdLkuZT506kzu1s+V/RonKZ5ez8AsE7ZZY79s32eihmZrlomLqRNAI8CHwYOALskbQzIg6Wr4mIO1PXfwaYTH2LhYhY27ERN6lemaVn9WY2DLLM6K8GDkfEyxHxNvAocH2d628BHunE4DrBZZZmNuyyBPoJ4PXU10eSY0tIuhhYDTyVOny2pBlJuyVN13jcbck1M3Nzc9lGnpHLLM1s2HW6vHID8FhEpHMlFycLBJ8AHpD0nsoHRcRDETEVEVMrVqzo6ICqlVkCHHv7uPP0ZjYUsgT6WeDC1NcXJMeq2UBF2iYiZpP/vgw8w+n5+66bnpzgCzdcyfjY6GnHjx5b9KKsmQ2FLIF+D7BG0mpJZ1IK5kuqZyRdBiwHvpU6tlzSWcnn5wPXAAcrH9tt05MT/MRZS9ed3fvGzIZBw6qbiDgu6XZgFzACfDkiDki6F5iJiHLQ3wA8GqcX5r8X+JKkk5ReVLakq3V6yYuyZjasMu2MjYhvAN+oOPb5iq/vqfK4PwKubGN8HbNyfIzZKkF9mcSOfbMutTSzwipsr5tKtRZlT0Q4V29mhTY0gb68KDsiLTnnXL2ZFdnQBHooBfuTNXr7OFdvZkU1VIEeam+UKufqzcyKZugCvXP1ZjZshi7QO1dvZsNm6AI9OFdvZsNlKAM9uNmZmQ2PoQ30bnZmZsNiaAO9m52Z2bAY2kAPbnZmZsNhqAM91F58nZ1f8KzezAph6AN9vcVXp3DMrAiGPtDXWpQFp3DMrBgytSkusnJ74ju27a963nX1Zjbohn5GD6VgP+EeOGZWUA70CffAMbOicqBPuAeOmRWVA31KvR44Lrc0s0HlQF/B5ZZmVjQO9BVcbmlmRTP05ZWVXG5pZkXjGX0VLrc0syJxoK/B5ZZmVhQO9DW43NLMisKBvg6XW5pZETjQN+BySzMbdA70Dbjc0swGXaZAL2m9pEOSDkvaVOX8/ZL2Jx8vSZpPnbtV0neTj1s7OPaeKOfqa5l1uaWZ9bmGgV7SCPAg8BHgcuAWSZenr4mIOyNibUSsBf4D8Hjy2POAu4H3A1cDd0ta3tF/QQ/UK7cUOH1jZn0ty4z+auBwRLwcEW8DjwLX17n+FuCR5PN1wJMR8VZEHAWeBNa3M+C8bFx3KUvrbyDA6Rsz62tZAv0E8Hrq6yPJsSUkXQysBp5q9rH9bnpygur1N67AMbP+1unF2A3AYxFxopkHSbpN0oykmbm5uQ4PqXNqpW/AFThm1r+yBPpZ4MLU1xckx6rZwDtpm8yPjYiHImIqIqZWrFiRYUj5aFSBc9f25xzszazvZAn0e4A1klZLOpNSMN9ZeZGky4DlwLdSh3cB10panizCXpscG0iNKnDcHsHM+lHDQB8Rx4HbKQXoF4HtEXFA0r2SrktdugF4NOKdraQR8RbwW5ReLPYA9ybHBla9Chxwbb2Z9R9FjS3+eZmamoqZmZm8h1HXjn2zbH78BRYWay9FPHDz2lMtj83Muk3S3oiYqnbOO2NbUK/hWZlTOGbWLxzoWzQ9OcF9N13l9ghm1vcc6NuQpT2CZ/VmljcH+jY1Wpx1CsfM8uZA3wHucGlm/cyBvgOcwjGzfuZA3yFO4ZhZv3Kg7yCncMysHznQd5BTOGbWjxzoO8wpHDPrNw70XeAUjpn1Ewf6LnAKx8z6iQN9lziFY2b9woG+i5zCMbN+cEbeAyiycpviO7btr3r+jfmFHo7GzIaVZ/RdVi+Fs0xy+sbMus6BvgdqpXB860Ez6wUH+h6od6MS5+rNrNsc6HtkenKCkzVu2+hySzPrJgf6Hlrpckszy4EDfQ81Kre8a/tzDvZm1nEur+yhRuWW5cXZ9LVmZu3yjL7HGu2Y9czezDrNgT4H9VI44LJLM+ssB/oc1Cu3LHPZpZl1igN9TqYnJ7jvpqvqzuxn5xe4ZstTntmbWVsc6HOUZWY/O7/gNI6ZtcWBPmflmX3tUO80jpm1x4G+D0xPTlB9z+w7vHvWzFqVKdBLWi/pkKTDkjbVuOYmSQclHZD0tdTxE5L2Jx87OzXwoqlXclnmFI6ZtaJhoJc0AjwIfAS4HLhF0uUV16wBNgPXRMQVwB2p0wsRsTb5uK5jIy+YRiWX4Bp7M2tNlhn91cDhiHg5It4GHgWur7jm08CDEXEUICJ+1NlhFl95YbbRzN419mbWrCyBfgJ4PfX1keRY2iXAJZKelbRb0vrUubMlzSTHp6v9AEm3JdfMzM3NNTP+QpmenODZTb/Mn235WMPds16cNbOsOrUYewawBvggcAvwsKTx5NzFETEFfAJ4QNJ7Kh8cEQ9FxFRETK1YsaJDQxpsjVI5Xpw1s6yyBPpZ4MLU1xckx9KOADsjYjEiXgFeohT4iYjZ5L8vA88Ak22OeShkqbF3CsfMssgS6PcAayStlnQmsAGorJ7ZQWk2j6TzKaVyXpa0XNJZqePXAAc7M/Tia7R71ouzZpZFwzbFEXFc0u3ALmAE+HJEHJB0LzATETuTc9dKOgicADZGxJuSfhH4kqSTlF5UtkSEA30T3NrYzNqlqHF7u7xMTU3FzMxM3sPoO9dseYrZ+YWa50ck7rvpKgd7syElaW+yHrqEd8YOCLc2NrNWOdAPiKytjZ2zN7NKDvQDJEtrY8/szaySA/2A8czezJrlm4MPoPKC6+bHX2Bh8UTVa1yNY5a/Hftm2brrEG/ML7ByfIyN6y7N5fnoGf2A8u0Izfrbjn2zbH78BWbnFwjyvYmQA/0Ay5Kzf6NOSaaZdc/WXYeWvOPOa/LlQD/gGs3sl0nO1ZvloNYkK4/JlwN9AdSb2bsKxywfK2t0oK11vJsc6Aui3szeuXqz3qu2yXFsdISN6y7t+Vgc6AtkenKCkzVaWritsVlvpW8mJEq3C/3CDVfmUnXj8sqCWTk+VrMnjsstzXprenKiL55vntEXTL2eOE7hmA0nz+gLplFb43odMM2smDyjL6DpyYma95wVOFdvNmQc6Atq47pLqVZZH+A+OGZDxoG+oKYnJ6h1SxnX1psNFwf6AquVvgF3uDQbJg70Bea7Upn1hx37Zrlmy1Os3vQE12x5qufPOVfdFFi5Aueu7c9xosZGqvLMPn29mXVOuYtlucFZuYsl9O455xl9wfmuVGb56oculg70Q8C9683y0w9dLB3oh0SWmb374Zh1Xj90sXSgHyJZZvZO4VgR5bkY2g9dLBU1FunyMjU1FTMzM3kPo9AqF4cqjUjcd9NVXpy1Qqj19778nFHu/vgVXfs7T98v9tyxUSSYP7bYtXvHStobEVPVznlGP4TKM/taTkRwx7b9TN77h57d28CrthgKcPTYYtfewVbeL3Z+YZH/t3iSX/3ARQDcuW1/T99ZONAPqXr9cMq6+UQwa6RT6ZZ6i57dKkKoVWnz1d2v5XKzcAf6IdZoQxW4GsfyUTkjnp1f4I5t+/nrm59gVZOBv9GiZzeqX2p9z8pEea+eXw70QyzL4iyUnmTNPrnM2lEr3XIyiZSz8wvcuW0/n9vxQsPv1WhC043ql2a+Zy+eX5kCvaT1kg5JOixpU41rbpJ0UNIBSV9LHb9V0neTj1s7NXDrjCxll2W9fKtpwy3LLDuAr+5+reHfY3lCMz42uuRct6pfqr241J9Odff51TDQSxoBHgQ+AlwO3CLp8opr1gCbgWsi4grgjuT4ecDdwPuBq4G7JS3v5D/A2lfviVDJzdCsF7LOiAOqpj4q8/sA++++lgduXtuTe7hWu1/sr37gotxSpVl63VwNHI6IlwEkPQpcDxxMXfNp4MGIOAoQET9Kjq8DnoyIt5LHPgmsBx7pzPCtU8r3ttyxb7bm3anKyi0Tyo8za0W6/LCy5HDjukvrlgCnpWf/O/bNcs/OA8wvLJ46Vtlbpld/s9V+1tTF5zV8fnVjzSBL6mYCeD319ZHkWNolwCWSnpW0W9L6Jh6LpNskzUiamZubyz5667gs1Tjgmb1lV616ptpiazpt0cy7zPLsv/w900G+LMtMuRebqrI8v7qxZtCpxdgzgDXAB4FbgIcljWd9cEQ8FBFTETG1YsWKDg3JWpWlGgfcDM0aqxXQf/PrB6qWH6YnD9OTE6elW2o59vbxU+8O6r0DqNfio9ELTyfVe351a80gS+pmFrgw9fUFybG0I8C3I2IReEXSS5QC/yyl4J9+7DOtDtZ6o/x2c+uuQw1vJu42x1ZPrXryWgG5WlownQKplpo5emyRjf/jORZPNt7lXyvlWK/DZKf/riufXyMSJyKY6NKOWcjQAkHSGcBLwIcoBe49wCci4kDqmvXALRFxq6TzgX3AWkprJXuBn08u/WPgfeWcfTVugdB/GrVMgNJMpFsLW9Z/yjPodKCqFrBWb3qi5i0t65kYH+PZTb9c9dw1W55qOAFp9nvXGqeAV7Z8rOWf1UtttUCIiOPA7cAu4EVge0QckHSvpOuSy3YBb0o6CDwNbIyIN5OA/luUXhz2APfWC/LWn7K2Ob5n54Ga56040mkO4NRNbcr/Tac9Ws0311uQbHexstrj+6HDZDe5qZlllmVm3+1GUZa/rDNqAWOjyzi2eLLpnzEicTKiagOwbszoq/1tD9q7VDc1s47IMrM/emzRDdEKLuuMOqClIA+ldwe1FkWzFguMj402bA9crrS5c9t+zjpjGcvPGe16jX0eHOitKeWdtI044BdXN9IZIxJK/lupsjQyy4RjbHSEe667YsmmpXTwrtZhcv7YIr/4nvOA1jtM5n0j8Gp8c3Br2vTkBL/59QMcPba0XrlSuQNm+XGWv3b7pDezmSmLdIpk9aYnql5T+S6iPMYsfeZr/XuqVdoE8Oz33llGbPZG3v1wI/BqPKO3ltz98SsyvX0Gb67qJ9VmsUePLWauHU/Xq5dn1I2a4tWz/JzR02bZzSyKVmsz8MDNa9n3+WszBdWsKahm2hL0w43Aq/GM3lpSfiJV1jTX4rYJ+duxb5a7tj93qjqmmsp9EZWz/796+ziLJ96psknPxnfsm+XObftrllOOLhPvOvuMuu8eqr1bqLeJqNWWBjv2zbIsKQfNIuuLQj/cCLwaB3prWbo/TpaA781V+SnP5LMEtvKL8syrb/F7e2dPBd16rQXKfwv1+rhsvbHx7SnTm4mq9cDphGZ+F2VZ1yVWjo9VrQjKu0zTgd7a1kzA98w+H43aA1RaWDzBI99+PVMwTM9WJ2oEuonxscz/v7vdeKzZ38XoMnHs7eOs3vREwxeeZt+RlNVr8NYJztFbx6R7kzTaXOWcfe/s2DfbUt151hlverZarfQxa/+WXlWr1EujTIyP8WsfuOhUbx0BiyfjtHWMetVk1dYNGpVp9qLPjmf01nH1KiLKPLPvvqwptXZUBvFWUy+9rFaplV5Jb6RqtDmwXjVZs+9IetFnx4HeuqL8B1pv8c85++7JsotZlMoJxysWWbOqtQu6ldRLL5uKZUmvZEnvdGp8vVjAdaC3rvHMPj9ZAtX9N689bfNQoxtiVDrnzDM69v+sl9UqWd51tFtl04xeLOA60FtXeWafj0YBqHJxdHpyIlNb6mZ+RjN6Xa3S6F1HrfFUu65drS7gNsOLsdZ1WW5A7puYdE65RryWWkEkaw+Zsk4G4XYWcbshy+8iXY3TzuJxKwu4zXL3SuuZLBt2yrp5E4aiyrL42qi7aNYUTjc6O3a7xLDd8fzSZSt4+k/nqm4eg/y7XdbrXulAbz2VZZEwzW2PG8sS4Eck7rup8YYlqN0GuF7r4GFT63dU74Yp3VYv0DtHbz2VJWefdvTYIndu28/Mq2/xr6ev7PbwBk7WF86TEZkDc62ccZHa9raj3r6EvFsd1OJAbz2XpRonLYD/vvs1nnj++011WSyKeimNrLs8m8mn96INwaAqv7DWknerg1qcurHcNJOzr2aZ4GScns+vdi/T8nlYekPmXtyYuR21ZuzllFa9JmJlno13Tr27W+X9e3aO3vpWszn7WsZGR/iV902c1oQrbRnQ6F5HeT9Rq6kXWEaXlV6kTtZ5CnuNo7Ma3ew8zwmDA731tfQsvLxbsxUjTbSdrfc9+mnBcVWNG3E04gDfHVnvV5vH79+LsdbXKjevfG7HC3x192tNB/x2g3z6e+R1Z6DK/u/Naqa6xpqX9e5a/XZnNc/orS+1m7/vlF7O8DuRxhLwypaPdW5QtkT6xbjRX2cvyy09o7eB02xlTrf0cobfbJ/0avq16qNI0u9AG6Vy+qXc0i0QrG9V2xqe7hXeawuLJ7hj235WdaFfeqs949PybBkwrBq1SuiXF16nbmxgNZpNjY+N8uPjJ2uWJk5PTmReXKulE4tuzaRsRpcJxJKWwl58zU+tncm9ruKql7rxjN4GVqPZ1P9dWFzyjuCBm9ey7/PXnnryNdvIq1J50a3V2X15LSJrymbrjVex9e9fVfffZL2VvrNaNxuTtcMzehto9RZtsy6EpRfXlrVYotlKtUuzi6959lGx/ufFWCusWou2zeSr04trrVa+tHIDlWYWX51/t3ZkSt1IWi/pkKTDkjZVOf8pSXOS9icf/zh17kTq+M5ODt4MOtvPu/J7jY+NMjpSu7d7WvnWclnVq8gYXSaWnzPal2kAGzwNZ/SSRoAHgQ8DR4A9knZGxMGKS7dFxO1VvsVCRKxte6RmdbRyn9Ks36uZnbtZy+nKNwepliYakdh6ozc9WedkSd1cDRyOiJcBJD0KXA9UBnqzQqoW+GutCyyT2LFvtm6QLqeHqj2+H/vt2ODLkrqZAF5PfX0kOVbpVyQ9L+kxSRemjp8taUbSbknTbYzVrC/UuzVio1si1quyGZEc5K0rOlVe+XVgVUT8HPAk8JXUuYuTleBPAA9Iek/lgyXdlrwYzMzNzXVoSGbdU87lj1S5N2utXH29mTw0d3MQs2ZkSd3MAukZ+gXJsVMi4s3Ul78D/Hbq3Gzy35clPQNMAt+rePxDwENQKq/MPnyz/ExPTnBnjfurljdhNVO62S+7KK14sgT6PcAaSaspBfgNlGbnp0h6d0R8P/nyOuDF5Phy4FhE/FjS+cA1pF4EzAbdyvGxmjtrL/9Xf8DiyTi1i7VekHf5pHVTw9RNRBwHbgd2UQrg2yPigKR7JV2XXPZZSQckPQd8FvhUcvy9wExy/GlgS5VqHbOBtXHdpdQqvjy2eHJJq4JqnJu3bvPOWLM2tXpzEHCVjXWOe92YdVE73TQd5K0XHOjN2tRqY7SJ8TEHeesJB3qzNpVLLcebuPWfF1+tlxzozTog3aq2Wm09lBZd3bvG8uDulWYdVK+bpoO75cWB3qzDysG8vFGqFzcWN6vHgd6sCzrZTdOsXc7Rm5kVnAO9mVnBOdCbmRWcA72ZWcE50JuZFVzfNTWTNAe8mvc4EucDf573INrg8ednkMcOHn/eWhn/xRGxotqJvgv0/UTSTK1ucIPA48/PII8dPP68dXr8Tt2YmRWcA72ZWcE50Nf3UN4DaJPHn59BHjt4/Hnr6PidozczKzjP6M3MCs6B3sys4BzozcwKbijbFEu6DLgeKPeRnQV2RsSL+Y2qNZL+FnA18CcR8Yd5j8fM+s/Qzegl/UvgUUDAd5IPAY9I2pTn2LKQ9J3U558G/iPw14C7B2H8li9J50raIulPJb0l6U1JLybHxvMeXyOS1qc+P1fSf5H0vKSvSfqZPMeWhaQzJP0TSd9Mxv28pD+Q9E8lZb/pcLM/d9iqbiS9BFwREYsVx88EDkTEmnxGlo2kfRExmXy+B/hoRMxJ+glgd0Rcme8IG5N0LrAZmAZ+GgjgR8DvA1siYj63wWUgaX1EfDP5/Fzg3wF/E/gT4M6I+GGe46tH0i7gKeArEfGD5NjPArcCH4qIa/McXyOS/jgifj75/HeAHwAPAzcAfycipnMcXkOSHgHmga8AR5LDF1D6/Z8XETd34+cO3YweOAmsrHL83cm5frdM0nJJP0XphXoOICL+Cjie79Ay2w4cBT4YEedFxE8Bv5Qc257ryLL5t6nP7wO+D3wc2AN8KZcRZbcqIr5YDvIAEfGDiPgicHGO42rFVER8LiJejYj7gVV5DyiD90XEr0fE7og4knzsjohfBya79UOHMUd/B/C/JX0XeD05dhHwN4Db8xpUE84F9lJKN4Wkd0fE9yW9Kzk2CFYlgeWUJPB8UdI/zGlMrZqKiLXJ5/dLujXPwWTwqqR/QWlG/0OAJOXxKd55PvSzn5b0zyn9rf+kJMU7aYlBmLi+JelG4Pci4iSApGXAjZQmOl0xdIE+Ir4p6RJKC5jpxdg9EXEiv5FlExGrapw6Cfy9Hg6lHQ42+bkZ2AT8n+R3HsAPgZ3ATXkOLKOHKa1JQSn9cT4wl6Sf9uc1qCZsAL4IPChpPjk2DjydnOuKocvRW/4kLacUbK6nlKOHd4LNlojo2symEyTdXXHoPyXrJD8L/HZEfDKPcWWVVJ1dQGlN5y9Tx0+tPfSzZPwTwLcHdPzvp/QC+z3gMuAXgIMR8Y2u/UwHeusnkv5BRPzXvMfRqn4fv6TPAr8BvAisBf5ZRPx+cu7UQme/kvQZSinWQR3/3cBHKGVTnqSUWXgG+DCwKyL+TVd+rgO99RNJr0XERXmPo1X9Pn5JLwC/EBF/KWkV8BjwuxHx79MVXf2qIONfC5xFqWLogoj4C0ljlN6h/Fw3fu7Q5egtf5Ker3UKGIRa6EEe/7JyuiMi/kzSB4HHJF3MYCzmD/r4jydrgcckfS8i/gIgIhYkda3qz4He8vAzwDqWVhkI+KPeD6dpgzz+H0paGxH7AZKZ8d8Fvgz0/R4MBn/8b0s6JyKOAe8rH0z2YzjQW6H8T+Bd5SdrmqRnej6a5g3y+D9JxX6LiDgOfFJSv+8BgMEf/9+OiB8DlMsrE6OUNk11hXP0ZmYF1+81v2Zm1iYHejOzgnOgNzMrOAd6M7OCc6A3Myu4/w/8eG0f70RE7AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(steps,y_values)\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only local\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"only local\")\n",
+    "steps = np.arange(-2.0, 30, 0.2)\n",
+    "x_values_l=[]\n",
+    "y_values_l=[]\n",
+    "# for i in range(len(steps[:-1])):\n",
+    "#     try:\n",
+    "#         if pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) & (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[1] < 1e-3:\n",
+    "#             x_values_l.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_l.append(pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i])&  ~(data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[0])\n",
+    "#         else:\n",
+    "#             x_values_l.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_l.append(0)\n",
+    "\n",
+    "#     except:\n",
+    "#         x_values_l.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#         y_values_l.append(0)\n",
+    "\n",
+    "for i in range(len(steps)):\n",
+    "    try:\n",
+    "        if pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) ][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) ][\"beta\"]))[1] < 1e-3:\n",
+    "            x_values_l.append(steps[i].astype(str))\n",
+    "            y_values_l.append(pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) ][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))[0])\n",
+    "        else:\n",
+    "            x_values_l.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "            y_values_l.append(0)\n",
+    "\n",
+    "    except:\n",
+    "        x_values_l.append(steps[i].astype(str))\n",
+    "        y_values_l.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only distal\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"only distal\")\n",
+    "steps = np.arange(-2.0, 30, 0.2)\n",
+    "x_values_d=[]\n",
+    "y_values_d=[]\n",
+    "# for i in range(len(steps[:-1])):\n",
+    "#     try:\n",
+    "#         if pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) & (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[1] < 1e-3:\n",
+    "#             x_values_d.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_d.append(pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i])&  (data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[0])\n",
+    "#         else:\n",
+    "#             x_values_d.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_d.append(0)\n",
+    "#     except:\n",
+    "#         x_values_d.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#         y_values_d.append(0)\n",
+    "\n",
+    "for i in range(len(steps)):\n",
+    "    try:\n",
+    "        if pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) ][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) ][\"beta\"]))[1] < 1e-3:\n",
+    "            x_values_d.append(steps[i].astype(str))\n",
+    "            y_values_d.append(pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i])&  (data1[\"RsID\"].isin(distal_rsids)) ][\"beta\"]))[0])\n",
+    "        else:\n",
+    "            x_values_d.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "            y_values_d.append(0)\n",
+    "    except:\n",
+    "        x_values_d.append(steps[i].astype(str))\n",
+    "        y_values_d.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newx=[]\n",
+    "newy=[]\n",
+    "for i in range(len(x_values_l)):\n",
+    "    if y_values_l[i] !=0 and y_values[i] !=0:\n",
+    "        \n",
+    "        if y_values_l[i] >= y_values[i]+0.01:\n",
+    "            newy.append(1)\n",
+    "            newx.append(x_values_l[i])\n",
+    "        else:\n",
+    "            newy.append(-1)\n",
+    "            newx.append(x_values_l[i])\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fad386752d0>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(newx,newy, c=\"red\", s=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PZv369VxwwQX1/R38PPnkk5x11ln1ZcTz8/O56667OPDAA6moqKgfHB977DFWr15NWVkZ999/P7t3744q886dO7n55ptZuHAhFRUVrFq1ihdeeAGA6upqxo4dy5o1ayguLuaRRx5pdO4xxxxDt27d6ntLzJkzJ2LpjtLSUkaMGAE4ivD5559n/fr1rF27lltvvRWA66+/nhtuuIFVq1bxt7/9jWnTpjFgwAB+9KMfccMNN1BRUdGk3tSjjz7K2WefXb8+cuTI+r+/0TKS+XtoKaYg4sWNZvrk7rvNFxECdXVw6qnQty+ccoqz3hpGjBjB6tWr+fLLLznggAMYN24cZWVlLFu2rH5w++c//9loUPNYunQp//Ef/wHAueeeG7GA3qhRo5gzZw4zZ85k3bp1ERvwANx///31T/3btm2LWYJ71apVnHLKKfTs2ZOcnBwmT55c7yvo3LlzfYvQESNGRKzSOm3aNObMmUNtbS3z5s2rL0Hux186vFu3bnTp0oUrr7yS5557rn42UFJSwrXXXkt+fj7nnXceX375ZX0RwEj85S9/oaysrL4hEVjZ8NaS7N9DSzEFES9uNFOvm282X0QIVFXBihWwf7/z2tq0lk6dOnH00Uczd+5cxo8fT1FREYsWLWLz5s31dYDeeOMNRo8e3aLrFxcXs3TpUvr06cOUKVN4/PHHmxyzePFiSkpKeO2111izZg0FBQWNynYn+nlEBIDs7OyIXd8uvPBCXn75ZV588UVGjBhBjx49mhzjLx2ek5PDG2+8wUUXXcSLL75Yb3qqq6tj5cqVVFRUUFFRwY4dO6L29y4pKeHXv/418+fPb1QKxMqGt45k/x5aiikI4pzKudFMuf37my8iBHr1gvHjISfHee3Vq/XXLCoq4p577qG4uJiioiIeeughCgoKEBHWr1/PiSeeSHZ2dpPziouLefLJJwF4+eWX+eyzz5oc8+GHH9K7d29++MMfMm3aNN58803AGcj37dsHwBdffEH37t3Jzc1l48aNrFy5Mqa8o0ePZsmSJezatYva2lqeeuopJkyYEPfn7dKlC2eddRY//vGPo1aG9ZcO37NnD1988QXnnHMO9913X30nuTPPPJPf/e539ed4Zqtg6fDy8nKuvvpq5s+fT6/AP8zKhreOMH4PLaHDK4iEp3IhdT3r6IjAokWwfTssXuyst5aioiIqKysZN24cvXv3pkuXLvXmpZdffjlqKOZtt93G0qVLGTx4MM899xz9+/dvcszixYsZPnw4BQUFzJs3j+uvvx6Aq666imHDhjF58mQmTZrE/v37GThwIDNmzGDs2LEx5T3iiCO46667OPXUUxk+fDgjRozgO9+JVb6sKZMnTyYrKytqZNa5555b3wDpq6++4tvf/jbDhg3j5JNP5t577wUcs1hZWRnDhg1j0KBBPPTQQwD827/9G88//3y9k3r69Ons2bOHiy++uN4c5bFo0SLOPffchGQ3Ggjj99AiVLVdLCNGjNCW8NFHqjk5quC8fvRRMyfMmqUK+s0dd+jy5cu1urq6Rfdt72zYsCHVIsRk4sSJunPnzlSLkXRmz56tt956a8xjCgsL9bPPPgtVjqKiIv30008j7kv370ZHAyjTKONqhy/d6E3lVqyAceMcM5NqDI3tTt3LBw2ipKQEcHoEG5nFq6++mmoRks4FF1zAe++9x8KFC2Me95vf/IatW7dy6KGHhiJHVVUVN954Y0TnvpFZhNowqC1pTcMgzwdx6aWOohg/3pnexSjPT01NDeXl5RQUFJg/IgLWFMaIhn030ouUNAzKJLKynCWRqAFLnDMMo71jCsIl4agBS5xrlvYyOzWSh30n4iMdkuQg3IZBGYUXNVBV5SiHZqMGvMS5LVsocbWJ+SIa6NKlC7t376ZHjx718ftGx0ZV2b17N126dEm1KGmNF1kZr7k7TExB+MjKgt694zzYdVb3uuQSJm7bZolzAfr27cv27dsJq5GTkZl06dKFvn37plqMtCZSklzc41KSMSd1kjCntWEYyUDVycnyZhBh50HEclLbDKK17NoFc+awbtAgSlwFZaYmwzBaSsLm7hAxJ3VrcX0RBRUVVqPJMIyk4Jm7U+2+sxlEgLq6BDW364voPHUqhXl51NTUUFpaaqYmwzCSgjcm5eU5Bou2nFXYDMJHi0rsukX8AJg9m3WLFlk5cMMwkoI3JvXpAz16tH35b1MQPlpVYtdMTYZhJBlvTKqthS++aPvy36YgfLSqxK7bL6Lz1VfXO6ktgc4wjNbgjUnZ2dCtW9uX/zYfhI9WRQ/4TU04tfKtmJ9hGK3BPyZ5Poi8PPjkk7bxRdgMIkCrowfcEhwF/fqZqckwjFbjjUnZ2dCzJ5x2muOLmDABKivDLcdhCiLZuL6I3HnzrJifYRhJxe8nXbYM+vcP12ltCiLZuL4IL/zVC3s1X4RhGK3F75MQCd9pbQoi2VjYq2EYIeFvRVpUFL7T2hREFFpdbtfCXg3DCIGsLDj88LbpWW0KIgItSpgLYmGvhmGESFuU4zAFEYFWJcx5eKamvDygIezVTE2GYSSTMJsLhaogRGSSiGwSkc0iMiPC/vtEpMJd3hGRz337rhCRd93lijDlDNKqhLkgFvZqGEZIJMXaEYPQEuVEJBt4ADgD2A6sEpH5qrrBO0ZVb/Ad/xOgwH1/GHAbMBJQYLV77mdhydtY9iSW2/XCXoGCa66xnhGGYSSNsJsLhTmDGA1sVtX3VXUv8DTwnRjHXwY85b4/C3hVVT91lcKrwKQQZW1C0ux7vrBXMzMZhpFMkmrtiECYpTb6ANt869uBMZEOFJGjgKOBhTHO7RPhvKuAqwD69+/feonDwPNF7NrFqMWLyRk5kqFmZjIMIwmE3VwoXZzUlwLPqmptIiep6sOqOlJVR/bs2TMUwZLmAJozh8633sqYDY6FzSKaDMNIBmFGM4WpIHYA/Xzrfd1tkbiUBvNSoueGRlIdQGZqMgwjwwhTQawCjhORo0WkM44SmB88SEROBLoDr/k2LwDOFJHuItIdONPd1qYkJdzVIy/PURJz5lhEk2EYGUFoCkJV9wPX4gzsbwPPqOp6EbldRM7zHXop8LRqgxFHVT8FfoWjZFYBt7vb2pSkO4AChfzATE2GYaQvofaDUNWXgJcC234RWJ8Z5dzHgMdCEy4Oku4Acgv4ea/WM8IwjHQmXZzUaYvnAFJNgrM6UMjPMzWdcMIJNpMwDCPtMAURB0nPVgyYmjZt2mROa8Mw0g5rORoHSc9WDJiaPGe1N5OwTGvDMNIBm0HEgd9ZPW6cY2ZKpqkpt6bGZhKGYaQdpiDiwHNWb93qvO/XL7mmJubMAZyZhIW/GoaRLpiCiJOsLGdJWl4ENCTPnXdeo5kEWPirYRipxxREAiQ9L8IzNc2f32gmYZnWhmGkA+akTgB/XkReHnzySZLzI9yZRMEll4CZmgzDSDE2g0iQrCzo2RNOOy2JYa+BmYQX/mqRTEZHJcwuaUb8mIJoAUmt0eTHV9APoKamxnwRRocj7C5p6Uy6KcZmFYSIZInI99pCmEwh6WGvHtbH2jDCewBLIyIpgnRUjM0qCFWtA25qA1kyhtDCXj0CfaytFIfRkQi7S1qqiaYI0lExxmtiKhGRn4lIPxE5zFtClSzNCSXs1cNKcRgdGO8BbPt2WLw4nEY4qcSvCEpLYcMGZyaRjoox3iimS9zXa3zbFPhWcsXJLLx/6IoVjU1Nya766kUzWVST0VHwimTW1SUxWjCF1NU1VIX2xo3SUjj4YCgocNYXLQq3fWhLiGsGoapHR1g6tHKAEE1NUUpxWFST0ZFIR5t8S/B/jgkT4KOPYOFCqKiA6urGMwmR8NqHtoS4o5hEZLyI/LuIfN9bwhQsUwiamvxTxlYTKMVhUU1GRyIdbfItwf85li2D/v2dMPmBA52ZQ3Z2w0wi3RRhXApCRP4M3AOcDIxyl5EhypVReFNG/z96wgSorGylogiU4li3aJH5IowOQ6Rowdra9AoDjQf/+CDSoPB27XIsEP6ZRNopQlVtdsFpGSrxHJuqZcSIEZpKamtV161TzcnxPBHO++JiZ1+rmDVLFfSbO+7Q5cuXa3V1dVJkNox0p7ZWdedO53eUna3arVsSf1dtSG2tamWlI7cnf12ds6+uLvL2tgIo0yjjqmgcqlhE/gpcp6qV4aqrljNy5EgtKytLqQyqzhSxtNSZJqo6Tz/bt7eyf8SuXY6ZaepUyMujpqaG8vJy6xthdAg+/tix3+/f37AtKb+rFOB3Vvv9DNG2twUislpVI1qE4vVB5AEbRGSBiMz3luSJ2D7wh+cVFYXQP8IS6IwOiN9E061bCAmqbYgXnRVUAtG2p5p4FcRM4HzgTuA3vsUIkJUFhx/eNLppwgTYscOJYGjxl9oS6IwOiPfgtWMH7N4dYoJqEkm3khktJd4w1yXARqCru7ztbjOiEIxuWrbMmSYfeWQrvtSWQGe0IxIZRL0n7OzsEBNUk0BdnROc0h7CcyH+KKbvAW8AFwPfA14XkYvCFKw94J8ae6i24ksdKObndaCzmYSRTsQz8LcmxyG0WmitxPtM/fo5D4TpqMASJV4T08+BUap6hap+HxgN/Gd4YrUPgj4Jb1uL0+itl7WR5gSTwiorm4am1tU5uULB3KF4Q1hDr4XWQrx8h9rahgCVdCmZ0VLiVRBZqvqJb313Aud2aDyfxOLFsHOnsyxc6JQPaHFMt/WyNkKmJTb04MC/bJkzePfo0aAwduxwFEh+Phx0UEPuUH6+c1yfPs6gWlsb+16h1kJrIf6ZTVERbNuW+bWk4h3k/+lGME0RkSnAP4CXwhOr/ZGVBUcc4XyJTjvN+SH4fziRnrSiEqGXdUFBAeXl5ezatcvMTUaraIn5xzvHP/CLON/pL75oUBhHHeW81tbCnj1QUuIkiXnH1dbCypXOANvcfdOpuJ1XM2rhQsdisGSJ82CYycoB4ndSTwf+AAxzl4dV9eYwBWuv+Keh/h9O8EkrpsKI0MvaC31dsGCBmZuMVtFciYtIswv/93rPHigvdwZ5LzTVrzA880thIRQXN/jpunZtuN6qVc3PCNKl6qtfoZ52mtNxMtMVQz3RMuj8C3AQkO2+PwE4D+gUx3mTgE3AZmBGlGO+B2wA1gNP+rbXAhXuMr+5e6U6kzpevKxJLys0O1tVpCH72lv8WaNFRU426f79qh995Hv9qEo/+s/f6/6PqvT996t12bLlWlVVZdnWRquIldlbW9uwz/te1tVFPqe2tuH76s8iLipy1r3resft26c6blzqMopbykcfNVRQyMlx1j28z5bOn4UYmdTxKojVQC7QB/gA+CvwRDPnZAPv4ZQE7wysAQYFjjkOKAe6u+u9fPv2xCObt2SKglCN/MOJV2FEfO1aq9myX8eO+Eb373fuUV1dbYrCaDHRBjb/YOgNiMEHmGiDYTyDZSYMqEGiKVS/Mk3n0iCxFES8PghR1Rrgu8B/q+rFwOBmzhkNbFbV91V1L/A08J3AMT8EHlDVzwC0sSO83eKP6faS6rwkIC/iKdLU3LPRNnn9SqjVbFau7lRvu7Vsa6M1eN9R1cbmpEiF5/wVSmOZV+LJFk7XjOJoBH0PflNXe6hGG7eCEJFxwGQcBzU4M4RY9AG2+da3u9v8HA8cLyKlIrJSRCb59nURkTJ3+/lRhLrKPaasKhP/+i6JKozga9eDFKd/k7DqDaVq5gOWbW20mkghq3V18PTTToSO9730VyjN4J9hwjTne0gnJ3qLiTa18C9AMTAfuNld/xZwfzPnXAT80bd+OfD7wDEvAs8DnYCjcRTKoe6+Pr57bQGOiXW/TDIxJYrfJBXpdd8+1XH9t2sOe7Wo+1qtpJfW3T1LVVWXL1+uM2fO1OXLl6f4UxiZRtCcFKymum9f9AqlHYFYvgePTDCZ0RoTk4hkA+ep6nmqererVN5X1euaOXUH0M+33tfd5mc7jgN6n6p+ALyD45dAVXd49wIWAx02yN8/w4j0mpMDy1cdwNZbH0aOOYZ+WTuZ8Pz1VFbCsGEFnHTS2eTnd9g/n9FCguYkf+TdihXODNeb8aY6kigevOirZPWUiGeGkGkmsyDNKghVrcVpFJQoq4DjRORoEekMXIozC/HzAnAKgIjk4Zic3heR7iJygG97IU6kkxGFrF55ZF17DSsqctlfl82ylZ3o16eOfn268N3vjmbSpANYtsxMTUb8BCsB+Kup+gfETBgEPXOQP/+otdnX6RJmGyY5cR5X7pb3/itQ7W1U1eeinaCq+0XkWmABjr/iMVVdLyK340xp5rv7zhSRDThhrdNVdbeIjAf+ICJ1OErsLlU1BdEM/mbodbVQq1l88ZXzmLRihfDUU2sQgZNPLkyxpEam4K9OXFXlpODs2pWavgWtIZh/BA0+k9b0lPCUY3sl3oZBcyJsVlX9QfJFahnp0DAoHfCiKi757l5KV2Zz8EGwpxoOOhiqa4RRo/YzY0YFp502hJqa3Iz7oRtGS6itdWZBb7zhlPaornYeptrrk38ixGoYFJeCyARMQTTG61CVN2c2b9/yJwqy1rC/LhtQROrIzVW++SaHceNg3rz2URbAMCLhmZdKS2HUKFi6FD79tPWzoFR2gUsmsRREXCYmEekCXImT+9DF255OMwijMfVT32lTGZwF45/9htKyA6jTLFSzqa52HgyWLVP69xfGj3fMCFlWgtFoB/gHb795qazMUQ6tNQt5SmfFCtr1byfej/Rn4HDgLGAJTkTSV2EJZSSRvDzkpuksuvABtuuRFB29jexsOOigWkSckplOyWVlw4b0qKtvGInirw8VLDaYl5f8fIT2kAQXD/EqiGNV9T+BalX9E3AuMCY8sYxkk3XlVA6f9TMW/eNf7Pg/D1C5bicvvLCaYcO+QKSWLl1qKShoXCiwsrKVLVINow0IKoSPP27ca+LttyNnOse6XnNhsO0iCS4O4lUQ+9zXz0VkCNANaKd/knaKWwE268X59P7VtXR9dh7nnTea0tLOzJ27hm++yW5UWfaww5z2qK1qkWoYbUDwad5ryuX1migoiL/KarylzjtCiCvEryAeFpHuOF3k5uPkJMwKTSojPAK9JA7+uobLLz+JsWPryMqqQ0SprYUvv3QOV23o+GUzifZPSxoFpZrg03zv3s7gXVHhRCvFawYKNjxq7pyw8z9qampSXiYn3n4Qf1TVz1R1iap+S1V7qepDYQtnhECEXhIicOedK7nhhnsZNuxLsrPhkEMaTvGewmwm0b5pTZ/oVOJ/mve6NYrA4MHxm4GCDY/Sod91OhTcjEtBiEhvEXlURF521weJyJXhimaESmAmMeKoflxwwTiWL+/Ejh1OpMfOnbB2bWJPYUbmksmO16wsx4R02mkNCk41PjOQf+bgNTxavTr1/a7TopVwtCJN/gV4Gaexzxp3PQdYF8+5bbW052J9oTJrlvOQNGtWxN2xmscY7YtM/1/HUzwviNezIViIsLKy6bUyofBeSyAJ/SDyVPUZoM5VKvtxSmMYmU5gJlGzdWsju2cynXGRiqUlu4Ca0TJi9TXIFFoSWRSpVerixY5vwbvWuHHO/kw0v7WWeBVEtYj0wGk6gIiMBb4ITSqj7Qj4JD65++4mds9ozWMScWhGKpY2YULkbf5+3KY4wqe99FRuycOMX6kUFjp+C5GGa23d6rzv39+J8MtE81uriDa18C/ASUAp8Ln7+g4wLJ5z22oxE1MrqapSnTVLqz/8UJcvb9rbOtiLePv2yL2JoxHsLQCqWVlNt0Vqr5ruLRsznZaYZtoTsUxH0Vqs+ntqZzokwcS0AaexzyrgY+ARV0kY7QV3JpHbvz+FhYVs2rSp0UzC78BctgyOOqrhicprORlr6u3vLeBFSNXVOREjzbVX9RKe0inUNhPDQSNRV+d8ho6Q9BXE+x+KRA9X9c8wiorgww9T77xuS+JVEI8DJwJ3Ar/D6dvw57CEMlLIrl0we3Z9y1IvgiJS8xjVxi0nvUE8klnIm7Lv2OFktua4VcD27HHi1ZtrrxpvqG2kgTvo52iJ2cp/jcrK9mGP9kxL/fo5f4utWzPT99ASWpIQt2SJ813M1EivFhFtauFfgA3xbEvlYiamJOGLaqqurm5iZvK3mPRMS/4oEL9ZyNvvtUb1puSxomUitVddt65hmp+d7axHmt77zWCeSSoYpdISs1Wka4ikv0kmkunE//f1/13T+XOEQUvNapke6RUJYpiY4lUQfwHG+tbHAI/Hc25bLaYgkoTri1DXBxGpn3Vw4KmtbTzYRPInBAfkREIGvR9lJOUTTYbsbEeZRfJ9+GWLpGyCskW7RjoPEn6lNnas6t69sZV5un6OsGjNQN/ewl2ToSDexglx3eIude62dcDaeK4R9mIKIslUVek3d9yhK198sYnDOhLBQdz/lO0fUFv6lBpJCQUd5tnZqocc4uwTcfbt2BF5UIymbCLNQqIpqMrKxjOddBo0PvrIkdf7W3XtGvl/EmtG1t5pbwN9S0mGgjgq1hLPNcJeTEEkGZ+pKdpMIojffOGZopL5lOofqP0DnX89K6vxwBjJ1BU0r/iPW7MmsjkraPryK5N0jLaqq3NmDtFmTh115mA0JZaCsI5yRmR27YLf/x6Amh/8gPJt2zjhhBPYtGkTBQUF5ObmNnuJ+q52SexjXN9S9RK393Zdg7PZS2oSabwvJ8dxMvqbxKg6zsngNTyH+J49jVtTRmoI8/HHjpNz//6GbZHu1VYEO5xFarM5bhw884yT65CJvaU7IjU1NZSXl8f9u0uUWB3l2mEPJCMp5OU5Mai//CW58+ZFDH1tDi/BLjs7eVUvs7Kc9qheZElRUUMI4rZtTqRJcF+k0E1/dIoXOeUNqnv2QElJQw2qaCG2/siubt3aPkw0WpMcL9kwKwuWL3cix7wosSVLnL9fMv8nRriktGhftKlFpi1mYgoBz2G9cWPMJLpUEsuOHK+NORid5ZmJ4jEfRTI9JSJ3S30XQV/Jzp1NTWbpYu4yWkcwmjDZ0FofRCYspiBCJFDQL16fRKbRXHRWIk725hRX0HE+dqyjLOLFH1mVna26dm1T/0xHC101WkYsBWEmJqN5AgX9gkl07YVgA5isrMR6CoBj6mkukc5fIM7LGF+5Ek4+uXEdqljuQb956+CD4aSTGpLdYpnWDCMhommOTFtsBtEGBGYSYU9904VETFXBp3gvEspvSvJHY3XtqlEjjJqrcRVthmPhm0YiYDMIIykEZhLrFi2ipKSE119/PeWtEcMk3taS/pmBasPTfX5+42q1H33klNX2GjONG9e0DlWsGlf+GkKRZjhht8LsaKRD689UYQrCiJ9AafCCigomTpyIiHQIRdEcwcJu5eVOJFSkQd8rq52T40QaRapDFaxxpdq0hpBq8vp1GJHxooja8vudLkopJ6V3NzKTqVMB6Dx1KoV5edTU1NCpUyf27dtHSUkJAIWFhamUMCV4obNeLgI4CqO0tCG3oq6ucaG33r0bh+56eSNVVQ25Hl6hwnHjnNSUYLG43r1Tk3fRUSjo148+n3zCji+/pKSiAgj/++0pJf+9ws6HiEg021MyFmASsAnYDMyIcsz3cMqJrwee9G2/AnjXXa5o7l7mg0gBvrpNqh3HJ5EIkbLL481gjlRexLKg4yP4XWzVd9P1vX1zxx1t9v2OJG9Y0YPE8EGENoMQkWzgAeAMYDuwSkTmq+oG3zHHAbcAhar6mYj0crcfBtwGjMTpYrfaPfezsOQ1WsCcOXDTTc776dPJzc2lsLCwfnrcpk86aYrnD4DGs4R4Mpj9UVRexreXyFdR0dD9zGiK9wS+d+9eOnfuzL59+1iyZAnQgqf/wIy5LfB+S368qMETTjihzX5fYZqYRgObVfV9ABF5GvgOzmzB44fAA97Ar6qfuNvPAl5V1U/dc1/FmY08FaK8RqK4PxzPac1558H8+awbNIiSsrL6H6cpigb8CiMePLOVV15kxQpHYZhyiI03mHpmz+LiYoqLi9m3bx81NTWJfR8935vbK4WpU51tbYynNEpLSxspvzB/X2EqiD7ANt/6dpwy4X6OBxCRUiAbmKmq/4xybp/wRDVahPfDmT3bmUksXgwvvUTBHXewf+LE+h+nKYrWEfRRWP2k5vHPZjt16kRBQUH9rKJTp04xZxFRbf2BGXOqCCo/CM8nkmondQ5wHHAK0BdYKiJD4z1ZRK4CrgLo379/GPIZ8eCfSZxyijmvQyLR2UeHZtcumDOH3KlT679z3sDaXIJnJAcx0PA9915TRCTlFxZhKogdQD/fel93m5/twOuqug/4QETewVEYO3CUhv/cxcEbqOrDwMPgVHNNluBGgngzCWj0ZBX8Irel7dRo3zQb0RPhaT+SXT/SNU844QQggiLxf88TlScEmvs8ySDMPIhVwHEicrSIdAYuBeYHjnkBVxGISB6Oyel9YAFwpoh0F5HuwJnuNiMT8Gy1mzbB7Nnk1tS0qBqsYUTLB2g2NyGQ1MmuXc3ey7vmpk2b6gfeeHMRUlpxNURCm0Go6n4RuRZnYM8GHlPV9SJyO05Y1XwaFMEGoBaYrqq7AUTkVzhKBuB2z2FtZADe05vrk6C6Gg46iIJLLoGJE20mYcRNNHNPs3b4oH/M/Q4ydSo1ubmNnvajzRyimpoiEK/5KtMI1Qehqi8BLwW2/cL3XoEb3SV47mPAY2HKZ4REwCdBdTXcdBO5QOH06fVRGGA+iY6If0DetGlTzEZU0QbeuO3w3nfR/Q4ClI8fH1cIbCKDfluYe1KBdZQzwsd1GHrhgTU1Nbz++uuICKNHj7ZZRAfDe0A49thj2bx5c/3rxIkTow6yQaWS8OzT9x30ZhCeYiguLu7QUXaxOsqlOorJ6AgE4shzp06lc+fOlJSUoKod+sfZEfEnfA0YMKDRq2d6BBqZgTxzz5YtW9i8eTMQefYZ1VnsczDnuufWbN3KgL/+lV7HHEOuRUFGxBSE0Xb4IksKrrkGwHIlOiB+c0yem3CWl5fXyPQINDJDBpVKNLNPIn6D3HnzGPDggzBgQErzGtIZUxBG2+HzTeQ+8ACF7nTfciUMiGzz995HUirxXqOegKmztXkNkUylrQ13TUlBvhhYuW+j7QiUC2fOnPof/ujRo5noi3BKdZljIzL+sNNkl6TOzc2tz3gG50Eh0UHS+z7FzI2YM8dZ976PQYXjhWk3ExpbXl7O0qVLWbJkSb3MrQ13TbdwWZtBGG3P1KlOVEl1tfMjzMtLSZ0ZI3H8voA+ffokVAAvnqfjRExECRNtxuDNLNxaYlRXwy9/6eyLYXoqKChg7969iEiTmUtLw13TLVzWFITR9uTlOTHpN93kvPp+hG1ZZ8aIH38UkecoPvLIIxvN+ppT5vEM/qEOkNEyoYN5O7fd1jjJLlCcz6/oTj311EaXam24a7qFy5qCMFJDsBKs+yO08hzpiX9wv+CCCxrNBKLltQRnDPEM/ikZIIN5O55C8JLsoJFiCXWWk2ZYHoSRWrwf4W231We6+p/WvMEnVoy8ET6xzEPR9qXkfxd0RIdwrUQcyW3hdG7tPSwPwkhfgpmuvpII5OWlpEmK0YB/8Ik2yEdrFNWW9nRPzlGLF9P51ludjdH8B/EqkSgmqXhmOZ48XjJemD61MGc0piCM1OJPojvooEYlEfxd6qw8R9sSHOCg+b97cKBqyw6D3r1zRo5kzKxZsUNX26CvgydPcXExE329USD5398wFbEpCCM9CCqKgG/CZhJtS3CAi2fwiTZQtYXN3rvn0IICOPfc2Ae3QV8H/9/Cy48Iq3dDmH4b80EY6Ynnm5g1q9FTnjeT6Oj1c8KmNXbt4LnplvxlNMZ8EEbmEeUpz8Jg24bWPJVGMzUZmYcpCCM9idIovi3bLXYkWl0t1Ue6JXulI5kyqzIFYaQ3nkMxEN0UdIAmY2DriASd0c1VS40HmzE0T6bkUpiCMNKbCA1fIiUteQOblehIjKAzurlqqWlDsDxGMvIe2oBm+16nGaYgjPQmgeimAQMG1PsmtmzZwgUXXGBO0mYIRttA7GqpaUOwPAZkRMnuTJk5eJiCMDKDYI9haJQn4RzidKvbsWMHmzdvpry8nMLCwqT+KDNN2TTnW8hYc1Ck8hgZQKb5ZyzM1cgs4jAtROt53FwP5FiDadBWnylhtsH2nlayJL1IhwcOC3M12g9RZhJ+omVfe+vRHLHR/BknnHACCxYsYPPmzW2SGZssampq2Lt3LxMmTGDIkCGNfAvpMDAZDd+5dPWdmYIwMpMo1WD9RKvRH6kHsr92UCR/xubNmzn22GMZM2ZMxMzYdBhwgzJ4DW0mTpxIXl5evQmutLQ0oRIajUhGMbxkFtTLcNI9r8cUhJGZJDCTiLQe7IEcTOjyBlOv5Lj39O0N/sEw2xYPuEkk6GuJZO9uSQmNRiSjjlHwGrEURqYokxbKmfZ5ParaLpYRI0ao0QGpqlKdNUt148bGr1VVzZ5aXV2ty5cv1+rq6hbffvny5Tpz5kxduHBho2sl49qJ4t2zqqoq6r1bLZf3966qavy+Jdfw/le33aYKzvsgs2ZF3xdNrlQQr5xpCFCmUcbVlA/syVpMQXRwvB/oOec0vMY5WLRm0Aye660vWrSokeKINWgnG09pLV++PHkXjTQAt3ZQ9M6/7bbog3u8A39bDtCRZEq1gmoFsRSEmZiM9oHfJwFObPycOc2bMGhdbHrQjBWtzHOYiXwt6dyWMJFMS62tiuo/Py+vSVkVIGrJlZjXCpso2f2ZkIeRKKYgjPaB/wf6pz81hMLOnt20CX1AYdQPqP36OcdHCqGNM3M3WpnnoOMbkpeTEfR/hJLb4B+A/X+/1gyKwUE1ln8j2qAc7Vph0kx2f7si2tQiGQswCdgEbAZmRNg/BagCKtxlmm9frW/7/ObuZSYmowlBE0ZzNu+gmcrbX1XV2HTVnFkkBvH4CeIlmv8jdILmnGSZV2Jdx9sXy1/R1mSwWckPqfBBANnAe8C3gM7AGmBQ4JgpwO+jnL8nkfuZgjCaEPwBN2fzjubw9galc86JrmQSHCyS4SdIiiO8Obnjsbcn0/7fEnmMVpEqBTEOWOBbvwW4JXCMKQij7QgOLs0NNrEUSnOROM1cu82inIJyBuVpTmnGM/gnc9DO4GigTCVVCuIi4I++9cuDysBVEJXAWuBZoJ9v336gDFgJnN/c/UxBGAnT3GAUz8CXqBkrCURVLrEijYJms+A5nrzBz5FA2HBSsBlCm5POCqIHcID7/mpgoW9fH/f1W8AW4JgI97jKVSJl/fv3D+0PaLRTkjEYNWfGamagbclMIqp5KpLCa24GETzOb05LtoJL5O9tiqLNSFsTU+D4bOCLKPvmAhfFup/NIIwWk8zBKJrCiOLcTsQX0ayDO1ETWiz5w5g5JGI+MlNTm5EqBZEDvA8c7XNSDw4cc4Tv/QXASvd9d9/MIg94N+jgDi6mIIwWE+Zg1IyvIpEZRFRlEk0xRDJzhakAgrRGYWXKDCJT5IxBShSEc1/OAd5xo5l+7m67HTjPff9/gfWu8lgEnOhuHw+sc7evA65s7l6mIIwW05Y/8gj3iqYkomVpN1EmQQUXy/HcnE8imZ+tI8wC2sFnjKUgQk2UU9WXgJcC237he38LjukpeN4KYGiYshlGPW2ZZBUhM7h806aIJZ8jlYKOmAAXzCIOZihHOjaMRjvBRLe2zG5OFe38M1rDIMNIBV4V2lmzqLnmmkYZ0V5Tn2CmdJNmP+lW6TTd5DHiwhoGGUa64XvyDJZ89vepiFkKOhmlt5NJO61H1JExBWEYqSDCYBqtE17U2kqZYt6wmUXGkpVqAQyjQ+P5Inbtqt9UUFAQu5mPdw44SibdB11vpjNnTqolMRLEFIRhpJIIg6c3Y4haDjzdB9yg0ps6FWbNSv+ZTjKIoPAzGTMxGUYqScRM5C85Hu85qSDoG+lIvol08wu1ElMQhpFKEhk8M2XwyRTfSBgk87Onge/GTEyGkWqimSUy1VTjKb10942EQTI/exqYEm0GYRipJlq3tEjb03nmYCSXNJiJmYIwjFQTrYVlR2ptaTQl3n7cIWIKwjBSjX8g8GYKsbYbHYsU+p7MB2EY6YLfYe33PXRkm36mk4yw1xT6nkxBGEa6kQbOSSNJJON/mcIHBDMxGUa6kQbOSSNJZPj/0qq5GoZhdGBiVXM1E5NhGEayaSclN0xBGIZhJJt24kcyH4RhGEayyXDfg4cpCMMwjGTTTgoUmonJMAzDiIgpCMMwDCMipiAMwzCMiJiCMAzDCJsMDXs1BWEYhhE2GRr2alFMhmEYYZOhYa82gzAMwwibZBTcS4GZyhSEYRhGJpACM5WZmAzDMDKBFJipQp1BiMgkEdkkIptFZEaE/VNEpEpEKtxlmm/fFSLyrrtcEaachmEYaU8K+kKENoMQkWzgAeAMYDuwSkTmq+qGwKHzVPXawLmHAbcBIwEFVrvnfhaWvIZhGEZjwpxBjAY2q+r7qroXeBr4TpznngW8qqqfukrhVWBSSHIahmEYEQjTB9EH2OZb3w6MiXDchSJSDLwD3KCq26Kc2yd4oohcBVzlru4RkU3JEDwJ5AGZlRHTGJM/dWSy7GDyp5qWyH9UtB2pdlL/P+ApVf1GRK4G/gScFu/Jqvow8HBYwrUUESmL1qEpEzD5U0cmyw4mf6pJtvxhmph2AP18633dbfWo6m5V/cZd/SMwIt5zDcMwjHAJU0GsAo4TkaNFpDNwKTDff4CIHOFbPQ94232/ADhTRLqLSHfgTHebYRiG0UaEZmJS1f0ici3OwJ4NPKaq60XkdqBMVecD14nIecB+4FNginvupyLyKxwlA3C7qn4alqwhkHZmrwQx+VNHJssOJn+qSar8oqrJvJ5hGIbRTrBSG4ZhGEZETEEYhmEYETEFYRiGYUQk1XkQGYWInIiTDe4l7e0A5qvq29HPSk9E5GScbPe3VPWVVMtjGEb6YTOIOBGRm3HKhQjwhrsI8FSkQoTphoi84Xv/Q+D3QFfgtkyQ30gtItJNRO4SkY0i8qmI7BaRt91th6ZavuYQkUm+991E5FERWSsiT4pI71TKFg8ikiMiV4vIP12514rIyyLyIxHpFNp9LYopPkTkHWCwqu4LbO8MrFfV41IjWXyISLmqFrjvVwHnqGqViBwErFTVoamVsHlEpBtwC3A+0AunkOMnwN+Bu1T185QJFwciMklV/+m+7wbcC4wC3sIpM/NxKuWLhYgsABYCf1LVj9xthwNXAKer6pmplK85RORNVT3Jff9H4CPgEeC7wARVPT+F4jWLiDwFfI5TbWK7u7kvzt//MFW9JIz72gwifuqAIyNsP8Ldl+5kuYmHPXAeDKoAVLUaJw8lE3gG+Aw4RVUPU9UewKnutmdSKll83Ol7/xugEvg3nHyfP6REovgZoKp3e8oBQFU/UtW7iVHLJ00Zqaq3quqHqnofMCDVAsXBCFX9saquVNXt7rJSVX8MFIR1U/NBxM9Pgf8RkXdpKCTYHzgWuDbaSWlEN2A1jllMReQIVa0UkYPdbZnAAHdAqscdsO4WkR+kSKaWMlJV893392VAz5MPReQmnBnExwCuaWYKjQtrpiu9RORGnO/6ISIi2mA+yYQH5U9F5GLgb6paByAiWcDFOA9IoWAKIk5U9Z8icjyOY9fvpF6lqrWpkyw+VHVAlF11wAVtKEprsEEqdVwCzACWuH9zBT7GKZ/zvVQKFieP4PjcwDHT5AFVrpmsIlVCJcClwN3AAyLyubvtUGCRuy8UzAdhZAxuXa4ZOJFkvdzN3iB1V7o3lBKR2wKbHnT9QIcDs1T1+6mQK17cKL6+OD6rPb7t9b6VdMaVvw/weobKPwZHMb8HnAiMAzao6kuh3dMUhNEeEJGpqtp23dyTTLrLLyLXAdfgFNTMB65X1b+7++odwOmKiPwExxScqfLfBpyNY/V5FceSsRinY+cCVf11KPc1BWG0B0Rkq6r2T7UcLSXd5ReRdcA4Vd0jIgOAZ4E/q+p/+SPk0pV2In8+cABOBFZfVf1SRA7EmRENC+O+5oMwMgYRWRttF5AJseyZLH+WZ5ZR1S0icgrwrIgcRWYEOWS6/PtdX2eNiLynql8CqOq/RCS0KEpTEEYm0RunX3nQ1yDAirYXJ2EyWf6PRSRfVSsA3CfxbwOPAWmfQ0Pmy79XRHJVtYaGxmpePo0pCMMAXgQO9n7kfkRkcZtLkziZLP/3CeTLqOp+4Psiku45HJD58hd73Te9MFeXTjjJcqFgPgjDMAwjIukee20YhmGkCFMQhmEYRkRMQbRDRGRP80c1e40eIrJIRPaIyO8D+0aIyDoR2Swi94uIuNvnisgHIlLhVv28zXfOYhHZ5O6rEJGLItxTRGShiBwS/Bwico6IvONGnbQZrtwjW3juT0Uk17fe6v9LhHtMCf5/4jhni4jkRdg+U0R+5r6/R0ROi3L+7SIysWUSJx9xKpqmdZJhpmJOaiMaXwP/CQxxFz//DfwQeB14CZgEvOzum66qz4pIF2CDiDyuqh+4+yaralmMe54DrPFC+DxE5HTgfuAsVf2wNR+qjfkp8BegJt4TRCTHdZ6mmt/hlKdYGNyhqr9Ixg1EJDsZZWpU9aFkyGM0xWYQHQQRyReRleLUkX/eLVuBiIxyt1WIyGwReQucKq+quhxHUfivcwRwiFtJUoHHccpvB+nivlYnIOZknNLd/vsV4wxU31bV90RkgCeju/9nIjLTfb9YRO4TkTJxehWMEpHnRORdEbnDPeYgEfmHiKwRkbdE5BJ3+y9EZJW77WFvVuS7T5Y7Q/Ku84KIrBaR9SJyVfCDiJN5fCSwSEQW+bb/2r33SnH7ELjXfUhEXgdmicgx4tT9Xy0iy8QpEYGIXOzKt0ZElvpud6R7/LsiMst3r8vcmd5bItKoyKHvmJ+7M7PlwAnedlcR9xCnDEjwnLneDNCdjfxSRN507+XJerCIzHG3rRWRC93te0TkNyKyBhgnIv8hIm+4378/iEi2e9x/u//H9SLyS9+97xKRDe4173G3+Wc+i0Xkbvea74hIkbs9V0Secc99XkRelxbODDsUqmpLO1uAPRG2rcWpew9wO/Bb9/1bOBmmAHfhdJjznzcF+L1vfSRQ4lsvAl50388FPsApfrYHuNN33GJgk7uvAugRQcYPga6+9X3Ap8Aw37YBfhmBnwEzffe4231/PbATpxz7ATg19HsAFwKP+M7v5r4e5tv2Z+DffNccCzwF/Nx3zGHu64Hu3zDS59kC5PnW1XfdWcCtvr/bi0C2u/4/wHHu+zHAQvf9OqCP+/5Q3//nfZxqvV3cv2E/HOW0FeiJYylYCJzvlwsnnn4dkAscAmwGfuaT9xHgwgifay5wke9aP3Hf/y/gj+77u3G/Y+56d9/f4Hvu+4HA/wM6uesPAt8P/H2z3f/BMPf/t4mG6EvvbzDTk9s99jfu+3Nwv6s435M/uO+H4IS8jkz1bzXdF5tBdADESaY5VFWXuJv+BBSL0wmsq6q+5m5/Mgm3m65OGevDgdNFZLxv32RVzXeX3RHOPUxVv/Kt78NJILsygfvPd1/X4TRyqlQnfvx9nIFzHXCG+5RZpKpfuMef6j5VrgNOAwb7rvkHHKXkr3dznfsUvNK9bjwNo/biKAJwSq8P8O37q6rWilN+fTzwVxGpcO99hHtMKTBXnI6A2b5z/0dVv1DVr4ENOP0ZRgGLVbVKHZPVE0BxQJ4i4HlVrVHHrDc/sP8TIvdACfJchM80EXjAO0AbCinWAn9z35+Oo6RWuZ/1dOBb7r7vicibQDnO/2IQ8AXOjPZREfku0U13keQ5GacjJKr6Fs4Dk9EMpiCMRNmBU9HTo6+7rRHqlDVYjPPDjJf94tS496jDKSU9WkT+j3cMjb+3XWjMN75zv/FtrwNyVPUd4CQcRXGHa1rqgvP0epE6nfUeCVx3BY4C6QIgTpmGiTgzr+E4g1hQjkjsU/cRFmeg9PsAPVNcFvC5T5Hmq+pAAFX9EXArjkJaLU7zJ/9njnTd1tAF+Fccx3n3j+feX2uD30FwSrd7n/MEVZ0pIkfjPPGfrk6NoX8AXVxFNxqnjtK3gWgVWBORx4iBKYgOgPuU/JlnjwUuB5ao06LzK3HKCEMcdeVVtRL4UkTGunb67xPwG4DjbMUxj7yXgKibaHiC9O5XA5wLTBaRK3HKe/cSJ8rqAJyBIm5E5EigRlX/AszGURbe4L7LfYIPRlg9iuOMf8b9XN2Az1S1xrW5j41yu69o6EEQF+6T/AfiNIfxIruGu++PUdXX1XESV+Eoimi8AUwQkTzXrn8ZsCRwzFLgfBE5UES64nS383M8jvmsJbyKU/0VV/buEY75H+AiEenlHnOYOFFqh+AozC9cP83Z7v6DcUyCLwE3AMMTkKcUt2+FiAwiM8prpBzTru2TXBHZ7lu/Fycd/yFxwi7fB6a6+64EHhGn4NcSnGk84DggcX6snUXkfOBMVd2AY2uei2N/f5mGCCaA2SJyK9AZZwB4jvj5B3AKji28HlX9VJym80txBsbbcQbAHcDGBK4PzsAw2/28+4Afq+rnIvIIzmD4EU4L0Eao6r2uqe7POHb/H4nI2zhKbWWUez0M/FNEdqrqqQnIOBn4b/fv2AnHNLLGlfs4nCfv/3G35Ue6gDrdAmfgNJQR4B/qlrf2HfOmiMxzr/OJ/3OLSCecbomxos5icQdOc5u3cJ7kf0ngu6CqG9zP+Io7c9wHXKOqK0WkHOd/uw1ncAdH2f7dnckJcGMC8jwI/ElENrjXXY/vu25ExkptdHBE5GDXHIQ7oByhqtenSJYjgMdV9YxU3N9oQEQuAE5S1f9MtSzJwJ1FdVLVr0XkGKAEOEFV96ZYtLTGZhDGuSJyC8534UOcp+OU4D71PiIih2ggF8Joc3KA36RaiCSSixNy3Aln9vG/TDk0j80gDMMwjIiYk9owDMOIiCkIwzAMIyKmIAzDMIyImIIwDMMwImIKwjAMw4iIKQjDMAwjIv8fK8XDhnXZJ7QAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(steps,y_values,c=\"grey\", s=1, label=\"full set\")\n",
+    "plt.scatter(steps,y_values_l, c=\"red\", s=1, label=\"local only (set1)\")\n",
+    "plt.scatter(steps,y_values_d, c=\"blue\", s=5, label=\"w/ distal only (set2)\")\n",
+    "plt.ylim((0.5,0.8))\n",
+    "#plt.xlim((3,30))\n",
+    "\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Log10BF (Kumsaka threshold) increasing\")\n",
+    "plt.ylabel(\"pearsonr\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = np.arange(0, 70, 1)\n",
+    "x_values=[]\n",
+    "y_values=[]\n",
+    "for i in range(len(steps)):\n",
+    "    if pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) ][\"Beta\"]), abs(data1[(data1[\"log10p\"]>steps[i])][\"beta\"]))[1] < 1e-3:\n",
+    "        x_values.append(steps[i].astype(str))\n",
+    "        y_values.append(pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) ][\"Beta\"]), abs(data1[(data1[\"log10p\"]>steps[i]) ][\"beta\"]))[0])\n",
+    "    else:\n",
+    "        x_values.append(steps[i].astype(str))\n",
+    "        y_values.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only local\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"only local\")\n",
+    "steps = np.arange(0, 70, 1)\n",
+    "x_values_l=[]\n",
+    "y_values_l=[]\n",
+    "# for i in range(len(steps[:-1])):\n",
+    "#     try:\n",
+    "#         if pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) & (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[1] < 1e-3:\n",
+    "#             x_values_l.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_l.append(pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i])&  ~(data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[0])\n",
+    "#         else:\n",
+    "#             x_values_l.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_l.append(0)\n",
+    "\n",
+    "#     except:\n",
+    "#         x_values_l.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#         y_values_l.append(0)\n",
+    "\n",
+    "for i in range(len(steps)):\n",
+    "    try:\n",
+    "        if pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) ][\"Beta\"]), abs(data1[(data1[\"log10p\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) ][\"beta\"]))[1] < 1e-3:\n",
+    "            x_values_l.append(steps[i].astype(str))\n",
+    "            y_values_l.append(pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids)) ][\"Beta\"]), abs(data1[(data1[\"log10p\"]>steps[i]) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))[0])\n",
+    "        else:\n",
+    "            x_values_l.append(steps[i].astype(str))\n",
+    "            y_values_l.append(0)\n",
+    "\n",
+    "    except:\n",
+    "        x_values_l.append(steps[i].astype(str))\n",
+    "        y_values_l.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "only distal\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"only distal\")\n",
+    "steps = np.arange(0, 70, 1)\n",
+    "x_values_d=[]\n",
+    "y_values_d=[]\n",
+    "# for i in range(len(steps[:-1])):\n",
+    "#     try:\n",
+    "#         if pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) & (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[1] < 1e-3:\n",
+    "#             x_values_d.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_d.append(pearsonr(abs(data1[(data1[\"Log10_BF\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>steps[i])&  (data1[\"RsID\"].isin(distal_rsids)) &  (data1[\"Log10_BF\"]<=steps[i+1])][\"beta\"]))[0])\n",
+    "#         else:\n",
+    "#             x_values_d.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#             y_values_d.append(0)\n",
+    "#     except:\n",
+    "#         x_values_d.append(steps[i].astype(str)+\":\"+steps[i+1].astype(str))\n",
+    "#         y_values_d.append(0)\n",
+    "\n",
+    "for i in range(len(steps)):\n",
+    "    try:\n",
+    "        if pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) ][\"Beta\"]), abs(data1[(data1[\"log10p\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids)) ][\"beta\"]))[1] < 1e-3:\n",
+    "            x_values_d.append(steps[i].astype(str))\n",
+    "            y_values_d.append(pearsonr(abs(data1[(data1[\"log10p\"]>steps[i]) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"log10p\"]>steps[i])&  (data1[\"RsID\"].isin(distal_rsids)) ][\"beta\"]))[0])\n",
+    "        else:\n",
+    "            x_values_d.append(steps[i].astype(str))\n",
+    "            y_values_d.append(0)\n",
+    "    except:\n",
+    "        x_values_d.append(steps[i].astype(str))\n",
+    "        y_values_d.append(0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(steps,y_values,c=\"grey\", s=1, label=\"full\")\n",
+    "plt.scatter(steps,y_values_l, c=\"red\", s=1, label=\"local only (set1)\")\n",
+    "plt.scatter(steps,y_values_d, c=\"blue\", s=5, label=\"w/ distal (set2)\")\n",
+    "plt.ylim((0.3,1.0))\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Log10 pvalue (Kaur) increasing\")\n",
+    "plt.ylabel(\"pearsonr\")\n",
+    "#plt.xlim((7,70))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format.kumsaka.susie.final.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format.kumsaka.susie.final.ipynb
new file mode 100644
index 00000000..74b1b446
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format.kumsaka.susie.final.ipynb
@@ -0,0 +1,1344 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n1 = pd.read_csv(\"local.eu.effects.tsv\",sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['molecular_trait_id', 'chromosome_x', 'position', 'ref', 'alt',\n",
+       "       'variant', 'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac',\n",
+       "       'an', 'r2', 'molecular_trait_object_id', 'gene_id', 'median_tpm',\n",
+       "       'rsid', 'position0', 'chromosome_y', 'gene_start', 'gene_end',\n",
+       "       'phenotype_id'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(843015, 24)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n2 = merged_dataframe_n1[['chromosome_x', 'position0', 'position', 'ref', 'alt', 'molecular_trait_id',\n",
+    "       'variant', 'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac',\n",
+    "       'an', 'r2', 'molecular_trait_object_id', 'gene_id', 'median_tpm',\n",
+    "       'rsid',  'gene_start', 'gene_end',\n",
+    "       'phenotype_id']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_dataframe_n2['chromosome_x'] = \"chr\"+merged_dataframe_n2['chromosome_x'].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(843015, 23)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0       1       2\n",
+      "0  chr1   10024   10432\n",
+      "1  chr1   29074   29455\n",
+      "2  chr1  181106  181306\n",
+      "3  chr1  183652  184006\n",
+      "4  chr1  186816  187016\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pybedtools\n",
+    "peaks_data_merged=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/merged.peaks.eu.top.50k.bed\", sep=\"\\t\", header=None)\n",
+    "print(peaks_data_merged.head())\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(merged_dataframe_n2)\n",
+    "peak_bed = pybedtools.BedTool.from_dataframe(peaks_data_merged) # this is a merged peaks file\n",
+    "vars_in_bed = beds_var.intersect(peak_bed, c=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn = vars_in_bed.to_dataframe(names=merged_dataframe_n2.columns.tolist() + [\"count\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn2 = vars_in_bedn[vars_in_bedn[\"count\"]>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8385"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(vars_in_bedn2[\"pvalue\"]<1e-7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100211"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(vars_in_bedn2[\"pvalue\"]>1e-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130270, 24)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn2 = vars_in_bedn2.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130270, 24)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chromosome_x</th>\n",
+       "      <th>position0</th>\n",
+       "      <th>position</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>ma_samples</th>\n",
+       "      <th>maf</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>...</th>\n",
+       "      <th>an</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>29367</td>\n",
+       "      <td>29368</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>chr1_29368_G_A</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.038462</td>\n",
+       "      <td>0.162300</td>\n",
+       "      <td>...</td>\n",
+       "      <td>182</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs796745295</td>\n",
+       "      <td>28924</td>\n",
+       "      <td>29435</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>181112</td>\n",
+       "      <td>181113</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>chr1_181113_A_G</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.412088</td>\n",
+       "      <td>0.008885</td>\n",
+       "      <td>...</td>\n",
+       "      <td>182</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs1383295402</td>\n",
+       "      <td>180749</td>\n",
+       "      <td>181528</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>183667</td>\n",
+       "      <td>183668</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>chr1_183668_G_C</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.677535</td>\n",
+       "      <td>...</td>\n",
+       "      <td>182</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs1375358516</td>\n",
+       "      <td>182645</td>\n",
+       "      <td>184537</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>183855</td>\n",
+       "      <td>183856</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>chr1_183856_A_G</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.038462</td>\n",
+       "      <td>0.990356</td>\n",
+       "      <td>...</td>\n",
+       "      <td>182</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs1260198881</td>\n",
+       "      <td>182645</td>\n",
+       "      <td>184537</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>186834</td>\n",
+       "      <td>186835</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>chr1_186835_A_G</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0.423077</td>\n",
+       "      <td>0.025945</td>\n",
+       "      <td>...</td>\n",
+       "      <td>182</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs1364805962</td>\n",
+       "      <td>186367</td>\n",
+       "      <td>187110</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chromosome_x  position0  position ref alt molecular_trait_id  \\\n",
+       "1          chr1      29367     29368   G   A         Interval_2   \n",
+       "2          chr1     181112    181113   A   G         Interval_3   \n",
+       "16         chr1     183667    183668   G   C         Interval_4   \n",
+       "17         chr1     183855    183856   A   G         Interval_4   \n",
+       "21         chr1     186834    186835   A   G         Interval_5   \n",
+       "\n",
+       "            variant  ma_samples       maf    pvalue  ...   an  r2  \\\n",
+       "1    chr1_29368_G_A           7  0.038462  0.162300  ...  182   .   \n",
+       "2   chr1_181113_A_G          75  0.412088  0.008885  ...  182   .   \n",
+       "16  chr1_183668_G_C           9  0.049451  0.677535  ...  182   .   \n",
+       "17  chr1_183856_A_G           7  0.038462  0.990356  ...  182   .   \n",
+       "21  chr1_186835_A_G          77  0.423077  0.025945  ...  182   .   \n",
+       "\n",
+       "   molecular_trait_object_id     gene_id  median_tpm          rsid gene_start  \\\n",
+       "1                 Interval_2  Interval_2           .   rs796745295      28924   \n",
+       "2                 Interval_3  Interval_3           .  rs1383295402     180749   \n",
+       "16                Interval_4  Interval_4           .  rs1375358516     182645   \n",
+       "17                Interval_4  Interval_4           .  rs1260198881     182645   \n",
+       "21                Interval_5  Interval_5           .  rs1364805962     186367   \n",
+       "\n",
+       "   gene_end phenotype_id count  \n",
+       "1     29435   Interval_2     1  \n",
+       "2    181528   Interval_3     1  \n",
+       "16   184537   Interval_4     1  \n",
+       "17   184537   Interval_4     1  \n",
+       "21   187110   Interval_5     1  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#vars_in_bedn2[[\"chromosome_x\", \"position\", \"ref\", \"alt\", \"variant\"]].to_csv(\"kumsaka.2018.kaur.scores.peak.filtered.bed\", sep='\\t', header=False, index=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "130268"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(vars_in_bedn2[\"variant\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atac_df = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/kaur_caqtls_lcl_latest/ATAC/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "dnase_df = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/kaur_caqtls_lcl_latest/DNASE_NEW/summary.mean.variant_scores_new_3.tsv\", sep=\"\\t\", header=0)\n",
+    "atac_df_50m = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/kaur_caqtls_lcl_latest/ATAC_50M/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130270, 23)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atac_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130270, 23)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atac_df_50m.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130270, 24)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dnase_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'abs_logfc.mean', 'abs_logfc.mean.pval', 'jsd.mean', 'jsd.mean.pval',\n",
+       "       'logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean.pval',\n",
+       "       'max_percentile.mean', 'max_percentile.mean.pval',\n",
+       "       'logfc_x_max_percentile.mean', 'abs_logfc_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_max_percentile.mean.pval', 'jsd_x_max_percentile.mean',\n",
+       "       'jsd_x_max_percentile.mean.pval',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'logfc_x_jsd_x_max_percentile.mean'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atac_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df = atac_df[['chr', 'pos', 'allele1', 'allele2', 'rsid']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  import sys\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  if __name__ == '__main__':\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_df[\"ATAC.572M.logfc.mean\"] = atac_df['logfc.mean']\n",
+    "merged_df[\"ATAC.572M.jsd.mean\"] = atac_df['jsd.mean']\n",
+    "merged_df[\"ATAC.572M.CRS\"] = atac_df['abs_logfc_x_jsd_x_max_percentile.mean']\n",
+    "merged_df[\"ATAC.50M.logfc.mean\"] = atac_df_50m['logfc.mean']\n",
+    "merged_df[\"ATAC.50M.jsd.mean\"] = atac_df_50m['jsd.mean']\n",
+    "merged_df[\"ATAC.50M.CRS\"] = atac_df_50m['abs_logfc_x_jsd_x_max_percentile.mean']\n",
+    "merged_df[\"DNASE.logfc.mean\"] = dnase_df['logfc.mean']\n",
+    "merged_df[\"DNASE.jsd.mean\"] = dnase_df['jsd.mean']\n",
+    "merged_df[\"DNASE.CRS\"] = dnase_df['abs_logfc_x_jsd_x_max_percentile.mean']\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle as pkl\n",
+    "import os\n",
+    "import numpy as np\n",
+    "\n",
+    "mean_change=[]\n",
+    "rsids=[]\n",
+    "\n",
+    "enf_dir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/kaur_caqtls_lcl_latest/enformer_preds_small_window/splitss/\"\n",
+    "for diri in [\"splitaa\", \"splitab\", \"splitac\", \"splitad\"]:\n",
+    "    enformer_recompute = pkl.load(open(enf_dir+diri+\"predictions_at_snp.pkl\",'rb'))\n",
+    "    for i in range(enformer_recompute[\"ref_logcount_preds\"].shape[0]):\n",
+    "        mean_change.append(np.log2( (enformer_recompute[\"ref_logcount_preds\"][i,440:456].sum()+1) / (enformer_recompute[\"alt_logcount_preds\"][i,440:456].sum()+1)))\n",
+    "        rsids.append(enformer_recompute[\"rsids\"][i])\n",
+    "mean_change = np.array(mean_change)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_df[\"enformer_recomputed\"] = mean_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "130270"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(mean_change)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    }
+   ],
+   "source": [
+    "newdf = vars_in_bedn2.reset_index()\n",
+    "merged_df[\"pvalue\"] =newdf[\"pvalue\"]\n",
+    "merged_df[\"beta\"] =newdf[\"beta\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "enf_preds = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/kaur_eu_caqtls/enformer_predictions.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>740285</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs193160839</td>\n",
+       "      <td>-0.002522</td>\n",
+       "      <td>-0.003365</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos ref alt         rsid  DNASE_SAD  DNASE_SAR\n",
+       "0  chr1  740285   G   A  rs193160839  -0.002522  -0.003365"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf_preds.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(126606, 7)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf_preds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'ref', 'alt', 'rsid', 'DNASE_SAD', 'DNASE_SAR'], dtype='object')"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf_preds.columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'ATAC.572M.logfc.mean',\n",
+       "       'ATAC.572M.jsd.mean', 'ATAC.572M.CRS', 'ATAC.50M.logfc.mean',\n",
+       "       'ATAC.50M.jsd.mean', 'ATAC.50M.CRS', 'DNASE.logfc.mean',\n",
+       "       'DNASE.jsd.mean', 'DNASE.CRS', 'enformer_recomputed', 'pvalue', 'beta'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_df[\"dbsnp_rsdi\"] = vars_in_bedn2.reset_index()[\"rsid\"]\n",
+    "#merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>dbsnp_rsdi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>804905</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs193160839</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     chr     pos allele2 allele1   dbsnp_rsdi\n",
+       "10  chr1  804905       A       G  rs193160839"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 = merged_df[['chr', 'pos', 'allele2', 'allele1', 'dbsnp_rsdi']].sort_values('dbsnp_rsdi')\n",
+    "temp1[temp1[\"dbsnp_rsdi\"]==\"rs193160839\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>740285</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs193160839</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos ref alt         rsid\n",
+       "0  chr1  740285   G   A  rs193160839"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 = enf_preds[['chr', 'pos', 'ref', 'alt', 'rsid']]\n",
+    "temp1[temp1[\"rsid\"]==\"rs193160839\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df1 = merged_df.merge(enf_preds, right_on=['chr', 'ref', 'alt', 'rsid'], left_on=['chr', 'allele2', 'allele1', 'dbsnp_rsdi'], how=\"inner\")\n",
+    "merged_df1[\"DNASE_SAD\"] = merged_df1[\"DNASE_SAD\"]*-1\n",
+    "merged_df1[\"DNASE_SAR\"] = merged_df1[\"DNASE_SAR\"]*-1\n",
+    "merged_df2 = merged_df.merge(enf_preds, right_on=['chr', 'ref', 'alt', 'rsid'], left_on=['chr', 'allele1', 'allele2', 'dbsnp_rsdi'], how=\"inner\")\n",
+    "\n",
+    "merged_df_f = pd.concat([merged_df1, merged_df2]).drop_duplicates(['chr', 'pos_x', 'allele1', 'allele2', 'rsid_x'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'ATAC.572M.logfc.mean',\n",
+       "       'ATAC.572M.jsd.mean', 'ATAC.572M.CRS', 'ATAC.50M.logfc.mean',\n",
+       "       'ATAC.50M.jsd.mean', 'ATAC.50M.CRS', 'DNASE.logfc.mean',\n",
+       "       'DNASE.jsd.mean', 'DNASE.CRS', 'enformer_recomputed', 'pvalue', 'beta',\n",
+       "       'dbsnp_rsdi', 'pos_y', 'ref', 'alt', 'rsid_y', 'DNASE_SAD',\n",
+       "       'DNASE_SAR'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df_f.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(125682, 24)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df_f.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df_f.to_csv(\"kaur.caqtls.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130270, 24)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chromosome_x', 'position0', 'position', 'ref', 'alt',\n",
+       "       'molecular_trait_id', 'variant', 'ma_samples', 'maf', 'pvalue', 'beta',\n",
+       "       'se', 'type', 'ac', 'an', 'r2', 'molecular_trait_object_id', 'gene_id',\n",
+       "       'median_tpm', 'rsid', 'gene_start', 'gene_end', 'phenotype_id',\n",
+       "       'count'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chromosome_x</th>\n",
+       "      <th>position0</th>\n",
+       "      <th>position</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>ma_samples</th>\n",
+       "      <th>maf</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>...</th>\n",
+       "      <th>an</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>29367</td>\n",
+       "      <td>29368</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>chr1_29368_G_A</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.038462</td>\n",
+       "      <td>0.1623</td>\n",
+       "      <td>...</td>\n",
+       "      <td>182</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs796745295</td>\n",
+       "      <td>28924</td>\n",
+       "      <td>29435</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chromosome_x  position0  position ref alt molecular_trait_id  \\\n",
+       "1         chr1      29367     29368   G   A         Interval_2   \n",
+       "\n",
+       "          variant  ma_samples       maf  pvalue  ...   an  r2  \\\n",
+       "1  chr1_29368_G_A           7  0.038462  0.1623  ...  182   .   \n",
+       "\n",
+       "  molecular_trait_object_id     gene_id  median_tpm         rsid gene_start  \\\n",
+       "1                Interval_2  Interval_2           .  rs796745295      28924   \n",
+       "\n",
+       "  gene_end phenotype_id count  \n",
+       "1    29435   Interval_2     1  \n",
+       "\n",
+       "[1 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#vars_in_bedn2[['chromosome_x', 'position', 'ref', 'alt', 'variant', 'rsid']].to_csv(\"/mnt/lab_data2/anusri/enformer/kaur_eu_caqtls/rsids.txt\", sep='\\t', header=False, index=False)\n",
+    "                                                                   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls-Copy1.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls-Copy1.ipynb
new file mode 100644
index 00000000..59a8d5da
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls-Copy1.ipynb
@@ -0,0 +1,4354 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"all_caqtls.scores.tsv\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.abs_logfc', 'Esan.mean.jsd', 'Esan.mean.abs_logfc_x_jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'Esan.mean.max_percentile', 'Esan.mean.abs_logfc.pval',\n",
+       "       'Esan.mean.jsd.pval', 'Esan.mean.abs_logfc_x_jsd.pval',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile.pval', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.abs_logfc', 'Mende.mean.jsd', 'Mende.mean.abs_logfc.pval',\n",
+       "       'Mende.mean.jsd.pval', 'Yoruba.mean.logfc', 'Yoruba.mean.abs_logfc',\n",
+       "       'Yoruba.mean.jsd', 'Yoruba.mean.abs_logfc.pval', 'Yoruba.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Luhya.mean.logfc', 'Luhya.mean.abs_logfc', 'Luhya.mean.jsd',\n",
+       "       'Luhya.mean.abs_logfc.pval', 'Luhya.mean.jsd.pval',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.abs_logfc', 'Gambian.mean.jsd',\n",
+       "       'Gambian.mean.abs_logfc.pval', 'Gambian.mean.jsd.pval',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.abs_logfc', 'Maasai.mean.jsd',\n",
+       "       'Maasai.mean.abs_logfc.pval', 'Maasai.mean.jsd.pval', 'afr.mean.logfc',\n",
+       "       'afr.mean.abs_logfc', 'afr.mean.jsd', 'eur.mean.logfc',\n",
+       "       'eur.mean.abs_logfc', 'eur.mean.jsd', 'afr.mean.abs_logfc.pval',\n",
+       "       'afr.mean.jsd.pval', 'eur.mean.abs_logfc.pval', 'eur.mean.jsd.pval',\n",
+       "       'region', 'snp', 'distance', 'pval', 'beta', 'se', 'abs_beta'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "data_dnase = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/dnase_preds/DNASE_NEW/summary.mean.variant_scores_new_3.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/atac/ATAC/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac_50m = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/atac_50M/ATAC_50M/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vals = ['GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc', 'GM12878.ENCSR637XSC.mean.jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for val in vals:\n",
+    "    #print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        data[val] = data_atac[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        data[val] = data_atac[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for val in vals:\n",
+    "    #print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR637XSC.sub.50M\")] = data_atac_50m[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR637XSC.sub.50M\")] = data_atac_50m[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for val in vals:\n",
+    "    #print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR000EMT\")] = data_dnase[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR000EMT\")] = data_dnase[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 97)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.columns[0:70].tolist()+data.columns[77:].tolist()+data.columns[70:77].tolist()].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>0.202449</td>\n",
+       "      <td>0.006546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>0.202449</td>\n",
+       "      <td>0.006546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>0.137133</td>\n",
+       "      <td>0.250142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>0.174397</td>\n",
+       "      <td>0.164112</td>\n",
+       "      <td>0.174397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>0.098145</td>\n",
+       "      <td>0.130176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219377</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627388</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50627388_G_A</td>\n",
+       "      <td>22:50627308:50627508</td>\n",
+       "      <td>22_50627388_G_A</td>\n",
+       "      <td>79</td>\n",
+       "      <td>0.405697</td>\n",
+       "      <td>-0.136940</td>\n",
+       "      <td>0.163901</td>\n",
+       "      <td>0.136940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219378</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628124</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>609</td>\n",
+       "      <td>0.742636</td>\n",
+       "      <td>0.021773</td>\n",
+       "      <td>0.066098</td>\n",
+       "      <td>0.021773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219379</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628493</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50628493_G_A</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628493_G_A</td>\n",
+       "      <td>978</td>\n",
+       "      <td>0.729436</td>\n",
+       "      <td>0.028973</td>\n",
+       "      <td>0.083502</td>\n",
+       "      <td>0.028973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219380</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628562</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_50628562_C_T</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628562_C_T</td>\n",
+       "      <td>1047</td>\n",
+       "      <td>0.191678</td>\n",
+       "      <td>-0.076209</td>\n",
+       "      <td>0.057921</td>\n",
+       "      <td>0.076209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219381</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50783303</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22_50783303_T_C</td>\n",
+       "      <td>22:50782926:50783896</td>\n",
+       "      <td>22_50783303_T_C</td>\n",
+       "      <td>376</td>\n",
+       "      <td>0.210785</td>\n",
+       "      <td>-0.130570</td>\n",
+       "      <td>0.103578</td>\n",
+       "      <td>0.130570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>219382 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       pos allele1 allele2             rsid  \\\n",
+       "0        chr1    866281       C       T     1_866281_C_T   \n",
+       "1        chr1    866300       A       C     1_866300_A_C   \n",
+       "2        chr1    870176       T       A     1_870176_T_A   \n",
+       "3        chr1    904947       G       A     1_904947_G_A   \n",
+       "4        chr1    905373       C       T     1_905373_T_C   \n",
+       "...       ...       ...     ...     ...              ...   \n",
+       "219377  chr22  50627388       G       A  22_50627388_G_A   \n",
+       "219378  chr22  50628124       C       G  22_50628124_C_G   \n",
+       "219379  chr22  50628493       G       A  22_50628493_G_A   \n",
+       "219380  chr22  50628562       C       T  22_50628562_C_T   \n",
+       "219381  chr22  50783303       T       C  22_50783303_T_C   \n",
+       "\n",
+       "                      region              snp  distance      pval      beta  \\\n",
+       "0            1:866099:866351     1_866281_C_T       181  0.974280  0.006546   \n",
+       "1            1:866099:866351     1_866300_A_C       200  0.974280  0.006546   \n",
+       "2            1:869800:870282     1_870176_T_A       375  0.071532 -0.250142   \n",
+       "3            1:904938:905544     1_904947_G_A         8  0.290840  0.174397   \n",
+       "4            1:904938:905544     1_905373_T_C       434  0.188152 -0.130176   \n",
+       "...                      ...              ...       ...       ...       ...   \n",
+       "219377  22:50627308:50627508  22_50627388_G_A        79  0.405697 -0.136940   \n",
+       "219378  22:50627514:50628822  22_50628124_C_G       609  0.742636  0.021773   \n",
+       "219379  22:50627514:50628822  22_50628493_G_A       978  0.729436  0.028973   \n",
+       "219380  22:50627514:50628822  22_50628562_C_T      1047  0.191678 -0.076209   \n",
+       "219381  22:50782926:50783896  22_50783303_T_C       376  0.210785 -0.130570   \n",
+       "\n",
+       "              se  abs_beta  \n",
+       "0       0.202449  0.006546  \n",
+       "1       0.202449  0.006546  \n",
+       "2       0.137133  0.250142  \n",
+       "3       0.164112  0.174397  \n",
+       "4       0.098145  0.130176  \n",
+       "...          ...       ...  \n",
+       "219377  0.163901  0.136940  \n",
+       "219378  0.066098  0.021773  \n",
+       "219379  0.083502  0.028973  \n",
+       "219380  0.057921  0.076209  \n",
+       "219381  0.103578  0.130570  \n",
+       "\n",
+       "[219382 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.columns[0:5].tolist()+data.columns[70:77].tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'afr', 'Yoruba', 'Maasai', 'Luhya', 'Mende', 'Esan', 'GM12878', 'eur', 'Gambian'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "anscs = data.columns[5:70].tolist()+data.columns[77:].tolist()\n",
+    "anscs = [ans.split(\".\")[0] for ans in anscs]\n",
+    "anscs = set(anscs)\n",
+    "print(anscs)\n",
+    "\n",
+    "sub_list = []\n",
+    "for ans in anscs:\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\".ENCSR637XSC\", \".ENCSR000EMT\", \".ENCSR637XSC.sub.50M\", \".ENCSR095QNB\"]:\n",
+    "            sub_list.append(ans+title+\".mean.logfc\")\n",
+    "            data[ans+title+\".mean.sign.jsd\"] = np.sign(data[ans+title+\".mean.logfc\"]) * data[ans+title+\".mean.jsd\"]\n",
+    "            sub_list.append(ans+title+\".mean.sign.jsd\")\n",
+    "            sub_list.append(ans+title+\".mean.abs_logfc_x_jsd_x_max_percentile\")\n",
+    "            sub_list.append(ans+title+\".mean.abs_logfc_x_jsd_x_max_percentile.pval\")\n",
+    "    else:\n",
+    "        sub_list.append(ans+\".mean.logfc\")\n",
+    "        data[ans+\".mean.sign.jsd\"] = np.sign(data[ans+\".mean.logfc\"]) * data[ans+\".mean.jsd\"]\n",
+    "        sub_list.append(ans+\".mean.sign.jsd\")        \n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'region', 'snp', 'distance',\n",
+       "       'pval', 'beta', 'se', 'abs_beta', 'afr.mean.logfc', 'afr.mean.sign.jsd',\n",
+       "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd', 'Maasai.mean.logfc',\n",
+       "       'Maasai.mean.sign.jsd', 'Luhya.mean.logfc', 'Luhya.mean.sign.jsd',\n",
+       "       'Mende.mean.logfc', 'Mende.mean.sign.jsd', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.sign.jsd', 'GM12878.ENCSR637XSC.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd', 'Gambian.mean.logfc',\n",
+       "       'Gambian.mean.sign.jsd'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data[data.columns[0:5].tolist()+data.columns[70:77].tolist()+sub_list]\n",
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_data = pd.read_csv(\"af_caqtls/significant.caqtls.list.tsv\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.92178e-41\n",
+      "9.99633e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min(sig_data[\"pval\"]))\n",
+    "print(max(sig_data[\"pval\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906982</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>1:906596:907043</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>385</td>\n",
+       "      <td>4.802840e-06</td>\n",
+       "      <td>-0.520262</td>\n",
+       "      <td>0.106740</td>\n",
+       "      <td>0.520262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>921203</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>1:921132:921332</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6.892040e-06</td>\n",
+       "      <td>0.905971</td>\n",
+       "      <td>0.189412</td>\n",
+       "      <td>0.905971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1:975872:976361</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>342</td>\n",
+       "      <td>2.622420e-11</td>\n",
+       "      <td>-0.615266</td>\n",
+       "      <td>0.080637</td>\n",
+       "      <td>0.615266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>1:1038791:1039035</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.169820e-11</td>\n",
+       "      <td>0.700579</td>\n",
+       "      <td>0.089787</td>\n",
+       "      <td>0.700579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1:1038791:1039035</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1.169820e-11</td>\n",
+       "      <td>0.700579</td>\n",
+       "      <td>0.089787</td>\n",
+       "      <td>0.700579</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr      pos allele1 allele2           rsid             region  \\\n",
+       "0  chr1   906982       C       T   1_906982_C_T    1:906596:907043   \n",
+       "1  chr1   921203       G       A   1_921203_G_A    1:921132:921332   \n",
+       "2  chr1   976215       A       G   1_976215_A_G    1:975872:976361   \n",
+       "3  chr1  1038800       T       G  1_1038800_G_T  1:1038791:1039035   \n",
+       "4  chr1  1038819       T       C  1_1038819_C_T  1:1038791:1039035   \n",
+       "\n",
+       "             snp  distance          pval      beta        se  abs_beta  \n",
+       "0   1_906982_C_T       385  4.802840e-06 -0.520262  0.106740  0.520262  \n",
+       "1   1_921203_G_A        70  6.892040e-06  0.905971  0.189412  0.905971  \n",
+       "2   1_976215_A_G       342  2.622420e-11 -0.615266  0.080637  0.615266  \n",
+       "3  1_1038800_G_T         8  1.169820e-11  0.700579  0.089787  0.700579  \n",
+       "4  1_1038819_C_T        27  1.169820e-11  0.700579  0.089787  0.700579  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_data.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"label\"] = data[\"rsid\"].isin(sig_data[\"rsid\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 45)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.99633e-06\n",
+      "2.92178e-41\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(max(data[data[\"label\"]][\"pval\"]))\n",
+    "print(min(data[data[\"label\"]][\"pval\"]))\n",
+    "data[\"pval\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.sign.jsd</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866280</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.406902</td>\n",
+       "      <td>-0.031931</td>\n",
+       "      <td>-0.009045</td>\n",
+       "      <td>0.000162</td>\n",
+       "      <td>0.192846</td>\n",
+       "      <td>-0.037148</td>\n",
+       "      <td>-0.009848</td>\n",
+       "      <td>-0.010779</td>\n",
+       "      <td>-0.011243</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866299</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.686779</td>\n",
+       "      <td>-0.001747</td>\n",
+       "      <td>-0.008332</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.328184</td>\n",
+       "      <td>0.009631</td>\n",
+       "      <td>0.010377</td>\n",
+       "      <td>0.007855</td>\n",
+       "      <td>0.009833</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870175</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.273741</td>\n",
+       "      <td>0.036457</td>\n",
+       "      <td>0.019025</td>\n",
+       "      <td>0.001262</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>0.066168</td>\n",
+       "      <td>0.015354</td>\n",
+       "      <td>0.023853</td>\n",
+       "      <td>0.011588</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904946</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.355028</td>\n",
+       "      <td>-0.025704</td>\n",
+       "      <td>-0.011829</td>\n",
+       "      <td>0.000317</td>\n",
+       "      <td>0.131444</td>\n",
+       "      <td>-0.018358</td>\n",
+       "      <td>-0.011800</td>\n",
+       "      <td>-0.024516</td>\n",
+       "      <td>-0.008703</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905372</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.058782</td>\n",
+       "      <td>-0.127411</td>\n",
+       "      <td>-0.025277</td>\n",
+       "      <td>0.003640</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>0.121536</td>\n",
+       "      <td>0.024607</td>\n",
+       "      <td>-0.057097</td>\n",
+       "      <td>-0.012910</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos0     pos allele1 allele2          rsid           region  \\\n",
+       "0  chr1  866280  866281       C       T  1_866281_C_T  1:866099:866351   \n",
+       "1  chr1  866299  866300       A       C  1_866300_A_C  1:866099:866351   \n",
+       "2  chr1  870175  870176       T       A  1_870176_T_A  1:869800:870282   \n",
+       "3  chr1  904946  904947       G       A  1_904947_G_A  1:904938:905544   \n",
+       "4  chr1  905372  905373       C       T  1_905373_T_C  1:904938:905544   \n",
+       "\n",
+       "            snp  distance      pval  ...  \\\n",
+       "0  1_866281_C_T       181  0.974280  ...   \n",
+       "1  1_866300_A_C       200  0.974280  ...   \n",
+       "2  1_870176_T_A       375  0.071532  ...   \n",
+       "3  1_904947_G_A         8  0.290840  ...   \n",
+       "4  1_905373_T_C       434  0.188152  ...   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.406902                        \n",
+       "1                                           0.686779                        \n",
+       "2                                           0.273741                        \n",
+       "3                                           0.355028                        \n",
+       "4                                           0.058782                        \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.logfc  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0                       -0.031931                          -0.009045   \n",
+       "1                       -0.001747                          -0.008332   \n",
+       "2                        0.036457                           0.019025   \n",
+       "3                       -0.025704                          -0.011829   \n",
+       "4                       -0.127411                          -0.025277   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.000162           \n",
+       "1                                           0.000052           \n",
+       "2                                           0.001262           \n",
+       "3                                           0.000317           \n",
+       "4                                           0.003640           \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.192846                \n",
+       "1                                           0.328184                \n",
+       "2                                           0.048612                \n",
+       "3                                           0.131444                \n",
+       "4                                           0.018781                \n",
+       "\n",
+       "   eur.mean.logfc  eur.mean.sign.jsd  Gambian.mean.logfc  \\\n",
+       "0       -0.037148          -0.009848           -0.010779   \n",
+       "1        0.009631           0.010377            0.007855   \n",
+       "2        0.066168           0.015354            0.023853   \n",
+       "3       -0.018358          -0.011800           -0.024516   \n",
+       "4        0.121536           0.024607           -0.057097   \n",
+       "\n",
+       "   Gambian.mean.sign.jsd  label  \n",
+       "0              -0.011243  False  \n",
+       "1               0.009833  False  \n",
+       "2               0.011588  False  \n",
+       "3              -0.008703  False  \n",
+       "4              -0.012910  False  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"pos0\"] = data[\"pos\"] - 1\n",
+    "selected_beds = data[[\"chr\", \"pos0\", \"pos\"]+data.columns[2:-1].tolist()]\n",
+    "selected_beds.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybedtools\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "peak_bed = pybedtools.BedTool('/mnt/lab_data2/anusri/chromatin-atlas-anvil/data/afgr_data/peak_files/merged.peaks.af.top.50k.bed')\n",
+    "sig_vars_in_bed = beds_var.intersect(peak_bed, c=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sig_vars_in_bed.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed = sig_vars_in_bed.to_dataframe(names=selected_beds.columns.tolist() + [\"count\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed[\"in_peaks\"] = sig_vars_in_bed[\"count\"]>0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.sign.jsd</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "      <th>label</th>\n",
+       "      <th>count</th>\n",
+       "      <th>in_peaks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866280</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.97428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.009045</td>\n",
+       "      <td>0.000162</td>\n",
+       "      <td>0.192846</td>\n",
+       "      <td>-0.037148</td>\n",
+       "      <td>-0.009848</td>\n",
+       "      <td>-0.010779</td>\n",
+       "      <td>-0.011243</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos0     pos allele1 allele2          rsid           region  \\\n",
+       "0  chr1  866280  866281       C       T  1_866281_C_T  1:866099:866351   \n",
+       "\n",
+       "            snp  distance     pval  ...  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0  1_866281_C_T       181  0.97428  ...                          -0.009045   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.000162           \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.192846                \n",
+       "\n",
+       "   eur.mean.logfc  eur.mean.sign.jsd  Gambian.mean.logfc  \\\n",
+       "0       -0.037148          -0.009848           -0.010779   \n",
+       "\n",
+       "   Gambian.mean.sign.jsd  label  count  in_peaks  \n",
+       "0              -0.011243  False      0     False  \n",
+       "\n",
+       "[1 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90158"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sig_vars_in_bed[\"in_peaks\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6827"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sig_vars_in_bed[sig_vars_in_bed[\"in_peaks\"]][\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(90158,)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed[sig_vars_in_bed[\"in_peaks\"]][\"label\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 48)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sig_vars_in_bed[['chr', 'pos', 'allele1', 'allele2', \"label\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2_new_filter.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pybedtools\n",
+    "# beds_var = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "# peak_bed = pybedtools.BedTool('af_caqtls/merged.peaks.af.bed')\n",
+    "# sig_vars_in_bed = beds_var.intersect(peak_bed, wa=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sig_vars_in_bed = sig_vars_in_bed.to_dataframe(names=[\"chr\", \"pos0\", \"pos\"]+data.columns[2:-1].tolist()).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sum(sig_vars_in_bed[\"label\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sig_vars_in_bed[['chr', 'pos', 'allele1', 'allele2', \"label\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#enforer_recomputed_preds\n",
+    "import pickle as pkl\n",
+    "import os\n",
+    "\n",
+    "mean_change=[]\n",
+    "rsids=[]\n",
+    "\n",
+    "enf_dir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/afr_caqtls_window/\"\n",
+    "for diri in [\"splitaa\", \"splitab\", \"splitac\"]:\n",
+    "    enformer_recompute = pkl.load(open(enf_dir+diri+\"/predictions_at_snp.pkl\",'rb'))\n",
+    "    for i in range(enformer_recompute[\"ref_logcount_preds\"].shape[0]):\n",
+    "        mean_change.append(np.log2( (enformer_recompute[\"ref_logcount_preds\"][i,440:456].sum()+1) / (enformer_recompute[\"alt_logcount_preds\"][i,440:456].sum()+1)))\n",
+    "        rsids.append(enformer_recompute[\"rsids\"][i])\n",
+    "mean_change = np.array(mean_change)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(116499,)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_change.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'chr1_870175_T_A_False'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rsids[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 48)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.sign.jsd</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "      <th>label</th>\n",
+       "      <th>count</th>\n",
+       "      <th>in_peaks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866280</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.009045</td>\n",
+       "      <td>0.000162</td>\n",
+       "      <td>0.192846</td>\n",
+       "      <td>-0.037148</td>\n",
+       "      <td>-0.009848</td>\n",
+       "      <td>-0.010779</td>\n",
+       "      <td>-0.011243</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866299</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.008332</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.328184</td>\n",
+       "      <td>0.009631</td>\n",
+       "      <td>0.010377</td>\n",
+       "      <td>0.007855</td>\n",
+       "      <td>0.009833</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870175</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.019025</td>\n",
+       "      <td>0.001262</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>0.066168</td>\n",
+       "      <td>0.015354</td>\n",
+       "      <td>0.023853</td>\n",
+       "      <td>0.011588</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904946</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011829</td>\n",
+       "      <td>0.000317</td>\n",
+       "      <td>0.131444</td>\n",
+       "      <td>-0.018358</td>\n",
+       "      <td>-0.011800</td>\n",
+       "      <td>-0.024516</td>\n",
+       "      <td>-0.008703</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905372</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.025277</td>\n",
+       "      <td>0.003640</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>0.121536</td>\n",
+       "      <td>0.024607</td>\n",
+       "      <td>-0.057097</td>\n",
+       "      <td>-0.012910</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos0     pos allele1 allele2          rsid           region  \\\n",
+       "0  chr1  866280  866281       C       T  1_866281_C_T  1:866099:866351   \n",
+       "1  chr1  866299  866300       A       C  1_866300_A_C  1:866099:866351   \n",
+       "2  chr1  870175  870176       T       A  1_870176_T_A  1:869800:870282   \n",
+       "3  chr1  904946  904947       G       A  1_904947_G_A  1:904938:905544   \n",
+       "4  chr1  905372  905373       C       T  1_905373_T_C  1:904938:905544   \n",
+       "\n",
+       "            snp  distance      pval  ...  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0  1_866281_C_T       181  0.974280  ...                          -0.009045   \n",
+       "1  1_866300_A_C       200  0.974280  ...                          -0.008332   \n",
+       "2  1_870176_T_A       375  0.071532  ...                           0.019025   \n",
+       "3  1_904947_G_A         8  0.290840  ...                          -0.011829   \n",
+       "4  1_905373_T_C       434  0.188152  ...                          -0.025277   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.000162           \n",
+       "1                                           0.000052           \n",
+       "2                                           0.001262           \n",
+       "3                                           0.000317           \n",
+       "4                                           0.003640           \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.192846                \n",
+       "1                                           0.328184                \n",
+       "2                                           0.048612                \n",
+       "3                                           0.131444                \n",
+       "4                                           0.018781                \n",
+       "\n",
+       "   eur.mean.logfc  eur.mean.sign.jsd  Gambian.mean.logfc  \\\n",
+       "0       -0.037148          -0.009848           -0.010779   \n",
+       "1        0.009631           0.010377            0.007855   \n",
+       "2        0.066168           0.015354            0.023853   \n",
+       "3       -0.018358          -0.011800           -0.024516   \n",
+       "4        0.121536           0.024607           -0.057097   \n",
+       "\n",
+       "   Gambian.mean.sign.jsd  label  count  in_peaks  \n",
+       "0              -0.011243  False      0     False  \n",
+       "1               0.009833  False      0     False  \n",
+       "2               0.011588  False      0     False  \n",
+       "3              -0.008703  False      0     False  \n",
+       "4              -0.012910  False      1      True  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_source = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(116499, 5)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig_source.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906870</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906982</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>921203</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos allele1 allele2  label\n",
+       "0  chr1  870176       T       A  False\n",
+       "1  chr1  905373       C       T  False\n",
+       "2  chr1  906870       A       G  False\n",
+       "3  chr1  906982       C       T   True\n",
+       "4  chr1  921203       G       A   True"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig_source.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_source[\"enformer_recomputed\"] = mean_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected_beds_new = data.merge(orig_source, how='left', right_on=['chr', 'pos', 'allele1', 'allele2', \"label\"], left_on=['chr', 'pos', 'allele1', 'allele2', \"label\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 46)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 46)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116499"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(selected_beds_new[\"enformer_recomputed\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = selected_beds_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.sign.jsd</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "      <th>label</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.009045</td>\n",
+       "      <td>0.000162</td>\n",
+       "      <td>0.192846</td>\n",
+       "      <td>-0.037148</td>\n",
+       "      <td>-0.009848</td>\n",
+       "      <td>-0.010779</td>\n",
+       "      <td>-0.011243</td>\n",
+       "      <td>False</td>\n",
+       "      <td>866280</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.008332</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.328184</td>\n",
+       "      <td>0.009631</td>\n",
+       "      <td>0.010377</td>\n",
+       "      <td>0.007855</td>\n",
+       "      <td>0.009833</td>\n",
+       "      <td>False</td>\n",
+       "      <td>866299</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.019025</td>\n",
+       "      <td>0.001262</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>0.066168</td>\n",
+       "      <td>0.015354</td>\n",
+       "      <td>0.023853</td>\n",
+       "      <td>0.011588</td>\n",
+       "      <td>False</td>\n",
+       "      <td>870175</td>\n",
+       "      <td>-0.002115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>0.174397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011829</td>\n",
+       "      <td>0.000317</td>\n",
+       "      <td>0.131444</td>\n",
+       "      <td>-0.018358</td>\n",
+       "      <td>-0.011800</td>\n",
+       "      <td>-0.024516</td>\n",
+       "      <td>-0.008703</td>\n",
+       "      <td>False</td>\n",
+       "      <td>904946</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.025277</td>\n",
+       "      <td>0.003640</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>0.121536</td>\n",
+       "      <td>0.024607</td>\n",
+       "      <td>-0.057097</td>\n",
+       "      <td>-0.012910</td>\n",
+       "      <td>False</td>\n",
+       "      <td>905372</td>\n",
+       "      <td>-0.014033</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos allele1 allele2          rsid           region           snp  \\\n",
+       "0  chr1  866281       C       T  1_866281_C_T  1:866099:866351  1_866281_C_T   \n",
+       "1  chr1  866300       A       C  1_866300_A_C  1:866099:866351  1_866300_A_C   \n",
+       "2  chr1  870176       T       A  1_870176_T_A  1:869800:870282  1_870176_T_A   \n",
+       "3  chr1  904947       G       A  1_904947_G_A  1:904938:905544  1_904947_G_A   \n",
+       "4  chr1  905373       C       T  1_905373_T_C  1:904938:905544  1_905373_T_C   \n",
+       "\n",
+       "   distance      pval      beta  ...  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0       181  0.974280  0.006546  ...                          -0.009045   \n",
+       "1       200  0.974280  0.006546  ...                          -0.008332   \n",
+       "2       375  0.071532 -0.250142  ...                           0.019025   \n",
+       "3         8  0.290840  0.174397  ...                          -0.011829   \n",
+       "4       434  0.188152 -0.130176  ...                          -0.025277   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.000162           \n",
+       "1                                           0.000052           \n",
+       "2                                           0.001262           \n",
+       "3                                           0.000317           \n",
+       "4                                           0.003640           \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.192846                \n",
+       "1                                           0.328184                \n",
+       "2                                           0.048612                \n",
+       "3                                           0.131444                \n",
+       "4                                           0.018781                \n",
+       "\n",
+       "   eur.mean.logfc  eur.mean.sign.jsd  Gambian.mean.logfc  \\\n",
+       "0       -0.037148          -0.009848           -0.010779   \n",
+       "1        0.009631           0.010377            0.007855   \n",
+       "2        0.066168           0.015354            0.023853   \n",
+       "3       -0.018358          -0.011800           -0.024516   \n",
+       "4        0.121536           0.024607           -0.057097   \n",
+       "\n",
+       "   Gambian.mean.sign.jsd  label    pos0  enformer_recomputed  \n",
+       "0              -0.011243  False  866280                  NaN  \n",
+       "1               0.009833  False  866299                  NaN  \n",
+       "2               0.011588  False  870175            -0.002115  \n",
+       "3              -0.008703  False  904946                  NaN  \n",
+       "4              -0.012910  False  905372            -0.014033  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 48)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"in_peaks\"] = sig_vars_in_bed[c]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116499"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(data[\"enformer_recomputed\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "67362"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((~np.isnan(data[\"enformer_recomputed\"]))&(data[\"in_peaks\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22796"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((np.isnan(data[\"enformer_recomputed\"]))&(data[\"in_peaks\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116499"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(data[\"enformer_recomputed\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data[(np.isnan(data[\"enformer_recomputed\"]))&(data[\"in_peaks\"])][['chr', 'pos', 'allele1', 'allele2', \"label\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2_new_filter.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#data[['chr', 'pos', 'allele1', 'allele2', \"label\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2_new_filter_full.tsv\", sep=\"\\t\", header=True, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#enforer_recomputed_preds\n",
+    "import pickle as pkl\n",
+    "import os\n",
+    "\n",
+    "mean_change=[]\n",
+    "rsids=[]\n",
+    "\n",
+    "enf_dir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/afr_caqtls_window_new_filter/\"\n",
+    "for diri in [\"\"]:\n",
+    "    enformer_recompute = pkl.load(open(enf_dir+diri+\"/predictions_at_snp.pkl\",'rb'))\n",
+    "    for i in range(enformer_recompute[\"ref_logcount_preds\"].shape[0]):\n",
+    "        mean_change.append(np.log2( (enformer_recompute[\"ref_logcount_preds\"][i,440:456].sum()+1) / (enformer_recompute[\"alt_logcount_preds\"][i,440:456].sum()+1)))\n",
+    "        rsids.append(enformer_recompute[\"rsids\"][i])\n",
+    "mean_change = np.array(mean_change)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(22796,)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_change.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_source2 = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2_new_filter.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(22796, 5)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig_source2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_source2[\"enformer_recomputed\"] = mean_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected_beds_new_2 = data.merge(orig_source2, how='left', right_on=['chr', 'pos', 'allele1', 'allele2', \"label\"], left_on=['chr', 'pos', 'allele1', 'allele2', \"label\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = selected_beds_new_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.loc[np.isnan(data[\"enformer_recomputed_x\"]),\"enformer_recomputed_x\"] = data[np.isnan(data[\"enformer_recomputed_x\"])][\"enformer_recomputed_y\"] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((np.isnan(data[\"enformer_recomputed_x\"]))&(data[\"in_peaks\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219592, 7)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rsid_map = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/convert_hg38_to_hg19/hg38_rsids2_new_filter_full.bed\", sep=\"\\t\", header=None)\n",
+    "rsid_map[\"rsid\"] = rsid_map[0] + \"_\" + rsid_map[2].astype(str) + \"_\" + rsid_map[3] + \"_\" + rsid_map[4] \n",
+    "rsid_map_dict = {}\n",
+    "for i,r in rsid_map.iterrows():\n",
+    "    rsid_map_dict[r[9]] = r[\"rsid\"]\n",
+    "enformer_predictions = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/enformer_predictions2_full.tsv\", sep=\"\\t\", header=0).drop_duplicates()\n",
+    "enformer_predictions.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>801661</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12132974</td>\n",
+       "      <td>0.000051</td>\n",
+       "      <td>0.000068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>801680</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs12134490</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>805556</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.000184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>840327</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs28625089</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.000002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>840753</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>-0.000095</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos ref alt        rsid  DNASE_SAD  DNASE_SAR\n",
+       "0  chr1  801661   C   T  rs12132974   0.000051   0.000068\n",
+       "1  chr1  801680   A   C  rs12134490   0.000039   0.000052\n",
+       "2  chr1  805556   T   A  rs72631880  -0.000139  -0.000184\n",
+       "3  chr1  840327   G   A  rs28625089  -0.000002  -0.000002\n",
+       "4  chr1  840753   T   C   rs4970382  -0.000071  -0.000095"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_predictions[\"rsid1\"] = enformer_predictions[\"rsid\"].map(rsid_map_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(ilocs[0], value, pi)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(219131, 12)\n",
+      "(219382, 62)\n"
+     ]
+    }
+   ],
+   "source": [
+    "enformer_predictions[\"RA1\"] = enformer_predictions[\"ref\"]+\"_\"+enformer_predictions[\"alt\"]\n",
+    "enformer_predictions[\"RA3\"] = enformer_predictions[\"alt\"]+\"_\"+enformer_predictions[\"ref\"]\n",
+    "enformer_predictions[\"RA2\"] = enformer_predictions[\"rsid1\"].str.split(\"_\").str[2:4].apply('_'.join)\n",
+    "enformer_predictions[\"id_hg19\"] = enformer_predictions[\"chr\"]+\"_\"+enformer_predictions[\"pos\"].astype(str)+\"_\"+enformer_predictions[\"ref\"]+\"_\"+enformer_predictions[\"alt\"]\n",
+    "\n",
+    "# Enformer predicitons are scores as alt/ref but the ground truth is with respect to allel2 by allele1 in rsid column\n",
+    "# when alt is allele2 keep the sign, when alt is allele1 flip the allele\n",
+    "enformer_predictions1 = enformer_predictions[enformer_predictions[\"RA1\"] == enformer_predictions[\"RA2\"]]\n",
+    "enformer_predictions2 = enformer_predictions[enformer_predictions[\"RA3\"] == enformer_predictions[\"RA2\"]]\n",
+    "enformer_predictions2.loc[:,\"DNASE_SAD\"] = -1*enformer_predictions2[\"DNASE_SAD\"] \n",
+    "enformer_predictions2.loc[:,\"DNASE_SAR\"] = -1*enformer_predictions2[\"DNASE_SAR\"] \n",
+    "\n",
+    "enformer_predictions_new = pd.concat([enformer_predictions2, enformer_predictions1])\n",
+    "\n",
+    "print(enformer_predictions_new.shape)\n",
+    "\n",
+    "data[\"rsid_temp\"] = data[\"chr\"] + \"_\" + data[\"pos\"].astype(str) + \"_\" + data[\"allele1\"] + \"_\" + data[\"allele2\"]\n",
+    "\n",
+    "dataf = data.merge(enformer_predictions_new, left_on=\"rsid_temp\", right_on=\"rsid1\", how=\"left\").drop_duplicates()\n",
+    "\n",
+    "print(dataf.shape)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "219131"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(dataf[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90079"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(dataf[\"DNASE_SAD\"]) & dataf[\"in_peaks\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6826"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[~np.isnan(dataf[\"DNASE_SAD\"]) & dataf[\"in_peaks\"]][\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "219131"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(dataf[\"DNASE_SAR\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "219131"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(dataf[\"DNASE_SAR\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11084"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11084"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "198353"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"pval\"]>1e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.06"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.round(sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"label\"])/sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"pval\"]>1e-3),2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_x</th>\n",
+       "      <th>pos_x</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid_x</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid_y</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>rsid1</th>\n",
+       "      <th>RA1</th>\n",
+       "      <th>RA3</th>\n",
+       "      <th>RA2</th>\n",
+       "      <th>id_hg19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12132974</td>\n",
+       "      <td>0.000051</td>\n",
+       "      <td>0.000068</td>\n",
+       "      <td>chr1_866281_C_T</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>T_C</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>chr1_801661_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>...</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs12134490</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>chr1_866300_A_C</td>\n",
+       "      <td>A_C</td>\n",
+       "      <td>C_A</td>\n",
+       "      <td>A_C</td>\n",
+       "      <td>chr1_801680_A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.000184</td>\n",
+       "      <td>chr1_870176_T_A</td>\n",
+       "      <td>T_A</td>\n",
+       "      <td>A_T</td>\n",
+       "      <td>T_A</td>\n",
+       "      <td>chr1_805556_T_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>0.174397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs28625089</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>chr1_904947_G_A</td>\n",
+       "      <td>G_A</td>\n",
+       "      <td>A_G</td>\n",
+       "      <td>G_A</td>\n",
+       "      <td>chr1_840327_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>0.000071</td>\n",
+       "      <td>0.000095</td>\n",
+       "      <td>chr1_905373_C_T</td>\n",
+       "      <td>T_C</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>chr1_840753_T_C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 62 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chr_x   pos_x allele1 allele2        rsid_x           region           snp  \\\n",
+       "0  chr1  866281       C       T  1_866281_C_T  1:866099:866351  1_866281_C_T   \n",
+       "1  chr1  866300       A       C  1_866300_A_C  1:866099:866351  1_866300_A_C   \n",
+       "2  chr1  870176       T       A  1_870176_T_A  1:869800:870282  1_870176_T_A   \n",
+       "3  chr1  904947       G       A  1_904947_G_A  1:904938:905544  1_904947_G_A   \n",
+       "4  chr1  905373       C       T  1_905373_T_C  1:904938:905544  1_905373_T_C   \n",
+       "\n",
+       "   distance      pval      beta  ...  ref  alt      rsid_y  DNASE_SAD  \\\n",
+       "0       181  0.974280  0.006546  ...    C    T  rs12132974   0.000051   \n",
+       "1       200  0.974280  0.006546  ...    A    C  rs12134490   0.000039   \n",
+       "2       375  0.071532 -0.250142  ...    T    A  rs72631880  -0.000139   \n",
+       "3         8  0.290840  0.174397  ...    G    A  rs28625089  -0.000002   \n",
+       "4       434  0.188152 -0.130176  ...    T    C   rs4970382   0.000071   \n",
+       "\n",
+       "   DNASE_SAR            rsid1  RA1  RA3  RA2          id_hg19  \n",
+       "0   0.000068  chr1_866281_C_T  C_T  T_C  C_T  chr1_801661_C_T  \n",
+       "1   0.000052  chr1_866300_A_C  A_C  C_A  A_C  chr1_801680_A_C  \n",
+       "2  -0.000184  chr1_870176_T_A  T_A  A_T  T_A  chr1_805556_T_A  \n",
+       "3  -0.000002  chr1_904947_G_A  G_A  A_G  G_A  chr1_840327_G_A  \n",
+       "4   0.000095  chr1_905373_C_T  T_C  C_T  C_T  chr1_840753_T_C  \n",
+       "\n",
+       "[5 rows x 62 columns]"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybedtools\n",
+    "\n",
+    "fft = selected_beds[\"region\"].str.split(\":\", expand=True)\n",
+    "fft[0] = \"chr\"+fft[0]\n",
+    "beds_var1 = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "peak_bed1 = pybedtools.BedTool.from_dataframe(fft)\n",
+    "temps = beds_var1.intersect(peak_bed1, c=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temps1 = temps.to_dataframe(names=selected_beds.columns.tolist()+[\"counts\"]).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "218803"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(temps1[\"counts\"] > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf[\"IsUsed\"] = False\n",
+    "# dataf.loc[(~np.isnan(dataf[\"DNASE_SAR\"])) & (dataf[\"label\"]==1) & (temps1[\"counts\"] > 0), \"IsUsed\"] = True\n",
+    "# dataf.loc[(~np.isnan(dataf[\"DNASE_SAR\"])) & (dataf[\"pval\"]>1e-3) & (temps1[\"counts\"] > 0), \"IsUsed\"] = True\n",
+    "dataf.loc[(~np.isnan(dataf[\"DNASE_SAR\"])) & (dataf[\"label\"]==1) & dataf[\"in_peaks\"] &  (temps1[\"counts\"] > 0), \"IsUsed\"] = True\n",
+    "dataf.loc[(~np.isnan(dataf[\"DNASE_SAR\"])) & (dataf[\"pval\"]>1e-3) & dataf[\"in_peaks\"] & (temps1[\"counts\"] > 0), \"IsUsed\"] = True\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'afr.mean.logfc',\n",
+       "       'afr.mean.sign.jsd', 'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', 'Luhya.mean.logfc',\n",
+       "       'Luhya.mean.sign.jsd', 'Mende.mean.logfc', 'Mende.mean.sign.jsd',\n",
+       "       'Esan.mean.logfc', 'Esan.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd', 'Gambian.mean.logfc',\n",
+       "       'Gambian.mean.sign.jsd', 'label', 'pos0', 'enformer_recomputed_x',\n",
+       "       'in_peaks', 'enformer_recomputed_y', 'rsid_temp', 'chr_y', 'pos_y',\n",
+       "       'ref', 'alt', 'rsid_y', 'DNASE_SAD', 'DNASE_SAR', 'rsid1', 'RA1', 'RA3',\n",
+       "       'RA2', 'id_hg19', 'IsUsed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "89909"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((temps1[\"counts\"] > 0) & (~np.isnan(dataf[\"DNASE_SAR\"])) & dataf[\"in_peaks\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6821"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[(temps1[\"counts\"] > 0) & (~np.isnan(dataf[\"DNASE_SAR\"])) & dataf[\"in_peaks\"]][\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "77999"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((temps1[\"counts\"] > 0) & (~np.isnan(dataf[\"DNASE_SAR\"])) & dataf[\"in_peaks\"] & (dataf[\"pval\"]>1e-3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "84820"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90158"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[\"in_peaks\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 63)"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "84820"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[\"IsUsed\"]&dataf[\"in_peaks\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6827"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[dataf[\"in_peaks\"]][\"label\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83331"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[dataf[\"in_peaks\"]][\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(dataf[\"IsUsed\"]) # 109707"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(dataf[dataf[\"IsUsed\"]][\"label\"]==1) # 8366"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(dataf[dataf[\"IsUsed\"]][\"label\"]==0) # 8366"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Yoruba\n",
+      "Maasai\n",
+      "Luhya\n",
+      "Mende\n",
+      "Esan\n",
+      "GM12878\n",
+      ".ENCSR637XSC\n",
+      ".ENCSR000EMT\n",
+      ".ENCSR637XSC.sub.50M\n",
+      ".ENCSR095QNB\n",
+      "Gambian\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the ground truths are flipped when rsid does not match allele2 and allele\n",
+    "index=(dataf[\"allele1\"] != (dataf[\"rsid_x\"].str.split('_').str[2]))\n",
+    "index=np.array(index.values)\n",
+    "dataf.loc[index,\"enformer_recomputed_x\"] = -1 * dataf.loc[index,\"enformer_recomputed_x\"]\n",
+    "for ans in anscs:\n",
+    "    if ans==\"eur\":\n",
+    "        continue\n",
+    "    if ans==\"afr\":\n",
+    "        continue       \n",
+    "    print(ans)\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\".ENCSR637XSC\", \".ENCSR000EMT\", \".ENCSR637XSC.sub.50M\", \".ENCSR095QNB\" ]:\n",
+    "            print(title)\n",
+    "            dataf.loc[index,ans+title+\".mean.sign.jsd\"] = -1 * dataf.loc[index,ans+title+\".mean.sign.jsd\"]\n",
+    "            dataf.loc[index,ans+title+\".mean.logfc\"] = -1 * dataf.loc[index,ans+title+\".mean.logfc\"]\n",
+    "            \n",
+    "    else:\n",
+    "        title=\"\"\n",
+    "        dataf.loc[index,ans+title+\".mean.sign.jsd\"] = -1 * dataf.loc[index,ans+title+\".mean.sign.jsd\"]\n",
+    "        dataf.loc[index,ans+title+\".mean.logfc\"] = -1 * dataf.loc[index,ans+title+\".mean.logfc\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf.loc[index,\"DNASE_SAD\"] = -1 * dataf.loc[index,\"DNASE_SAD\"]\n",
+    "dataf.loc[index,\"DNASE_SAR\"] = -1 * dataf.loc[index,\"DNASE_SAR\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'afr.mean.logfc',\n",
+       "       'afr.mean.sign.jsd', 'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', 'Luhya.mean.logfc',\n",
+       "       'Luhya.mean.sign.jsd', 'Mende.mean.logfc', 'Mende.mean.sign.jsd',\n",
+       "       'Esan.mean.logfc', 'Esan.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd', 'Gambian.mean.logfc',\n",
+       "       'Gambian.mean.sign.jsd', 'label', 'pos0', 'enformer_recomputed_x',\n",
+       "       'in_peaks', 'enformer_recomputed_y', 'rsid_temp', 'chr_y', 'pos_y',\n",
+       "       'ref', 'alt', 'rsid_y', 'DNASE_SAD', 'DNASE_SAR', 'rsid1', 'RA1', 'RA3',\n",
+       "       'RA2', 'id_hg19', 'IsUsed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          rs12132974\n",
+       "1          rs12134490\n",
+       "2          rs72631880\n",
+       "3          rs28625089\n",
+       "4           rs4970382\n",
+       "             ...     \n",
+       "219377      rs6151410\n",
+       "219378      rs6151406\n",
+       "219379       rs131715\n",
+       "219380       rs762668\n",
+       "219381    rs115055839\n",
+       "Name: rsid_y, Length: 219382, dtype: object"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf[\"rsid_y\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'afr.mean.logfc',\n",
+       "       'afr.mean.sign.jsd', 'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', 'Luhya.mean.logfc',\n",
+       "       'Luhya.mean.sign.jsd', 'Mende.mean.logfc', 'Mende.mean.sign.jsd',\n",
+       "       'Esan.mean.logfc', 'Esan.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd', 'Gambian.mean.logfc',\n",
+       "       'Gambian.mean.sign.jsd', 'label', 'pos0', 'enformer_recomputed_x',\n",
+       "       'in_peaks', 'enformer_recomputed_y', 'rsid_temp', 'chr_y', 'pos_y',\n",
+       "       'ref', 'alt', 'rsid_y', 'DNASE_SAD', 'DNASE_SAR', 'rsid1', 'RA1', 'RA3',\n",
+       "       'RA2', 'id_hg19', 'IsUsed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafinal = dataf[['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'label',  'IsUsed', 'pval', 'beta','rsid_y', 'region', 'snp',\n",
+    "       'distance', 'se', 'abs_beta', 'DNASE_SAD', 'DNASE_SAR', 'enformer_recomputed_x',\n",
+    "       'Gambian.mean.logfc', 'Gambian.mean.sign.jsd',\n",
+    "        'Esan.mean.logfc', 'Esan.mean.sign.jsd', \n",
+    "        'Mende.mean.logfc', 'Mende.mean.sign.jsd',\n",
+    "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+    "        'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', \n",
+    "        'Luhya.mean.logfc', 'Luhya.mean.sign.jsd', \n",
+    "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'chr_x': 'var.chr_hg38',\n",
+       " 'pos_x': 'var.pos_hg38',\n",
+       " 'IsUsed': 'var.isused',\n",
+       " 'rsid_x': 'var.snp_id',\n",
+       " 'rsid_y': 'var.dbsnp_rsid',\n",
+       " 'region': 'var.region',\n",
+       " 'snp': 'var.snp',\n",
+       " 'distance': 'var.distance',\n",
+       " 'se': 'var.se',\n",
+       " 'abs_beta': 'var.abs_beta',\n",
+       " 'pval': 'var.pval',\n",
+       " 'beta': 'var.beta',\n",
+       " 'label': 'var.label',\n",
+       " 'allele1': 'var.allele1',\n",
+       " 'allele2': 'var.allele2',\n",
+       " 'enformer_recomputed_x': 'enformer.encsr000emt.variantscore.local_logfc',\n",
+       " 'DNASE_SAR': 'enformer.encsr000emt.variantscore.global_sar',\n",
+       " 'DNASE_SAD': 'enformer.encsr000emt.variantscore.global_sad',\n",
+       " 'Gambian.mean.logfc': 'chrombpnet.gambian.variantscore.logfc',\n",
+       " 'Gambian.mean.sign.jsd': 'chrombpnet.gambian.variantscore.jsd',\n",
+       " 'Esan.mean.logfc': 'chrombpnet.esan.variantscore.logfc',\n",
+       " 'Esan.mean.sign.jsd': 'chrombpnet.esan.variantscore.jsd',\n",
+       " 'Mende.mean.logfc': 'chrombpnet.mende.variantscore.logfc',\n",
+       " 'Mende.mean.sign.jsd': 'chrombpnet.mende.variantscore.jsd',\n",
+       " 'Yoruba.mean.logfc': 'chrombpnet.yoruba.variantscore.logfc',\n",
+       " 'Yoruba.mean.sign.jsd': 'chrombpnet.yoruba.variantscore.jsd',\n",
+       " 'Maasai.mean.logfc': 'chrombpnet.maasai.variantscore.logfc',\n",
+       " 'Maasai.mean.sign.jsd': 'chrombpnet.maasai.variantscore.jsd',\n",
+       " 'Luhya.mean.logfc': 'chrombpnet.luhya.variantscore.logfc',\n",
+       " 'Luhya.mean.sign.jsd': 'chrombpnet.luhya.variantscore.jsd',\n",
+       " 'GM12878.ENCSR637XSC.mean.logfc': 'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       " 'GM12878.ENCSR637XSC.mean.sign.jsd': 'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       " 'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile': 'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       " 'GM12878.ENCSR000EMT.mean.logfc': 'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       " 'GM12878.ENCSR000EMT.mean.sign.jsd': 'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       " 'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile': 'chrombpnet.encsr000emt.variantscore.csr',\n",
+       " 'GM12878.ENCSR637XSC.sub.50M.mean.logfc': 'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       " 'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd': 'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       " 'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile': 'chrombpnet.encsr637xsc_50m.variantscore.csr'}"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rename_dict = {\"chr_x\": \"var.chr_hg38\", \"pos_x\": \"var.pos_hg38\", \"IsUsed\":\"var.isused\", \"rsid_x\": \"var.snp_id\", \"rsid_y\": \"var.dbsnp_rsid\", \n",
+    "               'region': 'var.region' , 'snp': 'var.snp', 'distance': 'var.distance', 'se': 'var.se', 'abs_beta': 'var.abs_beta',\n",
+    "               'pval': 'var.pval', 'beta': 'var.beta', 'label': 'var.label', 'allele1':'var.allele1', 'allele2': 'var.allele2',\n",
+    "               'enformer_recomputed_x':\"enformer.encsr000emt.variantscore.local_logfc\", \"DNASE_SAR\": \"enformer.encsr000emt.variantscore.global_sar\", \"DNASE_SAD\": \"enformer.encsr000emt.variantscore.global_sad\"}\n",
+    "\n",
+    "anc_data = ['Gambian.mean.logfc', 'Gambian.mean.sign.jsd',\n",
+    "        'Esan.mean.logfc', 'Esan.mean.sign.jsd', \n",
+    "        'Mende.mean.logfc', 'Mende.mean.sign.jsd',\n",
+    "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+    "        'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', \n",
+    "        'Luhya.mean.logfc', 'Luhya.mean.sign.jsd', \n",
+    "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "        ]\n",
+    "\n",
+    "for dt in anc_data:\n",
+    "    vals = dt.split(\".\")\n",
+    "    if vals[0] == \"GM12878\":\n",
+    "        popn=vals[1].lower()\n",
+    "        if vals[2] == \"sub\":\n",
+    "            popn=vals[1].lower()+\"_50m\"\n",
+    "    else:\n",
+    "        popn=vals[0].lower()\n",
+    "    if vals[-1] == \"logfc\":\n",
+    "        sc=\"logfc\"\n",
+    "    elif vals[-1] == \"jsd\":\n",
+    "        sc=\"jsd\"\n",
+    "    elif vals[-1] == \"abs_logfc_x_jsd_x_max_percentile\":\n",
+    "        sc=\"csr\"   \n",
+    "    elif vals[-1] == \"pval\" and vals[-2] == \"abs_logfc_x_jsd_x_max_percentile\":\n",
+    "        sc=\"pval\"\n",
+    "    else:\n",
+    "        print(vals[-1])\n",
+    "    rename_dict[dt] = \"chrombpnet.\"+popn+\".variantscore.\"+sc\n",
+    "        \n",
+    "rename_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafinal = datafinal.rename(columns=rename_dict)\n",
+    "\n",
+    "datafinal.to_csv(\"/oak/stanford/groups/akundaje/anusri/chrombpnet_zenodo_uploads_reformatted/Variant_effect_prediction_benchmarking/lcl.caqtls.african.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# f = open(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Afr.CaQTLS.README\", \"w\")\n",
+    "# for line in datafinal.columns:\n",
+    "#     f.write(line+\"\\n\")\n",
+    "# f.close()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename_dict = {\"chr_x\": \"chr_hg38\", \"pos_x\": \"pos_hg38\", \"DNASE_SAR\": \"enformer.encsr000emt.variantscore.sar\", \"DNASE_SAD\": \"enformer.encsr000emt.variantscore.sad\", \"IsUsed1\":\"IsUsed\", \"rsid_x\": \"snp_id\", \"rsid_y\": \"dbsnp_rsid\"}\n",
+    "\n",
+    "# datafinal = datafinal.rename(columns=rename_dict)\n",
+    "\n",
+    "# datafinal.to_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Afr.CaQTLS.tsv\", sep=\"\\t\", header=True, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed', 'pval', 'beta', 'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafinal.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109707"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(datafinal[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 45)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafinal.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datafinal[datafinal[\"IsUsed\"]][\"distance\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datafinal[\"distance\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fft = datafinal[\"region\"].str.split(\":\", expand=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fft.head()\n",
+    "fft[0] = \"chr\"+fft[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "width = fft[2].astype(int) - fft[1].astype(int) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "width.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866099</td>\n",
+       "      <td>866351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866099</td>\n",
+       "      <td>866351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>869800</td>\n",
+       "      <td>870282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904938</td>\n",
+       "      <td>905544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904938</td>\n",
+       "      <td>905544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219377</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627308</td>\n",
+       "      <td>50627508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219378</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627514</td>\n",
+       "      <td>50628822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219379</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627514</td>\n",
+       "      <td>50628822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219380</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627514</td>\n",
+       "      <td>50628822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219381</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50782926</td>\n",
+       "      <td>50783896</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>219382 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0         1         2\n",
+       "0        chr1    866099    866351\n",
+       "1        chr1    866099    866351\n",
+       "2        chr1    869800    870282\n",
+       "3        chr1    904938    905544\n",
+       "4        chr1    904938    905544\n",
+       "...       ...       ...       ...\n",
+       "219377  chr22  50627308  50627508\n",
+       "219378  chr22  50627514  50628822\n",
+       "219379  chr22  50627514  50628822\n",
+       "219380  chr22  50627514  50628822\n",
+       "219381  chr22  50782926  50783896\n",
+       "\n",
+       "[219382 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "chr1\t866280\t866281\tC\tT\t1_866281_C_T\t1:866099:866351\t1_866281_C_T\t181\t0.97428\t0.00654566\t0.202449\t0.00654566\t-0.0198358239733333\t-0.0092910501638771\t-0.0371476132\t-0.0098478139667772\t-0.0107790358\t-0.0112429074557623\t-0.03047294212\t-0.007854980010384\t-0.0274319003999999\t-0.0104460740301044\t-0.015266906\t-0.0074909427884077\t3.842012393515388e-05\t0.47469310823965\t-0.00869365584\t-0.0048307100337268\t2.439625108697341e-05\t0.5218877975662511\t-0.027134499\t-0.0065437883004904\t4.429526404646882e-05\t0.4069019535301472\t-0.0319308548\t-0.0090450750534331\t0.0001624065727458\t0.1928464912342504\t-0.0172759259\t-0.0108619390346573\t-0.01244030388\t-0.0089491793240438\t-0.02061483574\t-0.006391221128311\tFalse\t2\n",
+      " "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "temps.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "218803"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(temps1[\"counts\"]>0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 45)"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafinal.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temps1[\"IsUsed\"]  = datafinal[\"IsUsed\"] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109707"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1\n",
+    "sum(temps1[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109646"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(temps1[temps1[\"IsUsed\"]][\"counts\"] > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos0', 'pos', 'allele1', 'allele2', 'rsid', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'afr.mean.logfc',\n",
+       "       'afr.mean.sign.jsd', 'eur.mean.logfc', 'eur.mean.sign.jsd',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.sign.jsd', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.sign.jsd', 'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Esan.mean.logfc', 'Esan.mean.sign.jsd', 'Maasai.mean.logfc',\n",
+       "       'Maasai.mean.sign.jsd', 'Luhya.mean.logfc', 'Luhya.mean.sign.jsd',\n",
+       "       'label', 'counts'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls.ipynb
new file mode 100644
index 00000000..cfe405b8
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls.ipynb
@@ -0,0 +1,3396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"all_caqtls.scores.tsv\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.abs_logfc', 'Esan.mean.jsd', 'Esan.mean.abs_logfc_x_jsd',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'Esan.mean.max_percentile', 'Esan.mean.abs_logfc.pval',\n",
+       "       'Esan.mean.jsd.pval', 'Esan.mean.abs_logfc_x_jsd.pval',\n",
+       "       'Esan.mean.abs_logfc_x_jsd_x_max_percentile.pval', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.abs_logfc', 'Mende.mean.jsd', 'Mende.mean.abs_logfc.pval',\n",
+       "       'Mende.mean.jsd.pval', 'Yoruba.mean.logfc', 'Yoruba.mean.abs_logfc',\n",
+       "       'Yoruba.mean.jsd', 'Yoruba.mean.abs_logfc.pval', 'Yoruba.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Luhya.mean.logfc', 'Luhya.mean.abs_logfc', 'Luhya.mean.jsd',\n",
+       "       'Luhya.mean.abs_logfc.pval', 'Luhya.mean.jsd.pval',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.abs_logfc', 'Gambian.mean.jsd',\n",
+       "       'Gambian.mean.abs_logfc.pval', 'Gambian.mean.jsd.pval',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.abs_logfc', 'Maasai.mean.jsd',\n",
+       "       'Maasai.mean.abs_logfc.pval', 'Maasai.mean.jsd.pval', 'afr.mean.logfc',\n",
+       "       'afr.mean.abs_logfc', 'afr.mean.jsd', 'eur.mean.logfc',\n",
+       "       'eur.mean.abs_logfc', 'eur.mean.jsd', 'afr.mean.abs_logfc.pval',\n",
+       "       'afr.mean.jsd.pval', 'eur.mean.abs_logfc.pval', 'eur.mean.jsd.pval',\n",
+       "       'region', 'snp', 'distance', 'pval', 'beta', 'se', 'abs_beta'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "data_dnase = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/dnase_preds/DNASE_NEW/summary.mean.variant_scores_new_3.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/atac/ATAC/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac_50m = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds/atac_50M/ATAC_50M/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vals = ['GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc', 'GM12878.ENCSR637XSC.mean.jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for val in vals:\n",
+    "    #print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        data[val] = data_atac[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        data[val] = data_atac[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for val in vals:\n",
+    "    #print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR637XSC.sub.50M\")] = data_atac_50m[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR637XSC.sub.50M\")] = data_atac_50m[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for val in vals:\n",
+    "    #print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR000EMT\")] = data_dnase[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        data[val.replace(\"ENCSR637XSC\", \"ENCSR000EMT\")] = data_dnase[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 97)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.columns[0:70].tolist()+data.columns[77:].tolist()+data.columns[70:77].tolist()].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>0.202449</td>\n",
+       "      <td>0.006546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>0.202449</td>\n",
+       "      <td>0.006546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>0.137133</td>\n",
+       "      <td>0.250142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>0.174397</td>\n",
+       "      <td>0.164112</td>\n",
+       "      <td>0.174397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>0.098145</td>\n",
+       "      <td>0.130176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219377</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627388</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50627388_G_A</td>\n",
+       "      <td>22:50627308:50627508</td>\n",
+       "      <td>22_50627388_G_A</td>\n",
+       "      <td>79</td>\n",
+       "      <td>0.405697</td>\n",
+       "      <td>-0.136940</td>\n",
+       "      <td>0.163901</td>\n",
+       "      <td>0.136940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219378</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628124</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>609</td>\n",
+       "      <td>0.742636</td>\n",
+       "      <td>0.021773</td>\n",
+       "      <td>0.066098</td>\n",
+       "      <td>0.021773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219379</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628493</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50628493_G_A</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628493_G_A</td>\n",
+       "      <td>978</td>\n",
+       "      <td>0.729436</td>\n",
+       "      <td>0.028973</td>\n",
+       "      <td>0.083502</td>\n",
+       "      <td>0.028973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219380</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628562</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_50628562_C_T</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628562_C_T</td>\n",
+       "      <td>1047</td>\n",
+       "      <td>0.191678</td>\n",
+       "      <td>-0.076209</td>\n",
+       "      <td>0.057921</td>\n",
+       "      <td>0.076209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219381</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50783303</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22_50783303_T_C</td>\n",
+       "      <td>22:50782926:50783896</td>\n",
+       "      <td>22_50783303_T_C</td>\n",
+       "      <td>376</td>\n",
+       "      <td>0.210785</td>\n",
+       "      <td>-0.130570</td>\n",
+       "      <td>0.103578</td>\n",
+       "      <td>0.130570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>219382 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       pos allele1 allele2             rsid  \\\n",
+       "0        chr1    866281       C       T     1_866281_C_T   \n",
+       "1        chr1    866300       A       C     1_866300_A_C   \n",
+       "2        chr1    870176       T       A     1_870176_T_A   \n",
+       "3        chr1    904947       G       A     1_904947_G_A   \n",
+       "4        chr1    905373       C       T     1_905373_T_C   \n",
+       "...       ...       ...     ...     ...              ...   \n",
+       "219377  chr22  50627388       G       A  22_50627388_G_A   \n",
+       "219378  chr22  50628124       C       G  22_50628124_C_G   \n",
+       "219379  chr22  50628493       G       A  22_50628493_G_A   \n",
+       "219380  chr22  50628562       C       T  22_50628562_C_T   \n",
+       "219381  chr22  50783303       T       C  22_50783303_T_C   \n",
+       "\n",
+       "                      region              snp  distance      pval      beta  \\\n",
+       "0            1:866099:866351     1_866281_C_T       181  0.974280  0.006546   \n",
+       "1            1:866099:866351     1_866300_A_C       200  0.974280  0.006546   \n",
+       "2            1:869800:870282     1_870176_T_A       375  0.071532 -0.250142   \n",
+       "3            1:904938:905544     1_904947_G_A         8  0.290840  0.174397   \n",
+       "4            1:904938:905544     1_905373_T_C       434  0.188152 -0.130176   \n",
+       "...                      ...              ...       ...       ...       ...   \n",
+       "219377  22:50627308:50627508  22_50627388_G_A        79  0.405697 -0.136940   \n",
+       "219378  22:50627514:50628822  22_50628124_C_G       609  0.742636  0.021773   \n",
+       "219379  22:50627514:50628822  22_50628493_G_A       978  0.729436  0.028973   \n",
+       "219380  22:50627514:50628822  22_50628562_C_T      1047  0.191678 -0.076209   \n",
+       "219381  22:50782926:50783896  22_50783303_T_C       376  0.210785 -0.130570   \n",
+       "\n",
+       "              se  abs_beta  \n",
+       "0       0.202449  0.006546  \n",
+       "1       0.202449  0.006546  \n",
+       "2       0.137133  0.250142  \n",
+       "3       0.164112  0.174397  \n",
+       "4       0.098145  0.130176  \n",
+       "...          ...       ...  \n",
+       "219377  0.163901  0.136940  \n",
+       "219378  0.066098  0.021773  \n",
+       "219379  0.083502  0.028973  \n",
+       "219380  0.057921  0.076209  \n",
+       "219381  0.103578  0.130570  \n",
+       "\n",
+       "[219382 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.columns[0:5].tolist()+data.columns[70:77].tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Gambian', 'Luhya', 'Yoruba', 'Esan', 'GM12878', 'afr', 'Mende', 'Maasai', 'eur'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "anscs = data.columns[5:70].tolist()+data.columns[77:].tolist()\n",
+    "anscs = [ans.split(\".\")[0] for ans in anscs]\n",
+    "anscs = set(anscs)\n",
+    "print(anscs)\n",
+    "\n",
+    "sub_list = []\n",
+    "for ans in anscs:\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\".ENCSR637XSC\", \".ENCSR000EMT\", \".ENCSR637XSC.sub.50M\", \".ENCSR095QNB\"]:\n",
+    "            sub_list.append(ans+title+\".mean.logfc\")\n",
+    "            data[ans+title+\".mean.sign.jsd\"] = np.sign(data[ans+title+\".mean.logfc\"]) * data[ans+title+\".mean.jsd\"]\n",
+    "            sub_list.append(ans+title+\".mean.sign.jsd\")\n",
+    "            sub_list.append(ans+title+\".mean.abs_logfc_x_jsd_x_max_percentile\")\n",
+    "            sub_list.append(ans+title+\".mean.abs_logfc_x_jsd_x_max_percentile.pval\")\n",
+    "    else:\n",
+    "        sub_list.append(ans+\".mean.logfc\")\n",
+    "        data[ans+\".mean.sign.jsd\"] = np.sign(data[ans+\".mean.logfc\"]) * data[ans+\".mean.jsd\"]\n",
+    "        sub_list.append(ans+\".mean.sign.jsd\")        \n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'region', 'snp', 'distance',\n",
+       "       'pval', 'beta', 'se', 'abs_beta', 'Gambian.mean.logfc',\n",
+       "       'Gambian.mean.sign.jsd', 'Luhya.mean.logfc', 'Luhya.mean.sign.jsd',\n",
+       "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.sign.jsd', 'GM12878.ENCSR637XSC.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'afr.mean.logfc', 'afr.mean.sign.jsd', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.sign.jsd', 'Maasai.mean.logfc', 'Maasai.mean.sign.jsd',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data[data.columns[0:5].tolist()+data.columns[70:77].tolist()+sub_list]\n",
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_data = pd.read_csv(\"af_caqtls/significant.caqtls.list.tsv\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.92178e-41\n",
+      "9.99633e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min(sig_data[\"pval\"]))\n",
+    "print(max(sig_data[\"pval\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906982</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>1:906596:907043</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>385</td>\n",
+       "      <td>4.802840e-06</td>\n",
+       "      <td>-0.520262</td>\n",
+       "      <td>0.106740</td>\n",
+       "      <td>0.520262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>921203</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>1:921132:921332</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6.892040e-06</td>\n",
+       "      <td>0.905971</td>\n",
+       "      <td>0.189412</td>\n",
+       "      <td>0.905971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1:975872:976361</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>342</td>\n",
+       "      <td>2.622420e-11</td>\n",
+       "      <td>-0.615266</td>\n",
+       "      <td>0.080637</td>\n",
+       "      <td>0.615266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>1:1038791:1039035</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.169820e-11</td>\n",
+       "      <td>0.700579</td>\n",
+       "      <td>0.089787</td>\n",
+       "      <td>0.700579</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1:1038791:1039035</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1.169820e-11</td>\n",
+       "      <td>0.700579</td>\n",
+       "      <td>0.089787</td>\n",
+       "      <td>0.700579</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr      pos allele1 allele2           rsid             region  \\\n",
+       "0  chr1   906982       C       T   1_906982_C_T    1:906596:907043   \n",
+       "1  chr1   921203       G       A   1_921203_G_A    1:921132:921332   \n",
+       "2  chr1   976215       A       G   1_976215_A_G    1:975872:976361   \n",
+       "3  chr1  1038800       T       G  1_1038800_G_T  1:1038791:1039035   \n",
+       "4  chr1  1038819       T       C  1_1038819_C_T  1:1038791:1039035   \n",
+       "\n",
+       "             snp  distance          pval      beta        se  abs_beta  \n",
+       "0   1_906982_C_T       385  4.802840e-06 -0.520262  0.106740  0.520262  \n",
+       "1   1_921203_G_A        70  6.892040e-06  0.905971  0.189412  0.905971  \n",
+       "2   1_976215_A_G       342  2.622420e-11 -0.615266  0.080637  0.615266  \n",
+       "3  1_1038800_G_T         8  1.169820e-11  0.700579  0.089787  0.700579  \n",
+       "4  1_1038819_C_T        27  1.169820e-11  0.700579  0.089787  0.700579  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_data.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"label\"] = data[\"rsid\"].isin(sig_data[\"rsid\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 45)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.99633e-06\n",
+      "2.92178e-41\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(max(data[data[\"label\"]][\"pval\"]))\n",
+    "print(min(data[data[\"label\"]][\"pval\"]))\n",
+    "data[\"pval\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>afr.mean.logfc</th>\n",
+       "      <th>afr.mean.sign.jsd</th>\n",
+       "      <th>Mende.mean.logfc</th>\n",
+       "      <th>Mende.mean.sign.jsd</th>\n",
+       "      <th>Maasai.mean.logfc</th>\n",
+       "      <th>Maasai.mean.sign.jsd</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.sign.jsd</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866280</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.192846</td>\n",
+       "      <td>-0.019836</td>\n",
+       "      <td>-0.009291</td>\n",
+       "      <td>-0.030473</td>\n",
+       "      <td>-0.007855</td>\n",
+       "      <td>-0.012440</td>\n",
+       "      <td>-0.008949</td>\n",
+       "      <td>-0.037148</td>\n",
+       "      <td>-0.009848</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866299</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.328184</td>\n",
+       "      <td>-0.000810</td>\n",
+       "      <td>-0.008199</td>\n",
+       "      <td>0.006495</td>\n",
+       "      <td>0.007980</td>\n",
+       "      <td>0.003003</td>\n",
+       "      <td>0.007669</td>\n",
+       "      <td>0.009631</td>\n",
+       "      <td>0.010377</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870175</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>0.005320</td>\n",
+       "      <td>0.012242</td>\n",
+       "      <td>-0.005758</td>\n",
+       "      <td>-0.012707</td>\n",
+       "      <td>0.010434</td>\n",
+       "      <td>0.010721</td>\n",
+       "      <td>0.066168</td>\n",
+       "      <td>0.015354</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904946</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.131444</td>\n",
+       "      <td>-0.025393</td>\n",
+       "      <td>-0.010448</td>\n",
+       "      <td>-0.004891</td>\n",
+       "      <td>-0.012478</td>\n",
+       "      <td>-0.027632</td>\n",
+       "      <td>-0.010494</td>\n",
+       "      <td>-0.018358</td>\n",
+       "      <td>-0.011800</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905372</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>0.085157</td>\n",
+       "      <td>0.015007</td>\n",
+       "      <td>-0.070206</td>\n",
+       "      <td>-0.015290</td>\n",
+       "      <td>-0.084056</td>\n",
+       "      <td>-0.013616</td>\n",
+       "      <td>0.121536</td>\n",
+       "      <td>0.024607</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos0     pos allele1 allele2          rsid           region  \\\n",
+       "0  chr1  866280  866281       C       T  1_866281_C_T  1:866099:866351   \n",
+       "1  chr1  866299  866300       A       C  1_866300_A_C  1:866099:866351   \n",
+       "2  chr1  870175  870176       T       A  1_870176_T_A  1:869800:870282   \n",
+       "3  chr1  904946  904947       G       A  1_904947_G_A  1:904938:905544   \n",
+       "4  chr1  905372  905373       C       T  1_905373_T_C  1:904938:905544   \n",
+       "\n",
+       "            snp  distance      pval  ...  \\\n",
+       "0  1_866281_C_T       181  0.974280  ...   \n",
+       "1  1_866300_A_C       200  0.974280  ...   \n",
+       "2  1_870176_T_A       375  0.071532  ...   \n",
+       "3  1_904947_G_A         8  0.290840  ...   \n",
+       "4  1_905373_T_C       434  0.188152  ...   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.192846                \n",
+       "1                                           0.328184                \n",
+       "2                                           0.048612                \n",
+       "3                                           0.131444                \n",
+       "4                                           0.018781                \n",
+       "\n",
+       "   afr.mean.logfc  afr.mean.sign.jsd  Mende.mean.logfc  Mende.mean.sign.jsd  \\\n",
+       "0       -0.019836          -0.009291         -0.030473            -0.007855   \n",
+       "1       -0.000810          -0.008199          0.006495             0.007980   \n",
+       "2        0.005320           0.012242         -0.005758            -0.012707   \n",
+       "3       -0.025393          -0.010448         -0.004891            -0.012478   \n",
+       "4        0.085157           0.015007         -0.070206            -0.015290   \n",
+       "\n",
+       "   Maasai.mean.logfc  Maasai.mean.sign.jsd  eur.mean.logfc  eur.mean.sign.jsd  \\\n",
+       "0          -0.012440             -0.008949       -0.037148          -0.009848   \n",
+       "1           0.003003              0.007669        0.009631           0.010377   \n",
+       "2           0.010434              0.010721        0.066168           0.015354   \n",
+       "3          -0.027632             -0.010494       -0.018358          -0.011800   \n",
+       "4          -0.084056             -0.013616        0.121536           0.024607   \n",
+       "\n",
+       "   label  \n",
+       "0  False  \n",
+       "1  False  \n",
+       "2  False  \n",
+       "3  False  \n",
+       "4  False  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"pos0\"] = data[\"pos\"] - 1\n",
+    "selected_beds = data[[\"chr\", \"pos0\", \"pos\"]+data.columns[2:-1].tolist()]\n",
+    "selected_beds.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pybedtools\n",
+    "# beds_var = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "# peak_bed = pybedtools.BedTool('af_caqtls/merged.peaks.af.bed')\n",
+    "# sig_vars_in_bed = beds_var.intersect(peak_bed, wa=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sig_vars_in_bed = sig_vars_in_bed.to_dataframe(names=[\"chr\", \"pos0\", \"pos\"]+data.columns[2:-1].tolist()).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sum(sig_vars_in_bed[\"label\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sig_vars_in_bed[['chr', 'pos', 'allele1', 'allele2', \"label\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#enforer_recomputed_preds\n",
+    "import pickle as pkl\n",
+    "import os\n",
+    "\n",
+    "mean_change=[]\n",
+    "rsids=[]\n",
+    "\n",
+    "enf_dir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/afr_caqtls_window/\"\n",
+    "for diri in [\"splitaa\", \"splitab\", \"splitac\"]:\n",
+    "    enformer_recompute = pkl.load(open(enf_dir+diri+\"/predictions_at_snp.pkl\",'rb'))\n",
+    "    for i in range(enformer_recompute[\"ref_logcount_preds\"].shape[0]):\n",
+    "        mean_change.append(np.log2( (enformer_recompute[\"ref_logcount_preds\"][i,440:456].sum()+1) / (enformer_recompute[\"alt_logcount_preds\"][i,440:456].sum()+1)))\n",
+    "        rsids.append(enformer_recompute[\"rsids\"][i])\n",
+    "mean_change = np.array(mean_change)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(116499,)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_change.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'chr1_870175_T_A_False'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rsids[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 46)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>afr.mean.logfc</th>\n",
+       "      <th>afr.mean.sign.jsd</th>\n",
+       "      <th>Mende.mean.logfc</th>\n",
+       "      <th>Mende.mean.sign.jsd</th>\n",
+       "      <th>Maasai.mean.logfc</th>\n",
+       "      <th>Maasai.mean.sign.jsd</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.sign.jsd</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866280</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.192846</td>\n",
+       "      <td>-0.019836</td>\n",
+       "      <td>-0.009291</td>\n",
+       "      <td>-0.030473</td>\n",
+       "      <td>-0.007855</td>\n",
+       "      <td>-0.012440</td>\n",
+       "      <td>-0.008949</td>\n",
+       "      <td>-0.037148</td>\n",
+       "      <td>-0.009848</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866299</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.328184</td>\n",
+       "      <td>-0.000810</td>\n",
+       "      <td>-0.008199</td>\n",
+       "      <td>0.006495</td>\n",
+       "      <td>0.007980</td>\n",
+       "      <td>0.003003</td>\n",
+       "      <td>0.007669</td>\n",
+       "      <td>0.009631</td>\n",
+       "      <td>0.010377</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870175</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>0.005320</td>\n",
+       "      <td>0.012242</td>\n",
+       "      <td>-0.005758</td>\n",
+       "      <td>-0.012707</td>\n",
+       "      <td>0.010434</td>\n",
+       "      <td>0.010721</td>\n",
+       "      <td>0.066168</td>\n",
+       "      <td>0.015354</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904946</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.131444</td>\n",
+       "      <td>-0.025393</td>\n",
+       "      <td>-0.010448</td>\n",
+       "      <td>-0.004891</td>\n",
+       "      <td>-0.012478</td>\n",
+       "      <td>-0.027632</td>\n",
+       "      <td>-0.010494</td>\n",
+       "      <td>-0.018358</td>\n",
+       "      <td>-0.011800</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905372</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>0.085157</td>\n",
+       "      <td>0.015007</td>\n",
+       "      <td>-0.070206</td>\n",
+       "      <td>-0.015290</td>\n",
+       "      <td>-0.084056</td>\n",
+       "      <td>-0.013616</td>\n",
+       "      <td>0.121536</td>\n",
+       "      <td>0.024607</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos0     pos allele1 allele2          rsid           region  \\\n",
+       "0  chr1  866280  866281       C       T  1_866281_C_T  1:866099:866351   \n",
+       "1  chr1  866299  866300       A       C  1_866300_A_C  1:866099:866351   \n",
+       "2  chr1  870175  870176       T       A  1_870176_T_A  1:869800:870282   \n",
+       "3  chr1  904946  904947       G       A  1_904947_G_A  1:904938:905544   \n",
+       "4  chr1  905372  905373       C       T  1_905373_T_C  1:904938:905544   \n",
+       "\n",
+       "            snp  distance      pval  ...  \\\n",
+       "0  1_866281_C_T       181  0.974280  ...   \n",
+       "1  1_866300_A_C       200  0.974280  ...   \n",
+       "2  1_870176_T_A       375  0.071532  ...   \n",
+       "3  1_904947_G_A         8  0.290840  ...   \n",
+       "4  1_905373_T_C       434  0.188152  ...   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.192846                \n",
+       "1                                           0.328184                \n",
+       "2                                           0.048612                \n",
+       "3                                           0.131444                \n",
+       "4                                           0.018781                \n",
+       "\n",
+       "   afr.mean.logfc  afr.mean.sign.jsd  Mende.mean.logfc  Mende.mean.sign.jsd  \\\n",
+       "0       -0.019836          -0.009291         -0.030473            -0.007855   \n",
+       "1       -0.000810          -0.008199          0.006495             0.007980   \n",
+       "2        0.005320           0.012242         -0.005758            -0.012707   \n",
+       "3       -0.025393          -0.010448         -0.004891            -0.012478   \n",
+       "4        0.085157           0.015007         -0.070206            -0.015290   \n",
+       "\n",
+       "   Maasai.mean.logfc  Maasai.mean.sign.jsd  eur.mean.logfc  eur.mean.sign.jsd  \\\n",
+       "0          -0.012440             -0.008949       -0.037148          -0.009848   \n",
+       "1           0.003003              0.007669        0.009631           0.010377   \n",
+       "2           0.010434              0.010721        0.066168           0.015354   \n",
+       "3          -0.027632             -0.010494       -0.018358          -0.011800   \n",
+       "4          -0.084056             -0.013616        0.121536           0.024607   \n",
+       "\n",
+       "   label  \n",
+       "0  False  \n",
+       "1  False  \n",
+       "2  False  \n",
+       "3  False  \n",
+       "4  False  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_source = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/source2.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(116499, 5)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig_source.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906870</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906982</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>921203</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos allele1 allele2  label\n",
+       "0  chr1  870176       T       A  False\n",
+       "1  chr1  905373       C       T  False\n",
+       "2  chr1  906870       A       G  False\n",
+       "3  chr1  906982       C       T   True\n",
+       "4  chr1  921203       G       A   True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig_source.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_source[\"enformer_recomputed\"] = mean_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected_beds_new = data.merge(orig_source, how='left', right_on=['chr', 'pos', 'allele1', 'allele2', \"label\"], left_on=['chr', 'pos', 'allele1', 'allele2', \"label\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 46)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 46)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_beds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116499"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(selected_beds_new[\"enformer_recomputed\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = selected_beds_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(116604, 7)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rsid_map = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/convert_hg38_to_hg19/hg38_rsids2.bed\", sep=\"\\t\", header=None)\n",
+    "rsid_map[\"rsid\"] = rsid_map[0] + \"_\" + rsid_map[2].astype(str) + \"_\" + rsid_map[3] + \"_\" + rsid_map[4] \n",
+    "rsid_map_dict = {}\n",
+    "for i,r in rsid_map.iterrows():\n",
+    "    rsid_map_dict[r[9]] = r[\"rsid\"]\n",
+    "enformer_predictions = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/enformer_predictions2.tsv\", sep=\"\\t\", header=0).drop_duplicates()\n",
+    "enformer_predictions.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>805556</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.000184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>840753</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>-0.000095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>842250</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs148658266</td>\n",
+       "      <td>-0.000669</td>\n",
+       "      <td>-0.000892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>842362</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs28540380</td>\n",
+       "      <td>-0.000453</td>\n",
+       "      <td>-0.000604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>856583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>0.000342</td>\n",
+       "      <td>0.000450</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos ref alt         rsid  DNASE_SAD  DNASE_SAR\n",
+       "0  chr1  805556   T   A   rs72631880  -0.000139  -0.000184\n",
+       "1  chr1  840753   T   C    rs4970382  -0.000071  -0.000095\n",
+       "2  chr1  842250   A   G  rs148658266  -0.000669  -0.000892\n",
+       "3  chr1  842362   C   T   rs28540380  -0.000453  -0.000604\n",
+       "4  chr1  856583   G   A  rs141671872   0.000342   0.000450"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_predictions.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_predictions[\"rsid1\"] = enformer_predictions[\"rsid\"].map(rsid_map_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(ilocs[0], value, pi)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(116358, 12)\n",
+      "(219382, 60)\n"
+     ]
+    }
+   ],
+   "source": [
+    "enformer_predictions[\"RA1\"] = enformer_predictions[\"ref\"]+\"_\"+enformer_predictions[\"alt\"]\n",
+    "enformer_predictions[\"RA3\"] = enformer_predictions[\"alt\"]+\"_\"+enformer_predictions[\"ref\"]\n",
+    "enformer_predictions[\"RA2\"] = enformer_predictions[\"rsid1\"].str.split(\"_\").str[2:4].apply('_'.join)\n",
+    "enformer_predictions[\"id_hg19\"] = enformer_predictions[\"chr\"]+\"_\"+enformer_predictions[\"pos\"].astype(str)+\"_\"+enformer_predictions[\"ref\"]+\"_\"+enformer_predictions[\"alt\"]\n",
+    "\n",
+    "# Enformer predicitons are scores as alt/ref but the ground truth is with respect to allel2 by allele1 in rsid column\n",
+    "# when alt is allele2 keep the sign, when alt is allele1 flip the allele\n",
+    "enformer_predictions1 = enformer_predictions[enformer_predictions[\"RA1\"] == enformer_predictions[\"RA2\"]]\n",
+    "enformer_predictions2 = enformer_predictions[enformer_predictions[\"RA3\"] == enformer_predictions[\"RA2\"]]\n",
+    "enformer_predictions2.loc[:,\"DNASE_SAD\"] = -1*enformer_predictions2[\"DNASE_SAD\"] \n",
+    "enformer_predictions2.loc[:,\"DNASE_SAR\"] = -1*enformer_predictions2[\"DNASE_SAR\"] \n",
+    "\n",
+    "enformer_predictions_new = pd.concat([enformer_predictions2, enformer_predictions1])\n",
+    "\n",
+    "print(enformer_predictions_new.shape)\n",
+    "\n",
+    "data[\"rsid_temp\"] = data[\"chr\"] + \"_\" + data[\"pos\"].astype(str) + \"_\" + data[\"allele1\"] + \"_\" + data[\"allele2\"]\n",
+    "\n",
+    "dataf = data.merge(enformer_predictions_new, left_on=\"rsid_temp\", right_on=\"rsid1\", how=\"left\").drop_duplicates()\n",
+    "\n",
+    "print(dataf.shape)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116358"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(dataf[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116358"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(dataf[\"DNASE_SAR\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8366"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8366"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101341"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"pval\"]>1e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.round(sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"label\"])/sum(dataf[~np.isnan(dataf[\"DNASE_SAR\"])][\"pval\"]>1e-3),2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_x</th>\n",
+       "      <th>pos_x</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid_x</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid_y</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>RA1</th>\n",
+       "      <th>RA3</th>\n",
+       "      <th>rsid1</th>\n",
+       "      <th>RA2</th>\n",
+       "      <th>id_hg19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866281</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866281_C_T</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866300</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>1:866099:866351</td>\n",
+       "      <td>1_866300_A_C</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.974280</td>\n",
+       "      <td>0.006546</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.000184</td>\n",
+       "      <td>T_A</td>\n",
+       "      <td>A_T</td>\n",
+       "      <td>chr1_870176_T_A</td>\n",
+       "      <td>T_A</td>\n",
+       "      <td>chr1_805556_T_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904947</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_904947_G_A</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.290840</td>\n",
+       "      <td>0.174397</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>0.000071</td>\n",
+       "      <td>0.000095</td>\n",
+       "      <td>T_C</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>chr1_905373_C_T</td>\n",
+       "      <td>C_T</td>\n",
+       "      <td>chr1_840753_T_C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 60 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chr_x   pos_x allele1 allele2        rsid_x           region           snp  \\\n",
+       "0  chr1  866281       C       T  1_866281_C_T  1:866099:866351  1_866281_C_T   \n",
+       "1  chr1  866300       A       C  1_866300_A_C  1:866099:866351  1_866300_A_C   \n",
+       "2  chr1  870176       T       A  1_870176_T_A  1:869800:870282  1_870176_T_A   \n",
+       "3  chr1  904947       G       A  1_904947_G_A  1:904938:905544  1_904947_G_A   \n",
+       "4  chr1  905373       C       T  1_905373_T_C  1:904938:905544  1_905373_T_C   \n",
+       "\n",
+       "   distance      pval      beta  ...  ref  alt      rsid_y  DNASE_SAD  \\\n",
+       "0       181  0.974280  0.006546  ...  NaN  NaN         NaN        NaN   \n",
+       "1       200  0.974280  0.006546  ...  NaN  NaN         NaN        NaN   \n",
+       "2       375  0.071532 -0.250142  ...    T    A  rs72631880  -0.000139   \n",
+       "3         8  0.290840  0.174397  ...  NaN  NaN         NaN        NaN   \n",
+       "4       434  0.188152 -0.130176  ...    T    C   rs4970382   0.000071   \n",
+       "\n",
+       "   DNASE_SAR  RA1  RA3            rsid1  RA2          id_hg19  \n",
+       "0        NaN  NaN  NaN              NaN  NaN              NaN  \n",
+       "1        NaN  NaN  NaN              NaN  NaN              NaN  \n",
+       "2  -0.000184  T_A  A_T  chr1_870176_T_A  T_A  chr1_805556_T_A  \n",
+       "3        NaN  NaN  NaN              NaN  NaN              NaN  \n",
+       "4   0.000095  T_C  C_T  chr1_905373_C_T  C_T  chr1_840753_T_C  \n",
+       "\n",
+       "[5 rows x 60 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybedtools\n",
+    "\n",
+    "fft = selected_beds[\"region\"].str.split(\":\", expand=True)\n",
+    "fft[0] = \"chr\"+fft[0]\n",
+    "beds_var1 = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "peak_bed1 = pybedtools.BedTool.from_dataframe(fft)\n",
+    "temps = beds_var1.intersect(peak_bed1, c=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temps1 = temps.to_dataframe(names=selected_beds.columns.tolist()+[\"counts\"]).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "218803"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(temps1[\"counts\"] > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf[\"IsUsed\"] = False\n",
+    "dataf.loc[(~np.isnan(dataf[\"DNASE_SAR\"])) & (dataf[\"label\"]==1) & (temps1[\"counts\"] > 0), \"IsUsed\"] = True\n",
+    "dataf.loc[(~np.isnan(dataf[\"DNASE_SAR\"])) & (dataf[\"pval\"]>1e-3) & (temps1[\"counts\"] > 0), \"IsUsed\"] = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109646"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8363"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[dataf[\"IsUsed\"]][\"label\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101283"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[dataf[\"IsUsed\"]][\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(dataf[\"IsUsed\"]) # 109707"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(dataf[dataf[\"IsUsed\"]][\"label\"]==1) # 8366"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(dataf[dataf[\"IsUsed\"]][\"label\"]==0) # 8366"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gambian\n",
+      "Luhya\n",
+      "Yoruba\n",
+      "Esan\n",
+      "GM12878\n",
+      ".ENCSR637XSC\n",
+      ".ENCSR000EMT\n",
+      ".ENCSR637XSC.sub.50M\n",
+      ".ENCSR095QNB\n",
+      "Mende\n",
+      "Maasai\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the ground truths are flipped when rsid does not match allele2 and allele\n",
+    "index=(dataf[\"allele1\"] != (dataf[\"rsid_x\"].str.split('_').str[2]))\n",
+    "index=np.array(index.values)\n",
+    "dataf.loc[index,\"enformer_recomputed\"] = -1 * dataf.loc[index,\"enformer_recomputed\"]\n",
+    "for ans in anscs:\n",
+    "    if ans==\"eur\":\n",
+    "        continue\n",
+    "    if ans==\"afr\":\n",
+    "        continue       \n",
+    "    print(ans)\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\".ENCSR637XSC\", \".ENCSR000EMT\", \".ENCSR637XSC.sub.50M\", \".ENCSR095QNB\" ]:\n",
+    "            print(title)\n",
+    "            dataf.loc[index,ans+title+\".mean.sign.jsd\"] = -1 * dataf.loc[index,ans+title+\".mean.sign.jsd\"]\n",
+    "            dataf.loc[index,ans+title+\".mean.logfc\"] = -1 * dataf.loc[index,ans+title+\".mean.logfc\"]\n",
+    "            \n",
+    "    else:\n",
+    "        title=\"\"\n",
+    "        dataf.loc[index,ans+title+\".mean.sign.jsd\"] = -1 * dataf.loc[index,ans+title+\".mean.sign.jsd\"]\n",
+    "        dataf.loc[index,ans+title+\".mean.logfc\"] = -1 * dataf.loc[index,ans+title+\".mean.logfc\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf.loc[index,\"DNASE_SAD\"] = -1 * dataf.loc[index,\"DNASE_SAD\"]\n",
+    "dataf.loc[index,\"DNASE_SAR\"] = -1 * dataf.loc[index,\"DNASE_SAR\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'Gambian.mean.logfc',\n",
+       "       'Gambian.mean.sign.jsd', 'Luhya.mean.logfc', 'Luhya.mean.sign.jsd',\n",
+       "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.sign.jsd', 'GM12878.ENCSR637XSC.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'afr.mean.logfc', 'afr.mean.sign.jsd', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.sign.jsd', 'Maasai.mean.logfc', 'Maasai.mean.sign.jsd',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd', 'label', 'pos0',\n",
+       "       'enformer_recomputed', 'rsid_temp', 'chr_y', 'pos_y', 'ref', 'alt',\n",
+       "       'rsid_y', 'DNASE_SAD', 'DNASE_SAR', 'RA1', 'RA3', 'rsid1', 'RA2',\n",
+       "       'id_hg19', 'IsUsed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                NaN\n",
+       "1                NaN\n",
+       "2         rs72631880\n",
+       "3                NaN\n",
+       "4          rs4970382\n",
+       "             ...    \n",
+       "219377           NaN\n",
+       "219378     rs6151406\n",
+       "219379           NaN\n",
+       "219380           NaN\n",
+       "219381           NaN\n",
+       "Name: rsid_y, Length: 219382, dtype: object"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf[\"rsid_y\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'Gambian.mean.logfc',\n",
+       "       'Gambian.mean.sign.jsd', 'Luhya.mean.logfc', 'Luhya.mean.sign.jsd',\n",
+       "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.sign.jsd', 'GM12878.ENCSR637XSC.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'afr.mean.logfc', 'afr.mean.sign.jsd', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.sign.jsd', 'Maasai.mean.logfc', 'Maasai.mean.sign.jsd',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd', 'label', 'pos0',\n",
+       "       'enformer_recomputed', 'rsid_temp', 'chr_y', 'pos_y', 'ref', 'alt',\n",
+       "       'rsid_y', 'DNASE_SAD', 'DNASE_SAR', 'RA1', 'RA3', 'rsid1', 'RA2',\n",
+       "       'id_hg19', 'IsUsed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafinal = dataf[['chr_x', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'label',  'IsUsed', 'pval', 'beta', 'DNASE_SAD', 'DNASE_SAR', 'enformer_recomputed',\n",
+    "       'Gambian.mean.logfc', 'Gambian.mean.sign.jsd',\n",
+    "        'Esan.mean.logfc', 'Esan.mean.sign.jsd', \n",
+    "        'Mende.mean.logfc', 'Mende.mean.sign.jsd',\n",
+    "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+    "        'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', \n",
+    "        'Luhya.mean.logfc', 'Luhya.mean.sign.jsd', \n",
+    "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "        'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "        'rsid_y', 'region', 'snp',\n",
+    "       'distance', 'se', 'abs_beta', 'id_hg19']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'chr_x': 'chr_hg38',\n",
+       " 'pos_x': 'pos_hg38',\n",
+       " 'DNASE_SAR': 'enformer.encsr000emt.variantscore.sar',\n",
+       " 'DNASE_SAD': 'enformer.encsr000emt.variantscore.sad',\n",
+       " 'IsUsed1': 'IsUsed',\n",
+       " 'rsid_x': 'snp_id',\n",
+       " 'rsid_y': 'dbsnp_rsid',\n",
+       " 'Gambian.mean.logfc': 'chrombpnet.gambian.variantscore.logfc',\n",
+       " 'Gambian.mean.sign.jsd': 'chrombpnet.gambian.variantscore.jsd',\n",
+       " 'Esan.mean.logfc': 'chrombpnet.esan.variantscore.logfc',\n",
+       " 'Esan.mean.sign.jsd': 'chrombpnet.esan.variantscore.jsd',\n",
+       " 'Mende.mean.logfc': 'chrombpnet.mende.variantscore.logfc',\n",
+       " 'Mende.mean.sign.jsd': 'chrombpnet.mende.variantscore.jsd',\n",
+       " 'Yoruba.mean.logfc': 'chrombpnet.yoruba.variantscore.logfc',\n",
+       " 'Yoruba.mean.sign.jsd': 'chrombpnet.yoruba.variantscore.jsd',\n",
+       " 'Maasai.mean.logfc': 'chrombpnet.maasai.variantscore.logfc',\n",
+       " 'Maasai.mean.sign.jsd': 'chrombpnet.maasai.variantscore.jsd',\n",
+       " 'Luhya.mean.logfc': 'chrombpnet.luhya.variantscore.logfc',\n",
+       " 'Luhya.mean.sign.jsd': 'chrombpnet.luhya.variantscore.jsd',\n",
+       " 'GM12878.ENCSR637XSC.mean.logfc': 'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       " 'GM12878.ENCSR637XSC.mean.sign.jsd': 'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       " 'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile': 'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       " 'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval': 'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       " 'GM12878.ENCSR000EMT.mean.logfc': 'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       " 'GM12878.ENCSR000EMT.mean.sign.jsd': 'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       " 'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile': 'chrombpnet.encsr000emt.variantscore.csr',\n",
+       " 'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval': 'chrombpnet.encsr000emt.variantscore.pval',\n",
+       " 'GM12878.ENCSR637XSC.sub.50M.mean.logfc': 'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       " 'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd': 'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       " 'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile': 'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       " 'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval': 'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       " 'GM12878.ENCSR095QNB.mean.logfc': 'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       " 'GM12878.ENCSR095QNB.mean.sign.jsd': 'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       " 'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile': 'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       " 'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval': 'chrombpnet.encsr095qnb.variantscore.pval'}"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rename_dict = {\"chr_x\": \"chr_hg38\", \"pos_x\": \"pos_hg38\", \"DNASE_SAR\": \"enformer.encsr000emt.variantscore.sar\", \"DNASE_SAD\": \"enformer.encsr000emt.variantscore.sad\", \"IsUsed1\":\"IsUsed\", \"rsid_x\": \"snp_id\", \"rsid_y\": \"dbsnp_rsid\"}\n",
+    "\n",
+    "anc_data = ['Gambian.mean.logfc', 'Gambian.mean.sign.jsd',\n",
+    "        'Esan.mean.logfc', 'Esan.mean.sign.jsd', \n",
+    "        'Mende.mean.logfc', 'Mende.mean.sign.jsd',\n",
+    "       'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+    "        'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', \n",
+    "        'Luhya.mean.logfc', 'Luhya.mean.sign.jsd', \n",
+    "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "        'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+    "        ]\n",
+    "\n",
+    "for dt in anc_data:\n",
+    "    vals = dt.split(\".\")\n",
+    "    if vals[0] == \"GM12878\":\n",
+    "        popn=vals[1].lower()\n",
+    "        if vals[2] == \"sub\":\n",
+    "            popn=vals[1].lower()+\"_50m\"\n",
+    "    else:\n",
+    "        popn=vals[0].lower()\n",
+    "    if vals[-1] == \"logfc\":\n",
+    "        sc=\"logfc\"\n",
+    "    elif vals[-1] == \"jsd\":\n",
+    "        sc=\"jsd\"\n",
+    "    elif vals[-1] == \"abs_logfc_x_jsd_x_max_percentile\":\n",
+    "        sc=\"csr\"   \n",
+    "    elif vals[-1] == \"pval\" and vals[-2] == \"abs_logfc_x_jsd_x_max_percentile\":\n",
+    "        sc=\"pval\"\n",
+    "    else:\n",
+    "        print(vals[-1])\n",
+    "    rename_dict[dt] = \"chrombpnet.\"+popn+\".variantscore.\"+sc\n",
+    "        \n",
+    "rename_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafinal = datafinal.rename(columns=rename_dict)\n",
+    "\n",
+    "datafinal.to_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = open(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Afr.CaQTLS.README\", \"w\")\n",
+    "for line in datafinal.columns:\n",
+    "    f.write(line+\"\\n\")\n",
+    "f.close()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename_dict = {\"chr_x\": \"chr_hg38\", \"pos_x\": \"pos_hg38\", \"DNASE_SAR\": \"enformer.encsr000emt.variantscore.sar\", \"DNASE_SAD\": \"enformer.encsr000emt.variantscore.sad\", \"IsUsed1\":\"IsUsed\", \"rsid_x\": \"snp_id\", \"rsid_y\": \"dbsnp_rsid\"}\n",
+    "\n",
+    "# datafinal = datafinal.rename(columns=rename_dict)\n",
+    "\n",
+    "# datafinal.to_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Afr.CaQTLS.tsv\", sep=\"\\t\", header=True, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed', 'pval', 'beta', 'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafinal.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109707"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(datafinal[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 45)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafinal.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datafinal[datafinal[\"IsUsed\"]][\"distance\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datafinal[\"distance\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fft = datafinal[\"region\"].str.split(\":\", expand=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fft.head()\n",
+    "fft[0] = \"chr\"+fft[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "width = fft[2].astype(int) - fft[1].astype(int) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "width.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866099</td>\n",
+       "      <td>866351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>866099</td>\n",
+       "      <td>866351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>869800</td>\n",
+       "      <td>870282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904938</td>\n",
+       "      <td>905544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>904938</td>\n",
+       "      <td>905544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219377</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627308</td>\n",
+       "      <td>50627508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219378</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627514</td>\n",
+       "      <td>50628822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219379</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627514</td>\n",
+       "      <td>50628822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219380</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50627514</td>\n",
+       "      <td>50628822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219381</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50782926</td>\n",
+       "      <td>50783896</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>219382 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0         1         2\n",
+       "0        chr1    866099    866351\n",
+       "1        chr1    866099    866351\n",
+       "2        chr1    869800    870282\n",
+       "3        chr1    904938    905544\n",
+       "4        chr1    904938    905544\n",
+       "...       ...       ...       ...\n",
+       "219377  chr22  50627308  50627508\n",
+       "219378  chr22  50627514  50628822\n",
+       "219379  chr22  50627514  50628822\n",
+       "219380  chr22  50627514  50628822\n",
+       "219381  chr22  50782926  50783896\n",
+       "\n",
+       "[219382 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "chr1\t866280\t866281\tC\tT\t1_866281_C_T\t1:866099:866351\t1_866281_C_T\t181\t0.97428\t0.00654566\t0.202449\t0.00654566\t-0.0198358239733333\t-0.0092910501638771\t-0.0371476132\t-0.0098478139667772\t-0.0107790358\t-0.0112429074557623\t-0.03047294212\t-0.007854980010384\t-0.0274319003999999\t-0.0104460740301044\t-0.015266906\t-0.0074909427884077\t3.842012393515388e-05\t0.47469310823965\t-0.00869365584\t-0.0048307100337268\t2.439625108697341e-05\t0.5218877975662511\t-0.027134499\t-0.0065437883004904\t4.429526404646882e-05\t0.4069019535301472\t-0.0319308548\t-0.0090450750534331\t0.0001624065727458\t0.1928464912342504\t-0.0172759259\t-0.0108619390346573\t-0.01244030388\t-0.0089491793240438\t-0.02061483574\t-0.006391221128311\tFalse\t2\n",
+      " "
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "temps.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "218803"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(temps1[\"counts\"]>0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 47)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 45)"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafinal.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temps1[\"IsUsed\"]  = datafinal[\"IsUsed\"] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109707"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1\n",
+    "sum(temps1[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109646"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(temps1[temps1[\"IsUsed\"]][\"counts\"] > 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos0', 'pos', 'allele1', 'allele2', 'rsid', 'region', 'snp',\n",
+       "       'distance', 'pval', 'beta', 'se', 'abs_beta', 'afr.mean.logfc',\n",
+       "       'afr.mean.sign.jsd', 'eur.mean.logfc', 'eur.mean.sign.jsd',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.sign.jsd', 'Mende.mean.logfc',\n",
+       "       'Mende.mean.sign.jsd', 'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Esan.mean.logfc', 'Esan.mean.sign.jsd', 'Maasai.mean.logfc',\n",
+       "       'Maasai.mean.sign.jsd', 'Luhya.mean.logfc', 'Luhya.mean.sign.jsd',\n",
+       "       'label', 'counts'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temps1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls_asb.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls_asb.ipynb
new file mode 100644
index 00000000..b1f850ad
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_af_caqtls_asb.ipynb
@@ -0,0 +1,2791 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asb_sig = pd.read_csv(\"sig.asb.scores.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>Esan.mean.logfc</th>\n",
+       "      <th>Esan.mean.abs_logfc</th>\n",
+       "      <th>Esan.mean.jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>...</th>\n",
+       "      <th>afr.mean.jsd</th>\n",
+       "      <th>eur.mean.logfc</th>\n",
+       "      <th>eur.mean.abs_logfc</th>\n",
+       "      <th>eur.mean.jsd</th>\n",
+       "      <th>afr.mean.abs_logfc.pval</th>\n",
+       "      <th>afr.mean.jsd.pval</th>\n",
+       "      <th>eur.mean.abs_logfc.pval</th>\n",
+       "      <th>eur.mean.jsd.pval</th>\n",
+       "      <th>variantID</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>0.148617</td>\n",
+       "      <td>0.186169</td>\n",
+       "      <td>0.029973</td>\n",
+       "      <td>0.005416</td>\n",
+       "      <td>0.003405</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.036430</td>\n",
+       "      <td>-0.027498</td>\n",
+       "      <td>0.218047</td>\n",
+       "      <td>0.052142</td>\n",
+       "      <td>0.048619</td>\n",
+       "      <td>0.019607</td>\n",
+       "      <td>0.048772</td>\n",
+       "      <td>0.015193</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1000079</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>0.012582</td>\n",
+       "      <td>0.017991</td>\n",
+       "      <td>0.000220</td>\n",
+       "      <td>0.000198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.021034</td>\n",
+       "      <td>0.004492</td>\n",
+       "      <td>0.028228</td>\n",
+       "      <td>0.019831</td>\n",
+       "      <td>0.457881</td>\n",
+       "      <td>0.074831</td>\n",
+       "      <td>0.783580</td>\n",
+       "      <td>0.160148</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>-2.087074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>0.013834</td>\n",
+       "      <td>0.078046</td>\n",
+       "      <td>0.014067</td>\n",
+       "      <td>0.001314</td>\n",
+       "      <td>0.000522</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.014347</td>\n",
+       "      <td>-0.002473</td>\n",
+       "      <td>0.040055</td>\n",
+       "      <td>0.015031</td>\n",
+       "      <td>0.230560</td>\n",
+       "      <td>0.212956</td>\n",
+       "      <td>0.603360</td>\n",
+       "      <td>0.309351</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>3.172063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>0.139453</td>\n",
+       "      <td>0.139453</td>\n",
+       "      <td>0.021787</td>\n",
+       "      <td>0.003160</td>\n",
+       "      <td>0.001386</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.020315</td>\n",
+       "      <td>-0.208648</td>\n",
+       "      <td>0.208648</td>\n",
+       "      <td>0.021750</td>\n",
+       "      <td>0.071184</td>\n",
+       "      <td>0.081884</td>\n",
+       "      <td>0.053131</td>\n",
+       "      <td>0.128314</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>-0.354500</td>\n",
+       "      <td>0.354500</td>\n",
+       "      <td>0.029879</td>\n",
+       "      <td>0.010773</td>\n",
+       "      <td>0.006039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.032463</td>\n",
+       "      <td>0.552679</td>\n",
+       "      <td>0.552679</td>\n",
+       "      <td>0.034941</td>\n",
+       "      <td>0.006371</td>\n",
+       "      <td>0.025877</td>\n",
+       "      <td>0.006414</td>\n",
+       "      <td>0.041549</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr      pos allele1 allele2           rsid  Esan.mean.logfc  \\\n",
+       "0  chr1   976215       A       G   1_976215_A_G         0.148617   \n",
+       "1  chr1  1000079       A       G  1_1000079_A_G         0.001207   \n",
+       "2  chr1  1038800       T       G  1_1038800_G_T         0.013834   \n",
+       "3  chr1  1038819       T       C  1_1038819_C_T         0.139453   \n",
+       "4  chr1  1038845       G       A  1_1038845_A_G        -0.354500   \n",
+       "\n",
+       "   Esan.mean.abs_logfc  Esan.mean.jsd  Esan.mean.abs_logfc_x_jsd  \\\n",
+       "0             0.186169       0.029973                   0.005416   \n",
+       "1             0.012582       0.017991                   0.000220   \n",
+       "2             0.078046       0.014067                   0.001314   \n",
+       "3             0.139453       0.021787                   0.003160   \n",
+       "4             0.354500       0.029879                   0.010773   \n",
+       "\n",
+       "   Esan.mean.abs_logfc_x_jsd_x_max_percentile  ...  afr.mean.jsd  \\\n",
+       "0                                    0.003405  ...      0.036430   \n",
+       "1                                    0.000198  ...      0.021034   \n",
+       "2                                    0.000522  ...      0.014347   \n",
+       "3                                    0.001386  ...      0.020315   \n",
+       "4                                    0.006039  ...      0.032463   \n",
+       "\n",
+       "   eur.mean.logfc  eur.mean.abs_logfc  eur.mean.jsd  afr.mean.abs_logfc.pval  \\\n",
+       "0       -0.027498            0.218047      0.052142                 0.048619   \n",
+       "1        0.004492            0.028228      0.019831                 0.457881   \n",
+       "2       -0.002473            0.040055      0.015031                 0.230560   \n",
+       "3       -0.208648            0.208648      0.021750                 0.071184   \n",
+       "4        0.552679            0.552679      0.034941                 0.006371   \n",
+       "\n",
+       "   afr.mean.jsd.pval  eur.mean.abs_logfc.pval  eur.mean.jsd.pval  \\\n",
+       "0           0.019607                 0.048772           0.015193   \n",
+       "1           0.074831                 0.783580           0.160148   \n",
+       "2           0.212956                 0.603360           0.309351   \n",
+       "3           0.081884                 0.053131           0.128314   \n",
+       "4           0.025877                 0.006414           0.041549   \n",
+       "\n",
+       "       variantID  meanLog2FC  \n",
+       "0   1_976215_A_G   -1.852605  \n",
+       "1  1_1000079_A_G   -2.087074  \n",
+       "2  1_1038800_G_T    3.172063  \n",
+       "3  1_1038819_C_T    2.390945  \n",
+       "4  1_1038845_A_G    2.163432  \n",
+       "\n",
+       "[5 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7559, 72)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atac_model=pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds_asb/atac/ATAC/summary.mean.variant_scores_new_2.tsv\", sep='\\t', header=0)\n",
+    "atac_model_50m=pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds_asb/atac_50M/ATAC_50M/summary.mean.variant_scores_new_2.tsv\", sep='\\t', header=0)\n",
+    "dnase_model=pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/chrombpnet_preds_asb/dnase_predss/DNASE/summary.mean.variant_scores_new_2.tsv\", sep='\\t', header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GM12878.ENCSR637XSC.mean.logfc\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc\n",
+      "GM12878.ENCSR637XSC.mean.jsd\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile\n",
+      "GM12878.ENCSR637XSC.mean.max_percentile\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc.pval\n",
+      "GM12878.ENCSR637XSC.mean.jsd.pval\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval\n"
+     ]
+    }
+   ],
+   "source": [
+    "vals = ['GM12878.ENCSR637XSC.mean.logfc', 'GM12878.ENCSR637XSC.mean.abs_logfc', 'GM12878.ENCSR637XSC.mean.jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.max_percentile',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.jsd.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval',\n",
+    "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval']\n",
+    "\n",
+    "for val in vals:\n",
+    "    print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        asb_sig[val] = atac_model[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        asb_sig[val] = atac_model[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GM12878.ENCSR637XSC.mean.logfc\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc\n",
+      "GM12878.ENCSR637XSC.mean.jsd\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile\n",
+      "GM12878.ENCSR637XSC.mean.max_percentile\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc.pval\n",
+      "GM12878.ENCSR637XSC.mean.jsd.pval\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval\n"
+     ]
+    }
+   ],
+   "source": [
+    "for val in vals:\n",
+    "    print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        asb_sig[val.replace(\"ENCSR637XSC\", \"ENCSR000EMT\")] = dnase_model[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        asb_sig[val.replace(\"ENCSR637XSC\", \"ENCSR000EMT\")] = dnase_model[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GM12878.ENCSR637XSC.mean.logfc\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc\n",
+      "GM12878.ENCSR637XSC.mean.jsd\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile\n",
+      "GM12878.ENCSR637XSC.mean.max_percentile\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc.pval\n",
+      "GM12878.ENCSR637XSC.mean.jsd.pval\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd.pval\n",
+      "GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval\n"
+     ]
+    }
+   ],
+   "source": [
+    "for val in vals:\n",
+    "    print(val)\n",
+    "    if \"pval\" not in val:\n",
+    "        asb_sig[val.replace(\"ENCSR637XSC\", \"ENCSR637XSC.sub.50M\")] = atac_model_50m[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\")+\".mean\"]\n",
+    "    else:\n",
+    "        asb_sig[val.replace(\"ENCSR637XSC\", \"ENCSR637XSC.sub.50M\")] = atac_model_50m[val.replace(\"GM12878.ENCSR637XSC.mean.\", \"\").replace(\".pval\",\".mean.pval\")]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7559, 92)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig[asb_sig.columns[0:70].tolist()+asb_sig.columns[72:].tolist()+asb_sig.columns[70:72].tolist()].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>Esan.mean.logfc</th>\n",
+       "      <th>Esan.mean.abs_logfc</th>\n",
+       "      <th>Esan.mean.jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd</th>\n",
+       "      <th>Esan.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.jsd</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.max_percentile</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc.pval</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.jsd.pval</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd.pval</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>variantID</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>0.148617</td>\n",
+       "      <td>0.186169</td>\n",
+       "      <td>0.029973</td>\n",
+       "      <td>0.005416</td>\n",
+       "      <td>0.003405</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.038511</td>\n",
+       "      <td>0.002535</td>\n",
+       "      <td>0.001846</td>\n",
+       "      <td>0.726685</td>\n",
+       "      <td>0.188135</td>\n",
+       "      <td>0.038268</td>\n",
+       "      <td>0.099167</td>\n",
+       "      <td>0.031981</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1000079</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>0.012582</td>\n",
+       "      <td>0.017991</td>\n",
+       "      <td>0.000220</td>\n",
+       "      <td>0.000198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.013705</td>\n",
+       "      <td>0.000280</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.886727</td>\n",
+       "      <td>0.603316</td>\n",
+       "      <td>0.265932</td>\n",
+       "      <td>0.502734</td>\n",
+       "      <td>0.186599</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>-2.087074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>0.013834</td>\n",
+       "      <td>0.078046</td>\n",
+       "      <td>0.014067</td>\n",
+       "      <td>0.001314</td>\n",
+       "      <td>0.000522</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.016815</td>\n",
+       "      <td>0.000682</td>\n",
+       "      <td>0.000430</td>\n",
+       "      <td>0.615778</td>\n",
+       "      <td>0.377284</td>\n",
+       "      <td>0.193226</td>\n",
+       "      <td>0.289714</td>\n",
+       "      <td>0.121757</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>3.172063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>0.139453</td>\n",
+       "      <td>0.139453</td>\n",
+       "      <td>0.021787</td>\n",
+       "      <td>0.003160</td>\n",
+       "      <td>0.001386</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.020354</td>\n",
+       "      <td>0.002322</td>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.629579</td>\n",
+       "      <td>0.111893</td>\n",
+       "      <td>0.140336</td>\n",
+       "      <td>0.112243</td>\n",
+       "      <td>0.041210</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>-0.354500</td>\n",
+       "      <td>0.354500</td>\n",
+       "      <td>0.029879</td>\n",
+       "      <td>0.010773</td>\n",
+       "      <td>0.006039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.036387</td>\n",
+       "      <td>0.013667</td>\n",
+       "      <td>0.010299</td>\n",
+       "      <td>0.757558</td>\n",
+       "      <td>0.010294</td>\n",
+       "      <td>0.041947</td>\n",
+       "      <td>0.018396</td>\n",
+       "      <td>0.005023</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7554</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>45413743</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22_45413743_C_A</td>\n",
+       "      <td>0.060161</td>\n",
+       "      <td>0.066485</td>\n",
+       "      <td>0.019021</td>\n",
+       "      <td>0.001365</td>\n",
+       "      <td>0.001260</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.013788</td>\n",
+       "      <td>0.000329</td>\n",
+       "      <td>0.000294</td>\n",
+       "      <td>0.886294</td>\n",
+       "      <td>0.552125</td>\n",
+       "      <td>0.265274</td>\n",
+       "      <td>0.436014</td>\n",
+       "      <td>0.150906</td>\n",
+       "      <td>22_45413743_C_A</td>\n",
+       "      <td>-2.318303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7555</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>46773373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_46773373_C_T</td>\n",
+       "      <td>0.418584</td>\n",
+       "      <td>0.418584</td>\n",
+       "      <td>0.053580</td>\n",
+       "      <td>0.026134</td>\n",
+       "      <td>0.003178</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.030801</td>\n",
+       "      <td>0.008759</td>\n",
+       "      <td>0.001172</td>\n",
+       "      <td>0.100323</td>\n",
+       "      <td>0.030670</td>\n",
+       "      <td>0.064458</td>\n",
+       "      <td>0.038888</td>\n",
+       "      <td>0.071740</td>\n",
+       "      <td>22_46773373_C_T</td>\n",
+       "      <td>2.662931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7556</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>47001539</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_47001539_G_A</td>\n",
+       "      <td>0.629396</td>\n",
+       "      <td>0.629396</td>\n",
+       "      <td>0.048494</td>\n",
+       "      <td>0.031881</td>\n",
+       "      <td>0.024380</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.052056</td>\n",
+       "      <td>0.021613</td>\n",
+       "      <td>0.014751</td>\n",
+       "      <td>0.686682</td>\n",
+       "      <td>0.007990</td>\n",
+       "      <td>0.017456</td>\n",
+       "      <td>0.010411</td>\n",
+       "      <td>0.003098</td>\n",
+       "      <td>22_47001539_G_A</td>\n",
+       "      <td>-2.249763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7557</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>47918714</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_47918714_G_A</td>\n",
+       "      <td>0.603185</td>\n",
+       "      <td>0.603185</td>\n",
+       "      <td>0.044448</td>\n",
+       "      <td>0.029488</td>\n",
+       "      <td>0.020895</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.039984</td>\n",
+       "      <td>0.019479</td>\n",
+       "      <td>0.011643</td>\n",
+       "      <td>0.559056</td>\n",
+       "      <td>0.006763</td>\n",
+       "      <td>0.034914</td>\n",
+       "      <td>0.013736</td>\n",
+       "      <td>0.005346</td>\n",
+       "      <td>22_47918714_G_A</td>\n",
+       "      <td>3.353036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7558</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50017166</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_50017166_G_T</td>\n",
+       "      <td>-0.159627</td>\n",
+       "      <td>0.159627</td>\n",
+       "      <td>0.021944</td>\n",
+       "      <td>0.005232</td>\n",
+       "      <td>0.002373</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.034851</td>\n",
+       "      <td>0.012855</td>\n",
+       "      <td>0.008549</td>\n",
+       "      <td>0.614280</td>\n",
+       "      <td>0.017486</td>\n",
+       "      <td>0.047819</td>\n",
+       "      <td>0.025550</td>\n",
+       "      <td>0.009083</td>\n",
+       "      <td>22_50017166_G_T</td>\n",
+       "      <td>-2.075202</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7559 rows × 92 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        chr       pos allele1 allele2             rsid  Esan.mean.logfc  \\\n",
+       "0      chr1    976215       A       G     1_976215_A_G         0.148617   \n",
+       "1      chr1   1000079       A       G    1_1000079_A_G         0.001207   \n",
+       "2      chr1   1038800       T       G    1_1038800_G_T         0.013834   \n",
+       "3      chr1   1038819       T       C    1_1038819_C_T         0.139453   \n",
+       "4      chr1   1038845       G       A    1_1038845_A_G        -0.354500   \n",
+       "...     ...       ...     ...     ...              ...              ...   \n",
+       "7554  chr22  45413743       A       C  22_45413743_C_A         0.060161   \n",
+       "7555  chr22  46773373       C       T  22_46773373_C_T         0.418584   \n",
+       "7556  chr22  47001539       A       G  22_47001539_G_A         0.629396   \n",
+       "7557  chr22  47918714       G       A  22_47918714_G_A         0.603185   \n",
+       "7558  chr22  50017166       G       T  22_50017166_G_T        -0.159627   \n",
+       "\n",
+       "      Esan.mean.abs_logfc  Esan.mean.jsd  Esan.mean.abs_logfc_x_jsd  \\\n",
+       "0                0.186169       0.029973                   0.005416   \n",
+       "1                0.012582       0.017991                   0.000220   \n",
+       "2                0.078046       0.014067                   0.001314   \n",
+       "3                0.139453       0.021787                   0.003160   \n",
+       "4                0.354500       0.029879                   0.010773   \n",
+       "...                   ...            ...                        ...   \n",
+       "7554             0.066485       0.019021                   0.001365   \n",
+       "7555             0.418584       0.053580                   0.026134   \n",
+       "7556             0.629396       0.048494                   0.031881   \n",
+       "7557             0.603185       0.044448                   0.029488   \n",
+       "7558             0.159627       0.021944                   0.005232   \n",
+       "\n",
+       "      Esan.mean.abs_logfc_x_jsd_x_max_percentile  ...  \\\n",
+       "0                                       0.003405  ...   \n",
+       "1                                       0.000198  ...   \n",
+       "2                                       0.000522  ...   \n",
+       "3                                       0.001386  ...   \n",
+       "4                                       0.006039  ...   \n",
+       "...                                          ...  ...   \n",
+       "7554                                    0.001260  ...   \n",
+       "7555                                    0.003178  ...   \n",
+       "7556                                    0.024380  ...   \n",
+       "7557                                    0.020895  ...   \n",
+       "7558                                    0.002373  ...   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.jsd  \\\n",
+       "0                                 0.038511   \n",
+       "1                                 0.013705   \n",
+       "2                                 0.016815   \n",
+       "3                                 0.020354   \n",
+       "4                                 0.036387   \n",
+       "...                                    ...   \n",
+       "7554                              0.013788   \n",
+       "7555                              0.030801   \n",
+       "7556                              0.052056   \n",
+       "7557                              0.039984   \n",
+       "7558                              0.034851   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd  \\\n",
+       "0                                             0.002535   \n",
+       "1                                             0.000280   \n",
+       "2                                             0.000682   \n",
+       "3                                             0.002322   \n",
+       "4                                             0.013667   \n",
+       "...                                                ...   \n",
+       "7554                                          0.000329   \n",
+       "7555                                          0.008759   \n",
+       "7556                                          0.021613   \n",
+       "7557                                          0.019479   \n",
+       "7558                                          0.012855   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                              0.001846                   \n",
+       "1                                              0.000256                   \n",
+       "2                                              0.000430                   \n",
+       "3                                              0.001482                   \n",
+       "4                                              0.010299                   \n",
+       "...                                                 ...                   \n",
+       "7554                                           0.000294                   \n",
+       "7555                                           0.001172                   \n",
+       "7556                                           0.014751                   \n",
+       "7557                                           0.011643                   \n",
+       "7558                                           0.008549                   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.max_percentile  \\\n",
+       "0                                            0.726685   \n",
+       "1                                            0.886727   \n",
+       "2                                            0.615778   \n",
+       "3                                            0.629579   \n",
+       "4                                            0.757558   \n",
+       "...                                               ...   \n",
+       "7554                                         0.886294   \n",
+       "7555                                         0.100323   \n",
+       "7556                                         0.686682   \n",
+       "7557                                         0.559056   \n",
+       "7558                                         0.614280   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc.pval  \\\n",
+       "0                                            0.188135   \n",
+       "1                                            0.603316   \n",
+       "2                                            0.377284   \n",
+       "3                                            0.111893   \n",
+       "4                                            0.010294   \n",
+       "...                                               ...   \n",
+       "7554                                         0.552125   \n",
+       "7555                                         0.030670   \n",
+       "7556                                         0.007990   \n",
+       "7557                                         0.006763   \n",
+       "7558                                         0.017486   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.jsd.pval  \\\n",
+       "0                                      0.038268   \n",
+       "1                                      0.265932   \n",
+       "2                                      0.193226   \n",
+       "3                                      0.140336   \n",
+       "4                                      0.041947   \n",
+       "...                                         ...   \n",
+       "7554                                   0.265274   \n",
+       "7555                                   0.064458   \n",
+       "7556                                   0.017456   \n",
+       "7557                                   0.034914   \n",
+       "7558                                   0.047819   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd.pval  \\\n",
+       "0                                              0.099167       \n",
+       "1                                              0.502734       \n",
+       "2                                              0.289714       \n",
+       "3                                              0.112243       \n",
+       "4                                              0.018396       \n",
+       "...                                                 ...       \n",
+       "7554                                           0.436014       \n",
+       "7555                                           0.038888       \n",
+       "7556                                           0.010411       \n",
+       "7557                                           0.013736       \n",
+       "7558                                           0.025550       \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                              0.031981                        \n",
+       "1                                              0.186599                        \n",
+       "2                                              0.121757                        \n",
+       "3                                              0.041210                        \n",
+       "4                                              0.005023                        \n",
+       "...                                                 ...                        \n",
+       "7554                                           0.150906                        \n",
+       "7555                                           0.071740                        \n",
+       "7556                                           0.003098                        \n",
+       "7557                                           0.005346                        \n",
+       "7558                                           0.009083                        \n",
+       "\n",
+       "            variantID  meanLog2FC  \n",
+       "0        1_976215_A_G   -1.852605  \n",
+       "1       1_1000079_A_G   -2.087074  \n",
+       "2       1_1038800_G_T    3.172063  \n",
+       "3       1_1038819_C_T    2.390945  \n",
+       "4       1_1038845_A_G    2.163432  \n",
+       "...               ...         ...  \n",
+       "7554  22_45413743_C_A   -2.318303  \n",
+       "7555  22_46773373_C_T    2.662931  \n",
+       "7556  22_47001539_G_A   -2.249763  \n",
+       "7557  22_47918714_G_A    3.353036  \n",
+       "7558  22_50017166_G_T   -2.075202  \n",
+       "\n",
+       "[7559 rows x 92 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig[asb_sig.columns[0:70].tolist()+asb_sig.columns[72:].tolist()+asb_sig.columns[70:72].tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Maasai', 'Esan', 'Luhya', 'Mende', 'afr', 'Yoruba', 'eur', 'GM12878', 'Gambian'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "anscs = asb_sig.columns[5:70].tolist()+asb_sig.columns[72:].tolist()\n",
+    "anscs = [ans.split(\".\")[0] for ans in anscs]\n",
+    "anscs = set(anscs)\n",
+    "print(anscs)\n",
+    "\n",
+    "sub_list = []\n",
+    "for ans in anscs:\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\".ENCSR637XSC\", \".ENCSR000EMT\", \".ENCSR637XSC.sub.50M\", \".ENCSR095QNB\"]:\n",
+    "            sub_list.append(ans+title+\".mean.logfc\")\n",
+    "            asb_sig[ans+title+\".mean.sign.jsd\"] = np.sign(asb_sig[ans+title+\".mean.logfc\"]) * asb_sig[ans+title+\".mean.jsd\"]\n",
+    "            sub_list.append(ans+title+\".mean.sign.jsd\")\n",
+    "            sub_list.append(ans+title+\".mean.abs_logfc_x_jsd_x_max_percentile\")\n",
+    "            sub_list.append(ans+title+\".mean.abs_logfc_x_jsd_x_max_percentile.pval\")\n",
+    "    else:\n",
+    "        sub_list.append(ans+\".mean.logfc\")\n",
+    "        asb_sig[ans+\".mean.sign.jsd\"] = np.sign(asb_sig[ans+\".mean.logfc\"]) * asb_sig[ans+\".mean.jsd\"]\n",
+    "        sub_list.append(ans+\".mean.sign.jsd\")        \n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asb_sig = asb_sig[asb_sig.columns[0:5].tolist()+asb_sig.columns[70:72].tolist()+sub_list]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>variantID</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "      <th>Maasai.mean.logfc</th>\n",
+       "      <th>Maasai.mean.sign.jsd</th>\n",
+       "      <th>Esan.mean.logfc</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "      <td>0.077883</td>\n",
+       "      <td>0.032739</td>\n",
+       "      <td>0.148617</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.075894</td>\n",
+       "      <td>0.038511</td>\n",
+       "      <td>0.001846</td>\n",
+       "      <td>0.031981</td>\n",
+       "      <td>0.155960</td>\n",
+       "      <td>0.042078</td>\n",
+       "      <td>0.004710</td>\n",
+       "      <td>0.014595</td>\n",
+       "      <td>0.034014</td>\n",
+       "      <td>0.045668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1000079</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>-2.087074</td>\n",
+       "      <td>0.005979</td>\n",
+       "      <td>0.015850</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005725</td>\n",
+       "      <td>0.013705</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.186599</td>\n",
+       "      <td>-0.019343</td>\n",
+       "      <td>-0.020097</td>\n",
+       "      <td>0.000387</td>\n",
+       "      <td>0.115818</td>\n",
+       "      <td>0.026338</td>\n",
+       "      <td>0.028657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>3.172063</td>\n",
+       "      <td>-0.029561</td>\n",
+       "      <td>-0.011263</td>\n",
+       "      <td>0.013834</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.035529</td>\n",
+       "      <td>-0.016815</td>\n",
+       "      <td>0.000430</td>\n",
+       "      <td>0.121757</td>\n",
+       "      <td>-0.008690</td>\n",
+       "      <td>-0.013938</td>\n",
+       "      <td>0.000514</td>\n",
+       "      <td>0.095791</td>\n",
+       "      <td>-0.052296</td>\n",
+       "      <td>-0.017729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "      <td>0.082168</td>\n",
+       "      <td>0.015764</td>\n",
+       "      <td>0.139453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.109847</td>\n",
+       "      <td>0.020354</td>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.041210</td>\n",
+       "      <td>0.188001</td>\n",
+       "      <td>0.022832</td>\n",
+       "      <td>0.004286</td>\n",
+       "      <td>0.016022</td>\n",
+       "      <td>0.104596</td>\n",
+       "      <td>0.023049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "      <td>-0.404463</td>\n",
+       "      <td>-0.031904</td>\n",
+       "      <td>-0.354500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.377451</td>\n",
+       "      <td>-0.036387</td>\n",
+       "      <td>0.010299</td>\n",
+       "      <td>0.005023</td>\n",
+       "      <td>-0.529357</td>\n",
+       "      <td>-0.033111</td>\n",
+       "      <td>0.017407</td>\n",
+       "      <td>0.003498</td>\n",
+       "      <td>-0.347899</td>\n",
+       "      <td>-0.032427</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr      pos allele1 allele2           rsid      variantID  meanLog2FC  \\\n",
+       "0  chr1   976215       A       G   1_976215_A_G   1_976215_A_G   -1.852605   \n",
+       "1  chr1  1000079       A       G  1_1000079_A_G  1_1000079_A_G   -2.087074   \n",
+       "2  chr1  1038800       T       G  1_1038800_G_T  1_1038800_G_T    3.172063   \n",
+       "3  chr1  1038819       T       C  1_1038819_C_T  1_1038819_C_T    2.390945   \n",
+       "4  chr1  1038845       G       A  1_1038845_A_G  1_1038845_A_G    2.163432   \n",
+       "\n",
+       "   Maasai.mean.logfc  Maasai.mean.sign.jsd  Esan.mean.logfc  ...  \\\n",
+       "0           0.077883              0.032739         0.148617  ...   \n",
+       "1           0.005979              0.015850         0.001207  ...   \n",
+       "2          -0.029561             -0.011263         0.013834  ...   \n",
+       "3           0.082168              0.015764         0.139453  ...   \n",
+       "4          -0.404463             -0.031904        -0.354500  ...   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.logfc  \\\n",
+       "0                                0.075894   \n",
+       "1                                0.005725   \n",
+       "2                               -0.035529   \n",
+       "3                                0.109847   \n",
+       "4                               -0.377451   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd  \\\n",
+       "0                                   0.038511   \n",
+       "1                                   0.013705   \n",
+       "2                                  -0.016815   \n",
+       "3                                   0.020354   \n",
+       "4                                  -0.036387   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.001846                   \n",
+       "1                                           0.000256                   \n",
+       "2                                           0.000430                   \n",
+       "3                                           0.001482                   \n",
+       "4                                           0.010299                   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.031981                        \n",
+       "1                                           0.186599                        \n",
+       "2                                           0.121757                        \n",
+       "3                                           0.041210                        \n",
+       "4                                           0.005023                        \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.logfc  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0                        0.155960                           0.042078   \n",
+       "1                       -0.019343                          -0.020097   \n",
+       "2                       -0.008690                          -0.013938   \n",
+       "3                        0.188001                           0.022832   \n",
+       "4                       -0.529357                          -0.033111   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.004710           \n",
+       "1                                           0.000387           \n",
+       "2                                           0.000514           \n",
+       "3                                           0.004286           \n",
+       "4                                           0.017407           \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.014595                \n",
+       "1                                           0.115818                \n",
+       "2                                           0.095791                \n",
+       "3                                           0.016022                \n",
+       "4                                           0.003498                \n",
+       "\n",
+       "   Gambian.mean.logfc  Gambian.mean.sign.jsd  \n",
+       "0            0.034014               0.045668  \n",
+       "1            0.026338               0.028657  \n",
+       "2           -0.052296              -0.017729  \n",
+       "3            0.104596               0.023049  \n",
+       "4           -0.347899              -0.032427  \n",
+       "\n",
+       "[5 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>variantID</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "      <th>Maasai.mean.logfc</th>\n",
+       "      <th>Maasai.mean.sign.jsd</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976214</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "      <td>0.077883</td>\n",
+       "      <td>0.032739</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.075894</td>\n",
+       "      <td>0.038511</td>\n",
+       "      <td>0.001846</td>\n",
+       "      <td>0.031981</td>\n",
+       "      <td>0.155960</td>\n",
+       "      <td>0.042078</td>\n",
+       "      <td>0.004710</td>\n",
+       "      <td>0.014595</td>\n",
+       "      <td>0.034014</td>\n",
+       "      <td>0.045668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1000078</td>\n",
+       "      <td>1000079</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>-2.087074</td>\n",
+       "      <td>0.005979</td>\n",
+       "      <td>0.015850</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005725</td>\n",
+       "      <td>0.013705</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.186599</td>\n",
+       "      <td>-0.019343</td>\n",
+       "      <td>-0.020097</td>\n",
+       "      <td>0.000387</td>\n",
+       "      <td>0.115818</td>\n",
+       "      <td>0.026338</td>\n",
+       "      <td>0.028657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038799</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>3.172063</td>\n",
+       "      <td>-0.029561</td>\n",
+       "      <td>-0.011263</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.035529</td>\n",
+       "      <td>-0.016815</td>\n",
+       "      <td>0.000430</td>\n",
+       "      <td>0.121757</td>\n",
+       "      <td>-0.008690</td>\n",
+       "      <td>-0.013938</td>\n",
+       "      <td>0.000514</td>\n",
+       "      <td>0.095791</td>\n",
+       "      <td>-0.052296</td>\n",
+       "      <td>-0.017729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038818</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "      <td>0.082168</td>\n",
+       "      <td>0.015764</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.109847</td>\n",
+       "      <td>0.020354</td>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.041210</td>\n",
+       "      <td>0.188001</td>\n",
+       "      <td>0.022832</td>\n",
+       "      <td>0.004286</td>\n",
+       "      <td>0.016022</td>\n",
+       "      <td>0.104596</td>\n",
+       "      <td>0.023049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038844</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "      <td>-0.404463</td>\n",
+       "      <td>-0.031904</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.377451</td>\n",
+       "      <td>-0.036387</td>\n",
+       "      <td>0.010299</td>\n",
+       "      <td>0.005023</td>\n",
+       "      <td>-0.529357</td>\n",
+       "      <td>-0.033111</td>\n",
+       "      <td>0.017407</td>\n",
+       "      <td>0.003498</td>\n",
+       "      <td>-0.347899</td>\n",
+       "      <td>-0.032427</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos0      pos allele1 allele2           rsid      variantID  \\\n",
+       "0  chr1   976214   976215       A       G   1_976215_A_G   1_976215_A_G   \n",
+       "1  chr1  1000078  1000079       A       G  1_1000079_A_G  1_1000079_A_G   \n",
+       "2  chr1  1038799  1038800       T       G  1_1038800_G_T  1_1038800_G_T   \n",
+       "3  chr1  1038818  1038819       T       C  1_1038819_C_T  1_1038819_C_T   \n",
+       "4  chr1  1038844  1038845       G       A  1_1038845_A_G  1_1038845_A_G   \n",
+       "\n",
+       "   meanLog2FC  Maasai.mean.logfc  Maasai.mean.sign.jsd  ...  \\\n",
+       "0   -1.852605           0.077883              0.032739  ...   \n",
+       "1   -2.087074           0.005979              0.015850  ...   \n",
+       "2    3.172063          -0.029561             -0.011263  ...   \n",
+       "3    2.390945           0.082168              0.015764  ...   \n",
+       "4    2.163432          -0.404463             -0.031904  ...   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.logfc  \\\n",
+       "0                                0.075894   \n",
+       "1                                0.005725   \n",
+       "2                               -0.035529   \n",
+       "3                                0.109847   \n",
+       "4                               -0.377451   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd  \\\n",
+       "0                                   0.038511   \n",
+       "1                                   0.013705   \n",
+       "2                                  -0.016815   \n",
+       "3                                   0.020354   \n",
+       "4                                  -0.036387   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.001846                   \n",
+       "1                                           0.000256                   \n",
+       "2                                           0.000430                   \n",
+       "3                                           0.001482                   \n",
+       "4                                           0.010299                   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.031981                        \n",
+       "1                                           0.186599                        \n",
+       "2                                           0.121757                        \n",
+       "3                                           0.041210                        \n",
+       "4                                           0.005023                        \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.logfc  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0                        0.155960                           0.042078   \n",
+       "1                       -0.019343                          -0.020097   \n",
+       "2                       -0.008690                          -0.013938   \n",
+       "3                        0.188001                           0.022832   \n",
+       "4                       -0.529357                          -0.033111   \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                           0.004710           \n",
+       "1                                           0.000387           \n",
+       "2                                           0.000514           \n",
+       "3                                           0.004286           \n",
+       "4                                           0.017407           \n",
+       "\n",
+       "   GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                           0.014595                \n",
+       "1                                           0.115818                \n",
+       "2                                           0.095791                \n",
+       "3                                           0.016022                \n",
+       "4                                           0.003498                \n",
+       "\n",
+       "   Gambian.mean.logfc  Gambian.mean.sign.jsd  \n",
+       "0            0.034014               0.045668  \n",
+       "1            0.026338               0.028657  \n",
+       "2           -0.052296              -0.017729  \n",
+       "3            0.104596               0.023049  \n",
+       "4           -0.347899              -0.032427  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig[\"pos0\"] = asb_sig[\"pos\"] - 1\n",
+    "selected_beds = asb_sig[[\"chr\", \"pos0\", \"pos\"]+asb_sig.columns[2:-1].tolist()]\n",
+    "selected_beds.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybedtools\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "peak_bed = pybedtools.BedTool('af_caqtls/merged.peaks.af.bed')\n",
+    "sig_vars_in_bed = beds_var.intersect(peak_bed, wa=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed = sig_vars_in_bed.to_dataframe(names=[\"chr\", \"pos0\", \"pos\"]+asb_sig.columns[2:-1].tolist()).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5897, 40)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7559, 40)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_sig.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>variantID</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "      <th>Maasai.mean.logfc</th>\n",
+       "      <th>Maasai.mean.sign.jsd</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976214</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "      <td>0.077883</td>\n",
+       "      <td>0.032739</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.075894</td>\n",
+       "      <td>0.038511</td>\n",
+       "      <td>0.001846</td>\n",
+       "      <td>0.031981</td>\n",
+       "      <td>0.155960</td>\n",
+       "      <td>0.042078</td>\n",
+       "      <td>0.004710</td>\n",
+       "      <td>0.014595</td>\n",
+       "      <td>0.034014</td>\n",
+       "      <td>0.045668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038818</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "      <td>-0.082168</td>\n",
+       "      <td>-0.015764</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.109847</td>\n",
+       "      <td>-0.020354</td>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.041210</td>\n",
+       "      <td>-0.188001</td>\n",
+       "      <td>-0.022832</td>\n",
+       "      <td>0.004286</td>\n",
+       "      <td>0.016022</td>\n",
+       "      <td>-0.104596</td>\n",
+       "      <td>-0.023049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038844</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "      <td>0.404463</td>\n",
+       "      <td>0.031904</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.377451</td>\n",
+       "      <td>0.036387</td>\n",
+       "      <td>0.010299</td>\n",
+       "      <td>0.005023</td>\n",
+       "      <td>0.529357</td>\n",
+       "      <td>0.033111</td>\n",
+       "      <td>0.017407</td>\n",
+       "      <td>0.003498</td>\n",
+       "      <td>0.347899</td>\n",
+       "      <td>0.032427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038915</td>\n",
+       "      <td>1038916</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038916_A_G</td>\n",
+       "      <td>1_1038916_A_G</td>\n",
+       "      <td>2.062560</td>\n",
+       "      <td>0.185842</td>\n",
+       "      <td>0.019453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.067928</td>\n",
+       "      <td>0.010322</td>\n",
+       "      <td>0.000640</td>\n",
+       "      <td>0.084937</td>\n",
+       "      <td>0.174469</td>\n",
+       "      <td>0.022507</td>\n",
+       "      <td>0.004714</td>\n",
+       "      <td>0.014584</td>\n",
+       "      <td>0.191463</td>\n",
+       "      <td>0.025868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038974</td>\n",
+       "      <td>1038975</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038975_G_A</td>\n",
+       "      <td>1_1038975_G_A</td>\n",
+       "      <td>1.898451</td>\n",
+       "      <td>-0.076651</td>\n",
+       "      <td>-0.012948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.105511</td>\n",
+       "      <td>-0.015877</td>\n",
+       "      <td>0.001204</td>\n",
+       "      <td>0.054009</td>\n",
+       "      <td>-0.104905</td>\n",
+       "      <td>-0.015173</td>\n",
+       "      <td>0.002105</td>\n",
+       "      <td>0.031294</td>\n",
+       "      <td>-0.094641</td>\n",
+       "      <td>-0.013230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5892</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>44181279</td>\n",
+       "      <td>44181280</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_44181280_T_A</td>\n",
+       "      <td>22_44181280_T_A</td>\n",
+       "      <td>1.341581</td>\n",
+       "      <td>0.425672</td>\n",
+       "      <td>0.041464</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.296146</td>\n",
+       "      <td>0.031406</td>\n",
+       "      <td>0.007874</td>\n",
+       "      <td>0.007204</td>\n",
+       "      <td>0.484061</td>\n",
+       "      <td>0.048967</td>\n",
+       "      <td>0.020562</td>\n",
+       "      <td>0.002856</td>\n",
+       "      <td>0.309772</td>\n",
+       "      <td>0.037904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5893</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>45413742</td>\n",
+       "      <td>45413743</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22_45413743_C_A</td>\n",
+       "      <td>22_45413743_C_A</td>\n",
+       "      <td>-2.318303</td>\n",
+       "      <td>-0.015314</td>\n",
+       "      <td>-0.020732</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.019085</td>\n",
+       "      <td>-0.013788</td>\n",
+       "      <td>0.000294</td>\n",
+       "      <td>0.150906</td>\n",
+       "      <td>-0.004601</td>\n",
+       "      <td>-0.019122</td>\n",
+       "      <td>0.000923</td>\n",
+       "      <td>0.062391</td>\n",
+       "      <td>0.013612</td>\n",
+       "      <td>0.017659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5894</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>47001538</td>\n",
+       "      <td>47001539</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_47001539_G_A</td>\n",
+       "      <td>22_47001539_G_A</td>\n",
+       "      <td>-2.249763</td>\n",
+       "      <td>-0.667845</td>\n",
+       "      <td>-0.050768</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.415233</td>\n",
+       "      <td>-0.052056</td>\n",
+       "      <td>0.014751</td>\n",
+       "      <td>0.003098</td>\n",
+       "      <td>-0.677426</td>\n",
+       "      <td>-0.063648</td>\n",
+       "      <td>0.033886</td>\n",
+       "      <td>0.001455</td>\n",
+       "      <td>-0.463897</td>\n",
+       "      <td>-0.056886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5895</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>47918713</td>\n",
+       "      <td>47918714</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_47918714_G_A</td>\n",
+       "      <td>22_47918714_G_A</td>\n",
+       "      <td>3.353036</td>\n",
+       "      <td>0.575336</td>\n",
+       "      <td>0.045299</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.450246</td>\n",
+       "      <td>0.039984</td>\n",
+       "      <td>0.011643</td>\n",
+       "      <td>0.005346</td>\n",
+       "      <td>0.849161</td>\n",
+       "      <td>0.049301</td>\n",
+       "      <td>0.038834</td>\n",
+       "      <td>0.001195</td>\n",
+       "      <td>0.567306</td>\n",
+       "      <td>0.041571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5896</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50017165</td>\n",
+       "      <td>50017166</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_50017166_G_T</td>\n",
+       "      <td>22_50017166_G_T</td>\n",
+       "      <td>-2.075202</td>\n",
+       "      <td>-0.303815</td>\n",
+       "      <td>-0.026495</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.320167</td>\n",
+       "      <td>-0.034851</td>\n",
+       "      <td>0.008549</td>\n",
+       "      <td>0.009083</td>\n",
+       "      <td>-0.878319</td>\n",
+       "      <td>-0.049162</td>\n",
+       "      <td>0.036566</td>\n",
+       "      <td>0.001305</td>\n",
+       "      <td>-0.357370</td>\n",
+       "      <td>-0.031085</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5897 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        chr      pos0       pos allele1 allele2             rsid  \\\n",
+       "0      chr1    976214    976215       A       G     1_976215_A_G   \n",
+       "1      chr1   1038818   1038819       T       C    1_1038819_C_T   \n",
+       "2      chr1   1038844   1038845       G       A    1_1038845_A_G   \n",
+       "3      chr1   1038915   1038916       G       A    1_1038916_A_G   \n",
+       "4      chr1   1038974   1038975       A       G    1_1038975_G_A   \n",
+       "...     ...       ...       ...     ...     ...              ...   \n",
+       "5892  chr22  44181279  44181280       T       A  22_44181280_T_A   \n",
+       "5893  chr22  45413742  45413743       A       C  22_45413743_C_A   \n",
+       "5894  chr22  47001538  47001539       A       G  22_47001539_G_A   \n",
+       "5895  chr22  47918713  47918714       G       A  22_47918714_G_A   \n",
+       "5896  chr22  50017165  50017166       G       T  22_50017166_G_T   \n",
+       "\n",
+       "            variantID  meanLog2FC  Maasai.mean.logfc  Maasai.mean.sign.jsd  \\\n",
+       "0        1_976215_A_G   -1.852605           0.077883              0.032739   \n",
+       "1       1_1038819_C_T    2.390945          -0.082168             -0.015764   \n",
+       "2       1_1038845_A_G    2.163432           0.404463              0.031904   \n",
+       "3       1_1038916_A_G    2.062560           0.185842              0.019453   \n",
+       "4       1_1038975_G_A    1.898451          -0.076651             -0.012948   \n",
+       "...               ...         ...                ...                   ...   \n",
+       "5892  22_44181280_T_A    1.341581           0.425672              0.041464   \n",
+       "5893  22_45413743_C_A   -2.318303          -0.015314             -0.020732   \n",
+       "5894  22_47001539_G_A   -2.249763          -0.667845             -0.050768   \n",
+       "5895  22_47918714_G_A    3.353036           0.575336              0.045299   \n",
+       "5896  22_50017166_G_T   -2.075202          -0.303815             -0.026495   \n",
+       "\n",
+       "      ...  GM12878.ENCSR637XSC.sub.50M.mean.logfc  \\\n",
+       "0     ...                                0.075894   \n",
+       "1     ...                               -0.109847   \n",
+       "2     ...                                0.377451   \n",
+       "3     ...                                0.067928   \n",
+       "4     ...                               -0.105511   \n",
+       "...   ...                                     ...   \n",
+       "5892  ...                                0.296146   \n",
+       "5893  ...                               -0.019085   \n",
+       "5894  ...                               -0.415233   \n",
+       "5895  ...                                0.450246   \n",
+       "5896  ...                               -0.320167   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd  \\\n",
+       "0                                      0.038511   \n",
+       "1                                     -0.020354   \n",
+       "2                                      0.036387   \n",
+       "3                                      0.010322   \n",
+       "4                                     -0.015877   \n",
+       "...                                         ...   \n",
+       "5892                                   0.031406   \n",
+       "5893                                  -0.013788   \n",
+       "5894                                  -0.052056   \n",
+       "5895                                   0.039984   \n",
+       "5896                                  -0.034851   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                              0.001846                   \n",
+       "1                                              0.001482                   \n",
+       "2                                              0.010299                   \n",
+       "3                                              0.000640                   \n",
+       "4                                              0.001204                   \n",
+       "...                                                 ...                   \n",
+       "5892                                           0.007874                   \n",
+       "5893                                           0.000294                   \n",
+       "5894                                           0.014751                   \n",
+       "5895                                           0.011643                   \n",
+       "5896                                           0.008549                   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                              0.031981                        \n",
+       "1                                              0.041210                        \n",
+       "2                                              0.005023                        \n",
+       "3                                              0.084937                        \n",
+       "4                                              0.054009                        \n",
+       "...                                                 ...                        \n",
+       "5892                                           0.007204                        \n",
+       "5893                                           0.150906                        \n",
+       "5894                                           0.003098                        \n",
+       "5895                                           0.005346                        \n",
+       "5896                                           0.009083                        \n",
+       "\n",
+       "      GM12878.ENCSR095QNB.mean.logfc  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0                           0.155960                           0.042078   \n",
+       "1                          -0.188001                          -0.022832   \n",
+       "2                           0.529357                           0.033111   \n",
+       "3                           0.174469                           0.022507   \n",
+       "4                          -0.104905                          -0.015173   \n",
+       "...                              ...                                ...   \n",
+       "5892                        0.484061                           0.048967   \n",
+       "5893                       -0.004601                          -0.019122   \n",
+       "5894                       -0.677426                          -0.063648   \n",
+       "5895                        0.849161                           0.049301   \n",
+       "5896                       -0.878319                          -0.049162   \n",
+       "\n",
+       "      GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                              0.004710           \n",
+       "1                                              0.004286           \n",
+       "2                                              0.017407           \n",
+       "3                                              0.004714           \n",
+       "4                                              0.002105           \n",
+       "...                                                 ...           \n",
+       "5892                                           0.020562           \n",
+       "5893                                           0.000923           \n",
+       "5894                                           0.033886           \n",
+       "5895                                           0.038834           \n",
+       "5896                                           0.036566           \n",
+       "\n",
+       "      GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                              0.014595                \n",
+       "1                                              0.016022                \n",
+       "2                                              0.003498                \n",
+       "3                                              0.014584                \n",
+       "4                                              0.031294                \n",
+       "...                                                 ...                \n",
+       "5892                                           0.002856                \n",
+       "5893                                           0.062391                \n",
+       "5894                                           0.001455                \n",
+       "5895                                           0.001195                \n",
+       "5896                                           0.001305                \n",
+       "\n",
+       "      Gambian.mean.logfc  Gambian.mean.sign.jsd  \n",
+       "0               0.034014               0.045668  \n",
+       "1              -0.104596              -0.023049  \n",
+       "2               0.347899               0.032427  \n",
+       "3               0.191463               0.025868  \n",
+       "4              -0.094641              -0.013230  \n",
+       "...                  ...                    ...  \n",
+       "5892            0.309772               0.037904  \n",
+       "5893            0.013612               0.017659  \n",
+       "5894           -0.463897              -0.056886  \n",
+       "5895            0.567306               0.041571  \n",
+       "5896           -0.357370              -0.031085  \n",
+       "\n",
+       "[5897 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asb_sig[\"IsUsed\"] = asb_sig[\"rsid\"].isin(sig_vars_in_bed[\"rsid\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5897"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(asb_sig[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maasai\n",
+      "Esan\n",
+      "Luhya\n",
+      "Mende\n",
+      "Yoruba\n",
+      "GM12878\n",
+      ".ENCSR637XSC\n",
+      ".ENCSR000EMT\n",
+      ".ENCSR637XSC.sub.50M\n",
+      ".ENCSR095QNB\n",
+      "Gambian\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the ground truths are flipped when rsid does not match allele2 and allele\n",
+    "dataf = asb_sig\n",
+    "index=(dataf[\"allele1\"] != (dataf[\"rsid\"].str.split('_').str[2]))\n",
+    "index=np.array(index.values)\n",
+    "for ans in anscs:\n",
+    "    if ans==\"eur\":\n",
+    "        continue\n",
+    "    if ans==\"afr\":\n",
+    "        continue       \n",
+    "    print(ans)\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\".ENCSR637XSC\", \".ENCSR000EMT\", \".ENCSR637XSC.sub.50M\", \".ENCSR095QNB\" ]:\n",
+    "            print(title)\n",
+    "            dataf.loc[index,ans+title+\".mean.sign.jsd\"] = -1 * dataf.loc[index,ans+title+\".mean.sign.jsd\"]\n",
+    "            dataf.loc[index,ans+title+\".mean.logfc\"] = -1 * dataf.loc[index,ans+title+\".mean.logfc\"]\n",
+    "    else:\n",
+    "        title=\"\"\n",
+    "        dataf.loc[index,ans+title+\".mean.sign.jsd\"] = -1 * dataf.loc[index,ans+title+\".mean.sign.jsd\"]\n",
+    "        dataf.loc[index,ans+title+\".mean.logfc\"] = -1 * dataf.loc[index,ans+title+\".mean.logfc\"]\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>variantID</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "      <th>Maasai.mean.logfc</th>\n",
+       "      <th>Maasai.mean.sign.jsd</th>\n",
+       "      <th>Esan.mean.logfc</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile</th>\n",
+       "      <th>GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval</th>\n",
+       "      <th>Gambian.mean.logfc</th>\n",
+       "      <th>Gambian.mean.sign.jsd</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "      <td>0.077883</td>\n",
+       "      <td>0.032739</td>\n",
+       "      <td>0.148617</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001846</td>\n",
+       "      <td>0.031981</td>\n",
+       "      <td>0.155960</td>\n",
+       "      <td>0.042078</td>\n",
+       "      <td>0.004710</td>\n",
+       "      <td>0.014595</td>\n",
+       "      <td>0.034014</td>\n",
+       "      <td>0.045668</td>\n",
+       "      <td>976214</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1000079</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>1_1000079_A_G</td>\n",
+       "      <td>-2.087074</td>\n",
+       "      <td>0.005979</td>\n",
+       "      <td>0.015850</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.186599</td>\n",
+       "      <td>-0.019343</td>\n",
+       "      <td>-0.020097</td>\n",
+       "      <td>0.000387</td>\n",
+       "      <td>0.115818</td>\n",
+       "      <td>0.026338</td>\n",
+       "      <td>0.028657</td>\n",
+       "      <td>1000078</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038800</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>1_1038800_G_T</td>\n",
+       "      <td>3.172063</td>\n",
+       "      <td>0.029561</td>\n",
+       "      <td>0.011263</td>\n",
+       "      <td>-0.013834</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000430</td>\n",
+       "      <td>0.121757</td>\n",
+       "      <td>0.008690</td>\n",
+       "      <td>0.013938</td>\n",
+       "      <td>0.000514</td>\n",
+       "      <td>0.095791</td>\n",
+       "      <td>0.052296</td>\n",
+       "      <td>0.017729</td>\n",
+       "      <td>1038799</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "      <td>-0.082168</td>\n",
+       "      <td>-0.015764</td>\n",
+       "      <td>-0.139453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.041210</td>\n",
+       "      <td>-0.188001</td>\n",
+       "      <td>-0.022832</td>\n",
+       "      <td>0.004286</td>\n",
+       "      <td>0.016022</td>\n",
+       "      <td>-0.104596</td>\n",
+       "      <td>-0.023049</td>\n",
+       "      <td>1038818</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "      <td>0.404463</td>\n",
+       "      <td>0.031904</td>\n",
+       "      <td>0.354500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.010299</td>\n",
+       "      <td>0.005023</td>\n",
+       "      <td>0.529357</td>\n",
+       "      <td>0.033111</td>\n",
+       "      <td>0.017407</td>\n",
+       "      <td>0.003498</td>\n",
+       "      <td>0.347899</td>\n",
+       "      <td>0.032427</td>\n",
+       "      <td>1038844</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7554</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>45413743</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22_45413743_C_A</td>\n",
+       "      <td>22_45413743_C_A</td>\n",
+       "      <td>-2.318303</td>\n",
+       "      <td>-0.015314</td>\n",
+       "      <td>-0.020732</td>\n",
+       "      <td>-0.060161</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000294</td>\n",
+       "      <td>0.150906</td>\n",
+       "      <td>-0.004601</td>\n",
+       "      <td>-0.019122</td>\n",
+       "      <td>0.000923</td>\n",
+       "      <td>0.062391</td>\n",
+       "      <td>0.013612</td>\n",
+       "      <td>0.017659</td>\n",
+       "      <td>45413742</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7555</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>46773373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_46773373_C_T</td>\n",
+       "      <td>22_46773373_C_T</td>\n",
+       "      <td>2.662931</td>\n",
+       "      <td>0.651388</td>\n",
+       "      <td>0.053668</td>\n",
+       "      <td>0.418584</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001172</td>\n",
+       "      <td>0.071740</td>\n",
+       "      <td>0.432264</td>\n",
+       "      <td>0.049010</td>\n",
+       "      <td>0.009501</td>\n",
+       "      <td>0.007041</td>\n",
+       "      <td>0.539538</td>\n",
+       "      <td>0.055531</td>\n",
+       "      <td>46773372</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7556</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>47001539</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_47001539_G_A</td>\n",
+       "      <td>22_47001539_G_A</td>\n",
+       "      <td>-2.249763</td>\n",
+       "      <td>-0.667845</td>\n",
+       "      <td>-0.050768</td>\n",
+       "      <td>-0.629396</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.014751</td>\n",
+       "      <td>0.003098</td>\n",
+       "      <td>-0.677426</td>\n",
+       "      <td>-0.063648</td>\n",
+       "      <td>0.033886</td>\n",
+       "      <td>0.001455</td>\n",
+       "      <td>-0.463897</td>\n",
+       "      <td>-0.056886</td>\n",
+       "      <td>47001538</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7557</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>47918714</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_47918714_G_A</td>\n",
+       "      <td>22_47918714_G_A</td>\n",
+       "      <td>3.353036</td>\n",
+       "      <td>0.575336</td>\n",
+       "      <td>0.045299</td>\n",
+       "      <td>0.603185</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.011643</td>\n",
+       "      <td>0.005346</td>\n",
+       "      <td>0.849161</td>\n",
+       "      <td>0.049301</td>\n",
+       "      <td>0.038834</td>\n",
+       "      <td>0.001195</td>\n",
+       "      <td>0.567306</td>\n",
+       "      <td>0.041571</td>\n",
+       "      <td>47918713</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7558</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50017166</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_50017166_G_T</td>\n",
+       "      <td>22_50017166_G_T</td>\n",
+       "      <td>-2.075202</td>\n",
+       "      <td>-0.303815</td>\n",
+       "      <td>-0.026495</td>\n",
+       "      <td>-0.159627</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.008549</td>\n",
+       "      <td>0.009083</td>\n",
+       "      <td>-0.878319</td>\n",
+       "      <td>-0.049162</td>\n",
+       "      <td>0.036566</td>\n",
+       "      <td>0.001305</td>\n",
+       "      <td>-0.357370</td>\n",
+       "      <td>-0.031085</td>\n",
+       "      <td>50017165</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7559 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        chr       pos allele1 allele2             rsid        variantID  \\\n",
+       "0      chr1    976215       A       G     1_976215_A_G     1_976215_A_G   \n",
+       "1      chr1   1000079       A       G    1_1000079_A_G    1_1000079_A_G   \n",
+       "2      chr1   1038800       T       G    1_1038800_G_T    1_1038800_G_T   \n",
+       "3      chr1   1038819       T       C    1_1038819_C_T    1_1038819_C_T   \n",
+       "4      chr1   1038845       G       A    1_1038845_A_G    1_1038845_A_G   \n",
+       "...     ...       ...     ...     ...              ...              ...   \n",
+       "7554  chr22  45413743       A       C  22_45413743_C_A  22_45413743_C_A   \n",
+       "7555  chr22  46773373       C       T  22_46773373_C_T  22_46773373_C_T   \n",
+       "7556  chr22  47001539       A       G  22_47001539_G_A  22_47001539_G_A   \n",
+       "7557  chr22  47918714       G       A  22_47918714_G_A  22_47918714_G_A   \n",
+       "7558  chr22  50017166       G       T  22_50017166_G_T  22_50017166_G_T   \n",
+       "\n",
+       "      meanLog2FC  Maasai.mean.logfc  Maasai.mean.sign.jsd  Esan.mean.logfc  \\\n",
+       "0      -1.852605           0.077883              0.032739         0.148617   \n",
+       "1      -2.087074           0.005979              0.015850         0.001207   \n",
+       "2       3.172063           0.029561              0.011263        -0.013834   \n",
+       "3       2.390945          -0.082168             -0.015764        -0.139453   \n",
+       "4       2.163432           0.404463              0.031904         0.354500   \n",
+       "...          ...                ...                   ...              ...   \n",
+       "7554   -2.318303          -0.015314             -0.020732        -0.060161   \n",
+       "7555    2.662931           0.651388              0.053668         0.418584   \n",
+       "7556   -2.249763          -0.667845             -0.050768        -0.629396   \n",
+       "7557    3.353036           0.575336              0.045299         0.603185   \n",
+       "7558   -2.075202          -0.303815             -0.026495        -0.159627   \n",
+       "\n",
+       "      ...  GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0     ...                                           0.001846                   \n",
+       "1     ...                                           0.000256                   \n",
+       "2     ...                                           0.000430                   \n",
+       "3     ...                                           0.001482                   \n",
+       "4     ...                                           0.010299                   \n",
+       "...   ...                                                ...                   \n",
+       "7554  ...                                           0.000294                   \n",
+       "7555  ...                                           0.001172                   \n",
+       "7556  ...                                           0.014751                   \n",
+       "7557  ...                                           0.011643                   \n",
+       "7558  ...                                           0.008549                   \n",
+       "\n",
+       "      GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                              0.031981                        \n",
+       "1                                              0.186599                        \n",
+       "2                                              0.121757                        \n",
+       "3                                              0.041210                        \n",
+       "4                                              0.005023                        \n",
+       "...                                                 ...                        \n",
+       "7554                                           0.150906                        \n",
+       "7555                                           0.071740                        \n",
+       "7556                                           0.003098                        \n",
+       "7557                                           0.005346                        \n",
+       "7558                                           0.009083                        \n",
+       "\n",
+       "      GM12878.ENCSR095QNB.mean.logfc  GM12878.ENCSR095QNB.mean.sign.jsd  \\\n",
+       "0                           0.155960                           0.042078   \n",
+       "1                          -0.019343                          -0.020097   \n",
+       "2                           0.008690                           0.013938   \n",
+       "3                          -0.188001                          -0.022832   \n",
+       "4                           0.529357                           0.033111   \n",
+       "...                              ...                                ...   \n",
+       "7554                       -0.004601                          -0.019122   \n",
+       "7555                        0.432264                           0.049010   \n",
+       "7556                       -0.677426                          -0.063648   \n",
+       "7557                        0.849161                           0.049301   \n",
+       "7558                       -0.878319                          -0.049162   \n",
+       "\n",
+       "      GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile  \\\n",
+       "0                                              0.004710           \n",
+       "1                                              0.000387           \n",
+       "2                                              0.000514           \n",
+       "3                                              0.004286           \n",
+       "4                                              0.017407           \n",
+       "...                                                 ...           \n",
+       "7554                                           0.000923           \n",
+       "7555                                           0.009501           \n",
+       "7556                                           0.033886           \n",
+       "7557                                           0.038834           \n",
+       "7558                                           0.036566           \n",
+       "\n",
+       "      GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval  \\\n",
+       "0                                              0.014595                \n",
+       "1                                              0.115818                \n",
+       "2                                              0.095791                \n",
+       "3                                              0.016022                \n",
+       "4                                              0.003498                \n",
+       "...                                                 ...                \n",
+       "7554                                           0.062391                \n",
+       "7555                                           0.007041                \n",
+       "7556                                           0.001455                \n",
+       "7557                                           0.001195                \n",
+       "7558                                           0.001305                \n",
+       "\n",
+       "      Gambian.mean.logfc  Gambian.mean.sign.jsd      pos0  IsUsed  \n",
+       "0               0.034014               0.045668    976214    True  \n",
+       "1               0.026338               0.028657   1000078   False  \n",
+       "2               0.052296               0.017729   1038799   False  \n",
+       "3              -0.104596              -0.023049   1038818    True  \n",
+       "4               0.347899               0.032427   1038844    True  \n",
+       "...                  ...                    ...       ...     ...  \n",
+       "7554            0.013612               0.017659  45413742    True  \n",
+       "7555            0.539538               0.055531  46773372   False  \n",
+       "7556           -0.463897              -0.056886  47001538    True  \n",
+       "7557            0.567306               0.041571  47918713    True  \n",
+       "7558           -0.357370              -0.031085  50017165    True  \n",
+       "\n",
+       "[7559 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'variantID', 'meanLog2FC',\n",
+       "       'Maasai.mean.logfc', 'Maasai.mean.sign.jsd', 'Esan.mean.logfc',\n",
+       "       'Esan.mean.sign.jsd', 'Luhya.mean.logfc', 'Luhya.mean.sign.jsd',\n",
+       "       'Mende.mean.logfc', 'Mende.mean.sign.jsd', 'afr.mean.logfc',\n",
+       "       'afr.mean.sign.jsd', 'Yoruba.mean.logfc', 'Yoruba.mean.sign.jsd',\n",
+       "       'eur.mean.logfc', 'eur.mean.sign.jsd', 'GM12878.ENCSR637XSC.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR000EMT.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR637XSC.sub.50M.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'GM12878.ENCSR095QNB.mean.logfc', 'GM12878.ENCSR095QNB.mean.sign.jsd',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile',\n",
+       "       'GM12878.ENCSR095QNB.mean.abs_logfc_x_jsd_x_max_percentile.pval',\n",
+       "       'Gambian.mean.logfc', 'Gambian.mean.sign.jsd', 'pos0', 'IsUsed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafinal = dataf[['chr', 'pos', 'allele1', 'allele2',  'IsUsed', 'variantID', 'meanLog2FC',\n",
+    "       'GM12878.ENCSR637XSC.mean.logfc',\n",
+    "       'GM12878.ENCSR637XSC.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR000EMT.mean.logfc', 'GM12878.ENCSR000EMT.mean.sign.jsd',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.logfc',\n",
+    "       'GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "       ]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_dict = {\"chr\": \"chr_hg38\", \"pos\": \"pos_hg38\", 'variantID':\"snp_id\"}\n",
+    "\n",
+    "datafinal = datafinal.rename(columns=rename_dict)\n",
+    "\n",
+    "datafinal.to_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Afr.ASB.CaQTLS.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7559"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dataf[\"rsid\"] == dataf[\"variantID\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_blood_traits.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_blood_traits.ipynb
new file mode 100644
index 00000000..d46ca175
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_blood_traits.ipynb
@@ -0,0 +1,1420 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scored_variants = pd.read_csv(\"/mnt/lab_data2/anusri/signed_variant_scorer/variant-scorer/output/blood_traits/ATAC/summary.mean.variant_scores.tsv\", sep=\"\\t\", header=0)\n",
+    "blood_variants=pd.read_csv(\"/mnt/lab_data2/anusri/signed_variant_scorer/variant-scorer/output/blood_traits/SNP_info_table.tsv\", sep=\"\\t\", header=0)\n",
+    "scored_variants_dnase = pd.read_csv(\"/mnt/lab_data2/anusri/signed_variant_scorer/variant-scorer/output/blood_traits/DNASE/summary.mean.variant_scores.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "scored_variants[\"sign.jsd.mean\"] = scored_variants[\"jsd.mean\"] * np.sign(scored_variants[\"logfc.mean\"])\n",
+    "scored_variants = scored_variants[['chr', 'pos', 'allele1', 'allele2', 'variant_id', 'logfc.mean', \"sign.jsd.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", 'abs_logfc_x_jsd_x_max_percentile.mean.pval']]\n",
+    "\n",
+    "scored_variants_dnase[\"sign.jsd.mean\"] = scored_variants_dnase[\"jsd.mean\"] * np.sign(scored_variants_dnase[\"logfc.mean\"])\n",
+    "scored_variants_dnase = scored_variants_dnase[['chr', 'pos', 'allele1', 'allele2', 'variant_id', 'logfc.mean', \"sign.jsd.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", 'abs_logfc_x_jsd_x_max_percentile.mean.pval']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final = scored_variants.merge(blood_variants, how=\"left\", left_on=[\"variant_id\", \"allele1\", \"allele2\"], right_on=[\"rsid\", \"allele1\", \"allele2\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'variant_id', 'logfc.mean',\n",
+       "       'sign.jsd.mean', 'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid', 'traits', 'IDs',\n",
+       "       'maxPIP'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11916124, 13)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11916124, 9)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "scored_variants.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12951, 6)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "blood_variants.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11916124, 9)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scored_variants_dnase.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_dict = {'logfc.mean': 'ENCSR637XSC.logfc.mean' , 'sign.jsd.mean': 'ENCSR637XSC.sign_jsd.mean' ,'abs_logfc_x_jsd_x_max_percentile.mean': 'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean', 'abs_logfc_x_jsd_x_max_percentile.mean.pval': 'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval'}\n",
+    "\n",
+    "final = final.rename(columns=rename_dict)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for key in rename_dict:\n",
+    "    val = rename_dict[key]\n",
+    "    val = val.replace(\"ENCSR637XSC.\", \"ENCSR000EMT.\")\n",
+    "    final[val] = scored_variants_dnase[key]\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>variant_id</th>\n",
+       "      <th>ENCSR637XSC.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sign_jsd.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>traits</th>\n",
+       "      <th>IDs</th>\n",
+       "      <th>maxPIP</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.sign_jsd.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10352</td>\n",
+       "      <td>T</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>rs150218945</td>\n",
+       "      <td>-0.036835</td>\n",
+       "      <td>-0.016749</td>\n",
+       "      <td>0.000497</td>\n",
+       "      <td>0.055084</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.004799</td>\n",
+       "      <td>0.013399</td>\n",
+       "      <td>0.000059</td>\n",
+       "      <td>0.219019</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos allele1 allele2   variant_id  ENCSR637XSC.logfc.mean  \\\n",
+       "0  chr1  10352       T      TA  rs150218945               -0.036835   \n",
+       "\n",
+       "   ENCSR637XSC.sign_jsd.mean  \\\n",
+       "0                  -0.016749   \n",
+       "\n",
+       "   ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.000497   \n",
+       "\n",
+       "   ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval rsid traits  IDs  \\\n",
+       "0                                           0.055084       NaN    NaN  NaN   \n",
+       "\n",
+       "   maxPIP  ENCSR000EMT.logfc.mean  ENCSR000EMT.sign_jsd.mean  \\\n",
+       "0     NaN                0.004799                   0.013399   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.000059   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \n",
+       "0                                           0.219019       "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final.to_csv(\"../final_figures_new/k562.gwas_variants.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>variant_id</th>\n",
+       "      <th>ENCSR637XSC.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sign_jsd.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>traits</th>\n",
+       "      <th>IDs</th>\n",
+       "      <th>maxPIP</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.sign_jsd.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10352</td>\n",
+       "      <td>T</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>rs150218945</td>\n",
+       "      <td>-0.036835</td>\n",
+       "      <td>-0.016749</td>\n",
+       "      <td>0.000497</td>\n",
+       "      <td>0.055084</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.004799</td>\n",
+       "      <td>0.013399</td>\n",
+       "      <td>0.000059</td>\n",
+       "      <td>0.219019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>11012</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs544419019</td>\n",
+       "      <td>-0.010156</td>\n",
+       "      <td>-0.022090</td>\n",
+       "      <td>0.000218</td>\n",
+       "      <td>0.116639</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.008337</td>\n",
+       "      <td>0.031942</td>\n",
+       "      <td>0.000489</td>\n",
+       "      <td>0.061075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13110</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs540538026</td>\n",
+       "      <td>-0.005588</td>\n",
+       "      <td>-0.003792</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.703089</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.000662</td>\n",
+       "      <td>-0.004676</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.727699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13116</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs62635286</td>\n",
+       "      <td>0.006778</td>\n",
+       "      <td>0.005039</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>0.649554</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.007013</td>\n",
+       "      <td>0.005918</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.620976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13118</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs200579949</td>\n",
+       "      <td>0.028640</td>\n",
+       "      <td>0.006264</td>\n",
+       "      <td>0.000050</td>\n",
+       "      <td>0.334598</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013721</td>\n",
+       "      <td>0.006579</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.504230</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos allele1 allele2   variant_id  ENCSR637XSC.logfc.mean  \\\n",
+       "0  chr1  10352       T      TA  rs150218945               -0.036835   \n",
+       "1  chr1  11012       C       G  rs544419019               -0.010156   \n",
+       "2  chr1  13110       G       A  rs540538026               -0.005588   \n",
+       "3  chr1  13116       T       G   rs62635286                0.006778   \n",
+       "4  chr1  13118       A       G  rs200579949                0.028640   \n",
+       "\n",
+       "   ENCSR637XSC.sign_jsd.mean  \\\n",
+       "0                  -0.016749   \n",
+       "1                  -0.022090   \n",
+       "2                  -0.003792   \n",
+       "3                   0.005039   \n",
+       "4                   0.006264   \n",
+       "\n",
+       "   ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.000497   \n",
+       "1                                           0.000218   \n",
+       "2                                           0.000005   \n",
+       "3                                           0.000011   \n",
+       "4                                           0.000050   \n",
+       "\n",
+       "   ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval rsid traits  IDs  \\\n",
+       "0                                           0.055084       NaN    NaN  NaN   \n",
+       "1                                           0.116639       NaN    NaN  NaN   \n",
+       "2                                           0.703089       NaN    NaN  NaN   \n",
+       "3                                           0.649554       NaN    NaN  NaN   \n",
+       "4                                           0.334598       NaN    NaN  NaN   \n",
+       "\n",
+       "   maxPIP  ENCSR000EMT.logfc.mean  ENCSR000EMT.sign_jsd.mean  \\\n",
+       "0     NaN                0.004799                   0.013399   \n",
+       "1     NaN                0.008337                   0.031942   \n",
+       "2     NaN               -0.000662                  -0.004676   \n",
+       "3     NaN                0.007013                   0.005918   \n",
+       "4     NaN                0.013721                   0.006579   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.000059   \n",
+       "1                                           0.000489   \n",
+       "2                                           0.000001   \n",
+       "3                                           0.000002   \n",
+       "4                                           0.000006   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \n",
+       "0                                           0.219019       \n",
+       "1                                           0.061075       \n",
+       "2                                           0.727699       \n",
+       "3                                           0.620976       \n",
+       "4                                           0.504230       "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11916124, 17)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/blood_traits/enformer_predictions.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>CAGE_SAD_4828</th>\n",
+       "      <th>CAGE_SAR_4828</th>\n",
+       "      <th>CAGE_SAD_5111</th>\n",
+       "      <th>CAGE_SAR_5111</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>11012</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs544419019</td>\n",
+       "      <td>0.000217</td>\n",
+       "      <td>0.000304</td>\n",
+       "      <td>0.001164</td>\n",
+       "      <td>0.001593</td>\n",
+       "      <td>0.002567</td>\n",
+       "      <td>0.003328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13110</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs540538026</td>\n",
+       "      <td>-0.000036</td>\n",
+       "      <td>-0.000051</td>\n",
+       "      <td>-0.000100</td>\n",
+       "      <td>-0.000137</td>\n",
+       "      <td>-0.000312</td>\n",
+       "      <td>-0.000402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13116</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs62635286</td>\n",
+       "      <td>-0.000013</td>\n",
+       "      <td>-0.000018</td>\n",
+       "      <td>-0.000108</td>\n",
+       "      <td>-0.000148</td>\n",
+       "      <td>-0.000253</td>\n",
+       "      <td>-0.000327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13118</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs200579949</td>\n",
+       "      <td>0.000020</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.000077</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>0.000263</td>\n",
+       "      <td>0.000340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13273</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs531730856</td>\n",
+       "      <td>0.000056</td>\n",
+       "      <td>0.000078</td>\n",
+       "      <td>0.000225</td>\n",
+       "      <td>0.000305</td>\n",
+       "      <td>0.000451</td>\n",
+       "      <td>0.000569</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos ref alt         rsid  DNASE_SAD  DNASE_SAR  CAGE_SAD_4828  \\\n",
+       "0  chr1  11012   C   G  rs544419019   0.000217   0.000304       0.001164   \n",
+       "1  chr1  13110   G   A  rs540538026  -0.000036  -0.000051      -0.000100   \n",
+       "2  chr1  13116   T   G   rs62635286  -0.000013  -0.000018      -0.000108   \n",
+       "3  chr1  13118   A   G  rs200579949   0.000020   0.000028       0.000077   \n",
+       "4  chr1  13273   G   C  rs531730856   0.000056   0.000078       0.000225   \n",
+       "\n",
+       "   CAGE_SAR_4828  CAGE_SAD_5111  CAGE_SAR_5111  \n",
+       "0       0.001593       0.002567       0.003328  \n",
+       "1      -0.000137      -0.000312      -0.000402  \n",
+       "2      -0.000148      -0.000253      -0.000327  \n",
+       "3       0.000105       0.000263       0.000340  \n",
+       "4       0.000305       0.000451       0.000569  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9840768, 11)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11916124"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(final[\"variant_id\"].isin(enformer[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    True\n",
+       "1    True\n",
+       "2    True\n",
+       "3    True\n",
+       "4    True\n",
+       "Name: variant_id, dtype: bool"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final[\"variant_id\"].isin(enformer[\"rsid\"])[0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dffinal1 = final.merge(enformer, right_on=[\"chr\", \"rsid\", \"ref\", \"alt\"], left_on=[\"chr\", \"variant_id\", \"allele1\", \"allele2\"], how=\"left\")\n",
+    "dffinal2 = final.merge(enformer, right_on=[\"chr\", \"rsid\", \"ref\", \"alt\"], left_on=[\"chr\", \"variant_id\", \"allele2\", \"allele1\"], how=\"left\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11916124, 27)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dffinal1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11916124, 27)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dffinal2.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3689054"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.isnan(dffinal1[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10314492"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.isnan(dffinal2[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2087422"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.isnan(dffinal1[\"DNASE_SAD\"]) & np.isnan(dffinal2[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9828702"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "11916124-2087422"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dffinal1[np.isnan(dffinal1[\"DNASE_SAD\"])][\"DNASE_SAD\"] = dffinal2[np.isnan(dffinal1[\"DNASE_SAD\"])][\"DNASE_SAD\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dffinal1[np.isnan(dffinal1[\"DNASE_SAR\"])][\"DNASE_SAR\"] = dffinal2[np.isnan(dffinal1[\"DNASE_SAR\"])][\"DNASE_SAR\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "chr                                                       11916124\n",
+       "pos_x                                                     11916124\n",
+       "allele1                                                   11916124\n",
+       "allele2                                                   11916124\n",
+       "variant_id                                                11916124\n",
+       "ENCSR637XSC.logfc.mean                                    11916124\n",
+       "ENCSR637XSC.sign_jsd.mean                                 11916124\n",
+       "ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean         11916124\n",
+       "ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval    11916124\n",
+       "rsid_x                                                       12937\n",
+       "traits                                                       12937\n",
+       "IDs                                                          12937\n",
+       "maxPIP                                                       12937\n",
+       "ENCSR000EMT.logfc.mean                                    11916124\n",
+       "ENCSR000EMT.sign_jsd.mean                                 11916124\n",
+       "ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean         11916124\n",
+       "ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval    11916124\n",
+       "pos_y                                                      8227070\n",
+       "ref                                                        8227070\n",
+       "alt                                                        8227070\n",
+       "rsid_y                                                     8227070\n",
+       "DNASE_SAD                                                  8227070\n",
+       "DNASE_SAR                                                  8227070\n",
+       "CAGE_SAD_4828                                              8227070\n",
+       "CAGE_SAR_4828                                              8227070\n",
+       "CAGE_SAD_5111                                              8227070\n",
+       "CAGE_SAR_5111                                              8227070\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dffinal1.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2917"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(np.isnan(dffinal1[~np.isnan(dffinal1[\"maxPIP\"])][\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bg = pd.read_csv(\"/oak/stanford/groups/akundaje/projects/igvf-fcc/data/rosa/background_distal_noncoding_alleles_position_match.txt\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>REF</th>\n",
+       "      <th>ALT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10351</td>\n",
+       "      <td>10352.0</td>\n",
+       "      <td>rs150218945</td>\n",
+       "      <td>T</td>\n",
+       "      <td>TA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>11011</td>\n",
+       "      <td>11012.0</td>\n",
+       "      <td>rs544419019</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13109</td>\n",
+       "      <td>13110.0</td>\n",
+       "      <td>rs540538026</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13115</td>\n",
+       "      <td>13116.0</td>\n",
+       "      <td>rs62635286</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>13117</td>\n",
+       "      <td>13118.0</td>\n",
+       "      <td>rs200579949</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr  start      end         rsid REF ALT\n",
+       "0  chr1  10351  10352.0  rs150218945   T  TA\n",
+       "1  chr1  11011  11012.0  rs544419019   C   G\n",
+       "2  chr1  13109  13110.0  rs540538026   G   A\n",
+       "3  chr1  13115  13116.0   rs62635286   T   G\n",
+       "4  chr1  13117  13118.0  rs200579949   A   G"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bg.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bg[\"rsid\"].str.contains(\",\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bg[\"rsid\"].str.contains(\";\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigs = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/blood_traits/SNP_info_table.tsv\", sep=\"\\t\", header=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>traits</th>\n",
+       "      <th>IDs</th>\n",
+       "      <th>maxPIP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rs10000067</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>MCH</td>\n",
+       "      <td>MCH_rs10000067</td>\n",
+       "      <td>0.007929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rs10001067</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>MCV</td>\n",
+       "      <td>MCV_rs10001067</td>\n",
+       "      <td>0.021491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rs10001396</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>MCH</td>\n",
+       "      <td>MCH_rs10001396</td>\n",
+       "      <td>0.010357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>rs10002448</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>MCH</td>\n",
+       "      <td>MCH_rs10002448</td>\n",
+       "      <td>0.012410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>rs10002971</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>MCHC</td>\n",
+       "      <td>MCHC_rs10002971</td>\n",
+       "      <td>0.019088</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         rsid allele1 allele2 traits              IDs    maxPIP\n",
+       "0  rs10000067       T       C    MCH   MCH_rs10000067  0.007929\n",
+       "1  rs10001067       C       G    MCV   MCV_rs10001067  0.021491\n",
+       "2  rs10001396       C       G    MCH   MCH_rs10001396  0.010357\n",
+       "3  rs10002448       G       A    MCH   MCH_rs10002448  0.012410\n",
+       "4  rs10002971       A       C   MCHC  MCHC_rs10002971  0.019088"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sigs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12954"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bg[[\"rsid\", \"REF\", \"ALT\"]].isin(sigs[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9850361, 6)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bg.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12951, 6)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sigs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp = bg.merge(sigs, left_on=[\"rsid\", \"REF\", \"ALT\"], right_on=[\"rsid\", \"allele1\", \"allele2\"], how=\"left\")\n",
+    "temp1 = bg.merge(sigs, left_on=[\"rsid\", \"REF\", \"ALT\"], right_on=[\"rsid\", \"allele2\", \"allele1\"], how=\"left\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9850361, 11)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9850361, 6)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bg.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "10140"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp[np.isnan(temp[\"maxPIP\"])][\"maxPIP\"] = temp1[np.isnan(temp[\"maxPIP\"])][\"maxPIP\"]\n",
+    "\n",
+    "sum(~np.isnan(temp[\"maxPIP\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_cagi.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_cagi.ipynb
new file mode 100644
index 00000000..f4f7d3c7
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_cagi.ipynb
@@ -0,0 +1,3291 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from scipy import stats\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Locus</th>\n",
+       "      <th>deltaSVM</th>\n",
+       "      <th>Enf.CAGE.Agnostic</th>\n",
+       "      <th>Enf.CAGE</th>\n",
+       "      <th>Enf.DNase.Agnostic</th>\n",
+       "      <th>Enf.DNase</th>\n",
+       "      <th>Enformer</th>\n",
+       "      <th>G3</th>\n",
+       "      <th>G7</th>\n",
+       "      <th>dSVM LASSO</th>\n",
+       "      <th>dSVM RF</th>\n",
+       "      <th>dSVM (1 DHS)</th>\n",
+       "      <th>G5</th>\n",
+       "      <th>CADD</th>\n",
+       "      <th>Inter-replicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F9</td>\n",
+       "      <td>0.483518</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>0.573</td>\n",
+       "      <td>0.291</td>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.624</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.557</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.489</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>-0.086</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GP1BB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>0.555</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.563</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.261</td>\n",
+       "      <td>0.371</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>-0.083</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HBB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.550</td>\n",
+       "      <td>0.578</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.446</td>\n",
+       "      <td>0.416</td>\n",
+       "      <td>0.445</td>\n",
+       "      <td>0.328</td>\n",
+       "      <td>0.393</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HBG1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.178</td>\n",
+       "      <td>0.302</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.189</td>\n",
+       "      <td>0.474</td>\n",
+       "      <td>0.571</td>\n",
+       "      <td>0.591</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>0.342</td>\n",
+       "      <td>0.479</td>\n",
+       "      <td>0.174</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HNF4A</td>\n",
+       "      <td>0.091578</td>\n",
+       "      <td>0.173</td>\n",
+       "      <td>0.156</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>0.288</td>\n",
+       "      <td>0.335</td>\n",
+       "      <td>0.339</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.225</td>\n",
+       "      <td>0.151</td>\n",
+       "      <td>0.134</td>\n",
+       "      <td>0.191</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.89</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Locus  deltaSVM  Enf.CAGE.Agnostic  Enf.CAGE  Enf.DNase.Agnostic  \\\n",
+       "0     F9  0.483518              0.245     0.252               0.573   \n",
+       "1  GP1BB       NaN              0.370     0.469               0.555   \n",
+       "2    HBB       NaN              0.491     0.596               0.550   \n",
+       "3   HBG1       NaN              0.178     0.302               0.104   \n",
+       "4  HNF4A  0.091578              0.173     0.156               0.170   \n",
+       "\n",
+       "   Enf.DNase  Enformer     G3     G7  dSVM LASSO  dSVM RF  dSVM (1 DHS)  \\\n",
+       "0      0.291     0.572  0.624  0.391       0.557    0.566         0.489   \n",
+       "1      0.564     0.563  0.556  0.566       0.261    0.371         0.421   \n",
+       "2      0.578     0.600  0.446  0.416       0.445    0.328         0.393   \n",
+       "3      0.189     0.474  0.571  0.591       0.397    0.439         0.342   \n",
+       "4      0.288     0.335  0.339  0.397       0.225    0.151         0.134   \n",
+       "\n",
+       "      G5   CADD  Inter-replicate  \n",
+       "0  0.564 -0.086             0.88  \n",
+       "1  0.348 -0.083             0.88  \n",
+       "2  0.392  0.049             0.77  \n",
+       "3  0.479  0.174             0.92  \n",
+       "4  0.191  0.111             0.89  "
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/CAGI/output/\"\n",
+    "dirs = os.listdir(odir)\n",
+    "\n",
+    "enformer = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer/allGroupsBenchmark.txt\", sep=\"\\t\", header=0)\n",
+    "enformer.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Locus</th>\n",
+       "      <th>deltaSVM</th>\n",
+       "      <th>Enf.CAGE.Agnostic</th>\n",
+       "      <th>Enf.CAGE</th>\n",
+       "      <th>Enf.DNase.Agnostic</th>\n",
+       "      <th>Enf.DNase</th>\n",
+       "      <th>Enformer</th>\n",
+       "      <th>G3</th>\n",
+       "      <th>G7</th>\n",
+       "      <th>dSVM LASSO</th>\n",
+       "      <th>dSVM RF</th>\n",
+       "      <th>dSVM (1 DHS)</th>\n",
+       "      <th>G5</th>\n",
+       "      <th>CADD</th>\n",
+       "      <th>Inter-replicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F9</td>\n",
+       "      <td>0.483518</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>0.573</td>\n",
+       "      <td>0.291</td>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.624</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.557</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.489</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>-0.086</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GP1BB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>0.555</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.563</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.261</td>\n",
+       "      <td>0.371</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>-0.083</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HBB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.550</td>\n",
+       "      <td>0.578</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.446</td>\n",
+       "      <td>0.416</td>\n",
+       "      <td>0.445</td>\n",
+       "      <td>0.328</td>\n",
+       "      <td>0.393</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HBG1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.178</td>\n",
+       "      <td>0.302</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.189</td>\n",
+       "      <td>0.474</td>\n",
+       "      <td>0.571</td>\n",
+       "      <td>0.591</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>0.342</td>\n",
+       "      <td>0.479</td>\n",
+       "      <td>0.174</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HNF4A</td>\n",
+       "      <td>0.091578</td>\n",
+       "      <td>0.173</td>\n",
+       "      <td>0.156</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>0.288</td>\n",
+       "      <td>0.335</td>\n",
+       "      <td>0.339</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.225</td>\n",
+       "      <td>0.151</td>\n",
+       "      <td>0.134</td>\n",
+       "      <td>0.191</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.89</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Locus  deltaSVM  Enf.CAGE.Agnostic  Enf.CAGE  Enf.DNase.Agnostic  \\\n",
+       "0     F9  0.483518              0.245     0.252               0.573   \n",
+       "1  GP1BB       NaN              0.370     0.469               0.555   \n",
+       "2    HBB       NaN              0.491     0.596               0.550   \n",
+       "3   HBG1       NaN              0.178     0.302               0.104   \n",
+       "4  HNF4A  0.091578              0.173     0.156               0.170   \n",
+       "\n",
+       "   Enf.DNase  Enformer     G3     G7  dSVM LASSO  dSVM RF  dSVM (1 DHS)  \\\n",
+       "0      0.291     0.572  0.624  0.391       0.557    0.566         0.489   \n",
+       "1      0.564     0.563  0.556  0.566       0.261    0.371         0.421   \n",
+       "2      0.578     0.600  0.446  0.416       0.445    0.328         0.393   \n",
+       "3      0.189     0.474  0.571  0.591       0.397    0.439         0.342   \n",
+       "4      0.288     0.335  0.339  0.397       0.225    0.151         0.134   \n",
+       "\n",
+       "      G5   CADD  Inter-replicate  \n",
+       "0  0.564 -0.086             0.88  \n",
+       "1  0.348 -0.083             0.88  \n",
+       "2  0.392  0.049             0.77  \n",
+       "3  0.479  0.174             0.92  \n",
+       "4  0.191  0.111             0.89  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cagi_df_list = []\n",
+    "\n",
+    "for idir in dirs: \n",
+    "    idir=str(idir)\n",
+    "    ifile=odir+idir+\"/summary.mean.variant_scores_new_2.tsv\"\n",
+    "    if os.path.isfile(ifile):\n",
+    "        if not ifile.endswith(\"tsv\"):\n",
+    "            continue\n",
+    "        data = pd.read_csv(ifile, sep=\"\\t\", header=0)\n",
+    "        #print(data.head())\n",
+    "        data[[\"chr_temp\", \"pos_temp\", \"ref_temp\", \"alt_temp\", \"label\", \"Confidence\", \"challenge\"]] = data[\"rsid\"].str.split(\",\", expand=True)\n",
+    "        #print(data.columns)\n",
+    "        data[\"sign_jsd.mean\"] = data[\"jsd.mean\"] * np.sign(data[\"logfc.mean\"])\n",
+    "        data[\"model_used\"] = idir\n",
+    "        data = data[['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean', \"sign_jsd.mean\", 'abs_logfc_x_max_percentile.mean',\n",
+    "       'abs_logfc_x_max_percentile.mean.pval', \"label\", \"Confidence\", \"challenge\", \"model_used\"]]\n",
+    "        cagi_df_list.append(data)\n",
+    "\n",
+    "cagi_df = pd.concat(cagi_df_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>challenge</th>\n",
+       "      <th>model_used</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.143521</td>\n",
+       "      <td>-0.024822</td>\n",
+       "      <td>0.044314</td>\n",
+       "      <td>0.038392</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.131439</td>\n",
+       "      <td>-0.023676</td>\n",
+       "      <td>0.040458</td>\n",
+       "      <td>0.041713</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.086450</td>\n",
+       "      <td>-0.023025</td>\n",
+       "      <td>0.026278</td>\n",
+       "      <td>0.065491</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.013275</td>\n",
+       "      <td>-0.027508</td>\n",
+       "      <td>0.011885</td>\n",
+       "      <td>0.166936</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.033312</td>\n",
+       "      <td>0.020898</td>\n",
+       "      <td>0.011562</td>\n",
+       "      <td>0.171765</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr       pos allele1 allele2  \\\n",
+       "0  chr6  37775290       G       A   \n",
+       "1  chr6  37775290       G       C   \n",
+       "2  chr6  37775290       G       T   \n",
+       "3  chr6  37775291       T       A   \n",
+       "4  chr6  37775291       T       C   \n",
+       "\n",
+       "                                             rsid  logfc.mean  sign_jsd.mean  \\\n",
+       "0   6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.143521      -0.024822   \n",
+       "1   6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv   -0.131439      -0.023676   \n",
+       "2   6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv   -0.086450      -0.023025   \n",
+       "3  6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv   -0.013275      -0.027508   \n",
+       "4   6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv    0.033312       0.020898   \n",
+       "\n",
+       "   abs_logfc_x_max_percentile.mean  abs_logfc_x_max_percentile.mean.pval  \\\n",
+       "0                         0.044314                              0.038392   \n",
+       "1                         0.040458                              0.041713   \n",
+       "2                         0.026278                              0.065491   \n",
+       "3                         0.011885                              0.166936   \n",
+       "4                         0.011562                              0.171765   \n",
+       "\n",
+       "   label Confidence             challenge            model_used  \n",
+       "0   0.03       0.01  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "1   0.03       0.01  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "2   0.07       0.01  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "3  -0.07       0.01  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "4   0.03       0.01  challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get enformer preds\n",
+    "# first get enformer preds with hg19 coords\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "odir = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/data/\"\n",
+    "dirs = os.listdir(odir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_HBG1.tsv\n",
+      "challenge_PKLR.tsv\n",
+      "challenge_MYCrs6983267.tsv\n",
+      "challenge_TERT-GBM.tsv\n",
+      "challenge_MSMB.tsv\n",
+      "challenge_F9.tsv\n",
+      "challenge_IRF6.tsv\n",
+      "challenge_HNF4A.tsv\n",
+      "challenge_IRF4.tsv\n",
+      "challenge_SORT1.tsv\n",
+      "challenge_ZFAND3.tsv\n",
+      "challenge_HBB.tsv\n",
+      "challenge_LDLR.tsv\n",
+      "challenge_TERT-HEK293T.tsv\n",
+      "challenge_GP1BB.tsv\n"
+     ]
+    }
+   ],
+   "source": [
+    "enf_dfs= []\n",
+    "for idir in dirs:\n",
+    "    idir=str(idir)\n",
+    "    ifile=odir+idir\n",
+    "    if os.path.isfile(ifile):\n",
+    "            #print(ifile)\n",
+    "#             if idir in [\"challenge_MSMB.tsv\"]: # MSMB ref and alt also dont match\n",
+    "#                 continue\n",
+    "            if not ifile.endswith(\"tsv\"):\n",
+    "                continue\n",
+    "            if not idir.startswith(\"challenge\"):\n",
+    "                continue\n",
+    "            print(idir)\n",
+    "            orig = pd.read_csv(ifile, sep=\"\\t\", header=None, comment=\"#\")\n",
+    "            orig[0] = \"chr\"+orig[0].astype(str)\n",
+    "            orig.columns = [\"chr_hg19\", \"pos_hg19\", \"ref\", \"alt\", \"label\", \"Confidence\"]\n",
+    "            ifile1 = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/CAGI5_train_test_hg19_to_hg38_liftover/\"\n",
+    "            liftoverd = ifile1 + \"withChr.\"+idir\n",
+    "            lft_data = pd.read_csv(liftoverd, sep=\"\\t\", header=None)\n",
+    "            lft_data.columns = [\"chr_hg38\", \"pos_hg38\", \"ref_hg38\", \"alt_hg38\", \"label_hg38\", \"Confidence_hg38\"]\n",
+    "            assert(lft_data.shape[0]==orig.shape[0])\n",
+    "            \n",
+    "            combined = pd.concat([orig, lft_data],axis=1)\n",
+    "            #print(combined.head())\n",
+    "            \n",
+    "#             assert((combined[\"ref\"]==combined[\"ref_hg38\"]).all())\n",
+    "#             assert((combined[\"alt\"]==combined[\"alt_hg38\"]).all())\n",
+    "            assert((combined[\"label\"]==combined[\"label_hg38\"]).all())\n",
+    "            assert((combined[\"Confidence\"]==combined[\"Confidence_hg38\"]).all())\n",
+    "            \n",
+    "            enf_f = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/alldata/\"+idir.replace(\"challenge_\",\"\").replace(\".tsv\",\"\")+\".ZigaDataLocal.csv\"\n",
+    "            enf_f = pd.read_csv(enf_f, sep=\",\", header=0)\n",
+    "            #print(combined.drop_duplicates().shape[0], combined.shape[0])\n",
+    "            #print(enf_f.head(1))\n",
+    "            #assert(enf_f.shape[0] == combined.shape[0])\n",
+    "            #combined[[\"chr_hg19\", \"pos_hg19\", \"chr_hg38\", \"pos_hg38\", \"ref\", \"alt\", \"label\", \"Confidence\"]]\n",
+    "            #print(enf_f.columns)\n",
+    "            #enf_with_orig = combined.merge(enf_f, right_on=[\"data.Position\", \"data.Value\"], left_on=[\"pos_hg38\", \"label\"], how=\"left\")\n",
+    "            #enf_with_orig[\"challenge\"] = idir\n",
+    "            #print(enf_with_orig.shape)\n",
+    "            #print(sum(~np.isnan(enf_with_orig[\"data.Value\"])))\n",
+    "            enf_dfs.append(combined)\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enf_df = pd.concat(enf_dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>ref_hg38</th>\n",
+       "      <th>alt_hg38</th>\n",
+       "      <th>label_hg38</th>\n",
+       "      <th>Confidence_hg38</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271035</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249805</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271035</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249805</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271035</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249805</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271036</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249806</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271036</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249806</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>864</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711172</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723649</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>865</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711172</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723649</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>866</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711172</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723649</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>867</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711173</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723650</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>868</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711173</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723650</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13813 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr_hg19  pos_hg19 ref alt  label  Confidence chr_hg38  pos_hg38 ref_hg38  \\\n",
+       "0      chr11   5271035   G   A   0.19        0.06    chr11   5249805        G   \n",
+       "1      chr11   5271035   G   C   0.07        0.01    chr11   5249805        G   \n",
+       "2      chr11   5271035   G   T  -0.72        0.05    chr11   5249805        G   \n",
+       "3      chr11   5271036   G   A   0.14        0.04    chr11   5249806        G   \n",
+       "4      chr11   5271036   G   C   0.09        0.00    chr11   5249806        G   \n",
+       "..       ...       ...  ..  ..    ...         ...      ...       ...      ...   \n",
+       "864    chr22  19711172   G   A  -0.02        0.01    chr22  19723649        G   \n",
+       "865    chr22  19711172   G   C   0.02        0.00    chr22  19723649        G   \n",
+       "866    chr22  19711172   G   T   0.09        0.01    chr22  19723649        G   \n",
+       "867    chr22  19711173   T   C   0.00        0.00    chr22  19723650        T   \n",
+       "868    chr22  19711173   T   G   0.01        0.00    chr22  19723650        T   \n",
+       "\n",
+       "    alt_hg38  label_hg38  Confidence_hg38  \n",
+       "0          A        0.19             0.06  \n",
+       "1          C        0.07             0.01  \n",
+       "2          T       -0.72             0.05  \n",
+       "3          A        0.14             0.04  \n",
+       "4          C        0.09             0.00  \n",
+       "..       ...         ...              ...  \n",
+       "864        A       -0.02             0.01  \n",
+       "865        C        0.02             0.00  \n",
+       "866        T        0.09             0.01  \n",
+       "867        C        0.00             0.00  \n",
+       "868        G        0.01             0.00  \n",
+       "\n",
+       "[13813 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f182c55e350>"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(enf_df[\"label\"],enf_df[\"label_hg38\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>ref_hg38</th>\n",
+       "      <th>alt_hg38</th>\n",
+       "      <th>label_hg38</th>\n",
+       "      <th>Confidence_hg38</th>\n",
+       "      <th>data.Value</th>\n",
+       "      <th>dnase</th>\n",
+       "      <th>data.Position</th>\n",
+       "      <th>challenge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271035</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249805</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>-0.069240</td>\n",
+       "      <td>5249805.0</td>\n",
+       "      <td>challenge_HBG1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271035</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249805</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.011861</td>\n",
+       "      <td>5249805.0</td>\n",
+       "      <td>challenge_HBG1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271035</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249805</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>0.035393</td>\n",
+       "      <td>5249805.0</td>\n",
+       "      <td>challenge_HBG1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271036</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249806</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>-0.174114</td>\n",
+       "      <td>5249806.0</td>\n",
+       "      <td>challenge_HBG1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5271036</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr11</td>\n",
+       "      <td>5249806</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.010160</td>\n",
+       "      <td>5249806.0</td>\n",
+       "      <td>challenge_HBG1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>908</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711172</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723649</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.267801</td>\n",
+       "      <td>19723649.0</td>\n",
+       "      <td>challenge_GP1BB.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>909</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711172</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723649</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.337786</td>\n",
+       "      <td>19723649.0</td>\n",
+       "      <td>challenge_GP1BB.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>910</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711172</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723649</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.458304</td>\n",
+       "      <td>19723649.0</td>\n",
+       "      <td>challenge_GP1BB.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>911</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711173</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723650</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.487951</td>\n",
+       "      <td>19723650.0</td>\n",
+       "      <td>challenge_GP1BB.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>912</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19711173</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>19723650</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.568563</td>\n",
+       "      <td>19723650.0</td>\n",
+       "      <td>challenge_GP1BB.tsv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13171 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr_hg19  pos_hg19 ref alt  label  Confidence chr_hg38  pos_hg38 ref_hg38  \\\n",
+       "0      chr11   5271035   G   A   0.19        0.06    chr11   5249805        G   \n",
+       "1      chr11   5271035   G   C   0.07        0.01    chr11   5249805        G   \n",
+       "2      chr11   5271035   G   T  -0.72        0.05    chr11   5249805        G   \n",
+       "3      chr11   5271036   G   A   0.14        0.04    chr11   5249806        G   \n",
+       "4      chr11   5271036   G   C   0.09        0.00    chr11   5249806        G   \n",
+       "..       ...       ...  ..  ..    ...         ...      ...       ...      ...   \n",
+       "908    chr22  19711172   G   A  -0.02        0.01    chr22  19723649        G   \n",
+       "909    chr22  19711172   G   C   0.02        0.00    chr22  19723649        G   \n",
+       "910    chr22  19711172   G   T   0.09        0.01    chr22  19723649        G   \n",
+       "911    chr22  19711173   T   C   0.00        0.00    chr22  19723650        T   \n",
+       "912    chr22  19711173   T   G   0.01        0.00    chr22  19723650        T   \n",
+       "\n",
+       "    alt_hg38  label_hg38  Confidence_hg38  data.Value     dnase  \\\n",
+       "0          A        0.19             0.06        0.19 -0.069240   \n",
+       "1          C        0.07             0.01        0.07  0.011861   \n",
+       "2          T       -0.72             0.05       -0.72  0.035393   \n",
+       "3          A        0.14             0.04        0.14 -0.174114   \n",
+       "4          C        0.09             0.00        0.09  0.010160   \n",
+       "..       ...         ...              ...         ...       ...   \n",
+       "908        A       -0.02             0.01       -0.02  0.267801   \n",
+       "909        C        0.02             0.00        0.02  0.337786   \n",
+       "910        T        0.09             0.01        0.09  0.458304   \n",
+       "911        C        0.00             0.00        0.00  0.487951   \n",
+       "912        G        0.01             0.00        0.01  0.568563   \n",
+       "\n",
+       "     data.Position            challenge  \n",
+       "0        5249805.0   challenge_HBG1.tsv  \n",
+       "1        5249805.0   challenge_HBG1.tsv  \n",
+       "2        5249805.0   challenge_HBG1.tsv  \n",
+       "3        5249806.0   challenge_HBG1.tsv  \n",
+       "4        5249806.0   challenge_HBG1.tsv  \n",
+       "..             ...                  ...  \n",
+       "908     19723649.0  challenge_GP1BB.tsv  \n",
+       "909     19723649.0  challenge_GP1BB.tsv  \n",
+       "910     19723649.0  challenge_GP1BB.tsv  \n",
+       "911     19723650.0  challenge_GP1BB.tsv  \n",
+       "912     19723650.0  challenge_GP1BB.tsv  \n",
+       "\n",
+       "[13171 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "MergeError",
+     "evalue": "Merge keys are not unique in right dataset; not a many-to-one merge",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-95-8e9bd3827191>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mcagi_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pos\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcagi_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pos\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mcagi_df_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcagi_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menf_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"chr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pos\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"allele1\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"allele2\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"label\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Confidence\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"challenge\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"chr_hg19\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pos_hg19\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ref\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"alt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"label\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Confidence\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"challenge\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"left\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"many_to_one\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m   9201\u001b[0m             \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9202\u001b[0m             \u001b[0mindicator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindicator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9203\u001b[0;31m             \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9204\u001b[0m         )\n\u001b[1;32m   9205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0mindicator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindicator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m         \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     )\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    707\u001b[0m         \u001b[0;31m# are in fact unique.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalidate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_validate\u001b[0;34m(self, validate)\u001b[0m\n\u001b[1;32m   1440\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mright_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m                 raise MergeError(\n\u001b[0;32m-> 1442\u001b[0;31m                     \u001b[0;34m\"Merge keys are not unique in right dataset; \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m                     \u001b[0;34m\"not a many-to-one merge\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m                 )\n",
+      "\u001b[0;31mMergeError\u001b[0m: Merge keys are not unique in right dataset; not a many-to-one merge"
+     ]
+    }
+   ],
+   "source": [
+    "enf_df[\"label\"] = enf_df[\"label\"].astype(float)\n",
+    "enf_df[\"Confidence\"] = enf_df[\"Confidence\"].astype(float)\n",
+    "cagi_df[\"label\"] = cagi_df[\"label\"].astype(float)\n",
+    "cagi_df[\"Confidence\"] = cagi_df[\"Confidence\"].astype(float)\n",
+    "enf_df[\"pos_hg19\"] = enf_df[\"pos_hg19\"].astype(int)\n",
+    "cagi_df[\"pos\"] = cagi_df[\"pos\"].astype(int)\n",
+    "\n",
+    "cagi_df_new = cagi_df.merge(enf_df, left_on=[\"chr\", \"pos\", \"allele1\", \"allele2\", \"label\", \"Confidence\", \"challenge\"], right_on=[\"chr_hg19\", \"pos_hg19\", \"ref\", \"alt\", \"label\", \"Confidence\", \"challenge\"], how=\"left\", validate=\"many_to_one\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(81883, 27)"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df_new.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28422, 13)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13171, 16)"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp=enf_df.sort_values([\"chr_hg19\", \"pos_hg19\", \"ref\", \"alt\", \"label\", \"Confidence\", \"challenge\"])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>ref_hg38</th>\n",
+       "      <th>alt_hg38</th>\n",
+       "      <th>label_hg38</th>\n",
+       "      <th>Confidence_hg38</th>\n",
+       "      <th>data.Value</th>\n",
+       "      <th>dnase</th>\n",
+       "      <th>data.Position</th>\n",
+       "      <th>challenge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109274653</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>-0.120252</td>\n",
+       "      <td>109274653.0</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109274653</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>-0.148276</td>\n",
+       "      <td>109274653.0</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109274653</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>-0.120252</td>\n",
+       "      <td>109274653.0</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109274653</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>-0.148276</td>\n",
+       "      <td>109274653.0</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817277</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109274655</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.582183</td>\n",
+       "      <td>109274655.0</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612868</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-0.16</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chrX</td>\n",
+       "      <td>139530709</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-0.16</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.16</td>\n",
+       "      <td>-2.321797</td>\n",
+       "      <td>139530709.0</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>chrX</td>\n",
+       "      <td>139530710</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-3.545758</td>\n",
+       "      <td>139530710.0</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>chrX</td>\n",
+       "      <td>139530710</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-3.537784</td>\n",
+       "      <td>139530710.0</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chrX</td>\n",
+       "      <td>139530710</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-3.545758</td>\n",
+       "      <td>139530710.0</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>chrX</td>\n",
+       "      <td>139530710</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-3.537784</td>\n",
+       "      <td>139530710.0</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1389 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr_hg19   pos_hg19 ref alt  label  Confidence chr_hg38   pos_hg38  \\\n",
+       "5       chr1  109817275   A   C   0.03        0.00     chr1  109274653   \n",
+       "6       chr1  109817275   A   C   0.03        0.00     chr1  109274653   \n",
+       "7       chr1  109817275   A   G   0.03        0.00     chr1  109274653   \n",
+       "8       chr1  109817275   A   G   0.03        0.00     chr1  109274653   \n",
+       "14      chr1  109817277   C   G   0.02        0.01     chr1  109274655   \n",
+       "..       ...        ...  ..  ..    ...         ...      ...        ...   \n",
+       "632     chrX  138612868   T   G  -0.16        0.01     chrX  139530709   \n",
+       "633     chrX  138612869   G   A  -0.12        0.10     chrX  139530710   \n",
+       "634     chrX  138612869   G   A  -0.12        0.10     chrX  139530710   \n",
+       "636     chrX  138612869   G   T  -0.12        0.01     chrX  139530710   \n",
+       "637     chrX  138612869   G   T  -0.12        0.01     chrX  139530710   \n",
+       "\n",
+       "    ref_hg38 alt_hg38  label_hg38  Confidence_hg38  data.Value     dnase  \\\n",
+       "5          A        C        0.03             0.00        0.03 -0.120252   \n",
+       "6          A        C        0.03             0.00        0.03 -0.148276   \n",
+       "7          A        G        0.03             0.00        0.03 -0.120252   \n",
+       "8          A        G        0.03             0.00        0.03 -0.148276   \n",
+       "14         C        G        0.02             0.01        0.02  0.582183   \n",
+       "..       ...      ...         ...              ...         ...       ...   \n",
+       "632        T        G       -0.16             0.01       -0.16 -2.321797   \n",
+       "633        G        A       -0.12             0.10       -0.12 -3.545758   \n",
+       "634        G        A       -0.12             0.10       -0.12 -3.537784   \n",
+       "636        G        T       -0.12             0.01       -0.12 -3.545758   \n",
+       "637        G        T       -0.12             0.01       -0.12 -3.537784   \n",
+       "\n",
+       "     data.Position            challenge  \n",
+       "5      109274653.0  challenge_SORT1.tsv  \n",
+       "6      109274653.0  challenge_SORT1.tsv  \n",
+       "7      109274653.0  challenge_SORT1.tsv  \n",
+       "8      109274653.0  challenge_SORT1.tsv  \n",
+       "14     109274655.0  challenge_SORT1.tsv  \n",
+       "..             ...                  ...  \n",
+       "632    139530709.0     challenge_F9.tsv  \n",
+       "633    139530710.0     challenge_F9.tsv  \n",
+       "634    139530710.0     challenge_F9.tsv  \n",
+       "636    139530710.0     challenge_F9.tsv  \n",
+       "637    139530710.0     challenge_F9.tsv  \n",
+       "\n",
+       "[1389 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp[temp[[\"chr_hg19\", \"pos_hg19\", \"ref\", \"alt\", \"label\", \"Confidence\", \"challenge\"]].duplicated(keep=False)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enf_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>challenge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817275</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817277</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>632</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612868</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-0.16</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>633</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612869</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1389 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr_hg19   pos_hg19 ref alt  label  Confidence            challenge\n",
+       "5       chr1  109817275   A   C   0.03        0.00  challenge_SORT1.tsv\n",
+       "6       chr1  109817275   A   C   0.03        0.00  challenge_SORT1.tsv\n",
+       "7       chr1  109817275   A   G   0.03        0.00  challenge_SORT1.tsv\n",
+       "8       chr1  109817275   A   G   0.03        0.00  challenge_SORT1.tsv\n",
+       "14      chr1  109817277   C   G   0.02        0.01  challenge_SORT1.tsv\n",
+       "..       ...        ...  ..  ..    ...         ...                  ...\n",
+       "632     chrX  138612868   T   G  -0.16        0.01     challenge_F9.tsv\n",
+       "633     chrX  138612869   G   A  -0.12        0.10     challenge_F9.tsv\n",
+       "634     chrX  138612869   G   A  -0.12        0.10     challenge_F9.tsv\n",
+       "636     chrX  138612869   G   T  -0.12        0.01     challenge_F9.tsv\n",
+       "637     chrX  138612869   G   T  -0.12        0.01     challenge_F9.tsv\n",
+       "\n",
+       "[1389 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>challenge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817273</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.23</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817273</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817274</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.05</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817274</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109817274</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_SORT1.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612907</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-0.13</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612907</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612908</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.13</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612908</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>138612908</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-0.61</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>challenge_F9.tsv</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13171 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr_hg19   pos_hg19 ref alt  label  Confidence            challenge\n",
+       "0       chr1  109817273   C   A  -0.23        0.02  challenge_SORT1.tsv\n",
+       "1       chr1  109817273   C   G  -0.07        0.01  challenge_SORT1.tsv\n",
+       "2       chr1  109817274   G   A  -0.05        0.01  challenge_SORT1.tsv\n",
+       "3       chr1  109817274   G   C   0.06        0.01  challenge_SORT1.tsv\n",
+       "4       chr1  109817274   G   T   0.04        0.01  challenge_SORT1.tsv\n",
+       "..       ...        ...  ..  ..    ...         ...                  ...\n",
+       "702     chrX  138612907   A   G  -0.13        0.02     challenge_F9.tsv\n",
+       "703     chrX  138612907   A   T  -0.02        0.01     challenge_F9.tsv\n",
+       "704     chrX  138612908   T   A  -0.13        0.08     challenge_F9.tsv\n",
+       "705     chrX  138612908   T   C  -0.12        0.01     challenge_F9.tsv\n",
+       "706     chrX  138612908   T   G  -0.61        0.05     challenge_F9.tsv\n",
+       "\n",
+       "[13171 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp[[\"chr_hg19\", \"pos_hg19\", \"ref\", \"alt\", \"label\", \"Confidence\", \"challenge\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import deepdish as dd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datat = dd.io.load(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/sad.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39170,)"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datat[\"pos\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39170,)"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datat['pos'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['SAD', 'SADR', 'SADR_pct', 'SAD_pct', 'alt_allele', 'chr', 'percentiles', 'pos', 'ref_allele', 'snp', 'target_ids', 'target_labels'])"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datat.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_preds = pd.DataFrame()\n",
+    "\n",
+    "enformer_preds[\"chr\"] = datat[\"chr\"].astype(str)\n",
+    "enformer_preds[\"pos\"] = datat[\"pos\"]\n",
+    "enformer_preds[\"ref\"] = datat[\"ref_allele\"].astype(str)\n",
+    "enformer_preds[\"alt\"] = datat[\"alt_allele\"].astype(str)\n",
+    "\n",
+    "enformer_preds[\"rsid\"] = enformer_preds[\"chr\"].astype(str) + '_' +  enformer_preds[\"pos\"].astype(str) + \"_\" + enformer_preds[\"ref\"] + '_' +  enformer_preds[\"alt\"]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr2_60494940_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr2_60494940_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr2_60494940_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494941</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr2_60494941_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494941</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr2_60494941_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39165</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791602</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr7_156791602_G_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39166</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791602</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr7_156791602_G_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39167</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791603</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr7_156791603_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39168</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791603</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr7_156791603_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39169</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791603</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr7_156791603_C_T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>39170 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        chr        pos ref alt                rsid\n",
+       "0      chr2   60494940   C   A   chr2_60494940_C_A\n",
+       "1      chr2   60494940   C   G   chr2_60494940_C_G\n",
+       "2      chr2   60494940   C   T   chr2_60494940_C_T\n",
+       "3      chr2   60494941   C   A   chr2_60494941_C_A\n",
+       "4      chr2   60494941   C   G   chr2_60494941_C_G\n",
+       "...     ...        ...  ..  ..                 ...\n",
+       "39165  chr7  156791602   G   C  chr7_156791602_G_C\n",
+       "39166  chr7  156791602   G   T  chr7_156791602_G_T\n",
+       "39167  chr7  156791603   C   A  chr7_156791603_C_A\n",
+       "39168  chr7  156791603   C   G  chr7_156791603_C_G\n",
+       "39169  chr7  156791603   C   T  chr7_156791603_C_T\n",
+       "\n",
+       "[39170 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>challenge</th>\n",
+       "      <th>model_used</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.143521</td>\n",
+       "      <td>-0.024822</td>\n",
+       "      <td>0.044314</td>\n",
+       "      <td>0.038392</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.131439</td>\n",
+       "      <td>-0.023676</td>\n",
+       "      <td>0.040458</td>\n",
+       "      <td>0.041713</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.086450</td>\n",
+       "      <td>-0.023025</td>\n",
+       "      <td>0.026278</td>\n",
+       "      <td>0.065491</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.013275</td>\n",
+       "      <td>-0.027508</td>\n",
+       "      <td>0.011885</td>\n",
+       "      <td>0.166936</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.033312</td>\n",
+       "      <td>0.020898</td>\n",
+       "      <td>0.011562</td>\n",
+       "      <td>0.171765</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1242</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775848</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775848,G,C,0.19,0.03,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.012688</td>\n",
+       "      <td>0.011428</td>\n",
+       "      <td>0.006799</td>\n",
+       "      <td>0.309386</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1243</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775848</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775848,G,T,0.02,0.0,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.014364</td>\n",
+       "      <td>-0.010755</td>\n",
+       "      <td>0.008316</td>\n",
+       "      <td>0.339582</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1244</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775849,C,A,0.0,0.0,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.009251</td>\n",
+       "      <td>-0.012372</td>\n",
+       "      <td>0.007680</td>\n",
+       "      <td>0.323677</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1245</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>6,37775849,C,G,-0.02,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.007366</td>\n",
+       "      <td>0.012662</td>\n",
+       "      <td>0.004491</td>\n",
+       "      <td>0.411898</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1246</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775849,C,T,0.03,0.02,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.011113</td>\n",
+       "      <td>-0.013638</td>\n",
+       "      <td>0.010171</td>\n",
+       "      <td>0.325728</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>28422 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr       pos allele1 allele2  \\\n",
+       "0     chr6  37775290       G       A   \n",
+       "1     chr6  37775290       G       C   \n",
+       "2     chr6  37775290       G       T   \n",
+       "3     chr6  37775291       T       A   \n",
+       "4     chr6  37775291       T       C   \n",
+       "...    ...       ...     ...     ...   \n",
+       "1242  chr6  37775848       G       C   \n",
+       "1243  chr6  37775848       G       T   \n",
+       "1244  chr6  37775849       C       A   \n",
+       "1245  chr6  37775849       C       G   \n",
+       "1246  chr6  37775849       C       T   \n",
+       "\n",
+       "                                                rsid  logfc.mean  \\\n",
+       "0      6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.143521   \n",
+       "1      6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv   -0.131439   \n",
+       "2      6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv   -0.086450   \n",
+       "3     6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv   -0.013275   \n",
+       "4      6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv    0.033312   \n",
+       "...                                              ...         ...   \n",
+       "1242   6,37775848,G,C,0.19,0.03,challenge_ZFAND3.tsv    0.012688   \n",
+       "1243    6,37775848,G,T,0.02,0.0,challenge_ZFAND3.tsv   -0.014364   \n",
+       "1244     6,37775849,C,A,0.0,0.0,challenge_ZFAND3.tsv   -0.009251   \n",
+       "1245  6,37775849,C,G,-0.02,0.01,challenge_ZFAND3.tsv    0.007366   \n",
+       "1246   6,37775849,C,T,0.03,0.02,challenge_ZFAND3.tsv   -0.011113   \n",
+       "\n",
+       "      sign_jsd.mean  abs_logfc_x_max_percentile.mean  \\\n",
+       "0         -0.024822                         0.044314   \n",
+       "1         -0.023676                         0.040458   \n",
+       "2         -0.023025                         0.026278   \n",
+       "3         -0.027508                         0.011885   \n",
+       "4          0.020898                         0.011562   \n",
+       "...             ...                              ...   \n",
+       "1242       0.011428                         0.006799   \n",
+       "1243      -0.010755                         0.008316   \n",
+       "1244      -0.012372                         0.007680   \n",
+       "1245       0.012662                         0.004491   \n",
+       "1246      -0.013638                         0.010171   \n",
+       "\n",
+       "      abs_logfc_x_max_percentile.mean.pval  label  Confidence  \\\n",
+       "0                                 0.038392   0.03        0.01   \n",
+       "1                                 0.041713   0.03        0.01   \n",
+       "2                                 0.065491   0.07        0.01   \n",
+       "3                                 0.166936  -0.07        0.01   \n",
+       "4                                 0.171765   0.03        0.01   \n",
+       "...                                    ...    ...         ...   \n",
+       "1242                              0.309386   0.19        0.03   \n",
+       "1243                              0.339582   0.02        0.00   \n",
+       "1244                              0.323677   0.00        0.00   \n",
+       "1245                              0.411898  -0.02        0.01   \n",
+       "1246                              0.325728   0.03        0.02   \n",
+       "\n",
+       "                 challenge            model_used  \n",
+       "0     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "1     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "2     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "3     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "4     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "...                    ...                   ...  \n",
+       "1242  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1243  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1244  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1245  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1246  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "\n",
+       "[28422 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>genome</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>file</th>\n",
+       "      <th>clip</th>\n",
+       "      <th>scale</th>\n",
+       "      <th>sum_stat</th>\n",
+       "      <th>description</th>\n",
+       "      <th>encode_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [index, genome, identifier, file, clip, scale, sum_stat, description, encode_id]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_labesls[enformer_labesls[\"encode_id\"] == \"ENCSR539WBA\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       ENCSR000EIJ\n",
+       "1       ENCSR000EIK\n",
+       "2       ENCSR000EIL\n",
+       "3       ENCSR000EIP\n",
+       "4       ENCSR000EIS\n",
+       "           ...     \n",
+       "5308      CNhs14239\n",
+       "5309      CNhs14240\n",
+       "5310      CNhs14241\n",
+       "5311      CNhs14244\n",
+       "5312      CNhs14245\n",
+       "Name: encode_id, Length: 5313, dtype: object"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_labesls[\"encode_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_labesls = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/target_humans.txt\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_labesls[\"encode_id\"] = enformer_labesls[\"file\"].str.split(\"/\", expand=True)[8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "      <th>challenge</th>\n",
+       "      <th>model_used</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.143521</td>\n",
+       "      <td>-0.024822</td>\n",
+       "      <td>0.044314</td>\n",
+       "      <td>0.038392</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.131439</td>\n",
+       "      <td>-0.023676</td>\n",
+       "      <td>0.040458</td>\n",
+       "      <td>0.041713</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.086450</td>\n",
+       "      <td>-0.023025</td>\n",
+       "      <td>0.026278</td>\n",
+       "      <td>0.065491</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.013275</td>\n",
+       "      <td>-0.027508</td>\n",
+       "      <td>0.011885</td>\n",
+       "      <td>0.166936</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.033312</td>\n",
+       "      <td>0.020898</td>\n",
+       "      <td>0.011562</td>\n",
+       "      <td>0.171765</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR056PFI_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1242</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775848</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775848,G,C,0.19,0.03,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.012688</td>\n",
+       "      <td>0.011428</td>\n",
+       "      <td>0.006799</td>\n",
+       "      <td>0.309386</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1243</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775848</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775848,G,T,0.02,0.0,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.014364</td>\n",
+       "      <td>-0.010755</td>\n",
+       "      <td>0.008316</td>\n",
+       "      <td>0.339582</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1244</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775849,C,A,0.0,0.0,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.009251</td>\n",
+       "      <td>-0.012372</td>\n",
+       "      <td>0.007680</td>\n",
+       "      <td>0.323677</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1245</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>6,37775849,C,G,-0.02,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.007366</td>\n",
+       "      <td>0.012662</td>\n",
+       "      <td>0.004491</td>\n",
+       "      <td>0.411898</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1246</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775849</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775849,C,T,0.03,0.02,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.011113</td>\n",
+       "      <td>-0.013638</td>\n",
+       "      <td>0.010171</td>\n",
+       "      <td>0.325728</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>ENCSR539WBA_pancreas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>28422 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr       pos allele1 allele2  \\\n",
+       "0     chr6  37775290       G       A   \n",
+       "1     chr6  37775290       G       C   \n",
+       "2     chr6  37775290       G       T   \n",
+       "3     chr6  37775291       T       A   \n",
+       "4     chr6  37775291       T       C   \n",
+       "...    ...       ...     ...     ...   \n",
+       "1242  chr6  37775848       G       C   \n",
+       "1243  chr6  37775848       G       T   \n",
+       "1244  chr6  37775849       C       A   \n",
+       "1245  chr6  37775849       C       G   \n",
+       "1246  chr6  37775849       C       T   \n",
+       "\n",
+       "                                                rsid  logfc.mean  \\\n",
+       "0      6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.143521   \n",
+       "1      6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv   -0.131439   \n",
+       "2      6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv   -0.086450   \n",
+       "3     6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv   -0.013275   \n",
+       "4      6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv    0.033312   \n",
+       "...                                              ...         ...   \n",
+       "1242   6,37775848,G,C,0.19,0.03,challenge_ZFAND3.tsv    0.012688   \n",
+       "1243    6,37775848,G,T,0.02,0.0,challenge_ZFAND3.tsv   -0.014364   \n",
+       "1244     6,37775849,C,A,0.0,0.0,challenge_ZFAND3.tsv   -0.009251   \n",
+       "1245  6,37775849,C,G,-0.02,0.01,challenge_ZFAND3.tsv    0.007366   \n",
+       "1246   6,37775849,C,T,0.03,0.02,challenge_ZFAND3.tsv   -0.011113   \n",
+       "\n",
+       "      sign_jsd.mean  abs_logfc_x_max_percentile.mean  \\\n",
+       "0         -0.024822                         0.044314   \n",
+       "1         -0.023676                         0.040458   \n",
+       "2         -0.023025                         0.026278   \n",
+       "3         -0.027508                         0.011885   \n",
+       "4          0.020898                         0.011562   \n",
+       "...             ...                              ...   \n",
+       "1242       0.011428                         0.006799   \n",
+       "1243      -0.010755                         0.008316   \n",
+       "1244      -0.012372                         0.007680   \n",
+       "1245       0.012662                         0.004491   \n",
+       "1246      -0.013638                         0.010171   \n",
+       "\n",
+       "      abs_logfc_x_max_percentile.mean.pval  label  Confidence  \\\n",
+       "0                                 0.038392   0.03        0.01   \n",
+       "1                                 0.041713   0.03        0.01   \n",
+       "2                                 0.065491   0.07        0.01   \n",
+       "3                                 0.166936  -0.07        0.01   \n",
+       "4                                 0.171765   0.03        0.01   \n",
+       "...                                    ...    ...         ...   \n",
+       "1242                              0.309386   0.19        0.03   \n",
+       "1243                              0.339582   0.02        0.00   \n",
+       "1244                              0.323677   0.00        0.00   \n",
+       "1245                              0.411898  -0.02        0.01   \n",
+       "1246                              0.325728   0.03        0.02   \n",
+       "\n",
+       "                 challenge            model_used  \n",
+       "0     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "1     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "2     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "3     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "4     challenge_ZFAND3.tsv  ENCSR056PFI_pancreas  \n",
+       "...                    ...                   ...  \n",
+       "1242  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1243  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1244  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1245  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "1246  challenge_ZFAND3.tsv  ENCSR539WBA_pancreas  \n",
+       "\n",
+       "[28422 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13238, 4)"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cagi_df[[\"chr\", \"pos\", \"allele1\", \"allele2\"]].drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39170, 5)"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_preds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr2_60494940_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr2_60494940_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr2_60494940_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494941</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr2_60494941_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>60494941</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr2_60494941_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39165</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791602</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr7_156791602_G_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39166</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791602</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr7_156791602_G_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39167</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791603</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr7_156791603_C_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39168</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791603</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr7_156791603_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39169</th>\n",
+       "      <td>chr7</td>\n",
+       "      <td>156791603</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr7_156791603_C_T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>39170 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        chr        pos ref alt                rsid\n",
+       "0      chr2   60494940   C   A   chr2_60494940_C_A\n",
+       "1      chr2   60494940   C   G   chr2_60494940_C_G\n",
+       "2      chr2   60494940   C   T   chr2_60494940_C_T\n",
+       "3      chr2   60494941   C   A   chr2_60494941_C_A\n",
+       "4      chr2   60494941   C   G   chr2_60494941_C_G\n",
+       "...     ...        ...  ..  ..                 ...\n",
+       "39165  chr7  156791602   G   C  chr7_156791602_G_C\n",
+       "39166  chr7  156791602   G   T  chr7_156791602_G_T\n",
+       "39167  chr7  156791603   C   A  chr7_156791603_C_A\n",
+       "39168  chr7  156791603   C   G  chr7_156791603_C_G\n",
+       "39169  chr7  156791603   C   T  chr7_156791603_C_T\n",
+       "\n",
+       "[39170 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13787, 17)"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#enformer_preds.merge(cagi_df, right_on=[\"chr\", \"pos\", \"allele1\", \"allele2\"], left_on=[\"chr\", \"pos\", \"ref\", \"alt\"], how=\"inner\").drop_duplicates().shape\n",
+    "enformer_preds.merge(enf_df, right_on=[\"chr_hg38\", \"pos_hg38\", \"ref_hg38\", \"alt_hg38\"], left_on=[\"chr\", \"pos\", \"ref\", \"alt\"], how=\"inner\").drop_duplicates().shape\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39170, 5)"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_preds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13813, 12)"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf_df.shape\n",
+    "#enformer_preds.merge(cagi_df, right_on=[\"chr\", \"pos\", \"allele1\", \"allele2\"], left_on=[\"chr\", \"pos\", \"ref\", \"alt\"], how=\"inner\").drop_duplicates().shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_dsqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_dsqtls.ipynb
new file mode 100644
index 00000000..1524fff7
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_dsqtls.ipynb
@@ -0,0 +1,1691 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28309, 18)\n"
+     ]
+    }
+   ],
+   "source": [
+    "gkm_svm = pd.read_csv(\"/mnt/lab_data3/anusri/histone_expts/all_qtl_analysis/gm12878_sequence_sets/test_set/deltasv,/41588_2015_BFng3331_MOESM26_ESM.csv\", header=0,sep=',')\n",
+    "print(gkm_svm.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chrom_hg18</th>\n",
+       "      <th>pos_hg18</th>\n",
+       "      <th>SNPname1</th>\n",
+       "      <th>chrom_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>SNPname2</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>gkm_SVM</th>\n",
+       "      <th>kmer_SVM</th>\n",
+       "      <th>abs_gkm_SVM</th>\n",
+       "      <th>abs_kmer_SVM</th>\n",
+       "      <th>CADD</th>\n",
+       "      <th>GWAVA_region</th>\n",
+       "      <th>GWAVA_tss</th>\n",
+       "      <th>GWAVA_unmatched</th>\n",
+       "      <th>GERP</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>846446</td>\n",
+       "      <td>chr1.846446</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>856583</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2.653531</td>\n",
+       "      <td>7.938784</td>\n",
+       "      <td>2.653531</td>\n",
+       "      <td>7.938784</td>\n",
+       "      <td>-1.522533</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>-0.682</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>901458</td>\n",
+       "      <td>chr1.901458</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>911595</td>\n",
+       "      <td>rs7417106</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2.821422</td>\n",
+       "      <td>6.027648</td>\n",
+       "      <td>2.821422</td>\n",
+       "      <td>6.027648</td>\n",
+       "      <td>1.739152</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>2.150</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1176365</td>\n",
+       "      <td>chr1.1176365</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-7.167236</td>\n",
+       "      <td>-10.870018</td>\n",
+       "      <td>7.167236</td>\n",
+       "      <td>10.870018</td>\n",
+       "      <td>-0.091378</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.451</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1217275</td>\n",
+       "      <td>chr1.1217275</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1227412</td>\n",
+       "      <td>rs112571439</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-3.957382</td>\n",
+       "      <td>2.387491</td>\n",
+       "      <td>3.957382</td>\n",
+       "      <td>2.387491</td>\n",
+       "      <td>1.127472</td>\n",
+       "      <td>0.34</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>2.430</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1580438</td>\n",
+       "      <td>chr1.1580438</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1590575</td>\n",
+       "      <td>rs9661285</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.785596</td>\n",
+       "      <td>-6.827102</td>\n",
+       "      <td>4.785596</td>\n",
+       "      <td>6.827102</td>\n",
+       "      <td>0.665464</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>1.640</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chrom_hg18  pos_hg18      SNPname1 chrom_hg19  pos_hg19     SNPname2  \\\n",
+       "0       chr1    846446   chr1.846446       chr1    856583  rs141671872   \n",
+       "1       chr1    901458   chr1.901458       chr1    911595    rs7417106   \n",
+       "2       chr1   1176365  chr1.1176365       chr1   1186502    rs6603785   \n",
+       "3       chr1   1217275  chr1.1217275       chr1   1227412  rs112571439   \n",
+       "4       chr1   1580438  chr1.1580438       chr1   1590575    rs9661285   \n",
+       "\n",
+       "  allele1 allele2   gkm_SVM   kmer_SVM  abs_gkm_SVM  abs_kmer_SVM      CADD  \\\n",
+       "0       A       G  2.653531   7.938784     2.653531      7.938784 -1.522533   \n",
+       "1       G       A  2.821422   6.027648     2.821422      6.027648  1.739152   \n",
+       "2       T       A -7.167236 -10.870018     7.167236     10.870018 -0.091378   \n",
+       "3       A       G -3.957382   2.387491     3.957382      2.387491  1.127472   \n",
+       "4       A       G -4.785596  -6.827102     4.785596      6.827102  0.665464   \n",
+       "\n",
+       "   GWAVA_region  GWAVA_tss  GWAVA_unmatched   GERP  label  \n",
+       "0          0.48       0.53             0.75 -0.682      1  \n",
+       "1          0.29       0.36             0.89  2.150      1  \n",
+       "2          0.42       0.49             0.66  0.451      1  \n",
+       "3          0.34       0.43             0.49  2.430      1  \n",
+       "4          0.38       0.35             0.86  1.640      1  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gkm_svm.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "574"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(gkm_svm[\"label\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27735"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(gkm_svm[\"label\"]==-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "es = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/GSE31388_dsQtlTable.txt.gz\", sep=\"\\t\")\n",
+    "enformer_preds = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/dsqtls/process/fetch_enformer/enformer_predictions.tsv\", sep=\"\\t\", header=0)\n",
+    "gkm_svm['rsid'] = gkm_svm[\"chrom_hg19\"].astype(str) + \"_\" + gkm_svm[\"pos_hg19\"].astype(str) + \"_\" + gkm_svm[\"allele1\"].astype(str) + \"_\" + gkm_svm[\"allele2\"].astype(str)  \n",
+    "es[\"rsid\"] = es[\"Chr\"]+\".\"+es[\"SNP\"].astype(str)\n",
+    "gkm_svm = gkm_svm.merge(es, left_on=[\"SNPname1\"], right_on='rsid', how=\"left\").drop_duplicates('SNPname1')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28309, 48)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gkm_svm.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gkm_svm = gkm_svm[[\"chrom_hg19\", \"pos_hg19\", \"allele1\", \"allele2\",  \"SNPname2\", \"gkm_SVM\", \"Estimate\", \"label\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chrom_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>SNPname2</th>\n",
+       "      <th>gkm_SVM</th>\n",
+       "      <th>Estimate</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>856583</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>2.653531</td>\n",
+       "      <td>0.9301</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chrom_hg19  pos_hg19 allele1 allele2     SNPname2   gkm_SVM  Estimate  label\n",
+       "0       chr1    856583       A       G  rs141671872  2.653531    0.9301      1"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gkm_svm.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>842250</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs148658266</td>\n",
+       "      <td>-0.000669</td>\n",
+       "      <td>-0.000892</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos ref alt         rsid  DNASE_SAD  DNASE_SAR\n",
+       "0  chr1  842250   A   G  rs148658266  -0.000669  -0.000892"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_preds.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_enf_1 = enformer_preds.merge(gkm_svm, left_on=[\"rsid\", \"chr\", \"pos\", \"ref\", \"alt\"], right_on=[\"SNPname2\", \"chrom_hg19\", \"pos_hg19\", \"allele2\", \"allele1\"])\n",
+    "merged_enf_2 = enformer_preds.merge(gkm_svm, left_on=[\"rsid\", \"chr\", \"pos\", \"ref\", \"alt\"], right_on=[\"SNPname2\", \"chrom_hg19\", \"pos_hg19\", \"allele1\", \"allele2\"])\n",
+    "merged_enf_2[\"DNASE_SAD\"] = merged_enf_2[\"DNASE_SAD\"]*-1\n",
+    "merged_enf_2[\"DNASE_SAR\"] = merged_enf_2[\"DNASE_SAR\"]*-1\n",
+    "merged_enf = pd.concat([merged_enf_1, merged_enf_2]).drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>chrom_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>SNPname2</th>\n",
+       "      <th>gkm_SVM</th>\n",
+       "      <th>Estimate</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>842250</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs148658266</td>\n",
+       "      <td>-0.000669</td>\n",
+       "      <td>-0.000892</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>842250</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs148658266</td>\n",
+       "      <td>-3.62651</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos ref alt         rsid  DNASE_SAD  DNASE_SAR chrom_hg19  \\\n",
+       "0  chr1  842250   A   G  rs148658266  -0.000669  -0.000892       chr1   \n",
+       "\n",
+       "   pos_hg19 allele1 allele2     SNPname2  gkm_SVM  Estimate  label  \n",
+       "0    842250       G       A  rs148658266 -3.62651       NaN     -1  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_enf.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "27373\n",
+      "560\n",
+      "26813\n"
+     ]
+    }
+   ],
+   "source": [
+    "rsid_samples = merged_enf[\"rsid\"].unique()\n",
+    "gkm_svm[\"IsUsed\"] = False\n",
+    "gkm_svm.loc[gkm_svm[\"SNPname2\"].isin(rsid_samples),\"IsUsed\"] = True\n",
+    "\n",
+    "print(sum(gkm_svm[\"IsUsed\"]))\n",
+    "print(sum(gkm_svm[\"label\"]==1 & gkm_svm[\"IsUsed\"]))\n",
+    "print(sum((gkm_svm[\"label\"]==-1) & gkm_svm[\"IsUsed\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(28309, 11)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = gkm_svm.merge(merged_enf[[\"chrom_hg19\", \"pos_hg19\", \"allele1\", \"allele2\", \"DNASE_SAD\", \"DNASE_SAR\"]] , on=[\"chrom_hg19\", \"pos_hg19\", \"allele1\", \"allele2\"], how=\"left\")\n",
+    "print(data.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "936"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "sum(~data[\"IsUsed\"] & np.isnan(data[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chrom_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>SNPname2</th>\n",
+       "      <th>gkm_SVM</th>\n",
+       "      <th>Estimate</th>\n",
+       "      <th>label</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>856583</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>2.653531</td>\n",
+       "      <td>0.9301</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.000342</td>\n",
+       "      <td>0.000450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>911595</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs7417106</td>\n",
+       "      <td>2.821422</td>\n",
+       "      <td>-0.6285</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.001241</td>\n",
+       "      <td>-0.001526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>-7.167236</td>\n",
+       "      <td>-0.7363</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.006530</td>\n",
+       "      <td>-0.007840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1227412</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs112571439</td>\n",
+       "      <td>-3.957382</td>\n",
+       "      <td>-0.9236</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.001878</td>\n",
+       "      <td>-0.002290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1590575</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs9661285</td>\n",
+       "      <td>-4.785596</td>\n",
+       "      <td>-1.0890</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.008736</td>\n",
+       "      <td>-0.010230</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chrom_hg19  pos_hg19 allele1 allele2     SNPname2   gkm_SVM  Estimate  \\\n",
+       "0       chr1    856583       A       G  rs141671872  2.653531    0.9301   \n",
+       "1       chr1    911595       G       A    rs7417106  2.821422   -0.6285   \n",
+       "2       chr1   1186502       T       A    rs6603785 -7.167236   -0.7363   \n",
+       "3       chr1   1227412       A       G  rs112571439 -3.957382   -0.9236   \n",
+       "4       chr1   1590575       A       G    rs9661285 -4.785596   -1.0890   \n",
+       "\n",
+       "   label  IsUsed  DNASE_SAD  DNASE_SAR  \n",
+       "0      1    True   0.000342   0.000450  \n",
+       "1      1    True  -0.001241  -0.001526  \n",
+       "2      1    True  -0.006530  -0.007840  \n",
+       "3      1    True  -0.001878  -0.002290  \n",
+       "4      1    True  -0.008736  -0.010230  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atac_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/ATAC/summary.mean.variant_scores_new_2.tsv\"\n",
+    "dnase_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/DNASE_NEW/summary.mean.variant_scores_new_3.tsv\"\n",
+    "atac_preds_50m=\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/ATAC_50M/summary.mean.variant_scores_new_2.tsv\"\n",
+    "\n",
+    "atac_preds_250m=\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/ATAC_250M/summary.mean.variant_scores_new_2.tsv\"\n",
+    "atac_preds_100m=\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/ATAC_100M/summary.mean.variant_scores_new_2.tsv\"\n",
+    "atac_preds_25m=\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/ATAC_25M/summary.mean.variant_scores_new_2.tsv\"\n",
+    "atac_preds_5m=\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/ATAC_5M/summary.mean.variant_scores_new_2.tsv\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_sel_columns_and_append(df_f, out_df, title):\n",
+    "    \n",
+    "    keys = [\"abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "        \"abs_logfc_x_jsd_x_max_percentile.mean.pval\",\n",
+    "        \"abs_logfc_x_jsd.mean\" , \n",
+    "        \"jsd_x_max_percentile.mean\", \n",
+    "        \"abs_logfc_x_max_percentile.mean\", \n",
+    "        \"jsd.mean\", \n",
+    "        \"abs_logfc.mean\",\n",
+    "        \"max_percentile.mean\"]\n",
+    "    \n",
+    "    df = pd.read_csv(df_f, sep='\\t', header=0)\n",
+    "\n",
+    "    for key in keys:\n",
+    "        \n",
+    "        out_df.loc[:,title+\".\"+key] = df[key]\n",
+    "    \n",
+    "    \n",
+    "    out_df[title+\".\"+\"logfc.mean\"] = df[\"logfc.mean\"]*-1\n",
+    "    out_df[title+\".\"+\"sign.jsd.mean\"] = np.sign(df[\"logfc.mean\"])*df[\"jsd.mean\"]*-1\n",
+    "    \n",
+    "    return out_df\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data[['chrom_hg19', 'pos_hg19', 'allele1', 'allele2', 'SNPname2', 'Estimate', 'label',  'IsUsed', 'gkm_SVM', 'DNASE_SAD', 'DNASE_SAR']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1667: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self.obj[key] = value\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = get_sel_columns_and_append(atac_preds, data, \"ENCSR637XSC\")\n",
+    "data = get_sel_columns_and_append(dnase_preds, data, \"ENCSR000EMT\")\n",
+    "data = get_sel_columns_and_append(atac_preds_50m, data, \"ENCSR637XSC.sub.50M\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_sel_columns_and_append(df_f, out_df, title):\n",
+    "    \n",
+    "    keys = [\"abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "        \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]\n",
+    "    \n",
+    "    df = pd.read_csv(df_f, sep='\\t', header=0)\n",
+    "\n",
+    "    for key in keys:\n",
+    "        \n",
+    "        out_df.loc[:,title+\".\"+key] = df[key]\n",
+    "    \n",
+    "    \n",
+    "    out_df[title+\".\"+\"logfc.mean\"] = df[\"logfc.mean\"]*-1\n",
+    "    out_df[title+\".\"+\"sign.jsd.mean\"] = np.sign(df[\"logfc.mean\"])*df[\"jsd.mean\"]*-1\n",
+    "        \n",
+    "    return out_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = get_sel_columns_and_append(atac_preds_250m, data, \"ENCSR637XSC.sub.250M\")\n",
+    "data = get_sel_columns_and_append(atac_preds_100m, data, \"ENCSR637XSC.sub.100M\")\n",
+    "data = get_sel_columns_and_append(atac_preds_25m, data, \"ENCSR637XSC.sub.25M\")\n",
+    "data = get_sel_columns_and_append(atac_preds_5m, data, \"ENCSR637XSC.sub.5M\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28309, 57)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chrom_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>SNPname2</th>\n",
+       "      <th>Estimate</th>\n",
+       "      <th>label</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>gkm_SVM</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.100M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.100M.sign.jsd.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.25M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.25M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.25M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.25M.sign.jsd.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.5M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.5M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.5M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.5M.sign.jsd.mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>856583</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>0.9301</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.653531</td>\n",
+       "      <td>0.000342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.61987</td>\n",
+       "      <td>0.091883</td>\n",
+       "      <td>0.00484</td>\n",
+       "      <td>0.018692</td>\n",
+       "      <td>0.291935</td>\n",
+       "      <td>0.067401</td>\n",
+       "      <td>0.00136</td>\n",
+       "      <td>0.065265</td>\n",
+       "      <td>0.065098</td>\n",
+       "      <td>0.049902</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 57 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chrom_hg19  pos_hg19 allele1 allele2     SNPname2  Estimate  label  IsUsed  \\\n",
+       "0       chr1    856583       A       G  rs141671872    0.9301      1    True   \n",
+       "\n",
+       "    gkm_SVM  DNASE_SAD  ...  ENCSR637XSC.sub.100M.logfc.mean  \\\n",
+       "0  2.653531   0.000342  ...                          0.61987   \n",
+       "\n",
+       "   ENCSR637XSC.sub.100M.sign.jsd.mean  \\\n",
+       "0                            0.091883   \n",
+       "\n",
+       "   ENCSR637XSC.sub.25M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                            0.00484           \n",
+       "\n",
+       "   ENCSR637XSC.sub.25M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                           0.018692                \n",
+       "\n",
+       "   ENCSR637XSC.sub.25M.logfc.mean  ENCSR637XSC.sub.25M.sign.jsd.mean  \\\n",
+       "0                        0.291935                           0.067401   \n",
+       "\n",
+       "   ENCSR637XSC.sub.5M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                            0.00136          \n",
+       "\n",
+       "   ENCSR637XSC.sub.5M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                           0.065265               \n",
+       "\n",
+       "   ENCSR637XSC.sub.5M.logfc.mean  ENCSR637XSC.sub.5M.sign.jsd.mean  \n",
+       "0                       0.065098                          0.049902  \n",
+       "\n",
+       "[1 rows x 57 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chrom_hg19', 'pos_hg19', 'allele1', 'allele2', 'SNPname2', 'Estimate',\n",
+       "       'label', 'IsUsed', 'gkm_SVM', 'DNASE_SAD', 'DNASE_SAR',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd.mean',\n",
+       "       'ENCSR637XSC.jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_max_percentile.mean', 'ENCSR637XSC.jsd.mean',\n",
+       "       'ENCSR637XSC.abs_logfc.mean', 'ENCSR637XSC.max_percentile.mean',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.sign.jsd.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd.mean',\n",
+       "       'ENCSR000EMT.jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_max_percentile.mean', 'ENCSR000EMT.jsd.mean',\n",
+       "       'ENCSR000EMT.abs_logfc.mean', 'ENCSR000EMT.max_percentile.mean',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.sign.jsd.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd.mean',\n",
+       "       'ENCSR637XSC.sub.50M.jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.jsd.mean', 'ENCSR637XSC.sub.50M.abs_logfc.mean',\n",
+       "       'ENCSR637XSC.sub.50M.max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.sign.jsd.mean',\n",
+       "       'ENCSR637XSC.sub.250M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.250M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.250M.logfc.mean', 'ENCSR637XSC.sub.250M.sign.jsd.mean',\n",
+       "       'ENCSR637XSC.sub.100M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.100M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.100M.logfc.mean', 'ENCSR637XSC.sub.100M.sign.jsd.mean',\n",
+       "       'ENCSR637XSC.sub.25M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.25M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.25M.logfc.mean', 'ENCSR637XSC.sub.25M.sign.jsd.mean',\n",
+       "       'ENCSR637XSC.sub.5M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.5M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.5M.logfc.mean', 'ENCSR637XSC.sub.5M.sign.jsd.mean'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle as pkl\n",
+    "import os\n",
+    "enformer_recompute = pkl.load(open(os.path.join(\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/enformer_preds_small_window/predictions_at_snp.pkl\"),'rb'))\n",
+    "mean_change=[]\n",
+    "for i in range(data.shape[0]):\n",
+    "    mean_change.append(np.log2( (enformer_recompute[\"ref_logcount_preds\"][i,440:456].sum()+1) / (enformer_recompute[\"alt_logcount_preds\"][i,440:456].sum()+1)))\n",
+    "    \n",
+    "mean_change = np.array(mean_change)\n",
+    "                       \n",
+    "      \n",
+    "data[\"pred.enformer.encsr000emt.varscore.recomputed\"] = mean_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dict_rename = {\"chrom_hg19\": \"var.chrom\",\n",
+    "                            \"SNPname2\": \"var.rsid\",\n",
+    "                            \"DNASE_SAD\": \"pred.enformer.encsr000emt.varscore.sad\",\n",
+    "                            \"DNASE_SAR\": \"pred.enformer.encsr000emt.varscore.sar\",\n",
+    "                            \"pos_hg19\": \"var.pos\",\n",
+    "                            \"allele1\": \"var.allele1\",\n",
+    "                            \"allele2\": \"var.allele2\",\n",
+    "                            \"Estimate\": \"obs.estimate\",\n",
+    "                            \"label\" : \"var.label\",\n",
+    "                            \"IsUsed\": \"var.isused\",\n",
+    "                            \"gkm_SVM\": \"pred.gkmsvm.encsr000emt.varscore.deltasvm\",\n",
+    "                            \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\": \"pred.chrombpnet.encsr637xsc.varscore.crs\",\n",
+    "                            \"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\": \"pred.chrombpnet.encsr000emt.varscore.crs\",\n",
+    "                            \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\": \"pred.chrombpnet.encsr637xsc_50m.varscore.crs\",\n",
+    "\n",
+    "                            \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval\": \"pred.chrombpnet.encsr637xsc.varscore.pval\",\n",
+    "                            \"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval\": \"pred.chrombpnet.encsr000emt.varscore.pval\",\n",
+    "                            \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval\": \"pred.chrombpnet.encsr637xsc_50m.varscore.pval\",\n",
+    "\n",
+    "                            \"ENCSR637XSC.logfc.mean\": \"pred.chrombpnet.encsr637xsc.varscore.logfc\",\n",
+    "                            \"ENCSR000EMT.logfc.mean\": \"pred.chrombpnet.encsr000emt.varscore.logfc\",\n",
+    "                            \"ENCSR637XSC.sub.50M.logfc.mean\": \"pred.chrombpnet.encsr637xsc_50m.varscore.logfc\",\n",
+    "\n",
+    "                            \"ENCSR637XSC.sign.jsd.mean\": \"pred.chrombpnet.encsr637xsc.varscore.jsd\",\n",
+    "                            \"ENCSR000EMT.sign.jsd.mean\": \"pred.chrombpnet.encsr000emt.varscore.jsd\",\n",
+    "                            \"ENCSR637XSC.sub.50M.sign.jsd.mean\": \"pred.chrombpnet.encsr637xsc_50m.varscore.jsd\",\n",
+    "\n",
+    "                            \"ENCSR637XSC.max_percentile.mean\": \"pred.chrombpnet.encsr637xsc.varscore.aaq\",\n",
+    "                            \"ENCSR000EMT.max_percentile.mean\": \"pred.chrombpnet.encsr000emt.varscore.aaq\",\n",
+    "                            \"ENCSR637XSC.sub.50M.max_percentile.mean\": \"pred.chrombpnet.encsr637xsc_50m.varscore.aaq\",\n",
+    "               \n",
+    "                            \"ENCSR637XSC.abs_logfc_x_jsd.mean\": \"pred.chrombpnet.encsr637xsc.varscore.logfc_x_jsd\",\n",
+    "                            \"ENCSR000EMT.abs_logfc_x_jsd.mean\": \"pred.chrombpnet.encsr000emt.varscore.logfc_x_jsd\",\n",
+    "                            \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd.mean\": \"pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_jsd\",\n",
+    "\n",
+    "                            \"ENCSR637XSC.jsd_x_max_percentile.mean\": \"pred.chrombpnet.encsr637xsc.varscore.jsd_x_aaq\",\n",
+    "                            \"ENCSR000EMT.jsd_x_max_percentile.mean\": \"pred.chrombpnet.encsr000emt.varscore.jsd_x_aaq\",\n",
+    "                            \"ENCSR637XSC.sub.50M.jsd_x_max_percentile.mean\": \"pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq\",\n",
+    "               \n",
+    "                            \"ENCSR637XSC.abs_logfc_x_max_percentile.mean\": \"pred.chrombpnet.encsr637xsc.varscore.logfc_x_aaq\",\n",
+    "                            \"ENCSR000EMT.abs_logfc_x_max_percentile.mean\": \"pred.chrombpnet.encsr000emt.varscore.logfc_x_aaq\",\n",
+    "                            \"ENCSR637XSC.sub.50M.abs_logfc_x_max_percentile.mean\": \"pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq\"}\n",
+    "\n",
+    "\n",
+    "keyds = list(dict_rename.keys())\n",
+    "for key in keyds:\n",
+    "    if \"50M\" in key:\n",
+    "        for sub in [\"250M\", \"100M\", \"25M\", \"5M\"]:\n",
+    "            #print(dict_rename[key].replace(\"50M\".lower(), sub.lower()))\n",
+    "            dict_rename[key.replace(\"50M\", sub)] = dict_rename[key].replace(\"50M\".lower(), sub.lower())\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dict_rename"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.rename(columns=dict_rename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['var.chrom', 'var.pos', 'var.allele1', 'var.allele2', 'var.rsid',\n",
+       "       'obs.estimate', 'var.label', 'var.isused',\n",
+       "       'pred.gkmsvm.encsr000emt.varscore.deltasvm',\n",
+       "       'pred.enformer.encsr000emt.varscore.sad',\n",
+       "       'pred.enformer.encsr000emt.varscore.sar',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.crs',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.pval',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_jsd',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.jsd_x_aaq',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_aaq',\n",
+       "       'ENCSR637XSC.jsd.mean', 'ENCSR637XSC.abs_logfc.mean',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.aaq',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.logfc',\n",
+       "       'pred.chrombpnet.encsr637xsc.varscore.jsd',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.crs',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.pval',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.logfc_x_jsd',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.jsd_x_aaq',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.logfc_x_aaq',\n",
+       "       'ENCSR000EMT.jsd.mean', 'ENCSR000EMT.abs_logfc.mean',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.aaq',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.logfc',\n",
+       "       'pred.chrombpnet.encsr000emt.varscore.jsd',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.crs',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.pval',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_jsd',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq',\n",
+       "       'ENCSR637XSC.sub.50M.jsd.mean', 'ENCSR637XSC.sub.50M.abs_logfc.mean',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.aaq',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc',\n",
+       "       'pred.chrombpnet.encsr637xsc_50m.varscore.jsd',\n",
+       "       'pred.chrombpnet.encsr637xsc_250m.varscore.crs',\n",
+       "       'pred.chrombpnet.encsr637xsc_250m.varscore.pval',\n",
+       "       'pred.chrombpnet.encsr637xsc_250m.varscore.logfc',\n",
+       "       'pred.chrombpnet.encsr637xsc_250m.varscore.jsd',\n",
+       "       'pred.chrombpnet.encsr637xsc_100m.varscore.crs',\n",
+       "       'pred.chrombpnet.encsr637xsc_100m.varscore.pval',\n",
+       "       'pred.chrombpnet.encsr637xsc_100m.varscore.logfc',\n",
+       "       'pred.chrombpnet.encsr637xsc_100m.varscore.jsd',\n",
+       "       'pred.chrombpnet.encsr637xsc_25m.varscore.crs',\n",
+       "       'pred.chrombpnet.encsr637xsc_25m.varscore.pval',\n",
+       "       'pred.chrombpnet.encsr637xsc_25m.varscore.logfc',\n",
+       "       'pred.chrombpnet.encsr637xsc_25m.varscore.jsd',\n",
+       "       'pred.chrombpnet.encsr637xsc_5m.varscore.crs',\n",
+       "       'pred.chrombpnet.encsr637xsc_5m.varscore.pval',\n",
+       "       'pred.chrombpnet.encsr637xsc_5m.varscore.logfc',\n",
+       "       'pred.chrombpnet.encsr637xsc_5m.varscore.jsd',\n",
+       "       'pred.enformer.encsr000emt.varscore.recomputed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "use_cols = ['var.chrom', 'var.pos', 'var.allele1', 'var.allele2', 'var.rsid', 'var.label', 'var.isused',\n",
+    "       'obs.estimate', \n",
+    "       'pred.gkmsvm.encsr000emt.varscore.deltasvm',\n",
+    "       'pred.enformer.encsr000emt.varscore.sad',\n",
+    "       'pred.enformer.encsr000emt.varscore.sar',\n",
+    "       'pred.enformer.encsr000emt.varscore.recomputed',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr000emt.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.pval',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.aaq',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.logfc_x_jsd',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.jsd_x_aaq',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.logfc_x_aaq', \n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.pval',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_jsd',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.jsd_x_aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_aaq', \n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.pval',\n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.pval',\n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.pval',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_jsd',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq', \n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.pval',\n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.pval'  ]\n",
+    "\n",
+    "\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf = data[use_cols]\n",
+    "\n",
+    "#data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>var.chrom</th>\n",
+       "      <th>var.pos</th>\n",
+       "      <th>var.allele1</th>\n",
+       "      <th>var.allele2</th>\n",
+       "      <th>var.rsid</th>\n",
+       "      <th>var.label</th>\n",
+       "      <th>var.isused</th>\n",
+       "      <th>obs.estimate</th>\n",
+       "      <th>pred.gkmsvm.encsr000emt.varscore.deltasvm</th>\n",
+       "      <th>pred.enformer.encsr000emt.varscore.sad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.logfc</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.jsd</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.crs</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.pval</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.logfc</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.jsd</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.crs</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>856583</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.9301</td>\n",
+       "      <td>2.653531</td>\n",
+       "      <td>0.000342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.021877</td>\n",
+       "      <td>0.127298</td>\n",
+       "      <td>0.291935</td>\n",
+       "      <td>0.067401</td>\n",
+       "      <td>0.004840</td>\n",
+       "      <td>0.018692</td>\n",
+       "      <td>0.065098</td>\n",
+       "      <td>0.049902</td>\n",
+       "      <td>0.001360</td>\n",
+       "      <td>0.065265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>911595</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs7417106</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.6285</td>\n",
+       "      <td>2.821422</td>\n",
+       "      <td>-0.001241</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.027762</td>\n",
+       "      <td>0.056287</td>\n",
+       "      <td>0.093192</td>\n",
+       "      <td>0.036053</td>\n",
+       "      <td>0.002239</td>\n",
+       "      <td>0.033704</td>\n",
+       "      <td>0.045314</td>\n",
+       "      <td>0.034489</td>\n",
+       "      <td>0.000898</td>\n",
+       "      <td>0.077782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.7363</td>\n",
+       "      <td>-7.167236</td>\n",
+       "      <td>-0.006530</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.029658</td>\n",
+       "      <td>0.349964</td>\n",
+       "      <td>-0.402188</td>\n",
+       "      <td>-0.038791</td>\n",
+       "      <td>0.008839</td>\n",
+       "      <td>0.006710</td>\n",
+       "      <td>-0.163023</td>\n",
+       "      <td>-0.039080</td>\n",
+       "      <td>0.002210</td>\n",
+       "      <td>0.029520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1227412</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs112571439</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-0.9236</td>\n",
+       "      <td>-3.957382</td>\n",
+       "      <td>-0.001878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.137196</td>\n",
+       "      <td>0.518532</td>\n",
+       "      <td>-0.622704</td>\n",
+       "      <td>-0.222396</td>\n",
+       "      <td>0.086945</td>\n",
+       "      <td>0.000056</td>\n",
+       "      <td>-0.259064</td>\n",
+       "      <td>-0.172960</td>\n",
+       "      <td>0.027583</td>\n",
+       "      <td>0.000195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1590575</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs9661285</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1.0890</td>\n",
+       "      <td>-4.785596</td>\n",
+       "      <td>-0.008736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.053680</td>\n",
+       "      <td>0.103350</td>\n",
+       "      <td>-0.061906</td>\n",
+       "      <td>-0.050589</td>\n",
+       "      <td>0.003275</td>\n",
+       "      <td>0.027075</td>\n",
+       "      <td>-0.032300</td>\n",
+       "      <td>-0.023135</td>\n",
+       "      <td>0.000802</td>\n",
+       "      <td>0.084252</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  var.chrom  var.pos var.allele1 var.allele2     var.rsid  var.label  \\\n",
+       "0      chr1   856583           A           G  rs141671872          1   \n",
+       "1      chr1   911595           G           A    rs7417106          1   \n",
+       "2      chr1  1186502           T           A    rs6603785          1   \n",
+       "3      chr1  1227412           A           G  rs112571439          1   \n",
+       "4      chr1  1590575           A           G    rs9661285          1   \n",
+       "\n",
+       "   var.isused  obs.estimate  pred.gkmsvm.encsr000emt.varscore.deltasvm  \\\n",
+       "0        True        0.9301                                   2.653531   \n",
+       "1        True       -0.6285                                   2.821422   \n",
+       "2        True       -0.7363                                  -7.167236   \n",
+       "3        True       -0.9236                                  -3.957382   \n",
+       "4        True       -1.0890                                  -4.785596   \n",
+       "\n",
+       "   pred.enformer.encsr000emt.varscore.sad  ...  \\\n",
+       "0                                0.000342  ...   \n",
+       "1                               -0.001241  ...   \n",
+       "2                               -0.006530  ...   \n",
+       "3                               -0.001878  ...   \n",
+       "4                               -0.008736  ...   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq  \\\n",
+       "0                                           0.021877    \n",
+       "1                                           0.027762    \n",
+       "2                                           0.029658    \n",
+       "3                                           0.137196    \n",
+       "4                                           0.053680    \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq  \\\n",
+       "0                                           0.127298      \n",
+       "1                                           0.056287      \n",
+       "2                                           0.349964      \n",
+       "3                                           0.518532      \n",
+       "4                                           0.103350      \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.logfc  \\\n",
+       "0                                        0.291935   \n",
+       "1                                        0.093192   \n",
+       "2                                       -0.402188   \n",
+       "3                                       -0.622704   \n",
+       "4                                       -0.061906   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.jsd  \\\n",
+       "0                                      0.067401   \n",
+       "1                                      0.036053   \n",
+       "2                                     -0.038791   \n",
+       "3                                     -0.222396   \n",
+       "4                                     -0.050589   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.crs  \\\n",
+       "0                                      0.004840   \n",
+       "1                                      0.002239   \n",
+       "2                                      0.008839   \n",
+       "3                                      0.086945   \n",
+       "4                                      0.003275   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.pval  \\\n",
+       "0                                       0.018692   \n",
+       "1                                       0.033704   \n",
+       "2                                       0.006710   \n",
+       "3                                       0.000056   \n",
+       "4                                       0.027075   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.logfc  \\\n",
+       "0                                       0.065098   \n",
+       "1                                       0.045314   \n",
+       "2                                      -0.163023   \n",
+       "3                                      -0.259064   \n",
+       "4                                      -0.032300   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.jsd  \\\n",
+       "0                                     0.049902   \n",
+       "1                                     0.034489   \n",
+       "2                                    -0.039080   \n",
+       "3                                    -0.172960   \n",
+       "4                                    -0.023135   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.crs  \\\n",
+       "0                                     0.001360   \n",
+       "1                                     0.000898   \n",
+       "2                                     0.002210   \n",
+       "3                                     0.027583   \n",
+       "4                                     0.000802   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.pval  \n",
+       "0                                      0.065265  \n",
+       "1                                      0.077782  \n",
+       "2                                      0.029520  \n",
+       "3                                      0.000195  \n",
+       "4                                      0.084252  \n",
+       "\n",
+       "[5 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert (sum(dataf.columns.isin(use_cols))==dataf.shape[1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf.to_csv(\"../final_figures_new/dsqtls.lcls.benchmarking.tsv\", sep='\\t', header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "readme_data = {'var.chrom' : \"Chromosome of the variant (GRCh37)\", \n",
+    "        'var.pos': \"Position of the variant (GRCh37, 1-based)\"\n",
+    "        'var.allele1': \"Allele 1 for the variant\", \n",
+    "        'var.allele2': \"Allele 2 for the variant\",        \n",
+    "        'var.rsid': \"dbSNP rsid identifier\", \n",
+    "       'var.label': \"True if variant has significant observed effect size\", \n",
+    "       'var.isused': \"True if variant has enformer predictions\",\n",
+    "       'obs.estimate': \"Observed effect size\", \n",
+    "       'pred.gkmsvm.encsr000emt.varscore.deltasvm',\n",
+    "       'pred.enformer.encsr000emt.varscore.sad',\n",
+    "       'pred.enformer.encsr000emt.varscore.sar',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr000emt.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.pval',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.aaq',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.logfc_x_jsd',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.jsd_x_aaq',\n",
+    "       'pred.chrombpnet.encsr000emt.varscore.logfc_x_aaq', \n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.pval',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_jsd',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.jsd_x_aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_aaq', \n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_250m.varscore.pval',\n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_100m.varscore.pval',\n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.pval',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_jsd',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq', \n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_25m.varscore.pval',\n",
+    "\n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.jsd', \n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc_5m.varscore.pval'  }\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_eu_caqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_eu_caqtls.ipynb
new file mode 100644
index 00000000..d90997d9
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_eu_caqtls.ipynb
@@ -0,0 +1,3245 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dnase = pd.read_csv(\"c/DNASE_NEW/summary.mean.variant_scores_new_3.tsv\", sep=\"\\t\", header=0)\n",
+    "data_dnase[['Chrx', 'Posx', 'REF', 'ALT']] = data_dnase['rsid'].str.split(\"_\", expand=True)\n",
+    "data_dnase[\"jsd_sign.mean\"] = data_dnase[\"jsd.mean\"]*np.sign(data_dnase[\"logfc.mean\"])\n",
+    "vals = [\"chr\", \"pos\", \"allele1\", \"allele2\", \"rsid\", \"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]\n",
+    "data_dnase = data_dnase[vals]\n",
+    "\n",
+    "data_atac = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/ATAC/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac[['Chrx', 'Posx', 'REF', 'ALT']] = data_atac['rsid'].str.split(\"_\", expand=True)\n",
+    "data_atac[\"jsd_sign.mean\"] = data_atac[\"jsd.mean\"]*np.sign(data_atac[\"logfc.mean\"])\n",
+    "data_atac = data_atac[vals]\n",
+    "\n",
+    "data_atac_50m = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/chrombpnet_preds/atac_50M_model/ATAC_50M/summary.mean.variant_scores_new_2.tsv\", sep=\"\\t\", header=0)\n",
+    "data_atac_50m[['Chrx', 'Posx', 'REF', 'ALT']] = data_atac_50m['rsid'].str.split(\"_\", expand=True)\n",
+    "data_atac_50m[\"jsd_sign.mean\"] = data_atac_50m[\"jsd.mean\"]*np.sign(data_atac_50m[\"logfc.mean\"])\n",
+    "data_atac_50m = data_atac_50m[vals]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 9)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "data_atac.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 9)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dnase.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 9)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_atac_50m.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>jsd_sign.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_875770_A_G</td>\n",
+       "      <td>-0.007064</td>\n",
+       "      <td>-0.010017</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>0.241273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_930533_C_G</td>\n",
+       "      <td>0.067004</td>\n",
+       "      <td>0.013255</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>0.248824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_565286_C_T</td>\n",
+       "      <td>0.025990</td>\n",
+       "      <td>0.009459</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.528703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1_569204_T_C</td>\n",
+       "      <td>-0.040934</td>\n",
+       "      <td>-0.018886</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.078036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_748141_G_A</td>\n",
+       "      <td>-0.034312</td>\n",
+       "      <td>-0.008248</td>\n",
+       "      <td>0.000203</td>\n",
+       "      <td>0.153993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226341</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr22_51021271_T_C</td>\n",
+       "      <td>-0.018318</td>\n",
+       "      <td>-0.012623</td>\n",
+       "      <td>0.000513</td>\n",
+       "      <td>0.080871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226342</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_51059483_G_A</td>\n",
+       "      <td>-0.044313</td>\n",
+       "      <td>-0.012971</td>\n",
+       "      <td>0.000739</td>\n",
+       "      <td>0.084632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226343</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr22_51066552_C_G</td>\n",
+       "      <td>-0.085015</td>\n",
+       "      <td>-0.019822</td>\n",
+       "      <td>0.001514</td>\n",
+       "      <td>0.043477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226344</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_51082407_T_A</td>\n",
+       "      <td>-0.030337</td>\n",
+       "      <td>-0.004881</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.402063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226345</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22_51222052_G_A</td>\n",
+       "      <td>-0.002690</td>\n",
+       "      <td>-0.019944</td>\n",
+       "      <td>0.001427</td>\n",
+       "      <td>0.037118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>226346 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       pos allele1 allele2                rsid  logfc.mean  \\\n",
+       "0        chr1    875770       A       G     chr1_875770_A_G   -0.007064   \n",
+       "1        chr1    930533       C       G     chr1_930533_C_G    0.067004   \n",
+       "2        chr1    565286       C       T     chr1_565286_C_T    0.025990   \n",
+       "3        chr1    569204       T       C     chr1_569204_T_C   -0.040934   \n",
+       "4        chr1    748141       G       A     chr1_748141_G_A   -0.034312   \n",
+       "...       ...       ...     ...     ...                 ...         ...   \n",
+       "226341  chr22  51021271       T       C  chr22_51021271_T_C   -0.018318   \n",
+       "226342  chr22  51059483       G       A  chr22_51059483_G_A   -0.044313   \n",
+       "226343  chr22  51066552       C       G  chr22_51066552_C_G   -0.085015   \n",
+       "226344  chr22  51082407       T       A  chr22_51082407_T_A   -0.030337   \n",
+       "226345  chr22  51222052       G       A  chr22_51222052_G_A   -0.002690   \n",
+       "\n",
+       "        jsd_sign.mean  abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0           -0.010017                               0.000145   \n",
+       "1            0.013255                               0.000072   \n",
+       "2            0.009459                               0.000012   \n",
+       "3           -0.018886                               0.000509   \n",
+       "4           -0.008248                               0.000203   \n",
+       "...               ...                                    ...   \n",
+       "226341      -0.012623                               0.000513   \n",
+       "226342      -0.012971                               0.000739   \n",
+       "226343      -0.019822                               0.001514   \n",
+       "226344      -0.004881                               0.000029   \n",
+       "226345      -0.019944                               0.001427   \n",
+       "\n",
+       "        abs_logfc_x_jsd_x_max_percentile.mean.pval  \n",
+       "0                                         0.241273  \n",
+       "1                                         0.248824  \n",
+       "2                                         0.528703  \n",
+       "3                                         0.078036  \n",
+       "4                                         0.153993  \n",
+       "...                                            ...  \n",
+       "226341                                    0.080871  \n",
+       "226342                                    0.084632  \n",
+       "226343                                    0.043477  \n",
+       "226344                                    0.402063  \n",
+       "226345                                    0.037118  \n",
+       "\n",
+       "[226346 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_atac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/lead_caQTL_variants.tsv.gz\", sep=\"\\t\", header=0)\n",
+    "qvals[\"chr\"] = \"chr\"+qvals[\"Chr\"].astype(str) \n",
+    "qvals = qvals[~qvals['Alt'].str.contains('<CN')]\n",
+    "qvals = qvals[~qvals['Alt'].str.contains(',')]\n",
+    "qvals = qvals[qvals['Alt'].str.len()==1]\n",
+    "qvals = qvals[qvals['Ref'].str.len()==1]\n",
+    "qvals = qvals.reset_index()\n",
+    "#qvals = qvals[qvals[\"Inside_Peak\"]==1]\n",
+    "#qvals = qvals.sort_values('Log10_BF')\n",
+    "#.drop_duplicates([\"chr\", \"Ref\", \"Alt\", \"Pos\"], keep='last')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['index', 'Peak', 'Chr', 'Pos', 'RsID', 'Ref', 'Alt', 'AF',\n",
+       "       'Inside_Peak', 'P_Lead', 'Beta', 'SE', 'Log10_BF', 'chr'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals = qvals[[\"chr\", 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+    "       'P_Lead', 'Beta', 'SE', 'Log10_BF' ]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>0.188153</td>\n",
+       "      <td>3.085214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>0.140264</td>\n",
+       "      <td>-0.671608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.05005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988758</td>\n",
+       "      <td>0.347965</td>\n",
+       "      <td>0.059578</td>\n",
+       "      <td>4.597219</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     Pos Ref Alt         RsID  Peak       AF  Inside_Peak    P_Lead  \\\n",
+       "0  chr1  875770   A   G    rs4970379     3  0.62500            2  0.044994   \n",
+       "1  chr1  930533   C   G    rs3128110     5  0.35435            0  0.099392   \n",
+       "2  chr1  565286   C   T    rs1578391     7  0.59780            1  0.998561   \n",
+       "3  chr1  569204   T   C  rs112660509     8  0.40125            1  0.477613   \n",
+       "4  chr1  748141   G   A    rs2427893    10  0.05005            0  0.988758   \n",
+       "\n",
+       "       Beta        SE  Log10_BF  \n",
+       "0  0.155446  0.057836 -0.232537  \n",
+       "1  0.174020  0.040562  1.751829  \n",
+       "2  0.886884  0.188153  3.085214  \n",
+       "3  0.251458  0.140264 -0.671608  \n",
+       "4  0.347965  0.059578  4.597219  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 12)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(226346, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "rename_list=[\"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]\n",
+    "\n",
+    "rename_df={}\n",
+    "for val in rename_list:\n",
+    "    qvals[\"ENCSR637XSC.\"+val] = data_atac[val]\n",
+    "    \n",
+    "\n",
+    "print(qvals.shape)\n",
+    "#print(qvals.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_df={}\n",
+    "for val in rename_list:\n",
+    "    qvals[\"ENCSR637XSC.sub.50M.\"+val] = data_atac_50m[val]\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>ENCSR637XSC.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.jsd_sign.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.jsd_sign.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "      <td>-0.007064</td>\n",
+       "      <td>-0.010017</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>0.241273</td>\n",
+       "      <td>0.001895</td>\n",
+       "      <td>0.010946</td>\n",
+       "      <td>0.000367</td>\n",
+       "      <td>0.121047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "      <td>0.067004</td>\n",
+       "      <td>0.013255</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>0.248824</td>\n",
+       "      <td>0.057168</td>\n",
+       "      <td>0.009227</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.451472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>0.188153</td>\n",
+       "      <td>3.085214</td>\n",
+       "      <td>0.025990</td>\n",
+       "      <td>0.009459</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.528703</td>\n",
+       "      <td>0.014488</td>\n",
+       "      <td>0.010097</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.379356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>0.140264</td>\n",
+       "      <td>-0.671608</td>\n",
+       "      <td>-0.040934</td>\n",
+       "      <td>-0.018886</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.078036</td>\n",
+       "      <td>-0.028313</td>\n",
+       "      <td>-0.008258</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>0.191231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.05005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988758</td>\n",
+       "      <td>0.347965</td>\n",
+       "      <td>0.059578</td>\n",
+       "      <td>4.597219</td>\n",
+       "      <td>-0.034312</td>\n",
+       "      <td>-0.008248</td>\n",
+       "      <td>0.000203</td>\n",
+       "      <td>0.153993</td>\n",
+       "      <td>0.035548</td>\n",
+       "      <td>0.010029</td>\n",
+       "      <td>0.000281</td>\n",
+       "      <td>0.159250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226341</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs540889063</td>\n",
+       "      <td>277122</td>\n",
+       "      <td>0.05500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.362698</td>\n",
+       "      <td>0.010717</td>\n",
+       "      <td>0.018974</td>\n",
+       "      <td>-2.153040</td>\n",
+       "      <td>-0.018318</td>\n",
+       "      <td>-0.012623</td>\n",
+       "      <td>0.000513</td>\n",
+       "      <td>0.080871</td>\n",
+       "      <td>-0.002501</td>\n",
+       "      <td>-0.011443</td>\n",
+       "      <td>0.000436</td>\n",
+       "      <td>0.108499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226342</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>277123</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.553273</td>\n",
+       "      <td>-0.061982</td>\n",
+       "      <td>0.032636</td>\n",
+       "      <td>-1.221145</td>\n",
+       "      <td>-0.044313</td>\n",
+       "      <td>-0.012971</td>\n",
+       "      <td>0.000739</td>\n",
+       "      <td>0.084632</td>\n",
+       "      <td>-0.029992</td>\n",
+       "      <td>-0.013178</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>0.102590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226343</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>277124</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.921861</td>\n",
+       "      <td>0.087364</td>\n",
+       "      <td>0.030622</td>\n",
+       "      <td>-0.324830</td>\n",
+       "      <td>-0.085015</td>\n",
+       "      <td>-0.019822</td>\n",
+       "      <td>0.001514</td>\n",
+       "      <td>0.043477</td>\n",
+       "      <td>-0.068190</td>\n",
+       "      <td>-0.022175</td>\n",
+       "      <td>0.001367</td>\n",
+       "      <td>0.036352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226344</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62241515</td>\n",
+       "      <td>277125</td>\n",
+       "      <td>0.42505</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.315251</td>\n",
+       "      <td>-0.016347</td>\n",
+       "      <td>0.055462</td>\n",
+       "      <td>-1.737317</td>\n",
+       "      <td>-0.030337</td>\n",
+       "      <td>-0.004881</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.402063</td>\n",
+       "      <td>-0.012709</td>\n",
+       "      <td>-0.005298</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.593201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226345</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2879915</td>\n",
+       "      <td>277128</td>\n",
+       "      <td>0.26020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.726414</td>\n",
+       "      <td>-0.027808</td>\n",
+       "      <td>0.038060</td>\n",
+       "      <td>-1.804563</td>\n",
+       "      <td>-0.002690</td>\n",
+       "      <td>-0.019944</td>\n",
+       "      <td>0.001427</td>\n",
+       "      <td>0.037118</td>\n",
+       "      <td>-0.013154</td>\n",
+       "      <td>-0.018966</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.167751</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>226346 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       Pos Ref Alt         RsID    Peak       AF  Inside_Peak  \\\n",
+       "0        chr1    875770   A   G    rs4970379       3  0.62500            2   \n",
+       "1        chr1    930533   C   G    rs3128110       5  0.35435            0   \n",
+       "2        chr1    565286   C   T    rs1578391       7  0.59780            1   \n",
+       "3        chr1    569204   T   C  rs112660509       8  0.40125            1   \n",
+       "4        chr1    748141   G   A    rs2427893      10  0.05005            0   \n",
+       "...       ...       ...  ..  ..          ...     ...      ...          ...   \n",
+       "226341  chr22  51021271   T   C  rs540889063  277122  0.05500            1   \n",
+       "226342  chr22  51059483   G   A   rs76733141  277123  0.06000            1   \n",
+       "226343  chr22  51066552   C   G    rs6151406  277124  0.06000            1   \n",
+       "226344  chr22  51082407   T   A   rs62241515  277125  0.42505            1   \n",
+       "226345  chr22  51222052   G   A    rs2879915  277128  0.26020            1   \n",
+       "\n",
+       "          P_Lead      Beta        SE  Log10_BF  ENCSR637XSC.logfc.mean  \\\n",
+       "0       0.044994  0.155446  0.057836 -0.232537               -0.007064   \n",
+       "1       0.099392  0.174020  0.040562  1.751829                0.067004   \n",
+       "2       0.998561  0.886884  0.188153  3.085214                0.025990   \n",
+       "3       0.477613  0.251458  0.140264 -0.671608               -0.040934   \n",
+       "4       0.988758  0.347965  0.059578  4.597219               -0.034312   \n",
+       "...          ...       ...       ...       ...                     ...   \n",
+       "226341  0.362698  0.010717  0.018974 -2.153040               -0.018318   \n",
+       "226342  0.553273 -0.061982  0.032636 -1.221145               -0.044313   \n",
+       "226343  0.921861  0.087364  0.030622 -0.324830               -0.085015   \n",
+       "226344  0.315251 -0.016347  0.055462 -1.737317               -0.030337   \n",
+       "226345  0.726414 -0.027808  0.038060 -1.804563               -0.002690   \n",
+       "\n",
+       "        ENCSR637XSC.jsd_sign.mean  \\\n",
+       "0                       -0.010017   \n",
+       "1                        0.013255   \n",
+       "2                        0.009459   \n",
+       "3                       -0.018886   \n",
+       "4                       -0.008248   \n",
+       "...                           ...   \n",
+       "226341                  -0.012623   \n",
+       "226342                  -0.012971   \n",
+       "226343                  -0.019822   \n",
+       "226344                  -0.004881   \n",
+       "226345                  -0.019944   \n",
+       "\n",
+       "        ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                                0.000145   \n",
+       "1                                                0.000072   \n",
+       "2                                                0.000012   \n",
+       "3                                                0.000509   \n",
+       "4                                                0.000203   \n",
+       "...                                                   ...   \n",
+       "226341                                           0.000513   \n",
+       "226342                                           0.000739   \n",
+       "226343                                           0.001514   \n",
+       "226344                                           0.000029   \n",
+       "226345                                           0.001427   \n",
+       "\n",
+       "        ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                                0.241273        \n",
+       "1                                                0.248824        \n",
+       "2                                                0.528703        \n",
+       "3                                                0.078036        \n",
+       "4                                                0.153993        \n",
+       "...                                                   ...        \n",
+       "226341                                           0.080871        \n",
+       "226342                                           0.084632        \n",
+       "226343                                           0.043477        \n",
+       "226344                                           0.402063        \n",
+       "226345                                           0.037118        \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.logfc.mean  ENCSR637XSC.sub.50M.jsd_sign.mean  \\\n",
+       "0                             0.001895                           0.010946   \n",
+       "1                             0.057168                           0.009227   \n",
+       "2                             0.014488                           0.010097   \n",
+       "3                            -0.028313                          -0.008258   \n",
+       "4                             0.035548                           0.010029   \n",
+       "...                                ...                                ...   \n",
+       "226341                       -0.002501                          -0.011443   \n",
+       "226342                       -0.029992                          -0.013178   \n",
+       "226343                       -0.068190                          -0.022175   \n",
+       "226344                       -0.012709                          -0.005298   \n",
+       "226345                       -0.013154                          -0.018966   \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                                0.000367           \n",
+       "1                                                0.000016           \n",
+       "2                                                0.000022           \n",
+       "3                                                0.000155           \n",
+       "4                                                0.000281           \n",
+       "...                                                   ...           \n",
+       "226341                                           0.000436           \n",
+       "226342                                           0.000332           \n",
+       "226343                                           0.001367           \n",
+       "226344                                           0.000005           \n",
+       "226345                                           0.000224           \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \n",
+       "0                                                0.121047               \n",
+       "1                                                0.451472               \n",
+       "2                                                0.379356               \n",
+       "3                                                0.191231               \n",
+       "4                                                0.159250               \n",
+       "...                                                   ...               \n",
+       "226341                                           0.108499               \n",
+       "226342                                           0.102590               \n",
+       "226343                                           0.036352               \n",
+       "226344                                           0.593201               \n",
+       "226345                                           0.167751               \n",
+       "\n",
+       "[226346 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_df={}\n",
+    "for val in rename_list:\n",
+    "    qvals[\"ENCSR000EMT.\"+val] = data_dnase[val]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 24)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.jsd_sign.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>0.241273</td>\n",
+       "      <td>0.001895</td>\n",
+       "      <td>0.010946</td>\n",
+       "      <td>0.000367</td>\n",
+       "      <td>0.121047</td>\n",
+       "      <td>-0.018701</td>\n",
+       "      <td>-0.013757</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>0.505239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>0.248824</td>\n",
+       "      <td>0.057168</td>\n",
+       "      <td>0.009227</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.451472</td>\n",
+       "      <td>0.032293</td>\n",
+       "      <td>0.018814</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>0.580367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.528703</td>\n",
+       "      <td>0.014488</td>\n",
+       "      <td>0.010097</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.379356</td>\n",
+       "      <td>-0.045242</td>\n",
+       "      <td>-0.016605</td>\n",
+       "      <td>0.000053</td>\n",
+       "      <td>0.505239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.078036</td>\n",
+       "      <td>-0.028313</td>\n",
+       "      <td>-0.008258</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>0.191231</td>\n",
+       "      <td>0.027531</td>\n",
+       "      <td>0.017267</td>\n",
+       "      <td>0.000273</td>\n",
+       "      <td>0.580367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.05005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988758</td>\n",
+       "      <td>0.347965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000203</td>\n",
+       "      <td>0.153993</td>\n",
+       "      <td>0.035548</td>\n",
+       "      <td>0.010029</td>\n",
+       "      <td>0.000281</td>\n",
+       "      <td>0.159250</td>\n",
+       "      <td>-0.028731</td>\n",
+       "      <td>-0.013637</td>\n",
+       "      <td>0.000132</td>\n",
+       "      <td>0.439836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226341</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs540889063</td>\n",
+       "      <td>277122</td>\n",
+       "      <td>0.05500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.362698</td>\n",
+       "      <td>0.010717</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000513</td>\n",
+       "      <td>0.080871</td>\n",
+       "      <td>-0.002501</td>\n",
+       "      <td>-0.011443</td>\n",
+       "      <td>0.000436</td>\n",
+       "      <td>0.108499</td>\n",
+       "      <td>-0.024413</td>\n",
+       "      <td>-0.012631</td>\n",
+       "      <td>0.000679</td>\n",
+       "      <td>0.382899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226342</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>277123</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.553273</td>\n",
+       "      <td>-0.061982</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000739</td>\n",
+       "      <td>0.084632</td>\n",
+       "      <td>-0.029992</td>\n",
+       "      <td>-0.013178</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>0.102590</td>\n",
+       "      <td>-0.041708</td>\n",
+       "      <td>-0.018304</td>\n",
+       "      <td>0.000700</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226343</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>277124</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.921861</td>\n",
+       "      <td>0.087364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001514</td>\n",
+       "      <td>0.043477</td>\n",
+       "      <td>-0.068190</td>\n",
+       "      <td>-0.022175</td>\n",
+       "      <td>0.001367</td>\n",
+       "      <td>0.036352</td>\n",
+       "      <td>-0.041029</td>\n",
+       "      <td>-0.026228</td>\n",
+       "      <td>0.000862</td>\n",
+       "      <td>0.382899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226344</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62241515</td>\n",
+       "      <td>277125</td>\n",
+       "      <td>0.42505</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.315251</td>\n",
+       "      <td>-0.016347</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.402063</td>\n",
+       "      <td>-0.012709</td>\n",
+       "      <td>-0.005298</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.593201</td>\n",
+       "      <td>-0.011280</td>\n",
+       "      <td>-0.005821</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226345</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2879915</td>\n",
+       "      <td>277128</td>\n",
+       "      <td>0.26020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.726414</td>\n",
+       "      <td>-0.027808</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001427</td>\n",
+       "      <td>0.037118</td>\n",
+       "      <td>-0.013154</td>\n",
+       "      <td>-0.018966</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.167751</td>\n",
+       "      <td>-0.012639</td>\n",
+       "      <td>-0.023930</td>\n",
+       "      <td>0.001191</td>\n",
+       "      <td>0.382899</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>226346 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       Pos Ref Alt         RsID    Peak       AF  Inside_Peak  \\\n",
+       "0        chr1    875770   A   G    rs4970379       3  0.62500            2   \n",
+       "1        chr1    930533   C   G    rs3128110       5  0.35435            0   \n",
+       "2        chr1    565286   C   T    rs1578391       7  0.59780            1   \n",
+       "3        chr1    569204   T   C  rs112660509       8  0.40125            1   \n",
+       "4        chr1    748141   G   A    rs2427893      10  0.05005            0   \n",
+       "...       ...       ...  ..  ..          ...     ...      ...          ...   \n",
+       "226341  chr22  51021271   T   C  rs540889063  277122  0.05500            1   \n",
+       "226342  chr22  51059483   G   A   rs76733141  277123  0.06000            1   \n",
+       "226343  chr22  51066552   C   G    rs6151406  277124  0.06000            1   \n",
+       "226344  chr22  51082407   T   A   rs62241515  277125  0.42505            1   \n",
+       "226345  chr22  51222052   G   A    rs2879915  277128  0.26020            1   \n",
+       "\n",
+       "          P_Lead      Beta  ...  \\\n",
+       "0       0.044994  0.155446  ...   \n",
+       "1       0.099392  0.174020  ...   \n",
+       "2       0.998561  0.886884  ...   \n",
+       "3       0.477613  0.251458  ...   \n",
+       "4       0.988758  0.347965  ...   \n",
+       "...          ...       ...  ...   \n",
+       "226341  0.362698  0.010717  ...   \n",
+       "226342  0.553273 -0.061982  ...   \n",
+       "226343  0.921861  0.087364  ...   \n",
+       "226344  0.315251 -0.016347  ...   \n",
+       "226345  0.726414 -0.027808  ...   \n",
+       "\n",
+       "        ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                                0.000145   \n",
+       "1                                                0.000072   \n",
+       "2                                                0.000012   \n",
+       "3                                                0.000509   \n",
+       "4                                                0.000203   \n",
+       "...                                                   ...   \n",
+       "226341                                           0.000513   \n",
+       "226342                                           0.000739   \n",
+       "226343                                           0.001514   \n",
+       "226344                                           0.000029   \n",
+       "226345                                           0.001427   \n",
+       "\n",
+       "        ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                                0.241273        \n",
+       "1                                                0.248824        \n",
+       "2                                                0.528703        \n",
+       "3                                                0.078036        \n",
+       "4                                                0.153993        \n",
+       "...                                                   ...        \n",
+       "226341                                           0.080871        \n",
+       "226342                                           0.084632        \n",
+       "226343                                           0.043477        \n",
+       "226344                                           0.402063        \n",
+       "226345                                           0.037118        \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.logfc.mean  ENCSR637XSC.sub.50M.jsd_sign.mean  \\\n",
+       "0                             0.001895                           0.010946   \n",
+       "1                             0.057168                           0.009227   \n",
+       "2                             0.014488                           0.010097   \n",
+       "3                            -0.028313                          -0.008258   \n",
+       "4                             0.035548                           0.010029   \n",
+       "...                                ...                                ...   \n",
+       "226341                       -0.002501                          -0.011443   \n",
+       "226342                       -0.029992                          -0.013178   \n",
+       "226343                       -0.068190                          -0.022175   \n",
+       "226344                       -0.012709                          -0.005298   \n",
+       "226345                       -0.013154                          -0.018966   \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                                0.000367           \n",
+       "1                                                0.000016           \n",
+       "2                                                0.000022           \n",
+       "3                                                0.000155           \n",
+       "4                                                0.000281           \n",
+       "...                                                   ...           \n",
+       "226341                                           0.000436           \n",
+       "226342                                           0.000332           \n",
+       "226343                                           0.001367           \n",
+       "226344                                           0.000005           \n",
+       "226345                                           0.000224           \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                                0.121047                \n",
+       "1                                                0.451472                \n",
+       "2                                                0.379356                \n",
+       "3                                                0.191231                \n",
+       "4                                                0.159250                \n",
+       "...                                                   ...                \n",
+       "226341                                           0.108499                \n",
+       "226342                                           0.102590                \n",
+       "226343                                           0.036352                \n",
+       "226344                                           0.593201                \n",
+       "226345                                           0.167751                \n",
+       "\n",
+       "        ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "0                    -0.018701                  -0.013757   \n",
+       "1                     0.032293                   0.018814   \n",
+       "2                    -0.045242                  -0.016605   \n",
+       "3                     0.027531                   0.017267   \n",
+       "4                    -0.028731                  -0.013637   \n",
+       "...                        ...                        ...   \n",
+       "226341               -0.024413                  -0.012631   \n",
+       "226342               -0.041708                  -0.018304   \n",
+       "226343               -0.041029                  -0.026228   \n",
+       "226344               -0.011280                  -0.005821   \n",
+       "226345               -0.012639                  -0.023930   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                                0.000165   \n",
+       "1                                                0.000010   \n",
+       "2                                                0.000053   \n",
+       "3                                                0.000273   \n",
+       "4                                                0.000132   \n",
+       "...                                                   ...   \n",
+       "226341                                           0.000679   \n",
+       "226342                                           0.000700   \n",
+       "226343                                           0.000862   \n",
+       "226344                                           0.000002   \n",
+       "226345                                           0.001191   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \n",
+       "0                                                0.505239       \n",
+       "1                                                0.580367       \n",
+       "2                                                0.505239       \n",
+       "3                                                0.580367       \n",
+       "4                                                0.439836       \n",
+       "...                                                   ...       \n",
+       "226341                                           0.382899       \n",
+       "226342                                           0.333333       \n",
+       "226343                                           0.382899       \n",
+       "226344                                           0.666667       \n",
+       "226345                                           0.382899       \n",
+       "\n",
+       "[226346 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.jsd_sign.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000012</td>\n",
+       "      <td>0.528703</td>\n",
+       "      <td>0.014488</td>\n",
+       "      <td>0.010097</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.379356</td>\n",
+       "      <td>-0.045242</td>\n",
+       "      <td>-0.016605</td>\n",
+       "      <td>5.337650e-05</td>\n",
+       "      <td>0.505239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.078036</td>\n",
+       "      <td>-0.028313</td>\n",
+       "      <td>-0.008258</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>0.191231</td>\n",
+       "      <td>0.027531</td>\n",
+       "      <td>0.017267</td>\n",
+       "      <td>2.734630e-04</td>\n",
+       "      <td>0.580367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>726944</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131979</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.74175</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.953905</td>\n",
+       "      <td>-0.079065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000152</td>\n",
+       "      <td>0.208504</td>\n",
+       "      <td>0.040965</td>\n",
+       "      <td>0.024367</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.802258</td>\n",
+       "      <td>0.026410</td>\n",
+       "      <td>0.010944</td>\n",
+       "      <td>2.107834e-05</td>\n",
+       "      <td>0.580367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>752721</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131972</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.84500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.890193</td>\n",
+       "      <td>-0.011374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.012484</td>\n",
+       "      <td>0.004105</td>\n",
+       "      <td>0.369473</td>\n",
+       "      <td>0.047510</td>\n",
+       "      <td>0.012412</td>\n",
+       "      <td>0.003650</td>\n",
+       "      <td>0.338401</td>\n",
+       "      <td>0.047837</td>\n",
+       "      <td>1.246855e-02</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>755940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs3115857</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.59380</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302008</td>\n",
+       "      <td>-0.077224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000031</td>\n",
+       "      <td>0.386575</td>\n",
+       "      <td>-0.055487</td>\n",
+       "      <td>-0.008350</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.674640</td>\n",
+       "      <td>-0.026630</td>\n",
+       "      <td>-0.011550</td>\n",
+       "      <td>2.854755e-07</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226341</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs540889063</td>\n",
+       "      <td>277122</td>\n",
+       "      <td>0.05500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.362698</td>\n",
+       "      <td>0.010717</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000513</td>\n",
+       "      <td>0.080871</td>\n",
+       "      <td>-0.002501</td>\n",
+       "      <td>-0.011443</td>\n",
+       "      <td>0.000436</td>\n",
+       "      <td>0.108499</td>\n",
+       "      <td>-0.024413</td>\n",
+       "      <td>-0.012631</td>\n",
+       "      <td>6.785306e-04</td>\n",
+       "      <td>0.382899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226342</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>277123</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.553273</td>\n",
+       "      <td>-0.061982</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000739</td>\n",
+       "      <td>0.084632</td>\n",
+       "      <td>-0.029992</td>\n",
+       "      <td>-0.013178</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>0.102590</td>\n",
+       "      <td>-0.041708</td>\n",
+       "      <td>-0.018304</td>\n",
+       "      <td>7.000692e-04</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226343</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>277124</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.921861</td>\n",
+       "      <td>0.087364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001514</td>\n",
+       "      <td>0.043477</td>\n",
+       "      <td>-0.068190</td>\n",
+       "      <td>-0.022175</td>\n",
+       "      <td>0.001367</td>\n",
+       "      <td>0.036352</td>\n",
+       "      <td>-0.041029</td>\n",
+       "      <td>-0.026228</td>\n",
+       "      <td>8.617072e-04</td>\n",
+       "      <td>0.382899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226344</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62241515</td>\n",
+       "      <td>277125</td>\n",
+       "      <td>0.42505</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.315251</td>\n",
+       "      <td>-0.016347</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.402063</td>\n",
+       "      <td>-0.012709</td>\n",
+       "      <td>-0.005298</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.593201</td>\n",
+       "      <td>-0.011280</td>\n",
+       "      <td>-0.005821</td>\n",
+       "      <td>2.323380e-06</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226345</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2879915</td>\n",
+       "      <td>277128</td>\n",
+       "      <td>0.26020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.726414</td>\n",
+       "      <td>-0.027808</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001427</td>\n",
+       "      <td>0.037118</td>\n",
+       "      <td>-0.013154</td>\n",
+       "      <td>-0.018966</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.167751</td>\n",
+       "      <td>-0.012639</td>\n",
+       "      <td>-0.023930</td>\n",
+       "      <td>1.190772e-03</td>\n",
+       "      <td>0.382899</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>156731 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       Pos Ref Alt         RsID    Peak       AF  Inside_Peak  \\\n",
+       "2        chr1    565286   C   T    rs1578391       7  0.59780            1   \n",
+       "3        chr1    569204   T   C  rs112660509       8  0.40125            1   \n",
+       "6        chr1    726944   C   G    rs3131979      14  0.74175            1   \n",
+       "7        chr1    752721   A   G    rs3131972      17  0.84500            1   \n",
+       "8        chr1    755940   C   T    rs3115857      18  0.59380            1   \n",
+       "...       ...       ...  ..  ..          ...     ...      ...          ...   \n",
+       "226341  chr22  51021271   T   C  rs540889063  277122  0.05500            1   \n",
+       "226342  chr22  51059483   G   A   rs76733141  277123  0.06000            1   \n",
+       "226343  chr22  51066552   C   G    rs6151406  277124  0.06000            1   \n",
+       "226344  chr22  51082407   T   A   rs62241515  277125  0.42505            1   \n",
+       "226345  chr22  51222052   G   A    rs2879915  277128  0.26020            1   \n",
+       "\n",
+       "          P_Lead      Beta  ...  \\\n",
+       "2       0.998561  0.886884  ...   \n",
+       "3       0.477613  0.251458  ...   \n",
+       "6       0.953905 -0.079065  ...   \n",
+       "7       0.890193 -0.011374  ...   \n",
+       "8       0.302008 -0.077224  ...   \n",
+       "...          ...       ...  ...   \n",
+       "226341  0.362698  0.010717  ...   \n",
+       "226342  0.553273 -0.061982  ...   \n",
+       "226343  0.921861  0.087364  ...   \n",
+       "226344  0.315251 -0.016347  ...   \n",
+       "226345  0.726414 -0.027808  ...   \n",
+       "\n",
+       "        ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                                0.000012   \n",
+       "3                                                0.000509   \n",
+       "6                                                0.000152   \n",
+       "7                                                0.012484   \n",
+       "8                                                0.000031   \n",
+       "...                                                   ...   \n",
+       "226341                                           0.000513   \n",
+       "226342                                           0.000739   \n",
+       "226343                                           0.001514   \n",
+       "226344                                           0.000029   \n",
+       "226345                                           0.001427   \n",
+       "\n",
+       "        ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                                0.528703        \n",
+       "3                                                0.078036        \n",
+       "6                                                0.208504        \n",
+       "7                                                0.004105        \n",
+       "8                                                0.386575        \n",
+       "...                                                   ...        \n",
+       "226341                                           0.080871        \n",
+       "226342                                           0.084632        \n",
+       "226343                                           0.043477        \n",
+       "226344                                           0.402063        \n",
+       "226345                                           0.037118        \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.logfc.mean  ENCSR637XSC.sub.50M.jsd_sign.mean  \\\n",
+       "2                             0.014488                           0.010097   \n",
+       "3                            -0.028313                          -0.008258   \n",
+       "6                             0.040965                           0.024367   \n",
+       "7                             0.369473                           0.047510   \n",
+       "8                            -0.055487                          -0.008350   \n",
+       "...                                ...                                ...   \n",
+       "226341                       -0.002501                          -0.011443   \n",
+       "226342                       -0.029992                          -0.013178   \n",
+       "226343                       -0.068190                          -0.022175   \n",
+       "226344                       -0.012709                          -0.005298   \n",
+       "226345                       -0.013154                          -0.018966   \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                                0.000022           \n",
+       "3                                                0.000155           \n",
+       "6                                                0.000001           \n",
+       "7                                                0.012412           \n",
+       "8                                                0.000004           \n",
+       "...                                                   ...           \n",
+       "226341                                           0.000436           \n",
+       "226342                                           0.000332           \n",
+       "226343                                           0.001367           \n",
+       "226344                                           0.000005           \n",
+       "226345                                           0.000224           \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                                0.379356                \n",
+       "3                                                0.191231                \n",
+       "6                                                0.802258                \n",
+       "7                                                0.003650                \n",
+       "8                                                0.674640                \n",
+       "...                                                   ...                \n",
+       "226341                                           0.108499                \n",
+       "226342                                           0.102590                \n",
+       "226343                                           0.036352                \n",
+       "226344                                           0.593201                \n",
+       "226345                                           0.167751                \n",
+       "\n",
+       "        ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "2                    -0.045242                  -0.016605   \n",
+       "3                     0.027531                   0.017267   \n",
+       "6                     0.026410                   0.010944   \n",
+       "7                     0.338401                   0.047837   \n",
+       "8                    -0.026630                  -0.011550   \n",
+       "...                        ...                        ...   \n",
+       "226341               -0.024413                  -0.012631   \n",
+       "226342               -0.041708                  -0.018304   \n",
+       "226343               -0.041029                  -0.026228   \n",
+       "226344               -0.011280                  -0.005821   \n",
+       "226345               -0.012639                  -0.023930   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                            5.337650e-05   \n",
+       "3                                            2.734630e-04   \n",
+       "6                                            2.107834e-05   \n",
+       "7                                            1.246855e-02   \n",
+       "8                                            2.854755e-07   \n",
+       "...                                                   ...   \n",
+       "226341                                       6.785306e-04   \n",
+       "226342                                       7.000692e-04   \n",
+       "226343                                       8.617072e-04   \n",
+       "226344                                       2.323380e-06   \n",
+       "226345                                       1.190772e-03   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \n",
+       "2                                                0.505239       \n",
+       "3                                                0.580367       \n",
+       "6                                                0.580367       \n",
+       "7                                                0.333333       \n",
+       "8                                                0.666667       \n",
+       "...                                                   ...       \n",
+       "226341                                           0.382899       \n",
+       "226342                                           0.333333       \n",
+       "226343                                           0.382899       \n",
+       "226344                                           0.666667       \n",
+       "226345                                           0.382899       \n",
+       "\n",
+       "[156731 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals[qvals[\"Inside_Peak\"]==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#qvals[qvals[\"Inside_Peak\"]==1][['chr', 'Pos', 'Ref', 'Alt', 'RsID']].to_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/enformer_preds_small_window/metad_data.tsv\", sep=\"\\t\", header=False, index=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#enforer_recomputed_preds\n",
+    "import pickle as pkl\n",
+    "import os\n",
+    "\n",
+    "mean_change=[]\n",
+    "rsids=[]\n",
+    "\n",
+    "enf_dir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/enformer_preds_small_window/\"\n",
+    "for diri in [\"splitaa\", \"splitab\"]:\n",
+    "    enformer_recompute = pkl.load(open(enf_dir+diri+\"/predictions_at_snp.pkl\",'rb'))\n",
+    "    for i in range(enformer_recompute[\"ref_logcount_preds\"].shape[0]):\n",
+    "        mean_change.append(np.log2( (enformer_recompute[\"ref_logcount_preds\"][i,440:456].sum()+1) / (enformer_recompute[\"alt_logcount_preds\"][i,440:456].sum()+1)))\n",
+    "        rsids.append(enformer_recompute[\"rsids\"][i])\n",
+    "mean_change = np.array(mean_change)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(156731,)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_change.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(156731, 24)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals[qvals[\"Inside_Peak\"]==1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals[\"enformer_recomputed\"]= np.nan\n",
+    "qvals.loc[qvals[\"Inside_Peak\"]==1,\"enformer_recomputed\"] = mean_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.jsd_sign.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.528703</td>\n",
+       "      <td>0.014488</td>\n",
+       "      <td>0.010097</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.379356</td>\n",
+       "      <td>-0.045242</td>\n",
+       "      <td>-0.016605</td>\n",
+       "      <td>5.337650e-05</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>-0.001945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.078036</td>\n",
+       "      <td>-0.028313</td>\n",
+       "      <td>-0.008258</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>0.191231</td>\n",
+       "      <td>0.027531</td>\n",
+       "      <td>0.017267</td>\n",
+       "      <td>2.734630e-04</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>0.001667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>726944</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131979</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.74175</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.953905</td>\n",
+       "      <td>-0.079065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.208504</td>\n",
+       "      <td>0.040965</td>\n",
+       "      <td>0.024367</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.802258</td>\n",
+       "      <td>0.026410</td>\n",
+       "      <td>0.010944</td>\n",
+       "      <td>2.107834e-05</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>-0.001038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>752721</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131972</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.84500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.890193</td>\n",
+       "      <td>-0.011374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.004105</td>\n",
+       "      <td>0.369473</td>\n",
+       "      <td>0.047510</td>\n",
+       "      <td>0.012412</td>\n",
+       "      <td>0.003650</td>\n",
+       "      <td>0.338401</td>\n",
+       "      <td>0.047837</td>\n",
+       "      <td>1.246855e-02</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.344077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>755940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs3115857</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.59380</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302008</td>\n",
+       "      <td>-0.077224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.386575</td>\n",
+       "      <td>-0.055487</td>\n",
+       "      <td>-0.008350</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.674640</td>\n",
+       "      <td>-0.026630</td>\n",
+       "      <td>-0.011550</td>\n",
+       "      <td>2.854755e-07</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>-0.007762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226341</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs540889063</td>\n",
+       "      <td>277122</td>\n",
+       "      <td>0.05500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.362698</td>\n",
+       "      <td>0.010717</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.080871</td>\n",
+       "      <td>-0.002501</td>\n",
+       "      <td>-0.011443</td>\n",
+       "      <td>0.000436</td>\n",
+       "      <td>0.108499</td>\n",
+       "      <td>-0.024413</td>\n",
+       "      <td>-0.012631</td>\n",
+       "      <td>6.785306e-04</td>\n",
+       "      <td>0.382899</td>\n",
+       "      <td>-0.006601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226342</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>277123</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.553273</td>\n",
+       "      <td>-0.061982</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.084632</td>\n",
+       "      <td>-0.029992</td>\n",
+       "      <td>-0.013178</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>0.102590</td>\n",
+       "      <td>-0.041708</td>\n",
+       "      <td>-0.018304</td>\n",
+       "      <td>7.000692e-04</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.040270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226343</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>277124</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.921861</td>\n",
+       "      <td>0.087364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.043477</td>\n",
+       "      <td>-0.068190</td>\n",
+       "      <td>-0.022175</td>\n",
+       "      <td>0.001367</td>\n",
+       "      <td>0.036352</td>\n",
+       "      <td>-0.041029</td>\n",
+       "      <td>-0.026228</td>\n",
+       "      <td>8.617072e-04</td>\n",
+       "      <td>0.382899</td>\n",
+       "      <td>0.042600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226344</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62241515</td>\n",
+       "      <td>277125</td>\n",
+       "      <td>0.42505</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.315251</td>\n",
+       "      <td>-0.016347</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.402063</td>\n",
+       "      <td>-0.012709</td>\n",
+       "      <td>-0.005298</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.593201</td>\n",
+       "      <td>-0.011280</td>\n",
+       "      <td>-0.005821</td>\n",
+       "      <td>2.323380e-06</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.003503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226345</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2879915</td>\n",
+       "      <td>277128</td>\n",
+       "      <td>0.26020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.726414</td>\n",
+       "      <td>-0.027808</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.037118</td>\n",
+       "      <td>-0.013154</td>\n",
+       "      <td>-0.018966</td>\n",
+       "      <td>0.000224</td>\n",
+       "      <td>0.167751</td>\n",
+       "      <td>-0.012639</td>\n",
+       "      <td>-0.023930</td>\n",
+       "      <td>1.190772e-03</td>\n",
+       "      <td>0.382899</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>156731 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       Pos Ref Alt         RsID    Peak       AF  Inside_Peak  \\\n",
+       "2        chr1    565286   C   T    rs1578391       7  0.59780            1   \n",
+       "3        chr1    569204   T   C  rs112660509       8  0.40125            1   \n",
+       "6        chr1    726944   C   G    rs3131979      14  0.74175            1   \n",
+       "7        chr1    752721   A   G    rs3131972      17  0.84500            1   \n",
+       "8        chr1    755940   C   T    rs3115857      18  0.59380            1   \n",
+       "...       ...       ...  ..  ..          ...     ...      ...          ...   \n",
+       "226341  chr22  51021271   T   C  rs540889063  277122  0.05500            1   \n",
+       "226342  chr22  51059483   G   A   rs76733141  277123  0.06000            1   \n",
+       "226343  chr22  51066552   C   G    rs6151406  277124  0.06000            1   \n",
+       "226344  chr22  51082407   T   A   rs62241515  277125  0.42505            1   \n",
+       "226345  chr22  51222052   G   A    rs2879915  277128  0.26020            1   \n",
+       "\n",
+       "          P_Lead      Beta  ...  \\\n",
+       "2       0.998561  0.886884  ...   \n",
+       "3       0.477613  0.251458  ...   \n",
+       "6       0.953905 -0.079065  ...   \n",
+       "7       0.890193 -0.011374  ...   \n",
+       "8       0.302008 -0.077224  ...   \n",
+       "...          ...       ...  ...   \n",
+       "226341  0.362698  0.010717  ...   \n",
+       "226342  0.553273 -0.061982  ...   \n",
+       "226343  0.921861  0.087364  ...   \n",
+       "226344  0.315251 -0.016347  ...   \n",
+       "226345  0.726414 -0.027808  ...   \n",
+       "\n",
+       "        ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                                0.528703        \n",
+       "3                                                0.078036        \n",
+       "6                                                0.208504        \n",
+       "7                                                0.004105        \n",
+       "8                                                0.386575        \n",
+       "...                                                   ...        \n",
+       "226341                                           0.080871        \n",
+       "226342                                           0.084632        \n",
+       "226343                                           0.043477        \n",
+       "226344                                           0.402063        \n",
+       "226345                                           0.037118        \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.logfc.mean  ENCSR637XSC.sub.50M.jsd_sign.mean  \\\n",
+       "2                             0.014488                           0.010097   \n",
+       "3                            -0.028313                          -0.008258   \n",
+       "6                             0.040965                           0.024367   \n",
+       "7                             0.369473                           0.047510   \n",
+       "8                            -0.055487                          -0.008350   \n",
+       "...                                ...                                ...   \n",
+       "226341                       -0.002501                          -0.011443   \n",
+       "226342                       -0.029992                          -0.013178   \n",
+       "226343                       -0.068190                          -0.022175   \n",
+       "226344                       -0.012709                          -0.005298   \n",
+       "226345                       -0.013154                          -0.018966   \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                                0.000022           \n",
+       "3                                                0.000155           \n",
+       "6                                                0.000001           \n",
+       "7                                                0.012412           \n",
+       "8                                                0.000004           \n",
+       "...                                                   ...           \n",
+       "226341                                           0.000436           \n",
+       "226342                                           0.000332           \n",
+       "226343                                           0.001367           \n",
+       "226344                                           0.000005           \n",
+       "226345                                           0.000224           \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                                0.379356                \n",
+       "3                                                0.191231                \n",
+       "6                                                0.802258                \n",
+       "7                                                0.003650                \n",
+       "8                                                0.674640                \n",
+       "...                                                   ...                \n",
+       "226341                                           0.108499                \n",
+       "226342                                           0.102590                \n",
+       "226343                                           0.036352                \n",
+       "226344                                           0.593201                \n",
+       "226345                                           0.167751                \n",
+       "\n",
+       "        ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "2                    -0.045242                  -0.016605   \n",
+       "3                     0.027531                   0.017267   \n",
+       "6                     0.026410                   0.010944   \n",
+       "7                     0.338401                   0.047837   \n",
+       "8                    -0.026630                  -0.011550   \n",
+       "...                        ...                        ...   \n",
+       "226341               -0.024413                  -0.012631   \n",
+       "226342               -0.041708                  -0.018304   \n",
+       "226343               -0.041029                  -0.026228   \n",
+       "226344               -0.011280                  -0.005821   \n",
+       "226345               -0.012639                  -0.023930   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                            5.337650e-05   \n",
+       "3                                            2.734630e-04   \n",
+       "6                                            2.107834e-05   \n",
+       "7                                            1.246855e-02   \n",
+       "8                                            2.854755e-07   \n",
+       "...                                                   ...   \n",
+       "226341                                       6.785306e-04   \n",
+       "226342                                       7.000692e-04   \n",
+       "226343                                       8.617072e-04   \n",
+       "226344                                       2.323380e-06   \n",
+       "226345                                       1.190772e-03   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                                0.505239        \n",
+       "3                                                0.580367        \n",
+       "6                                                0.580367        \n",
+       "7                                                0.333333        \n",
+       "8                                                0.666667        \n",
+       "...                                                   ...        \n",
+       "226341                                           0.382899        \n",
+       "226342                                           0.333333        \n",
+       "226343                                           0.382899        \n",
+       "226344                                           0.666667        \n",
+       "226345                                           0.382899        \n",
+       "\n",
+       "        enformer_recomputed  \n",
+       "2                 -0.001945  \n",
+       "3                  0.001667  \n",
+       "6                 -0.001038  \n",
+       "7                 -0.344077  \n",
+       "8                 -0.007762  \n",
+       "...                     ...  \n",
+       "226341            -0.006601  \n",
+       "226342             0.040270  \n",
+       "226343             0.042600  \n",
+       "226344             0.003503  \n",
+       "226345             0.000000  \n",
+       "\n",
+       "[156731 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals[qvals[\"Inside_Peak\"]==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qvals.to_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Eu.CaQTLS.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "             "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>0.155446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>0.174020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>0.886884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>0.251458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>0.347965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226341</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51021271</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs540889063</td>\n",
+       "      <td>0.010717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226342</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51059483</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>-0.061982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226343</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51066552</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>0.087364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226344</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51082407</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62241515</td>\n",
+       "      <td>-0.016347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226345</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>51222052</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2879915</td>\n",
+       "      <td>-0.027808</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>226346 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       Pos Ref Alt         RsID      Beta\n",
+       "0        chr1    875770   A   G    rs4970379  0.155446\n",
+       "1        chr1    930533   C   G    rs3128110  0.174020\n",
+       "2        chr1    565286   C   T    rs1578391  0.886884\n",
+       "3        chr1    569204   T   C  rs112660509  0.251458\n",
+       "4        chr1    748141   G   A    rs2427893  0.347965\n",
+       "...       ...       ...  ..  ..          ...       ...\n",
+       "226341  chr22  51021271   T   C  rs540889063  0.010717\n",
+       "226342  chr22  51059483   G   A   rs76733141 -0.061982\n",
+       "226343  chr22  51066552   C   G    rs6151406  0.087364\n",
+       "226344  chr22  51082407   T   A   rs62241515 -0.016347\n",
+       "226345  chr22  51222052   G   A    rs2879915 -0.027808\n",
+       "\n",
+       "[226346 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals[[\"chr\", \"Pos\", \"Ref\", \"Alt\" , \"RsID\", \"Beta\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#qvals[[\"chr\", \"Pos\", \"Ref\", \"Alt\" , \"RsID\", \"Beta\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/source2.tsv\", sep=\"\\t\", header=True, index=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/enformer_predictions2.tsv\", sep='\\t', header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>0.000011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>0.000026</td>\n",
+       "      <td>0.000036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>726944</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131979</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>-0.000028</td>\n",
+       "      <td>-0.000037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>752721</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs3131972</td>\n",
+       "      <td>-0.002493</td>\n",
+       "      <td>-0.003319</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     pos ref alt         rsid  DNASE_SAD  DNASE_SAR\n",
+       "0  chr1  565286   C   T    rs1578391   0.000008   0.000011\n",
+       "1  chr1  569204   T   C  rs112660509   0.000026   0.000036\n",
+       "2  chr1  726944   C   G    rs3131979   0.000100   0.000133\n",
+       "3  chr1  748141   G   A    rs2427893  -0.000028  -0.000037\n",
+       "4  chr1  752721   G   A    rs3131972  -0.002493  -0.003319"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(177812, 7)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(177812, 7)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer.drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.jsd_sign.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.5978</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.528703</td>\n",
+       "      <td>0.014488</td>\n",
+       "      <td>0.010097</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.379356</td>\n",
+       "      <td>-0.045242</td>\n",
+       "      <td>-0.016605</td>\n",
+       "      <td>0.000053</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>-0.001945</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     Pos Ref Alt       RsID  Peak      AF  Inside_Peak    P_Lead  \\\n",
+       "2  chr1  565286   C   T  rs1578391     7  0.5978            1  0.998561   \n",
+       "\n",
+       "       Beta  ...  ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2  0.886884  ...                                           0.528703        \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.logfc.mean  ENCSR637XSC.sub.50M.jsd_sign.mean  \\\n",
+       "2                        0.014488                           0.010097   \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                           0.000022           \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                           0.379356                \n",
+       "\n",
+       "   ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "2               -0.045242                  -0.016605   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                           0.000053   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  enformer_recomputed  \n",
+       "2                                           0.505239                 -0.001945  \n",
+       "\n",
+       "[1 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals[qvals[\"RsID\"]==\"rs1578391\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enf1 = qvals.merge(enformer, left_on=['chr', 'Pos', 'Ref', 'Alt', 'RsID'], right_on=[\"chr\", \"pos\" ,\"ref\", \"alt\", \"rsid\"], how=\"left\")\n",
+    "\n",
+    "enf2 = qvals.merge(enformer, left_on=['chr', 'Pos', 'Ref', 'Alt', 'RsID'], right_on=[\"chr\", \"pos\" ,\"alt\", \"ref\", \"rsid\"], how=\"left\")\n",
+    "\n",
+    "enf2[\"DNASE_SAD\"] = -1*enf2[\"DNASE_SAD\"]\n",
+    "enf2[\"DNASE_SAR\"] = -1*enf2[\"DNASE_SAR\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "170092"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(enf1[\"DNASE_SAD\"]) & np.isnan(enf2[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "170092"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(enf1[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals[\"DNASE_SAD\"] = enf1[\"DNASE_SAD\"]\n",
+    "qvals[\"DNASE_SAR\"] = enf1[\"DNASE_SAR\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals.loc[~np.isnan(enf2[\"DNASE_SAD\"]), \"DNASE_SAD\"] =  enf2[\"DNASE_SAD\"]\n",
+    "qvals.loc[~np.isnan(enf2[\"DNASE_SAR\"]),\"DNASE_SAR\"] =  enf2[\"DNASE_SAR\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 31)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enf2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals[\"IsUsed\"] = ~np.isnan(qvals[\"DNASE_SAD\"])\n",
+    "qvals.to_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=True, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "226345"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(qvals[\"IsUsed\"] )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_kumasaka_susie.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_kumasaka_susie.ipynb
new file mode 100644
index 00000000..c1a67edc
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_kumasaka_susie.ipynb
@@ -0,0 +1,4594 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "data_sus = pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/QTD100018.all.tsv.gz\", header=0, sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>position</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>ma_samples</th>\n",
+       "      <th>maf</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>type</th>\n",
+       "      <th>ac</th>\n",
+       "      <th>an</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.861729</td>\n",
+       "      <td>-0.052510</td>\n",
+       "      <td>0.300473</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.534432</td>\n",
+       "      <td>-0.213047</td>\n",
+       "      <td>0.341387</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.727965</td>\n",
+       "      <td>-0.087366</td>\n",
+       "      <td>0.250258</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.908891</td>\n",
+       "      <td>-0.032622</td>\n",
+       "      <td>0.284130</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.367211</td>\n",
+       "      <td>0.260117</td>\n",
+       "      <td>0.286773</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  molecular_trait_id  chromosome  position ref alt         variant  \\\n",
+       "0         Interval_1           1     10583   G   A  chr1_10583_G_A   \n",
+       "1         Interval_2           1     10583   G   A  chr1_10583_G_A   \n",
+       "2         Interval_3           1     10583   G   A  chr1_10583_G_A   \n",
+       "3         Interval_4           1     10583   G   A  chr1_10583_G_A   \n",
+       "4         Interval_5           1     10583   G   A  chr1_10583_G_A   \n",
+       "\n",
+       "   ma_samples       maf    pvalue      beta        se type  ac   an  r2  \\\n",
+       "0           9  0.049451  0.861729 -0.052510  0.300473  SNP   9  182 NaN   \n",
+       "1           9  0.049451  0.534432 -0.213047  0.341387  SNP   9  182 NaN   \n",
+       "2           9  0.049451  0.727965 -0.087366  0.250258  SNP   9  182 NaN   \n",
+       "3           9  0.049451  0.908891 -0.032622  0.284130  SNP   9  182 NaN   \n",
+       "4           9  0.049451  0.367211  0.260117  0.286773  SNP   9  182 NaN   \n",
+       "\n",
+       "  molecular_trait_object_id     gene_id  median_tpm        rsid  \n",
+       "0                Interval_1  Interval_1         NaN  rs58108140  \n",
+       "1                Interval_2  Interval_2         NaN  rs58108140  \n",
+       "2                Interval_3  Interval_3         NaN  rs58108140  \n",
+       "3                Interval_4  Interval_4         NaN  rs58108140  \n",
+       "4                Interval_5  Interval_5         NaN  rs58108140  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_sus[\"position0\"] = data_sus[\"position\"] - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['molecular_trait_id', 'chromosome', 'position', 'ref', 'alt', 'variant',\n",
+       "       'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac', 'an', 'r2',\n",
+       "       'molecular_trait_object_id', 'gene_id', 'median_tpm', 'rsid',\n",
+       "       'position0'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>position0</th>\n",
+       "      <th>position</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>ma_samples</th>\n",
+       "      <th>maf</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>type</th>\n",
+       "      <th>ac</th>\n",
+       "      <th>an</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10582</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.861729</td>\n",
+       "      <td>-0.052510</td>\n",
+       "      <td>0.300473</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "      <td>Interval_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10582</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.534432</td>\n",
+       "      <td>-0.213047</td>\n",
+       "      <td>0.341387</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "      <td>Interval_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10582</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.727965</td>\n",
+       "      <td>-0.087366</td>\n",
+       "      <td>0.250258</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "      <td>Interval_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10582</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.908891</td>\n",
+       "      <td>-0.032622</td>\n",
+       "      <td>0.284130</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>Interval_4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "      <td>Interval_4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10582</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.367211</td>\n",
+       "      <td>0.260117</td>\n",
+       "      <td>0.286773</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>Interval_5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "      <td>Interval_5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344879628</th>\n",
+       "      <td>9</td>\n",
+       "      <td>138315492</td>\n",
+       "      <td>138315493</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr9_138315493_G_T</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.076923</td>\n",
+       "      <td>0.467407</td>\n",
+       "      <td>0.227731</td>\n",
+       "      <td>0.311820</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>14</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1386837028</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344879629</th>\n",
+       "      <td>9</td>\n",
+       "      <td>138318263</td>\n",
+       "      <td>138318264</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9_138318264_C_A</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.131868</td>\n",
+       "      <td>0.623327</td>\n",
+       "      <td>-0.108922</td>\n",
+       "      <td>0.220881</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>24</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241022</td>\n",
+       "      <td>Interval_241022</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1460832765</td>\n",
+       "      <td>Interval_241022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344879630</th>\n",
+       "      <td>9</td>\n",
+       "      <td>138318263</td>\n",
+       "      <td>138318264</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9_138318264_C_A</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.131868</td>\n",
+       "      <td>0.814942</td>\n",
+       "      <td>-0.057632</td>\n",
+       "      <td>0.245391</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>24</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1460832765</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344879631</th>\n",
+       "      <td>9</td>\n",
+       "      <td>138318559</td>\n",
+       "      <td>138318560</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr9_138318560_A_G</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.747654</td>\n",
+       "      <td>-0.105536</td>\n",
+       "      <td>0.326847</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241022</td>\n",
+       "      <td>Interval_241022</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1248605022</td>\n",
+       "      <td>Interval_241022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344879632</th>\n",
+       "      <td>9</td>\n",
+       "      <td>138318559</td>\n",
+       "      <td>138318560</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr9_138318560_A_G</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.541544</td>\n",
+       "      <td>0.222004</td>\n",
+       "      <td>0.362036</td>\n",
+       "      <td>SNP</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1248605022</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>344879633 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           chromosome  position0   position ref alt             variant  \\\n",
+       "0                   1      10582      10583   G   A      chr1_10583_G_A   \n",
+       "1                   1      10582      10583   G   A      chr1_10583_G_A   \n",
+       "2                   1      10582      10583   G   A      chr1_10583_G_A   \n",
+       "3                   1      10582      10583   G   A      chr1_10583_G_A   \n",
+       "4                   1      10582      10583   G   A      chr1_10583_G_A   \n",
+       "...               ...        ...        ...  ..  ..                 ...   \n",
+       "344879628           9  138315492  138315493   G   T  chr9_138315493_G_T   \n",
+       "344879629           9  138318263  138318264   C   A  chr9_138318264_C_A   \n",
+       "344879630           9  138318263  138318264   C   A  chr9_138318264_C_A   \n",
+       "344879631           9  138318559  138318560   A   G  chr9_138318560_A_G   \n",
+       "344879632           9  138318559  138318560   A   G  chr9_138318560_A_G   \n",
+       "\n",
+       "           ma_samples       maf    pvalue      beta        se type  ac   an  \\\n",
+       "0                   9  0.049451  0.861729 -0.052510  0.300473  SNP   9  182   \n",
+       "1                   9  0.049451  0.534432 -0.213047  0.341387  SNP   9  182   \n",
+       "2                   9  0.049451  0.727965 -0.087366  0.250258  SNP   9  182   \n",
+       "3                   9  0.049451  0.908891 -0.032622  0.284130  SNP   9  182   \n",
+       "4                   9  0.049451  0.367211  0.260117  0.286773  SNP   9  182   \n",
+       "...               ...       ...       ...       ...       ...  ...  ..  ...   \n",
+       "344879628          14  0.076923  0.467407  0.227731  0.311820  SNP  14  182   \n",
+       "344879629          24  0.131868  0.623327 -0.108922  0.220881  SNP  24  182   \n",
+       "344879630          24  0.131868  0.814942 -0.057632  0.245391  SNP  24  182   \n",
+       "344879631           9  0.049451  0.747654 -0.105536  0.326847  SNP   9  182   \n",
+       "344879632           9  0.049451  0.541544  0.222004  0.362036  SNP   9  182   \n",
+       "\n",
+       "           r2 molecular_trait_object_id          gene_id  median_tpm  \\\n",
+       "0         NaN                Interval_1       Interval_1         NaN   \n",
+       "1         NaN                Interval_2       Interval_2         NaN   \n",
+       "2         NaN                Interval_3       Interval_3         NaN   \n",
+       "3         NaN                Interval_4       Interval_4         NaN   \n",
+       "4         NaN                Interval_5       Interval_5         NaN   \n",
+       "...        ..                       ...              ...         ...   \n",
+       "344879628 NaN           Interval_241023  Interval_241023         NaN   \n",
+       "344879629 NaN           Interval_241022  Interval_241022         NaN   \n",
+       "344879630 NaN           Interval_241023  Interval_241023         NaN   \n",
+       "344879631 NaN           Interval_241022  Interval_241022         NaN   \n",
+       "344879632 NaN           Interval_241023  Interval_241023         NaN   \n",
+       "\n",
+       "                   rsid molecular_trait_id  \n",
+       "0            rs58108140         Interval_1  \n",
+       "1            rs58108140         Interval_2  \n",
+       "2            rs58108140         Interval_3  \n",
+       "3            rs58108140         Interval_4  \n",
+       "4            rs58108140         Interval_5  \n",
+       "...                 ...                ...  \n",
+       "344879628  rs1386837028    Interval_241023  \n",
+       "344879629  rs1460832765    Interval_241022  \n",
+       "344879630  rs1460832765    Interval_241023  \n",
+       "344879631  rs1248605022    Interval_241022  \n",
+       "344879632  rs1248605022    Interval_241023  \n",
+       "\n",
+       "[344879633 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus = data_sus[['chromosome', 'position0', 'position', 'ref', 'alt', 'variant',\n",
+    "       'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac', 'an', 'r2',\n",
+    "       'molecular_trait_object_id', 'gene_id', 'median_tpm', 'rsid',\n",
+    "       'molecular_trait_id']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  phenotype_id    quant_id    group_id     gene_id chromosome  gene_start  \\\n",
+      "0   Interval_1  Interval_1  Interval_1  Interval_1          1       10034   \n",
+      "1   Interval_2  Interval_2  Interval_2  Interval_2          1       28924   \n",
+      "2   Interval_3  Interval_3  Interval_3  Interval_3          1      180749   \n",
+      "3   Interval_4  Interval_4  Interval_4  Interval_4          1      182645   \n",
+      "4   Interval_5  Interval_5  Interval_5  Interval_5          1      186367   \n",
+      "\n",
+      "   gene_end  strand   gene_name       gene_type  phenotype_pos  \\\n",
+      "0     10617       1  Interval_1  open_chromatin          10326   \n",
+      "1     29435       1  Interval_2  open_chromatin          29180   \n",
+      "2    181528       1  Interval_3  open_chromatin         181138   \n",
+      "3    184537       1  Interval_4  open_chromatin         183591   \n",
+      "4    187110       1  Interval_5  open_chromatin         186738   \n",
+      "\n",
+      "   phenotype_length  \n",
+      "0               584  \n",
+      "1               512  \n",
+      "2               780  \n",
+      "3              1893  \n",
+      "4               744  \n"
+     ]
+    }
+   ],
+   "source": [
+    "peaks_data=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/peak_metadata/QTD100018_peak_metadata.tsv.gz\", sep=\"\\t\", header=0)\n",
+    "print(peaks_data.head())\n",
+    "peaks_data = peaks_data[[\"chromosome\", \"gene_start\", \"gene_end\", \"phenotype_pos\", \"phenotype_id\"]]\n",
+    "peaks_data[\"chromosome\"] = \"chr\"+peaks_data[\"chromosome\"] .astype(str)\n",
+    "peaks_data[\"center\"]  =  ((peaks_data[\"gene_start\"] + peaks_data[\"gene_end\"])/2).astype(int)\n",
+    "# peaks_data[\"start\"] = peaks_data[\"center\"] - 100\n",
+    "# peaks_data[\"end\"] = peaks_data[\"center\"] + 100\n",
+    "peaks_data = peaks_data[['chromosome', 'gene_start', 'gene_end', \"phenotype_id\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe = data_sus.merge(peaks_data, right_on=['phenotype_id'], left_on=[\"molecular_trait_id\"], how='left')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n = merged_dataframe[((merged_dataframe[\"position\"].astype(int) >= merged_dataframe[\"gene_start\"].astype(int)) & (merged_dataframe[\"position\"].astype(int) <= merged_dataframe[\"gene_end\"].astype(int)))]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n.to_csv(\"eu_qtls.inpeaks.effects.tsv\", sep=\"\\t\", index=False, header=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1071080, 24)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(344879633, 20)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#data_sus.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n1 = merged_dataframe_n[merged_dataframe_n[\"ref\"].str.len()==1]\n",
+    "merged_dataframe_n1 = merged_dataframe_n1[merged_dataframe_n1[\"alt\"].str.len()==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(843015, 24)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n1.to_csv(\"local.eu.effects.tsv\",sep=\"\\t\", index=False, header=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['molecular_trait_id', 'chromosome_x', 'position', 'ref', 'alt',\n",
+       "       'variant', 'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac',\n",
+       "       'an', 'r2', 'molecular_trait_object_id', 'gene_id', 'median_tpm',\n",
+       "       'rsid', 'position0', 'chromosome_y', 'gene_start', 'gene_end',\n",
+       "       'phenotype_id'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_n2 = merged_dataframe_n1[['chromosome_x', 'position0', 'position', 'ref', 'alt', 'molecular_trait_id',\n",
+    "       'variant', 'ma_samples', 'maf', 'pvalue', 'beta', 'se', 'type', 'ac',\n",
+    "       'an', 'r2', 'molecular_trait_object_id', 'gene_id', 'median_tpm',\n",
+    "       'rsid',  'gene_start', 'gene_end',\n",
+    "       'phenotype_id']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_dataframe_n2['chromosome_x'] = \"chr\"+merged_dataframe_n2['chromosome_x'].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chromosome_x</th>\n",
+       "      <th>position0</th>\n",
+       "      <th>position</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>ma_samples</th>\n",
+       "      <th>maf</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ac</th>\n",
+       "      <th>an</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10582</td>\n",
+       "      <td>10583</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>chr1_10583_G_A</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.049451</td>\n",
+       "      <td>0.861729</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>Interval_1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs58108140</td>\n",
+       "      <td>10034</td>\n",
+       "      <td>10617</td>\n",
+       "      <td>Interval_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>29367</td>\n",
+       "      <td>29368</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>chr1_29368_G_A</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.038462</td>\n",
+       "      <td>0.162300</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>Interval_2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs796745295</td>\n",
+       "      <td>28924</td>\n",
+       "      <td>29435</td>\n",
+       "      <td>Interval_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1295</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>181112</td>\n",
+       "      <td>181113</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>chr1_181113_A_G</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.412088</td>\n",
+       "      <td>0.008885</td>\n",
+       "      <td>...</td>\n",
+       "      <td>107</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1383295402</td>\n",
+       "      <td>180749</td>\n",
+       "      <td>181528</td>\n",
+       "      <td>Interval_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1311</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>181392</td>\n",
+       "      <td>181393</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>chr1_181393_G_C</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.054945</td>\n",
+       "      <td>0.467159</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1255535955</td>\n",
+       "      <td>180749</td>\n",
+       "      <td>181528</td>\n",
+       "      <td>Interval_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1319</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>181412</td>\n",
+       "      <td>181413</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>chr1_181413_A_T</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.032967</td>\n",
+       "      <td>0.582758</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>Interval_3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1480963775</td>\n",
+       "      <td>180749</td>\n",
+       "      <td>181528</td>\n",
+       "      <td>Interval_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344878518</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>138150600</td>\n",
+       "      <td>138150601</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>chr9_138150601_G_T</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.000413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1337303724</td>\n",
+       "      <td>138149834</td>\n",
+       "      <td>138151081</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344878520</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>138150603</td>\n",
+       "      <td>138150604</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>chr9_138150604_G_C</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.000413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs1229955401</td>\n",
+       "      <td>138149834</td>\n",
+       "      <td>138151081</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344878522</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>138150624</td>\n",
+       "      <td>138150625</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>chr9_138150625_G_A</td>\n",
+       "      <td>62</td>\n",
+       "      <td>0.384615</td>\n",
+       "      <td>0.099104</td>\n",
+       "      <td>...</td>\n",
+       "      <td>70</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs11137389</td>\n",
+       "      <td>138149834</td>\n",
+       "      <td>138151081</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344878524</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>138150742</td>\n",
+       "      <td>138150743</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>chr9_138150743_A_G</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.000413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs56981042</td>\n",
+       "      <td>138149834</td>\n",
+       "      <td>138151081</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344878526</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>138151063</td>\n",
+       "      <td>138151064</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>chr9_138151064_C_G</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.104396</td>\n",
+       "      <td>0.010551</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>182</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>rs369255085</td>\n",
+       "      <td>138149834</td>\n",
+       "      <td>138151081</td>\n",
+       "      <td>Interval_241023</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>843015 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chromosome_x  position0   position ref alt molecular_trait_id  \\\n",
+       "0                 chr1      10582      10583   G   A         Interval_1   \n",
+       "235               chr1      29367      29368   G   A         Interval_2   \n",
+       "1295              chr1     181112     181113   A   G         Interval_3   \n",
+       "1311              chr1     181392     181393   G   C         Interval_3   \n",
+       "1319              chr1     181412     181413   A   T         Interval_3   \n",
+       "...                ...        ...        ...  ..  ..                ...   \n",
+       "344878518         chr9  138150600  138150601   G   T    Interval_241023   \n",
+       "344878520         chr9  138150603  138150604   G   C    Interval_241023   \n",
+       "344878522         chr9  138150624  138150625   G   A    Interval_241023   \n",
+       "344878524         chr9  138150742  138150743   A   G    Interval_241023   \n",
+       "344878526         chr9  138151063  138151064   C   G    Interval_241023   \n",
+       "\n",
+       "                      variant  ma_samples       maf    pvalue  ...   ac   an  \\\n",
+       "0              chr1_10583_G_A           9  0.049451  0.861729  ...    9  182   \n",
+       "235            chr1_29368_G_A           7  0.038462  0.162300  ...    7  182   \n",
+       "1295          chr1_181113_A_G          75  0.412088  0.008885  ...  107  182   \n",
+       "1311          chr1_181393_G_C          10  0.054945  0.467159  ...   10  182   \n",
+       "1319          chr1_181413_A_T           6  0.032967  0.582758  ...    6  182   \n",
+       "...                       ...         ...       ...       ...  ...  ...  ...   \n",
+       "344878518  chr9_138150601_G_T          28  0.153846  0.000413  ...   28  182   \n",
+       "344878520  chr9_138150604_G_C          28  0.153846  0.000413  ...   28  182   \n",
+       "344878522  chr9_138150625_G_A          62  0.384615  0.099104  ...   70  182   \n",
+       "344878524  chr9_138150743_A_G          28  0.153846  0.000413  ...   28  182   \n",
+       "344878526  chr9_138151064_C_G          19  0.104396  0.010551  ...   19  182   \n",
+       "\n",
+       "           r2  molecular_trait_object_id          gene_id  median_tpm  \\\n",
+       "0         NaN                 Interval_1       Interval_1         NaN   \n",
+       "235       NaN                 Interval_2       Interval_2         NaN   \n",
+       "1295      NaN                 Interval_3       Interval_3         NaN   \n",
+       "1311      NaN                 Interval_3       Interval_3         NaN   \n",
+       "1319      NaN                 Interval_3       Interval_3         NaN   \n",
+       "...        ..                        ...              ...         ...   \n",
+       "344878518 NaN            Interval_241023  Interval_241023         NaN   \n",
+       "344878520 NaN            Interval_241023  Interval_241023         NaN   \n",
+       "344878522 NaN            Interval_241023  Interval_241023         NaN   \n",
+       "344878524 NaN            Interval_241023  Interval_241023         NaN   \n",
+       "344878526 NaN            Interval_241023  Interval_241023         NaN   \n",
+       "\n",
+       "                   rsid gene_start   gene_end     phenotype_id  \n",
+       "0            rs58108140      10034      10617       Interval_1  \n",
+       "235         rs796745295      28924      29435       Interval_2  \n",
+       "1295       rs1383295402     180749     181528       Interval_3  \n",
+       "1311       rs1255535955     180749     181528       Interval_3  \n",
+       "1319       rs1480963775     180749     181528       Interval_3  \n",
+       "...                 ...        ...        ...              ...  \n",
+       "344878518  rs1337303724  138149834  138151081  Interval_241023  \n",
+       "344878520  rs1229955401  138149834  138151081  Interval_241023  \n",
+       "344878522    rs11137389  138149834  138151081  Interval_241023  \n",
+       "344878524    rs56981042  138149834  138151081  Interval_241023  \n",
+       "344878526   rs369255085  138149834  138151081  Interval_241023  \n",
+       "\n",
+       "[843015 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_n2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      0       1       2\n",
+      "0  chr1   10024   10442\n",
+      "1  chr1   13224   13424\n",
+      "2  chr1   29073   29455\n",
+      "3  chr1   96551   96751\n",
+      "4  chr1  181000  181390\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pybedtools\n",
+    "peaks_data_merged=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/merged.peaks.eu.top.50k.bed\", sep=\"\\t\", header=None)\n",
+    "print(peaks_data_merged.head())\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(merged_dataframe_n2)\n",
+    "peak_bed = pybedtools.BedTool.from_dataframe(peaks_data_merged) # this is a merged peaks file\n",
+    "vars_in_bed = beds_var.intersect(peak_bed, c=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn = vars_in_bed.to_dataframe(names=merged_dataframe_n2.columns.tolist() + [\"count\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn2 = vars_in_bedn[vars_in_bedn[\"count\"]>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chromosome_x', 'position0', 'position', 'ref', 'alt',\n",
+       "       'molecular_trait_id', 'variant', 'ma_samples', 'maf', 'pvalue', 'beta',\n",
+       "       'se', 'type', 'ac', 'an', 'r2', 'molecular_trait_object_id', 'gene_id',\n",
+       "       'median_tpm', 'rsid', 'gene_start', 'gene_end', 'phenotype_id', 'count',\n",
+       "       'corrected_pvalue'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 324,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn2.to_csv(\"eu.inpeaks.kaur.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2[\"pvalue\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11188"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(vars_in_bedn2[\"pvalue\"]<1e-7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "277176"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(vars_in_bedn2[\"pvalue\"]>1e-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(324911, 24)"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_in_bedn2[\"pvalue\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "324911"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(vars_in_bedn2[\"variant\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "324909"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(vars_in_bedn2[\"variant\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "permuted_set=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/QTD100018.permuted.tsv.gz\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(241014, 11)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(permuted_set.shape)\n",
+    "import numpy as np\n",
+    "permuted_set = permuted_set[~np.isnan(permuted_set[\"p_beta\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "permuted_set[\"p_beta\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.000944917"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "permuted_set[\"p_beta\"].quantile(0.05)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(324911, 25)"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30623, 24)"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2[vars_in_bedn2[\"pvalue\"]<permuted_set[\"p_beta\"].quantile(0.05)].shape  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final number of significant effects after FDR: 30623\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from statsmodels.stats.multitest import multipletests\n",
+    "\n",
+    "significant_effects=vars_in_bedn2[vars_in_bedn2[\"pvalue\"]<permuted_set[\"p_beta\"].quantile(0.05)]\n",
+    "# FDR Correction\n",
+    "_, corrected_pvalues, _, _ = multipletests(significant_effects['pvalue'], alpha=0.01, method='fdr_bh')\n",
+    "\n",
+    "# Filter based on corrected p-values\n",
+    "significant_effects['corrected_pvalue'] = corrected_pvalues\n",
+    "final_significant_effects = significant_effects[significant_effects['corrected_pvalue'] <= 0.01]\n",
+    "print(f\"Final number of significant effects after FDR: {len(final_significant_effects)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.60570709e-04, 9.63276034e-05, 2.65811619e-09, ...,\n",
+       "       2.88574273e-05, 2.10523537e-07, 1.32878151e-05])"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corrected_pvalues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 519,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(226346, 28)\n",
+      "(226346, 28)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "eus = pd.read_csv(\"../final_figures_new/Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n",
+    "#eus = eus[eus[\"IsUsed\"] & eus[\"Inside_Peak\"]==1]\n",
+    "\n",
+    "#eus = eus[eus[\"Inside_Peak\"]==2]\n",
+    "print(eus.shape)\n",
+    "#eus = eus[(eus[\"Log10_BF\"]>3) | (eus[\"Log10_BF\"]<-1)]\n",
+    "print(eus.shape)\n",
+    "eus[\"labeln\"] = 1\n",
+    "eus.loc[eus[\"Log10_BF\"]>3,\"labeln\"]=1\n",
+    "eus.loc[eus[\"Log10_BF\"]<-1,\"labeln\"]=-1\n",
+    "#eus = eus[['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'labeln']]\n",
+    "eus[\"my_rsid\"] = eus[\"chr\"]+\".\"+eus[\"Pos\"].astype(str)\n",
+    "\n",
+    "datan = eus.merge(vars_in_bedn2, left_on=[\"Ref\", \"Alt\", \"RsID\"], right_on=[\"ref\", \"alt\", \"rsid\"], how=\"inner\").drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 520,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(117142, 55)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(datan.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 521,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(226346, 30)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(eus.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 522,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>r2</th>\n",
+       "      <th>molecular_trait_object_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>median_tpm</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "      <th>count</th>\n",
+       "      <th>corrected_pvalue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.</td>\n",
+       "      <td>Interval_26</td>\n",
+       "      <td>Interval_26</td>\n",
+       "      <td>.</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>939341</td>\n",
+       "      <td>942673</td>\n",
+       "      <td>Interval_26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.643614</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 55 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     Pos Ref Alt       RsID  Peak     AF  Inside_Peak    P_Lead  \\\n",
+       "0  chr1  875770   A   G  rs4970379     3  0.625            2  0.044994   \n",
+       "\n",
+       "       Beta  ...  r2  molecular_trait_object_id      gene_id  median_tpm  \\\n",
+       "0  0.155446  ...   .                Interval_26  Interval_26           .   \n",
+       "\n",
+       "        rsid  gene_start  gene_end  phenotype_id  count  corrected_pvalue  \n",
+       "0  rs4970379      939341    942673   Interval_26      1          0.643614  \n",
+       "\n",
+       "[1 rows x 55 columns]"
+      ]
+     },
+     "execution_count": 522,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datan.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 523,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16341\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1 = datan[datan[\"Inside_Peak\"]==1]\n",
+    "# distal_rsids = list(set(datan[(datan[\"Inside_Peak\"]==2) & (datan[\"Log10_BF\"]>-1)][\"RsID\"].values))\n",
+    "# data1 = pd.concat([data1[data1[\"Log10_BF\"]<-1],  data1[data1[\"RsID\"].isin(distal_rsids)]]).drop_duplicates()\n",
+    "\n",
+    "data1[\"log10p\"] = -1*np.log(data1[\"pvalue\"])\n",
+    "\n",
+    "from scipy.stats import rankdata\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "rank_kumasaka = rankdata(abs(data1[\"Beta\"]))\n",
+    "rank_kaur = rankdata(abs(data1[\"beta\"]))\n",
+    "colors = np.array(['grey']*data1.shape[0])\n",
+    "threshold=3\n",
+    "#colors[data1[\"Log10_BF\"]>threshold] = 'r'\n",
+    "#colors[data1[\"RsID\"].isin(dbsnp)] = 'blue'\n",
+    "th=5\n",
+    "colors[data1[\"log10p\"]>th] = 'r'\n",
+    "\n",
+    "# plt.scatter(rank_kumasaka[data1[\"Log10_BF\"]<-1], rank_kaur[data1[\"Log10_BF\"]<-1], alpha=0.5, s=1, color=colors[data1[\"Log10_BF\"]<-1])\n",
+    "# plt.scatter(rank_kumasaka[data1[\"Log10_BF\"]>threshold], rank_kaur[data1[\"Log10_BF\"]>threshold], s=0.5, color=colors[data1[\"Log10_BF\"]>threshold])\n",
+    "\n",
+    "plt.scatter(rank_kumasaka[data1[\"log10p\"]<2], rank_kaur[data1[\"log10p\"]<2], alpha=0.1, s=1, color=colors[data1[\"log10p\"]<2])\n",
+    "plt.scatter(rank_kumasaka[data1[\"log10p\"]>th], rank_kaur[data1[\"log10p\"]>th], s=0.5, color=colors[data1[\"log10p\"]>th])\n",
+    "\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"linear model kaur beta\")\n",
+    "\n",
+    "print(sum(data1[\"log10p\"]>th))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 575,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55598\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7651\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1 = datan[datan[\"Inside_Peak\"]==1]\n",
+    "# distal_rsids = list(set(datan[(datan[\"Inside_Peak\"]==2) & (datan[\"Log10_BF\"]>-1)][\"RsID\"].values))\n",
+    "# data1 = pd.concat([data1[data1[\"Log10_BF\"]<-1],  data1[data1[\"RsID\"].isin(distal_rsids)]]).drop_duplicates()\n",
+    "print(sum(data1[\"Log10_BF\"]<-1))\n",
+    "data1[\"log10p\"] = -1*np.log(data1[\"pvalue\"])\n",
+    "\n",
+    "from scipy.stats import rankdata\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "rank_kumasaka = rankdata(abs(data1[\"Beta\"]))\n",
+    "rank_kaur = rankdata(abs(data1[\"beta\"]))\n",
+    "colors = np.array(['grey']*data1.shape[0])\n",
+    "threshold=3\n",
+    "colors[data1[\"Log10_BF\"]>threshold] = 'r'\n",
+    "#colors[data1[\"RsID\"].isin(dbsnp)] = 'blue'\n",
+    "# th=5\n",
+    "# colors[data1[\"log10p\"]>th] = 'r'\n",
+    "\n",
+    "plt.scatter(rank_kumasaka[data1[\"Log10_BF\"]<-1], rank_kaur[data1[\"Log10_BF\"]<-1], alpha=0.1, s=1, color=colors[data1[\"Log10_BF\"]<-1])\n",
+    "plt.scatter(rank_kumasaka[data1[\"Log10_BF\"]>threshold], rank_kaur[data1[\"Log10_BF\"]>threshold], s=0.5, color=colors[data1[\"Log10_BF\"]>threshold])\n",
+    "\n",
+    "# plt.scatter(rank_kumasaka[data1[\"log10p\"]<2], rank_kaur[data1[\"log10p\"]<2], alpha=0.5, s=1, color=colors[data1[\"log10p\"]<2])\n",
+    "# plt.scatter(rank_kumasaka[data1[\"log10p\"]>th], rank_kaur[data1[\"log10p\"]>th], s=0.5, color=colors[data1[\"log10p\"]>th])\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"linear model kaur beta\")\n",
+    "\n",
+    "\n",
+    "print(sum(data1[\"Log10_BF\"]>threshold))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 576,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7525696609368799, 0.0)"
+      ]
+     },
+     "execution_count": 576,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "#distal_rsids = list(set(datan[(datan[\"Inside_Peak\"]==2) & (datan[\"Log10_BF\"]>-1)][\"RsID\"].values))\n",
+    "distal_rsids = list(set(datan[(datan[\"Inside_Peak\"]==2)]))\n",
+    "\n",
+    "plt.scatter(abs(data1[data1[\"log10p\"]>th][\"Beta\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]), s=1, c='r')\n",
+    "#plt.scatter((data1[data1[\"log10p\"]>th][\"Beta\"]), (data1[data1[\"log10p\"]>th][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"log10p\"]>th][\"beta\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"log10p\"]>th][\"beta\"]),abs(data1[data1[\"log10p\"]>th][\"Beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 577,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7525696609368799, 0.0)"
+      ]
+     },
+     "execution_count": 577,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "distal_rsids = list(set(datan[(datan[\"Inside_Peak\"]==2) & (datan[\"Log10_BF\"]>3)][\"RsID\"].values))\n",
+    "#distal_rsids = list(set(datan[(datan[\"Inside_Peak\"]==2)][\"RsID\"].values))\n",
+    "\n",
+    "plt.scatter(abs(data1[(data1[\"log10p\"]>th) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"log10p\"]>th) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]), s=1, c='grey')\n",
+    "plt.scatter(abs(data1[(data1[\"log10p\"]>th) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"log10p\"]>th) &  (data1[\"RsID\"].isin(distal_rsids))][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"log10p\"]>th][\"beta\"]), abs(data1[data1[\"log10p\"]>th][\"beta\"]),  c='k')\n",
+    "\n",
+    "pearsonr(abs(data1[data1[\"log10p\"]>th][\"beta\"]),abs(data1[data1[\"log10p\"]>th][\"Beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 578,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7479207429626905, 0.0)"
+      ]
+     },
+     "execution_count": 578,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(data1[(data1[\"log10p\"]>th) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"log10p\"]>th) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 579,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6765798015696909, 4.0138740501326115e-77)"
+      ]
+     },
+     "execution_count": 579,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(data1[(data1[\"log10p\"]>th) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"log10p\"]>th) &  (data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 580,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "567"
+      ]
+     },
+     "execution_count": 580,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(((data1[\"log10p\"]>th) &  (data1[\"RsID\"].isin(distal_rsids))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 581,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15774"
+      ]
+     },
+     "execution_count": 581,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(((data1[\"log10p\"]>th) &  (~data1[\"RsID\"].isin(distal_rsids))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 582,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 582,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1[data1[\"log10p\"]>th][\"Log10_BF\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 583,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.685591245174656"
+      ]
+     },
+     "execution_count": 583,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1[data1[\"log10p\"]>th][\"Log10_BF\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 584,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 584,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1[data1[\"Log10_BF\"]>3][\"log10p\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 585,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6596580884202183, 0.0)"
+      ]
+     },
+     "execution_count": 585,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "plt.scatter(abs(data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "#plt.scatter((data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]), (data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]),  c='k')\n",
+    "plt.xlim((0,2))\n",
+    "plt.ylim((0,2))\n",
+    "pearsonr(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]),abs(data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 587,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.6596580884202183, 0.0)"
+      ]
+     },
+     "execution_count": 587,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "# plt.scatter(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]), s=1, c='r')\n",
+    "print(threshold)\n",
+    "plt.scatter(abs(data1[(data1[\"Log10_BF\"]>threshold) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>threshold) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]), s=1, c='grey')\n",
+    "plt.scatter(abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"RsID\"].isin(distal_rsids))][\"beta\"]), s=1, c='r')\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]),  c='k')\n",
+    "\n",
+    "pearsonr(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]),abs(data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 588,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6751055820277331, 0.0)"
+      ]
+     },
+     "execution_count": 588,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "# plt.scatter(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"Beta\"]), s=1, c='r')\n",
+    "\n",
+    "plt.scatter(abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"log10p\"]>th)][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"log10p\"]>th)][\"beta\"]), s=1, c='grey')\n",
+    "\n",
+    "plt.xlabel(\"Kumsaka beta\")\n",
+    "plt.ylabel(\"Kaur beta\")\n",
+    "plt.plot(abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]), abs(data1[data1[\"Log10_BF\"]>threshold][\"beta\"]),  c='k')\n",
+    "\n",
+    "\n",
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"log10p\"]>th)][\"beta\"]),abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"log10p\"]>th)][\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 589,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5879311558375122, 0.0)"
+      ]
+     },
+     "execution_count": 589,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>-1) &  (data1[\"Log10_BF\"]<3) &  (data1[\"log10p\"]>th) ][\"beta\"]),abs(data1[(data1[\"Log10_BF\"]>-1) &  (data1[\"Log10_BF\"]<3) &  (data1[\"log10p\"]>th) ][\"Beta\"]))\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 590,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20122"
+      ]
+     },
+     "execution_count": 590,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((data1[\"Log10_BF\"]>-1) &  (data1[\"Log10_BF\"]<3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 591,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27773"
+      ]
+     },
+     "execution_count": 591,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(data1[\"Log10_BF\"]>-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 592,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "75720"
+      ]
+     },
+     "execution_count": 592,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(data1[\"Log10_BF\"]<3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 593,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.652425052431501, 0.0)"
+      ]
+     },
+     "execution_count": 593,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>threshold) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>threshold) &  ~(data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 594,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6512906689395656, 9.958685808671734e-67)"
+      ]
+     },
+     "execution_count": 594,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"RsID\"].isin(distal_rsids))][\"Beta\"]), abs(data1[(data1[\"Log10_BF\"]>threshold) &  (data1[\"RsID\"].isin(distal_rsids))][\"beta\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 567,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35"
+      ]
+     },
+     "execution_count": 567,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(((data1[\"Log10_BF\"]>threshold) &  (data1[\"RsID\"].isin(distal_rsids))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 568,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 568,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(((data1[\"Log10_BF\"]>threshold) &  (~data1[\"RsID\"].isin(distal_rsids))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 545,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7525696609368799, 0.0)"
+      ]
+     },
+     "execution_count": 545,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "pearsonr(abs(data1[data1[\"log10p\"]>th][\"beta\"]),abs(data1[data1[\"log10p\"]>th][\"Beta\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 546,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed', 'DNASE_SAD', 'DNASE_SAR', 'IsUsed', 'labeln',\n",
+       "       'my_rsid', 'chromosome_x', 'position0', 'position', 'ref', 'alt',\n",
+       "       'molecular_trait_id', 'variant', 'ma_samples', 'maf', 'pvalue', 'beta',\n",
+       "       'se', 'type', 'ac', 'an', 'r2', 'molecular_trait_object_id', 'gene_id',\n",
+       "       'median_tpm', 'rsid', 'gene_start', 'gene_end', 'phenotype_id', 'count',\n",
+       "       'corrected_pvalue', 'log10p'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 546,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 602,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1\n",
+      "27773 83371\n",
+      "0.333\n",
+      "0.6074843663265156\n",
+      "1\n",
+      "11986 67584\n",
+      "0.177\n",
+      "0.6083424333560723\n",
+      "3\n",
+      "7651 63249\n",
+      "0.121\n",
+      "0.6002182901679083\n",
+      "5\n",
+      "5364 60962\n",
+      "0.088\n",
+      "0.597397039345766\n",
+      "7\n",
+      "4001 59599\n",
+      "0.067\n",
+      "0.5932573934435035\n",
+      "9\n",
+      "3056 58654\n",
+      "0.052\n",
+      "0.5891967076160054\n",
+      "11\n",
+      "2361 57959\n",
+      "0.041\n",
+      "0.5872969229422946\n",
+      "13\n",
+      "1813 57411\n",
+      "0.032\n",
+      "0.5782921689509902\n",
+      "15\n",
+      "1362 56960\n",
+      "0.024\n",
+      "0.5782080968876514\n",
+      "17\n",
+      "1027 56625\n",
+      "0.018\n",
+      "0.5704353642350793\n",
+      "19\n",
+      "730 56328\n",
+      "0.013\n",
+      "0.5573568805438123\n",
+      "21\n",
+      "509 56107\n",
+      "0.009\n",
+      "0.5249392947353748\n",
+      "23\n",
+      "349 55947\n",
+      "0.006\n",
+      "0.5139081225709863\n",
+      "25\n",
+      "226 55824\n",
+      "0.004\n",
+      "0.4941736372829072\n",
+      "27\n",
+      "140 55738\n",
+      "0.003\n",
+      "0.43857139342808676\n",
+      "29\n",
+      "77 55675\n",
+      "0.001\n",
+      "0.3861476091000745\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "random_bc = []\n",
+    "chrombpnet = {}\n",
+    "enformer_dnase_roc = []\n",
+    "enformer_dnase_roc_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "caqtls=datan\n",
+    "caqtls[\"log10p\"] = -1*np.log(caqtls[\"pvalue\"])\n",
+    "# distal_rsids = list(set(caqtls[(caqtls[\"Inside_Peak\"]==2) & (caqtls[\"Log10_BF\"]>-1)][\"RsID\"].values))\n",
+    "# caqtls = pd.concat([caqtls[caqtls[\"Log10_BF\"]<-1],  caqtls[~caqtls[\"RsID\"].isin(distal_rsids)]]).drop_duplicates()\n",
+    "\n",
+    "for threshold in np.arange(-1,30,2):\n",
+    "    print(threshold)\n",
+    "    \n",
+    "    \n",
+    "    #dataf = caqtls[(caqtls[\"log10p\"]<2) | (caqtls[\"log10p\"]>threshold)]\n",
+    "    dataf = caqtls[(caqtls[\"Log10_BF\"]<-1) | (caqtls[\"Log10_BF\"]>threshold)]\n",
+    "\n",
+    "    dataf = dataf[dataf['IsUsed'] & (dataf['Inside_Peak']==1)]\n",
+    "    dataf[\"label\"] = 0\n",
+    "    dataf.loc[dataf[\"Log10_BF\"]>threshold, \"label\"] = 1\n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(sigs, total)\n",
+    "    \n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,3))\n",
+    "    print(random_bc[-1])\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n",
+    "    print(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer_recomputed\"]))\n",
+    "    enformer_dnase_roc.append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"DNASE_SAR\"]))\n",
+    "    enformer_dnase_roc_sar.append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 604,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f518cb071d0>"
+      ]
+     },
+     "execution_count": 604,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "colors = [\"blue\", \"blue\"]\n",
+    "alphas = [0.5, 1.0]\n",
+    "idx=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\"]:\n",
+    "    ax.scatter(np.arange(-1,30,2).astype(str), chrombpnet[key], label=key, c=\"blue\", alpha=alphas[idx])\n",
+    "    idx+=1\n",
+    "    \n",
+    "key=\"ChromBPNet DNase (68M)\"\n",
+    "ax.scatter(np.arange(-1,30,2).astype(str), chrombpnet[key], label=key)\n",
+    "ax.scatter(np.arange(-1,30,2).astype(str), enformer_dnase_roc_sar, label=\"Enformer DNase (reported) (68M)\")\n",
+    "ax.scatter(np.arange(-1,30,2).astype(str), enformer_dnase_roc, label=\"Enformer DNase (recomputed) (68M)\")\n",
+    "\n",
+    "ax.scatter(np.arange(-1,30,2).astype(str), random_bc, label=\"random\")\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Average precision\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/eu_caqtls_auprc_compare.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 549,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         1.121576\n",
+       "1         1.121576\n",
+       "2         0.402735\n",
+       "3         0.437440\n",
+       "4         0.159633\n",
+       "            ...   \n",
+       "117137    2.056851\n",
+       "117138    2.247179\n",
+       "117139    2.706052\n",
+       "117140    2.689417\n",
+       "117141    3.385294\n",
+       "Name: log10p, Length: 117142, dtype: float64"
+      ]
+     },
+     "execution_count": 549,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls[\"log10p\"] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed', 'DNASE_SAD', 'DNASE_SAR', 'IsUsed', 'labeln',\n",
+       "       'my_rsid', 'chromosome_x', 'position0', 'position', 'ref', 'alt',\n",
+       "       'molecular_trait_id', 'variant', 'ma_samples', 'maf', 'pvalue', 'beta',\n",
+       "       'se', 'type', 'ac', 'an', 'r2', 'molecular_trait_object_id', 'gene_id',\n",
+       "       'median_tpm', 'rsid', 'gene_start', 'gene_end', 'phenotype_id', 'count',\n",
+       "       'corrected_pvalue', 'log10p'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed', 'DNASE_SAD', 'DNASE_SAR', 'IsUsed', 'labeln',\n",
+       "       'my_rsid'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#eus.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1         0.162300\n",
+       "2         0.008885\n",
+       "12        0.455387\n",
+       "13        0.738469\n",
+       "14        0.484093\n",
+       "            ...   \n",
+       "842996    0.003631\n",
+       "842997    0.820080\n",
+       "843007    0.248117\n",
+       "843008    0.248117\n",
+       "843009    0.112737\n",
+       "Name: pvalue, Length: 324911, dtype: float64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars_in_bedn2[\"pvalue\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "credible_sets=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/susie_merged/QTD100018.credible_sets.tsv.gz\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>cs_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>cs_size</th>\n",
+       "      <th>pip</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>z</th>\n",
+       "      <th>cs_min_r2</th>\n",
+       "      <th>region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100002782_A_AT</td>\n",
+       "      <td>rs531537001</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.185236</td>\n",
+       "      <td>7.604570e-10</td>\n",
+       "      <td>-0.614320</td>\n",
+       "      <td>0.087505</td>\n",
+       "      <td>-7.388146</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100002782_A_AT</td>\n",
+       "      <td>rs35227168</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.185236</td>\n",
+       "      <td>7.604570e-10</td>\n",
+       "      <td>-0.614320</td>\n",
+       "      <td>0.087505</td>\n",
+       "      <td>-7.388146</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Interval_461</td>\n",
+       "      <td>Interval_461</td>\n",
+       "      <td>Interval_461_L1</td>\n",
+       "      <td>chr1_10000458_T_C</td>\n",
+       "      <td>rs3003377</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.014039</td>\n",
+       "      <td>9.104810e-10</td>\n",
+       "      <td>0.619787</td>\n",
+       "      <td>0.088806</td>\n",
+       "      <td>7.545987</td>\n",
+       "      <td>0.937097</td>\n",
+       "      <td>chr1:9743190-10143190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100007048_A_G</td>\n",
+       "      <td>rs534252</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.019619</td>\n",
+       "      <td>6.170090e-09</td>\n",
+       "      <td>-0.614765</td>\n",
+       "      <td>0.094044</td>\n",
+       "      <td>-6.877001</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047_L1</td>\n",
+       "      <td>chr1_100007048_A_G</td>\n",
+       "      <td>rs534252</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0.019667</td>\n",
+       "      <td>1.091410e-10</td>\n",
+       "      <td>-1.036960</td>\n",
+       "      <td>0.138943</td>\n",
+       "      <td>-8.093380</td>\n",
+       "      <td>0.824131</td>\n",
+       "      <td>chr1:99849456-100249456</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  molecular_trait_id        gene_id             cs_id              variant  \\\n",
+       "0      Interval_7046  Interval_7046  Interval_7046_L1  chr1_100002782_A_AT   \n",
+       "1      Interval_7046  Interval_7046  Interval_7046_L1  chr1_100002782_A_AT   \n",
+       "2       Interval_461   Interval_461   Interval_461_L1    chr1_10000458_T_C   \n",
+       "3      Interval_7046  Interval_7046  Interval_7046_L1   chr1_100007048_A_G   \n",
+       "4      Interval_7047  Interval_7047  Interval_7047_L1   chr1_100007048_A_G   \n",
+       "\n",
+       "          rsid  cs_size       pip        pvalue      beta        se         z  \\\n",
+       "0  rs531537001       66  0.185236  7.604570e-10 -0.614320  0.087505 -7.388146   \n",
+       "1   rs35227168       66  0.185236  7.604570e-10 -0.614320  0.087505 -7.388146   \n",
+       "2    rs3003377       30  0.014039  9.104810e-10  0.619787  0.088806  7.545987   \n",
+       "3     rs534252       66  0.019619  6.170090e-09 -0.614765  0.094044 -6.877001   \n",
+       "4     rs534252       80  0.019667  1.091410e-10 -1.036960  0.138943 -8.093380   \n",
+       "\n",
+       "   cs_min_r2                   region  \n",
+       "0   0.930934  chr1:99846814-100246814  \n",
+       "1   0.930934  chr1:99846814-100246814  \n",
+       "2   0.937097    chr1:9743190-10143190  \n",
+       "3   0.930934  chr1:99846814-100246814  \n",
+       "4   0.824131  chr1:99849456-100249456  "
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "credible_sets.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "credible_sets[[\"chr\", \"pos\", \"ref\", \"alt\"]] = credible_sets[\"variant\"].str.split(\"_\", expand=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_dataframe_cs = credible_sets.merge(peaks_data, right_on=['phenotype_id'], left_on=[\"molecular_trait_id\"], how='left')\n",
+    "merged_dataframe_cs = merged_dataframe_cs[((merged_dataframe_cs[\"pos\"].astype(int) >= merged_dataframe_cs[\"gene_start\"].astype(int)) & (merged_dataframe_cs[\"pos\"].astype(int) <= merged_dataframe_cs[\"gene_end\"].astype(int)))]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20479, 21)"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>cs_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>cs_size</th>\n",
+       "      <th>pip</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cs_min_r2</th>\n",
+       "      <th>region</th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100047012_G_A</td>\n",
+       "      <td>rs554742</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.019619</td>\n",
+       "      <td>6.170090e-09</td>\n",
+       "      <td>-0.614765</td>\n",
+       "      <td>0.094044</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100047012</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100046355</td>\n",
+       "      <td>100047272</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   molecular_trait_id        gene_id             cs_id             variant  \\\n",
+       "44      Interval_7046  Interval_7046  Interval_7046_L1  chr1_100047012_G_A   \n",
+       "\n",
+       "        rsid  cs_size       pip        pvalue      beta        se  ...  \\\n",
+       "44  rs554742       66  0.019619  6.170090e-09 -0.614765  0.094044  ...   \n",
+       "\n",
+       "    cs_min_r2                   region   chr        pos ref alt chromosome  \\\n",
+       "44   0.930934  chr1:99846814-100246814  chr1  100047012   G   A       chr1   \n",
+       "\n",
+       "   gene_start   gene_end   phenotype_id  \n",
+       "44  100046355  100047272  Interval_7046  \n",
+       "\n",
+       "[1 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2829"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(merged_dataframe_cs[\"pip\"]>0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>cs_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>cs_size</th>\n",
+       "      <th>pip</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cs_min_r2</th>\n",
+       "      <th>region</th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100047012_G_A</td>\n",
+       "      <td>rs554742</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.019619</td>\n",
+       "      <td>6.170090e-09</td>\n",
+       "      <td>-0.614765</td>\n",
+       "      <td>0.094044</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100047012</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100046355</td>\n",
+       "      <td>100047272</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047_L1</td>\n",
+       "      <td>chr1_100050364_C_T</td>\n",
+       "      <td>rs581097</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0.019667</td>\n",
+       "      <td>1.091410e-10</td>\n",
+       "      <td>-1.036960</td>\n",
+       "      <td>0.138943</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.824131</td>\n",
+       "      <td>chr1:99849456-100249456</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100050364</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100048413</td>\n",
+       "      <td>100050498</td>\n",
+       "      <td>Interval_7047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072_L1</td>\n",
+       "      <td>chr1_100364923_T_C</td>\n",
+       "      <td>rs12085272</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>2.862930e-08</td>\n",
+       "      <td>-0.819940</td>\n",
+       "      <td>0.132737</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.499912</td>\n",
+       "      <td>chr1:100165228-100565228</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364923</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364560</td>\n",
+       "      <td>100365896</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072_L1</td>\n",
+       "      <td>chr1_100365383_G_A</td>\n",
+       "      <td>rs28361193</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>2.862930e-08</td>\n",
+       "      <td>-0.819940</td>\n",
+       "      <td>0.132737</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.499912</td>\n",
+       "      <td>chr1:100165228-100565228</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100365383</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364560</td>\n",
+       "      <td>100365896</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072_L1</td>\n",
+       "      <td>chr1_100365547_C_T</td>\n",
+       "      <td>rs7551111</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>2.862930e-08</td>\n",
+       "      <td>-0.819940</td>\n",
+       "      <td>0.132737</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.499912</td>\n",
+       "      <td>chr1:100165228-100565228</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100365547</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364560</td>\n",
+       "      <td>100365896</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446064</th>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230_L1</td>\n",
+       "      <td>chr9_99569991_T_G</td>\n",
+       "      <td>rs10988864</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.151737</td>\n",
+       "      <td>1.222270e-15</td>\n",
+       "      <td>1.166170</td>\n",
+       "      <td>0.116173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921079</td>\n",
+       "      <td>chr9:99370478-99770478</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99569991</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99569299</td>\n",
+       "      <td>99571657</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446066</th>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230_L1</td>\n",
+       "      <td>chr9_99571495_G_A</td>\n",
+       "      <td>rs10819686</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.151737</td>\n",
+       "      <td>1.222270e-15</td>\n",
+       "      <td>1.166170</td>\n",
+       "      <td>0.116173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921079</td>\n",
+       "      <td>chr9:99370478-99770478</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99571495</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99569299</td>\n",
+       "      <td>99571657</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446077</th>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231_L1</td>\n",
+       "      <td>chr9_99574024_C_A</td>\n",
+       "      <td>rs10120175</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.098729</td>\n",
+       "      <td>1.468140e-16</td>\n",
+       "      <td>1.098210</td>\n",
+       "      <td>0.104363</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921499</td>\n",
+       "      <td>chr9:99374150-99774150</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99574024</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99573883</td>\n",
+       "      <td>99574416</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446079</th>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231_L1</td>\n",
+       "      <td>chr9_99574121_T_A</td>\n",
+       "      <td>rs11794098</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.098729</td>\n",
+       "      <td>1.468140e-16</td>\n",
+       "      <td>1.098210</td>\n",
+       "      <td>0.104363</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921499</td>\n",
+       "      <td>chr9:99374150-99774150</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99574121</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99573883</td>\n",
+       "      <td>99574416</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446082</th>\n",
+       "      <td>Interval_238255</td>\n",
+       "      <td>Interval_238255</td>\n",
+       "      <td>Interval_238255_L1</td>\n",
+       "      <td>chr9_99834039_G_A</td>\n",
+       "      <td>rs2900223</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.401042</td>\n",
+       "      <td>2.143160e-11</td>\n",
+       "      <td>0.941993</td>\n",
+       "      <td>0.120278</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.974002</td>\n",
+       "      <td>chr9:99634216-100034216</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99834039</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99833646</td>\n",
+       "      <td>99834785</td>\n",
+       "      <td>Interval_238255</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20479 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       molecular_trait_id          gene_id               cs_id  \\\n",
+       "44          Interval_7046    Interval_7046    Interval_7046_L1   \n",
+       "47          Interval_7047    Interval_7047    Interval_7047_L1   \n",
+       "170         Interval_7072    Interval_7072    Interval_7072_L1   \n",
+       "172         Interval_7072    Interval_7072    Interval_7072_L1   \n",
+       "174         Interval_7072    Interval_7072    Interval_7072_L1   \n",
+       "...                   ...              ...                 ...   \n",
+       "446064    Interval_238230  Interval_238230  Interval_238230_L1   \n",
+       "446066    Interval_238230  Interval_238230  Interval_238230_L1   \n",
+       "446077    Interval_238231  Interval_238231  Interval_238231_L1   \n",
+       "446079    Interval_238231  Interval_238231  Interval_238231_L1   \n",
+       "446082    Interval_238255  Interval_238255  Interval_238255_L1   \n",
+       "\n",
+       "                   variant        rsid  cs_size       pip        pvalue  \\\n",
+       "44      chr1_100047012_G_A    rs554742       66  0.019619  6.170090e-09   \n",
+       "47      chr1_100050364_C_T    rs581097       80  0.019667  1.091410e-10   \n",
+       "170     chr1_100364923_T_C  rs12085272       32  0.109903  2.862930e-08   \n",
+       "172     chr1_100365383_G_A  rs28361193       32  0.109903  2.862930e-08   \n",
+       "174     chr1_100365547_C_T   rs7551111       32  0.109903  2.862930e-08   \n",
+       "...                    ...         ...      ...       ...           ...   \n",
+       "446064   chr9_99569991_T_G  rs10988864       17  0.151737  1.222270e-15   \n",
+       "446066   chr9_99571495_G_A  rs10819686       17  0.151737  1.222270e-15   \n",
+       "446077   chr9_99574024_C_A  rs10120175       25  0.098729  1.468140e-16   \n",
+       "446079   chr9_99574121_T_A  rs11794098       25  0.098729  1.468140e-16   \n",
+       "446082   chr9_99834039_G_A   rs2900223        3  0.401042  2.143160e-11   \n",
+       "\n",
+       "            beta        se  ...  cs_min_r2                    region   chr  \\\n",
+       "44     -0.614765  0.094044  ...   0.930934   chr1:99846814-100246814  chr1   \n",
+       "47     -1.036960  0.138943  ...   0.824131   chr1:99849456-100249456  chr1   \n",
+       "170    -0.819940  0.132737  ...   0.499912  chr1:100165228-100565228  chr1   \n",
+       "172    -0.819940  0.132737  ...   0.499912  chr1:100165228-100565228  chr1   \n",
+       "174    -0.819940  0.132737  ...   0.499912  chr1:100165228-100565228  chr1   \n",
+       "...          ...       ...  ...        ...                       ...   ...   \n",
+       "446064  1.166170  0.116173  ...   0.921079    chr9:99370478-99770478  chr9   \n",
+       "446066  1.166170  0.116173  ...   0.921079    chr9:99370478-99770478  chr9   \n",
+       "446077  1.098210  0.104363  ...   0.921499    chr9:99374150-99774150  chr9   \n",
+       "446079  1.098210  0.104363  ...   0.921499    chr9:99374150-99774150  chr9   \n",
+       "446082  0.941993  0.120278  ...   0.974002   chr9:99634216-100034216  chr9   \n",
+       "\n",
+       "              pos ref alt chromosome gene_start   gene_end     phenotype_id  \n",
+       "44      100047012   G   A       chr1  100046355  100047272    Interval_7046  \n",
+       "47      100050364   C   T       chr1  100048413  100050498    Interval_7047  \n",
+       "170     100364923   T   C       chr1  100364560  100365896    Interval_7072  \n",
+       "172     100365383   G   A       chr1  100364560  100365896    Interval_7072  \n",
+       "174     100365547   C   T       chr1  100364560  100365896    Interval_7072  \n",
+       "...           ...  ..  ..        ...        ...        ...              ...  \n",
+       "446064   99569991   T   G       chr9   99569299   99571657  Interval_238230  \n",
+       "446066   99571495   G   A       chr9   99569299   99571657  Interval_238230  \n",
+       "446077   99574024   C   A       chr9   99573883   99574416  Interval_238231  \n",
+       "446079   99574121   T   A       chr9   99573883   99574416  Interval_238231  \n",
+       "446082   99834039   G   A       chr9   99833646   99834785  Interval_238255  \n",
+       "\n",
+       "[20479 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20479, 21)"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.945248"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs[merged_dataframe_cs[\"pip\"]>0.2][\"pvalue\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.496945"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs[merged_dataframe_cs[\"pip\"]>0.8][\"pvalue\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.7680099999999997e-39"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs[merged_dataframe_cs[\"pip\"]>0.8][\"pvalue\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>cs_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>cs_size</th>\n",
+       "      <th>pip</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cs_min_r2</th>\n",
+       "      <th>region</th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>gene_start</th>\n",
+       "      <th>gene_end</th>\n",
+       "      <th>phenotype_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100047012_G_A</td>\n",
+       "      <td>rs554742</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.019619</td>\n",
+       "      <td>6.170090e-09</td>\n",
+       "      <td>-0.614765</td>\n",
+       "      <td>0.094044</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100047012</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100046355</td>\n",
+       "      <td>100047272</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047_L1</td>\n",
+       "      <td>chr1_100050364_C_T</td>\n",
+       "      <td>rs581097</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0.019667</td>\n",
+       "      <td>1.091410e-10</td>\n",
+       "      <td>-1.036960</td>\n",
+       "      <td>0.138943</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.824131</td>\n",
+       "      <td>chr1:99849456-100249456</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100050364</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100048413</td>\n",
+       "      <td>100050498</td>\n",
+       "      <td>Interval_7047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072_L1</td>\n",
+       "      <td>chr1_100364923_T_C</td>\n",
+       "      <td>rs12085272</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>2.862930e-08</td>\n",
+       "      <td>-0.819940</td>\n",
+       "      <td>0.132737</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.499912</td>\n",
+       "      <td>chr1:100165228-100565228</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364923</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364560</td>\n",
+       "      <td>100365896</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072_L1</td>\n",
+       "      <td>chr1_100365383_G_A</td>\n",
+       "      <td>rs28361193</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>2.862930e-08</td>\n",
+       "      <td>-0.819940</td>\n",
+       "      <td>0.132737</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.499912</td>\n",
+       "      <td>chr1:100165228-100565228</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100365383</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364560</td>\n",
+       "      <td>100365896</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "      <td>Interval_7072_L1</td>\n",
+       "      <td>chr1_100365547_C_T</td>\n",
+       "      <td>rs7551111</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.109903</td>\n",
+       "      <td>2.862930e-08</td>\n",
+       "      <td>-0.819940</td>\n",
+       "      <td>0.132737</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.499912</td>\n",
+       "      <td>chr1:100165228-100565228</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100365547</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>100364560</td>\n",
+       "      <td>100365896</td>\n",
+       "      <td>Interval_7072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446064</th>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230_L1</td>\n",
+       "      <td>chr9_99569991_T_G</td>\n",
+       "      <td>rs10988864</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.151737</td>\n",
+       "      <td>1.222270e-15</td>\n",
+       "      <td>1.166170</td>\n",
+       "      <td>0.116173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921079</td>\n",
+       "      <td>chr9:99370478-99770478</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99569991</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99569299</td>\n",
+       "      <td>99571657</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446066</th>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "      <td>Interval_238230_L1</td>\n",
+       "      <td>chr9_99571495_G_A</td>\n",
+       "      <td>rs10819686</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.151737</td>\n",
+       "      <td>1.222270e-15</td>\n",
+       "      <td>1.166170</td>\n",
+       "      <td>0.116173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921079</td>\n",
+       "      <td>chr9:99370478-99770478</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99571495</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99569299</td>\n",
+       "      <td>99571657</td>\n",
+       "      <td>Interval_238230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446077</th>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231_L1</td>\n",
+       "      <td>chr9_99574024_C_A</td>\n",
+       "      <td>rs10120175</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.098729</td>\n",
+       "      <td>1.468140e-16</td>\n",
+       "      <td>1.098210</td>\n",
+       "      <td>0.104363</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921499</td>\n",
+       "      <td>chr9:99374150-99774150</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99574024</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99573883</td>\n",
+       "      <td>99574416</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446079</th>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "      <td>Interval_238231_L1</td>\n",
+       "      <td>chr9_99574121_T_A</td>\n",
+       "      <td>rs11794098</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.098729</td>\n",
+       "      <td>1.468140e-16</td>\n",
+       "      <td>1.098210</td>\n",
+       "      <td>0.104363</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.921499</td>\n",
+       "      <td>chr9:99374150-99774150</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99574121</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99573883</td>\n",
+       "      <td>99574416</td>\n",
+       "      <td>Interval_238231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>446082</th>\n",
+       "      <td>Interval_238255</td>\n",
+       "      <td>Interval_238255</td>\n",
+       "      <td>Interval_238255_L1</td>\n",
+       "      <td>chr9_99834039_G_A</td>\n",
+       "      <td>rs2900223</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.401042</td>\n",
+       "      <td>2.143160e-11</td>\n",
+       "      <td>0.941993</td>\n",
+       "      <td>0.120278</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.974002</td>\n",
+       "      <td>chr9:99634216-100034216</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99834039</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99833646</td>\n",
+       "      <td>99834785</td>\n",
+       "      <td>Interval_238255</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20479 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       molecular_trait_id          gene_id               cs_id  \\\n",
+       "44          Interval_7046    Interval_7046    Interval_7046_L1   \n",
+       "47          Interval_7047    Interval_7047    Interval_7047_L1   \n",
+       "170         Interval_7072    Interval_7072    Interval_7072_L1   \n",
+       "172         Interval_7072    Interval_7072    Interval_7072_L1   \n",
+       "174         Interval_7072    Interval_7072    Interval_7072_L1   \n",
+       "...                   ...              ...                 ...   \n",
+       "446064    Interval_238230  Interval_238230  Interval_238230_L1   \n",
+       "446066    Interval_238230  Interval_238230  Interval_238230_L1   \n",
+       "446077    Interval_238231  Interval_238231  Interval_238231_L1   \n",
+       "446079    Interval_238231  Interval_238231  Interval_238231_L1   \n",
+       "446082    Interval_238255  Interval_238255  Interval_238255_L1   \n",
+       "\n",
+       "                   variant        rsid  cs_size       pip        pvalue  \\\n",
+       "44      chr1_100047012_G_A    rs554742       66  0.019619  6.170090e-09   \n",
+       "47      chr1_100050364_C_T    rs581097       80  0.019667  1.091410e-10   \n",
+       "170     chr1_100364923_T_C  rs12085272       32  0.109903  2.862930e-08   \n",
+       "172     chr1_100365383_G_A  rs28361193       32  0.109903  2.862930e-08   \n",
+       "174     chr1_100365547_C_T   rs7551111       32  0.109903  2.862930e-08   \n",
+       "...                    ...         ...      ...       ...           ...   \n",
+       "446064   chr9_99569991_T_G  rs10988864       17  0.151737  1.222270e-15   \n",
+       "446066   chr9_99571495_G_A  rs10819686       17  0.151737  1.222270e-15   \n",
+       "446077   chr9_99574024_C_A  rs10120175       25  0.098729  1.468140e-16   \n",
+       "446079   chr9_99574121_T_A  rs11794098       25  0.098729  1.468140e-16   \n",
+       "446082   chr9_99834039_G_A   rs2900223        3  0.401042  2.143160e-11   \n",
+       "\n",
+       "            beta        se  ...  cs_min_r2                    region   chr  \\\n",
+       "44     -0.614765  0.094044  ...   0.930934   chr1:99846814-100246814  chr1   \n",
+       "47     -1.036960  0.138943  ...   0.824131   chr1:99849456-100249456  chr1   \n",
+       "170    -0.819940  0.132737  ...   0.499912  chr1:100165228-100565228  chr1   \n",
+       "172    -0.819940  0.132737  ...   0.499912  chr1:100165228-100565228  chr1   \n",
+       "174    -0.819940  0.132737  ...   0.499912  chr1:100165228-100565228  chr1   \n",
+       "...          ...       ...  ...        ...                       ...   ...   \n",
+       "446064  1.166170  0.116173  ...   0.921079    chr9:99370478-99770478  chr9   \n",
+       "446066  1.166170  0.116173  ...   0.921079    chr9:99370478-99770478  chr9   \n",
+       "446077  1.098210  0.104363  ...   0.921499    chr9:99374150-99774150  chr9   \n",
+       "446079  1.098210  0.104363  ...   0.921499    chr9:99374150-99774150  chr9   \n",
+       "446082  0.941993  0.120278  ...   0.974002   chr9:99634216-100034216  chr9   \n",
+       "\n",
+       "              pos ref alt chromosome gene_start   gene_end     phenotype_id  \n",
+       "44      100047012   G   A       chr1  100046355  100047272    Interval_7046  \n",
+       "47      100050364   C   T       chr1  100048413  100050498    Interval_7047  \n",
+       "170     100364923   T   C       chr1  100364560  100365896    Interval_7072  \n",
+       "172     100365383   G   A       chr1  100364560  100365896    Interval_7072  \n",
+       "174     100365547   C   T       chr1  100364560  100365896    Interval_7072  \n",
+       "...           ...  ..  ..        ...        ...        ...              ...  \n",
+       "446064   99569991   T   G       chr9   99569299   99571657  Interval_238230  \n",
+       "446066   99571495   G   A       chr9   99569299   99571657  Interval_238230  \n",
+       "446077   99574024   C   A       chr9   99573883   99574416  Interval_238231  \n",
+       "446079   99574121   T   A       chr9   99573883   99574416  Interval_238231  \n",
+       "446082   99834039   G   A       chr9   99833646   99834785  Interval_238255  \n",
+       "\n",
+       "[20479 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_dataframe_cs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maindir=\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/AFGR_peaks/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "beds=[]\n",
+    "for file in os.listdir(maindir):\n",
+    "        peak_bed=maindir+file\n",
+    "        #print(peak_bed)\n",
+    "\n",
+    "        bed = pd.read_csv(peak_bed, sep=\"\\t\", header=None)\n",
+    "        \n",
+    "        beds.append(bed.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'distribution of peak numbers in individuals')"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(beds)\n",
+    "plt.xlabel(\"distribution of peak numbers in individuals\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maindir=\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/Kumasaka_peaks/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.00419 1734.14\n",
+      "1.00598 1350.88\n",
+      "1.00728 1598.17\n",
+      "1.00512 1753.85\n",
+      "1.00577 1877.15\n",
+      "1.00469 1601.48\n",
+      "1.01169 1836.61\n",
+      "1.00233 1681.27\n",
+      "1.00705 1457.42\n",
+      "1.00486 1649.68\n",
+      "1.00658 1646.65\n",
+      "1.00796 1722.62\n",
+      "1.00218 1548.92\n",
+      "1.00072 1763.57\n",
+      "1.00017 1719.11\n",
+      "1.00394 1762.41\n",
+      "1.00663 1428.21\n",
+      "1.00232 1883.21\n",
+      "1.00309 1689.62\n",
+      "1.00104 1822.03\n",
+      "1.00233 1803.08\n",
+      "1.00275 1738.02\n",
+      "1.0089 1757.59\n",
+      "1.00039 1703.21\n",
+      "1.00183 1943.97\n",
+      "1.00428 1868.7\n",
+      "1.00706 1749.02\n",
+      "1.00498 1752.51\n",
+      "1.00178 1772.96\n",
+      "1.00219 1461.13\n",
+      "1.00942 1596.44\n",
+      "1.00859 1773.86\n",
+      "1.0048 1153.32\n",
+      "1.00853 1748.41\n",
+      "1.00544 1936.77\n",
+      "1.00897 1771.97\n",
+      "1.00229 1798.15\n",
+      "1.00575 1708.68\n",
+      "1.00648 1709.01\n",
+      "1.00362 1547.62\n",
+      "1.00594 1752.96\n",
+      "1.00206 1418.84\n",
+      "1.00747 1898.87\n",
+      "1.00381 1747.32\n",
+      "1.00959 1841.26\n",
+      "1.00487 1561.88\n",
+      "1.00618 1799.48\n",
+      "1.00819 1318.43\n",
+      "1.00181 1990.2\n",
+      "1.00142 1786.38\n",
+      "1.0011 1576.59\n",
+      "1.00145 1730.47\n",
+      "1.00543 1811.55\n",
+      "1.01242 1693.81\n",
+      "1.00334 1788.99\n",
+      "1.00471 1592.44\n",
+      "1.00327 1629.4\n",
+      "1.00042 1517.79\n",
+      "1.00921 1602.57\n",
+      "1.01109 1745.25\n",
+      "1.00736 1651.13\n",
+      "1.00762 1693.27\n",
+      "1.0101 1634.97\n",
+      "1.00667 1713.66\n",
+      "1.00369 1507.39\n",
+      "1.00602 1717.49\n",
+      "1.00375 1505.5\n",
+      "1.00745 1693.28\n",
+      "1.01672 1668.8\n",
+      "1.00083 1767.99\n",
+      "1.00181 1902.65\n",
+      "1.01131 583.508\n",
+      "1.00982 1750.62\n",
+      "1.0032 1712.65\n",
+      "1.00481 1805.66\n",
+      "1.0017 1625.0\n",
+      "1.00243 1725.4\n",
+      "1.00662 1773.49\n",
+      "1.00427 1718.54\n",
+      "1.00559 1754.5\n",
+      "1.00888 1604.83\n",
+      "1.00333 1591.04\n",
+      "1.00683 1812.22\n",
+      "1.00234 1969.04\n",
+      "1.00639 1842.58\n",
+      "1.00246 1755.53\n",
+      "1.00013 1781.88\n",
+      "1.00914 1859.89\n",
+      "1.00616 2053.99\n",
+      "1.0042 759.547\n",
+      "1.00384 1864.7\n",
+      "1.00599 2379.61\n",
+      "1.00784 1921.16\n",
+      "1.00772 1809.75\n",
+      "1.00737 1917.23\n",
+      "1.00377 1750.25\n",
+      "1.00295 1941.43\n",
+      "1.00852 1749.21\n",
+      "1.00719 1204.84\n",
+      "1.00982 2282.62\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "beds=[]\n",
+    "for file in os.listdir(maindir):\n",
+    "        peak_bed=maindir+file\n",
+    "        #print(peak_bed)\n",
+    "\n",
+    "        bed = pd.read_csv(peak_bed, sep=\"\\t\", header=None)\n",
+    "        \n",
+    "        beds.append(bed.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'distribution of peak numbers in individuals')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(beds)\n",
+    "plt.xlabel(\"distribution of peak numbers in individuals\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maindir=\"/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/pooled_Gambian.formatted.peaks.bed\n",
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/pooled_Maasai.formatted.peaks.bed\n",
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/GM12878.ENCSR637XSC.formatted.peaks.bed\n",
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/pooled_Mende.formatted.peaks.bed\n",
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/GM12878.ENCSR095QNB.formatted.peaks.bed\n",
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/pooled_Luhya.formatted.peaks.bed\n",
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/pooled_Esan.formatted.peaks.bed\n",
+      "/oak/stanford/groups/akundaje/projects/african_omics/outs/10_15_2022_per_ancestry_scores/peaks/formatted/pooled_Yoruba.formatted.peaks.bed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "beds_afr=[]\n",
+    "names=[]\n",
+    "for file in os.listdir(maindir):\n",
+    "        peak_bed=maindir+file\n",
+    "        print(peak_bed)\n",
+    "        names.append(file.replace(\".formatted.peaks.bed\", \"\"))\n",
+    "        bed = pd.read_csv(peak_bed, sep=\"\\t\", header=None)\n",
+    "        beds_afr.append(bed.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Number of peaks')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(names, beds_afr)\n",
+    "plt.xticks(rotation=90, ha='right')\n",
+    "\n",
+    "plt.xlabel(\"ancestry group\")\n",
+    "plt.ylabel(\"Number of peaks\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_microglia_caqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_microglia_caqtls.ipynb
new file mode 100644
index 00000000..010154c2
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_microglia_caqtls.ipynb
@@ -0,0 +1,933 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta=pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/microglia_caqtls/feb11_2024/caqtls_microglia_formatted.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  CHROM          POS non-effect_allele effect_allele Top_SNP_perPeak  \\\n",
+      "0  chr2  199884843.0                 A             G        rs281785   \n",
+      "1  chr2  200357680.0                 A             T       rs2033804   \n",
+      "2  chr2  200656660.0                 T             C       rs2287012   \n",
+      "3  chr1  172585461.0                 A             G      rs10752994   \n",
+      "4  chr1  172625478.0                 C             T      rs12123755   \n",
+      "\n",
+      "   Z_score_fixed  \n",
+      "0       10.11000  \n",
+      "1       -3.48053  \n",
+      "2       -5.07707  \n",
+      "3      -11.41800  \n",
+      "4        8.27532  \n"
+     ]
+    }
+   ],
+   "source": [
+    "meta = meta[[\"CHROM\", \"POS\", \"non-effect_allele\", \"effect_allele\", \"Top_SNP_perPeak\", \"Z_score_fixed\"]]\n",
+    "print(meta.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks=pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/microglia_caqtls/feb11_2024/PeakInfoDetailed_210832_OCRs.csv\", sep=\",\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Chr   start     end   PeakID  distanceToTSS annotationSimple  \\\n",
+      "0  chr1  817105  817588  Peak_13            0.0         Promoter   \n",
+      "1  chr1  819868  820628  Peak_14        -2157.0         Promoter   \n",
+      "2  chr1  820873  821328  Peak_15        -3162.0           Intron   \n",
+      "3  chr1  826886  828002  Peak_16            0.0         Promoter   \n",
+      "4  chr1  869728  870232  Peak_17            0.0         Promoter   \n",
+      "\n",
+      "                                          annotation Associated.Gene.Name  \\\n",
+      "0                                   Promoter (<=1kb)               FAM87B   \n",
+      "1                                   Promoter (2-3kb)           AL669831.4   \n",
+      "2  Intron (ENST00000635509/ENSG00000230021, intro...           AL669831.4   \n",
+      "3                                   Promoter (<=1kb)            LINC00115   \n",
+      "4                                   Promoter (<=1kb)               FAM41C   \n",
+      "\n",
+      "                            Gene.type  \n",
+      "0                             lincRNA  \n",
+      "1  transcribed_unprocessed_pseudogene  \n",
+      "2  transcribed_unprocessed_pseudogene  \n",
+      "3                             lincRNA  \n",
+      "4                             lincRNA  \n",
+      "(210832, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(peaks.head())\n",
+    "print(peaks.shape)\n",
+    "#peaks[\"start\"] = ((peaks[\"start\"]+peaks[\"end\"]) // 2) - 100\n",
+    "#peaks[\"end\"] = peaks[\"start\"]  + 200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4978, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "preds = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/microglia_scatac/ATAC_new/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "preds[\"sign.jsd.mean\"] = preds[\"jsd.mean\"]*np.sign(preds[\"logfc.mean\"])\n",
+    "preds = preds[[\"chr\", \"pos\", \"rsid\",\"allele1\", \"allele2\", \"logfc.mean\",  \"sign.jsd.mean\"]]\n",
+    "preds.head()\n",
+    "print(preds.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4979, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(meta.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4978, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "meta = meta[~np.isnan(meta[\"POS\"])]\n",
+    "print(meta.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4978, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "meta[\"POS\"] = meta[\"POS\"].astype(int) \n",
+    "preds[\"pos\"] = preds[\"pos\"].astype(int) \n",
+    "final = meta.merge(preds, left_on=[\"CHROM\", \"POS\", \"effect_allele\", \"non-effect_allele\"], right_on=[\"chr\", \"pos\", \"allele1\", \"allele2\"]).drop_duplicates()\n",
+    "print(final.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHROM</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>non-effect_allele</th>\n",
+       "      <th>effect_allele</th>\n",
+       "      <th>Top_SNP_perPeak</th>\n",
+       "      <th>Z_score_fixed</th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign.jsd.mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>199884843</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs281785</td>\n",
+       "      <td>10.11000</td>\n",
+       "      <td>chr2</td>\n",
+       "      <td>199884843</td>\n",
+       "      <td>chr2_199884843.0_G_A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.032774</td>\n",
+       "      <td>0.016354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200357680</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs2033804</td>\n",
+       "      <td>-3.48053</td>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200357680</td>\n",
+       "      <td>chr2_200357680.0_T_A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.008075</td>\n",
+       "      <td>-0.009565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200656660</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs2287012</td>\n",
+       "      <td>-5.07707</td>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200656660</td>\n",
+       "      <td>chr2_200656660.0_C_T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.003838</td>\n",
+       "      <td>0.004759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172585461</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs10752994</td>\n",
+       "      <td>-11.41800</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172585461</td>\n",
+       "      <td>chr1_172585461.0_G_A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.005939</td>\n",
+       "      <td>0.004529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172625478</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12123755</td>\n",
+       "      <td>8.27532</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172625478</td>\n",
+       "      <td>chr1_172625478.0_T_C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-0.441283</td>\n",
+       "      <td>-0.047415</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  CHROM        POS non-effect_allele effect_allele Top_SNP_perPeak  \\\n",
+       "0  chr2  199884843                 A             G        rs281785   \n",
+       "1  chr2  200357680                 A             T       rs2033804   \n",
+       "2  chr2  200656660                 T             C       rs2287012   \n",
+       "3  chr1  172585461                 A             G      rs10752994   \n",
+       "4  chr1  172625478                 C             T      rs12123755   \n",
+       "\n",
+       "   Z_score_fixed   chr        pos                  rsid allele1 allele2  \\\n",
+       "0       10.11000  chr2  199884843  chr2_199884843.0_G_A       G       A   \n",
+       "1       -3.48053  chr2  200357680  chr2_200357680.0_T_A       T       A   \n",
+       "2       -5.07707  chr2  200656660  chr2_200656660.0_C_T       C       T   \n",
+       "3      -11.41800  chr1  172585461  chr1_172585461.0_G_A       G       A   \n",
+       "4        8.27532  chr1  172625478  chr1_172625478.0_T_C       T       C   \n",
+       "\n",
+       "   logfc.mean  sign.jsd.mean  \n",
+       "0    0.032774       0.016354  \n",
+       "1   -0.008075      -0.009565  \n",
+       "2    0.003838       0.004759  \n",
+       "3    0.005939       0.004529  \n",
+       "4   -0.441283      -0.047415  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['CHROM', 'POS', 'non-effect_allele', 'effect_allele', 'Top_SNP_perPeak',\n",
+       "       'Z_score_fixed', 'chr', 'pos', 'rsid', 'allele1', 'allele2',\n",
+       "       'logfc.mean', 'sign.jsd.mean'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = ['CHROM', \"POS0\", 'POS', 'non-effect_allele', 'effect_allele', 'Top_SNP_perPeak',\n",
+    "       'Z_score_fixed', 'logfc.mean', 'sign.jsd.mean']\n",
+    "final[\"POS0\"] = final[\"POS\"] - 1\n",
+    "finalf = final[cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>PeakID</th>\n",
+       "      <th>distanceToTSS</th>\n",
+       "      <th>annotationSimple</th>\n",
+       "      <th>annotation</th>\n",
+       "      <th>Associated.Gene.Name</th>\n",
+       "      <th>Gene.type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>817105</td>\n",
+       "      <td>817588</td>\n",
+       "      <td>Peak_13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Promoter</td>\n",
+       "      <td>Promoter (&lt;=1kb)</td>\n",
+       "      <td>FAM87B</td>\n",
+       "      <td>lincRNA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>819868</td>\n",
+       "      <td>820628</td>\n",
+       "      <td>Peak_14</td>\n",
+       "      <td>-2157.0</td>\n",
+       "      <td>Promoter</td>\n",
+       "      <td>Promoter (2-3kb)</td>\n",
+       "      <td>AL669831.4</td>\n",
+       "      <td>transcribed_unprocessed_pseudogene</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>820873</td>\n",
+       "      <td>821328</td>\n",
+       "      <td>Peak_15</td>\n",
+       "      <td>-3162.0</td>\n",
+       "      <td>Intron</td>\n",
+       "      <td>Intron (ENST00000635509/ENSG00000230021, intro...</td>\n",
+       "      <td>AL669831.4</td>\n",
+       "      <td>transcribed_unprocessed_pseudogene</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>826886</td>\n",
+       "      <td>828002</td>\n",
+       "      <td>Peak_16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Promoter</td>\n",
+       "      <td>Promoter (&lt;=1kb)</td>\n",
+       "      <td>LINC00115</td>\n",
+       "      <td>lincRNA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>869728</td>\n",
+       "      <td>870232</td>\n",
+       "      <td>Peak_17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Promoter</td>\n",
+       "      <td>Promoter (&lt;=1kb)</td>\n",
+       "      <td>FAM41C</td>\n",
+       "      <td>lincRNA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210827</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>21439802</td>\n",
+       "      <td>21440929</td>\n",
+       "      <td>Peak_215075</td>\n",
+       "      <td>-2558.0</td>\n",
+       "      <td>Promoter</td>\n",
+       "      <td>Promoter (2-3kb)</td>\n",
+       "      <td>RBMY2TP</td>\n",
+       "      <td>unprocessed_pseudogene</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210828</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>21481590</td>\n",
+       "      <td>21481950</td>\n",
+       "      <td>Peak_215076</td>\n",
+       "      <td>-11569.0</td>\n",
+       "      <td>Distal Intergenic</td>\n",
+       "      <td>Distal Intergenic</td>\n",
+       "      <td>RBMY1HP</td>\n",
+       "      <td>unprocessed_pseudogene</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210829</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>26403079</td>\n",
+       "      <td>26403631</td>\n",
+       "      <td>Peak_215077</td>\n",
+       "      <td>2469.0</td>\n",
+       "      <td>Promoter</td>\n",
+       "      <td>Promoter (2-3kb)</td>\n",
+       "      <td>CYCSP48</td>\n",
+       "      <td>processed_pseudogene</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210830</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>26436428</td>\n",
+       "      <td>26436771</td>\n",
+       "      <td>Peak_215078</td>\n",
+       "      <td>-17211.0</td>\n",
+       "      <td>Distal Intergenic</td>\n",
+       "      <td>Distal Intergenic</td>\n",
+       "      <td>AC013734.1</td>\n",
+       "      <td>processed_pseudogene</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210831</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>26440922</td>\n",
+       "      <td>26441463</td>\n",
+       "      <td>Peak_215079</td>\n",
+       "      <td>-12519.0</td>\n",
+       "      <td>Distal Intergenic</td>\n",
+       "      <td>Distal Intergenic</td>\n",
+       "      <td>AC013734.1</td>\n",
+       "      <td>processed_pseudogene</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>210832 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Chr     start       end       PeakID  distanceToTSS  \\\n",
+       "0       chr1    817105    817588      Peak_13            0.0   \n",
+       "1       chr1    819868    820628      Peak_14        -2157.0   \n",
+       "2       chr1    820873    821328      Peak_15        -3162.0   \n",
+       "3       chr1    826886    828002      Peak_16            0.0   \n",
+       "4       chr1    869728    870232      Peak_17            0.0   \n",
+       "...      ...       ...       ...          ...            ...   \n",
+       "210827  chrY  21439802  21440929  Peak_215075        -2558.0   \n",
+       "210828  chrY  21481590  21481950  Peak_215076       -11569.0   \n",
+       "210829  chrY  26403079  26403631  Peak_215077         2469.0   \n",
+       "210830  chrY  26436428  26436771  Peak_215078       -17211.0   \n",
+       "210831  chrY  26440922  26441463  Peak_215079       -12519.0   \n",
+       "\n",
+       "         annotationSimple                                         annotation  \\\n",
+       "0                Promoter                                   Promoter (<=1kb)   \n",
+       "1                Promoter                                   Promoter (2-3kb)   \n",
+       "2                  Intron  Intron (ENST00000635509/ENSG00000230021, intro...   \n",
+       "3                Promoter                                   Promoter (<=1kb)   \n",
+       "4                Promoter                                   Promoter (<=1kb)   \n",
+       "...                   ...                                                ...   \n",
+       "210827           Promoter                                   Promoter (2-3kb)   \n",
+       "210828  Distal Intergenic                                  Distal Intergenic   \n",
+       "210829           Promoter                                   Promoter (2-3kb)   \n",
+       "210830  Distal Intergenic                                  Distal Intergenic   \n",
+       "210831  Distal Intergenic                                  Distal Intergenic   \n",
+       "\n",
+       "       Associated.Gene.Name                           Gene.type  \n",
+       "0                    FAM87B                             lincRNA  \n",
+       "1                AL669831.4  transcribed_unprocessed_pseudogene  \n",
+       "2                AL669831.4  transcribed_unprocessed_pseudogene  \n",
+       "3                 LINC00115                             lincRNA  \n",
+       "4                    FAM41C                             lincRNA  \n",
+       "...                     ...                                 ...  \n",
+       "210827              RBMY2TP              unprocessed_pseudogene  \n",
+       "210828              RBMY1HP              unprocessed_pseudogene  \n",
+       "210829              CYCSP48                processed_pseudogene  \n",
+       "210830           AC013734.1                processed_pseudogene  \n",
+       "210831           AC013734.1                processed_pseudogene  \n",
+       "\n",
+       "[210832 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybedtools\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(finalf)\n",
+    "peak_bed =  pybedtools.BedTool.from_dataframe(peaks)\n",
+    "sig_vars_in_bed = beds_var.intersect(peak_bed, c=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed = sig_vars_in_bed.to_dataframe(names=cols+[\"IsUsed\"]).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHROM</th>\n",
+       "      <th>POS0</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>non-effect_allele</th>\n",
+       "      <th>effect_allele</th>\n",
+       "      <th>Top_SNP_perPeak</th>\n",
+       "      <th>Z_score_fixed</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign.jsd.mean</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>199884842</td>\n",
+       "      <td>199884843</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs281785</td>\n",
+       "      <td>10.11000</td>\n",
+       "      <td>0.032774</td>\n",
+       "      <td>0.016354</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200357679</td>\n",
+       "      <td>200357680</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs2033804</td>\n",
+       "      <td>-3.48053</td>\n",
+       "      <td>-0.008075</td>\n",
+       "      <td>-0.009565</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200656659</td>\n",
+       "      <td>200656660</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs2287012</td>\n",
+       "      <td>-5.07707</td>\n",
+       "      <td>0.003838</td>\n",
+       "      <td>0.004759</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172585460</td>\n",
+       "      <td>172585461</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs10752994</td>\n",
+       "      <td>-11.41800</td>\n",
+       "      <td>0.005939</td>\n",
+       "      <td>0.004529</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172625477</td>\n",
+       "      <td>172625478</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12123755</td>\n",
+       "      <td>8.27532</td>\n",
+       "      <td>-0.441283</td>\n",
+       "      <td>-0.047415</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  CHROM       POS0        POS non-effect_allele effect_allele Top_SNP_perPeak  \\\n",
+       "0  chr2  199884842  199884843                 A             G        rs281785   \n",
+       "1  chr2  200357679  200357680                 A             T       rs2033804   \n",
+       "2  chr2  200656659  200656660                 T             C       rs2287012   \n",
+       "3  chr1  172585460  172585461                 A             G      rs10752994   \n",
+       "4  chr1  172625477  172625478                 C             T      rs12123755   \n",
+       "\n",
+       "   Z_score_fixed  logfc.mean  sign.jsd.mean  IsUsed  \n",
+       "0       10.11000    0.032774       0.016354       0  \n",
+       "1       -3.48053   -0.008075      -0.009565       0  \n",
+       "2       -5.07707    0.003838       0.004759       0  \n",
+       "3      -11.41800    0.005939       0.004529       0  \n",
+       "4        8.27532   -0.441283      -0.047415       1  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed[\"IsUsed\"] = sig_vars_in_bed[\"IsUsed\"] > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "956"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sig_vars_in_bed[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4978, 10)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed[\"logfc.mean\"] = -1*sig_vars_in_bed[\"logfc.mean\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed[\"sign.jsd.mean\"] = -1*sig_vars_in_bed[\"sign.jsd.mean\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed.to_csv(\"../final_figures_new/microglia.caqtls.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fc4e7472350>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df=sig_vars_in_bed[sig_vars_in_bed[\"IsUsed\"]]\n",
+    "plt.scatter(df[\"Z_score_fixed\"], df[\"logfc.mean\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6123604665289462, 1.836191629240661e-99)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "scipy.stats.pearsonr(df[\"Z_score_fixed\"], df[\"logfc.mean\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_smc_caqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_smc_caqtls.ipynb
new file mode 100644
index 00000000..35807b5b
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_smc_caqtls.ipynb
@@ -0,0 +1,1914 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig = pd.read_csv(\"/oak/stanford/groups/akundaje/projects/cad/data/clint_data/rasqual_results/revisions/smc/SMC_rasqual_results_FDR_0.05_083121.txt\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Feature_ID</th>\n",
+       "      <th>rsID</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Freq</th>\n",
+       "      <th>HWE_Chisquare</th>\n",
+       "      <th>Imp_quality</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Distance to TSS</th>\n",
+       "      <th>Nearest PromoterID</th>\n",
+       "      <th>Entrez ID</th>\n",
+       "      <th>Nearest Unigene</th>\n",
+       "      <th>Nearest Refseq</th>\n",
+       "      <th>Nearest Ensembl</th>\n",
+       "      <th>Gene Name</th>\n",
+       "      <th>Gene Alias</th>\n",
+       "      <th>Gene Description</th>\n",
+       "      <th>Gene Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1.1069225.1069626</td>\n",
+       "      <td>rs74048003</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.2000</td>\n",
+       "      <td>0.156250</td>\n",
+       "      <td>0.999916</td>\n",
+       "      <td>-4.920961</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4881</td>\n",
+       "      <td>NM_001205252</td>\n",
+       "      <td>401934.0</td>\n",
+       "      <td>Hs.568137</td>\n",
+       "      <td>NM_001205252</td>\n",
+       "      <td>ENSG00000237330</td>\n",
+       "      <td>RNF223</td>\n",
+       "      <td>-</td>\n",
+       "      <td>ring finger protein 223</td>\n",
+       "      <td>protein-coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1.108118484.108118885</td>\n",
+       "      <td>rs665659</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.7250</td>\n",
+       "      <td>0.000393</td>\n",
+       "      <td>0.999935</td>\n",
+       "      <td>-4.118572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>74124</td>\n",
+       "      <td>NM_213651</td>\n",
+       "      <td>29957.0</td>\n",
+       "      <td>Hs.656870</td>\n",
+       "      <td>NM_013386</td>\n",
+       "      <td>ENSG00000085491</td>\n",
+       "      <td>SLC25A24</td>\n",
+       "      <td>APC1|SCAMC-1|SCAMC1</td>\n",
+       "      <td>solute carrier family 25 member 24</td>\n",
+       "      <td>protein-coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1.108721337.108721738</td>\n",
+       "      <td>rs4970806</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108721447</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.6625</td>\n",
+       "      <td>5.930399</td>\n",
+       "      <td>0.999939</td>\n",
+       "      <td>-9.266900</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8604</td>\n",
+       "      <td>NM_001144937</td>\n",
+       "      <td>163479.0</td>\n",
+       "      <td>Hs.258253</td>\n",
+       "      <td>NM_173532</td>\n",
+       "      <td>ENSG00000143107</td>\n",
+       "      <td>FNDC7</td>\n",
+       "      <td>-</td>\n",
+       "      <td>fibronectin type III domain containing 7</td>\n",
+       "      <td>protein-coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1.108841698.108842099</td>\n",
+       "      <td>rs34272714</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108842308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.1375</td>\n",
+       "      <td>1.016593</td>\n",
+       "      <td>0.999885</td>\n",
+       "      <td>-4.721082</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15205</td>\n",
+       "      <td>NM_152763</td>\n",
+       "      <td>254268.0</td>\n",
+       "      <td>Hs.729441</td>\n",
+       "      <td>NM_152763</td>\n",
+       "      <td>ENSG00000162641</td>\n",
+       "      <td>AKNAD1</td>\n",
+       "      <td>C1orf62</td>\n",
+       "      <td>AKNA domain containing 1</td>\n",
+       "      <td>protein-coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1.109274794.109275195</td>\n",
+       "      <td>rs611917</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109272630</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>5.566376</td>\n",
+       "      <td>0.999931</td>\n",
+       "      <td>-3.006430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8149</td>\n",
+       "      <td>NM_001350237</td>\n",
+       "      <td>84722.0</td>\n",
+       "      <td>Hs.405925</td>\n",
+       "      <td>NM_032636</td>\n",
+       "      <td>ENSG00000134222</td>\n",
+       "      <td>PSRC1</td>\n",
+       "      <td>DDA3|FP3214</td>\n",
+       "      <td>proline and serine rich coiled-coil 1</td>\n",
+       "      <td>protein-coding</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Feature_ID        rsID Chromosome  SNP_position Ref_allele  \\\n",
+       "0      chr1.1069225.1069626  rs74048003       chr1       1067007          A   \n",
+       "1  chr1.108118484.108118885    rs665659       chr1     108121219          C   \n",
+       "2  chr1.108721337.108721738   rs4970806       chr1     108721447          C   \n",
+       "3  chr1.108841698.108842099  rs34272714       chr1     108842308          G   \n",
+       "4  chr1.109274794.109275195    rs611917       chr1     109272630          A   \n",
+       "\n",
+       "  Alt_allele    Freq  HWE_Chisquare  Imp_quality  Log10_BH_Q  ...  \\\n",
+       "0          G  0.2000       0.156250     0.999916   -4.920961  ...   \n",
+       "1          G  0.7250       0.000393     0.999935   -4.118572  ...   \n",
+       "2          T  0.6625       5.930399     0.999939   -9.266900  ...   \n",
+       "3          C  0.1375       1.016593     0.999885   -4.721082  ...   \n",
+       "4          G  0.2750       5.566376     0.999931   -3.006430  ...   \n",
+       "\n",
+       "   Distance to TSS  Nearest PromoterID  Entrez ID  Nearest Unigene  \\\n",
+       "0             4881        NM_001205252   401934.0        Hs.568137   \n",
+       "1            74124           NM_213651    29957.0        Hs.656870   \n",
+       "2             8604        NM_001144937   163479.0        Hs.258253   \n",
+       "3            15205           NM_152763   254268.0        Hs.729441   \n",
+       "4             8149        NM_001350237    84722.0        Hs.405925   \n",
+       "\n",
+       "   Nearest Refseq  Nearest Ensembl  Gene Name           Gene Alias  \\\n",
+       "0    NM_001205252  ENSG00000237330     RNF223                    -   \n",
+       "1       NM_013386  ENSG00000085491   SLC25A24  APC1|SCAMC-1|SCAMC1   \n",
+       "2       NM_173532  ENSG00000143107      FNDC7                    -   \n",
+       "3       NM_152763  ENSG00000162641     AKNAD1              C1orf62   \n",
+       "4       NM_032636  ENSG00000134222      PSRC1          DDA3|FP3214   \n",
+       "\n",
+       "                           Gene Description       Gene Type  \n",
+       "0                   ring finger protein 223  protein-coding  \n",
+       "1        solute carrier family 25 member 24  protein-coding  \n",
+       "2  fibronectin type III domain containing 7  protein-coding  \n",
+       "3                  AKNA domain containing 1  protein-coding  \n",
+       "4     proline and serine rich coiled-coil 1  protein-coding  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Feature_ID', 'rsID', 'Chromosome', 'SNP_position', 'Ref_allele',\n",
+       "       'Alt_allele', 'Freq', 'HWE_Chisquare', 'Imp_quality', 'Log10_BH_Q',\n",
+       "       'Chisquare', 'Effect_size', 'Delta', 'Phi', 'Overdispersion',\n",
+       "       'SNP_id_region', 'Num_feature_SNPs', 'Num_tested_SNPs',\n",
+       "       'Num_iterations_null', 'Num_iterations_alt', 'Random_ties',\n",
+       "       'Log_likelihood_null', 'Convergence_status', 'Sq_corr_fSNPs',\n",
+       "       'Sq_corr_rSNP', 'P', 'q', 'keep', 'PeakID', 'Chr', 'Start', 'End',\n",
+       "       'Strand', 'Peak Score', 'Focus Ratio/Region Size', 'Annotation',\n",
+       "       'Detailed Annotation', 'Distance to TSS', 'Nearest PromoterID',\n",
+       "       'Entrez ID', 'Nearest Unigene', 'Nearest Refseq', 'Nearest Ensembl',\n",
+       "       'Gene Name', 'Gene Alias', 'Gene Description', 'Gene Type'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/smc_caqtls/ATAC_sig_only_new/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1984, 23)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "preds[\"jsd.sign.mean\"] = np.sign(preds[\"logfc.mean\"])* preds[\"jsd.mean\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>abs_logfc.mean</th>\n",
+       "      <th>abs_logfc.mean.pval</th>\n",
+       "      <th>jsd.mean</th>\n",
+       "      <th>jsd.mean.pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>max_percentile.mean.pval</th>\n",
+       "      <th>logfc_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean.pval</th>\n",
+       "      <th>jsd_x_max_percentile.mean</th>\n",
+       "      <th>jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>jsd.sign.mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1.1069225.1069626,rs74048003,chr1,1067007,A...</td>\n",
+       "      <td>0.022735</td>\n",
+       "      <td>0.027306</td>\n",
+       "      <td>0.525433</td>\n",
+       "      <td>0.008771</td>\n",
+       "      <td>0.578309</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.071857</td>\n",
+       "      <td>0.015619</td>\n",
+       "      <td>0.019011</td>\n",
+       "      <td>0.247607</td>\n",
+       "      <td>0.005870</td>\n",
+       "      <td>0.175994</td>\n",
+       "      <td>0.000209</td>\n",
+       "      <td>0.302285</td>\n",
+       "      <td>0.000184</td>\n",
+       "      <td>0.008771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1.108118484.108118885,rs665659,chr1,1081212...</td>\n",
+       "      <td>0.021728</td>\n",
+       "      <td>0.026806</td>\n",
+       "      <td>0.533548</td>\n",
+       "      <td>0.009595</td>\n",
+       "      <td>0.513887</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.097398</td>\n",
+       "      <td>0.013720</td>\n",
+       "      <td>0.016709</td>\n",
+       "      <td>0.288160</td>\n",
+       "      <td>0.005725</td>\n",
+       "      <td>0.178482</td>\n",
+       "      <td>0.000164</td>\n",
+       "      <td>0.334105</td>\n",
+       "      <td>0.000141</td>\n",
+       "      <td>0.009595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108721447</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1.108721337.108721738,rs4970806,chr1,108721...</td>\n",
+       "      <td>0.289341</td>\n",
+       "      <td>0.289341</td>\n",
+       "      <td>0.034743</td>\n",
+       "      <td>0.031146</td>\n",
+       "      <td>0.047851</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.138296</td>\n",
+       "      <td>0.148480</td>\n",
+       "      <td>0.148480</td>\n",
+       "      <td>0.018484</td>\n",
+       "      <td>0.015681</td>\n",
+       "      <td>0.031848</td>\n",
+       "      <td>0.004771</td>\n",
+       "      <td>0.023252</td>\n",
+       "      <td>0.004771</td>\n",
+       "      <td>0.031146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108842308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1.108841698.108842099,rs34272714,chr1,10884...</td>\n",
+       "      <td>-0.293381</td>\n",
+       "      <td>0.293381</td>\n",
+       "      <td>0.031649</td>\n",
+       "      <td>0.041953</td>\n",
+       "      <td>0.023738</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.193577</td>\n",
+       "      <td>-0.127705</td>\n",
+       "      <td>0.127705</td>\n",
+       "      <td>0.023055</td>\n",
+       "      <td>0.018106</td>\n",
+       "      <td>0.024531</td>\n",
+       "      <td>0.005598</td>\n",
+       "      <td>0.019474</td>\n",
+       "      <td>-0.005598</td>\n",
+       "      <td>-0.041953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109272630</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1.109274794.109275195,rs611917,chr1,1092726...</td>\n",
+       "      <td>0.100375</td>\n",
+       "      <td>0.100375</td>\n",
+       "      <td>0.171206</td>\n",
+       "      <td>0.016716</td>\n",
+       "      <td>0.188809</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.139277</td>\n",
+       "      <td>0.051331</td>\n",
+       "      <td>0.051331</td>\n",
+       "      <td>0.086704</td>\n",
+       "      <td>0.008469</td>\n",
+       "      <td>0.103948</td>\n",
+       "      <td>0.000975</td>\n",
+       "      <td>0.102504</td>\n",
+       "      <td>0.000975</td>\n",
+       "      <td>0.016716</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr        pos allele1 allele2  \\\n",
+       "0  chr1    1067007       A       G   \n",
+       "1  chr1  108121219       C       G   \n",
+       "2  chr1  108721447       C       T   \n",
+       "3  chr1  108842308       G       C   \n",
+       "4  chr1  109272630       A       G   \n",
+       "\n",
+       "                                                rsid  logfc.mean  \\\n",
+       "0  chr1.1069225.1069626,rs74048003,chr1,1067007,A...    0.022735   \n",
+       "1  chr1.108118484.108118885,rs665659,chr1,1081212...    0.021728   \n",
+       "2  chr1.108721337.108721738,rs4970806,chr1,108721...    0.289341   \n",
+       "3  chr1.108841698.108842099,rs34272714,chr1,10884...   -0.293381   \n",
+       "4  chr1.109274794.109275195,rs611917,chr1,1092726...    0.100375   \n",
+       "\n",
+       "   abs_logfc.mean  abs_logfc.mean.pval  jsd.mean  jsd.mean.pval  ...  \\\n",
+       "0        0.027306             0.525433  0.008771       0.578309  ...   \n",
+       "1        0.026806             0.533548  0.009595       0.513887  ...   \n",
+       "2        0.289341             0.034743  0.031146       0.047851  ...   \n",
+       "3        0.293381             0.031649  0.041953       0.023738  ...   \n",
+       "4        0.100375             0.171206  0.016716       0.188809  ...   \n",
+       "\n",
+       "   max_percentile.mean.pval  logfc_x_max_percentile.mean  \\\n",
+       "0                  0.071857                     0.015619   \n",
+       "1                  0.097398                     0.013720   \n",
+       "2                  0.138296                     0.148480   \n",
+       "3                  0.193577                    -0.127705   \n",
+       "4                  0.139277                     0.051331   \n",
+       "\n",
+       "   abs_logfc_x_max_percentile.mean  abs_logfc_x_max_percentile.mean.pval  \\\n",
+       "0                         0.019011                              0.247607   \n",
+       "1                         0.016709                              0.288160   \n",
+       "2                         0.148480                              0.018484   \n",
+       "3                         0.127705                              0.023055   \n",
+       "4                         0.051331                              0.086704   \n",
+       "\n",
+       "   jsd_x_max_percentile.mean  jsd_x_max_percentile.mean.pval  \\\n",
+       "0                   0.005870                        0.175994   \n",
+       "1                   0.005725                        0.178482   \n",
+       "2                   0.015681                        0.031848   \n",
+       "3                   0.018106                        0.024531   \n",
+       "4                   0.008469                        0.103948   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                               0.000209   \n",
+       "1                               0.000164   \n",
+       "2                               0.004771   \n",
+       "3                               0.005598   \n",
+       "4                               0.000975   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                    0.302285   \n",
+       "1                                    0.334105   \n",
+       "2                                    0.023252   \n",
+       "3                                    0.019474   \n",
+       "4                                    0.102504   \n",
+       "\n",
+       "   logfc_x_jsd_x_max_percentile.mean  jsd.sign.mean  \n",
+       "0                           0.000184       0.008771  \n",
+       "1                           0.000141       0.009595  \n",
+       "2                           0.004771       0.031146  \n",
+       "3                          -0.005598      -0.041953  \n",
+       "4                           0.000975       0.016716  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = preds[[\"chr\", \"pos\", \"allele1\", \"allele2\", \"logfc.mean\", \"jsd.sign.mean\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig = orig.merge(preds, left_on=['Chromosome', 'SNP_position', 'Ref_allele','Alt_allele'], right_on=[\"chr\", \"pos\", \"allele1\", \"allele2\"]).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Feature_ID', 'rsID', 'Chromosome', 'SNP_position', 'Ref_allele',\n",
+       "       'Alt_allele', 'Freq', 'HWE_Chisquare', 'Imp_quality', 'Log10_BH_Q',\n",
+       "       'Chisquare', 'Effect_size', 'Delta', 'Phi', 'Overdispersion',\n",
+       "       'SNP_id_region', 'Num_feature_SNPs', 'Num_tested_SNPs',\n",
+       "       'Num_iterations_null', 'Num_iterations_alt', 'Random_ties',\n",
+       "       'Log_likelihood_null', 'Convergence_status', 'Sq_corr_fSNPs',\n",
+       "       'Sq_corr_rSNP', 'P', 'q', 'keep', 'PeakID', 'Chr', 'Start', 'End',\n",
+       "       'Strand', 'Peak Score', 'Focus Ratio/Region Size', 'Annotation',\n",
+       "       'Detailed Annotation', 'Distance to TSS', 'Nearest PromoterID',\n",
+       "       'Entrez ID', 'Nearest Unigene', 'Nearest Refseq', 'Nearest Ensembl',\n",
+       "       'Gene Name', 'Gene Alias', 'Gene Description', 'Gene Type', 'chr',\n",
+       "       'pos', 'allele1', 'allele2', 'logfc.mean', 'jsd.sign.mean'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig = orig[['Chromosome', 'SNP_position', 'Ref_allele', 'Alt_allele', 'Log10_BH_Q', 'Effect_size', 'logfc.mean', 'jsd.sign.mean']]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_bed = pd.read_csv(\"/oak/stanford/groups/akundaje/projects/cad/outs/7_30_2023_human_all_models/peaks/original/SMC.peaks.bed\", sep=\"\\t\", header=None)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>624982</td>\n",
+       "      <td>SMC_m2PE_peak_1</td>\n",
+       "      <td>1978</td>\n",
+       "      <td>.</td>\n",
+       "      <td>8.30810</td>\n",
+       "      <td>200.48422</td>\n",
+       "      <td>197.87073</td>\n",
+       "      <td>181479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>639033</td>\n",
+       "      <td>773662</td>\n",
+       "      <td>SMC_m2PE_peak_2</td>\n",
+       "      <td>140</td>\n",
+       "      <td>.</td>\n",
+       "      <td>2.44222</td>\n",
+       "      <td>15.88614</td>\n",
+       "      <td>14.09891</td>\n",
+       "      <td>131441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>778301</td>\n",
+       "      <td>779293</td>\n",
+       "      <td>SMC_m2PE_peak_3</td>\n",
+       "      <td>13192</td>\n",
+       "      <td>.</td>\n",
+       "      <td>12.68989</td>\n",
+       "      <td>1324.53931</td>\n",
+       "      <td>1319.24121</td>\n",
+       "      <td>428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>783770</td>\n",
+       "      <td>822546</td>\n",
+       "      <td>SMC_m2PE_peak_4</td>\n",
+       "      <td>4017</td>\n",
+       "      <td>.</td>\n",
+       "      <td>10.95845</td>\n",
+       "      <td>404.78445</td>\n",
+       "      <td>401.77557</td>\n",
+       "      <td>33595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>826698</td>\n",
+       "      <td>827834</td>\n",
+       "      <td>SMC_m2PE_peak_5</td>\n",
+       "      <td>11889</td>\n",
+       "      <td>.</td>\n",
+       "      <td>14.98692</td>\n",
+       "      <td>1193.70923</td>\n",
+       "      <td>1188.91516</td>\n",
+       "      <td>827</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0       1       2                3      4  5         6           7  \\\n",
+       "0  chr1       2  624982  SMC_m2PE_peak_1   1978  .   8.30810   200.48422   \n",
+       "1  chr1  639033  773662  SMC_m2PE_peak_2    140  .   2.44222    15.88614   \n",
+       "2  chr1  778301  779293  SMC_m2PE_peak_3  13192  .  12.68989  1324.53931   \n",
+       "3  chr1  783770  822546  SMC_m2PE_peak_4   4017  .  10.95845   404.78445   \n",
+       "4  chr1  826698  827834  SMC_m2PE_peak_5  11889  .  14.98692  1193.70923   \n",
+       "\n",
+       "            8       9  \n",
+       "0   197.87073  181479  \n",
+       "1    14.09891  131441  \n",
+       "2  1319.24121     428  \n",
+       "3   401.77557   33595  \n",
+       "4  1188.91516     827  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks_bed.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>Effect_size</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>jsd.sign.mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067006</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.920961</td>\n",
+       "      <td>0.200715</td>\n",
+       "      <td>0.022735</td>\n",
+       "      <td>0.008771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121218</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.118572</td>\n",
+       "      <td>0.228169</td>\n",
+       "      <td>0.021728</td>\n",
+       "      <td>0.009595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108721446</td>\n",
+       "      <td>108721447</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-9.266900</td>\n",
+       "      <td>0.764114</td>\n",
+       "      <td>0.289341</td>\n",
+       "      <td>0.031146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108842307</td>\n",
+       "      <td>108842308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.721082</td>\n",
+       "      <td>0.242912</td>\n",
+       "      <td>-0.293381</td>\n",
+       "      <td>-0.041953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109272629</td>\n",
+       "      <td>109272630</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-3.006430</td>\n",
+       "      <td>0.709092</td>\n",
+       "      <td>0.100375</td>\n",
+       "      <td>0.016716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2033</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>96159156</td>\n",
+       "      <td>96159157</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-3.026381</td>\n",
+       "      <td>0.806026</td>\n",
+       "      <td>-0.104170</td>\n",
+       "      <td>-0.024419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2034</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>96178224</td>\n",
+       "      <td>96178225</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-4.596132</td>\n",
+       "      <td>0.787369</td>\n",
+       "      <td>0.016330</td>\n",
+       "      <td>0.007556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2035</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>97227899</td>\n",
+       "      <td>97227900</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-2.961709</td>\n",
+       "      <td>0.405945</td>\n",
+       "      <td>-0.000896</td>\n",
+       "      <td>-0.007822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2036</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>98873906</td>\n",
+       "      <td>98873907</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.182089</td>\n",
+       "      <td>0.608833</td>\n",
+       "      <td>-0.040235</td>\n",
+       "      <td>-0.008717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2037</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99444663</td>\n",
+       "      <td>99444664</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-4.616533</td>\n",
+       "      <td>0.279315</td>\n",
+       "      <td>0.060264</td>\n",
+       "      <td>0.012065</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1984 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chromosome       pos0  SNP_position Ref_allele Alt_allele  Log10_BH_Q  \\\n",
+       "0          chr1    1067006       1067007          A          G   -4.920961   \n",
+       "1          chr1  108121218     108121219          C          G   -4.118572   \n",
+       "2          chr1  108721446     108721447          C          T   -9.266900   \n",
+       "3          chr1  108842307     108842308          G          C   -4.721082   \n",
+       "4          chr1  109272629     109272630          A          G   -3.006430   \n",
+       "...         ...        ...           ...        ...        ...         ...   \n",
+       "2033       chr9   96159156      96159157          C          T   -3.026381   \n",
+       "2034       chr9   96178224      96178225          G          A   -4.596132   \n",
+       "2035       chr9   97227899      97227900          A          T   -2.961709   \n",
+       "2036       chr9   98873906      98873907          T          C   -4.182089   \n",
+       "2037       chr9   99444663      99444664          G          A   -4.616533   \n",
+       "\n",
+       "      Effect_size  logfc.mean  jsd.sign.mean  \n",
+       "0        0.200715    0.022735       0.008771  \n",
+       "1        0.228169    0.021728       0.009595  \n",
+       "2        0.764114    0.289341       0.031146  \n",
+       "3        0.242912   -0.293381      -0.041953  \n",
+       "4        0.709092    0.100375       0.016716  \n",
+       "...           ...         ...            ...  \n",
+       "2033     0.806026   -0.104170      -0.024419  \n",
+       "2034     0.787369    0.016330       0.007556  \n",
+       "2035     0.405945   -0.000896      -0.007822  \n",
+       "2036     0.608833   -0.040235      -0.008717  \n",
+       "2037     0.279315    0.060264       0.012065  \n",
+       "\n",
+       "[1984 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orig[\"pos0\"] = orig[\"SNP_position\"] - 1\n",
+    "selected_beds = orig[['Chromosome', \"pos0\", 'SNP_position', 'Ref_allele', 'Alt_allele', 'Log10_BH_Q', 'Effect_size', 'logfc.mean', 'jsd.sign.mean']]\n",
+    "\n",
+    "selected_beds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_bed[1] = peaks_bed[1] + peaks_bed[9] - 100\n",
+    "peaks_bed[2] = peaks_bed[1] + 200\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>181381</td>\n",
+       "      <td>181581</td>\n",
+       "      <td>SMC_m2PE_peak_1</td>\n",
+       "      <td>1978</td>\n",
+       "      <td>.</td>\n",
+       "      <td>8.30810</td>\n",
+       "      <td>200.48422</td>\n",
+       "      <td>197.87073</td>\n",
+       "      <td>181479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>770374</td>\n",
+       "      <td>770574</td>\n",
+       "      <td>SMC_m2PE_peak_2</td>\n",
+       "      <td>140</td>\n",
+       "      <td>.</td>\n",
+       "      <td>2.44222</td>\n",
+       "      <td>15.88614</td>\n",
+       "      <td>14.09891</td>\n",
+       "      <td>131441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>778629</td>\n",
+       "      <td>778829</td>\n",
+       "      <td>SMC_m2PE_peak_3</td>\n",
+       "      <td>13192</td>\n",
+       "      <td>.</td>\n",
+       "      <td>12.68989</td>\n",
+       "      <td>1324.53931</td>\n",
+       "      <td>1319.24121</td>\n",
+       "      <td>428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>817265</td>\n",
+       "      <td>817465</td>\n",
+       "      <td>SMC_m2PE_peak_4</td>\n",
+       "      <td>4017</td>\n",
+       "      <td>.</td>\n",
+       "      <td>10.95845</td>\n",
+       "      <td>404.78445</td>\n",
+       "      <td>401.77557</td>\n",
+       "      <td>33595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>827425</td>\n",
+       "      <td>827625</td>\n",
+       "      <td>SMC_m2PE_peak_5</td>\n",
+       "      <td>11889</td>\n",
+       "      <td>.</td>\n",
+       "      <td>14.98692</td>\n",
+       "      <td>1193.70923</td>\n",
+       "      <td>1188.91516</td>\n",
+       "      <td>827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122115</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>56831818</td>\n",
+       "      <td>56832018</td>\n",
+       "      <td>SMC_m2PE_peak_122116</td>\n",
+       "      <td>66</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.44943</td>\n",
+       "      <td>7.93707</td>\n",
+       "      <td>6.62112</td>\n",
+       "      <td>223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122116</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>56836744</td>\n",
+       "      <td>56836944</td>\n",
+       "      <td>SMC_m2PE_peak_122117</td>\n",
+       "      <td>163</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.74992</td>\n",
+       "      <td>18.21555</td>\n",
+       "      <td>16.36313</td>\n",
+       "      <td>135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122117</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>56850861</td>\n",
+       "      <td>56851061</td>\n",
+       "      <td>SMC_m2PE_peak_122118</td>\n",
+       "      <td>236</td>\n",
+       "      <td>.</td>\n",
+       "      <td>2.47251</td>\n",
+       "      <td>25.62169</td>\n",
+       "      <td>23.66050</td>\n",
+       "      <td>373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122118</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>56870843</td>\n",
+       "      <td>56871043</td>\n",
+       "      <td>SMC_m2PE_peak_122119</td>\n",
+       "      <td>292</td>\n",
+       "      <td>.</td>\n",
+       "      <td>3.14725</td>\n",
+       "      <td>31.30095</td>\n",
+       "      <td>29.28865</td>\n",
+       "      <td>73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122119</th>\n",
+       "      <td>chrY</td>\n",
+       "      <td>56873684</td>\n",
+       "      <td>56873884</td>\n",
+       "      <td>SMC_m2PE_peak_122120</td>\n",
+       "      <td>39</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1.68549</td>\n",
+       "      <td>5.09318</td>\n",
+       "      <td>3.92608</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>122120 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0         1         2                     3      4  5         6  \\\n",
+       "0       chr1    181381    181581       SMC_m2PE_peak_1   1978  .   8.30810   \n",
+       "1       chr1    770374    770574       SMC_m2PE_peak_2    140  .   2.44222   \n",
+       "2       chr1    778629    778829       SMC_m2PE_peak_3  13192  .  12.68989   \n",
+       "3       chr1    817265    817465       SMC_m2PE_peak_4   4017  .  10.95845   \n",
+       "4       chr1    827425    827625       SMC_m2PE_peak_5  11889  .  14.98692   \n",
+       "...      ...       ...       ...                   ...    ... ..       ...   \n",
+       "122115  chrY  56831818  56832018  SMC_m2PE_peak_122116     66  .   1.44943   \n",
+       "122116  chrY  56836744  56836944  SMC_m2PE_peak_122117    163  .   1.74992   \n",
+       "122117  chrY  56850861  56851061  SMC_m2PE_peak_122118    236  .   2.47251   \n",
+       "122118  chrY  56870843  56871043  SMC_m2PE_peak_122119    292  .   3.14725   \n",
+       "122119  chrY  56873684  56873884  SMC_m2PE_peak_122120     39  .   1.68549   \n",
+       "\n",
+       "                 7           8       9  \n",
+       "0        200.48422   197.87073  181479  \n",
+       "1         15.88614    14.09891  131441  \n",
+       "2       1324.53931  1319.24121     428  \n",
+       "3        404.78445   401.77557   33595  \n",
+       "4       1193.70923  1188.91516     827  \n",
+       "...            ...         ...     ...  \n",
+       "122115     7.93707     6.62112     223  \n",
+       "122116    18.21555    16.36313     135  \n",
+       "122117    25.62169    23.66050     373  \n",
+       "122118    31.30095    29.28865      73  \n",
+       "122119     5.09318     3.92608      36  \n",
+       "\n",
+       "[122120 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks_bed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybedtools\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "peak_bed =  pybedtools.BedTool.from_dataframe(peaks_bed)\n",
+    "sig_vars_in_bed = beds_var.intersect(peak_bed, c=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed = sig_vars_in_bed.to_dataframe(names=['Chromosome', \"pos0\", 'SNP_position', 'Ref_allele', 'Alt_allele', 'Log10_BH_Q', 'Effect_size', 'logfc.mean', 'jsd.sign.mean', \"hits\"]).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>Effect_size</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>jsd.sign.mean</th>\n",
+       "      <th>hits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067006</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.920961</td>\n",
+       "      <td>0.200715</td>\n",
+       "      <td>0.022735</td>\n",
+       "      <td>0.008771</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121218</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.118572</td>\n",
+       "      <td>0.228169</td>\n",
+       "      <td>0.021728</td>\n",
+       "      <td>0.009595</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108721446</td>\n",
+       "      <td>108721447</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-9.266900</td>\n",
+       "      <td>0.764114</td>\n",
+       "      <td>0.289341</td>\n",
+       "      <td>0.031146</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108842307</td>\n",
+       "      <td>108842308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.721082</td>\n",
+       "      <td>0.242912</td>\n",
+       "      <td>-0.293381</td>\n",
+       "      <td>-0.041953</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109272629</td>\n",
+       "      <td>109272630</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-3.006430</td>\n",
+       "      <td>0.709092</td>\n",
+       "      <td>0.100375</td>\n",
+       "      <td>0.016716</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Chromosome       pos0  SNP_position Ref_allele Alt_allele  Log10_BH_Q  \\\n",
+       "0       chr1    1067006       1067007          A          G   -4.920961   \n",
+       "1       chr1  108121218     108121219          C          G   -4.118572   \n",
+       "2       chr1  108721446     108721447          C          T   -9.266900   \n",
+       "3       chr1  108842307     108842308          G          C   -4.721082   \n",
+       "4       chr1  109272629     109272630          A          G   -3.006430   \n",
+       "\n",
+       "   Effect_size  logfc.mean  jsd.sign.mean  hits  \n",
+       "0     0.200715    0.022735       0.008771     0  \n",
+       "1     0.228169    0.021728       0.009595     0  \n",
+       "2     0.764114    0.289341       0.031146     1  \n",
+       "3     0.242912   -0.293381      -0.041953     0  \n",
+       "4     0.709092    0.100375       0.016716     0  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1984, 10)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>Effect_size</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>jsd.sign.mean</th>\n",
+       "      <th>hits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067006</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.920961</td>\n",
+       "      <td>0.200715</td>\n",
+       "      <td>0.022735</td>\n",
+       "      <td>0.008771</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121218</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.118572</td>\n",
+       "      <td>0.228169</td>\n",
+       "      <td>0.021728</td>\n",
+       "      <td>0.009595</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108721446</td>\n",
+       "      <td>108721447</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-9.266900</td>\n",
+       "      <td>0.764114</td>\n",
+       "      <td>0.289341</td>\n",
+       "      <td>0.031146</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108842307</td>\n",
+       "      <td>108842308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.721082</td>\n",
+       "      <td>0.242912</td>\n",
+       "      <td>-0.293381</td>\n",
+       "      <td>-0.041953</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109272629</td>\n",
+       "      <td>109272630</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-3.006430</td>\n",
+       "      <td>0.709092</td>\n",
+       "      <td>0.100375</td>\n",
+       "      <td>0.016716</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>96159156</td>\n",
+       "      <td>96159157</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-3.026381</td>\n",
+       "      <td>0.806026</td>\n",
+       "      <td>-0.104170</td>\n",
+       "      <td>-0.024419</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>96178224</td>\n",
+       "      <td>96178225</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-4.596132</td>\n",
+       "      <td>0.787369</td>\n",
+       "      <td>0.016330</td>\n",
+       "      <td>0.007556</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1981</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>97227899</td>\n",
+       "      <td>97227900</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-2.961709</td>\n",
+       "      <td>0.405945</td>\n",
+       "      <td>-0.000896</td>\n",
+       "      <td>-0.007822</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>98873906</td>\n",
+       "      <td>98873907</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.182089</td>\n",
+       "      <td>0.608833</td>\n",
+       "      <td>-0.040235</td>\n",
+       "      <td>-0.008717</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99444663</td>\n",
+       "      <td>99444664</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-4.616533</td>\n",
+       "      <td>0.279315</td>\n",
+       "      <td>0.060264</td>\n",
+       "      <td>0.012065</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1984 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chromosome       pos0  SNP_position Ref_allele Alt_allele  Log10_BH_Q  \\\n",
+       "0          chr1    1067006       1067007          A          G   -4.920961   \n",
+       "1          chr1  108121218     108121219          C          G   -4.118572   \n",
+       "2          chr1  108721446     108721447          C          T   -9.266900   \n",
+       "3          chr1  108842307     108842308          G          C   -4.721082   \n",
+       "4          chr1  109272629     109272630          A          G   -3.006430   \n",
+       "...         ...        ...           ...        ...        ...         ...   \n",
+       "1979       chr9   96159156      96159157          C          T   -3.026381   \n",
+       "1980       chr9   96178224      96178225          G          A   -4.596132   \n",
+       "1981       chr9   97227899      97227900          A          T   -2.961709   \n",
+       "1982       chr9   98873906      98873907          T          C   -4.182089   \n",
+       "1983       chr9   99444663      99444664          G          A   -4.616533   \n",
+       "\n",
+       "      Effect_size  logfc.mean  jsd.sign.mean  hits  \n",
+       "0        0.200715    0.022735       0.008771     0  \n",
+       "1        0.228169    0.021728       0.009595     0  \n",
+       "2        0.764114    0.289341       0.031146     1  \n",
+       "3        0.242912   -0.293381      -0.041953     0  \n",
+       "4        0.709092    0.100375       0.016716     0  \n",
+       "...           ...         ...            ...   ...  \n",
+       "1979     0.806026   -0.104170      -0.024419     0  \n",
+       "1980     0.787369    0.016330       0.007556     0  \n",
+       "1981     0.405945   -0.000896      -0.007822     0  \n",
+       "1982     0.608833   -0.040235      -0.008717     0  \n",
+       "1983     0.279315    0.060264       0.012065     0  \n",
+       "\n",
+       "[1984 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed[\"IsUsed\"] = sig_vars_in_bed[\"hits\"] > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>Effect_size</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>jsd.sign.mean</th>\n",
+       "      <th>hits</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067006</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.920961</td>\n",
+       "      <td>0.200715</td>\n",
+       "      <td>0.022735</td>\n",
+       "      <td>0.008771</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121218</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.118572</td>\n",
+       "      <td>0.228169</td>\n",
+       "      <td>0.021728</td>\n",
+       "      <td>0.009595</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108721446</td>\n",
+       "      <td>108721447</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-9.266900</td>\n",
+       "      <td>0.764114</td>\n",
+       "      <td>0.289341</td>\n",
+       "      <td>0.031146</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108842307</td>\n",
+       "      <td>108842308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.721082</td>\n",
+       "      <td>0.242912</td>\n",
+       "      <td>-0.293381</td>\n",
+       "      <td>-0.041953</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109272629</td>\n",
+       "      <td>109272630</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-3.006430</td>\n",
+       "      <td>0.709092</td>\n",
+       "      <td>0.100375</td>\n",
+       "      <td>0.016716</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>96159156</td>\n",
+       "      <td>96159157</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-3.026381</td>\n",
+       "      <td>0.806026</td>\n",
+       "      <td>-0.104170</td>\n",
+       "      <td>-0.024419</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>96178224</td>\n",
+       "      <td>96178225</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-4.596132</td>\n",
+       "      <td>0.787369</td>\n",
+       "      <td>0.016330</td>\n",
+       "      <td>0.007556</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1981</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>97227899</td>\n",
+       "      <td>97227900</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-2.961709</td>\n",
+       "      <td>0.405945</td>\n",
+       "      <td>-0.000896</td>\n",
+       "      <td>-0.007822</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>98873906</td>\n",
+       "      <td>98873907</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.182089</td>\n",
+       "      <td>0.608833</td>\n",
+       "      <td>-0.040235</td>\n",
+       "      <td>-0.008717</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99444663</td>\n",
+       "      <td>99444664</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-4.616533</td>\n",
+       "      <td>0.279315</td>\n",
+       "      <td>0.060264</td>\n",
+       "      <td>0.012065</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1984 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chromosome       pos0  SNP_position Ref_allele Alt_allele  Log10_BH_Q  \\\n",
+       "0          chr1    1067006       1067007          A          G   -4.920961   \n",
+       "1          chr1  108121218     108121219          C          G   -4.118572   \n",
+       "2          chr1  108721446     108721447          C          T   -9.266900   \n",
+       "3          chr1  108842307     108842308          G          C   -4.721082   \n",
+       "4          chr1  109272629     109272630          A          G   -3.006430   \n",
+       "...         ...        ...           ...        ...        ...         ...   \n",
+       "1979       chr9   96159156      96159157          C          T   -3.026381   \n",
+       "1980       chr9   96178224      96178225          G          A   -4.596132   \n",
+       "1981       chr9   97227899      97227900          A          T   -2.961709   \n",
+       "1982       chr9   98873906      98873907          T          C   -4.182089   \n",
+       "1983       chr9   99444663      99444664          G          A   -4.616533   \n",
+       "\n",
+       "      Effect_size  logfc.mean  jsd.sign.mean  hits  IsUsed  \n",
+       "0        0.200715    0.022735       0.008771     0   False  \n",
+       "1        0.228169    0.021728       0.009595     0   False  \n",
+       "2        0.764114    0.289341       0.031146     1    True  \n",
+       "3        0.242912   -0.293381      -0.041953     0   False  \n",
+       "4        0.709092    0.100375       0.016716     0   False  \n",
+       "...           ...         ...            ...   ...     ...  \n",
+       "1979     0.806026   -0.104170      -0.024419     0   False  \n",
+       "1980     0.787369    0.016330       0.007556     0   False  \n",
+       "1981     0.405945   -0.000896      -0.007822     0   False  \n",
+       "1982     0.608833   -0.040235      -0.008717     0   False  \n",
+       "1983     0.279315    0.060264       0.012065     0   False  \n",
+       "\n",
+       "[1984 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols=['Chromosome', 'SNP_position', 'Ref_allele', 'Alt_allele', 'Log10_BH_Q', 'Effect_size', 'logfc.mean', 'jsd.sign.mean', \"IsUsed\"]\n",
+    "\n",
+    "sig_vars_in_bed[cols].to_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures_new/smc.caqtls.tsv\", sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures/format_spi1_bqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures/format_spi1_bqtls.ipynb
new file mode 100644
index 00000000..7a05d0c8
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures/format_spi1_bqtls.ipynb
@@ -0,0 +1,1769 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps=pd.read_csv(\"/mnt/lab_data/kundaje/users/anusri/QTLs/bQTLs/src/pu1.txt\",header=0,sep='\\t')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atac_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/ATAC/summary.mean.variant_scores_new.tsv\"\n",
+    "atac_preds_50m=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/ATAC_50M/summary.mean.variant_scores_new.tsv\"\n",
+    "dnase_preds=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/DNASE/summary.mean.variant_scores_new.tsv\"\n",
+    "\n",
+    "atac_preds_50m = pd.read_csv(atac_preds_50m, sep=\"\\t\", header=0)\n",
+    "atac_preds = pd.read_csv(atac_preds, sep=\"\\t\", header=0)\n",
+    "dnase_preds = pd.read_csv(dnase_preds, sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>position</th>\n",
+       "      <th>Depth</th>\n",
+       "      <th>ALTdepth</th>\n",
+       "      <th>REFDepth</th>\n",
+       "      <th>ALTallele</th>\n",
+       "      <th>POSTallele</th>\n",
+       "      <th>POSTfreq</th>\n",
+       "      <th>prechipfreq</th>\n",
+       "      <th>pvalue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>18967176</td>\n",
+       "      <td>538</td>\n",
+       "      <td>418</td>\n",
+       "      <td>120</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.223048</td>\n",
+       "      <td>0.866699</td>\n",
+       "      <td>2.331853e-251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>176935913</td>\n",
+       "      <td>626</td>\n",
+       "      <td>597</td>\n",
+       "      <td>29</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.046326</td>\n",
+       "      <td>0.543949</td>\n",
+       "      <td>4.449643e-162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>144534083</td>\n",
+       "      <td>618</td>\n",
+       "      <td>368</td>\n",
+       "      <td>250</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.404531</td>\n",
+       "      <td>0.057295</td>\n",
+       "      <td>5.863065e-141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>19015381</td>\n",
+       "      <td>144</td>\n",
+       "      <td>143</td>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.006944</td>\n",
+       "      <td>0.872850</td>\n",
+       "      <td>1.040250e-126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>17231724</td>\n",
+       "      <td>2477</td>\n",
+       "      <td>1321</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.466694</td>\n",
+       "      <td>0.255195</td>\n",
+       "      <td>9.915948e-114</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chr   position  Depth  ALTdepth  REFDepth ALTallele POSTallele  POSTfreq  \\\n",
+       "0  chr17   18967176    538       418       120         G          A  0.223048   \n",
+       "1   chr4  176935913    626       597        29         A          C  0.046326   \n",
+       "2   chr1  144534083    618       368       250         T          C  0.404531   \n",
+       "3  chr17   19015381    144       143         1         A          T  0.006944   \n",
+       "4   chr1   17231724   2477      1321      1156         G          A  0.466694   \n",
+       "\n",
+       "   prechipfreq         pvalue  \n",
+       "0     0.866699  2.331853e-251  \n",
+       "1     0.543949  4.449643e-162  \n",
+       "2     0.057295  5.863065e-141  \n",
+       "3     0.872850  1.040250e-126  \n",
+       "4     0.255195  9.915948e-114  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 10)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(999799, 23)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(atac_preds.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "atac_preds[\"jsd_sign.mean\"] = atac_preds[\"jsd.mean\"] * np.sign(atac_preds[\"logfc.mean\"])\n",
+    "atac_preds_50m[\"jsd_sign.mean\"] = atac_preds_50m[\"jsd.mean\"] * np.sign(atac_preds_50m[\"logfc.mean\"])\n",
+    "dnase_preds[\"jsd_sign.mean\"] = dnase_preds[\"jsd.mean\"] * np.sign(dnase_preds[\"logfc.mean\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>abs_logfc.mean</th>\n",
+       "      <th>abs_logfc.mean.pval</th>\n",
+       "      <th>jsd.mean</th>\n",
+       "      <th>jsd.mean.pval</th>\n",
+       "      <th>...</th>\n",
+       "      <th>max_percentile.mean.pval</th>\n",
+       "      <th>logfc_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_max_percentile.mean.pval</th>\n",
+       "      <th>jsd_x_max_percentile.mean</th>\n",
+       "      <th>jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>jsd_sign.mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>18967176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr17_18967176_A_G</td>\n",
+       "      <td>0.311478</td>\n",
+       "      <td>0.311478</td>\n",
+       "      <td>0.017033</td>\n",
+       "      <td>0.052623</td>\n",
+       "      <td>0.011653</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.024523</td>\n",
+       "      <td>0.263315</td>\n",
+       "      <td>0.263315</td>\n",
+       "      <td>0.003379</td>\n",
+       "      <td>0.043836</td>\n",
+       "      <td>0.002004</td>\n",
+       "      <td>0.013618</td>\n",
+       "      <td>0.003758</td>\n",
+       "      <td>0.013618</td>\n",
+       "      <td>0.052623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>176935913</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr4_176935913_C_A</td>\n",
+       "      <td>1.523892</td>\n",
+       "      <td>1.523892</td>\n",
+       "      <td>0.000073</td>\n",
+       "      <td>0.181628</td>\n",
+       "      <td>0.000215</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.115946</td>\n",
+       "      <td>0.737156</td>\n",
+       "      <td>0.737156</td>\n",
+       "      <td>0.000233</td>\n",
+       "      <td>0.082352</td>\n",
+       "      <td>0.000300</td>\n",
+       "      <td>0.129340</td>\n",
+       "      <td>0.000075</td>\n",
+       "      <td>0.129340</td>\n",
+       "      <td>0.181628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>144534083</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_144534083_C_T</td>\n",
+       "      <td>-0.233717</td>\n",
+       "      <td>0.233717</td>\n",
+       "      <td>0.035638</td>\n",
+       "      <td>0.025101</td>\n",
+       "      <td>0.064513</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.021040</td>\n",
+       "      <td>-0.201860</td>\n",
+       "      <td>0.201860</td>\n",
+       "      <td>0.006923</td>\n",
+       "      <td>0.021170</td>\n",
+       "      <td>0.010960</td>\n",
+       "      <td>0.004999</td>\n",
+       "      <td>0.012593</td>\n",
+       "      <td>-0.004999</td>\n",
+       "      <td>-0.025101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>19015381</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr17_19015381_T_A</td>\n",
+       "      <td>-0.045305</td>\n",
+       "      <td>0.045305</td>\n",
+       "      <td>0.378414</td>\n",
+       "      <td>0.011349</td>\n",
+       "      <td>0.319737</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003597</td>\n",
+       "      <td>-0.042740</td>\n",
+       "      <td>0.042740</td>\n",
+       "      <td>0.083991</td>\n",
+       "      <td>0.010690</td>\n",
+       "      <td>0.045718</td>\n",
+       "      <td>0.000677</td>\n",
+       "      <td>0.109582</td>\n",
+       "      <td>-0.000677</td>\n",
+       "      <td>-0.011349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>17231724</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_17231724_A_G</td>\n",
+       "      <td>0.008116</td>\n",
+       "      <td>0.046234</td>\n",
+       "      <td>0.346740</td>\n",
+       "      <td>0.008417</td>\n",
+       "      <td>0.447890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005079</td>\n",
+       "      <td>0.006339</td>\n",
+       "      <td>0.042423</td>\n",
+       "      <td>0.074615</td>\n",
+       "      <td>0.007741</td>\n",
+       "      <td>0.066760</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>0.120123</td>\n",
+       "      <td>-0.000015</td>\n",
+       "      <td>0.008417</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     chr        pos allele1 allele2                rsid  logfc.mean  \\\n",
+       "0  chr17   18967176       A       G  chr17_18967176_A_G    0.311478   \n",
+       "1   chr4  176935913       C       A  chr4_176935913_C_A    1.523892   \n",
+       "2   chr1  144534083       C       T  chr1_144534083_C_T   -0.233717   \n",
+       "3  chr17   19015381       T       A  chr17_19015381_T_A   -0.045305   \n",
+       "4   chr1   17231724       A       G   chr1_17231724_A_G    0.008116   \n",
+       "\n",
+       "   abs_logfc.mean  abs_logfc.mean.pval  jsd.mean  jsd.mean.pval  ...  \\\n",
+       "0        0.311478             0.017033  0.052623       0.011653  ...   \n",
+       "1        1.523892             0.000073  0.181628       0.000215  ...   \n",
+       "2        0.233717             0.035638  0.025101       0.064513  ...   \n",
+       "3        0.045305             0.378414  0.011349       0.319737  ...   \n",
+       "4        0.046234             0.346740  0.008417       0.447890  ...   \n",
+       "\n",
+       "   max_percentile.mean.pval  logfc_x_max_percentile.mean  \\\n",
+       "0                  0.024523                     0.263315   \n",
+       "1                  0.115946                     0.737156   \n",
+       "2                  0.021040                    -0.201860   \n",
+       "3                  0.003597                    -0.042740   \n",
+       "4                  0.005079                     0.006339   \n",
+       "\n",
+       "   abs_logfc_x_max_percentile.mean  abs_logfc_x_max_percentile.mean.pval  \\\n",
+       "0                         0.263315                              0.003379   \n",
+       "1                         0.737156                              0.000233   \n",
+       "2                         0.201860                              0.006923   \n",
+       "3                         0.042740                              0.083991   \n",
+       "4                         0.042423                              0.074615   \n",
+       "\n",
+       "   jsd_x_max_percentile.mean  jsd_x_max_percentile.mean.pval  \\\n",
+       "0                   0.043836                        0.002004   \n",
+       "1                   0.082352                        0.000300   \n",
+       "2                   0.021170                        0.010960   \n",
+       "3                   0.010690                        0.045718   \n",
+       "4                   0.007741                        0.066760   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                               0.013618   \n",
+       "1                               0.129340   \n",
+       "2                               0.004999   \n",
+       "3                               0.000677   \n",
+       "4                               0.000381   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                    0.003758   \n",
+       "1                                    0.000075   \n",
+       "2                                    0.012593   \n",
+       "3                                    0.109582   \n",
+       "4                                    0.120123   \n",
+       "\n",
+       "   logfc_x_jsd_x_max_percentile.mean  jsd_sign.mean  \n",
+       "0                           0.013618       0.052623  \n",
+       "1                           0.129340       0.181628  \n",
+       "2                          -0.004999      -0.025101  \n",
+       "3                          -0.000677      -0.011349  \n",
+       "4                          -0.000015       0.008417  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atac_preds.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atac_preds = atac_preds[[\"chr\", \"pos\", \"allele1\", \"allele2\", \"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]]\n",
+    "\n",
+    "atac_preds_50m = atac_preds_50m[[\"chr\", \"pos\", \"allele1\", \"allele2\", \"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]]\n",
+    "\n",
+    "#atac_preds_50m = atac_preds_50m[[\"chr\", \"pos\", \"allele1\", \"allele2\", \"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]]\n",
+    "\n",
+    "dnase_preds = dnase_preds[[\"chr\", \"pos\", \"allele1\", \"allele2\", \"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_list=[\"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]\n",
+    "\n",
+    "rename_df={}\n",
+    "for val in rename_list:\n",
+    "    snps[\"ENCSR637XSC.\"+val] = atac_preds[val]\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_list=[\"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]\n",
+    "\n",
+    "rename_df={}\n",
+    "for val in rename_list:\n",
+    "    snps[\"ENCSR637XSC.sub.50M.\"+val] = atac_preds_50m[val]\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rename_list=[\"logfc.mean\", \"jsd_sign.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean\", \"abs_logfc_x_jsd_x_max_percentile.mean.pval\"]\n",
+    "\n",
+    "rename_df={}\n",
+    "for val in rename_list:\n",
+    "    snps[\"ENCSR000EMT.\"+val] = dnase_preds[val]\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#snps[[\"Chr\", \"position\", \"ALTallele\",  \"POSTallele\", \"POSTfreq\", \"prechipfreq\"]].to_csv(\"/mnt/lab_data2/anusri/enformer/bqtls/source_new.tsv\", sep=\"\\t\", header=True, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4834, 22)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps[snps[\"pvalue\"]<1e-4].shape\n",
+    "\n",
+    "\n",
+    "#[\"Chr\", \"position\", \"ALTallele\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Chr', 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+       "       'POSTallele', 'POSTfreq', 'prechipfreq', 'pvalue',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps.head(1).columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# temp = snps[snps[\"pvalue\"]<1e-4][[\"Chr\", \"position\", \"POSTallele\", \"ALTallele\"]]\n",
+    "# temp[\"rsid\"] = snps[\"Chr\"]+\"_\"+snps[\"position\"].astype(str)+\"_\"+snps[\"ALTallele\"]+\"_\"+snps[\"POSTallele\"]\n",
+    "# temp.to_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/pu1_sig.tsv\", sep='\\t', header=False, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>position</th>\n",
+       "      <th>POSTallele</th>\n",
+       "      <th>ALTallele</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>18967176</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr17_18967176_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>176935913</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr4_176935913_A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>144534083</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1_144534083_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>19015381</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr17_19015381_A_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>17231724</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1_17231724_G_A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chr   position POSTallele ALTallele                rsid\n",
+       "0  chr17   18967176          A         G  chr17_18967176_G_A\n",
+       "1   chr4  176935913          C         A  chr4_176935913_A_C\n",
+       "2   chr1  144534083          C         T  chr1_144534083_T_C\n",
+       "3  chr17   19015381          T         A  chr17_19015381_A_T\n",
+       "4   chr1   17231724          A         G   chr1_17231724_G_A"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# temp.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#enforer_recomputed_preds\n",
+    "import pickle as pkl\n",
+    "import os\n",
+    "\n",
+    "mean_change=[]\n",
+    "rsids=[]\n",
+    "\n",
+    "enf_dir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/enformer_preds_small_window/\"\n",
+    "\n",
+    "enformer_recompute = pkl.load(open(enf_dir+\"/predictions_at_snp.pkl\",'rb'))\n",
+    "for i in range(enformer_recompute[\"ref_logcount_preds\"].shape[0]):\n",
+    "        mean_change.append(np.log2( (enformer_recompute[\"ref_logcount_preds\"][i,440:456].sum()+1) / (enformer_recompute[\"alt_logcount_preds\"][i,440:456].sum()+1)))\n",
+    "        rsids.append(enformer_recompute[\"rsids\"][i])\n",
+    "mean_change = np.array(mean_change)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps[\"enformer_recomputed\"] = np.nan\n",
+    "snps.loc[snps[\"pvalue\"]<1e-4, \"enformer_recomputed\"] = mean_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       0.001704\n",
+       "1      -0.990719\n",
+       "2       0.143367\n",
+       "3      -0.016722\n",
+       "4      -0.075683\n",
+       "          ...   \n",
+       "4829   -0.013837\n",
+       "4830   -0.001946\n",
+       "4831    0.131631\n",
+       "4832   -0.004711\n",
+       "4833    0.160228\n",
+       "Name: enformer_recomputed, Length: 4834, dtype: float64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps[snps[\"pvalue\"]<1e-4][\"enformer_recomputed\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Chr', 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+       "       'POSTallele', 'POSTfreq', 'prechipfreq', 'pvalue',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enformer_preds = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/bqtls/enformer_predictions_new.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>14320</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs7210283</td>\n",
+       "      <td>0.000003</td>\n",
+       "      <td>0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>14678</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs113247765</td>\n",
+       "      <td>-0.000057</td>\n",
+       "      <td>-0.000080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>35514</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs8066551</td>\n",
+       "      <td>-0.000435</td>\n",
+       "      <td>-0.000601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>35724</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs6565729</td>\n",
+       "      <td>0.000144</td>\n",
+       "      <td>0.000199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>48103</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs113075245</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.000003</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     chr    pos ref alt         rsid  DNASE_SAD  DNASE_SAR\n",
+       "0  chr17  14320   G   A    rs7210283   0.000003   0.000004\n",
+       "1  chr17  14678   A   C  rs113247765  -0.000057  -0.000080\n",
+       "2  chr17  35514   A   C    rs8066551  -0.000435  -0.000601\n",
+       "3  chr17  35724   G   C    rs6565729   0.000144   0.000199\n",
+       "4  chr17  48103   C   T  rs113075245  -0.000002  -0.000003"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "enformer_preds.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>position</th>\n",
+       "      <th>Depth</th>\n",
+       "      <th>ALTdepth</th>\n",
+       "      <th>REFDepth</th>\n",
+       "      <th>ALTallele</th>\n",
+       "      <th>POSTallele</th>\n",
+       "      <th>POSTfreq</th>\n",
+       "      <th>prechipfreq</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.jsd_sign.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>18967176</td>\n",
+       "      <td>538</td>\n",
+       "      <td>418</td>\n",
+       "      <td>120</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.223048</td>\n",
+       "      <td>0.866699</td>\n",
+       "      <td>2.331853e-251</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003758</td>\n",
+       "      <td>0.292126</td>\n",
+       "      <td>0.048841</td>\n",
+       "      <td>0.012187</td>\n",
+       "      <td>0.00344</td>\n",
+       "      <td>0.36072</td>\n",
+       "      <td>0.052333</td>\n",
+       "      <td>0.017846</td>\n",
+       "      <td>0.001838</td>\n",
+       "      <td>0.001704</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chr  position  Depth  ALTdepth  REFDepth ALTallele POSTallele  POSTfreq  \\\n",
+       "0  chr17  18967176    538       418       120         G          A  0.223048   \n",
+       "\n",
+       "   prechipfreq         pvalue  ...  \\\n",
+       "0     0.866699  2.331853e-251  ...   \n",
+       "\n",
+       "   ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                           0.003758        \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.logfc.mean  ENCSR637XSC.sub.50M.jsd_sign.mean  \\\n",
+       "0                        0.292126                           0.048841   \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.012187           \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                            0.00344                \n",
+       "\n",
+       "   ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "0                 0.36072                   0.052333   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.017846   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  enformer_recomputed  \n",
+       "0                                           0.001838                  0.001704  \n",
+       "\n",
+       "[1 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = snps.merge(enformer_preds, right_on=[\"chr\", \"pos\", \"ref\", \"alt\"], left_on=[\"Chr\", \"position\",  \"POSTallele\", \"ALTallele\"], how=\"left\")\n",
+    "df2 = snps.merge(enformer_preds, right_on=[\"chr\", \"pos\", \"alt\", \"ref\"], left_on=[\"Chr\", \"position\",  \"POSTallele\", \"ALTallele\"], how=\"left\")\n",
+    "df2[\"DNASE_SAD\"] = df2[\"DNASE_SAD\"] * -1\n",
+    "df2[\"DNASE_SAR\"] = df2[\"DNASE_SAR\"] * -1\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 30)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 30)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "881752"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(df1[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105717"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(df2[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "df1[np.isnan(df1[\"DNASE_SAD\"])][\"DNASE_SAD\"] = df2[np.isnan(df1[\"DNASE_SAD\"])][\"DNASE_SAD\"]\n",
+    "df1[np.isnan(df1[\"DNASE_SAD\"])][\"DNASE_SAR\"] = df2[np.isnan(df1[\"DNASE_SAD\"])][\"DNASE_SAR\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Chr', 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+       "       'POSTallele', 'POSTfreq', 'prechipfreq', 'pvalue',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed', 'chr', 'pos', 'ref', 'alt', 'rsid', 'DNASE_SAD',\n",
+       "       'DNASE_SAR'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dff = df1[['Chr', 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele', \"enformer_recomputed\",\n",
+    "       'POSTallele', 'POSTfreq', 'prechipfreq', 'pvalue',\n",
+    "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+    "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+    "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "        'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+    "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid', 'DNASE_SAD', 'DNASE_SAR']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_bed = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/bqtls_lcl/pu1/ENCFF306SRV.bed.gz\", sep=\"\\t\", header=None)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chrX</td>\n",
+       "      <td>16137892</td>\n",
+       "      <td>16137990</td>\n",
+       "      <td>.</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>.</td>\n",
+       "      <td>4.12871</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.53677</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0         1         2  3     4  5        6    7        8   9\n",
+       "0  chrX  16137892  16137990  .  1000  .  4.12871 -1.0  0.53677  70"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks_bed.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_bed[1] = peaks_bed[1] + peaks_bed[9] - 100\n",
+    "peaks_bed[2] = peaks_bed[1] + 200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dff[\"pos0\"] = dff[\"position\"] - 1\n",
+    "selected_beds = dff[['Chr', \"pos0\", 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+    "       'POSTallele', 'POSTfreq', 'prechipfreq', 'pvalue',\n",
+    "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+    "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+    "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+    "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid', 'DNASE_SAD', 'DNASE_SAR']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybedtools\n",
+    "beds_var = pybedtools.BedTool.from_dataframe(selected_beds)\n",
+    "peak_bed =  pybedtools.BedTool.from_dataframe(peaks_bed)\n",
+    "sig_vars_in_bed = beds_var.intersect(peak_bed, c=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed = sig_vars_in_bed.to_dataframe(names=['Chr', \"pos0\", 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+    "       'POSTallele', 'POSTfreq', 'prechipfreq', 'pvalue',\n",
+    "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+    "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+    "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "        'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+    "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+    "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid', 'DNASE_SAD', 'DNASE_SAR', \"count\"]).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_vars_in_bed[\"count\"] = sig_vars_in_bed[\"count\"] > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25778"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sig_vars_in_bed[\"count\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 27)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Chr', 'pos0', 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+       "       'POSTallele', 'POSTfreq', 'prechipfreq', 'pvalue',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid',\n",
+       "       'DNASE_SAD', 'DNASE_SAR', 'count'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_vars_in_bed[sig_vars_in_bed[\"count\"]].columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1037"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sig_vars_in_bed[sig_vars_in_bed[\"count\"]]['pvalue'] < 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "881752"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(dff[\"DNASE_SAD\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 27)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dff.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    }
+   ],
+   "source": [
+    "#dff[\"IsUsed\"] = (~np.isnan(dff[\"DNASE_SAD\"])) & (sig_vars_in_bed[\"count\"])\n",
+    "dff[\"IsUsed\"] = ~np.isnan(dff[\"DNASE_SAD\"])\n",
+    "dff[\"InPeaks\"] = (~np.isnan(dff[\"DNASE_SAD\"])) & (sig_vars_in_bed[\"count\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dff.to_csv(\"../final_figures_new/spi1_bqtls.new.tsv\",sep=\"\\t\", header=True, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "881752"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dff[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 28)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dff.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "881752"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dff[\"IsUsed\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dff[\"IsUsed\"] = (~np.isnan(dff[\"DNASE_SAD\"])) & (sig_vars_in_bed[\"count\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22627"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dff[\"InPeaks\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3818"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dff[\"IsUsed\"] & (dff[\"pvalue\"]<1e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3818"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((dff[dff[\"IsUsed\"]][\"pvalue\"]<1e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 22)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4834"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(snps[\"pvalue\"] < 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3818"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dff[dff[\"IsUsed\"]][\"pvalue\"] < 1e-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled.ipynb
new file mode 100644
index 00000000..35eac67a
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled.ipynb
@@ -0,0 +1,32 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled1.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled1.ipynb
new file mode 100644
index 00000000..7fec5150
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled1.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled2.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled2.ipynb
new file mode 100644
index 00000000..7fec5150
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/Untitled2.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/afr.caqtls_scoring-Copy1.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/afr.caqtls_scoring-Copy1.ipynb
new file mode 100644
index 00000000..794745d9
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/afr.caqtls_scoring-Copy1.ipynb
@@ -0,0 +1,3534 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import scipy.stats\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls = pd.read_csv(\"Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n",
+    "caqtls = caqtls[caqtls[\"IsUsed\"] & caqtls[\"Inside_Peak\"]==1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = afrs[afrs[\"IsUsed\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>snp_id</th>\n",
+       "      <th>label</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>enformer.encsr000emt.variantscore.sad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.logfc</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.jsd</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.csr</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.pval</th>\n",
+       "      <th>dbsnp_rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.036457</td>\n",
+       "      <td>0.019025</td>\n",
+       "      <td>0.001262</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.137133</td>\n",
+       "      <td>0.250142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.127411</td>\n",
+       "      <td>0.025277</td>\n",
+       "      <td>0.003640</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.098145</td>\n",
+       "      <td>0.130176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906870</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_906870_A_G</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.151325</td>\n",
+       "      <td>-0.335979</td>\n",
+       "      <td>-0.000669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.850441</td>\n",
+       "      <td>-0.048693</td>\n",
+       "      <td>0.024808</td>\n",
+       "      <td>0.002238</td>\n",
+       "      <td>rs148658266</td>\n",
+       "      <td>1:906596:907043</td>\n",
+       "      <td>1_906870_A_G</td>\n",
+       "      <td>273</td>\n",
+       "      <td>0.232118</td>\n",
+       "      <td>0.335979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906982</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>-0.520262</td>\n",
+       "      <td>-0.000453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.406960</td>\n",
+       "      <td>-0.029040</td>\n",
+       "      <td>0.009527</td>\n",
+       "      <td>0.007020</td>\n",
+       "      <td>rs28540380</td>\n",
+       "      <td>1:906596:907043</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>385</td>\n",
+       "      <td>0.106740</td>\n",
+       "      <td>0.520262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>921203</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.905971</td>\n",
+       "      <td>0.000342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.730897</td>\n",
+       "      <td>0.085904</td>\n",
+       "      <td>0.025396</td>\n",
+       "      <td>0.002171</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>1:921132:921332</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0.189412</td>\n",
+       "      <td>0.905971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219372</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50583387</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_50583387_C_G</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.717574</td>\n",
+       "      <td>-0.039875</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.020411</td>\n",
+       "      <td>0.012694</td>\n",
+       "      <td>0.000294</td>\n",
+       "      <td>0.137602</td>\n",
+       "      <td>rs112714656</td>\n",
+       "      <td>22:50583202:50583631</td>\n",
+       "      <td>22_50583387_C_G</td>\n",
+       "      <td>184</td>\n",
+       "      <td>0.109887</td>\n",
+       "      <td>0.039875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219373</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50620690</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_50620690_G_T</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.178047</td>\n",
+       "      <td>0.101003</td>\n",
+       "      <td>-0.001514</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.028894</td>\n",
+       "      <td>0.022597</td>\n",
+       "      <td>0.001665</td>\n",
+       "      <td>0.038481</td>\n",
+       "      <td>rs3091400</td>\n",
+       "      <td>22:50620470:50621609</td>\n",
+       "      <td>22_50620690_G_T</td>\n",
+       "      <td>219</td>\n",
+       "      <td>0.074396</td>\n",
+       "      <td>0.101003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219374</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50621055</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50621055_G_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.411079</td>\n",
+       "      <td>0.094050</td>\n",
+       "      <td>-0.000848</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.038317</td>\n",
+       "      <td>-0.024680</td>\n",
+       "      <td>0.002349</td>\n",
+       "      <td>0.028349</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>22:50620470:50621609</td>\n",
+       "      <td>22_50621055_G_A</td>\n",
+       "      <td>584</td>\n",
+       "      <td>0.113873</td>\n",
+       "      <td>0.094050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219375</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50621446</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22_50621446_T_C</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.217021</td>\n",
+       "      <td>-0.123608</td>\n",
+       "      <td>0.000378</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.011861</td>\n",
+       "      <td>-0.008688</td>\n",
+       "      <td>0.000134</td>\n",
+       "      <td>0.213307</td>\n",
+       "      <td>rs75395540</td>\n",
+       "      <td>22:50620470:50621609</td>\n",
+       "      <td>22_50621446_T_C</td>\n",
+       "      <td>975</td>\n",
+       "      <td>0.099411</td>\n",
+       "      <td>0.123608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219378</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628124</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.742636</td>\n",
+       "      <td>0.021773</td>\n",
+       "      <td>-0.000557</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.039441</td>\n",
+       "      <td>-0.025766</td>\n",
+       "      <td>0.002166</td>\n",
+       "      <td>0.030504</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>609</td>\n",
+       "      <td>0.066098</td>\n",
+       "      <td>0.021773</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>109646 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr_hg38  pos_hg38 allele1 allele2           snp_id  label  IsUsed  \\\n",
+       "2          chr1    870176       T       A     1_870176_T_A  False    True   \n",
+       "4          chr1    905373       C       T     1_905373_T_C  False    True   \n",
+       "8          chr1    906870       A       G     1_906870_A_G  False    True   \n",
+       "9          chr1    906982       C       T     1_906982_C_T   True    True   \n",
+       "12         chr1    921203       G       A     1_921203_G_A   True    True   \n",
+       "...         ...       ...     ...     ...              ...    ...     ...   \n",
+       "219372    chr22  50583387       C       G  22_50583387_C_G  False    True   \n",
+       "219373    chr22  50620690       T       G  22_50620690_G_T  False    True   \n",
+       "219374    chr22  50621055       G       A  22_50621055_G_A  False    True   \n",
+       "219375    chr22  50621446       T       C  22_50621446_T_C  False    True   \n",
+       "219378    chr22  50628124       C       G  22_50628124_C_G  False    True   \n",
+       "\n",
+       "            pval      beta  enformer.encsr000emt.variantscore.sad  ...  \\\n",
+       "2       0.071532 -0.250142                              -0.000139  ...   \n",
+       "4       0.188152 -0.130176                              -0.000071  ...   \n",
+       "8       0.151325 -0.335979                              -0.000669  ...   \n",
+       "9       0.000005 -0.520262                              -0.000453  ...   \n",
+       "12      0.000007  0.905971                               0.000342  ...   \n",
+       "...          ...       ...                                    ...  ...   \n",
+       "219372  0.717574 -0.039875                               0.000002  ...   \n",
+       "219373  0.178047  0.101003                              -0.001514  ...   \n",
+       "219374  0.411079  0.094050                              -0.000848  ...   \n",
+       "219375  0.217021 -0.123608                               0.000378  ...   \n",
+       "219378  0.742636  0.021773                              -0.000557  ...   \n",
+       "\n",
+       "        chrombpnet.encsr095qnb.variantscore.logfc  \\\n",
+       "2                                        0.036457   \n",
+       "4                                        0.127411   \n",
+       "8                                       -0.850441   \n",
+       "9                                       -0.406960   \n",
+       "12                                       0.730897   \n",
+       "...                                           ...   \n",
+       "219372                                   0.020411   \n",
+       "219373                                   0.028894   \n",
+       "219374                                  -0.038317   \n",
+       "219375                                  -0.011861   \n",
+       "219378                                  -0.039441   \n",
+       "\n",
+       "        chrombpnet.encsr095qnb.variantscore.jsd  \\\n",
+       "2                                      0.019025   \n",
+       "4                                      0.025277   \n",
+       "8                                     -0.048693   \n",
+       "9                                     -0.029040   \n",
+       "12                                     0.085904   \n",
+       "...                                         ...   \n",
+       "219372                                 0.012694   \n",
+       "219373                                 0.022597   \n",
+       "219374                                -0.024680   \n",
+       "219375                                -0.008688   \n",
+       "219378                                -0.025766   \n",
+       "\n",
+       "        chrombpnet.encsr095qnb.variantscore.csr  \\\n",
+       "2                                      0.001262   \n",
+       "4                                      0.003640   \n",
+       "8                                      0.024808   \n",
+       "9                                      0.009527   \n",
+       "12                                     0.025396   \n",
+       "...                                         ...   \n",
+       "219372                                 0.000294   \n",
+       "219373                                 0.001665   \n",
+       "219374                                 0.002349   \n",
+       "219375                                 0.000134   \n",
+       "219378                                 0.002166   \n",
+       "\n",
+       "        chrombpnet.encsr095qnb.variantscore.pval   dbsnp_rsid  \\\n",
+       "2                                       0.048612   rs72631880   \n",
+       "4                                       0.018781    rs4970382   \n",
+       "8                                       0.002238  rs148658266   \n",
+       "9                                       0.007020   rs28540380   \n",
+       "12                                      0.002171  rs141671872   \n",
+       "...                                          ...          ...   \n",
+       "219372                                  0.137602  rs112714656   \n",
+       "219373                                  0.038481    rs3091400   \n",
+       "219374                                  0.028349   rs76733141   \n",
+       "219375                                  0.213307   rs75395540   \n",
+       "219378                                  0.030504    rs6151406   \n",
+       "\n",
+       "                      region              snp  distance        se  abs_beta  \n",
+       "2            1:869800:870282     1_870176_T_A       375  0.137133  0.250142  \n",
+       "4            1:904938:905544     1_905373_T_C       434  0.098145  0.130176  \n",
+       "8            1:906596:907043     1_906870_A_G       273  0.232118  0.335979  \n",
+       "9            1:906596:907043     1_906982_C_T       385  0.106740  0.520262  \n",
+       "12           1:921132:921332     1_921203_G_A        70  0.189412  0.905971  \n",
+       "...                      ...              ...       ...       ...       ...  \n",
+       "219372  22:50583202:50583631  22_50583387_C_G       184  0.109887  0.039875  \n",
+       "219373  22:50620470:50621609  22_50620690_G_T       219  0.074396  0.101003  \n",
+       "219374  22:50620470:50621609  22_50621055_G_A       584  0.113873  0.094050  \n",
+       "219375  22:50620470:50621609  22_50621446_T_C       975  0.099411  0.123608  \n",
+       "219378  22:50627514:50628822  22_50628124_C_G       609  0.066098  0.021773  \n",
+       "\n",
+       "[109646 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8363"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101283"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00100034"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(afrs[afrs[\"label\"]==0][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.999995"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(afrs[afrs[\"label\"]==0][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.97713e-06"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.92178e-41"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109646"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==1)+sum(afrs[\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(109646, 46)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08257061895875913"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==1)/sum(afrs[\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.97713e-06"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.92178e-41"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed', 'pval', 'beta', 'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar', 'enformer_recomputed',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUPRC (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Precision')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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T3Lr+sypQABmASZIk5ZKNGzfm9RB0PvroIxYtWpQn105ISODrr7/WtmUA9urItSBMkiRJkl4WlpaW9O7dO0+ubewpS+nV8FI8HZkXkgTy6UhJkiRJknKNDMLSEfcYtm/P61FIkiRJkpRfySBMkiRJkiQpD8ggTJIkSZIkKQ/IICwddhaXqXhjdl4PQ5Kkl9S1a9fo3r079vb2eHl50aZNG86cOcO2bdt4/fXXc/3627Zto1ixYri7u+Pq6kqzZs24ceMGoGZ2L126NO7u7tSsWZPp06c/1TXu3bvHlClTnmmcY8aMYf78+Ub3dejQQcsXtn79ei3be5EiRXBwcMDd3Z23334bgOXLl6MoCqGhobo+goOD8fPzw8HBAQ8PD/r378/Dhw8NrnX48GGtCkDqz87d3Z3hw4drx61btw4HBweqVq3KmDFjtPZGjRpRqVIlUqd96tChg5Z5/+bNm0bzoqXH2PuJiIigYMGC2t/bhx9+mOUSQefPn6dOnTpUrVqVbt26ERcXZ3BMcHCw9p7d3NwMEsYmJibi4eHxXL5/XxUyCEtHYdNHuER8AUmyirckSdkjhKBjx440atSIc+fOcfDgQUaPHm2QqT0jiYnP/n9Pcu3Io0ePUqtWLSZPnqztSy5btG3bNr777rtsjS1ZTgRh69evp0WLFkb7PnjwIFFRUYSHh9OyZUst27u3tzfz588nJCSEv//+G1CTtjZo0ECXQf769et06dKFsWPHcvr0aQ4fPkyrVq2MZvkfNWqUVsoHUj67kJAQLTFpYmIin3zyCWvXruXkyZMEBARw8uRJ7ZzixYtrRbjv3bunS6JaunRpypcvn6Ui3em9H0ArmH706FFOnjypFXXPzKBBg/jyyy85e/YsJUqU0GpLpubs7MyBAwcICQlh3bp1fPDBB7oyWL/++qvMv5fDZBCWmVxKZitJ0vOhKLnzlZGtW7dibm6uy5ru5uamZYuPiYmhc+fOODo60qtXL232xNbWlkGDBuHp6akVe3ZxccHZ2ZlBgwZpfRUpUoRvv/0WJycnmjVrRnBwMI0aNaJKlSqsXLnSYDxCCKKjoylRooTBvjJlymBvb8+FCxfo27cvAwYMoF69elSpUoXFixdrx40bN45atWrh6urKkCFDAPD39+fcuXO4u7sbZMwfN26cVhrnyy+/pEmTJgBs2bJFK259//594uLijJa5Wbp0Ke3ataN79+7plixKFhMTw86dO5k5c6bu2MmTJ9OnTx98fHy0ts6dOxvUfIyOjubo0aO4ublleJ3g4GCqVq1KlSpVKFCgAN27d2fFihXa/tRjXbp0qUFW/Q4dOqQ765eV95OamZkZ9erV05VbSo8Qgi1bttC5c2cA+vTpYzR4K1SoEGZmauaq2NhYXeb7yMhIVq9enSfJbPMzGYRJkiTlsOPHj+Pl5ZXu/sOHDzNp0iROnjxJeHi4bnbE2tqaQ4cO4efnx6BBg9iyZQshISHs379f+8H54MEDmjRpwokTJ7CysuKHH35g48aNLFu2TJu1gZSyRZUqVWLTpk3069fPYCzh4eGEh4dTtWpVAK5evcrOnTtZtWoV/v7+AGzYsIGwsDCCg4MJCQnh4MGD7NixgzFjxmgzM+PGjdP16+vrq9XKPHDgADExMcTHxxMUFISfnx8AmzZtMlrMGlLqM/bo0SPT+ogrVqygVatWVK9eHWtra61od2Z/D8kOHDig1W1MtmfPHtzc3GjdujUnTpwA1JqZFSumlDy2sbHh8uWUksdNmzZlx44dJCYmEhgYSLdu3XR9ent7Z6l+aHrvJ7WHDx+yefNmXFxciI6ONlqk293dnZMnT3L79m2KFy+uBVhpx53avn37cHJywsXFhalTp2rnfPHFF/z888+YmMiwISfJTzMz8zpApOE/AEmSXg5C5M7Xs6hduzY2NjaYmJjg7u5ORESEti/5B/f+/ftp1KgRpUuXxszMjF69erFjxw4AChQooK0vcnFxoWHDhpibm+Pi4qLrK/mW2qVLl3jnnXcYOHCgtm/BggW4u7vTo0cP/vrrL0qWLAmoszUmJibUrFlTu0W5YcMGNmzYgIeHB56enoSGhhIWFpbhe/Ty8uLgwYPcv38fCwsLfHx8OHDgAEFBQdqM4Lp162jdurXBudevXycsLIwGDRpQvXp1zM3NOX78eLrXCggIoHv37oA6G5VZ0JbW1atXdbNxnp6eXLhwgSNHjvDZZ58ZrfdojKmpKQ0aNCAwMJBHjx5ha2ur21+mTBmuXLmSaT8ZvZ/kmcf69evTtm1bWrdujZWVldEi3SEhIdSsWTNLY09Wp04dTpw4wf79+xk9ejSxsbGsWrWKMmXKZCmglbJHZszPTEQQzGgCnx+Fh7ehaAWwKpv5eZIkvbKcnJx0t/LSsrCw0F6bmprq1t0ULlw40/7Nzc21W0UmJiZafyYmJrq+Umvfvj2dOnXStrt168Yff/yR4diSb5MKIRg8eDAffPCB7tjUAZ+xMdrZ2TFnzhzq1auHq6srW7du5ezZs9q6ouDgYP7880+DcxcuXMjdu3exs7MD1NuWAQEBjBw50uDYO3fusGXLFo4dO4aiKCQmJqIoCuPGjcPJyYmDBw9mWvi8YMGCxMbGattFixbVXrdp04aPP/6YW7duUaFCBS5duqTti4yMpEKFCrq+unfvTseOHRk6dKjBdWJjYylYsGCGY8no/UDKmrDUoqOjtcA2rX///ZcaNWpw7949EhISMDMzMzrutGrUqEGRIkU4fvw4u3btYuXKlaxZs4bY2Fju37/PW2+9xT///JNhH1Lm5ExYVv3qCtMbw4TqsHNSXo9GkqQXWJMmTXj8+DHTpk3T2o4ePZqlW1HJateuzfbt27l16xaJiYkEBATQsGHDpx7Tzp07sbe3f6pzW7ZsyaxZs4iJiQHU23I3btzAysrK6CL3ZL6+vowfPx4/Pz98fX2ZOnUqHh4eKIrCiRMncHR0xNTU1OC8gIAA1q1bR0REBBERERw8eDDdtVGLFy+md+/eXLhwgYiICC5duoSdnR1BQUF8+umnzJ07l3379mnHL1261OAhhBo1aujWVl27dk0LQIODg0lKSsLa2ppatWoRFhbG+fPniYuLIzAwkPbt2xu858GDB9Ojh2H55DNnzmi3PS9fvmz0VmxG7yc9mc2EKYpC48aNtV8M5s6dazQwPX/+vBbEX7hwgdDQUGxtbRk9ejSRkZFEREQQGBhIkyZNZACWQ2QQ9jQ2DcnrEUiS9AJTFIVly5axadMm7O3tcXJyYvDgwZQrVy7LfZQvX54xY8bQuHFj3Nzc8PLyynRGJ63kNWFubm7MmzePCRMmZPetANCiRQt69uyJj48PLi4udO7cmejoaKytralfvz7Ozs4GC/NBDUiuXr2Kj48PZcuWxdLSUpuxWbt2rdGUDREREVy4cEFLTQFgZ2dHsWLFdMFUsoCAADp27Khr69SpEwEBAZQtW5bAwEC++eYbHBwcqFGjBuvXr8fKykp3vKOjI1FRUVpAuXjxYpydnXFzc2PAgAEEBgaiKApmZmb88ccftGzZkho1atC1a1ecnJx0fSmKwjfffEOpUqUMxrp161batm0LqLdAk9dbZfX9PIuxY8cyceJEqlatyu3bt7V0HCtXrtTWEe7cuRM3Nzfc3d3p2LEjU6ZMMfo+pJyjiJfs6T9vb29x4MCBXOt/yIReLC11lDeiYxhx6076B353FQoUyrVxSJL09E6dOiUfpX/BNW/enL///pvy5cvn9VAA+OWXX7CyssrVp//8/PxYsWIFJUqU4I8//qBSpUoGM2nSyy2d/3vSfZ5argl7Wg9uQAHbvB6FJEnSS2njxo15PQSdjz76iEWLFuVa/zdv3uSrr77S0oR8+umnuXYt6eUhb0dmh3PKolZ+zTifjCRJkvTysLS0pHfv3rnWf+nSpbP8lKX06pBBWFYVKAKdZ+nbzm7Om7FIkiRJkvTSk0FYVtTsAO9tVV9Xa5nS/s+bRg+XJEmSJEnKjAzC0rHCqghzj3blaLMT0HUulK6u7ug4VX/gob+f/+AkSZIkSXrpySAsA+NuNeJGrA1bt8JHH8GDB0ChklDCLuWglZ9BwuM8G6MkSZIkSS8nGYRlIC7JjD59oEkTmDoV/v33yY4+aQrkHl3w3McmSdKL7dq1a3Tv3h17e3u8vLxo06YNZ86cYdu2bbz++uu5fv1t27ZRrFgx3N3dcXV1pVmzZty4cQOAOXPmULp0adzd3alZsybTp09/qmvcu3ePKVOmPNM4x4wZY1DUOnl8Hh4eVKtWjZYtW7J7925tf9++falQoQKPH6u/AN+6dcugRNCkSZOwtLQkKipKa3v48CG9evXSiqI3aNBAS0Bramqqq7k4ZswYo+P94osvtPJRQgi+//57qlevTo0aNbSC5VFRUbRr1w43NzecnJyYPXs2oOZAUxSFH374Qevv1q1bmJuba09L/vHHH8yalWb9cQY6dOigy6kGMHToUCpUqIC7uzvOzs5Gi7qnZ+7cuVSrVo1q1aoxd+5co8f8+OOPuLq64u7uTosWLbRSTCtWrNDavb292blzZ5av+8oSQrxUX15eXiI3/TS+p3Ce4yyc5zgL21ordNXitm9PdeDMlkIMKap+jbXL1TFJkpQ9J0+ezNPrJyUlibp164o///xTawsJCRE7duwQW7duFW3bts20j4SEhGcaQ9rr+Pv7i59++kkIIcTs2bPFJ598IoQQ4vr166JUqVLi2rVr2b7G+fPnhZOT0zONs1GjRuLGjRu6ttTjE0KILVu2iLJly2p/r3369BEVK1YUU6ZMEUIIcfPmTVG5cmVdH7Vr1xYNGjQQs2bN0tpGjRolvvzyS207NDRUxMbGCiGEKFy4cKZjvXXrlqhTp462PWvWLNG7d2+RmJgohFA/SyGEGDlypBg4cKAQQogbN26IEiVKiMePH4vz588LOzs74e7urvUxZcoU4ebmpr3fBw8e6PZn5O7du8LGxkY4OjqKc+fOae1DhgwR48aNE0Ko/xasra21MWbk9u3bws7OTty+fVvcuXNH2NnZiTt37hgcFxUVpb3+9ddfxQcffCCEECI6OlokJSUJIYQ4cuSIcHBwyNL7yE/S+b8n3ZhG5gnLQFycPhmrn1+qja7zYHxV9bVVebh6BEo7gpkFkiS9OGz9V+dKvxFj2qa7b+vWrZibm/Phhx9qbW5ualqbbdu2ERMTQ+fOnTl+/DheXl78888/KIqCra0t3bp1Y+PGjQwcOBAhBKNGjUIIQdu2bRk7diwARYoU4aOPPmLNmjWUL1+eUaNGMXDgQC5evMikSZMMEoAKIYiOjqZq1aoGYy1Tpgz29vZcuHCBQYMGUbRoUQ4cOMC1a9f4+eef6dy5MwDjxo1j4cKFPH78mI4dOzJs2DD8/f21gtLNmzfX6hsmH29hYcGAAQP48ssvOXLkCFu2bGHLli3MnDmT+fPnc//+feLi4nTFs41p3Lgx77//PtOmTeOXX34B1BmpX375hffee8/g+HPnzhETE8OUKVMYOXIk77zzDqBmqa9cubJ2nIODQ4bXTWvJkiW6LP9//vkn//77LyYmJtpnCWrW/OjoaIQQxMTEULJkSS07fqFChahRowYHDhzA29ubBQsW0LVrV202qVChQtja2hIcHEzt2rUzHM/SpUtp166dVhngu+++MzimRo0amJmZcevWLW186Vm/fj3NmzfXirk3b96cdevWGZRgSl1b88GDB1od0yJFihhtl9Inb0dmIC7OUrf9ZAZaVaQ0NPhKfX39OPzlByPKwL2Lz2+AkiS9kJKDq/QcPnyYSZMmcfLkScLDw9m1a5e2z9ramkOHDuHn58egQYPYsmULISEh7N+/n+XLlwPqD7gmTZpw4sQJrKys+OGHH9i4cSPLli3TStBAStmiSpUqsWnTJvr162cwlvDwcMLDw7UA7erVq+zcuZNVq1bh7+8PwIYNGwgLCyM4OJiQkBAOHjzIjh07GDNmjFZQOnUABmrJouR6hwcOHCAmJob4+HiCgoLwe/Ib7aZNm4zWTzTG09OT0NBQbbtSpUo0aNCAefPmGRwbGBhI9+7d8fX15fTp01qtyH79+jF27Fh8fHz44YcfCAsL08559OiR7nbkggWGy0x27dql+3s9d+4cCxYswNvbm9atW2v9ffrpp5w6dYrXXnsNFxcXfv31Vy1QA7XId2BgIJcuXcLU1JTXXntNdx1vb+8s1RkNCAigR48e9OjRI92yRvv27cPExITSpUszf/583XtM/koOtC9fvkzFihW1c21sbLh8+bLRfr///nsqVqzI/PnzGT58uNa+bNkyHB0dadu2bbZuq76q5ExYBuLi9UHYkSNpZsPiHxmetGmoYT4xSZLyTEYzVnmldu3a2NjYAODu7k5ERAQNGjQAoFu3bgDs37+fRo0aabNEvXr1YseOHXTo0IECBQpoMzIuLi5YWFhgbm6Oi4sLERER2nV8fX1ZtWoVoNYOHDhwIFOnqk94L1iwgJ07d2JhYcFff/2lzX506NABExMTatasqQUvGzZsYMOGDXh4eAAQExNDWFgYlSpVSvc9enl5cfDgQe7fv4+FhQWenp4cOHCAoKAgbe3UunXrtFmqzAgjJfYGDx7MG2+8odVjTBYQEMCyZcswMTGhU6dOLFq0iE8//RR3d3fCw8PZsGEDmzZtolatWuzZs4caNWpQsGBBQkJCMhzD1atXdbN2jx8/xtLSkgMHDrB06VL69etHUFAQ69evx93dnS1btnDu3DmaN2+u1cwEaNWqFT/++CNly5bV/r5TK1OmjC7gNOb69euEhYXRoEEDFEXB3Nyc48ePawXCf/nlF/755x+srKxYsGABiqLQq1cvevXqlWG/WTVy5EhGjhzJ6NGj+eOPPxg2bBgAHTt2pGPHjuzYsYMff/yRTZs25cj18is5E5aBxIQC+u3ENAfUed/wpMuHcm9AkiS9FJycnDh48GC6+y0sUpYtmJqakpCQoG0XLlw40/7Nzc21Wz0mJiZafyYmJrq+Umvfvr22oBzUYC8kJIR9+/bpCkanHlty4COEYPDgwYSEhBASEsLZs2e1AtAZjdHOzo45c+ZQr149fH192bp1K2fPntVq62Xllluyw4cPG9Tkq1atGu7u7ixcuFBrO3bsGGFhYTRv3hxbW1sCAwN1s0RFihThzTffZMqUKbz11lusWbMmS9cHKFiwILGxsdq2jY0Nb76p5ovs2LEjR48eBWD27Nm8+eabKIpC1apVsbOz0wVVBQoUwMvLiwkTJmizUKnFxsZSsGDBDMeycOFC7t69i52dHba2tkREROje55dffklISAhBQUFaAJjZTFiFChW4dOmS1kdkZCQVKlTIcBy9evViyZIlBu1+fn6Eh4dz69atDM9/1ckgzEDKb1uJCfr1XV9+CU8eLlIVq4iBhxkU/ZYk6ZXQpEkTHj9+zLRp07S2o0ePZukWU7LatWuzfft2bt26RWJiIgEBATRs2PCpx7Rz507s7e2f6tyWLVsya9Ys7UnCy5cvc+PGDaysrIiOjk73PF9fX8aPH4+fnx++vr5MnToVDw8PFEXhxIkTODo6Ympqmun1t2/fzrRp04yu//r+++8ZP368th0QEMDQoUOJiIggIiKCK1eucOXKFS5cuMCuXbu4e/cuAHFxcZw8eVK3RiwzNWrU4OzZs9p2hw4d2Lp1qzbG6tXVfJKVKlVi82a1osr169c5ffo0VapU0fX19ddfM3bsWG0GMrUzZ85oM1p//PEHf/zxh8ExAQEBrFu3TnufBw8eJDAwMMPx9+rVSwukU38tXrwYUP+eN2zYwN27d7l79y4bNmygZcuWBv2kvo27YsUKHB0dATh79qwWuB86dIjHjx9jbW2d4ZhedTIIS0MoKUGYSDL8zyHVOlswNYcPguDjfVDjyULYx1Fw5XAuj1KSpBeZoigsW7aMTZs2YW9vj5OTE4MHD6ZcuXJZ7qN8+fKMGTOGxo0b4+bmhpeXF2+88Ua2xpG8JszNzY158+YxYcKE7L4VAFq0aEHPnj3x8fHBxcWFzp07Ex0djbW1NfXr18fZ2Zlvv/3W4DxfX1+uXr2Kj48PZcuWxdLSUpuVWbt2rW6Re1oLFizA3d2d6tWrM2rUKJYsWWIwEwbqrKOnp6e2HRgYqJvZA3WWKjAwkHPnztGwYUNcXFzw8PDA29ubTp3UmsBp14Qlr4dLrW3btmzbtk3b9vf3Z8mSJbi4uDB48GBmzJgBqCkcdu/ejYuLC02bNmXs2LGUKlXKYNx9+vQx+t537dpF8+bNAQgNDTUIZCIiIrhw4YIuNYWdnR3FihVj3759RvvMipIlS/Ljjz9Sq1YtatWqxU8//aQFif379+fAgQPa+3Z2dsbV1ZUNGzbw66+/AuqDC87Ozri7u/PJJ59ot0Gl9CnG7rO/yLy9vUXyN0Ju+HFiF5Zbq9PGYd8tA9MSJD0sQPxtK+0Yox/Z2U3wz5MC30Vt4KsTuTZGSZIydurUKaM/sKUXR/Pmzfn7778pX758Xg8lWxo0aMCqVasoXrx4rvR/+PBhJk6cqD1w8Prrr7N06VIKFCiQyZnSiyCd/3vSjUTlTFgaQklZ+GVeLJZyPffyWv+UdRQODhAXZ+REu1S3Ce5HwtBicCc8F0cqSZL08tq4ceNLF4ABTJgwgYsXc+8p+Fu3bvG///1P2161apUMwPIxGYSlIUgJwkyKx2ivn6Sm4fRpsLAwMhtmag7Nh+vbfvPIpVFKkiRJeaFOnTq4urrmWv/JDxRIrwYZhKWRpCRpr4u4pjwlkjaVjdEHkOp/btgWH2vYJkmSJEnSK08GYWmkngmzKJPy1M+Th14yNzgSan+Qsn1sYfrHSpIkSZL0ypJBWBpJStpkYKpT16J02x9/nE4HFlbQcmTK9qG/c2hkkiRJkiTlJzIIS0Okuh2ZWoc/9dXgnzyJbJypOZR7smYgcj/M75JDo5Mk6WVhamqqS3kwZsyYDI9//PgxzZo1S7dkzotq7969Bvm7IiIiKFiwIB4eHtSoUYPatWszZ84cbf+cOXMwMTHRkpsCODs767L9h4SEoCgK69at0/U9cuRInJyccHV1xd3dXUvJ0KhRIxwcHAwSkKa1fPlyXZmdl8mkSZN4+PBhts7Ztm0br7/+OqAu8k9d1iqttJ/NwoULqVmzJk5OTvTs2VNrHzhwIE5OTtSoUYMBAwZoucFsbW11lQFArQiRnPPs2LFj9O3bN8tjnzRpEpaWlkRFpUyCbNu2jWLFiuHu7k6NGjW0TP1ZcfDgQVxcXKhatapu3KmtWLFC+97y9vZm586Un/2p/02nrc/6tGTZIgPGg7Bss6kF1578BxO2IWf6lCTppZGVMjipHT6s5hfMzjmJiYlZSnaaFUIIhBC6GodZkV6+L3t7e+09hYeH8+abbyKE0MoU2djYMHLkyHQDzoCAABo0aEBAQIDW/549e1i1ahWHDh3CwsKCW7duEZfqcfX58+fj7e2d4Xh//vlnVq5cadCekJCgFdl+UU2aNIm33nqLQoUKPdX5bdu25ccff8Tf399oH6k/m7CwMEaPHs2uXbsoUaIEN55kKt+9eze7du3SAugGDRqwfft2GjVqBEB0dDSXLl2iYsWKnDp1Ste/i4sLkZGRXLx4McOSV8kCAgKoVasWS5cu1ZW3Si7H9eDBA9zd3WnXrp0uV1x6PvroI6ZPn06dOnVo06YN69ato3Xr1rpjmjZtSvv27VEUhaNHj9K1a1et2kF2/01nhZwJM5AqMjZ2a1IRKAXisbAw3KVT71P9dtyDZx6ZJEkvP1tbW4YMGYKnpycuLi6EhoZy48YN3nrrLfbv34+7uzvnzp1j8+bNeHh44OLiQr9+/Xj8+LF2/qBBg/D09GTRokXY2toyePBg7Tf3Q4cO0bJlS+zt7bU6kQDjxo2jVq1auLq6MmTIEECdsXJwcODtt9/G2dlZV7Jm//79WkmeFStWULBgQeLi4oiNjdVlf9+8eTPNmjXL8D1XqVKFiRMnajUjQc1/deLECU6fPm1wvBCCRYsWMWfOHDZu3KiVCrp69SqlSpXSSiuVKlXKoPh1Rs6cOYOFhYWWOLVv3758+OGH1KlTh4EDB3Lu3DlatWqFl5cXvr6+2g/f69ev07FjR9zc3HBzc2P37t0ATJw4EWdnZ5ydnZk0aZL2mTo6OtK3b1+qV69Or1692LRpE/Xr16datWoEBwcDMHToUHr37o2Pjw/VqlVj+vTpgH7mCtRi4HPmzOG3337jypUrNG7cmMaNGwNqTU8fHx88PT3p0qWLVtFg3bp1ODo64unpydKlS7W+FEWhUaNGWj3RjD6b6dOn88knn1CiRAlArWeZ3EdsbCxxcXE8fvyY+Ph4ypYtq/XTtWtXLbBOLjCeWrt27TLN7A9qcfSYmBhGjBiRbnHywoUL4+XlpatikJ6rV69y//596tati6IovP322yxfvtzguCJFimgJZh88eJDryWZlEGYgdZ6wEP0u00QqD1xDpS83UKL844y7KVkFvruSsn0j42KskiTlkqHFcucrE2kzsKee8SlVqhSHDh3io48+Yvz48ZQpU4YZM2bg6+tLSEgIFSpUoG/fvixYsIBjx46RkJDAn3/+qZ1vbW3NoUOH6N69O6CWyQkJCcHX15e+ffuyePFi9u7dqwVbGzZsICwsjODgYEJCQjh48KBWRzIsLIyPP/6YEydO6Er4eHh4aL/1BwUF4ezszP79+9m3bx916tQB1JxW5ubmFCuW+efh6empq59oYmLCwIEDGTVqlMGxu3fvxs7ODnt7exo1asTq1asBNXP/pUuXqF69Oh9//DHbt2/XnderVy/t8zaWwX/Xrl0GMyaRkZHs3r2biRMn8v777/P7779z8OBBxo8fz8dPFv8OGDCAhg0bcuTIEQ4dOqTVBp09ezb79u1j7969TJ8+XZv5O3v2LF9//TWhoaGEhoby77//snPnTsaPH697v0ePHmXLli3s2bOH4cOHc+XKFdIzYMAAXnvtNbZu3crWrVu5desWI0aMYNOmTRw6dAhvb28mTpxIbGws7733Hv/99x8HDx7k2rVrun68vb2Nls9K+9mcOXOGM2fOUL9+ferWravdFvbx8aFx48aUL1+e8uXL07JlS11y0k6dOmmB33///Ue7du2ydP20AgMD6d69O76+vpw+fVorJp/a7du32bt3L05OTpw+fdpoXUx3d3fu3bvH5cuXsbGx0c61sbHh8uXLRq+9bNkyHB0dadu2LbNmzdLaY2Nj8fb2pm7dukYDuKfxYs+95olUM2EmKeklYiNLULxeSrQdZXqPtWvL0ro1jBwJgYGwcyfo/i8qkKoQ74wm8NYSqJrxb4ySJOUPGd26SJ5h8vLy0s1UJDt9+jR2dnZaLcI+ffowefJkvvjiC0Atvp1a8voUFxcXYmJisLKywsrKCgsLC+7du8eGDRvYsGEDHh5q7sKYmBjCwsKoVKkSlStX1pW/SWZmZoa9vT2nTp0iODiYr776ih07dpCYmKit+9mwYQMtWrTI0udhbP1Nz549GTlyJOfPn9e1BwQEaAFm9+7d+fvvv+nUqRNFihTh4MGDBAUFsXXrVrp168aYMWO0dUaZ3Y68evUqpUuX1rV16dIFU1NTYmJi2L17N126pKzhTZ593LJlC3//rT5kZWpqSrFixdi5cycdO3bUCq6/+eabBAUF0b59e+zs7HBxcQHU8kRNmzZFURRcXFx0697eeOMNChYsSMGCBWncuDHBwcFZzsS/d+9eTp48Sf369QG1FqaPjw+hoaHY2dlRrVo1AN566y1dDdMyZcoYDfbSfjYJCQmEhYWxbds2IiMj8fPz49ixY9y6dYtTp04RGRkJqHnNUhcJt7a2pkSJEgQGBlKjRg2D257pXT+tgIAAli1bhomJCZ06dWLRokV8+ql6hykoKAgPDw9MTEzw9/fHyckJyN6t/Ix07NiRjh07smPHDn788Uc2bdoEwIULF6hQoQLh4eE0adIEFxeXp67HmkwGYWmI1GvCRMpai6SHBSjicUHbtrS7SXBwWZo2hR9/VJO3duwIW7Zk0Pk/nWBoVAYHSJKU417Af3PJt9NMTU1JMJp0MGPJP/jT9mdiYqK9Tt5OSEhACMHgwYP54IMPdOdFREQY9JWan58fa9euxdzcnGbNmtG3b18SExMZN24coK4H++qrr7I05sOHDxuUczEzM9MKWSdLTExkyZIlrFixgpEjRyKE4Pbt20RHR2NlZYWpqSmNGjWiUaNGuLi4MHfu3Cwv9i5YsKBukTekfJZJSUkUL148R36Qp/07SP33k/rvO+2tLkVRMDMzIykp5edQ8q3YtIQQNG/e3OBWXWbjj42NpWDBggbtaT8bGxsb6tSpg7m5ufYLQXJQVrduXYoUKQJA69at2bNnj25Bfrdu3fjkk090D2Nkdv3Ujh07RlhYmFY/My4uDjs7Oy0IS14Tltrp06cNfjlJtm3bNipUqKAFjqDOgFaoUCHDcfj5+REeHs6tW7coVaqUdnyVKlVo1KgRhw8ffuYgTN6OTCOBeO11YmwFkmLVOLVQ9euYFkzZV9TrAhuP3mLTppTs+Vu3QqqHfVSf7NdvRx7MjWFLkpSPODg4EBERoa11mTdvHg0bNszkrPS1bNmSWbNmaWuGLl++rC20zoivry+TJk3Cx8eH0qVLc/v2bU6fPo2zszNCCI4ePYq7u3um/URERPDNN9/w2WefGezr27cvmzZt4ubNm4C6xszV1ZVLly5phao7derEsmXLOH36NGFhYdq5ISEhuluomalRo0a664eKFi2KnZ0dixYtAtQg58iRI4C6WDv5dnBiYiJRUVH4+vqyfPlyHj58yIMHD1i2bJnBk4GZWbFiBbGxsdy+fZtt27ZRq1YtKleuzMmTJ3n8+DH37t1j8+bN2vFWVlZER6v5K+vWrcuuXbu09/PgwQPOnDmDo6MjERERnDt3DsAgSDtz5oz2tGJGn02HDh20YuW3bt3izJkzVKlShUqVKrF9+3YSEhKIj49n+/btBsF1x44dGThwIC1btjS4TurrBwcH8/bbbxscExAQwNChQ4mIiCAiIoIrV65w5coVLly4YHBsMgcHB0JCQox+FS9enPLly1O0aFH27t2LEIK///6bN954w6Cfs2fParO2hw4d4vHjx1hbW3P37l1tZvTWrVvs2rWLmjVrpjuerJJBWBpJqabMRVwpkuLSnyw8W+g0bdvq2/buVf9MSIBHjyCxZHV9Jv0l7+bkcCVJekGlXRPm7++f5XMtLS2ZPXs2Xbp0wcXFBRMTEz788MOnHkuLFi3o2bMnPj4+uLi40LlzZ+2HeUbq1KnD9evX8fPzA8DV1RUXFxcUReHgwYN4eHiku3D53LlzWoqKrl27MmDAAN0TbskKFCjAgAEDtKAwICCAjh076o7p1KkTAQEBxMTE0KdPH2rWrImrqysnT55k6NCh2nGp14QZe1jAz8+Pw4cPG701CurtzJkzZ+Lm5oaTkxMrVqwA4Ndff2Xr1q24uLjg5eXFyZMn8fT0pG/fvtSuXZs6derQv39/7XZvVrm6utK4cWPq1q3Ljz/+yGuvvUbFihXp2rUrzs7OdO3aVdfn+++/T6tWrWjcuDGlS5dmzpw59OjRA1dXV+1WpKWlJdOmTaNt27Z4enpqC+qTbd26lbZpf3AZ+WxatmyJtbU1NWvWpHHjxowbNw5ra2s6d+6Mvb09Li4u2oMKadd9WVlZMWjQIKM1L1Nf/+LFi0ZnxQIDAw2+Bzp27JilBf0ZmTJlCv3796dq1arY29trT0ZOnTpVe4hlyZIlODs74+7uzieffMKCBQtQFIVTp07h7e2Nm5sbjRs3xt/fP0eCMCW9b8YXlbe3tzhw4ECu9f/Zb7XYVkyd/n0Y8QHx96phYpH+7YILY9uQukB6v37w55/qrcl1W+Ko+PlGACIsU3Ks0GsxVGueK+OXJAlOnTpl8Nu5lLNGjBhB1apVtbVbL4vPP/+cdu3aZfpEZ24bOnQoRYoU4Ztvvnlu17x+/To9e/bUza6lltufzePHj2nYsCE7d+7EzMyMb7/9lt69e+dqLc7nLZ3/e9J9xFLOhKWRNiTNKAADMC2iTk8GBECDRgnMCXxModIP2W9xQAvAAGYnpJqWPb8jp4YrSZKUJ3744YeXLgAD+O6777Kd8DS/uHjxIhMmTEh3f25/NhcvXmTMmDFaPrZx48blqwDsaciZsDQ++c2bHcXUwOphxPskPqqi2x9/txCmheK04Oz+AVt+/rYkNaua03P6vnT7tSCO05Z91Q37JtB7Wa6MX5IkORMmSVLekDNhz0ik+ahijtnothPuFeLezpRq3kW9Ixix9VCGARjAYwpwOulJX+e2kJQEX3wBkybB7ds5MHBJkiRJkl4qMghLQ+gy5qtBV2oJUYWIPmCb5f5aF07JWVNJSXkaydwskV9/hS+/hFKlQFHUPGOSJEmSJL0aZBCWhkizlfTYjMeXi2stCXcLAQpRe43nBimjlOSXTl6cG9WGQwPbMvWnstxepybteyPuf9pxPzc3LKLq6wt//fXs70GSJEmSpBefDMLSSEqzRk7Em3JjcS2cXiuKQ1kr3vFTi47e21nN6Pn7x9bhzdrlMDVRSM5T9+ic+ojwGVFRO+5rnz+Mnv/zz8/4BiRJkiRJeinIICyNtI8pJMWbMfW3Aqwe4Mv6L/2YNM4cIUAkmNIgqhF3Ntfgws9tuDihFRd+bgNC/UgPH4bk2rmJMZZc/EUt7bE9MeVJEPHgjtqXAEdHta1Pn1x+g5IkPRempqa6PGFjxozJ8PjHjx/TrFkzgzqTL7q9e/fy3nvv6doiIiIoWLCgliesdu3auuzpc+bMwcTEhKOpsls7OzvrSvqEhISgKIpWszDZyJEjcXJywtXVFXd3d/btU9fjNmrUCAcHB+3z7ty5s9HxLl++nOHDhz/ju86eiIgI/v3332yfl1wHFNTyTakT1abVuXNnwsPDATXD/Pvvv0/16tVxdHRkyZIlgPp0YuPGjfHw8MDV1ZU1a9YAakZ5RVGYMWOG1l/y5z9+/HgAvvnmG7ZkWBJGz93d3eDp2b59+2JnZ4e7uzuenp7s2bMny/2NHj2aqlWr4uDgwPr1640e06tXLxwcHHB2dqZfv37Ex6sJ1ufPn6/luKtXr56WhPeFIIR4qb68vLxEbnr3V3fhPMdZOM9xFlWG/SoK2l8Tjx4ZPzYpSYixY4UwNU0OpVK+TEwM2yoPWiWa+/8pxJCiKV+PH2j9TZ6sHvfggfHrSZKUNSdPnszrIYjChQtn6/g9e/aIpk2bZuuchISEbB2fkaSkJJGYmJjt83766SexePFiXdv58+eFk5OTtn3u3Dnh5uYmZs2aJYQQYvbs2aJixYqia9eu2jFOTk7i/Pnz2vbAgQNFgwYNxNtvv6217d69W9StW1fExsYKIYS4efOmuHz5shBCiIYNG4r9+/dnOl4fHx9x8+ZNg/b4+PgsvNvsi4+PF1u3bhVt27bN9rl9+vQRixYtEkIIsW3bNtG/f3+jxx0/flx06NBB2/7pp5/E999/L4QQIjExUXu/7733npgyZYoQQogTJ06IypUrCyGE2Lp1q3B2dhbNmzfX+hg4cKBwc3MT48aNE0IIERERodufkZMnTwpnZ2fx2muviZiYGKPvZ/369cLFxSVL/Z04cUK4urqK2NhYER4eLqpUqWL0e3/16tUiKSlJJCUlie7du2vvddeuXeLOnTtCCCHWrFkjateunaXrPo10/u9JN6aRM2FpCCX1XJgg9lJJLC2NH6soMHAg3L8PZcvq9yWX/vrf/+CTT9TXl6c11N2SBGBUee1l8nHplL+SJCkfsLW1ZciQIXh6euLi4kJoaCg3btzgrbfeYv/+/bi7u3Pu3Dk2b96Mh4cHLi4u9OvXTyuZYmtry6BBg/D09GTRokXY2toyePBg3N3d8fb25tChQ7Rs2RJ7e3stCzioOZlq1aqFq6srQ4YMAdQZGgcHB95++22cnZ25dOmSdvz+/fu1QuMrVqygYMGCxMXFERsbS5UqKal7Nm/enGlyzypVqjBx4kR+++03re3111/nxIkTnD592uB4IQSLFi1izpw5bNy4UaufePXqVUqVKqXVYixVqhSvvfZalj/7M2fOYGFhQalSpQB1ZubDDz+kTp06DBw4kHPnztGqVSu8vLzw9fUlNDRUd5y3tzfVq1fX6hbGxsbyzjvv4OLigoeHB1u3bgXUmb727dvTpEkTmjZtir+/P0FBQbi7u/PLL7+QmJjIt99+q/19/PVkMbAQgk8//RQHBweaNWumKy3l6+vLpk2bjNYanT9/vq4Ez6xZsxg8eDCg1qtMfr+KonD//n0AoqKidJ9d5cqViY2N5fr16wghWLdunZZRPnn/7du3uXbtWqafc0BAAL1796ZFixZa1YG0/Pz80i0hldaKFSvo3r07FhYW2NnZUbVqVYKDgw2Oa9OmDYqioCgKtWvX1mpF1qtXjxIlSgBquafUNSTzmizgnUba25EizjzTcwoVguTvy7QVPPr2hZIl4dNPoUSJItT5Bd6L+4rpBSamHBS2UZdBf9UqtQ5l48ZP9x4kSUrhMtclV/o91udYhvuTyxYlGzx4sFZguFSpUhw6dIgpU6Ywfvx4ZsyYwYwZMxg/fjyrVq0iNjaWRo0asXnzZqpXr87bb7/Nn3/+yRdffAGAtbU1hw4dAsDf359KlSoREhLCl19+Sd++fdm1axexsbE4Ozvz4YcfsmHDBsLCwggODkYIQfv27dmxYweVKlUiLCyMuXPnUrduXd34PTw8tGLQQUFBODs7s3//fhISEqhTpw6g1tAzNzenWLFimX5enp6eWlADanAwcOBARo0axdy5c3XH7t69Gzs7O+zt7WnUqBGrV6+mU6dOtGjRguHDh1O9enWaNWtGt27ddDU1e/XqpZXBad68uVZoPNmuXbvw9PTUtUVGRrJ7925MTU1p2rQpU6dOpVq1auzbt4+PP/5YuwUXERFBcHAw586do3Hjxpw9e5bJkyejKArHjh0jNDSUFi1acObMGUCtO3j06FFKlizJtm3btL9bgGnTplGsWDH279/P48ePqV+/Pi1atODw4cOcPn2akydPcv36dWrWrEm/fv20z6tq1aocOXIELy8vg/fVo0cPAO7duwfAjz/+yLZt27C3t+ePP/6gbNmyDB06lBYtWvD777/z4MEDNm3apOunc+fOLFq0CA8PDzw9PXWFyJP/Dnft2kWnTp0y+qtmwYIFbNy4kdDQUH7//Xd69uxpcMx///2Hi4v6b3PcuHHMnz/f4Bg/Pz9+++03Ll++rPv+tLGx4fLly+lePz4+nnnz5vHrr78a7Js5c6YuuMxrciYsrTRB1JPv5yw7cUK/bWOjBmmOjimzZRuTvPk5PtV01/zOjBmdxB+p1up36KDWn5Qk6eVUsGBBXRHhbqmmuJNnmLy8vHTroJKdPn0aOzs7qldXcxL26dOHHTtSKm10SzNd3r59ewBcXFyoU6cOVlZWlC5dGgsLC+7du8eGDRvYsGGD9sM1NDRUW19UuXJlgwAMwMzMDHt7e06dOkVwcDBfffUVO3bsICgoSCtUvWHDBlq0aJGlz0MYSQzes2dP9u7dy/nz53XtAQEB2nqi7t27a0WoixQpwsGDB5k2bRqlS5emW7duurVm8+fP1z7vtAEYqDNppUuX1rV16dIFU1NTYmJi2L17N126dMHd3Z0PPviAq1evasd17doVExMTqlWrRpUqVQgNDWXnzp289dZbADg6OlK5cmUtCGvevDklS5Y0+lls2LCBv//+G3d3d+rUqcPt27cJCwtjx44d9OjRA1NTU1577TWaNGmiO69MmTJcuXIlw/eVkJBAZGQk9erV49ChQ/j4+GilkQICAujbty+RkZGsWbOG3r17k5R82+bJe1y0aBEBAQFaUJeV66d24MABSpUqRaVKlWjatCmHDx/mzp072v5vv/0Wd3d3pk2bxsyZM7U2Y4W3U8+cZsfHH3+Mn5+fQUH1rVu3MnPmTMaOHftU/eYGOROWhj5PWAJZ+AVPp2ZNiIqC33+Hzz4z3P9hQ3umbj/HlMQ36PpgD7bFLwLw++hrDP/lNczM1ODr/n0wfzIJl5AApqZP+YYk6RWX2YxVXkieYTA1NTV6eykzhQsXNtqfiYmJbvbCxMSEhIQEhBAMHjyYDz74QHdeRESEQV+p+fn5sXbtWszNzWnWrBl9+/YlMTFRC3DWrl3LV199laUxHz582CCTuJmZGV9//bXuh2JiYiJLlixhxYoVjBw5EiEEt2/fJjo6GisrK0xNTWnUqBGNGjXCxcWFuXPn0rdv3yyNoWDBgkRFRenakt9/UlISxYsX12b/0kpbqDy9wuVp+zVGCMHvv/9Oy5Ytde3JC+XTExsba7TgdcGCBbVbttbW1hQqVEgL9Lt06aIFOzNnztQedPDx8SE2NpZbt25p/ZQrVw5zc3M2btzIr7/+yu7du7N0/dQCAgIIDQ3F1tYWgPv377NkyRLt4Y1x48YZPDSR2UxYhQoVdLfKIyMjqVChgtHrDxs2jJs3b2q3eJMdPXqU/v37s3btWqytrTN8D89Trs6EKYrSSlGU04qinFUUxd/I/kqKomxVFOWwoihHFUVpk5vjyZJU/64symb8DyI9RYvC99+rf6bVyrmc9vqTsJQng8oWvkH//sZnv8zM4KOPUrajouAVLX0mSa8EBwcHIiIitDUz8+bN0912y66WLVsya9YsYmJiALh8+bJuvVF6fH19mTRpEj4+PpQuXZrbt29z+vRpnJ2dEUJw9OhR3S3X9ERERPDNN9/wmZHfTPv27cumTZu4efMmoK4xc3V15dKlS0RERHDhwgU6derEsmXLOH36tO4JwZCQECpXrpzFTwFq1KiR7jqkokWLYmdnx6JFiwA1UEr9FN2iRYtISkri3LlzhIeH4+DggK+vrxY8nDlzhosXL+Lg4GDQt5WVFdHR0dp2y5Yt+fPPP7Wn986cOcODBw/w8/NjwYIFJCYmcvXqVW2NWbIzZ87g7Oyc4ftSFIV27dqxbds2QP08a9asCUClSpW04t2nTp0iNjbWYGZw+PDhjB07FlMjv/mnvv7gwYNZtkxffi8pKYmFCxdy7NgxIiIiiIiIYMWKFdpMZnoymwlr3749gYGBPH78mPPnzxMWFkbt2rUN+pkxYwbr168nICAAE5OU8ObixYu8+eabzJs3T5tdflHk2kyYoiimwGSgORAJ7FcUZaUQ4mSqw34AFgoh/lQUpSawBrDNrTFlReqZMFOLzP+Tyq7qZYtor1evVmCo+vrQBw05cMWdt5ZO5/Rtw2+SqVNTUl4k++cfiI6Gnj2NB3ySJOWdtGvCWrVqlWmaimSWlpbMnj2bLl26kJCQQK1atfjwww+feiwtWrTg1KlT+Pj4AOptvX/++cfoD9rU6tSpw/Xr1/Hz8wPA1dWVa9euoSgKBw4cwMPDI90ZoXPnzuHh4UFsbCxWVlYMGDDA6IxVgQIFGDBgAJ9//jmgzqR07NhRd0ynTp34888/cXJy4rPPPuPevXuYmZlRtWpVpk2bph2Xek1YqVKlDNY8+fn58fXXXyOEMDru+fPn89FHHzFixAji4+Pp3r07bm5ugBrA1K5dm/v37zN16lQsLS35+OOP+eijj3BxccHMzIw5c+YYrKNK/txMTU1xc3Ojb9++fP7550RERODp6YkQgtKlS7N8+XI6duzIli1bqFmzJpUqVdL+vgCuX79OwYIFKVeunEH/bdu2Zdu2bdoDEmPHjqV379588cUXlC5dmtmzZwMwYcIE3nvvPX755RcURWHOnDkGn0O9evUM+gd1ndXZs2fx9larwBw7dky7DZ4sKCiIChUq6Bb8+/n5cfLkSd2t3exycnKia9eu1KxZEzMzMyZPnqx977Zp04YZM2bw2muv8eGHH1K5cmXtc3vzzTf56aefGD58OLdv3+bjjz8G1BnY3KxBnR25VsBbURQfYKgQouWT7cEAQojRqY75CwgXQox9cvwEIYTx74AncruAd5/JrhwqkvKZ5PStDCEEdoPVGbY9g5tQ/hfDf1BWoyN5nGDBw8cFWL8eXn89836PHgWX3Fl/LEkvHVnAO/eNGDGCqlWrGuSCetF9/vnntGvXLtMnOlPr27cvr7/+erq5x56HX375haJFi/Luu+8a7Hv06BGNGzdm165dmQbWT2vZsmUcOnSI//1PrfzSsmXLdPN1vcpepALeFYBLqbYjn7SlNhR4S1GUSNRZMCOrqJ4vfYqKnJf6t467D+LhG8Pke9GDbYj7sTRxBxbQti1kcgseAFdXeIZfNCRJkrLlhx9+eOkCMIDvvvuOhy/heo7ixYvTJ51s3gULFmTYsGEZPjH4rBISEvj666+1bRmA5Yy8fjqyBzBHCGEDtAHmKYpiMCZFUd5XFOWAoigHktcN5JbcDcH0Lt55CEXKQNd5UN7dYH+hde8DELPxD8SQYiRNb4mIuoIQcCX8HkmJ+tFmsqZTkiTplVe2bFmD22iZmTNnTp7OggG88847mJmlv4KoZcuWVKpUKdeu36VLF4oXL55r/b+qcjMIuwykzkxq86QttXeBhQBCiD2AJVAqbUdCiGlCCG8hhHfaRYQvI+cK6gIuC7MnH3/N9vDBdmhlZL3I0GKYbPweACVyL0ysAUOLUX5uZZQtw3UL+fv3h1T5+iRJkiRJeoHlZhC2H6imKIqdoigFgO7AyjTHXASaAiiKUgM1CMvdqa7MZPzUcY44flnNWPzOnP36HXU/gqFRTC17L2sd7ZyIqYkgPj7lluXKtJ+wJEmSJEkvpFwLwoQQCcCnwHrgFOpTkCcURRmuKEryXPDXwHuKohwBAoC+IreeFMgikeaG5LZL23gYn3vrBw5euGPQ1q69wokbjrq2rsduQe33DTsYVhyzEcU4ExqvNSkKjB8Pq1dDYmKOD1mSJEmSpByQa09H5pbcfjryrT+dOVJIPx3Wxq4NY/1yLsPu2RsxNJu4XduOGNNWtz8pCawL38Wm6BUeJ1gQdqcqAF9/rQZXxD3U1ZwEwOsdlPaT0r1mTAxkkDtQkvIV+XSkJEl54UV6OjLfWHM+Z1e8Vy1TRLe9+9wt3baJCazZUoLjN5y0AAxgwgQoXx4atShESNWZ+k4PzmbKlPSveeMGvGTxtiS91ExNTXF3d8fZ2Zl27dppNf2e1Zw5c/j0009zpK/UbG1tddnTc1qRIur/e1euXMnVRe7u7u4GT2327dsXOzs73N3d8fT0ZM+ePVnub/To0VStWhUHB4d0nwg8f/48derUoWrVqnTr1o24uDhATRLauHFjPDw8cHV1zTQjvvTqkUFYFpiZ5HxO27Wfp9S06jl9n8F+Hx81M76/P1SpktJ+7Rps3w4evTvTc8kMogvW1PZ99KFACDXY2rhRbXNzg/ffV/swMZHljyTpeUmuHXn8+HFKlizJ5MmT83pIL4TXXnuNxYsX50rfp06dIjExkaCgIB48eKDbN27cOEJCQhgzZoxB+ab0nDx5ksDAQE6cOMG6dev4+OOPSTSyxmPQoEF8+eWXnD17lhIlSmhlgkaMGEHXrl05fPgwgYGBWrJQSUomgzADhtNFhcwK5fhVapTPPMV90aIwejScOwdGKlUQcLwLr/20IaVh2QeQpP4H0ayZGowdPAipEkqTlARGCstLkpSLfHx8tBxOwcHB+Pj44OHhQb169Th9+jSgznC9+eabtGrVimrVqjFw4EDt/NmzZ1O9enVq167Nrl27tPaIiAiaNGmCq6srTZs25eJFtRZt3759+eijj6hbty5VqlRh27Zt9OvXjxo1amRYZ/Hnn3/GxcWF2rVra2Vw/vvvP+rUqYOHhwfNmjXj+vXrAGzfvh13d3fc3d3x8PDQyvKMGzeOWrVq4erqypAhQwyuERERoZW+yeg9b9iwAR8fHzw9PenSpYtWcikjAQEB9O7dmxYtWrBixQqjx/j5+aVbuiitFStW0L17dywsLLCzs6Nq1aoEBwfrjhFCsGXLFm12r0+fPixfvhxQ80Lev68+iBUVFaXLJC9JIAt4GzB2x66gWRaypT6Fab29eH/eQQBs/VdToXhBdg5qbLScxrFjEB4OI0aAg4M6QwYQE2eVctDRBeoXQPcAcGyDqak6K9aqVcoi/S++UL8++ogMb2FKUn5wyjF31obVCD2VpeMSExPZvHmzlunc0dGRoKAgzMzM2LRpE9999x1LliwB1FqIhw8fxsLCAgcHBz777DPMzMwYMmQIBw8epFixYtrtLYDPPvuMPn360KdPH2bNmsWAAQO0AODu3bvs2bOHlStX0r59e3bt2sWMGTOoVasWISEhRms+FitWjGPHjvH333/zxRdfsGrVKho0aMDevXtRFIUZM2bw888/M2HCBMaPH8/kyZOpX78+MTExWFpasmHDBsLCwggODkYIQfv27dmxY4dW9sgYY++5YMGCjBgxgk2bNlG4cGHGjh3LxIkT+emnnzL8rBcsWMDGjRsJDQ3l999/p2fPngbH/Pfff7g8KS+SWeHoy5cvU7duXa3dxsbGICHq7du3KV68uJbDK/UxQ4cOpUWLFvz+++88ePDAoIySJMmZsCwoZJ7zM2EA5mb6j//yvUe0/jUo3eOrVIFZs2DQINi7N6V9yn7DMhYE9tBeNmumFgZ/+BBS3wX480/1Scpu3dQ/05RrkyTpGSTXjixXrhzXr1+nefPmgDoj0qVLF5ydnfnyyy85ceKEdk7Tpk0pVqwYlpaW1KxZkwsXLrBv3z4aNWpE6dKlKVCgAN26ddOO37NnjxZo9O7dm507d2r72rVrh6IouLi4ULZsWVxcXDAxMcHJyYmIiAijY+7Ro4f2Z/K6qcjISFq2bImLiwvjxo3Txlu/fn2++uorfvvtN62W44YNG9iwYQMeHh54enoSGhqqK7htjLH3vHfvXk6ePEn9+vVxd3dn7ty5XLhwIcN+Dhw4QKlSpahUqRJNmzbl8OHD3LmT8vT5t99+i7u7O9OmTdNuF2ZWOPpZBQQE0LdvXyIjI1mzZg29e/cmKSkpR/qW8gc5E5aGMPIMQ27NhDWqbph4NvRaNIlJAlOTjBOW1akDt25BqVLwyZoJfFxrZobHg5pLrFMndXF/6hJHCxeqfy5fDmvXQuvW2XkXkvRiy+qMVU5LXhP28OFDWrZsyeTJkxkwYAA//vgjjRs3ZtmyZURERNCoUSPtnNTFn01NTUlInY05m5L7MjEx0fVrYmKSbr+pZ+GTX3/22Wd89dVXtG/fnm3btjF06FAA/P39adu2LWvWrKF+/fqsX78eIQSDBw/O8pqr1OOElPcshKB58+YEBARkuZ+AgABCQ0OxtbUF4P79+yxZsoT33nsPUGe90j4QkNlMWIUKFbh0KaX6XmRkJBUq6KvvWVtbc+/ePRISEjAzM9MdM3PmTNatWweot6RjY2O5desWZcqUyfL7kvI3OROWBbmxJgzU/+QmdnUDoIuXjdbe9a+sPbljba31hDIsCmVYFIl91qYcsMf4vcYrVyD5/7ZPP4Vx41L2tWkD6fySLEnSUyhUqBC//fYbEyZMICEhgaioKO2H9Jw5czI9v06dOmzfvp3bt28THx/PokWLtH316tUjMDAQgPnz5+Pr65teN1myYMEC7U8fHx8A3Xjnzp2rHXvu3DlcXFwYNGgQtWrVIjQ0lJYtWzJr1ixt/dbly5e5ceNGtsdRt25ddu3apa3devDgAWfOnAFg8ODBLFu2THd8UlISCxcu5NixY0RERBAREcGKFSsyDeIymwlr3749gYGBPH78mPPnzxMWFkbt2rV1fSiKQuPGjbWHDebOncsbT0qXVKpUic2bNwPqQwOxsbHkh6ovUs6RQZgBIwvzc+l2JMCbnjZEjGnLuC5uWtvBC3dJSMzalPWIEfptsyr1UjbWD4b4WKPnde+uLtz//Xf45htIvX62SRNo3x4ePcry25AkKQPJKQoCAgIYOHAggwcPxsPDI0szXeXLl2fo0KH4+PhQv359XQ6i33//ndmzZ+Pq6sq8efP49Rmfurl79y6urq78+uuv/PLLL4C6rqlLly54eXlRqlRKVblJkybh7OyMq6sr5ubmtG7dmhYtWtCzZ098fHxwcXGhc+fO2oL97ChdujRz5syhR48euLq64uPjQ2hoKADHjh2jXLlyuuODgoKoUKGCbuG7n58fJ0+e5Grqaf9scnJyomvXrtSsWZNWrVoxefJkTJ88Yt6mTRuuXLkCoK1Zq1q1Krdv39bW/02YMIHp06fj5uZGjx49mDNnjtE1v9KrSyZrTaPH1JocL6jP4+Ba2pX5bQynrHPatB3nGLVG/Y+mndtr/N7DI8vnpv53/WeP8XxY/X8pDf3WQ6W6hielsWiRGoydSnX3Zt8+SPOLnyS98GSy1vyrZcuW6ebrkqS8JpO15oKjN48+l+u8U99Oe/3fkStkJ0COTTXh9VHAN/qds1rC0GLw0LBEUmpduuhnxEBdeyZJkvSikAGYlJ/IICyNsgl59+SKuakJbVxSptntBq8hNj5rxR8tLGD69JTtlst3Gh40xSfTfrp1gydpbQB14b8kSZIkSTlPBmFp9L+Qtx/J8Df0WVmn7QjP8rn9+6e83nDEhQEXz8BHu1MaY66pM2KJGa9DsbJS14vNmgVeXurifUmSJEmScpYMwtIomqDQ716UQbvLXBceJeT+SvVSRSx02xM3nuH4ZcPxpGf27JTXv88uC2Wd4BN9hmf+Z01WODjA+vUwebK65szRUc03Jkkvg5dtvaskSS+3p/k/RwZhaShGc+argq8Gp7svJ0WMaavbfv33ndyINv6UY1q9eum3Hz4ESjvAmzOyPY6KFfXbp0/DjBnwpDatJL2wLC0tuX37tgzEJEl6LoQQ3L59G0tLy2ydJ5O1ZkNh88LP7Vobv/Sj+S87tO3aIzcbBGfGmJurhb+LFVO3p01TSxTh2gXK1ICp9dUd145BOZcM+6pYETZvhqZNU9qSyx2BWodSPm0tvYhsbGyIjIzk5s2beT0USZJeEZaWltjY2GR+YCoyCEtDUQTXzIx/LM8zCKtW1ooTw1riNCTlSaCzN2KoWqZIpucWTVUb/Msv1XJH7dsDZWqm7JjaAAZFQMESGfbVpIm6Pgxgzx6olyoNmYkJ3L4NJUtm4Q1J0nNkbm6OnZ1d5gdKkiTlIXk70og1RYwHWyKDW5W5obCFGeu+SMmA3Wzidk5cydr6MHv7lNdvvAHBwahRU+rEs2NtUyKsLPDxgbQJvvfsUWfDvL2z3I0kSZIkScggLFvyYn2JY7miuu22v+3E1n91pudt2aLfrlNHvX3IoAj9jmHF4UzW8+706QPXr6uvBw+GP/5QXx88qAZjTyp0SJIkSZKUCRmEpZHRwvwkkTc5xAa3djRo+2bRkQzPqVTJcJLL1BSEqQUMTTOb9m9XNXXF7XOQlHlesjJl1L5HjYJvv9Xva9YM5DIcSZIkScqcDMIyMbDWQO313cd382QMHzS0Z1Zf/f2+xQcjsfVfzZV7GafNSBsQmZioM1Z3Pr1tePDvnjA8ewu8mjSBJzV/AejRQ124f+1atrqRJEmSpFeODMIyUbloZe31v6f+zbNxNHEsS8SYttiUKKhrbzR+W4bnlSoFt24ZtluXNuPhwCj43EhJpuNLsjW2rl3VmTEhYPVqWLIEypfPVheSJEmS9MqRQVgaiiIolJRy27Fu+ZTC1y1tW+bFkHR2DmrCF82qadtxCUl0nbqHew/TT95lbQ379xu2Fy4MF+5Xhh9uQLVU721xv2wt2E8tdckjRYG7eTN5KEmSJEkvPBmEGVEoKSUAKWBagDZ2bQB4mPAwr4ak80Wz6qz8tL62HRxxB/fhGzl55T7HIqOMPkDg7Q1nz8K8efp2W9sn68R6LYSqzVJ2nHu6FfZpL12ypFpcXObMlCRJkiQ9GYSloSAMFuefvH0SgDHBY/JiSEa52hQ3aGvzWxDt/tjJyNWnjJ5jbw9vvWVYesjEBBYtAjr8mdL4TydY991TjS0x1dr+r76C3r3Va6xc+VTdSZIkSVK+JIMwIxLSpIGPuB+hvb4Ufek5jyZ9+75ryh89PQzaZ+w8n+HTk6amalb91Lp2BYqUgcKlUxr3ToabZ7I9LhMTdeYrKQm++QYWL1bb33hDzohJkiRJUjIZhBmRQPq1eNadX/ccR5KxskUted31NaPljJKfnlx6KNLouUWLGs5MKQrw7Vl4/ZeUxsm1IO7pbsMqirpAf8SIlDYT+R0nSZIkSYAMwgwoiiAhg3qIxS2LP7exZEf4qDa6dWLJvlqY/oxYu3bwKE2Gi8ePAe9+ULpGSuOo8jC++pNsr9k3cKB++9Chp+pGkiRJkvIVGYSlYWoKiWlmwixNU6qiF7co/pxHlDUmJgquNsU5+EMzPvCrotuXUYZ9S0sICkrZ1matPtmrPzDmOgwvAXsmZ3ts5uZw717K9rFj2e5CkiRJkvIdGYSloSBITDMTtuyNZdprC1OL5zyi7LEuYsHgNjVY9VkDXXtGgViDVIeOGAEPHkB4OPCTkfwS67+DeW9me1zFiqn1K/39YdIk9VZlxYrZ7kaSJEmS8g0ZhBmRmGZhvo2VDQVMCgAw/9T8vBhStjlXKMaw9k66trqj0k878WaquKpIEfVJyhpOJmqJI/9L+gX75zbDsJLwwEgW2AzUqqU+LRkSom5HRsLIkdnqQpIkSZLyDRmEZcBUMdVexyWpyVB3X9lNTFxMXg0pW/rUsyX0f6207Wv3Y7kfG2/02EWLDNtCQ2H5csCyqLpgv8uclJ0iEcbZw+pvsjWm0qUhMDBl+4cf1Fkx+dSkJEmS9KqRQZiBlGjAzMTM6BE3Ht54XoN5ZpbmpgS+n5L133XoBh48TjA4zsRETaqaVseOapD033+AU0d4d6P+gP3TIXh6tsbUrRts2JCy/dFHhrnLJEmSJCm/k0FYBlLPhKVmYfZirwtLq24Va9329KBwo8dZWKTUgOzTR7+vfXs1GIsuXhu+PgNuPVJ2rsnebBhA8+bqk5g7d8K4cerifUmSJEl6lcggzIjP79wD4Gvvr7W2cQ3Haa+TnjJVQ146P7qN9nrSpjBi4xMzOBrmzFEX6KdVtCjMW14WOk6FTjNTdpxale0xFSigllNq1kwN8CpXljNikiRJ0qtDBmFpKIqgf9R9tl+IpKtDV629lW3K2qq5J+fmxdCeiaIoDGlXU9t2/HEd0emsD0tWqJA6K7YqTXz19tvqTBbOnVIaF/SCq+nnJEtPXBzsfZIN4+JFdUZs7dpsdyNJkiRJLx0ZhKWjZAazXQtOL3iOI8k579S30237L81awq62bQ0Lf2/aBEWsFB43TJUO/y+/bCd0tbKCzz7Tt7VpAzNnwpgXp1SnJEmSJOU4GYSlkbZ4d36TusTR6qNXsfVfTZMJ2zI976231Fmxv/5KaXvwACybfEaMyycpjcNLZHtG7Lff1NjN01Pd/ucfNU/Z4MHqbcqLF7PVnSRJkiS9FGQQlkaBAunvs7ZUF7i/5/LecxpN7hj9potuO/zmAz6YdyBL577/PixcqG+z6pwm2ddfftkek6LAwYNw4wb07AmjRqXsq1xZrUEpSZIkSfmJDMLSKFw4/X3dHbs/v4Hkoh61KxE0sLGubf2J6zyKy3ixfrIuXdLm9VJQht0D+6YpTdeOP9XYSpdWA7LINHXHr12Ti/YlSZKk/EUGYWlkdDsyKFItsjjj2IznNZxcU7FkISLGtGXG295aW42f1mVY3igtIaBFi+QthfpTl6bsnGpYTDw7KlRQ+7/xJCXbZ5+pi/aXL3+mbiVJkiTphSGDsGw4eusoACIfrRtrVrOsQdvgLC7YB/2TjLt3w+KT7VMahhaDI4GGJ2VD6dJqMHbgyd3Sjh2fqTtJkiRJemHIICytDOrn1ChZQ3t9+s7p5zGa5+LEsJY0dkipDRkQfJGw69FZOtfEBI6litm6LkqTvmPZBzkxRIoXT3mdprSnJEmSJL2UZBCWDWULp8wadf6vcx6OJGcVtjBj9ju1Wf9FyoL65r/swNZ/NUIIRCaFHZ2d1acbR40CgYm6Piy1o0YKU2bTmjX67aFDn7lLSZIkScpTMgjLhjfs39Bt54e1Yak5lLOitXM5XZvd4DXYDV7D0JUnMjxXUdSUEnfugLm5gjIsKmXn0v45Mr4LF1JeDxuWI11KkiRJUp6RQZiB9Gd9KlpV1G3/euhXIqMj0zn65fTnW14GKSwA5uyO4O1ZwZmeX6KEmgX/wQP4edcArT1uStMMzsqaSpVg1y719eHDauCnKPpi4JIkSZL0spBBWDaUL2KYrGrFuRV0WN6BkBshz39AuaRH7UqcHtGKopZmuvYdZ25mWuooWaFCULrbD9p2gRsHOD9nXAZnZE29euqyveHDU9patlSLgUuSJEnSy0TJbL3Pi8bb21scOJC1xKJPZXQlePzkVtrQKN2u+KR4POd5pnvqmjfXGMyW5QcXbj+g4bhtALzpUYGJ3dyzfG4xyyii/Ctp29djK1J29LFnXl0vBLi4wIlUd0m7dYPAZ3sYU5IkSZJyWro/8ORMWDaYm5izodMGPMsYD8TaLG1D1OMoo/teZpWtUzLYLj18OdOF+qlFxRaj/j97tO2ylpdgWHE1fcUzUBQ4niYfbI0asG4dhIU9U9eSJEmS9FzIICybyhcpz7QW09Ld3yCwwXMczfMzuLWj9rrWyE3ZOnfX2ZqE1ltnuOMZAzFIySFWvTq8+y7066e+/u23Z+5akiRJknKVDMIMZD7LY2FqQQGTDIpM5kMdPSpor2/FxNFq0o5sne/YwoeVnlF8smZ8Tg8NLy84fVotOXX1qtr2+ecyn5gkSZL0YpNB2FOKS4pLd9/Lts4uK8oUtWT2O7W07dBr0dkqcQTQvj189vd7mAy7m9I4tBjEP8qRMZYoAb/+qm/7+usc6VqSJEmScpwMwp5SnXJ1dNtmJilPEl6KvvS8h/NcNHYoQ+D7dXVttv6ribz7MMt9ODpCfEKab7uR5Ywf/BQGDFATxyarVCn9YyVJkiQpL8kgLK0szmL93PBnShUsRV+nvuzsvpNd3Xdp+xJEQm6NLs/VrWJNxJi2urYGY7cydfu5LPdhagrrvK/p2oa2+CNHxgfqbciICPV1ly5qvclevXKse0mSJEnKETIIe0olLUuytetWvvb+mmIWxShkXoiSliUBuBZzLZOzX34nhrXUbY9ZG5qt25OtXi/IhtrXte2h9b5nzdycq8dZubIaTyuKmtj133/V140b59glJEmSJOmZyCDMwNOv57oTeweADzZ9wMHrB7X2NeFrGLh9IDFxMc88uhdFYQszgxkxIFuBWIs2ltypNULbbnO+NnfPZX1GLSvKl9eXO9q2TQ3GFj17OUtJkiRJeia5GoQpitJKUZTTiqKcVRTFP51juiqKclJRlBOKovybm+N5nvqu68uvh34lLjGOQUGDWBuxls0XN+f1sHJcxJi2nBnRWtdm67+asOvRWTq/ZNvPiCzaTtsuMc8TFvSGmBs5NsbEROicqt76u+9Cu3bg6gpt2uTYZSRJkiQpW3ItCFMUxRSYDLQGagI9FEWpmeaYasBgoL4Qwgn4IrfG8zzUr1Bftz3j2Ax+2v2Ttm1pZvm8h/RcFDAzIfh7fW3I5r/s4OuFR7J0vs1X/7DrYqoHHU6thKXv5dj4TEzUmS8hYOdO8PeHI0cgOhrWrlWTvObDB1olSZKkF1xuzoTVBs4KIcKFEHFAIPBGmmPeAyYLIe4CCCFybvrjaT3DT+PRDUYbtK0OT7k9lx9TVyQrY2XJuVH6aaUlhyKx9V/NrJ3nMz3faWKaKtzh27i6bkFODhGA+vWhalU1t1jy4v3QUDVQkyRJkqTnKTd/9FQAUudqiHzSllp1oLqiKLsURdmrKEqrXBxPrithWYI1Hdeku//4rePp7ssPTE0UIsa05edOrrr24atOcjM64wrbxYtD0k9RvDYhVGsrv/d9TJQkhg/Xp53ICWZmcOWKvm3gwJy9hiRJkiRlJK9//zcDqgGNgB7AdEVRiqc9SFGU9xVFOaAoyoGbN2/m8pCebbaqYtGKHOtzjOH1hhvsu3D/gpEz8p+utSoSPqoNjRxKa221Rm7C1n81o9eeSvc8ExPo8X55ui6arbUlDSnBkCFqWoucVr68fuIzPBwS8m92EUmSJOkFk6UgTFGU+oqibFQU5YyiKOGKopxXFCU8k9MuAxVTbds8aUstElgphIgXQpwHzqAGZTpCiGlCCG8hhHfp0qXT7n4hdazWkY2dNzK56WRcS6szQ9sit+XtoJ4jExOFOe/UNmj/a3s4tv6rmbPL+C3KCRNgwfE3dW1iSDEKmj2kT59cGSr376spLBYtgrFj4XLa71JJkiRJygVZnQmbCUwEGgC1AO8nf2ZkP1BNURQ7RVEKAN2BlWmOWY46C4aiKKVQb09mFty9NMoVLoefjR+1ymb2UeVfEWPaGmTZBxj630kmbjzDtahYg32KAgzSzxo+/L48f/8tsLXN+TFaWUGPHjBrFvzwA9jYwNSpOX8dSZIkSUotq0FYlBBirRDihhDidvJXRicIIRKAT4H1wClgoRDihKIowxVFaf/ksPXAbUVRTgJbgW8z6zfX5cLi+bRPTb5qkrPsh49qQ2vnlBJFv20Oo+7ozWw/Y+QWc8Hi8Ln+6crg/o25eCEJT8/cGWfyQn2Ajz5Sg8EZM9QUF5IkSZKU05SsPLGnKMoYwBRYCmgrrIUQh3JvaMZ5e3uLAwcO5N4FRpSDhCcFpYdG5UiX1x5co/ni5gAcefsIJkpeL8XLO0II7AYbf3jh/Og2KIqib0xKguElDI495L0Nz9c9cnx8S5dCp076tkWL9HnGJEmSJCkblPR2ZDUaqIN6C3IUMOHJ1/hnH9eroXTBlHVsr8ri/PQoivoE5eEfm/NNi+q6fXaD13D9fprbkyYm0N0wh6/ngUbETjBcc/as3nxTXSOW7Lvv1NqTZ8/m+KUkSZKkV1yWgjAhRGMjX01ye3B5I+dvR5qapDzat//a/hzv/2VUonABPm1SjXVf+Ora64zaTK8Ze/UHO7aFIfeg82xds2X0adzLHc3xsVlZqXelhYDhw2H5cqhW7claNUmSJEnKIVl9OrKYoigTk9NEKIoyQVGUYrk9uPxowoEJeT2EF4pjuaIEDdRX1d519jaJSWmCYUUB5zd5PDiKQiOvas0hH/rC0GIk/dUYknJ+8ZaJif5WpKLAsWM5fhlJkiTpFZTV25GzgGig65Ov+8DsDM94WeVSVvt6r9UD4GHCw3ydOf9pVCxZiIgxbVn/hZ/WZv/dGqMJXi0s4GF8ISKtXte1m1w9BMNLwq5fczQYUxSISVN33dU155PHSpIkSa+erAZh9kKIIU9KEIULIYYBVXJzYPnNyAYjtdePEzPOHv+qcihnpduuNXITe84Zf1jW5uv5iA5G8khs/EkNxi7sybFxFS5sGIjt3Wv8WEmSJEnKqqwGYY8URWmQvKEoSn3gUe4MKX8qVbCU9vpc1DmO3jzK+ajMayq+aiLGtNVt95i+l8lbja+KV9x7EOsfhTIsip+2fqffObsVJOZc+vvChdVJ0q++giVLoF69HOtakiRJekVlNUWFOzAXKIb6qOUdoK8Q4khG5+WGXE9R8b/SkBinvs6hFBXJXOa6GLRNajyJppWa5uh18oNx60OZvPWctr3043p4VjJMVZHs/HmoUgXGNf+eb+r9kbJj4HkoVDJHx5aQAK1bw6ZN8PAhFCyYo91LkiRJ+cuzpagQQoQIIdwAV8BFCOGRFwFYfvTF1i/kGjEjvm3pyE+v19S235yymx1nbqb7WdnZwa1b8O3GkfodP9vl6IwYwKBBagAGUKhQjnYtSZIkvUIyDMIURXnryZ9fKYryFdAf6J9qO//JxYAoeXF+Wlsubcm1a77M+jWw47ceKQlZ354VnG6iVwBra3XBvDIsitsPU82a/dslR8c1dKh+W1EgOjpHLyFJkiS9AjKbCSv85E+rdL6kbPijyR/UKV+Hno49mdd6ntb+xdYv8m5QL7j2bq8ZtNn6r073eEVR4+hS4yJSGs9t4cH9+BwbU3IesdRekrrykiRJ0gskS2vCXiS5viZseClIevIDO4fXhKXVa3Uvjt5Sk40ubrcYh5IOuXq9l13q4Kt+VWvm9zcsDJ4sMhJcq93hziA7rS2+WDXMv8zZ753kBK5nz8KWLfD+++r2jRsyMJMkSZKAZ10TpijKz4qiFFUUxVxRlM2KotxMvlWZ/zy/oPTv1n9rr8/cPfPcrvuySv3k5K6zGdd5t7GB6/dLsjw05RzzqDBmfLEkR8ckBISFqbdC//svpb1MmVy9sy1JkiTlA1lNUdFCCHEfeB2IAKoC3+bWoF4Vpiam1C2vzuasPb82j0fzclj6ccq6umYTt2d4rLk5dAj8l8/Ppsx+9S/ej441/svRAKlqVSheHObP17ebmMDduzl3HUmSJCl/yWoQZvbkz7bAIiFE7t6ne4XEP7n1efXB1UyOlABdmoqzN2IyXB+W7Nd/qrE4abq2vazbW9Qsc5r4nFsmBqhrxRLSPIi5YQM8eiQz7EuSJEmGshqErVIUJRTwAjYrilIaiM29YeWh53wPKXkm7Oy9s1x7cO25XvtlFfy9Pq+arf9qbP1Xcz82/aiq8/CuUP8LbfvUJ7UpWugRgwbl7NhMTdVvoY4d1W1HR3jrLbU9p4M+SZIk6eWW1Txh/kA9wFsIEQ88AN7IzYG9KrzKemmvmy9uLgOxLChjZcmhH5sbtLsO3cCW0Ovpn9h8GMKuobb56PtyHF26kaFDcn6aaulSNRh78EB9DVCgACTmfI1xSZIk6SWVWZ6wJk/+fBNoBLzx5HUr1KAsH3q+M2E1rWvqti/cv/Bcr/+yKlm4AKdHtKKjRwVde785B7D1X830HeFGz1N6L9Ntr+3VmaFKCUoXvsXDhzk/zrTljczM5K1JSZIkSZXZTFjytEE7I1+v5+K4XhmFzQszpekUbfvU7VN5OJqXi4WZKb90cydiTFs+a1JVt2/kmlPY+q8mNj7N1JOJqZp6xPcbXfPNb+2580MNLc9YTkq7TszUNGf7lyRJkl5OGQZhQoghT/58x8hXv+czxPzP18aX9vbtAfj10K/ExMXk8YhePl+3cGDVZw0M2h1/XMfQlScMT2j6Iwy5R6J5Ma3JpugVxJBiDG08hj17cm5syevE+j35FzN9ulwfJkmSJGU9T9goRVGKp9ouoSjKiFwbVV7Ko+ROre1aA5AgEvAJ8OG7oO/yZBwvM+cKxYgY01aXTwxgzu4I409RKgqm31+EH/U5x4Y1Gs3cz2bSuXPOjm/mTNi/H1q0gD59UhK9SpIkSa+mrD4d2VoIcS95QwhxF2iTKyN6RXmU8dBt/xf+H8dvHc+j0bz8Isa0ZcH7+oz66aazMDWDH2+T2Gmu1jT19a9Y7FyMlfNzNnWItzc8fgwBAeq2oqjZ9iVJkqRXT1aDMFNFUSySNxRFKQhYZHD8SyxvZsIKmxfmXed3dW09VvdgTPAYHsQ/yJMxvezqVLHm/Gj97wq2/qtZcjCShMQ0q+NNzTB16QD91uua24c5IvbPzNFxVasGJUvqt99+W2bYlyRJetVkNQibj5of7F1FUd4FNgJzMzlHyqYvvL7gU/dPdW3zT82n7r/p10iUMqYoCmEjW+vavl50hKrfr+XB4wTDEyrVhSH3uFzpk5Q+Vn9FzCj3HB3XrVvwTapnA+bNg48+ytFLSJIkSS+4rOYJGwuMAGo8+fqfEOLn3BzYq+oDtw841PuQQbvMH/b0zE1NODa0hUG705D1JCYZmX5SFCr0G8Wk2yl1JovEnadl1U0cOZIzY1IUGDcOtm1Tt7t1gz//hNdfV/f16ZMz15EkSZJeXIrI4j0QRVEqA9WEEJsURSkEmAohonN1dEZ4e3uLAwcOZH7g0xpaLNXrvKvOFH4vnN1XdjN2/1it7Wuvr+nr3DfPxpQfxCUkUf0HfZ3O5Z/Ux71icaPH/zExhk/vp+QiazBrHbsu+RAcDLVq5ezY7t2DEilVmXBxgaNHc/YakiRJ0nOX7mNYWX068j1gMfDXk6YKwPJnHpaUrirFq/BWzbfwLOOptU04OIGsBs2ScQXMTDib5vZkh8m7OHvDeFqQT78qwv0Ws7Ttnf1a0bByELVrQ+PG6iL7nFK8OOzYkbJ97Jg6Kxb93H/VkSRJkp6HrK4J+wSoD9wHEEKEAWVya1B5ysRM/2ceG1xnsG67/fL2HL5xmCQh064/LTNTEyLGtGV8FzetrdnE7cbziQFF63WC1uO07W19X8eu+Hm2bQNLy5xdUO/rCzGp4sFSpdTblpIkSVL+k9Ug7LEQIi55Q1EUM/LqMcLc9vZKKFkF+vyX1yMBwLGkI8f6HKNtFTX3VcT9CN5e+7ZcrJ8DOnvZ0KN2JW073XxiAHXeh1Qlj8I/d2dkk2EAmGT1X1EWFS6sBnb29uoC/iFDYOJE2LIlZ68jSZIk5a2s/vjYrijKd0BBRVGaA4uAFyNKyWm29WHAYaj8YpXG/KnuT7rtRwmPuHj/Yh6NJv8Y/aYLo9900bUNCDhs/GD7JlClkbb5ne9ExJBiVCx6CUUxLE/0rM6eVTPrr1yp3qZs2lS9PRkjCypIkiTlC1kNwgYBN4FjwAfAGuCH3BqUZKiQeSG2d9vOey7vaW191slH6HJCj9qVOD2ilba98sgVjkWm81DG2yugVn9d08UvnRFDirGi51uEhubs2MzM1OBuxYqUNisrNTCTJEmSXm6ZPh2pKIopcEII4fh8hpSxXH868iXQd11fDl4/CEBPx54G68akpxNx6wGNxm/Ttld8Uh+3dJ6aBGBmC7i0z+iuh9/cplCRnF1X6OcHQUEp2927p2TelyRJkl5YT/90pBAiETitKEqlzI6Vno+xvilpK/4N/ZcNERvycDT5h22pwrRxKadtvzF5FzvDbqV/wrsb4DPDnG4AhcZbU6Lg3RxdtL9jB/z9t/q6ShUYNAhmzICdO3PuGpIkSdLzk6U8YYqi7AA8gGBAq6EjhGife0MzTs6EqW4+vEmTRU10bX81/4t6r71Ya9leRjOCwhmx+pRB+8pP6+NqUzzd82pVOMj+9/R/J8qwKExNIS4u5xbwx8fDrl1quaMOHSD5n0NsLFjk02JikiRJL7F0Z8KyGoQ1NNYuhNj+DIN6KjIIS7H14lYGbB2ga9vYeSPlCpdL5wwpq9J7SnJEB2feqls5w3OT/umGydl12rYyTF1flpSkLqzPKXfugLW1vq1KFTh3LueuIUmSJD2zpwvCFEWxBD4EqqIuyp8phMjhZ8CyRwZhei5zXQza/mz2Jw0qNMiD0eQvjxMS6T0zmODzdwz22VoXYtu3jdM/OXXlBaDA/24Sn1Qgx4t0x8ZCwYL6tvBwsLPL2etIkiRJT+2pg7AFQDwQBLQGLgghPs/x4WWDDMIMRcdFUy9AfxtyYqOJNK/cPI9GlP+sO36ND/85aHRfxJi2ho1xD2DUa7omZdg9QMnxQAz0s2JJSXDwoFr2SN6elCRJynNPvTC/phDiLSHEX0BnwDdHhyXlCKsCVkxtNpU65epobSP2jsjDEeU/rZzLsXdwU5o6GhaKsPVfzdHIe/qSUgUKG9QeFUOKA+otSReXnM20X7KkGnzdvw/r16uL9i0tYdKknLuGJEmSlLMyC8Lik1/k9W1IKWP1K9RnRssZvF3zbQDuxN4hPjE+k7Ok7ChXzJKZfWsR/H1Tiljo00+0/2MXdoPXYOu/mvuxqT73Ifd0x63r1RGA48fVhfr37+fc+BRFzSEWHJySXf/LL9X2/ftz7jqSJElSzsjsdmQiKU9DKkBB4OGT10IIUTTXR5iGvB2ZsYSkBDzmeWjbBc0KsqvHLsxNzPNwVPlXegv4dTnGkhJheEltX1RsUax/Pk+iUAO511+H/3K4/sTWrdBE/6AmCxdCly45ex1JkiQpU093O1IIYSqEKPrky0oIYZbq9XMPwKTMmaUpPP4o4RHNFjXLo9HkfxFj2hIxpi1hI1vr2t+YvIvu0/aoGyamunxixSzvk/CTNT81HAPAqlU5+9QkQOPG6u3OQYPU7fffV29P3roFjx/n7LUkSZKkp5OlFBUvEjkTljkhBNsubTNIXzG31VwszCywK2pHIfNCeTO4fM7pp3U8iEvUtYX+rxWW5qbw8A78rH9sse6MTey7XEvbzo1/jiEhatoKd3fo2BGOHcu9a0mSJEkGni1P2ItEBmFZdznmMq2WtDJotypgxe4eu/NgRK+GyLsPaTB2q67NvWJxAt+vqwZje6fCukG6/TUn7+PULbUyWG79k5w5E/rry15y6BB4eBg/XpIkScoRT1+2SHp5VShSgXmt5xm0R8dF4zLXhV8P/ZoHo8r/bEoU4tyoNrq2kEv3cPxxHRtPXoe6H4LvN7r9Jz+pw98d3gcEigLLl+f8uN59F06c0Ld5esLGjTl/LUmSJClzcibsFXEn9g7D9wxn88XNBvsKmBSgv2t/PnL7KA9Glr8ZK4H0YUN7/Furs16s9Yd9f+r2V/v9EGfv2DNyJHz3Xe6M6++/oU8fde3YnDlQSVaGlSRJyi3ydqSkSkhK4J+T/zDh4ASDfW3s2jDWb6yRs6RntSLkMp8HhujatCSvq7+B/dMNzrH79Qgf+ttqi+tzQ1KSmi7DzQ2KFlXXi8mATJIkKUfJIEzSe5z4mPmn5nP81nE2Xki5HzWjxQw8y3hibipTWuS0badv0He2PmHX9m8bUdm6sLphZFYMYEfJqfgN6JErYxIC3n4b/vnHsF2SJEnKETIIk9IXERVBu+XtdG2y/mTuiE9Motr3a3Vt/33aABebJ7UmI3bCHMMySBfMm1L5+6W5MqbVq9VcZak1bQqbNuXK5SRJkl41cmG+lD7bYrZ87fW1ru2jTR8RciMkbwaUj5mbmhAxpi0mqf5JtvtjJ4lJT34Zsm2gljsaco/bNb7Ujqkcv5mkf3vmypjatlVnvh48SGnbvBliYnLlcpIkSdITMgiTAOjr3Jf9vfYzu+Vsra332t6cjzqfh6PKv8JHt2X4G07atv13a/QHKArW3YaypPQOrcnkzGoYWgyuHsmVMRUqBEePqq9XroQiRdQksooib09KkiTlBhmESRpLM0u8y3mzt+dera398vZ5OKL87W0fW932zrBbBsd0+sSNYmMu6hv/8lODsWOLc3xMyYXF27VTs+wnMzGBv/7K8ctJkiS90mQQJhkobF6YH+v+qG2fu3cuD0eTv6XOJ/bWzH3Y+q8mKUk/7RQVW4wioy4bnrzkXTUYS0rKlbGNTfOg7Icfqov4JUmSpJwhgzDJqK4OXbXXHVZ0YOHphXk4mvzL1ETh7361dW1VvluDrf9qftl4RmuLiSvCnpZRKMOimHX4LX0nw0vAA8NZtGdVooQ6KxYcnNI2b55aAkmSJEl6dvLpSCldK86u4IddP+ja3rB/g+/qfCdrT+awG9Gx1B5pmEgXUuUTQy2+bWkJxSzucc+/sv7AgeehUMlcGd/Zs1CtGvz7L3TuDIGBUKAAdOuWK5eTJEnKT2SKCunp7Lq8iw83fahrU1A42udoHo0of7sZ/ZhaIw1zQ5we0QoLM1MAFi+GLl3AVEkg4Sdr3XEXPP+icvvuuTrG6Gg1sWuypCR18b4kSZJklExRIT2d+hXqc+TtI/zW+DccSjgAIBCsCl+VxyPLn0pbWRAxpq1u9gvA4Yd1xMYnAupMlBCwcLEZyrAo5h1JmY6qfOgDdZ1YxM5cG+P9+/ptExN1hk6SJEnKHjkTJmWLy1wX7fXWrlspVbBUHo4m/7P1X63bDv1fKyzNTXVtQsCMNz7jPc+/DTvwvwSWRQ3bn9GjR2pKi9TkjJgkSZJReTMTpihKK0VRTiuKclZRFP8MjuukKIpQFMU7N8cjPbs1b6bks2q8sDEuc12YdXxWHo4of4sY05aCqYIuxx/XGRyjKPDeyt+J87/Df6db6XeOqQibh+f4uAoW1OcOK1MGbtzI8ctIkiTla7kWhCmKYgpMBloDNYEeiqLUNHKcFfA5sC+3xiLlnIpWFVn+xnJd2y8Hf+Fh/MO8GdAr4NT/9IHVxA2njR5XwNKUVn8voMbkYP2OoAnqLcrEhBwfmxCwcaNa+LtMGWjTBi4byaYhSZIkGcrNmbDawFkhRLgQIg4IBN4wctz/gLFAbC6ORcpB9sXtWdNxDQNrDdTa6vxbh6sxV/NwVPlb6jViv205i89o409SmpvDqZsOrKsbRZ0ZaY75n7XRc55Vs2ZqAPbGGxAWBjY26tOUkiRJUsZyMwirAFxKtR35pE2jKIonUFEIoV/4koaiKO8rinJAUZQDN2/ezPmRStlWsWhFetfsTU/HlHqGLZa0YMmZJXk4qvxtx7eNtddXo2LZEno93WNbtYIh07xRhkXp2lf26E50dO6Mr379lOCrWjX1NunXX2d8jiRJ0qssz56OVBTFBJgIZPrftBBimhDCWwjhXbp06dwfnJRlg2oP4iuvr7TtoXuG8sPOHzI4Q3palawLEZrq1mS/OQdYd/xause3aQMnT4Lp8DtaW3uHtVhNKAaXD+X4+AYNgp5paoxPnAhTp+b4pSRJkvKF3AzCLgMVU23bPGlLZgU4A9sURYkA6gIr5eL8l4uJYsI7zu8wxGeI1rbi3Ar+OfkPsQnyDnNOszQ3ZfY7tbTtD/85SPs/0k9HUaMGJCaZwqAI/Y7pjdV1Yjn8dPT8+WqXJ06ktPXvr/55K+eT+kuSJL3Uci1FhaIoZsAZoClq8LUf6CmEOJHO8duAb4QQGeafkCkqXlyPEx/j/Y8+ht7ZfSfFLIrl0Yjyr982hzExVVkjgODvm1LGyjL9kxITCPuiFtWsw3XNlzofp6JzxXROejb790OtJzFjcvqKqCh9sldJkqR87vmnqBBCJACfAuuBU8BCIcQJRVGGK4rSPreuK+UdC1MLAtsG6toaBDZg+J7hcq1YDhvQtBpnR7bWtdUeuZluf+1J/yRTM6r+dthgnVjFxc782+ld/vwz58eZHICtXJnSVqwYyN+jJEmSZLJWKRckJCVw/NZxeq/tbbDvWJ9jeTCi/C1tQleAkJ+aU7xQgXTPSUqC3f1b0qDSXl37J5v/YnJQzpc9EgKKFzfMti8TvEqS9AqQtSOl5y8oMoiPN39s0D612VTqV6ifByPKvxISk6j6/VqD9r2Dm1KuWPq3KBMjD2M6o5HhjiH3ciU6+v57GDUqZfvzz2HSpBy/jCRJ0otEBmFS3hFC4Pq3q64tqFsQxS2L582A8ikhBHaD1xjdt+4LXxzKWqGkE1iJg3+j/PeZru3xt9exKJzBGrOnlJgIZmZQoQJERqoFyX/7DbZuBVPTzM+XJEl6ycggTMpb92LvMWb/GFaHp9w6O/L2EUwUWUM+pz2MS6DmT+vT3Z+2OHgyIUAZpn+Iosy4s1yPKZ1rtwzj4mDwYDWVRfIYJEmS8pm8qR0pScmKWxZnjO8YBtcerLW5/e3G6TvGS/BIT69QATMixrTl1PBWRvfb+q/maOQ9g3ZFgbhv9XnHbnxbldcd0g/onlWBAuqsWOoxyEBMkqRXhZwJk54rY7cm5YxY7ouNTzRa/Pv0iFZYmBneAxTDSqKIRH3jT3fBJHf+nmrXVtNZJBs8WL92TJIk6SUmZ8KkF4OiKBzrc4y1b6YsInf7200mds1lluamRIxpyxvur+naHX4wDMwAlCF3SOgwU984vAR37+TOL2170mTWcHHJlctIkiS9UGQQJuUJGysbPvf8XNuuNb9WBkdLOeXX7h4c+KGZrs3WfzX3HsYZHGvm3pk7A/Q5xUr8VhxFESQmGhz+TExN1duQCxao26+/DleuqLcn1xo+9ClJkpQvyNuRUp4auXckgadTEryufXMtNlY2eTiiV0fa/GL1q1ozv39dwwOFgGHFDZqLjrlM1KMiubJoPylJ/6RkiRJw5076x0uSJL3A5O1I6cX0fd3vqVs+5Qd/66Wtuf7geh6O6NVxYlhL3faus7ex9V9N+z92ovvlTFHUvGFp3PevoD5NufxjSEzI0bGZmMD6VM8D3L2rDiPtbUtJkqSXmZwJk/JcbEIsH236iAPXU/5e65avy6gGoyhdqHQejuzVEPUoHrdhG4zuW/JRPbwql0hpeBxD/NohmIfMMDy4nCt8GJSjY3v0CKpVg8uXU9r+/Rd69MjRy0iSJOUmmSdMevFtubiFz7d+rmv7u/XfeJTxyKMRvVou33tE/TFbjO4LeK8uPvbWurbP6vzF760HGh786QEoVS1HxzZ7NvTrB40bw4YNsHkzXL0Kffvm6GUkSZJygwzCpJdDkkhi3fl1DAoapLXNbTUXz7KeeTiqV0t8YhK1Rm7i3sN4XbuiwPnRbQ3abIpGculLJ8OOhkYZtj2je/cgPh4WLYJPPgEHBzhxQmbalyTphSaDMOnlcvL2Sbqt6qZr+8rrK95xfiePRvRqmrz1LOPW6xPqznu3Nr7VUm4Tt2oFR/ZFcfXzSoYddA8AxzY5OiYhDNOVffABTJmSa2nMJEmSnoUMwqSXj7FArHLRygyvNxznUs4UMC2QRyN7taSX6LWzlw3ju7hp23v3go8PiCHFDI7Fshh8fhQKFs+RMcXFQaNGhgv1w8KgatUcuYQkSVJOkUGY9PIKvRNKl/+6GLS/5/IeAzwH5MGIXk2j15zirx3hujbrwgU4+GNzXVuXLmB75VfGNf/JeEcFisBHu6CE7TOP6Z9/oHdv9XWlSmoxcC8vdVvOikmS9IKQQZj0crsbe5fPt37O4RuHDfat6LCCKsWq5MGoXk1X7j2iXpoF/OdHt0FJlTAsIgLs7KB8katc+dox/c7enA6uXZ95TFu2wNmz8N57amqL1q3V9seP1fqUkiRJeUgGYVL+cevRLRovbKxr61WjF196fYmFqUUejerVYuwWpbmpwpkRrXXB2K1bUPrJ8rEW9ptY/1Yn4x3m0CL+hATo1AlWrtS337mjJnyVJEnKAzIIk/Kf4KvBfL39a+49vgdA/Qr1+crrK6oVr6YLBKTckzbrPkCJQuYc/qmFru3BAyhSJGXbt9IudryTZsF+/81g4/3MYzK2cB/UtWJhYc/cvSRJUnbJIEzKnx7EP2DCgQksOrNI1/6B6wd86vFpHo3q1XLpzkN8f95q0L7LvwkVihfUttMGRxamscT+UNawww+CoLzrM48rPh6aNIGdO9VUFj17wk/pLFOTJEnKRTIIk/K3g9cPMjZ4LKfunNK1L2u/jKol5ONyz8PDuARq/rTeoP1/bzjR28dW1zZtmppWAqC782ICOr1rvNPKDaDBl/CaOxQu9VTjevAAfvsN/P1h+nT1upcvw2uvPVV3kiRJ2SWDMOnV8DD+IX3W9SH0TqjW9q7zu3zu+bm8RfmcGMstBnByeEsKFTDTtpNTWiT7ou5kfmn5XSa9K/DJPijtkO1xxcaCi4u6gD/Z22/DnDnkShFySZKkJ2QQJr1adl/Zzeh9o4m4HwFAs0rNmNBoAiaKzFvwPCQlCd6du5+tp2/q2t+pb8vAlo4ULKCmuN+zB+rV05+7ZsIKWid8BQ9vZXwR/0tgWTRb4/L0hMOGD9gyciR8l1n8J0mS9HRkECa9euKT4vn90O/MPjEbAMeSjnSp3oVyhcvhW8FXzow9B0II7AavMWhf+7kvNcqnBFDG/irmz1fXcQEQFQlz2sLdCMMDn+LJyp07wddXfe3sDA0bwh9/ZLsbSZKkrJBBmPTqWhO+RleLEqBCkQo8SnjE/+r/Dz8bvzwa2avj9LVoWk7aoWtb9VkDnCukZNe/dElNuGqMlu9LCJjXAcK36Q/oMgecOmZ7XNevQ0AAfPGFut26Naxbp7aXKZPt7iRJkoyRQZj0art0/xKLwxaz58oeg8X7xSyKEfU4in/b/ItLaZc8GuGrYfuZm/SZFaxr+7ChPf6tUxK6fvaZ8Vmpjz+GyZOfbAgBw4rrD2g1Fup++NRjO3YMXNM8lHnrFlhbP3WXkiRJIIMwSUrxKOERS84sYXX4as7eO0tsYqy2r3P1zpgqprSo3IJa5WrJW5a54Mile7wxeZdBe8SYtrrtq1ehRg2ISnO3Ufdf1rktMC/NDNiQe0+10l4IKFoUYmIM923aBE2bZrtLSZIkkEGYJBn3MP4hx28dxz/In5uP9IvIS1qWxK20G71r9qZWuVp5NML868z1aFr8or9Fmbb8EejXbyWLiwNz8ycbF/fCrJb6A744DsUrPtW44uKgTx8IDFS3GzeGvn3VJyklSZKeggzCJCkjD+MfsvvKbpafXU5h88IERQYRHR+t7e9QtQOfun9K2cJGkotKTy0pSVDlO/3C/fLFLNnt30QXjMXFgYWRilS3b0PJkhi/PQnQPQAc2xi2Z9Hx4+q1PT3h3j21LmXXrjKlhSRJ2SKDMEnKjrjEOA5cO8CIfSO4FH1Ja3/H6R0+cPuAwuaF83B0+YsQgobjtnHxzkNdu7mpwoHvm1OskLnW1qIFbNyoP3/4cPjxxycb6wbD3imGFyleGQaEGK9nlAXHj8OQIbB0qbp99y4UL/5UXUmS9OqRQZgkPQ0hBKvCVzFszzAeJz7W2vu79Kefcz+sCljl4ejyl+v3Y6kzarNB+7sN7Pjx9Zradnq1IU+eVNeQAbBmIAT/ZfxC/TZApTrZGltkJFRMc3dz/Xo1KJQkScqEDMIk6VltvriZL7Z+oWtzsnain3M/WtjKn8Y55d7DONyHbzRob+f2Gr/38NC2T5xQc3ylVrgwREenul14IxSmpBNwfbgLyjkb35cOLy84dEjflpQkb09KkpQhGYRJUk5IEknMOTGHLRe3cOTmEd2+4fWG0/H/7d13eJRVvsDx78mk904gkJBQpSMKqLiUVUD2Irqu7VoW+7rrste665XL9dpX9OquXpW1rH0VURErFkDECpGaUEJNIz2ZtOlz7h9vCISZSQaFTJL5fZ4nz8ycc973PS/nyeTHeU8ZcuxrVQnvVu+s5Kp/rvdIP3rw/v/+L9x6a/syLtdRvWVbl8HbPvanXFR3TI8pv/4apkwx3t94Izz0kDGrUgghfJAgTIjjrbCukF+v+HW7tD7RfXjvvPdkzNhx9Ob6Iv789tZ2aVvvnklcZFi7tEMLrR7J6QST6agTFqyApVe0T5v1IJz2+2Oq1/LlcO65Rvy2aROMH2+MGTtf4nAhRHsShAlxohTUFHDxBxe3S0uISGBY0jD++ou/khqVGqCa9S4D//KhR1rh/ecQZjJ6sbSGceNgy5b2ZS65xFgV38PdCZ5p134BmROO6fnizp0w/PBaswwZYqTJI0ohRCsJwoQ4kbTW/GvHv1i6cyl7zHva5d007iauG3OdbB7+M2mtOe+pb9hcXN8u/fRBKbx27aS2R5Rms++Zi/HxsGqVMbYLgIObYYmXbavGXQbneZll6cMTT8CCBe3TwsKgoQEiI/0+jRCid5IgTIiu4tZu3tz5Jg98/0C79PMHn8/8kfPJTcwNUM16B6fLzeC7PvZIT4wOY+N/nd0WjL3xBlx6qe/zVFZCWhpGF9o718HWtzwLTb8Lpt7hV73sdmPbo507D6c98ojneDUhRNCRIEyIruZwO7ju0+vYVbeLRvvhhV8vHnYxCycvDGDNeoftBxs4529feaS/+/vTGZ+V1PbZ7Ya33oKFC2H37vZlf/MbI1hrGzfmtMF9Hezc/Z8HITy6w3pVVRmbf8+ceXgz8L59ZW0xIYKYBGFCBEqTvYl1petY9M0iLE4LACcln8Rj0x8jMzYzwLXr+cwtDsbe86lHurctkABuuAH+8Q/P89TXQ8KhYWLmUnhshGchgAGT4OqVnQ76OvTVungx/PnPh9OXLoULL+zwUCFE7yJBmBCB5nK7WLJlCU9vfrotLTM2E7PNzMrfrCQ+XNY5+Dk+L6jg2pc9vxtW3TqV3LTYdmkVFcZg+vr69mVjYo7awNvlhO+fhk999FyevwTGXtJhvbyt8g9QVOS5AKwQoleSIEyI7uK93e+x8GvPP+qhKpQLhl7A6f1OZ/qA6V57cUTH3G7NbW9t5p2NpR55kWEhbFo0k8iww2tWlJVBppfOyC1bYPTooxLtzfDEBGg86HnAvP8zBvP7aLO6Orj/fnj00cNpTzwBN93kz10JIXo4CcKE6G4sTguvFrzKKwWvUGera5eXEZPB7IGzuWHMDcSGx/o4g/DFbHFwwysb+G5vrdf8N66fzOTclHZp3uKn6mpISTkqsbkavnsavnrE+8XHXQZzFkO451pxbrexttiHHx5eUHbzZmNpjSVL4Prr/bg5IURPI0GYEN1Zi6OFDRUbeLngZQrrCqm1tg8ekiOTmT9yPucNPo+kyCQfZxHe5JeZueiZb2m2uzzy1t4+nayUwwPtfXU+NjUZjyo9FK+HD2+B8i1eMoHYPnDdakjwPvZvzx4YPLh92qWXwuuvez+dEKJHkiBMiJ7C5Xbx/LbneW7rc20D+Q9RKAYlDmJq/6lcNeoqEiK8LDgqvNJa88LX+7n3gwKv+WeP6MPi34whxBl+eID+UV54Aa64AkJDvWTuWQVrHoLi7z3zcqfBFcu9Rnm33db+MeUhsvq+EL2GBGFC9EQ7anfw9KanKWosIiYspt1+lSEqhDGpY+gb05eosKi2sWThpvAA1rhnuGPZZpZuKPGZv+XumcRHhvHww+1nNh7JYulgIdamSvjnHKgpbJ+uTHDXQQiN8DjkyA3JlYIPPoA5c/y4GSFEdydBmBC9gdlm5tXtr7KxYiN5FXk4tbNdfnRoNH+Z+BfmDppLaIi37hpxpGfX7uXRz3Zidbh9lrnn3JGE7h/IZZd5z7/hBnjmGR8Hm0vho9thp+eWS4RGwqQbYMZ/genwPpgFBXDNNfDaa5CbC08+aSxz8eOPXsanCSF6AgnChOhtmuxN5FXkUVBbwOaqzRwwH6CkyejdSYxI5PMLPyfC5NnjIryzOV2c8dAqqpvsHnkj+8Xz4YIzaW6GqVMhL8/z+FGj4KGHjN4rj6eOLifc20EEFRoFMxbC6e2nS1ZXG9crOOIJakmJ9xmdQohuS4IwIXo7h9vB33/8Oy/mv9iWdufEO5k3eB4xYd5GlQtf6lvs/Mebm1izs6pd+rgBiTx9+cmkRkfx8ccwb57346dMMR4neowtc7uhNA/WLobCld4Pjs2Aaz+HxAFoDZdd5n0D8lWrYPr0Y783IUSXkyBMiGCxumg1C1Yf3k06NSqVsWljuXLElZzc5+QA1qzn0VqTc+dHPvN/uOuXfPpeJDfeeNQir0coLPScAdnG7YZty4y9K72ZeAOcfQ92Hcm558LKI+K2Z5+Fa6+FlhaIiup0AX8hROBIECZEMHG4HXy490Ne3/4622u3t8tbOGkhFw27SBaDPQZf7qrity/84DN/fFYi7/7+DFavhquuggMHPMskJBiLtvr8Z7fUwYtzoWKr9/xTrkaf8wh/vtPE4sVG/FZUBGvWwPz5xubheXk+Zm4KIQJJgjAhgpFbu3lq01PkVeSxoaL9782i0xYxpd8U+sb2DVDteqbSegtnPLTKa96CGYO5ZeYwwHew9eijcMstnVyk4SAsvRJKfAR+F74II89n0SK49972WbffDg8/3Mn5hRBdSYIwIYKd2WbmpfyXeHbrs+3ScxNymTVwFrMHziYnIUd6yPykteatDSXc8bbnQq2bF80kIdqY8Wi1woQJ7QfXg7FQa25uJxdxu+DNK7zPrgS4+FWKYuaSne2ZJRuFC9FtSBAmhDDYXDaW7VrGdwe/Y335epodzW15SRFJxIXHceO4G5mTM4cQFRLAmvYcVY02Tr3/c4/0Sydm8eCvjU0oq6ogPd3z2EWL4O67/RzTVZoHz87wmlU57UX6TD+PQ9/3H3wAv/qV8dgyRJpRiECSIEwI4cnhdrC6aDVfFH3B12VfY7aZ2/LSotKICYvh9V+9Tlx4XABr2XOMu+dT6lscHulxEaGsum0aqbERLFoE993n/fiKCu+BmofHRoO5yGtWc86vefTAP1m0CLQ2FpS12/3seRNCnAgShAkhOqa15puyb3gp/yW2VG9p6yGLNEXyx/F/ZHbObNKj/YkQgpvWmqfW7GHxyp1e8+88Zzg3TB3E6tUww3unFnfeCQ884MfFnDb4/hn4bJHX7PI+lzDv0Tv4Yc+gtrSaGkhO9uPcQojjRYIwIYT/XG4Xr21/jSc3Pdm2f6VCMTptNNMHTOfCoRfKvpV+qGy0cuvSzVQ22NhZ0eiR/8EfpzCibwIXXADLl3sen5RkDOSfP9+Px5XFP8DzZ3vNarZHM+Pl9/mh9JS2tC1bYPRo/+9FCPGTBSYIU0rNBv4GmIDntNYPHZV/C3At4ASqgKu11l4mdx8mQZgQXafF0cKq4lV8tv8z1pWuw+42VpMPUSGMSh3FFSOuIDYslhEpI0iOlO6VjhTXtnDmw6u95v159nB+N3UQq1bB5ZdDeblnmREjYMUKGDTIM68dtws2vQ4V+fD9016LXP/JEipTz2P5B5GUlhqPK3NyjvGGhBD+6vogTCllAnYBZwMlwHrgUq11wRFlpgPfa61blFI3AtO01hd3dF4JwoQIjGZHM6uKVvFY3mNUWao88lMiUxiaNJQxaWOYNXAWQ5KGBKCW3Z/F7uL8p75mR7lnz9h1Z+Zw7Zm52M2RvPIKLFxojOvyZscOGDask4u53bDxZVh5F9i9ryb7IY9w/r2/xeEO5/TT4fHH4dRTj+2ehBAdCkgQdhpwt9Z6VuvnOwG01g/6KD8eeFJrfUZH55UgTIjA21S5idXFq1lVtIoIUwQlTSXtZlkCnJx+MjeOu5EJfSYQFhLm40zB7b1NpfzpjU1e80JDFO/ddAbZ8Qk88AA86PWbE8aOhS+/9LJF0tHcbnj6NGMcWd0+r0UWf/NH7vjsXkAxdy4sWwbh4X7fjhDCu4AEYb8BZmutr239fAUwSWt9k4/yTwLlWmuPeUNKqeuB6wGysrImHPC2HLUQImC01pQ1l7GjdgdritewfPfydvnx4fE8MOUBTut3GuEm+at+tB3lDVz27PfUNHtuHg6QHBPO1KFpLJgyknsWhvHii97Ps2CB0ZPV6fgxrWHdY/DFPYDn34BaSyKzX32H+/45gZkzobQUUlKMmZZCiGPWvYMwpdTlwE3AVK21raPzSk+YEN1fcWMxD37/INtrt1NtqW6XNzd3Lr8e8msm9JkgC8N6sa+6md+9kud1IP8hV0zO5qzUk5gx1YTb7b3MBRfAc89BYqIfF935CXx8O9R7X/biNd7i8v85G1AMGQKffw5ZWX6cVwgB3flxpFLqLOAJjACssrPzShAmRM/ybdm3PL/teQrrCqm11ral94/tz4ysGUzMmMiEPhOIDY8NYC27p9pmO18VVvl8ZHnInOH9Kd+QwfIlqWinySO/rAz6+rM7lbUBvn0SvvyrzyLjl6xlU/lYQNYeE8JPAQnCQjEG5v8SKMUYmP/vWuv8I8qMB5Zh9JgV+nNeCcKE6LmKG4t5t/Bdlu9e3m5wv0mZGJs2lszYTOYOmsspGafIODIvXvnuAP+1fFun5Zz1UTT8OJDG9Tkc+f1/TFsZNdfA8hvBXAyVBV6L3P/Vrcz6nz9xypQEtPZz1X8hgk/AlqiYAzyOsUTFC1rr+5VS9wAbtNYrlFKfA6OBg62HFGmtz+3onBKECdHzOd1ONlRs4IeDP7C+fD3bqrfh1M62/OjQaLLis4gOjWZOzhymZ02XhWKPsr+6mcUrd/LRtoM+Z1AeYjuYQOXSibith8fj/eUvcP31fi5N4XbBvy6Bwk9916c+i493n4Ur4xQuve88UjJi/LwTIXo9WaxVCNF9NdobeX/P++wz72Nd6TpKmko8ypycfjLDkoeREZPBuLRxjE8fL2PKjrK7spHPCir56yc7fJY5+PLp2A8meaRv3AjjxvlxEacNNr4CH97aaVFrRH/C579JSN9RfpxYiF5LgjAhRM+gtabGWsOXxV9SZ6tjVdEqtlVvQx81iy8qNIqzs8/m7OyzmZI5hdCQ0ADVuHtqtjm55/0C3txQ7DVfa6j9ZDRNW9qPsD/lFJg5E265xZgR2amyjbD0SmzNVsLs1YQoHzMFptyMnvafqFCZHSuCjgRhQoieq95aT35NPjvrdlLWVMan+z+lzlbXrkx6VDrnDj6XYcnDGJo0lOy4bEwhnoPUg5Hbrbn7/Xxe/tb78j72yjgql07E1ex9DYq774ZFi/wb8+VywWOL9nFb2LgOy5lvKiMhVR5ZiqAgQZgQovdwuBysKVnDJ/s+Ib8mn9KmUo8ysWGxzMiawZUjriQ3MVcG+reqaLBy3csb2FJi9prftKU/tZ+PRDu89yxedx2cdx7MmgWmTmJcrcF6YCtRH14HVdu9ltnZMJYBF1xH9Gn/DhI0i95JgjAhRO+ktWZ/w37ya/IpqClgd91uNldtpsXZ0lYmIyaDaf2nMSVzCpP6TiIyVFYdBdhb1cRFS77F5nTTaHV65A/YeyrrlqWCDvF5jvvug4sugiF+7FL1ztP5jNl+GYOTva/YD6CvWonKnuxX/YXoISQIE0IEl42VG/nX9n/xddnXNNgb2uX9ov8vGJQwiOHJw5mRNUOCMuDbPTU8tWY3XxVW+ywzKXwE61/PZt8e30HZhg0wYULn19vy+Q6+eOQl5gz+lGGpuz3ydzeNJmtcDuFz/geSZTEy0aNJECaECE5u7WZL1RbWla7jn9v+id3tfWugK0dcyZCkIczNnRvUY8ncbs2yvBLueHtLh+WGhg4gdsco3n3be0A2eDBER8MTT8AvftHxNR0OCPt4Aa6Nb2DysWmKffzvCD/rdohJ9es+hOhGJAgTQgitNXvq97CqeBXflH1DXkWeR5lIUyQjUkaQk5BDalQql510GUmRnks6BIuNRXXcunQze6ubfZaJCA1hVPhA3r1viM+xZIsWGT+djSPL+6qGvGdf4lep/yAz/qD3QiFhMO9JGHuJv7chRCBJECaEEN58U/YNX5d+TV5FHhUtFR57XQKMTx9PYkQiFw69kMn9JgftIH+tNW+sL+bOd7Z2WjbXfBKrn2m/Yj/AmDHwpz/B/PkQ4vupJgB1dfDRfS8ytOFFcjOqSTF5WW4jfSRknw7j/h36jZdl+0V3JEGYEEJ0RmtNZUsle8x7WFe6jo0VG9lW036boOTIZM7KOovxfcYzMmUk2fHZhKhOooleyOly896mMvLLGthUXMePRfU+y7r29qMuP52WXRkee1vGx8Pf/gbnnAN9+ng/XmtoaIB4VU7RilcJyXuBAQmeM2LbnHkrTF/YeZQnRNeQIEwIIX4Ks83MF0VfUNpUymcHPmOfuf3MvpiwGFIiU2hyNDEkaQjT+k8jJyGHUzNOJdwUXAuTFpQ1MOfvXzE4PZbdlU1ey2gNuBXWohSat/WnuaAfR/+NeuwxuPpqI0A7Wn4+jBoFUaEtnD1oNaf2+5ELTlrBSWm7vFdq/OUw8XroO/bn3ZwQP50EYUII8XNprdleu521JWspqCmgoKaAipYKr2XDQsKIDYvlnJxzGJk6krSoNIYnDw+a8WVut2ZjcT0vf7ufLSVm9nUwpky7FI0bs2lYn4OrIdojf84cuP12mDbtcFpDgxGovf228TlEuVi58DHOCrm344oNnQ2zHjBmXMqjS9E1JAgTQogTodZaS3FjMUUNRdRYalhdvBqzzcwe8x6v5VOjUsmKyyLMFMbFwy4mMzaTrLgsYsNju7jmXa/B6mDtripW5lfw/uYyn+UaN2bRtGUA9vIEfP39mj4d/vu/jZmXjz8O27YZjzVjY6Gyws1tM99kRs5a5o97vYMaKRh3GcT1gUm/g1jZJF6cEBKECSFEV6q31rO1equxgGz9bipaKthesx2ry+q1fP/Y/qRFpzEwfiCDEgfRP7Y/fWP7kpuQ22vXMXO63Hy8rZzn1+1jU3G9z3LapWj4IRfzd4PRdu+zL2fNgttugxkzjKFgixfDrl3w3HMAmpmDvmDJv93MwMSizisWnQJzFsPwf4PQiJ90b0IcQYIwIYQINLd2U9pYyqaqTewz76OwvpDSplL2m/fjcDt8HpcZm8nAhIHkxOeQk5BD35i+jEsfR1x4XBfW/sTLLzPzh9d+JMwUQqGPMWWH9C0fwaY1cVgPeF83bPRouOcemDcPDhwwess++giWLIHcHE1m/ds0rHuT5OpP/atc8iC46GXIGHWMdyWEBGFCCNFtOVwOipuKqbHUsLN2J0WNRXxV8hU2l406ax1O7bmlEBjbMaVHpRMdFo1LuxiaNJRhScMYEDeA7PhsUqJSeuzMTa016/fX8fjnuzBbHOSXNXRY3lEVj7UsAeuBFCx70tH29suIREXB/fcb48gSEoy0I4eEmZSTkenbeW7uH6m1JDFr8KqOKzhgMvzqEegzSsaWic5IECaEED2Rw+2gpLGEfeZ97DPvaxtzVtJUgtPtPTg7JMIUQf/Y/gxNGkpEaARDk4Yyue9kBiYM7JFrnZlbHLyVV0xFg5Vnv/K9/+QhbkcIlr3puBojsZcnYNmTjtt6eMbqxIlw2mnGgP+33oKSEli71shbsAAef6CW5h8/RG94kThzJ393+k+EKTfDwDMgMuFn3KXohSQIE0KI3sThdlDcUEyDvYEaSw11trq2cWdbq7fi0i7MNrPXY03KRL/YfgxJHEJKVAqT+04mJyGHgfEDCTP1nODM5dasLayitM5CYUUj3+6t4aDZ6nUz8qPZK+NwW8KwlSdSv3YYuEMYOxbmzoULL4ShQyEyEjZtgrPOgpoaULg5d9hHvH3RFZhC3B1fIHcaDJkJOVMhZTCE9c5xfcIvEoQJIUSwabI3caDxAGtL1rK1aisazT7zPsqaytD4/u6f3HcymbGZDEkaQkZ0BhkxGWTHZ/eYGZyNVgc7yhtZtqGEL3dVkZkURUWDlZI6S6fH2ivjaNmVga00CVtJMtpp4uabYepUeOYZ+OSTI0tr8l9/gxH1T0J8P+oLfiQxzPcG6EQkwMjz4JeLZA/M4CJBmBBCCIPNZWO/eT9ritdQa63lx8ofKW0spdHR2OFxmbGZDEocREpkCuPSx5GTkEO/mH6kRqX2iE3P65rtfLTtIPurm9lT1cyqHZWEh4Zgd3bSqwWYvx1Eww+5uK3hjBwJzc3Qty+89BIMGQJWqzHubEbOGsb0yWdivzwuHf1255UaeCYMOwdGXyhLZPReEoQJIYTomMPloLixmAMNByhrLmN3/W6qWqo42HyQfeZ9PmdwhqpQ0qPT6R/Xn2HJwxicOJjchFxyE3OJD/ey7H0302h1sK20gZe/3c/H28pJj4vAYtU0Ouw+j2kpTKe5IBNbaRKu5ghwh9C3L8TFGUtjHCk2vJF9K5aRuvd5qOh8300GnglxGcYSGYNmQGT3/zcUHZIgTAghxE/ndDvZWbuTjZUbqbJUUdFSwX7zfsqby6mx1vg8LjEikZTIFFKiUtpeM2IyiA+PJyMmg5GpI7ttoGZucfDq9wd46Zv9NFqdWByuTo9x1EbjaorEZQnHumUgrup4Thocxg8/gMkEd9wBjz7i4rT+PzAjZy1XjXuVnCQ/1i4LizbGmaUOgTEXQ/oImZXZc0gQJoQQ4sSwuWwcbDrIHvMe9tbvbXvdZ97nc3HaI0WYIkiLSiM7IZtBCYPoE92HhIgE4sPjiY+IN17D40mKTArofpxaa3ZVNPHa9wdYvbOS4trOx5gdyVkfTYq1D1VbU6kuicDdHGH0oqEIC7FzzoTtvHXr04Tv/Qh8TKpoJywGHM0wYh4oE5xyNWSfIRuXdz8ShAkhhOhabu2mzlpHjbWGaks1NRbjtaypjEZHI8WNxeys3YnNZfP7nPHh8aRGpRIbHtsWnMWHx5MQkUBaVBqpUamkRqcar1GpRJhO/Ir3LXYnPx6o58tdlXyxvZK91c2E6zCsThchYZ2PNwNw781k9NBwfnteHGcOTuOJhyN48EFFZKiFRVP/yn9MepqoMCuby0cxNHUPUaGdBIARCXDeUzB0FvSgGa+9lARhQgghuh+tNRanhYPNBymsK6SkqYRqSzVmm5kGewMNtgYa7A2YbWbMdnOna6MdLSYshsSIRBIiEkgITyA7Ppvs+GycbidZ8VkoFGnRaSRFJpEcmUxUaNRxvT+ny82Bmhae/aCad/JKaWzShEa4MSV3PAniSG5HCC3b+2EtSsFWlsgpw2L45KUCEso/pqiwkYjtr9Entsr3CaJTITELrGZjAsDgX0LaMFnPrOtIECaEEKJn01pTY62h3lpPo6OxLUBrsDdQb6un2lJNdUs11ZZqqixV1FhqfO424EtUaBTJkcmkRKa0BWZtP1GH3x8a3/ZzdiSoqNDctbiB/LoqDlBGeFoj2q1QIf7/XdaWcKwHE3DUxmCviCejuYkXpt7MyX23EBve7OdZFEyYD/3GGz8Zo2W82fElQZgQQojg4tZumhxNmK1m6m311FprKawvZEP5BhIiEsiryCMzNhOL00KdrY4aS02He3h6E6pCObnPySRFJpEUkWS8tr5PjkwmJSqF1KhU4sPjUZ0ENm63sfRFdTW895GDdzYc5IDrIKa0BuyWENzWMMLT/e9Bw2FifGoz1w84wBkVbxDfuBMAszWehMiOt4ECILYPTLkFTr1GHmn+PBKECSGEEB3RWtPsaKbWWkuttZYaaw211lrqrHVGmuVw+u763cd07lAVSnyEMXbt0OPRxIhEEsITSIxs//nQ+8TIxHZj2lwuKC/XHCh38NGXLZTVW3n702ZiRpXgbg4nMrvW7/pEYmNSyA4WsJxIbMSk59I3xkJE+Y/Q0Ri90ChjPbOxl0C/k6HfOGM5DdERCcKEEEKI46nF0dI2hq3eWk+drY46a11br1uNxQjiqi3VNDmafvJ1RqSMwKRMxIXH0S+2nxGgHfGTFJmEbklk3RfxbPo+hn1lLg7a6qmKKwE3RA+rQDtDUKH+TRLIVWX8IfQ9hjpLGR2x1/+KZk6AtOEwdDZkny67AhwmQZgQQggRKHaXHbPNeCxqtpnb3tfb6jHbWz9bjc8N9oZj7mk7UkxYDC63i7CQMEJDQhkeNx6s8bgscewuaqbxYH8sTUk0O8MxZbXgdkaiVCi+YoUI7GSoWgapMs41fUMizZweso1w1fm6aQCERkLOL6DuAIy9GAZMMvbTjEkPluU0JAgTQgghehKX20W1pZqixiIqWypxaRctjpZ2AdyhXjezzUyjvZFGe2OH+4L6EmmKxEQYza5GIluGo+xpNFhCINSCy5WOdsUc/nHGoF3RoMNRuBmn9vBvpu+YZVqPQmPTYeSGlPt/8Yh44xHngMkQGm68Dp1lzN7sHRMEJAgTQgghersjJyPU2eqwOq1tM0g3Vm4EoKK5gipLFXvNe4kNi8XqtGJ3+96iyScdgnaG43ZEo8KtaGcc2pGEdkeiXZFEu0z0c7cwRNfyCwooc6eTRAt9XDZGUEWiy02U1r4jlCOdNBcyxsDk30NEz9hI/ggShAkhhBDC06EJCRUtFdRaa2mwN9Bkb2KveS9hIWE02Buos9ZRZ6szxr61vj/WmaTeKLeJcFcY8W43fd0Wkt02orSmxmRipM2OQ8Eom50kl5s4t5tY7Sbe5SZGa6yOOCr0UCwxI+ifGUriyAmEDpoCUcndbb9NCcJ6A7f9J/xPRQghhDjOtNa4tBOL04LZZqbOWofNaaPZ0UyTs5kGm5l6mxlz6xg4u8uBS7uwuazUWOqpaa6nwVGHW/30QC5Ea6K1JsatiXG7jVftJrr1NcHtJtnlwmZLIcoZyq7GSejQTOKT0uibOZwJY7MYPjiD0KjI4/gv45XPICz0RF9ZHD97z5mDo7Q00NUQQggh2glv/YkLWA0ODfA3ecmzA3Ymstoj58PsEOatzD+RFeuQBGE9iAoLQ4XJgnlCCCGEPuqT1uDWh97rI0q0vqqjjyHgA/8lCOtBBn3ycaCrIIQQQvQaJzl+/ri2nyMoFugQQgghhDhaWICfLkkQJoQQQggRABKECSGEEEIEgARhQgghhBABIEGYEEIIIUQASBAmhBBCCBEAEoQJIYQQQgSABGFCCCGEEAEgQZgQQgghRABIECaEEEIIEQAShAkhhBBCBIAEYUIIIYQQASBBmBBCCCFEACitdaDrcEyUUlXAgRN8mVSg+gRfQxw7aZfuR9qke5J26X6kTbqnrmiXaq31bG8ZPS4I6wpKqQ1a61MCXQ/RnrRL9yNt0j1Ju3Q/0ibdU6DbRR5HCiGEEEIEgARhQgghhBABIEGYd/8IdAWEV9Iu3Y+0Sfck7dL9SJt0TwFtFxkTJoQQQggRANITJoQQQggRAEEdhCmlZiuldiqldiul/uIlP0Ip9WZr/vdKqYEBqGbQ8aNdblFKFSiltiilvlBKZQeinsGkszY5otwFSimtlJJZYCeYP22ilLqo9XclXyn1elfXMRj58f2VpZRarZTa2PodNicQ9QwmSqkXlFKVSqltPvKVUurvrW22RSl1clfVLWiDMKWUCfg/4BxgBHCpUmrEUcWuAeq01oOBx4C/dm0tg4+f7bIROEVrPQZYBjzctbUMLn62CUqpOOBPwPddW8Pg40+bKKWGAHcCZ2itRwL/0dX1DDZ+/q4sBJZqrccDlwBPdW0tg9KLgNd1ulqdAwxp/bkeeLoL6gQEcRAGTAR2a633aq3twBvAvKPKzANean2/DPilUkp1YR2DUaftorVerbVuaf34HdC/i+sYbPz5XQG4F+M/KtaurFyQ8qdNrgP+T2tdB6C1ruziOgYjf9pFA/Gt7xOAsi6sX1DSWq8FajsoMg94WRu+AxKVUn27om7BHIRlAsVHfC5pTfNaRmvtBMxASpfULnj50y5Hugb4+ITWSHTaJq3d9wO01h92ZcWCmD+/J0OBoUqpr5VS3ymlOuoJEMeHP+1yN3C5UqoE+Aj4Y9dUTXTgWP/uHDehXXERIU4EpdTlwCnA1EDXJZgppUKA/wXmB7gqor1QjMcr0zB6i9cqpUZrresDWSnBpcCLWutHlVKnAa8opUZprd2BrpjoesHcE1YKDDjic//WNK9llFKhGF3HNV1Su+DlT7uglDoLuAs4V2tt66K6BavO2iQOGAWsUUrtByYDK2Rw/gnlz+9JCbBCa+3QWu8DdmEEZeLE8addrgGWAmitvwUiMfYvFIHj19+dEyGYg7D1wBClVI5SKhxjgOSKo8qsAH7b+v43wCotC6udaJ22i1JqPLAEIwCTcS4nXodtorU2a61TtdYDtdYDMcbpnau13hCY6gYFf76/lmP0gqGUSsV4PLm3C+sYjPxplyLglwBKqZMwgrCqLq2lONoK4MrWWZKTAbPW+mBXXDhoH0dqrZ1KqZuAlYAJeEFrna+UugfYoLVeATyP0VW8G2NQ3yWBq3Fw8LNdFgOxwFut8ySKtNbnBqzSvZyfbSK6kJ9tshKYqZQqAFzA7Vpr6ck/gfxsl1uBZ5VSN2MM0p8v/7k/sZRS/8L4D0lq61i8/wbCALTWz2CMzZsD7AZagKu6rG7S9kIIIYQQXS+YH0cKIYQQQgSMBGFCCCGEEAEgQZgQQgghRABIECaEEEIIEQAShAkhhBBCBIAEYUKIXkUp5VJKbVJKbVNKva+USjzO59/fuu4WSqmm43luIURwkSBMCNHbWLTW47TWozDW9/tDoCskhBDeSBAmhOjNvqV1I16l1CCl1CdKqTyl1FdKqeGt6X2UUu8qpTa3/pzemr68tWy+Uur6AN6DEKKXCtoV84UQvZtSyoSxPczzrUn/AH6ntS5USk0CngJmAH8HvtRan996TGxr+au11rVKqShgvVLqbVlxXghxPEkQJoTobaKUUpswesC2A58ppWKB0zm81RVAROvrDOBKAK21CzC3pi9QSp3f+n4AxubXEoQJIY4bCcKEEL2NRWs9TikVjbGH3x+AF4F6rfU4f06glJoGnAWcprVuUUqtwdhoWQghjhsZEyaE6JW01i3AAowNk1uAfUqpCwGUYWxr0S+AG1vTTUqpBCABqGsNwIYDk7v8BoQQvZ4EYUKIXktrvRHYAlwKXAZco5TaDOQD81qL/QmYrpTaCuQBI4BPgFCl1HbgIeC7rq67EKL3U1rrQNdBCCGEECLoSE+YEEIIIUQASBAmhBBCCBEAEoQJIYQQQgSABGFCCCGEEAEgQZgQQgghRABIECaEEEIIEQAShAkhhBBCBIAEYUIIIYQQAfD/YzD4cGy92/8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"chrombpnet.encsr637xsc.variantscore.csr\"\n",
+    "\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (572M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"chrombpnet.encsr637xsc_50m.variantscore.csr\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"chrombpnet.encsr000emt.variantscore.csr\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ DNASE (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "\n",
+    "\n",
+    "keyd=\"enformer_recomputed\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "title=\"Enformer w/ DNASE (recomputed) (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "# keyd=\"enformer.encsr000emt.variantscore.sad\"\n",
+    "# label=afrs[\"label\"].astype(int)\n",
+    "# score=abs(afrs[keyd])\n",
+    "# title=\"Enformer w/ DNASE SAD (50M)\"\n",
+    "\n",
+    "# fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "# roc_auc = metrics.average_precision_score(label, score)\n",
+    "# ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"enformer.encsr000emt.variantscore.sar\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "title=\"Enformer w/ DNASE (reported) (68M)\"\n",
+    "\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "enformer_auc = roc_auc\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "roc_auc=sum(afrs[\"label\"]==1)/len(afrs[\"label\"])\n",
+    "ax.plot(fpr_chrombpnet, [sum(afrs[\"label\"]==1)/len(afrs[\"label\"])]*len(fpr_chrombpnet), linewidth=2, label=\"Random baseline, AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc='upper right')\n",
+    "ax.set_xlabel(\"Recall\")\n",
+    "ax.set_ylabel(\"Precision\")\n",
+    "\n",
+    "#plt.savefig(\"subfigs/afr_caqtls_auprc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1e-06\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:35: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sig 6597\n",
+      "107880\n",
+      "0.061\n",
+      "1e-09\n",
+      "sig 3650\n",
+      "104933\n",
+      "0.035\n",
+      "1e-12\n",
+      "sig 2245\n",
+      "103528\n",
+      "0.022\n",
+      "1e-15\n",
+      "sig 1413\n",
+      "102696\n",
+      "0.014\n",
+      "1e-18\n",
+      "sig 904\n",
+      "102187\n",
+      "0.009\n",
+      "2.5800991659088344e-19\n",
+      "sig 798\n",
+      "102081\n",
+      "0.008\n",
+      "4.990607165261473e-19\n",
+      "sig 861\n",
+      "102144\n",
+      "0.008\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "random_bc = []\n",
+    "chrombpnet = {}\n",
+    "enformer_dnase_roc = []\n",
+    "enformer_dnase_roc_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(6,25,3):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "    print(threshold)\n",
+    "    dataf1 = afrs[afrs[\"pval\"]<threshold]\n",
+    "    dataf1[\"label\"] = 1\n",
+    "    dataf1 = dataf1[[\"chrombpnet.encsr000emt.variantscore.csr\", \n",
+    "                    \"chrombpnet.encsr637xsc.variantscore.csr\",\n",
+    "                     \"chrombpnet.encsr637xsc_50m.variantscore.csr\",\n",
+    "                    \"enformer.encsr000emt.variantscore.sar\",  \"enformer_recomputed\", \"label\"]]\n",
+    "    \n",
+    "    dataf2 = afrs[afrs[\"label\"]==0]\n",
+    "    dataf2 = dataf2[[\"chrombpnet.encsr000emt.variantscore.csr\", \n",
+    "                    \"chrombpnet.encsr637xsc.variantscore.csr\",\n",
+    "                     \"chrombpnet.encsr637xsc_50m.variantscore.csr\",\n",
+    "                    \"enformer.encsr000emt.variantscore.sar\",  \"enformer_recomputed\", \"label\"]]\n",
+    "\n",
+    "#     dataf2 = caqtls[(caqtls[\"Log10_BF\"]<-1)]\n",
+    "#     dataf2[\"chrombpnet.encsr637xsc.variantscore.csr\"] = caqtls[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]\n",
+    "#     dataf2[\"chrombpnet.encsr637xsc_50m.variantscore.csr\"] = caqtls[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]\n",
+    "#     dataf2[\"chrombpnet.encsr000emt.variantscore.csr\"] = caqtls[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"]\n",
+    "#     dataf2[\"enformer.encsr000emt.variantscore.sar\"] = caqtls[\"DNASE_SAR\"]\n",
+    "#     dataf2[\"enformer_recomputed\"] = caqtls[\"enformer_recomputed\"]\n",
+    "#     dataf2[\"label\"] = 0\n",
+    "    \n",
+    "    dataf = pd.concat([dataf1, dataf2])\n",
+    "    \n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(\"sig\",sigs)\n",
+    "    print(total)\n",
+    "    print(np.round(sigs/total,3))\n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,3))\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"chrombpnet.encsr000emt.variantscore.csr\"]))\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"chrombpnet.encsr637xsc.variantscore.csr\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.csr\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer_recomputed\"]))\n",
+    "    enformer_dnase_roc.append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer.encsr000emt.variantscore.sar\"]))\n",
+    "    enformer_dnase_roc_sar.append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd54b843b90>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "colors = [\"blue\", \"blue\"]\n",
+    "alphas = [0.5, 1.0]\n",
+    "idx=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], label=key, c=\"blue\", alpha=alphas[idx])\n",
+    "    idx+=1\n",
+    "    \n",
+    "key=\"ChromBPNet DNase (68M)\"\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], label=key)\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_dnase_roc_sar, label=\"Enformer DNase (reported) (68M)\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_dnase_roc, label=\"Enformer DNase (recomputed) (68M)\")\n",
+    "\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), random_bc, label=\"random\")\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10 pval\")\n",
+    "ax.set_ylabel(\"Average precision\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "\n",
+    "#plt.savefig(\"subfigs/afr_caqtls_auprc_compare.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.69\n",
+      "ChromBPNet ATAC (50M):  0.67\n",
+      "ChromBPNet DNase:  0.68\n",
+      "Enformer:  0.63\n",
+      "Enformer:  0.48\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet DNase:  0.0\n",
+      "Enformer:  0.0\n",
+      "Enformer:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataf = afrs\n",
+    "pearsonrs = []\n",
+    "\n",
+    "dataf['enformer_recomputed'] = -1*dataf['enformer_recomputed']\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer: \",np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer: \",np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.38\n",
+      "ChromBPNet ATAC (50M):  0.35\n",
+      "ChromBPNet DNase:  0.37\n",
+      "Enformer:  0.29\n",
+      "Enformer:  0.12\n",
+      "ChromBPNet ATAC:  3.385725386748927e-278\n",
+      "ChromBPNet ATAC:  3.224363455097045e-246\n",
+      "ChromBPNet DNase:  2.7850970670387657e-263\n",
+      "Enformer SAD:  9.244826958911604e-165\n",
+      "Enformer SAR:  4.206300602432501e-28\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonr_abs = []\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer SAD: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer SAR: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.68</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.48</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.35</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.37</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.12</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsonr                             method   scoring  score\n",
+       "0      0.69             ChromBPNet ATAC (500M)    signed  logfc\n",
+       "1      0.67              ChromBPNet ATAC (50M)    signed  logfc\n",
+       "2      0.68             ChromBPNet DNase (68M)    signed  logfc\n",
+       "3      0.63  Enformer (recomputed) DNase (68M)    signed  logfc\n",
+       "4      0.48    Enformer (reported) DNase (68M)    signed  logfc\n",
+       "5      0.38             ChromBPNet ATAC (500M)  unsigned  logfc\n",
+       "6      0.35              ChromBPNet ATAC (50M)  unsigned  logfc\n",
+       "7      0.37             ChromBPNet DNase (68M)  unsigned  logfc\n",
+       "8      0.29  Enformer (recomputed) DNase (68M)  unsigned  logfc\n",
+       "9      0.12    Enformer (reported) DNase (68M)  unsigned  logfc"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.DataFrame()\n",
+    "\n",
+    "df[\"pearsonr\"] = pearsonrs + pearsonr_abs\n",
+    "df[\"method\"] = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\", \"Enformer (recomputed) DNase (68M)\", \"Enformer (reported) DNase (68M)\"] + [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\", \"Enformer (recomputed) DNase (68M)\", \"Enformer (reported) DNase (68M)\"]\n",
+    "df[\"scoring\"] = [\"signed\"]*len(pearsonrs) + [\"unsigned\"]*len(pearsonr_abs)\n",
+    "df[\"score\"] = [\"logfc\"] * df.shape[0]\n",
+    "df[\"pearsonr\"] = df[\"pearsonr\"].astype(float)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.66\n",
+      "ChromBPNet ATAC (50M):  0.65\n",
+      "ChromBPNet DNase:  0.67\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet DNase:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataf = afrs\n",
+    "pearsonrs = []\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.32\n",
+      "ChromBPNet ATAC (50M):  0.31\n",
+      "ChromBPNet DNase:  0.33\n",
+      "ChromBPNet ATAC:  1.3703003278465646e-193\n",
+      "ChromBPNet ATAC:  3.874155173698438e-181\n",
+      "ChromBPNet DNase:  2.1062604760967424e-206\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonr_abs = []\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.68</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.48</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsonr                  method   scoring score\n",
+       "0      0.69  ChromBPNet ATAC (500M)    signed   jsd\n",
+       "1      0.67   ChromBPNet ATAC (50M)    signed   jsd\n",
+       "2      0.68  ChromBPNet DNase (68M)    signed   jsd\n",
+       "3      0.63  ChromBPNet ATAC (500M)  unsigned   jsd\n",
+       "4      0.48   ChromBPNet ATAC (50M)  unsigned   jsd\n",
+       "5      0.38  ChromBPNet DNase (68M)  unsigned   jsd"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1= pd.DataFrame()\n",
+    "\n",
+    "df1[\"pearsonr\"] = pearsonrs + pearsonr_abs\n",
+    "df1[\"method\"] = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"] + [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]\n",
+    "df1[\"scoring\"] = [\"signed\"]*len(pearsonrs) + [\"unsigned\"]*len(pearsonr_abs)\n",
+    "df1[\"score\"] = [\"jsd\"] * df1.shape[0]\n",
+    "df1[\"pearsonr\"] = df[\"pearsonr\"].astype(float)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat((df, df1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.68</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.48</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.35</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.37</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.12</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.68</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.48</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsonr                             method   scoring  score\n",
+       "0      0.69             ChromBPNet ATAC (500M)    signed  logfc\n",
+       "1      0.67              ChromBPNet ATAC (50M)    signed  logfc\n",
+       "2      0.68             ChromBPNet DNase (68M)    signed  logfc\n",
+       "3      0.63  Enformer (recomputed) DNase (68M)    signed  logfc\n",
+       "4      0.48    Enformer (reported) DNase (68M)    signed  logfc\n",
+       "5      0.38             ChromBPNet ATAC (500M)  unsigned  logfc\n",
+       "6      0.35              ChromBPNet ATAC (50M)  unsigned  logfc\n",
+       "7      0.37             ChromBPNet DNase (68M)  unsigned  logfc\n",
+       "8      0.29  Enformer (recomputed) DNase (68M)  unsigned  logfc\n",
+       "9      0.12    Enformer (reported) DNase (68M)  unsigned  logfc\n",
+       "0      0.69             ChromBPNet ATAC (500M)    signed    jsd\n",
+       "1      0.67              ChromBPNet ATAC (50M)    signed    jsd\n",
+       "2      0.68             ChromBPNet DNase (68M)    signed    jsd\n",
+       "3      0.63             ChromBPNet ATAC (500M)  unsigned    jsd\n",
+       "4      0.48              ChromBPNet ATAC (50M)  unsigned    jsd\n",
+       "5      0.38             ChromBPNet DNase (68M)  unsigned    jsd"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    0.69\n",
+      "1    0.67\n",
+      "2    0.68\n",
+      "3    0.63\n",
+      "4    0.48\n",
+      "5    0.38\n",
+      "6    0.35\n",
+      "7    0.37\n",
+      "8    0.29\n",
+      "9    0.12\n",
+      "0    0.69\n",
+      "1    0.67\n",
+      "2    0.68\n",
+      "3    0.63\n",
+      "4    0.48\n",
+      "5    0.38\n",
+      "Name: pearsonr, dtype: float64\n",
+      "{'ChromBPNet DNase (68M)', 'Enformer (recomputed) DNase (68M)', 'ChromBPNet ATAC (50M)', 'Enformer (reported) DNase (68M)', 'ChromBPNet ATAC (500M)'}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f2466985a90>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "print(df[\"pearsonr\"])\n",
+    "df[\"scoring-methods\"] = df[\"scoring\"] + \"_\" +  df[\"score\"]\n",
+    "ax = sns.barplot(data=df, x=\"method\", y=\"pearsonr\", hue=\"scoring-methods\")\n",
+    "print(set(df[\"method\"]))\n",
+    "xtix = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\", \"Enformer (recomputed) DNase (68M)\", \"Enformer (reported) DNase (68M)\"]\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "ax.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6900002588055183, 0.0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Pearsonr = 0.69')"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = afrs[afrs[\"label\"]==1][\"chrombpnet.encsr637xsc.variantscore.logfc\"]\n",
+    "data[\"Beta\"] = afrs[afrs[\"label\"]==1][\"beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"green\", rasterized=True)\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"green\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "\n",
+    "#plt.savefig(\"subfigs/afr_caqtls_pearsonr.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.4729085713163014, 0.0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Pearsonr = 0.47')"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = dataf[dataf[\"label\"]==1][\"enformer.encsr000emt.variantscore.sad\"]\n",
+    "data[\"Beta\"] = dataf[dataf[\"label\"]==1][\"beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"Red\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#ffcccb\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1e-06\n",
+      "1e-09\n",
+      "1e-12\n",
+      "1e-15\n",
+      "1e-18\n",
+      "2.5800991659088344e-19\n",
+      "4.990607165261473e-19\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "\n",
+    "\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "enformer = []\n",
+    "enformer_unsigned = []\n",
+    "enformer_sar = []\n",
+    "enformer_unsigned_sar = []\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(6,25,3):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "    print(threshold)\n",
+    "    dataf = afrs[afrs[\"pval\"]<threshold]\n",
+    "\n",
+    "      \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr000emt.variantscore.logfc\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr000emt.variantscore.logfc\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.logfc\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.logfc\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer_recomputed\"], dataf[\"beta\"])\n",
+    "    enformer.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer_recomputed\"]), abs(dataf[\"beta\"]))\n",
+    "    enformer_unsigned.append(pear[0])\n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer.encsr000emt.variantscore.sar\"], dataf[\"beta\"])\n",
+    "    enformer_sar.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer.encsr000emt.variantscore.sar\"]), abs(dataf[\"beta\"]))\n",
+    "    enformer_unsigned_sar.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f24793d9f50>"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    print(chrombpnet[key])\n",
+    "\n",
+    "\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase (reported) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase (reported) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10pval\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/afr_caqtls_pearsonr_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1e-06\n",
+      "1e-09\n",
+      "1e-12\n",
+      "1e-15\n",
+      "1e-18\n",
+      "2.5800991659088344e-19\n",
+      "4.990607165261473e-19\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "\n",
+    "\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(6,25,3):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "    print(threshold)\n",
+    "    dataf = afrs[afrs[\"pval\"]<threshold]\n",
+    "\n",
+    "      \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr000emt.variantscore.jsd\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr000emt.variantscore.jsd\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.jsd\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.jsd\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f2466b2c890>"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/afr_caqtls_pearsonr_compare_jsd.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (across-ancestry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed', 'pval', 'beta', 'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf = afrs[afrs[\"label\"]==1]\n",
+    "dataf.columns\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'esan', 'luhya', 'encsr637xsc_50m', 'mende', 'yoruba', 'maasai', 'encsr095qnb', 'gambian', 'encsr000emt', 'encsr637xsc'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "anscs = dataf.columns[11:-6].tolist()\n",
+    "anscs = [ans.split(\".\")[1] for ans in anscs]\n",
+    "anscs = set(anscs)\n",
+    "print(anscs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>esan</th>\n",
+       "      <th>luhya</th>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <th>mende</th>\n",
+       "      <th>yoruba</th>\n",
+       "      <th>maasai</th>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <th>gambian</th>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <th>encsr637xsc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ancestry</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>esan</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.967219</td>\n",
+       "      <td>0.931925</td>\n",
+       "      <td>0.968802</td>\n",
+       "      <td>0.969716</td>\n",
+       "      <td>0.961665</td>\n",
+       "      <td>0.926250</td>\n",
+       "      <td>0.965039</td>\n",
+       "      <td>0.938818</td>\n",
+       "      <td>0.947346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>luhya</th>\n",
+       "      <td>0.967219</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.933962</td>\n",
+       "      <td>0.969444</td>\n",
+       "      <td>0.970310</td>\n",
+       "      <td>0.968970</td>\n",
+       "      <td>0.929342</td>\n",
+       "      <td>0.969731</td>\n",
+       "      <td>0.939227</td>\n",
+       "      <td>0.948864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <td>0.931925</td>\n",
+       "      <td>0.933962</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.938928</td>\n",
+       "      <td>0.927798</td>\n",
+       "      <td>0.929949</td>\n",
+       "      <td>0.957399</td>\n",
+       "      <td>0.934693</td>\n",
+       "      <td>0.951317</td>\n",
+       "      <td>0.965801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mende</th>\n",
+       "      <td>0.968802</td>\n",
+       "      <td>0.969444</td>\n",
+       "      <td>0.938928</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.968804</td>\n",
+       "      <td>0.965864</td>\n",
+       "      <td>0.933363</td>\n",
+       "      <td>0.972593</td>\n",
+       "      <td>0.946330</td>\n",
+       "      <td>0.953242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yoruba</th>\n",
+       "      <td>0.969716</td>\n",
+       "      <td>0.970310</td>\n",
+       "      <td>0.927798</td>\n",
+       "      <td>0.968804</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.964676</td>\n",
+       "      <td>0.927679</td>\n",
+       "      <td>0.968421</td>\n",
+       "      <td>0.934047</td>\n",
+       "      <td>0.947957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>maasai</th>\n",
+       "      <td>0.961665</td>\n",
+       "      <td>0.968970</td>\n",
+       "      <td>0.929949</td>\n",
+       "      <td>0.965864</td>\n",
+       "      <td>0.964676</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.920529</td>\n",
+       "      <td>0.967941</td>\n",
+       "      <td>0.942865</td>\n",
+       "      <td>0.941758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <td>0.926250</td>\n",
+       "      <td>0.929342</td>\n",
+       "      <td>0.957399</td>\n",
+       "      <td>0.933363</td>\n",
+       "      <td>0.927679</td>\n",
+       "      <td>0.920529</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.931529</td>\n",
+       "      <td>0.922027</td>\n",
+       "      <td>0.961778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gambian</th>\n",
+       "      <td>0.965039</td>\n",
+       "      <td>0.969731</td>\n",
+       "      <td>0.934693</td>\n",
+       "      <td>0.972593</td>\n",
+       "      <td>0.968421</td>\n",
+       "      <td>0.967941</td>\n",
+       "      <td>0.931529</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.946232</td>\n",
+       "      <td>0.953597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <td>0.938818</td>\n",
+       "      <td>0.939227</td>\n",
+       "      <td>0.951317</td>\n",
+       "      <td>0.946330</td>\n",
+       "      <td>0.934047</td>\n",
+       "      <td>0.942865</td>\n",
+       "      <td>0.922027</td>\n",
+       "      <td>0.946232</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.942114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc</th>\n",
+       "      <td>0.947346</td>\n",
+       "      <td>0.948864</td>\n",
+       "      <td>0.965801</td>\n",
+       "      <td>0.953242</td>\n",
+       "      <td>0.947957</td>\n",
+       "      <td>0.941758</td>\n",
+       "      <td>0.961778</td>\n",
+       "      <td>0.953597</td>\n",
+       "      <td>0.942114</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     esan     luhya  encsr637xsc_50m     mende    yoruba  \\\n",
+       "ancestry                                                                   \n",
+       "esan             1.000000  0.967219         0.931925  0.968802  0.969716   \n",
+       "luhya            0.967219  1.000000         0.933962  0.969444  0.970310   \n",
+       "encsr637xsc_50m  0.931925  0.933962         1.000000  0.938928  0.927798   \n",
+       "mende            0.968802  0.969444         0.938928  1.000000  0.968804   \n",
+       "yoruba           0.969716  0.970310         0.927798  0.968804  1.000000   \n",
+       "maasai           0.961665  0.968970         0.929949  0.965864  0.964676   \n",
+       "encsr095qnb      0.926250  0.929342         0.957399  0.933363  0.927679   \n",
+       "gambian          0.965039  0.969731         0.934693  0.972593  0.968421   \n",
+       "encsr000emt      0.938818  0.939227         0.951317  0.946330  0.934047   \n",
+       "encsr637xsc      0.947346  0.948864         0.965801  0.953242  0.947957   \n",
+       "\n",
+       "                   maasai  encsr095qnb   gambian  encsr000emt  encsr637xsc  \n",
+       "ancestry                                                                    \n",
+       "esan             0.961665     0.926250  0.965039     0.938818     0.947346  \n",
+       "luhya            0.968970     0.929342  0.969731     0.939227     0.948864  \n",
+       "encsr637xsc_50m  0.929949     0.957399  0.934693     0.951317     0.965801  \n",
+       "mende            0.965864     0.933363  0.972593     0.946330     0.953242  \n",
+       "yoruba           0.964676     0.927679  0.968421     0.934047     0.947957  \n",
+       "maasai           1.000000     0.920529  0.967941     0.942865     0.941758  \n",
+       "encsr095qnb      0.920529     1.000000  0.931529     0.922027     0.961778  \n",
+       "gambian          0.967941     0.931529  1.000000     0.946232     0.953597  \n",
+       "encsr000emt      0.942865     0.922027  0.946232     1.000000     0.942114  \n",
+       "encsr637xsc      0.941758     0.961778  0.953597     0.942114     1.000000  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "ancestries = []\n",
+    "\n",
+    "for ans in anscs:\n",
+    "    if ans==\"eur\":\n",
+    "        continue\n",
+    "    if ans==\"afr\":\n",
+    "        continue\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\"encsr637xsc\", \"encsr000emt\", \"encsr637xsc_50m\", \"encsr000emt.ENCSR095QNB\" ]:\n",
+    "            ancestries.append(ans+title)\n",
+    "    else:\n",
+    "        ancestries.append(ans)\n",
+    "    \n",
+    "abs_logfc_dict = {x: [] for x in ancestries}\n",
+    "abs_logfc_dict['ancestry'] = []\n",
+    "\n",
+    "for outer_ancestry in ancestries:\n",
+    "    abs_logfc_dict['ancestry'].append(outer_ancestry)\n",
+    "    for ancestry in ancestries:\n",
+    "        #print(\"chrombpnet.encsr637xsc.variantscore.logfc\".replace(\"encsr637xsc\", ancestry))\n",
+    "\n",
+    "        abs_logfc_dict[ancestry].append(scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\".replace(\"encsr637xsc\", outer_ancestry)],\n",
+    "                                                 dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\".replace(\"encsr637xsc\", ancestry)])[0])\n",
+    "        \n",
+    "abs_logfc_df = pd.DataFrame(abs_logfc_dict)\n",
+    "abs_logfc_df.set_index('ancestry', inplace=True)\n",
+    "\n",
+    "abs_logfc_df    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.clustermap(abs_logfc_df, annot=True, cmap=\"crest\")\n",
+    "g.fig.set_figwidth(12)\n",
+    "g.fig.set_figheight(12)\n",
+    "g.fig.suptitle('Ancestry vs. Ancestry ChromBPNet Absolute LogFC Pearson Correlation', fontsize=18, y=1.05)\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_ancestry_logfc.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "esan\n",
+      "luhya\n",
+      "encsr637xsc_50m\n",
+      "mende\n",
+      "yoruba\n",
+      "maasai\n",
+      "encsr095qnb\n",
+      "gambian\n",
+      "encsr000emt\n",
+      "encsr637xsc\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>esan</th>\n",
+       "      <th>luhya</th>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <th>mende</th>\n",
+       "      <th>yoruba</th>\n",
+       "      <th>maasai</th>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <th>gambian</th>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <th>encsr637xsc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ancestry</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>esan</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.960769</td>\n",
+       "      <td>0.944884</td>\n",
+       "      <td>0.963115</td>\n",
+       "      <td>0.961965</td>\n",
+       "      <td>0.958645</td>\n",
+       "      <td>0.941344</td>\n",
+       "      <td>0.961508</td>\n",
+       "      <td>0.923923</td>\n",
+       "      <td>0.945075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>luhya</th>\n",
+       "      <td>0.960769</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.946419</td>\n",
+       "      <td>0.963569</td>\n",
+       "      <td>0.965634</td>\n",
+       "      <td>0.964181</td>\n",
+       "      <td>0.940539</td>\n",
+       "      <td>0.965961</td>\n",
+       "      <td>0.926727</td>\n",
+       "      <td>0.948717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <td>0.944884</td>\n",
+       "      <td>0.946419</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.946116</td>\n",
+       "      <td>0.945635</td>\n",
+       "      <td>0.945881</td>\n",
+       "      <td>0.950975</td>\n",
+       "      <td>0.950496</td>\n",
+       "      <td>0.937292</td>\n",
+       "      <td>0.969292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mende</th>\n",
+       "      <td>0.963115</td>\n",
+       "      <td>0.963569</td>\n",
+       "      <td>0.946116</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.966076</td>\n",
+       "      <td>0.962352</td>\n",
+       "      <td>0.942669</td>\n",
+       "      <td>0.969161</td>\n",
+       "      <td>0.932825</td>\n",
+       "      <td>0.947684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yoruba</th>\n",
+       "      <td>0.961965</td>\n",
+       "      <td>0.965634</td>\n",
+       "      <td>0.945635</td>\n",
+       "      <td>0.966076</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.960864</td>\n",
+       "      <td>0.944662</td>\n",
+       "      <td>0.965081</td>\n",
+       "      <td>0.925010</td>\n",
+       "      <td>0.950900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>maasai</th>\n",
+       "      <td>0.958645</td>\n",
+       "      <td>0.964181</td>\n",
+       "      <td>0.945881</td>\n",
+       "      <td>0.962352</td>\n",
+       "      <td>0.960864</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.937487</td>\n",
+       "      <td>0.967005</td>\n",
+       "      <td>0.929940</td>\n",
+       "      <td>0.945719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <td>0.941344</td>\n",
+       "      <td>0.940539</td>\n",
+       "      <td>0.950975</td>\n",
+       "      <td>0.942669</td>\n",
+       "      <td>0.944662</td>\n",
+       "      <td>0.937487</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.945148</td>\n",
+       "      <td>0.924156</td>\n",
+       "      <td>0.957086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gambian</th>\n",
+       "      <td>0.961508</td>\n",
+       "      <td>0.965961</td>\n",
+       "      <td>0.950496</td>\n",
+       "      <td>0.969161</td>\n",
+       "      <td>0.965081</td>\n",
+       "      <td>0.967005</td>\n",
+       "      <td>0.945148</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.935592</td>\n",
+       "      <td>0.950797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <td>0.923923</td>\n",
+       "      <td>0.926727</td>\n",
+       "      <td>0.937292</td>\n",
+       "      <td>0.932825</td>\n",
+       "      <td>0.925010</td>\n",
+       "      <td>0.929940</td>\n",
+       "      <td>0.924156</td>\n",
+       "      <td>0.935592</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.926494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc</th>\n",
+       "      <td>0.945075</td>\n",
+       "      <td>0.948717</td>\n",
+       "      <td>0.969292</td>\n",
+       "      <td>0.947684</td>\n",
+       "      <td>0.950900</td>\n",
+       "      <td>0.945719</td>\n",
+       "      <td>0.957086</td>\n",
+       "      <td>0.950797</td>\n",
+       "      <td>0.926494</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     esan     luhya  encsr637xsc_50m     mende    yoruba  \\\n",
+       "ancestry                                                                   \n",
+       "esan             1.000000  0.960769         0.944884  0.963115  0.961965   \n",
+       "luhya            0.960769  1.000000         0.946419  0.963569  0.965634   \n",
+       "encsr637xsc_50m  0.944884  0.946419         1.000000  0.946116  0.945635   \n",
+       "mende            0.963115  0.963569         0.946116  1.000000  0.966076   \n",
+       "yoruba           0.961965  0.965634         0.945635  0.966076  1.000000   \n",
+       "maasai           0.958645  0.964181         0.945881  0.962352  0.960864   \n",
+       "encsr095qnb      0.941344  0.940539         0.950975  0.942669  0.944662   \n",
+       "gambian          0.961508  0.965961         0.950496  0.969161  0.965081   \n",
+       "encsr000emt      0.923923  0.926727         0.937292  0.932825  0.925010   \n",
+       "encsr637xsc      0.945075  0.948717         0.969292  0.947684  0.950900   \n",
+       "\n",
+       "                   maasai  encsr095qnb   gambian  encsr000emt  encsr637xsc  \n",
+       "ancestry                                                                    \n",
+       "esan             0.958645     0.941344  0.961508     0.923923     0.945075  \n",
+       "luhya            0.964181     0.940539  0.965961     0.926727     0.948717  \n",
+       "encsr637xsc_50m  0.945881     0.950975  0.950496     0.937292     0.969292  \n",
+       "mende            0.962352     0.942669  0.969161     0.932825     0.947684  \n",
+       "yoruba           0.960864     0.944662  0.965081     0.925010     0.950900  \n",
+       "maasai           1.000000     0.937487  0.967005     0.929940     0.945719  \n",
+       "encsr095qnb      0.937487     1.000000  0.945148     0.924156     0.957086  \n",
+       "gambian          0.967005     0.945148  1.000000     0.935592     0.950797  \n",
+       "encsr000emt      0.929940     0.924156  0.935592     1.000000     0.926494  \n",
+       "encsr637xsc      0.945719     0.957086  0.950797     0.926494     1.000000  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "ancestries = []\n",
+    "\n",
+    "for ans in anscs:\n",
+    "    if ans==\"eur\":\n",
+    "        continue\n",
+    "    if ans==\"afr\":\n",
+    "        continue\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\"encsr637xsc\", \"encsr000emt\", \"encsr637xsc_50m\", \"encsr000emt.ENCSR095QNB\" ]:\n",
+    "            ancestries.append(ans+title)\n",
+    "    else:\n",
+    "        ancestries.append(ans)\n",
+    "    \n",
+    "abs_logfc_dict = {x: [] for x in ancestries}\n",
+    "abs_logfc_dict['ancestry'] = []\n",
+    "\n",
+    "for outer_ancestry in ancestries:\n",
+    "    print(outer_ancestry)\n",
+    "    abs_logfc_dict['ancestry'].append(outer_ancestry)\n",
+    "    for ancestry in ancestries:\n",
+    "        abs_logfc_dict[ancestry].append(scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\".replace(\"encsr637xsc\", outer_ancestry)],\n",
+    "                                                 dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\".replace(\"encsr637xsc\", ancestry)])[0])\n",
+    "        \n",
+    "abs_logfc_df = pd.DataFrame(abs_logfc_dict)\n",
+    "abs_logfc_df.set_index('ancestry', inplace=True)\n",
+    "\n",
+    "abs_logfc_df    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.clustermap(abs_logfc_df, annot=True, cmap=\"crest\")\n",
+    "g.fig.set_figwidth(12)\n",
+    "g.fig.set_figheight(12)\n",
+    "g.fig.suptitle('Ancestry vs. Ancestry ChromBPNet Absolute JSD Pearson Correlation', fontsize=18, y=1.05)\n",
+    "plt.savefig(\"subfigs/afr_caqtls_ancestry_jsd.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8363, 45)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (ASB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asb_scores = pd.read_csv(\"Afr.ASB.CaQTLS.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asb_scores = asb_scores[asb_scores[\"IsUsed\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>snp_id</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "      <th>GM12878.ENCSR637XSC.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR637XSC.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR000EMT.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR000EMT.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "      <td>-0.079329</td>\n",
+       "      <td>-0.061612</td>\n",
+       "      <td>0.051729</td>\n",
+       "      <td>0.032590</td>\n",
+       "      <td>0.075894</td>\n",
+       "      <td>0.038511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "      <td>-0.223319</td>\n",
+       "      <td>-0.023942</td>\n",
+       "      <td>-0.044806</td>\n",
+       "      <td>-0.014620</td>\n",
+       "      <td>-0.109847</td>\n",
+       "      <td>-0.020354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "      <td>0.441510</td>\n",
+       "      <td>0.035797</td>\n",
+       "      <td>0.253871</td>\n",
+       "      <td>0.045086</td>\n",
+       "      <td>0.377451</td>\n",
+       "      <td>0.036387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038916</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038916_A_G</td>\n",
+       "      <td>1_1038916_A_G</td>\n",
+       "      <td>2.062560</td>\n",
+       "      <td>0.083078</td>\n",
+       "      <td>0.013802</td>\n",
+       "      <td>0.193501</td>\n",
+       "      <td>0.028406</td>\n",
+       "      <td>0.067928</td>\n",
+       "      <td>0.010322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038975</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038975_G_A</td>\n",
+       "      <td>1_1038975_G_A</td>\n",
+       "      <td>1.898451</td>\n",
+       "      <td>-0.210283</td>\n",
+       "      <td>-0.012515</td>\n",
+       "      <td>-0.028861</td>\n",
+       "      <td>-0.012718</td>\n",
+       "      <td>-0.105511</td>\n",
+       "      <td>-0.015877</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chr_hg38  pos_hg38 allele1 allele2  IsUsed           rsid         snp_id  \\\n",
+       "0     chr1    976215       A       G    True   1_976215_A_G   1_976215_A_G   \n",
+       "3     chr1   1038819       T       C    True  1_1038819_C_T  1_1038819_C_T   \n",
+       "4     chr1   1038845       G       A    True  1_1038845_A_G  1_1038845_A_G   \n",
+       "5     chr1   1038916       G       A    True  1_1038916_A_G  1_1038916_A_G   \n",
+       "6     chr1   1038975       A       G    True  1_1038975_G_A  1_1038975_G_A   \n",
+       "\n",
+       "   meanLog2FC  GM12878.ENCSR637XSC.mean.logfc  \\\n",
+       "0   -1.852605                       -0.079329   \n",
+       "3    2.390945                       -0.223319   \n",
+       "4    2.163432                        0.441510   \n",
+       "5    2.062560                        0.083078   \n",
+       "6    1.898451                       -0.210283   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.mean.sign.jsd  GM12878.ENCSR000EMT.mean.logfc  \\\n",
+       "0                          -0.061612                        0.051729   \n",
+       "3                          -0.023942                       -0.044806   \n",
+       "4                           0.035797                        0.253871   \n",
+       "5                           0.013802                        0.193501   \n",
+       "6                          -0.012515                       -0.028861   \n",
+       "\n",
+       "   GM12878.ENCSR000EMT.mean.sign.jsd  GM12878.ENCSR637XSC.sub.50M.mean.logfc  \\\n",
+       "0                           0.032590                                0.075894   \n",
+       "3                          -0.014620                               -0.109847   \n",
+       "4                           0.045086                                0.377451   \n",
+       "5                           0.028406                                0.067928   \n",
+       "6                          -0.012718                               -0.105511   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd  \n",
+       "0                                   0.038511  \n",
+       "3                                  -0.020354  \n",
+       "4                                   0.036387  \n",
+       "5                                   0.010322  \n",
+       "6                                  -0.015877  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.68\n",
+      "ChromBPNet ATAC (50M):  0.67\n",
+      "ChromBPNet DNase (68M):  0.66\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC (50M):  0.0\n",
+      "ChromBPNet DNase (68M):  0.0\n",
+      "ChromBPNet ATAC:  0.66\n",
+      "ChromBPNet ATAC (50M):  0.66\n",
+      "ChromBPNet DNase (68M):  0.67\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC (50M):  0.0\n",
+      "ChromBPNet DNase (68M):  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonrs = []\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.27\n",
+      "ChromBPNet ATAC (50M):  0.28\n",
+      "ChromBPNet DNase (68M):  0.27\n",
+      "ChromBPNet ATAC:  1.5398565297413478e-102\n",
+      "ChromBPNet ATAC (50M):  2.3953461418137785e-109\n",
+      "ChromBPNet DNase (68M):  4.024120257115456e-96\n",
+      "ChromBPNet ATAC:  0.26\n",
+      "ChromBPNet ATAC (50M):  0.26\n",
+      "ChromBPNet DNase (68M):  0.26\n",
+      "ChromBPNet ATAC:  4.329032818186739e-95\n",
+      "ChromBPNet ATAC (50M):  2.240949272090948e-90\n",
+      "ChromBPNet DNase (68M):  8.346832432824987e-93\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonrs_abs = []\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.68</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed-logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed-logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed-logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned-logc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.28</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned-logc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned-logc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.26</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.26</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.26</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned-jsd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    pearsonr                  method        scoring\n",
+       "0       0.68  ChromBPNet ATAC (500M)   signed-logfc\n",
+       "1       0.67   ChromBPNet ATAC (50M)   signed-logfc\n",
+       "2       0.66  ChromBPNet DNase (68M)   signed-logfc\n",
+       "3       0.66  ChromBPNet ATAC (500M)     signed-jsd\n",
+       "4       0.66   ChromBPNet ATAC (50M)     signed-jsd\n",
+       "5       0.67  ChromBPNet DNase (68M)     signed-jsd\n",
+       "6       0.27  ChromBPNet ATAC (500M)  unsigned-logc\n",
+       "7       0.28   ChromBPNet ATAC (50M)  unsigned-logc\n",
+       "8       0.27  ChromBPNet DNase (68M)  unsigned-logc\n",
+       "9       0.26  ChromBPNet ATAC (500M)   unsigned-jsd\n",
+       "10      0.26   ChromBPNet ATAC (50M)   unsigned-jsd\n",
+       "11      0.26  ChromBPNet DNase (68M)   unsigned-jsd"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.DataFrame()\n",
+    "\n",
+    "df[\"pearsonr\"] = pearsonrs + pearsonrs_abs\n",
+    "df[\"method\"] = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]*2 + [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]*2\n",
+    "df[\"scoring\"] = [\"signed-logfc\"]*3 + [\"signed-jsd\"]*3 + [\"unsigned-logc\"]*3 + [\"unsigned-jsd\"]*3\n",
+    "\n",
+    "df[\"pearsonr\"] = df[\"pearsonr\"].astype(float)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     0.68\n",
+      "1     0.67\n",
+      "2     0.66\n",
+      "3     0.66\n",
+      "4     0.66\n",
+      "5     0.67\n",
+      "6     0.27\n",
+      "7     0.28\n",
+      "8     0.27\n",
+      "9     0.26\n",
+      "10    0.26\n",
+      "11    0.26\n",
+      "Name: pearsonr, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "plt.figure(figsize=(13,6))\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "print(df[\"pearsonr\"])\n",
+    "ax = sns.barplot(data=df, x=\"method\", y=\"pearsonr\", hue=\"scoring\")\n",
+    "xtix = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "plt.savefig(\"subfigs/afr_caqtls_asb_pearsonr.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6848214217530895, 0.0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = asb_scores['GM12878.ENCSR637XSC.mean.logfc']\n",
+    "data[\"Beta\"] = asb_scores[\"meanLog2FC\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"#893f45\", rasterized=True)\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#893f45\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_asb.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/afr.caqtls_scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/afr.caqtls_scoring.ipynb
new file mode 100644
index 00000000..cd6748bf
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/afr.caqtls_scoring.ipynb
@@ -0,0 +1,3550 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import scipy.stats\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new_filter.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(219382, 48)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls = pd.read_csv(\"Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n",
+    "caqtls = caqtls[caqtls[\"IsUsed\"] & caqtls[\"Inside_Peak\"]==1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = afrs[afrs[\"IsUsed\"] & afrs[\"in_peaks\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>snp_id</th>\n",
+       "      <th>label</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>in_peaks</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.jsd</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.csr</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.pval</th>\n",
+       "      <th>dbsnp_rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "      <th>id_hg19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.025277</td>\n",
+       "      <td>0.003640</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.098145</td>\n",
+       "      <td>0.130176</td>\n",
+       "      <td>chr1_840753_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>959193</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_959193_G_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.570435</td>\n",
+       "      <td>-0.048551</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.010437</td>\n",
+       "      <td>0.000299</td>\n",
+       "      <td>0.136079</td>\n",
+       "      <td>rs13303010</td>\n",
+       "      <td>1:958757:959472</td>\n",
+       "      <td>1_959193_G_A</td>\n",
+       "      <td>435</td>\n",
+       "      <td>0.085245</td>\n",
+       "      <td>0.048551</td>\n",
+       "      <td>chr1_894573_A_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>959339</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_959339_C_T</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.401773</td>\n",
+       "      <td>0.071807</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.021064</td>\n",
+       "      <td>0.001762</td>\n",
+       "      <td>0.036620</td>\n",
+       "      <td>rs187243360</td>\n",
+       "      <td>1:958757:959472</td>\n",
+       "      <td>1_959339_C_T</td>\n",
+       "      <td>581</td>\n",
+       "      <td>0.085227</td>\n",
+       "      <td>0.071807</td>\n",
+       "      <td>chr1_894719_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>960509</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_960509_A_T</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.786217</td>\n",
+       "      <td>-0.024701</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.018586</td>\n",
+       "      <td>0.001606</td>\n",
+       "      <td>0.039693</td>\n",
+       "      <td>rs72891151</td>\n",
+       "      <td>1:960449:961416</td>\n",
+       "      <td>1_960509_A_T</td>\n",
+       "      <td>59</td>\n",
+       "      <td>0.090793</td>\n",
+       "      <td>0.024701</td>\n",
+       "      <td>chr1_895889_A_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>960684</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_960684_C_G</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.316227</td>\n",
+       "      <td>0.077054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.017779</td>\n",
+       "      <td>0.000444</td>\n",
+       "      <td>0.105717</td>\n",
+       "      <td>rs113034360</td>\n",
+       "      <td>1:960449:961416</td>\n",
+       "      <td>1_960684_C_G</td>\n",
+       "      <td>234</td>\n",
+       "      <td>0.076444</td>\n",
+       "      <td>0.077054</td>\n",
+       "      <td>chr1_896064_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219374</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50621055</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50621055_G_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.411079</td>\n",
+       "      <td>0.094050</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.024680</td>\n",
+       "      <td>0.002349</td>\n",
+       "      <td>0.028349</td>\n",
+       "      <td>rs76733141</td>\n",
+       "      <td>22:50620470:50621609</td>\n",
+       "      <td>22_50621055_G_A</td>\n",
+       "      <td>584</td>\n",
+       "      <td>0.113873</td>\n",
+       "      <td>0.094050</td>\n",
+       "      <td>chr22_51059483_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219376</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50621621</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50621621_G_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.203090</td>\n",
+       "      <td>0.131361</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.010963</td>\n",
+       "      <td>0.000183</td>\n",
+       "      <td>0.181022</td>\n",
+       "      <td>rs11912237</td>\n",
+       "      <td>22:50621616:50621880</td>\n",
+       "      <td>22_50621621_G_A</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.102438</td>\n",
+       "      <td>0.131361</td>\n",
+       "      <td>chr22_51060049_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219378</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628124</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.742636</td>\n",
+       "      <td>0.021773</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.025766</td>\n",
+       "      <td>0.002166</td>\n",
+       "      <td>0.030504</td>\n",
+       "      <td>rs6151406</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628124_C_G</td>\n",
+       "      <td>609</td>\n",
+       "      <td>0.066098</td>\n",
+       "      <td>0.021773</td>\n",
+       "      <td>chr22_51066552_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219379</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628493</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>22_50628493_G_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.729436</td>\n",
+       "      <td>0.028973</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.017223</td>\n",
+       "      <td>0.002840</td>\n",
+       "      <td>0.023777</td>\n",
+       "      <td>rs131715</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628493_G_A</td>\n",
+       "      <td>978</td>\n",
+       "      <td>0.083502</td>\n",
+       "      <td>0.028973</td>\n",
+       "      <td>chr22_51066921_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219380</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50628562</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>22_50628562_C_T</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.191678</td>\n",
+       "      <td>-0.076209</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.009933</td>\n",
+       "      <td>0.000280</td>\n",
+       "      <td>0.141806</td>\n",
+       "      <td>rs762668</td>\n",
+       "      <td>22:50627514:50628822</td>\n",
+       "      <td>22_50628562_C_T</td>\n",
+       "      <td>1047</td>\n",
+       "      <td>0.057921</td>\n",
+       "      <td>0.076209</td>\n",
+       "      <td>chr22_51066990_C_T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>84820 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr_hg38  pos_hg38 allele1 allele2           snp_id  label  IsUsed  \\\n",
+       "4          chr1    905373       C       T     1_905373_T_C  False    True   \n",
+       "13         chr1    959193       G       A     1_959193_G_A  False    True   \n",
+       "14         chr1    959339       C       T     1_959339_C_T  False    True   \n",
+       "16         chr1    960509       A       T     1_960509_A_T  False    True   \n",
+       "17         chr1    960684       C       G     1_960684_C_G  False    True   \n",
+       "...         ...       ...     ...     ...              ...    ...     ...   \n",
+       "219374    chr22  50621055       G       A  22_50621055_G_A  False    True   \n",
+       "219376    chr22  50621621       G       A  22_50621621_G_A  False    True   \n",
+       "219378    chr22  50628124       C       G  22_50628124_C_G  False    True   \n",
+       "219379    chr22  50628493       G       A  22_50628493_G_A  False    True   \n",
+       "219380    chr22  50628562       C       T  22_50628562_C_T  False    True   \n",
+       "\n",
+       "        in_peaks      pval      beta  ...  \\\n",
+       "4           True  0.188152 -0.130176  ...   \n",
+       "13          True  0.570435 -0.048551  ...   \n",
+       "14          True  0.401773  0.071807  ...   \n",
+       "16          True  0.786217 -0.024701  ...   \n",
+       "17          True  0.316227  0.077054  ...   \n",
+       "...          ...       ...       ...  ...   \n",
+       "219374      True  0.411079  0.094050  ...   \n",
+       "219376      True  0.203090  0.131361  ...   \n",
+       "219378      True  0.742636  0.021773  ...   \n",
+       "219379      True  0.729436  0.028973  ...   \n",
+       "219380      True  0.191678 -0.076209  ...   \n",
+       "\n",
+       "        chrombpnet.encsr095qnb.variantscore.jsd  \\\n",
+       "4                                      0.025277   \n",
+       "13                                    -0.010437   \n",
+       "14                                     0.021064   \n",
+       "16                                     0.018586   \n",
+       "17                                    -0.017779   \n",
+       "...                                         ...   \n",
+       "219374                                -0.024680   \n",
+       "219376                                -0.010963   \n",
+       "219378                                -0.025766   \n",
+       "219379                                 0.017223   \n",
+       "219380                                -0.009933   \n",
+       "\n",
+       "        chrombpnet.encsr095qnb.variantscore.csr  \\\n",
+       "4                                      0.003640   \n",
+       "13                                     0.000299   \n",
+       "14                                     0.001762   \n",
+       "16                                     0.001606   \n",
+       "17                                     0.000444   \n",
+       "...                                         ...   \n",
+       "219374                                 0.002349   \n",
+       "219376                                 0.000183   \n",
+       "219378                                 0.002166   \n",
+       "219379                                 0.002840   \n",
+       "219380                                 0.000280   \n",
+       "\n",
+       "        chrombpnet.encsr095qnb.variantscore.pval   dbsnp_rsid  \\\n",
+       "4                                       0.018781    rs4970382   \n",
+       "13                                      0.136079   rs13303010   \n",
+       "14                                      0.036620  rs187243360   \n",
+       "16                                      0.039693   rs72891151   \n",
+       "17                                      0.105717  rs113034360   \n",
+       "...                                          ...          ...   \n",
+       "219374                                  0.028349   rs76733141   \n",
+       "219376                                  0.181022   rs11912237   \n",
+       "219378                                  0.030504    rs6151406   \n",
+       "219379                                  0.023777     rs131715   \n",
+       "219380                                  0.141806     rs762668   \n",
+       "\n",
+       "                      region              snp  distance        se  abs_beta  \\\n",
+       "4            1:904938:905544     1_905373_T_C       434  0.098145  0.130176   \n",
+       "13           1:958757:959472     1_959193_G_A       435  0.085245  0.048551   \n",
+       "14           1:958757:959472     1_959339_C_T       581  0.085227  0.071807   \n",
+       "16           1:960449:961416     1_960509_A_T        59  0.090793  0.024701   \n",
+       "17           1:960449:961416     1_960684_C_G       234  0.076444  0.077054   \n",
+       "...                      ...              ...       ...       ...       ...   \n",
+       "219374  22:50620470:50621609  22_50621055_G_A       584  0.113873  0.094050   \n",
+       "219376  22:50621616:50621880  22_50621621_G_A         4  0.102438  0.131361   \n",
+       "219378  22:50627514:50628822  22_50628124_C_G       609  0.066098  0.021773   \n",
+       "219379  22:50627514:50628822  22_50628493_G_A       978  0.083502  0.028973   \n",
+       "219380  22:50627514:50628822  22_50628562_C_T      1047  0.057921  0.076209   \n",
+       "\n",
+       "                   id_hg19  \n",
+       "4          chr1_840753_T_C  \n",
+       "13         chr1_894573_A_G  \n",
+       "14         chr1_894719_C_T  \n",
+       "16         chr1_895889_A_T  \n",
+       "17         chr1_896064_C_G  \n",
+       "...                    ...  \n",
+       "219374  chr22_51059483_G_A  \n",
+       "219376  chr22_51060049_G_A  \n",
+       "219378  chr22_51066552_C_G  \n",
+       "219379  chr22_51066921_G_A  \n",
+       "219380  chr22_51066990_C_T  \n",
+       "\n",
+       "[84820 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6821"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "77999"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00100034"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(afrs[afrs[\"label\"]==0][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.999996"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(afrs[afrs[\"label\"]==0][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.94985e-06"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.92178e-41"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "84820"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==1)+sum(afrs[\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(84820, 48)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08744983910050129"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(afrs[\"label\"]==1)/sum(afrs[\"label\"]==0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.94985e-06"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.92178e-41"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(afrs[afrs[\"label\"]==1][\"pval\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed', 'in_peaks', 'pval', 'beta',\n",
+       "       'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar', 'enformer_recomputed_x',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta', 'id_hg19'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs[\"enformer_recomputed\"] = afrs[\"enformer_recomputed_x\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUPRC (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"chrombpnet.encsr637xsc.variantscore.csr\"\n",
+    "\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (572M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"chrombpnet.encsr637xsc_50m.variantscore.csr\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"chrombpnet.encsr000emt.variantscore.csr\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ DNASE (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "\n",
+    "\n",
+    "keyd=\"enformer_recomputed\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "title=\"Enformer w/ DNASE (recomputed) (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "# keyd=\"enformer.encsr000emt.variantscore.sad\"\n",
+    "# label=afrs[\"label\"].astype(int)\n",
+    "# score=abs(afrs[keyd])\n",
+    "# title=\"Enformer w/ DNASE SAD (50M)\"\n",
+    "\n",
+    "# fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "# roc_auc = metrics.average_precision_score(label, score)\n",
+    "# ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"enformer.encsr000emt.variantscore.sar\"\n",
+    "label=afrs[\"label\"].astype(int)\n",
+    "score=abs(afrs[keyd])\n",
+    "title=\"Enformer w/ DNASE (reported) (68M)\"\n",
+    "\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "enformer_auc = roc_auc\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "roc_auc=sum(afrs[\"label\"]==1)/len(afrs[\"label\"])\n",
+    "ax.plot(fpr_chrombpnet, [sum(afrs[\"label\"]==1)/len(afrs[\"label\"])]*len(fpr_chrombpnet), linewidth=2, label=\"Random baseline, AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc='upper right')\n",
+    "ax.set_xlabel(\"Recall\")\n",
+    "ax.set_ylabel(\"Precision\")\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_auprc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.94985e-06\n",
+      "sig 6819\n",
+      "84818\n",
+      "0.08\n",
+      "9.94985e-09\n",
+      "sig 3705\n",
+      "81704\n",
+      "0.045\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:35: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.94985e-12\n",
+      "sig 2257\n",
+      "80256\n",
+      "0.028\n",
+      "9.94985e-15\n",
+      "sig 1471\n",
+      "79470\n",
+      "0.019\n",
+      "9.94985e-18\n",
+      "sig 931\n",
+      "78930\n",
+      "0.012\n",
+      "2.5671599685918016e-18\n",
+      "sig 834\n",
+      "78833\n",
+      "0.011\n",
+      "4.965579270327687e-18\n",
+      "sig 884\n",
+      "78883\n",
+      "0.011\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "random_bc = []\n",
+    "chrombpnet = {}\n",
+    "enformer_dnase_roc = []\n",
+    "enformer_dnase_roc_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(6,25,3):\n",
+    "    threshold = 9.94985 / (10**(threshold))\n",
+    "    print(threshold)\n",
+    "    dataf1 = afrs[afrs[\"pval\"]<threshold]\n",
+    "    dataf1[\"label\"] = 1\n",
+    "    dataf1 = dataf1[[\"chrombpnet.encsr000emt.variantscore.csr\", \n",
+    "                    \"chrombpnet.encsr637xsc.variantscore.csr\",\n",
+    "                     \"chrombpnet.encsr637xsc_50m.variantscore.csr\",\n",
+    "                    \"enformer.encsr000emt.variantscore.sar\",  \"enformer_recomputed\", \"label\"]]\n",
+    "    \n",
+    "    dataf2 = afrs[afrs[\"label\"]==0]\n",
+    "    dataf2 = dataf2[[\"chrombpnet.encsr000emt.variantscore.csr\", \n",
+    "                    \"chrombpnet.encsr637xsc.variantscore.csr\",\n",
+    "                     \"chrombpnet.encsr637xsc_50m.variantscore.csr\",\n",
+    "                    \"enformer.encsr000emt.variantscore.sar\",  \"enformer_recomputed\", \"label\"]]\n",
+    "\n",
+    "#     dataf2 = caqtls[(caqtls[\"Log10_BF\"]<-1)]\n",
+    "#     dataf2[\"chrombpnet.encsr637xsc.variantscore.csr\"] = caqtls[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]\n",
+    "#     dataf2[\"chrombpnet.encsr637xsc_50m.variantscore.csr\"] = caqtls[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]\n",
+    "#     dataf2[\"chrombpnet.encsr000emt.variantscore.csr\"] = caqtls[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"]\n",
+    "#     dataf2[\"enformer.encsr000emt.variantscore.sar\"] = caqtls[\"DNASE_SAR\"]\n",
+    "#     dataf2[\"enformer_recomputed\"] = caqtls[\"enformer_recomputed\"]\n",
+    "#     dataf2[\"label\"] = 0\n",
+    "    \n",
+    "    dataf = pd.concat([dataf1, dataf2])\n",
+    "    \n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(\"sig\",sigs)\n",
+    "    print(total)\n",
+    "    print(np.round(sigs/total,3))\n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,3))\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"chrombpnet.encsr000emt.variantscore.csr\"]))\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"chrombpnet.encsr637xsc.variantscore.csr\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.csr\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer_recomputed\"]))\n",
+    "    enformer_dnase_roc.append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer.encsr000emt.variantscore.sar\"]))\n",
+    "    enformer_dnase_roc_sar.append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "colors = [\"blue\", \"blue\"]\n",
+    "alphas = [0.5, 1.0]\n",
+    "idx=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], label=key, c=\"blue\", alpha=alphas[idx])\n",
+    "    idx+=1\n",
+    "    \n",
+    "key=\"ChromBPNet DNase (68M)\"\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], label=key)\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_dnase_roc_sar, label=\"Enformer DNase (reported) (68M)\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_dnase_roc, label=\"Enformer DNase (recomputed) (68M)\")\n",
+    "\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), random_bc, label=\"random\")\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10 pval\")\n",
+    "ax.set_ylabel(\"Average precision\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_auprc_compare.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.69\n",
+      "ChromBPNet ATAC (50M):  0.66\n",
+      "ChromBPNet DNase:  0.67\n",
+      "Enformer:  0.63\n",
+      "Enformer:  0.49\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet DNase:  0.0\n",
+      "Enformer:  0.0\n",
+      "Enformer:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataf = afrs\n",
+    "pearsonrs = []\n",
+    "\n",
+    "dataf['enformer_recomputed'] = -1*dataf['enformer_recomputed']\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer: \",np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer: \",np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.38\n",
+      "ChromBPNet ATAC (50M):  0.37\n",
+      "ChromBPNet DNase:  0.38\n",
+      "Enformer:  0.29\n",
+      "Enformer:  0.11\n",
+      "ChromBPNet ATAC:  1.9237148658787595e-238\n",
+      "ChromBPNet ATAC:  2.301462737345662e-220\n",
+      "ChromBPNet DNase:  8.332655777196384e-227\n",
+      "Enformer SAD:  7.284298367169e-129\n",
+      "Enformer SAR:  2.3616044014841508e-18\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonr_abs = []\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.logfc'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer_recomputed'\n",
+    "print(\"Enformer SAD: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='enformer.encsr000emt.variantscore.sar'\n",
+    "print(\"Enformer SAR: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.49</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.37</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.11</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsonr                             method   scoring  score\n",
+       "0      0.69             ChromBPNet ATAC (500M)    signed  logfc\n",
+       "1      0.66              ChromBPNet ATAC (50M)    signed  logfc\n",
+       "2      0.67             ChromBPNet DNase (68M)    signed  logfc\n",
+       "3      0.63  Enformer (recomputed) DNase (68M)    signed  logfc\n",
+       "4      0.49    Enformer (reported) DNase (68M)    signed  logfc\n",
+       "5      0.38             ChromBPNet ATAC (500M)  unsigned  logfc\n",
+       "6      0.37              ChromBPNet ATAC (50M)  unsigned  logfc\n",
+       "7      0.38             ChromBPNet DNase (68M)  unsigned  logfc\n",
+       "8      0.29  Enformer (recomputed) DNase (68M)  unsigned  logfc\n",
+       "9      0.11    Enformer (reported) DNase (68M)  unsigned  logfc"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.DataFrame()\n",
+    "\n",
+    "df[\"pearsonr\"] = pearsonrs + pearsonr_abs\n",
+    "df[\"method\"] = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\", \"Enformer (recomputed) DNase (68M)\", \"Enformer (reported) DNase (68M)\"] + [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\", \"Enformer (recomputed) DNase (68M)\", \"Enformer (reported) DNase (68M)\"]\n",
+    "df[\"scoring\"] = [\"signed\"]*len(pearsonrs) + [\"unsigned\"]*len(pearsonr_abs)\n",
+    "df[\"score\"] = [\"logfc\"] * df.shape[0]\n",
+    "df[\"pearsonr\"] = df[\"pearsonr\"].astype(float)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.65\n",
+      "ChromBPNet ATAC (50M):  0.64\n",
+      "ChromBPNet DNase:  0.67\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet DNase:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataf = afrs\n",
+    "pearsonrs = []\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(dataf[keyd][dataf[\"label\"]==1], dataf[\"beta\"][dataf[\"label\"]==1])[1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.34\n",
+      "ChromBPNet ATAC (50M):  0.32\n",
+      "ChromBPNet DNase:  0.34\n",
+      "ChromBPNet ATAC:  1.2898078859520728e-179\n",
+      "ChromBPNet ATAC:  1.8304213920818472e-167\n",
+      "ChromBPNet DNase:  8.492320480992387e-188\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonr_abs = []\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "pearsonr_abs.append(np.round(scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='chrombpnet.encsr637xsc.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr637xsc_50m.variantscore.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n",
+    "keyd='chrombpnet.encsr000emt.variantscore.jsd'\n",
+    "print(\"ChromBPNet DNase: \", scipy.stats.pearsonr(abs(dataf[keyd])[dataf[\"label\"]==1], abs(dataf[\"beta\"])[dataf[\"label\"]==1])[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.49</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsonr                  method   scoring score\n",
+       "0      0.69  ChromBPNet ATAC (500M)    signed   jsd\n",
+       "1      0.66   ChromBPNet ATAC (50M)    signed   jsd\n",
+       "2      0.67  ChromBPNet DNase (68M)    signed   jsd\n",
+       "3      0.63  ChromBPNet ATAC (500M)  unsigned   jsd\n",
+       "4      0.49   ChromBPNet ATAC (50M)  unsigned   jsd\n",
+       "5      0.38  ChromBPNet DNase (68M)  unsigned   jsd"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1= pd.DataFrame()\n",
+    "\n",
+    "df1[\"pearsonr\"] = pearsonrs + pearsonr_abs\n",
+    "df1[\"method\"] = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"] + [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]\n",
+    "df1[\"scoring\"] = [\"signed\"]*len(pearsonrs) + [\"unsigned\"]*len(pearsonr_abs)\n",
+    "df1[\"score\"] = [\"jsd\"] * df1.shape[0]\n",
+    "df1[\"pearsonr\"] = df[\"pearsonr\"].astype(float)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat((df, df1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.49</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.37</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>Enformer (recomputed) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.11</td>\n",
+       "      <td>Enformer (reported) DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.69</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.63</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.49</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.38</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned</td>\n",
+       "      <td>jsd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsonr                             method   scoring  score\n",
+       "0      0.69             ChromBPNet ATAC (500M)    signed  logfc\n",
+       "1      0.66              ChromBPNet ATAC (50M)    signed  logfc\n",
+       "2      0.67             ChromBPNet DNase (68M)    signed  logfc\n",
+       "3      0.63  Enformer (recomputed) DNase (68M)    signed  logfc\n",
+       "4      0.49    Enformer (reported) DNase (68M)    signed  logfc\n",
+       "5      0.38             ChromBPNet ATAC (500M)  unsigned  logfc\n",
+       "6      0.37              ChromBPNet ATAC (50M)  unsigned  logfc\n",
+       "7      0.38             ChromBPNet DNase (68M)  unsigned  logfc\n",
+       "8      0.29  Enformer (recomputed) DNase (68M)  unsigned  logfc\n",
+       "9      0.11    Enformer (reported) DNase (68M)  unsigned  logfc\n",
+       "0      0.69             ChromBPNet ATAC (500M)    signed    jsd\n",
+       "1      0.66              ChromBPNet ATAC (50M)    signed    jsd\n",
+       "2      0.67             ChromBPNet DNase (68M)    signed    jsd\n",
+       "3      0.63             ChromBPNet ATAC (500M)  unsigned    jsd\n",
+       "4      0.49              ChromBPNet ATAC (50M)  unsigned    jsd\n",
+       "5      0.38             ChromBPNet DNase (68M)  unsigned    jsd"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    0.69\n",
+      "1    0.66\n",
+      "2    0.67\n",
+      "3    0.63\n",
+      "4    0.49\n",
+      "5    0.38\n",
+      "6    0.37\n",
+      "7    0.38\n",
+      "8    0.29\n",
+      "9    0.11\n",
+      "0    0.69\n",
+      "1    0.66\n",
+      "2    0.67\n",
+      "3    0.63\n",
+      "4    0.49\n",
+      "5    0.38\n",
+      "Name: pearsonr, dtype: float64\n",
+      "{'ChromBPNet ATAC (500M)', 'ChromBPNet DNase (68M)', 'Enformer (recomputed) DNase (68M)', 'ChromBPNet ATAC (50M)', 'Enformer (reported) DNase (68M)'}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f1ca35ae210>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "print(df[\"pearsonr\"])\n",
+    "df[\"scoring-methods\"] = df[\"scoring\"] + \"_\" +  df[\"score\"]\n",
+    "ax = sns.barplot(data=df, x=\"method\", y=\"pearsonr\", hue=\"scoring-methods\")\n",
+    "print(set(df[\"method\"]))\n",
+    "xtix = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\", \"Enformer (recomputed) DNase (68M)\", \"Enformer (reported) DNase (68M)\"]\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "ax.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6863082060539054, 0.0)\n",
+      "(0.38392145980073195, 1.9237148658787595e-238)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0zL+f/Xca+hrYVffHFNaRyBb9F0dWADIy9wijGred3M4vPvkFCreCv5z/lwiCgMFgwGAwoFarRz2n19WLfchOUWIRm2Zu4pdHf8n+hv3YzDbmWOeMc8NELPPHix/nneZ3gPHCeyLr3WQysfvybl44/QL/ufg/82dFf0ZJSgm5hlweLXhUCjrD1d0/X6TQ7U6v7L1eZAUgI3MXMpEgK7GWUJxYjMvlYm3aWhTDCirzKjGbzej1ehQKxbjzlFhL0Kq0HGg8wNY5WymxlmDWmqXdwFhBPtL33zncSY29BqvJOm4XMFl/ny2ztoAAj818jBlxM/B4PDyU9RChUIiuri6MRiNGo/Gq7p8vksZ5tefejcpBVgAyMnch4/r0H9/BV3O/SnZUNqIoUpxSzMLchZx1nOX7h79/VaH25NwnMWlM0g5iUfoi6vrqWJyxGJvZBvxxdzHS9/8XC/8Cp9dJU38T22u3jzr/ZP19RvrpIwJ388zNTI+dzqfNn/LW4beonFbJ4tTFNBRM3CBuqq6h6+0cejfWB8gKQEbmNmYyq3OqGTCPTXuMnZ/sZOfJnSg8Cv6m/G8wGo2oVOGf/lSEWom1hKb+Jp6tfpYnZj8hCW+AZ8qeGdXQbWTmT6TgbHvt9nECdSodPsemke5q2cXPTv8MFIAIrb2t/PrEr7GZbZ8r2HutzqFjr/HdGHOQFYCMzG3M1TpsTia4RVEkPzqfvyr5KwKBAJV5lYhKkU1zNmE2m0cdO1aoTaZYRvbzf6bsGamVRMSyP2Y/xg+qf0BTfxPLspYhIkqN2yaq3h25A8gwZ0z4mpOlb24q2sTwwDCXBy6z+8JukoxJo4rablTB12S9jo7Zj03YlO5OdBHJCkBG5jZmqh02ISz4h4eHGR4e5kznGfY17uPhWQ9z//T7KZtRNuH5x1rLkymWJ2Y/If07kWW/o3YHuy7sYn3B+mtOBxu7/smOvVoap5gisvvibuYNzyMmFEMoFJJiGDeq4Guya/95lPLtiqwAZGRuYyazOkcJwyuCf2hoiFAohFarZU/bHp47+RwhfYgFWQuAqVmoEwm9Y/ZjfNDyAc+UPTOpZT/yeRNZ8SNff0nGEg5fOsySjCVsr90+5cEwIxEEgY1zNxIUgqy0raS7u5uYmBi0Wu0Nc9VMpiCuRynf7txSBSAIwr8BlUCXKIpFt3ItMjK3M5NZly6Xi8HBQYLBIFqtlqioKDQaDUuyltAwMNrHfrUB7lcb8TjyecB1WesjFVfkPA2OBnZd2CX9C7C5ePOodtFTUVYl1hJKKkvw+/04HA76+vqIjo6+6QVfV2t7fadY/hFu9Q7g18D/BV66xeuQkbmtGWtd+v1+nE4nte217Gvcx6MljzIvZZ50/MhAbSRIuqV4C0O+IZSCkuePPs8Lx14AwsrgmXefYXf9boZ8Q+OaxE1k2S7JmHw4i2Tp25bw4okXpXWMbU89sk31WDfQ9bhT1Go18fHxUjVxKBQiKipqqpf2nuaWKgBRFN8XBCHzVq5BRuZ24modLCOW+dDQEIODgwiCwN5Le/nxyR9T76nnGc0zo9IZI4Hanad2Sv56k8bED4/8kKcXPc22hdsk4Xtp4BJltjLMWvO4NUxk3UeGswz5hjBrzTi9TlbmreRwy2GUgpLnPn6OXlcvGoWGNXlrWGpbOmpXAGHFlFGcwfba7RTGF/J48eP0uHo4bj9+VXfKyPWN3cn09/czODiIRqOZtMOozB+51TsAGRmZEVytg+WspFn09/fj8XjQ6/U0uBoYFAf509I/5WzPWXae2vnHuAAiScYk8mPzcXqdkrUvZdLM3CRN6VpqWyoJ8k0zN101gBvpLBoIBdg6ZytmrZl3mt+hxl5D53Anuy7sYuucrWxbuI0eVw+/O/c7Sq2lfNDygZTvP5FLaX3BetoG2qix1xBviOfHFT+eNMNm5POVgpLqlmqUgjJcpGY24/P5GBwcHKUA7sQMnS+D214BCIKwFdgKkJGRcYtXIyNzc5ks9fHrRV/ng/MfsPv8br42+2ssTFnIzk938sKxF6jMq6TP3ce5nnOjZuxGCrJW5q2kc7iTafHT2F67nWnx03i2+lmsJiu/rPnlhG6fka89kpGdRbct3CZ1+iy1lrIqb5Xk0imxlnDcfhydSjdu6MxE1v1S21I+uvTRpH3/Rwr9kc//ycc/ocZew4yEGUA4OHxx8CKvn3idjQs3UppaOu75sgL4I7e9AhBF8QXgBYC5c+eK1zhcRuaOZqJCpK/P+DppqjR+dP5H/PTUTwkZQyzMXjjKp/7rE79m14VdpESlYNKYpOBvxA/f1N/E3vq9NDgaqO2s5VDTIdbkrWFt/lq6hrtGtWoY2ahtLGMHtIx060zUemKsYpnIxTXkG+KjSx+xaeamUQVnIyuEx2YZRV4jyZhEqbWUJGOSdP4fffwjPr74MSF9SFIAd2KGzpfBba8AZGTuVXbU7uAnH/8EhUvBU4ue4tF5jxIyhiQhNlL4JhmTeHrR0wD88MgPUQrhqVwft35M20AbJcklxOnjGPQNolVpqcipYMO0Deyu280bF94g25I9rplb5DVGMlmmy2TPudogeRGRH1T/gF0XdlFmC9cp1PXVTdhqerLX3TRzE0ExKO0wdtTuoGmgiTJbGY8WPnrN59/r3Oo00J3AA0C8IAhtwDOiKP7qVq5JRuZ2YfPMzSjcCtbkrCE2NpZkTTLz0ueNO26ku2dL8RaCYlDy+1fmVVJjr6HUWsqQb4iqpirKs8qZa53LHOsc6nvrqcyrZFr8NCmrZ7JagOePPi+5cyKKJzJpbF7KPNYXrJd2HhHBP+QbkuIPY9spbK/dTlN/EyuyV9Ax3MG5nnOsn7aeaE00g75B3jj/xrh5A9cKUG8p3oISJa5+F8FA8KrPlbn1WUBTm0YtI3OPIYoimbpM/nL+XxIbG4tGo5n02IncI8ftxzFpTBQmFEqWf0Z0BqvzVtM20IbD4+CVU6/wafunODwOaciLP+SnydEk+dQBqc3Drgu7KLWWEhSDkjUfiQdEAsCR5m47andwrucc6dHprM5bzVLbUklh9Lh62Hlqp6QsIi6spv4mznWfY8A3wK4LuxARxymAqQyB2XlqJ6d7T7PrzC4W5y2e8nPvRWQXkIzMbYYoilK2j9lsRqfTXdV6nci9Ebnvuwe+S7OzmeqWapZnLyfZlIxKUDE3ZS5vXngTh8dBRnQGT8x+ghxLDo2ORnbX78YdcBMSQ5Kbpqm/iQ0FG8iLzWNRxiKe2v8US21LpXjAtPhpeAIeChMKgXCdQG1nLTX2GmrsNUyLmwYgKYyIIomkttrMNilVtcXZAvyx/cTI9z4VX77T6+Ro91GK4ooIBAJS47vPEwe40buGYCjIiY4TVDVVUdVUxYsbXiTZlPyFz/t5kRWAjMxthCiKHK47zJun3+Srs7/KopRFwOe3Xp1eJw6PQxL+JzpOEKuPZXfdbnbX72Z13moK4wuxmW1sqNjAG+ffwOV30THcgSfo4X9W/0+0Si2L0xYTEkNsmrmJnad2Ut9XT6Ojke+XfZ8SawkbXtlA+2A7vz/7e0qtpRy+dJguVxfT46eTYc4Ij6TMWIxSUJIfl49GqRnnLhq5g9kwbYNURTzSjbS5ePM13/OTc5/EpDJRmVaJy+UiOjpaum5TuXaTpZx+HgUgiiL1ffVUNYYF/rvN79Ln7pMef6fpHb4+8+vXfd4bhawAZGRuE4LBIA6HgzdPv8kvTv+CkCnEopywApiq9fraudd46eRLVOZXUtdTh0ljItmYTMdwBzmWHIoSi6jrraMwvpAyWxm2aBs/PPJDya2zYdoGbGYbO0/t5FzPOXbX7w7HD/xD7K3fK71O+2A7NfYaEo2JmDQmKvMr+f3Z39M+2M73qr7Hw9MfHlUk9sMjP+RpniYoBsmIzuBQ0yESDYlkmDOkHQZM3FxtbNHaVILNJdYS+vr6cLvdkgKYKpOlnE4V+6BdsvCrGqtoHWgdd0xWTBblWeXkWHKua203GlkByMjcBvh8PhwOB6FQiIdLHyZkCo0SOhP11hlbBQvw5vk3aRto472m9zjfex4Af8iP1WTFrDXT4Gigxl5DgiGB6pZqojXRPD7rcZbalvLaudf4zcnfkGHOQKvUcmngEqvzVhOvjydWF8uwbRiloGRF7grMWjMzEmbgDXp54eNwAPrvy/+e71V9j/0N+9GpdLyx8Q0pePz0oqdHBaZ9QR/ne87z/Xe/z576PVIX0ZGMLFoba31vKd5yTUtdr9fj8Xjw+XwTxlAmc+9MlnI6GU6Pk+qWag41HqKqqYqz3WfHHZNgSOChrIcozyqnPLucbEv2Vc/5ZSErABmZW8zw8DADAwMolUri4+Oxqq0syFwwYd/5kQ3VkoxJozJsIJxaWWOvIT8un4cyHyIoBjnddRqVQsWZ7jMUJxYTEkPMTJpJSAwRIsTprtPE6eO42HeRN+vepMxWhkljYnHaYjqGOuhx9WAftFPdUg2AXq3nxxU/5qn9T3Go6ZAkvEusJTw590l0Kp3kv//t6d9yofcCc61zsegsVOZVsn7aenbX7WbXhV2sy19HRU4Fz5Q9c12xDYCn9j/FwcaDNDgapNcbqUQiQn8iBTByfsHI6zfZ647EE/DwUetHklvns/bPCImhUccY1UbKMsvCAj+rnJlJM1EI40du3mpkBSAjc4sIhUJSsFen0xETEzNqLu9k83Yfn/U4xzuOUxBXQGVeJVqllrUvr2X9tPVYTVZW560mIzqDx4oeY3vtdg40HmBZ1jK6XF14g16qmqpQCAoONh5kdd5qHsp8iEXpi+gc6qTUWkqKKYUzPWfwBrwcajrEiuwVdLo6Kc8qJy06bZxbJFJZvCB1AdUt1Tw8/WEyzBk8tf8pzveep7qlGp1KR4+rR1JOz5Q9g1lrRiEoiNaOd9GMTTudaLcT6XcUyT4a21FUqVSiVCoJBALjzj92fsHVCIaCHO84TlVjFYeaDnH40mE8Ac+oY1QKFYvSFkkW/vzU+WiUk2du3S7ICkBG5hbg9Xo5Un+EPXV7pNYOYxkraCP5/pV5lVRkV9Dv6ZcydqqaqvAEPPiCPt6/9D7DvmHO957nftv9LMtaRm5sLkpFOGMnJIb+uBNImMk/LP8HHn/9cU51n8Kis5AWnUafpw+VQsX6gvU8MfsJXj/3OoIgsGHaBkREvv3WtzFrzSyxLeH5o89jH7JLdQYA57rPcbDxILOTZ7Mufx0PZj7Iic4TzEiYwRLbEl45/Qp97j6yLdn89NOfolVqpSygka0sSq3hSt66vrpx1nqJNTyYZrKOohDuFOrz+a56bcda+6IoUtdbR1VTFYcaD/Fe83s4PI5x55iVNEsS+Pfb7sekMV3nt+DWIysAGZkvEVEUpW6ee+r38M+n/llq7TCWsX7/pbalUofP/Lh8VuatxD5oJzs2G5VCRaIxEX/QT6m1FKvJytmes7j9bg41HUIhKOgYDrtzTBoT77W8R429RgpCCoKARWchwxzut/Xk3Cf56NJHOL1ORFGUhrvH6eMAJOF8ruccPa4eZibMpCixCJ1KR44lhx5XD8WJxfym9jdU5FTQPtjOiydfZNvCbRxuOUyNvYbqlmo2FGxg28JtUnxgyDcktbIYGUQea61fbX7BWDeQx+MZlQ468tpGaB9sl1w6VU1VtA20jfs8si3ZGNVGTnWd4snSJ/lF5S8+79fgtkFWADIyXxKhUAiHw4HX60Wv17NpwSYGVAMoBSWvn3udw5cOT2iR7qjdwcHGg/S6ejFrzVRkV7AofREHLh7AFXBR31uPKIpYTVaa+5uZFjeNuSlzcQfcxBviKbOVUZhQSFFCERunb6Tmcg376/czf8Z8osQoPjj3AStTVvKG8w00Hg0ftn2Io9eBw+ugfagdR7+DdxrfYXnucjbN2IRSqaTR0UhIDGFQG5ieMB29Ss9/W/rfeGr/U1Ku/7r8dazJW0PrQCs2s00KBK/KW4VGqUGr1LImfw1zrHN45dQrPFn6JJcHLiMIQnhmwZU+QpGitoliITB6uM1YZWAwGBgcHMTpdBIbG4sgCAD0e/r51fFfsf3kdkm5jSXRmMhDWQ+xLGsZ5dnlZMZkctx+fMIh93cqsgKQkfkSiEytCgaDxMTEYDAYsFgsmE6F+/Ov71sv9cAZqwCWZCzhTPcZHB4Hb9a9yaaiTfz8s5+jU+moaqpiefZyDjYeRKVUsbd+LxU5FRg1RpIMSahEFdqAFr1Xj8fv4Z/e+Sd0Kh19zj7wQa+ql331++gc7kQUROamzWXAP8AFxwXOdp/lQduDPJj+IMqgkrVZa0lRpKBUKNmYv5H/V/v/aHY2k2BI4M/m/xkQtr5VChXxhniCYhC1Qs3C1IU8OfdJttdu54VjL0iukgONByhKLOJc9zme+/g5lmUt41DTIUqtpcToYqT3P9ZaP2Y/hlJQsnXO1mvOH1YoFERHR9PZ28kHlz/gk65PeKf5HY62Hx0XuDWoDSQZk/jKtK/wxOwnKEoskhTGZGu505EVgIzMTcbr9eJwOBAEgbi4uFEZKZEpXVGaKDYUbKAwvnBc5s/bF99mf8N+1uWvY1PRJkRRRKPUEBSDPDr9UWL1sfRZ+8iIzmBF1gp6BnrYXbubzy59xlenfZXpMdOxD9tpd7fT6+1FqVRijjbT6G7EorKg9qs5P3ieGnsNLpWLwuRCjC6jNGP3vPs89d56TjhPsN68njfPvsnva39PcUwx54PnuTx8Wer3X2ItYXvtdvY37KcipwJRFOkc7pTeK4Q7lB65dIStc7ZK/6/Mq8Sit7A6dzUqhUrq7jkREet/28JtE6ZvQjhwW2Ovkdw6H7Z+OGHgNsmYxNr8tfzJrD/h1dOv8s+f/jMAM5NmfsFP/c5AVgAyEnKzrBuPx+PB4XCgVCqJi4tDqVRKj0Wut1lr5odHfkiptZTd9eH0SKWg5JdHf4lZa8ais1BqLSUzJpMeVw91fXUkGhPZd3EfjxQ+QttAGzPiZ2Dvs2NT27DF2ChJKSEvLo/HZj3G//7of9Pv66e6rZqK7Api9bHMT51PVVMVlwYuEaOLITUqlcL4QpSCkhOdJ0gwJOAL+thxegcbZ2wEAbx4uTBwgRcvvEiHrwOP08ODaQ9SY69hsfWPPXeW2JbQ4GigMr+SPXV7eOPCGyQZk3h+7fOUWEv49lvf5oVjL7B1zlYOtxyWir2CYpCvFn6VD1o+YNPMTVfN01cKSpxep9TGenbybC70XuBnn/6MQ03hwG2/p3/c51EUX8SSlCVU5FWwPH85Ro1Rekyv0qNUKO8a985UkBWAjITcLOvGEhH+arWa2NjYUSme8MfrvXXOVr4z/zu0OltZk7+GHEsOTq+TI21HiNXHMitpFmW2MpSCkn5PPzX2Gr5Z8k3W5a/D7XPzQcMHPJj6IJ9e/pRiazFqnZo4fxznhs7xev3rWKOsROuiWZ69nFhDLGe6z9Dn6eNrhV9jT/0eymxlVLdUY9KYONdzDofHQZ+7j8KEQjbP3ExIDGHSmPAEPPyg+gfsb9jPqtxVIMCZ4TMcajlEnD6OFGMKv7/4e+xDdnZd2IVZaybRmChl8kQEekShmbVmkqOSWZa1jJSoFP5i0V8Af2wDHRk7CePz9CPupEvOSygVSt5rfo9h//C4zyDHkiNl6jyU9RAWrQWn04nH48E75MVgMUhunrvNvTMVZAUgIyEPzbhxRNw+kwl/GH29t9dul/ryby7ezCunXiEtOo19F/dhM9t48Ssvctx+nH/+5J8ptZYSq4nlh0t/yP+s+p8Mxw+Tm5hLQBfAoDXw6tlXcRNODfUGvCSbkuke7sZqsvJS7Us8NuMxOoY62F23mzfr3iQYChIUgzQ4GqhuqWZZ1jJSo1NxuB04vU6GfEPU2Gswaoy0DbRRkVOBWqGmqb+JYDDI4rzFGPVGXjvxGrX9teQm5VJqLcWoNtLv6cekMTHsG5aC2enmdIoSi1icsZjfnfkdDo+Do+1Hx12fJRnhnUSkZ5DD7eC95veoaqpiT/0eAN5ueHvc8wriCvjufd+lPKscW4wNCCufv//g76XdxNDQEAMDAxxpOMIfGv4wapdxL+2EZQUgI/FFLaB76YdzNfx+P319fahUqkmFP4yevDVWGTz38XM8PutxSq2lCIIguTr+rPTPeOXYK8yKnsU/vvePtHpaSUhIQKlX8s38b/LauddYlbuKmYkzERCwRlmxD9rDQV5E1uWvY8g3RHVLNU/MeoJSaylFiUUcsx+jc7iT5dnL6RzuRCkoiTfEkxmTidPrxKQxkW3OJhgK8vD0h6nrqSMkhthdv5v1Bet5bO5j/GD/DzjTdga9Vk9adBrD/mF2ntnJsqxlzE2ZS/tgO7OTZnO25ywqQcW++n2jKpfH8m7zu+y6sIuz3Wf5m3f+hnM958YFbjVKDcVJxagVagrjClEqlPzpvD+dMJNq5G7CZDLh9/t5/ePX+adT/yTdP9GxdzOyApC5YdxLP5zJCAaD9PX1oVAorir8YfT1GjsEHcJ98hGhub+ZV2pfYcenO1iTtYb/uuC/8uyHz3K05yhHO45Sai3FF/LhCrjodfdyefAySkHJoaZDbCjYwKykWZzuOk37UDvJpmSmJ0ynPKucBEMCRQlF1HbVolaqsegsJJuS6XP3EWeIo7arllh9LDazjYK4AtoG21AICn5/9vc8PP1hFIKCoBjEarKy4/QOTFEm7rfdz7zkeTy17CmO248Tb4hnScYSXjwZHkuZHZMt9SKy6Cw8OfdJrCYrW4q3EAgF2HFqB/9a86/4gj5Odp4EoL6vXromCkGBRWfh0RmP4nA7eOXMK9yfcT/AhIHhCBPFDTQaDatzVxPSj+67dC/thGUFIHPDuJd+OBMhiiJ9fX2IojhpwHdsK4OR/0Zo6m+iwdFAYUIhva5eYojhwqULfNL+CX2ePkIXQ/T7+pmRNINUcyoWnQWVQsW57nN0DneSZEyiMKEQEZFkUzI//vjHbC7ajCfooWOoA1EU6XZ1c7LzJL3uXmrsNazIDjd4SzGloFfpAUg2JUvtIv7Xg/8LQGr2FggFCIQCVLdUS2mbVU1VrMxYSYezA7/fT4m1ZNTYx/UF61mTtwaX30W8IZ7TXaeZ55hHZkwmz1Y/y3vN7+H0OkddCwGBBEMCJq2JRkcjZRll9Hp60av0LC9ajjvgZqltKTazbcJrGWFk3MCkMVFiLaGmtYa9F/eyeeHm6+oFdDchK4B7lJvhrpnsh3OvuIYcDgd+v5/Y2FjUavWoxybaHY28XiOv0UsnXwrXBPggTZ2G3W+nw9fBzMyZ6Iw6Pm3/lBp7jdSbp8vVRedQJ/6Qn0RjIoeaDuEP+YnWRnN54DLfmf8dLvZdpDC+kEZHIwcaD1BmK2Na/DS6hrtIMiWREZ2BQW2Q+gc99/FzbJqxieXZy2kbaOMnH/+EJRlL0Cg0bJqxiTM9Z5gWN401eWtQCkqyLFmoFCqyzdmUxJXgdrtRq9W8cuoVRFFkQ8EGvl/2fX7x2S84232W2s5aOoY72HNxz4TXsjC+kB88+AMezHyQOEMcb5x/g1+f+DWV+ZWc6z4nucqkKWQVG8bVCowdYSmloWYs4ak9T6H2qvnlqV8SMoaYkzLnhn8f7gRkBXAHcSMF6WSVlDdDQF+Pa+hOVRaRzJLIBK+RRAqXvlH8DQKhAH918K/o9/Tz5NwnKbGW8Nq513ih5gX2N+wHYMv0Lei8OiwaC82Dzaj0KkxaExqVhmZnM0vSl2A1WbHoLXQPd0vtG+L18RjUBtRKNfGGeAY9g+yq38XKnJW83fA2q3JXUZgQ9pPHG+LpcfVwtucsVpOVOdY57KnfQ4uzhSW2JfS6etEoNQz6Bsk0ZyIIAm9deIu36t9iff56ko3JJJuS6RjqQK/W80DmAygFJc99/Bz62XqWeZZxcfgiJztPcqH3Aka1kY1/2Ehdb924a2dUG7nfdj+PTH+EJFMS1c3VnO0+S7+nn7/74O/YUryFDdM2jBsPCYxz60Su98gRlpEYRqRS+Kl9T/Hy0Zf56vSvsnXB1nt2xwqyArijuJE+9omCjpOddypC+WrHXI9r6E6MIwwNDTE8PIzRaMRoNI57PPKe1hes56Xal0alXZZYS3jp5EvYh+xUZFag8qjo7O4kGAqiMWlI0iXRPtBOUWIRp7tO0+3qRqPUsLt+N/dn3I9aqabX1cve+r1sKtpEfV89qVGpHO84zsKUhazJWUNqdCplGWVEaaLoHu6ma7iLfRf3UWotZXr8dARB4PClw+y6sAtf0EdIDOEJeKTsn/KscmYlzSIYClJqLQ27Y/obCYpBqpqqKLOV8dGlj9hSvAV/yE9GVAbf3PtN3re/z4BvYNz1UClUxOhi6HH1MNc6F6vJijXKyumu01I+/+763fhDfkkpjt0pLclYwuFLh6VpYZ3DnVJL6ciuY13+OgriCgD44ZEfAuEGbusy1qFwK9g4fyOlaaU362txRyArgDuIG+ljn8hdM9l5pyKUr3bM9fhU77Q4QqSXv16vx2w2T3hM5L0stS0lyZiERWehIK6AJRlL+O6B72JQGViatJQ+Zx9HLh2hPbGdJl8Taq8aAQGD2kC/u59YXSxalZZSayluv5skUxK/qf0Ny7OWsyFnAz6Xj+NNx4mzxZGjzqGhrYFzPedwx7vJjM6k29GN1Wxlesx0ko3JxOhipKCx1WRl44yNpESlUGOvYXbybLwBL4nGRC4PXqaurw6VEBYXRrWRlKgU7EN2Hsp8CLVSzZt1b/Juy7uc7DiJP+Qf9f4FBOL0ccQb4/EH/eTF5pEdk02TsykcsFYoMWvNvN3wtlRABlCZX8mMhBlMi5/Ghlc28MTsJ9hXv48Xjr1AXW8du+t38/Sip1lfsB5/0M/3qr7Hk3OfxOl18mbdm2yds5X/s+L/cNx+nKAYZPPMzfT19ZEfm8//rvzfEw6JudeQFcAdxM0KTk3Ua2WkNT8VoTz2mM/ryrmTAnAulwun0yn18p+Mke8pw5zBjtodrMxdyb8d/zeUopIPL37Ig+kPcqj1EEvzljLMMDUdNaREpZBpyeRC7wVMmrDVXWOvIRgM0jHYQaI6kbXpa3E6nUQboonSRLEgeQFajZa4qDjSlemYokzE6mPpd/dzxH6EB1UPcqT1CPPT5hMfFc9wYJiihCJ2ntnJ6tzV9Lh6qG6pRqPUkGxMJiUqhUAogD/o55zjHImGcMfRtOg0jtmPcbrr9LjUTIA4bRx6hR6rxcpn7Z8xP20+MxNmcqb7jDRmsjyrHI1Sw5r8NXx2+TNyLDm8ceGN8L9XponV9dTx+7O/p2O4g1+f+DX5sfmUWkuZkTCD/Lh8Ns3cBDBqEtnjsx+nc7iTVXmrRl3//v5+XC4XFotFFv5XkBWAzDjGWvNTEcpjj7kTXTnXg8vlor+/H61Wi8ViGdc0bDIi16XR0ci79e9SkVrB0tSl6KJ0FNuKidJG8UDWA4iiSLo5nYa+BlKjUlEpVZQklZCsTSZJnURyVDIHLhxgTcEaAgRIjEvkbN9ZUuJS+M2Z37CpaBP9w/0ISoE2dxszk2YyxBD51nyGFEPkx+TT2d/J+03vMzdrLmW2MuIMcZjUJgRBYF7KPD65/AkGtYGOoQ78ej8dQx009DVwyH9o3Psyqo1oVVqyYrJYlLqILEMWHzZ+yKzsWVh0FvJj89nXsI+K7ApSolIwa830e/qlBni7Luzi6UVPs3XOVpSCkuP24+yo3SF1Fq3IqeCJ2U9gM9sIikEeK3ps1Pdq5CSyD1o++GNw+ErcwOVy4XK5iIqKQq/Xf+HP/25hSgpAEISvAv8IJALClZsoiuL1TVuWuSP4vG4YURQ5evkor9S+wuK0xQilAl/L/RqDg4OIoigdMxJBEEbdFArFqNvIVMrbhZHCf2SL4ZFctY8NSnodvSxPXU6UIQqtUcvH9o9JMCQgCAJ76vYgInKy4yTvX3qf0uRSRJ+IWTCDB+oG6tDr9ZQVluFWuQn4A7QOt1J9qZo1eWtYlrWM9sF2+tx9JBmT6HJ1cbbrLIeaDqFWqjnQcgCdVke0JpplGcsIBoJkxWfRNtTGyc6TWE1W9tTtwRVw0T7YPmGLBb1KT4Y5A71KT6IxkZLkEsm6L7OV0eHpoN/TT6ezE61Ky7nuc5Qkl4ya7xtp87zUtpQcSw6bZm5i56mdvNP8DhDuKRRxdf2nOf+JDdM2cMx+bMLPZGSQeGxKaDAYxOl0otVqiYqKmtJnfKcmI1wvU90B/B9grSiK527mYmQ+PzfyC3s1i18URQKBAMFgkEAgMOoWCoV49ZNX2Xl6Jwq3gm2LtgEwODgoCcmxwlIUxXFKYSSCIEij/ZRKJSqVCpVKJf19tUKrm0GkhcDVhD9MvAN67dxrbD++nQJDARe7L2IxWzjmPEY66ZQkl3C84zgqhYr9Dfv5Vsm30Cv1LEtbRro2nURD2MKv6qiiLKuMpKgkOoY66O3vxRf0MSNhBuvy1xGljSLFlMLlwctolBoONh4Mp3wmTCMgBojTx3F/xv1EaaOI1cdS762nprmGzOFMavpqMGlN1HbWjns/KoWKOH0cGqWG1oFWKnIqKIgroNHRSGN/I06vE7PWzKPTH6XXHV7TqZ5TxBhjaPe1U2OvYeucrZPm20eE9ws14RnHg75BDrccllJep8VNY8O0DVPaWY79/g4ODgJMGqOZ6ud3NzJVBdApC//bm5vxhQ2FQvh8Pvx+v3QLBoOjjokIZZ1Oh1Kp5OE5DxPShXi0+FGSkpIky/54x/EJFdQx+zF2nNrB5pmbmZU0C1EUCYVChEIhgsHgqJvH4yEUGu1vjuwS1Gq1pBzUavUN3zmIosjAwADDw8Po9XpiYmKu6vYZuYuKKOfGrkZ6Hb2IRpEFuQvw4qU/1C/18J+dNJultqXMSJjB2fazGEUjxpCROFMcLd4WBlQDbCnZQrerG3/IT2FCIf2efuyDdl458wqrclex49QOVueuxqg2YtFZWJe/DqPaSI+rh35PP9kx2QDsOLWDFFMKIiLddNPV1wUgFWIpBAUJhgRidDFkRGdwsOkgD2U+hEqpCu8sTEkc7ziOUWOkxl6DRWfhUNOhcE//s4d4fNbjfGPON8g2ZHOo8xDzSucRo42h4jcVPDLjEb4151sTXreOoQ5q7DWkR6fz/bLvoxTCYyxHXs8h35DkJrrWdz0YDOJyuTCZTKMmgsGNy1y7k5mqAjgqCMJvgTcAb+ROURRfuxmLkrl+vugXNhQK4ff7Rwn8iLAXBAGVSoVGo5GEbMQKH2uBL4hawIKsBePOP5mCitwfGQV4rR3MSMUwcifi9XpxuVzScQqFYpRCiNym6qsfSTAYpL+/H6/Xi8lkIioq6prnifT5ae5v5sUTL9LR18FDqQ9xLnCOXZd28Sexf4LVZGVW0ixpnKPD4+Cts28RK8RiCpnwiB4OtB9AG6PFG/LS5+6jqb+JqqYqSq2ltA+2kxebR7IpmZU5K8mPy8cX9BFriOVc9znJdx6ji6HJ0YTL7+L1C68z4AmnZrYPtY9ac4wmBr/PT1FCEdmJ2Qz5hnir/i1sZhvLspahUWk43XU6fN7sCg40HuBPiv+EUmsp0+KnoVQoWZmzEp1KR7IxmUenP8rOT3byXv173F9wP/V99VQ1VREUg1zsvSj58UcK4jX5a3AH3KzJXyNVEj9/9Hl2ntoJhL87MboYPrn8CQpBMe75Y787Xm9YXE3k979RmWt3MlNVANGAC1gx4j4RkBXAbcL1fmEj1v3IWwS1Wo1Goxn171iBd70up8kUVORvpaCUcrWvdr5IbGBspW3kPUWUV8Qt5fF4JMUgCMKo9xTZKUwmzEOhEC6Xi6GhIURRlCZ5TYWIcGnoa+CDug9YYVuBI+ggOiaa+4X7MaqNnO05i8vvotvVjVqhRulXcuzSMRanL2ZYMYxSq2Rp1lLUSjUx2hiG/cNoVVoqcirIiM6gy9XF5cHLUk5/sDtIoiERg9JAsjGZ+anzOW4/zqBvEHfAPW6NsfpYkozhXdr8lPkUJxVz4NwBzIKZQ2cOUVFUwerc1Zg0JvRqPWqFmmRTMhtnbCRGFxN+zVCQafHTeP/S+yQZk8i0ZJI/kM8/fvSP+EN+FmQuoG2gjQezH6RloAWAvNg8jtrDE7kiefvVLdX0untJNCSyv2E/MxJmSNcx0hYbwor1RMcJHB4HZ7rPjLrWE313AoGA9LmP5V6x8q/GlBSAKIr/4WYvRObmIooifr8fr9eLx+PB7w/nakd+HFFRUWg0GjQazZSs5Ehr3wZHA4/PfpzDLYdZYlsS/vdKkU5EOVxNWUQU13H7cZxe57it/fUoGoVCgVarRavVjro/GAxKOxufz8fw8LAUd4i8/8iORhAE6Vr5fD5CoRBarRaz2TzOhXA1thRvgRDMi51HnjaP+dnzOdR6CEVQQZ+7j5aBFjRKTbh/TuZKrIIVQSMwO302thQbNR01JKmTiNZGo1PpaHY2c6jpEJuKNjHoHZTm2GaaM1mXvw6VoMIddPNZ+2dcGriEL+gbtyaj2kiUNorixGIONB5gWdYyBn2D7Lu4j0xzJic7T9LuaycuNo751vmoXCoONR5iRd4Kkk3JtA60sqd+D6XWUqn/T2ToyvT46Qz6BvnZpz/jsaLH2LZwG1uKt/DLz37Jey3vYTFZuOS7RFVTFRqlBp1Kx7mec9LnXmOvwaQxoVVq2bZwW7hdw/6nmBY/jdNdpznQeICCuAJ21O7gQOMBKnIq+Oacb/7xWjOxII98l0VRvOvHO34eppoFpAO+CcwApDp3URT/401al8wNIOI393q9eL1eSehddF7kzYtvstC2kCPtR9gyawsl8aN/CK+de42XTr7EE7OfkAJ0UhWmbQlKQYnNbBuVxtfoaKR1oJVGRyNvXHgDCP/IJGvY0SBVa46lxFqCSROejxsUgze0Na9SqUSv10tugEgge2RsY2x8IRLXMBgMnytnvCi+iO/O+S6iKJKVnMXfH/l7dl3YxZq8NaSb09lbv5c/mfknrEhdQa4uF6VSydmhs3R4OxB7RWxmGz3uHmJ1sdgH7SRHJVNmK2PIO8Tu+t3MTpqNWqGmvq+eHlcP3a7ucWvQKDVEaaKYkTgDRLCarGTFZNE62MrqvNVolBpsZhvlWeVkmDMIhAIkG5MpshbxaeBTLFoL69PXsyx7GXNtc/npxz9lZc5KsmOy6ff2h4vBBu2j5vjqVDreuvAWuzaFvxdmnZk5GXPwerxYTVZKraXkxuZS11uHSqFie+12npz7JAA6lQ69So/D4+Dti2/zwrEXeLz4cXQqHRsKNjA3ZS4ftn7IN4q/wV8s/IsJeyqNRa1WI4oiXq+Xs46z90Rmz/UwVZPmN8B5oAL4AbAZkIPCtyERYeb1eiW3Tp2jjt0Nu3mk+BHmZczjx6d+zHPHnmP98JVB5MJ44So1JOOPGRojt+ovnnyRpxc9TX5cvpTGB9A60EqOJUeyAEVR5L60+zibdZZQIMT249uZHjsdGJ0SKggCG6dthCBsnL6RQCCAQqFg88zNwI3dpkes/rFugUhGUiRw/Xnx+Xz09fUhCAKt/lb+/sjfEwgFWJa1jNaBVkqSS1idvRqVR4Xb5abf1E+vp5dkczKiQuTti29L7SLKs8rJjc2lsa+Rwy2HKY4vJk4bR21nLSHGBMRRkKRPIl4bDyHIjMlErVLjxcuexj2szV/LsY5jiIhSdpBaoaZrOBwAzjRn0uvu5VzPOV77+mts2LkBhaBg76m9XOi9QLOzGYfHQVAMcrDxIOVZ5cy1ziUlKgWAGF0MH7Z+yMzE8Dzdf635V850nyEmKoZ6ez35mvywEvMNsb9hP+sL1kvC+Pm1z/PU/qc41HSIGnsNTy96mm0Lt9Hj6pGqevfU75G6ik51h6jValGpVAwMDLDj5A6e+2RiY+JeSfscy1QVQK4oio8IgrBeFMUXBUF4GfjgZi5MZupEhL7b7SYQCABIbp3z/ef5yamf0NTfhKgVWZC5YFRrghxLzoTVu0/MfgJA+heQtuozEmawbcE2FqQs4KNLH2HVWnn2vmd5ev/TNNobWRK/BLWgxtnj5N3ud3n71Nv4Bn2kW9KpTKukt7d3wveRqk7lqdlPAfD+2ffZV7+P1XmreXrW0yiVSvr6+iZMB/0iwnokX1TwQzjo+Pua3/NW/Vs8OvdRPr78sVQpW55Vznst7xGnjsPhcWA0G0lPSqfV3Up1SziHP1Yfy9q8tSQZk8LVt93nONNxhj5vH0GCHO85Pur1zBozKcYUBFFgRvwMVKjwBrwIosDhlsPMiJ9BWlQaX0v/GgFfgKrmKh6Z8Qil1lJSTCnEGmKJN8ZjH7TjDripsdeQYkrhv+z7L8xKnsWHlz7kRPsJUqNSUYaULE5bTJQmCl/QR15sHgvSFvBvx/+NSwOXSDYmE2+IZ/209QD84dwf2N+wX3qtOQlzaPA2sCpvFVqVllZnKzXtNWyv3c6W4i1Sz/5SaymLMhbxbtO7DHgH+PP5f84Ts5+gxRmOIYz8Tl5rhygIAs2eZn539HfMz5gvGSZjuVfSPscyVQUQae7RLwhCEdBBuChM5hYRCARwuVx4PB5J6Gs0GqkbZSQN8v8e+r/surCLx4sfZ8g3JPnXR3YAFRF5av9T+EN+mvub+UH1D/h+2fd5Y+MbiKLIZ62f8UrtKyxLXUZUKIq1uWvJj83nn4/8MyfsJ1B71FxMuohz0MnjMx/HH/JT01WDX+FHpVDR5GniSM8RMpIy2N26G5PZRLOzmZ2nd7K5eDOiKPLyqZfZPHMzlfmViKLI3mN7+eXpXyJqRObY5kiZPxG/fARBENBoNJLvf6Jg35eF1+ulr6+Pt+rf4nfNv2NQPYhZayYkhlAKSs70nKHX2UuDu4GP2j/CrXHjE30Y1UbKs8qJ1kTz/qX3CYaCDHgHcAVc417DorNg0piYlTQLb8BLQXwBve5e6nrrMEeZaXW2sr95PxtnbGSabRo5MTl0DnQSp4+joaeBypRKkpRJ9Gp7idJE0eRoonO4U8rs2TRjEwO+AeyDds50n6Hb3c3szNm0D7dzuu102KWCyLS4aZi1Zg5fOkyLs4VEYyLTE6bzjVnfkATow9MfRqPQEK2LJlmXzIn2E5waPIVSoeSS8xK7LuzCHXBLDd9GDsV5av9TfNT6kZTJBEzYEXQqgdzfnvstL5x6gSfFJ/lfq//XhBlB92pAeKoK4AVBECzA3wJvAibg+zdtVXcpX3SbGclpjgRxI8LPZDKh0+kmLYoqtZbS7e5mb/1eqQPlSIsHwtOUNhRsoGOgg9Mdp8kx5DCQO8Du87tx+928dv412m3trCtcx1sX32J++nw6Ah2gB7fWze8u/Y5dLeHtuYDA4e7DxFpiKYgrYHrKdLITszFrzfzoyI9ACQ2OBt6qf4uQEBbmb9a/SUgI8bWZXwNgc+lmRLXI5uLNowp4Itdw4/SNzIifIQVrBwbCqY0qlQqj0YjBYLhhO4OpEHH7qFQqHpv3GB6tB6sp3Aenxl4THozug2x9NvHmeMpjyhnwDqBEycnOk3S7uicM3OqUOnJjcxEROdN9hnkp81Ar1aRGp/J8zfPhVtAiJBmTqO+rx6g2sixrGTG6GADaXe3sadrDN4q/gS5aR2pUKmpRjdFv5MXjL/LVGV9FpVCFYw3DdjxBD4XxhTi9TrQqLTPiZ5BoSoQg+AN+csw5fNjxIQVxBZzqOkWGOYPipGLO95ync7iTnad2crT9KHvq9/DE7CfIi8vjuY+fozS5lAxlBuc7z5NoTqQwvpDhrGEesD3AjIQZE2aHBUIB4g3x2IfsbK/dPmns6FodbLcUbwER1mas5ZPGT3ir7a1xv8F7NSA81Syg/3flv9VA9s1bzt3NVLaZY5WEKIq43W7cbreU0zyRpT/ZOawmKx1DHWRGZ7J1zlaW2MLZFUttS8MFNaKSHHMOj2Q9wvzY+dSF6phhnIEqqGJ33W6OdB5hlnUWs7Nmc3b4LO4mN7su7KJisAKNUoMz4AQB5lvn4/a4mRM7h2ZHM9+c9k0S9Am8f+F9MqIzsEXbyDBmsNG2EXFIZGnsUpRpSsqTykEEpUvJothFPP3a01QWVHJ56DJtnW289MlLeIu9zEmbg1qtDl/DT54DIWwxRggGg1ItgNPpZHBwkJiYmHG9+W8GgUBAck+1+Fo43HqYNXlr+MO5P7A4bTFp0WmYBTNDQ0N4VV52X9pNn7uPIf/QuHNpFVqiVFFkW7IZDg5zpucMRUlFuH1uipOK0Sg1HLMfwxvwsi5/Ha0DrRxoPMDqvNWkRafROdTJoaZDODyO8MwAnYU1eWvodfdyoPkAXpsXtaCmKLaIZcFlpBpTOdN3hpzYHPRqPYFQgIt9F/GH/FS3VLMufx11vXXMTpqNQWegva99VOFXqbWU1KhUfEEfXcNdDPuGqe2qlaz6x2c/TqOjkRxLDr29vSzPWM4F5wX06nCw93zveX694dfjrsPI7LCIi2gsI7/nY3e0P6j+AU39TcAfdxYul4tn9j3Dv5z+F+k17nWmmgWUBPwdkCKK4ipBEKYDi0RR/NVNXd1dxlS2mZKSCEG2IRu3200oFEKpVEqNrK6Vjhg5R2RYRkpUCj+v+Tlb52zlcPNhfvbJzxC8AtHBaLYf205lbiXdg92c1Z7lt/W/5cHsBznVfYppcdMYYICm4SbSLekISoHV2atRBpWEAiFUARVt9jYGDAM4e50EXAFault4++LbrMlbw7mec8xODrsPah21DIvDKHVKPu7+mHwxnxZPC++0v0O2JZv9bfvxKX283/I+olKk1dnKu03vUhRfhCqoIkOXgSAIbMjYgOAT2FS0adR7ViqVGAwGDAYDPp8Pp9NJX18fZrN5wh79NwpRFHE4HADExsbyo3d+RHVLNWe6z7C/YT89rh7sTju+oI9eb++oQfAQrn8wa82YFCayDFmkWdLQGXQMB8I5/4vSF9HU30S3q5v06HREUSTDnMGe+rBV3+PuYfPMzfR7+jnfc54yWxlalZYYbQw6tY5fHf8Vq3JXEW+ID/vio1I4032GDkMHroCLrv4/zgZIi07DG/RS1VTF14u+zqrcVZg0Jl4+/TK+oA9NUMNMy0y+XvR1hn3DbC7ajEqpYsO0Dfzq2K/YXb+bDQUbeHj6w+hUOirzKnnxxIthgyGngv7+flL0KTxge4DWgVYpnhRhoh1yRBEcsx/jqf1PjRL2kVkAESLG1ZBviF0XdrGhYMOo35pGo2F13mpCutA95+qZjKm6gH4N/Dvw36/8XQf8FpAVwHVwrbbLoijyaMGjKNwKVqeuxuVyodPpMBqN10xFHLflJRy0feHYC6zPX095ejnRoWhKokv4iu0rzEmYQ0ZsBqJapNvbzXs976GOUlM5PdyLvaaxhrSoNNL16Ry+eJjF1sUYlAaOXjxKeXI5H7R+gFlvJi8hD0Ej0DrYygc9H6CP0ZNvy6dD7GBf6z4esj2EAgXvt76PQqFAQKC2qxaryUqCIYGAGKDb3c2Gwg30+/pZO2MtBWkFdIe6WT1rNQIC1gQr33n3OzxW+BgPpj9IjjkHQRAYHBzEZDKNc/VoNBri4+NxOBw4nU6p8OtmMDg4KI2BVCgVnO85z+XBy3QMdaAUlNTYa8Y9x6w1kxqViklrQitoEd0icdo4EuMSafe0k6JIob6vnmnx07g8cJmqpiqWZS0jx5LDey3vMT1+Ovdn3E+3q5t9F/dxf8b9aFVa7s+4n9rOWmxmGyc6T5BjyWFj0UZcPhd6lZ4UUwoJhgQSDAmoVCpO9pwk3hDP6tzVWPQW4nRxmLQmEgwJuANuzFozTq+T5dnLybZk89ua36INadFb9Oyq28WK7BVYTVbeaXqHWH0sfz7/z9Eqw/MKvjXnW/z5vj/HrDVTkV3BypyVvHfuPT5o/YD7jPdRmV9JSAzxlcKvSNdlZKfURGMiT859UhL2kWljEZ77+DkpU2jsQPedp3ZSai0lLzZv1O9NFEUK4gtYmLeQc/3nJIVyL+8EpqoA4kVRfFUQhO8BiKIYEAQheK0nyYSZzPcvWfsi5EfnMzw8TLomnacXP43RaESv10+52VnkXHW9dWRbstEqtVRkVaAP6mlztOEf9tOn7eMP/X9gT8seomOjMQ2Y2FK6hfea3+Py8GU6XZ0oRAVFcUWsT19PopiIVq/FmG5Eq9HyZsObPJTzELvadpFsTuay+zKxMbEEhSBmjZkNWRvQo+doy1EenvYwa1PXUmAuoMXZwqacTeRYcuh2daNMUWLVWOnwdRDwBEiNTuV012mOtR+jJLmEPc49vNP2DsvylvF6/eusyF6BfcjOr878ijxrHjMTZzI4OMjg4CAej4f4+PhxSkAQBGJiYujq6mJ4ePiq/fo/L36/n7fOvMWL515EUIf7HU2Ujx+njSPdnI5SocSit5BgTGDn6Z38p5L/hG/Qh1vhxqP24BW8xGhjpH44KaYUkoxJVGRXkByVjH3QTkpUCjqVjnRzOv2eflblriJGG8Pp7nBf/oiy8If8DHgHwmmcF/dSZisjLTqNcz3nqGqqYk32GsrTy9Hr9AyHhuke7iYYCuL0Oanvq6fGXsO6/HXE6GJwepyYVCbuT7kfQS2gUWrYOmcr3qCX012nMaqNvH/pfdblr+PNujep66vjmbJnaHW2EhSD9Lh7uNB9gURdIvdl3UdTfxO763aPa9m8pXgLDY4GQmJIGt6uFJRUt1RzX/p944R9pLPoWFeQ0+vkocyHpFkBESIuVMmVeA9m/YxlqgpgWBCEOMLtHxAEYSHg/KIvLgjCSuCfASXw/0RR/Icves5bzUTCfrIv2+aZmxH8ApVplZKlesl3id/V/o4ts7YgDoye1TvRuSP3LbUtpcHREHYXDHRjUVhobW9FQOCdS++wIHMBHWIHbe42luctR6fWST14LjkuMTQ4hE1rwzngpDnQzIdtH7IgYwFBRZCshCxEQeShgofw+X0cbj7Mg2kPkqxPZtAziNvjJtmQTIAAQSHIirwVNA43Un25Go/aQ1VLFcuyl9E70ItZa6Yt1EaMJgZVtIqWvhaMPiNBXZD1M9djH7SjElVU2iqxaq08nPEweQl5fCp+yoHGA2QezeT5tc/T5Glix/EdVKZXUqIumVDAR/oBjW1g90XoHu7mnaZ3qGqq4sDFA1J7g5FEaaKI0cVgM9nwu/wsyF6ABw8KQcHFvosMeAfYVLSJjt4OdKKOQeUgbze8zf0Z9yMIAtPipkkD3z9t/5RpcdOI1oQHvHe5ulAr1HgCHoqTijnTdYaUqBR63D3hISi5q1Ar1MyIn0G0NhqT1gSEu3mGQiGmJ0yHIOQb82nobeC1uteoyK2QBsUbNUZSo1Kl+cLne85z3H4cRZqCT9s/ZXbWbJJUSazIXcGRS0e4L/0+el29knupIqeCDHMG3z34XUqtpZzuPs1x+3FsKhsCAn6tn4rsCvLi8oBwKnKEEmsJz5Q9w85TO9k6Jzyr95dHfyk1h/vG7G9I8YCR8Z+xCQ0vHHuBbQu3jfqtBYNBhoaGpLqAUQPi7+GdwFQVwDbC2T85giB8CCQAD3+RFxYEQQn8DFgOtAGfCYLwpiiKZ7/Ieb9Mpirsl2QsocHRMOrL7vP5SFens23ONjQaDdHR0Wg0Gv6+5u957pPnaOhvIMmYxAvHXkApKNleu33UNlhE5NXTr1Jjr8EaZWVf/T7+euFf8+KnLxKliuKC4wJuhRurxUqpopSEqAR6Xb0kGBIoSizCPmCnMquSeDGegeAASdFJpMSkcKrvFIYoA4sNi7HF2GhwNKBCRVN3E7lRudQ763kk+xEa+hsQEbEYLZzpPYNL6yJaF01dbx2x+lg6fZ0syV5CRkwGy1TLiNJE8erZV1mWtUyadgVwtOMoqdGpuEU3BqOB/oF+CuIK0Cl1nOs8hx4959rOMcc6h0AogEVnAa4UpV2uJuQPsffiXr6+6OsT/oBDodB1tXAYy5BviPdb3qeqsYpDTYcmbJWsVWq5L/0+Hp7+MOXZ5fzr0X/lM/tnBIYCZMZk0jjYSIuzhRmJM3B4HEyLm0b3cDe1l2qZb5uPLc5GhaKCeEM8O07vwKKz0O/p5/LgZWrsNaRGpdLt6uZQ0yGWZy/HF/BxsPEgsfpYgmKQ9sF2UqJS6HP3oRJUUuHURcdFZiTMIBgKsvfiXh7IeAAzZgx+A16/l5SkFB7SPURubC5ltjKKE4tpcbZg0pjINGfS7+2n0FJImjKNNFMaPpuPGEMMH7V9xMW+i6iVaoKhIMVJxbQNttEx3MH+hv0EQ0EONR1CRKQoroh1aetQokRpVNLsbCY3Nlcq6sqx5EhT0iK/o5Gf48rclbQ4W/CH/FI8YeRvCyaOrY38fygUoq+vD1EUiY6Olp5fYi3h2299mxeOvcCQb4jn1z7/ub8ndypTzQI6JghCGVBAeBjMBVEU/dd42rWYD1wURbERQBCEV4D1wB2jACYS9hN9Gd+++Da7LuwiyZjE+oL1UlthpVJJbGzsqEyVyDa4qb+J/Nh8ti3cJvnyt87ZKm2Df/LxT7g8GPYPL89cjiFo4GL7RZ554Bn+88H/zBttb7AqdxXRumhitOHZryqFir0X96IKqvC4PNT11KFMU3Ko7RDzMuZx2XGZQ81Xyvq1MbT0tPBZ42eoU9QMeAa4GLrIux3vcn/2/aRb00k3p3PMfgxbnE1qc5BiSsFqsiIgkGBIoMZeQ3ZMNmqlmtV5q8m15KJX6bHF2HD73SzPXk6SMQlHr4P2wXYcHgfD/mGa+5vp8HaQoE/g+KXjKAUl77e9j0Vn4bkjz3G843jYzeEboLm7GaFWmLARWCAQmHIDNwBf0McnbZ9Q1VRFVVMVH7d9TCAUGHWMTqVjcepiFiYuZG3RWualzUOp+GM2Vperiw9bPuRrGV8jKyGLE44TZJozsQ/aqbHXEKuLRafQsTR9KWq9GsRwZ06L3kKptRSdSocv6CPJmESZrYwEYwJGlRGL3oLb7yY3NpegGMThdnCw8aBUaGXRWZgWP40B3wAtzhaqmqpQCSqUISX/seA/oggqsDvsdPg66Hf1o/frOdh4EK1SS3VLNTqVTirc6tX28mnzp1RkhHPwm33N+BV+4g3xzEiYgVlrJhAKUN9Xz4B3AJ1KR05MDuVZ5ZSmlCIgkGXIor+vn4/aPqI0qxRFUCFZ8/6gnxXZK1Ar1Dx/9HkpmDv2Mzx86bBUNfzE7CdGFS5GGBkoHmuQRTK0gsEgFotlXCzIrDVTai3FrJ36rIC7ialmASmB1UDmleesuNI067mrPvHqpAKtI/5uA8b1ERYEYSuwFSAjI+MLvNzUmWq+/kTCfqJ8YulLpjbT09OD3+/HaDQSHR09znfd1N+EgMADtgfYNHMTIiKvnHqFrXO2sjJvJYdbDgNhH/f0+OmogioKDYUctR/l5bqXOTF4gpiocKfGOEMcx+zH0Kv1dA51Mjd5LmvS1mAQDSSbkzFHm0mOSWaxZjFx+jiyYrIQEMiJyuF022kKEgpYmLoQtOHAal5SHh61h/bhdtQqtWSZJRmSKM8qx6KzsP3UdlZkh5vGDngHwjsUkxWdWsewbxin1yk1AjOqjWTFZNEx1BGefGUtJcGQgMPt4EDjAanx2YqCFaSqU1mlWYVZZ+ZQ4yEONR2iPKucQCDAqZ5TGCyGcf3hh4eHEQThqiMAQ2KI2s5aqhrDAv/9lvfHTcBSCkrmpc7jocyHWJa9jEXpi3ANuAgEAiQmjq+HTDImsTRtKdHaaPq8fXQPd7MobRFdw12syF5Bx3BYsSmCCg6eP8i87HlYTVbO9pwlwZCARWch2ZRMXV8d1S3VmDQm0qPT6XH1SD7+Q02H2FK8hbV5a7HoLejVevpcfZzoPEG/u5+FSQupSK0gW53NGxfeYHHGYlyiiwPtB3go+yFyLDkMeAcotZYSpY0KZwFFpbEuex0p2hScLieP5D8CGggFQ+gVejr6Ouh19eIKuKjrrSNeH091SzVqRbguYdA7SL+7H3uvndqmWjTJGtQ6NTMzZ5JuSadjqIN1+etYk7eG35/7PQcaD9Dr7qUosYitc7aOGv4+Mi4Q+XdsBpXUm+pK88FAKMBPP/0pdb11PPvAsxSYCxgYGEAQBGJjY8c1CYRwHCEoBsfFC+4Vpro3fgvwAKdgTAOSm4woii8ALwDMnTt38tFRN5CpBoimWjyyaeYmgsEga9LXEAgExln9I/nNyd/wxoU32FCwgRJrCU/tf4rnPn6ObQu3cbjlsLSu/Lh8jjYdJdeQS1AIYogxsKdpDx6Fh5yYcF+eQDDA9MTpNDuaOWk/SZY6i0HXIBqzBnWUGqfDSfPlZhIMCbxy5hXKM8s53nyc2IxYYvQxOBVO3rr8FuVZ5WRbsjnXcw6tSktaVJqUL15jr8GsNVPVVMWmok3cn3E/cfo4EowJdA93S03CAqEAQ76hcDqhSkO/p58cSw5N/U0kGhIps5VhNVlJNCUy7Bvm/oz7GfQOsrt+N1/J+goXBy+yv30/s62zKUwoZFnWMnqGeyhOKOb+nPupH6gfVSwUKZrT6/XjaiUaHY0cajxEVVMV7zS9Q4+rZ9znMD1hOsuyllGeXU6ZrQyzbrSFOBgcnNS1tGnmJi50XWB3/W5KMkuosdcQp4/jQOMBtszcgoCAUqFEKSjZkLMBpVoZTlUVISs2i1/U/ILHZz1OIBhgde5qIFw41+3qDmcDXcnZ73X1okRJ10BXuHdSSMHJ9pMsTl1MnDIOu2jHEmWhOKOYqJgo3C43j8x4hEHfIO2D7TQ7m0nSJ5GoSWRAOYDOo6PQWMjZvrO8e/ldFmQuIFYZS5eri2hNtBSYjtZGUxhfiEFlYFnWMrpcXQghAR06LndcJkudRUFCAaYYE0qVEqvWSq+7lzfr3mRT0SZ21+/GPmSnMq+SOEMcxzuOsyxrmeQWAqTxjyMNse8e+C7VLdUoBaWU67/rwi4aHA1S2meZrYz2/nZ2frqTVdmr2Ne4j41zN5KsTZ7ws7reArC7rWfQVBVAmiiKxTf4tS8D6SNf48p9t5wltvE++y/CrKRZpJamEgqFiIuLu2pKYrYlmzJbGTmWHI7ZjzHkG+LpRU+zKGMRRy4d4Tvzv8OQb4ju/m4+bvqY+bb5tHnbmJ86n9Wq1bQNtKFT6Yg3xJNoTKSpr4loTTSPZT6GJ+ghOzWbTncnlwYuUd1SzYrsFcxKmgUiLIhbgDlgRq1Xs/vCbioNlWyeuRmHx0H7UDtvN4SblEVromkZaGF20mySjEnkxeVJ2+guZRenu0+T6EpEp9Jh0VmI0kRJed9WkxVv0Bt2hehjOdh4kG/P+TYqhYpB3yD2Djv5sflolBoUgoK5yXMxK8woTUpW5a0iWhtNIBhgRsIMLouXsQ/Z2Tx/M4dbD4/aiTmd4RyFqKgouoa7JAu/qqmK5v7mcdc9PTqd8uxylmUt46Gsh7BGWa/5uU42yrLEWkJBQgGDzkGyDFmUJpeSbclmbd5aAqEA6eZ0mvqb2Nu0l9W21RxrOMai1EWcunwKY9BIZXolglegd7iXw62HWZK+hOKEYob0Q9gH7cSEYmgfbmfIPUSsLpZEQyIXBy6SbcnGneAmOiqa04OnebP9TQLGAPsv7WdT1CaG/cN0DXVxuuM0K20rydXmkm5I53jzcbIsWbR72onXxCMYBZbmLiXeEE+Pq4fqlmq2LdxGeaCc6QnTCYQCdLm6qOutY27CXM54zpCly0KhUJA2Iw2DwYDL4cIdchOjiKF9qJ1pcdMotZaiUWo4032GrJgsnil7BkAK7I7t9TO2i6wn6AHAG/Syo3YH/qCfipwKKvMrybHksDhlMZ82fsqwfpjKvErebH6T504+R0gfojSt9Jqf51S427KHpqoA9gmCsEIUxQM38LU/A/IEQcgiLPg3Al+/gef/3BxuOTwuRe16GGslOJ1OgsHgNYU/hIOO1S3VUu/zF469EG6vIAi80/wO6dHpvHnhTR5Of5iFGQtRG9TUtIRnuc5PmY8ChdSYy+Fx4A16MSvMXPZdZm/7Xso15agVamwWG6vzVpMRnUH7YDsul4t2RTsDDHCx/yLLspYRZ4ijub+Z6pZqHp3+KGvy1hCji0EphBuweQIevEEv9b31ALgDbhINiSgFJQcaD7AyZyValRZXwEWsPpbKvEpERKkoKSsmi1JrqeSvXpa1jJLkEi70XqDL1cX0uOlkaDMwqow8f+F5VuatpNfVS5Yli9aeVuLUcWyv306KNWVUVkhXfxdv173Npz2f8n7r+5zqOjXuOsfqY3kw80HKs8pZlr2M3Njc62odoVKppBbbEz1vU/EmgoEgpZZS9Go9eYl52IfszEqaxZG2I3S7ulmRvYKi5CICigAZsRkoNAr0Cj0WrYVL/ZcojivGLJjRqXS8fOxl5qXM4+P2jwkFQ3x4+UMWpi8kNjqWKF0UuqCObkU3uigdvcFejFojc5PnkqBJ4Gs5XyPfkI/ao0an0WFT2cg153LEfgSTwUSMJYYOsYP9zfupyKlgf8N+Hi58mARDAl8t/Gq4dqOzNhxTCKlYZltGXWsdFy5fwBK04Aq4GFIMYdAbCBHivqz7aHe3o1KocLgdvFX/FklzwvGMpbalxOnjRlnQIiI7T+3E6XXyTNkzUm+qwoRCKnIqCIQCbK/djjfgldpqPDn3SWkHkGXI4tklzxIIBFiSuASTyYTBYCA6Npqh0NTHR06Fu61n0FQVwMfA64IgKAg3hhMAURTF6M/7wldqCf4M2E84DfTfRFE883nPdyP5oh/ySCtheux03G63NHBlIkYqjCfnPimNRgSkgLA/5KfGXsO8lHl8JecrdDg6mJYxDa/oDRfkRFlZlLEIgJSoFOalzuN3Z37HgcYD+OP9zEqdxf3a+0kyJfHiyRdZkb2CA40HWJu/Fl/AB0HwiB7izfEY/UbMejP+oJ/c2Fy0Si0xuhhcfhc7Tu2Q3DqZ5kzSzGlc6LnAtLhpaBQagoogOZYcVuWuwqw1S7NqAamDpNPrlIZ/TIufBoQ7ZXa5ujjVdYpuVzeJhkRStalc7L/IoGGQsqwyREQCYoBjbcdo7Wxlw4wN/OnCP+WxGY9JmTpVTVV8cvmTcYFbvUrPUttSyrPKKc8qp8RagkL4/APljUYjbrebwcFBKbNkJCXWEkrWlODxeCh3lvOjwz+iq7uLo4GjPF7yOC+deomm/iZOdJzgQNMBej29xOhi+EPDH1ievZxcay61jlq6fd34PX6KsosoTC5kWDtMUUoRg6pBplmncaz9GMX6YjSCBk1Qg07U4XV7Sdem41P6SFWlsqx4GW/Wv0mnqxONVsP+tv3cr7qf97vfJyomigRTAsc6jrEiewVp0WlsKNjAXy/9a2Ynz8bv97Pj6A50IR3rU9ZTZCniSOMR4o3xzMqYxdL8pbQNtaFSqPjk8ic4PA5anC3sb9jPtoXb+MuFf0leXN6oCt4lGUvYXrud5v5mDl86jFqh5tP2T3F4HJg0JqnCd0PBBikAvCRjCUdaj7B1zla+XfptpsdOJ9+QzwPx4cymSN2HXq+XFPJkMya+CHdbz6CpKoDngEXAKXGyfe/nQBTFvcDeG3W+G8UX/ZBHKpBIto/JZJr0+JEKY3Px5lHreKbsGbbXbketUNM51Ik74Oax6Y/xafOnLC5YzNv1bzM7eTb9nn6OXDrCD4/8kG0Lt/GtOd8i3hAfLhpSplPfV0+3r5s4fRyrc1cTq4/l0emPMugbZH/jflamrESn1OERPAz5hzDrzfgCPhweB7OSZtE+2E6MPoYyW5nUTqDJ2YQn4JEyUUwaE9HaaLaf2s6j0x9FpVBRkV1Bflw+Z7rOUNVUBUCyKRmNUoM/5KdnuIf9jfvZPHMzCkFBsikZBQrSdel0OjrpCfRgUpmoaqpide5qZsbPxOlwkqRJ4tzgOXwDPn5+9Od4Ap5R11QpKJmfOj8s8LPLWZS2CK1qfBDw86LRaDAYDAwNDSEIglSRPHb3p9Pp0Gg0fHX2V2kdauX9hvfJ0mfxFzP+gh2nd+AOuXkk+xGMWiMx+hg0IQ3ZUdlkR2fTPdBNgiUBk9rExd6LXO65TE1TDaaAiTghDrvdTt2lOmICMdS017AobREmjQmUcK7/HNWXq3EKTmoGa0iOSqaHHorMRSzNWkpRYhEKhQJRFOkc7mRm/EySDcn84rNf8PiMx0lTpdHR0YEoinwl+yscbj1Mc6gZj8rDytkrefXMqxy6dIiM+AyeX/s8337r21S3VFORU4FKoRrV639kh8/nPn5OapMR8d1X5lVS3VJNZV7lqEreDHOGlPW2/eR2fvbJz/izkj8jRZFCb28vq7JWESTI12Z/jYSEhAk/p7vNYr/RTFUBtAKnb6Twv5uJfOlFUaSjo+OanSlHfkm3126X+viMLHo5bj/O+d7zdA9389uzv+Ur2V/hxeMvsqsu3IFz14Vdo9JEj9mP8UHLBxTGF3Kq6RQf2z9mRdEKhr3DZJgz+GXNL3lsxmPoVDo2z9yMJqAhXhnPWddZqpqqWJG9Ak/Aw/uX3gdg38V9/Nm8PyNgCuD2u5mZMJNB3yCJxkSpF028IZ5oTXQ4aKo1E62LpnO4k2HfMLmxuQBoVVo6hjqoaqrCZ/Nh1ppZnr2ckBjiYONBVuWsIt+YT9AfpIcePuz6kOioaIoSinC4Hfy+/fdcdl0elxECEKeLY0POBpbnLGfV9HC84GYS6VI6ODiI2+0mOjp6wqEjCoWCRTmLON1/GqfgJDcll9nps3n1/Ku8efZNHpn2CIvTFvPqmVc50X4C77CX7r5uPmz9kML4QsxaM6cvnyY+M57lGctJj0nnM/tnLEhbwCLlIqxRVgSDgFar5cUzL1KeVc7slNnEx8YTDAXZcXoHq/NWh2MwRitppjSWpCxBE9BwuvM0n7V9RnF8MWqzmnW2dSxNXYpCocBoNHLOcY6agRqaA83sa92HoBP40ZofUd9bj8PjkNInIzvXpbalfNDywYTFi5EU50AoMCqtc6ltKSlRKdK5pMyc6ZsojC3E6/WyNnUtiiIFa7LWoNVq0ev1lFnLeGDmA1f9jO42i/1GM1UF0Ai8JwjCPsAbufMLpoHe9YRCIURRvKbff6IvqSfo4aPWj1AKSunxZ8qe4dnqZ8MVnOdeAw2sL1hPma0MT8DDvNR5fGvOtzhmPyb5R5dlLcOkM7EsfRnRqmheOf0K3yj+BiuyVyCKIq+ff51SaylF8UU0O5uxqC2UJpeSGZOJy++SUjO/XvR1Wp2ttA22UWOv4f6M+0mPDqf2KQQFBxsPUpFdgcasYcg3hDvopqUzHNTrHO4Ewvnz0dposmOyUQkqpiVMkzpNtg20MTd5LlGBKLqcXYhakZ5AD2nRaXzY+iEDvoFx102v0rM4fTEPZT3EJcclNmZvZHridBISEr6UVtARt4NOp2NgYIC+vj7Wpq1F8Ak8Ou3RcfGBvfV7ebvpbXQaHfMy5xHUBXl80eNsmrmJUCjEtMFpBPQBzFozZq2Z+dr5aNVaUqNTcWldzMudR3VLNW2BNj7o/oDC9EIyEzM513OOkBhiQdICul3d5MXmoRSVxGvjyY/Nx+P2sCxjGdMN01mTs4b8uHwAYolFEVCgVWrRa/QIGoHfnfkd6SnpHD16lCW2JVLx1Z+W/mm4WCyhWOomKwgCZ7vP8sb5N8JtqQGb2caGig3Sex65u/1xxY+lHW1EQURibB+1fMR7Te+BH763+Ht8d/Z3CQaDOJ1OVCoVJRkl6KP1vHruVczxZkosslC/EUxVATRduWmu3O5ZricN7PNsmEZWKEp95Ec89visx3m+5nneb3ifZZnLSIhOoKqpCvuQnd11u/nWnG/x/NHnaepvknLOlUYlBZYCMmMyWZ0XTis80HiANXlrWJO3BqvJSutAK93ubmaqZlIUVcSFngukRacB4Pa76fP0MSNhBnq1ntSoVKJ10Tg9Tvo9/UyLD2d4WPQWhnxD1NhrSDQmolaqSY9Op3WgVepvsyxrGanRqcToY7jQe4G3L74NQKouFU1AwxnPGS67L9Pv7R93bXRKHelR6TxS9Aj2ITv/fuLfmZU0i79e+tc4HA48Hg8Wi+VLnQMAoNPp0Gq1eDweinXFFMQVIIoidrtdGjivVquxGW3MT5pPRlQGO07uGNWyIOIeeXrR09T11fHy8ZdZk7eGXefC6Y37G/eH5+3WvcXqnNX86ew/JRQIkReXx/nL53G6nXzW8Bn5hnyifFEMDQ/x2vnX2Fy0mZ8+9NPw9LRCpbSe0z2n2d2+myXTltBX34dZa2ZxxmIsURap8DASf1pfsJ4VuSvCtQaePqloq8HRwO763fhDfooTiyfMjhnrgol8v0OhkDTXwufzMdw/TFtHG64YF16vVxrwo9FopHTbf3v/38JVu/57s2r3ZjDVSuBnb/ZC7hSuJw0s0sht5ASrqTI2GBzh8KXD2IfsLM1bik1n43DLYT7s+pA1eWswa80ctx/HrDWjVqixRlnxBDzE6mN5x/4Os12z6Rvsk4ZzR2miiNJEcazjGDX2GsqzyjkzeAab1kZtWy1pRWmkm9PRqDRUNVWhVWnZW7+XtXlrMSgN9AR7qLHXkGPJIVYfy9mes1h0FsqzypkeP53arlpCYohsSzbBUJBvFH+DBEMCZ7rOkGnOpHu4m4zoDI5ePsrb7rcnbJUcFIMUmgvpdnazMm8l3f5u5qXOw2a2YdFZ2FK8RZqXEBUVdcsmgkUKzvR6PaIo4vP58Pl8koBzu92st61H7VOzOj2shBUzFaxJXcN7Z97D4/Tw3VnfZU3aGi6bLyO4BFReFY9lPsZ623pyNblkmbNQupQofUocDgfVLdU4bU6Otx5nbupcrCYrO87sYGPRRh6f9zhak5aFtoX8uPbHLMlcwuGWw5Lh8ttzv5VajkTm7G6auYmM4gyp8HBV3qpR7pwN0zZw3H5c+l62OFvwBDzYh+zYzLYJxy2KiBACv9cvdU71+/2c6Twjjfwsshbx9TlfR2/Us3HWRpKTJ87Zv9erdm8Gn7tJiiAIW68Uad1TXE9QSaFQoFQq8fuvv2vGyPL2vzr4V5ztPss353xzVBOrg2cPsjR5KUXWIqIN0fzwyA+JN8RLx5zvOY9BbWDnmZ2UZ5XT5++jubOZ+KhwjcDZnrMsTltMkimJJ2Y9gV6lp1HRSLYlG5PGxMDgAHqTHqWgpMxWRmF8Id6Al6LEIk51nUIhKFiWtYxEYyL+kJ8EQwKx+lhOdZ3C6XXS5+7DorPg8IRbFixIXcCnlz/lfO95FIJCmpU7kiRjEtHaaCw6C/mx+Tj6HWSbsmmLa6PT10nncCevnnmVKE0UT859kuLEYrq7u6XJaLcDghD2x5/pOyPtFmclzyIhIYFF+YsIhUKEQiFKM0sRRZHvHfwer5x7hQczH0ShVFDTUUOHq4P8uHxUChUZ8RmsmbGGH1T/gP1t+6nIreCx4seItkRj0VtYbg6301AICnKsOURHR/O7+t+xJOePLpyIoIfwd2tL8RaGfENYdBaEAoHG/ka2124HkAoPJxrBONJdWWItwWa2SS6d2cmzCQaDuN1u/H4/gUCAnUd2svPUThQuBdsWb0OpVKLRaNjbspd/Of0vhIwhHix+kISEBO7Lu++q1/Ver9q9GXz+LlnhVNB7jusNKmk0Gny+P476O2Y/xiunXsHpdfLk3Cevea4dtTuosdeEKyAVyvCc3itVkG9eeJOnip9iU/4m9rTukToollhL2F67nbfq3+I7876DP+QnLzaPYCDIosAibAYbDe4GFqUu4mLfRanJ2MHGg5RnlXO89zjDw8M0dzWzJHMJvUIvDo+Ds91npUyeblc3ScYkDjUdkqZHlVpLSTQmhl1fIngCHi45L9Hn6UOtUPPJ5U+k9xUR/rHaWPq9/ZRYwxWzMxNnIggChxoPYdPYiFJGgTZcH3Gw8SDLs5cDUGOv4SdHfkKyMplV2atYUrjkS3f9XItRu8WKEhQKxYQ7FFO0iWJbMRfdF3m9+XW2zN/CRfdFeoI97K3bi1vlxmqysjRvKaJGlD7jj9o/4h+P/CPbFm5j48yNPH/0eR7KfAh3wM1HrR/R5w43QNtQsIE1eWuAP3bfjKRI7r24F5vZFp4HPEKwTmbgRFygm2duZmbCTArMBXx/4ffx+/1S1hCElaBKpWJD0QZEjcijxY+SnJwsfUab526W3stUmcr4Rznge318bgUgiqLshJsCOp0Ot9uNz+dDo9Gwo3YH1S3V1Nhr6Bzu5JmyZ646JGZL8RY0Cg1apZbK/PCwlh21O2jqb2JdwToeLnmY39f8np+f/jlPLnySpv4mnq1+ljJbGZV5lfR7+6lqqkKj1NA20Ea6Np22vjbebnub+7PCQ0UqciqYY52DzWyjdaCVooQiWgdaSTImYVaaOXTxEMUZxSQZwz1/5qbMpaW/hdSoVHRKHdYoKzG6GDQKDe2D7XzY9iGfXv5UqtwciVFlJFGbSGZUJp90fMLagrW0D7YzM2kmMboYqQBsacJS9IKe3zX/jqXKpVh04UZpiYZEBn2D4UpiRSxt3jYCqsA1s0EmI+KL9nq9UtvoiPBSqVSSD/rzKJfJdotjP+OI4HV6ndJ9z5Q9w48/+jGl1lIUKEYFUseef0nGEinov75gPWatWeoiGukMeq7n3KT993dd2EV+XL7Ud2rINzRqnZuLNkvzl1/+9GV+c+I3KIYVJC9Mlq6XWq3GYDCgVqulOIMgCGHLPn+8ZX8js3PuturcL5OpNoP7O+D/iKLYf+VvC/CUKIp/cxPXdleg1WpRKBQMDw+j0WjYUrwFpaAkPTpd2nZPOCSGP/5Ittdu50DjAYoSi4DRgqXEWoLX5UWhVNDv6+fNC2+y68Iuhn3DHGo6xLr8dZRaS0kyJuEL+tjXtI9v53+bivQK0mPTMagMPPvgs/z7iX+nqb8pbOGLEBSDJJuS8QV8lKeXIygEvH4vJo2Js91ncfld9Lh6ONR0CJvZRq+7VxIcgCT8NUoNRpWRNH0aheZCuoa6qOmtYbp1OuuK1tHj7iHRlEh1SzX+kJ9kQzJxxOFRhwO65apyzLrwBC2X38XDMx5GREQICOQb8hE0AhvnbLzuz0UURYaGhhgaGkIURZRKpRRsjMxhjsRuIgIuMlks8plei8mE3ESf8djjSqwlPDzjYX517Fckm5J5etHT41wfJdYSRESeefcZLg1cYkX2Cpr6m6jIrmDbwm0stS0lNzZ30jbJEUUT6bC5/eR2Djcf5nTnaaLEKISQwPaT21EMKfjLRX8JwNrctQB8ZcZXpO6aSqXylu6+5Fz/z89UdwCrRFH868gfoig6BEFYDcgK4Bqc6DzBy5+9zJrMNSwxLbnmsOuJ+hCNvW+swJiTMYe3zr3Fr07+is3Fm6nIqSDRmMj6gvVU5lVy+NJhXH4XRYlF6FQ6OrwdHG09it1vJ90cbsfk9IS7dEYavw0HhgkGg5zpP8Pi5MW0dLYQo4nB6XNyafgSlwcv0+/pB5B6uAAoBEV49KAugXhNPDmmnPCQF6UKr+AlLz0PrVmLUqGkvjc8eWpT0SYSjYnEaeJQ+9W09rdijjFztj8cVN5xagfLs5fz5Nwn+fWJX5Njzgl3eYwSeLT0Ucl3PVXrLxgM0tfXh9/vR6/XYzKZJnTNBIPBUcHc4eFhycURsXQjSuF6dglTFVgZ5gxUChX1ffUUxBVMqkwuDVzCorNQklyCP+SXJmUBo3z4kdqUQCBAMBgkEAiQY8zhv8/77/j9fipTKlG71MyzzKMyqxKFUoFCoWAgMECbv4256XNJSUmhbEYZcPu4XuRc/8/PVBWAUhAErSiKXgBBEPTAjSurvIvZUbuDfzr+TwgBgZkpM4mLi0MQhEm/tBP1IbpWbyKtVsvqvNUMKgaJNkbzlcKv8EHLB/zlwr9k56mdNDub6XP30evupdvVTY4ph/vS7kMfrWcgMMCPP/oxCYYEvEEv/qCfYf8wvpCPnuFwls+Ad4CLfRdRCAqC4ujpWgICycZkolRRXHZeZnnacmxRNpr7m+l19dKp6iQ6Jpoudxf7m/azJm8N8cZ4/EE/hfGFpEWl0e/pp+piFd/I+wadrk5Sk1JpGWxBr9KjUWlYnrWcuda5vHTyJd668Bbr0tdxtucseWl5vHzq5eva/vv9fmk4yNW6skJ40HwkswfCOwO/34/X68Xn8+HxeHC5XOHrIAhoNBpJIWg0mkl3CVP1Ze+o3cEbF96g1FpKsmnizJhIMNesNfNY0WOjuqFGhPzIWzAYHJWeHHF3aTQa5mXPY1H+IsmqB9jVuotfHv8lBqOB+bb5o15bdr3c+UxVAewAqgRB+Pcrf/8H4MWbs6S7i4iV93DBw/h8PoaGhoiKirrm8RNt2yMVvs8ffR6z1ixZekqlkoL4AkyXwn1Ptnq3Ytaa+eXRXzLoG8ThcZBoTKSqqYryrHISoxPRe/XUD9VzyXMpPJDF46BjqINjHceYnjCdHlcPva5eAOr7ws3eIsLforEQr4lneux0PB4PebF5DPmH6DJ0EaOL4azzLL3+XuJi4ni96XVpQHekjUSrs5XMmEz8IT9BMYh/0M/Xs79On7ePvZf38oDmAaqaqii1lhIIhVtaPzLjEZr7m9F6tZSll5GdnM2WkoldG5MRGQ4CTKkx31hGCvmR54ykNnq9XsmlBGEFEtkdRGIJJ7tO8vKpl8dZzSOL9yAsUJfYltDoaCTDnCF1yISwIooI+AJzAT964EcEAgFCoRBdXV0TCvmIi0un04VrAq78PbZV9liutluRXS93PldVABGrXxTFfxQE4SSw7MpD/1MUxf03f3l3PiOtvf7+foaGhtDpdJMKn8n8wSP7qbxw7AVKraVSg6uI62F+ynzWD63HorNwqOkQNfYavl70dSw6CzMSZ0i9dtxBN10DXfR6ejHrzRxqOkS2JZuOoQ4AznaPHsqmV+lJiUohx5JDMBREpVCxv3E/tngb73S9g2gUOdh0kMdmPIbD76AwtZDzPedJNiVLQt+sNYMAg75Bul3dWPQW2gfaCXqCNHQ1sNC2kIzkDB5UPsis5FnoVDqMaiPne88jIvKTIz/hW4Xf4p+W/xMWi2XUkBcR8ZpuoJFjAT+P8J+MSLB45C4h4jKKpEJGFAPAKx+/wvZT21G4FWSWZSIIAgqFgpePvoy9187Xcr7G13K+xsDAAB/Wfch7F97jm7O/yUD/AE9//DRrcv9YyRtBEAQu9F1gT/0e1heup9haPErAfxEf/dXcK7Lr5c7nWjuAI8AcQRB+I4rinwBvfwlrumuJjo7G4/EwODhIbGzsuMdHugAinRPHWoojt/yL0hfx1P6nuC/tPo6cP0JvsJddF3ZhKDJQlFhEkikJpzfcF7/V2crBxoPcl34fCYYE+unnrPMsAz3hFguNjkbpNTRKjeRyyIrJorqlmhhdDEExSFVzFevy17E8ezk5lhwEhUB+bD5KpZI+dx8HGg9QnlXOrKRZ0vSvaE00aqWaPlcfMboYauw1FMUVYQgYyInNId4QT1AdpHO4k0NNh9CqtJRnlfNZ+2ekRaWhElTUX67n5wM/5y/K/oIUfcqo63YtV4Qoijgcjim35P4iRGoARk6firiOAoEAXyn+CqJSZF3+Ovx+v9QuZE3mGgSfwOq81WQZsxgeHqYyuxIhKLA2by1vXXiLX9X+ClEp8j+y/odUYxK5/aj2R1Lv+yUFS27a+5O5u7iWAtAIgvB1YLEgCF8d+6Aoiq/dnGXdGVxvEEyhUGAwGBgeHiYUCo3zEY8UZMAooTbytZ5f+zzH7Md45t1n2F2/m/qceo42HGXljJXS0I2u4S5CYohpcdPoGu7ik8ufkGRM4qPWjyasuE0yJZGgTyA5Kpk4fRwuv4s3LrzBA7YHWJ27mgRjAnH6OBSCgixLFo19jfR7+sm15HLk8hHmJM9h2D8stZ842XkSg9qA2+/G4XEw6BvkQOMBVuWuYrVtNW6nm+Ptx0ENCo2CPfV7WJ27mmVZ4U1mdUs1uy7sYvOMzai8KlQGFbtadpFSl8KCrNGTQ6/lihgYGMDr9WKxWCZtyX0zGek6Wpy7mMW5i8cdY7VaeXDmg+Pue6DoAQCiLFGE9CE2F2+esOBNdsfIfB6upQCeBDYDMcDaMY+JwD2tAK43CHbMfowdx3dQmVY54YzSq/n/x75WJPvjkcJHMClNfHXaV3kw/0HM7WYMagMfXvqQi46LHGw8OGHFbawuliRNEhnmDPa37sdqsmI1WbEP2dEqtRjVRtbmryUoBjGqjXQNd9Ht6uZg40FEUeRQ0yHKbGX0unupsdeQH5dPXW8dpdbwQPCZSTM5031GmvVblFjEcttynA4n+TH5OP1OyqeXc6r3FJnazPD8AJ2ZM93hNhH5sflUZFaQqkqlvr+eaWnTyEzLnFDAXc0V4XK5GB4exmQyXXU28O3OtdwtE3VHlZG5FldVAKIoHgYOC4JwVBTFX31Ja7pjuF6ra0ftDn762U9RuBUszFs47vGxP/KrNdXaUrwFf8jPhZ4LnHGcwaqz0namjfcvvY/D45CeFxH+UZoo0qLTMGlMGNVG9Go9Ry4eIduYTUVmBWaDGYvOwu763UyLm8agb5BobTTne84DSPGEMlsZWpWWMlsZqVGppJvTidPH0evqDa+n9wIqhYpgKEheXB4AakFNyB3CO+glUZ9In9hHk6eJJGUSRQlFGNVG2gbb2Hl6J6vzVof7E7Ud40zrGfSpevZc3kNCUsJ1NwDzer04nU60Wu1VA+93A3JGjsznQZhKx8qJ3D+Ak/CAmK4bvqpJmDt3rnj06NEv6+VuOMftx9l+fDuVaZUsyl901RTEq9E+2E5VYxX/cPgfONtzdsJjDGoDLr+LmQnh1gpqpZoae43U02fYP0y/q5+YUAzRumg+6fuE/Ph8RFHEFXCx68IuVueuRqVQEaWNom2gjYK4ArxBL72uXhSCAqPGiNPjRERk38V9bJyxkSHfECIi/qAftaAmPyqfxq5GihKLON57HDQQZ4yjY6hDaj2RFpWGO+CmwdHAnKQ52PvsFEQX0DLYwoqiFZzvPc8SW7iZ2ZKMJbx98e1RWVAT4ff76e3tRaFQEB8fP6XCrTuZkXUlV6ssv11y92W+dCbMAphqGug3CU8Ee/fK3w8ANUCWIAg/EEXxN194eXcg1/tjKrGWUGAuoL+/X6o6nex8I4PAWZYs3mt+Txp5eK7n3LjnKlCQF5sXbpU8YKeyoDI8ECahkPdb3icUCpEalYpWqaXF2cK+i/vCswK0JuxOOxq/hssDlxnyDzEvZR4bCjaQFp1Go6MxXLlsTictOo2LfRdJiUqhub+ZaF00bYNtLExdyPLs5fS6eznYeJA12WvI1edyqv0Uje5G8hLzaPQ1EhcTR6+7l5OdJylOKg6PPrTk4vK76BzupCCmgH5HPzWXakiYlsCrj78qZa9E2iVHWheUWksRBIFnq5/lidlPjKqPiKR7CoJAbGzsXS38R35nRraJiDCR61DeKchEmKoCUAOFoih2AgiCkAS8BCwA3gfuSQXweX5Mfr9fysue7HyBUID2wXZ+f+73/Nvxf8PpdY7z8SoEBQmGBDqHO3lixhPEEcdj8x/jd+d/x4nOE8TqYyWBUNdTx6ftnzI3ZS4nO07S5epiVe4q4vRxtA608snlT1idupoUUwrN3mYATBoTDnd4qPyJzhPYom283/I+qdGpfHz5YxIMCZzsOEmiMZEh3xCJukQUQQVl8WVYFVY8Xg/TU6bT7Gqm8/9v7+7DorzPRd9/fwwwwAwMKCigMgIBMb5ExLjU+hKrDaliwDZNYnFXV5ulrrXPlXN2UtO1Vq/VrHRd+6zdk7bX3mft06q7e+/Ehc1rqyRqNGoao42mAVTUKCDy4gsBFRgY3hme88fMPBlgQIzADHJ/rovLgXkYfvMIv/t5fi/33VOLAwelt0v1tNNN7U3O+YREjRhTDJ9Xfc6kaZMwB5tJmJxAdFR0r6WLSxOcu6GzUrOcOYqMFr649QX7y/bj6HHoaYvnTprba7mnt0D7IBnsd7CopgiDMuhJAkEmi0VvQ/3rmOru/F3qgGmaptUrpe491/ED4uv8MbmTwnl2bo4eh54cLjosmh2FO+h0ODOIugujBKgA4sxx3Gi+wbJpyzhx7QS5c3IpbyhnUcIizlec550L79ChOiisKWRWzCxePPwiG+duZPG0xZTWl9LS2UJXTxePxj/KNds16tvqMQWZ+O6c77J86nI+LfuUVlsrzaqZsuYyHMpB4ZfOIi4RxgjeL3ufZ2c/y8PRD9Pe0U5sVCwHSg6wcupK4s3xaGiEBocSbArG5rBxreUaRyq+qhecFp1GvDmecKOzDkHu7FxuNN3AGmTlr9P+mnbaKagv0NNT7L20l5PVzhz2J6u/2g3tngvYd3kfhgADaRPT+MWnv4Ae+EnGT+jp6WHChAk+qw0wmgb7HXQHB3fRGZC1+6K3oQaAj5VS+4F3XJ8/5fqaCWgciYaNBff6x9TT00NXVxdms5nLty9z7OoxjlYc5ePKj/W8Op5mTJyB0WAkdUIq/2nxfyI0KJS84jyWWZfxaNWj2Dvt5JfkozTFZ6Wf8dhDj1Gv1fP8wucxGoy8eupV/bXyS5yVpY5XHSckMIQPr37I6sTVHK04yvKE5bxjfwdToInPb3/OmrA1zIuYh0NzkJiQyM2mm1hCLaybso6upi7aO9sp+rKIRVMWsTx+OQkTEiizldGqtfLxzY95OvJpOh2dBAcGk5WShcVowRRsouROibNiWFkhax5agwkT3c3dXOEKLz72IsYQI3nFeRiUgUPlhyiuK+ZwuXO/obeOzp2v/kzNGbq6u3gy4Ul9rb8vlnv6wmC/g3K1L+5mqJPACvgO4N5h8mfgD6NdJH4sTwLfaLrBodJDfFDyAae+PMVN+81+x0yNmEpUSBS5c3L50fwfER0WPegkHkBecR7dPd2cu3qOyLBI8qudHX13TzezY2bT0t1Cl6OLtq42bB02zEYz08KnYe+yY2u30dDWgCnYxJsX3+TZWc/S3t1OiCEEs8HMuZvniA2LZVr4NGpbajEFmzAFm2jubsbebaeTThIiE7jdepvS+lJmRc+iqbOJaRHTyHwok7cuvMWt1lscqzhGZlImceFxhASGcKPxBvWN9Tw84WFutt3k0PVDZKZkMjtmNo3tjXw75du8dvY1KhorSJ+cjjHQyIL4BRwoO9BvvB+ceW/u3LmjX/mPl85fiHvw9SeBNU3TlFIngU6c6///Mtqdv78Y6sRvQ1uDc+K2wjlx615O6WmSaRKrElexOmk1qxJXYY209vo5/3riXzEogz7G6y4Ec7XhKqV3SpkVMwt7p51vp3ybrpYuIgwRtAe1E2uO5c/X/kx7dzthQWHcaL6BKcjEJ9WfsMK6gmu2a0SFRLGvZB+ZyZlU3a7i2VnP0tLZQkRIBHvO7+HbD32b+Oh48kvyWZW4imNVx/T6xFPCp9DS06LnFpo9aTZt3W04NIdeA3jf5X0EqABCAkPITMrEGGgkIjiC76V8j/3n99MR2sEtxy2mTprKUsNSrjddp6WzheNVxzEHm/Xi4fZOO7uKdlFlq9LvBjwDgHT+Qnx9Q60H8DTwKvAxzkjyb0qp7ZqmvTuCbfNLA026tXW18edrf9ZX6hTWFPbbgGUOMrNkyhLWzFjDqqRVzIqZNWCOFvfP2TJ/Cy8seoGlVmfRj4rGCuLD49lftp+27jaOVRzDHGzmV2t/xc8O/oyPyj9iw9wNxIfHc7j8ME+mPsmcSXPodHSSEZdBvDmeQEMgASqAjLgMrBYr3T3d1NhrOF51nLUpa1mVuIr02HTsnXaemvkUE8MmEhoYSoQxAoUzd01iVCIKRbAhmIt1FwkODObtL97Wh5XWpa6jqaOJ+PB4AlUgX9R8gaHFwIGOA/z2/G9ZmbqS/NJ8fVlqdVM1j0x6hBkTZ+jB1Z022xxsZmbMTEICQ3olRZPOX4j7M9Q5gJ8Cj7rX/CulYoCjwLgLAO6hl2dnP8tn1z/Tr/D/XP1nOhwdvY4NNgSzeOpiViWu4puJ32RqwFQmWCYMaVNS36IvLx5+kYrGChIjE9k8bzNBAUFoaGSlZLFx7kYu1l/EoAxsnLmRaFM062euZ1bMLAzKwKunXuUfvvEP1LXUERYYRl1bHRNCJzDJNAmDMhBnjkNDY23KWh6a8BDVtmqu1F/h7S/eZsPsDRTcLCBtYhrXm68zd9JcyhvLcfQ4mB83n8KaQo5VHGPTI5tYlbiKuPA41qasxdHj4HjVcbKSsogkktjAWD658QmdIZ08t+g5llmXMdnsXM1j67BxsOwgQQFBeoU0b8sbn5v/nH5+NE2jvr5+3I35CzGchhoAAvps+LoDPLiLq73QNI1Lty/xxoU3yC/JZ1fRrl4VsMCZG39e7DzmTJqDvdPOi0teZMk0Z96XtrY2Ghoa+qV/8MbbuL+9067XbXUX437j/Bt6sre3L77N6S9PkxaVxqErhyitL+XlFS9Taaskuz6bG803qGutI9gQTG1LLe+Xvu8sN+haXVRWX0ZadBpnvzyLKchEO+38TfrfcNN+k8KaQiabJmMKMnHp9iV94ripo4lnZz/rXOEzMQ1jgJEb9hvEhMUQY4wh1BpKVEAUxiAjyqhYELaAhMgEllqXcqLqhF4Tee/lvVTZqqhorNArpN1tiW1DQwNdXV3S+QtxH4YaAA4ppQ4Db7g+fwY4ODJN8h/Xm67rK3WOXT1Gjb2m3zEpE1JYlbiKVUmrWDl9JRPDJvLi4RfZXbyb6ZHT9QDQ3t5OQEDAXTsrd174LkcXrxx/hZdXvMye4j3sKtrVbzlfXnEeu4p2YQ42Y++0c/rWaZIjkpkeOp1ORyf/9Kd/IiYsBovRQlNnE+mx6cybPI+ztWdJjEwkyBCExWjhSv0VjlcdJyggiONVx3k86XE+vPohK6wraGhvYHXiaq43XyfWFEtdax3fn/19Gjsa6dF6ePfiuxgDnZvLyhvLudNyB1ujjbZQZ0nFN6reIDc9l9iQWAJaArjacJUjV47w28Lf6u/jZNVJauw1zsLnrqpng61gsdvttLe3ExERMaSAOtbJ7l0xUoY6CbxdKfVdwF3deZemaXtHrlm+c7DsIPtL93Os4hild0r7PR8dGk20KZpnZj3DD9N/SIIlod8x3jqvjo6OXp3VQH/Ue4r3kF+ST2ZyJtcaruk7Xe2ddgzKwJmaM/ou4WXWZfrXF8QvoLallsnmyVytuIrZZObAtQNkxGUQGRKpF1iZGDqR13JeY+v7W9lVtIunH36ayebJrE5cTaw5llWJq8iIzyDQEMhDUQ9R3lBOdFg0De0NJEUloZRCQ+Ng2UE9UGTEZdDR1UFhRSHZSdm0dLUQagrl0p1LrJ6xmidSnuBk9UkaOxo5fPUw61LWOec1Epby4uEXWWZdRnlDOV2OLl47+xpWi3XA5Y1dXV00NzcTEhLiNSvmg0h274qRMuRtkpqm/QH4wwi2xS/8ruh37L38VWwLDw5nZeJK51V+4ioejnnY68Rt3w7d8w/VXa3JMwAM9EftzvcfFRJFUECQvvnJHGzW1/WX1pdS0VgBoH89e0Y2+SX5TJ4/mfRp6bS2tvJd63cJDA0kzhKHKcjE9MivsmlajBYy4jIICghiz/k9PJf+HJWNlRyrOOYs+FJ+mB/M/QFzYuZQfKuYeZPnUW2r5mbzTaJDo1mbspaJoRPZ+shWuju6SYtMI6I7glZa+eO1P7LMuozEqER9Nc+vT/+aH8z9ARlxGSRGJfKrzF/p6R0ANs3bxM6CnRwsOUhyVPKAef0bGxsJCAggMjLy6/4Xjzmynl+MlLtVBGsGr3lmFc7VoREj0iofWpOyhsb2Rn1YZ0H8AgID7h4nB7tK6+pybpb23Jk60B91elw65mAzv/j0F2xfvJ3Uian6MfZOO2X1ZeSX5JM9I5uNczdS2VhJeUM5K6zOQt2PTnmUd794l0/KP+Fb8d8iMiiSjpYOKhsqUUpRcLOAV46/QlZqFg7NwcyYmdg6bNTaa6lrreN7M79Ha3crT6Y+SWRIJJfuXOJg2UFWJa7iVustpkZM5fcXfk9uWi6tTa20OlqZHjmdT2o+obqjmsenPs5zkc/pBWvyivOYGT2T7BnZpMemY+uwsTJxZb9zkFecR429hk1zN2HvtHOm5ox+Ht3B9amHnsIaan3g8/v0Jbt3xUi5WzroBzuHrhfPzX+u12qToRrsKs3hcNbS9cz/4/lH3ffuwZ33ZknCEnLScvTnJ4ROoKqxiv8w9z/wnZnf0ecB8kvy6e7p5kDZAbocXQQGBPKNxG9Q1VLFlJApfC/1e3S3d3Op/hL7L+3nvfL3aO1q5ZvTv0np7VImhE4gJiwGcJZsPFR+iFWJqzAajGyet5nunm6mmKcwJXQKTa1NPJ3wNO0t7dxqu0XB7QKWBC7hSNURslKyqLJVYbVY2TBnA3nFeRy5eoSLty7qa/g9i9t769jc6/7NwWZ9Avznx39OdUM1AS0B/OPKfyQkJETGxYUYBg92pqxRNNBVWlFNEb8v+D1rrWuJi4vz+nzfYuCnrp3ietN1Pq3+lJy0HP3uwj3mvipxFSeqTpCTlsPGuRu52nAVU5CJjLgMosOiudl8kzUpazhedZz06ensv76foNAgrly5whrLGnKTcukx9PBp1ac4cHCw/CDfSvoWMWExWC1WokKjCA4I5q/i/4rVU1fz4fkP2XtmL2seWoPNbmPqhKnERsXScqeFtZPW6sNJYUFhvP3F23q94rToNIprizEoA5nJmWSlZpEclcxS61I9T1HfHDVnas4A6PMde4r38F7Jezw9/WnWpq7FYrEAMi4uxHCQADDC9hTv4TdFv0F1Kr6R9o1+q4D2FO+horFCH9IBsHXYKKwp1Hfeur8+M2YmwYZgZsXM4pnZz1BUU8RrZ1+jrbuNrp4uJpkmcaftDkcrjmIIMHCz+SZ/uPQHauw1JEUmMTV2KjGRMZTfKqe+qZ5Lty+xPGE566asI6g7iPr6eoLbg4kKjGLv5b20NLRwuPgwqdGpLJm+hAatgT/d/hProtZx6/YtDpUfYs1DawDIiMvgZvNNvj/7+wQZgtg4dyOvHH+FutY6JoVN4mjFUVInphIUEMSp6lMDdt7uIbBXT72KQ3OQOycX1a5Ym7SWJalL9KEfGRcX4v5JABhhG+duBA3WTlmL3W7vVwy+74YvgG0LtmEONuvPed5deA5PvfThSxTXFtPQ3kCcOY6tGVs5de0UQQFBWEIsaJrG4fLDZM/IZtO8TZyoOsEzc5+h0lbJvov7iIqMIiQwBEuAhfaudm633UZTGsHGYJYkL6GiqYLKrkqqe6o5XHmYpx9+mszkTKZETOFm800y4jKYEDaBvOI8ffgnPjxeX9+/ed5mXjv7GrNiZtHV08U12zXKG8qxWqxsX7ydDXM29DoX7mEdz6WgiaGJvPDoC1gsll7BU8bFhbh/EgBGmLujam5uprm5mZaWFkwmU7/nvX3P3dg6bPq6/czkTF4/9zr5Jfk8nvQ4VxuvkjYxjZwZOcyYOIMPyj7QO+b/evq/svvCbr6V9C3+cPkPrLCuINIYySe3PiE0PJRJYZPITcvld0W/wxxkZlrENCabJqOUYsOcDWho5F/OJzIkkvUz1zMpbBLLrMt47exr5JfkMytmlp6pMycth63vb+V41XGeX/i883tL8kmdmIqG1mso6M3zb3K86jgGZeCXj/8Sm81Ga2sr4eHhvc6ZEGJ4SAAYJWazma6uLmw2G0opwsLCvB53L5Ob2xZso7allorGCo5XHaeisYLM5Exq7DUkRCRgtVixddg4WnGUwppCfWJVKcXyhOXEmePInZ1LSFAIjh4HG2ZvINgQzJKEJXxa/Sn2DjtRxijSYtIouVzC/rL9GA1GzMFmimqKSIpKQkPD3mnn0+pP2TxvM8lRyf2GZbYt2KanfMhKzQJgmXVZv3F8fegrNoPGxkba2toIDw9/4Ov5CuErEgBGiVKKqKgo6uvraWxsxOFweO3Y7mVyMz0unU2PbOL1c6/zmPUxALJSs/j8xud6zVyAN86/oecFOlNzhpy0HA6VHaKisYLJ5sl0ObrYXbybzORMDpcfZlLYJMrqy/Tsoe9+8S4xYTGsSlxFVEgUz8x+Ri/N2N7dzuHyw/rEr7eyhJ67lq82XKWutU6v4AXok8LfTvk25iAzOdYc2traiIiIGDebvYTwBZ8EAKXU94B/BmYCCzVNG5NJ/u91KaK7Rq3NZqO5uRmHw4HFYum1sexeJzcPXTlEfkk+kcZIrjdd58qdK3R0d1DcWEyACuDp2U9j67ChUHxU+REAz855lorGCn0Xb0ZcBtkzsgkPDiczOZPallp6tB49e+jF2xfRNI1jFcf0TVovr3gZgO6ebnJm5JAyIYXF0xbrQzqeNY3T49KdCetuXdRTPnhumNM3hLkqejkcDiIjIwkNDb3X/xIhxD3w1R3ABZwFZnb66OcPi6+zFFEpRWRkJAEBAdjtdpRS+tJG9+u417/3XSrpjXsJZkN7A4U1hUwMnUhIYAiHrx4myBCk5xF6IvkJCmsKmWSaxP8o/B8kRSWx5qE1TDJNYtuCbRTcLODti29jDDQy2TSZyabJBKgAmjqb9Aykj0x+BFuHTd+k5d7lmxadxoGyA9S21LK7eDf2Tju1LbW9lramx6WzbcE29l3a16++8ca5G8EB66ato6enRxK8CTFKfBIANE27BAyYC3+suJ+liBERzk3Udrsdg8HQb6jjbsHFffcxM3ompfWlZKVm0aP1UN1UjTXCSs6MHJZbl3O04ijbMrYRQAArrCu40XyDLkcXtS21TAidwITQCewo2EGVrYqjFUdZnbha34B2puaMnnHUarFyQjuhb9LyvMJ/5fgr5Jfks+mRTWyZv4UORwcVjRVsmruJrp4utr6/lW0LtpFgSaCxo5H8knyiw6L19zXDMoMfp/8Yg8HAhAkTHvhC7kL4C/lLuw/3uxQxIiKC7u5uPbmZZ8d3t+DiDhDuHEDJUcnMjJ5Jjb2GxKhEAgMC+ajiIz1pW1hQGPNi53Gh7gKPTH6E4rpijlw9ginIRG1LLQvjF7I6cTXGQKO+yaxvxtG+qRvcASorJYv27naWJizl0q1LnK44TfrkdO603eHCrQsU1hQCzrxFffc8uFdHGY1GoqKixlWKByF8bcQCgFLqKBDr5amfapqWfw+vswXYApCQ0D/z5lhnsVioq6ujpaXF61DQQNwd6DLrsl4rbxyaQy8C85MlPwEgOiya1869xpOpT9LY3sit1lvUttSSmZyJOdjMwSsHmRYxjf+48D9youoEadFp5LyZw+Z5m/vtU/Bsk0EZsHXYKL1TyuHyw8yKmdUrb1F1UzVzYuZgDjb3mpTeOHcj82Ln0djYSGtrK2FhYf3mQoQQI2/EAoCmaauH6XV2AbvAWRR+OF7TnxgMBkJDQ2ltbSUiImLATtBbttG+E62Ank7BoTl4ZvYz+ucxYTGU3Cmhq8eZmG7RlEWEBIYQGhjKlvlb9D0COWk5rH9zPfkl+SgUe5/di4ZGXnGe/vrgrE9cWl9KfomzCP2W+Vu8lnJcZl3GiaoTeiGb9Lh0PatnW1sbZrNZHw6723sWQgwvGQLyA0ajkdbWVrq7u3tlDPXkOSfg7vg9C8b3LaOYOzeXvOI8Km2VnKw6ye3W27xX+h5rUtYw2TQZgFdPvcoLi15g57qv5uKLaoqIDIkkIy6DBEtCr1xFBmUgrziPjXM36iksMpMzudp4lWmWafzsTz/T01R43i14FnEHsNlsQ1rmKfl+hBhZvloGuh74NyAGOKCUOqtpWqYv2nI/husK1Z0l1OFwDBgAvI2/uwvGu5/z7DDdWTWvNlzlWtM1FsYvZHnCcqKMUbx66tVeV+2e9hTvoejLIn3lz/8++79pbG9kW8Y2iuuK9ayenkNQJ6pOcLXhKvvL9tPc2UxXT9eA56OpqUnf3Xu3Nf6S70eIkeWrVUB7gTFfUWy4r1AHGwP3Niew1LqUk1Un9c89O8y3LrzFCusKYs2x7CvZx6yYWUSFRnH+1nn9qj0pKqnfa/Yd8//nj/9ZLxdZ11pHZnJmv/mAnLQc9l3eR3dPt34H4E1rayt2ux2TyTSk3b2S70eIkSVDQPdhuK5Qu7u7gd71Agbj7hhf+vAlPXeOe07AraG9geNVx7FarL3uEnYU7CAkMIS5k+b2S8b2x0t/ZPe53Wyet1nveGdGOwvGLJyykIu3LpKVmtVvPgCcQSDBksCe4j1e29zZ2YnNZsNoNA445i+EGF0SAO7DcF2hdnZ2EhAQMOD6d29DTUU1RVy8fZGuHueafuh9R+KZUdRzfsBitHCo/BBJUUn9Xnv3ud365i13Z77MuowerYfbrbf1jV9Hrh6hvKGcl1e8TEVjhR40TlSd8HpHVHCjgLc+e4usGVksm7lMVvsI4SckAPgBd8H4gTr6nx//uV4D2P31PcV7OFh2kBXWFfqkrruS2Mzomfpkrefx7nmDpKgkfe+Ae67A3mln87zNRBoj0dB468Jb+tCPuwaxxWjB3mlnftx8Xj/3OslRyXpOIICXV7yMQRmobanlpQ9fYsOcDcyLncdbn79F3vk8ekJ6WDFrxWifXiHEACQA+Njn1z7n7b+8zcLkhey5vKdfR7+zYKe+1NJzqMldPN5zff3J6pN6Z5xfko+9067fBXgmXjtUdojti7ezeNpi8kvyyYjLoMPRgdViZWLYRH59+tdkpWRRWFNInDmO/WX7yZ6Rja3Dxq6iXb0mn6tsVQD6sFFecR67i3fryeGmh0xnTdIaeoJ62Jguk7lC+BMJAD721rm3+P2F33Ol4wr5ZflkpWTpWTvT49L1XD8pE1L65c/fumArOwt28sb5N4D+m8PcG8IAcufmAnCo7BC7inbpHfqZL88QZ47jzJfOtA/2Tjs/WfITunq6yCKL7LTsXoXpPYeVwLkkNTkqGavFqrfB3mnHEmwhOzGbtrY2Hk16lJVzVw45v5EQYnRIAPCxpx5+CtWhWDJzCUkTk/RO26E5SI9LZ8OcDTg0h15k3XMvgHt9/vKE5ZTWl/LyipfJnZurDyNVNlaSXZ/dK/f+9sXbyZ6RTUVjBakTUlmduFpfyum+ws+ekc31pusU1hSSOjG1V4rnisYKXjn+Cpvnbe5Vrxi+mhPZkbWjVz5/93JPWdcvhH+RAOBjj8Q9QkJIAjExMax/eL2+i9ezHKR7J+4y6zI9/cKb59+korGCrJQsJoZN1MfkAX2SNnVCqj7WnxadRmZyJqnRqXowWZywmJNVJ7FarORk5vTavftp9adkxGX0W+HkOVHsLkpv77QTFBDE1ve3kpmcyanSU6xJWsOjSY/2Wusv6/qF8C8SAHzEPeH7/dnfJ07F0dbWRlBQkNeVRZ5XzoA+Dr86cTVLE5Zy6MqhXjV23ROz7rH6pdal7CzYqW/iyojL4FeZv9Lz8AcGBOpX9e6rfc/du56T05vnbQbQ/3UXcT9acZQLNRdoutNEbUstKlixcu7KXu9D1vUL4V8kAPiIZ6f+T3/1T7S0tBAWFkbxreJ+K4G8XTnr8wDvb9UDQnpcOn+89EcUiucXPs9jiY9xsuokp6pPERgQyOrE1QQGBJJXnKeXcty+eDtf3PqC/WX7USg+KPtAn1h2p5xwrxQC+FXmr/qldsidk0tgZyDppnSUQfFu1bukJKSM/EkUQtwXCQBejEYSMs9OPTw8nLa2NpqamryOk/e9cvZ8HBUSxeNJj9Pp6ORMzRl9iCZnRg6vn32disYKFk1ZxIyJM/Sx/o1zN7KjYIceOH40/0cYAgzEmmPZUbhDX8EDzuGk+XHzvaaNAPhL9V94p/Ad1iSv4UDlAY7cOMLaGWvZtmDbiJw3IcTwkQDgxWhMVvbt1MPDw2lqauKZNGcahaGOk9fYa7jTdocPr35IsCFYH5pxd+ZZKVk8kfJEr7F++KqSmMVoIScth5y0HF768CVWWFcwPXK6/vPLG8p5/dzrvLDohV7t7enpobm5mXc+f4fdF3bTY+xh46Mb0YyarPIRYoyQAOCFLyYrTSYTra2tTA+czi8f/+WQ00JPNk2msb2R6NDofp15RlwGM6Nncqr6lJ4ywj2ss8y6TF9d5H5dW4eNhfEL9dTN4Nzc5VlvAKC9vR2bzYbD4WD93PX0mHrY+Ej/egFCCP8mAcALX3RkSikiIiKor6+ntbUVk8nU63l3x++5c9e9YsfWYeu1IQzotXx0R8EOCmsKmRUzi5f/9DLVTdXYO+290kC7awf3vdJ35xeqbKwk71we6xPXkxSeRGBgINHR0cTHx7MkZckInx0hxEiQAOBHQkJCCAkJoampieDg4F6poT3X8b+w6IVem7w8O3I3zyDmzgtkUAZ2F+9mhXUFFqOl1/Gedz2edxnun1t5u5K6O3Xc/PImzy97nkVJiySnjxBjnASAYTCck8aRkZHcunWL27dvY7FYCAsLA5wds3sPgHuC1XO/wGDcV/FLEpawpWNLv7sFz2Og9xxI7pxcVIdifvR88rvzebfyXeLj4lmcvPi+3qcQwvckAAyD4Zo09twbMD1kOo2Njfry0Dkxc8jTvirQ7k7tUGmr7Jf4bbD2ue8W+qZl8DzGHVRyZ+diNVp5YcELmEwm0qanEVkY2StVxWDvQyaDhfBvEgCGwXBNGnt2wr98/Je0tbXR0tLCZ+Wf8UHZByyIX8CPH/kx65PWs6doD/9W8G+U15eTX+rcmTtQZ+utfX2DVt9CMI9MfoQ7d+7Q2dlJVFQUoaGhzLfMx3ze3CtVxWDvw50yWoKAEP5JAsAwGK5JY89OWCnFZdtlZ+1fzcBvLvyGvw38W37yjZ/Q3d1N1rQsAtoDSI9LJyUkhZzEHFpbWwkNDe03Nu/ZPnfRl6zUrF6FYjyP0TSNhoYGzn95ngPVB9g4fyPpoQNvSvP2PsobyqlorCCvOE8CgBB+Smmadvej/MSCBQu0goICXzdj1LhTNWyZv6VXFk5N0+ju7tY/urq66OzspKenh6CgID6p+4R/P//vesI2Tzlv5pBfkk/2jGz2Pbuv38/UNI2TpSd578J7NAc0s7N4Jy8seqFXQrihOFNz5q5DU0KIUeN1xYbcAfixvsMybkopgoKC+hWQb29vp76+nrcL3ia/8quEbZ765vLxpGkajY2NvHfhPX574bf83eK/Y8v8LXcd8/dG9gQI4f8kAPixe+1EQ0JCCAwMZP3M9XQYO3p18p4Ts/ue3ddvEtjhcNDQ0EBnZyffmfcdesw9bJizgTfOv8FHlR/p7RFCPDgkAHgxVlextLW10d3dzbpZ60idlsqe4j1YLdZ+q3x6fa5BakQqTU1NAERFRREf/9UyT/cmsoy4DJ+9LyHEyJAA4MVYLFyiaRpNTU0EBQVx2XaZf/nkX/Tykp6ZPxdPW8yLh19kacJSVJdi3bR12Gw2jEYjkZGRGAyGXq/rWVxeCPFgkQDghb8XLvF2h9LS0oLD4SAqKorf/+n35Jd8VV5yZ8FOdhXtYvvi7fz7mX/nZsNNAloCeGHRCwQFBREeHk5ISIjXnyVj+UI8uCQAeDHSnd79DjF5u0Mpri1m79m9PJXxFN+f/X3QoNvRzX879d/42/S/5cV5L9LS0MLpstOssK4ge1Y2EydOxGg0fu12jdWhMiGEkwQAH7jfISZvdyj7yvZR+GUhhiIDzy96nrXxa9lVuIv1CesJ6Azgx0t/zN6yvVS1V/HN2d9k6Yyld23X3Tr4+3kfEjyE8D0JAD4w0BBTUU0ROwt26rl6BuoYvd2h2LpsfHz7Y1KnpGKxWDhYeJD3q99n0bRFJJgTiI2N5fDHhzlQeYBAYyB/k/E3d22Xu4N3Zx7t21nfz1DZWJxnEeJBIwHABwYaYnKnZHZX5LqXjtFzstZkMpG7IJdmmnslfhtsD4C3drk7ds/Mo30rk33dztvf51mEGA9kJ7AfOVNzhh0FO+56BzDawyeD7eqVoRwhxgTZCezv0uPSe+X2H6hzHe3hk8Gu9GUoR4ixSwKAHxuocx3p4ZN7uaofrC1ydyCEf5MA4McG6lzdV+R90zkMl3u5qpe7AyHGLgkAfuxuk6wj1cH2DTxf90peJnqF8G8SAMYwbx3scAy79A08XzfQyC5iIfybBIAxwlvH7q2DHaizvp/AMJxX8jIvIIT/kAAwRgz1Knyg4Rt7p51dRbvu+v3eDOeVvMwLCOE/JACMEUO9Ch9o+Gb74u29SkD6iswLCOE/fLIRTCn1KrAO6ATKgb/WNK3xbt/3oG8EGwlSmlEIwQAbwQJGuxUuR4DZmqbNBUqBf/BROx5oRTVF0vkLIQbkkwCgadqHmqZ1uz49DUz1RTsG4l5ff6bmzJhuh3v4J684b5hbJoR4EPjDHMAPgbd83QhP/jJRORJpo4UQwm3EAoBS6igQ6+Wpn2qalu865qdAN7BnkNfZAmwBSEhIGIGW9ucvHef9tkPW4QshBuOzbKBKqc3AVmCVpmmtQ/kemQQWQoivxX+ygSqlngBeAlYMtfMXQggxvHy1Cui/A+HAEaXUWaXUDh+1Q7j4y8S3EGL0+OQOQNO0h3zxc8XA/GXiWwgxevxhFZC4RyORT8dfJr6FEKNHAsAYNBJX67JiSIjxRwLAGDTQ1bpk2hRC3AsJAGPQQFfrMo4vhLgXEgAeIDKOL4S4FxIAHiAyji+EuBe+2gcghBDCxyQACCHEOCUBQAghxikJAEIIMU5JABBCiHFKAoAQQoxTEgCEEGKckgAghBDjlAQAIYQYpyQACCHEOCUBQAghxikJAA84KfUohBiIJIN7wEmKaCHEQCQAPOAkRbQQYiASAEbZaFftkhTRQoiBSAAYZTIkI4TwFxIARpkMyQgh/IUEgFEmQzJCCH8hy0CFEGKckgAghBDjlAQAIYQYpyQACCHEOCUBQAghxikJAEIIMU5JABgFkpBNCOGPZB/AKJDdv0IIfyQBYBTI7l8hhD+SADAKfL37d7QT0AkhxgYJAA8oz05fhqCEEN5IAHhAeXb6MgQlhPBGAsADyrPT9/UQlBDCP/kkACil/gXIBnqAOmCzpmk3fdGWB5V0+kKIu/HVPoBXNU2bq2naPGA/8DMftUMIIcYtnwQATdOaPD41AZov2iGEEOOZz+YAlFL/GfgBYANW+qodQggxXilNG5mLb6XUUSDWy1M/1TQt3+O4fwBCNE17eYDX2QJsAUhISMioqqoaieYKIcSDTHn94kgFgKFSSiUABzVNm323YxcsWKAVFBSMQquEEOKB4jUA+GQOQCmV4vFpNnDZF+0QQojxzFdzAP9FKTUD5zLQKmCbj9ohhBDjls+HgO6FUuoWzoARDdz2cXP68sc2gX+2yx/bBP7ZLn9sE0i77oU/tOm2pmlP9P3imAoAbkqpAk3TFvi6HZ78sU3gn+3yxzaBf7bLH9sE0q574Y9tcpOCMEIIMU5JABBCiHFqrAaAXb5ugBf+2Cbwz3b5Y5vAP9vlj20Cade98Mc2AWN0DkAIIcT9G6t3AEIIIe7TmAgASqlXlVKXlVLFSqm9SqnIAY57QilVopS6opT6+xFu0/eUUheVUj1KqQFn+JVSlUqp80qps0qpEd/GfA/tGs1zNUEpdUQpVeb6N2qA4xyu83RWKfXeCLZn0PeulDIqpd5yPf+ZUmr6SLXlHtq0WSl1y+P8PDcKbfpfSqk6pdSFAZ5XSqn/19XmYqXU/JFu0xDb9ZhSyuZxrkY827BSappS6k9KqS9cf3//p5djfHK+BqVpmt9/AI8Dga7HvwB+4eUYA1AOJAHBwDng4RFs00xgBvAxsGCQ4yqB6FE8V3dtlw/O1f8D/L3r8d97+/9zPWcfhfNz1/cO/B2ww/X4WeAtP2jTZuC/j9bvketnLgfmAxcGeH4N8AHONAOLgM/8pF2PAftH+VzFAfNdj8OBUi//hz45X4N9jIk7AE3TPtQ0rdv16WlgqpfDFgJXNE27qmlaJ/AmzjQTI9WmS5qmlYzU639dQ2zXqJ4r12u/7nr8OpAzgj/rboby3j3b+y6wSinlNZfKKLZp1Gma9glQP8gh2cBuzek0EKmUivODdo06TdNqNE0rcj1uBi4BU/oc5pPzNZgxEQD6+CHOKNrXFOCax+fX6f8f4Asa8KFSqtCV2dQfjPa5mqxpWo3r8ZfA5AGOC1FKFSilTiulckaoLUN57/oxrgsPGzBxhNoz1DYBfNc1dPCuUmraCLZnqPz1bw5gsVLqnFLqA6XUrNH8wa4hw3Tgsz5P+d358puawENJH62U+inQDezxlzYNwVJN024opSYBR5RSl11XML5u17AarE2en2iapimlBlp6ZnWdqyTgI6XUeU3Tyoe7rWPU+8AbmqZ1KKW24rxD+aaP2+SvinD+LtmVUmuAfUDK4N8yPJRSZuAPwP+l9S585Zf8JgBomrZ6sOeVUpuBLGCV5hpQ6+MG4HlVNNX1tRFr0xBf44br3zql1F6ct/v3FQCGoV2jeq6UUrVKqThN02pct7x1A7yG+1xdVUp9jPMqargDwFDeu/uY60qpQMAC3BnmdtxTmzRN8/z5v8M5r+Jrw/57NBw8O15N0w4qpX6jlIrWNG1E8/EopYJwdv57NE37o5dD/O58jYkhIKXUE8BLwJOaprUOcNjnQIpSKlEpFYxz8m7EVpIMhVLKpJQKdz/GOZntdeXCKBvtc/UesMn1eBPQ7y5FKRWllDK6HkcD3wC+GIG2DOW9e7b3KeCjAS46Rq1NfcaKn8Q5xuxr7wE/cK1uWQTYPIb6fEYpFeues1FKLcTZz41kAMf18/4ncEnTtF8PcJj/nS9fz0IP5QO4gnPs7Kzrw71CIx5nMRnPWfZSnFeNPx3hNq3HOYbXAdQCh/u2CeeqjnOuj4sj3aahtssH52oicAwoA44CE1xfXwD8zvV4CXDeda7OAz8awfb0e+/Az3FeYACEAO+4fu/+AiSNwv/b3dr0r67foXPAn4C0UWjTG0AN0OX6nfoRztTt21zPK+D/c7X5PIOshhvldv0fHufqNLBkFNq0FOd8X7FHP7XGH87XYB+yE1gIIcapMTEEJIQQYvhJABBCiHFKAoAQQoxTEgCEEGKckgAghBDjlAQAIQahvspQek4pVaSUWnKX4yOVUn83Wu0T4n7IMlAhBqGUsmuaZnY9zgT+UdO0FYMcPx1nJsrZo9REIb42uQMQYugigAb3J0qp7Uqpz10J2l5xffm/AMmuu4ZXlVJmpdQx193DeaWUz7N8CuHmN7mAhPBToUqpszh3B8fhSsCmlHocZ4KxhTh3eL6nlFqOs97BbE3T5rmOCwTWa5rW5EpxcVop9Z4mt97CD0gAEGJwbR6d+WJgt1JqNs68To8DZ1zHmXEGhOo+36+A/9sVHHpwpv+djDMtthA+JQFAiCHSNO2U6yo+BmfH/q+apu30PEb1Lx+Z6zo+Q9O0LqVUJc67CSF8TuYAhBgipVQazvKNd4DDwA9d+d9RSk1x1XxoxlkS0M0C1Lk6/5WAdZSbLcSA5A5AiMG55wDAedW/SdM0B84qbzOBU67Mw3Zgo6Zp5UqpPytnwfIPcNawfl8pdR4oAC6P+jsQYgCyDFQIIcYpGQISQohxSgKAEEKMUxIAhBBinJIAIIQQ45QEACGEGKckAAghxDglAUAIIcYpCQBCCDFO/f8dsy3vLuZwowAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = afrs[afrs[\"label\"]==1][\"chrombpnet.encsr637xsc.variantscore.logfc\"]\n",
+    "data[\"Beta\"] = afrs[afrs[\"label\"]==1][\"beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"green\", rasterized=True)\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"green\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_pearsonr.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.5245842317293431, 4.963181862567866e-55)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Pearsonr = 0.52')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = dataf[dataf[\"label\"]==1][\"enformer.encsr000emt.variantscore.sad\"]\n",
+    "data[\"Beta\"] = dataf[dataf[\"label\"]==1][\"beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"Red\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#ffcccb\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1e-06\n",
+      "1e-09\n",
+      "1e-12\n",
+      "1e-15\n",
+      "1e-18\n",
+      "2.5800991659088344e-19\n",
+      "4.990607165261473e-19\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "\n",
+    "\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "enformer = []\n",
+    "enformer_unsigned = []\n",
+    "enformer_sar = []\n",
+    "enformer_unsigned_sar = []\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(6,25,3):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "    print(threshold)\n",
+    "    dataf = afrs[afrs[\"pval\"]<threshold]\n",
+    "\n",
+    "      \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr000emt.variantscore.logfc\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr000emt.variantscore.logfc\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.logfc\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.logfc\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer_recomputed\"], dataf[\"beta\"])\n",
+    "    enformer.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer_recomputed\"]), abs(dataf[\"beta\"]))\n",
+    "    enformer_unsigned.append(pear[0])\n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer.encsr000emt.variantscore.sar\"], dataf[\"beta\"])\n",
+    "    enformer_sar.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer.encsr000emt.variantscore.sar\"]), abs(dataf[\"beta\"]))\n",
+    "    enformer_unsigned_sar.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.6983690193932983, 0.7189548152927936, 0.7295209713273648, 0.7373501709489557, 0.7442226433050187, 0.7469131420307702, 0.7446218027253718]\n",
+      "[0.6743140122287863, 0.6955496292567133, 0.7058122936418465, 0.7135073170669227, 0.7199681600724328, 0.7236989890628697, 0.7204562995516648]\n",
+      "[0.6827702620416447, 0.7050972038615901, 0.7177419947236471, 0.7275573594743634, 0.7347780426308606, 0.7380142624179875, 0.7353225305932436]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    print(chrombpnet[key])\n",
+    "\n",
+    "\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase (reported) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase (reported) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10pval\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_pearsonr_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1e-06\n",
+      "1e-09\n",
+      "1e-12\n",
+      "1e-15\n",
+      "1e-18\n",
+      "2.5800991659088344e-19\n",
+      "4.990607165261473e-19\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "\n",
+    "\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(6,25,3):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "    print(threshold)\n",
+    "    dataf = afrs[afrs[\"pval\"]<threshold]\n",
+    "\n",
+    "      \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr000emt.variantscore.jsd\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr000emt.variantscore.jsd\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.jsd\"], dataf[\"beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"chrombpnet.encsr637xsc_50m.variantscore.jsd\"]), abs(dataf[\"beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "# ax.scatter(np.arange(6,25,3).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "# ax.scatter(np.arange(6,25,3).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,3).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "# ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "# ax.scatter(np.arange(6,25,3).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_pearsonr_compare_jsd.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (across-ancestry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed', 'in_peaks', 'pval', 'beta',\n",
+       "       'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar', 'enformer_recomputed_x',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta', 'id_hg19', 'enformer_recomputed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf = afrs[afrs[\"label\"]==1]\n",
+    "dataf.columns\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# anscs = dataf.columns[11:-6].tolist()\n",
+    "# anscs = [ans.split(\".\")[1] for ans in anscs]\n",
+    "# anscs = set(anscs)\n",
+    "# print(anscs)\n",
+    "anscs = {'esan', 'luhya', 'encsr637xsc_50m', 'mende', 'yoruba', 'maasai', 'encsr095qnb', 'gambian', 'encsr000emt', 'encsr637xsc'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>esan</th>\n",
+       "      <th>maasai</th>\n",
+       "      <th>yoruba</th>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <th>gambian</th>\n",
+       "      <th>encsr637xsc</th>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <th>luhya</th>\n",
+       "      <th>mende</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ancestry</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>esan</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.962213</td>\n",
+       "      <td>0.970371</td>\n",
+       "      <td>0.928157</td>\n",
+       "      <td>0.946298</td>\n",
+       "      <td>0.965898</td>\n",
+       "      <td>0.949169</td>\n",
+       "      <td>0.933165</td>\n",
+       "      <td>0.968656</td>\n",
+       "      <td>0.969244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>maasai</th>\n",
+       "      <td>0.962213</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.965313</td>\n",
+       "      <td>0.922630</td>\n",
+       "      <td>0.949146</td>\n",
+       "      <td>0.968507</td>\n",
+       "      <td>0.945036</td>\n",
+       "      <td>0.932632</td>\n",
+       "      <td>0.969797</td>\n",
+       "      <td>0.966338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yoruba</th>\n",
+       "      <td>0.970371</td>\n",
+       "      <td>0.965313</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.929201</td>\n",
+       "      <td>0.942193</td>\n",
+       "      <td>0.968802</td>\n",
+       "      <td>0.950110</td>\n",
+       "      <td>0.930489</td>\n",
+       "      <td>0.971073</td>\n",
+       "      <td>0.969247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <td>0.928157</td>\n",
+       "      <td>0.922630</td>\n",
+       "      <td>0.929201</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.934232</td>\n",
+       "      <td>0.932394</td>\n",
+       "      <td>0.960970</td>\n",
+       "      <td>0.958289</td>\n",
+       "      <td>0.931905</td>\n",
+       "      <td>0.934637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <td>0.946298</td>\n",
+       "      <td>0.949146</td>\n",
+       "      <td>0.942193</td>\n",
+       "      <td>0.934232</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.952491</td>\n",
+       "      <td>0.952417</td>\n",
+       "      <td>0.958030</td>\n",
+       "      <td>0.946711</td>\n",
+       "      <td>0.951359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gambian</th>\n",
+       "      <td>0.965898</td>\n",
+       "      <td>0.968507</td>\n",
+       "      <td>0.968802</td>\n",
+       "      <td>0.932394</td>\n",
+       "      <td>0.952491</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.955324</td>\n",
+       "      <td>0.937421</td>\n",
+       "      <td>0.970706</td>\n",
+       "      <td>0.972954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc</th>\n",
+       "      <td>0.949169</td>\n",
+       "      <td>0.945036</td>\n",
+       "      <td>0.950110</td>\n",
+       "      <td>0.960970</td>\n",
+       "      <td>0.952417</td>\n",
+       "      <td>0.955324</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.966690</td>\n",
+       "      <td>0.951238</td>\n",
+       "      <td>0.954590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <td>0.933165</td>\n",
+       "      <td>0.932632</td>\n",
+       "      <td>0.930489</td>\n",
+       "      <td>0.958289</td>\n",
+       "      <td>0.958030</td>\n",
+       "      <td>0.937421</td>\n",
+       "      <td>0.966690</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.937484</td>\n",
+       "      <td>0.940570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>luhya</th>\n",
+       "      <td>0.968656</td>\n",
+       "      <td>0.969797</td>\n",
+       "      <td>0.971073</td>\n",
+       "      <td>0.931905</td>\n",
+       "      <td>0.946711</td>\n",
+       "      <td>0.970706</td>\n",
+       "      <td>0.951238</td>\n",
+       "      <td>0.937484</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.970165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mende</th>\n",
+       "      <td>0.969244</td>\n",
+       "      <td>0.966338</td>\n",
+       "      <td>0.969247</td>\n",
+       "      <td>0.934637</td>\n",
+       "      <td>0.951359</td>\n",
+       "      <td>0.972954</td>\n",
+       "      <td>0.954590</td>\n",
+       "      <td>0.940570</td>\n",
+       "      <td>0.970165</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     esan    maasai    yoruba  encsr095qnb  encsr000emt  \\\n",
+       "ancestry                                                                  \n",
+       "esan             1.000000  0.962213  0.970371     0.928157     0.946298   \n",
+       "maasai           0.962213  1.000000  0.965313     0.922630     0.949146   \n",
+       "yoruba           0.970371  0.965313  1.000000     0.929201     0.942193   \n",
+       "encsr095qnb      0.928157  0.922630  0.929201     1.000000     0.934232   \n",
+       "encsr000emt      0.946298  0.949146  0.942193     0.934232     1.000000   \n",
+       "gambian          0.965898  0.968507  0.968802     0.932394     0.952491   \n",
+       "encsr637xsc      0.949169  0.945036  0.950110     0.960970     0.952417   \n",
+       "encsr637xsc_50m  0.933165  0.932632  0.930489     0.958289     0.958030   \n",
+       "luhya            0.968656  0.969797  0.971073     0.931905     0.946711   \n",
+       "mende            0.969244  0.966338  0.969247     0.934637     0.951359   \n",
+       "\n",
+       "                  gambian  encsr637xsc  encsr637xsc_50m     luhya     mende  \n",
+       "ancestry                                                                     \n",
+       "esan             0.965898     0.949169         0.933165  0.968656  0.969244  \n",
+       "maasai           0.968507     0.945036         0.932632  0.969797  0.966338  \n",
+       "yoruba           0.968802     0.950110         0.930489  0.971073  0.969247  \n",
+       "encsr095qnb      0.932394     0.960970         0.958289  0.931905  0.934637  \n",
+       "encsr000emt      0.952491     0.952417         0.958030  0.946711  0.951359  \n",
+       "gambian          1.000000     0.955324         0.937421  0.970706  0.972954  \n",
+       "encsr637xsc      0.955324     1.000000         0.966690  0.951238  0.954590  \n",
+       "encsr637xsc_50m  0.937421     0.966690         1.000000  0.937484  0.940570  \n",
+       "luhya            0.970706     0.951238         0.937484  1.000000  0.970165  \n",
+       "mende            0.972954     0.954590         0.940570  0.970165  1.000000  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "ancestries = []\n",
+    "\n",
+    "for ans in anscs:\n",
+    "    if ans==\"eur\":\n",
+    "        continue\n",
+    "    if ans==\"afr\":\n",
+    "        continue\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\"encsr637xsc\", \"encsr000emt\", \"encsr637xsc_50m\", \"encsr000emt.ENCSR095QNB\" ]:\n",
+    "            ancestries.append(ans+title)\n",
+    "    else:\n",
+    "        ancestries.append(ans)\n",
+    "    \n",
+    "abs_logfc_dict = {x: [] for x in ancestries}\n",
+    "abs_logfc_dict['ancestry'] = []\n",
+    "\n",
+    "for outer_ancestry in ancestries:\n",
+    "    abs_logfc_dict['ancestry'].append(outer_ancestry)\n",
+    "    for ancestry in ancestries:\n",
+    "        #print(\"chrombpnet.encsr637xsc.variantscore.logfc\".replace(\"encsr637xsc\", ancestry))\n",
+    "\n",
+    "        abs_logfc_dict[ancestry].append(scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\".replace(\"encsr637xsc\", outer_ancestry)],\n",
+    "                                                 dataf[\"chrombpnet.encsr637xsc.variantscore.logfc\".replace(\"encsr637xsc\", ancestry)])[0])\n",
+    "        \n",
+    "abs_logfc_df = pd.DataFrame(abs_logfc_dict)\n",
+    "abs_logfc_df.set_index('ancestry', inplace=True)\n",
+    "\n",
+    "abs_logfc_df    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Ancestry vs. Ancestry ChromBPNet Absolute LogFC Pearson Correlation')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.clustermap(abs_logfc_df, annot=True, cmap=\"crest\")\n",
+    "g.fig.set_figwidth(12)\n",
+    "g.fig.set_figheight(12)\n",
+    "g.fig.suptitle('Ancestry vs. Ancestry ChromBPNet Absolute LogFC Pearson Correlation', fontsize=18, y=1.05)\n",
+    "\n",
+    "#plt.savefig(\"subfigs/afr_caqtls_ancestry_logfc.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "esan\n",
+      "maasai\n",
+      "yoruba\n",
+      "encsr095qnb\n",
+      "encsr000emt\n",
+      "gambian\n",
+      "encsr637xsc\n",
+      "encsr637xsc_50m\n",
+      "luhya\n",
+      "mende\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>esan</th>\n",
+       "      <th>maasai</th>\n",
+       "      <th>yoruba</th>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <th>gambian</th>\n",
+       "      <th>encsr637xsc</th>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <th>luhya</th>\n",
+       "      <th>mende</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ancestry</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>esan</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.956347</td>\n",
+       "      <td>0.959848</td>\n",
+       "      <td>0.939054</td>\n",
+       "      <td>0.925145</td>\n",
+       "      <td>0.959481</td>\n",
+       "      <td>0.942877</td>\n",
+       "      <td>0.942284</td>\n",
+       "      <td>0.959814</td>\n",
+       "      <td>0.960258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>maasai</th>\n",
+       "      <td>0.956347</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.960483</td>\n",
+       "      <td>0.935312</td>\n",
+       "      <td>0.930455</td>\n",
+       "      <td>0.966666</td>\n",
+       "      <td>0.946207</td>\n",
+       "      <td>0.946024</td>\n",
+       "      <td>0.965104</td>\n",
+       "      <td>0.962058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yoruba</th>\n",
+       "      <td>0.959848</td>\n",
+       "      <td>0.960483</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.943358</td>\n",
+       "      <td>0.931674</td>\n",
+       "      <td>0.963991</td>\n",
+       "      <td>0.950856</td>\n",
+       "      <td>0.946661</td>\n",
+       "      <td>0.966400</td>\n",
+       "      <td>0.964158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr095qnb</th>\n",
+       "      <td>0.939054</td>\n",
+       "      <td>0.935312</td>\n",
+       "      <td>0.943358</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.931924</td>\n",
+       "      <td>0.942130</td>\n",
+       "      <td>0.953984</td>\n",
+       "      <td>0.949909</td>\n",
+       "      <td>0.939205</td>\n",
+       "      <td>0.940732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr000emt</th>\n",
+       "      <td>0.925145</td>\n",
+       "      <td>0.930455</td>\n",
+       "      <td>0.931674</td>\n",
+       "      <td>0.931924</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.936890</td>\n",
+       "      <td>0.934557</td>\n",
+       "      <td>0.940561</td>\n",
+       "      <td>0.927627</td>\n",
+       "      <td>0.934885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gambian</th>\n",
+       "      <td>0.959481</td>\n",
+       "      <td>0.966666</td>\n",
+       "      <td>0.963991</td>\n",
+       "      <td>0.942130</td>\n",
+       "      <td>0.936890</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.950917</td>\n",
+       "      <td>0.950066</td>\n",
+       "      <td>0.966610</td>\n",
+       "      <td>0.968532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc</th>\n",
+       "      <td>0.942877</td>\n",
+       "      <td>0.946207</td>\n",
+       "      <td>0.950856</td>\n",
+       "      <td>0.953984</td>\n",
+       "      <td>0.934557</td>\n",
+       "      <td>0.950917</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.970156</td>\n",
+       "      <td>0.948869</td>\n",
+       "      <td>0.947742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>encsr637xsc_50m</th>\n",
+       "      <td>0.942284</td>\n",
+       "      <td>0.946024</td>\n",
+       "      <td>0.946661</td>\n",
+       "      <td>0.949909</td>\n",
+       "      <td>0.940561</td>\n",
+       "      <td>0.950066</td>\n",
+       "      <td>0.970156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.946535</td>\n",
+       "      <td>0.946391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>luhya</th>\n",
+       "      <td>0.959814</td>\n",
+       "      <td>0.965104</td>\n",
+       "      <td>0.966400</td>\n",
+       "      <td>0.939205</td>\n",
+       "      <td>0.927627</td>\n",
+       "      <td>0.966610</td>\n",
+       "      <td>0.948869</td>\n",
+       "      <td>0.946535</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.964346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mende</th>\n",
+       "      <td>0.960258</td>\n",
+       "      <td>0.962058</td>\n",
+       "      <td>0.964158</td>\n",
+       "      <td>0.940732</td>\n",
+       "      <td>0.934885</td>\n",
+       "      <td>0.968532</td>\n",
+       "      <td>0.947742</td>\n",
+       "      <td>0.946391</td>\n",
+       "      <td>0.964346</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     esan    maasai    yoruba  encsr095qnb  encsr000emt  \\\n",
+       "ancestry                                                                  \n",
+       "esan             1.000000  0.956347  0.959848     0.939054     0.925145   \n",
+       "maasai           0.956347  1.000000  0.960483     0.935312     0.930455   \n",
+       "yoruba           0.959848  0.960483  1.000000     0.943358     0.931674   \n",
+       "encsr095qnb      0.939054  0.935312  0.943358     1.000000     0.931924   \n",
+       "encsr000emt      0.925145  0.930455  0.931674     0.931924     1.000000   \n",
+       "gambian          0.959481  0.966666  0.963991     0.942130     0.936890   \n",
+       "encsr637xsc      0.942877  0.946207  0.950856     0.953984     0.934557   \n",
+       "encsr637xsc_50m  0.942284  0.946024  0.946661     0.949909     0.940561   \n",
+       "luhya            0.959814  0.965104  0.966400     0.939205     0.927627   \n",
+       "mende            0.960258  0.962058  0.964158     0.940732     0.934885   \n",
+       "\n",
+       "                  gambian  encsr637xsc  encsr637xsc_50m     luhya     mende  \n",
+       "ancestry                                                                     \n",
+       "esan             0.959481     0.942877         0.942284  0.959814  0.960258  \n",
+       "maasai           0.966666     0.946207         0.946024  0.965104  0.962058  \n",
+       "yoruba           0.963991     0.950856         0.946661  0.966400  0.964158  \n",
+       "encsr095qnb      0.942130     0.953984         0.949909  0.939205  0.940732  \n",
+       "encsr000emt      0.936890     0.934557         0.940561  0.927627  0.934885  \n",
+       "gambian          1.000000     0.950917         0.950066  0.966610  0.968532  \n",
+       "encsr637xsc      0.950917     1.000000         0.970156  0.948869  0.947742  \n",
+       "encsr637xsc_50m  0.950066     0.970156         1.000000  0.946535  0.946391  \n",
+       "luhya            0.966610     0.948869         0.946535  1.000000  0.964346  \n",
+       "mende            0.968532     0.947742         0.946391  0.964346  1.000000  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "ancestries = []\n",
+    "\n",
+    "for ans in anscs:\n",
+    "    if ans==\"eur\":\n",
+    "        continue\n",
+    "    if ans==\"afr\":\n",
+    "        continue\n",
+    "    if ans == \"GM12878\":\n",
+    "        for title in [\"encsr637xsc\", \"encsr000emt\", \"encsr637xsc_50m\", \"encsr000emt.ENCSR095QNB\" ]:\n",
+    "            ancestries.append(ans+title)\n",
+    "    else:\n",
+    "        ancestries.append(ans)\n",
+    "    \n",
+    "abs_logfc_dict = {x: [] for x in ancestries}\n",
+    "abs_logfc_dict['ancestry'] = []\n",
+    "\n",
+    "for outer_ancestry in ancestries:\n",
+    "    print(outer_ancestry)\n",
+    "    abs_logfc_dict['ancestry'].append(outer_ancestry)\n",
+    "    for ancestry in ancestries:\n",
+    "        abs_logfc_dict[ancestry].append(scipy.stats.pearsonr(dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\".replace(\"encsr637xsc\", outer_ancestry)],\n",
+    "                                                 dataf[\"chrombpnet.encsr637xsc.variantscore.jsd\".replace(\"encsr637xsc\", ancestry)])[0])\n",
+    "        \n",
+    "abs_logfc_df = pd.DataFrame(abs_logfc_dict)\n",
+    "abs_logfc_df.set_index('ancestry', inplace=True)\n",
+    "\n",
+    "abs_logfc_df    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.05, 'Ancestry vs. Ancestry ChromBPNet Absolute JSD Pearson Correlation')"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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XHyD8nJovpeq/QrwDv7YAzhtJ6gG8Hdtm8/pT/8bMhh02ygMtP8j0/+Cd9bklbNotfl0mH26hzrlFwHzg+rAd3Ci8g/2nwsp94D8fD2w1r+/1z82sX/3CiGRmI4FewDvV7em36Sd4O+RbDvLSg7Vpy7DnPfEOij47TDX64h0gFXBgm07wy7T1/z+IdyD0ClBk3hCpXzOzlMO8R21FzrkdtSc6b8CKZ4EvmX/PEN6Ztm4cWSJb/cVf3/rAkX2m1epaH7v6/xfXMa96Wrda09fULoh3RbeuedXT66rPCufcO/7f68653+BdpexK3aNvfUJNojQK6OycO/lQiaV/QPRHYKSZfekgxY54/3C0vsA2k1fHtCX+/+p2+SvePvBvQLGZTTGz/wtbF+HQ7Zzn16F2OzeUvnj77aUc+HlXby9H9Xk750LOuQeccyfhnQQZi9dlKwN4ysxG1HOR9T04X0vNOjoa6Omc6+Fqul/Va10zs2Tz7plbj9d9batftjoRqeum/7q284ZcRyL2Qc652t/TB3M0+72j2V8d7HsYYJUL657s+8L7A/Pud/yJmS3Hu89qm//66pFsv8hgDV2BTc657XXMW4z3edY+pqrP94Q0It3jE0Bm1hU4E++L7mA7oBs4cAjh2jujiMXWtxr+/9/j9Smvy/6diHPuJ2b2GN6l45F4Q+z+0Mx+45y76yCvr8vOuiY65zaY2Zt4fdF/hJcEjgJ+5w7dbzjcU3if2VnAO3hXf/YBEaNkOee+Yma/xbu3YCTwA+BeM/uucy7iNzDqofog7X5q7gkJd4OZ3VVHLAdr09rtWd094VAM74vkukOUWQTgnFvhJ3tn+3+j8fp7/9zMRjnnDtqfupY629P3KF4XhJvw1rNb8LqcHcmQ7dW/KVP7TOWRONLPFA5d/2Pxnoead0TbrHPuE/NuMj6rjtlbD3Hm9lB+jden/kEzq+u+tnrtH47S0W4zh+Wc2+afmBlJTVL4MN76fYFz7uOjrXTtt6pr4sGuIByC+cs6n4OvL3Ud2NaLf9XyHbxk8zO8bfSreN25jlT1b/ksO8Ly5YdZR+u7rj2DN2rho3j3lm7D+8yqh3Kv64TxAdt5Q64jdSQO1Q65zTvnysxsHd7Vy0T3Be/JOoRD7ffqmncs9gd/wOvC/BxeklqI191xCN7+qLFP9B9qny1RpMQnmL6KtyO5Fa8Pbm2/xOsC8sejWPZKvC/Qkw9TrvqHAvcd6YGT84bZ/gvwFzNLwLtE/SMz+73freZwB+aH8yheYnUpNQe8R3J1oNozePf63GRmH+J1lXvb1fGbJf4VokXAb80sHe8s86/M7JFa3ZIOy79KciVe95G6bsgehHez8sUc+Q3q4ZbjHRCdhHfz6sGswDuDPtsdwRDhzrldeN0rpwCYN5rcG3h9+L9dXewo6lu9/Fwz+xS4xcz+g3cz7SvOGx3qcD7Eu4n4UjNrGXbGtDFUnwnsz4GjYfWrVaaxNMEbqOKYcM6Vmncz/cP4I7LVUt/9Q2NuM3058OrnAe3iH3xO9/8w78c35+F1+7qQmitxdXWp64N3IHa4di72l51Za72u6yrAoT6jFXhdlNc75+q6otUQZvv/s470BX5X6svwuonVOeLYUTjidc3fV1+Ed8/GN2rNq9399LAaaR2pr5fxErgbOfgAH+HC91e1T1gdq/1VvY8X6nAjMMM5d034RP8qb231/d5ZDZxnZul19EDoh3clTb8XdZxQV7eA8bthjce7z+HfzrkXa//hdREaeDRdyfwv36l497Ac8EUQdj/Hp3gH/t8wswO+pP3L0pn+4zTzh4IOe59KarqcVF+irj7YzuTovIF3w+HX8Q7GPnTOLT3SFztv9K+pePdPXY/XJSNixBq/X3FcrdftwPuCa4E36ARm1tS8YVM7HcFbX4N3qf8fB2nPX+GdRTvYvVuHUz3CzIMWNnR1WEzVbfoU3j6jrlGNMLPw7iJ1daWsvg8qvP1CQEbYe9TXv/AOVP+C99n++0he5PdVvxevi8JzdXXB8/v3P2jH/l6tt/H6rN8R/r7+4zvwPpO3j/F7HpR5Q9An4R2QHUt/w+uG9HMOTKqOeP/gC/nTj3Tb/yLbzPcscgj3bLyrnMuqk4aDrN9L8bpGZQL4J2s+wuuOOSBseUbND25OOkwc1Vfsa+9rf1BH2UPtH6uvgj5odQyjHb7t1oeZnWxm7Q8y+1L//5KDzK+9rEy8kb5SgQf8eyKOhfqsa9Vn6a1WmfZ4vRCOWCOuI/X1G7wr978xszrv6zKzc82sOoGYjJco/DD8e9r/TL6K16X5cF3fD6e++4O67OPAdksicsTQavU9lngF77vv7lrLPx/vJOpkV/dIuRKDdMUneM7FGzb4UFcyXsK7J+MWYO5RvMfteDvrqWb2JN4BUyLecM1r8X4fwJk3pPF7wOd+N7bFeAf/PfCSh3vwbjQ+E3jUzF7C694QAobifdF84pyr7vKwBO/m+m+Z2U68q1mFzrnqGzsPyTm3z6/HT/xJPz6K2J/EO0v8e7yzkq/Umn8T3oHTJLyrY3vwunmNA54P61qQhZfYfYB3b8qh3IJ3kFZnFwDn3E4zm4p39SLLeTfbHzHn3Bzzhiu9C5hvZs/hXQ3pinfW/BRgh3PuRTN7HLjdvN+Ieh3vLFc23o3RPag5E/2Wme3Au9F+A17f//H4I+CEvf1svDOsfzWzj/C+vN47xI3ztT2NdxXuBrzkss7fEzlI3I+ZWUe8HyldaWbP4K1jcXjJ1Jfxht6uM9E7Ws65HX53y0eAT8z//Qi8z6cH3shMJQd5+Rc1xMxu8B83xzuLexveevqTg77qKDjndpvZT6lp721h8+qzfwBvPbkd+JvfdW4P3r6hrnuf4IttM02AmWb2LF5i/A28/Vv473j9y0+I3sI78EsErvbLPxVW7jt42/hMM6seqvgivP3BM865w62vz+LdL/eomfXBuwJ0HgfeT1DdtWolcI2ZrcK7F6/cOfeac26ueb9fMgFYYGYv4J0Eao+3r70Ab4S2+joHL5l6i5qrqGl4+7SL8YbF/0MdrzvfjycO78TWYLwrPSnAb51zvz2KutSpPuua3xXsLbxukBV434+d8U6WraF+92c01jpSL865LWZ2Ed5okLPM7BW8Ln2leCO1nYc3lPg3/fLLzOu6/SNghv/9UD2cdTJw/SG63h1pneq7P6jLi8DX/fq9g3dP0M2E7XfCzMW7f+peM8vAOxG1xh04uFO1J/BOlt5l3s95zPDr9S287exojiUkWlwMDC2nv2P3h3fGzAEDD1NuGV7ikOg/d4QNjRpWbjx1DPuId+D+D7xhonfjbfxvETb0pl+us19urV9uG16i9BDQ0S/T1S+Th7fzLfcf30/YcJB+2QvwrhxU+vWa7k8fQ60hKA8Sd2e8g+tS6hi29Ag+32Z+DA74Vx3zT8ZLjlb6cZTidZv5AdA8rFyX8Pof4v36++VeOky5a/1yP/afT6DWcJ9hZdfW9b7+Mj7ESy7L8c5O/pGwIUz9cjfiJTSlfjusxes+cXVYmVvxrlps8dt9M16XtzNrLasFXpJe4LfL/nWNQwx1XWsZ//Ff99Oj3GZy/DZb48ezE1iId8DW83DbwsHqykG2qbD5l+GdQCj3/z4CLq1He9XZxtQxrDF1D2e9D68f/MvAsDqW74DXj+Dzq36/v9Yxr3ogFEet4azDtsdD7h/8cnF4o/vlh60ndW7rfPFtpj/eFcQt/vowBxhb67WX450Jz8e7r6wI7+D1ijre5yS8EyTFftk8vIPI+FrlDtaep+Jtl5V4JxoexTuRcMD6hXeS4kN/fXJ1rJMX4nUhrq7LBryr2N84gnbu6i/zT7Xa/l68H4fd4C+zHO+g9fdAu4PEWP23249pDl63yEGHq0cd28YB69VByh7putYK78rxJv8zX4i3PxtPre3/YG3WgOvIE/iDvh1kez3o/qaO8pl4J31y8U7i7fbr+iJwcR3lb8Xblivx9v1vAyPrUw8Osi87ijY64HPH+y75LV6SWYnXfe5uvHtMD9hf4CUyS/z32V9nDnIsgXcF+SG8bm+78fad/8Ub8CW8XJ2vP1z76a/x/qp/c0PkhOBfnt8A/Mc59/XDlZfYZ2Z/wzv72MUd+CNyInIMmNlJeEP33u+cuy/K1REROSq6x0dONN/E+0HTI7mpU2KcmaXhdXObqqRHpEGd5v9fGNVaiIh8AbrHR04I/o2anYAfAtOcc8f6Rm5pRP6NwIPxuisk490HISLHmH8/yCl4ozFuxB+lUUTkeKTER04Uz+L1+53JwX+4UI4fV+L1T98IfMsdu99NEZFIvwC6493Q/QN37EZbExFpdLrHR0REREREAk/3+IiIiIiISOAp8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgKfEREREREZHAU+IjIiIiIiKBp8RHREREREQCT4mPiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOAp8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgKfEREREREZHAU+IjIiIiIiKBp8RHREREREQCT4mPiIiIiIgEnhIfEREREREJvBM68TGzx8ys0MwWHWS+mdmfzWylmX1uZkPC5n3FzFb4f19pvFqLiIiIiEh9ndCJD/AEcN4h5p8P9PT/bgP+DmBmmcB9wKnAKcB9ZpbRoDUVEREREZGjdkInPs65GUDxIYpcAjzlPLOBdDNrD4wD3nbOFTvntgNvc+gESkREREREoqhJfV8wcMgPXENUpCEs+vQPX8e7UlPtUefco/VYRBawIex5vj/tYNNFRERERCQG1TvxOZ74SU59Eh0REREREQmg+ic+cdYA1YhZG4GOYc+z/WkbgTG1pk9vtFqJiIiIiEi91D/xOaHyHiYDt5vZRLyBDEqcc5vNbBrwYNiABucC90SrkieynJycu4GEaNdDRAKnMjc391fRroSIiBw7R5H4BCfzMbNn8a7ctDKzfLyR2poCOOf+AUwBLgBWAjuBr/rzis3sF8Bcf1H3O+cONUiCNJyE3NzcCdGuhIgES05OzoRo10FERI6teic+LkDjwDnnrj3MfAd8+yDzHgMea4h6iYiIiIjIsXVCX/EREREREZETg+7xERERERGRwNOobiIiIiIiEnj1v8enIWohIiIiIiLSgHTFR0REREREAk+DG4iIiIiISOBpcAMREREREQm8+t/joys+IiIiIiJynNE9PiIiIiIiEnga1U1ERERERAJPV3xERERERCTwNKqbiIiIiIgE3lEMbtAQ1RAREREREWk4uuIjIiIiIiKBp+GsRUREREQk8PQDpiIiIiIiEnj1v+KjUd1EREREROQ4oys+IiIiIiISeLrHR0REREREAk+juomIiIiISODpd3xERERERCTwdMVHREREREQC7yhGdWuIaoiIiIiIiDSc+l/x0bBuIiIiIiJynNEVHxERERERCTzd4yMiIiIiIoGnUd1EJGbl5OTcDSREux5yQuqSk5MzIdqVkBNOZW5u7q+iXQmRoDqKKz4NUAsRkbol5ObmToh2JUREGoOSbZGGpa5uIiIiIiISeOrqJiIiIiIigVf/Kz5xynxEREREROT4Uv8rPg1RCxERERERkQZU/1/liTuO/g7DzM4zs2VmttLM7q5jfmcze9fMPjez6WaWHTbvN2a22MzyzOzPZrr5SUREREQkVtU/8TE7fv4OGYbFA48A5wP9gGvNrF+tYr8DnnLODQLuBx7yXzscGAEMAgYAw4DR9f4sRURERESkUZzIgxucAqx0zq0GMLOJwCXAkrAy/YDv+4/fB17xHzu83xZphjfAd1OgoOGrLCIiIiIiR+MorvgcR3+HlgVsCHue708L9xlwuf/4MiDFzFo65z7GS4Q2+3/TnHN5h31HERERERGJikB3dTOz28wsN+zvtnpGeycw2sw+xevKthHYZ2Y9gL5ANl6ydJaZjaz3ZykiIiIiIo3iKH7AtAFq0UCcc48Cjx5k9kagY9jzbH9a+Os34V/xMbNk4Arn3A4zuxWY7ZwL+fOmAqcDM49tBCIiIiIicizU+4qPs+Pn7zDmAj3NrKuZNQOuASaHFzCzVmZW/RndAzzmP16PdyWoiZk1xbsapK5uIiIiIiIx6oS9x8c5txe4HZiGl7Q875xbbGb3m9nFfrExwDIzWw60BR7wp78IrAIW4t0H9Jlz7rUj+wBFRERERKSxHUVXt+Oor9thOOemAFNqTftZ2OMX8ZKc2q/bB3y9wSsoIiIiIiLHRKDv8REREREREYGjSXzq3zlOREREREQkqpTGiIiIiIhI4NX7io/Fqa+biIiIiIgcX3TFR0REREREAk/3+IiIiIiISODVv6uberqJiIiIiMhxpv5XfERERESOIzk5OXcDCdGuxxHokpOTMyHalTgClbm5ub+KdiVE6ktd3URERCToEnJzcydEuxJBcZwkZyIHOIquburrJiIiIiIixxfd4yMiIiIiIoFX/65uSnxEREREROQ4oys+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwNPgBiIiIiIiEnj6HR8REREREQm8eic+ccp7RERERETkOKOubiIiIiIiEnga3EBERERERAKv/olPQ9RCRERERESkAdU/8YlriGqIyMHk5OTcDSREux5R0iUnJ2dCtCsRRZW5ubm/inYlREREgkCDG4jEvoTc3NwJ0a6ENL4TPOkTERE5pnT9RkREREREAk9d3UREREREJPCOoqub+rqJiIiIiMjxRddvREREREQk8DS4gYiIiIiIBJ7u8RERERERkcDTD5iKiIiIiEjg1T/xUeYjIsfQCf4DrYdzov+A68Hoh11FRKTe1NVNRKJNP9Aq9aJkUEREjsYJ3dXNzM4D/gTEA/92zv2q1vzOwGNAa6AYuME5l+/P6wT8G+gIOOAC59zaxqu9iEjdToCraEG/EqYrWiIiDeAouroFI/Uxs3jgEWAskA/MNbPJzrklYcV+BzzlnHvSzM4CHgJu9Oc9BTzgnHvbzJKBqkasvojIoegq2nEs4EmdiEjUnMj3+JwCrHTOrQYws4nAJUB44tMP+L7/+H3gFb9sP6CJc+5tAOdcqJHqLCIiIiIiR+FETnyygA1hz/OBU2uV+Qy4HK873GVAipm1BHoBO8zsZaAr8A5wt3NuX4PXWkREGlUUug5GoyufuteJSOAFOvExs9uA28ImPeqce7Qei7gT+KuZjQdmABuBfXif20hgMLAeeA4YD/zni9daRERiTOC7Dqp7nYicCAKd+PhJzsESnY14AxNUy/anhb9+E94VH/z7eK5wzu0ws3xgQVg3uVeA01DiIyIiIiISkwKd+BzGXKCnmXXFS3iuAa4LL2BmrYBi51wVcA/eCG/Vr003s9bOuSLgLCC30WouIiIiIiL1Uu/EJy4giY9zbq+Z3Q5MwxvO+jHn3GIzux/Idc5NBsYAD5mZw+vq9m3/tfvM7E7gXfOGuZsH/CsacYhI7DsB7hHR/SEiIhLz6p34BOmHfJxzU4Aptab9LOzxi8CLB3nt28CgBq2giARFoO8R0f0hIiJyPDhhr/iIiIiIBMEJcFUZdGVZjoET9gdMRURERAIi0FeVQVeW5dg4obu6iYiIiIjIiUFd3UREREREJPBO5OGsRURERETkBKHER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOAdReKjzEdERERERI4v+gFTEREREREJPHV1ExERERGRwFPiIyIiIiIigafER0REREREAq/+iU9D1EJERERERKQB1X9wg7iGqIaIiIiIiEjDUVc3EREREREJPHV1ExERERGRwFNXNxERERERCTx1dRMRERERkcBTVzcREREREQm8+nd1U+YjIiIiIiLHmfpf8dE9PiIiIiIicpxRVzcREREREQk8dXUTEREREZHA06huIiIiIiISeLriIyIiIiIigacrPiIiIiIiEnhKfEREREREJPDU1U1ERERERAKv3r/KE2fHz9/hmNl5ZrbMzFaa2d11zO9sZu+a2edmNt3MsmvNTzWzfDP7a30/RxERERERaTz1TnzMjp+/Q8dh8cAjwPlAP+BaM+tXq9jvgKecc4OA+4GHas3/BTCjvp+hiIiIiIg0rhP5is8pwErn3Grn3G5gInBJrTL9gPf8x++HzzezoUBb4K36foYiIiIiItK4TuTEJwvYEPY8358W7jPgcv/xZUCKmbU0szjg98Cd9f38RERERESk8dW/q9vx9Gd2m5nlhv3dVs9w7wRGm9mnwGhgI7AP+BYwxTmXX8/liYiIiIhIFAR6VDfn3KPAoweZvRHoGPY8258W/vpN+Fd8zCwZuMI5t8PMTgdGmtm3gGSgmZmFnHMHDJAgIiIiIiLRF+jE5zDmAj3NrCtewnMNcF14ATNrBRQ756qAe4DHAJxz14eVGQ/kKOkREREREYldJ+yobs65vcDtwDQgD3jeObfYzO43s4v9YmOAZWa2HG8ggwfq+3mJiIiIiEj0nchXfHDOTQGm1Jr2s7DHLwIvHmYZTwBPNED1RERERETkGDmKxCdAmY+IiIiIiJwQ6p34KO8REREREZHjTb0Tn3glPiIiIiIicpyp/xWfhqiFiIiIiIhIAzqhBzcQEREREZETgxIfEREREREJPA1uICIiIiIigafBDUREREREJPDU1U1ERERERAJPo7qJiIiIiEjgmXOuXi/4zfQ/1+8FUfSjMf+nPE1ERERERI6iq1tcQ1RDgmLgkB8cN4nx0brn352iXYUGtXVnsM8XbC6Pdg0aXkJ8tGvQsFKaRbsGDSutebRr0PC274p2DRrWzj3RrkHDKioP/Fc9f7n4O8H+MjxB1T/xaYhaiIiIiIiINCANbiAiIiIiIoGnxEdERERERAJPP2AqIiIiIiKBpys+IiIiIiISePodHxERERERCbx6Jz7xynxEREREROQ4o65uIiIiIiISeBrcQEREREREAk9XfEREREREJPCU+IiIiIiISOCpq5uIiIiIiASeRnUTEREREZHAO4qubq4h6iEiIiIiItJg6p34XDfkO7rmIyIiIiIix5W4aFdARERERESkoSnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPDq/QOmIrHg/vuuZtTIvhQXh7j8qt9FuzpHZeWC9bz51CyqqqoYcmY/zrhkSMT8HUVlTP7ne5SXVpCYnMDl3z6H1JbJ7Cgq47k/TMU5R9XeKk4ZN5CcsQOiFMWhrVu4jlnPzqLKVdFvZD+GXjA0Yn7p1lLee/w9KkOVNE9qztivjSU5M5nSraVMfcSPcV8Vg84exIAxsRdjr1aduajPKOLMmJu/mA/WzIuYn56QwhUDziGpWSIVeyp57vO3KN0V2j+/eXwzvnfGDSwpXMXkvA8au/qHtWnxOua9OAtXVUX3Ef3of25k+5VvK2X2/95jV6iSZknNGf6VsbTISKZ8Wykz/jUVV+Vw+6roNWYQPUfGXvsBrF+0jo8mejH2GdmPwedHxli2rZTpT7xHZZm3jp51i7eOlm0r5a2/eTFW7atiwFmD6BeD6+jqz9fx7v+8+AaN7sdpX4qMr2RrKVP//R4VZZUkJDXnom+MJSUzmZKtpUz601Rwjn37qhgydhCDz4q9+DYsWsfHfvv1HtmPk+tovxlh7TcmrP3eDmu//jHafuBth7kvzMK5KnoM70f/cZExhqq3wzJvOxwx3tsOQ9tKmfGo14ZV+6roNXoQvUbFXox9W3fmioGjiTPj43WLeXtlbsT8jMQUrj95LMnNE9m5u5Kn5k9jR2XNfjShSTN+fOYNLNyymhcWTm/cystxR4mPHJdefW0uzz43iwfuvzbaVTkqVVVVTHl8Bjf++EuktkzmX/e+SO+hXWidnbm/zNtPf8Sgkb05eXQf1izK592Js7ns2+eQktGCW+6/giZN49lduYe//XAivYd2JSUzKYoRHaiqqooZT8/g4h9cTHJGMi/84gW6ntyVzA41MX70/Ef0Gd6HPiP6kJ+Xz8cvfczYW8eSlJ7ElT++kvim8eyu3M3En02k60ldScqInRgN4+K+Y/hP7iRKK0N8+/SryStcQ2F58f4yF/Q+g0835TF/01K6ZWZzXq/hPL/wrf3zx/Y8jTXbN0aj+odVVVVF7vMzOOuOi0lMT2bab14ge2BX0trXtN/8SR/R9dQ+dDutD1uW5bPg1Y8ZPn4sCWlJnPsDr/32VO5mygMTyRrYlRbpsdN+4MX44TMzuPB7F5OUkczLD7xAl5O6khG2js5+4SN6nd6H3sP7sDEvnzmTPuasW8bSIi2JS++uifH5CRPpfHJXkmIoxqqqKt55agZX/ehiUjKTeeq+F+gxpCutsmrim/7sRwwY0YcBI/uwbkk+Hzz/MRd9YyzJ6Unc8LMr/f3Mbh778UR6DO5KSgxtg9Xtd4Hffq888AKda7XfJy98RM/T+9DLb7+5kz7mTL/9LglrvxdjsP3Ai3HuczM46/8upkV6Mm/++gWyB0Vuh5++/BHdwrbDT1/9mBHjx5KYlsS4O2tifOOXE8keFFvboWF8edAYHvl4EjsqQvxw1DUs3LKaLaGa/ehl/UcyJz+PORvy6NUqmy/1Hc5/P63Zj17Y5zRWbdsUjeo3CjPLBJ4DugBrgaucc9vNzIA/ARcAO4Hxzrn5/mu+AvzEX8QvnXNPHoN6dAGGO+ee+aLLiiZ1dZPj0rz5qykp2Rntahy1jSsLyWyXRkbbNOKbxNP/9B4szV0TUaYov5iuA7IA6NI/i6XzvPnxTeJp0jQegL179uGca9zKH6HC1YWktUkjrbUXY89TerLm08gYizcXk9XXizGrTxZrFtTEGO/HWLW3KiZj7JjWlm07d7C9opR9rorPNq+gb5tuEWXaJGeyqjgfgNXF+RHzO6S2JrlZC1ZsXd+o9T5S29YWktw6jeRWXvt1HtqT/M8j2690czFte3vt17ZXFvkLj5/2AyhcU0hq6zRS/XW0x7CerF0QGeP2TcVk9fFi7NAna//88Bj37a2CGIxx86pC0tukkd7Gi6/vaT1ZOT8yvq2biunUz4uvU9+s/fPD9zP79lThqmIvvqJa7dd9WE/W1dF+HcLab91B2i9W19FtawtJaZ1GSth2uOGzyBhLthTTtlfYdvj58bMdds5oy9byErbt9Paj8zYuZ2C7yP1ou+RMlhdtAGD51vyI+R3T2pDSvAVLi9Y1ar0bi5nFA3cD7zrnegLv+s8Bzgd6+n+3AX/3X5MJ3AecCpwC3GdmGcegOl2A647BcqJKiY9IFJRtLye1ZfL+56ktkynbXh5Rpm3nVuTNWQ3A0rmr2V2xh51llQCUbCvj7z+ayMO3P8WIiwfH3NUegNCOEMmZNTEmZyRTviMyxlYdW7F6nhfj6vmr2VO5h8qQF2NZcRkT75vIkz98kiHnD4mpqz0AqQnJlIR1tyitDJGWEFnHzWVb6d+mOwD923QnoUkzWjRNwIALe49kyrJZjVnleqnYESIpo6b9WqQns7NW+6Vnt2LDAq/98j9bzd7KPezy2698exlTHpjIKz95kn5jh8TUWeZqO2uto0l1rKMtO7ZizXwvxjWfRq6joeIyXpgwkafvepKTzhsSc1cLQttDpITtZ1IyD9zPtOnYiuW5Xnwrclezu3IPFf5+pnRbGY/fO5G/f+9JTr1oSExd7QEoP8L2W+u339o62u+lCRN5JkbbD7ztsEX4dpiRTEVJre0wq2Y73LCg1nZYXMYbv5zIpHufpN+5sbcdpicks72ibP/zHZUh0hOTI8psLN3KSe17AHBS++4kNm2+fz96Wf+RvLI4OvtRM7vBzOaY2QIz+6eZxZtZyMweMLPPzGy2mbX1y7Y1s0n+9M/MbLiZJZnZG/7zRWZ2tV92rZn92szmA18GLgGqr9g8CVzqP74EeMp5ZgPpZtYeGAe87Zwrds5tB94GzvOXfa6ZfWxm883sBTNLDnvPh/xYcs1siJlNM7NVZvYN//1+BYz0y3yvgT/eBnPUXd1ycnLuBhKOYV0kEEZHuwKBce71w5nyxAw++2AZnfq2JyUzibg4AyCtZQrf/M01lBWXM/EPU+l3SneS01tEucb1N+LLI5jxzAyWfriUDr06kJSRhPkxpmSmcM3Pr6F8ezlTHplC96HdaZF2fMU4ZdksLu47mqFZ/VizfSMllSGqXBWndRrEsqK1Eff7HI8GXzaC3OdnsGb2Ulr36EBiek37JWWkcMG917BzRzkzHp1Cx8HdSUw9vtoP4LQvj2DWMzNY9tFS2vfqQFJYjMmZKXx5wjWU7yhn2iNT6Da0Oy2OsxjHXDuCd56awaKZS+nYpwPJYdtgassUvvrANZRtL2fSn6bQe1h3ko6zbfDUL4/go2dmsPyjpbSro/2u8Nvv7Uem0PU4bD+AIZePIPe5GayevZQ2tbfDzBQu/Im/Hf5zCp2Ow+1w0uKZXDVoDKd26suqbRvZXlGGc1WM7DKIxYVrI+73OZbM7Da8KynVHnXOPerP6wtcDYxwzu0xs78B1wNJwGzn3L1m9hvgVuCXwJ+BD5xzl/lXcZLxkpFNzrkL/WWmhb3XNufcEH/6P5xzm/3pW4C2/uMsYEPYa/L9aXVON7NWeN3fznHOlZvZXcD3gfv9cuudcyeb2cPAE8AIvOP8RcA/8K403emcu+jIP8XY80Xu8UnIzc2dcKwqIsEwcMgP7ot2HY4HKRlJlG4Lu1qwLXTA2dSUzCSu/v75AOyu3EPenNUkJDU/oEyb7EzWL9tMv1O7N3zF6yE5PZlQcU2Moe2hA86oJmUkcf63q2Pczar5q2jeovkBZTI7ZLJpxSZ65PRo+IofIe8KT9hVu4RkSiojz8SW7Srn6QVTAGgW35QBbXtQuXc3ndLa0SUji9M6DaJZfFPi4+LZtXcP01Z81KgxHEpiejLl22vab+eO0AFni1ukJzHqNq/99lTuZsOCVTSr1X4t0pNIb59J0cpNdBoSO+0H3lWs8HW0vK51ND2Jcd+qiXHNvDrW0fQkMrMy2bJiE92Gxk6MyRnJlIXtZ8qK69jPZCRx2XdqtsFlc1cduJ/JSKJ1Vib5yzbR+5TYiS/pCNtvbFj7rT1I+2XEYPuBtx3uDN8Ot4dITKtjO/x6TYzrD7IdpnWIve1wR2WIjMSU/c/TE5LZURGZyJTuKuffc98AvP3oSe17ULF3N10y29M9M4uRXQbRPL4p8XFx7Nq7h8l5Hx6TuvlJzqMHmX02MBSY691qQyJQCOwGXvfLzAPG+o/PAm7yl7sPKDGzhcDvzezXwOvOuZlhy3/uIHVyZna0fRZPA/oBH/p1bgZ8HDZ/sv9/IZDsnCsDysxsl5mlH+V7xhx1dROJgqzubdi2pYTthaXs27uPxR+vpPfQrhFldpZW7O9XP/PVeQwe0xfwkqQ9u/cCUBGqZP2yzbRsn96o9T8Sbbq2oaSghNIiL8YVc1bQ5eQuEWUqympinD9lPn3P8GIMFYfY68dYWV7J5pWbyWh3LLooHzv5pQW0apFORmIq8RbHSe17kle4OqJMdXcMgDFdc8jduBiA5xa+xa9nPM5vZjzBlGWz+HRTXkwlPQAtO7ehrLCE0Fav/dbNW0HWwC4RZSpDNe235K35dD/da7+d22vab/fOSopWbya1bWy1H0CbLm0oKaxZR1fOXUHnk7pElAlfRz+dOp/edayju8or2bJiM2kxFmP7bm3YXlDCDj++vNkr6DG4S0SZnWHxzX5tPgNHefGVFdfsZyrLK8lfvpnM9rEVX+subSgNa79Vc1fQqVb7VYbFt2DqfHodov3SY6z9oO7tMHtQl4gy4dvh4ml1b4e7dlZStGozKTEW4/odBbROSqdlC28/OjSrFwsLIvejSc1q9qPn9sxh9volADw1fxr3vfMYE955nFeWzGRu/tJjlvQcAQOedM6d7P/1ds5NAPa4mpup9nGICwzOueXAELxE45dm9rOw2eFn0Qr8Lmz4/wv96RuBjmHlsv1pB5tueF3gquvczzl3S1i5Xf7/qrDH1c8DMxhaYAKRE8uvH7yBYUO7k56exDtTf8oj/5jGpFfnRLtaRywuPo4Lxo/kfw+9hqtynDymD206ZvL+C3Po0LU1vXO6sjZvE+9OnA1A574duOCrowAo2ridt/73IWaGc47hF51M204toxlOneLi4xh5/UgmPzwZV+Xoe0ZfWma15JNXPqFNlzZ0PbkrG5dtZPZLs8GgQ68OjL7e6yq5ffN2Pny+5gts8LjBtMyOrRirnGNy3nRuHnoJZnHkblxMYXkx5/Q4lY0lheQVraFbZjbjeg4HHGu2b+LVJdOjXe0jFhcfR85VI3n/Ea/9up3el/QOLfn89U/I7NSG7EFdKVy+kQWTZ2MGbXp0IOcqr/1Ktmzn05c/9L5mHfQ9ezDpWbHVfuDFeMZ1I5nyx8k45+g9oi+ZWS2Z++ontO7chi4nd2Xz8o188vJsDGjfqwNnXOfFuGPLdj5+vibGQTG4jsbFx3HOTSN54TdefANH9aVVdktmvvQJ7bq2oeeQrmzI28gHL3jxZffpwNibvPi2bdrO+8/WbIPDLhhM646xF9/w60YytVb75frt1/nkrmxavpG5L3v70fa9OjAirP0+qdV+mTHWfuBvh1eP5L2/etthd387/Oy1T2jZOWw7fNXbj7bp0YFhV9dsh/NfCtsOzxlMRoxth1XO8cLC6XzrtEsxM2avX8KWsmIu6H0a63cUsKhgDT1bZvOlviMAx8ptG2NlyOp3gVfN7GHnXKE/oEDKYcp/E/hjWFe3JKDYOfc/M9sBfO0gr50MfAXvHpuvAK+GTb/dzCbiDWRQ4pzbbGbTgAfDBjQ4F7gHiAceMbMezrmVZpYEZPkJ2JEoO0yMxwU72lE+cnJyJqirm9Q2cMgPYm/YmGPsnn93inYVGtTWnXb4QsexzeWHL3O8S4iPdg0aVkqzaNegYaU1P3yZ4932XYcvczzbuSfaNWhYReWB/6rnLxd/55Bfhv5gBPfg9Z7aA3wbeMc5Vz1gwJXARc658f4gB48C3fCuBH0TSAV+i3dFZQ/wTedcrpmtBXKcc1v95bQEngc6AevwhrMu9oez/ivevUI7ga8653L919wM/Niv6gPOucf96WcBvwaq9zI/cc5NDn9PMxvvP77df81aIAcoAaYBLYEnnHMP1+8TjQ264iMiIiIiUg/Ouec48F6c5LD5LwIv+o8L8EZhq21aHcvtUuv5Nrx7imqXc3jJVl11ewx4rI7p7wHDDvWezrkn8AY3qKs+Z9X1fscT3eMjIiIiIiKBp8RHREREREQCT4mPiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOAp8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgNYl2BSRY7vl3p2hXocE99LX10a5Cg2r91Y7RrkKD2rXLRbsKDa4q4CHGWbRrIF/UqX2Dfd61bFe0a9CwtA3K8SrYex4RERERERGU+IiIiIiIyAlAiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgKfEREREREZHAU+IjIiIiIiKBp8RHREREREQCT4mPiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOA1iXYFRA5m5YL1vPnULKqqqhhyZj/OuGRIxPwdRWVM/ud7lJdWkJicwOXfPofUlsnsKCrjuT9MxTlH1d4qThk3kJyxA6IUxdG7/76rGTWyL8XFIS6/6nfRrs5RGdapM7ePGk2cxTFlySKenZcbMb9tSgo/PHssaYmJlFXu4sG33mRreQiANskp3Hn2ObROTsHhuGfyqxSUlUYjjIM6tUtnvnvWGOIsjtcWLuJ/c+ZGzG+bmsKPx51LeotESisruf+NNykKefG1TUnh7nFjaZOSjAPufOkVtpTGVnyndenMd88eQ7zFMfnzRfy3VnztUlO49zw/vopKJtSK757zxtI2JRnn4PsxGB8Evw2DHl+f1p25tN9o4syYvWEx762K3MdkJKZw9aCxJDdLZOeeSp5eMI2SytD++c2bNOOuUTewqGA1Ly+e3si1PzL923bmmkFejDPXLubN5ZExZiamMH7oWFKaJ1K+u5L/5E5je0VNjAlNmnH/2Bv4dNNqnv1seiPX/vD6t+nMVX58s9YtZlod8X1lyFiS/fgey53GjsrI+CaccwMLNq1m4ufTG7n2crxR4iMxqaqqiimPz+DGH3+J1JbJ/OveF+k9tAutszP3l3n76Y8YNLI3J4/uw5pF+bw7cTaXffscUjJacMv9V9CkaTy7K/fwtx9OpPfQrqRkJkUxovp79bW5PPvcLB64/9poV+WoxJnxnTFn8sNXXqYoFOLvV1/LR6tXs2578f4y3xgxkreW5vHW0jwGZ2dz6/ARPPT2NADuHjuOp3PnMG/DehKaNsU5F61Q6hRnxg/OOYvvvvAyhWVl/PuG65i1ahVrt9XEd/voUby5JI+pi5cwpGNHvjHyDH4x9U0AfnLBOJ6aPYe569aT2LQpVbEY39iz+M7zXnyP3XgdM2vFd8eYUUxdnMeUxUsY2qkj3xx1BvdP8eL72QXjeCKG44MTpA0DHJ9hXN5/DP/4ZBIllSG+d8Y1LC5YTUGoJr4v9R1Jbn4euRvz6NEymwt7D+eZz97aP//8XqexunhTNKp/RAzjupPG8PCsSWyvCHHvmdfw2ebVbC6rifHLA0fy8fo8Pl6fR5/W2VzWfziP5dbEeEm/01i+NTZjNIxrTxrDHz/04rvnzGv4vFZ8Vw4cyccb8pi9Po/erbz4Hp9XE9/FfU9jRYzGJ7FHXd0kJm1cWUhmuzQy2qYR3ySe/qf3YGnumogyRfnFdB2QBUCX/lksnefNj28ST5Om8QDs3bMv5g6Yj9S8+aspKdkZ7WoctT5t27FxRwmbS0vZW1XFe8uXM7xb94gynTNb8mn+BgA+zc9neLdu3vSMTOLjjHkb1gNQuWcPu/bubdwADqNvu3bkb9/BppIS9lZV8e7SZYzsHhlf15Ytmbfei2H+hg2M7OHF16VlJvEWx9x13ryKGIyvX/vI+N5ZuoxRPSLj69KyJbl+fPPWb2BUeHxxsR0fBL8Ngx5fp/S2bN1ZQnFFKftcFZ9uWs6Att0iyrRLzmTlNm8fs3JbfsT87NQ2pDRvwbKt6xq13vXRNbMtReUlbN3pxTg3fzknt4+MsUNqJkuLvBiXFuVHzO+U3obU5i1YUhCbMXbNbEthWHy5+cs5qVZ87VMyWebHt2xrfsT8TultSE1owZLC2IxPYo8SH4lJZdvLSW2ZvP95astkyraXR5Rp27kVeXNWA7B07mp2V+xhZ1klACXbyvj7jyby8O1PMeLiwcfd1Z4gaJWURGGobP/zraEyWidHtsOqrUWM7N4DgJHdu5PUrDmpCQlkZ2QQ2rWLn19wEf+85jq+PuIM4swatf6H0zolmcKymvgKQyFapyRHlFlRVMTonj0BGN2zB0nNvfg6+vE9ePFFPH7j9Xx79MjYiy+5VnxlIVonR8a3srCIMXXE18mP76FLLuLJm67n9hiMD06ANgx4fGkJyeyoqIlvR2WItITI+DaVbmVgO28fM7BddxKaNqdF0wQMuLjfSCbnzWrMKtdbekIyxWExbq8IkZ4YGeOGkq0M6eDFOLhDdxKbNiepmRfjVQNH8uKi2I0xPSGZ7bXjq9WG+SVbGXyQ+K4cMJIXF0YnPjO7wczmmNkCM/unmcWb2RNmtsjMFprZ9/xyt5rZXDP7zMxeMrMW/vQnzOzPZvaRma02syujEsgJRomPHLfOvX446/I28c+7n2dt3iZSMpOIi/O+mNNapvDN31zD/z18PZ/NWEZox/F75STI/vHhTE7Kyuaf11zHoA7ZFIXK2FfliDdjYIcs/jFrBt987lnap6Yxrm+/aFe33h6ZPoPB2Vk8fuP1nJydTWFZGVXOER8Xx0nZWfz1g5l87X/P0CEtjQv6H3/x/WX6DAZ3zOLJm65ncMcD4/vL9Jnc/N9n6JCexoUDjr/4IPhtGPT4JufNpHvLLL5/xrV0z8xiR0UZVa6KEZ0HkVe4NuJ+n+PVCwtn0qtVFj8961p6tcpiux/jmG6DWLhlbcT9PsejFxd58d175rX0bFkT3+hug1hUsDbifp/GYmZ9gauBEc65k4F9wE+ALOfcAOfcQOBxv/jLzrlhzrmTgDzglrBFtQfOAC4CftVY9T+R6R6fo5STk3M3kBDtesSa7z964zFZTkpGEqXbanZmpdtCpGREXi1IyUzi6u+fD8Duyj3kzVlNQlLzA8q0yc5k/bLN9Ds1souHNKyt5eW0SU7Z/7xVcgpFocirdtvKy7lvyusAJDRtyqgePSjfvYuiUIhVW4vY7N9I/eHqVfRt156pLG68AA6jqCxEm5Sa+NokJ1NUFvkFvLW8nB9P9uJLbNqUMb16ENq1i6KyMlYUFrGppASAGStX0b99O1gUQ/GFasWXkrz/pvdqW8vLuefVmvjO9OMrrB3filUM6NCO1xbGTnxwArRhwOMrqQyRnlgTX3pC8gGJTOmucp6Y9wYAzeKbMqhdDyr37qZzRnu6ZWYxovMgmjVpShOLY9fePbyx7MNGjeFwdlSGyAyLMSMxmR21EpmSynL+/okXY/P4pgzN6kHFnt10z2xPj1ZZjOk2iOZNmtIkzovx5cWxE+OOyhAZteOrPDC+f4TFN8SPr1tme3q2zGJ010EkNGlKfFwcu/btYdIxis/MbgNuC5v0qHPuUf/x2cBQYK55V0ITgTeBbmb2F+ANoPpGpAFm9ksgHUgGpoUt8xXnXBWwxMzaHpOKyyEp8Tl6Cbm5uROiXYlY88z8P913LJaT1b0N27aUsL2wlNTMJBZ/vJLLbx8bUWanP5qbxRkzX53H4DF9AS9JSkxJoGmzJlSEKlm/bDOnXXDSsaiW1MPSgi1kpafTLjWVraEQZ/XqxQPTpkaUSU1IoKyyEgdcN3QYU5d4B1XLCgtIbt6ctIRESiorGJzdkWWFBVGI4uCWbtlCdkYG7dNSKSoLcXaf3vz8jcj40hITKK3w4rvx1GG84R805m3x4ktPTGRHRQVDO3Vk6ZbYii9v8xY6hsV3Tp/e3Pf6weO76dRhvL7wEPEVxFZ8EPw2DHp8G0oKaJ2UTmZiKiWVIQZ36MV/P30zokxS0wR27vHiO7tHDnPylwDw9IKaY89h2X3pmNY25pIegLXbC2iTnE6rFqlsrwgxLLsX/54bGWNyswTKd3sxnt87h1lrvRj/nVsT4/BOfemc0Tamkh6oia9li1R2VITIye7Ff2rFl9QsgZ1+fOf1zuHDdV58j4XFd3qnvnROb3vMkh4AP8l59CCzDXjSOXdPxESze4FxwDeAq4CbgSeAS51zn5nZeGBM2Et21VqmNDAlPhKT4uLjuGD8SP730Gu4KsfJY/rQpmMm778whw5dW9M7pytr8zbx7sTZAHTu24ELvjoKgKKN23nrfx9iZjjnGH7RybTt1DKa4RyVXz94A8OGdic9PYl3pv6UR/4xjUmvzol2tY5YlXP85YP3+fXFlxEfZ0xdspi1xcWMP/U0lhcW8tGa1Zyclc3Xho/AOfh800b+PP39/a/9x6yZ/O6yyzGM5UWFvLF4UZQjirTPOR5+9z3+cMXlxMcZry9czJpt2/jaiNNZuqWAWatWM7hjR74x0ovvs/x8fv9uTXyPfDCDP111BYaxrKCAyZ8vjHJEkfY5x+/feY8/Xnk5cWHx3TridPL8+IZ07Mg3R3nxLcjP53fv1MT3l+kz+MvVXnxLCwp49bPYig9OjDYMcnxVzvHyouncdsqlxJkxJ38JBaFizut1Ght2FLC4cA3dW2ZzYZ8ROOdYXbyRl2J0yOqDqXKOZxZM57sjLsXM+HDdEjaVFXNx39NYt6OAzzavoVerbC4fMAKcY/m2jTyzYHq0q33Eqpxj4mfT+c6IS4nDi29zWTFf6nsa67YX8PmWNfRulc2l/UcAjhVbN8bKkNzvAq+a2cPOuUIzywRSgO3OuZfMbBnwP79sCrDZzJoC1wMbo1NlAbCjHfEqJydnwol8xeNEj/9gnpn/p+NzCLV6eOhr66NdhQbV+qsdo12FBrVrV+BXUaoCHmKczose907tG+xbjMt2Hb7M8exE2Ab/edl3DhmlmV0N3IN3v/we4PvAw9TcP3+Pc26qmX0T+BFQBHwCpDjnxpvZE8DrzrkX/eWFnHPJSIPSFR8RERERkXpwzj0HPFdr8pA6yv0d+Hsd08fXeq6kpxEE+5SLiIiIiIgISnxEREREROQEoMRHREREREQCT4mPiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOAp8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgNYl2BSRYtu60aFehwbX+asdoV6FBFT2+IdpVaFDuog7RrkKDa5EY8O2wSbDj27nTRbsKDS61ebDbsKA02G0Y7NaTINMVHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOAp8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgKfEREREREZHAU+IjIiIiIiKBp8RHREREREQCT4mPiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAq9JtCsgcjDrFq5j1rOzqHJV9BvZj6EXDI2YX7q1lPcef4/KUCXNk5oz9mtjSc5MpnRrKVMfmYpzjqp9VQw6exADxgyIUhQHN6xTZ24fNZo4i2PKkkU8Oy83Yn7blBR+ePZY0hITKavcxYNvvcnW8hAAbZJTuPPsc2idnILDcc/kVykoK41GGEft/vuuZtTIvhQXh7j8qt9FuzpHZUSPLtx9wZnEm/HS/EX8Z+aciPnt01L4xWXjyGzRgpKKSu5+aQoFpSGGde3IXeeN2V+ua6tMfvjCG7y3dGUjR3Bop3Xrwg/OGUNcXByvLljIU7PnRsxvl5rCTy8cR3qLREorKrnvtakUlnnraNvUFO69YCxtU1JwwPeen8TmkthbR0/r0pnvnj2GeItj8ueL+O+cA2O897xz98c44Y03KQr5MaakcM95Y2mbkoxz8P2XXmFLaWzFOLx7F+46z2vDSfMX8tiHkfG1T0vh5xePIyMpkZKKSn78steGw7p05M5xo/eX69oqk7tefIP3l61q7BAOadPidcx/cRauqoruI/rR79zI74nybaV88r+a74nTvzKWFhnJ++fvqdjNG798huxB3ci5elRjV/+IDGrfmRuHjCbOjOmrFvNaXuR3RasWKdx66lhSExIJ7ark7x9Po7jCW0f/e/UdbCjZBsDW8jL+MPO1Rq//4RwuvpYtUrjt1LGkJCRSXiu+p8Li2xaj8UlsUeIjMamqqooZT8/g4h9cTHJGMi/84gW6ntyVzA6Z+8t89PxH9Bnehz4j+pCfl8/HL33M2FvHkpSexJU/vpL4pvHsrtzNxJ9NpOtJXUnKSIpiRJHizPjOmDP54SsvUxQK8ferr+Wj1atZt714f5lvjBjJW0vzeGtpHoOzs7l1+AgeensaAHePHcfTuXOYt2E9CU2b4pyLVihH7dXX5vLsc7N44P5ro12VoxJnxk8uOptbn3yRLaVlPPf163l/6UpWF9W04Z3jRjN5wRImL1jCKV078t1zRnLPy1OZu2YDV/79vwCkJiYw9Ts389GqtVGKpG5xZvzo3LO4feJLFJaW8eT465m5YhVrttXE952zRjNl0RLeWLiEnM4d+daYM5jw2psATLjoPB7/6BPmrF1PYtOmVMXgOhpnxg/GnsV3nn+ZwrIyHrvxOmauWsXasBjvGDOKqYvzmLJ4CUM7deSbo87g/ilejD+7YBxPzJ7D3HWxGWOcGT++4Cy+/t+XKCgt45lbr2f6slWs3loT3/fHjua1z5fw2mdLOKVLR75z9hnc+8qbzF27gav/+T8AUhMSeP3/bubjVeuiFUqdqqqqmPf8DM6842IS05N56zcvkDWwK2nta74nPp30EV1O7UO30/qwZVk+n736MaePH7t//uevf0KbHh2iUf0jYmaMHzqGh96fRHFFiF+cew3zN65mY2lNG143eCSz1uYxc00e/dpmc/VJw/n77LcA2L1vLz9+85loVf+wzIyvDB3Dr/z47j/3GuZtXM2mQ8R31UnD+UdYfPfGcHwNzcz+DfzBObck2nU5Xqirm8SkwtWFpLVJI611GvFN4ul5Sk/WfLomokzx5mKy+mYBkNUnizULvPnxTeKJbxoPQNXeqphMCvq0bcfGHSVsLi1lb1UV7y1fzvBu3SPKdM5syaf5GwD4ND+f4d26edMzMomPM+ZtWA9A5Z497Nq7t3EDOAbmzV9NScnOaFfjqA3Mbsf64h3kby9h774qpi5cxll9ekSU6d6mJXNWe204Z80GzuzT/YDlnNuvJzNXrKVyT2y1Yf8O7cjfvoNNO0rYW1XFW3lLGdUrsv5dW2Uyd623Huau28Cont78ri0ziY+LY44/ryJG19F+7f0YS7wY31m6jFE9ImPs0rIlueu9OOat38CoHt386V6Mc9fFbowDstqxoXgHG/02fHPxUsbUWge7t85kzhovhjlrNxwwH2Bsv57MWrGGyhiLr3htIcmt00hu5X1PdBrak/zPI78nSjYX07a39z3RtlcW+Qtr5hevL6SybCft+nRs1HrXR/fMthSESigqL2VfVRWz1y9naHa3iDJZaZksLvD2M0sK8g+YH8uCHl9Dc859TUlP/SjxkZgU2hEiObOmO0JyRjLlO8ojyrTq2IrV81YDsHr+avZU7qEyVAlAWXEZE++byJM/fJIh5w+Jqas9AK2SkigMle1/vjVURuvkyDqu2lrEyO7egfTI7t1Jatac1IQEsjMyCO3axc8vuIh/XnMdXx9xBnFmjVp/gTYpyWwpqWnDgtIy2qQmR5RZtqWIc/p5bXhO3x4kJzQnLTEhosz5A/swdeHShq9wPbVOTqagtCa+wrIQrVNSIsqsKNzKmb17AjCmVw+Sm3vxdcr01tFfX/4l/vvVG7jjzFExuY62Tk6msKxWjMmRbbiysIgxPb0YR/fsQVJzbzvs5G+HD11yEU/edD23jx4ZczG2SUlmS3gbloZoW6sNlxVs5ey+Xnxn96lpw3DnDejNm4tibx3duSMU0W2tRXoyFbW+JzKyW5G/wPueyP9sNXsr97ArVImrcnz68ocMvmxEo9a5vjJbJLNtZ00bFu8MkZEYuY6u376VYdnefiYnuzuJTZuT3Mxrw6bxTfjFudfw87FXMTQr9hKGjBbJFH/B+O4/9xomRCE+M+tiZkvN7AkzW25mT5vZOWb2oZmtMLNT/L+PzexTM/vIzHqHvXammc33/4b709ub2QwzW2Bmi8xspD/972aWa2aLzeznYXWYbmY5jRr4cU5d3eS4NeLLI5jxzAyWfriUDr06kJSRhMV5Bx4pmSlc8/NrKN9ezpRHptB9aHdapLWIco3r5x8fzuT/Rp/JuL79+HzjRopCZeyrcsSbMbBDFl+f+DQFZWX87LwLGNe3H1OXLI52laWW3037gHsvPJtLBg9g3tp8tpSURXSHapWcRM+2rfhw5droVfIL+NN7H/DDc8/iooH9+XRDPgWl/joaF8fJ2Vnc8Pj/KCgp5YFLL+Kigf2Z/PmiaFe53v4yfQY/OOdMLhzQj0/zN1JY5rVhfFwcJ2Vn8ZUnn6agtJRfXHwhFw7ox2sLj6/t8A9vfcA9F5zFJSf1Z956rw2rqiLX0R5tWvFRjHVzO1InXzaCec/PYPXspbTp0YHEdO97YsWMhbTv3zkicTpePb1gJuOHjmFUt74sLdxI8c4yqlwVAN+Z/BjbK8ppnZTKvWddwYaSbRSGSqJc4/p5ZsFMvjJ0DCO79WVZrfi+Gxbfj6MTXw/gy8DNwFzgOuAM4GLgx8BNwEjn3F4zOwd4ELgCKATGOucqzawn8CyQ479+mnPuATOLB6oPXO51zhX70941s0HOuc8bL8zgUOLTgHJycu4GEg5bMEBu+uNNx2Q5yenJhIpD+5+HtodISo+8IpKUkcT53z4fgN2Vu1k1fxXNWzQ/oExmh0w2rdhEj5zIbkjRtLW8nDbJNWdeWyWnUBSKPFO5rbyc+6a8DkBC06aM6tGD8t27KAqFWLW1iM3+TdQfrl5F33btmcrxdcB1vCssC9EuraYN26amUFgaiihTVFbOdydOBiCxWVPO6deTsspd++efN6AX7+atZG9VVeNUuh6KQiHaptbE1yYlmaKwqyMAW0Pl3PWydzNxYtOmnNm7J6FduygsC7G8sIhNO7wDkA9WrGRAh/YQY1/TRaEQbVJqxRiKbMOt5eXc86q3HSY2bcqZvXr4MZaxorCITSVejDNWrGJAh3YxlfgUloVoF96GqckU1GrDolA533++pg3P6duTsl016+i5/Xvx3tLYXEdbpCezc3tNe+3cESKx1vdEi/QkRt7mfU/sqdzNhgWraNaiOVvXbKFo1WZWzljEnl17qNq3jybNm3Lypac3agyHU7wzRMsWNW2Y2SKZ7RWR6+iOinL+OOsNAJo3acopHXuwc89uALZXeN8rReWl5BXm0yWjdUwlPtt3hsg8gvj+FBbfsEPE1/kYxmdmtwG3hU161Dn3aK1ia5xzC/3yi4F3nXPOzBYCXYA04Ek/uXFAU/91TYG/mtnJwD6glz99LvCYmTUFXnHOLfCnX+XXpwnQHuhHzO1Rjw9KfBpWQm5u7oRoV6Ix/XnWn+87Fstp07UNJQUllBaVkpSRxIo5Kxh729iIMhVlFSQkJWBxxvwp8+l7Rl8AQsUhEpITaNKsCZXllWxeuZmTzz35WFTrmFlasIWs9HTapaayNRTirF69eGDa1IgyqQkJlFVW4oDrhg7bf0VnWWGB1x0lIZGSygoGZ3dkWWFBFKI4sS3auIVOmelkpadSUBbi/IG9+dELUyLKpLdIpKSiAufg1pGnMOnTyCse5w/swx/fmdWY1T5iSzZtoWNGOh3SUiksC3Fu3z78dHJkfGmJCZRWeOvo+NNP4bXPvXV0yeYtpDRvTnpiIjsqKsjp3JG8zbG3juZt3kLHjAzap6VSVBbinD69ue/1yO0wPMabTh3G635ik7fF2w6rYxzaqSNLC2IrxsUbt9Cppb+OloY4r38f7nm51jqamECJH98tI0/hlU8jE7fzB/Thz+/G5jqa2bkNZYUlhLaWkpiexPp5Kxg+PvJ7YleogmYtvO+JJW/Np9vp3vfE8K+eu7/M6o/zKF5fFHNJD8Dq4gLapaTTOimV4ooQp3XqxSMfvRlRJrlZAuW7vTa8uF8O01d7t3y0aNqc3fv2srdqH8nNEujVugOv582LQhQHV1d8fztMfB80Unx+klM70altV9jjqrDnVXjH2L8A3nfOXWZmXYDp/vzvAQXASXi3nVT67znDzEYBFwJPmNkfgJnAncAw59x2M3uCE+yk+rGkxEdiUlx8HCOvH8nkhyfjqhx9z+hLy6yWfPLKJ7Tp0oauJ3dl47KNzH5pNhh06NWB0dd7Q69u37ydD5//cP+yBo8bTMvsltEKpU5VzvGXD97n1xdfRnycMXXJYtYWFzP+1NNYXljIR2tWc3JWNl8bPgLn4PNNG/nz9Pf3v/Yfs2byu8suxzCWFxXyxuLjrwvRrx+8gWFDu5OensQ7U3/KI/+YxqRX5xz+hTFiX5XjwTfe4583XUF8XByT5i9iVdE2vn3WcBZvLGD6slUM65LNd8eOxDnHvHUb+eXr7+5/fYf0VNqlpZC7dkMUozi4fc7x27ff58/XXEGcGa99vojVW7dx28jh5G3ewsyVqxnayRvJDeDT9fn85q33AG8d/dN7H/DIdVdiGEu3FPDKgoXRDKdO+5zj9++8xx+vvJy4OOP1hYtZs20bt444nbwtBcxatZohHTvyzVHedrggP5/fvVOzHf5l+gz+cvUVXowFBbz6WWzFuM85HpryPn+/wWvDVxZ46+i3xgxn8aYtfLB8NTldOvJ/Z3ttOG9dPg9OeW//6zukpdIuNXbX0bj4OHKuGsn0R7zviW6n9yWtQ0s+f/0TMju1IXtQVwqWb+SzybMxg9Y9OpBz1ejDLziGVDnHE7nTuWvMpcSZ8cHqJWwsLeaKgaexpriA+RvX+CO5jcA5x9KijTyROx3wBgW4ZdhZVDlHnBmTl+RGjAYXC6qc48nc6fzoEPH1DYtvWa34bg6L77UluRGjwcWINGCj/3h8ren5zrkqM/sKEA9gZp396f8ys+bAEOAzoBwoMbO2wPnUJFBST3a0I17l5ORMONGuZoQ7kvhPxM/oz7P+HHtDqB1jr3wae10+jqWix2PzIOdYcRfF7tC1x0qLxNi6yf5Yi28S7Ph27gz8bpTLRgT7vOuKomC3YbC3QM//rv3OIcP0r+C87pwb4D9/wn/+YvU84FbgSbzE5Q3gBudcF7/r20t43d/eBL7tnEv2k6AfAnuAEHCTc26Nv+zhwAagBJjsnHvCzKYDdzrnIn/8SA4q2HseEREREZFjzDm3FhgQ9nz8Qeb1CnvZT/z5K4BBYdPv8qc/iZco1X6v8bWn+dPH1L/mJzYNZy0iIiIiIoGnxEdERERERALvuOnqFoNDQ3eJdgVEREREROTIHDeJDzE2NHROTs6EaNdBRERERESOjLq6iYiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOAp8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgKfEREREREZHAU+IjIiIiIiKBp8RHREREREQCr0m0KyDBsrk82jVoeLt2uWhXoUG5izpEuwoNyl7fFO0qNLjS84Ldhi0So12DhrVvX7D3MQCby4IdY6gi2jVoWHEW7RqIHB1d8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgKfEREREREZHAU+IjIiIiIiKBp8RHREREREQCT4mPiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCaRLsCIgfTq1VnLuozijgz5uYv5oM18yLmpyekcMWAc0hqlkjFnkqe+/wtSneF9s9vHt+M751xA0sKVzE574PGrv5hndqlM989awxxFsdrCxfxvzlzI+a3TU3hx+POJb1FIqWVldz/xpsUhbz42qakcPe4sbRJScYBd770CltKS6MQxaGN6NGFuy84k3gzXpq/iP/MnBMxv31aCr+4bByZLVpQUlHJ3S9NoaA0xLCuHbnrvDH7y3VtlckPX3iD95aubOQIjt79913NqJF9KS4OcflVv4t2dY7KGT27cO9FZxIXZ7w4dxH/mhHZfh3SU3jgipr2++HzXvud2q0jd18wZn+5bq0z+f7EN3g3L/bab3j3Ltw5bgzxFsekTxfyxEeR22H7tBTu+9I4MlokUlJRyU9emUphmbcdtktN4acXjaVdWgrOwR3PTmJzSWxth0HfBvu36cxVg0YTZ8asdYuZtjw3Yn5mYgpfGTKW5OaJlO+u5LHcaeyorPmeSGjSjAnn3MCCTauZ+Pn0Rq79kRmc1ZlbT/NifHv5Yl76PDLG1kkp3DFyLGkJiZTtquThD6axbacX48vj72Dd9m0AbC0v44F3Xmv0+h/O4KzO3HKqF987yxfz8sID47v9jLGkJiQS2lXJH2fUxPfiV+5gvR9fUXkZD70be/FJbFHiIzHJMC7uO4b/5E6itDLEt0+/mrzCNRSWF+8vc0HvM/h0Ux7zNy2lW2Y25/UazvML39o/f2zP01izfWM0qn9YcWb84Jyz+O4LL1NYVsa/b7iOWatWsXZbTXy3jx7Fm0vymLp4CUM6duQbI8/gF1PfBOAnF4zjqdlzmLtuPYlNm1LlXLRCOag4M35y0dnc+uSLbCkt47mvX8/7S1eyuqgmxjvHjWbygiVMXrCEU7p25LvnjOSel6cyd80Grvz7fwFITUxg6ndu5qNVa6MUydF59bW5PPvcLB64/9poV+WoxJnxs4vP5ubHXqSgtIwXvnU97y1dyarCmvb70fmjeXX+El75dAmnduvI98eN5K4XpvLJ6g1c9lev/dISE5j2g5v5cOXaKEVycHFm3HXeWXzr6ZcoKC3jf1+7ng+Wr2LN1poYv3vOaF7/fAmvf76EYV06csdZZ/DTV73t8P5LzuM/sz7hkzXeduhibDsM+jZoGNeeNIY/fjiJ7RUh7jnzGj7fvJrNZTXxXTlwJB9vyGP2+jx6t8rmsv7DeXxezffExX1PY8XWTdGo/hGJM+Prp4/hvmmT2FYe4ncXX8Oc9avZsKMmxq+eMpL3V+bx/so8BrbP5sac4fxxhhfj7n17+d6rz0Sr+ocVZ8Ztp41hwrRJbNsZ4jdf8uLLL6mJb/ywkUxfVRPfDUOH86eZNfF9f3LsxldfZhZyziXXMf1+YIZz7p0oVCtQ1NVNYlLHtLZs27mD7RWl7HNVfLZ5BX3bdIso0yY5k1XF+QCsLs6PmN8htTXJzVqwYuv6Rq33kerbrh3523ewqaSEvVVVvLt0GSO7d48o07VlS+at9+o/f8MGRvbw4uvSMpN4i2PuOm9exZ497Nq7t3EDOAIDs9uxvngH+dtL2LuviqkLl3FWnx4RZbq3acmc1RsAmLNmA2f26X7Acs7t15OZK9ZSuSf2YjyUefNXU1KyM9rVOGqDstuxfpvXfnv2VTHl82Wc3ffA9pvtt98nqzdwdt8D22/cgJ7MXB6b7Tegg7cdbtzhbYfTFi9lTO/IGLq1zmTuWm9bm7t2A6P9+V1bZRIfF8cna2q2w8oY2w6Dvg12zWxLYXkJW3d63xO5+cs5qX3k90T7lEyWFXnxLduaHzG/U3obUhNasKRwXaPWuz56tmrLltISCspK2VtVxczVyzmlU2SMHdMzWbjZi3Hh5nxOrTU/lvVs1ZbNZSUUhLz4ZtURX3Z6Jp+HxVd7/onAOfczJT3HhhIfiUmpCcmUhHVHKK0MkZaQFFFmc9lW+rfxvqT7t+lOQpNmtGiagAEX9h7JlGWzGrPK9dI6JZnCsrL9zwtDIVqnRJ7kWVFUxOiePQEY3bMHSc2bk5qQQMeMDEK7dvHgxRfx+I3X8+3RI4kza9T6H4k2KclsKamJsaC0jDapkTEu21LEOf28A7Fz+vYgOaE5aYkJEWXOH9iHqQuXNnyFJULbtGQ2h7XflpIy2tbRfmP7e+03tr/Xfum12u+CQX1447PYbL/WqclsKQ3bDktDtElJiSizvGArZ/XxtsOz+vQgubm3jnZumUGoche/+/KXeObWG/ju2aNibjsM+jaYnpDM9oqa+LZXhEhPiIwvv2Qrgzt48Q3u0J3Eps1JauZ9T1w5YCQvLozd7wmAlknJbC2viXFbeYiWLSJjXFO8ldM6ezGe1rk7LZo1J6W514bN4pvw+4uv4TcXXRWTCVFmi1rx7QzRMikyvrXFWzn9EPH99kvX8KsLr2r0hMjMfmpmy8xslpk9a2Z3mtmtZjbXzD4zs5fMrIVf9gkz+7uZzTaz1WY2xsweM7M8M3ui1nIfNrPFZvaumbUOe/2V/uOf+e+xyMweNfN2PGY23cx+bWZzzGy5mY1s1A/kOKHER45bU5bNomtmFnecfi1dM7MoqQxR5ao4rdMglhWtjbjf53j0yPQZDM7O4vEbr+fk7GwKy8qoco74uDhOys7irx/M5Gv/e4YOaWlc0L9ftKt7VH437QNyunTkhW/eSE6XjmwpKYvottcqOYmebVvFZDcpgd9M+YBhXTvy8u03Mqyr1377wtqvdUoSvdq1YtaKtdGr5Bf08NsfMLRzNs/cegNDOmVTUFrGvipvOzy5UxYPvz2DG//9NFkZaXzppP7Rrm69BX0bfHHRTHq1yuLeM6+lZ8sstleUUeWqGN1tEIsK1kbc73O8emLOTAa0y+LhS65lQLsstpZ7MQJ87fnH+MHkifz+gze55dTRtEtJi3Jt6++JuTPp3y6L3198Lf39+Pb58d32wmP88LWJPPzBm9xySuPFZ2bDgCuAk4DzgRx/1svOuWHOuZOAPOCWsJdlAKcD3wMmAw8D/YGBZnayXyYJyHXO9Qc+AO6r4+3/6r/HACARuChsXhPn3CnAdw/y2hOe7vGJspycnLuBhMMWPE6MfeCmY7Ic7wpPzVkf7wpQeUSZsl3lPL1gCgDN4psyoG0PKvfuplNaO7pkZHFap0E0i29KfFw8u/buYdqKj45J3Y6ForLIM8ttkpMpKov8At5aXs6PJ78OQGLTpozp1YPQrl0UlZWxorCITSUlAMxYuYr+7dvBosWNF8ARKCwL0S6tJsa2qSkUlkbGWFRWzncnTgYgsVlTzunXk7LKXfvnnzegF+/mrWRvVVXjVFr2KygJ0T6s/dqlpVBQq/0Ky8r5v6e99mvRrCnn9q/VfgN78c7i2G2/otIQ7VLDtsPUyCuxAFtD5dz5gnfDdGLTppzdtyehXbsoLA2xvKCIjTu87XD6spUMzGrPq41X/cMK+ja4ozJERmJNfBmJyQckMiWV5fzjkzcAaB7flCFZPajYs5tume3p2TKL0V0HkdCkKfFxcezat4dJiz9s1BgOZ1t5iFZJNTG2TEref2N/teKKcn71nhdjQpOmnN6lB+W7d3vzdnrfmwVlpSzakk+3lq3ZUlbSSLU/vOKdteJrkcy28sj4tleU8+uw+E7r3IOdteMLefF1zTx28ZnZbcBtYZMedc496j8eAbzqnKsEKs2selSFAWb2SyAdSAamhb3+NeecM7OFQIFzbqH/PouBLsACoAp4zi//P+DlOqp2ppn9CGgBZAKLger3ry4/z1+m1KLEJ/oScnNzJ0S7EsfKPdP+fEzOMOSXFtCqRToZiamUVoY4qX1PJn42LaJMi6YJVOypxAFjuuaQu9E78H8ubICDIR36kp3WJqaSHoClW7aQnZFB+7RUispCnN2nNz9/Y2pEmbTEBEorvPhuPHUYb/iJTd6WApKbNyc9MZEdFRUM7dSRpVsKohDFoS3auIVOmelkpadSUBbi/IG9+dELUyLKpLdIpKSiAufg1pGnMOnTRRHzzx/Yhz++E9tdUYJq4cYtdG6VTlZGKoWlIS4Y1Js7nzt4+902+hRemhfZfhcO6sPDb8Vu+y3etIWOmel0SPdiHNe/Dz+eVCvGxARK/O3w5jNO4dUFi/e/NiWhOektEtmxs4JhXTqyZHNsbYdB3wbXbi+gTXI6LVuksqMiRE52L/4z982IMknNEti522u/83rn8OG6JQA8llvzfXJ6p750Tm8bc0kPwIqtBbRPS6dNcirFO0OM7NaL30+PjDGleQKhXV6MV56Uw7vLvRiTmjVn19697K3aR0rzBPq26cCkhfPqeJfoWbG1gPapNfGd0a0XD39w8PiuGJTDeyvqjq9P22Mbn5/kPHrYgpGeAC51zn1mZuOBMWHzqs8oVIU9rn5+sOPxiBFTzCwB+BuQ45zbYGYTiDx5Xr3cfYdY5glNH4rEpCrnmJw3nZuHXoJZHLkbF1NYXsw5PU5lY0kheUVr6JaZzbiewwHHmu2beHXJ9GhX+4jtc46H332PP1xxOfFxxusLF7Nm2za+NuJ0lm4pYNaq1Qzu2JFvjByBc/BZfj6/f/d9wPtsHvlgBn+66goMY1lBAZM/XxjliA60r8rx4Bvv8c+briA+Lo5J8xexqmgb3z5rOIs3FjB92SqGdcnmu2NH4pxj3rqN/PL1d/e/vkN6Ku3SUshduyGKURy9Xz94A8OGdic9PYl3pv6UR/4xjUmvzjn8C2PEvirHLya/x3++egVxFsdL8xaxsnAbd5wznEX5Bby/dBWndsvme+eOBBxz12zk/sk17ZeVnkr7tBTmrInd9tvnHL9+830eue4K4syY/NkiVhdt4xujh7Nk8xZmLF/N0C4duePMM3DA/PX5/Grqe4C3HT789gf884YrwYy8zQW8PD+2tsOgb4NVzjHxs+l8Z8SlxGF8uG4Jm8uK+VLf01i3vYDPt6yhd6tsLu0/AnCs2LqRZz+bHu1q10uVczz68XQmjLuUODPeXbGEDTuKuW7waazcWsCcDWu8kdyGjsDhWLJlI//4eDrgDXrwzeFn4XAYxkuf50aMBhcLqpzjX7Onc9+5kfFd68c3d8MaBrTL5oacEeAciws28qgfX7YfX5VzxJnx8ue5EaPBNbAPgX+a2UN4x9IX4SVJKcBmM2sKXA/Ud2jZOOBKYCJwHVD7rEN1krPVzJL9si8eVQQnKDva4TdzcnImNOaVisZ+v8M5kvocqzLHk3um/Tm2xnNtADMW7ot2FRpUSWnsdWk5luz12B269ljZc16HaFehQbVIjK1BBI613XsCvxtlxOBgn3fdsj3aNWhYccHeBAGY9NXvHDJK/2rLdUABUAi8iZcE/QgoAj4BUpxz4/0BDF53zr1oZl38xwP85YTPC+ElUOf6y7zaOVdUq8wvgWuBLcByYJ1zboKZTQfudM7lmlkrvHuFuhyrzyMogr3nERERERE59n7nJxwtgBnAPOfcfODvtQs658aHPV4LDDjIvAN+w6eOMj8BflJHmTFhj7eie3zqpMRHRERERKR+HjWzfnjdz570kx6JcUp8RERERETqwTl3XbTrIPWn3/EREREREZHAU+IjIiIiIiKBp8RHREREREQCT4mPiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8PQ7PkevMicnZ8JhynRphHqIiIiIiMhhKPE5Srm5ub86XJkjSIxERERERKQRqKubiIiIiIgEnhIfEREREREJPCU+IiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCaRLsCJ4KcnJy7gYSDzO7SiFVpcAnx0a5Bw6ty0a5Bw2qRaNGuQoMqPa9DtKvQ4Jq+uSnaVWhQ7rKsaFehQTVpEuxtEKBpwL8rEppFuwYNq0nA20+CS4lP40jIzc2dUNeMnJycOqeLiIiIiMixo65uIiIiIiISeEp8REREREQk8JT4iIiIiIhI4CnxERERERGRwFPiIyIiIiIigafER0REREREAk+Jj4iIiIiIBJ4SHxERERERCTwlPiIiIiIiEnhKfEREREREJPCU+IiIiIiISOAp8RERERERkcBT4iMiIiIiIoGnxEdERERERAJPiY+IiIiIiASeEh8REREREQk8JT4iIiIiIhJ4SnxERERERCTwlPiIiIiIiEjgKfEREREREZHAU+IjIiIiIiKBp8RHREREREQCr0m0KyByMJsWr2Pei7NwVVV0H9GP/ucOjZhfvq2U2f97j12hSpolNWf4V8bSIiOZ8m2lzPjXVFyVw+2roteYQfQcOSBKURzcaV06892zxxBvcUz+fBH/nTM3Yn671BTuPe9c0lskUlpRyYQ33qQoFAKgbUoK95w3lrYpyTgH33/pFbaUlkYjjEM6rVsXfnDOGOLi4nh1wUKemn1gjD+9cNz+GO97bSqFZX6MqSnce8FY2qak4IDvPT+JzSWxFeMZPbtw70VnEhdnvDh3Ef+aMSdifof0FB64YhyZLVpQUlHJD5+fQkFpiFO7deTuC8bsL9etdSbfn/gG7+atbOQIvpj777uaUSP7Ulwc4vKrfhft6hyV4d278MNxY4izOF75dCGPfxS5jrZPS+G+L40jw19H732lZh1tl5rCzy4aS9u0FHBw+7Oxt44O796FH43ztsFJny7k8Q8PjG/CxTXx/XhSZHz3fWksbVO9bfCOZyaxKcbi69u6M1cOHE2cGR+tW8zbK3Mj5mckpnDDyWNJbp7Izt2VPDl/GjsqQ/vnJzRpxr1n3sDnW1bzwsLpjVv5I3RS+86MH+bF+N7Kxby6ODLGVkkpfOO0saQmJBLaXclfP5xG8U4vxmevu4P1O7YBsHVnGb+d/lqj1/9wBrXvzI1DvPimr1rMa3m14muRwq2n+vHtquTvH0+juMKL779X38GGEj++8jL+MDP24pPYosRHYlJVVRW5z8/grDsuJjE9mWm/eYHsgV1Ja5+5v8z8SR/R9dQ+dDutD1uW5bPg1Y8ZPn4sCWlJnPuDK4lvGs+eyt1MeWAiWQO70iI9KYoRRYoz4wdjz+I7z79MYVkZj914HTNXrWLttuL9Ze4YM4qpi/OYsngJQzt15JujzuD+KW8C8LMLxvHE7DnMXbeexKZNqXIuWqEcVJwZPzr3LG6f+BKFpWU8Of56Zq5YxZqwGL9z1mimLFrCGwuXkNO5I98acwYTXvNinHDReTz+0SfMWRubMcaZ8bOLz+bmx16koLSMF751Pe8tXcmqwpr4fnT+aF6dv4RXPl3Cqd068v1xI7nrhal8snoDl/31vwCkJSYw7Qc38+HKtVGK5Oi9+tpcnn1uFg/cf220q3JU4sy4+7yz+ObTL1FQWsbTX7ueD5avYvXWmjb83jmjeePzJbz2+RKGdenIHWedwU9f9dbRX1xyHv+e9QmfrPHWUReD6+g955/FN/4XFt+yyPi+P3Y0r39WE9//nX0GP3nFi++Xl3rxzV4dm/EZxlWDxvDXjyexoyLED0ddw8Itq9kSqonvsv4jmZOfxycb8ujVKpuL+w7nqU/f2j//wj6nsWrbpmhU/4iYGTefMoYH3p3Etp0hHjr/GnLzV7OxpCbGG4eMZMaaPGaszqN/22yuPXk4j3zkxbh7317umvJMtKp/WGbG+KFjeOj9SRRXhPjFudcwf+NqNpbWxHfd4JHMWpvHzDV59GubzdUnDefvs2vi+/GbsRvfF2Vm44Ec59zt0a5LUKirm8SkbWsLSW6dRnKrNOKbxNN5aE/yP18TUaZ0czFte2cB0LZXFvkLvfnxTeKJbxoPQNXeqpj7sgbo174d+dt3sKmkhL1VVbyzdBmjenSPKNOlZUty168HYN76DYzq0c2fnkl8XBxz13nzKvbsYdfevY0bwBHo38GPcYcX41t5SxnVKzLGrq0ymbvWiyN33QZG9fTmd/VjnLM2dmMclN2O9dt2kL+9hD37qpjy+TLO7tsjokz3Ni2ZvXoDAJ+s3sDZfbsfsJxxA3oyc/laKvfEVnxHYt781ZSU7Ix2NY7agA7t2LB9Bxv9dXTa4qWM6R3ZRt1aZ+5fD+eu3bB/frdW3jr6yZqadbQyxtbRAVlHEF+rQ8c3e3Xsxtcloy1by0vYtrOUfa6K+RuXM6hdt4gy7ZMzWVbkbYPLt+YzMGx+x7Q2pDZvQV7Rukatd330aNmWgrISCkOl7Kuq4qO1yxmWHRljVlomi7d4MS4uyCen1vxY1j2zLQWhEorKvfhmr1/O0LriK/DiW1KQf8B8kfpQ4iMxqWJHiKSM5P3PW6Qns3NHeUSZ9OxWbFiwGoD8z1azt3IPu0KVAJRvL2PKAxN55SdP0m/skJi62gPQOjmZwrKy/c8Ly0K0Tk6OKLOysIgxPXsCMLpnD5KaNyc1IYFOGRmEdu3ioUsu4smbruf20SOJM2vU+h+J1snJFJTWijElJaLMisKtnNnbi3FMrx4kN29OWmICnTK9GH99+Zf471dv4I4zR8VcjG3TktlcUhPflpIy2qZGtuGyLUWM7e8lQ2P79yA5oTnpiQkRZS4Y1Ic3Plva8BWWA7RJjVxHC0oPXEeXF2zlrD7eOnpWn7B1tGUGZZW7+N2Xv8Szt97Ad8+OvXW0TUoyW0oi42tTR3xn1xFfZz++33/5S0y89Qa+d07sxZeWkMz2ipr4tleGSEuM3AY3lm7l5PbeNnhS++4kNm1OUtMEDLi8/0gmLZ7VmFWut8wWyWzbWRPjtp0hMlpExrhu+1ZO6ejFeErH7rRo1pzkZt5+pml8Ex48/xp+Oe6qmEyIasdXvDNERq02XL99K8Oyvfhysr02DI/vF+dew8/HXsXQrMaNz8y6mNlSM3vCzJab2dNmdo6ZfWhmK8zsFDNLMrPHzGyOmX1qZpf4rx1vZi+b2Zt+2d+ELfer/vLmACPCprc2s5fMbK7/N6KOaslhKPGR49bgy0ZQuGITUx96joIVm0hMT8LivC/mpIwULrj3Gr404QZWf7KUitLj76z0X6bPYHDHLJ686XoGd8ymsKyMKueIj4vjpOws/jJ9Jjf/9xk6pKdx4YB+0a7uUfnTex8wpFM2//3qDQzplE1BaRn7qrwYT87O4k/vzWD8E0+TlZ7GRQP7R7u69fabKR8wrGtHXr79RoZ17ciWkjL2hV2BbJ2SRK92rZi1Ym30KimH9PDbHzC0czbP3noDQ8PW0SZxcQzulMXDb8/ghn8/TXZGGhefdPyto3/w45t46w3kdPbiq/K3wcGdsvjD2zO4/t9Pk3Wcxjdp8Ux6tMrirtHX0qNlFtsryqhyVYzsMojFhWsj7vc5Xv1v/kz6tc3iVxdcS9+2WWwr92IE+Pakx/jx1In8+cM3+UrOaNomp0W5tvX39IKZ9G2TxQPnXUvfNlkU76yJ7zuTH+Onb03krx+9yY1DRtOm8ePrAfwe6OP/XQecAdwJ/Bi4F3jPOXcKcCbwWzOrPhN7MnA1MBC42sw6mll74Od4Cc8ZQPiX+5+Ah51zw4ArgH83bGjBpHt8oq8yJydnQrQrcax86Vc3HZPlJKYnU7695gtp547QAVdtWqQnMeq28wHYU7mbDQtW0axF8wPKpLfPpGjlJjoNieyGFE1Focgzr21SkvcPXFBta3k597z6OgCJTZtyZq8ehHbtorCsjBWFRWwqKQFgxopVDOjQjtcWLm68AI5AUShE29RaMYZd5QLYGirnrpe9m1ETmzblzN49/RhDLC8sYtMOL8YPVqxkQIf28Hnj1f9wCkpCtE+ria9dWgoFpZFtWFhWzv89PRmAFs2acm7/npRV7to//7yBvXhn8Ur2VlU1TqUlQmFp5DraNvXAdbQoVM6dL9Sso2f39dbRgtIQywuK2Oivo+8vW8nArPaNV/kjUFgWol1aZHyFdcT3g1rxlfnxLQuPb+lKBmW355UFjVb9wyqpDJGRWBNfRkIyJRWR22DJrnL+PfcNAJrFN+Xk9j2o2Lubrpnt6Z6Zxcgug2ge35T4uDh27d3D5LwPGzWGwyneGaJli5oYW7ZIZvvOyBi3V5Tz+xlejM2bNOXUjj3YuWf3/nkAhaFSlhTk0yWzNQWhkkaq/eHVji+zRTLba7Xhjopy/jirJr5T6oivqLyUvMJ8umS0pvAYxWdmtwG3hU161Dn3aK1ia5xzC/3yi4F3nXPOzBYCXYBs4GIzu9MvnwB08h+/65wr8V+7BOgMtAKmO+eK/OnPAb388ucA/azmymuqmSU7547/7L0RKfGJstzc3F9Fuw7H0s/f+fN9x2I5LTu3oaywhNDWUhLTk1g3bwXDx4+NKFMZqqB5iwQszljy1ny6n94XgJ3bQzRLSqBJsybs3llJ0erN9Dnr5GNRrWMmb/MWOmZk0D4tlaKyEOf06c19r0+NKJOWmEBpRSUOuOnUYbzuJzZ5WwpIbt6c9MREdlRUMLRTR5YWFEQhikNbsmkLHTPS6ZCWSmFZiHP79uGnk6dElAmPcfzpp/Da516MSzZvISUsxpzOHcnbHFsxLty4hc6t0snKSKWwNMQFg3pz53OR8aW3SKSkogLn4LbRp/DSvEUR8y8c1IeH34rtrjZBtnjTFjplptMh3WvDcf37cM+kWm2YmECJv47efMYpvLpg8f7XpiQ0J6NFItt3VjCsS0eWxNg6unjjgfH9+BDx3XLGKbwSHl/zmvhO6dqRJZtiK751OwponZROyxap7KgIMSSrF0/MfzOiTFKzBHbu9uIb1zOH2euXAPDk/Gn7y5zasS+d0tvGXNIDsGpbAe1S0mmdlEpxRYjhXXrx51mRMaY0TyC0y4vx0v45vL/KizGpWXN27d3L3qp9pDRPoHfrDkxeMi8KURzc6uLI+E7r1ItHPoqML7lZAuV+G17cL4fpq734WjRtzu59XnzJzRLo1boDr+cdu/j8JKd2olPbrrDHVWHPq/COsfcBVzjnloW/yMxOrfXafRz+mDwOOM05V3mYcnIISnwkJsXFx5Fz1Ujef2QyrsrR7fS+pHdoyeevf0JmpzZkD+pK4fL/Z+++46usz/+Pv66wsglhQxgCIqAiQhiCKKK4a11VnLW12trW2vbXYafWLm3tsrXDr7XaqnVTEYtYUQRcEBFlyggrgQSSQJKTwcr1++M+IYOpJjknN+/n4+HDnHPfJ7nefE5yznXuz/2581k8/W3MoNugXmRfcToApQXbee+5N8AAh6FnnkxG786xDdTIXnd+88qr/P7yS0lIMGYsWca64mJumnAKKwoKmb82l5F9+nDLaRNwh8V5edz7ymsA1Ljzxzlz+eOVl2EYKwsLef79JTFOtL+97vz6f69x39TLSDDjhQ+WkltUzM0Tx7NiSwHz1uQyqm+wkhvAexvz+NXLrwJBxj+8+jr3X315kLGgkP8sjq+Me2ucn05/lb9/7jISLIFn313Kmq3F3HrWeJbmFfLayrWMHZDFN86eCDgL1+Vz1/TZ+x7fOyOdnh3TWLBuU+xCfEL3/OJaRo8aSEZGCq/M/BH3/3UW055fcPgHxom97tzz0mv8+ergOfr8+0vJ3VbMLaePZ/mWAl5flUt2/z7cesapOLBoYx6/nFn3HP3t/17nr9dejpmxYkshzy2Ks+eoO3fPfI2/XBPNt3gpa7cVc8uk8SzfXJfva5ODfO9uaJjvd6+8zt+uC34HV2wp5Nk4y1fjzlNL5vCVcRdjZry9cTkF5SVccNw4Nu4oZEnhOo7tnMVFQycAzprifJ5aMie2RX9ENe48tHAO3z/z4uhyz8vJKy3hM8PHkVtSyLt56xjWPYurRkzAcVZuzefvC+YA0Ds9k5vGTsZxDOP5ZTkNVoOLBzXuPJwzh+9OCvK9nruc/LISLjtxHOtKClmUvy66ktsE3J2V2/J5OGcOECx6cOPoydS4k2DG9OU5DVaDixOzgFvN7NbokaCT3f29Q+z/DvAHM+sMlAGfAd6PbnsZuBX4NYCZjXD3xc1XejjZx13xKjs7+86cnJw7m7ac+Pl5TaG25tZY+8f1k1fui78l1JrYS+/tjXUJzWrvnnAPYVl5uPMBtHspfpfnbQptL+kd6xKaVRwuRNnkTh3RJtYlNKtt8XW5oybXNtzDB8BjV912yNU8zKw/MMPdT4jefjh6+5nabcBo4PfAeIIjNuvc/cLGy1Sb2QzgXnefY2afA74H7AAWA7vc/atm1gW4HxhKcOBirrt/qQkjHxV0xEdERERE5CNw9/XACfVu33CQbV88wGMfBh6ud/vCel//A/jHAR5TRLAYgnwCWtVNRERERERCT42PiIiIiIiEnhofEREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhIq2Bm75rZV8ys00d9rBofERERERFpLa4EegELzewJMzvHzA553aVaanxERERERKRVcPc17v4DYDDwOPAQsMHMfmJmmYd6rBofERERERFpNcxsOPAb4NfAs8BngDLg1UM9rm3zlyYiIiIiIvLJmdm7wA7g78Dt7r4zuukdM5twqMeq8RERERERkbhnZgnAs+7+iwNtd/dLD/V4TXUTEREREZG45+41wCGbm0NR4yMiIiIiIq3FK2b2LTPrY2aZtf8dyQM11U1ERERERFqLK6P//0q9+xwYcLgHqvEREREREZHWYqi7V9e/w8wSj+SBmuomIiIiIiKtxZtHeN9+dMRHRERERETimpn1AHoDSWZ2MmDRTelA8pF8DzU+IiIiIiIS784BbgCyCC5eWtv4lAPfP5JvoMZHRERERETimrs/AjxiZpe5+7Mf53voHB8REREREWktssws3QIPmtkiMzv7SB6oxkdERERERFqLz7t7GXA20Bm4Drj7SB6oxkdERERERFqL2nN7zgf+6e7L6t13SGp8RERERESktXjXzF4maHxmmVkaUHMkD9TiBiIiIiIi0lrcCIwAct290sw6A587kgeq8Wle1dnZ2XcC/WNcR4tJax/rCppfwhEdTG3F2oY7YHJSrCtofn5J71iX0Kz2TMuPdQnNyi/sFesSml3V7lhX0Lyqd8W6guaVkhjrCuQo58Aw4ELgLiAFOKJnpaa6NaOcnJy7c3Jy7gTWx7gUEREREZEw+DNwCnBV9HY5cP+RPFBHfEREREREpLUY6+4jzew9AHffbmZHNOdIR3xERERERKS12G1mbQimvGFmXTnCxQ3U+IiIiIiISGtxHzAN6GZmPwfmA784kgdqqpuIiIiIiLQK7v6Ymb0LnElw/Z6L3X3FkTxWjY+IiIiIiLQKZjYOWObu90dvp5vZWHd/53CP1VQ3ERERERFpLf4CROrdjkTvOyw1PiIiIiIi0lqYu3vtDXev4QhnsanxERERERGR1iLXzL5mZu2i/90G5B7JA9X4iIiIiIhIa/ElYDyQD+QBY4Gbj+SBWtxARERERERaBXffCkz9OI9V4yMiIiIiIq1C9IKlNwH9qdfLuPvnD/dYNT4iIiIiItJaPA/MA14B9n6UB6rxERERERGR1iLZ3b/7cR6oxQ1ERERERKS1mGFm53+cB6rxERERERGR1uI2guanyszKzKzczMqO5IGa6iYiIiIiIq2Cu6eZWSZwLJD4UR6rxkdERERERFoFM/sCwVGfLGAxMA54EzjzcI/VVDcREREREWktbgNGAxvc/QzgZKD0SB6oxkdERERERFqLanevBjCzDu6+EjjuSB6oqW4iIiIiItJa5JlZBvAf4H9mth3YcCQPVOMjIiIiIiKtgrtfEv3yTjN7DegIvHQkj1XjIyIiIiIirY67v/5R9lfjI3Fr49INvPnEfLymhiETh3HyeaMabC8vLmPOw69SXV5Nh5QOTL5xCqmZqZQXl/Hyn2fiNU7N3hpOmDycYZNOiFGKgxvbvx9fnzyJBEvghSVLeXTBwgbbu6en8f1zziYjOYmy6mruevEltkUiwba0NG4/Zwrd0lJx4FvP/oeCsiNawr5Fjevfj6+fOYk2lsD0D5byr0YZe6Sn8YNzoxmrqrmzUcbvnTuF7mmpuMM34zDj+IH9+dY5Qb5p7y3h4Tcb5uvZMY07PnUOnZKTKK2q5of/mcnW8iBfj/Q0fnThFHp0TMMdbv33NLaUxl++b58TPEf/894S/nGIfGVV1fygUb4fXziF7h3TwOGrcZjvcO6640pOmziUkpIIl15xb6zL+VgmDOrP7eefQRsznl20lL/PW9Bge8+Oafz0knPITE6mtKqa25/9L4VlEUYf04fvnjtp337HdMnk20+/yKsr17RwgkM7oXs/rjrpdMyMeeuWMXNVToPtnZPT+NyoKaR2SKJiVzUPLpzF9qrIvu2Jbdvz0ynX8t6WXB5fPKeFqz8yJ/fux03jTifBjP+tWsazHzTM2DUljVsnTqFjYhLlO6v53euzKK4MMj53w61s2F4MQFFFOT9/5YUWr/9whvfsx/WjgnyvrV3GC8sb5uuSnMbN46aQ3iGJyK5q/vzmLEqiY/jo1FvZWBrkK64o5zdz4y+fxJdP0vhUZ2dn39lUhRyB/i34syTGampqeOPxuVzwjYtI6ZTKcz9/mv4nHUOnXpn79nn76TcZfMoQjhs/hPwVeSyY9haTb5xCcscULr79ctq0a8Pu6l08decT9BtxDCkZKTFM1FCCGf/vrMl8/enn2FpezoPXXs38tWtZX1yyb5+vnn4aLy1fwcxlyxnZpw9fmngqP50ZHMn94fnn8M+3F7Bww0aS2rWjxj1WUQ4qwYz/N2Uytz0VZHzouquZ1yjjrZNOY+ayFfx32XJG9e3DLaedyl3/DTL++PxzeDiOMyaY8d1zJ/Plx56lsKycR79wDa+vWsu6orp8Xz/rdGZ8sJwZHyxndP8+3Dr5VH70fJDvrk+fy9/nv8M764J8Hof5bj93MrdE8z0WzZdbL983zjqdFz9YzgsHyPfTT5/Lg3Gc70g8/8JC/v3kfH5+11WxLuVjSTDjhxeeyU2PPENBWTlPfvEaXlu5htxtdWP4rXNOZ/ri5UxfvJwxx/Th62dN5HvPzWThuk1c/pd/AZCelMjM2z7Pm2vXxyjJgRnGNSMm8Zv509heGeFHk6eyeEsuW8rr8l1x4kTe3LCCNzeuYEjXLC47fjwP5ry8b/slx49jVdHmWJR/RBLM+OIpk7hj1jSKKyLce9FUFmzMZdOOuoyfGzOR19as4LU1KzixZxbXZY/n93ODjLv27uEbzz8eq/IPy8z4XPYkfvnqNIqrIvzsnKksysslv6wu3zUjJzJv3QrmrVvBsO5ZXDliPH95qy7f92fGb76PyszmAN9y95zD7Ssfz8dufHJycu5uykIOp4WbLImxreu2kt61I+ldOwIwaPSxrF+8rkHjs31zCadcMQGAXkN6M+vP/wWgTds2+/bZu6cG4vAN19AePcjbvoPNpcHqi7NXfsjEgQMbNAXHdO7MH+cER3AXbdrE3Rd/CoD+nTNpYwks3LARgKrdu1u4+iMzrGfDjK+s/JDTBjXM2L9zZ/7wWpDx3Y2buKd+xoT4znhCryBf/o4g36xlK5l03MAGjc+Arpn89n9zAFi4fhO/ueIiIPj0vE1CAu+si+98mw6QL7dRvt/Uy/fbaL4BrSDfkXh3US69enaKdRkf24lZPdhYsoO87cEYzlzyIZOHDCJ3W91Rn4HdOvPrl4LfwQXrNnHfVZ/e7/ucPexY5q1eT/XuPS1T+BEakNmdrRWlFFUERxIX5K3i5F4D2PJh3XO0Z3omT3wwF4CV2/L46ikX7tvWL6Mb6R2SWVq4gX6durds8Ufo2C7dKSgrpbA8yDgvdxVj+g5o0Pj0ycjkoQVBxiVb8vj+mRce8HvFo0Gdu1MYKWVrdAzf2rCKUVkDyF9el693eib/WhTkW16YxzdPaz35GjOztu4eX79IRxktZy1xqXJHhNTM1H23UzqlUrGjosE+nft0Yd2iXADWvZfL7urdVEeqAYiUlPP0nU/w2Hcf4aRzR8bV0R6ArmmpbC0v33d7ayRC17TUBvus3raN0489FoDTjx1ESocOpCcm0qdTJyI7d/KLiy7kH9ddw1dOn0iCWYvWfyS6pjbKWB6ha2rDjGu2bmPSATL2jWb85acv5JHrr+GrcZixa3oqBWX18pVF6JaW1mCfVYVFTB4S5Js8ZBCpHTrQMSmRfp07Eaneyb2f+RSP33QtXz/ztLjL1y09lcJ6+QrLInQ9wnx9O3eiPJrv33Ga72jQLS2VgtL6Y1hOt/SGv4MfFmzjrGGDADhr6CBSE4MxrO+8E4cwc8nK5i/4I8pISqWksi7f9qoIGUkN823aUcSo3kG+kb0GktSuAyntEzHgiuETeWrJ/JYs+SPrnJJKUUVdxuKKCJ2TG2ZcV1LEuH5BxnH9BpLcvgNpHYIxbN+mLb+5aCq/uvAKxvYd0HKFH6FOSakU18tXUhkhs1G+DTuKGNMnyDc6ayDJ7TqQ2j7I165NW352zlR+cvYVZGe1XD4zu8vMvl7v9s/N7DYz+7WZLTWzJWZ2ZXTbJDObZ2bTgeVm1t/MltZ77LfM7M563/46M1sc/T5jovuMMbO3zOw9M3vTzI5o6WbZnxofabXGfWYCm1dt5pm7nmTLqs2kZKRgCcGbq9TMND5z51Sm/vxaVr25ksqyyhhX+9HdP2cuJ2f15h/XXcOIrCy2lpdT406bhAROyurNn16fxxcefZxeHTty/vHDYl3ux/LHOXM5uU9vHrn+Gk7us3/GP86Zx+f/9Ti9MjpywQmtL+Pv/vc6o/pl8fhN1zKybxaFZeXsrQnyjejbm9/9by7XPfgYvTt15FMnHR/rcj+y2nz/vulaRtXL1zYhgZOj+a598DGyOnXkolaY72hw76zXye7fh6dvuY7s/n0oKC1vMK20S2oKx3bvwhtr1seuyE/g6SXzGNylN3eceRXHde1NSWU5NV7DGQOHs6RgfYPzfVqrhxfM44Qevfndp6/ihB69KaoIMgJ84amH+H/Tn+A3r7/EjWNPp0daxxhX+9E99t48hnTrzS/OvYqh3XtTXFmX72vPP8QPZz3B/W+8xHUjT6dbaovlewi4HsDMEoCpQB4wAjgJOAv4tZn1jO4/ErjN3QcfwfdOdvcRwJejPwdgJTDR3U8Gfgz8omliHH20uEELy87Ovh1IPOyOrdTVv72+Sb5PckYqkZK6F6SK7ZH9jtqkZKRwzpfPA2B39S7WvbuWDskd9tsns3cmBas3M2DUoCaprSlsK294dKBbairbyhu+ABdVVPD96TMASGrXjkmDBxHZuZNt5eWs3rpt3xSyuWvWcnzPHrB0WcsFOALbIo0ypqXuW7igVlFFBd97vi7jGdGMWxtnXL2WE3r14IUl8ZNxW1mEHun18qU3PMIFUBSp4FtPByfbJrVrx5lDjw3ylUVYVbht3zSyOR+u4cTePXm+5co/rK1lEbrXy9c9PZVtjfJtO0i+wkb5Xovmk5a1tTxCj471xzCNrWUNfwe3lVfw9SemA5DUvh1nDTuW8uqd+7afe8JgZq9Yw56ampYp+iPYURUhM7kuX6ekVHY0amR2VFfw57dfBKBDm3aM7DWIqt27GJjZk2O79OaMAcPp0LYdbRMS2LlnN88ufaNFMxxOcUWELil1GTunpO5buKBWSVUFd78aZExs245T+g+iYteuYFtlMFOisLyMpQV5DOjclYLyI7rAfYvYXhWhc718mcmplDTKt6Oqgt/Pi45h23aM7jOIyt27oo8P8m2tKGP51jz6d+rK1kjT5DOzm4Gb6931gLs/AODu682s2MxOBroD7wGnAv92971AoZm9DowGyoAF7r7uCH/0v6M/Y66ZpUevV5MGPGJmxwIOtPvkCY9OanxaXmJOTs6dsS6iufx27n13NMX36da/G6VbSynbVkZKpxTWLFzNmV+Y0mCfqvIqElMSsQTjvZmLOO7UoQBESiIkpibStn1bdlZUU7B6CyeeNaIpymoyKwsKyOrUiZ4d09lWHuHMIcfxkxdnNtinY1IiZVXVOHDd2NG8GG1sVhQUktqhAxlJSeyoqmJU3z6sLCiMQYpDW7GlgD71Mp415DjumHHwjNePHc2MJYfIWBhfGZdtLqBPZga9MtLZWhbhnOOH8P1p/22wT0ZSIqXRfJ8/dQzPL16277FpiR3ISE5iR2UVo/v3YfmW+MvXt1G+733EfJ2Sk9gep/mOBkvzgzHsnZFOYXmE8048ju883WgMk5MorarCHW6aOIZp7y1tsP28E4fw+1ficzrYuu2FdE/NoEtyOturIozJGswDCxpeyiO1fSIVu4Ln6PlDspm/YTkA/7dw1r59JvQbSr9O3eOu6QFYXVRIz44ZdEtNp6QywsQBg/nNnIYZ0zokEtkZZLz8pGxmrwoyprTvwM49e9hTs5e0DokM7daLaUvejUGKg1tbXEiPtAy6pqRTUhXhlH6D+dObB8/36WHZvL42mq9dB3burct3XNdezFjedPmiTc4Dh9jlQeAGoAfBkZkph9i3/lz9PTSccdX4w/DGJyY78FPgNXe/xMz6A3MO8bPkENT4SFxKaJPAqVdP5L+/n467c9yEoWT27szC59+ha79u9B9xDFtW5fPOc29jQM/BvTj16tMB2FGwnbeeegMMcBh+zsl0zuoc0zyN7XXnd7Nf5beXXUqbBGPGkmWsKy7mCxNOYWVBIfPX5nJynz58aeIE3OH9vDx+M/s1AGrcuf/1ufzhisswjA8LC5n+wZIYJ9rfXnd+88qr/P7yS0mol/GmCaewIppxZJ8+3HJakHFxXh73vlKX8Y9z5vLHK4OMKwsLef79+Mq41517XnqN+6++jAQzpr+/lNxtxXzp9PEs31LA3FW5jOrfh1vPOBUHFm3M4+6ZrwJBvt/973X+du3lYMaKLYU8tyg+8/05mu/5aL5bovleX5VLdqN8v6yX77f/e52/Xns5Fqf5jsQ9v7iW0aMGkpGRwiszf8T9f53FtOcXHP6BcWJvjfOLF1/lb9dfRpuEBKYtWsrabcV8ZfJ4luUXMufDtYzun8XXp0zE3Xl3Qz4/mzF73+N7ZaTTo2MaOes3xTDFwdW489jiOXzj1ItJMGP++uVsLi/h08PGsX57Ie9vWcdxXbO47IQJuDurivJ5LE6XrD6YGnceeGsOd54TZJy9ejmbdpRw9cnjWFNUyIJN64KV3EZNwHGWF+Tz17fmAMGiB7eMn4zjGMazH+Q0WBQhHtS483DOHG4/I8g3J3c5+aUlXH7iOHJLClmUv46h3bKYOiIYw5Vb8/lHzhwAenXM5MYxk3F3zIzpy3IarAbXAqYBdxEcfbmaoIH5opk9AmQCpwHfBoY0elwh0M3MOgMR4EIaXnzzSuA1MzsVKHX3UjPrCORHt9/QPHGODtZalhjNzs6+s7UeKalfe2vOcSR+O/e+1vGE+gSeXbA31iU0q5qQj+DO6pAHZP+PC8Nmz7T8w+/UivmFvWJdQrMbe1K4P3ctip/ZZM0iJbQT9us8fvVth12Rxcz+Cuxw99vNzIBfAecR/Bn+mbs/aWaTCJaovrDe474G3EbQzOQC6939zuhy1ouB0wkaqs+7+wIzOwV4hODI0YvAte7ev4miHlXC/ZdHRERERKSJRRc1GAd8BsCDIwnfjv63j7vPodHUNHe/D7iv8fd090kH+lnu/hZQf2GEH37swo9yWtVNREREROQImdkwYA0w291Xx7oeOXI64iMiIiIicoTcfTkQfxdGksPSER8REREREQk9NT4iIiIiIhJ6anxERERERCT01PiIiIiIiEjoqfEREREREZHQU+MjIiIiIiKhp8ZHRERERERCT42PiIiIiIiEnhofEREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqHXNtYFHCWqs7Oz74x+3T+GdTS7jh1iXYF8UpWVHusSmtXeveHOB9C2rcW6hGblF/aKdQnNymZsjnUJzS59TN9Yl9CsSvXuSiQu6VezBeTk5Nxd+3W9BkhERERERFqIprqJiIiIiEjoqfEREREREZHQU+MjIiIiIiKhp8ZHRERERERCT42PiIiIiIiEnhofEREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQm9trEuQORgcj/YwOxH5+M1NQw/fRjjPjWqwfbSojJmPvgqVeXVJKZ04MIvTSEtM5XSojKm/WEmuLN3bw0jpwzn5MknxCjFwY3t34+vT55EgiXwwpKlPLpgYYPt3dPT+P45Z5ORnERZdTV3vfgS2yKRYFtaGrefM4Vuaak48K1n/0NBWVkMUhza+IH9+e65k0hISGDaoiU89EbDjD07pvGTi86hU0oSpVXVfP+5mWwtjzC6fx++dc7p+/Y7pksm333mRV77cG1LRzikCYP6c/v5Z9DGjGcXLeXv8xY02N6zYxo/veQcMpOTKa2q5vZn/0thWYTRx/Thu+dO2rffMV0y+fbTL/LqyjUtnODQxg/sz3fOiY7fe0v4xwHG786LzqFTchJlVdV8f1owfgA90tO441NT6J6ehgO3Pj6NzaXx9xwN+xgezl13XMlpE4dSUhLh0ivujXU5H9mQrv245PjTMTPe2biM2WtzGmzvlJTG1JOmkNo+icrd1Tz63ixKqyP7tndo257bT7+WJYW5PLd0TgtXf2RG9OrH50afToIZs9cs4z9LG2bskpLGV8ZPIT0xicjOav4wfxYllUHGJ6+9lY07igEoqijnntdeaPH6D2d4z35cPyrI99raZbywvFG+5DRuHjeF9A5JRHZV8+c3Z1FSFeR7dOqtbCwN8hVXlPObufGXT+KLGh+JSzU1Nbzyz7lc8Z2LSMtM5Z93PM2gkcfQpXfmvn3m/PtNTpgwhBMmDmHD8jxef+otLvzSFFIzUrj2x5fTtl0bdlXv4qHvP8Ggk48hrVNKDBM1lGDG/ztrMl9/+jm2lpfz4LVXM3/tWtYXl+zb56unn8ZLy1cwc9lyRvbpw5cmnspPZ74EwA/PP4d/vr2AhRs2ktSuHTXusYpyUAlmfP/8yXzxX89SWFbO4zddw5wP15JbVJfxm1NO54UPlvPC+8sZ078Pt515Kj/4z0ssXL+JK//2KADpiYnM+NrneWvthlhFOaAEM3544Znc9MgzFJSV8+QXr+G1lWvI3VaX71vnnM70xcuZvng5Y47pw9fPmsj3npvJwnWbuPwv/wIgPSmRmbd9njfXro9RkgNLMON7503mS48G4/fYF67h9QOM34z3l/PCB8sZ3b8PXzvzVH74n+A5+rOLz+XB+e/wdm7wHPU4fY6GeQyPxPMvLOTfT87n53ddFetSPjLDuOyESfz1nWnsqIrwjYlTWVqYS2GkbvwuGjqRnLwVLMxbwaDOWVw4ZDyPLX553/bzjxvH2pLNsSj/iCSY8YWxk7jrf9MoqYxw9/lTydmUS15pXcbPjprInLUreD13BSf0yOKak8fzxzeCjLv27uHbMx6PVfmHZWZ8LnsSv3x1GsVVEX52zlQW5eWSX1aX75qRE5m3bgXz1q1gWPcsrhwxnr+8VZfv+zNjk8/MIu6eeph91gPZ7l7UMlXJ4Wiqm8SlLWu3ktGtIxndOtKmbRuGjjuWNYvWNdinaHMJfYf1BqDv0N77trdp24a27doAsHd3DV4Tf2+4hvboQd72HWwuLWVPTQ2zV37IxIEDG+xzTOfOvLtxIwCLNm1i4qABAPTvnEkbS2DhhmBb1e7d7Nyzp2UDHIETevdgU8kO8ncEGV9atpJJQxpmHNg1kwXrghwL1m/abzvAlGHHMn/1OqrjLOOJWT3YWLKDvO2l7Nlbw8wlHzJ5yKAG+wzs1pkFuZsAWLBuE2ccIN/Zw45l3ur1VO+Or3wn9O7Bpu114zdr2UomHdew/gFdMlmwPhi/hes37ds+oEsmbRISeDu37jkab+MH4R/DI/HuolxKSytjXcbH0jejO0UVpRRXlrHXa3gvfxUndB/QYJ8eaZmsLgrGb01xXoPtWR27kdo+mQ+3xdeHKvUN6tydgvJStkbK2FNTwxvrVzG6T8OMWRmZLC0IMi4tyNtvezwb1Lk7hZFStlaUsbemhrc2rGJUVsP6e6dnsqwwyLe8MG+/7SIfhRofiUuR7RHSOtd9kJKWmUr59ooG+3Tr04VVObkArM7JZVf1bqrKqwEoKy7nHz94gr984xHGXjgyro72AHRNS2Vrefm+21sjEbqmNfzgaPW2bZx+7LEAnH7sIFI6dCA9MZE+nToR2bmTX1x0If+47hq+cvpEEsxatP4j0S0tlYKyehnLInRPS2uwz4eFRZw5NMh45pBBpHboQMekxAb7nHvCcby0dGXzF/wRdUtLpaC0Ll9hWTnd0huO4YcF2zhrWPBG+qyhg0hN3D/feScOYeaS1pAvQrdG47eqsIgzhwTjN7ne+PXr3Iny6p385jOf4ombruUbZ50Wv8/REI9h2GUkpbKjum78SqsjdExqOH75ZUUM7xmM34k9BpLYrgPJ7RIx4NPDJjJ9xfyWLPkjy0xOpaiiLmNxZYTM5IYZ128vYmzfIOPYvgNJbt+B1A7Bc7R9m7bcc/5UfnHeFXHZEHVKSqW4Xr6SA+TbsKOIMX2CfKOzBpLcrgOp7YN87dq05WfnTOUnZ19BdowaIjObZGYz6t3+k5ndUG+XW81skZktMbMhZpZgZqvNrGt0/wQzW2NmXc3sU2b2jpm9Z2avmFn3ls4Tdmp8pNWadNUENq3czMM/fJJNH24mtVMKlhC8uUrvnMbnfj6Vm359LUvnr6SiFX6ief+cuZyc1Zt/XHcNI7Ky2FpeTo07bRISOCmrN396fR5fePRxenXsyPnHD4t1uR/Lb19+nex+WTx587WM6p9FYVk5NfWO0HVJTWFQty68GWfT3I7UvbNeJ7t/H56+5Tqy+/ehoLS8wbTELqkpHNu9C2+sWR+7Ij+B3/7vdUb1y+KJm64lu1/d+LVJSODkvr357f/mcs2Dj9G7U0cuOun4WJf7sYR9DMNu+vJ5DMzszf+beBWDOvdmR1U5NV7DhP7DWbF1fYPzfVqrf+bM4/juvfn1hVcxrHtviivKqampAeCWZx/iu/99gt/Pe4nPjT6d7qkdY1ztR/fYe/MY0q03vzj3KoZ2701xZTCGAF97/iF+OOsJ7n/jJa4beTrd4jNfkbuPBP4CfMvda4BHgWui288C3nf3bcB8YJy7nww8AXwnFgWHmc7xaXnV2dnZd8a6iOZyy/3XN8n3Se2USnlx3QtSeUlkv6M2aZ1SuOS28wDYVb2LDxeuJTGlw377dO2dSd6HmzluTMMpLLG0rbzhp+fdUlPZVt7wBbioooLvTw8+REpq145JgwcR2bmTbeXlrN66jc2lpQDMXbOW43v2gKXLWi7AEdhaHqFHer2M6akU1jvKBbAtUsE3nwpORk1q146zhh5L+c6d+7afffxgXl25hj3RF/F4srU8Qo+Odfm6p6extazhGG4rr+DrT0wHIKl9O84adizl1XX5zj1hMLNXtJZ8DY9SQjB+/+/puvE7Mzp+hWURPizcRv6O4Dn62so1DM/qyX8Wt1j5RyTsYxh2O6oiZCTWjV/HxFRKqxqOX9nOCv7x7osAtG/TjuE9BlG9Zxf9O/VkQGZvJvQbTvu27WhrCezas5sZK99o0QyHU1IZoUtKXcbOyan7Fi6otb2qgl+/HmRMbNuOcX0HUbl7V/D4qmCmxNZIGcsK8jgmsyuFkdIWqv7wtldF6FwvX+YB8u2oquD384J8Hdq2Y3Sfunzba/NVlLF8ax79O3VlaxPlM7ObgZvr3fWAuz/wMb7Vc9H/vwtcGv36IeB54PfA54F/RO/PAp40s55Ae6DhHH/5xNT4tLCcnJy7Y11Dc/r7O/fd0RTfp+eAbmwvLGXHtjLSOqWw4u3VfOqWKQ32qSyvIiklEUsw3n5hESeeNhQImqTE1ETatW9LdUU1eau2kH3uiKYoq8msLCggq1MnenZMZ1t5hDOHHMdPXpzZYJ+OSYmUVVXjwHVjR/NitLFZUVBIaocOZCQlsaOqilF9+7CyoDAGKQ5tWX4BfTtn0DsjncKyCOceP4TvPfffBvtkJCVSGs1448Qx/Oe9hs3beScM4b7Z8TkVZWl+AX0zo/nKI5x34nF85+lG+ZKTKK2qwh1umjiGae8tbbD9vBOH8PtX4jPfsmi+XhnpbC2LcM7xQ/j+tEOM36lj+M/iYPyWbS4grUMHOiUnsb2yijHH9GH55vh7joZ9DMNuU2khXVMyyExKp7Q6wsm9B/Poopca7JPSLpHK3cFz9KxB2byzaTkAj743a98+o7OG0ieje9w1PQBrigvpmZZBt9R0SiojTOg/mN/Pa5gxrUMikZ1BxktOyObVNUHGlPYd2LlnD3tq9pLWIZEh3Xrx/LJ3Y5Di4NYWF9IjLYOuKemUVEU4pd9g/vTmwfN9elg2r6+N5mvXgZ176/Id17UXM5Y3Xb5ok3Mkjc4eGs6gSmy0vfaTkr1E33e7+yYzKzSzycAY6o7+/BH4rbtPN7NJwJ0fq3g5KDU+EpcS2iRw1vUTefpX03F3TjxtKF2yOjPv2XfocUw3jh15DJtW5PP6029jQNaQXky5Plj+uHjzdl77d90L2OjzT6Zrn84xSnJge9353exX+e1ll9ImwZixZBnriov5woRTWFlQyPy1uZzcpw9fmjgBd3g/L4/fzH4NgBp37n99Ln+44jIM48PCQqZ/sCTGifa3151f/vc1/nLtZSSY8Z/FS1m7rZgvTxrPss0FvL4ql+zoSmAA727I4xf/fXXf43t1TKdHeho56zfFKsIh7a1xfvHiq/zt+stok5DAtEVBvq9MHs+y/ELmfLiW0f2z+PqUibg7727I52czZu97fK+MdHp0jON87tw98zX+ck0wfs9Hx++WSeNZXn/8Jp+KE4zfL2cG41fjzu9eeZ2/XXc5hrFiSyHPLorD52jIx/BI3POLaxk9aiAZGSm8MvNH3P/XWUx7fsHhHxgHatx5dtkcvjj2YhLMeGfTcgoiJZw7eBybSgtZVriOQV2yuGDIBNyd3JJ8nonTJasPpsadBxfM4YdnBRlfXbOcvNISrjxpHGuLC8nJW8fx3bO4ZuQEHGd5YT4PvjMHgKyOmdw8bjLujpkxbWlOg9Xg4kGNOw/nzOH2M4J8c3KXk19awuUnjiO3pJBF+esY2i2LqSOCMVy5NZ9/5MwBoFfHTG4cU5dv+rKcBqvBtaANwDAz6wAkAWcSTFk7nAcJprz9y933Ru/rCORHv/5sUxcqYPG4xOiBZGdn35mTk3NnrOuQQ/v7O/e1jifUJ/DQvL2H36kVi1SEewj37g13PoC2beNvIYGmtGdPuMfQZsTv8spN5ay7+sa6hGa1Kb76iybX/ij42Pzxq2875B/S+stZm9mvgEsIpqZFgOnu/nD95azNLBu4190nRR/TDigGxrj7yuh9nwZ+B2wHXgVG1+4vTeMoeOqKiIiIiDSd+tfwcffvcICFCNy9f72vc4BJ9TafRLCowcp6+zxPcO6PNBM1PiIiIiIiLcTMbgduoe7cHmkhWs5aRERERKSFuPvd7t7P3bUySgtT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQk9NT4iIiIiIhJ6anxERERERCT01PiIiIiIiEjoqfEREREREZHQU+MjIiIiIiKhp8ZHRERERERCT42PiIiIiIiEnhofEREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnptY12AhMv2nbGuoPmNHRruzwvSO1isS2hWW8o91iU0u3ZtYl1B86raHesKmlf6mL6xLqHZvfLjjbEuoVn1vrlPrEtoVkmJ4X6dkPAK9zs4ERERERER1PiIiIiIiMhRQI2PiIiIiIiEnhofEREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQk9NT4iIiIiIhJ6anxERERERCT01PiIiIiIiEjoqfEREREREZHQaxvrAkQOZtPSDbz1xHy8pobjJg5jxHmjGmwvLy5j7sOvUl1eTYeUDky6cQqpmamUF5fxvz/PxGucmr01HD95OMMmnRCjFAc3pGs/Lh52OglmvL1pGa+uzWmwvVNSGlcOn0Jq+yQqd1fz2OJZlFZH9m3v0LY93z3tWpYW5vLcsjktXP2R2bxsA4ueCcZw4IRhDDu74RhWFJfxzqOvUh0JxvCUz04huVPqvu27q3bx4s8eJ2v4ALKvPK2lyz+s47v144rhwRjO37CMWasajmFmUhqfHTmF1A5JVOyq5qGcWeyoN4aJbdtz51nXsnhzLk98MKeFqz+8oV37cfmJQb43Nyzjf2v2f45eOyLIV7mrmkcW7Z/vB2dcywcFuTy9ZE7LFn+ETujej6tOOh0zY966ZcxsNIadk9P43Ki6MXxw4Sy2VzXM+NMp1/LellweXzynhas/vCFd+3HJ8UG+dzYuY/YB/s5MPanu78yj7+3/d+b2069lSWEuzy2d08LVf3J33XElp00cSklJhEuvuDfW5Xwso/r045YJp5NgCby0YilPLW44ht1S0/jGpClkJCVRvnMnv5r9EkUVwRi+ePPXWF9SDMC2SBl3vvRCi9d/OCf37seNY4K/M6+sXsZzSxrm65qSxlcnTCE9MYnIrmp+P3cWxZVBvmeuv5WNO2rzlfPLV+Mvn8QXNT4Sl2pqanjj8bmc/42LSOmUyn9+/jT9TjqGTr0y9+3zztNvcuwpQxg8fgj5K/JYOO0tzrhxCskdU/j07ZfTpl0bdlfv4pk7n6DfiGNIyUiJYaKGDOPS4yfx13emUVod4RunTmVZYS6FkZJ9+3xq6ERy8laQk7+CQZ2zuOC48Tz+/sv7tp83eBy5JZtjUf4Rqamp4d2n5nLGrReRlJHKy796mt4nHkPHnnVj+N60N+k/dggDxg2h4MM83n/+LU65Ycq+7R/MeIdug3rFovzDMoyrTprE79+YxvaqCN87YyofbMllS3ndGF5+4kTe2rSCtzeu4LguWVxy/Hj+8W7dGF40dByri+JzDA3jiuGT+NNb09hRFeHbp01lSUEuBfWeo5ccP5EFeSt4Z9MKBnfJ4qKh4/nne3X5LhgyjrXF8ZkPgozXjJjEb+ZPY3tlhB9NnsriRmN4xYkTeXPDCt7cuIIhXbO47PjxPJhTl/GS48exKo7H8LITgr8zO6oifGPiVJY2+jtzUfTvzMK84O/MhUPG89jiunznHzeOtXH8d+Zwnn9hIf9+cj4/v+uqWJfysSSY8ZVTz+D7M56jqCLCfZdexdsbctm4vW4MbzplIrNXreCVVSs4qVcWnxs7gV+/OguAXXv38JVnHotV+YeVYMbNYydx58vTKK6M8KsLp7JgYy55pXX5bhg9kTlrV/Da2hWc2COLa0eN5w/zguforr17+Ob0x2NVfosws0zgSaA/sB64wt23m1kn4CFgIFANfN7dl0Yfsx4oB/YCe9w9+xP8/Ii7px5+z9ZBU90kLm1bt5X0rh1J79qRNm3bMHD0sWxYvK7BPts3l9BrSG8Aeg3pvW97m7ZtaNOuDQB799Tg7i1b/BHom9GdospSSqrK2Os1vLd5FSd0H9Bgnx6pmawp3gTAmuK8Btuz0ruR1iGZD4s2tGjdH0XJ+q2kdu1IapdgDPuOOpa8DxqOYemWErofF4xh98G9yVtSt71k41aqyyvpMaRPi9Z9pI7J7M7WilKKKoMxzMlbxUk9G45hz7RMPtwWjOGHRXkNtvfN6EZ6YjLLt8bnGPbv1J2iilKKo/kW5a9ieI9G+VLr8q0qyuPEetv7dOxGeodkVmyLz3wAA2rHsCLIuCBvFSf3apQxPZMV0Ywrt+Uxot72fhlBxngdw74ZDcfwvfwD/J1Jy2R10UH+znTsRmr7ZD6M4zE8nHcX5VJaWhnrMj6247r1YEtZKQXlZeypqeH1tas4pf/ABvv07dSZxfnBGL6/OY9x/Qcc6FvFpWO7dGdLeSmFkSDf/HWrGNO3Yf1ZHTP5YEuQb0lBHmP6tJ58n5SZtQFuB2a7+7HA7OhtgO8Di919OHA98IdGDz/D3Ud8kqYnjNT4SFyq2BEhNbPuA4aUTqlU7KhosE/nPl1YvygXgPXv5bK7ejfVkWoAIiXlPHvnEzz+3Uc46dyRcXW0B6BjYio7qsr33d5RHaFjYsMPVDaXFXFij0EAnNhjIIntOpDcLhEDLho2kekr5rdkyR9Z5Y5Ig2lryRmpVDUaw05ZXchbHIxh3vu57Knezc5INV7jvPfcG5x8yYQWrfmjyEhMZXu9MdxeFSGj0RjmlRZxcq9gDE/uNZCkdh1IaR+M4eUnTOSZJfE7hh0b56uO0DGpYb78siJG9AzyndQzmi/6HL30+IlMWxa/+QAyklIpqWw0ho0ybtpRxKjeQcaRjcbwiuETeSqOxzAjKZUd1XX5Sg8yhsN7HvjvzKdbwd+ZsOucksK2SN0YFkXK6ZzS8PUst3gbEwYEYzjhmIGktO9AWodEANq3act9l17F7y65cr+GKR5kJqdSVFGXr7giQufkhs/R9duLOKVfkG9c34EkN8r36wuncvcFV+zXMDU3M7vWzBaY2WIz+5uZtTGziJn93MzeN7O3zax7dN/uZjYtev/7ZjbezFLM7MXo7aVmdmV03/Vmdo+ZLQI+A3waeCT6Yx8BLo5+PQx4FcDdVwL9a3/eIWoeVa+GX5tZ7RGiG8zsOTN7ycxWm9mvGj3ud2a2zMxmm1nXJvkHjJGjeqpbdnb27UBirOsIk6m/vb7FftbYz0zgzcfnsurNlfQY3IuUjBQswQBIzUzjsjunUrGjgv/d/1+OGTWQ5PTkFqutKUxfMY9LT5jE6Kyh5Jbks6OqnBqvYUK/4azYur7BPPzWasQlE3j3qbnkvr2SboN6kRQdw9Vzl9Dz+H4NGqfW6Jml87jqpEmc0ncoq4vy2R4dw9MHDGdp4foG58O0RtOWzeMzwycxtu9Q1hTX5ZvYfzjLtrb+fABPL5nH1SMmMaHfUFYV5VNSGWQ8Y+BwlhSsb3C+T2s0ffk8LjvQ35n+4fk7E3b/99Y8vnLqGUw5bhhLt+SzLVJOTXSmw/WP/Z3iigp6pKVzz0WXs76kiC1lpTGu+KN5eOE8bh43iTMGDWV5YT5FFeXs9RoAbn7mIUoqK+iems5d517Gxu3FFJQ3TT4zuxm4ud5dD7j7A9FtQ4ErgQnuvtvM/gxcA6QAb7v7D6LNw03Az4D7gNfd/ZLoUZxU4Fxgs7tfEP2eHev9rGJ3Hxm9/6/uviV6fwFQ29y8D1wKzDOzMUA/IAsoBBx42cwc+Ftt3cA/gK+6+1wz+3WjyCOAk4GdwIdm9kd33xTNlOPu3zCzHwN3AF/9iP+cceOobnyAxJycnDtjXUSY3Dv3vjua4vukZKQSKal7wa3YHtnvqE1KRgpTvnweALurd7H+3bV0SO6w3z6demdSsHozA0YNaorSmkRpdYSMpLR9tzMSU/d7g1G2s4KH330RgPZt2jG8xyCq9+yiX6eeDMjszYR+w2nfth1tLYGde3bz4odvtGiGw0nOSKVye12myh0RkhqNYXJGChNvrhvDTYvX0j65A0XrCti2dgtr5i5l987d1OzdS9sO7Rhx8SktmuFQdlRH6FRvDDslpe73Rr+0uoK/vhOMYYc27RjZexBVu3cxILMnx3buzenHDCexbTvaJCSwc+9upi2LnzEsbZwvMZXSRm/yS3dW8ODCuufoiJ6DqNqzi2MyezIwszcT+w+nQ5tovj27mb4ifvIB7KiKkJncaAwbZdxRXcGf3643hr2CMRyY2ZNju/TmjAHD6dC2HW2jGZ9dGj8Zd1RFyEisy9fxAGNYtrOCfxzg70z/A/yd2bVnNzNWxk++o0FxRQVdU+vGsEtqGsUVDY+cl1RW8NOXZwCQ2LYdE44ZRMWunfseD1BQXsYHm/MY2KVrXDU+JZURuqTU5euckrpv4YJa26squOe14Dma2LYd4/oNonLXrujjg3yFkTKWFuRxTGbXJmt8os3CAwfZfCYwClhoZgBJwFZgFzAjus+7QO1Jq5MJpqPh7nuBUjNbAvzGzO4BZrj7vHrf/8mD1OTRZgbgbuAPZrYYWAK8R3BOD8Cp7p5vZt2A/5nZSuADIMPd50b3+RdwXr1vP9vdSwHMbDlBI7UJqKlXz6PAcwf5N2kVjvbGR+JU1/7dKNtaStm2MlI6pbB24WrO+MKUBvtUl1fRISURSzAWz1zE4FOHAhApiZCYmkjb9m3ZWVFNweotnHjWiBikOLhNpYV0TckgMymd0uoIJ/cazL/ee6nBPintEqncXY0DZw7KZkHecgAeWzxr3z6js4bSp2P3uGt6ADL7daN8aymRojKSMlLY+O5qxt/QcAx3RqponxyM4fKXFzHglGAMx3/u7H375L61gpKN2+Kq6QFYv72QbqkZdE5OZ0dVhOyswfx9YaMxbJ9I5a5gDM89Lps3NgRj+FBO3Rie0nco/TK6x1XTA7BhR/Acrc03svdgHl508HznHJvN2xuDfI8sqss3ts9Q+mZ0j7umB2Dd9kK6p2bQJTmd7VURxmQN5oEFDTOmtk+kIprx/CHZzI+O4f8trMs4od9Q+nXqHldNDxzg70zvwTzaeAzr/Z05a1A272wK8j36XqO/Mxnd1fTEwIdbC+jVMYPuaekUV0Q4feBg7pk9s8E+6YmJlFcHY3jlyNG8vHIZAKntO7Bzzx521+wlPTGRYT168nSjFeFibXVRIT3TM+iWmk5JZYRTjxnM7+Y2fI6mdUgksjPId9mJ2by6OniOpkTz7anZS1qHRIZ068W0Je+2VOkGPOLu32twp9m3vO7E4r0c4n22u68ys5HA+cDPzGy2u98V3Vy/uy00s57uvsXMehI0WLh7GfC56M81YB2QG92WH/3/VjObBowhaHwOZWe9rw9Ve/ydOP0RqPGRuJTQJoHxV09k5u+n4+4cN2Eomb07k/P8O3Tt141+I45h86p8Fj73NgA9B/diwtWnA7CjYDvvPPVG8GfJYfg5J5OZ1TmGafZX485zS+dw85iLSTBjQd5yCiMlnDt4HJt2FLJs6zoGds7igiETcHdyS/J5Nk6XrD6YhDYJZF8xkTn3T8drnAGnDKVjr858MOMdMvt2I2v4MRSuyuf96W9jBl0H9SL7itNjXfYRq3HniffncNuEi0nAeGPDcraUl/CpoePYsL2QDwrWcVyXLC4+fgLgrC7K59/vz4l12Uesxp2nlszhK+Muxsx4e+NyCspLuOC4cWzcUciSwnUc2zmLi4YG+dYU5/PUkjmxLfojqnHnscVz+Mapwe/h/PXL2VxewqeHjWP99kLe37KO47pmcdkJwe/hqqJ8HovDJasPpsadZ5fN4Ytjg3zvbFpOQe3fmdJClhWuY1CXhn9nnmmFS1Yfyj2/uJbRowaSkZHCKzN/xP1/ncW05xfEuqwjVuPOn+e/xs8vuIQEM17+cBkbtpdwXfY4Vm/bytsbchkeXcnNHZZuyef+ea8B0KdTJl877UzcHTPjqfdyGqwGFw9q3Pm/t+dwx5TgOTp7zXI27SjhqhHjWFNcyMJN6zihRxbXjpoA7iwrzOeBt+cAwaIHt4yfTI07CWY8tySnwWpwzWw28LyZ/S7aXGQCaYfZ/xbg9/WmuqUAJe7+qJntAL5wkMdOBz5LcITns8DzAGaWAVS6+67oY+e6e5mZpQAJ7l4e/fps4C5332FmO8zsVHefTzA170gkAJcDTwBXA636xD+LxxWvDiQ7O/vOpp6W1hzf82h379z7WscT6hPYXB7uiOkdLNYlNKstIR8/gOiihqFVtTvWFTSv9A6H36e1e+XHG2NdQrPqfXN8rkbZVJISw/06ATDthtsOGTK6GMH3CBqD3cBXgFdql342s8uBC939huiiAw8AAwiOptwCpAO/JphKthu4xd1zoktRZ7t7UfT7dAaeAvoCGwiWsy4xs1MIFjtwYBlwY3SZ6wHAtGiZbYHH3f3n0e81imAJbAdeBs539xPM7Iboz/xqdL8ZwL3uPsfMItHazyY42nSlu2/7WP+ocUBHfEREREREPgJ3f5L9z8VJrbf9GeCZ6NeFBKuzNTar8R3u3r/R7WKCc4oa7/cWMPgA9+cCJx2k5ndrt5lZf4Jpdrj7w8DD9fa7sN7XrXuVoUa0nLWIiIiIiISejviIiIiIiBxF3H09cEKs62hpOuIjIiIiIiKhp8ZHRERERERCT42PiIiIiIiEnhofEREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQk9NT4iIiIiIhJ6bWNdgIRL5e5YV9D8ynfGuoLmVVjmsS6hWUWqYl1B80tsH+sKmlf1rlhX0LxKj4JX5t4394l1Cc0q/4FNsS6hWVVP6RnrEkQ+Fh3xERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQk9NT4iIiIiIhJ6bWNdwEdQnZ2dfWcTf8/+Tfz9REREREQkDrWaxicnJ+fupv6ezdBIiYiIiIhIHNJUNxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREKv1VzHR44+m5dtIOfp+bjXMGj8MI4/Z1SD7ZHiMt5+9FV2llfTPqUDE26YQnKnVCLFZcx9YCa4U7O3hsGnD2fwaSfEKMXBHd+9H1OHn06CGfPWL+OlVTkNtmcmpXHDqCmkdUiiYlc1f8+ZxfaqyL7tiW3bc9eUa3lvcy7/fn9OC1d/ZIb37Md1I4OMc9Yu44UVDTN2SU7jprFTSE9MIrKzmr+8NYuSaMZ/XXkrm0qLASiqKOe3815o8foP5+Te/bhpXJDvf6uW8ewHDfN1TUnj1olT6JiYRPnOan73+iyKK4N8z91wKxu21+X7+Svxl++knv24YXSQ79U1y3h+WaPxS0njS+Oi47ermj+9MYuSaL5/X30rG3dE81WW8+s58ZcPwj+GI3r143PRMZy9Zhn/Wbr/GH5lfN3v4B/m143hk9fWG8OKcu55Lf7yjerTj1smnE6CJfDSiqU8tbhhvm6paXxj0hQykpIo37mTX81+iaKKIN+LN3+N9SVBvm2RMu58Kf7yHc5dd1zJaROHUlIS4dIr7o11OR/LxMH9+eFFk2ljxlMLl/DAnAUNtvfKSOeXnzmHzJRkSiur+daTL1JQGmHsgD784FNn7NtvQNdMvv74DF5ZvqalI0grosZH4lJNTQ0Ln5zL5K9dRHJGKi/d8zRZw4+hY8/Mffu899ybDBg7hAHjhlDwYR7vPf8WE26YQlLHFM751uW0adeG3dW7ePFnT5A1/BiSM1JimKghw7j6pEn8bv40tldF+MEZU3l/Sy5bykv27fOZEyfy1sYVvLVxBUO6ZnHJ8eN5KOflfds/PWwcq4o2x6L8I2Jm3DBqEr98bRolVRF+evZUFuXnkl9Wl/Hqkycyf/0K5q1bwbDuWVx50nj+8naQcdfePXz/pcdjVf5hJZjxxVMmccesaRRXRLj3oqks2JjLph11+T43ZiKvrVnBa2tWcGLPLK7LHs/v59bl+8bz8ZvPzPj8mEn8fPY0iisj/PK8qeTk5ZJfWpfvupETmbtuBXNzV3B89yyuGjGe+9+sy/fd/8ZvPgj/GCaY8YWxk7jrf9MoqYxw9/lTydmUS169MfzsqInMWbuC13NXcEKPLK45eTx/fKMu37dnxHe+r5x6Bt+f8RxFFRHuu/Qq3t6Qy8btdfluOmUis1et4JVVKzipVxafGzuBX786CwjyfeWZx2JVfpN4/oWF/PvJ+fz8rqtiXcrHkmDGnRefxQ0PPk1BaTnPfvVaXl2+ljVbi/ftc/sFp/Ofd5czbdEyxg3sw/87dyLffnIm7+Ru4qI//BOAjkmJvPKdG5m/en2MkjQvM7sV+AqwF3jR3b9jZmOAB2p3Ae5092lmdhzwZL2HDwB+7O6/b8ma45WmuklcKl6/lbSuHUnr0pE2bdvQb9SxbHp/XYN9SgtK6D64NwDdB/cm74Nge5u2bWjTrg0ANXtqcPeWLf4IHJPZnW0VpRRVlrHXa1iYt4oRPQc02KdXeiYrt20CYOW2vAbb+2Z0I71DMssLN7Ro3R/FwMzuFEZK2VZRxt6aGt7euIpRWQ0z9u6YybLCIOPywrz9tsezY7t0p6CslMLyMvbU1DAvdxVj+jasv09GJku2BPmWbMljbN/Wk29Q5+4UlpeyNRKM35vrVzH6QONXEORbVphHdisaPzg6xrAgOoZ7amp4Y/0qRvdpWH9WRiZLo2O4tCBvv+3x7LhuPdhSVkpBdPxeX7uKU/oPbLBP306dWZwf5Ht/cx7j+reefEfi3UW5lJZWxrqMj214nx5sKN7OppJSdu+t4cX3V3LmsIZjOKh7Z95auxGAt9du4qxhg/b7PueeOJi5H66jeveeFqm7pZhZGzM7A/g0cJK7Hw/UHtpbCmS7+wjgXOBvZtbW3T909xHR+0cBlcC0lq8+PqnxkbhUtSNCcqfUfbeTO6VSVVrRYJ+M3l3YtDgXgE2Lc9lTvZudkWoAKkrKefFnTzDtB48w7OyRcXW0ByAjMZWSqvJ9t7dXRchISm2wz6bSIkb2Cv7An9xrIEntOpDSPhEDrjhxIs8snd+SJX9kmcmpFFfWZSypjNCpUcaN24sYnRVkzM4KMqa2TwSgXZu2/PTsqfxkyhWM6h1/b1Y6p6RSVFGXr7giQufkhvnWlRQxrl+Qb1y/gSS370BahyBf+zZt+c1FU/nVhVfE5ZvpxuNXXBmhU6N8G7YXMaZPkG9MnyBf/fH7xXlT+dk5V8RtQ3Q0jGGDfJURMhvlW7+9iLF9g3xj+0bHsF6+e86fyi/OuyIuG6LOKSlsi9TlK4qU0zml4d/63OJtTBgQ5JtwzEBSGo3ffZdexe8uuXK/hklaRo+OaWzZUTeGBaURundMa7DPys3bOOeEYwE4+/hjSU3sQEZyYoN9LjhpCDMWr2z+gusxs2vNbIGZLTazv0WblIiZ/dzM3jezt82se3Tf7mY2LXr/+2Y23sxSzOzF6O2lZnZldN/1ZnaPmS0CPgPcAtzt7jsB3H1r9P+V7l7b6SUCB/qU90xgrbtvMLO2ZrbQzCZFf84vzezn0a/vNrPlZvaBmd17sJqb6Z+yRWmqm7RaIy+dQM6Tc8l9eyXdBvUiKSMFSzAAUjLTuOCHU6ncUcHcv/2XvicPJCk9OcYVfzRPL5nH1SdNYny/oawqymd7VTk1XsOkAcNZUrC+wfk+rdVji+dxw6hJnDZgKCu35lNSGWQEuG36Q2yvqqBrSjo/mHwZm0qL2RopjXHFH83DC+Zx8ymTOPPYoSwryKeooi7fF556iJLKCrqnpfPTcy9jw/ZiCspbV75HF83j86MncfrAoazYmk9xvXxfmRaMX7fUdH501mVs2lFMYSsbPwj/GP4zZx5fGDOJMwYNZXlhdAxrgny3PPsQJdExvPPsy9i4vfWN4f+9NY+vnHoGU44bxtIt+WyLlFMTnQVw/WN/p7iigh5p6dxz0eWsLyliS1nrync0uPvFOdxx8ZlcOup4Fq7Lo6C0nL01de/xu6alcFyPLsxbtb7FajKzocCVwAR3321mfwauAVKAt939B2b2K+Am4GfAfcDr7n6JmbUBUgmO0mx29wui37NjvR9R7O4jo/ffDkyMNinVwLfcfWF021jgIaAfcF29RqjWVODfAO6+x8xuAJ6JTp07FxhrZp2BS4Ah7u5mlhF97IFqbvXU+MSJ7Ozs2wk69lbtonuub5Lvk5SRSuX2ujf2ldsjJHVs+EleckYKp33xPAB2V+9i4+K1tE/usN8+HXtlsm3NZvqO3P/weKzsqI6QmVT3qVanpFR2NGpkSqsr+Ms7LwLQoU07RvUeRNXuXQzM7MmgLr2ZNGA4Hdq2o21CAjv37Oa5ZW+0aIbDKamM0Dm5LmNmcup+zdqOqgp+Pz+asW07xvQZROXuXQBsrwqO8G2rKGPF1jz6d+oaV41PcUWELil1+TqnpO476b1WSVUFd78a5Ets245T+g+iYleQr6QyyFdYXsbSgjwGdO4aV2+aG49f5+RUtjfKt72qgt/MrRu/sQcYv62RMpYX5tE/s2vcvWk+GsawQb7k1H0LF9TaXlXBr1+vyzeub90YltQbw2UFeRwTZ2NYXFFB19S6fF1S0yiuaDgzoKSygp++PAMI8k04ZhAVu3buezxAQXkZH2zOY2CXrmp8WlhBaTk9M+rGsEfHVApLyxvss7W8gq/8azoAye3bcc6Jgymv3rlv+/nDj+PlZavZE23Ym4qZ3QzcXO+uB9y99pyaMwmmkS00M4AkYCuwC5gR3eddYEr068nA9QDuvhcoNbMlwG/M7B5ghrvPq/ez6p+j0xbIBMYBo4GnzGyAB94Bjo82Yo+Y2Ux3r47W3x64CPhe7Tdy92Vm9q9ojae4+y4zKyVoqP5uZjPq1b9fzR/hny9uqfGJH4k5OTl3xrqIT+qu2ffd0RTfp3O/bpRvLSVSVEZSRgob3l3NhM9NabBPdaSKDsmJWIKxbNYiBp4yFAiapPYpibRt35adldVsW7uFIZNHNEVZTWb99kK6pWbQJTmd7VURRmcN5sGFLzXYJ7V9IhW7qnHgvOOymb9+OQAP5szat8/4vkPp16l73DU9ALklhfRIy6BrSjolVRHG9R3M/W8ePONFw7KZkxtkTG7XgV1797CnZi+p7RMZ3LUXM1a8G4MUB7e6qJCeHTPolppOSWWEiQMG85s5DfOldUgksjPId/lJ2cxeFeRLad+BnXuCfGkdEhnarRfTlsRXvrXFDcdvfP/B3Df/4PkuPj6b19YeON9xXXsxfXl85YPwj+Ga4kJ6ptXlm9B/ML+fd/B8l5yQzatrDpxvSLdePL8svvJ9uLWAXh0z6J6WTnFFhNMHDuae2TMb7JOemEh5dZDvypGjeXnlMgBSo/l21+wlPTGRYT168nSjFeGk+S3JK6B/505kdepIYVk5F5w0hG8+8WKDfTolJ7Gjqgp3+OIZY3lm4dIG2y8cMYR7Z86jqUWbnAcOstmAR9z9ew3uNPuW151YvJdDvM9291VmNhI4H/iZmc1297uim+t38HnAc9Hvu8DMaoAuwLZ632uFmUWAE4DaJ/J5wCJ3L2z0o08EdgDdoo/dE10o4UzgcuCrBE1PKKnxkbiU0CaB7Csn8uqfpuM1zsBThpLRqzPvv/AOnft1I2v4MWxdlc/i598Gg26DejH6ytMBKC3YzqJn3wj+LDkMPetkOvXuHNtAjdS48/jiOXx9wsWYGW9sWM7m8hIuGjqODTsKeX/LOgZ3yeLSEyaAO6uK83l88ZxYl/2R1LjzcM4cvjvpYhLMeD13OfllJVx24jjWlRSyKH9ddCW3Cbg7K7fl83DOHCA4af7G0ZOpcSfBjOnLcxqsBhcPatx54K053HlOkG/26uVs2lHC1SePY01RIQs2rQtWARs1AcdZXpDPX9+aAwQnzN8yfjKOYxjPfpDTYCWxeFDjzkML5/D9My+OLke+nLzSEj4zfBy5JYW8mxeM31Ujgnwrt+bz9wVzAOidnslNY+vyPb8sp8FqcPHiaBjDBxfM4YdnXRxdkjwYwytPGsfa4kJy8tZxfPcsrhkZzVeYz4PvzAEgq2MmN4+bjLtjZkxbmtNgNbh4UOPOn+e/xs8vuIQEM17+cBkbtpdwXfY4Vm/bytsbchkeXcnNHZZuyef+ea8B0KdTJl877cx9+Z56L6fBanCtxT2/uJbRowaSkZHCKzN/xP1/ncW05xcc/oFxYm+N85PnZ/PQjZfRJiGBZxYuYU1hMbdNmcCSvAJeXbGWsdGV3Nydhevy+Ml/Zu97fO9O6fTomMaCdZtauvTZwPNm9jt332pmmUDaYfa/Bfh9vWljKUCJuz9qZjuALxzksf8BzgBeM7PBQHugyMyOATZFG5d+wBBgfb3HXUV0mlstM7uU4OjRacCMaMOzB0h29/+a2RtA7sFqdvdWf9TH4nHFq5aSnZ19Z7wcZYmnWj6Ju2bfF/on1KbScEes3Hn4fVqzSFWsK2h+ie1jXUHzqt4V6wqaV7uj4CPJSGW4/47mP9Dib8RbVPWUnrEuodmtvudbdqjt0cUIvkewUNhuguWmX3H31Oj2y4EL3f2G6CIHDxAsLb2XoKFIB34N1EQff4u755jZeoLV2oqi36c9wXk8Iwim0n3L3V81s+uA26OPrQHucvf/RB+TAmwEBtQ2K2bWBXgTONPdN5nZ1wim690OPE9wuoUB97r7Iweq2d3f+pj/nHHjKPjzKiIiIiLSdNz9SRqeiwP1FgBw92eAZ6JfFxIsSd3YrMZ3uHv/Rrd3AdceYL9/Af86SG0VQOdG9xUBg+vdvq/e5jEH+B4Hq7lV03LWIiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQk9NT4iIiIiIhJ6anxERERERCT01PiIiIiIiEjoqfEREREREZHQU+MjIiIiIiKhp8ZHRERERERCT42PiIiIiIiEnhofEREREREJvbaxLiDGqrOzs++MdRFR/WNdQFPYVuGxLqHZJVisK2heIY8X+vEDaNsm1hU0r5TEWFcgn1RSYrh/Eaun9Ix1Cc0q8X9bYl1C87sn1gVIcziqG5+cnJy7Y11DrThqwEREREREQkdT3UREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQk9NT4iIiIiIhJ6anxERERERCT01PiIiIiIiEjoqfEREREREZHQU+MjIiIiIiKhp8ZHRERERERCT42PiIiIiIiEnhofEREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhF7bWBcgcjBDu/bjshNPJ8GMtzYs439rchps75SUxjUjppDaIYnKXdX8c9EsdlRH9m1PbNue759xLUsKcnl6yZyWLf4IHN+tH1cMD/LN37CMWasa5stMSuOzI4N8FbuqeShn/3x3nnUtizfn8sQHc1q4+iMzvGc/rhsZZJyzdhkvrGiYsXNyGjePnUJaYhIVO6v5y1uzKKkKMv7zylvZVFoMQHFFOb+d90KL1384J/fux41jg3yvrFrGc0sa5uuaksZXT51CemISkZ3V/H7uLIorg3zPfPZWNm4P8m2rKOeXs+Mv3+HGr0tyGjeNrctXf/z+VW/8iuJ0/CDIeP2oIONra5fxwvL9M948bgrpHZKI7Krmz2/WZXx06q1srPcc/c3c+MsY9nwn9+7HjWOiv4OrD/I7OCH6HN3V6Hfw+lvZuCP6Oxgp55evxl8+gImD+/PDiybTxoynFi7hgTkLGmzvlZHOLz9zDpkpyZRWVvOtJ1+koDTC2AF9+MGnzti334CumXz98Rm8snxNS0f4RO6640pOmziUkpIIl15xb6zLkVZOjY/EJcP4zPBJ3P/WNHZURfj2aVNZUpBLQaRk3z6XHD+RBXkrWLBpBYO7ZPGpoeP513sv79t+wZBxrC3eHIvyD8swrjppEr9/YxrbqyJ874ypfLAlly3ldfkuP3Eib21awdsbV3BclywuOX48/3i3Lt9FQ8exuig+8wGYGZ8dNYm7X5tGSVWEu86eyrv5uWwuq8t49ckTmb9+BfPWrWBY9yyuOGk8f307yLhr7x5+8NLjsSr/sBLMuHncJO6cNY3iygi/+tRUFmzMJa+0Lt8NoycyZ+0KXluzghN7ZnHtqPH8YV5dvm9Oj998ZsYNoybxy+j4/fTsqSzKzyX/EON35Unj+Uu98ft+HI8fBBk/lz2JX746jeKqCD87ZyqL8hpmvGbkROatq5dxxHj+8la9jDPjN2PY8yWYcfPYSdz5cvR38MJD/A6uXcGJPVrX7yAEGe+8+CxuePBpCkrLefar1/Lq8rWs2Vq8b5/bLzid/7y7nGmLljFuYB/+37kT+faTM3kndxMX/eGfAHRMSuSV79zI/NXrY5Tk43v+hYX8+8n5/Pyuq2JdioSAprpJXOrXqTtFFaUUV5ax12t4N38VJ/YY0GCfHqmZrNq2CYBVRXkNtvfp2I20Dsms3LahRes+UsdkdmdrRSlF0Xw5eas4qWfDfD3TMvkwmu/DorwG2/tmdCM9MZnlW+MzH8DAzO4URkrZVlHG3poa3t64ilFZDTP27pjJssIg4/LCvP22x7Nju3RnS3kphZEy9tTUMD93FWP6Nqw/KyOTD7YE+ZZsydtvezwL+/gBDOocZNwazfjWhgNkTG+9GcOeb7/fwXUH+B3sWO93sCCPMX1aTz6A4X16sKF4O5tKStm9t4YX31/JmcMGNthnUPfOvLV2IwBvr93EWcMG7fd9zj1xMHM/XEf17j0tUndTendRLqWllbEuI6bM7FYzW2lmy8zsV9H7xpjZ4uh/75vZJdH7j6t3/2IzKzOzrzdzfevNbEn05+XUuz/TzP5nZquj/+/UnHUcCTU+EpcyElPZXlW+7/aO6ggZSakN9skvK+KknsEf+JN6DiSpXQeS2yViBEeD/rNsfkuW/JE0zre9KkJGYsN8eaVFnNwryHdyryBfSvsg3+UnTOSZJfGbD6BTciollXUZSyojdGo0hhu3FzE6K8iYnRVkTG2fCEC7Nm256+yp3DnlCkb1jr83K5nJqRRV1OUrrozQOaVhvvUlRZzSL8g3rt9Aktt3IK1DkK99m7b8+lNTufuCK+KyIcpMTqX4E47fT8+eyk/idPwAOiWlUlzRMGNmcsOMG3YUMaZPkHF01kCSG2X82TlT+cnZV5Adhw1D2PPt9ztYEaFzo3zrt9f7Hex7gN/BC+P3dxCgR8c0tuyoy1hQGqF7x7QG+6zcvI1zTjgWgLOPP5bUxA5kJCc22OeCk4YwY/HK5i9YmpSZtTGzM4BPAye5+/FA7Xy/pUC2u48AzgX+ZmZt3f1Ddx8RvX8UUAlMa4Fyz4j+3Ox6990OzHb3Y4HZ0dsxpalu0mpNWzaPK4ZPYmzfoawtzmd7VTnuNUzsP5xlW9c3OB+mNXpm6TyuOmkSp/QdyuqiIF+N13D6gOEsLWz9+QAeXzyPz46axMQBQ/lwaz4llUFGgK9Pf4jtVRV0TUnn+5MvY1NpMVsjpTGu+KN5eOE8bj5lEmcMGsrywnyKKsrZG81389MPUVJZQffUdO469zI2bi+moLx15Xts8TxuGDWJ0wYMZWWj8but3vj9oJWOH8Bj783jhuxJnHbMUFZuy6e4XsavPR9k7JaSzg/OvIyNO1pfxrDne3jhPG4ed5DfwWda/+8gwN0vzuGOi8/k0lHHs3BdHgWl5eyt8X3bu6alcFyPLsxbtT52RYaQmV0LfA1oD7wDfBkoBf4AXAhUAZ9290Iz6w78FajtsG8B3geeArKANsBP3f1JM1sPPAlMAX4FXArc7e47Adx9a/T/9Q+DJQLO/s4E1rr7BjNrC7wFfNvd55jZL4Ead/+Bmd0NXATsAV52928dqGZ3f/Mj/jN9GpgU/foRYA7wXTO7Ezgm+r37At8AxgHnAfnAp9x990f8WUdEjU/8qM7Ozr4z1kV8Uqf8+Lom+T47qiN0Sqr7VCsjMZUdVQ3f6JftrODBhS8C0L5NO07qOYiqPbvon9mTgZm9mdh/OB3atKNNQgI79+xm+oo3mqS2ptA4X6ek1P0amdLqCv76TpCvQ5t2jOw9iKrduxiQ2ZNjO/fm9GOGk9g2mm/vbqYti598ANsrI2Qm12XMTE5le6Mx3FFVwR/mRzO2bcfoPoOo3L0reHxVBQDbKspYsTWPfp26xtWbrpLKCF1S6vJ1Tk6luKJhvu1VFdzzapAvsW07xvUbROWuXdHHB/kKI2UsLcjjmMyucfWmq6QyQucjGL/f1xu/MYcYv/5xNn4QHGntnNIwY0nlATLOO/RzdGtFGcvjMGPY8+33O5iSum/hglrbqyq457XW+TsIUFBaTs+Muow9OqZSWFreYJ+t5RV85V/TAUhu345zThxMefXOfdvPH34cLy9bzZ6ampYpOiTM7Gbg5np3PeDuD0S3DQWuBCa4+24z+zNwDZACvB1tJn4F3AT8DLgPeN3dLzGzNkAqwVGaze5+QfR7dqz3s4rdfWT0/tuBiWb2c6Aa+Ja7L4xuGws8BPQDrnP3xnMZpwL/BnD3PWZ2A/CMmd0a/fljzawzcAkwxN3dzDKijz1QzQfjwMtm5sDfav+dgO7uviX6dQHQvd5jBgJnAMMIGrLL3P07ZjYNuAD4zyF+3semxidO5OTk3B3rGprCrdP/cEdTfJ+NOwrpmpJB5+R0dlRFGNV7MA8veqnBPintE6ncVY0DZx+bzdsblwPwz0Wz9u0zts9Q+mZ0j6umB2D99kK6pdbly84azN8XHjzfucdl88aGIN9DOXX5Tuk7lH4Z3eOu6QHILSmkR1oGXVPSKamKMK7vYP78ZsOMqe0TqYhmvGhYNq/nBhmT23Vg19497KnZS2r7RAZ37cWMFe/GIMXBrS4qpGd6Bt1S0ympjHDqgMH87vWG+dI6JBLZGeS7bHg2r64O8qW078DOPUG+tA6JDOnei2lL4ivfgcbv/sOM35xWNH4Aa4sbZjyl32D+9ObBx/DTw7J5fW10DNt1YOfeujE8rmsvZiyPr4xhz7ff7+Axg/nd3EP8Dp54iN/BbvH3OwiwJK+A/p07kdWpI4Vl5Vxw0hC++cSLDfbplJzEjqoq3OGLZ4zlmYVLG2y/cMQQ7p05ryXLDoXom/cHDrL5TIJpZAvNDCAJ2ArsAmZE93mX4KgNwGTg+uj33QuUmtkS4Ddmdg8ww93rD9KT9b5uC2QSHBEZDTxlZgM88A5wfLQRe8TMZrp7NYCZtSc4ivO9epmWmdm/ojWe4u67zKyUoKH6u5nNqFf/fjUf4p/rVHfPN7NuwP/MbKW7z62/Q7Spqn9Uama0aVxCcMSr9pd3CdD/ED/rE1HjI3Gpxp2nl8zhy+Muxsx4e+NyCspLOP+4cWzcUcjSwnUc2zmLTw2dADhrivPjcsnqg6lx54n353DbhItJwHhjw3K2lJfwqaHj2LC9kA8K1nFclywuPj7It7oon3+/PyfWZX8kNe48kjOH70y6mAQzXs9dTn5ZCZedOI51JYUsyl/H0O5ZXHnSBNydD7fl83DOHCA4af7zoydT406CGS8sz2mwGlw8qHHn/96ewx1nB/lmr17Oph0lXHXyONYUFbJw0zpO6JHFtdkTwJ1lhfk88NYcIFj04Jbxdfme+yCnwUpU8aDGnYdz5vDdQ4zfsHrjt7LR+N1Yb/ymL89psJJYvKjNePsZQcY5ucvJLy3h8hPHkVv7HO2WxdQR0Yxb8/lHNGOvjpncOGYy7o6ZMX1Z/GU8GvL939tzuGNK9HdwTfR3cMQ41hTX+x0cVe938O05QLDoQYPfwSXx9zsIsLfG+cnzs3noxstok5DAMwuXsKawmNumTGBJXgGvrljL2OhKbu7OwnV5/OQ/s/c9vnendHp0TGPBuk0xTPHJ3POLaxk9aiAZGSm8MvNH3P/XWUx7fsHhH9i8DHjE3b/X4E6zb7l77Zv7vRzifba7rzKzkcD5wM/MbLa73xXdXFFv1zzguej3XWBmNUAXYFu977XCzCLACUDt4gLnAYvcvbDRjz4R2AF0iz52j5mNIWjmLge+StD0HDF3z4/+f2v0iM0YYC5QaGY93X2LmfUkaA5r1U7dqzGz3fX+3Wpoxv7E6n6OyCd36/Q/hP4JtWtvrCtoXhXVsa6geYU9H0Cj85pDRy9brV/VrlhX0LyWrmiW0xPiRuL/thx+p1ZuyaLf2MG2mdkw4HmCqW5bzSwTSAOWuXtqdJ/LgQvd/QYze4JgCtzv600bSwFK3L3azC4EvuDuF0fP8cl296Lo9/kS0Mvdf2xmgwkWCehLcFRkU7Rx6UcwXWx4vcc9Acxy93/Uq/tS4IvArQRHdsYQnNeTHM3REch1984Hqtnd9zvqY2YpQIK7l0e//h9wl7u/ZGa/Jpi2d3d0yl5mdDrbnUDE3e+Nfo9IvX+3Btuamo74iIiIiIgcIXdfbmY/JDivJQHYDXzlEA+5DXjAzG4kOBJ0C5AO/Dp6BGd39L4DeQh4yMyWEkyl+2x02tipwO1mtpvgKMmX6zU9KQTT7L5Y+03MrAtwN3Cmu28ysz8RLMRwO/C8mSUSHMn65iFqfusA9XUHpkWn/LUFHnf32mlrdxNMzbsR2ABccYh/oxahIz7SpHTEp/UL+xGRsOcDHfGR+KcjPq3b0X7ER1ovXcdHRERERERCT1PdRERERETkoKLLXs8+wKYz3b24pev5uNT4iIiIiIjIQUWbmxGxruOT0lQ3EREREREJPTU+IiIiIiISemp8REREREQk9NT4iIiIiIhI6KnxERERERGR0FPjIyIiIiIioafGR0REREREQk+Nj4iIiIiIhJ4aHxERERERCT01PiIiIiIiEnpqfEREREREJPTU+IiIiIiISOip8RERERERkdBT4yMiIiIiIqGnxkdEREREREJPjY+IiIiIiISeGh8REREREQk9NT4iIiIiIhJ6anxERERERCT01PiIiIiIiEjombvHugaRj83Mbnb3B2JdR3MKe8aw54PwZ1S+1i/sGZWv9TsaMkrz0xEfae1ujnUBLSDsGcOeD8KfUflav7BnVL7W72jIKM1MjY+IiIiIiISeGh8REREREQk9NT7S2h0N833DnjHs+SD8GZWv9Qt7RuVr/Y6GjNLMtLiBiIiIiIiEno74iIiIiIhI6KnxERERERGR0FPjI62Omd12JPeJSPMzs+RY1yAiInIkdI6PtDpmtsjdRza67z13PzlWNcmRMbMh7r7SzEYeaLu7L2rpmpqDmU0A7gT6AW0BA9zdB8SyrqZkZuOBB4FUd+9rZicBX3T3L8e4tCZhZscCvwSGAYm194dsDLsC32X/jJNjVlQTMbPOBL+DEwAH5gN3uXtxLOtqSmY2Dljm7uXR2+nAUHd/J7aVNR0zawNcAPQn+FsKgLv/NlY1SevW9vC7iMQHM7sKuBo4xsym19uUBpTEpqqmZ2aXAvcA3QjeMNe+aU6PaWFN45sEF6H7zQG2OdDq33BF/R34BvAusDfGtTSX3wHnANMB3P19MzsttiU1qX8AdxDkPAP4HOGbJfEY8CTBG8svAZ8FtsW0oqbzBDAXuCx6+xqCrGfFrKKm9xeg/odIkQPc19q9AFQDS4CaGNciIaAjPtJqmFk/4BiCT2Fvr7epHPjA3ffEpLAmZmZrgE+5+4pY1yIfj5m94+5jY11Hc6rNWP9oq5m97+4nxbq2pmBm77r7KDNb4u4n1r8v1rU1lXoZP3D34dH7Frr76FjX9kmZ2VJ3P6HRffvGMgzMbLG7j2h0376xDIOw5ZHY0xEfaTXcfQOwATgF9h3Wr30OpxOeoz6FR0PTY2YnsP8Um3/GrqIm9ZqZ/Rp4DthZe2dYpvJFbYpOd3MzawfcBoTpebvTzBKA1Wb2VSAfSI1xTU1td/T/W8zsAmAzkBnDeprSy2Y2FXgqevtyYFYM62kOuWb2NYKjPABfBnJjWE9zmGlmZ7v7y7EuRMJBR3yk1TGzm4G7CA5/1xCy8yfM7A9AD+A/NHzT/FysampqZnYHMImg8fkvcB4w390vj2VdTcXMXjvA3R6GcydqmVkX4A8EU4cMeBm4LSznUJjZaIJGLgP4KdAR+JW7vx3LupqSmV0IzAP6AH8k+ADpJ+4+/ZAPjGNmVk4wbdaAFOqmRyUAkZBMGQbAzLoB9xFMEXZgNvB1d98a08KakJldAjxKMH67CdfUb4kBNT7S6pjZauAUdy+KdS3Nwcz+cYC73d0/3+LFNBMzWwKcBLzn7ieZWXfgUXefEuPSRBqIHln22hPIw8TMOoelUZVwMrN1wKeBJa43rNIENNVNWqO1QGWsi2gu7v65WNfQAqrcvcbM9kTfWG4l+NQ5NKJTh46n4VS+u2JXUdMwsz8SfLp8QO7+tRYsp9mYWTbBAgdp0dulwOfd/d2YFta03jazxQQ5Z4btjaWZ9aZuZUUA3H1u7CpqWmb2K+BnQBXwEjAc+Ia7PxrTwprWJmBp2J6bEjtqfKQ1+h7wppm9Q8OpYGF5w5UI3Mj+b5pDc8QHyDGzDOD/CFY+iwBvxbSiJmRmfwWSCVYDe5Dg/IIFMS2q6eRE/z+BYKrik9HbnwGWx6Si5vEQ8GV3nwdgZqcSNAhhOtF6MMFUxc8D95nZU8DD7r4qtmV9cmZ2D3AlwXOydmVFJ1jpLSzOdvfvRKeDrQcuJcgXpsYnF5hjZjNp+Hqv5azlY9FUN2l1zGwBwTUZGixv6e6PxKyoJmRmTwMrCZbuvotgGdYV7h7Ki7SaWX8g3d0/iHUtTaV2JaJ6/08l+ER9Yqxraypm9jZwau1qitEFDua5+7jYVtY0DnRtsANdQywszOwMgjfMKcD7wO3u3mo/jDCzD4Hh7r7zsDu3UrUr15nZg8Az7v5SmFZWhH3ng+7H3X/S0rVIOOiIj7RG7dz9m7EuohkNcvfPmNmn3f0RM3uc4ATk0Ihe4HOxu1cApwIjzewP0ZX7wqAq+v9KM+sFFAM9Y1hPc+hEw9UUU6P3tWr1Lq77upn9Dfg3wZGCK4E5saqrOUQv8nktcB1QCNxKcF2mEcDTBJcPaK1ygXbUO0oQQjPMbCXB35tbohekrY5xTU1KDY40NTU+0hrNjK7s9gIND32HZTnr2iVmd0SXfC4guJhpmPwFOMnMTgL+H8F0sH8Cp8e0qqYzIzqV79fAIoI3zg/GtKKmdzfwXnQFOwNOA+6MaUVNo/HFdet/4hy2KRJvAf8CLnb3vHr350Sna7ZmlcBiM5tNCKdEA7j77dHzfErdfa+ZVRIsBNDqmVlHgmntFxO8/jnBuaDPA3e7+46YFSetmqa6SasTXeWlsTAtZ/0F4FngROBhgk/Sf+zurf2NyD61U4bM7MdAvrv/PazTiMysA5Do7qWxrqWpmVkPoPZCre+4e0Es65GPxswsrCeNm9lnD3R/WKZEA5jZT4E73X1v9HY68IcwLJBjZrOAV4FHav+uRP/e3ABMdvezY1ietGJqfESkxZnZ6wSrEH2O4EjBVuD91n5VdTOb7O6vmtmlB9oepmsxQbhXzYo25fsJw8p8tcxsMPAtoD8NxzA015sKMzP7JXA2wd/R7sCfgD+6+59iWlgTMLMP3f24j7pN5HA01U1aHTNLBr4J9HX3m83sWOA4d58R49KahJndRrB6VDnBqmcjCU40DtOVq68kWLzhRncvMLO+BNPCWrvTCT6l/NQBtjkQmsan3qpZy6hbZCRMq2ZV1Ps6EbiQ4IKmYfI08FeCaZh7D7NvqxK9VtiBPtmtvQBmq1+dz92/Z2avAO8A24HT3H1NjMtqKhvM7DsER3wKAaLXe7uBYIlrkY9FR3yk1TGzJwmWQL4+uqJNMvCmu4+IbWVNo3ZVHjM7B/gS8EPgX2GcBiat19GwalZ90SmLs9x9UqxraSpm9q67j4p1Hc0heu4LBOcwQbA6JgTnFxKGhVTM7DSCPI8STI3uRPBh0uaYFtYEzKwTcDvBOUu157gWEiy+cU+IzumVFqYjPtIaDXT3K83sKgB3rzQzi3VRTag2ywXAP919WcjyYWbjgD8CQ4H2QBsg4u4dY1pYE4mulnUHwYp1TrD8+l3uXhzTwprW0bBqVn3JQFasi2gKZpYZ/fIFM/syMI3wLRQzpdFy5LdHzyO8PWYVNb17gc+4+3KA6BTbV4EhMa2qCbj7duC70f9EmowaH2mNdplZEtFpDGY2kHC9+Xo3emLnAIIX6zTqXa8oJP4ETCWYapMNXE9wMcWweIJgytdl0dvXEFzo86yYVdT0Qr1qVqOpUm2ArgTX1QqDdwmy1X6g8u1625zgb09rZ2Y2wd3fiN4YDyTEuKamdkrtwgYQnEMYPX8ydKIXEB4DLA3ZtG9pYZrqJq2OmU0hmP41DHiZ4AryN7j7nFjW1VTMLIEgXyd3/0b0/Jd+tVeQDwMzy3H37NoLfEbv2++Cka1V7YUFG923pLUv3lBf2FfNMrN+9W7uAQprL9Yq8c/MRgEPAR0JGrztwOfdfVFMC2sCZvY1YJq7h/ZcFzNb4O5jol/fBHyF4Mjk2cAL7n53LOuT1kuNj7RK0alE4whe0N5296IYl9RkzOwvBEd4Jrv70Ohc55fdfXSMS2syZjaX4OjHgwTXKdpC0LyG4orjZvZbYAHwVPSuy4Ex7v6t2FXV9KJHXvu6+4exrqUpmVkbYJm7t/opQ4diZtcf6H53/2dL19JcoteDIUzLyZtZKcHiG2sJLrD7tLtvi21VTav+B2FmthA43923mVkKwWt+aD5EkpYVtsO+EnJm1tHMrgQ+CxwHpBB8GhsmY939K0SvwB2d69w+tiU1uesI/v58leAFvA9108JaLTMrN7My4CbgcWBX9L8ngJtjWVtTM7NPAYsJliXHzEaY2fSYFtVEotOHPowebQ2z0fX+m0hwAdqLYllQMzgRuDE6UyAscgnON/spMApYbmYvmdlno1OjwyDBzDpFP+S02sbO3SsI32u+tCCd4yOtRvTTyTsIprflR+8+A/iFmf0kRJ9S7o5+4lx7DlNXQnaOT70VlaqBn8Sylqbk7mF503Ek7iSYcz8HwN0Xm1kYzg2p1QlYZmYLqLe0tbuHpjFw91vr3zazDIImvdU6xBSpO81sVEimSLm71xC8Fr5sZu2A84CrCBY86BrL4ppIR4Jz0QxwM+vp7lvMLJW6c9NEPjI1PtKa/AAY5e476t8ZnQr2DhCWxuc+ghfqbmb2c4JpUj+MbUlNK3rtpV8SnKeVWHu/u4fmjbOZDWf/C0OG5jo+wG53L2204GCYGvQfxbqAGKgAjol1EZ9Qu3pf30ywuts2M7sXeBsIQ+PT4JfO3XcTLPM8PXp5h1bP3fsfZFMNcEntDTPrFJ0VIXJE1PhIa2Ic+IJ0NYToEyB3f8zM3gXOJMh1sbuH7cKJ/yA4evc7gqN2nyNEU2/N7CFgOPtf3DNMjc8yM7saaBNtZL8GvBnjmpqMu78evWBi7bl1C9x9ayxrampm9gJ1f1MTCD6IeOrgj2gVEqIfhiXQaIqUmYVlitSVB9vg7pUtWUhLi+ZbV++u2QQX+RY5IlrcQFqN6CpSPyY4vF+7mk1fYArwU3d/OEalyUdUe+HE+iudheliima23N2HxbqO5hT9ZPkHBKssAcwi+D0MxdLyZnYF8GuCqXxGcA7Mt939mVjW1ZTM7PR6N/cAG9w9L1b1NAUzW0/dh2EOTKg3RWp+iC50nQDg7jVm1h44AVgfkmswHbEwrQYqLUONj7Qq0U/yzgF6R+/KJ7iaug51tyJm9ibBxT2fIbjgXj5wt7sfF9PCmoiZ/R34Te2FBcPIzLIJGp/+1M0e8NrlyVs7M3ufYJrU1ujtrsArYVl58GgTbdS7u/u6w+4c58zsYuBvBA3el4DvAxGCBX9ucfcXYlddy4pelFZHfOSIqfGRVic6/WRf4+PuhbGsRz46MxsNrAAyCFYmSgd+5e7vxLKuphL9JH06wVLdO4l++hyWpgDAzD4EvgUspd65PfUWrmjVGl93KfoJ+/thWEbXzMo58LTh2udpeguX1OSijWoWsBfIdfdIjEtqMmb2HsFiBknA+8Bod/8weu2pZ909O6YFtiA1PvJR6RwfaTXMbATwV4LVXvIIXqSzzGwH8OUwXJjuKOLAv4B+1J2M/H8E58WEwd8JluxeQrhO+K9vW8g/WZ5pZrMIrpMCwXkV/41hPU0mzKsPmtkwggVi+hNMhX6PYKGY14HbwnI9H3cvADCzjbXX0XL3DbVT4I4ioTm/V1qGGh9pTR4Gvtj4qICZjSM4WV5TUFqPx4BvE97GYJu7h+KaNodwh5k9SHBy8b7zekK0cl0e8BbBuT0AD7j7tBjWI0fmIeCz0SMgY4CvuPvY6NLWfydYJbPVM7OE6JLWn693XxtCds236Ov7Mncvj95OB4bWex9wZsyKk1ZJU92k1TCz1e5+7EG2rXH3QS1dk3w8Zjbf3U+NdR3Nxcz+TDCN7wXC2RRgZo8CQ2i0cp27f/7gj2o9zOxnwFRgEcGb6VmuF8y4Z2bv1z8Pq/5UKDNb4e5DY1dd04hOFV7i7tWN7u8PnOruj8aksGYQndY3svZ3L3pEK0fT2+TjUuMjrYaZ3QcMJLheT+2qbn2A64F17v7VWNUmH42ZnUlwsb1QHi0ws38c4O7QNAUQnOMTlsUoDsaCixSdTbDcejbBUs9/d/e1MS1MDsrMniOY3vYqcCnQyd0/H73I59KwPmfNrLO7F8e6jqZmZosbr8RnZh+E6XxJaVma6iathrt/zczOAz5Nw1Xd7nf3UMy9P4p8juBoQTtCeJ0bd/9crGtoAW+a2bAwr1zn7m5mBQSLVOwBOgHPmNn/3P07sa1ODuLzBKucfY/gxP/bovcnA5+NVVFNyczuBu5196Lo6opPATXR5u56d389thU2qVwz+xrwl+jtLwO5MaxHWjkd8RGRFhf2owVmlgjcCBwPJNbeH7IjPisIjsCuI4Qr15nZbQRHk4uAB4H/uPvu6FSb1e4+MKYFylGr0fXPXgO+4+4LzWww8HiYVnUzs24Ei1VMJvhwbDbw9bBdTFhajo74SKthZh0JPsX7NNCd4I/gVuB5gmvA7IhddfIRhf1owb+AlQTXnLoLuIZg+e4wOTfWBTSzTODSxstzRy8YeWGMapLDOEpeJ9qaWVt33wMkuftCAHdfZWYdYlxbk4o2OFNjXYeEh474SKsRXVr2VeCRekt59gBuACa7+9mHeLjEkaPgaMF77n5y7Vz06BSUee4+Lta1iYTZIV4nPgucGYbXCTO7FfgUcDdwGsEUzOcIjooMcPfrYlhekzKzXwE/A6qAlwguefCNMC3gIC1LjY+0GoeaHhX2qVNhE73Q3n5CdPHLBe4+xszmEsxJLwAWuPuAGJcmEmpHy+uEmU0CbgEGE8ze2QT8B/iHu++OWWFNrHZxAzO7BLgQ+CYwt/7KfSIfhaa6SWuywcy+Q/BJXiGAmXUnOOKz6VAPlPgSlgbnEB4ws07AD4HpQCrwo9iWJHJUOFpeJ+YC3dz9qVgX0sxq36deADzt7qXBYosiH8/RdoVfad2uBDoDr5tZiZmVAHMI5uJfEcvCRBrpSN0SyPcD9wB7zGxELIsSOQocFa8T0YuXHg0rC84ws5XAKGC2mXUFqg/zGJGD0lQ3aVWiKyp9xt2fjHUtIgdjZo8TND0vRO+6EPgA6E/wqeWvYlSaSOgdLa8T0WWti4AngYra+929JGZFNQMzywRK3X2vmaUAabXnb4l8VGp8pNUxs5wwLdcp4RM9t+d8d49Eb6cCLxKshPauuw+LZX0iYXc0vE6Y2boD3O1hOpfQzH4K3Onue6O304E/HCXXSpNmoHN8pDV6xcy+Rcg/5ZJWrRvBanW1dgPd3b3KzHYe5DEi0nRC/zrh7sfEuoYW0BZYYGafI1ie/E/AH2NbkrRmOuIjrc7R8CmXtG5m9iPgEoJrh0Cw9Ox04DfAA+5+TaxqEzkaHA2vE2b2GeAldy83sx8CI4Gfuvt7MS6tSZnZmcAMYDtwmruviXFJ0oqp8RERaQZmlg1MiN58w91zYlmPiIRLveuEnUpwrZtfAz9297ExLq3JmNlpwF+AR4ETCa5ZdKO7b45pYdJqaVU3aXXM7DNmlhb9+odm9pyZnRzrukTqc/ccd/9D9D81PSIt6Ch5ndgb/f8FBEeSXwTax7Ce5nAvwUIVv3T3q4H/I7hArcjHosZHWqMfRQ/tnwqcBfwd+GuMaxIRkfhxNLxO5JvZ3wiW8P6vmXUgfO/rTnH35bU33P056o6ki3xkYfsFkaPD0fApl4iIfHxHw+vEFcAs4Bx330FwraJvx7SiJmJmXzOzPrWrudXn7sWxqEnCQY2PtEZHw6dcIiLy8R0NrxPDgf+5++ro7QqgNIb1NKWfAu+Y2Twz+3L0wqUin5gWN5BWx8ySCa6HssTdV5tZT+BEd385xqWJiEgcOBpeJ8zsPWCkR9/IRS/cmuPuI2Nb2ScXzTaKYJrilcBFwLvAv4Hn3L08huVJK6br+EhrVPspV+0fvjB9yiUiIp/c0fA6YV7v02t3rzGzsLyvc3evAV4GXjazdsB5wFUECx7oCJB8LGE77CtHh78AkXq3I9H7RERE4Oh4nciNngvTLvrfbUBurItqIlb/hrvvdvfp7n4V0C9GNUkIqPGR1mi/T7nQ0UsREalzNLxOfAkYD+QDecBY4OaYVtR0rjzYBnevbMlCJFzU+EhrFOZPuURE5JML/euEu29196nu3s3du7v71e6+NdZ1NQV3X2VmCdHzljCz9mY20swyY12btG5a3EBaHTPrBtwHTAYcmA18PSx/8EVE5JM5Gl4noiud3QT0p97RLHf/fKxqaipmdjHwN6CG4MjW9wmmKx4H3OLuL8SuOmnN1PiIiIiItDJm9iYwj2C1s33Xu3H3Z2NWVBOJrup2HpAEvA+MdvcPzawf8Ky7Z8e0QGm1wjbfVY4CYf6US0REPrmj5HUi2d2/G+simou7FwCY2UZ3/zB634ba6W8iH4caH2mNnif4lOsV6n3KJSIiEnU0vE7MMLPz3f2/sS6kOZhZQnRRis/Xu68N0D52VUlrp6lu0uqY2WJ3HxHrOkREJD4dDa8TZlYOJAO7gN0ES0C7u6fHtLAmYGajCS4+W93o/v7Aqe7+aEwKk1ZPhwv/f3t382pVFYdx/PuUJgkVEglOoqJCiaJEMSIItCB00qCQCCKCogjqH7BBkRZUg+zFCho2yEEWmITloHKmoeBAIbAGOZMkLkIk3l+DczQJZ+fm2mvf7wcO52X0XO5g7WetvdZWj/Ym2dw6hCRpsBbDOHED8Azw5rTs3AU80jTRAqmqQ5cpPTdW1W+WHs3CFR91Z8yzXJKk2S2GcSLJLiannm2sqjVJVgD7q2p942gzS/IW8E5VnU6yDtjN5G9dCjxdVT80DahuueKjHo12lkuStCAWwzixoapeAv4CqKozjGf/y5aqOj39/DawtapuZ/I/fLddLPXO4qMefQjcDzw5/T4HfNAujiRpYBbDOHFuutm/4OJJdvNtIy2YJUkuHMB1bVUdgsmDTYFl7WKpdxYf9WjMs1ySpNkthnFiJ7AHWJlkO3AQ2NE20oL5CNiXZCPwbZL3kjyU5DXgaNto6pnHWatHY57lkiTNbvTjRFV9nuRnYBOTPUyPVdXxxrEWRFW9n+QY8CJwJ5Pr1TuAr4A3GkZT5yw+6tF/Z7keB7a1jSRJGpBFMU5U1QngROsc/5MfgZVVtbt1EI2Hp7qpS0lW8+8s14GxzHJJkhaG40T/khyuqnWtc2g8LD6SJEkanOmx1qeBL4CzF36vqj+ahVLXLD6SJEkanCS/XubnqqrbrngYjYLFR5IkSdLoeZy1JEmSBifJE0mum37eluTLJPe1zqV+WXwkC7aDTwAAAe9JREFUSZI0RK9W1VySB4GHgc+AjxtnUscsPpIkSRqi89P3LcCnVfUN43sQra4gi48kSZKG6FSST4CtwL4ky/DaVTPwcANJkiQNTpLlwKPAsar6Jckq4O6q2t84mjq1pHUASZIk6TLuAb6rqrnp97PAnw3zqHOu+EiSJGlwkhwB1tb0YjXJVcDhqlrbNpl65X2SkiRJGqLUJTP0VTWPdytpBhYfSZIkDdHJJC8nWTp9vQKcbB1K/bL4SJIkaYheAB4ATgG/AxuA55smUtfc4yNJkiRp9LxPUpIkSYOT5CbgOeAWLrlmrapnW2VS3yw+kiRJGqKvgZ+A74HzjbNoBLzVTZIkSYOT5GhV3ds6h8bDww0kSZI0RHuTbG4dQuPhio8kSZIGJ8kcsBz4GzgHBKiqur5pMHXLPT6SJEkaohuAp4Bbq+r1JDcDqxpnUsdc8ZEkSdLgJNkFzAMbq2pNkhXA/qpa3ziaOuWKjyRJkoZoQ1WtTXIEoKrOJLmmdSj1y8MNJEmSNETnklwNFFx8rs9820jqmcVHkiRJQ7QT2AOsTLIdOAjsaBtJPXOPjyRJkgYpyWpgE5MT3Q5U1fHGkdQxi48kSZKk0fNWN0mSJEmjZ/GRJEmSNHoWH0mSJEmjZ/GRJEmSNHoWH0mSJEmj9w/mv3+1Q4Gk5AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.clustermap(abs_logfc_df, annot=True, cmap=\"crest\")\n",
+    "g.fig.set_figwidth(12)\n",
+    "g.fig.set_figheight(12)\n",
+    "g.fig.suptitle('Ancestry vs. Ancestry ChromBPNet Absolute JSD Pearson Correlation', fontsize=18, y=1.05)\n",
+    "#plt.savefig(\"subfigs/afr_caqtls_ancestry_jsd.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6821, 49)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (ASB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asb_scores = pd.read_csv(\"Afr.ASB.CaQTLS.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "asb_scores = asb_scores[asb_scores[\"IsUsed\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>snp_id</th>\n",
+       "      <th>meanLog2FC</th>\n",
+       "      <th>GM12878.ENCSR637XSC.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR637XSC.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR000EMT.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR000EMT.mean.sign.jsd</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.logfc</th>\n",
+       "      <th>GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>976215</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>1_976215_A_G</td>\n",
+       "      <td>-1.852605</td>\n",
+       "      <td>-0.079329</td>\n",
+       "      <td>-0.061612</td>\n",
+       "      <td>0.051729</td>\n",
+       "      <td>0.032590</td>\n",
+       "      <td>0.075894</td>\n",
+       "      <td>0.038511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038819</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>1_1038819_C_T</td>\n",
+       "      <td>2.390945</td>\n",
+       "      <td>-0.223319</td>\n",
+       "      <td>-0.023942</td>\n",
+       "      <td>-0.044806</td>\n",
+       "      <td>-0.014620</td>\n",
+       "      <td>-0.109847</td>\n",
+       "      <td>-0.020354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038845</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>1_1038845_A_G</td>\n",
+       "      <td>2.163432</td>\n",
+       "      <td>0.441510</td>\n",
+       "      <td>0.035797</td>\n",
+       "      <td>0.253871</td>\n",
+       "      <td>0.045086</td>\n",
+       "      <td>0.377451</td>\n",
+       "      <td>0.036387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038916</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038916_A_G</td>\n",
+       "      <td>1_1038916_A_G</td>\n",
+       "      <td>2.062560</td>\n",
+       "      <td>0.083078</td>\n",
+       "      <td>0.013802</td>\n",
+       "      <td>0.193501</td>\n",
+       "      <td>0.028406</td>\n",
+       "      <td>0.067928</td>\n",
+       "      <td>0.010322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1038975</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1_1038975_G_A</td>\n",
+       "      <td>1_1038975_G_A</td>\n",
+       "      <td>1.898451</td>\n",
+       "      <td>-0.210283</td>\n",
+       "      <td>-0.012515</td>\n",
+       "      <td>-0.028861</td>\n",
+       "      <td>-0.012718</td>\n",
+       "      <td>-0.105511</td>\n",
+       "      <td>-0.015877</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chr_hg38  pos_hg38 allele1 allele2  IsUsed           rsid         snp_id  \\\n",
+       "0     chr1    976215       A       G    True   1_976215_A_G   1_976215_A_G   \n",
+       "3     chr1   1038819       T       C    True  1_1038819_C_T  1_1038819_C_T   \n",
+       "4     chr1   1038845       G       A    True  1_1038845_A_G  1_1038845_A_G   \n",
+       "5     chr1   1038916       G       A    True  1_1038916_A_G  1_1038916_A_G   \n",
+       "6     chr1   1038975       A       G    True  1_1038975_G_A  1_1038975_G_A   \n",
+       "\n",
+       "   meanLog2FC  GM12878.ENCSR637XSC.mean.logfc  \\\n",
+       "0   -1.852605                       -0.079329   \n",
+       "3    2.390945                       -0.223319   \n",
+       "4    2.163432                        0.441510   \n",
+       "5    2.062560                        0.083078   \n",
+       "6    1.898451                       -0.210283   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.mean.sign.jsd  GM12878.ENCSR000EMT.mean.logfc  \\\n",
+       "0                          -0.061612                        0.051729   \n",
+       "3                          -0.023942                       -0.044806   \n",
+       "4                           0.035797                        0.253871   \n",
+       "5                           0.013802                        0.193501   \n",
+       "6                          -0.012515                       -0.028861   \n",
+       "\n",
+       "   GM12878.ENCSR000EMT.mean.sign.jsd  GM12878.ENCSR637XSC.sub.50M.mean.logfc  \\\n",
+       "0                           0.032590                                0.075894   \n",
+       "3                          -0.014620                               -0.109847   \n",
+       "4                           0.045086                                0.377451   \n",
+       "5                           0.028406                                0.067928   \n",
+       "6                          -0.012718                               -0.105511   \n",
+       "\n",
+       "   GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd  \n",
+       "0                                   0.038511  \n",
+       "3                                  -0.020354  \n",
+       "4                                   0.036387  \n",
+       "5                                   0.010322  \n",
+       "6                                  -0.015877  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "asb_scores.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.68\n",
+      "ChromBPNet ATAC (50M):  0.67\n",
+      "ChromBPNet DNase (68M):  0.66\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC (50M):  0.0\n",
+      "ChromBPNet DNase (68M):  0.0\n",
+      "ChromBPNet ATAC:  0.66\n",
+      "ChromBPNet ATAC (50M):  0.66\n",
+      "ChromBPNet DNase (68M):  0.67\n",
+      "ChromBPNet ATAC:  0.0\n",
+      "ChromBPNet ATAC (50M):  0.0\n",
+      "ChromBPNet DNase (68M):  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonrs = []\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "pearsonrs.append(np.round(scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(asb_scores[keyd], asb_scores[\"meanLog2FC\"])[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet ATAC:  0.27\n",
+      "ChromBPNet ATAC (50M):  0.28\n",
+      "ChromBPNet DNase (68M):  0.27\n",
+      "ChromBPNet ATAC:  1.5398565297413478e-102\n",
+      "ChromBPNet ATAC (50M):  2.3953461418137785e-109\n",
+      "ChromBPNet DNase (68M):  4.024120257115456e-96\n",
+      "ChromBPNet ATAC:  0.26\n",
+      "ChromBPNet ATAC (50M):  0.26\n",
+      "ChromBPNet DNase (68M):  0.26\n",
+      "ChromBPNet ATAC:  4.329032818186739e-95\n",
+      "ChromBPNet ATAC (50M):  2.240949272090948e-90\n",
+      "ChromBPNet DNase (68M):  8.346832432824987e-93\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonrs_abs = []\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.logfc'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.logfc'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "pearsonrs_abs.append(np.round(scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[0],2))\n",
+    "\n",
+    "\n",
+    "keyd='GM12878.ENCSR637XSC.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC: \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR637XSC.sub.50M.mean.sign.jsd'\n",
+    "print(\"ChromBPNet ATAC (50M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n",
+    "keyd='GM12878.ENCSR000EMT.mean.sign.jsd'\n",
+    "print(\"ChromBPNet DNase (68M): \", scipy.stats.pearsonr(abs(asb_scores[keyd]), abs(asb_scores[\"meanLog2FC\"]))[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pearsonr</th>\n",
+       "      <th>method</th>\n",
+       "      <th>scoring</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.68</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed-logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed-logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed-logfc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>signed-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>signed-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.67</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>signed-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned-logc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.28</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned-logc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned-logc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.26</td>\n",
+       "      <td>ChromBPNet ATAC (500M)</td>\n",
+       "      <td>unsigned-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.26</td>\n",
+       "      <td>ChromBPNet ATAC (50M)</td>\n",
+       "      <td>unsigned-jsd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.26</td>\n",
+       "      <td>ChromBPNet DNase (68M)</td>\n",
+       "      <td>unsigned-jsd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    pearsonr                  method        scoring\n",
+       "0       0.68  ChromBPNet ATAC (500M)   signed-logfc\n",
+       "1       0.67   ChromBPNet ATAC (50M)   signed-logfc\n",
+       "2       0.66  ChromBPNet DNase (68M)   signed-logfc\n",
+       "3       0.66  ChromBPNet ATAC (500M)     signed-jsd\n",
+       "4       0.66   ChromBPNet ATAC (50M)     signed-jsd\n",
+       "5       0.67  ChromBPNet DNase (68M)     signed-jsd\n",
+       "6       0.27  ChromBPNet ATAC (500M)  unsigned-logc\n",
+       "7       0.28   ChromBPNet ATAC (50M)  unsigned-logc\n",
+       "8       0.27  ChromBPNet DNase (68M)  unsigned-logc\n",
+       "9       0.26  ChromBPNet ATAC (500M)   unsigned-jsd\n",
+       "10      0.26   ChromBPNet ATAC (50M)   unsigned-jsd\n",
+       "11      0.26  ChromBPNet DNase (68M)   unsigned-jsd"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.DataFrame()\n",
+    "\n",
+    "df[\"pearsonr\"] = pearsonrs + pearsonrs_abs\n",
+    "df[\"method\"] = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]*2 + [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]*2\n",
+    "df[\"scoring\"] = [\"signed-logfc\"]*3 + [\"signed-jsd\"]*3 + [\"unsigned-logc\"]*3 + [\"unsigned-jsd\"]*3\n",
+    "\n",
+    "df[\"pearsonr\"] = df[\"pearsonr\"].astype(float)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     0.68\n",
+      "1     0.67\n",
+      "2     0.66\n",
+      "3     0.66\n",
+      "4     0.66\n",
+      "5     0.67\n",
+      "6     0.27\n",
+      "7     0.28\n",
+      "8     0.27\n",
+      "9     0.26\n",
+      "10    0.26\n",
+      "11    0.26\n",
+      "Name: pearsonr, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "plt.figure(figsize=(13,6))\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "print(df[\"pearsonr\"])\n",
+    "ax = sns.barplot(data=df, x=\"method\", y=\"pearsonr\", hue=\"scoring\")\n",
+    "xtix = [\"ChromBPNet ATAC (500M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "plt.savefig(\"subfigs/afr_caqtls_asb_pearsonr.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6848214217530895, 0.0)\n",
+      "(0.27464139750304883, 1.5398565297413478e-102)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = asb_scores['GM12878.ENCSR637XSC.mean.logfc']\n",
+    "data[\"Beta\"] = asb_scores[\"meanLog2FC\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"#893f45\", rasterized=True)\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#893f45\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/afr_caqtls_asb.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi.scoring.old.temp.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi.scoring.old.temp.ipynb
new file mode 100644
index 00000000..f158157f
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi.scoring.old.temp.ipynb
@@ -0,0 +1,1292 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from scipy import stats\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Locus</th>\n",
+       "      <th>deltaSVM</th>\n",
+       "      <th>Enf.CAGE.Agnostic</th>\n",
+       "      <th>Enf.CAGE</th>\n",
+       "      <th>Enf.DNase.Agnostic</th>\n",
+       "      <th>Enf.DNase</th>\n",
+       "      <th>Enformer</th>\n",
+       "      <th>G3</th>\n",
+       "      <th>G7</th>\n",
+       "      <th>dSVM LASSO</th>\n",
+       "      <th>dSVM RF</th>\n",
+       "      <th>dSVM (1 DHS)</th>\n",
+       "      <th>G5</th>\n",
+       "      <th>CADD</th>\n",
+       "      <th>Inter-replicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F9</td>\n",
+       "      <td>0.483518</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>0.573</td>\n",
+       "      <td>0.291</td>\n",
+       "      <td>0.572</td>\n",
+       "      <td>0.624</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.557</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.489</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>-0.086</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GP1BB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>0.555</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.563</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.566</td>\n",
+       "      <td>0.261</td>\n",
+       "      <td>0.371</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>-0.083</td>\n",
+       "      <td>0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HBB</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.550</td>\n",
+       "      <td>0.578</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.446</td>\n",
+       "      <td>0.416</td>\n",
+       "      <td>0.445</td>\n",
+       "      <td>0.328</td>\n",
+       "      <td>0.393</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HBG1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.178</td>\n",
+       "      <td>0.302</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.189</td>\n",
+       "      <td>0.474</td>\n",
+       "      <td>0.571</td>\n",
+       "      <td>0.591</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>0.342</td>\n",
+       "      <td>0.479</td>\n",
+       "      <td>0.174</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HNF4A</td>\n",
+       "      <td>0.091578</td>\n",
+       "      <td>0.173</td>\n",
+       "      <td>0.156</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>0.288</td>\n",
+       "      <td>0.335</td>\n",
+       "      <td>0.339</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>0.225</td>\n",
+       "      <td>0.151</td>\n",
+       "      <td>0.134</td>\n",
+       "      <td>0.191</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.89</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Locus  deltaSVM  Enf.CAGE.Agnostic  Enf.CAGE  Enf.DNase.Agnostic  \\\n",
+       "0     F9  0.483518              0.245     0.252               0.573   \n",
+       "1  GP1BB       NaN              0.370     0.469               0.555   \n",
+       "2    HBB       NaN              0.491     0.596               0.550   \n",
+       "3   HBG1       NaN              0.178     0.302               0.104   \n",
+       "4  HNF4A  0.091578              0.173     0.156               0.170   \n",
+       "\n",
+       "   Enf.DNase  Enformer     G3     G7  dSVM LASSO  dSVM RF  dSVM (1 DHS)  \\\n",
+       "0      0.291     0.572  0.624  0.391       0.557    0.566         0.489   \n",
+       "1      0.564     0.563  0.556  0.566       0.261    0.371         0.421   \n",
+       "2      0.578     0.600  0.446  0.416       0.445    0.328         0.393   \n",
+       "3      0.189     0.474  0.571  0.591       0.397    0.439         0.342   \n",
+       "4      0.288     0.335  0.339  0.397       0.225    0.151         0.134   \n",
+       "\n",
+       "      G5   CADD  Inter-replicate  \n",
+       "0  0.564 -0.086             0.88  \n",
+       "1  0.348 -0.083             0.88  \n",
+       "2  0.392  0.049             0.77  \n",
+       "3  0.479  0.174             0.92  \n",
+       "4  0.191  0.111             0.89  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#odir=\"/srv/www/kundaje/anusri/chrombpnet_models_mpra/variant_scores/\"\n",
+    "odir=\"/mnt/lab_data2/anusri/variant-scorer/src/output/CAGI/output/\"\n",
+    "dirs = os.listdir(odir)\n",
+    "\n",
+    "enformer = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer/allGroupsBenchmark.txt\", sep=\"\\t\", header=0)\n",
+    "enformer.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_to_pear = {}\n",
+    "dataset_to_vals = {}\n",
+    "labels={}\n",
+    "\n",
+    "for idir in dirs: \n",
+    "    idir=str(idir)\n",
+    "    ifile=odir+idir+\"/summary.mean.variant_scores_new_2.tsv\"\n",
+    "    if os.path.isfile(ifile):\n",
+    "        if not ifile.endswith(\"tsv\"):\n",
+    "            continue\n",
+    "        data = pd.read_csv(ifile, sep=\"\\t\", header=0)\n",
+    "        #print(data.head())\n",
+    "        data[[\"chr_temp\", \"pos_temp\", \"ref_temp\", \"alt_temp\", \"label\", \"Confidence\", \"challenge\"]] = data[\"rsid\"].str.split(\",\", expand=True)\n",
+    "        grouped = data.groupby(\"challenge\")\n",
+    "        for g in list(grouped):\n",
+    "            df = g[1]\n",
+    "            if g[0] not in dataset_to_pear:\n",
+    "                    dataset_to_pear[g[0]] = []\n",
+    "                    dataset_to_vals[g[0]] = []\n",
+    "                    labels[g[0]] = df[\"label\"].astype(float).values\n",
+    "            #pears=stats.pearsonr(df[\"label\"].astype(float), df[\"jsd.mean\"].astype(float)*np.sign(df['logfc.mean'].astype(float)))[0]\n",
+    "            pears=stats.pearsonr(df[\"label\"].astype(float), df['logfc.mean'].astype(float))[0]\n",
+    "\n",
+    "            dataset_to_pear[g[0]].append(pears)\n",
+    "              "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.32193638902963084, 0.3414646145553015, 0.1887277639068928, 0.2891811987098261, 0.3984857758855108, 0.31174904895986527, 0.3192111247029, 0.44484781066968043, 0.2927436490770697, 0.2958126463496242]\n",
+      "[0.6841190910774478, 0.6860985124263778, 0.6853042263564131]\n",
+      "[0.8154299895742269]\n",
+      "[0.5213276765530211]\n",
+      "[0.5472988538268535]\n",
+      "[0.5416481138583874]\n",
+      "[0.8120202837314338]\n",
+      "[0.3275628031599529]\n",
+      "[0.5571818417723304]\n",
+      "[0.7608707755348065]\n",
+      "[0.33885305062040927]\n",
+      "[0.19856415224540452]\n",
+      "[0.5078362906951961]\n",
+      "[0.5553154805041812]\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_dataset_to_pear = {}\n",
+    "for key in dataset_to_pear:\n",
+    "    print(dataset_to_pear[key])\n",
+    "    best_dataset_to_pear[key] = np.max(dataset_to_pear[key])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'challenge_ZFAND3.tsv': 0.44484781066968043,\n",
+       " 'challenge_IRF6.tsv': 0.6860985124263778,\n",
+       " 'challenge_IRF4.tsv': 0.8154299895742269,\n",
+       " 'challenge_GP1BB.tsv': 0.5213276765530211,\n",
+       " 'challenge_HBB.tsv': 0.5472988538268535,\n",
+       " 'challenge_HBG1.tsv': 0.5416481138583874,\n",
+       " 'challenge_PKLR.tsv': 0.8120202837314338,\n",
+       " 'challenge_F9.tsv': 0.3275628031599529,\n",
+       " 'challenge_LDLR.tsv': 0.5571818417723304,\n",
+       " 'challenge_SORT1.tsv': 0.7608707755348065,\n",
+       " 'challenge_HNF4A.tsv': 0.33885305062040927,\n",
+       " 'challenge_MSMB.tsv': 0.19856415224540452,\n",
+       " 'challenge_MYCrs6983267.tsv': 0.5078362906951961,\n",
+       " 'challenge_TERT-HEK293T.tsv': 0.5553154805041812}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_dataset_to_pear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.DataFrame(best_dataset_to_pear.items(), columns=[\"gene\", 'correlation']) \n",
+    "data[\"gene\"] = data[\"gene\"].str.replace(\"challenge_\",\"\").str.replace(\".tsv\",\"\")\n",
+    "data.loc[len(data.index)] = [\"TERT-GBM\", 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TERT(GBM)\n",
+      "PKLR\n",
+      "LDLR\n",
+      "IRF4\n",
+      "TERT(HEK293T)\n",
+      "HBB\n",
+      "F9\n",
+      "GP1BB\n",
+      "IRF6\n",
+      "SORT1\n",
+      "ZFAND3\n",
+      "HBG1\n",
+      "MYC\n",
+      "HNF4A\n",
+      "MSMB\n"
+     ]
+    }
+   ],
+   "source": [
+    "keys = [\"TERT-GBM\", \"PKLR\", \"LDLR\", \"IRF4\", \"TERT-HEK293T\", \"HBB\", \"F9\", \"GP1BB\", \"IRF6\", \"SORT1\", \"ZFAND3\", \"HBG1\", \"MYCrs6983267\", \"HNF4A\", \"MSMB\"]\n",
+    "final_coorelations_chrombpnet = []\n",
+    "final_coorelations_enformer = []\n",
+    "keysl = []\n",
+    "for key in keys:\n",
+    "    final_coorelations_chrombpnet.append(data[data[\"gene\"]==key][\"correlation\"].values[0])\n",
+    "    if key==\"TERT-GBM\":\n",
+    "        key=\"TERT(GBM)\"\n",
+    "    if key==\"TERT-HEK293T\":\n",
+    "        key=\"TERT(HEK293T)\"\n",
+    "    if key==\"MYCrs6983267\":\n",
+    "        key=\"MYC\"\n",
+    "    print(key)\n",
+    "    keysl.append(key)\n",
+    "    final_coorelations_enformer.append(enformer[enformer[\"Locus\"]==key][\"Enf.DNase\"].values[0])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TERT(GBM) (0.0, nan)\n",
+      "PKLR (0.8120202837314338, 0.809)\n",
+      "LDLR (0.5571818417723304, 0.628)\n",
+      "IRF4 (0.8154299895742269, 0.714)\n",
+      "TERT(HEK293T) (0.5553154805041812, 0.587)\n",
+      "HBB (0.5472988538268535, 0.578)\n",
+      "F9 (0.3275628031599529, 0.291)\n",
+      "GP1BB (0.5213276765530211, 0.564)\n",
+      "IRF6 (0.6860985124263778, 0.648)\n",
+      "SORT1 (0.7608707755348065, 0.619)\n",
+      "ZFAND3 (0.44484781066968043, 0.45)\n",
+      "HBG1 (0.5416481138583874, 0.189)\n",
+      "MYC (0.5078362906951961, 0.559)\n",
+      "HNF4A (0.33885305062040927, 0.288)\n",
+      "MSMB (0.19856415224540452, 0.119)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(final_coorelations_chrombpnet,final_coorelations_enformer, s=5, c=\"g\")\n",
+    "ax.plot(final_coorelations_chrombpnet,final_coorelations_chrombpnet, c=\"r\",  alpha=0.2)\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keysl[i], (final_coorelations_chrombpnet[i], final_coorelations_enformer[i]))\n",
+    "    texts.append(plt.text(final_coorelations_chrombpnet[i], final_coorelations_enformer[i], keysl[i]))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/cagi_logfc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_to_pear = {}\n",
+    "dataset_to_vals = {}\n",
+    "labels={}\n",
+    "\n",
+    "for idir in dirs: \n",
+    "    idir=str(idir)\n",
+    "    ifile=odir+idir\n",
+    "    if os.path.isfile(ifile):\n",
+    "        if not ifile.endswith(\"tsv\"):\n",
+    "            continue\n",
+    "\n",
+    "        #print(ifile)\n",
+    "        data = pd.read_csv(ifile, sep=\"\\t\", header=0)\n",
+    "        data[[\"chr_temp\", \"pos_temp\", \"ref_temp\", \"alt_temp\", \"label\", \"Confidence\", \"challenge\"]] = data[\"rsid\"].str.split(\",\", expand=True)\n",
+    "        grouped = data.groupby(\"challenge\")\n",
+    "        for g in list(grouped):\n",
+    "            df = g[1]\n",
+    "            if g[0] not in dataset_to_pear:\n",
+    "                    dataset_to_pear[g[0]] = []\n",
+    "                    dataset_to_vals[g[0]] = []\n",
+    "                    labels[g[0]] = df[\"label\"].astype(float).values\n",
+    "            pears=stats.pearsonr(df[\"label\"].astype(float), df[\"jsd.mean\"].astype(float)*np.sign(df['logfc.mean'].astype(float)))[0]\n",
+    "\n",
+    "            dataset_to_pear[g[0]].append(pears)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.26121420522216976, 0.2956343407119622, 0.10199100387176496, 0.2716437637815248, 0.31924331847152715, 0.2828748779109874, 0.29365380659535606, 0.3968265961621204, 0.2636786272828819, 0.26991422573396806]\n",
+      "[0.5042496309112188, 0.4779495300404426, 0.555157194729831]\n",
+      "[0.7497534109732615]\n",
+      "[0.42540813836968683]\n",
+      "[0.5698413608177562]\n",
+      "[0.5017067346770685]\n",
+      "[0.7929239856356867]\n",
+      "[0.4529915899243378]\n",
+      "[0.5467644140523147]\n",
+      "[0.708269007627931]\n",
+      "[0.3101191262850677]\n",
+      "[0.2389035758206448]\n",
+      "[0.5084297791443301]\n",
+      "[0.4943933566895047]\n",
+      "TERT(GBM)\n",
+      "PKLR\n",
+      "LDLR\n",
+      "IRF4\n",
+      "TERT(HEK293T)\n",
+      "HBB\n",
+      "F9\n",
+      "GP1BB\n",
+      "IRF6\n",
+      "SORT1\n",
+      "ZFAND3\n",
+      "HBG1\n",
+      "MYC\n",
+      "HNF4A\n",
+      "MSMB\n",
+      "TERT(GBM) (0.0, nan)\n",
+      "PKLR (0.7929239856356867, 0.809)\n",
+      "LDLR (0.5467644140523147, 0.628)\n",
+      "IRF4 (0.7497534109732615, 0.714)\n",
+      "TERT(HEK293T) (0.4943933566895047, 0.587)\n",
+      "HBB (0.5698413608177562, 0.578)\n",
+      "F9 (0.4529915899243378, 0.291)\n",
+      "GP1BB (0.42540813836968683, 0.564)\n",
+      "IRF6 (0.555157194729831, 0.648)\n",
+      "SORT1 (0.708269007627931, 0.619)\n",
+      "ZFAND3 (0.3968265961621204, 0.45)\n",
+      "HBG1 (0.5017067346770685, 0.189)\n",
+      "MYC (0.5084297791443301, 0.559)\n",
+      "HNF4A (0.3101191262850677, 0.288)\n",
+      "MSMB (0.2389035758206448, 0.119)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  import sys\n",
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n",
+      "posx and posy should be finite values\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_dataset_to_pear = {}\n",
+    "for key in dataset_to_pear:\n",
+    "    print(dataset_to_pear[key])\n",
+    "    best_dataset_to_pear[key] = np.max(dataset_to_pear[key])\n",
+    "    \n",
+    "data = pd.DataFrame(best_dataset_to_pear.items(), columns=[\"gene\", 'correlation']) \n",
+    "data[\"gene\"] = data[\"gene\"].str.replace(\"challenge_\",\"\").str.replace(\".tsv\",\"\")\n",
+    "data.loc[len(data.index)] = [\"TERT-GBM\", 0]\n",
+    "\n",
+    "keys = [\"TERT-GBM\", \"PKLR\", \"LDLR\", \"IRF4\", \"TERT-HEK293T\", \"HBB\", \"F9\", \"GP1BB\", \"IRF6\", \"SORT1\", \"ZFAND3\", \"HBG1\", \"MYCrs6983267\", \"HNF4A\", \"MSMB\"]\n",
+    "final_coorelations_chrombpnet = []\n",
+    "final_coorelations_enformer = []\n",
+    "keysl = []\n",
+    "for key in keys:\n",
+    "    final_coorelations_chrombpnet.append(data[data[\"gene\"]==key][\"correlation\"].values[0])\n",
+    "    if key==\"TERT-GBM\":\n",
+    "        key=\"TERT(GBM)\"\n",
+    "    if key==\"TERT-HEK293T\":\n",
+    "        key=\"TERT(HEK293T)\"\n",
+    "    if key==\"MYCrs6983267\":\n",
+    "        key=\"MYC\"\n",
+    "    print(key)\n",
+    "    keysl.append(key)\n",
+    "    final_coorelations_enformer.append(enformer[enformer[\"Locus\"]==key][\"Enf.DNase\"].values[0])\n",
+    "\n",
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(final_coorelations_chrombpnet,final_coorelations_enformer, s=5, c=\"g\")\n",
+    "ax.plot(final_coorelations_chrombpnet,final_coorelations_chrombpnet, c=\"r\",  alpha=0.2)\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keysl[i], (final_coorelations_chrombpnet[i], final_coorelations_enformer[i]))\n",
+    "    texts.append(plt.text(final_coorelations_chrombpnet[i], final_coorelations_enformer[i], keysl[i]))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/cagi_jsd.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f9 = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/alldata/HBG1.ZigaDataLocal.csv\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>data.Value</th>\n",
+       "      <th>dnase</th>\n",
+       "      <th>data.Position</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.19</td>\n",
+       "      <td>-0.069240</td>\n",
+       "      <td>5249805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.011861</td>\n",
+       "      <td>5249805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>0.035393</td>\n",
+       "      <td>5249805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.14</td>\n",
+       "      <td>-0.174114</td>\n",
+       "      <td>5249806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.010160</td>\n",
+       "      <td>5249806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>-0.08</td>\n",
+       "      <td>-0.967332</td>\n",
+       "      <td>5250077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>0.13</td>\n",
+       "      <td>-0.653998</td>\n",
+       "      <td>5250077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>-0.04</td>\n",
+       "      <td>1.184885</td>\n",
+       "      <td>5250078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.003179</td>\n",
+       "      <td>5250078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>0.041097</td>\n",
+       "      <td>5250078</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>630 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     data.Value     dnase  data.Position\n",
+       "0          0.19 -0.069240        5249805\n",
+       "1          0.07  0.011861        5249805\n",
+       "2         -0.72  0.035393        5249805\n",
+       "3          0.14 -0.174114        5249806\n",
+       "4          0.09  0.010160        5249806\n",
+       "..          ...       ...            ...\n",
+       "625       -0.08 -0.967332        5250077\n",
+       "626        0.13 -0.653998        5250077\n",
+       "627       -0.04  1.184885        5250078\n",
+       "628        0.24  0.003179        5250078\n",
+       "629       -0.02  0.041097        5250078\n",
+       "\n",
+       "[630 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.18851068518283778, 1.8888239190707555e-06)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import seaborn as sns\n",
+    "fig, ax =  plt.subplots()\n",
+    "ax = sns.scatterplot(x=\"data.Value\", y=\"dnase\", data=f9, s=6, color=\"green\")\n",
+    "sns.regplot(x=\"data.Value\", y=\"dnase\", data=f9, scatter=False, ci=None, color=\"green\")\n",
+    "ax.set_xlabel(\"ground truth\")\n",
+    "ax.set_ylabel(\"Enformer scores\")\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(stats.pearsonr(f9[\"data.Value\"],f9[\"dnase\"])[0],2)))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "print(stats.pearsonr(f9[\"data.Value\"],f9[\"dnase\"]))\n",
+    "\n",
+    "plt.savefig(\"subfigs/cagi_enformer_hbg1.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f9 = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/alldata/HBG1.ZigaDataLocal.csv\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     0      1      2  \\\n",
+      "0     challenge_F9.tsv  fold0  HEPG2   \n",
+      "1   challenge_LDLR.tsv  fold0  HEPG2   \n",
+      "2  challenge_SORT1.tsv  fold0  HEPG2   \n",
+      "3  challenge_GP1BB.tsv  fold0   K562   \n",
+      "4    challenge_HBB.tsv  fold0   K562   \n",
+      "\n",
+      "                                                   3                 4  \n",
+      "0  /mnt/lab_data2/anusri/chrombpnet/results/chrom...  chrombpnet_model  \n",
+      "1  /mnt/lab_data2/anusri/chrombpnet/results/chrom...  chrombpnet_model  \n",
+      "2  /mnt/lab_data2/anusri/chrombpnet/results/chrom...  chrombpnet_model  \n",
+      "3  /mnt/lab_data2/anusri/chrombpnet/results/chrom...  chrombpnet_model  \n",
+      "4  /mnt/lab_data2/anusri/chrombpnet/results/chrom...  chrombpnet_model  \n",
+      "HEPG2..challenge_F9.tsv challenge_F9.tsv\n",
+      "HEPG2..challenge_F9.tsv\n",
+      "HEPG2..challenge_LDLR.tsv challenge_LDLR.tsv\n",
+      "HEPG2..challenge_LDLR.tsv\n",
+      "HEPG2..challenge_SORT1.tsv challenge_SORT1.tsv\n",
+      "HEPG2..challenge_SORT1.tsv\n",
+      "K562..challenge_GP1BB.tsv challenge_GP1BB.tsv\n",
+      "K562..challenge_GP1BB.tsv\n",
+      "K562..challenge_HBB.tsv challenge_HBB.tsv\n",
+      "K562..challenge_HBB.tsv\n",
+      "K562..challenge_HBG1.tsv challenge_HBG1.tsv\n",
+      "K562..challenge_HBG1.tsv\n",
+      "K562..challenge_PKLR.tsv challenge_PKLR.tsv\n",
+      "K562..challenge_PKLR.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv challenge_IRF4.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv\n",
+      "HEPG2..challenge_F9.tsv challenge_F9.tsv\n",
+      "HEPG2..challenge_F9.tsv\n",
+      "HEPG2..challenge_LDLR.tsv challenge_LDLR.tsv\n",
+      "HEPG2..challenge_LDLR.tsv\n",
+      "HEPG2..challenge_SORT1.tsv challenge_SORT1.tsv\n",
+      "HEPG2..challenge_SORT1.tsv\n",
+      "K562..challenge_GP1BB.tsv challenge_GP1BB.tsv\n",
+      "K562..challenge_GP1BB.tsv\n",
+      "K562..challenge_HBB.tsv challenge_HBB.tsv\n",
+      "K562..challenge_HBB.tsv\n",
+      "K562..challenge_HBG1.tsv challenge_HBG1.tsv\n",
+      "K562..challenge_HBG1.tsv\n",
+      "K562..challenge_PKLR.tsv challenge_PKLR.tsv\n",
+      "K562..challenge_PKLR.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv challenge_IRF4.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv\n",
+      "HEPG2..challenge_F9.tsv challenge_F9.tsv\n",
+      "HEPG2..challenge_F9.tsv\n",
+      "HEPG2..challenge_LDLR.tsv challenge_LDLR.tsv\n",
+      "HEPG2..challenge_LDLR.tsv\n",
+      "HEPG2..challenge_SORT1.tsv challenge_SORT1.tsv\n",
+      "HEPG2..challenge_SORT1.tsv\n",
+      "K562..challenge_GP1BB.tsv challenge_GP1BB.tsv\n",
+      "K562..challenge_GP1BB.tsv\n",
+      "K562..challenge_HBB.tsv challenge_HBB.tsv\n",
+      "K562..challenge_HBB.tsv\n",
+      "K562..challenge_HBG1.tsv challenge_HBG1.tsv\n",
+      "K562..challenge_HBG1.tsv\n",
+      "K562..challenge_PKLR.tsv challenge_PKLR.tsv\n",
+      "K562..challenge_PKLR.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv challenge_IRF4.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv\n",
+      "HEPG2..challenge_F9.tsv challenge_F9.tsv\n",
+      "HEPG2..challenge_F9.tsv\n",
+      "HEPG2..challenge_LDLR.tsv challenge_LDLR.tsv\n",
+      "HEPG2..challenge_LDLR.tsv\n",
+      "HEPG2..challenge_SORT1.tsv challenge_SORT1.tsv\n",
+      "HEPG2..challenge_SORT1.tsv\n",
+      "K562..challenge_GP1BB.tsv challenge_GP1BB.tsv\n",
+      "K562..challenge_GP1BB.tsv\n",
+      "K562..challenge_HBB.tsv challenge_HBB.tsv\n",
+      "K562..challenge_HBB.tsv\n",
+      "K562..challenge_HBG1.tsv challenge_HBG1.tsv\n",
+      "K562..challenge_HBG1.tsv\n",
+      "K562..challenge_PKLR.tsv challenge_PKLR.tsv\n",
+      "K562..challenge_PKLR.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv challenge_IRF4.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv\n",
+      "HEPG2..challenge_F9.tsv challenge_F9.tsv\n",
+      "HEPG2..challenge_F9.tsv\n",
+      "HEPG2..challenge_LDLR.tsv challenge_LDLR.tsv\n",
+      "HEPG2..challenge_LDLR.tsv\n",
+      "HEPG2..challenge_SORT1.tsv challenge_SORT1.tsv\n",
+      "HEPG2..challenge_SORT1.tsv\n",
+      "K562..challenge_GP1BB.tsv challenge_GP1BB.tsv\n",
+      "K562..challenge_GP1BB.tsv\n",
+      "K562..challenge_HBB.tsv challenge_HBB.tsv\n",
+      "K562..challenge_HBB.tsv\n",
+      "K562..challenge_HBG1.tsv challenge_HBG1.tsv\n",
+      "K562..challenge_HBG1.tsv\n",
+      "K562..challenge_PKLR.tsv challenge_PKLR.tsv\n",
+      "K562..challenge_PKLR.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR405YLV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR242RPQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR338NEI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR056PFI_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR828FVZ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR999VDH_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR539WBA_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR464TKV_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR236KPK_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv challenge_ZFAND3.tsv\n",
+      "ENCSR595HZQ_pancreas..challenge_ZFAND3.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR724CND_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR035RVH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000ELH_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv challenge_IRF6.tsv\n",
+      "ENCSR000EPQ_keratinocyte..challenge_IRF6.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv challenge_IRF4.tsv\n",
+      "ENCSR000FEK_SK-MEL..challenge_IRF4.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_HNF4A.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MSMB.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_TERT-HEK293T.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv challenge_MYCrs6983267.tsv\n",
+      "ENCSR000EJR_HEK293..challenge_MYCrs6983267.tsv\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "dataset = {}\n",
+    "\n",
+    "data = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/models_to_score.csv\",sep=',',header=None)\n",
+    "print(data.head())\n",
+    "\n",
+    "per_expt={}\n",
+    "pre_expt_gt={}\n",
+    "num_per_fold = {}\n",
+    "for i,r in data.iterrows():\n",
+    "        filename = r[0]\n",
+    "        fold = r[1]     \n",
+    "        title = r[2]\n",
+    "        modelpath = r[3]\n",
+    "        modelname = r[4]\n",
+    "        if str(modelpath) == \"nan\":\n",
+    "                continue\n",
+    "        outdir = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/output/\"+title+\"/\"+filename.replace(\".tsv\",\"\")+\"/\"\n",
+    "\n",
+    "        title = title+\"..\"+filename\n",
+    "        print(title,filename)\n",
+    "        print(title)\n",
+    "\n",
+    "        if os.path.isfile(outdir+fold+\"/snp_scores.tsv\"):\n",
+    "                if title not in per_expt:\n",
+    "                        per_expt[title] = {}\n",
+    "                        #per_expt[title][\"filename\"] = filename\n",
+    "\n",
+    "                        per_expt[title][\"probs_jsd_diff\"] = []\n",
+    "                        per_expt[title][\"log_counts_diff\"] = []\n",
+    "                        per_expt[title][\"log_probs_diff_abs_sum\"] = []\n",
+    "                        per_expt[title][\"jsd_and_counts_diff\"] = []\n",
+    "                        num_per_fold[title] = 0\n",
+    "\n",
+    "\n",
+    "                results = pd.read_csv(outdir+fold+\"/snp_scores.tsv\", sep=\"\\t\", header=0)\n",
+    "                #print(results.shape)\n",
+    "                per_expt[title][\"probs_jsd_diff\"].append(results[\"probs_jsd_diff\"])\n",
+    "                per_expt[title][\"log_counts_diff\"].append(results[\"log_counts_diff\"])\n",
+    "                per_expt[title][\"log_probs_diff_abs_sum\"].append(results[\"log_probs_diff_abs_sum\"])\n",
+    "                combine = np.abs(results[\"probs_jsd_diff\"]) * results[\"log_counts_diff\"]\n",
+    "                per_expt[title][\"jsd_and_counts_diff\"].append(combine)\n",
+    "                values = results[\"META_DATA\"].str.split(\",\").str[4].astype(float).tolist()\n",
+    "                #print(np.array(values).shape)\n",
+    "                num_per_fold[title]+=1\n",
+    "                if title not in pre_expt_gt:\n",
+    "                        pre_expt_gt[title] = values\n",
+    "                else:\n",
+    "                        assert(values==pre_expt_gt[title])\n",
+    "\n",
+    "                        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K562..challenge_HBG1.tsv\n",
+      "(0.539219789977537, 4.9428887935742324e-49)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#for title in per_expt:\n",
+    "print(title)\n",
+    "# if title !=\"K562..challenge_HBB.tsv\":\n",
+    "#     continue\n",
+    "title=\"K562..challenge_HBG1.tsv\"\n",
+    "filen = title.split(\"..\")[1]\n",
+    "gt_lines = open(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/data/\"+filen).readlines()\n",
+    "ensemble1 = np.mean(per_expt[title][\"probs_jsd_diff\"],axis=0)\n",
+    "ensemble2 = np.mean(per_expt[title][\"log_counts_diff\"],axis=0)\n",
+    "ensemble3 = np.mean(per_expt[title][\"log_probs_diff_abs_sum\"],axis=0)\n",
+    "ensemble4 = np.mean(per_expt[title][\"jsd_and_counts_diff\"],axis=0)\n",
+    "nums = len(ensemble1)\n",
+    "values = pre_expt_gt[title]\n",
+    "\n",
+    "\n",
+    "filename=title.split(\"..\")[1]\n",
+    "if filename not in dataset:\n",
+    "        dataset[filename] = []\n",
+    "\n",
+    "\n",
+    "dataset[filename].append(np.round(np.max([stats.pearsonr(ensemble4, values)[0],stats.pearsonr(ensemble1, values)[0], stats.pearsonr(ensemble2, values)[0], stats.pearsonr(ensemble3, values)[0]]),3))\n",
+    "scores_id = np.argmax([stats.pearsonr(ensemble1, values)[0], stats.pearsonr(ensemble2, values)[0], stats.pearsonr(ensemble3, values)[0], stats.pearsonr(ensemble4, values)[0]])\n",
+    "\n",
+    "print(stats.pearsonr(ensemble2, values))\n",
+    "scores_id = 1\n",
+    "out_title = [\"profile, jsd\", \"counts\", \"profile, per base\", \"profile and counts\"]\n",
+    "scores = [ensemble1, ensemble2, ensemble3, ensemble4]\n",
+    "plot_scores = scores[scores_id]\n",
+    "out_title_f = out_title[scores_id]\n",
+    "\n",
+    "import seaborn as sns\n",
+    "fig, ax =  plt.subplots()\n",
+    "data = pd.DataFrame()\n",
+    "data[\"Beta\"] = values\n",
+    "data[\"logfc.mean\"] = plot_scores\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"Red\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"red\")\n",
+    "ax.set_title(title+\" n=\"+str(nums)+\"/\"+str(len(gt_lines)-7)+\" r=\"+str(dataset[filename][-1]))\n",
+    "ax.set_xlabel(\"ground truth\")\n",
+    "ax.set_ylabel(\"chromBPNet predicted \"+out_title_f)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/cagi_chrombpnet_hbg1.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#for title in per_expt:\n",
+    "print(title)\n",
+    "# if title !=\"K562..challenge_HBB.tsv\":\n",
+    "#     continue\n",
+    "title=\"K562..challenge_HBG1.tsv\"\n",
+    "filen = title.split(\"..\")[1]\n",
+    "gt_lines = open(\"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/data/\"+filen).readlines()\n",
+    "ensemble1 = np.mean(per_expt[title][\"probs_jsd_diff\"],axis=0)\n",
+    "ensemble2 = np.mean(per_expt[title][\"log_counts_diff\"],axis=0)\n",
+    "ensemble3 = np.mean(per_expt[title][\"log_probs_diff_abs_sum\"],axis=0)\n",
+    "ensemble4 = np.mean(per_expt[title][\"jsd_and_counts_diff\"],axis=0)\n",
+    "nums = len(ensemble1)\n",
+    "values = pre_expt_gt[title]\n",
+    "\n",
+    "\n",
+    "filename=title.split(\"..\")[1]\n",
+    "if filename not in dataset:\n",
+    "        dataset[filename] = []\n",
+    "\n",
+    "\n",
+    "dataset[filename].append(np.round(np.max([stats.pearsonr(ensemble4, values)[0],stats.pearsonr(ensemble1, values)[0], stats.pearsonr(ensemble2, values)[0], stats.pearsonr(ensemble3, values)[0]]),3))\n",
+    "scores_id = np.argmax([stats.pearsonr(ensemble1, values)[0], stats.pearsonr(ensemble2, values)[0], stats.pearsonr(ensemble3, values)[0], stats.pearsonr(ensemble4, values)[0]])\n",
+    "\n",
+    "print(stats.pearsonr(ensemble2, values))\n",
+    "scores_id = 1\n",
+    "out_title = [\"profile, jsd\", \"counts\", \"profile, per base\", \"profile and counts\"]\n",
+    "scores = [ensemble1, ensemble2, ensemble3, ensemble4]\n",
+    "plot_scores = scores[scores_id]\n",
+    "out_title_f = out_title[scores_id]\n",
+    "\n",
+    "import seaborn as sns\n",
+    "fig, ax =  plt.subplots()\n",
+    "data = pd.DataFrame()\n",
+    "data[\"Beta\"] = values\n",
+    "data[\"logfc.mean\"] = plot_scores\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"Red\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"red\")\n",
+    "ax.set_title(title+\" n=\"+str(nums)+\"/\"+str(len(gt_lines)-7)+\" r=\"+str(dataset[filename][-1]))\n",
+    "ax.set_xlabel(\"ground truth\")\n",
+    "ax.set_ylabel(\"chromBPNet predicted \"+out_title_f)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/cagi_chrombpnet_hbg1.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi_scoring_all.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi_scoring_all.ipynb
new file mode 100644
index 00000000..52232f98
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi_scoring_all.ipynb
@@ -0,0 +1,857 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf = pd.read_csv(\"cagi.mpra.per_model.scores.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>ref_hg38</th>\n",
+       "      <th>alt_hg38</th>\n",
+       "      <th>label_hg38</th>\n",
+       "      <th>Confidence_hg38</th>\n",
+       "      <th>SAD</th>\n",
+       "      <th>SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.130221</td>\n",
+       "      <td>-0.029623</td>\n",
+       "      <td>0.000808</td>\n",
+       "      <td>0.057231</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807514</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.021439</td>\n",
+       "      <td>-0.025864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.079846</td>\n",
+       "      <td>-0.022760</td>\n",
+       "      <td>0.000452</td>\n",
+       "      <td>0.125456</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807514</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.013206</td>\n",
+       "      <td>-0.015869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.102046</td>\n",
+       "      <td>-0.031783</td>\n",
+       "      <td>0.000633</td>\n",
+       "      <td>0.065748</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807514</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.022903</td>\n",
+       "      <td>-0.027649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.015090</td>\n",
+       "      <td>0.021831</td>\n",
+       "      <td>0.000192</td>\n",
+       "      <td>0.174132</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807515</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.005508</td>\n",
+       "      <td>0.006592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.072389</td>\n",
+       "      <td>0.028991</td>\n",
+       "      <td>0.000554</td>\n",
+       "      <td>0.099254</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807515</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.028412</td>\n",
+       "      <td>0.033691</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr       pos allele1 allele2  \\\n",
+       "0  chr6  37775290       G       A   \n",
+       "1  chr6  37775290       G       C   \n",
+       "2  chr6  37775290       G       T   \n",
+       "3  chr6  37775291       T       A   \n",
+       "4  chr6  37775291       T       C   \n",
+       "\n",
+       "                                             rsid  logfc.mean  sign_jsd.mean  \\\n",
+       "0   6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.130221      -0.029623   \n",
+       "1   6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv   -0.079846      -0.022760   \n",
+       "2   6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv   -0.102046      -0.031783   \n",
+       "3  6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv    0.015090       0.021831   \n",
+       "4   6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv    0.072389       0.028991   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                               0.000808   \n",
+       "1                               0.000452   \n",
+       "2                               0.000633   \n",
+       "3                               0.000192   \n",
+       "4                               0.000554   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean.pval  label  ...  ref alt chr_hg38  \\\n",
+       "0                                    0.057231   0.03  ...    G   A     chr6   \n",
+       "1                                    0.125456   0.03  ...    G   C     chr6   \n",
+       "2                                    0.065748   0.07  ...    G   T     chr6   \n",
+       "3                                    0.174132  -0.07  ...    T   A     chr6   \n",
+       "4                                    0.099254   0.03  ...    T   C     chr6   \n",
+       "\n",
+       "   pos_hg38  ref_hg38 alt_hg38 label_hg38 Confidence_hg38       SAD       SAR  \n",
+       "0  37807514         G        A       0.03            0.01 -0.021439 -0.025864  \n",
+       "1  37807514         G        C       0.03            0.01 -0.013206 -0.015869  \n",
+       "2  37807514         G        T       0.07            0.01 -0.022903 -0.027649  \n",
+       "3  37807515         T        A      -0.07            0.01  0.005508  0.006592  \n",
+       "4  37807515         T        C       0.03            0.01  0.028412  0.033691  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'sign_jsd.mean', 'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval', 'label', 'Confidence',\n",
+       "       'challenge', 'model_used', 'chr_hg19', 'pos_hg19', 'ref', 'alt',\n",
+       "       'chr_hg38', 'pos_hg38', 'ref_hg38', 'alt_hg38', 'label_hg38',\n",
+       "       'Confidence_hg38', 'SAD', 'SAR'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "dataf = dataf[~np.isnan(dataf[\"SAD\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "challenges = list(set(dataf[\"challenge\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "enf_preds_dir = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/alldata/\"\n",
+    "\n",
+    "pear_dict_logfc = {}\n",
+    "pear_dict_jsd = {}\n",
+    "pear_dict_enf = {}\n",
+    "\n",
+    "for challenge in challenges:\n",
+    "    temp = dataf[dataf[\"challenge\"] == challenge]\n",
+    "    models = list(set(temp[\"model_used\"]))\n",
+    "    pear_dict_logfc[challenge] = []\n",
+    "    pear_dict_jsd[challenge] = []\n",
+    "    pear_dict_enf[challenge] = []\n",
+    "    for model in models:\n",
+    "        temp_new = temp[temp[\"model_used\"]==model].drop_duplicates()\n",
+    "        #print(temp_new.shape)\n",
+    "        #temp_new = temp_new[temp_new[\"Confidence\"]<0.1]\n",
+    "        #print(temp_new.shape)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"logfc.mean\"])\n",
+    "        pear_dict_logfc[challenge].append(pear)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"sign_jsd.mean\"])\n",
+    "        pear_dict_jsd[challenge].append(pear)\n",
+    "\n",
+    "        ifile = enf_preds_dir + challenge.replace(\"challenge_\",\"\").replace(\".tsv\",\"\") + \".ZigaDataLocal.csv\"\n",
+    "        dnf=pd.read_csv(ifile, sep=\",\", header=0)\n",
+    "        pear1 = scipy.stats.pearsonr(dnf[\"data.Value\"],dnf[\"dnase\"])\n",
+    "        \n",
+    "        pear_dict_enf[challenge].append(pear1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_IRF6.tsv\n",
+      "[(0.6863978095129647, 2.9969485291779e-189), (0.6855702910433198, 1.2787248261565636e-188), (0.6844426312373798, 9.162780560960268e-188)]\n",
+      "challenge_F9.tsv\n",
+      "[(0.3278058135324871, 3.3786553145326075e-18)]\n",
+      "challenge_LDLR.tsv\n",
+      "[(0.5571818417723304, 1.0332418194611135e-59)]\n",
+      "challenge_GP1BB.tsv\n",
+      "[(0.5215335208116025, 1.4335645617060139e-61)]\n",
+      "challenge_ZFAND3.tsv\n",
+      "[(0.44484781066968043, 1.2314636625275076e-61), (0.3984857758855108, 1.0109777074904528e-48), (0.2891811987098261, 1.9042474170665058e-25), (0.2958126463496242, 1.3278182288448838e-26), (0.3192111247029, 6.209806529740245e-31)]\n",
+      "challenge_MYCrs6983267.tsv\n",
+      "[(0.5078362906951961, 2.593999727387911e-90)]\n",
+      "challenge_HBB.tsv\n",
+      "[(0.5472988538268535, 4.0027204727308833e-35)]\n",
+      "challenge_TERT-HEK293T.tsv\n",
+      "[(0.5553154805041812, 7.931503234163681e-48)]\n",
+      "challenge_SORT1.tsv\n",
+      "[(0.7609120286314868, 5.326426982528549e-257)]\n",
+      "challenge_IRF4.tsv\n",
+      "[(0.8154299895742269, 1.5489669725325216e-232)]\n",
+      "challenge_HNF4A.tsv\n",
+      "[(0.34006638384182497, 1.423244593466684e-19)]\n",
+      "challenge_HBG1.tsv\n",
+      "[(0.5437910247812469, 9.119319172115673e-50)]\n",
+      "challenge_PKLR.tsv\n",
+      "[(0.8120143586029585, 8.990220357193962e-241)]\n",
+      "challenge_MSMB.tsv\n",
+      "[(0.19855880983217153, 2.1753443562357922e-13)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_pear_logfc = []\n",
+    "best_pear_jsd = []\n",
+    "best_pear_sad = []\n",
+    "keys = list(pear_dict_jsd.keys())\n",
+    "for key in keys:\n",
+    "    print(key)\n",
+    "    print(pear_dict_logfc[key])\n",
+    "    best_pear_logfc.append(np.max(pear_dict_logfc[key]))\n",
+    "    best_pear_jsd.append(np.max(pear_dict_jsd[key]))    \n",
+    "    best_pear_sad.append(np.max(pear_dict_enf[key])) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_IRF6.tsv (0.6863978095129647, 0.647739059957578)\n",
+      "challenge_F9.tsv (0.3278058135324871, 0.2910237971063166)\n",
+      "challenge_LDLR.tsv (0.5571818417723304, 0.6279168449537609)\n",
+      "challenge_GP1BB.tsv (0.5215335208116025, 0.5639564947233268)\n",
+      "challenge_ZFAND3.tsv (0.44484781066968043, 0.44980549021497984)\n",
+      "challenge_MYCrs6983267.tsv (0.5078362906951961, 0.5589049959383294)\n",
+      "challenge_HBB.tsv (0.5472988538268535, 0.5784344527267236)\n",
+      "challenge_TERT-HEK293T.tsv (0.5553154805041812, 0.5871151716773245)\n",
+      "challenge_SORT1.tsv (0.7609120286314868, 0.6187820217627544)\n",
+      "challenge_IRF4.tsv (0.8154299895742269, 0.7141790825255856)\n",
+      "challenge_HNF4A.tsv (0.34006638384182497, 0.288200934853065)\n",
+      "challenge_HBG1.tsv (0.5437910247812469, 0.18851068518283778)\n",
+      "challenge_PKLR.tsv (0.8120143586029585, 0.8091312664217033)\n",
+      "challenge_MSMB.tsv (0.19855880983217153, 0.1191656497017333)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(best_pear_logfc,best_pear_sad, s=5, c=\"g\")\n",
+    "ax.plot(best_pear_logfc,best_pear_logfc, c=\"r\",  alpha=0.2)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)-0.05, c=\"r\",  alpha=0.1)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)+0.05, c=\"r\",  alpha=0.1)\n",
+    "\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "ax.set_ylim((0,1.0))\n",
+    "ax.set_xlim((0,1.0))\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keys[i], (best_pear_logfc[i], best_pear_sad[i]))\n",
+    "    texts.append(plt.text(best_pear_logfc[i], best_pear_sad[i], keys[i].replace(\"challenge_\",\"\").replace(\".tsv\",\"\")))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/cagi_logfc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MannwhitneyuResult(statistic=97.0, pvalue=0.9816711400706458)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.mannwhitneyu(best_pear_logfc, best_pear_sad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5442135726135151"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(best_pear_logfc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5030618534104299"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(best_pear_sad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "enf_preds_dir = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/alldata/\"\n",
+    "\n",
+    "pear_dict_logfc = {}\n",
+    "pear_dict_jsd = {}\n",
+    "pear_dict_enf = {}\n",
+    "\n",
+    "for challenge in challenges:\n",
+    "    temp = dataf[dataf[\"challenge\"] == challenge]\n",
+    "    models = list(set(temp[\"model_used\"]))\n",
+    "    pear_dict_logfc[challenge] = []\n",
+    "    pear_dict_jsd[challenge] = []\n",
+    "    pear_dict_enf[challenge] = []\n",
+    "    for model in models:\n",
+    "        temp_new = temp[temp[\"model_used\"]==model].drop_duplicates()\n",
+    "        #print(temp_new.shape)\n",
+    "        #temp_new = temp_new[temp_new[\"Confidence\"]<0.1]\n",
+    "        #print(temp_new.shape)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"logfc.mean\"]))\n",
+    "        pear_dict_logfc[challenge].append(pear)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"sign_jsd.mean\"]))\n",
+    "        pear_dict_jsd[challenge].append(pear)\n",
+    "\n",
+    "        ifile = enf_preds_dir + challenge.replace(\"challenge_\",\"\").replace(\".tsv\",\"\") + \".ZigaDataLocal.csv\"\n",
+    "        dnf=pd.read_csv(ifile, sep=\",\", header=0)\n",
+    "        pear1 = scipy.stats.pearsonr(abs(dnf[\"data.Value\"]),abs(dnf[\"dnase\"]))\n",
+    "        \n",
+    "        pear_dict_enf[challenge].append(pear1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_pear_logfc = []\n",
+    "best_pear_jsd = []\n",
+    "best_pear_sad = []\n",
+    "keys = list(pear_dict_jsd.keys())\n",
+    "for key in keys:\n",
+    "    best_pear_logfc.append(np.max(pear_dict_logfc[key]))\n",
+    "    best_pear_jsd.append(np.max(pear_dict_jsd[key]))    \n",
+    "    best_pear_sad.append(np.max(pear_dict_enf[key])) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_IRF6.tsv (0.688024621814145, 0.6549134344255393)\n",
+      "challenge_F9.tsv (0.08162256518538383, 0.08819851923478136)\n",
+      "challenge_LDLR.tsv (0.6045974580705764, 0.579072736314436)\n",
+      "challenge_GP1BB.tsv (0.4954772436774826, 0.47205022981619266)\n",
+      "challenge_ZFAND3.tsv (0.41884266063861497, 0.4171465992089521)\n",
+      "challenge_MYCrs6983267.tsv (0.5365007062961231, 0.573783992279473)\n",
+      "challenge_HBB.tsv (0.4131418431199688, 0.4249386569940439)\n",
+      "challenge_TERT-HEK293T.tsv (0.43998778134423205, 0.46237325058903983)\n",
+      "challenge_SORT1.tsv (0.7337050220520509, 0.5220823990168146)\n",
+      "challenge_IRF4.tsv (0.8145174978461941, 0.6261232756990919)\n",
+      "challenge_HNF4A.tsv (0.2964168915155524, 0.29097554418302185)\n",
+      "challenge_HBG1.tsv (0.48106994565359995, 0.14587095143969553)\n",
+      "challenge_PKLR.tsv (0.8194964319535258, 0.7968832540705849)\n",
+      "challenge_MSMB.tsv (0.18064507834629845, 0.09576838651866845)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(best_pear_logfc,best_pear_sad, s=5, c=\"g\")\n",
+    "ax.plot(best_pear_logfc,best_pear_logfc, c=\"r\",  alpha=0.2)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)-0.05, c=\"r\",  alpha=0.1)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)+0.05, c=\"r\",  alpha=0.1)\n",
+    "\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "ax.set_ylim((0,1.0))\n",
+    "ax.set_xlim((0,1.0))\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keys[i], (best_pear_logfc[i], best_pear_sad[i]))\n",
+    "    texts.append(plt.text(best_pear_logfc[i], best_pear_sad[i], keys[i].replace(\"challenge_\",\"\").replace(\".tsv\",\"\")))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/cagi_logfc_abs.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.5437910247812469, 9.119319172115673e-50)\n",
+      "(0.18851068518283778, 1.8888239190707555e-06)\n",
+      "(0.19855880983217153, 2.1753443562357922e-13)\n",
+      "(0.1191656497017333, 0.0003614069297465672)\n",
+      "(0.8154299895742269, 1.5489669725325216e-232)\n",
+      "(0.7141790825255856, 1.8925793862734474e-152)\n",
+      "(0.7609120286314868, 5.326426982528549e-257)\n",
+      "(0.6187820217627544, 1.1822834552124175e-136)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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A/Xlqq+M4TtNg6VJzT917L7z3Xux4y5Zw7rkmGqedBi1a1HmrYgpIL+CDyOeVwFEJzjtfRI4H3gZ+oKofJLm2V6JKRKQKqALo27dvDprtOI7TyNi8GR56yKyNF16oXXbUUSYaF18MnTtndNs6Z2GJyIUi0i54/58i8qiIHJFRLdnzV6C/qh4GPA1kHOdQ1UmqWq6q5d26dct5Ax0nrzSmqaWNqa31pTE86+7d8Pe/w+jRsN9+8I1vxMSjd2+48UZ44w14+WWbaZWheEB6Fsh/qepDInIscDIW1L6TxNZCJqwC+kQ+9yYWLAdAVT+KfLwbuDVy7Qlx186tZ3scp+ERTi2Fhh/gbUxtrS8N+Vlff90sjXvvhTVrYsdbt4bzzoNx4+DEE6GsrN5VpSMgu4PXs4BJqjpbRH5W75phPjBYRAZggnAJcGn0BBHpoarhN3Au8Ebw/kngFyLSKfh8KnBjDtrkOA2LxjS1tDG1NZ6aGhOFMWPSE4RcPmumdSfio4/g/vtNOBYsqF12/PHmorrgAmjfvv7tjZCOgKwSkYnYbKlfisg+5GABoqruEpGrMDEoA/6iqq+JyM3AAlWdCXxXRM4FdgEfA+OCaz8WkZ9iIgRwcxhQd5wmRWOaWtqY2hpPphZFLp81W2vm88/hiSdMNGbNss8hAwfC2LFw+eX2Pk+IRuf7JjpBpA02w2mJqr4jIj2AQ1X1qby1Kk+Ul5frgnh1dhzHWbTIVqDXxwooRN2q8MorJhrTpsGGDbGydu3gootMOI47DkRy2cqEN6vTAlHVbSKyDjgWeAezBt7JZcscx3GKSjGtp3Tq/vBDE5nqanj11dhxETjlFHNRVVRAmzap75MLd1mEOgVERMIFe0OAe4AWwFTgmHrX7jiO4yRm+3aYMcNE48knbeFfyIEHWjB8zBjolXAFQ2JyHPxPJwbydWA4UAOgqqvDab2O4zhODgkTGFZXwwMP2PqNkE6dbEruuHFQXp6diyrHEx3SEZCdqqoiogAi8qWc1Ow4juMYYQLD6mp4JxIhaN4czjzT4hpnnw377FO/enLsqktHQB4MZmF1FJFvAP8O/ClnLXAcxylFtmyBRx6xtCLPPbd3AsNx48zi2HffYrWwTlIKiIgI8ABwIPAJFgf5kao+XYC2OY7jNC327IF582IJDLdujZV17x5LYHjYYcVrYwakFJDAdfWEqh6KpRJxHMdxMuWdd2IJDFesiB1v2RJGjYolMGzeuLZoSqe1NSLyZVWdX/epjuM4DgCbNsGDD5q18eKLtcuOOspcVBdfbMHxRko6AnIUcJmIrAC2YgtKNEhw6DhOUyDH6wNyRkNtVzJ27YI5c0w0Hn/cpuKG9OljK8PHjoUhQ4rWxFySjoCclvdWOI5TXDJdH1Cojr0hJy2M8uqrsdXh0QSGbdrA+eebi+rEE6FZvbNANSjSWYm+QkQOB44LDv1DVf+V32Y5jpMx9enUM10fUKiOvSEnaNywIZbAcOHC2mUjR8YSGLZrusvm0lmJ/j3gG8CjwaGpIjJJVX+X15Y5jpMZ9enUM10fUKiOPdcpRuprOe3cGUtgOHt27QSGgwbFEhgOGJC7Njdg0nFhXQEcpapbAUTkl8BLgAuI4zQkCjlab6yZd7MRWVUTnupquO8+S50e0q6dBcIrK+GYY3KdwLDBk46ACLE9QQjel9a35DiNgUw79WIHqItRfyYiu2aNtS8+gWGzZrEEhqNG1Z3AsAmTjoDcA/yviDwWfK4A/py3FjmOUxiKHaAuRv11iexnn8USGD71VO0EhgcfbKJx2WWZJTBswqQTRP+ViMzF0rkD/JuqNuCNgB2nwBR7JJ8u8e0sdoC62PWHpEpg2LlzLIHhiBEl56Kqi3SC6F8BXlPVmuBzexE5SlX/N++tc5zGQLFH8ukS385ixzFyVX+2Ar5iha0MnzIlcQLDyko466z6JzBswqTjwroTOCLyeUuCY45TujSUkXRdNJZ2ZkomAr5li+WgChMYRjniCBON0aOhW7f8tLWJkVYQXSP73qrqHhHJScIWETkd+A22J/rdqnpLXPkE4EpsF8T1wL+r6oqgbDewJDj1fVU9NxdtcpyMKfZIPl3SaWddo/mG6K6rSxj37IG5c2MJDLdti5V1727TbisrbYrutGmwcqULSJqkIwTLReS7mNUB8B/A8vpWLCJlwB+AU4CVwHwRmamqr0dOWwSUB9vqfgu4Fbg4KPtMVYfVtx2O40SoazSfqjwbccmFIEXToEd55x0TjXvvtf02QvbZJ5bA8NRTYwkMr766cbgiGxDpCMh44LfAfwIKPANU5aDuI4GlqrocQESmA6OALwREVaM25stAE7O9HacAZNJJ1zWaT1WeTSwoF/Gj6D0GDLBAeHW1BcajfPWrJhoXXZQ4gWGiZ2uIFlcDIp1ZWOuAS/JQdy/gg8jnlVjixmRcAfwt8rmViCzA3Fu3qOrjiS4SkSoCwevbt2992us4jZNMOum63FypyrOJsWRyTbLO/JJLYPlyWLIE9tsPduyIlfXpY6vDx46FAw5Iff9Ez9ZYJkgUiToze4nIrcHMqxYi8oyIrBeRgloCQX3lwG2Rw/1UtRy4FLhDRAYlulZVJ6lquaqWd3O/ptPQqakxV8qiDGbK13XNmDEwYQIce2zm906nrvAYwO23m0sp3XqGD7dr0umcw8586lT7/OqrcO21cO65lvn26adNPNq0McF45hl47z047zyYODG75w6/u6Y28SBHpOPCOlVVrxORrwPvAecBzwNT61n3KqBP5HPv4FgtRORk4IfASFX9YmihqquC1+XBOpXhwLJ6tslxikuiEW9dbpS6RsnhyDoXPv5EdcUfy9eofcwYC4CXldmajJqa2uUnnmguqvPPh7ZtU7c5XRrLBIkikY6AhOecBTykqpslN4tp5gODRWQAJhyXYNbEF4jIcGAicHrgSguPdwK2qeoOEekKHIMF2B2ncZPIpVNXB5jMDZSPhYOJ7hF/LNN66hLInTstcWGYwHDXrlhZnz5QVWUzqfr1S7/NTm5Q1ZQ/wC3Am9iMqBZAN+B/67ounR/gTOBtzHL4YXDsZuDc4P0cYC3wSvAzMzh+NDaF91/B6xXp1DdixAh1nKKzcKHqhAmqNTXpnV9Tk9n5IRMmqIK9ZlpnNkTrCN8/+mjd9UbbGbJnj+r8+apXXaXapYuVhz/t26seeqi9/8EP8vc8TpSEfWo6QfQbRORWYLOq7haRbdhsqVyI1xPAE3HHfhR5f3KS614EDs1FGxyn4GTqUsnWjRIdeU+dajGCZcvgppvy45aJPhfY+2XLLLcUpBd8f/JJ+PnPYdUqC4yHNGtmU27DBIZvvmnPVB+rwmdY1Zu0FgSq6seR91uxrW0dp2mS746lUC6V6PqIMWNinfmgQYV7ruOOs/pSPeuBB0J5Odx4oyUwjLZ76NBYAsOePWPHcxGb8BlW9SYnK8odp0mR744lH4HZRKIXfY7bbzfLo3t3C0IvWpS7NkTrvv322PHw/hUVe1+jCv/8p8U1HnwQPvkkVtaqlV1zzTWWXiRfCQw9NlJvXEAcJ57G0LGEnfaxx8ILL5goxIte/HMMH26zk267DXbvTi0gmVhhEyfCpEmWZ2rixNTnvvdeLIHh0qWx482bW+LCMIFhy5ap75MLfIZVvUknG+8zqvq1uo45TpOhoXUsqayLZcssVtCvn62JGD06dl30OcJ7HHecfQ7XhBx7LPz979Chg12bbGpuKjp0sGm1HTokLv/0U3jkEbM25s6tXeYJDBs1SQVERFoBbYCuwbTZ0I5sj60idxynEEyfDvPmmZURb10cdxxMnmyxjbIyO5aOO+ub3zSr4e23YdYsE4CoVZKJFTZ6tF0bFa/duy3bbXU1PPpo7QSGXbva/hqVlXDIIdl8I8mf0ykoqSyQbwLfB3oC0RU7nwC/z2ObHKe0ie8YN2+GhQutkw8JrYuaGkvRUVFhlki4Srsud1ZoNQwdasHp0AJJFs9IRRj0fu89+O1vLWYxZw58EMlUtM8+0L8/vPWW3fu22xLdKTM8CF50kgqIqv4G+I2IfEdVf1fANjlOaRPfMY4fb7GLsPOPxj+qq+Hdd+ErX4GBA2tbDKGbKhQiVbj/frjrLjjjDLMSLr7Yjk+blrjuRMQL3J//DH/8I9xzD2zcWPvco4+OJTB89930p96mY100hlhVEyedIPpfROQ/gb6qWiUig4Ehqjorz21znKZHtGMMO+74TjJR8Fs1Zl1E4x8zZsRmOUXvM3w4XHddbdfXtGn2eedOWLs2th4kTHGyZQt07Ahnnx2LlSQirH/5cmjRIrbOIxSP/faDK6+0fFSffmrnv/tu8thSXS63ZALS0GJVJUhaAgIsxFZ/g6UdeQhwAXEaLg3FPx7fjkSL7aB2G+MFQxV+8hOYORM2bIDvf9+OH3ecTcvdsQNeftmslKgorV1rrq+hQ+38MWNMTN54wzr97dvNuhkzxsRj505LQLhwYcwtFs8DD8Dzz0Pr1pbAMORLX4ILLjBrY+RIW/gH6eXfSiQWbl00CtIRkEGqerGIjAZQ29wpTxOzHSdH5Ms/nqkwxbcjVS6pRNeVlVmgu1MnOOcc+OgjKw/jE3/7m7mxTjvNBGX6dNtI6eabYcgQi3P06BFzZd16q60B+fxzWLPGrh040O7/7rvQq5fllxozJvasZ55pqdKrq+GVV2q389BDbb3GeefVTmAY/2yJnjF+Zlj0HLcuGgXpCMhOEWmNbSZFkDZ9R+pLHKfI5GsEm6kwJXJHxVsbkDjx4ZYtJgYzZljK8p07LdVH8+Yx91OHDnD88fb64osW19i2zYLWS5bASSeZa2nSJKsntFAuuMBmXw0eDP/3f3DQQVbfrl1w4YU2s+uNNyz9yR132LawIR072sZNixaZ+Iwdu/dzpxOMj58Z5jQ60hGQm4C/A31EZBqW+XZcPhvl1IOG4ropNvkawWYqTOm2I16YwkV/L79sbiFVm/66Z49ZFFOn2jmDB1tH/uab1rGvXm1uqNNPt/UdLVvawry1a22kHy76O+ssi1Pcc4/FRdq1s7qfesqEY/VqEyOwOlu0MKvl6KPhqKPg4INNgMaNs3NSueo8CN5kSSeZ4tMiUgN8BVsL8j1V3ZD3ljnZ4VMb80s6gpCpiNfUmKuqqiqxa2vLFuv0v/Y1EwcwEXnsMYtDzJljge8PP4RjjrHYRteu5r5q1cpShsyYYS6tFSvgiitg61azZgBOOcXcWW+9ZZ/D6bfNmkHnzrZB0+GH2+f+/eHXvzar4/HHre3f/CasW2dB/WXLTPASPU8236XToEm1kDB+/9clwWsbEemrqu/HX+PUg1xZDj6qKz6Zbgo1bZq5igYOjB2rqTFroVUri0+MHWuvInbexIkWUA/Xc7RrZ1N0d+wwQbnsMujSBV5/PbZnxuuvm2iEQnTooebmiqdtW7vnwQebe+vAA83lVVFhFseIESYqV19tQjFpkrnYevY0odq926yTqqr8CoRb20UnlQUyG4t7RAPmiu0Hsi9Qlsd2lR65shzqM6or9D9kY+8AkrU/XsRraixmMWtW7XxR4fUHHQSvvWadb4cOtnCwTRsThHDxYGhNlJXZ65lnmvtq7Vorb9/ezhkwAC691OIlbdrYDKwOHeCzzywesm6dWSrNm9cWj44dzdpYvtwE5qabbL1IuICxb18TjMMPN1fWihWWcn3YMBOWgQNN0K691u796acmeFEefdRyYI0bl3iGV6rvNBFubRedVAsJa+23ISL9geuBk4Ff5LdZJUhDsBwK/Q/Z2DuAZO2PTsNdsMDyQHXrZh1z586xGVHh9RUVFvSuqDALY8YMsyBGjoy5nTp1stjDK69YR712Ley7r7mq1q+36zt3ttd16yxwvnCh1bl7N/zrX/Dww+a6ChGxdg4bBr17m8XSqZNN4Y0uYDz2WJvV9e67JlArV9q9R42yacRTptj7GTNs//DNmy2ucthhtRcyTpkSWzOSTEAy+ZuI/s809sFIIyWdZIqDsT3JjwJuB76rqp/nu2ElR0PwBx97rPmwUy0iy4Z0R+oNhXQ7o1TtDzvC004zt9HJJ5vbqHVrE4RlyyxuMWqU7Zdx++3wne9Yp15RYe6gZs0sHrF6tSVLvPNOW6TXurV17K1b2306d7bpvfvtZwHwfv3MbbVrlwlOOPU3ytFHm9jsv799/tOfLHbRu7eJ0+OPm9XRvLlZIi1amHiMG2ezvUaMMIEBE6j4fT/atjVr63e/i21iFQbcw9dMv9N4ov8zudjv3cmYVDGQQzDhGIrtN36Fqu7OZeUicjrwG8wddreq3hJXvg8wBRgBfARcrKrvBWU3AlcAuzFRezKXbStJXnghtuFQshFiNqQaqTfEf/Z0YxjRfFTRkXZNjXWe115rwW6wWVGHHVY7+eH27SYu48ebkIhYAsLu3S2gvWqVuYuGDoX337c4yVln2Xnz5pnbCsxdNHiwrRd56ikTn7VrY7OowMSifXuLZ7RubWXPPGNWzNatZjksWWKztl57zfbnCGd4zZljQvjTn9rzVVTYM06daoMOMNGK/s0MH27Xrl5tlstdd5nrLrw2+n1FycXui07BSGWB/Av4AIuFHAkcGV0/qKrfrU/FIlIG/AE4BVgJzBeRmar6euS0K4CNqrq/iFwC/BK4WEQOBi7BxK0nMEdEDsi1wJUc+fonbCj/3HVZFqkWtqVyrcSXTZ9uLp6TTjJ3z5NPWgcbrhQ/4AAb7a9fb68ffmixh927bdQ/e7YlNuze3WILy5fHYhx9+pg4jRhhlsf69TbCf+ONWDxk9Wp7LSszF9eAATYdeNgwE48ePUyEmje3oPusWTYT68MPrc5DDrHz3nnHFhaOHGnC85OfxOIX8elU4r+X8LscOtRmeEVTvefKdRn/+2yIg5EmTioBuYJg8WCeOBJYqqrLAURkOrbXelRARgE/Dt4/DPw+WAU/CpiuqjuAd0VkaXC/l/LY3qZPvv4Ji/3PHXY0iTZdipJqYVsqf3u8QEaz544fbx3+jh02Wyncv6N7d4tV7NxpCQ1nzbKO+LvfNXfX6tVmZVRVmRWy334mPsuXW8B71y6zFN5/3+4TpX17i520bWsWzRFH2Cr2Xr1g/nyLc7Rubeft3GmJDnv1svUeobUxZIh93rjRpu4uXWrutMmTTUCi6eRD11X0ewm/y6oqE6BoqvdcDSgaewytCZAqiD5ZRLoB/bCOflOO6+6FWTghK7E4S8JzVHWXiGwGugTHX467NuEeJSJSBVQB9O0bPzO5gZCvAGC2921qAcloZ1ZVlXxL10QdW6IV1fH+9niBDGMDzZpZbGHr1pi1ceyxFl+YPduEYv/97f3++1vHfOih5m567TU7f8UKc12FazKWLTPrYdmy2nuHt2plgnDAAeaK7NnTrJWyMnNFvfWWpVtfuNDEq1OnWLC9RQuLfQwaZPc+5JBYLqtmzaye2bPh1FMtbhPNBvyPf8T+TqJJGTt0sO96/PjcuaniaSiWbQmTKgZyJTbbahkwQESqVHVmwVqWI1R1EjAJoLy8PJ8WVfbkaySV7X2b2sgu2tFMnZp8S9dEHVs2if5UzZ307rvWUW/ZYh13VZXFKb70JRuV77+/uZ7mzYtN11240MRh7VrrzAcPtk5+7VqzJnbujNVTVmaupX32sU79qKPMcvjsM7MaXn3V4hzNmtm9Dj3ULJdw2vC8eea66tnTZna99prVP3Cgdfxh6vXp0629PXpYe994o7b7qqzMzg1df2Vl9h1PmJDfv5+6hKipDYQaIKlcWN8HhqrqehEZCEwDcikgq4A+kc+9g2OJzlkpIs2BDlgwPZ1rGw8NLfbQ1EZ2iTqa+L01knUyib4LTTIOqamxzvaNN8wlddppNppfvdosg/HjLa/Ugw/aNN3Vq01EOnSwhYA9e8aC1l/9qpU/+6wJQpReveyaZs3MtbVundXVqZP9dOxo8Yv1683KOeAAeOklc0HNmWNTig8/3ESlf39zcb3wglkgffrErIbw+7jrLhObTp3ge9+LtSN0X23eHMu1dfvt9gy7dxf/76epDYQaIKkEZKeqrgdQ1eXBjKhcMh8YLCIDsM7/EuDSuHNmApVYbOMC4FlVVRGZCdwnIr/CguiDgf/LcfsKR6Ymfbojq2xdBcWOWeST+Gerq5NJZZVs2RLb6Gn4cJtl9OabsGmTdehdupgIvPqqBcfvv99G8ccfb+fMnm2Wx+DBdl379hZ32HdfC3pHhapdO7M2mjWzzn/TJqu/d2+zULp3Nwvio4/M6hg71qyWXr3MDbZwoZ1bUWExj4qKmMspXMNx2mkx8Ygu+jv9dLtXZWXtGWhg91m0qPaGV5n8/eTTSmhqA6EGSCoB6S0iv032ub6zsIKYxlXAk9g03r+o6msicjOwIHCX/Rm4NwiSf4yJDMF5D2IB913At0tqBpaPrHJHumtfoh1d2CGFrprQhdOpk1kEW7davOK++yx4vO++JhZbtljnH6ZOP/NM6+AXL7YOfvFiC5hH6d3bZnJ9+csmNosWWbxj0yabNdWjh1kc++1n036POMKu69rVRGXnTnNdrV5t54Xp2+MD4WBt+sc/rCy66G/QoOTTu+srAPn8W27KA6EGQioBuTbu88JcV66qTwBPxB37UeT9duDCJNf+HPh5rtvUKCjFkVUuR6rReyVa+5KorunTY7v73XprbJ3D7t0xF05VVUxAmje3gPPmzeZKOuUUG8WvX2+d/scf2zqQ++6LrQ7//PPY6vARI8wiOeIIW0W+bp3lpWre3ISjVSsLfofZd0ViiRY7dzZBAnjiCQt+jxxpQtGlS2JLoV+/WMyjpia2rqOy0t5D6gWT4f0ypRT/lpsQqWZhVReyIU2eXHaApTiyyqajSvadR++VqAOLbuYUdqrRqbnhvadPN6vijDPsWMeOsc2fPv/cBGLQIJvCO2yYubLWrbPFfvGrw9u1s3MPPdSm5h50kMUemje3n/79zU3Utaudv369iUGzZmbJDBtmwtOqlbUpnGnVsqXFU15+2e4ZziRLtIYiTL8SZv+dMCEmqvlKyV6Kf8tNiFSzsLoC3wY2Ytva3gYch83KulpVlxakhU0FdzvVj2w6qmTfefRe0Y4zPC+6niMMDkfzQoVZaJ991kSlbVsLai9caFbAmWfGtpNVtWOvv24L6qK0amXurUMOMfFp29bWecybZzO1RoywgPd991lOK7BV5yK2ve2551obw1lV69fb+aGV07y5Ccg//2nBc7A9QJJ9N6GVdfLJJh6ZphNJF58d1WRI5cK6D1hALEB9D5Z25DjgbuCEfDeuSeGmev1I1lGl6oySfed1BdLDn2hwODx23XXWyR5zjHW0PXpYBz57tnXcI0faNR07WjqQmhq7bygezZpZoPyIIyz+sXp1zM20apUFxE87zYLua9eaG+vCC83N1amTlYfTgNu3NxHavt0snm7dbCZXOLvqpZdsFfrJJ5vgXXBB6u8mamWFGYPzgQ+mmgypBKS7qv6/YOX3ClW9LTj+poh8uwBta1q4qZ47Eq14hrrXdaRK6hhu53rddRb4jnfrhPcLLYuhQ22EP2uWdc6ff27upd27LdngypW12xKmaT/nHKsrXPj3pS+Z4HTpYm6st96yczdtsus+/dS2qH3iCbM4OnUyd1WYJPH99y0xYpjb6vTTzerYuNHEI9z69vvfT/3dQMzKyvcgxwdTTYZUArIbIJg2uyGubE/+muQ4dVBXDCOd6+KTOnboYC6pXr1MMMK9w8N4QHhe9+42Qu/e3abfnnqqWSEzZuztomrRwhb2jRhhwfU5c2JpSHbtMtfVihUmBGFeqjZtLAjeubMt7mve3I6NGGHiMXmyCeHcuSZeI0daXRdcYPds2dLcW2edFUt+mIpi5JPywVSTIZWADAzWW0jkPcHnAXlvmeMkIz6GkW5nlEpsQvdNnz6xFeu/+pVl1I3GA0aPttH+V74CN9xgC/aiiJi4tG5t02UvucTiDosWmZXSu7ftEaJq1kaYW2rjRrMm+ve3fFV9+pjgVFSYlRJSUWGzosJpwM2bmwtr1iyzatq1s3UdBx6Y+DvIZu9yx0lCKgEZFXn/P3Fl8Z8dZ2/qEyxNdW0q0cj2uqj7JnrO6NExN9bmzRYMf+GFWKcb0quXWQ3dullKkYULrUN/++3adZ9zjlkq4YZMI0fa7Ktmzex4r14WCxk82MorK+36yZNjm01VVJh76oQTzBJp396ee8AAC/BPnQq//KUJVl2pWTKx4Dz47cSRahrvvEI2xGmC1Gd0m4s8XqEbKp0OL15cwvhHdbWtEn/ySfjNb2rvsdGzpwW7x461mESHDrGss2+8YUkTq6pq1xNmyW3b1lxSM2bYsfHj7d4bNpi4dOli033DzaYqKux+Gzdanb/9rZU9/rhZN/vsYwIHqUUhviwTC86tFSeOdHYkPBv4KZaVtznmwlJVbZ/ntjmNnfoES7MdGccnTky3w4sfXb/yCnzjG7FZVGAdfNu2NiuqstI6/1//2lxWM2aYWITrRn70I3NhxWf2XbbMhKZTJ7sumrE2nPm1ebPFM0aNqn19RUUsdUjz5rVFYOpUi9W0bWuCk+x5k+XxSgcPfjtx1CkgwB3AecAS1fr89TklR32CpdmOjBN1npkE2BcutFH+4sWxMhFbh1FZCV//eiwm0b69lYVJBZs3t5lQZWUW+0hUx7JlZrkMGWJupjBjbVTAKivN8hk3zjr8+N37Uq1dqetZ62NFePDbiSMdAfkAeNXFw2mwpLveIyTaWR90EPz1r5YmJNwqNmTIEOvMx4yxoHb8tdH7V1TAN78ZC8RPnGgWQfPmtpPfyJG2SHD4cMsz1a9f7b1Joqvf3347lpcKEm+ve/PNtXcCTLdzdyvCySHpCMh1wBMiMg/YER5U1V8lv8RxCkii9R4TJ8ZiEvEd69Sp5nqaM8c66k8/jZV16mTWQ2UlHHmkde533JHerKXx422dyPLlFgSfMMHSlzzxhC0EDGdVhbO6onuTRFe/z5ixt/sq+n7iRGv32WdnLgThd5VqX3LHSZN0BOTnwBagFdAyv81xckqpzZoJnzdcuzFiRO2No1auhHvvhcces89RN9XAgeZSOuccC0iHxKduT7RfevR7vukmE4ZQuK65xsRj6NBYADzMR7Vgga0679MnFjuBvWeDxf/uOnSwNSZdumT/XXlA3MkB6QhIT1U9JO8tcXJPNINsKXQSYad47bXmHurQwUb8U6daTOGZZ2oHkQ87zNJ8bN1q7qdE31HYqUfjG/H7pSeKwYQj/BYtYivPR46sLTyzZ9vsLoi9JorhxA8ERo82N9eiRfZs6ay2T/Zc7spy6kE6AvKEiJyqqk/lvTVObonPINvUCTvDiy+2Tru62kb4W7bEzunWzXYDrKy0lCB1Ebp8wvjGiBF7d9KJ9hSJ7sM+cqS1Kb5DP+ssWwR4+ulmoYSp1OPdb4lydYWWTrwApGtZeEDcyQHpCMi3gGtEZCcQ7nbj03gbA4XKbdRQCLeGvfBCixGElJWZK2vUKHjoIbMKMiX6XcZPDw73FDngAPjb36wdBx1k9Z1xRvJ9RsL1JUOHxqyaq6/e2/2WyFpIJgBuWTgFpE4BUdV2hWiIkwdKYZT5ySe2x3h1tXXkUY48Ek480VZ4d+9uI/psxANqB5/LysyyCFO7h5ZHmP59xIjYvuiDBlncY9o0qzvqUkxkuYSJHaOLEjOZAFkKv3OnwZCOBYKInAscH3ycq6qz8tckx6mDMPPs5MkWEN++PVbWs6etDB871qyAq6+2abOJ1lvUFWtIROgimjChdkqT22+3mMS6dZbQcNAgE4rjjotdc/bZtV2KiXZDHD5871TqHvB2GijprES/BfgyMC049D0ROUZVb8y2UhHpDDwA9AfeAy5S1Y1x5wwD7gTaY5mBf66qDwRlk4GRQJC7gXGq+kq27XFyTK5nf4X3O/poSzQ4darliwpp3doW+FVW2oK/srJYWfwoP1VnnE5HHe8i2rIltpZj+HDbIGrSJBOL0AKJ7j1+wAF73yORuynZ6nrHaUioasofYDHQLPK5DFhc13V13PNW4Ibg/Q3ALxOccwAwOHjfE1gDdAw+TwYuyLTeESNGqFMAJkxQBXutLxs2qJ50kt0v/ue441Tvvlt18+a9r1u40Oq/9tpYWxYutM9VVao1NXtfU1Nj5yUqS0b8s4b3eOyxzO+V6r6OU1wS9qnN0tSZjpH3HeojWAGjgOrgfTVQEX+Cqr6tqu8E71cD64BuOajbyTdjxqS/JWpIOO01THs+cyacf77ttfHss7Hzwk2Sli6F55+HK66wlCLx9witic2bY22ZNs0W7rVtG0sRsmhR7N7Dh+89jTZ6z0RtLiuD73zHLJHQCrnsMltFfuyxZi0lurYusvkO80mq78EpWdKJgfwCWCQiz2GJFI/HrIb60F1Vw913PgS6pzpZRI7EFjEuixz+uYj8CHgGs2Z2JLm2CqgC6Nu3bz2b7XxBtmnTkxGuDn/uOVvwt359rKxlS5vNNGGCdcrNkox7oi6o0HV1xhkWxJ46tfYiwHQTLabj8ho1ymIZbdvWnna7bFntdCOZ0NCC4R6HcRKQUkBEpBm2++BXsDgIwPWq+mFdNxaROcB+CYp+GP2gqioiSaeZiEgP4F6gUlXDnRBvxISnJTAJuB64OdH1qjopOIfy8nLP55UrMu1QkgnOhx/a8b/+1T6HI9wwgWGLFjY1dtAgOP74ve8bJT4TbxigDlemb9hg287Gn5vuPeOfZ8sWW7R49NG1Yx3RmEf0eK4pZKYBj8M4iUjm2wp/gAV1nZPpD/AW0CN43wN4K8l57YEaUsQ7gBOAWenU6zGQHPLoo6qjRpmvPx2iPv3PPlN94AHVM89ULSurHdcYMkT1F79Qff99uy7TuEQY+4jGIK69VnXECNXKyvrHFR55xJ577Njixyg8TuIUjoR9ajourDkicg02a2prRHg+roduzQQqgVuC1xnxJ4hIS+AxYIqqPhxX1kNV14iIYPGTV+vRFicbEk1BTcVll8Hq1bZta48esGlTrCw+gaFIrCxTV058WpGQ3bvNIujSJbtR9KOP2nTgnj3tuSsr945RFDr3mFsFTpFJR0AuDl6/HTmmwMB61HsL8KCIXAGsAC4CEJFyYLyqXhkcOx7oIiLjguvC6brTRKQbFpN5BRhfj7Y42ZBu5/XBB5bAsLo6tr0rWPD5jDOsI45PYJgu0Q5bg90HEyU7jE+7ng1TpphwnHmmxTzCzZ2i7SgrK2ycoKHFSZySI52V6ANyXamqfgR8LcHxBcCVwfupwNQk15+U6zY5GZKq89q61Ubs1dU2gyq6kvrww000Lr3UVofXh2nT4OmnLVh9wAGx/TTywbhxsdeKitrp0EOr5zvfMXGJrix3nCZMuivRj8YW/X1xvqpOyVObnMbInj02rba6Gh5+uHYCw333jSUwPPzw3NU5ZkxsplNVlbmUwnQikHp0Hu9uqsv9FLU4oLarLLqoMBO3nuM0ctJZiX4vMAhzFe0ODivgAuLYeowpU+xnxYrY8ZYt4dxzTTROOy3zHFTpxBPCrLSDBtm03RdeMLdYOgkk42eRZTqrLOrCi+5nXkrJK52SJx0LpBw4WNW3tHUCNm+2rLaJEhgedZSJxsUXQ+fO6d0vkVik26GHf5YvvZQ4eJ6MZOlE0u38E7nwPCbhlBjpCMir2HqONXWd6DRhdu+2eEN1NTz+eO0Ehr17w+WXWwLDAw/M/N6JxCLaoaeyRqL7bmSycju+s/fO33EyJqmAiMhfMVdVO+B1Efk/au+Jfm7+m+cUnddfN9G4915Lix7SujWcd54FlU88sX7B67r2u7j66uTWSLwryXGcgpHKAvmfgrXCaVh89BHcf78Jx4IFtcuOP95cVBdcEMtBVV/qGv2nci+55eA4RSOVgKzCclb9M3pQRI7F3VlNj88/hyeeMNGYNcs+hwwcGNtjY0DOZ3XXTaYikczlVZ+9QAq9SNBxGgGpBOQOLOdUPJuDsnPy0B6nkCxcaAkM9+yx+MaGDbGydu3goovM2jj22NqrwwtNpp13sgB8ffYC8WSCjrMXqQSku6ouiT+oqktEpH/+muTknTVrrEO85RZzV4WIwCmnmGhUVECbNkVrYi2mT6+9FWxdJHN5pXKF1TULy9OGOM5epBKQjinKWue4HU6+2b7d9tiYPBmefNKsjpD+/eGb37SZVL16FauFydm8ufZWsHWRzOWVyhVWl5vMYy2OsxepBGSBiHxDVf8UPSgiVwIL89ssp16ELp/LLjPhmDIFHnigdgLDzp1h9GizNsrL03NRZRMHyEXsYPx4X6DnOA2QVALyfeAxEbmMmGCUY3twfD3P7XLqw513wt13w1/+Uls0ysosGeC4cXDWWbEEhul28tnEAXIROyj06N8D5o6TFkkFRFXXAkeLyInAIcHh2ar6bLJrnCKydSs88ojNonruOTsWisewYbEEhvvuu/e1dXXyYYeaKNNtXTTG2IEHzB0nLdLJxvsc8FwB2uJkyp49FlwOExhu3Ror697dXFhjx9adwLCuTj7ZHhvp0BhjB41R9BynCKSVjddpYDz+uM2gev/92qvDW7a0dOJhAsPmaf5667OQrynSGEXPcYqAC0hjYdMmePBBszZefLF22Ve+Ektg2KlT7uv2DrV+eEzFaaK4gDRk5s+H//5v2LbNXFXRBIbt2sGFF8J118GQIbmtt1Q7vHw9t8dUnCZKUQRERDpje6z3B94DLlLVjQnO2w2EixnfDxM4isgAYDrQBZshdrmq7sx/ywvEa6+ZpfHHP9aOa7RpA+efb9bGiSdCs2b5qT/a4YVbxeZDTBqaUEV3OLzppty1qdRcgE7JUCwL5AbgGVW9RURuCD5fn+C8z1R1WILjvwR+rarTReQu4Argzry1tj6k20lu2BBLYLgwbpnNiBHw7W9bAsN27fLbXqjd4U2dmr/Rc0MbmUd3OBw0yMRz4kTo0MHWzDSWaciOUyCKJSCjgBOC99XAXBILyF6IiAAnAZdGrv8xDVVAUnWSO3fGEhjOnr13AsPKSlsdXugEhok6vHyMntMdmRfCUgnrGDfOxCMUz0mTTMB373YRcJw4iiUg3VU1nD70IdA9yXmtRGQBsAu4RVUfx9xWm1R1V3DOSqAB5t8IiO8kVa2zqq6G++6rnYuqfftYAsNjjskugWGuO9t8jp7TvXchLJVkU5W3bIlZII7j1CJvAiIic7CdDOP5YfSDqqqIJNsut5+qrhKRgcCzIrIEywacSTuqgCqAvn37ZnJpbgg7yTVr4LbbTDheey1W3qxZLIFhv362GHD9erj+essBNX58Zp1mQ3ML5YJCxBCSbWo1cWL+6nScRk7eBERVT05WJiJrRaSHqq4RkR7AuiT3WBW8LheRucBw4BGgo4g0D6yQ3tjeJcnaMQmYBFBeXl7Yfd23bzd/+uTJ8NRTtRMYHnSQCcdnn8G3vmWdVbjz3rJlsHKlxULats1MCJpiwDZXVlAq68zjFI6TMcVyYc0EKoFbgtcZ8SeISCdgm6ruEJGuwDHArYHF8hxwATYTK+H1BSFRh6QKL71klsYDD5gVERKfwPCaa+BPf7LAuKrlqqqqgjPOsLUeI0bEhKCmJr2ArneEyYm3zhraLDDHaWQUS0BuAR4UkSuAFcBFACJSDoxX1SuBg4CJIrIHaIbFQF4Prr8emC4iPwMWAX8u9AMAtTukzp0t6+2UKbB0aeyc5s0tgWFl5d4JDEPBiM52mjDB9uKoqNi7rlIP6Na3w4+3ziZOtO90yxZLZ+9i4jgZURQBUdWPgK8lOL4AuDJ4/yJwaJLrlwNH5rONdVJTAxs3mrXw/PMxIQkZPtxEY/To1AkMJ0yo3WGl2tCo1AO69Y3vxFtnnTrByJH22hRjR46TZ3wleqbs2QNz58J3vgOvv167rHt36+grK+HQhNoXI340nM6GRqUe0M11fGfjRlvhP2SITVbI5b0dpwQQ1cLGlYtJeXm5LliwILuL33nH4hr33mtJDENatjR3U2UlnHpq+gkMneKzaJG5Dt1t5Th1kXBNgfd2qdi0yQLh1dUWGI/y1a9aqvR8JTB08o9POHCceuECEs+uXTbltrrapuDu2BEr69PHRGPsWDjggOK10XEcpwHgAhKyZImJxrRp8OGHseNt2lgOqspKOOGE/CUwdBzHaWSUtoCsX2/pRKqrzR8e5cQTTTTOP98W8zmO4zi1KD0B2bnTEheGCQx37YqV7b+/uacuvxz69y9aEx3HcRoDpSUg778PPXrAxx/HjrVvb4Hwyko4+ujsEhg6juOUIKUlIOvX22uzZjbltrLS9hBv3bq47XIcx2mElJaAtGoFN98Ml10GPXsWuzWO4ziNmtKaUjR0KFx7rYtHSE2NZQCOn0DgOI6TBqVlgTi18fxPjuPUAxeQUqbQe4d4+nTHaVK4gJQyhU7l4RaP4zQpXECcwtEUd0t0nBLGBcQpHJ680HGaFKU1C8txHMfJGS4gjuM4TlYURUBEpLOIPC0i7wSve22oISInisgrkZ/tIlIRlE0WkXcjZcMK/QyO4zilTrEskBuAZ1R1MPBM8LkWqvqcqg5T1WHAScA24KnIKdeG5ar6SgHa7DiO40QoloCMAqqD99VARR3nXwD8TVW35bNRjQpfRe44TpEploB0V9U1wfsPge51nH8JcH/csZ+LyGIR+bWI7JPsQhGpEpEFIrJgfZhMsSkQrqmYOrXYLXEcp0TJ2zReEZkD7Jeg6IfRD6qqIqIp7tMDOBR4MnL4Rkx4WgKTgOuBmxNdr6qTgnMoLy9PWk+jw9dUOI5TZPImIKp6crIyEVkrIj1UdU0gEOtS3Ooi4DFV/Txy79B62SEi9wDX5KTRjQlfU+E4TpEplgtrJlAZvK8EZqQ4dzRx7qtAdBARweInr+a+iY7jOE4qiiUgtwCniMg7wMnBZ0SkXETuDk8Skf5AH2Be3PXTRGQJsAToCvws7ZqbevC5qT+f4zgNhqKkMlHVj4CvJTi+ALgy8vk9oFeC807KuvJiJvQrRDZaT1joOE6BKK1cWNu2QVkZVFXlN/icTCgK0bl7cN1xnAJRWgLy8cdw220wYUJ+R+fJhKIQnbsH1x3HKRClJSCdO9t+6PkenScTCu/cHcdpQohq01kaURfl5eW6YMGCYjfDcRynsSGJDno2XsdxHCcrXEAcx3GcrHABcRzHcbKitARk5cr0Ftj5YjzHcZw6KS0BWbs2vey1nunWcRynTkprGm/37ulN4fXFeI7jOHXi03gdx3GcuvBpvI7jOE7ucAFxHMdxssIFxHEcx8kKFxDHcRwnK1xAHMdxnKxwAXEcx3GywgXEcRzHyYqSWgciIuuBFcVuR57oCmwodiOKRCk/O5T285fys0Phnn+Dqp4ef7CkBKQpIyILVLW82O0oBqX87FDaz1/Kzw7Ff353YTmO4zhZ4QLiOI7jZIULSNNhUrEbUERK+dmhtJ+/lJ8divz8HgNxHMdxssItEMdxHCcrXEAcx3GcrHABaUKIyG0i8qaILBaRx0SkY7HbVChE5EIReU1E9ohISUzrFJHTReQtEVkqIjcUuz2FRET+IiLrROTVYrel0IhIHxF5TkReD/7mv1estriANC2eBg5R1cOAt4Ebi9yeQvIqcB7wfLEbUghEpAz4A3AGcDAwWkQOLm6rCspkYK+FbSXCLuBqVT0Y+Arw7WL97l1AmhCq+pSq7go+vgz0LmZ7ComqvqGqbxW7HQXkSGCpqi5X1Z3AdGBUkdtUMFT1eeDjYrejGKjqGlWtCd5/CrwB9CpGW1xAmi7/Dvyt2I1w8kYv4IPI55UUqRNxioeI9AeGA/9bjPqbF6NSJ3tEZA6wX4KiH6rqjOCcH2Jm7rRCti3fpPPsjlMqiEhb4BHg+6r6STHa4ALSyFDVk1OVi8g44Gzga9rEFvnU9ewlxiqgT+Rz7+CYUwKISAtMPKap6qPFaoe7sJoQInI6cB1wrqpuK3Z7nLwyHxgsIgNEpCVwCTCzyG1yCoCICPBn4A1V/VUx2+IC0rT4PdAOeFpEXhGRu4rdoEIhIl8XkZXAV4HZIvJksduUT4LJElcBT2JB1AdV9bXitqpwiMj9wEvAEBFZKSJXFLtNBeQY4HLgpOD//BURObMYDfFUJo7jOE5WuAXiOI7jZIULiOM4jpMVLiCO4zhOVriAOI7jOFnhAuI4juNkhQuI4zQgROTHInJNguMV2STME5H+InJp5PM4Efl9fdvpOOAC4jgZIyLFyOBQgWXd3Ys62tMfuDRFueNkjQuI40QQkf8K9th4QUTuD60BEZkrIneIyALgeyLyNRFZJCJLgr0p9gnOe09Eugbvy0VkbvD+x8F5c0VkuYh8N1LnD0XkbRF5ARiSoE1HA+cCtwWLxgYlaM9kEbkgcs2W4O0twHHBdT8IjvUUkb+LyDsicmuOv0KnhPBcWI4TICJfBs4HDgdaADXAwsgpLVW1XERaAe9g+cbeFpEpwLeAO+qo4kDgRCxbwFsicidwGJaGZBj2/xhfJ6r6oojMBGap6sNBW79oT/B5cpI6bwCuUdWzg/PGBXUNB3YE7fidqn6Q5HrHSYpbII4T4xhghqpuD/ZZ+Gtc+QPB6xDgXVV9O/hcDRyfxv1nq+oOVd0ArAO6A8cBj6nqtiCjaib5rB6o+5SEPKOqm1V1O/A60C/L+zgljguI46TP1jTO2UXs/6pVXNmOyPvd1N8DEG3PF/WKSDOgZYrrct0Op0RxAXGcGP8EzhGRVsFeC2cnOe8toL+I7B98vhyYF7x/DxgRvD8/jTqfBypEpLWItAPOSXLep5jrKxnRes/FXHDpXOc4WeMC4jgBqjofcyEtxnZzXAJsTnDeduDfgIdEZAmwBwgzH/8E+E0Q3N6dRp01mCvqX0Gd85OcOh24NgjcD0pQ/idgpIj8C8tIHFoni4HdIvKvSBDdcXKCZ+N1nAgi0lZVt4hIG8w6qAr3n3Ycpzbu+3Sc2kwKFuy1AqpdPBwnOW6BOI7jOFnhMRDHcRwnK1xAHMdxnKxwAXEcx3GywgXEcRzHyQoXEMdxHCcr/j+nKtwRuXV25gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "\n",
+    "enf_preds_dir = \"/mnt/lab_data2/anusri/chrombpnet/results/variant_data/CAGI/enformer_preds/alldata/\"\n",
+    "\n",
+    "pear_dict_logfc = {}\n",
+    "pear_dict_jsd = {}\n",
+    "pear_dict_enf = {}\n",
+    "import seaborn as sns\n",
+    "\n",
+    "for challenge in [\"challenge_HBG1.tsv\", \"challenge_MSMB.tsv\", \"challenge_IRF4.tsv\", \"challenge_SORT1.tsv\"]:\n",
+    "    temp = dataf[dataf[\"challenge\"] == challenge]\n",
+    "    models = list(set(temp[\"model_used\"]))\n",
+    "    pear_dict_logfc[challenge] = []\n",
+    "    pear_dict_jsd[challenge] = []\n",
+    "    pear_dict_enf[challenge] = []\n",
+    "    for model in models:\n",
+    "        temp_new = temp[temp[\"model_used\"]==model].drop_duplicates()\n",
+    "        #print(temp_new.shape)\n",
+    "        #temp_new = temp_new[temp_new[\"Confidence\"]<0.1]\n",
+    "        #print(temp_new.shape)\n",
+    "        \n",
+    "        challengen = challenge.replace(\"challenge_\",\"\").replace(\".tsv\",\"\")\n",
+    "                    \n",
+    "        fig, ax =  plt.subplots()\n",
+    "        ax = sns.scatterplot(x=\"label\", y=\"logfc.mean\", data=temp_new, s=6, color=\"red\")\n",
+    "        sns.regplot(x=\"label\", y=\"logfc.mean\", data=temp_new, scatter=False, ci=None, color=\"red\")\n",
+    "        ax.set_xlabel(\"ground truth\")\n",
+    "        ax.set_ylabel(\"ChromBPNet scores\")\n",
+    "        ax.set_title(challengen+\" ChromBPNet Pearsonr = \"+str(np.round(scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"logfc.mean\"])[0],2)))\n",
+    "        ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "        plt.savefig(\"subfigs/cagi_chrombpnet_\"+challengen+\".pdf\", transparent=True, dpi=300)\n",
+    "        print(scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"logfc.mean\"]))\n",
+    "                    \n",
+    "        pear_dict_logfc[challenge].append(pear)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"sign_jsd.mean\"])\n",
+    "        pear_dict_jsd[challenge].append(pear)\n",
+    "\n",
+    "        ifile = enf_preds_dir + challengen + \".ZigaDataLocal.csv\"\n",
+    "        dnf=pd.read_csv(ifile, sep=\",\", header=0)\n",
+    "        pear1 = scipy.stats.pearsonr(dnf[\"data.Value\"],dnf[\"dnase\"])\n",
+    "        \n",
+    "        pear_dict_enf[challenge].append(pear1)\n",
+    "\n",
+    "        fig, ax =  plt.subplots()\n",
+    "        ax = sns.scatterplot(x=\"data.Value\", y=\"dnase\", data=dnf, s=6, color=\"green\")\n",
+    "        sns.regplot(x=\"data.Value\", y=\"dnase\", data=dnf, scatter=False, ci=None, color=\"green\")\n",
+    "        ax.set_xlabel(\"ground truth\")\n",
+    "        ax.set_ylabel(\"Enformer scores\")\n",
+    "        ax.set_title(challengen+\" Enformer Pearsonr = \"+str(np.round(scipy.stats.pearsonr(dnf[\"data.Value\"],dnf[\"dnase\"])[0],2)))\n",
+    "        print(scipy.stats.pearsonr(dnf[\"data.Value\"],dnf[\"dnase\"]))\n",
+    "        ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "        plt.savefig(\"subfigs/cagi_enformer_\"+challengen+\".pdf\", transparent=True, dpi=300)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi_scoring_per_model.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi_scoring_per_model.ipynb
new file mode 100644
index 00000000..a305a8a9
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/cagi_scoring_per_model.ipynb
@@ -0,0 +1,1236 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf = pd.read_csv(\"cagi.mpra.per_model.scores.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign_jsd.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>ref_hg38</th>\n",
+       "      <th>alt_hg38</th>\n",
+       "      <th>label_hg38</th>\n",
+       "      <th>Confidence_hg38</th>\n",
+       "      <th>SAD</th>\n",
+       "      <th>SAR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.130221</td>\n",
+       "      <td>-0.029623</td>\n",
+       "      <td>0.000808</td>\n",
+       "      <td>0.057231</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807514</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.021439</td>\n",
+       "      <td>-0.025864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.079846</td>\n",
+       "      <td>-0.022760</td>\n",
+       "      <td>0.000452</td>\n",
+       "      <td>0.125456</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807514</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.013206</td>\n",
+       "      <td>-0.015869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.102046</td>\n",
+       "      <td>-0.031783</td>\n",
+       "      <td>0.000633</td>\n",
+       "      <td>0.065748</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807514</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>-0.022903</td>\n",
+       "      <td>-0.027649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.015090</td>\n",
+       "      <td>0.021831</td>\n",
+       "      <td>0.000192</td>\n",
+       "      <td>0.174132</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807515</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.005508</td>\n",
+       "      <td>0.006592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.072389</td>\n",
+       "      <td>0.028991</td>\n",
+       "      <td>0.000554</td>\n",
+       "      <td>0.099254</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37807515</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.028412</td>\n",
+       "      <td>0.033691</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr       pos allele1 allele2  \\\n",
+       "0  chr6  37775290       G       A   \n",
+       "1  chr6  37775290       G       C   \n",
+       "2  chr6  37775290       G       T   \n",
+       "3  chr6  37775291       T       A   \n",
+       "4  chr6  37775291       T       C   \n",
+       "\n",
+       "                                             rsid  logfc.mean  sign_jsd.mean  \\\n",
+       "0   6,37775290,G,A,0.03,0.01,challenge_ZFAND3.tsv   -0.130221      -0.029623   \n",
+       "1   6,37775290,G,C,0.03,0.01,challenge_ZFAND3.tsv   -0.079846      -0.022760   \n",
+       "2   6,37775290,G,T,0.07,0.01,challenge_ZFAND3.tsv   -0.102046      -0.031783   \n",
+       "3  6,37775291,T,A,-0.07,0.01,challenge_ZFAND3.tsv    0.015090       0.021831   \n",
+       "4   6,37775291,T,C,0.03,0.01,challenge_ZFAND3.tsv    0.072389       0.028991   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                               0.000808   \n",
+       "1                               0.000452   \n",
+       "2                               0.000633   \n",
+       "3                               0.000192   \n",
+       "4                               0.000554   \n",
+       "\n",
+       "   abs_logfc_x_jsd_x_max_percentile.mean.pval  label  ...  ref alt chr_hg38  \\\n",
+       "0                                    0.057231   0.03  ...    G   A     chr6   \n",
+       "1                                    0.125456   0.03  ...    G   C     chr6   \n",
+       "2                                    0.065748   0.07  ...    G   T     chr6   \n",
+       "3                                    0.174132  -0.07  ...    T   A     chr6   \n",
+       "4                                    0.099254   0.03  ...    T   C     chr6   \n",
+       "\n",
+       "   pos_hg38  ref_hg38 alt_hg38 label_hg38 Confidence_hg38       SAD       SAR  \n",
+       "0  37807514         G        A       0.03            0.01 -0.021439 -0.025864  \n",
+       "1  37807514         G        C       0.03            0.01 -0.013206 -0.015869  \n",
+       "2  37807514         G        T       0.07            0.01 -0.022903 -0.027649  \n",
+       "3  37807515         T        A      -0.07            0.01  0.005508  0.006592  \n",
+       "4  37807515         T        C       0.03            0.01  0.028412  0.033691  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'sign_jsd.mean', 'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval', 'label', 'Confidence',\n",
+       "       'challenge', 'model_used', 'chr_hg19', 'pos_hg19', 'ref', 'alt',\n",
+       "       'chr_hg38', 'pos_hg38', 'ref_hg38', 'alt_hg38', 'label_hg38',\n",
+       "       'Confidence_hg38', 'SAD', 'SAR'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "dataf = dataf[~np.isnan(dataf[\"SAD\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "challenges = list(set(dataf[\"challenge\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "pear_dict_logfc = {}\n",
+    "pear_dict_jsd = {}\n",
+    "pear_dict_enf = {}\n",
+    "pear_dict_enf_sar = {}\n",
+    "for challenge in challenges:\n",
+    "    temp = dataf[dataf[\"challenge\"] == challenge]\n",
+    "    models = list(set(temp[\"model_used\"]))\n",
+    "    pear_dict_logfc[challenge] = []\n",
+    "    pear_dict_jsd[challenge] = []\n",
+    "    pear_dict_enf[challenge] = []\n",
+    "    pear_dict_enf_sar[challenge] = []\n",
+    "    for model in models:\n",
+    "        temp_new = temp[temp[\"model_used\"]==model].drop_duplicates()\n",
+    "        #print(temp_new.shape)\n",
+    "        #temp_new = temp_new[temp_new[\"Confidence\"]<0.1]\n",
+    "        #print(temp_new.shape)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"logfc.mean\"])\n",
+    "        pear_dict_logfc[challenge].append(pear)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"sign_jsd.mean\"])\n",
+    "        pear_dict_jsd[challenge].append(pear)\n",
+    "\n",
+    "        pear1 = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"SAD\"])\n",
+    "        pear2 = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"SAR\"])\n",
+    "        \n",
+    "        pear_dict_enf[challenge].append(pear1)\n",
+    "        pear_dict_enf_sar[challenge].append(pear2)\n",
+    "\n",
+    "#         pear = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"logfc.mean\"]))\n",
+    "#         pear_dict_logfc[challenge].append(pear)\n",
+    "\n",
+    "#         pear = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"sign_jsd.mean\"]))\n",
+    "#         pear_dict_jsd[challenge].append(pear)\n",
+    "\n",
+    "#         pear1 = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"SAD\"]))\n",
+    "#         pear2 = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"SAR\"]))\n",
+    "        \n",
+    "#         pear_dict_enf[challenge].append(pear1)\n",
+    "#         pear_dict_enf_sar[challenge].append(pear2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_pear_logfc = []\n",
+    "best_pear_jsd = []\n",
+    "best_pear_sad = []\n",
+    "best_pear_sar = []\n",
+    "keys = list(pear_dict_jsd.keys())\n",
+    "for key in keys:\n",
+    "    best_pear_logfc.append(np.max(pear_dict_logfc[key]))\n",
+    "    best_pear_jsd.append(np.max(pear_dict_jsd[key]))    \n",
+    "    best_pear_sad.append(np.max(pear_dict_enf[key])) \n",
+    "    best_pear_sar.append(np.max(pear_dict_enf_sar[key]))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_MSMB.tsv (0.19855880983217153, 0.09520784539427939)\n",
+      "challenge_HBB.tsv (0.5472988538268535, 0.5777343256266372)\n",
+      "challenge_LDLR.tsv (0.5571818417723304, 0.5993377023399556)\n",
+      "challenge_SORT1.tsv (0.7609120286314868, 0.6128270356550973)\n",
+      "challenge_PKLR.tsv (0.8120143586029585, 0.7808898228177903)\n",
+      "challenge_IRF4.tsv (0.8154299895742269, 0.6194705136131787)\n",
+      "challenge_HBG1.tsv (0.5437910247812469, 0.17844376595518469)\n",
+      "challenge_MYCrs6983267.tsv (0.5078362906951961, 0.5271069007208296)\n",
+      "challenge_ZFAND3.tsv (0.44484781066968043, 0.4842148015081688)\n",
+      "challenge_IRF6.tsv (0.6863978095129647, 0.6589761892033787)\n",
+      "challenge_TERT-HEK293T.tsv (0.5553154805041812, 0.5952787463573234)\n",
+      "challenge_F9.tsv (0.3278058135324871, 0.28833966564695757)\n",
+      "challenge_GP1BB.tsv (0.5215335208116025, 0.5632784392741983)\n",
+      "challenge_HNF4A.tsv (0.34006638384182497, 0.2882221742640615)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(best_pear_logfc,best_pear_sad, s=5, c=\"g\")\n",
+    "ax.plot(best_pear_logfc,best_pear_logfc, c=\"r\",  alpha=0.2)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)-0.05, c=\"r\",  alpha=0.1)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)+0.05, c=\"r\",  alpha=0.1)\n",
+    "\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "ax.set_ylim((0,1.0))\n",
+    "ax.set_xlim((0,1.0))\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keys[i], (best_pear_logfc[i], best_pear_sad[i]))\n",
+    "    texts.append(plt.text(best_pear_logfc[i], best_pear_sad[i], keys[i].replace(\"challenge_\",\"\").replace(\".tsv\",\"\")))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "#plt.savefig(\"subfigs/cagi_logfc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_MSMB.tsv (0.19855880983217153, 0.11747552572198068)\n",
+      "challenge_HBB.tsv (0.5472988538268535, 0.5963431617909264)\n",
+      "challenge_LDLR.tsv (0.5571818417723304, 0.6400788914766323)\n",
+      "challenge_SORT1.tsv (0.7609120286314868, 0.6451592431151819)\n",
+      "challenge_PKLR.tsv (0.8120143586029585, 0.8131728750016707)\n",
+      "challenge_IRF4.tsv (0.8154299895742269, 0.7144655925355718)\n",
+      "challenge_HBG1.tsv (0.5437910247812469, 0.2306480685800073)\n",
+      "challenge_MYCrs6983267.tsv (0.5078362906951961, 0.531071916776147)\n",
+      "challenge_ZFAND3.tsv (0.44484781066968043, 0.49660231505400293)\n",
+      "challenge_IRF6.tsv (0.6863978095129647, 0.6491011671352478)\n",
+      "challenge_TERT-HEK293T.tsv (0.5553154805041812, 0.5844841342987368)\n",
+      "challenge_F9.tsv (0.3278058135324871, 0.29587888379366384)\n",
+      "challenge_GP1BB.tsv (0.5215335208116025, 0.5624264864931722)\n",
+      "challenge_HNF4A.tsv (0.34006638384182497, 0.28158864209788315)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(best_pear_logfc,best_pear_sar, s=5, c=\"g\")\n",
+    "ax.plot(best_pear_logfc,best_pear_logfc, c=\"r\",  alpha=0.2)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)-0.05, c=\"r\",  alpha=0.1)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)+0.05, c=\"r\",  alpha=0.1)\n",
+    "\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "ax.set_ylim((0,1.0))\n",
+    "ax.set_xlim((0,1.0))\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keys[i], (best_pear_logfc[i], best_pear_sar[i]))\n",
+    "    texts.append(plt.text(best_pear_logfc[i], best_pear_sar[i], keys[i].replace(\"challenge_\",\"\").replace(\".tsv\",\"\")))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "#plt.savefig(\"subfigs/cagi_logfc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_MSMB.tsv (0.23884388933617556, 0.09520784539427939)\n",
+      "challenge_HBB.tsv (0.5698413608177562, 0.5777343256266372)\n",
+      "challenge_LDLR.tsv (0.5467644140523147, 0.5993377023399556)\n",
+      "challenge_SORT1.tsv (0.708297065900647, 0.6128270356550973)\n",
+      "challenge_PKLR.tsv (0.7930240481527618, 0.7808898228177903)\n",
+      "challenge_IRF4.tsv (0.7497534109732615, 0.6194705136131787)\n",
+      "challenge_HBG1.tsv (0.503740228594714, 0.17844376595518469)\n",
+      "challenge_MYCrs6983267.tsv (0.5084297791443301, 0.5271069007208296)\n",
+      "challenge_ZFAND3.tsv (0.3968265961621204, 0.4842148015081688)\n",
+      "challenge_IRF6.tsv (0.5556701804309403, 0.6589761892033787)\n",
+      "challenge_TERT-HEK293T.tsv (0.4943933566895047, 0.5952787463573234)\n",
+      "challenge_F9.tsv (0.4535045167176045, 0.28833966564695757)\n",
+      "challenge_GP1BB.tsv (0.4261320559232507, 0.5632784392741983)\n",
+      "challenge_HNF4A.tsv (0.31176704261573024, 0.2882221742640615)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(best_pear_jsd,best_pear_sad, s=5, c=\"g\")\n",
+    "ax.plot(best_pear_jsd,best_pear_jsd, c=\"r\",  alpha=0.2)\n",
+    "ax.plot(best_pear_jsd,np.array(best_pear_jsd)-0.05, c=\"r\",  alpha=0.1)\n",
+    "ax.plot(best_pear_jsd,np.array(best_pear_jsd)+0.05, c=\"r\",  alpha=0.1)\n",
+    "\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "ax.set_ylim((0,1.0))\n",
+    "ax.set_xlim((0,1.0))\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keys[i], (best_pear_jsd[i], best_pear_sad[i]))\n",
+    "    texts.append(plt.text(best_pear_jsd[i], best_pear_sad[i], keys[i].replace(\"challenge_\",\"\").replace(\".tsv\",\"\")))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "#plt.savefig(\"subfigs/cagi_logfc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.stats\n",
+    "pear_dict_logfc = {}\n",
+    "pear_dict_jsd = {}\n",
+    "pear_dict_enf = {}\n",
+    "pear_dict_enf_sar = {}\n",
+    "for challenge in challenges:\n",
+    "    temp = dataf[dataf[\"challenge\"] == challenge]\n",
+    "    models = list(set(temp[\"model_used\"]))\n",
+    "    pear_dict_logfc[challenge] = []\n",
+    "    pear_dict_jsd[challenge] = []\n",
+    "    pear_dict_enf[challenge] = []\n",
+    "    pear_dict_enf_sar[challenge] = []\n",
+    "    for model in models:\n",
+    "        temp_new = temp[temp[\"model_used\"]==model].drop_duplicates()\n",
+    "        #print(temp_new.shape)\n",
+    "        #temp_new = temp_new[temp_new[\"Confidence\"]<0.1]\n",
+    "        #print(temp_new.shape)\n",
+    "\n",
+    "#         pear = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"logfc.mean\"])\n",
+    "#         pear_dict_logfc[challenge].append(pear)\n",
+    "\n",
+    "#         pear = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"sign_jsd.mean\"])\n",
+    "#         pear_dict_jsd[challenge].append(pear)\n",
+    "\n",
+    "#         pear1 = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"SAD\"])\n",
+    "#         pear2 = scipy.stats.pearsonr(temp_new[\"label\"],temp_new[\"SAR\"])\n",
+    "        \n",
+    "#         pear_dict_enf[challenge].append(pear1)\n",
+    "#         pear_dict_enf_sar[challenge].append(pear2)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"logfc.mean\"]))\n",
+    "        pear_dict_logfc[challenge].append(pear)\n",
+    "\n",
+    "        pear = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"sign_jsd.mean\"]))\n",
+    "        pear_dict_jsd[challenge].append(pear)\n",
+    "\n",
+    "        pear1 = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"SAD\"]))\n",
+    "        pear2 = scipy.stats.pearsonr(abs(temp_new[\"label\"]),abs(temp_new[\"SAR\"]))\n",
+    "        \n",
+    "        pear_dict_enf[challenge].append(pear1)\n",
+    "        pear_dict_enf_sar[challenge].append(pear2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_pear_logfc = []\n",
+    "best_pear_jsd = []\n",
+    "best_pear_sad = []\n",
+    "best_pear_sar = []\n",
+    "keys = list(pear_dict_jsd.keys())\n",
+    "for key in keys:\n",
+    "    best_pear_logfc.append(np.max(pear_dict_logfc[key]))\n",
+    "    best_pear_jsd.append(np.max(pear_dict_jsd[key]))    \n",
+    "    best_pear_sad.append(np.max(pear_dict_enf[key])) \n",
+    "    best_pear_sar.append(np.max(pear_dict_enf_sar[key]))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_MSMB.tsv (0.18064507834629845, 0.07812276670116146)\n",
+      "challenge_HBB.tsv (0.4131418431199688, 0.43519130169648945)\n",
+      "challenge_LDLR.tsv (0.6045974580705764, 0.5838372212163301)\n",
+      "challenge_SORT1.tsv (0.7337050220520509, 0.5465667737423907)\n",
+      "challenge_PKLR.tsv (0.8194964319535258, 0.7863479773817957)\n",
+      "challenge_IRF4.tsv (0.8145174978461941, 0.5045105003994057)\n",
+      "challenge_HBG1.tsv (0.48106994565359995, 0.09046884578192874)\n",
+      "challenge_MYCrs6983267.tsv (0.5365007062961231, 0.5369289876911911)\n",
+      "challenge_ZFAND3.tsv (0.41884266063861497, 0.4607256612021779)\n",
+      "challenge_IRF6.tsv (0.688024621814145, 0.667289171982701)\n",
+      "challenge_TERT-HEK293T.tsv (0.43998778134423205, 0.5081399724206273)\n",
+      "challenge_F9.tsv (0.08162256518538383, 0.09360957733433492)\n",
+      "challenge_GP1BB.tsv (0.4954772436774826, 0.5165704527315866)\n",
+      "challenge_HNF4A.tsv (0.2964168915155524, 0.28990987975578736)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(best_pear_logfc,best_pear_sad, s=5, c=\"g\")\n",
+    "ax.plot(best_pear_logfc,best_pear_logfc, c=\"r\",  alpha=0.2)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)-0.05, c=\"r\",  alpha=0.1)\n",
+    "ax.plot(best_pear_logfc,np.array(best_pear_logfc)+0.05, c=\"r\",  alpha=0.1)\n",
+    "\n",
+    "ax.set_xlabel(\"ChromBPNet correlations\")\n",
+    "ax.set_ylabel(\"Enformer cell-type specfic (untrained) correlations\")\n",
+    "ax.set_ylim((0,1.0))\n",
+    "ax.set_xlim((0,1.0))\n",
+    "\n",
+    "texts = []\n",
+    "for i, txt in enumerate(keys):\n",
+    "    print(keys[i], (best_pear_logfc[i], best_pear_sad[i]))\n",
+    "    texts.append(plt.text(best_pear_logfc[i], best_pear_sad[i], keys[i].replace(\"challenge_\",\"\").replace(\".tsv\",\"\")))\n",
+    "adjust_text(texts, only_move={'points':'y', 'texts':'y'}, arrowprops=dict(arrowstyle=\"->\", color='r', lw=0.5))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "#plt.savefig(\"subfigs/cagi_logfc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gt = dataf[[\"chr\", \"pos\", \"allele1\", \"allele2\",\"challenge\", \"label\", \"Confidence\"]].drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13215, 7)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>challenge</th>\n",
+       "      <th>label</th>\n",
+       "      <th>Confidence</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775290</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>-0.07</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>37775291</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>challenge_ZFAND3.tsv</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19688</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>396592</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>challenge_IRF4.tsv</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19689</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>396592</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>challenge_IRF4.tsv</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19690</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>396593</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>challenge_IRF4.tsv</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19691</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>396593</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>challenge_IRF4.tsv</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19692</th>\n",
+       "      <td>chr6</td>\n",
+       "      <td>396593</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>challenge_IRF4.tsv</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13215 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        chr       pos allele1 allele2             challenge  label  Confidence\n",
+       "0      chr6  37775290       G       A  challenge_ZFAND3.tsv   0.03        0.01\n",
+       "1      chr6  37775290       G       C  challenge_ZFAND3.tsv   0.03        0.01\n",
+       "2      chr6  37775290       G       T  challenge_ZFAND3.tsv   0.07        0.01\n",
+       "3      chr6  37775291       T       A  challenge_ZFAND3.tsv  -0.07        0.01\n",
+       "4      chr6  37775291       T       C  challenge_ZFAND3.tsv   0.03        0.01\n",
+       "...     ...       ...     ...     ...                   ...    ...         ...\n",
+       "19688  chr6    396592       G       C    challenge_IRF4.tsv   0.12        0.02\n",
+       "19689  chr6    396592       G       T    challenge_IRF4.tsv   0.11        0.03\n",
+       "19690  chr6    396593       T       A    challenge_IRF4.tsv   0.15        0.07\n",
+       "19691  chr6    396593       T       C    challenge_IRF4.tsv   0.03        0.02\n",
+       "19692  chr6    396593       T       G    challenge_IRF4.tsv   0.29        0.12\n",
+       "\n",
+       "[13215 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_MSMB.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_HBB.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_LDLR.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_SORT1.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_PKLR.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_IRF4.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_HBG1.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_MYCrs6983267.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_ZFAND3.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_IRF6.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_TERT-HEK293T.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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qkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkN2vKfCrkZnxYoSePOI3dJatDIwj3JVUm+nORIkn2juh1J0kuN5LRMkjOAPwF+EXgK+GKSg1X1xChuTxqlFk79nfzHI334z0naMKpz7lcAR6rqawBJZoHdgOEuaUWDfpGu5pfUuBv1f/pKVQ1/p8lbgauq6je76+8Afraq3r1kzh5gT3f1HwBfXsNNXQB8a53ljgt7GT+t9AHt9NJKHzCcXn6iql673IpNe7VMVe0H9q9nH0keqqqpIZW0qexl/LTSB7TTSyt9wOh7GdUTqvPAjiXXL+zGJEkbYFTh/kXgoiSvS/Iq4Frg4IhuS5J0ipGclqmqE0neDfwVcAbwkap6fAQ3ta7TOmPGXsZPK31AO7200geMuJeRPKEqSdpcvkNVkhpkuEtSg7ZUuCf5tSSPJ/m7JCu+hGgrfPRBkvOT3JvkK93X81aY92KSR7rL2DwpPeh7nOSsJJ/o1n8+yc5NKLOXHr3ckOR/LbkffnMz6hwkyUeSPJPksRXWJ8kHuz4fTXLZRtfYR48+ppMcX3J//IuNrrGvJDuS3J/kiS673rPMnNHcL1W1ZS7AP2TxDU9zwNQKc84Avgq8HngV8CXg4s2ufZk6/w2wr1veB/zxCvMWNrvWtXyPgX8G/Ptu+VrgE5td9zp6uQH40GbX2qOXfwRcBjy2wvqrgXuAAFcCn9/smtfYxzRw52bX2bOXbcBl3fKPAH+9zONrJPfLljpyr6onq2rQO1l/8NEHVfV/gJMffTBudgMHuuUDwDWbV8qq9fkeL+3vduDNSbKBNfa1VR4vA1XVA8B3TjNlN/DRWvQgcG6SbRtTXX89+tgyqurpqnq4W34OeBLYfsq0kdwvWyrce9oOfHPJ9ad46TdzHExW1dPd8t8AkyvMe3WSh5I8mOSajSltoD7f4x/MqaoTwHHgRzekutXp+3j5p92fzLcn2bHM+q1gq/xs9PFzSb6U5J4kb9jsYvroTk3+DPD5U1aN5H4Zu3/WkeQzwI8ts+r3quqOja5nPU7Xy9IrVVVJVnpN6k9U1XyS1wOfTXK4qr467Fp1Wv8V+HhVvZDkXSz+RfKmTa7p5exhFn8uFpJcDfwX4KLNLen0kkwAnwTeW1Xf24jbHLtwr6p/vM5djM1HH5yulyTHkmyrqqe7P8GeWWEf893XryWZY/E3/2aHe5/v8ck5TyU5EzgH+PbGlLcqA3upqqV1/zmLz5dsRWPzs7EeS8Oxqu5O8qdJLqiqsfxAsSSvZDHYP1ZVn1pmykjulxZPy2yVjz44CFzfLV8PvOSvkiTnJTmrW74AeCPj8bHJfb7HS/t7K/DZ6p49GjMDeznl/OevsnjedCs6CLyze3XGlcDxJacGt4wkP3by+ZskV7CYY+N44EBX563Ak1X1/hWmjeZ+2exnk1f5zPNbWDwf9QJwDPirbvzHgbtPefb5r1k8wv29za57hV5+FLgP+ArwGeD8bnwK+PNu+eeBwyy+guMwcONm13267zHwh8CvdsuvBv4zcAT4AvD6za55Hb38a+Dx7n64H/ipza55hT4+DjwN/N/u5+RG4LeA3+rWh8V/ovPV7vG07CvONvvSo493L7k/HgR+frNrPk0vvwAU8CjwSHe5eiPuFz9+QJIa1OJpGUl62TPcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+HxhFLAnYCWGfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_F9.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_GP1BB.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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W0pSBgQE6XUPZ3GMeyu5x3cbtpW27qA3dY2waajqeCjuwpta2fY0r+zjO9m6Zg+OXW9LPQ2l8BFgyYb3FaczMzNpotuG+FVibptcCd04Yf3+6a+Zi4EhEPN1kjWZmNkMN/+6R9B2gBiyQ9BTwKeBG4DZJ64GfAVel1e8CrgCGgReAD5RQs5mZNdAw3CPimmkWXTbFugFc22xRZmbWHL9D1cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQ+37enEzm5GlG7dPOb6he4x10ywzG9dUuEs6APwSeBkYi4iqpLOAW4GlwAHgqog43FyZZmY2E624LNMTEcsjoprmNwI7ImIZsCPNm5lZG5VxzX0VsCVNbwFWl7APMzM7CkXE7J8sPQkcBgL454jok/RcRMxPywUcHp+f9NxeoBegUqms6O/vn3UdrTA6OkpXV1dHayibezy+DI0cmXK8MhcOvtjmYtqs3T12L5rXvp0lrfhd7enpGZxw1eT3NPuC6lsjYkTSq4F7JD0ycWFEhKQp//WIiD6gD6BarUatVmuylOYMDAzQ6RrK5h6PL9O9aLqhe4xNQ3nfC9HuHg+sqbVtX+PK/l1t6rJMRIykn4eAO4ALgYOSFgKkn4eaLdLMzGZm1uEu6XRJZ4xPA28H9gJbgbVptbXAnc0WaWZmM9PM3z0V4I76ZXXmAP8WEf8p6cfAbZLWAz8Drmq+TDMzm4lZh3tEPAG8cYrxZ4HLminKzMya448fMDPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDeb+H2awFpvtcdbNjmc/czcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEO+z93MTnideC/Dhu4x1m3czoEb31nK9n3mbmaWIZ+523Gh6JnV+NmQ2YnOZ+5mZhkqLdwlrZT0qKRhSRvL2o+Zmb1SKeEu6STgq8DlwPnANZLOL2NfZmb2SmVdc78QGE5foo2kfmAV8HCrd9SqV7lnc622rFe5j2X+hESz44MiovUbla4EVkbEh9L8+4CLIuIjE9bpBXrT7B8Dj7a8kJlZADzT4RrK5h7z4B7z0IoeXxsR50y1oGN3y0REH9DXqf1PJml3RFQ7XUeZ3GMe3GMeyu6xrBdUR4AlE+YXpzEzM2uDssL9x8AySedKOgW4Gtha0r7MzGySUi7LRMSYpI8A3wdOAr4REfvK2FcLHTOXiErkHvPgHvNQao+lvKBqZmad5XeompllyOFuZpahEzbcJX1R0iOSHpJ0h6T506x3XH6MgqT3Ston6TeSpr3dStIBSUOS9kja3c4amzWDHo/LYwgg6SxJ90h6LP08c5r1Xk7HcI+k4+LmhUbHRdKpkm5Ny38kaWkHymxKgR7XSfrfCcfuQy3beUSckA/g7cCcNP0F4AtTrHMS8DjwOuAU4EHg/E7XXrC/P6H+5rABoHqU9Q4ACzpdb1k9Hs/HMNX/d8DGNL1xqt/TtGy007XOsK+GxwX4S+Cf0vTVwK2drruEHtcBXylj/yfsmXtE/CAixtLsLur34k/2249RiIj/A8Y/RuGYFxH7I6LT7/otVcEej9tjmKwCtqTpLcDqzpXSUkWOy8Tebwcuk6Q21tisjv7unbDhPskHgbunGF8E/M+E+afSWE4C+IGkwfSRELk53o9hJSKeTtM/ByrTrHeapN2Sdkla3Z7SmlLkuPx2nXQidgQ4uy3VtUbR370/T5eHb5e0ZIrls5L1l3VI+iHwh1Ms+mRE3JnW+SQwBny7nbW1QpH+CnhrRIxIejVwj6RHIuK+1lXZnBb1eEw7Wo8TZyIiJE137/Jr03F8HXCvpKGIeLzVtVrL/QfwnYh4SdKHqf+lcmkrNpx1uEfEnx1tuaR1wLuAyyJdAJvkmP4YhUb9FdzGSPp5SNId1P+UPGbCvQU9HtPHEI7eo6SDkhZGxNOSFgKHptnG+HF8QtIA8Cbq13uPVUWOy/g6T0maA8wDnm1PeS3RsMeImNjP16m/xtISJ+xlGUkrgb8B3h0RL0yzWtYfoyDpdElnjE9Tf5F5b2erarnj/RhuBdam6bXAK/5akXSmpFPT9ALgEkr4eO0WK3JcJvZ+JXDvNCdhx6qGPaZ/sMe9G9jfsr13+hXlDr6SPUz9etie9Bh/Vf41wF0T1rsC+C/qZ0Gf7HTdM+jvPdSv8b0EHAS+P7k/6q/iP5ge+46n/or2eDwfw1T72cAO4DHgh8BZabwKfD1N/ykwlI7jELC+03UX7O0VxwX4LPUTLoDTgH9P/68+ALyu0zWX0OPn0/97DwI7gde3at/++AEzswydsJdlzMxy5nA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEP/D1ktxrKYfAs2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "challenge_HNF4A.tsv\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ZFAND3', 'GP1BB', 'MYC', 'HBB', 'LDLR', 'TERT')"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for challenge in challenges:\n",
+    "    plt.figure()\n",
+    "    print(challenge)\n",
+    "    gt[gt[\"challenge\"] == challenge][\"label\"].hist()\n",
+    "    #print(np.max(abs(gt[gt[\"challenge\"] == challenge][\"label\"])))\n",
+    "    #print(np.median(abs(gt[gt[\"challenge\"] == challenge][\"Confidence\"])))\n",
+    "    plt.show()\n",
+    "    \n",
+    "\"MSMB\", \"F9\", \"HNF4A\", \"SORT1\", \"IRF6\", \"IRF4\", \"PKLR\"\n",
+    "\n",
+    "\"ZFAND3\",\"GP1BB\", \"MYC\", \"HBB\", \"LDLR\", \"TERT\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/consolidate_datasets.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/consolidate_datasets.ipynb
new file mode 100644
index 00000000..201d15a6
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/consolidate_datasets.ipynb
@@ -0,0 +1,2017 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #rsids to hg19 coordinates\n",
+    "# rsid_map = pd.read_csv(\"/mnt/lab_data2/anusri/enformer/eu_caqtls/convert_hg38_to_hg19/hg38_rsids2.bed\", sep=\"\\t\", header=None)\n",
+    "# rsid_map[\"rsid\"] = rsid_map[0] + \"_\" + rsid_map[2].astype(str) + \"_\" + rsid_map[3] + \"_\" + rsid_map[4] \n",
+    "# rsid_map_dict = {}\n",
+    "# for i,r in rsid_map.iterrows():\n",
+    "#     rsid_map_dict[r[9]] = r[\"rsid\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls = pd.read_csv(\"Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n",
+    "caqtls = caqtls[caqtls[\"IsUsed\"] & caqtls[\"Inside_Peak\"]==1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)\n",
+    "afrs = afrs[afrs[\"IsUsed\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>snp_id</th>\n",
+       "      <th>label</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>enformer.encsr000emt.variantscore.sad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.jsd</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.csr</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.pval</th>\n",
+       "      <th>dbsnp_rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "      <th>id_hg19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.019025</td>\n",
+       "      <td>0.001262</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.137133</td>\n",
+       "      <td>0.250142</td>\n",
+       "      <td>chr1_805556_T_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.025277</td>\n",
+       "      <td>0.003640</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.098145</td>\n",
+       "      <td>0.130176</td>\n",
+       "      <td>chr1_840753_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906870</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>1_906870_A_G</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.151325</td>\n",
+       "      <td>-0.335979</td>\n",
+       "      <td>-0.000669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.048693</td>\n",
+       "      <td>0.024808</td>\n",
+       "      <td>0.002238</td>\n",
+       "      <td>rs148658266</td>\n",
+       "      <td>1:906596:907043</td>\n",
+       "      <td>1_906870_A_G</td>\n",
+       "      <td>273</td>\n",
+       "      <td>0.232118</td>\n",
+       "      <td>0.335979</td>\n",
+       "      <td>chr1_842250_A_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906982</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>-0.520262</td>\n",
+       "      <td>-0.000453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.029040</td>\n",
+       "      <td>0.009527</td>\n",
+       "      <td>0.007020</td>\n",
+       "      <td>rs28540380</td>\n",
+       "      <td>1:906596:907043</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>385</td>\n",
+       "      <td>0.106740</td>\n",
+       "      <td>0.520262</td>\n",
+       "      <td>chr1_842362_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>921203</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.905971</td>\n",
+       "      <td>0.000342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.085904</td>\n",
+       "      <td>0.025396</td>\n",
+       "      <td>0.002171</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>1:921132:921332</td>\n",
+       "      <td>1_921203_G_A</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0.189412</td>\n",
+       "      <td>0.905971</td>\n",
+       "      <td>chr1_856583_G_A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chr_hg38  pos_hg38 allele1 allele2        snp_id  label  IsUsed      pval  \\\n",
+       "2      chr1    870176       T       A  1_870176_T_A  False    True  0.071532   \n",
+       "4      chr1    905373       C       T  1_905373_T_C  False    True  0.188152   \n",
+       "8      chr1    906870       A       G  1_906870_A_G  False    True  0.151325   \n",
+       "9      chr1    906982       C       T  1_906982_C_T   True    True  0.000005   \n",
+       "12     chr1    921203       G       A  1_921203_G_A   True    True  0.000007   \n",
+       "\n",
+       "        beta  enformer.encsr000emt.variantscore.sad  ...  \\\n",
+       "2  -0.250142                              -0.000139  ...   \n",
+       "4  -0.130176                              -0.000071  ...   \n",
+       "8  -0.335979                              -0.000669  ...   \n",
+       "9  -0.520262                              -0.000453  ...   \n",
+       "12  0.905971                               0.000342  ...   \n",
+       "\n",
+       "    chrombpnet.encsr095qnb.variantscore.jsd  \\\n",
+       "2                                  0.019025   \n",
+       "4                                  0.025277   \n",
+       "8                                 -0.048693   \n",
+       "9                                 -0.029040   \n",
+       "12                                 0.085904   \n",
+       "\n",
+       "    chrombpnet.encsr095qnb.variantscore.csr  \\\n",
+       "2                                  0.001262   \n",
+       "4                                  0.003640   \n",
+       "8                                  0.024808   \n",
+       "9                                  0.009527   \n",
+       "12                                 0.025396   \n",
+       "\n",
+       "    chrombpnet.encsr095qnb.variantscore.pval   dbsnp_rsid           region  \\\n",
+       "2                                   0.048612   rs72631880  1:869800:870282   \n",
+       "4                                   0.018781    rs4970382  1:904938:905544   \n",
+       "8                                   0.002238  rs148658266  1:906596:907043   \n",
+       "9                                   0.007020   rs28540380  1:906596:907043   \n",
+       "12                                  0.002171  rs141671872  1:921132:921332   \n",
+       "\n",
+       "             snp  distance        se  abs_beta          id_hg19  \n",
+       "2   1_870176_T_A       375  0.137133  0.250142  chr1_805556_T_A  \n",
+       "4   1_905373_T_C       434  0.098145  0.130176  chr1_840753_T_C  \n",
+       "8   1_906870_A_G       273  0.232118  0.335979  chr1_842250_A_G  \n",
+       "9   1_906982_C_T       385  0.106740  0.520262  chr1_842362_C_T  \n",
+       "12  1_921203_G_A        70  0.189412  0.905971  chr1_856583_G_A  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.head() # hg38"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.379356</td>\n",
+       "      <td>-0.045242</td>\n",
+       "      <td>-0.016605</td>\n",
+       "      <td>5.337650e-05</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>-0.001945</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>0.191231</td>\n",
+       "      <td>0.027531</td>\n",
+       "      <td>0.017267</td>\n",
+       "      <td>2.734630e-04</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>0.001667</td>\n",
+       "      <td>0.000026</td>\n",
+       "      <td>0.000036</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>726944</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131979</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.74175</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.953905</td>\n",
+       "      <td>-0.079065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.802258</td>\n",
+       "      <td>0.026410</td>\n",
+       "      <td>0.010944</td>\n",
+       "      <td>2.107834e-05</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>-0.001038</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>752721</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131972</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.84500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.890193</td>\n",
+       "      <td>-0.011374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.012412</td>\n",
+       "      <td>0.003650</td>\n",
+       "      <td>0.338401</td>\n",
+       "      <td>0.047837</td>\n",
+       "      <td>1.246855e-02</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.344077</td>\n",
+       "      <td>0.002493</td>\n",
+       "      <td>0.003319</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>755940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs3115857</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.59380</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302008</td>\n",
+       "      <td>-0.077224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.674640</td>\n",
+       "      <td>-0.026630</td>\n",
+       "      <td>-0.011550</td>\n",
+       "      <td>2.854755e-07</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>-0.007762</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     Pos Ref Alt         RsID  Peak       AF  Inside_Peak    P_Lead  \\\n",
+       "2  chr1  565286   C   T    rs1578391     7  0.59780            1  0.998561   \n",
+       "3  chr1  569204   T   C  rs112660509     8  0.40125            1  0.477613   \n",
+       "6  chr1  726944   C   G    rs3131979    14  0.74175            1  0.953905   \n",
+       "7  chr1  752721   A   G    rs3131972    17  0.84500            1  0.890193   \n",
+       "8  chr1  755940   C   T    rs3115857    18  0.59380            1  0.302008   \n",
+       "\n",
+       "       Beta  ...  ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2  0.886884  ...                                           0.000022           \n",
+       "3  0.251458  ...                                           0.000155           \n",
+       "6 -0.079065  ...                                           0.000001           \n",
+       "7 -0.011374  ...                                           0.012412           \n",
+       "8 -0.077224  ...                                           0.000004           \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                           0.379356                \n",
+       "3                                           0.191231                \n",
+       "6                                           0.802258                \n",
+       "7                                           0.003650                \n",
+       "8                                           0.674640                \n",
+       "\n",
+       "   ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "2               -0.045242                  -0.016605   \n",
+       "3                0.027531                   0.017267   \n",
+       "6                0.026410                   0.010944   \n",
+       "7                0.338401                   0.047837   \n",
+       "8               -0.026630                  -0.011550   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                       5.337650e-05   \n",
+       "3                                       2.734630e-04   \n",
+       "6                                       2.107834e-05   \n",
+       "7                                       1.246855e-02   \n",
+       "8                                       2.854755e-07   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                           0.505239        \n",
+       "3                                           0.580367        \n",
+       "6                                           0.580367        \n",
+       "7                                           0.333333        \n",
+       "8                                           0.666667        \n",
+       "\n",
+       "   enformer_recomputed  DNASE_SAD  DNASE_SAR  IsUsed  \n",
+       "2            -0.001945   0.000008   0.000011    True  \n",
+       "3             0.001667   0.000026   0.000036    True  \n",
+       "6            -0.001038   0.000100   0.000133    True  \n",
+       "7            -0.344077   0.002493   0.003319    True  \n",
+       "8            -0.007762   0.000022   0.000029    True  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.head() # hg19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#afrs[\"rsid1\"] = afrs[\"dbsnp_rsid\"].map(rsid_map_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#afrs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs[[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\"]] = afrs[\"id_hg19\"].str.split(\"_\", expand=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs.head()\n",
+    "afrs[\"pos_hg19\"] = afrs[\"pos_hg19\"].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.379356</td>\n",
+       "      <td>-0.045242</td>\n",
+       "      <td>-0.016605</td>\n",
+       "      <td>5.337650e-05</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>-0.001945</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000155</td>\n",
+       "      <td>0.191231</td>\n",
+       "      <td>0.027531</td>\n",
+       "      <td>0.017267</td>\n",
+       "      <td>2.734630e-04</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>0.001667</td>\n",
+       "      <td>0.000026</td>\n",
+       "      <td>0.000036</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>726944</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131979</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.74175</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.953905</td>\n",
+       "      <td>-0.079065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.802258</td>\n",
+       "      <td>0.026410</td>\n",
+       "      <td>0.010944</td>\n",
+       "      <td>2.107834e-05</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>-0.001038</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>752721</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131972</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.84500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.890193</td>\n",
+       "      <td>-0.011374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.012412</td>\n",
+       "      <td>0.003650</td>\n",
+       "      <td>0.338401</td>\n",
+       "      <td>0.047837</td>\n",
+       "      <td>1.246855e-02</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.344077</td>\n",
+       "      <td>0.002493</td>\n",
+       "      <td>0.003319</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>755940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs3115857</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.59380</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302008</td>\n",
+       "      <td>-0.077224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.674640</td>\n",
+       "      <td>-0.026630</td>\n",
+       "      <td>-0.011550</td>\n",
+       "      <td>2.854755e-07</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>-0.007762</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     Pos Ref Alt         RsID  Peak       AF  Inside_Peak    P_Lead  \\\n",
+       "2  chr1  565286   C   T    rs1578391     7  0.59780            1  0.998561   \n",
+       "3  chr1  569204   T   C  rs112660509     8  0.40125            1  0.477613   \n",
+       "6  chr1  726944   C   G    rs3131979    14  0.74175            1  0.953905   \n",
+       "7  chr1  752721   A   G    rs3131972    17  0.84500            1  0.890193   \n",
+       "8  chr1  755940   C   T    rs3115857    18  0.59380            1  0.302008   \n",
+       "\n",
+       "       Beta  ...  ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2  0.886884  ...                                           0.000022           \n",
+       "3  0.251458  ...                                           0.000155           \n",
+       "6 -0.079065  ...                                           0.000001           \n",
+       "7 -0.011374  ...                                           0.012412           \n",
+       "8 -0.077224  ...                                           0.000004           \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                           0.379356                \n",
+       "3                                           0.191231                \n",
+       "6                                           0.802258                \n",
+       "7                                           0.003650                \n",
+       "8                                           0.674640                \n",
+       "\n",
+       "   ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "2               -0.045242                  -0.016605   \n",
+       "3                0.027531                   0.017267   \n",
+       "6                0.026410                   0.010944   \n",
+       "7                0.338401                   0.047837   \n",
+       "8               -0.026630                  -0.011550   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                       5.337650e-05   \n",
+       "3                                       2.734630e-04   \n",
+       "6                                       2.107834e-05   \n",
+       "7                                       1.246855e-02   \n",
+       "8                                       2.854755e-07   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                           0.505239        \n",
+       "3                                           0.580367        \n",
+       "6                                           0.580367        \n",
+       "7                                           0.333333        \n",
+       "8                                           0.666667        \n",
+       "\n",
+       "   enformer_recomputed  DNASE_SAD  DNASE_SAR  IsUsed  \n",
+       "2            -0.001945   0.000008   0.000011    True  \n",
+       "3             0.001667   0.000026   0.000036    True  \n",
+       "6            -0.001038   0.000100   0.000133    True  \n",
+       "7            -0.344077   0.002493   0.003319    True  \n",
+       "8            -0.007762   0.000022   0.000029    True  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = afrs.merge(caqtls, right_on=[\"chr\", \"Pos\", \"Ref\", \"Alt\"], left_on=[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\"], how=\"left\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19353"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "sum(~np.isnan(df1[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(109646, 79)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = afrs.merge(caqtls, right_on=[\"chr\", \"Pos\", \"Alt\", \"Ref\"], left_on=[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\"], how=\"left\" )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7305"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(~np.isnan(df2[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df1[~np.isnan(df1[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"])],df2[~np.isnan(df2[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"])]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(26658, 79)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rsid_int = set(caqtls[caqtls[\"RsID\"].isin(afrs[\"dbsnp_rsid\"])][\"RsID\"].values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26658"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(rsid_int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(26658, 79)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls_ols=pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/rasqual_atac_1M\", sep='\\t', header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2087"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(caqtls_ols[caqtls_ols[1].isin( afrs[\"dbsnp_rsid\"])][1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4435"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(caqtls_ols[caqtls_ols[1].isin( caqtls[\"RsID\"])][1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>snp_id</th>\n",
+       "      <th>label</th>\n",
+       "      <th>IsUsed_x</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>enformer.encsr000emt.variantscore.sad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed_y</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>IsUsed_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000139</td>\n",
+       "      <td>0.195813</td>\n",
+       "      <td>-0.000459</td>\n",
+       "      <td>-0.008914</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>-0.002396</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.000184</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001306</td>\n",
+       "      <td>0.039847</td>\n",
+       "      <td>0.072689</td>\n",
+       "      <td>0.019863</td>\n",
+       "      <td>0.001272</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.015009</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>-0.000095</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>906982</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_906982_C_T</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>-0.520262</td>\n",
+       "      <td>-0.000453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001456</td>\n",
+       "      <td>0.033359</td>\n",
+       "      <td>-0.079933</td>\n",
+       "      <td>-0.026171</td>\n",
+       "      <td>0.001060</td>\n",
+       "      <td>0.382899</td>\n",
+       "      <td>0.176561</td>\n",
+       "      <td>-0.000453</td>\n",
+       "      <td>-0.000604</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1020217</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_1020217_G_T</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.975842</td>\n",
+       "      <td>-0.002104</td>\n",
+       "      <td>0.000293</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000197</td>\n",
+       "      <td>0.152159</td>\n",
+       "      <td>-0.000160</td>\n",
+       "      <td>-0.012468</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>0.000754</td>\n",
+       "      <td>0.000293</td>\n",
+       "      <td>0.000344</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1137871</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_1137871_C_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.493873</td>\n",
+       "      <td>-0.103324</td>\n",
+       "      <td>-0.000020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000031</td>\n",
+       "      <td>0.368320</td>\n",
+       "      <td>0.018878</td>\n",
+       "      <td>0.010569</td>\n",
+       "      <td>0.000123</td>\n",
+       "      <td>0.439836</td>\n",
+       "      <td>-0.000308</td>\n",
+       "      <td>-0.000020</td>\n",
+       "      <td>-0.000026</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 79 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   chr_hg38  pos_hg38 allele1 allele2         snp_id  label  IsUsed_x  \\\n",
+       "0      chr1    870176       T       A   1_870176_T_A  False      True   \n",
+       "1      chr1    905373       C       T   1_905373_T_C  False      True   \n",
+       "3      chr1    906982       C       T   1_906982_C_T   True      True   \n",
+       "30     chr1   1020217       G       T  1_1020217_G_T  False      True   \n",
+       "66     chr1   1137871       C       A  1_1137871_C_A  False      True   \n",
+       "\n",
+       "        pval      beta  enformer.encsr000emt.variantscore.sad  ...  \\\n",
+       "0   0.071532 -0.250142                              -0.000139  ...   \n",
+       "1   0.188152 -0.130176                              -0.000071  ...   \n",
+       "3   0.000005 -0.520262                              -0.000453  ...   \n",
+       "30  0.975842 -0.002104                               0.000293  ...   \n",
+       "66  0.493873 -0.103324                              -0.000020  ...   \n",
+       "\n",
+       "    ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                            0.000139           \n",
+       "1                                            0.001306           \n",
+       "3                                            0.001456           \n",
+       "30                                           0.000197           \n",
+       "66                                           0.000031           \n",
+       "\n",
+       "    ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                            0.195813                \n",
+       "1                                            0.039847                \n",
+       "3                                            0.033359                \n",
+       "30                                           0.152159                \n",
+       "66                                           0.368320                \n",
+       "\n",
+       "    ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "0                -0.000459                  -0.008914   \n",
+       "1                 0.072689                   0.019863   \n",
+       "3                -0.079933                  -0.026171   \n",
+       "30               -0.000160                  -0.012468   \n",
+       "66                0.018878                   0.010569   \n",
+       "\n",
+       "    ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                            0.000072   \n",
+       "1                                            0.001272   \n",
+       "3                                            0.001060   \n",
+       "30                                           0.000081   \n",
+       "66                                           0.000123   \n",
+       "\n",
+       "    ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                            0.505239        \n",
+       "1                                            0.333333        \n",
+       "3                                            0.382899        \n",
+       "30                                           0.580367        \n",
+       "66                                           0.439836        \n",
+       "\n",
+       "    enformer_recomputed_y  DNASE_SAD  DNASE_SAR  IsUsed_y  \n",
+       "0               -0.002396  -0.000139  -0.000184      True  \n",
+       "1                0.015009  -0.000071  -0.000095      True  \n",
+       "3                0.176561  -0.000453  -0.000604      True  \n",
+       "30               0.000754   0.000293   0.000344      True  \n",
+       "66              -0.000308  -0.000020  -0.000026      True  \n",
+       "\n",
+       "[5 rows x 79 columns]"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed_x', 'pval', 'beta', 'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar', 'enformer_recomputed_x',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta', 'id_hg19', 'chr_hg19', 'pos_hg19',\n",
+       "       'ref_hg19', 'alt_hg19', 'chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak',\n",
+       "       'AF', 'Inside_Peak', 'P_Lead', 'Beta', 'SE', 'Log10_BF',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed_y', 'DNASE_SAD', 'DNASE_SAR', 'IsUsed_y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14843"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(df[\"Log10_BF\"]<-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4170"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(df[\"Log10_BF\"]>3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(69, 79)"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 1 / (10**(6))\n",
+    "\n",
+    "df[(df[\"Log10_BF\"]<-1) & (df[\"label\"]==1)].drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2800, 79)"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 1 / (10**(6))\n",
+    "df[(df[\"Log10_BF\"]>3) & (df[\"label\"]==1)].drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1370, 79)"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df[\"Log10_BF\"]>3) & (df[\"pval\"]>1e-3)].drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg19</th>\n",
+       "      <th>pos_hg19</th>\n",
+       "      <th>ref_hg19</th>\n",
+       "      <th>alt_hg19</th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>dbsnp_rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>3839080</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>3922516</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs36197555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>15930251</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>15603756</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs72645889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>849</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>19986341</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>19659848</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12747702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1040</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>25021037</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>24694546</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs2885174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1259</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>28575121</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>28248610</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs530426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108106</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>18484205</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>18001439</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs5992955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109267</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>43157308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>42761302</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs6002949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109272</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>43165866</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>42769860</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs6002950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109275</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>43259541</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>42863535</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs5751390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109515</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>47330955</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>46935059</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs738406</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1370 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr_hg19  pos_hg19 ref_hg19 alt_hg19 chr_hg38  pos_hg38 allele1  \\\n",
+       "208        chr1   3839080        A        G     chr1   3922516       A   \n",
+       "684        chr1  15930251        T        G     chr1  15603756       T   \n",
+       "849        chr1  19986341        A        T     chr1  19659848       A   \n",
+       "1040       chr1  25021037        A        C     chr1  24694546       A   \n",
+       "1259       chr1  28575121        T        G     chr1  28248610       G   \n",
+       "...         ...       ...      ...      ...      ...       ...     ...   \n",
+       "108106    chr22  18484205        C        G    chr22  18001439       C   \n",
+       "109267    chr22  43157308        G        C    chr22  42761302       G   \n",
+       "109272    chr22  43165866        T        C    chr22  42769860       T   \n",
+       "109275    chr22  43259541        C        A    chr22  42863535       C   \n",
+       "109515    chr22  47330955        G        C    chr22  46935059       G   \n",
+       "\n",
+       "       allele2  dbsnp_rsid  \n",
+       "208          G  rs36197555  \n",
+       "684          G  rs72645889  \n",
+       "849          T  rs12747702  \n",
+       "1040         C   rs2885174  \n",
+       "1259         T    rs530426  \n",
+       "...        ...         ...  \n",
+       "108106       G   rs5992955  \n",
+       "109267       C   rs6002949  \n",
+       "109272       C   rs6002950  \n",
+       "109275       A   rs5751390  \n",
+       "109515       C    rs738406  \n",
+       "\n",
+       "[1370 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df[\"Log10_BF\"]>3) & (df[\"pval\"]>1e-3)].drop_duplicates()[[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\",\"chr_hg38\", \"pos_hg38\", \"allele1\", \"allele2\", \"dbsnp_rsid\"]]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2787"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(df[\"pval\"]<threshold)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23221"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(df[\"pval\"]>1e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(109646, 51)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(156730, 28)"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1370, 79)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f32b96d0950>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "tt = df[(df[\"Log10_BF\"]>3) & (df[\"pval\"]>1e-3)].drop_duplicates()\n",
+    "print(tt.shape)\n",
+    "plt.scatter(abs(tt[\"enformer_recomputed_y\"]),abs(tt[\"enformer_recomputed_x\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt('test_rsids.txt', tt['dbsnp_rsid'].values, fmt='%s')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr_hg38', 'pos_hg38', 'allele1', 'allele2', 'snp_id', 'label',\n",
+       "       'IsUsed_x', 'pval', 'beta', 'enformer.encsr000emt.variantscore.sad',\n",
+       "       'enformer.encsr000emt.variantscore.sar', 'enformer_recomputed_x',\n",
+       "       'chrombpnet.gambian.variantscore.logfc',\n",
+       "       'chrombpnet.gambian.variantscore.jsd',\n",
+       "       'chrombpnet.esan.variantscore.logfc',\n",
+       "       'chrombpnet.esan.variantscore.jsd',\n",
+       "       'chrombpnet.mende.variantscore.logfc',\n",
+       "       'chrombpnet.mende.variantscore.jsd',\n",
+       "       'chrombpnet.yoruba.variantscore.logfc',\n",
+       "       'chrombpnet.yoruba.variantscore.jsd',\n",
+       "       'chrombpnet.maasai.variantscore.logfc',\n",
+       "       'chrombpnet.maasai.variantscore.jsd',\n",
+       "       'chrombpnet.luhya.variantscore.logfc',\n",
+       "       'chrombpnet.luhya.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc.variantscore.pval',\n",
+       "       'chrombpnet.encsr000emt.variantscore.logfc',\n",
+       "       'chrombpnet.encsr000emt.variantscore.jsd',\n",
+       "       'chrombpnet.encsr000emt.variantscore.csr',\n",
+       "       'chrombpnet.encsr000emt.variantscore.pval',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.logfc',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.jsd',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.csr',\n",
+       "       'chrombpnet.encsr637xsc_50m.variantscore.pval',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.logfc',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.jsd',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.csr',\n",
+       "       'chrombpnet.encsr095qnb.variantscore.pval', 'dbsnp_rsid', 'region',\n",
+       "       'snp', 'distance', 'se', 'abs_beta', 'id_hg19', 'chr_hg19', 'pos_hg19',\n",
+       "       'ref_hg19', 'alt_hg19', 'chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak',\n",
+       "       'AF', 'Inside_Peak', 'P_Lead', 'Beta', 'SE', 'Log10_BF',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed_y', 'DNASE_SAD', 'DNASE_SAR', 'IsUsed_y'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tt.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f32ba0ca310>"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(abs(tt[\"chrombpnet.encsr637xsc.variantscore.csr\"]),abs(tt[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'ChromBPNet predictions')"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(abs(tt[\"enformer_recomputed_y\"]),abs(tt[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "plt.xlabel(\"Enformer predictions\")\n",
+    "plt.ylabel(\"ChromBPNet predictions\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'ChromBPNet predictions')"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tt = df[df[\"Log10_BF\"]<-1]\n",
+    "plt.scatter(abs(tt[\"enformer_recomputed_y\"]),abs(tt[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "plt.xlabel(\"Enformer predictions\")\n",
+    "plt.ylabel(\"ChromBPNet predictions\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors=np.array(['b'])\n",
+    "colors = 'r'\n",
+    "plt.scatter(abs(tt[\"enformer_recomputed_y\"]),abs(tt[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "plt.xlabel(\"Enformer predictions\")\n",
+    "plt.ylabel(\"ChromBPNet predictions\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tt[\"enformer_recomputed_y\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/debug_kumasaka_caqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/debug_kumasaka_caqtls.ipynb
new file mode 100644
index 00000000..dcfd4d97
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/debug_kumasaka_caqtls.ipynb
@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/lead_caQTL_variants.tsv.gz\", sep=\"\\t\", header=0)\n",
+    "qvals[\"chr\"] = \"chr\"+qvals[\"Chr\"].astype(str) \n",
+    "qvals = qvals[~qvals['Alt'].str.contains('<CN')]\n",
+    "qvals = qvals[~qvals['Alt'].str.contains(',')]\n",
+    "qvals = qvals[qvals['Alt'].str.len()==1]\n",
+    "qvals = qvals[qvals['Ref'].str.len()==1]\n",
+    "qvals = qvals.reset_index()\n",
+    "qvals['abs_Beta'] = abs(qvals['Beta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>chr</th>\n",
+       "      <th>abs_Beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>0.155446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>0.174020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>0.188153</td>\n",
+       "      <td>3.085214</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>0.886884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>569204</td>\n",
+       "      <td>rs112660509</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.40125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.251458</td>\n",
+       "      <td>0.140264</td>\n",
+       "      <td>-0.671608</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>0.251458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.05005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988758</td>\n",
+       "      <td>0.347965</td>\n",
+       "      <td>0.059578</td>\n",
+       "      <td>4.597219</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>0.347965</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index  Peak  Chr     Pos         RsID Ref Alt       AF  Inside_Peak  \\\n",
+       "0      2     3    1  875770    rs4970379   A   G  0.62500            2   \n",
+       "1      4     5    1  930533    rs3128110   C   G  0.35435            0   \n",
+       "2      6     7    1  565286    rs1578391   C   T  0.59780            1   \n",
+       "3      7     8    1  569204  rs112660509   T   C  0.40125            1   \n",
+       "4      9    10    1  748141    rs2427893   G   A  0.05005            0   \n",
+       "\n",
+       "     P_Lead      Beta        SE  Log10_BF   chr  abs_Beta  \n",
+       "0  0.044994  0.155446  0.057836 -0.232537  chr1  0.155446  \n",
+       "1  0.099392  0.174020  0.040562  1.751829  chr1  0.174020  \n",
+       "2  0.998561  0.886884  0.188153  3.085214  chr1  0.886884  \n",
+       "3  0.477613  0.251458  0.140264 -0.671608  chr1  0.251458  \n",
+       "4  0.988758  0.347965  0.059578  4.597219  chr1  0.347965  "
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inside_other_peaks = qvals[(qvals[\"Inside_Peak\"]==2) & (qvals[\"Log10_BF\"]>3)].drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inside_focal_peaks = qvals[(qvals[\"Inside_Peak\"]==1) & (qvals[\"Log10_BF\"]>3)].drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1742, 15)"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside_other_peaks.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9851, 15)"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside_focal_peaks.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>chr</th>\n",
+       "      <th>abs_Beta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.5978</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>0.188153</td>\n",
+       "      <td>3.085214</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>0.886884</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index  Peak  Chr     Pos       RsID Ref Alt      AF  Inside_Peak    P_Lead  \\\n",
+       "2      6     7    1  565286  rs1578391   C   T  0.5978            1  0.998561   \n",
+       "\n",
+       "       Beta        SE  Log10_BF   chr  abs_Beta  \n",
+       "2  0.886884  0.188153  3.085214  chr1  0.886884  "
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside_focal_peaks.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inside_other_peaks[\"Chr\"]=\"chr\"+inside_other_peaks[\"Chr\"].astype(str)\n",
+    "inside_focal_peaks[\"Chr\"]=\"chr\"+inside_focal_peaks[\"Chr\"].astype(str)\n",
+    "\n",
+    "inside_focal_peaks[\"rsid\"]=inside_focal_peaks[\"Chr\"].astype(str)+\"_\"+inside_focal_peaks[\"Pos\"].astype(str)+\"_\"+inside_focal_peaks[\"Ref\"]+\"_\"+inside_focal_peaks[\"Alt\"]\n",
+    "inside_other_peaks[\"rsid\"]=inside_other_peaks[\"Chr\"].astype(str)+\"_\"+inside_other_peaks[\"Pos\"].astype(str)+\"_\"+inside_other_peaks[\"Ref\"]+\"_\"+inside_other_peaks[\"Alt\"]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "973"
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(inside_other_peaks[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9851"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(inside_focal_peaks[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z1 = set(inside_focal_peaks[\"RsID\"]).intersection(set(inside_other_peaks[\"RsID\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "656"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(z1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = set(inside_focal_peaks[\"rsid\"]).intersection(set(inside_other_peaks[\"rsid\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "656"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "rsid\n",
+       "chr7_7860667_A_G       1\n",
+       "chr2_42826919_T_G      1\n",
+       "chr2_42357292_G_A      1\n",
+       "chr2_39955465_C_T      1\n",
+       "chr2_38079108_C_T      1\n",
+       "                      ..\n",
+       "chr1_181399618_G_T    11\n",
+       "chr12_87705214_G_A    14\n",
+       "chr10_20236731_A_G    18\n",
+       "chr13_75874528_T_C    34\n",
+       "chr3_192616700_T_G    47\n",
+       "Name: Peak, Length: 656, dtype: int64"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inside_other_peaks[inside_other_peaks.isin(z)].groupby('rsid')['Peak'].size().sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "newd = inside_other_peaks.sort_values('abs_Beta', ascending=False).drop_duplicates(['rsid'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>chr</th>\n",
+       "      <th>abs_Beta</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>77620</th>\n",
+       "      <td>102420</td>\n",
+       "      <td>97027</td>\n",
+       "      <td>chr4</td>\n",
+       "      <td>190156646</td>\n",
+       "      <td>rs34706863</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.998763</td>\n",
+       "      <td>1.972464</td>\n",
+       "      <td>0.092728</td>\n",
+       "      <td>35.001132</td>\n",
+       "      <td>chr4</td>\n",
+       "      <td>1.972464</td>\n",
+       "      <td>chr4_190156646_G_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205567</th>\n",
+       "      <td>269843</td>\n",
+       "      <td>251337</td>\n",
+       "      <td>chr17</td>\n",
+       "      <td>19091555</td>\n",
+       "      <td>rs200234160</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.0617</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.130740</td>\n",
+       "      <td>-1.725211</td>\n",
+       "      <td>0.137092</td>\n",
+       "      <td>18.966584</td>\n",
+       "      <td>chr17</td>\n",
+       "      <td>1.725211</td>\n",
+       "      <td>chr17_19091555_T_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26827</th>\n",
+       "      <td>35422</td>\n",
+       "      <td>33009</td>\n",
+       "      <td>chr2</td>\n",
+       "      <td>114413955</td>\n",
+       "      <td>rs12621065</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.5250</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.755553</td>\n",
+       "      <td>-1.699921</td>\n",
+       "      <td>0.147025</td>\n",
+       "      <td>16.850797</td>\n",
+       "      <td>chr2</td>\n",
+       "      <td>1.699921</td>\n",
+       "      <td>chr2_114413955_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142292</th>\n",
+       "      <td>186629</td>\n",
+       "      <td>176374</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>31153406</td>\n",
+       "      <td>rs10758026</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.4050</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.213319</td>\n",
+       "      <td>1.443859</td>\n",
+       "      <td>0.079542</td>\n",
+       "      <td>29.586316</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>1.443859</td>\n",
+       "      <td>chr9_31153406_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62801</th>\n",
+       "      <td>82724</td>\n",
+       "      <td>77907</td>\n",
+       "      <td>chr4</td>\n",
+       "      <td>52727094</td>\n",
+       "      <td>rs1875921</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.3350</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.210748</td>\n",
+       "      <td>1.443545</td>\n",
+       "      <td>0.105982</td>\n",
+       "      <td>21.001514</td>\n",
+       "      <td>chr4</td>\n",
+       "      <td>1.443545</td>\n",
+       "      <td>chr4_52727094_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126971</th>\n",
+       "      <td>167010</td>\n",
+       "      <td>157822</td>\n",
+       "      <td>chr8</td>\n",
+       "      <td>11718528</td>\n",
+       "      <td>rs1293288</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.5700</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.992249</td>\n",
+       "      <td>-0.072896</td>\n",
+       "      <td>0.013616</td>\n",
+       "      <td>3.066369</td>\n",
+       "      <td>chr8</td>\n",
+       "      <td>0.072896</td>\n",
+       "      <td>chr8_11718528_T_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16003</th>\n",
+       "      <td>21473</td>\n",
+       "      <td>19944</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>226593271</td>\n",
+       "      <td>rs7525191</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.2250</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.266814</td>\n",
+       "      <td>0.072875</td>\n",
+       "      <td>0.011974</td>\n",
+       "      <td>4.367032</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>0.072875</td>\n",
+       "      <td>chr1_226593271_T_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52236</th>\n",
+       "      <td>69106</td>\n",
+       "      <td>65036</td>\n",
+       "      <td>chr3</td>\n",
+       "      <td>144661471</td>\n",
+       "      <td>rs66492371</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.3550</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.426985</td>\n",
+       "      <td>-0.063148</td>\n",
+       "      <td>0.011782</td>\n",
+       "      <td>3.014309</td>\n",
+       "      <td>chr3</td>\n",
+       "      <td>0.063148</td>\n",
+       "      <td>chr3_144661471_A_C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148069</th>\n",
+       "      <td>194207</td>\n",
+       "      <td>183495</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>139796639</td>\n",
+       "      <td>rs10870141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.7400</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.594994</td>\n",
+       "      <td>0.062153</td>\n",
+       "      <td>0.010696</td>\n",
+       "      <td>3.798187</td>\n",
+       "      <td>chr9</td>\n",
+       "      <td>0.062153</td>\n",
+       "      <td>chr9_139796639_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112382</th>\n",
+       "      <td>147820</td>\n",
+       "      <td>139775</td>\n",
+       "      <td>chr7</td>\n",
+       "      <td>5532104</td>\n",
+       "      <td>rs35823801</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.168144</td>\n",
+       "      <td>-0.051879</td>\n",
+       "      <td>0.009562</td>\n",
+       "      <td>3.041991</td>\n",
+       "      <td>chr7</td>\n",
+       "      <td>0.051879</td>\n",
+       "      <td>chr7_5532104_G_A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>973 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         index    Peak    Chr        Pos         RsID Ref Alt      AF  \\\n",
+       "77620   102420   97027   chr4  190156646   rs34706863   G   T  0.2650   \n",
+       "205567  269843  251337  chr17   19091555  rs200234160   T   A  0.0617   \n",
+       "26827    35422   33009   chr2  114413955   rs12621065   T   C  0.5250   \n",
+       "142292  186629  176374   chr9   31153406   rs10758026   T   C  0.4050   \n",
+       "62801    82724   77907   chr4   52727094    rs1875921   T   C  0.3350   \n",
+       "...        ...     ...    ...        ...          ...  ..  ..     ...   \n",
+       "126971  167010  157822   chr8   11718528    rs1293288   T   C  0.5700   \n",
+       "16003    21473   19944   chr1  226593271    rs7525191   T   G  0.2250   \n",
+       "52236    69106   65036   chr3  144661471   rs66492371   A   C  0.3550   \n",
+       "148069  194207  183495   chr9  139796639   rs10870141   G   A  0.7400   \n",
+       "112382  147820  139775   chr7    5532104   rs35823801   G   A  0.2750   \n",
+       "\n",
+       "        Inside_Peak    P_Lead      Beta        SE   Log10_BF    chr  abs_Beta  \\\n",
+       "77620             2  0.998763  1.972464  0.092728  35.001132   chr4  1.972464   \n",
+       "205567            2  0.130740 -1.725211  0.137092  18.966584  chr17  1.725211   \n",
+       "26827             2  0.755553 -1.699921  0.147025  16.850797   chr2  1.699921   \n",
+       "142292            2  0.213319  1.443859  0.079542  29.586316   chr9  1.443859   \n",
+       "62801             2  0.210748  1.443545  0.105982  21.001514   chr4  1.443545   \n",
+       "...             ...       ...       ...       ...        ...    ...       ...   \n",
+       "126971            2  0.992249 -0.072896  0.013616   3.066369   chr8  0.072896   \n",
+       "16003             2  0.266814  0.072875  0.011974   4.367032   chr1  0.072875   \n",
+       "52236             2  0.426985 -0.063148  0.011782   3.014309   chr3  0.063148   \n",
+       "148069            2  0.594994  0.062153  0.010696   3.798187   chr9  0.062153   \n",
+       "112382            2  0.168144 -0.051879  0.009562   3.041991   chr7  0.051879   \n",
+       "\n",
+       "                      rsid  \n",
+       "77620   chr4_190156646_G_T  \n",
+       "205567  chr17_19091555_T_A  \n",
+       "26827   chr2_114413955_T_C  \n",
+       "142292   chr9_31153406_T_C  \n",
+       "62801    chr4_52727094_T_C  \n",
+       "...                    ...  \n",
+       "126971   chr8_11718528_T_C  \n",
+       "16003   chr1_226593271_T_G  \n",
+       "52236   chr3_144661471_A_C  \n",
+       "148069  chr9_139796639_G_A  \n",
+       "112382    chr7_5532104_G_A  \n",
+       "\n",
+       "[973 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "newd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#newd = inside_other_peaks.groupby('rsid')['abs_Beta'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#newd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newd = newd[newd['rsid'].isin(z)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newd = newd.sort_values('rsid')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = inside_focal_peaks[inside_focal_peaks[\"rsid\"].isin(z)]\n",
+    "df = df[[\"abs_Beta\", \"rsid\", \"Beta\"]].sort_values('rsid')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(656, 2)"
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"abs_Beta\", \"rsid\"]].sort_values('rsid').shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f15e1569dd0>"
+      ]
+     },
+     "execution_count": 249,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(df['abs_Beta'], newd['abs_Beta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'distal observed effect')"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(df['Beta'], newd['Beta'],s=1)\n",
+    "plt.xlabel(\"local observed effect\")\n",
+    "plt.ylabel(\"distal observed effect\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.4008546238537578, 1.0260601619452211e-26)"
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import pearsonr\n",
+    "pearsonr(df['abs_Beta'], newd['abs_Beta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8148285317030296, 5.230535437738494e-157)"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(df['Beta'], newd['Beta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3703204935701525, 1.0444693438062491e-83)"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/eu.caqtls_scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/eu.caqtls_scoring.ipynb
new file mode 100644
index 00000000..ab798da7
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/eu.caqtls_scoring.ipynb
@@ -0,0 +1,3893 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls = pd.read_csv(\"Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)\n",
+    "afrs = afrs[afrs[\"IsUsed\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(177813, 4)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls[[\"chr\", \"Pos\", \"Ref\", \"Alt\"]].drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 28)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# caqtls[caqtls[\"Inside_Peak\"]==2][[\"chr\", \"Pos\", \"Ref\", \"Alt\"]].drop_duplicates().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# distal_effects = caqtls[caqtls[\"Inside_Peak\"]==2][[\"chr\", \"Pos\", \"Ref\", \"Alt\"]].drop_duplicates()[[\"chr\", \"Pos\", \"Ref\", \"Alt\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# distal_effects.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# caqtls = caqtls[caqtls[\"IsUsed\"] & caqtls[\"Inside_Peak\"]==1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# caqtls.loc[:,[\"chr\", \"Pos\", \"Ref\", \"Alt\"]].isin(distal_effects[[\"chr\", \"Pos\", \"Ref\", \"Alt\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import numpy as np\n",
+    "# df = pd.merge(caqtls, distal_effects, on=[\"chr\", \"Pos\", \"Ref\", \"Alt\"], how='left', indicator='Exist')\n",
+    "# df['Exist'] = np.where(df.Exist == 'both', True, False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(df['Exist'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 28)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 28)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(41153, 28)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls[caqtls[\"Inside_Peak\"]==2].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000367</td>\n",
+       "      <td>0.121047</td>\n",
+       "      <td>-0.018701</td>\n",
+       "      <td>-0.013757</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000178</td>\n",
+       "      <td>0.000233</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.273108</td>\n",
+       "      <td>0.074079</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.015608</td>\n",
+       "      <td>0.002812</td>\n",
+       "      <td>-0.613550</td>\n",
+       "      <td>-0.059538</td>\n",
+       "      <td>0.021172</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.006530</td>\n",
+       "      <td>-0.007840</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.493991</td>\n",
+       "      <td>-0.046797</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.015608</td>\n",
+       "      <td>0.002812</td>\n",
+       "      <td>-0.613550</td>\n",
+       "      <td>-0.059538</td>\n",
+       "      <td>0.021172</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.006530</td>\n",
+       "      <td>-0.007840</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1590681</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs9661500</td>\n",
+       "      <td>125</td>\n",
+       "      <td>0.655</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.082582</td>\n",
+       "      <td>-0.023552</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000077</td>\n",
+       "      <td>0.249567</td>\n",
+       "      <td>0.037795</td>\n",
+       "      <td>0.024868</td>\n",
+       "      <td>0.001097</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000106</td>\n",
+       "      <td>0.000124</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>2508343</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs200553981</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.350</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.340515</td>\n",
+       "      <td>0.017197</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.011033</td>\n",
+       "      <td>0.004075</td>\n",
+       "      <td>0.177735</td>\n",
+       "      <td>0.039674</td>\n",
+       "      <td>0.003995</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.002804</td>\n",
+       "      <td>0.003008</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      chr      Pos Ref Alt         RsID  Peak     AF  Inside_Peak    P_Lead  \\\n",
+       "0    chr1   875770   A   G    rs4970379     3  0.625            2  0.044994   \n",
+       "44   chr1  1186502   A   T    rs6603785    66  0.165            2  0.273108   \n",
+       "45   chr1  1186502   A   T    rs6603785    67  0.165            2  0.493991   \n",
+       "84   chr1  1590681   G   A    rs9661500   125  0.655            2  0.082582   \n",
+       "144  chr1  2508343   G   C  rs200553981   194  0.350            2  0.340515   \n",
+       "\n",
+       "         Beta  ...  ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0    0.155446  ...                                           0.000367           \n",
+       "44   0.074079  ...                                           0.015608           \n",
+       "45  -0.046797  ...                                           0.015608           \n",
+       "84  -0.023552  ...                                           0.000077           \n",
+       "144  0.017197  ...                                           0.011033           \n",
+       "\n",
+       "     ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                             0.121047                \n",
+       "44                                            0.002812                \n",
+       "45                                            0.002812                \n",
+       "84                                            0.249567                \n",
+       "144                                           0.004075                \n",
+       "\n",
+       "     ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "0                 -0.018701                  -0.013757   \n",
+       "44                -0.613550                  -0.059538   \n",
+       "45                -0.613550                  -0.059538   \n",
+       "84                 0.037795                   0.024868   \n",
+       "144                0.177735                   0.039674   \n",
+       "\n",
+       "     ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                             0.000165   \n",
+       "44                                            0.021172   \n",
+       "45                                            0.021172   \n",
+       "84                                            0.001097   \n",
+       "144                                           0.003995   \n",
+       "\n",
+       "     ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "0                                             0.505239        \n",
+       "44                                            0.333333        \n",
+       "45                                            0.333333        \n",
+       "84                                            0.333333        \n",
+       "144                                           0.333333        \n",
+       "\n",
+       "     enformer_recomputed  DNASE_SAD  DNASE_SAR  IsUsed  \n",
+       "0                    NaN   0.000178   0.000233    True  \n",
+       "44                   NaN  -0.006530  -0.007840    True  \n",
+       "45                   NaN  -0.006530  -0.007840    True  \n",
+       "84                   NaN   0.000106   0.000124    True  \n",
+       "144                  NaN   0.002804   0.003008    True  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls[caqtls[\"Inside_Peak\"]==2].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['rs4970379', 'rs6603785', 'rs6603785', ..., 'rs138248257',\n",
+       "       'rs138248257', 'rs138248257'], dtype=object)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls[caqtls[\"Inside_Peak\"]==2][\"RsID\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(41153, 28)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls[caqtls[\"Inside_Peak\"]==2].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5321"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(caqtls[caqtls[\"Inside_Peak\"]==1][\"RsID\"].isin(caqtls[caqtls[\"Inside_Peak\"]==2][\"RsID\"].values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(caqtls[caqtls[\"Inside_Peak\"]==1][\"RsID\"].isin(caqtls[caqtls[\"Log10_BF\"]==0][\"RsID\"].values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls = pd.read_csv(\"Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "distal_rsids = list(set(caqtls[(caqtls[\"Inside_Peak\"]==2) & (caqtls[\"Log10_BF\"]>-1)][\"RsID\"].values))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5787"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(distal_rsids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#distal_rsids = list(set(caqtls[(caqtls[\"Inside_Peak\"]==2)][\"RsID\"].values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5787"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(distal_rsids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls = caqtls[caqtls[\"IsUsed\"] & caqtls[\"Inside_Peak\"]==1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2550"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(caqtls[caqtls[\"Log10_BF\"]>3][\"RsID\"].isin(distal_rsids))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9851"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(caqtls[caqtls[\"Log10_BF\"]>3][\"RsID\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2550"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "index = caqtls[caqtls[\"Log10_BF\"]>3][\"RsID\"].isin(distal_rsids)\n",
+    "len(set(caqtls[caqtls[\"Log10_BF\"]>3][index][\"RsID\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls1 = pd.concat([caqtls[caqtls[\"RsID\"].isin(distal_rsids)]]).drop_duplicates()\n",
+    "caqtls2 = pd.concat([caqtls[~caqtls[\"RsID\"].isin(distal_rsids)]]).drop_duplicates()\n",
+    "caqtls1 = caqtls1[caqtls1[\"Log10_BF\"]>3]\n",
+    "caqtls2 = caqtls2[caqtls2[\"Log10_BF\"]>3]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2550, 28)"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls1.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7301, 28)"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls2.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "5\n",
+      "7\n",
+      "9\n",
+      "11\n",
+      "13\n",
+      "15\n",
+      "17\n",
+      "19\n",
+      "21\n"
+     ]
+    }
+   ],
+   "source": [
+    "ths =[]\n",
+    "num_set1 = []\n",
+    "num_set2 = []\n",
+    "for threshold in np.arange(3,23,2):\n",
+    "    print(threshold)\n",
+    "    ths.append(threshold)\n",
+    "    #num_set1.append(caqtls1[caqtls1[\"Log10_BF\"]>threshold].shape[0])\n",
+    "    #num_set2.append(caqtls2[caqtls2[\"Log10_BF\"]>threshold].shape[0])\n",
+    "    \n",
+    "    num_set1.append(pearsonr(caqtls1[caqtls1[\"Log10_BF\"]>threshold]['Beta'], caqtls1[caqtls1[\"Log10_BF\"]>threshold]['ENCSR637XSC.logfc.mean']*1)[0])\n",
+    "    num_set2.append(pearsonr(caqtls2[caqtls2[\"Log10_BF\"]>threshold]['Beta'], caqtls2[caqtls2[\"Log10_BF\"]>threshold]['ENCSR637XSC.logfc.mean']*1)[0])\n",
+    "#    num_set1.append(pearsonr(abs(caqtls1[caqtls1[\"Log10_BF\"]>threshold]['Beta']), abs(caqtls1[caqtls1[\"Log10_BF\"]>threshold]['ENCSR637XSC.logfc.mean']))[0])\n",
+    "#    num_set2.append(pearsonr(abs(caqtls2[caqtls2[\"Log10_BF\"]>threshold]['Beta']), abs(caqtls2[caqtls2[\"Log10_BF\"]>threshold]['ENCSR637XSC.logfc.mean']))[0])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#num_set1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff8141b3850>"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(ths, num_set1, label=\"set 2\")\n",
+    "plt.scatter(ths, num_set2, label=\"set 1\")\n",
+    "plt.xlabel(\"inc significance threshold\")\n",
+    "#plt.ylabel(\"total significant variants at a threshold\")\n",
+    "plt.ylabel(\"unsigned Pearsonr (observed vs ChromBPNet)\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9851"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "7301+2550"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>84</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.995510</td>\n",
+       "      <td>-0.406159</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.560834e-02</td>\n",
+       "      <td>0.002812</td>\n",
+       "      <td>-0.613550</td>\n",
+       "      <td>-0.059538</td>\n",
+       "      <td>0.021172</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.955810</td>\n",
+       "      <td>-6.530000e-03</td>\n",
+       "      <td>-7.840000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>2508343</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs200553981</td>\n",
+       "      <td>213</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999924</td>\n",
+       "      <td>0.226942</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.103318e-02</td>\n",
+       "      <td>0.004075</td>\n",
+       "      <td>0.177735</td>\n",
+       "      <td>0.039674</td>\n",
+       "      <td>0.003995</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.182859</td>\n",
+       "      <td>2.804000e-03</td>\n",
+       "      <td>3.008000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>2615134</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs78125432</td>\n",
+       "      <td>223</td>\n",
+       "      <td>0.31310</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.366991</td>\n",
+       "      <td>0.704901</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.490562e-07</td>\n",
+       "      <td>0.816993</td>\n",
+       "      <td>0.042428</td>\n",
+       "      <td>0.014587</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>0.001052</td>\n",
+       "      <td>-3.600000e-07</td>\n",
+       "      <td>-5.000000e-07</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>218</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>3820005</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs10909828</td>\n",
+       "      <td>277</td>\n",
+       "      <td>0.34500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999966</td>\n",
+       "      <td>-0.654595</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.141986e-02</td>\n",
+       "      <td>0.000489</td>\n",
+       "      <td>-0.618973</td>\n",
+       "      <td>-0.086568</td>\n",
+       "      <td>0.024578</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.094186</td>\n",
+       "      <td>-3.593000e-04</td>\n",
+       "      <td>-4.478000e-04</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>4327635</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs2101576</td>\n",
+       "      <td>286</td>\n",
+       "      <td>0.55000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999782</td>\n",
+       "      <td>-0.666789</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.266398e-02</td>\n",
+       "      <td>0.001471</td>\n",
+       "      <td>-0.689615</td>\n",
+       "      <td>-0.048458</td>\n",
+       "      <td>0.024916</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.810135</td>\n",
+       "      <td>-3.294000e-03</td>\n",
+       "      <td>-4.560000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225992</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>42925131</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs2413698</td>\n",
+       "      <td>276699</td>\n",
+       "      <td>0.78985</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999988</td>\n",
+       "      <td>-1.477181</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.059423e-01</td>\n",
+       "      <td>0.000049</td>\n",
+       "      <td>-0.870185</td>\n",
+       "      <td>-0.141672</td>\n",
+       "      <td>0.049538</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>1.248557</td>\n",
+       "      <td>-8.030000e-03</td>\n",
+       "      <td>-9.970000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226103</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>44845516</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs133770</td>\n",
+       "      <td>276841</td>\n",
+       "      <td>0.45500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.297393</td>\n",
+       "      <td>-0.395199</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.842590e-03</td>\n",
+       "      <td>0.005253</td>\n",
+       "      <td>-0.337324</td>\n",
+       "      <td>-0.043263</td>\n",
+       "      <td>0.008894</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.376536</td>\n",
+       "      <td>-2.384000e-03</td>\n",
+       "      <td>-3.248000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226135</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>45642907</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs6006956</td>\n",
+       "      <td>276879</td>\n",
+       "      <td>0.50000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.748201</td>\n",
+       "      <td>0.619708</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.322327e-02</td>\n",
+       "      <td>0.003211</td>\n",
+       "      <td>0.437278</td>\n",
+       "      <td>0.067900</td>\n",
+       "      <td>0.017931</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.549798</td>\n",
+       "      <td>6.200000e-03</td>\n",
+       "      <td>7.607000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226225</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>48314463</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs5768156</td>\n",
+       "      <td>276995</td>\n",
+       "      <td>0.41000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.627204</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.164257e-02</td>\n",
+       "      <td>0.004596</td>\n",
+       "      <td>0.327306</td>\n",
+       "      <td>0.043628</td>\n",
+       "      <td>0.009750</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.280423</td>\n",
+       "      <td>2.985000e-03</td>\n",
+       "      <td>4.097000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226274</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50339008</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs138248257</td>\n",
+       "      <td>277045</td>\n",
+       "      <td>0.14000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.430608</td>\n",
+       "      <td>1.429469</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.167279e-03</td>\n",
+       "      <td>0.040831</td>\n",
+       "      <td>0.054643</td>\n",
+       "      <td>0.024913</td>\n",
+       "      <td>0.001414</td>\n",
+       "      <td>0.382899</td>\n",
+       "      <td>-0.180623</td>\n",
+       "      <td>1.306000e-03</td>\n",
+       "      <td>1.501000e-03</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2550 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          chr       Pos Ref Alt         RsID    Peak       AF  Inside_Peak  \\\n",
+       "56       chr1   1186502   A   T    rs6603785      84  0.16500            1   \n",
+       "162      chr1   2508343   G   C  rs200553981     213  0.35000            1   \n",
+       "173      chr1   2615134   C   G   rs78125432     223  0.31310            1   \n",
+       "218      chr1   3820005   G   A   rs10909828     277  0.34500            1   \n",
+       "226      chr1   4327635   C   T    rs2101576     286  0.55000            1   \n",
+       "...       ...       ...  ..  ..          ...     ...      ...          ...   \n",
+       "225992  chr22  42925131   A   G    rs2413698  276699  0.78985            1   \n",
+       "226103  chr22  44845516   T   C     rs133770  276841  0.45500            1   \n",
+       "226135  chr22  45642907   T   C    rs6006956  276879  0.50000            1   \n",
+       "226225  chr22  48314463   G   A    rs5768156  276995  0.41000            1   \n",
+       "226274  chr22  50339008   C   T  rs138248257  277045  0.14000            1   \n",
+       "\n",
+       "          P_Lead      Beta  ...  \\\n",
+       "56      0.995510 -0.406159  ...   \n",
+       "162     0.999924  0.226942  ...   \n",
+       "173     0.366991  0.704901  ...   \n",
+       "218     0.999966 -0.654595  ...   \n",
+       "226     0.999782 -0.666789  ...   \n",
+       "...          ...       ...  ...   \n",
+       "225992  0.999988 -1.477181  ...   \n",
+       "226103  0.297393 -0.395199  ...   \n",
+       "226135  0.748201  0.619708  ...   \n",
+       "226225  1.000000  1.627204  ...   \n",
+       "226274  0.430608  1.429469  ...   \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "56                                           1.560834e-02           \n",
+       "162                                          1.103318e-02           \n",
+       "173                                          7.490562e-07           \n",
+       "218                                          4.141986e-02           \n",
+       "226                                          2.266398e-02           \n",
+       "...                                                   ...           \n",
+       "225992                                       1.059423e-01           \n",
+       "226103                                       8.842590e-03           \n",
+       "226135                                       1.322327e-02           \n",
+       "226225                                       1.164257e-02           \n",
+       "226274                                       1.167279e-03           \n",
+       "\n",
+       "        ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "56                                               0.002812                \n",
+       "162                                              0.004075                \n",
+       "173                                              0.816993                \n",
+       "218                                              0.000489                \n",
+       "226                                              0.001471                \n",
+       "...                                                   ...                \n",
+       "225992                                           0.000049                \n",
+       "226103                                           0.005253                \n",
+       "226135                                           0.003211                \n",
+       "226225                                           0.004596                \n",
+       "226274                                           0.040831                \n",
+       "\n",
+       "        ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "56                   -0.613550                  -0.059538   \n",
+       "162                   0.177735                   0.039674   \n",
+       "173                   0.042428                   0.014587   \n",
+       "218                  -0.618973                  -0.086568   \n",
+       "226                  -0.689615                  -0.048458   \n",
+       "...                        ...                        ...   \n",
+       "225992               -0.870185                  -0.141672   \n",
+       "226103               -0.337324                  -0.043263   \n",
+       "226135                0.437278                   0.067900   \n",
+       "226225                0.327306                   0.043628   \n",
+       "226274                0.054643                   0.024913   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "56                                               0.021172   \n",
+       "162                                              0.003995   \n",
+       "173                                              0.000017   \n",
+       "218                                              0.024578   \n",
+       "226                                              0.024916   \n",
+       "...                                                   ...   \n",
+       "225992                                           0.049538   \n",
+       "226103                                           0.008894   \n",
+       "226135                                           0.017931   \n",
+       "226225                                           0.009750   \n",
+       "226274                                           0.001414   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "56                                               0.333333        \n",
+       "162                                              0.333333        \n",
+       "173                                              0.580367        \n",
+       "218                                              0.333333        \n",
+       "226                                              0.333333        \n",
+       "...                                                   ...        \n",
+       "225992                                           0.333333        \n",
+       "226103                                           0.333333        \n",
+       "226135                                           0.333333        \n",
+       "226225                                           0.333333        \n",
+       "226274                                           0.382899        \n",
+       "\n",
+       "        enformer_recomputed     DNASE_SAD     DNASE_SAR  IsUsed  \n",
+       "56                 0.955810 -6.530000e-03 -7.840000e-03    True  \n",
+       "162               -0.182859  2.804000e-03  3.008000e-03    True  \n",
+       "173                0.001052 -3.600000e-07 -5.000000e-07    True  \n",
+       "218                0.094186 -3.593000e-04 -4.478000e-04    True  \n",
+       "226                0.810135 -3.294000e-03 -4.560000e-03    True  \n",
+       "...                     ...           ...           ...     ...  \n",
+       "225992             1.248557 -8.030000e-03 -9.970000e-03    True  \n",
+       "226103             0.376536 -2.384000e-03 -3.248000e-03    True  \n",
+       "226135            -0.549798  6.200000e-03  7.607000e-03    True  \n",
+       "226225            -0.280423  2.985000e-03  4.097000e-03    True  \n",
+       "226274            -0.180623  1.306000e-03  1.501000e-03    True  \n",
+       "\n",
+       "[2550 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import pearsonr\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.31340149962392755, 3.155029122286477e-59)"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(caqtls1['Beta']), abs(caqtls1['enformer_recomputed']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.19695670888399872, 2.1534071315904045e-24)"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(caqtls2['Beta']), abs(caqtls2['enformer_recomputed']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.25434669792692144, 4.444001061181763e-40)"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(caqtls2['Beta']), abs(caqtls2['ENCSR637XSC.logfc.mean']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.383422273233087, 4.315239001906426e-90)"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pearsonr(abs(caqtls1['Beta']), abs(caqtls1['ENCSR637XSC.logfc.mean']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7ff813916690>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(abs(caqtls1['Beta']), abs(caqtls1['ENCSR637XSC.logfc.mean']), s=1, c='r')\n",
+    "plt.scatter(abs(caqtls2['Beta']), abs(caqtls2['ENCSR637XSC.logfc.mean']), s=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'observed Beta')"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(abs(caqtls1['Beta']),50, alpha=1, density=True, histtype=\"step\", cumulative=True, label=\"set 2\")\n",
+    "plt.hist(abs(caqtls2['Beta']),50, alpha=1, density=True, histtype=\"step\", cumulative=True, label=\"set 1\")\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"observed Beta\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11947, 28)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "#caqtls[(caqtls[\"Inside_Peak\"]==2) & (caqtls[\"Log10_BF\"]>-1)][\"RsID\"]\n",
+    "\n",
+    "\n",
+    "caqtls = pd.concat([caqtls[caqtls[\"Log10_BF\"]<-1],  caqtls[caqtls[\"RsID\"].isin(distal_rsids)]]).drop_duplicates()\n",
+    "caqtls = pd.concat([caqtls[caqtls[\"Log10_BF\"]<-1],  caqtls[caqtls[\"RsID\"].isin(distal_rsids)]]).drop_duplicates()\n",
+    "\n",
+    "# caqtls = caqtls[caqtls[\"IsUsed\"]]\n",
+    "# caqtls = caqtls.sort_values('Log10_BF').drop_duplicates([\"chr\", \"Ref\", \"Alt\", \"Pos\"], keep='last')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2550"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(caqtls[caqtls[\"Log10_BF\"]>3][\"RsID\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "111915"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(caqtls[caqtls[\"Log10_BF\"]<-1][\"RsID\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "119216"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "111915+7301"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# caqtls = caqtls[caqtls[\"Inside_Peak\"]==1]\n",
+    "# caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(153456, 28)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>726944</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131979</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.74175</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.953905</td>\n",
+       "      <td>-0.079065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.802258</td>\n",
+       "      <td>0.026410</td>\n",
+       "      <td>0.010944</td>\n",
+       "      <td>2.107834e-05</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>-0.001038</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>752721</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131972</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.84500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.890193</td>\n",
+       "      <td>-0.011374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.012412</td>\n",
+       "      <td>0.003650</td>\n",
+       "      <td>0.338401</td>\n",
+       "      <td>0.047837</td>\n",
+       "      <td>1.246855e-02</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.344077</td>\n",
+       "      <td>0.002493</td>\n",
+       "      <td>0.003319</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>755940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs3115857</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.59380</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302008</td>\n",
+       "      <td>-0.077224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.674640</td>\n",
+       "      <td>-0.026630</td>\n",
+       "      <td>-0.011550</td>\n",
+       "      <td>2.854755e-07</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>-0.007762</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>793429</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs200956863</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.72345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.407804</td>\n",
+       "      <td>0.006696</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000526</td>\n",
+       "      <td>0.072640</td>\n",
+       "      <td>0.055559</td>\n",
+       "      <td>0.018649</td>\n",
+       "      <td>7.959288e-04</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.012475</td>\n",
+       "      <td>0.000175</td>\n",
+       "      <td>0.000233</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>805556</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.07000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.665134</td>\n",
+       "      <td>-0.081155</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000139</td>\n",
+       "      <td>0.195813</td>\n",
+       "      <td>-0.000459</td>\n",
+       "      <td>-0.008914</td>\n",
+       "      <td>7.226519e-05</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>-0.002396</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>-0.000184</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     chr     Pos Ref Alt         RsID  Peak       AF  Inside_Peak    P_Lead  \\\n",
+       "6   chr1  726944   C   G    rs3131979    14  0.74175            1  0.953905   \n",
+       "7   chr1  752721   A   G    rs3131972    17  0.84500            1  0.890193   \n",
+       "8   chr1  755940   C   T    rs3115857    18  0.59380            1  0.302008   \n",
+       "10  chr1  793429   T   C  rs200956863    21  0.72345            1  0.407804   \n",
+       "12  chr1  805556   T   A   rs72631880    23  0.07000            1  0.665134   \n",
+       "\n",
+       "        Beta  ...  ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "6  -0.079065  ...                                           0.000001           \n",
+       "7  -0.011374  ...                                           0.012412           \n",
+       "8  -0.077224  ...                                           0.000004           \n",
+       "10  0.006696  ...                                           0.000526           \n",
+       "12 -0.081155  ...                                           0.000139           \n",
+       "\n",
+       "    ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "6                                            0.802258                \n",
+       "7                                            0.003650                \n",
+       "8                                            0.674640                \n",
+       "10                                           0.072640                \n",
+       "12                                           0.195813                \n",
+       "\n",
+       "    ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "6                 0.026410                   0.010944   \n",
+       "7                 0.338401                   0.047837   \n",
+       "8                -0.026630                  -0.011550   \n",
+       "10                0.055559                   0.018649   \n",
+       "12               -0.000459                  -0.008914   \n",
+       "\n",
+       "    ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "6                                        2.107834e-05   \n",
+       "7                                        1.246855e-02   \n",
+       "8                                        2.854755e-07   \n",
+       "10                                       7.959288e-04   \n",
+       "12                                       7.226519e-05   \n",
+       "\n",
+       "    ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "6                                            0.580367        \n",
+       "7                                            0.333333        \n",
+       "8                                            0.666667        \n",
+       "10                                           0.333333        \n",
+       "12                                           0.505239        \n",
+       "\n",
+       "    enformer_recomputed  DNASE_SAD  DNASE_SAR  IsUsed  \n",
+       "6             -0.001038   0.000100   0.000133    True  \n",
+       "7             -0.344077   0.002493   0.003319    True  \n",
+       "8             -0.007762   0.000022   0.000029    True  \n",
+       "10            -0.012475   0.000175   0.000233    True  \n",
+       "12            -0.002396  -0.000139  -0.000184    True  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+       "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+       "       'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'enformer_recomputed', 'DNASE_SAD', 'DNASE_SAR', 'IsUsed'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/peaks.bed.gz\", sep='\\t', header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>#Chr</th>\n",
+       "      <th>Pos_Left</th>\n",
+       "      <th>Pos_Right</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>GC</th>\n",
+       "      <th>Peak_Height</th>\n",
+       "      <th>P_caQTL</th>\n",
+       "      <th>PMR</th>\n",
+       "      <th>P_eQTL_Coloc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9866</td>\n",
+       "      <td>10709</td>\n",
+       "      <td>1</td>\n",
+       "      <td>57.6422</td>\n",
+       "      <td>4504</td>\n",
+       "      <td>0.005961</td>\n",
+       "      <td>0.000650</td>\n",
+       "      <td>0.001069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>16019</td>\n",
+       "      <td>16819</td>\n",
+       "      <td>2</td>\n",
+       "      <td>54.9313</td>\n",
+       "      <td>700</td>\n",
+       "      <td>0.002610</td>\n",
+       "      <td>0.000197</td>\n",
+       "      <td>0.000396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>28870</td>\n",
+       "      <td>29662</td>\n",
+       "      <td>3</td>\n",
+       "      <td>74.4010</td>\n",
+       "      <td>2234</td>\n",
+       "      <td>0.014269</td>\n",
+       "      <td>0.000830</td>\n",
+       "      <td>0.003304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>235424</td>\n",
+       "      <td>236104</td>\n",
+       "      <td>4</td>\n",
+       "      <td>41.4097</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>0.012361</td>\n",
+       "      <td>0.000636</td>\n",
+       "      <td>0.002777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>237485</td>\n",
+       "      <td>238046</td>\n",
+       "      <td>5</td>\n",
+       "      <td>46.9751</td>\n",
+       "      <td>4162</td>\n",
+       "      <td>0.024590</td>\n",
+       "      <td>0.000912</td>\n",
+       "      <td>0.007109</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   #Chr  Pos_Left  Pos_Right  Peak       GC  Peak_Height   P_caQTL       PMR  \\\n",
+       "0     1      9866      10709     1  57.6422         4504  0.005961  0.000650   \n",
+       "1     1     16019      16819     2  54.9313          700  0.002610  0.000197   \n",
+       "2     1     28870      29662     3  74.4010         2234  0.014269  0.000830   \n",
+       "3     1    235424     236104     4  41.4097         2003  0.012361  0.000636   \n",
+       "4     1    237485     238046     5  46.9751         4162  0.024590  0.000912   \n",
+       "\n",
+       "   P_eQTL_Coloc  \n",
+       "0      0.001069  \n",
+       "1      0.000396  \n",
+       "2      0.003304  \n",
+       "3      0.002777  \n",
+       "4      0.007109  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks[\"#Chr\"] = 'chr'+peaks[\"#Chr\"].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>#Chr</th>\n",
+       "      <th>Pos_Left</th>\n",
+       "      <th>Pos_Right</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>GC</th>\n",
+       "      <th>Peak_Height</th>\n",
+       "      <th>P_caQTL</th>\n",
+       "      <th>PMR</th>\n",
+       "      <th>P_eQTL_Coloc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>9866</td>\n",
+       "      <td>10709</td>\n",
+       "      <td>1</td>\n",
+       "      <td>57.6422</td>\n",
+       "      <td>4504</td>\n",
+       "      <td>0.005961</td>\n",
+       "      <td>0.000650</td>\n",
+       "      <td>0.001069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>16019</td>\n",
+       "      <td>16819</td>\n",
+       "      <td>2</td>\n",
+       "      <td>54.9313</td>\n",
+       "      <td>700</td>\n",
+       "      <td>0.002610</td>\n",
+       "      <td>0.000197</td>\n",
+       "      <td>0.000396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>28870</td>\n",
+       "      <td>29662</td>\n",
+       "      <td>3</td>\n",
+       "      <td>74.4010</td>\n",
+       "      <td>2234</td>\n",
+       "      <td>0.014269</td>\n",
+       "      <td>0.000830</td>\n",
+       "      <td>0.003304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>235424</td>\n",
+       "      <td>236104</td>\n",
+       "      <td>4</td>\n",
+       "      <td>41.4097</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>0.012361</td>\n",
+       "      <td>0.000636</td>\n",
+       "      <td>0.002777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>237485</td>\n",
+       "      <td>238046</td>\n",
+       "      <td>5</td>\n",
+       "      <td>46.9751</td>\n",
+       "      <td>4162</td>\n",
+       "      <td>0.024590</td>\n",
+       "      <td>0.000912</td>\n",
+       "      <td>0.007109</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   #Chr  Pos_Left  Pos_Right  Peak       GC  Peak_Height   P_caQTL       PMR  \\\n",
+       "0  chr1      9866      10709     1  57.6422         4504  0.005961  0.000650   \n",
+       "1  chr1     16019      16819     2  54.9313          700  0.002610  0.000197   \n",
+       "2  chr1     28870      29662     3  74.4010         2234  0.014269  0.000830   \n",
+       "3  chr1    235424     236104     4  41.4097         2003  0.012361  0.000636   \n",
+       "4  chr1    237485     238046     5  46.9751         4162  0.024590  0.000912   \n",
+       "\n",
+       "   P_eQTL_Coloc  \n",
+       "0      0.001069  \n",
+       "1      0.000396  \n",
+       "2      0.003304  \n",
+       "3      0.002777  \n",
+       "4      0.007109  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks['center'] = (peaks['Pos_Left']+peaks['Pos_Right'])/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277128, 10)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peaks.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks_ids = np.arange(277128)+1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "277128"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(peaks[\"Peak\"]==peaks_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls['Peak']=caqtls['Peak']-1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "277127"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls['Peak'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "caqtls['peak_cente'] = caqtls['Peak'].map(peaks['center'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2           565396.0\n",
+       "3           569416.5\n",
+       "6           726976.0\n",
+       "7           752591.5\n",
+       "8           756027.5\n",
+       "             ...    \n",
+       "226341    51021367.0\n",
+       "226342    51059604.0\n",
+       "226343    51066695.5\n",
+       "226344    51082338.0\n",
+       "226345    51222113.5\n",
+       "Name: peak_cente, Length: 151409, dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls['peak_cente']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var_center_dist = abs(caqtls['Pos']-caqtls['peak_cente'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#caqtls = caqtls[var_center_dist<=100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_center_dist.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(151409, 29)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "dbsnp = np.loadtxt('test_rsids.txt', dtype='str')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['rs36197555', 'rs72645889', 'rs12747702', ..., 'rs6002950',\n",
+       "       'rs5751390', 'rs738406'], dtype='<U11')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dbsnp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1078"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(caqtls['RsID'].isin(dbsnp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1078"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(caqtls['RsID'].isin(dbsnp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.stats import rankdata\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "rank_chrom_atac = rankdata(abs(caqtls[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "enf = rankdata(abs(caqtls[\"enformer_recomputed\"]))\n",
+    "colors = np.array(['grey']*caqtls.shape[0])\n",
+    "threshold=3\n",
+    "colors[caqtls[\"Log10_BF\"]>threshold] = 'r'\n",
+    "colors[caqtls[\"RsID\"].isin(dbsnp)] = 'blue'\n",
+    "plt.scatter(rank_chrom_atac[caqtls[\"Log10_BF\"]<-1], enf[caqtls[\"Log10_BF\"]<-1], alpha=0.5, s=1, color=colors[caqtls[\"Log10_BF\"]<-1])\n",
+    "#plt.scatter(rank_chrom_atac[caqtls[\"Log10_BF\"]>threshold], enf[caqtls[\"Log10_BF\"]>threshold], s=0.5, color=colors[caqtls[\"Log10_BF\"]>threshold])\n",
+    "#plt.scatter(rank_chrom_atac[(~caqtls[\"RsID\"].isin(dbsnp)) & (caqtls[\"Log10_BF\"]>threshold)], enf[(~caqtls[\"RsID\"].isin(dbsnp)) & (caqtls[\"Log10_BF\"]>threshold)], s=0.5, color=colors[(~caqtls[\"RsID\"].isin(dbsnp)) & (caqtls[\"Log10_BF\"]>threshold)])\n",
+    "plt.scatter(rank_chrom_atac[caqtls[\"RsID\"].isin(dbsnp)], enf[caqtls[\"RsID\"].isin(dbsnp)], s=0.5, color=colors[caqtls[\"RsID\"].isin(dbsnp)])\n",
+    "\n",
+    "plt.xlabel(\"chrombpnet (ATAC, 572M) rank\")\n",
+    "plt.ylabel(\"Enformer (DNase, 68M)  rank\")\n",
+    "plt.yscale('log')\n",
+    "plt.xscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(151409, 29)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(caqtls.shape)\n",
+    "#caqtls = caqtls[~caqtls[\"RsID\"].isin(dbsnp)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(151409, 29)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "caqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUPRC (Benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "2550 114465\n",
+      "0.022\n",
+      "0.4415095369386685\n",
+      "5\n",
+      "2271 114186\n",
+      "0.02\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:55: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(loc, value, pi)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.45755714633350997\n",
+      "7\n",
+      "2005 113920\n",
+      "0.018\n",
+      "0.4704244060146789\n",
+      "9\n",
+      "1769 113684\n",
+      "0.016\n",
+      "0.47143316836547994\n",
+      "11\n",
+      "1547 113462\n",
+      "0.014\n",
+      "0.4768632302057005\n",
+      "13\n",
+      "1304 113219\n",
+      "0.012\n",
+      "0.4774304577037799\n",
+      "15\n",
+      "1082 112997\n",
+      "0.01\n",
+      "0.4842363165605904\n",
+      "17\n",
+      "884 112799\n",
+      "0.008\n",
+      "0.47479501324872725\n",
+      "19\n",
+      "673 112588\n",
+      "0.006\n",
+      "0.46623846442743355\n",
+      "21\n",
+      "503 112418\n",
+      "0.004\n",
+      "0.443974664011063\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "random_bc = []\n",
+    "chrombpnet = {}\n",
+    "enformer_dnase_roc = []\n",
+    "enformer_dnase_roc_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(3,23,2):\n",
+    "    print(threshold)\n",
+    "    \n",
+    "#     dataf1 = caqtls[caqtls[\"Log10_BF\"]>threshold]\n",
+    "#     dataf1[\"label\"] = 1\n",
+    "#     dataf1 = dataf1[[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\", \n",
+    "#                     \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                      \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                     \"DNASE_SAR\", \"enformer_recomputed\", \"label\"]]\n",
+    "    \n",
+    "#     dataf2 = afrs[afrs[\"label\"]==0]\n",
+    "#     dataf2[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr637xsc.variantscore.csr\"]\n",
+    "#     dataf2[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr637xsc_50m.variantscore.csr\"]\n",
+    "#     dataf2[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr000emt.variantscore.csr\"]\n",
+    "#     dataf2[\"DNASE_SAR\"] = dataf2[\"enformer.encsr000emt.variantscore.sar\"]\n",
+    "#     dataf2[\"enformer_recomputed\"] = dataf2[\"enformer_recomputed\"]\n",
+    "#     #print(dataf2[\"enformer_recomputed\"])\n",
+    "#     dataf2 = dataf2[[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\", \n",
+    "#                     \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                     \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                     \"DNASE_SAR\",  \"enformer_recomputed\", \"label\"]]\n",
+    "\n",
+    "#     dataf = pd.concat([dataf1, dataf2])\n",
+    "    dataf = caqtls[(caqtls[\"Log10_BF\"]<-1) | (caqtls[\"Log10_BF\"]>threshold)]\n",
+    "    dataf[\"label\"] = 0\n",
+    "    dataf.loc[dataf[\"Log10_BF\"]>threshold, \"label\"] = 1\n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(sigs, total)\n",
+    "    \n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,3))\n",
+    "    print(random_bc[-1])\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n",
+    "    print(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer_recomputed\"]))\n",
+    "    enformer_dnase_roc.append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"DNASE_SAR\"]))\n",
+    "    enformer_dnase_roc_sar.append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff80f4c1850>"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "colors = [\"blue\", \"blue\"]\n",
+    "alphas = [0.5, 1.0]\n",
+    "idx=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\"]:\n",
+    "    ax.scatter(np.arange(3,23,2).astype(str), chrombpnet[key], label=key, c=\"blue\", alpha=alphas[idx])\n",
+    "    idx+=1\n",
+    "    \n",
+    "key=\"ChromBPNet DNase (68M)\"\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), chrombpnet[key], label=key)\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_dnase_roc_sar, label=\"Enformer DNase (reported) (68M)\")\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_dnase_roc, label=\"Enformer DNase (recomputed) (68M)\")\n",
+    "\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), random_bc, label=\"random\")\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Average precision\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/eu_caqtls_auprc_compare.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "9851 121766\n",
+      "0.081\n",
+      "0.5316466483102311\n",
+      "5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:55: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6722 118637\n",
+      "0.057\n",
+      "0.5255050166908499\n",
+      "7\n",
+      "4894 116809\n",
+      "0.042\n",
+      "0.5228369439220858\n",
+      "9\n",
+      "3689 115604\n",
+      "0.032\n",
+      "0.5176397006514095\n",
+      "11\n",
+      "2813 114728\n",
+      "0.025\n",
+      "0.5158678909863839\n",
+      "13\n",
+      "2135 114050\n",
+      "0.019\n",
+      "0.5070998068900187\n",
+      "15\n",
+      "1594 113509\n",
+      "0.014\n",
+      "0.5050438096240829\n",
+      "17\n",
+      "1194 113109\n",
+      "0.011\n",
+      "0.49408221158142424\n",
+      "19\n",
+      "844 112759\n",
+      "0.007\n",
+      "0.48310256455439116\n",
+      "21\n",
+      "597 112512\n",
+      "0.005\n",
+      "0.4512487487386577\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "random_bc = []\n",
+    "chrombpnet = {}\n",
+    "enformer_dnase_roc = []\n",
+    "enformer_dnase_roc_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(3,23,2):\n",
+    "    print(threshold)\n",
+    "    \n",
+    "#     dataf1 = caqtls[caqtls[\"Log10_BF\"]>threshold]\n",
+    "#     dataf1[\"label\"] = 1\n",
+    "#     dataf1 = dataf1[[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\", \n",
+    "#                     \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                      \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                     \"DNASE_SAR\", \"enformer_recomputed\", \"label\"]]\n",
+    "    \n",
+    "#     dataf2 = afrs[afrs[\"label\"]==0]\n",
+    "#     dataf2[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr637xsc.variantscore.csr\"]\n",
+    "#     dataf2[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr637xsc_50m.variantscore.csr\"]\n",
+    "#     dataf2[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr000emt.variantscore.csr\"]\n",
+    "#     dataf2[\"DNASE_SAR\"] = dataf2[\"enformer.encsr000emt.variantscore.sar\"]\n",
+    "#     dataf2[\"enformer_recomputed\"] = dataf2[\"enformer_recomputed\"]\n",
+    "#     #print(dataf2[\"enformer_recomputed\"])\n",
+    "#     dataf2 = dataf2[[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\", \n",
+    "#                     \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                     \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "#                     \"DNASE_SAR\",  \"enformer_recomputed\", \"label\"]]\n",
+    "\n",
+    "#     dataf = pd.concat([dataf1, dataf2])\n",
+    "    dataf = caqtls[(caqtls[\"Log10_BF\"]<-1) | (caqtls[\"Log10_BF\"]>threshold)]\n",
+    "    dataf[\"label\"] = 0\n",
+    "    dataf.loc[dataf[\"Log10_BF\"]>threshold, \"label\"] = 1\n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(sigs, total)\n",
+    "    \n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,3))\n",
+    "    print(random_bc[-1])\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n",
+    "    print(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer_recomputed\"]))\n",
+    "    enformer_dnase_roc.append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"DNASE_SAR\"]))\n",
+    "    enformer_dnase_roc_sar.append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fce9e907190>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+PyHETDQaDSsoKFDk5eV9DiDG2DXm/CvgDIBAxpg/Y8wWwEwAP+ge5JyXcc77cc79OOd+AE4DaBGAEAsROicJLYLtNGtNR28Gof379y+nAIQQ8xKJRLx///5l0A44GL/GXI1zztUA4gAcApAKYDfnPIUxtoIxZjQiIt1I+HRtYbw+3gCY9rO5CuXRItguIXQ6+h5MRAEIIcJo+rfWaqxh0nQMY2wkAD/D6znn29p7Huf8IICDzc4Z/VOac36PKX0hAhIqJwktgu0Si+8PumlNCGAd24EJIdar3ZEQxtjXANYDGA3gzqaPSDP3i9xOaBFsl7DW7cDWKCsrS/Lggw8O8vb2Dg0JCQkeO3ZsQFJSkt3+/fudx40bF2Du9vfv3+/s7Ox8h1wuV8hkMsXIkSNlOTk5EgDYuHGjm4uLS4RcLlcMHjw4ZMOGDf1050Ui0bCEhAT90FpgYGBIWlqabVttrVixYkBFRUWr7zW5ubkSiUQydN26df0B4IknnvDRtW1vbz9ULpcr5HK54p///KcLANx7772DIyIi5M3vs3TpUnd/f/8QuVyuCA0NDf7444/djLU3d+5c7x9//LEXAEyZMsVPKpWG6do4deqUAwAsWbLEXXcuMDAwRCwWD7tx44Y4IyPDZvjw4bLBgweHBAQEhKxcuXKA7r5Tpkzxc3BwGFJSUiIybIsxNiw3N1dSW1vLIiMjgxoaGtp6uW47pkzHRAIYxTlfwDl/oenjRXN3jNxGqDBfl3l4iBS/vT4el9c+gN9eH281AYg1FcvTaDSIiYkJuPvuuyuys7OTU1JSUteuXZtz/fp1G1PvoVarO92PyMjISpVKpUxPT1cOGTKkav369fo31Ojo6BKVSqU8fvx42qpVq6TZ2dkSAHB3d69fsWKFR0fa2bx5s3tlZWWr7zXbtm1ziYiIqNqzZ48rAHz99ddZKpVKefDgwYve3t51KpVKqVKplE899VRJYWGhODk52amiokKsVCr1wc+6dev6Hz58uPfZs2dTdf02VpIkLy9PfPbsWafJkydX6s6tWrXqmq6NkSNH1gDAypUrb+jOLV++/Nqdd95Z4e7u3mhjY4MNGzZcy8zMTDlz5kzqF198MeDs2bP2unt5e3vX7dy5sy8ANDY24uTJk84DBgxoAAB7e3s+duzY8s8//9y1I6+ftTMlCEmGducKIeZBhflIGyxdLO/0aTi8/DI8pk6F78svw+P0aXRqkc3+/fudJRIJf/XVVwt05+66666aSZMmVQJAVVWVeNKkSYP8/f1DYmJi/DUaDQBAKpWGzZ8/X6pQKIK//PJLl82bN7vKZDJFYGBgyPz58/XRpqOj45Bnn33WKyAgIGTkyJGyI0eOOEZFRQV5eXmFbd++vU/z/mg0GlRUVIhdXFxaRDZSqVTt4+NTl5GRYQsAEyZMKEtPT3c4f/68XfNr4+Pje99xxx1yhUIRPHny5EFlZWWiVatWDcjPz7cZO3asbPjw4TJjr8eePXtc169fn33jxg2bzMzMNgOxb775pu+9995b+sgjjxRv27ZN/2b+/vvvD9yyZctVV1dXDQC4urpqXnjhhSIjz3eZMGFCeVttNLdz507XadOmFQOAr69vw+jRo6sBwMXFRTN48OCarKwsfTD06KOPFn/33XeuAHDgwAHnO++8s1IikeijoalTp5bu2rWLghADpgQh/QAoGWOHGGM/6D7M3TFyGxFyESzpMnvP5WDU2sPwf/0ARq09jL3nctp/0i14802guvrmc9XV2vPmdvo0HN59F+6lpZB4eaGhtBSSd9+Fe2cCkaSkJIeIiIjq1h5PTU112LRpU3ZGRkZKVlaW3S+//NJL95ibm5taqVSmTpw4sXLZsmXSo0ePpiuVypRz5845ff31130BoKamRjRhwoTyjIyMFCcnp8a33npLeuLEifQ9e/ZkrFy5Uh+sJCYm9pLL5QpPT8/wEydOOMfFxRU274tSqbTNzs62UygUdQAgEomwcOHCvOXLl980GpKbmytZs2aNx/Hjx9OVSmXq0KFDq1euXOn+1ltv5Q8YMKDh2LFj6QkJCenN75+RkWFTUFBgM27cuOqYmJgSw8DCmN27d7s+/vjjxXPmzCmOj493BYDi4mJRVVWVWKFQ1Lf1XAA4depUr8jIyCrDc8uXL5fKZDLF008/7V1TU3NTwcOKigrR8ePH+zz++OMlze+VlpZmq1QqHceOHasfVQkKCqorKiqSFBQUiHfs2OH62GOPFRs+584776xJSkpyaq+ftxNTgpBlAB4GsAbABoMPQrpO+HRgUTKwrFT7mQKQbk2XGC2ntAYc/0uMZo5AxJLF8nbvRl8XFzT27YtGkQjo2xeNLi5o3L0bfc3VZlhYWNXgwYMbxGIxQkJCqjMzM/V/acfGxpYAwMmTJ51GjBhR4enpqbaxscGMGTOKjx071gsAbGxs+NSpU8sBICQkpGb06NEVdnZ2PCoqqiYnJ0d/L910TF5eXtJjjz1WFBcXp5//3Ldvn4tcLlfMnDlz0AcffHDV3d1dv9r52WefLfrjjz96qVQq/b2OHj3qlJmZaR8VFSWXy+WKXbt2uRmOELRm27ZtrjExMSUA8MQTTxR///33rQYh2dnZkqtXr9pPnDixMjw8vE4ikfAzZ87Yt3a9MTdu3LBxd3fXj/j84x//yLl06VLy+fPnU0tKSsRLliy5adR/165dfYYNG1Zp+P0DQFlZmejRRx8dvHbt2mzd6ItOdHR0yZdffun6xx9/OOlGt3QkEglsbGy44bqR2127u2M458cYY+7QLkgFgN8550YznxFCbg9CJkbz8dFOwRg7b25ZWbD18sJNKwl790ZjVhbafYNtTVhYWM3evXtdWnvczs5OP3wvFouhVqv1f507OztrjD/rfyQSCReJtO9xIpFIfz+xWIzGxkZm7DlTpkwpnTZt2mDdcXR0dMm2bduMhnk2NjaIi4vLW7Fihf4Nm3OO0aNHl+/bt+9ye/0z9P3337sWFBTY6EY18vPzbS5cuGAXFhZW1/zabdu2uZaXl4u9vb3DAKCyslK8bds2t48++ijH0dFRo1QqbdsbDbG3t9fU1NToAwBfX98GAHBwcOBz584t2rBhg7vh9bt373adPn36TaMZdXV17IEHHhg8bdq04jlz5pQ2byM2NrZk+PDhwVOnTi0Si8XNH0ZDQwNzdHSkLeJNTNkdMx3A7wCmAZgOIIExNtXcHSOEdF9CJkazZLE8Hx/Ul5fjpneS8nKIfXzQ7tB/a6Kjoyvq6+vZ+vXr++nOJSQkOPz000+92nqeoTFjxlQlJCQ45+bmStRqNfbs2eN6zz33VLb/TOOOHDnSy9fXt8Ubf2vi4uKKTp482bu4uFgCAPfcc09VYmJir+TkZDsAKC8vFyUlJdkBgJOTU2NZWVmL95qkpCS7qqoqcX5+flJOTs6FnJycC3FxcXlbt241Ohry3Xffuf7rX/+6qLs2ISFBqQvmXnrppdznnnvOt7i4WARoRyqM7Y4JCgqqTU9P169nuXr1qg2gXRcTHx/fNzg4WP8LXFRUJP7999+dH3vssVLdOY1Gg5kzZ/rKZLLaZcuW3TDWT5lMVv/3v/8956WXXipo/lheXp64b9++asNA83ZnypDQmwDu5JzP4ZzHQlsdd4l5u0UI6c6ETIw2ezawZQvg6wswpv28ZYswdWumT0dpSQnEpaUQazRAaSnEJSUQT5+O0lu9p0gkwg8//JB5+PDh3t7e3qEBAQEhr732mlQqlZq8d9PX17fh7bffzhk7dqwsODg4JCIiourxxx/vUJ90a0KCgoIUO3fudPvggw+M1Gkwzt7ens+bNy9fF4R4enqqN2/efGXmzJmDZDKZIjIyUn7hwgV7AJgzZ07hpEmTWixM3bp1q+tf/vKXm9ZazJw5s0Q3KmIoLS3NNicnx3b8+PH69Rxyubze2dm58fDhw06vvvpqwd13310+dOhQRWBgYMiIESPkxhLSxcTElB07dsxZdzxjxgx/mUymCAoKCikqKpK88847ubrHtm/f3nfMmDHlvXv31o8+/fLLL7327t3rdvLkSWfdFt5vv/22xWLfxYsXF4aEhLQI6n788cfe9957b1nrr+zthxnbxnTTBYxd4JyHGRyLAJw3PCekyMhInphImd1JJyXt1iZDK7um3Qo8YSmtQ+mA5sXyAG1itG6Yl6TF9MP58+evREREtFiE2ZrTp+Gwezf6ZmXB1scH9dOno3TECFAu/B5q2LBhQYcOHcro169fY/tXd62JEycOXr9+/bXw8HCTR52swfnz5/tFRET4GXvMlIypPzHGDgHY2XQ8A82yoBLSo+jSxOuytOrSxAMUiJhIF2i8dygN10tr4NnXAYvvD+puAUiXGDECNRR0WI/33nvvWmZmpm2/fv0E/ZnW1taymJiY0tstAGlPuyMhAMAYmwJgVNPhCc75v8zaqzbQSAjptPdDW6kQ7K3dmUOsSadHQgghndPZkRBwzr8H8H1XdooQi6E08YQQ0i20ujCVMXay6XMFY6zc4KOCMdahjHOEdCuUJp4QQrqFVkdCOOejmz47t3YNIT3ShKU3rwkBKE18N7f3XM5tsf6EkNuNKXlCBjPG7Jq+vocx9iJjrK/Ze0aIuVCa+B5FyOyshBBhmZIn5HsAjYyxAABbAHgD2GHWXhFibkKmiU/arV0Mu6yv9nPSbvO1ZYXays7aU2VlZUkefPDBQd7e3qEhISHBY8eODUhKSrLbv3+/87hx4wLM3f7+/fudnZ2d75DL5QqZTKYYOXKkLCcnRwIAGzdudHNxcYmQy+WKwYMHh2zYsKGf7rxIJBqWkJCgTwYTGBgYkpaW1mb22BUrVgyoqKho9b0mNzdXIpFIhq5bt64/ADzxxBM+urbt7e2H6vJx/POf/3QBgHvvvXdwRESEvPl9li5d6u7v7x8il8sVoaGhwcaSlQHA3LlzvX/88cdeADBlyhQ/qVQapmvj1KlTDoA2KdmTTz7p7ePjEyqTyRQnT550BLT5Shhjw1588UXP5v2PjY31AYA1a9b0/+CDD4y2TVoyJQjRcM7VAB4B8BHnfDGADpVyJuS2pdsOXJYNgP9vOzAFIiYTMjurEDQaDWJiYgLuvvvuiuzs7OSUlJTUtWvX5ly/fr3NCrKG1OoWBW87TFc7Jj09XTlkyJCq9evXD9A9Fh0dXaJSqZTHjx9PW7VqlTQ7O1sCAO7u7vUrVqzo0P//mzdvdq+srGz1vWbbtm0uERERVXv27HEFgK+//jpLpVIpDx48eNHb27tOpVIpVSqV8qmnniopLCwUJycnO1VUVIiVSqU++Fm3bl3/w4cP9z579myqrt/Gdn7m5eWJz5496zR58mR9dtlVq1Zd07UxcuTIGgDYs2dPn0uXLtlfuXIl+dNPP726YMECfZEAqVRa/8svv/Q17H9AQECt7viFF14o2rx5803p30nrTAlCGhhjswDMAbC/6ZzJ/1gIua39uuLmtSeA9vjXFZbpTw8kZHZWYz77DK6enggTiTDM0xNhn32GTpVi379/v7NEIuGvvvqqPq33XXfdVaMrdlZVVSWeNGnSIH9//5CYmBh/jUabsFMqlYbNnz9fqlAogr/88kuXzZs3u8pkMkVgYGDI/Pnz9QtkHB0dhzz77LNeAQEBISNHjpQdOXLEMSoqKsjLyyts+/btLbJ7ajQaVFRUiF1cXFpENlKpVO3j41OXkZFhCwATJkwoS09Pdzh//rxd82vj4+N733HHHXKFQhE8efLkQWVlZaJVq1YNyM/Ptxk7dmyLjKk6e/bscV2/fn32jRs3bDIzM9t8b/nmm2/63nvvvaWPPPJIsWHF3ffff3/gli1bruqKybm6umpeeOGFIiPPd5kwYUK7Gyv+/e9/9509e3aRSCTChAkTqsrLyyW6FO8ODg6agICAmuPHjzsC2vo3Dz/8sL6+jLOzs8bLy6vuyJEjjq3dn/yPKUHIUwDuArCac36ZMeYP4GvzdosQKyH0dmArnPpZfH8QHGxuLgTmYCPG4vuDzN72Z5/BddEi+ObmwpZzIDcXtosWwbczgUhSUpJDREREdWuPp6amOmzatCk7IyMjJSsry+6XX37R15Rxc3NTK5XK1IkTJ1YuW7ZMevTo0XSlUply7tw5p6+//rovANTU1IgmTJhQnpGRkeLk5NT41ltvSU+cOJG+Z8+ejJUrV+qDFV3adk9Pz/ATJ044x8XFtcidolQqbbOzs+0UCkUdoE05v3Dhwrzly5ffNBqSm5srWbNmjcfx48fTlUpl6tChQ6tXrlzp/tZbb+UPGDCg4dixY+kJCQnpze+fkZFhU1BQYDNu3LjqmJiYEsPAwpjdu3e7Pv7448Vz5swp1qV3Ly4uFlVVVYnbK14HAKdOneoVGRlZZXhu+fLlUplMpnj66ae9a2pqWNP3Y+Pn56e/n4eHR70uCAGAmTNnFn/zzTeuGRkZNmKxmHt6et6Ucn/o0KFVR48epU0dJmg3COGcKznnL3LOdzYdX+acv2v+rhFiBYTcDmylUz8PD5HinUfDIO3rAAZA2tdBsPTwK1ZAWlt78/+TtbUQrVgBszUeFhZWNXjw4AaxWIyQkJDqzMxM/bRDbGxsCQCcPHnSacSIERWenp5qGxsbzJgxo/jYsWO9AMDGxoZPnTq1HABCQkJqRo8eXWFnZ8ejoqJqcnJy9PfSTcfk5eUlPfbYY0VxcXH6X8p9+/a5yOVyxcyZMwd98MEHVw1L2T/77LNFf/zxRy+VSqW/19GjR50yMzPto6Ki5HK5XLFr1y63rKysdisNb9u2zTUmJqYEAJ544oni77//vtUgJDs7W3L16lX7iRMnVoaHh9dJJBJ+5swZe1NfVwC4ceOGjbu7u37E5x//+EfOpUuXks+fP59aUlIiXrJkycC2nq8zZcqU8mPHjvXeunWr65QpU4qbPz5gwAB1R6bXbmetbtFljO3mnE9njF0AYDi5xgBwznm42XtHSE8n5HbgtqZ+evjOn4eHSC2yJTcvD0bfSFs7b4qwsLAaXfVXYwwrrIrFYqjVan3WV2dnZ43xZ/2PRCLhIpE2bhKJRPr7icViNDY2tsggCwBTpkwpnTZt2mDdcXR0dMm2bduyjF1rY2ODuLi4vBUrVujfsDnnGD16dPm+ffsut9c/Q99//71rQUGBjW5UIz8/3+bChQt2YWFhLVKbb9u2zbW8vFzs7e0dBgCVlZXibdu2uX300Uc5jo6OGqVSadveaIi9vb2mpqZGH1T6+vo2AICDgwOfO3du0YYNG9wBwMPDo+HKlSv6n3Fubq6tr69vQ21tLWu6Dw8PD6/+9NNPB6akpCR/++23fQ3bqa2tFTk4OLT7syJtj4QsbPr8IIBogw/dMSGkPUJuB6ZMsF1u4EAYfVNr7bwpoqOjK+rr69n69ev76c4lJCQ4/PTTT73aep6hMWPGVCUkJDjn5uZK1Go19uzZ43rPPfdUtv9M444cOdLL19fX5JomcXFxRSdPnuytq6J7zz33VCUmJvZKTk62A4Dy8nJRUlKSHQA4OTk1lpWVtXivSUpKsquqqhLn5+cn5eTkXMjJybkQFxeXt3XrVqOjId99953rv/71r4u6axMSEpS6YO6ll17Kfe6553yLi4tFAFBWViYytjsmKCioNj09Xb+eRTfFotFoEB8f3zc4OLgGAGJiYkq3b9/uptFo8Ouvvzo5Ozs36gIWnddeey3v7bffvmY4SqSTnp5uFxoa2jNXTgus1SCEc55rcM0NzvlVzvlVAPkwUo+BENIKobYDUybYLrd0KXLs7XHTX7T29tAsXYpbTlIiEonwww8/ZB4+fLi3t7d3aEBAQMhrr70mlUqlDe0/W8vX17fh7bffzhk7dqwsODg4JCIiourxxx8v7Ug/dGtCgoKCFDt37nT74IMPjBRUMs7e3p7PmzcvXxeEeHp6qjdv3nxl5syZg2QymSIyMlJ+4cIFewCYM2dO4aRJk1osTN26davrX/7ylxLDczNnzizRjYoYSktLs83JybEdP368fj2HXC6vd3Z2bjx8+LDTq6++WnD33XeXDx06VBEYGBgyYsQIuUgkarE9JiYmpuzYsWP6tRozZszwl8lkiqCgoJCioiLJO++8kwsA06dPL/P19a3z9fUNnT9/vu+mTZuuNr9XZGRkrbHFrwBw5syZXjExMZRZ3ATtFrBjjCUCGMk5r286tgXwG+f8TgH61wIVsCOkFc2rAwPaqZ/bOxFbpwvYffYZXFesgDQvD7YDB6J+6VLkPPccWqwDID3DsGHDgg4dOpTRr1+/FiMYXeG3335zeO+99wbu3bu3Q1NT1qyzBewkugAEADjn9U2BCCGkO9EFGr+u0E7B9PHSrj25fQOQLvHccyimoMN6vPfee9cyMzNt+/XrZ5bpkvz8fJt3332X0vmayJQgpIAxFsM5/wEAGGMPAaAy2IR0R+HTKeggpA2GUzrm8Mgjj9A0TAeYEoQ8B2A7Y2wTtLtkrgGINWuvCCGEEGL12g1COOeZAEYwxno1Hd/yCmxCCCGEEB1Tqui6M8a+ALCHc17JGFMwxp4WoG+EkO7MCrOzEkKEZUra9q8AHAKgqxqYDuAlM/WHENITWGl2VkKIsEwJQvpxzncD2r3yTRV1zbK1iRDSQ1Bhvk7JysqSPPjgg4O8vb1DQ0JCgseOHRuQlJRkt3//fudx48YFmLv9/fv3Ozs7O98hl8sVMplMMXLkSFlOTo4EADZu3Ojm4uISIZfLFYMHDw7ZsGFDP915kUg0LCEhQV85MDAwMCQtLa3N3ZIrVqwYUFFRYfS9JioqKsjPzy9UJpMp/P39Q2JjY30KCwv1hYIYY8OeeeYZfaKbpUuXur/88suexu7VFTQaDUaMGCHTJT0rLCzUFxMcNGhQyH/+8x8nADh16pRDRESEXC6XK0JDQ4N1xeo2btzoxhgbtnfvXn0ukq+//rovY2zYP//5TxcAePDBBwdduHChRQHA25UpQUgVY8wNTanbGWMjAJSZtVeEkO6NsrPeMo1Gg5iYmIC77767Ijs7OzklJSV17dq1OR2pNaJWtyh422G62jHp6enKIUOGVK1fv36A7rHo6OgSlUqlPH78eNqqVauk2dnZEgBwd3evX7FihUfrd21p8+bN7pWVla2+12zbtu1Senq6MjU1VWlnZ6eZPHmyPgiztbXlBw8edMnNzTVlE0Wn7d69u09ISEiNrhrvvHnzvCdOnFh++fLlFKVSqbzjjjtqAWDx4sVeb7755nWVSqVcsmTJ9ddee81bd4/AwMCanTt36hOu7dq1yzUoKEgfsc+fPz9/9erVJtWouR2YEoS8DOAHAIMZY78B2AbgBbP2ihDSvd1G2Vm/OX3VNWr1f8L8Xz8wLGr1f8K+OX31livoAtpRCIlEwl999dUC3bm77rqrZtKkSZUAUFVVpf/rOyYmxl+j0SZslUqlYfPnz5cqFIrgL7/80mXz5s2uMplMERgYGDJ//nx9YR1HR8chzz77rFdAQEDIyJEjZUeOHHGMiooK8vLyCtu+fXuf5v3RaDSoqKgQu7i4tIhspFKp2sfHpy4jI8MWACZMmFCWnp7ucP78+RZ/ycfHx/e+44475AqFInjy5MmDysrKRKtWrRqQn59vM3bs2BYZU5uzt7fnn3766bXr16/b/ve//3UAALFYzGNjYwvWrFnj3vz6HTt29AkPD5cHBwcrRo4cKdMFSgcOHOgll8sVcrlcERwcrCgpKREBwJIlS9xDQ0ODZTKZYtGiRUZHU7Zv3+76yCOPlAJAUVGROCEhwfmll14q1PVPl+CMMYaysjIxAJSWlord3d31ubSGDx9eee7cOae6ujpWVlYmunLlil1ISIi+avKkSZMqT5w40buhweQEuVatzSCEMSYGMLbpYySAZwGEcM6TBOgbIaS7mrBUm43VkLkK81nQN6evuq7cr/TNr6iz5QDyK+psV+5X+nYmEElKSnKIiIiobu3x1NRUh02bNmVnZGSkZGVl2f3yyy/6mjJubm5qpVKZOnHixMply5ZJjx49mq5UKlPOnTvn9PXXX/cFgJqaGtGECRPKMzIyUpycnBrfeust6YkTJ9L37NmTsXLlSn2wokvb7unpGX7ixAnnuLi4FvmflEqlbXZ2tp1CoagDtCnnFy5cmLd8+fKbRkNyc3Mla9as8Th+/Hi6UqlMHTp0aPXKlSvd33rrrfwBAwY0HDt2LD0hISG9vddGIpEgODi4Ojk5WV8dd/Hixfnx8fGuRUVFYsNr77vvvso///xTlZqaqpw6dWqxrqDehg0bBm7cuPGqSqVSnj59WtWrVy9NfHx874yMDPukpKTU1NRU5Z9//un4448/tqjVc/bs2V6jRo2qArSp4l1dXdXTpk3zCw4OVsyYMcO3vLxcBAAbN27MXrp0qdfAgQPDlyxZ4rVhwwZ9cjLGGO6+++7y+Pj43jt27Og7adKkUsM2xGIxfH19a0+fPu3Y3utxO2gzCOGcNwKYxTlXc85TOOfJnHMK3wi53QlZmM+CNv56UVqn1tz0/2SdWiPa+OtFs5X0DQsLqxo8eHCDWCxGSEhIdWZmpn7NRWxsbAkAnDx50mnEiBEVnp6eahsbG8yYMaP42LFjvQDAxsaGT506tRwAQkJCakaPHl1hZ2fHo6KianJycvT30k3H5OXlJT322GNFcXFx+mGsffv2ucjlcsXMmTMHffDBB1cNi7Q9++yzRX/88UcvlUqlv9fRo0edMjMz7aOiouRyuVyxa9cut6ysrFvKrN28lIirq6tm2rRpRWvXrh1geP7y5cu2Y8aMCZTJZIqNGzcOVKlUDgAwYsSIyldeecV71apVAwoLC8U2Njb46aefeh8/fry3QqFQhISEKDIzM+1VKpU9mikrK5O4uLhoAECtVrPU1FTH559/viA1NVXp6OioWbJkyUAA2LhxY/933nknOy8vL2nNmjXZTz75pJ/hfWbPnl28a9cu1z179rjOmTOnRbbdfv36qbOzs02efrNmpkzH/MYY+5gxNoYxNlT3YcrNGWOTGGNpjLEMxtjrRh5/jjF2gTH2J2PsJGNM0eHvgBBiGUIV5rOggoo6o2+krZ03RVhYWM358+db/SvYzs5O/y4sFouhVqv19W+cnZ3bLQ8vkUi4SKT9r10kEunvJxaL0djYaLT46JQpU0oTEhL0iyl1a0KSkpJUsbGxpYbX2tjYIC4uLk838gBoA4fRo0eXq1QqpUqlUmZmZqbs3r27RdG39qjVaqSlpTmGh4fXGp5/4403buzYsaNfVVWV/j0rLi7OZ8GCBfnp6enKjz/++GpdXZ0IANasWZP3+eefX62pqRGNGTNGfu7cOXvOOV566aVcXf+ysrKSFy1a1GLkRywW88ZGbbzl5+dX7+7uXq/LsDpjxowS3c/t+++/d9O9LnPnzi1JSkpyMrzPuHHjqlUqlUNxcbEkPDy8RXXiuro6kaOjY7s/y9uBKUHIHQBCAKwAsKHpY317T2qaytkEYDIABYBZRoKMHZzzMM75HQDWAfiHyT0nhBAz6+9sV9+R86aIjo6uqK+vZ+vXr++nO5eQkODw008/tZgeaM2YMWOqEhISnHNzcyVqtRp79uxxveeee245keSRI0d6+fr6tnizbE1cXFzRyZMne+uq6N5zzz1ViYmJvZKTk+0AoLy8XJSUlGQHAE5OTo1lZWXtvtfU1dWxuLg4Lw8Pj/rhw4fftPXK3d29MTo6umTHjh3616yiokLs4+PTAABfffWVm+58SkqKXVRUVM3q1avzwsPDq5KTk+0nT55c/vXXX/fT9ePy5cs2ut1Ahvz9/WtTU1PtAMDHx0c9cODAet36l59//rl3UFBQLQD079+/4eDBg84AsG/fPmdfX9/a5vdauXLltZUrVxqtIXP58mW7oUOHmqV2TU9jSsbUcbd47ygAGZzzSwDAGNsF4CEASoN7G+bYd0LTDhxCCOkOXpwQmLNyv9LXcErGTiLSvDgh8JYLlIlEIvzwww+ZCxYs8P7www8H2tnZcS8vr7qPPvoo++rVqyaNsPj6+ja8/fbbOWPHjpVxztm9995b+vjjj5d2pB+6NSGcczg7Ozd++eWXV0x9rr29PZ83b17+kiVLvAHA09NTvXnz5iszZ84cVF9fzwDg7bffzgkPD6+bM2dO4aRJk2Tu7u71xtaFxMbGDrK1tdXU19eLxowZU/7jjz9mGGvzzTffzNu6dWt/g+Prs2bNGtynTx/16NGjK7KysuwAYN26dQNOnTrVmzHGg4KCaqZOnVrm4ODAU1JS7O+88045ADg6Omq2b99+WSqV3rQYd+LEiWU///yzc2hoaB0AfPTRR1mzZ88eVF9fz3x8fOp27tx5BQA+/fTTqy+//LL33/72N2ZnZ6f57LPPWoz6TJ8+3WgNmezsbImdnR338fHp/BYnK8Caz7+1uEC7PfdtAKOhDRJOAljBOS9q53lTAUzinP+16fgJAMM553HNrnse2h04tgDGc84vGrnXPADzAMDHx2fY1asdHuUjhNyeWkw/nD9//kpERITJRTi/OX3VdeOvF6UFFXW2/Z3t6l+cEJjz+Ahfqqprha5evWoza9Ysv1OnTrV4H+oqy5cvH9C7d2+Nsekga3X+/Pl+ERERfsYeM2Xv9S4AxwFMaTqeDeBbAPd2Rec455sAbGKMPQbgLQBzjFyzBcAWAIiMjKTREkKIYB4f4VtMQcftwdfXt2Hu3LmFxcXFIl2ukK7Wt2/fxgULFrT5R/ztxJQgxINzvtLgeBVjbIYJz8sB4G1w7NV0rjW7AHxqwn0JIYQQs/jrX/9aYs77L1y4kAIQA6YsTP2ZMTaTMSZq+pgObS2Z9pwBEMgY82eM2QKYCW3SMz3GWKDB4QMAzDYERgjpwahYHiFWyZSRkGegLVj3ddOxGNpU7s8C4Jzz3saexDlXM8bioA1YxAC+5JynMMZWAEjknP8AII4xdi+ABgAlMDIVQwi5zemK5elq1eiK5QFWuS2YkNuJKbtjnNu7po3nHgRwsNm5pQZfL7zVexNCbhNtFcujIISQHs2U6RhCCLEcKpZHiNWiIIQQ0r1ZYbE8sVg8TFdkTS6XK/7+97+3WVW1pqaGjRw5UiaXyxX/93//5yJUP9sSFRUV5OfnFyqTyRT+/v4hsbGxPoWFhfr6LoyxYc8884z+h7R06VL3l19+2WjhuK6g0WgwYsQIWXFxsaDvaxs3bnS7cuVKh1Kwp6Wl2QYGBoYAwO+//+4wZcoUv9au/e233xymT5/uqzvev3+/s1wuVwQEBITceeedQbrzy5cvHxAQEBASGBgYEh0d7V9dXc0A7c/Jw8MjTFcIEQDuvffewY6OjkMA4Pr165IxY8YYrs8UFAUhhJDuzQqL5dnZ2Wl0KcRVKpVyzZo1eW1df+rUKUcAUKlUymeeecak3RtqddflwtJoNNClMze0bdu2S+np6crU1FSlnZ2dZvLkyQG6x2xtbfnBgwddcnNzTVl72Gm7d+/uExISUtN8a21rfe8KarUa33zzTb+srKxbrgMTFRVVk5uba3vx4kWjiepWrVrlsWjRonwAKCwsFC9cuNBn3759GRkZGSl79+7NBLQZYLds2eL+559/Ki9evJjS2NjIPv/8c32RRWdn50ZdIcTCwkJxfn6+vr+enp5qd3f3hp9//tmpedtCMCkIYYyNZow91fR1f8aYv3m7RQghTSxdLO/MF65YLwvDsr7DsF4WhjNf3HIF3fZIpdKwRYsWeSoUimCZTKY4d+6cfU5OjuSpp57yv3DhgqNcLlekpKTY/fvf/3YODg5WyGQyxbRp0/xqamqY7vnz58+XKhSK4C+//NJFKpWGPf/881K5XK4IDQ0NPnnypOPo0aMDvb29Q9etW6fPPmqszH1aWpqtn59f6COPPOInk8lCDAvpNWdvb88//fTTa9evX7f973//6wBo67DExsYWrFmzxr359Tt27OgTHh4uDw4OVowcOVKWnZ0tAYADBw700o0OBQcHK0pKSkSt9a+57du3uz7yyCOlrfW9te/R398/JCYmxn/QoEEhkyZNGlRRUSECAFNe4y1btrgmJyc7xsbGDpLL5YrKykp24sQJxzvvvDMoJCQkePTo0YFXr161AYATJ044BgUFKYKCghT/+Mc/birGN3ny5NKtW7e2GOEqKSkRpaamOt511101APD555+7PvDAAyWBgYH1TX3RR5qNjY2sqqpK1NDQgJqaGpGXl5e+2Oyjjz5avH37dlcA+Oabb/pGR0eXGrbz8MMPl27bts0NFtBuEMIYexvAawDeaDplA+Abc3aKEEJuYqlieWe+cMWhN3xRecMW4EDlDVscesO3s4FIXV2dyHA6xnCKpV+/fmqlUpk6d+7cgrVr17pLpVL1J598clVX9dbf37/+2Wef9f/2228z09PTlWq1Gu+9954+oHBzc1MrlcrUefPmlQCAj49PvUqlUg4fPrxy7ty5fvv27ctMSEhQvfvuu54A0FaZ+6ysLLu4uLiCjIyMFJlM1ma9HIlEguDg4Ork5GR9ddrFixfnx8fHuxYVFYkNr73vvvsq//zzT1Vqaqpy6tSpxbpieBs2bBi4cePGqyqVSnn69GlVr169NG31z9DZs2d7jRo1qkp3bNj35ORk+9buceXKFfu4uLj8S5cupTg7O2vee++9/tXV1cyU13jBggXFoaGh1du2bbukUqmUNjY2ePHFF33+/e9/Z6akpKTOmTOn8JVXXpECwNNPP+33wQcfZKWlpSmb93348OFVp06darEJ5OTJk05BQUH6Vdnp6en2JSUlkqioqKCQkJDgjz/+2A0A/P39G55//vk8f3//8AEDBkQ4Ozs3Pvroo/q08RMnTqw4ffp0L12dodjY2JuS740aNarq999/N7l2UVcyZZjsEQBDAPwBAJzz64yxW94xQwghPcaxd6VQ1938x5q6ToRj70px59O3nEVVNx1j7LHHHnusBACioqKqf/jhhxZ/HZ8/f97ey8urTled9cknnyzatGnTAAD5ABAbG3vTdM306dNLASAsLKy6qqpK5OLionFxcdHY2tpqCgsLxYZl7gGgurpapFKp7AcNGlTv4eFRP2HChCqYqHkZEFdXV820adOK1q5dO8DBwUE/TXL58mXbhx9+2KugoMCmvr5e5O3tXQcAI0aMqHzllVe8p0+fXjxr1qySwYMHa1rr3+TJk28q2FdWViZxcXHRt2HY97a+x4EDB9ZPnDixCgCeeOKJoo0bNw44f/58eUdeY52kpCS7ixcvOowfP14GaKeC+vfv31BYWCiuqKgQ6/o8d+7cosOHD/cx6Kv6xo0bLaZ0cnJybNzc3PQjGmq1miUlJTmeOHEivaqqSjRixAj53XffXenh4aE+cOBA34yMjAtubm6NDzzwwKBPPvnEdcGCBcWAtrJyVFRU5f/93/+51tbWioKCgm4KKD09PdX5+fm3XBm6M0wJQuo555wxxgGAMWaReSNCCBFcZSv/Mbd2vgvY29tzQPvGoVarW9S+aY+zs/NNayJ09xOJRLC1tdVHCSKRCA0NDUxX5n7x4sU31TJJS0uz7Ui5ebVajbS0NMfw8PDrhuffeOONG0OHDlXMnDlTf/+4uDifhQsX5s2ePbts//79zitWrPAEgDVr1uQ9/PDDZf/+97/7jBkzRn7gwIGLrfWvObFYzBsbGyEWawddDPve1vfI2M0vcfNjY5q/xgbtsICAgJo///xTZXjecMGuMTU1NSJ7e/sW93R0dNTU1f0vCPby8qp3c3NT9+7dW9O7d2/N8OHDKxITEx0BwMfHp87T01MNaKdXTp061UsXhADA7Nmzi2fNmhWwePHi683bqa6uZnZ2dmZJU98eU9aE7GaMbQbQlzH2DID/APg/83aLEEK6gV4DjE9BtHZeABEREbU5OTm2ycnJdgCwbds2tzFjxlTc6v1MLXPflrq6OhYXF+fl4eFRP3z48JuSuri7uzdGR0eX7Nixo5/uXEVFhdjHx6cBAL766iv9WoSUlBS7qKiomtWrV+eFh4dXJScn25vaP39//9rU1FS7jn6Pubm5tv/5z3+cAO26kpEjR1Z25DXu1atXY1lZmRgAwsPDa4uLiyW6+9XV1bHExET7fv36NTo7OzceOnSoV9P3fNN0nlKptDOcdtEJCwurvXLliv57mjp1aunp06d7NTQ0oKKiQnTu3LleYWFhNX5+fvV//PFHr4qKCpFGo8Hhw4edg4ODaw3vdf/991e++OKLuXPnzm0xgpecnGwvk8latC8EU5KVrWeM3QegHEAQgKWc81/M3jNCCLG0sa/l4NAbvjdNyUjsNBj7Wlt1sNqlWxOiOx4/fnzZJ598YtI9HR0d+WeffXZl2rRpgxsbGxEREVH9yiuvFNxqXx599NFyY2XuJRJJu8VCY2NjB9na2mrq6+tFY8aMKf/xxx8zjF335ptv5m3durW/wfH1WbNmDe7Tp4969OjRFVlZWXYAsG7dugGnTp3qzRjjQUFBNVOnTi1zcHDgxvpnuCgTACZOnFj2888/O4eGhtZ15Hv08/Or/eijjwbMmzfPMTAwsPaVV14p6MhrHBsbW/jCCy/4Ll68WJOYmJi6a9euzBdffNGnoqJC3NjYyObPn38jMjKy9osvvrjy17/+1Y8xhnvuuafc8B6HDx/u/eCDD5Y1v/eQIUNqKyoqxCUlJSIXFxfN0KFDa++9994yuVweIhKJ8MQTTxTceeedtQAQHR1dEh4eHiyRSBASElL98ssv39RfkUiEFStW3DD2Pfzyyy/OkyZNatG+EFjzObzuLjIykicmJlq6G4SQnqHF2Pr58+evREREmF5G/cwXrjj2rhSV+bboNaAeY1/L6cx6EGIeV69etZk1a5bfqVOnTK5BlpaWZvvggw8GXrx4McWcfWtLTU0NGzFiRFBiYqLKxqblTt/ly5cPcHZ21rz88sum/852UGRkZNCPP/6Y0b9/f7PsZT5//ny/iIgIP2OPtTsSwhirANA8UikDkAjgb5zzS53uISGEdFd3Pl1MQUf35+vr2zB37tzC4uJiUfNcId1ZRkaG7erVq3OMBSAAsHjx4oJ//vOfZktQd/36dcnChQtvmCsAaU+7IyGMsZUArgHYAe1fFTMBDIZ2t8x8zvk9Zu7jTWgkhBDSAZ0fCSGEdEpbIyGmLEyN4Zxv5pxXcM7LOedbANzPOf8WQLdIH0wIIYSQnseUIKSaMTadMSZq+pgOQLfqtmctKCGEEEJIt2FKEDIbwBPQJmm50fT144wxBwBxZuwbIYQQQqyYKVt0LwGIbuXhk13bHUIIIYTcLkypHWPPGHueMfYJY+xL3YcQnSOEEGskFouHGdaO+fvf/z6wretramrYyJEjZc3rzFhSVFRUkJ+fX6hMJlP4+/uHxMbG+hhmBmWMDXvmmWe8dMdLly51f/nll40Wn+sKGo0GI0aMkBUXF/e46vCnTp1y+Pbbb/u0f+XNoqKigo4fP+4IACNHjpQVFBQYzcza/LUpLCwUT5o0aZC/v3/IoEGDQnTJ1U6dOuUQEREh1xU8PHLkiCMAbNy40Y0xNmzv3r36ki1ff/11X8bYMN3OnQcffHDQhQsXjCaLa4spP6yvAQwEcD+AYwC8ANxydj5CCLnd6WrH6D7WrFmT19b1p06dcgQAlUqlfOaZZ4zWLWlOrVa3f5GJNBoNGhtb7uDctm3bpfT0dGVqaqrSzs5OM3ny5ADdY7a2tvzgwYMuubm5Hcq+eqt2797dJyQkpKb59tzW+t6dJCYmOh44cKDDQYihWbNmFa1fv76/sceavzbz5s3znjhxYvnly5dTlEql8o477qgFgMWLF3u9+eab11UqlXLJkiXXX3vtNW/dPQIDA2t27typz/S6a9cuV8Msr/Pnz89fvXp1m8G0MaYEIQGc8yUAqjjnWwE8AGB4RxsihJCe6Nu0b13H7R4XFr41fNi43ePCvk37tlMVdNsilUrDFi1a5KlQKIJlMpni3Llz9jk5OZKnnnrK/8KFC45yuVyRkpJiZ0qZ+S+//NJFKpWGPf/881LdX7YnT550HD16dKC3t3founXr9G9YrZW59/PzC33kkUf8ZDJZSGZmZqv1cuzt7fmnn3567fr167b//e9/HQBtLZfY2NiCNWvWuDe/fseOHX3Cw8PlwcHBipEjR8qys7MlAHDgwIFeutGh4OBgRUlJiai1/jW3fft210ceeaS0tb63do+PP/7YTSaTKYKCghQPP/ywv+75I0aMkMlkMsVdd90lu3jxoi0ATJkyxW/27Nk+ERERci8vr7D9+/c7T5s2zW/QoEEhU6ZM8dPd09HRccjTTz/tHRAQEHLXXXfJrl+/LgFuHrnIzc2VSKXSsNraWvbOO+947tu3z0U30lVeXi6aNm2aX1hYWHBwcLDim2++6QsAlZWV7MEHHxw0aNCgkPvuu29wbW2tfgv6zJkzS+Pj4/Up8Ft7bYqKisQJCQnOL730UqHuZ9evX79GQFs3R5eCvrS0VOzu7q4vTzB8+PDKc+fOOdXV1bGysjLRlStX7EJCQqp1j0+aNKnyxIkTvRsaGtARpgQhujuWMsZCAfQBMKBDrRBCSA/0bdq3ruvOrPMtrCm05eAorCm0XXdmnW9nAxFd2nbdh+EUS79+/dRKpTJ17ty5BWvXrnWXSqXqTz755GpkZGSlSqVS+vv715tSZn7evHklAODj41OvUqmUw4cPr5w7d67fvn37MhMSElTvvvuuJwDEx8f3bq3MfVZWll1cXFxBRkZGikwma7NejkQiQXBwcHVycrK97tzixYvz4+PjXYuKim6aJrjvvvsq//zzT1Vqaqpy6tSpxStWrBgIABs2bBi4cePGqyqVSnn69GlVr169NG31z9DZs2d7jRo1Sl/x17DvycnJ9sbukZiYaL9+/XqPY8eOpaelpSk3b96cBQDz58/3mT17dlF6erpyxowZRfPnz9ePCJSVlUnOnTunWrt2bfbMmTMDFi9efOPixYspKpXK4dSpUw6AtiBdZGRkVUZGRsqoUaMqXn/99Vanoezt7fkbb7xxPTo6ukQ30vX3v//dY9y4ceUXLlxIPXHiRNpbb73lVV5eLlq/fv0ABwcHzaVLl1JWrVp1XalU6gvK9u/fv7G+vp7l5eW1mJIxfG3S0tJsXV1d1dOmTfMLDg5WzJgxw7e8vFwEABs3bsxeunSp18CBA8OXLFnitWHDBn0pAcYY7r777vL4+PjeO3bs6Dtp0qRSwzbEYjF8fX1rT58+7dja92qMKUHIFsaYC4C3APwAQAng3Y40QgghPdFn5z+T1jfW3/T/ZH1jveiz859JO3Pf5tMxhlMsjz32WAkAREVFVWdnZ7eYYz9//rx98zLzJ0+e1M/VNy8zP3369FIACAsLqx46dGiVi4uLxtPTU21ra6spLCwUG5a5DwkJUWRmZtqrVCp7APDw8KifMGFCFUzUPPmlq6urZtq0aUVr16696Q/Xy5cv244ZMyZQJpMpNm7cOFClUjkAwIgRIypfeeUV71WrVg0oLCwU29jYoK3+GSorK5O4uLjop2IM+97aPQ4dOtQ7Ojq6xMPDQw1oi+0BwLlz55zmzZtXDADz588vPnv2rD7oeeCBB0pFIhGGDh1a7ebm1hAVFVUjFoshk8lqMjMz7QBtnZa//vWvxQAwd+7cot9//71F0NSWo0eP9n7//fc95HK5YvTo0UF1dXUsIyPD9uTJk72eeOKJIgAYPnx4jUwmqzZ8npubmzorK6vFiJXha6NWq1lqaqrj888/X5Camqp0dHTULFmyZCAAbNy4sf8777yTnZeXl7RmzZrsJ5980s/wPrNnzy7etWuX6549e1znzJnTIotwv3791NnZ2cZTv7aizSCEMSYCUM45L+GcH+ecD+KcD+Ccb+5II4QQ0hMV1RQZnYJo7XxXsLe35wAgkUi4Wq1uv658M83LzOvuJxKJYGtrq48SRCIRGhoamK7MvS4gysrKSl60aFEhoC30Zmq7arUaaWlpjuHh4TdVb33jjTdu7Nixo19VVZX+/SYuLs5nwYIF+enp6cqPP/74qq5c/Zo1a/I+//zzqzU1NaIxY8bIz507Z99W/wyJxWJuuPbDsO+m3sMUutdTLBa3eD1b+3kxpj0tkUj0fayurm71Z8s5x3fffZeh629ubu6FoUOH1rZ2vU5dXR0z9jMzfG38/Pzq3d3d68ePH18FADNmzCg5f/68IwB8//33brGxsaUAMHfu3JKkpCQnw/uMGzeuWqVSORQXF0t0QXCz9kUd+Z0B2glCOOcaAK925IaEEGIt3BzcjE5BtHZeCB0pM2+Ktsrcm6quro7FxcV5eXh41A8fPvymkvDu7u6N0dHRJTt27OinO1dRUSH28fFpAICvvvpKv44hJSXFLioqqmb16tV54eHhVcnJyfam9s/f3782NTXV6O6M1u5x//33l+/bt89FN4Vx48YNMQAMGTKk6vPPP3cBgM2bN7tGRkZWduT10Gg00O0a+eqrr9yioqIqAMDb27vu999/dwKA7du366fgevfu3VhZWal/Px43blz5hg0b3DUa7fv5b7/95gAAo0ePrty+fbsrAJw5c8Y+PT3d0bDNgoICm6CgoBbBgeFr4+Pjox44cGD9+fPn7QDg559/7h0UFFQLAP379284ePCgMwDs27fP2dfXt0Xgs3LlymsrV640WvH58uXLdkOHDq0x9lhrTPlF+w9j7BUA3wLQD8txzqmgEyHEqj0X8VzOujPrfA2nZGzFtprnIp4z+p+wqXRrQnTH48ePL/vkk09MumdHysyboq0y9+09NzY2dpCtra2mvr5eNGbMmPIff/wxw9h1b775Zt7WrVv7GxxfnzVr1uA+ffqoR48eXZGVlWUHAOvWrRtw6tSp3owxHhQUVDN16tQyBwcHbqx/Uqn0pu0/EydOLPv555+dQ0NDW7wJt/Y9RkZG1v7tb3/LHTNmjFwkEvHQ0NDq77///spnn32WFRsb6/fhhx8OdHNzU2/btu1KB15SODg4aH7//Xen9957z9PNza0hPj7+EgC8/vrrN2bMmDHoq6++6n/fffeV6q6fPHlyxfr16z3kcrnib3/7W+7atWuvz5s3z0culys0Gg3z9vauO3LkSMYrr7ySP3PmTP9BgwaFBAQE1CoUCv178smTJx2HDBlSZawQXvPX5qOPPsqaPXv2oPr6eubj41O3c+fOKwDw6aefXn355Ze9//a3vzE7OzvNZ599drX5vaZPn15u7HvOzs6W2NnZcR8fnw5tyzKlgN1lI6c553xQRxrqKlTAjhDSAZ0uYPdt2reun53/TFpUU2Tr5uBW/1zEczkzgmbQH2HdzNWrV21mzZrld+rUqYuW7oujo+OQ6urqc0K2+dRTT3k//PDDpQ899FCLUTEhXpvly5cP6N27t8bYNFdbBexMyZjq3wX9I4SQHmlG0IxiCjq6P19f34a5c+cWFhcXi5rnCrkdhIaG1hgLQABhXpu+ffs2LliwoKijzzNlJMQRwMsAfDjn8xhjgQCCOOf7b62rnUMjIYSQDuj0SAghpHPaGgkxZYvuPwHUAxjZdJwDYFXXdI0QQgghtytTgpDBnPN1aEpaxjmvhpG/LgghhBBCOsKUIKSeMeYAgAMAY2wwgBarjwkhhBBCOsKULbrLAPwEwJsxth3AKABPmrFPhBBCCLkNtDsSwjn/GcCj0AYeOwFEcs6PmrdbhBBCTCWVSsOEqlZLSFdq95eWMbYPwA4AP3DOTa4hQAghpH0ajQacc4jFLeqOEWL1TFkTsh7AGABKxth3jLGpjLEWxYMIIcQaFe/c5XpxzN1hqcGKYRfH3B1WvHNXpyroAi1Lzc+YMcMvNDQ0OCAgIMSwzLxUKg1btGiRp0KhCJbJZIpz587ZA0BeXp541KhRgQEBASEzZszwNUy1sGzZMvfAwMCQwMDAkBUrVgzQtefv7x8yZcoUPz8/v9CYmBj/vXv3Og8dOlTu6+sbeuTIkQ5VPiWkq5gyHXOMc74AwCAAmwFMB5Bvys0ZY5MYY2mMsQzG2OtGHn+ZMaZkjCUxxn5ljPl29BsghBBzKd65yzV/7VpfdUGBLTiHuqDANn/tWt+uCEQMS81v2rQpOzk5OVWlUqX89ttvzgkJCQ666/r166dWKpWpc+fOLVi7dq07ALz++uued911V2VGRkbKI488Upqbm2sLACdOnHDcsWOH29mzZ1MTExNTt23b1l9XdyQ7O9v+tddeu5GZmZmcmZlpv337drfExETV6tWrr61evdqjs98PIbfClJEQNO2OmQLgOQB3AthqwnPEADYBmAxAAWAWY0zR7LJz0K4xCQfwHYB1pnedEELMq+iTT6S8qcKrDq+rExV98om0s/c2LDW/detWV4VCEaxQKBQXL160P3/+vH60+bHHHisBgKioqOrs7Gw7ADh9+rTz3LlziwBg5syZZb17924EgKNHj/b6y1/+Utq7d29Nnz59NA888EDJkSNHnAFAKpXWGZadHz9+fLmuJP21a9eMFn4jxNxMWROyG0AUtDtkPgZwrKm6bnuiAGRwzi813WcXgIcAKHUXcM6PGFx/GsDjpnedEELMS11YaNuR8x2hK3muUqlsP/74Y/ezZ8+m9u/fv3HKlCl+tbW1+sBHVzpeIpHw1krFm6J52XnDkvSNjY2U+4lYhCkjIV9Am7DsuaagYSRjbJMJz5MCyDY4vtZ0rjVPA/jR2AOMsXmMsUTGWGJBwS0XiySEkA6R9OtX35Hzt6KkpETs4OCgcXV1bczOzpYcPXq0T3vPGTFiRMVXX33lBgC7d+/uXV5eLgaAcePGVR48eLBvRUWFqLy8XHTw4EGXcePGGa0nQkh3YMqakEMAwhlj6xhjVwCsBKDqyk4wxh4HEAngvVb6sIVzHsk5j+zfv7+xSwghpMu5LViQw+zsbhr5ZXZ2GrcFC3K6qo277rqrJjQ0tHrw4MGh06dPHzRs2LDK9p6zdu3a67/99luvgICAkPj4eBcPD496ABg9enT1Y489VjR06NDgYcOGBT/xxBMFo0aNqumqvhLS1VotYMcYkwGY1fRRCOBbAK9wzk1aPMoYuwvAMs75/U3HbwAA5/ydZtfdC+AjAGM55+0ueKUCdoSQDuh0Abvinbtciz75RKouLLSV9OtX77ZgQY7rrJlUVZcQE7VVwK6tNSEqACcAPMg5zwAAxtiiDrR7BkAgY8wf2qJ3MwE8ZngBY2wItDtuJpkSgBBCiNBcZ80spqCDEPNoazrmUQC5AI4wxv6PMTYBHShcxzlXA4gDcAhAKoDdnPMUxtgKxlhM02XvAegFYA9j7E/G2A+39F0QQgghpMdpdSSEc74XwF7GmBO0u1peAjCAMfYpgH81pXNvE+f8IICDzc4tNfj63lvrNiGE3DKNRqNhIpHI+Fw0IaTLaDQaBqDVHbWmLEyt4pzv4JxHA/CCNrfHa13XRUIIEVRyQUFBn6b/HAkhZqLRaFhBQUEfAMmtXdOhgkec8xIAW5o+CCGkx1Gr1X/Ny8v7PC8vLxQmJmwkhNwSDYBktVr919YuoKqLhJDbyrBhw/IBxLR7ISHE7OivAEIIIYRYBAUhhBBCCLEICkIIIYQQYhEUhBBCCCHEIigIIYQQQohFUBBCCCGEEIugIIQQQgghFkFBCCGEEEIsgoIQQgghhFgEBSGEEEIIsQgKQgghhBBiERSEEEIIIcQiKAghhBBCiEVQEEIIIYQQi6AghBBCCCEWQUEIIYQQQiyCghBCCCGEWAQFIYQQQgixCApCCCGEEGIRFIQQQgghxCIoCCGEEEKIRVAQQgghhBCLoCCEEEIIIRZBQQghhBBCLIKCEEIIIYRYBAUhhBBCCLEICkIIIYQQYhEUhBBCCCHEIigIIYQQQohFUBBCCCGEEIugIIQQQgghFmHWIIQxNokxlsYYy2CMvW7k8bsZY38wxtSMsanm7AshhBBCuhezBSGMMTGATQAmA1AAmMUYUzS7LAvAkwB2mKsfhBBCCOmeJGa8dxSADM75JQBgjO0C8BAApe4CzvmVpsc0ZuwHIYQQQrohc07HSAFkGxxfazrXYYyxeYyxRMZYYkFBQZd0jhBCCCGW1SMWpnLOt3DOIznnkf3797d0dwghhBDSBcwZhOQA8DY49mo6RwghhBBi1iDkDIBAxpg/Y8wWwEwAP5ixPUIIIYT0IGYLQjjnagBxAA4BSAWwm3OewhhbwRiLAQDG2J2MsWsApgHYzBhLMVd/CCGEENK9MM65pfvQIZGRkTwxMdHS3SCE9AzM0h0ghLSuRyxMvRVl+/bh4vgJSA1W4OL4CSjbt8/SXSKEEEKIAXPmCbGYsn37kLtkKXhtLQBAff06cpcsBQD0iY62ZNcIIYQQ0sQqR0Ly3/9AH4Do8Npa5L//gWU6RAghhJAWrDIIUefmdug8IYQQQoRnlUGIxMOjQ+cJIYQQIjyrDEIGLHoJzN7+pnPM3h4DFr1kmQ4RQgghpAWrXJiqW3ya//4HUOfmQuLhgQGLXqJFqYQQQkg3QnlCCCHWjPKEENKNWeV0DCGEEEK6PwpCCCGEEGIRFIR0AcrOSgghhHScVS5MFRJlZyWEEEJuDY2EdBJlZyWEEEJuDQUhnUTZWQkhhJBbQ0FIJ1F2VkIIIeTWUBDSSZSdlRBCCLk1FIR0Up/oaHisXAGJpyfAGCSenvBYucJsi1JpJw4hhBBrQRlTe5DmO3EA7aiLOYMeQno4yphKSDdGIyE9CO3EIYQQYk0oCOlBaCcOIYQQa0JBSA9CO3EIIYRYEwpCehChd+LQIlhCCCHmRGnbexDd4tP89z+AOjcXEg8PDFj0klkWpVI6ekIIIeZGu2OIURfHT4D6+vUW5yWengg8/KsFekTILaHdMYR0YzQdQ4wSehEsTf0QQsjth4IQYpSQi2B1Uz/q69cBzvVTPxSIEEKIdaMghBgl5CJYS+Q/oZEXQgixPFqYSowSchGsJaZ+aNEtIYRYHgUhpFV9oqMFeVOWeHgYXwRrpvwnbY28UBBCCCHCoekYYnFC5z+hRbeEENI9UBBCLE7oSsTWvOiWAh5CSE9CeULIbUfIasRC5luhKstGUZ4QQroxGgkhtx0hR16EnPoRepeRkKMuNMJDiHWihanktmSNi26FDHiE3GFEu5kIsV5mHQlhjE1ijKUxxjIYY68bedyOMfZt0+MJjDE/c/aHEKEJuehWyLUuQo66WCKPDCFEGGYLQhhjYgCbAEwGoAAwizGmaHbZ0wBKOOcBAN4H8K65+kOIJQg59SNkwCPkqIvQu5kIIcIx53RMFIAMzvklAGCM7QLwEAClwTUPAVjW9PV3AD5mjDHe01bLEtIGoaZ+hEwwJ+Q0k9B5ZAghwjHndIwUQLbB8bWmc0av4ZyrAZQBcDNjnwixan2ioxF4+FcEpyoRePhXswU/Qo66CJ1HhhAinB6xMJUxNg/APADw8fGxcG8IIUKOugjZFiFEWGbLE8IYuwvAMs75/U3HbwAA5/wdg2sONV3zX8aYBEAegP5tTcdQnhBCSAdQnhBCujFzTsecARDIGPNnjNkCmAngh2bX/ABgTtPXUwEcpvUghBBCyO3BbNMxnHM1YywOwCEAYgBfcs5TGGMrACRyzn8A8AWArxljGQCKoQ1UCCGEEHIboLTthBBrRtMxhHRjlLadEEIIIRZBQQghhBBCLIKCEEIIIYRYBAUhhBBCCLEICkIIIYQQYhE9bncMY6wAwNUOPKUfgEIzdYfa6tltCd0etSV8W4Wc80nm6gwhpHN6XBDSUYyxRM55JLVFbVm6PWqrZ7VFCDE/mo4hhBBCiEVQEEIIIYQQi7gdgpAt1Ba11U3ao7Z6VluEEDOz+jUhhBBCCOmeboeREEIIIYR0Q1YbhDDG7BljvzPGzjPGUhhjy83c3hXG2AXG2J+MMbNV2GOMBTW1ofsoZ4y9ZMb2FjLGkptewy5thzH2JWMsnzGWbHBuWlNbGsZYl+2CaKWtlYyxpKbX8WfGmKcZ21rGGMsx+Ln9xYxtfWvQzhXG2J9mbCuCMfbfpt/9fYyx3l3Uljdj7AhjTNn0+7Cw6bxZfj8IIZZhtdMxjDEGwIlzXskYswFwEsBCzvlpM7V3BUAk51ywHBeMMTGAHADDOecdyZ1i6v1DAewCEAWgHsBPAJ7jnGd00f3vBlAJYBvnPLTpXDAADYDNAF7hnHdJQNdKW7055+VNX78IQME5f85MbS0DUMk5X9/Z+7fXVrPHNwAo45yvMEdbjLEz0P6cjjHG5gLw55wv6YK2PAB4cM7/YIw5AzgL4GEAHGb4/SCEWIbVjoRwrcqmQ5umD2uLuCYAyDRHANIkGEAC57yac64GcAzAo111c875cQDFzc6lcs7TuqqNdtoqNzh0Qhf9fhhry1zaaqspEJ8OYKcZ25IBON709S8ApnRRW7mc8z+avq4AkApAaq7fD0KIZVhtEAJoRwqahqLzAfzCOU8wY3McwM+MsbOMsXlmbMfQTHTRG0wrkgGMYYy5McYcAfwFgLcZ2xMcY2w1YywbwGwAS83cXFzT9M+XjDEXM7cFAGMA3OCcXzRjGykAHmr6ehrM8PvBGPMDMASAOf/9EkIswKqDEM55I+f8DgBeAKKaphfMZTTnfCiAyQCebxq6NhvGmC2AGAB7zNUG5zwVwLsAfoZ2KuZPAI3mas8SOOdvcs69AWwHEGfGpj4FMBjAHQByAWwwY1s6s2DeIBUA5gJYwBg7C8AZ2mm7LsMY6wXgewAvNRu5IoRYAasOQnQ456UAjgAwWw0JznlO0+d8AP+Cdh2FOU0G8Afn/IY5G+Gcf8E5H8Y5vxtACYB0c7ZnQdvRRVMJxnDObzQFxRoA/wcz/34wxiTQTp19a852OOcqzvlEzvkwaAOezK66d9Naru8BbOecx3fVfQkh3YfVBiGMsf6Msb5NXzsAuA+AykxtOTUtngNjzAnARGinMsxJiL9ywRgb0PTZB9o3tR3mblMojLFAg8OHYKbfj6a2PAwOH4H5fz/uBaDinF8zZyMGvx8iAG8B+KyL7ssAfAEglXP+j664JyGk+5FYugNm5AFga9MOEhGA3Zzz/WZqyx3Av7T/b0ICYAfn/CcztaULdO4D8Ky52jDwPWPMDUADgOebRpW6BGNsJ4B7APRjjF0D8Da0Cx8/AtAfwAHG2J+c8/vN1NZfGGNB0O62uAqg0ztj2mjrHsbYHdCuHbqCLvrZGWuLc/4FzLBeqJXvqxdj7PmmS+IB/LOLmhsF4AkAFwy2GP8dgB3M8PtBCLEMq92iSwghhJDuzWqnYwghhBDSvVEQQgghhBCLoCCEEEIIIRZBQQghhBBCLIKCEEIIIYRYBAUhxGIYY08yxj7uxPN/YoyVMsb2NzvvzxhLYIxlNFWUtW06b1jJVsUY+7QpvwUYY18xxqY2u08lCCGEmA0FIaQnew/aXBLNvQvgfc55ALRZXp82eOz9plT+CgBhAMaau5OEEEKMoyCEdAuMMT/G2OGmAm+/NmVoBWNsMGPsNGPsAmNsleHoBOf8VwAVze7DAIwH8F3Tqa3QloBvzhaAPbRBCiGEEAugIIR0Fx8B2Mo5D4e2jsvGpvMfAviQcx4GwJQU5G4ASjnn6qbjawCkBo8vasrAmQsgnXP+p8Fj7zVN1fxpkKWTEEKImVAQQrqLu/C/ujRfAxhtcF5XKbgr6tbopmMGAHBijM00eGwx5/wO3UcXtEUIIaQNFIQQQTHGnjcYafA0QxNFAPo2VZEFAC8AOc0v4pw3APgJwN1m6AMhhBATUBBCBMU532Qw0nDd4KFT0BZdA4DZAE40fX0awJSmrw1HLVq7PwdwBIBup8scAP9ufl3T2pFR6MLS84QQQjqGghDSXbwA4CnGWBK0O14WNp1/CcDLTecDAJTpnsAYOwHtVM0Extg1xpiumuprTc/JgHaNyBcG7ejWhCQDEAP4xGzfESGEkDZRFV3SrTHGHAHUcM550/qNWZzzhyzdL0IIIZ0naf8SQixqGICPm6ZPSgHMtWx3CCGEdBUaCSGEEEKIRdCaEEIIIYRYBAUhhBBCCLEICkIIIYQQYhEUhBBCCCHEIigIIYQQQohFUBBCCCGEEIv4f4N2STZatPCGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "colors = [\"blue\", \"blue\"]\n",
+    "alphas = [0.5, 1.0]\n",
+    "idx=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\"]:\n",
+    "    ax.scatter(np.arange(3,23,2).astype(str), chrombpnet[key], label=key, c=\"blue\", alpha=alphas[idx])\n",
+    "    idx+=1\n",
+    "    \n",
+    "key=\"ChromBPNet DNase (68M)\"\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), chrombpnet[key], label=key)\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_dnase_roc_sar, label=\"Enformer DNase (reported) (68M)\")\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_dnase_roc, label=\"Enformer DNase (recomputed) (68M)\")\n",
+    "\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), random_bc, label=\"random\")\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Average precision\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/eu_caqtls_auprc_compare.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(loc, value, pi)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Precision')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "dataf = caqtls[(caqtls[\"Log10_BF\"]<-1) | (caqtls[\"Log10_BF\"]>3)]\n",
+    "dataf[\"label\"] = 0\n",
+    "dataf.loc[dataf[\"Log10_BF\"]>3, \"label\"] = 1\n",
+    "\n",
+    "    \n",
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (572M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ DNASE (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"enformer_recomputed\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ DNASE SAD (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"DNASE_SAR\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ DNASE SAR (50M)\"\n",
+    "\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "enformer_auc = roc_auc\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "roc_auc=sum(dataf[\"label\"]==1)/len(dataf[\"label\"])\n",
+    "ax.plot(fpr_chrombpnet, [sum(dataf[\"label\"]==1)/len(dataf[\"label\"])]*len(fpr_chrombpnet), linewidth=2, label=\"Random baseline, AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc='upper right')\n",
+    "ax.set_xlabel(\"Recall\")\n",
+    "ax.set_ylabel(\"Precision\")\n",
+    "\n",
+    "#plt.savefig(\"subfigs/eu_caqtls_auprc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6868906658926365, 0.0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Pearsonr = 0.69')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = caqtls[caqtls[\"Log10_BF\"] > 3][\"ENCSR637XSC.logfc.mean\"]\n",
+    "data[\"Beta\"] = caqtls[caqtls[\"Log10_BF\"] > 3][\"Beta\"]\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"#2B1C97\" , rasterized=True)\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#2B1C97\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "#plt.savefig(\"subfigs/eu_caqtls_pearsonr.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "(0.665201152808693, 0.0)\n",
+      "(0.34850892927706606, 2.630986285282384e-279)\n",
+      "(0.6868906658926365, 0.0)\n",
+      "(0.373254896437749, 3e-323)\n",
+      "(0.6592033898590558, 0.0)\n",
+      "(0.3472522744775551, 3.540472364876723e-277)\n",
+      "(0.627801839294076, 0.0)\n",
+      "(0.3192540544109002, 3.112035561037733e-232)\n",
+      "(0.4589230068441387, 0.0)\n",
+      "(0.1176776945456202, 1.018370060074654e-31)\n",
+      "5\n",
+      "(0.6826615286957263, 0.0)\n",
+      "(0.324407859465878, 1.6214207694521943e-164)\n",
+      "(0.7035209829901686, 0.0)\n",
+      "(0.3492777529105701, 3.931635735369743e-192)\n",
+      "(0.6769824693057631, 0.0)\n",
+      "(0.32628370414750707, 1.6421597349614996e-166)\n",
+      "(0.6411690719027333, 0.0)\n",
+      "(0.28825067537231314, 8.870861194497183e-129)\n",
+      "(0.4777358871973751, 0.0)\n",
+      "(0.09238815482662387, 3.21069946085816e-14)\n",
+      "7\n",
+      "(0.6950902591089945, 0.0)\n",
+      "(0.30211172435536165, 8.05495353891432e-104)\n",
+      "(0.714697007575527, 0.0)\n",
+      "(0.32706838324688947, 2.2683993962838284e-122)\n",
+      "(0.6890682340849307, 0.0)\n",
+      "(0.30522182053278235, 4.912388307359105e-106)\n",
+      "(0.6518683819270539, 0.0)\n",
+      "(0.2602150926507698, 1.4463704172886153e-76)\n",
+      "(0.48778120533572766, 5.3383060250475896e-291)\n",
+      "(0.06884290185007744, 1.4316932327882974e-06)\n",
+      "9\n",
+      "(0.7014228450600566, 0.0)\n",
+      "(0.2856313536972103, 3.338529460126271e-70)\n",
+      "(0.7211907298407995, 0.0)\n",
+      "(0.3093373799658413, 1.2542419675848708e-82)\n",
+      "(0.6946289776991347, 0.0)\n",
+      "(0.28907747942451256, 6.166123619638952e-72)\n",
+      "(0.6635183293951822, 0.0)\n",
+      "(0.24599121087863077, 5.6379035748626e-52)\n",
+      "(0.499144336930447, 1.0175325393365094e-231)\n",
+      "(0.057053025500328466, 0.0005265229506653116)\n",
+      "11\n",
+      "(0.7075259979323208, 0.0)\n",
+      "(0.27089507341408525, 1.6607753892550684e-48)\n",
+      "(0.7284896036950416, 0.0)\n",
+      "(0.295502154315366, 8.550506665426216e-58)\n",
+      "(0.7014502498805992, 0.0)\n",
+      "(0.2767833137185822, 1.2106013169809058e-50)\n",
+      "(0.673364996127206, 0.0)\n",
+      "(0.23620481328127424, 5.738051629597827e-37)\n",
+      "(0.5046523097374483, 1.1395949327094489e-181)\n",
+      "(0.041480149971671765, 0.027808193571009995)\n",
+      "13\n",
+      "(0.7082521271184222, 0.0)\n",
+      "(0.2509125451534507, 5.204409853513289e-32)\n",
+      "(0.7301693997545102, 0.0)\n",
+      "(0.27605781731680057, 1.1991784149842266e-38)\n",
+      "(0.7032658547722237, 2.151557e-318)\n",
+      "(0.2625670160828593, 5.363115481776276e-35)\n",
+      "(0.6758214921561225, 6.274675047436831e-285)\n",
+      "(0.22211953980687146, 2.8332698433615437e-25)\n",
+      "(0.5081635039656659, 1.4889742623074987e-140)\n",
+      "(0.035625996462810956, 0.09982650815336373)\n",
+      "15\n",
+      "(0.7223787927381474, 2.4468489381949018e-257)\n",
+      "(0.24347385024090037, 6.09347737674947e-23)\n",
+      "(0.7413819086933173, 4.557933398306138e-278)\n",
+      "(0.26259713877391083, 1.5059943118213788e-26)\n",
+      "(0.7152589545555957, 5.1865028692858216e-250)\n",
+      "(0.24881791657257168, 6.41918750260712e-24)\n",
+      "(0.6853357367512125, 1.5618391990166726e-221)\n",
+      "(0.20642085115705344, 8.455452361573364e-17)\n",
+      "(0.5124008415355104, 2.0113856524220865e-107)\n",
+      "(0.01805825222424427, 0.47123713705700354)\n",
+      "17\n",
+      "(0.7254228485393548, 1.3693580472667745e-195)\n",
+      "(0.23318015869038994, 3.303322549575806e-16)\n",
+      "(0.7440993987894741, 4.772436893049345e-211)\n",
+      "(0.2471545369785267, 4.471788983711831e-18)\n",
+      "(0.7184364180936791, 3.927462371788624e-190)\n",
+      "(0.23859267277393376, 6.44727767946077e-17)\n",
+      "(0.6857609353874446, 1.1603795714018026e-166)\n",
+      "(0.19345309577485695, 1.5685074783991428e-11)\n",
+      "(0.506938916871917, 5.909474261603635e-79)\n",
+      "(-0.009931120662050286, 0.7317399008717101)\n",
+      "19\n",
+      "(0.7375987093261414, 9.42562414326714e-146)\n",
+      "(0.21557890190866796, 2.4803578820028113e-10)\n",
+      "(0.7520767922168495, 1.2761540554037136e-154)\n",
+      "(0.2241223940657413, 4.5356045772670874e-11)\n",
+      "(0.7290220552802559, 9.01938707228914e-141)\n",
+      "(0.2261687862324507, 2.987981722116067e-11)\n",
+      "(0.6897824318242196, 3.222481841439591e-120)\n",
+      "(0.1756991207576248, 2.794564069781792e-07)\n",
+      "(0.5106021921020437, 3.158873036327609e-57)\n",
+      "(-0.030072101163663212, 0.3829097374704071)\n",
+      "21\n",
+      "(0.7392373541166697, 3.054027651356299e-104)\n",
+      "(0.22560163422535967, 2.5081665140024487e-08)\n",
+      "(0.7526297995590513, 4.529398430472803e-110)\n",
+      "(0.24310963914077485, 1.7646813945676983e-09)\n",
+      "(0.7294244145864095, 3.407717594527533e-100)\n",
+      "(0.24347995687417626, 1.6645327502714675e-09)\n",
+      "(0.7012750904492621, 1.6464238665213796e-89)\n",
+      "(0.20084649265148435, 7.512734334537685e-07)\n",
+      "(0.5166539804666994, 4.784746767469464e-42)\n",
+      "(0.004058035061216381, 0.9211819869876746)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "enformer = []\n",
+    "enformer_unsigned = []\n",
+    "enformer_sar = []\n",
+    "enformer_unsigned_sar = []\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(3,23,2):\n",
+    "    print(threshold)\n",
+    "    dataf = caqtls[(caqtls[\"Log10_BF\"]>threshold)]\n",
+    "        \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR000EMT.logfc.mean\"], dataf[\"Beta\"])\n",
+    "    print(pear)\n",
+    "\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR000EMT.logfc.mean\"]), abs(dataf[\"Beta\"]))\n",
+    "    print(pear)\n",
+    "\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.logfc.mean\"], dataf[\"Beta\"])\n",
+    "    print(pear)\n",
+    "\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.logfc.mean\"]), abs(dataf[\"Beta\"]))\n",
+    "    print(pear)\n",
+    "\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.sub.50M.logfc.mean\"], dataf[\"Beta\"])\n",
+    "    print(pear)\n",
+    "\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.sub.50M.logfc.mean\"]), abs(dataf[\"Beta\"]))\n",
+    "    print(pear)\n",
+    "\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer_recomputed\"]*-1, dataf[\"Beta\"])\n",
+    "    print(pear)\n",
+    "\n",
+    "    enformer.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer_recomputed\"]), abs(dataf[\"Beta\"]))\n",
+    "    print(pear)\n",
+    "\n",
+    "    enformer_unsigned.append(pear[0])\n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"DNASE_SAR\"], dataf[\"Beta\"])\n",
+    "    print(pear)\n",
+    "    enformer_sar.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"DNASE_SAR\"]), abs(dataf[\"Beta\"]))\n",
+    "    print(pear)\n",
+    "    enformer_unsigned_sar.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f5764292c90>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(3,23,2).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase (provided) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(3,23,2).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase (provided) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/eu_caqtls_pearsonr_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "5\n",
+      "7\n",
+      "9\n",
+      "11\n",
+      "13\n",
+      "15\n",
+      "17\n",
+      "19\n",
+      "21\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "enformer = []\n",
+    "enformer_unsigned = []\n",
+    "enformer_sar = []\n",
+    "enformer_unsigned_sar = []\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(3,23,2):\n",
+    "    print(threshold)\n",
+    "    dataf = caqtls[(caqtls[\"Log10_BF\"]>threshold)]\n",
+    "        \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR000EMT.jsd_sign.mean\"], dataf[\"Beta\"])\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR000EMT.jsd_sign.mean\"]), abs(dataf[\"Beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.jsd_sign.mean\"], dataf[\"Beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.jsd_sign.mean\"]), abs(dataf[\"Beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.sub.50M.jsd_sign.mean\"], dataf[\"Beta\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.sub.50M.jsd_sign.mean\"]), abs(dataf[\"Beta\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer_recomputed\"]*-1, dataf[\"Beta\"])\n",
+    "    enformer.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer_recomputed\"]), abs(dataf[\"Beta\"]))\n",
+    "    enformer_unsigned.append(pear[0])\n",
+    "\n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"DNASE_SAR\"], dataf[\"Beta\"])\n",
+    "    enformer_sar.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"DNASE_SAR\"]), abs(dataf[\"Beta\"]))\n",
+    "    enformer_unsigned_sar.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f5762b27b90>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9BT927NhyqVQqvnjxYua6deu4eXl5HACwtbVtXLt2rf2j3Gfbtm22NTU1HX7XxMXFWfj5+dUePHjQEgB2796dK5VKxadOnbrp5OQkl0qlYqlUKn7zzTfL79+/z05PT+9VXV3NFovF6mBs48aNfc6dO2d2/fp1CdPv9jYmLioqYl+/fr3X6NGja5hj69aty2fuERISUg8AUVFR95hja9asyQ8MDKy2tbVV6unpISYmJj87Ozvj2rVrkm+//dbm+vXrhkxbTk5O8v379/cGAKVSicuXL5va2Ng0AYChoSE9ZMiQqh07dlg+yudH6BYJigiCIB6ivBysFStgW1n55H9nnjhxwpTD4dAffPBBCXNs0KBB9aNGjaoBgNraWvaoUaP6urm5icLDw91UKhUAgMvl+ixYsIArFAoF3333ncW2bdsseTye0NPTU7RgwQIu05axsXH/+fPnO3p4eIhCQkJ458+fNw4KCvJydHT02bt3r3nr/qhUKlRXV7MtLCzaRFpcLlfh7Owsl8lk+gAwbNiwyqysLKOUlBSD1ucePnzYrF+/fnyhUCgYPXp038rKSta6detsiouL9YYMGcIbOHAgr73P4+DBg5abNm3Ku3fvnl52dnangeGePXt6Dx8+vGLcuHFlcXFx6uDi888/t9u+fXuOpaWlCgAsLS1V//73v9uUjNqzZ4/FsGHDqjq7R2v79++3jIiIKAMAFxeXptDQ0DoAsLCwULm7u9fn5uaqg7Px48eXHTp0yBIATp48aRoYGFjD4XDU0dnEiRMr4uPjSVD0DCNBEUEQxEOcOgXThASYnDwJ0ydtKzU11cjPz6+uo9clEonR5s2b82QyWUZubq7BL7/8YsK8ZmVlpRCLxZKRI0fW/Oc//+H+/vvvWWKxOCM5ObnX7t27ewNAfX09a9iwYVUymSyjV69eyhUrVnAvXbqUdfDgQVlUVJQ6eEpKSjLh8/lCBwcH30uXLplGRkbeb90XsVisn5eXZyAUCuUAwGKx8N577xWtWbOmRbaosLCQs2HDBvuLFy9micViib+/f11UVJTtihUrim1sbJouXLiQlZiYmNW6fZlMpldSUqI3dOjQuvDw8HLNQKc9Bw4csJwxY0bZ7Nmzyw4fPmwJAGVlZaza2lq2UChs7OxaAEhISDAJCAio1Ty2Zs0aLo/HE/7rX/9yqq+vb1EWprq6mnXx4kXzGTNmlLduKzMzU18sFhsPGTJEnXXy8vKSl5aWckpKStj79u2znDZtWpnmNYGBgfWpqam9HtZP4ukhQRFBEEQnysvBOnECvd3dIT9+HL27I1vUGR8fn1p3d/cmNpsNkUhUl52drc5EzJo1qxwALl++3Cs4OLjawcFBoaenh8mTJ5dduHDBBAD09PToiRMnVgGASCSqDw0NrTYwMKCDgoLqCwoK1G0xw2dFRUWp06ZNK42MjHRkXjt+/LgFn88XTpkype8XX3yRY2trq2Remz9/fulff/1lIpVK1W39/vvvvbKzsw2DgoL4fD5fGB8fb6WZQelIXFycZXh4eDkAzJw5s+zHH3/sMCjKy8vj5OTkGI4cObLG19dXzuFw6GvXrhl2dH577t27p2dra6vOiH322WcFt27dSk9JSZGUl5ezV65caad5fnx8vPmAAQNqNN8/AFRWVrLGjx/vHh0dncdkpxhjx44t/+677yz/+uuvXkz2j8HhcKCnp0drzjsini1k80aCIIhOnDoFU4UCMDWFqqIC7JMnYTptGioftz0fH5/6I0eOWHT0uoGBgXq4hc1mQ6FQqLMXpqamqvav+huHw6FZrObvXBaLpW6PzWZDqVS2WyB3woQJFREREe7M87Fjx5bHxcXltneunp4eIiMji9auXasOIGiaRmhoaNXx48dvP6x/mn788UfLkpISPSbrU1xcrJeWlmbg4+Mjb31uXFycZVVVFdvJyckHAGpqathxcXFWX375ZYGxsbFKLBbrPyxbZGhoqKqvr1cHJC4uLk0AYGRkRM+ZM6c0JibGVvP8AwcOWE6aNKlFtkcul1Ovvfaae0RERNns2bMrWt9j1qxZ5QMHDhRMnDixlM1mt+lDU1MTZWxsTLaEeEaRaJUgCKIDTJbIxgYKALCxgeJJs0Vjx46tbmxspDZt2mTNHEtMTDQ6c+aMSWfXaQoLC6tNTEw0LSws5CgUChw8eNDy5Zdfrnn4le07f/68iYuLS5tApCORkZGlly9fNisrK+MAwMsvv1yblJRkkp6ebgAAVVVVrNTUVAMA6NWrl7KysrLN55WammpQW1vLLi4uTi0oKEgrKChIi4yMLNq1a1e72aJDhw5Z/vTTTzeZcxMTE8VMcLlo0aLCt99+26WsrIwFNGdy2lt95uXl1ZCVlaWeD5WTk6MHNM+rOnz4cG+BQFDPvFZaWsq+evWq6bRp0yqYYyqVClOmTHHh8XgN//nPf+61108ej9f48ccfFyxatKik9WtFRUXs3r17KzQDX+LZQoIigiCIDjBZIgMD0EDzfxUK4EnmFrFYLBw7diz73LlzZk5OTt4eHh6iDz/8kMvlcru8VtvFxaVp9erVBUOGDOEJBAKRn59f7YwZMyoepR/MnCIvLy/h/v37rb744ou8rl5raGhIz5s3r5gJihwcHBTbtm27M2XKlL48Hk8YEBDAT0tLMwSA2bNn3x81alSbida7du2y/Mc//tFirs6UKVPKmayRpszMTP2CggL9V155RT0fiM/nN5qamirPnTvX64MPPigZPHhwlb+/v9DT01MUHBzMb2+DzvDw8MoLFy6of3aTJ0924/F4Qi8vL1FpaSnnv//9byHz2t69e3uHhYVVmZmZqbNzv/zyi8mRI0esLl++bMos2f/hhx/aTF5ftmzZfZFI1CbIPH36tNnw4cMfO8tIaB/V3rLFZ1lAQACdlJT0tLtBEMTzoc1wUUpKyh0/P782k4rbs3Qp7G7fRpt5K25uaNi0CUXd0UFCtwYMGOB19uxZmbW1tfLhZ3evkSNHum/atCnf19e3y1k5QjtSUlKs/fz8XFsfJ3OKCIIgOkACn57n008/zc/Ozta3trauf/jZ3aehoYEKDw+vIAHRs40ERQRBEMQLQ3MITpcMDQ3pyMjINnsnEc8WMqeIIAiCIAgCJCgiCOJ5UV8IHHMH6smIFkEQ2kGCIoIgng9pUUDNHSA96mn3hCCIHooERQShCz01y6Gr91VfCNzeCUAF3NrZ8z5HgiCeCSQoIl5cugxUemqWQ1fvKy0KoB9sF0Mrn/vPMTc3lzNmzJi+Tk5O3iKRSDBkyBCP1NRUgxMnTpgOHTrUQ9v3P3HihKmpqWk/Pp8v5PF4wpCQEF5BQQEHAGJjY60sLCz8+Hy+0N3dXRQTE2PNHGexWAMSExONmHY8PT1FmZmZnZbzWLt2rU11dXWH3zWFhYUcDofjv3Hjxj4AMHPmTGfm3oaGhv7MfkA7d+60AIDhw4e7+/n58Vu3s2rVKls3NzcRn88Xent7C9rbvBEA5syZ43T69GkTAJgwYYIrl8v1Ye6RkJBgBDRv0vjGG284OTs7e/N4POHly5eNgeb9kiiKGvDuu+86tO7/rFmznAFgw4YNfb744ot27008+0hQRDw7dJ1N0dUXuq6zHD0te8PcR/WggoOq8bnOFqlUKoSHh3sMHjy4Oi8vLz0jI0MSHR1dcPfu3U4rxGtSKNoUtH9kTO2zrKwscf/+/Ws3bdpkw7w2duzYcqlUKr548WLmunXruHl5eRwAsLW1bVy7dq19x622tW3bNtuampoOv2vi4uIs/Pz8ag8ePGgJALt3786VSqXiU6dO3XRycpJLpVKxVCoVv/nmm+X3799np6en96qurmaLxWJ1MLZx48Y+586dM7t+/bqE6Xd7e/AVFRWxr1+/3mv06NHq3b/XrVuXz9wjJCSkHgAOHjxofuvWLcM7d+6kb926NWfhwoXOzPlcLrfxl19+6a3Zfw8Pjwbm+b///e/Sbdu2tSgXQjw/tBoUURQ1iqKoTIqiZBRF/V87r79BUVQJRVE3HjzmarM/xDNOl9kUXQYqus5y9LTsjeZ9GDrOFuXkQM/JCd65uU++jcmJEydMORwO/cEHH6jLQAwaNKieKR5aW1vLHjVqVF83NzdReHi4m0rV/N65XK7PggULuEKhUPDdd99ZbNu2zZLH4wk9PT1FCxYs4DJtGRsb958/f76jh4eHKCQkhHf+/HnjoKAgL0dHR5+9e/e22X1ZpVKhurqabWFh0SbS4nK5CmdnZ7lMJtMHgGHDhlVmZWUZpaSkGLQ+9/Dhw2b9+vXjC4VCwejRo/tWVlay1q1bZ1NcXKw3ZMiQNjtaMw4ePGi5adOmvHv37ullZ2d3Ghju2bOn9/DhwyvGjRtXFhcXp975+vPPP7fbvn17DlOc1dLSUvXvf/+7zfL3PXv2WAwbNqyqs3sAwNGjR3tPnz69lMViYdiwYbVVVVUcpiSIkZGRysPDo/7ixYvGQHP9ttdff11dH83U1FTl6OgoP3/+vPHD7kM8e7QWFFEUxQawGcBoAEIAUymKErZz6g80Tfd78Nihrf4Qj6mnZR0YuvpC13WWoydmbwqO/X0fhqoRyD/a/ffqwPLlsC8ogMHy5XB4+NmdS01NNfLz86vr6HWJRGK0efPmPJlMlpGbm2vwyy+/qGuiWVlZKcRisWTkyJE1//nPf7i///57llgszkhOTu61e/fu3gBQX1/PGjZsWJVMJsvo1auXcsWKFdxLly5lHTx4UBYVFaUOnpgyHw4ODr6XLl0yjYyMbLPLt1gs1s/LyzMQCoVyoLlEyXvvvVe0Zs2aFtmiwsJCzoYNG+wvXryYJRaLJf7+/nVRUVG2K1asKLaxsWm6cOFCVmJiYlbr9mUymV5JSYne0KFD68LDw8s1A532HDhwwHLGjBlls2fPLmPKgZSVlbFqa2vZDysGCwAJCQkmAQEBLfYpWrNmDZfH4wn/9a9/OdXX11MP3o+eq6uruj17e/tGJigCgClTppTt2bPHUiaT6bHZbNrBwaFFiRZ/f//a33///bFLwRBPjzYzRUEAZDRN36JpuhFAPIB/avF+hDb0tKwDoNsvdF1nOXpi9mZcPjCNbvsYl9/992pHTg70Dh2CNU0DBw/CujuyRZ3x8fGpdXd3b2Kz2RCJRHXZ2dnqYaJZs2aVA8Dly5d7BQcHVzs4OCj09PQwefLksgsXLpgAgJ6eHj1x4sQqABCJRPWhoaHVBgYGdFBQUH1BQYG6LWb4rKioKHXatGmlkZGRjsxrx48ft+Dz+cIpU6b0/eKLL3JsbW3VJTHmz59f+tdff5lIpVJ1W7///nuv7Oxsw6CgID6fzxfGx8db5ebmdjrXCADi4uIsw8PDywFg5syZZT/++GOHQVFeXh4nJyfHcOTIkTW+vr5yDodDX7t2rU0Jls7cu3dPz9bWVp0R++yzzwpu3bqVnpKSIikvL2evXLnSrivtTJgwoerChQtmu3btspwwYUJZ69dtbGwUjzIcSjw7tBkUcQFoFhjMf3CstQkURaVSFHWIoignLfaHeFQ9MesA6PYLXZdZjhcse6Mry5fD/sEIFlQq4EmzRT4+PvUpKSkdDq1oVlBns9lQKBTq+m2mpqaq9q/6G4fDoVms5r/aWSyWuj02mw2lUtmmFhwATJgwoSIxMVGd2WDmFKWmpkpnzZpVoXmunp4eIiMji9auXasOIGiaRmhoaBUzNyc7OzvjwIEDOQ/r648//mj5ww8/WHG5XJ/x48d7ZGZmGqWlpbUZmgOaA6iqqiq2k5OTD5fL9SkoKDCIi4uzsrS0VBkbG6s05xh1xNDQUFVfX6/+3nNxcWlisVgwMjKi58yZU3r9+vVeAGBvb990584ddXuFhYX6Li4uTRrt0L6+vnVbt261mzlzZjlaaWhoYBkZGT30Z0U8e572ROvjAFxpmvYF8AuAXe2dRFHUPIqikiiKSiopKWnvFEIbemLWAdDtF7ousxwvUPZGV5gsUVNTc2HZpiZQT5otGjt2bHVjYyO1adMma+ZYYmKi0ZkzZ0w6u05TWFhYbWJiomlhYSFHoVDg4MGDli+//HLNw69s3/nz501cXFy6XJMrMjKy9PLly2ZlZWUcAHj55Zdrk5KSTNLT0w0AoKqqipWammoAAL169VJWVla2+a5JTU01qK2tZRcXF6cWFBSkFRQUpEVGRhbt2rWr3WzRoUOHLH/66aebzLmJiYniI0eOWADAokWLCt9++22XsrIyFgBUVlay2lt95uXl1ZCVlaUOupghMZVKhcOHD/cWCAT1ABAeHl6xd+9eK5VKhd9++62XqampUjMoAoAPP/ywaPXq1fmaWTRGVlaWgbe3t05rqxHdQ5tBUQEAzcyP44NjajRNl9I0zfyPuAPAgPYaoml6O03TATRNB/Tp00crnX2u6GKeT0/OOvTUL/QXKHujK5pZIsaTZotYLBaOHTuWfe7cOTMnJydvDw8P0YcffsjlcrlND7+6mYuLS9Pq1asLhgwZwhMIBCI/P7/aGTNmVDxKP5g5RV5eXsL9+/dbffHFF3kPv6qZoaEhPW/evGImKHJwcFBs27btzpQpU/ryeDxhQEAAPy0tzRAAZs+efX/UqFFtJlrv2rXL8h//+EeLLMuUKVPKmblCmjIzM/ULCgr0NeuW8fn8RlNTU+W5c+d6ffDBByWDBw+u8vf3F3p6eoqCg4P5LBarzfKz8PDwygsXLqgzYpMnT3bj8XhCLy8vUWlpKee///1vIQBMmjSp0sXFRe7i4uK9YMECl82bN7fJegUEBDS0N5kbAK5du2YSHh7+0AndxLOHam/ZYrc0TFEcAFkAhqE5GLoGYBpN0xka59jTNF344M/jAHxI03RwZ+0GBATQSUlJWunzc+PqQkC2DfB8GwjcrL173Pq25ZcsSx9wn6u9exJE92szXJSSknLHz8+vzaTi9tjawre4GG3mhtjYoOnePaR2RwcJ3RowYIDX2bNnZdbW1m0yPN3hypUrRp9++qndkSNHbmujfaJ7pKSkWPv5+bm2Pq61CYM0TSsoiooEcBYAG8B3NE1nUBS1FkASTdPHALxLUVQ4AAWAMgBvaKs/PUbreT7eKwGjLs0NfDSdZR1IUES8IEjg0/N8+umn+dnZ2frW1tZaGd4qLi7W++STTwoefibxLNJapkhbntlMUX0h8EsoMOKKdoIUhmYGh2RuCOJhnihTRBBEz9RRpuhpT7TuOXSxdL2H7exLEARBEM8SEhR1B10tXX8GdvYlCIIgiJ6KBEXdQVdL18nqIoIgCILQGq3uzPpC6GhISxsToJ/3JeMEQRAE8QwjmaInRYa0CIJ4RLm5uZwxY8b0dXJy8haJRIIhQ4Z4pKamGpw4ccJ06NChHtq+/4kTJ0xNTU378fl8IY/HE4aEhPAKCgo4ABAbG2tlYWHhx+fzhe7u7qKYmBhr5jiLxRqQmJhoxLTj6ekpyszM7HQn6bVr19pUV1e3+10TFBTk5erq6s3j8YRubm6iWbNmOd+/f5/NvE5R1IC33npLXX5k1apVtkuWLHni+nMdUalUCA4O5jGbQN6/f19dnLdv376iX3/9tRcAJCQkGPn5+fH5fL7Q29tbwBR/jY2NtaIoasCRI0fUeyHt3r27N0VRA3bu3GkBAGPGjOnb0a7dxNPXs4MiXWxySIa0CIJ4BCqVCuHh4R6DBw+uzsvLS8/IyJBER0cXPEqtLIWiTUH7R8bUPsvKyhL379+/dtOmTTbMa0yZj4sXL2auW7eOm5eXxwEAW1vbxrVr19p33Gpb27Zts62pqenwuyYuLu5WVlaWWCKRiA0MDFSjR49WB4X6+vr0qVOnLAoLC3UyqnHgwAFzkUhUb2lpqQKAefPmOY0cObLq9u3bGWKxWNyvX78GAFi2bJnj8uXL70qlUvHKlSvvfvjhh+qNij09Pev379+v3oAyPj7e0svLS738f8GCBcXr16/X4hJl4kn07KBIFyvCeuruyARBqBWU1+sF//c377sV9U/85XzixAlTDodDf/DBB+qaRYMGDaofNWpUDQDU1taqsxPh4eFuqgdbanO5XJ8FCxZwhUKh4LvvvrPYtm2bJY/HE3p6eooWLFigritpbGzcf/78+Y4eHh6ikJAQ3vnz542DgoK8HB0dffbu3Wveuj8qlQrV1dVsCwuLNpEWl8tVODs7y2UymT4ADBs2rDIrK8soJSWlTabj8OHDZv369eMLhULB6NGj+1ZWVrLWrVtnU1xcrDdkyJA2O1q3ZmhoSG/dujX/7t27+n/88YcRALDZbHrWrFklGzZssG19/r59+8x9fX35AoFAGBISwmMCt5MnT5rw+Xwhn88XCgQCYXl5OQsAVq5caevt7S3g8XjCxYsXt5tt2rt3r+W4ceMqAKC0tJSdmJhoumjRovtM/5gNHymKQmVlJRsAKioq2La2tup/GQ8cOLAmOTm5l1wupyorK1l37twxEIlEdczro0aNqrl06ZJZU1OXNzAndKjnBkW6WhFGEESP9+lZqf29ygaDT89mPvHQTWpqqpGfn19dR69LJBKjzZs358lksozc3FyDX375RV0TzcrKSiEWiyUjR46s+c9//sP9/fffs8RicUZycnKv3bt39waA+vp61rBhw6pkMllGr169lCtWrOBeunQp6+DBg7KoqCh18MSU+XBwcPC9dOmSaWRkZJu9m8RisX5eXp6BUCiUA80lSt57772iNWvWtMgWFRYWcjZs2GB/8eLFLLFYLPH396+LioqyXbFiRbGNjU3ThQsXshITE7Me9tlwOBwIBIK69PR0Q+bYsmXLig8fPmxZWlrK1jx3xIgRNTdu3JBKJBLxxIkTy5gCtTExMXaxsbE5UqlU/Oeff0pNTExUhw8fNpPJZIapqakSiUQivnHjhvHp06fb1Jq7fv26yUsvvVQLNJcWsbS0VERERLgKBALh5MmTXaqqqlgAEBsbm7dq1SpHOzs735UrVzrGxMSoN2ukKAqDBw+uOnz4sNm+fft6jxo1qkLzHmw2Gy4uLg1//vlnh0WBiaen5wZFuloRRhBEj1ZQXq93Kq3ImgZwMq3QujuyRZ3x8fGpdXd3b2Kz2RCJRHXZ2dnqOTuzZs0qB4DLly/3Cg4OrnZwcFDo6elh8uTJZRcuXDABAD09PXrixIlVACASiepDQ0OrDQwM6KCgoPqCggJ1W8zwWVFRUeq0adNKIyMj1XN3jh8/bsHn84VTpkzp+8UXX+RoFj2dP39+6V9//WUilUrVbf3++++9srOzDYOCgvh8Pl8YHx9vlZub+9Cq9e1pvaGwpaWlKiIiojQ6OtpG8/jt27f1w8LCPHk8njA2NtZOKpUaAUBwcHDN0qVLndatW2dz//59tp6eHs6cOWN28eJFM6FQKBSJRMLs7GxDqVRqiFYqKys5FhYWKgBQKBSURCIxfuedd0okEonY2NhYtXLlSjsAiI2N7fPf//43r6ioKHXDhg15b7zxhqtmO9OnTy+Lj4+3PHjwoOXs2bPLWt/H2tpakZeX1+XhUkJ3emZQRDY5JAiim3x6VmqvevBFrVLReNJskY+PT31KSkqHWQIDAwN1VMBms6FQKNS7cpuamqrav+pvHA6HZrGa/2pnsVjq9thsNpRKZZsdvgFgwoQJFYmJierJwcycotTUVOmsWbMqNM/V09NDZGRkEZOZAZoDmdDQ0CqpVCqWSqXi7OzsjAMHDrQpovowCoUCmZmZxr6+vg2axz/66KN7+/bts66trVV/Z0VGRjovXLiwOCsrS/zVV1/lyOVyFgBs2LChaMeOHTn19fWssLAwfnJysiFN01i0aFEh07/c3Nz0xYsXt8mMsdlsWqlsjv9cXV0bbW1tG5kitJMnTy5nfm4//vijFfO5zJkzpzw1NbWXZjtDhw6tk0qlRmVlZRxfX185WpHL5SxjY+OH/iwJ3euZQRFZEUYQRDdgskQKFU0BgEJFU0+aLRo7dmx1Y2MjtWnTJmvmWGJiotGZM2faDOd0JCwsrDYxMdG0sLCQo1AocPDgQcuXX3655nH7dP78eRMXF5c2X94diYyMLL18+bJZWVkZBwBefvnl2qSkJJP09HQDAKiqqmKlpqYaAECvXr2UlZWVD/2ukcvlVGRkpKO9vX3jwIEDW9Qls7W1VY4dO7Z837596s+surqa7ezs3AQA33//vRVzPCMjwyAoKKh+/fr1Rb6+vrXp6emGo0ePrtq9e7c104/bt2/rMavtNLm5uTVIJBIDAHB2dlbY2dk1MvOnfv75ZzMvL68GAOjTp0/TqVOnTAHg+PHjpi4uLg2t24qKisqPiopqtwba7du3Dfz9/bVSe414Mj1znyJSzJQgiG6gmSViMNmizyf3y32cNlksFo4dO5a9cOFCp//97392BgYGtKOjo/zLL7/My8nJ6dKQk4uLS9Pq1asLhgwZwqNpmho+fHjFjBkzKh6lH8ycIpqmYWpqqvzuu+/udPVaQ0NDet68ecUrV650AgAHBwfFtm3b7kyZMqVvY2MjBQCrV68u8PX1lc+ePfv+qFGjeLa2to3tzSuaNWtWX319fVVjYyMrLCys6vTp07L27rl8+fKiXbt29dF4fnfq1Knu5ubmitDQ0Orc3FwDANi4caNNQkKCGUVRtJeXV/3EiRMrjYyM6IyMDMPAwEA+ABgbG6v27t17m8vltphcPnLkyMqff/7Z1NvbWw4AX375Ze706dP7NjY2Us7OzvL9+/ffAYCtW7fmLFmyxOn999+nDAwMVF9//XWbrNikSZOq2nsfeXl5HAMDA9rZ2fnJlxAS3Y4UhCUIoid7ooKwA6J+8S2tbWwz98Oql37T9ZUjUrujg8SzIycnR2/q1KmuCQkJN7V1jzVr1tiYmZmp2hu+I3Sno4KwPTNTRBAE0Q1I4PNicXFxaZozZ879srIyFrNXUXfr3bu3cuHChaXaaJt4ciQoIgiCIIgH5s6dW67N9t977z0SED3DeuZEa4IgCIIgiEdEgiKCIAiCIAiQoIggCIIgCAIACYoIgiAIgiAAkKCIIAhC59hs9gCmaCmfzxd+/PHHnVZNr6+vp0JCQnh8Pl/4zTffWOiqn50JCgrycnV19ebxeEI3NzfRrFmznO/fv6+uT0ZR1IC33npLXTpk1apVtkuWLHni2nEdUalUCA4O5pWVlen0ey02Ntbqzp07j1SyIzMzU9/T01MEAFevXjWaMGGCa0fnXrlyxWjSpEkuzPMTJ06Y8vl8oYeHhygwMNCLOb5mzRobDw8Pkaenp2js2LFudXV1FND8c7K3t/dhCgsDwPDhw92NjY37A8Ddu3c5YWFhno/S/56MBEUEQRA6ZmBgoGJKTkilUvGGDRs6rUGUkJBgDABSqVT81ltvdWl1lELRfXsDqlQqMOUvNMXFxd3KysoSSyQSsYGBgWr06NEezGv6+vr0qVOnLAoLC3WyyvnAgQPmIpGovvVS+o763h0UCgX27NljnZub+9h1zIKCguoLCwv1b9682e7GnevWrbNfvHhxMQDcv3+f/d577zkfP35cJpPJMo4cOZINNO/QvX37dtsbN26Ib968maFUKqkdO3ZYMm2YmpoqmcLC9+/fZxcXF6v76+DgoLC1tW36+eefe7W+94uIBEUEQRAPU5ujhyNO3qjN1eoXPJfL9Vm8eLGDUCgU8Hg8YXJysmFBQQHnzTffdEtLSzPm8/nCjIwMg6NHj5oKBAIhj8cTRkREuNbX11PM9QsWLOAKhULBd999Z8Hlcn3eeecdLp/PF3p7ewsuX75sHBoa6unk5OS9ceNG9e7QK1eutPX29hbweDzh4sWLHYDmbIarq6v3uHHjXHk8nkizMG1rhoaG9NatW/Pv3r2r/8cffxgBzXXEZs2aVbJhwwbb1ufv27fP3NfXly8QCIQhISG8vLw8DgCcPHnShMmeCQQCYXl5Oauj/rW2d+9ey3HjxlV01PeO3qObm5soPDzcrW/fvqJRo0b1ra6uZgFAVz7j7du3W6anpxvPmjWrL5/PF9bU1FCXLl0yDgwM9BKJRILQ0FDPnJwcPQC4dOmSsZeXl9DLy0v42WeftShuO3r06Ipdu3a1yQCWl5ezJBKJ8aBBg+oBYMeOHZavvfZauaenZ+ODvqgjX6VSSdXW1rKamppQX1/PcnR0bGJeGz9+fNnevXstAWDPnj29x44dW6F5n9dff70iLi7OCgQJigiCIB4qZbk96goMkLK8W4Z/5HI5S3P4THNIzNraWiEWiyVz5swpiY6OtuVyuYotW7bkMFXt3dzcGufPn+/2ww8/ZGdlZYkVCgU+/fRTdYBjZWWlEIvFknnz5pUDgLOzc6NUKhUPHDiwZs6cOa7Hjx/PTkxMlH7yyScOAHD48GEzmUxmmJqaKpFIJOIbN24Ynz592gQAcnNzDSIjI0tkMlkGj8drbP0+NHE4HAgEgrr09HR19flly5YVHz582LK0tJStee6IESNqbty4IZVIJOKJEyeWMcVlY2Ji7GJjY3OkUqn4zz//lJqYmKg665+m69evm7z00ku1zHPNvqenpxt21MadO3cMIyMji2/dupVhamqq+vTTT/vU1dVRXfmMFy5cWObt7V0XFxd3SyqVivX09PDuu+86Hz16NDsjI0Mye/bs+0uXLuUCwL/+9S/XL774IjczM1Pcuu8DBw6sTUhIMG19/PLly728vLzUNdKysrIMy8vLOUFBQV4ikUjw1VdfWQGAm5tb0zvvvFPk5ubma2Nj42dqaqocP368uszIyJEjq//8808Tpk7erFmzyjTv89JLL9VevXq1y7X3ejKyeSNBEERnanP0kHvIGqCB3IPW8Ft/F72erG4VM3zW3mvTpk0rB4CgoKC6Y8eOtckepKSkGDo6OsqZ6utvvPFG6ebNm20AFAPArFmzWgyvTZo0qQIAfHx86mpra1kWFhYqCwsLlb6+vur+/fvsM2fOmF28eNFMKBQKAaCuro4llUoN+/bt22hvb984bNiwWnRR67JRlpaWqoiIiNLo6GgbIyMj9bDW7du39V9//XXHkpISvcbGRpaTk5McAIKDg2uWLl3qNGnSpLKpU6eWu7u7qzrq3+jRo1sUwK2srORYWFio76HZ987eo52dXePIkSNrAWDmzJmlsbGxNikpKVWP8hkzUlNTDW7evGn0yiuv8IDmobs+ffo03b9/n11dXc1m+jxnzpzSc+fOmWv0VXHv3r02Q3AFBQV6VlZW6oyPQqGgUlNTjS9dupRVW1vLCg4O5g8ePLjG3t5ecfLkyd4ymSzNyspK+dprr/XdsmWL5cKFC8sAgMPh0EFBQTXffPONZUNDA8vLy6tFgOvg4KAoLi7uUt29nk6rQRFFUaMA/A8AG8AOmqajOzhvAoBDAAJpmiaFzQiCeHakLLcH/eC7llYBKcsdELL7sYrBdoWhoSENNH+RKRSKNrXbHsbU1LTFnBqmPRaLBX19fXXUwmKx0NTURNE0jUWLFhUuW7asRS2uzMxMfWNj4y6XulAoFMjMzDT29fW9q3n8o48+uufv7y+cMmWKuv3IyEjn9957r2j69OmVJ06cMF27dq0DAGzYsKHo9ddfrzx69Kh5WFgY/+TJkzc76l9rbDabViqVYLObk1Kafe/sPVJUy4+49fP2tP6MNe5DeXh41N+4cUOqeVxzAnp76uvrWYaGhm3aNDY2VsnlcvWIjqOjY6OVlZXCzMxMZWZmpho4cGB1UlKSMQA4OzvLHRwcFEDzcFhCQoIJExQBwPTp08umTp3qsWzZsrut71NXV0cZGBhopazJ80Zrw2cURbEBbAYwGoAQwFSKooTtnGcK4D0AidrqC0EQxGNhskR0U/M3Jd1EIfegtbbnFnXGz8+voaCgQD89Pd0AAOLi4qzCwsKqH7e90aNHV+3evdu6srKSBTRP2i0oKHik9yeXy6nIyEhHe3v7xoEDB9ZrvmZra6scO3Zs+b59+6yZY9XV1WxnZ+cmAPj+++/Vc1kyMjIMgoKC6tevX1/k6+tbm56ebtjV/rm5uTVIJBKDR32PhYWF+r/++msvoHleUkhISM2jfMYmJibKyspKNgD4+vo2lJWVcZj25HI5lZSUZGhtba00NTVVnj171uTBe7bUbEMsFhtoDpMxfHx8Gu7cuaN+TxMnTqz4888/TZqamlBdXc1KTk428fHxqXd1dW3866+/TKqrq1kqlQrnzp0zFQgEDZptvfrqqzXvvvtu4Zw5c8pa3yc9Pd2Qx+O1uf+L6KG/+A+Cmziapqc/YttBAGQ0Td960E48gH8CaJ0yjgLwCYBlj9g+QRCEdmlmiRjdkC1i5hQxz1955ZXKLVu2FHTlWmNjY/rrr7++ExER4a5UKuHn51e3dOnSksfty/jx46syMjIMAwMD+Q/aV+3du/c2h8OhH3btrFmz+urr66saGxtZYWFhVadPn5a1d97y5cuLdu3a1Ufj+d2pU6e6m5ubK0JDQ6tzc3MNAGDjxo02CQkJZhRF0V5eXvUTJ06sNDIyotvrn+YkYwAYOXJk5c8//2zq7e0tf5T36Orq2vDll1/azJs3z9jT07Nh6dKlJY/yGc+aNev+v//9b5dly5apkpKSJPHx8dnvvvuuc3V1NVupVFILFiy4FxAQ0PDtt9/emTt3ritFUXj55ZerNNs4d+6c2ZgxYypbt92/f/+G6upqdnl5OcvCwkLl7+/fMHz48Eo+ny9isViYOXNmSWBgYAMAjB07ttzX11fA4XAgEonqlixZ0qK/LBYLa9euvdfee/jll19MR40a1eb+LyKq9RhwuydR1GUAr9A03elEu1bXTAQwiqbpuQ+ezwQwkKbpSI1z/AEsp2l6AkVRvwNY+rDhs4CAADopiYywEQTRJW3GQlJSUu74+fl1OhSj9qOtL+TFbZdbG9g0YcK91CfvHtFdcnJy9KZOneqakJBws6vXZGZm6o8ZM8bz5s2bGdrsW2fq6+up4OBgr6SkJKmeXttftTVr1tiYmpqqlixZ0rXf2ccQEBDgdfr0aVmfPn20s3fBMyglJcXaz8/PtfXxrqZIbwG4QlHUMQDqSXc0TX/2uB2iKIoF4DMAb3Th3HkA5gGAs7Pz496SIAji0ZDA57nh4uLSNGfOnPtlZWWs1nsVPctkMpn++vXrC9oLiABg2bJlJTt37tTahp13797lvPfee/depICoM13NFK1u7zhN02s6uWYQgP/QNP3qg+cfPbjmvw+emwPIBsCsILADUAYgvLNsEckUEQTxCJ4sU0QQRI/0RJmizoKfTlwD4ElRlBuAAgBTAEzTaLMSgHriXVeHzwiCIAiCILShS0ERRVE8AEsBuGpeQ9P0Kx1dQ9O0gqKoSABn0bwk/zuapjMoiloLIImm6WNP0nGCIAiCIIju1NU5RQcBfA1gB4AujzvSNH0KwKlWx1Z1cO7LXW2XIAiCIAiiu3U1KFLQNL1Vqz0hCIIgCIJ4irq6eeNxiqIWUhRlT1GUJfPQas8IgiB6KDabPUCz9tnHH39s19n59fX1VEhICK91nbSnKSgoyMvV1dWbx+MJ3dzcRLNmzXLW3LmZoqgBb731liPzfNWqVbZLlizpltpx7VGpVAgODuaVlZU9dzU9ExISjH744Qfzh5/ZUlBQkNfFixeNASAkJIRXUlLS7s7ZrT+b+/fvs0eNGtXXzc1N1LdvXxGz2WRCQoKRn58fnykgfP78eWMAiI2NtaIoasCRI0fU9dl2797dm6KoAczKuDFjxvRNS0trd/PM50lXf3lmo3lzxQQA1x88yIRogiCIx8DUPmMeGzZsKOrs/ISEBGMAkEql4rfeeqvdulutKRRPVJ6tBZVKBaWy7cyJuLi4W1lZWWKJRCI2MDBQjR492oN5TV9fnz516pRFYWGhTnb/PnDggLlIJKpvvRy/o74/S5KSkoxPnjz5yEGRpqlTp5Zu2rSpT3uvtf5s5s2b5zRy5Miq27dvZ4jFYnG/fv0aAGDZsmWOy5cvvyuVSsUrV668++GHHzoxbXh6etbv379fnQyJj4+31NyFe8GCBcXr16/vNLh/HnQpKKJp2q2dR19td44gCOJZkFORo+f0uZN3bqV2y3twuVyfxYsXOwiFQgGPxxMmJycbFhQUcN588023tLQ0Yz6fL8zIyDA4evSoqUAgEPJ4PGFERIRrfX09xVy/YMECrlAoFHz33XcWXC7X55133uEy//K/fPmycWhoqKeTk5P3xo0b1V+gK1eutPX29hbweDzh4sWLHYDmjQ1dXV29x40b58rj8UTZ2dkdFgw1NDSkt27dmn/37l39P/74wwhorkU2a9askg0bNti2Pn/fvn3mvr6+fIFAIAwJCeHl5eVxAODkyZMmTPZMIBAIy8vLWR31r7W9e/dajhs3rqKjvnfUxldffWXF4/GEXl5ewtdff92NuT44OJjH4/GEgwYN4t28eVMfACZMmOA6ffp0Zz8/P76jo6PPiRMnTCMiIlz79u0rmjBhgivTprGxcf9//etfTh4eHqJBgwbx7t69ywFaZnYKCws5XC7Xp6Ghgfrvf//rcPz4cQsmE1hVVcWKiIhw9fHxEQgEAuGePXt6A0BNTQ01ZsyYvn379hWNGDHCvaGhQb3lxJQpUyoOHz6sLpnS0WdTWlrKTkxMNF20aNF95mdnbW2tBJrrvjElSyoqKti2trbqDZsHDhxYk5yc3Esul1OVlZWsO3fuGIhEojrm9VGjRtVcunTJrKmpCc+zLgVFFEXpURT1LkVRhx48IimKan+nKYIgiB5m+bnl9gVVBQbLzy3vluEfpswH89AcErO2tlaIxWLJnDlzSqKjo225XK5iy5YtOQEBATVSqVTs5ubWOH/+fLcffvghOysrS6xQKPDpp5+qAxwrKyuFWCyWzJs3rxwAnJ2dG6VSqXjgwIE1c+bMcT1+/Hh2YmKi9JNPPnEAgMOHD5vJZDLD1NRUiUQiEd+4ccP49OnTJgCQm5trEBkZWSKTyTJ4PF6nFQ04HA4EAkFdenq6IXNs2bJlxYcPH7YsLS1tMawzYsSImhs3bkglEol44sSJZWvXrrUDgJiYGLvY2NgcqVQq/vPPP6UmJiaqzvqn6fr16yYvvfSSenNhzb6np6cbttdGUlKS4aZNm+wvXLiQlZmZKd62bVsuACxYsMB5+vTppVlZWeLJkyeXLliwQJ0xqays5CQnJ0ujo6PzpkyZ4rFs2bJ7N2/ezJBKpUYJCQlGQHOB14CAgFqZTJbx0ksvVf/f//1fh783hoaG9EcffXR37Nix5Uwm8OOPP7YfOnRoVVpamuTSpUuZK1ascKyqqmJt2rTJxsjISHXr1q2MdevW3RWLxb2Ydvr06aNsbGykioqK2gyhaX42mZmZ+paWloqIiAhXgUAgnDx5sktVVRULAGJjY/NWrVrlaGdn57ty5UrHmJgYdekZiqIwePDgqsOHD5vt27ev96hRoyo078Fms+Hi4tLw559/Gnf0Xp8HXR0+2wpgAIAtDx4DHhwjCILo0XIqcvQOiQ9Z06BxMOOgdXdki1oPn2kOiU2bNq0cAIKCgury8vLazNFISUkxdHR0lPv6+soB4I033ii9fPmyeq7HrFmzWgyvTZo0qQIAfHx86vz9/WstLCxUDg4OCn19fdX9+/fZZ86cMbt48aKZUCgUikQiYXZ2tqFUKjUEAHt7+8Zhw4bVootabwZsaWmpioiIKI2OjrbRPH779m39sLAwTx6PJ4yNjbWTSqVGABAcHFyzdOlSp3Xr1tncv3+fraenh876p6myspJjYWGhHjrT7HtHbZw9e9Zs7Nix5fb29gqguXgtACQnJ/eaN29eGQAsWLCg7Pr16+og7LXXXqtgsVjw9/evs7KyagoKCqpns9ng8Xj12dnZBkBznbG5c+eWAcCcOXNKr1692iaI68zvv/9u9vnnn9vz+XxhaGiol1wup2Qymf7ly5dNZs6cWQoAAwcOrOfxeHWa11lZWSlyc3PbZPQ0PxuFQkFJJBLjd955p0QikYiNjY1VK1eutAOA2NjYPv/973/zioqKUjds2JD3xhtvuGq2M3369LL4+HjLgwcPWs6ePbtNYVlra2tFXl7ec50w6WpQFEjT9Gyaps89eLwJIFCbHSMIgngWLD+33F71oCisilahu7JFHTE0NKQBgMPh0AqFos2O3A9jamraYk4N0x6LxYK+vr46amGxWGhqaqJomsaiRYsKmQAtNzc3ffHixfeB5sKpXb2vQqFAZmamsa+vb4vq7B999NG9ffv2WdfW1qq/byIjI50XLlxYnJWVJf7qq69y5HI5CwA2bNhQtGPHjpz6+npWWFgYPzk52bCz/mlis9m05twhzb53tY2uYD5PNpvd5vPs6OdFUc2HORyOuo91dXUd/mxpmsahQ4dkTH8LCwvT/P39Gzo6nyGXy6n2fmaan42rq2ujra1t4yuvvFILAJMnTy5PSUkxBoAff/zRatasWRUAMGfOnPLU1NRemu0MHTq0TiqVGpWVlXGYoLzV/VmP8jvzLOpqUKSkKMqdeUJRVF88wn5FBEEQzyMmS9SkaqIAoEnVRHVXtuhx+fn5NRQUFOinp6cbAEBcXJxVWFhY9eO2N3r06Krdu3dbV1ZWsgDg9u3begUFBY/0/uRyORUZGelob2/fOHDgwHrN12xtbZVjx44t37dvn7qCQXV1NdvZ2bkJAL7//nv1PJiMjAyDoKCg+vXr1xf5+vrWpqenG3a1f25ubg0SiaTd1U8dtfHqq69WHT9+3IIZcrp37x4bAPr371+7Y8cOCwDYtm2bZUBAQE177XZEpVKBWZX1/fffWwUFBVUDgJOTk/zq1au9AGDv3r3qIVMzMzNlTU2N+vt46NChVTExMbYqVXN8ceXKFSMACA0Nrdm7d68lAFy7ds0wKyvLWPOeJSUlel5eXm2CFc3PxtnZWWFnZ9eYkpJiAAA///yzmZeXVwMA9OnTp+nUqVOmAHD8+HFTFxeXNoFYVFRUflRUVEHr4wBw+/ZtA39///r2XntedPUXfxmA8xRF3UJzLSEXAG9qrVcEQRDPAM0sEYPJFu0etzv3cdtl5hQxz1955ZXKLVu2tPtF05qxsTH99ddf34mIiHBXKpXw8/OrW7p0acnj9mX8+PFVGRkZhoGBgfwH7av27t17m8PhPLQw5qxZs/rq6+urGhsbWWFhYVWnT5+WtXfe8uXLi3bt2tVH4/ndqVOnupubmytCQ0Orc3NzDQBg48aNNgkJCWYURdFeXl71EydOrDQyMqLb6x+Xy22xvG7kyJGVP//8s6m3t3eboKCj9xgQENDw/vvvF4aFhfFZLBbt7e1d9+OPP975+uuvc2fNmuX6v//9z87KykoRFxd35xE+UhgZGamuXr3a69NPP3WwsrJqOnz48C0A+L//+797kydP7vv999/3GTFiRAVz/ujRo6s3bdpkz+fzhe+//35hdHT03Xnz5jnz+XyhSqWinJyc5OfPn5ctXbq0eMqUKW59+/YVeXh4NAiFQvXQ5uXLl4379+9f215h2dafzZdffpk7ffr0vo2NjZSzs7N8//79dwBg69atOUuWLHF6//33KQMDA9XXX3+d07qtSZMmVbX3nvPy8jgGBga0s7Nz9y17fAq6VBAWACiKMgDg9eBpJk3TbX7xdIEUhCUI4hE8UUFY2022vsW1xW2+ZWx62TTdW3ovtTs6SHSPnJwcvalTp7omJCTcfNp9MTY27l9XV5esy3u++eabTq+//nrFP//5zzZZQ118NmvWrLExMzNTPe6wpK49UUFYiqIiAJyhaTqVoqgVAPwpilpH0/Rf3dxPgiCIZwYJfJ4fLi4uTXPmzLlfVlbGar1X0YvA29u7vr2ACNDNZ9O7d2/lwoULS7XRti51KVNEUVQqTdO+FEWFAogCsAnAKpqmB2q7g62RTBFBEI/giTJFBEH0TB1liro80frBf18D8A1N0ycBdLiRF0EQBEEQxPOmq0FRAUVR2wBMBnDqwfyi566+DEEQBEEQREe6GthMAnAWwKs0TVcAsETzijSCIAiCIIge4aETrSmKYgP4i6ZpPnOMpulCAIXa7BhBEARBEIQuPTRTRNO0EkAmRVHOOugPQRAE0UX9+/fnP/ysrjl//rxxQECAl6urqzdTE6u6upq1ZMkSh1WrVrUp6trdlixZ4mBjY+PL5/OFbm5uounTpzszuzBPmDDBlcvl+vD5fKFQKBT8+uuvvZjjNjY2vkxBXKbIamf3uX//Pjs6OrrdavKM3bt396YoakBycrIhAPj6+vL5fL7Q3t7ex8LCwo+pWZeZmanf1NQECwsLv4ULF3I126isrGRNmzbNxcnJyVskEgmCgoK8zp0716v1vVQqFYKDg3llZWUsoLmoL4/HEzJFfJnzXnvttb7MfZnPAgB++uknM5FIJODxeEKRSCQ4duyYuuQLl8v1GTBggJfm/fh8vtDT01MEAFevXjXSLGRLdH34zAJABkVRv1EUdYx5aLNjBEEQROeSk5Ol3dFOXl4eZ/r06e7R0dH5d+7cSZdIJOJRo0ZVVVRUdOk7QqVSQbPExuN6++2370mlUrFMJsuQSqVGzO7KALBu3bp8qVQqXrduXcHChQtdmONsNpuOjY21br/FtkpLS9nffvutTWfnxMfHW/r7+9fExcVZAkBqaqpUKpWKNQu3SqVSsZeXV+NPP/1k5ubmJj9+/LgFswM1AEyfPt3VwsJCcefOnfSMjAxJXFzc7eLi4jajMwcOHDAXiUT1mkvlL1y4kCWVSsXp6ekS5tjJkydvMff9xz/+UT5mzJhyALCxsWk6efKkLCsrS/z999/fnjt3rptm+7W1tWyZTKYHAH/99VeLmnFBQUH1hYWF+jdv3iQLpx7oalC0EsAYAGsBxGg8nkmOnzmCWkO1eTh+5vi0u0YQxHNk6VLYTZgA19aPpUth97htZmZm6jP/UgeAVatW2S5ZssQBAIKCgrwWLFjA9fHxEbi6unqfOXPGBACSkpIMfXx8BHw+X8jj8YRpaWkGQPMmgQCgVCoxY8YMZzc3N1FISIjnkCFDPJgyE1wu12fx4sUOQqFQwOPxhEz2Q1NMTIzNpEmTSocPH67eIfnNN98sd3JyUgCARCIxCgoK8nJ0dPRZt26dDfM+XF1dvceNG+fK4/FE2dnZ+vPnz3f09PQU8Xg84TfffGMBACdOnDANDAz0GjZsmLujo6PPwoULuVu3brX08fER8Hg8YUZGRpvSHHK5nJLL5SwrK6s2uyOPGjWqWrNQ7vz584u3bt1q29TU1OazXrlypa23t7eAx+MJFy9e7AAA77//vmNeXp4Bn88Xzp8/v82XQmVlJevatWsmO3fuvPPTTz9ZdvBjVNu/f7/lwoUL7zk4ODT+9ttvvYDmciXJycm9/ve//xWw2c1F6/l8fuOUKVMqW1+/d+9ey3HjxlU87D4MlUqF48ePqwuyvvTSS/Wurq5NADBgwIAGuVzOYjJnAPD666+XMcFdXFyc5YQJE1oUch09enTFrl27LEAA6GJQRNP0hfYe2u7c4wr3Coc+u2Xgq8/Wxz+9/vmUekQQxPOof3/Us1igXVzQyDxYLND+/tBafSeFQkGlpaVJPvnkk7y1a9c6AMCXX37ZZ+HChfekUqk4NTVV4ubm1qh5TVxcnEVeXp6+TCbLiI+Pv52cnNyiKru1tbVCLBZL5syZUxIdHd1mKEwsFhsFBATUtT7OkMlkhhcuXMi6du2aZNOmTQ5yuZwCgNzcXIPIyMgSmUyWkZCQYJyWlmYkkUgyfvvtt6xVq1Y55uTk6AGAVCo1+u6773Jv3ryZfujQIausrCzDtLQ0ycyZM+/HxMSoszZff/21LZ/PF9rZ2fm5ubk1hISEtPmc4+Pje3t6eqqPu7i4NAYGBtZs2bLFSvO8w4cPm8lkMsPU1FSJRCIR37hxw/j06dMmMTEx+U5OTnKpVCretm1bfuv29+3b1/vll1+u9PX1lVtYWCguXbpk3PocRl1dHXXlyhWzKVOmVERERJTt2bPHEgBu3LhhKBQK6zich++PfP36dZOXXnqpVvPYsGHDPEUikWDTpk1tMmBnz541sba2bvLx8WlTVWLXrl0WIpGozsjISL0B4dSpU8uPHz9u8eDa3uPHj6/QvGbgwIG1CQkJpiAAdDEooigqmKKoaxRF1VAU1UhRlJKiqHbrnzwLVg5eCRbV8q2xKTZWDln5lHpEEMTz6B//QDWHA8jlzZtAyuWgOBzgtdfw2AVYHyYiIqIcAEJCQmrz8/P1AWDQoEG1MTEx9suXL7e7efOmvomJSYtddy9dumQyfvz4cjabDWdnZ0VwcHCL/k2bNq0cAIKCguo0syxdNXLkyAojIyPa3t5eYWlp2ZSfn88BAHt7+8Zhw4bVPuiD6aRJk8o4HA6cnJwUAwcOrLl8+bIxAPj4+NS6uLg0GRkZ0c7OzvLRo0dXAoCfn199bm6u+l+wzPBZSUlJSl1dHWv79u3qDMaKFSsc+Xy+cMeOHdbffvvtHc3+rVq1qjA2NtZOc/jqzJkzZhcvXjQTCoVCkUgkzM7ONpRKpW2yZK0dOHDAcurUqeUAMGHChLLdu3d3mC364YcfegcHB1ebmJjQM2bMKD979qyFQvFopb8qKys5FhYW6o5fvnxZKhaLJT///PPNb775xub06dMtAtw9e/a0yfYAzdnEVatWcb/55psW9cpsbGyU5ubmiu3bt1t4eHjUm5iYtNjR2t7eXnHv3r22BdNeUF0dPvsKwFQANwEYAZgLYLO2OvWk7E3t8Wa/N9XZIn22Pt7s9ybsTB47400QxAvIwgKqMWNQUVzcvFK3uBicsWNRYW6Oxy6VwOFwaM0v74aGhhZ/DxsaGtIPzoNSqaQA4O233y47evSozMjISDVmzBhPzcm0XaHRJq1QKNrs8i0QCOqTkpI6zIgYGBiogzA2mw2mDWNj4y59DprXs1gsdX9YLJb6PbY+f+TIkVUXL15sM6coISHhZmBgYIvq7T4+PnKhUFinOQxE0zQWLVpUyMzDyc3NTX9YXa579+6x//zzT9N33nnHhcvl+nz11Vd2recKaYqPj7e8cuWK2YMJzcLKykr28ePHzfr169cgkUiMuxIgsdlsWnM+lpubWxMAcLlcxWuvvVbxxx9/qCdnNzU14cyZMxazZs1qERRlZ2frTZw40ePbb7+9LRKJ2mSQJk6cWP7BBx+4TJ06tU0wVV9fzzI0NHzhyqJ0pMsbMNI0LQPApmlaSdP0TgCjtNetJ6eZLSJZIoIgHheTLaquBqs7skSOjo6KsrIyTlFREbu+vp46e/as+cOuEYvF+gKBQL5ixYriV199teLGjRtGmq+HhobWHDlyxEKpVCIvL4+TmJj4SEHT0qVLiw8cOGCluTpq165dvfPy8rpUHxMABg8eXH3o0CFLhUKBu3fvcq5evWoSFhZW+/Ar21KpVEhISDBxd3fvcuHx1atXF27evFn9L9/Ro0dX7d6927qyspIFALdv39YrKCjgmJubK2tra9v97tu9e7fFuHHjyu7evZtWUFCQVlRUlOro6Nh49uxZk9bnlpWVsa5du2aSn5+fWlBQkFZQUJAWHR2du2/fPkuRSCT39fWtXbJkiQMTUGVmZurHx8e3+Vm7ubk1SCQSAwCoqqpilZeXs5g/nz9/3szX11c9VHj06FGzvn37Nri7u6snUN2/f5/9j3/8w3PNmjX5I0eObPfznj59evk777xTNH78+DYjPGKx2MDLy0trw8HPm64GRXUURekDuEFR1EaKohY/wrVPBZMtYlEskiUiCOKxMdmi7GwYPGmWCGjOgrz//vuFgYGBgrCwMJ6Hh0fDw67Zs2ePJY/HE/H5fKFEIjGaP39+i8Kbs2fPLre3t2/08PAQTZ482U0kEtX17t27y8vBnJycFHFxcbeWLVvm6Orq6t23b1/R2bNnzXv37t3l9zpz5swKkUhULxAIRC+//DJvzZo1+c7Ozo80lsTMKeLxeCKVSoVly5YVd/XagICABpFIpJ4XNX78+KqIiIiywMBAPo/HE44bN869oqKCbWdnpxwwYECNp6enqPVE64MHD1qOHz++XPPYP//5z3JmrpCmvXv3WoSEhFRrzt+ZMmVKxa+//mpeX19P7dmz505xcbGei4uLt6enp2jmzJlu9vb2bWaDjxw5svLnn382BYD8/HxOcHAw38vLS+jv7y8YOXJkxcSJE9WBzP79+y0jIiJaZHs2btxok5uba/Df//7XgVmyX1BQ0CKYtbCwUK1fv76IydBpOnfunNmYMWPaTAB/UXW1IKwLgHtorne2GIA5gC0PskedXTcKwP8AsAHsoGk6utXrbwN4B8211WoAzKNpWtxZm49SELawuhChO0NxZc4VEhQRxIupWwrClpeDFRODPsuWoeRJgyJtqaysZJmbm6uKiorYgYGBgitXrkgfNSghdC8nJ0dv6tSprgkJCTd1fe/6+noqODjYKykpSaqn92JNK+qoIGyXUqM0TedQFGUEwJ6m6TVduebBTtibAYwAkA/gGkVRx1oFPftomv76wfnhAD5DNw7L2ZvaI/vd7O5qjiCIF5SFBVTr1uHe0+5HZ0aMGOFZVVXFbmpqopYtW1ZIAqLng4uLS9OcOXPul5WVsTT3KtIFmUymv379+oIXLSDqTJeCIoqixgLYhOZMkRtFUf0ArKVpOryTy4IAyGiavvWgjXgA/wSgDopomtYc3+wF4OFpqy4YuOFX3KtqOxRta2aAxI+Hd8ctCIIgnilXr17NfNp9IB7P3Llzyx9+Vvfz8fGRt7e0/0XW1XlB/0FzkFMBADRN3wDg1vHpAAAugDyN5/kPjrVAUdQ7FEVlA9gI4N0u9qdTIwS20GO3zJrrsSmMEJIhNIIgCIIg2tfVoKiJpunWE7G6JatD0/RmmqbdAXwIYEV751AUNY+iqCSKopJKSkoe2ua7wzzBoloGRWyKwrvDPLqjywRBEARB9EBdXW6ZQVHUNABsiqI80ZzRSXjINQUAnDSeOz441pF4AFvbe4Gm6e0AtgPNE60f1lkbM0NEDHDED0l5aFLS0GNTmBjgBBvTh+7b9cgcP3NEQXXbt8U15SJ/SZvNUgmCIAiCeEZ1NVP0bwAiAHIA+wBUAlj0kGuuAfCkKMrtwXL+KQBaFJF9EGAxXkPz5pDdQjNbpM0sESkpQhAEQRA9Q6dBEUVRhhRFLULzfJ9cAINomg6kaXoFTdOd7q1B07QCQCSAswAkAA7QNJ1BUdTaByvNACCSoqgMiqJuAFgCYPaTvZ2/MdkiioLWskQAKSlCEMTT079/f353tXX+/HnjgIAAL1dXV2+BQCCcPHmyS3V1NWvJkiUOq1atalMvrbstWbLEwcbGxpfP5wvd3NxE06dPd2Z2ep4wYYIrl8v14fP5QqFQKPj11197McdtbGx8mQKohYWFHC6X69PZfe7fv8+Ojo7u09k5u3fv7k1R1ACmeK6vry+fz+cL7e3tfSwsLPyY/YAyMzP1m5qaYGFh4bdw4cIWc2YrKytZ06ZNc3FycvIWiUSCoKAgL83NMRkqlQrBwcG8srIyFtBcwJfH4wn5fL7Q29tbwJx37949dkhIiKeLi4t3SEiIZ0lJCRsAYmNjrSiKGnDkyBH1hp1M/5miwGPGjOnLFBEmOvewTNEuAAEA0gCMRvMKtC6jafoUTdM8mqbdaZpe/+DYKpqmjz3483s0TYtomu5H0/RQmqYzHuM9tBEVBbzxBpB93BMGTcbIPuaBN95oPt7dSEkRgiCeluTkZGl3tJOXl8eZPn26e3R0dP6dO3fSJRKJeNSoUVUVFRVdGk1QqVTQLFXxuJjaZzKZLEMqlRqdOnWqTZmPdevWFSxcuNCFOc5ms+nY2Ng2hVM7Ulpayv72229tOjsnPj7e0t/fv4apLp+amiqVSqXijz766O7YsWPLmdIhXl5ejT/99JOZm5ubvHU5kOnTp7taWFgo7ty5k56RkSGJi4u7XVxc3GbKyoEDB8xFIlG95nL8CxcuZEmlUnF6erqEObZ69Wr7l19+uTonJyf95Zdfrl61apX6S8bT07N+//796g0m4+PjLTV3qV6wYEHx+vXryZdSFzzsF15I0/QMmqa3AZgIYLAO+vTE/PwAigK8nA0xxWQovJwNQVFAv37df6+BG37FsUuD0PRgR5AmBXDs0iAM3PBr99+MIAidsrWFL0VhQOuHrS18H7fNzMxMfU9PTxHzfNWqVbZLlixxAICgoCCvBQsWcH18fASurq7eZ86cMQGai336+PgIHuz2LGT+1W9sbNwfAJRKJWbMmOHs5uYmCgkJ8RwyZIgHkyXgcrk+ixcvdhAKhQIejydksh+aYmJibCZNmlQ6fPhwdZmIN998s9zJyUkBABKJxCgoKMjL0dHRZ926dTbM+3B1dfUeN26cK4/HE2VnZ+vPnz/f0dPTU8Tj8YTffPONBQCcOHHCNDAw0GvYsGHujo6OPgsXLuRu3brV0sfHR8Dj8YQZGRltMhhyuZySy+UsKyurNnstjRo1qlqzqO38+fOLt27datvU1GazaKxcudLW29tbwOPxhIsXL3YAgPfff98xLy/PgM/nC1vvaA00Z3iuXbtmsnPnzjs//fRTh8VgGfv377dcuHDhPQcHh8bffvutFwBkZGQYJCcn9/rf//5XwGazAQB8Pr9xypQpbXaO3rt3r+W4ceMqHnafM2fO9GZ2Mp8/f37p6dOn1XXeBg4cWJOcnNxLLpdTlZWVrDt37hho7u49atSomkuXLpm19xkRLT0sKFJ/gg+Gw54LQ4cC+vqA/MHuC3J58/OhQ7v/XiMEtjBiW6GXchhAU+ilHA5DtiVZ/k8QPcCIEajQ02u50lZPD/TIkc3bk2iDQqGg0tLSJJ988kne2rVrHQDgyy+/7LNw4cJ7UqlUnJqaKnFzc2vUvCYuLs4iLy9PXyaTZcTHx99OTk5uUavL2tpaIRaLJXPmzCmJjo5uMxQmFouNAgIC6lofZ8hkMsMLFy5kXbt2TbJp0yYHuVxOAUBubq5BZGRkiUwmy0hISDBOS0szkkgkGb/99lvWqlWrHHNycvQAQCqVGn333Xe5N2/eTD906JBVVlaWYVpammTmzJn3Y2Ji1FkbpsyHnZ2dn5ubW0NISEibmlzx8fG9PT091cddXFwaAwMDa7Zs2WKled7hw4fNZDKZYWpqqkQikYhv3LhhfPr0aZOYmJh8JycnuVQqFW/btq3Naph9+/b1fvnllyt9fX3lFhYWikuXLnVYKLeuro66cuWK2ZQpUyoiIiLKmHIgN27cMBQKhXUczsPXMl2/ft3kpZdealGzbNiwYZ4ikUiwadMmdQastLSU4+Li0gQATk5OTaWlperGKYrC4MGDqw4fPmy2b9++3qNGjarQbI/NZsPFxaXhzz//7PC9EM0eFhT5URRV9eBRDcCX+TNFUW0Kyz0rBAJg+3YgOhpYs6b5v9u3A/xuG33/GzOh27xpKji0LXo3TSXL/wmih1i/HoWsVn9LsljA+vW4q617RkRElANASEhIbX5+vj4ADBo0qDYmJsZ++fLldjdv3tQ3MTFpEahdunTJZPz48eVsNhvOzs6K4ODgFkVrp02bVg4AQUFBdZpZlq4aOXJkhZGREW1vb6+wtLRsys/P5wCAvb1947Bhw2of9MF00qRJZRwOB05OToqBAwfWXL582RgAfHx8al1cXJqMjIxoZ2dn+ejRoysBwM/Prz43N1e9UoUZPispKUmpq6tjbd++XZ0NWbFihSOfzxfu2LHD+ttvv72j2b9Vq1YVxsbG2mkOX505c8bs4sWLZkKhUCgSiYTZ2dmGUqn0oZNLDxw4YDl16tRyAJgwYULZ7t27O8wW/fDDD72Dg4OrTUxM6BkzZpSfPXvWQqF4tPxBZWUlx8LCQt3xy5cvS8ViseTnn3+++c0339icPn26TTFaFosFqtW2M9OnTy+Lj4+3PHjwoOXs2bPLWl9jbW2tyMvLI1tXP0SnQRFN02yaps0ePExpmuZo/NlMV518VOHhzZkhTfr6wD+1sCCMmdBtxLYCV74DhmxLrU7sJghCd1xc0DRxIu4z2SI9PdAREbjv7IzHzpxzOBxa88u7oaGhxd/DTNFODocDpVJJAcDbb79ddvToUZmRkZFqzJgxnseOHTPFI9Bok1YoFG3qwQkEgvqkpKQOswgGBgbqIIzNZoNpw9jYuEtlKTSvZ7FY6v6wWCz1e2x9/siRI6suXrzYZk5RQkLCzcDAwBYLfXx8fORCobBu165d6iCKpmksWrSokJn/k5ubm7548eJOa97du3eP/eeff5q+8847Llwu1+err76yaz1XSFN8fLzllStXzLhcrs+AAQOElZWV7OPHj5v169evQSKRGHclQGKz2bTmfCw3N7cmAOByuYrXXnut4o8//ugFAFZWVgom85aTk6NnaWnZovGhQ4fWSaVSo7KyMo6vr2+bXarlcjmrqz+vF9kzXen+ca1c2fyvOU1sdvNxbdDV8n+CIHRPM1vUHVkiR0dHRVlZGaeoqIhdX19PnT171vxh14jFYn2BQCBfsWJF8auvvlpx48YNI83XQ0NDa44cOWKhVCqRl5fHSUxMfKSgaenSpcUHDhyw0lwdtWvXrt55eXld3csOgwcPrj506JClQqHA3bt3OVevXjUJCwurffiVbalUKiQkJJi4u7t3uQTF6tWrCzdv3qyetzB69Oiq3bt3W1dWVrIA4Pbt23oFBQUcc3NzZW1tbbvffbt377YYN25c2d27d9MKCgrSioqKUh0dHRvPnj3bJltTVlbGunbtmkl+fn5qQUFBWkFBQVp0dHTuvn37LEUikdzX17d2yZIlDkxAlZmZqR8fH9/mZ+3m5tYgkUgMAKCqqopVXl7OYv58/vx5M19f33oAePXVVyu2bdtmBQDbtm2zaj1EBgBRUVH5UVFR7e4HePv2bQN/f/82w5FESz0yKLK3B9588+9skb5+83M7LU3z0dXyf8fPHEGtodo8HD9rM1eQIIhuwmSLKAp40iwR0JwFef/99wsDAwMFYWFhPA8Pj063NwGAPXv2WPJ4PBGfzxdKJBIjZsItY/bs2eX29vaNHh4eosmTJ7uJRKK63r17d3k5mJOTkyIuLu7WsmXLHF1dXb379u0rOnv2rHnv3r27nFmYOXNmhUgkqhcIBKKXX36Zt2bNmvxHLUrLzCni8XgilUqFZcuWFXf12oCAgAbNycXjx4+vioiIKAsMDOTzeDzhuHHj3CsqKth2dnbKAQMG1Hh6eopaT7Q+ePCg5fjx41vUIfvnP/9ZzswV0rR3716LkJCQaiMjI3UWbMqUKRW//vqreX19PbVnz547xcXFei4uLt6enp6imTNnutnb27eZ6Txy5MjKn3/+2RQA8vPzOcHBwXwvLy+hv7+/YOTIkRUTJ06sAoA1a9YUnj9/3szFxcX7999/N1uzZk1h67YmTZpUNXbs2OrWx/Py8jgGBgY0KRL8cBRNd0u1Dp0JCAigk5KSHnpeYSHQty/Q0AAYGQG3bmknKIqKArKzATmrAcmWf6B/2SAYqAzh7t79mamFJxfi2+Rv0aj8e46lPlsfc/vPxebXNnfvzQiiZ2gzNJOSknLHz8+v02GU1nJyoBcaCq8rVyB90qBIWyorK1nm5uaqoqIidmBgoODKlStS8iX47MvJydGbOnWqa0JCQrdtXtzamjVrbMzMzFQPGz58kaSkpFj7+fm5tj7eIzNFwN/ZIhZLu1kiXS7/JxtFEsTT4eKCprw8pD+rAREAjBgxwpPP5wtfeukl/rJlywpJQPR8cHFxaZozZ859ZvNGbejdu7cyMjKSBERd0OXx4ufRypXA2bPam0sENC/zP3myedm/gYF2l/8zG0Uy2SKyUSRBEIyrV69mPu0+EI9n7ty55Q8/6/G99957pQ8/iwB6eFBkb988tKVNAgFQ0M60tpMngfxurgc7cMOvKKgahCbDbwHq740ik1J+ReLHw7v3ZgRBEATxgunRQZEuhIcD334LNGpspaat5f8jBLb4IakRvZTDUMM+o9WNIh0/c0RBddtoj2vKRf6Sbo72CIIgCOIZ0GPnFOmKLpf/63KjyHCvcHU9N4Y+Wx//9NJCtEcQBEEQzwASFD0hXS7/1+VGkWRSN0EQBPGiIUFRN9DMFmlzk0hAdxtFMpO6mWyRNid1k/2XCOLx9O/fv9uKF50/f944ICDAy9XV1VsgEAgnT57sUl1dzVqyZInDqlWr2tRL625LlixxsLGx8eXz+UI3NzfR9OnTnZmdnidMmODK5XJ9+Hy+UCgUCn799ddezHEbGxvf+vp6CgAKCws5XC7Xp7P73L9/nx0dHd2no9fZbPYAPp8v9PDwEHl5eQlXr15ty/TjxIkTphRFDdi3b596E8ahQ4d6nDhx4pE2y3wUOTk5ekOHDlX/RZ+YmGjUr18/voeHh4jH4wnr6uooANi2bZslj8cT8ng8YVhYmGdhYSEHaP6MjIyM+jObQgLAnDlznCiKGlBYWMhpaGigAgICvEix2GYkKOoGulr+HxUFfPCuISwrHQEasKx0wgf/NkRUlHbup5kt0maWiAzVEcTjSU5OlnZHO3l5eZzp06e7R0dH59+5cyddIpGIR40aVVVRUdGl7wiVSgXNUhWPi6l9JpPJMqRSqdGpU6falPlYt25dwcKFC12Y42w2m46NjbVuv8W2SktL2d9++61NR68bGBiomD6cO3cu65dffjFfunSpA/O6ra1t0yeffGL/OO/vcWzYsMH2X//6130AaGpqwsyZM922bt2aI5PJMi5evJipr69PNzU14aOPPnK6cOFCVlZWllgkEtV/+umn6vfo5OQk379/f28AUCqVuHz5sqmNjU0T0FwCZsiQIVU7duzosMbbi4QERd1k5UrA1VW7WSJmT6QwS0+YUMYItfTQ2p5IAzf8ikHr/wKnYShAU2A3DEXwuusYuOHXbr8XGaojnlUDon7xdf2/kwNaPwZE/eL7uG1mZmbqe3p6ipjnq1atsl2yZIkDAAQFBXktWLCA6+PjI3B1dfU+c+aMCQAkJSUZ+vj4CB7s9ixMS0szAABjY+P+QPMX3YwZM5zd3NxEISEhnkOGDPHYuXOnBQBwuVyfxYsXOwiFQgGPxxMmJye3GW+PiYmxmTRpUunw4cPVZTnefPPNcicnJwUASCQSo6CgIC9HR0efdevW2TDvw9XV1XvcuHGuPB5PlJ2drT9//nxHT09PEY/HE37zzTcWQHN2JTAw0GvYsGHujo6OPgsXLuRu3brV0sfHR8Dj8YQZGRltCtTK5XJKLpezrKys2uy1NGrUqGrNorbz588v3rp1q217mY6VK1faent7C3g8nnDx4sUOAPD+++875uXlGfD5fGHrHa1b43K5ih07dtzZuXOnDVOuQyAQ1Jmamip/+umnNvU/ly5dau/t7S3w9PQUTZ061YW5Zt26dTbu7u4iHo8nHDNmTF+guYxHRESEq4+Pj0AgEAj37NnTu70+nDx50mLChAmVAHD48GFzgUBQP2jQoHoAsLOzU3I4HKhUKoqmaVRXV7NUKhWqqqpYDg4O6uU/48ePLzt06JDlg/ZMAwMDazgcjnrn5okTJ1bEx8eToAgkKOo2zPJ/bWWJgOa9j/T1AXajISYaDgW70VBreyKNENhCj91yUrcem9LKSjddDtURxKMI87Su4LCoFtv+c1gUPZjXp0Jb91QoFFRaWprkk08+yVu7dq0DAHz55Zd9Fi5ceE8qlYpTU1Mlbm5ujZrXxMXFWeTl5enLZLKM+Pj428nJyS1qdVlbWyvEYrFkzpw5JdHR0W2GwsRisVFAQEBd6+MMmUxmeOHChaxr165JNm3a5CCXyykAyM3NNYiMjCyRyWQZCQkJxmlpaUYSiSTjt99+y1q1apUjU8BUKpUafffdd7k3b95MP3TokFVWVpZhWlqaZObMmfdjYmLUGQ2mzIednZ2fm5tbQ0hISJtaXfHx8b09PT3Vx11cXBoDAwNrtmzZYqV53uHDh81kMplhamqqRCKRiG/cuGF8+vRpk5iYmHwnJye5VCoVb9u27aFLaYVCYaNSqURBQYF6tfby5csLN2zY0CZbtGzZsuL09HTJzZs3M+rr61lMrbPY2Fi79PR0cVZWlvj777/PAYCPP/7YfujQoVVpaWmSS5cuZa5YscKxqqqqxXeyVCrVNzc3VzClRDIzMw0oikJoaKinUCgUrFixwhZoLh3z2Wef5fr7+4tsbW19s7KyjBYtWqTerNHLy0teWlrKKSkpYe/bt89y2rRpZZr3CQwMrE9NTe0FggRFzxOBANi+HYiOBtasaf7v9u0Av9tmFfyNmbvEgSW48h1gw0Krc5h0NVQHkDlMRNcte5VfyMzhY7BYFJa96vVERWE7ExERUQ4AISEhtfn5+foAMGjQoNqYmBj75cuX2928eVPfxMSkRaB26dIlk/Hjx5ez2Ww4OzsrgoODW9S/mjZtWjkABAUF1WlmWbpq5MiRFUZGRrS9vb3C0tKyKT8/nwMA9vb2jcOGDat90AfTSZMmlXE4HDg5OSkGDhxYc/nyZWMA8PHxqXVxcWkyMjKinZ2d5aNHj64EAD8/v/rc3Fz12DkzfFZSUpJSV1fH2r59u7rq/YoVKxz5fL5wx44d1t9+++0dzf6tWrWqMDY21k6zmv2ZM2fMLl68aCYUCoUikUiYnZ1tKJVKu2VVyujRo2sAoHWh2NOnT5v6+vryeTyeMCEhwTQ9Pd0IALy8vOrHjRvntmXLFks9PT0aAH7//Xezzz//3J7P5wtDQ0O95HI5JZPJWswjyMvL07O0tFRnyxQKBXXt2jWTgwcP3k5MTMw8ceKExdGjR03lcjm1ffv2PomJieJ79+6lCoXC+o8//rhF0DZ27Njy7777zvKvv/7qNWrUqBrN1zgcDvT09GjNeUcvqhf+A3iehIf/vcqNoa09kZiVbnrs5i8EPTal1WK3TLaIRbG0niUic5iIruJaGDX9w8fuPpMt4rAo+jUf+/sOvY0eu4QGh8OhNb+8GxoaWvw9bGhoSD84D0qlkgKAt99+u+zo0aMyIyMj1ZgxYzyPHTv2SBN7NdqkFQpFm3pwAoGgPikpybij6w0MDNRBGJvNBtOGsbFxlwrGal7PYrHU/WGxWOr32Pr8kSNHVl28eLHNnKKEhISbgYGBLYro+vj4yIVCYd2uXbvUQRRN01i0aFGhVCoVS6VScW5ubvrj1P4Si8X6bDYbXC63xc/8o48+Kly3bp068Kirq6Pef/99l8OHD2dnZWWJZ8yYcZ/52Z4/f/7mO++8U/LXX38Z9+/fX9DU1ASapnHo0CEZ07/CwsI0f3//Fu/L2NhYJZfL1b8fjo6OjQMHDqy2t7dXmJqaqkaMGFGZlJRk/OeffxoBgEgkkrNYLEydOrUsMTGxReZn1qxZ5dHR0Q5DhgypYrPZbd5nU1MTZWxs/HwVQ9UCEhQ9R3S5JxKgu5VujJWDV8K1t6vW5xKROUzEo9DMFnVHlsjR0VFRVlbGKSoqYtfX11Nnz541f9g1YrFYXyAQyFesWFH86quvVty4ccNI8/XQ0NCaI0eOWCiVSuTl5XESExMfKWhaunRp8YEDB6zOnTun/iLdtWtX77y8vC5v8Dt48ODqQ4cOWSoUCty9e5dz9epVk7CwsNqHX9mWSqVCQkKCibu7u7yr16xevbpw8+bN6n9NjR49umr37t3WlZWVLAC4ffu2XkFBAcfc3FxZW1vbpe++u3fvct566y2XN998s5jV6i/f8ePHV1VWVrKlUqkRANTV1bEAwM7OTlFZWck6fvy4BdA83ys7O1t/7Nix1Zs3by6oqalhV1ZWsocOHVoVExNjywTIV65cMUIrPj4+8oKCAvW/4MaNG1cllUqNqqurWU1NTbhy5YqpSCRqcHFxaZLJZIZ3797lAM1ZMh6P1yLA4vF4jR9//HHBokWLSlrfp6ioiN27d2+FZvD6oiI7Wj9HmFVuzA7a2twTCfg7W7T3aq5Ws0QDN/yKe1XM332xCF53HQBga2aglfIlpIYc8SiYbNHRG3f7PGmWCGjOgrz//vuFgYGBAltb2yYPD4+Gh12zZ88eywMHDlhxOBy6T58+TVFRUYWar8+ePbv8119/NfXw8BDZ29s3ikSiut69e3d5OZiTk5MiLi7u1rJlyxxLS0v1WCwWHRwcXDN+/PiqrrYxc+bMioSEBBOBQCCiKIpes2ZNvrOzsyI1NbWrTeDrr7+2PXDggJVCoaAEAkHdsmXLirt6bUBAQINIJKrLyMgwBpqDloyMDMPAwEA+0Jx12bt3722RSCQfMGBAjaenp+iVV16pbD2vSC6Xs/h8vlChUFBsNpuePHly6erVq++1d88PP/ywcMaMGR4AYG1trZw+fXqJQCAQ9enTR+Hn51cLNA95TZs2za26uppN0zQ1d+7cYmtra2V0dPTdefPmOfP5fKFKpaKcnJzk58+fl2m2b2ZmpnJ2dpanp6cbeHt7y/v06aOMjIy8179/fwFFURg2bFjllClTKgFg2bJlhaGhoV4cDod2dHRs3Ldv3+3W/V22bFm7mbLTp0+bDR8+vLKrn3VPRtH08xUYBgQE0ElJSU+7G09NYSHQty/Q0AAYGQG3bmknKIqKap44Lmc1INnyD/QvGwQDlSHc3bs/M7XipzT8kJSHJuXfv4t6bAqTA52x7nXv7r3ZA4XVhegb2xcNigYYcYxw671bJCjqmdoMzaSkpNzx8/N7pGGUgvJ6vQlfJ3gdXhAifdKgSFsqKytZ5ubmqqKiInZgYKDgypUrUmdn52eyr0TXxcXF9U5KSjKOjY3V2jy2kSNHum/atCnf19e3y5m5511KSoq1n5+fa+vjZPjsOaOrPZGY5f9ezoaYYjIUXs6GWlv+rzlMx9D2cJ0u5zARzz+uhVHTnx8NS39WAyIAGDFihCefzxe+9NJL/GXLlhWSgKhnmDVrVoWrq2vjw898PA0NDVR4eHjFixQQdYZkip5DhYVAaChw5Yr2gqLqamDpUsDSEjAwAORyoKwMiIkBTEwefv2j0swWaTtLxCisLkTozlBcmXNFa0ERKaz71HVLpoggiJ6lo0yRVucUURQ1CsD/ALAB7KBpOrrV60sAzAWgAFACYA5N0zna7FNPwOyJpE0CAVDQ9rscJ08C+Vr4Ln93mCcOXs8HQOtkUjfQnC3Kfle7H2S4V7h67hKDrHR7NCSwJAhCV7Q2fEZRFBvAZgCjAQgBTKUoStjqtGQAATRN+wI4BGCjtvpDPBpdLv8H/p7UTVHQ6qRuXSMr3Z4c2UKBIAhd0eacoiAAMpqmb9E03QggHkCLv8Vomj5P0zSzi+qfAMjuec8IXS7/j4oC3ngDyD7uCYMmY2Qf88Abb0BrNd10iezW/eRIYEkQhK5oMyjiAsjTeJ7/4FhH/gXgtBb7QzwCZkI3ky3S5vJ/XU7qfhp0tVt3T92pmwSWBEHoyjOx+oyiqBkAAgB82sHr8yiKSqIoKqmkpM2+U4SWaGaLtLlJJFPTTf5g7YNcDq3VdAOa90Vy/b+TbR7aKHYL6G6lW08eZtJlGZjnSf/+/bulyM+JEydMTU1N+/H5fCHzOHLkSKcbQCYnJxvy+XyhQCBot6jr08Bmswfw+Xyhh4eHyMvLS7h69WpbpbJ5u6YTJ06YUhQ1YN++ferNMocOHepx4sSJR9ro8lHk5OToDR06VPsTJFv5v//7v0f+SyY2NtZq1qxZzgCwYcOGPl988YVVR+euXbvW5quvvlK/vn79ehs3NzeRh4eH6O2333YEmov6jh8/3pXH4wn79u0r+uijj9R9oihqwD//+U835nlTUxMsLCz8mM9q//795osWLXJ41PfQHbQZFBUAcNJ47vjgWAsURQ0HsBxAOE3T7S4JpGl6O03TATRNB/Tp00crnSXa0tXyf13WdAP+LnarSVvFbhm62K27Jw8zkS0U2pecnCztrrYCAgJqmJITUqlU/Prrr1d3dv7Bgwd7h4eHl0skErFIJHrocm6VSgUmQOkOTU1NbY4ZGBiopFKpWCaTZZw7dy7rl19+MV+6dKn6y9XW1rbpk08+aVPIVVs2bNhg+69//avNSsf2+t4dmM84Njb2id7jv//979Jt27a1KRwMNPd9z5491vPnzy8FgOPHj5uePHmyt1gsFstksoyVK1cWAcDOnTstGhsbWVlZWeKUlBRJXFxcn8zMTH0AMDIyUmVmZhrV1NRQAPDTTz+Z2draqj+UyZMnV549e7Z3dXW1zhM32rzhNQCeFEW5URSlD2AKgGOaJ1AU1R/ANjQHRF3euZTQnZUrAVdX7WWJAN1P6n5a+yJlv5ut1S/znj7MpKsyMC38aOuLfdSANo8fbX0ft8nMzEx9T09PEfN81apVtkuWLHEAgKCgIK8FCxZwfXx8BK6urt5nzpwxAYCkpCRDHx8fAZ/PF/J4PGFaWpoBABgbG/cHmktJzJgxw9nNzU0UEhLiOWTIEI+dO3daAACXy/VZvHixg1AoFPB4PGFycnKXVzFkZmbq9+3bVzRlyhQXDw8P0UsvveRZU1ND/fDDD+bbt2+3/f777/sMHDiQBwD/+c9/bD09PUWenp6itWvX2jDXu7q6eo8bN86Vx+OJzpw5Y+Lm5iaaMGGCq6urq3d4eLjbkSNHTP39/fkuLi7e58+fNwaAqqoqVkREhKuPj49AIBAI9+zZ0xtozma88sorHsHBwbyQkBCvzvrO5XIVO3bsuLNz504bppSGQCCoMzU1Vf70009mrc9funSpvbe3t8DT01M0depUF+aadevW2bi7u4t4PJ5wzJgxfTvrX2snT560mDBhQmV7fe/sPQ4bNsw9KCjIy8XFxfv9999XBzhd+YwnT57syuzKHR4e7gYAW7ZssWR+f6ZNm+aiUDRvYfW///3PytXV1dvHx0eQkJCg3mzF1NRU5ejoKGd+HpqOHz9u5uPjU6enpwcA2Lp1a58PPvig0MjIiGY+dwCgKAp1dXWspqYm1NbWUnp6erTmLuvDhw+vPHjwYG8A2L9/v+WECRPKmNdYLBZCQkKqf/jhh4eWwOluWguKaJpWAIgEcBaABMABmqYzKIpaS1FU+IPTPgVgAuAgRVE3KIo61kFzxFPCLP/XVpYI0H1NN10Xu9WlnjzMpIvAsu1NR1SA0mu5mRulR8N+ZIW2bqlQKKi0tDTJJ598krd27VoHAPjyyy/7LFy48J5UKhWnpqZK3NzcWmzmFxcXZ5GXl6cvk8ky4uPjbycnJ7fYTcza2lohFoslc+bMKYmOjm43A5CUlGSiOXzGDInl5uYavvvuu8UymSzD3NxcGRcXZzF58uTKWbNmlbz99tv3EhMTsy5dumS8b98+q+vXr0uSkpIkcXFxfZhaXrm5uQaRkZElMpksw8PDozEvL8/www8/vJednZ2enZ1tuHfvXqukpCTp+vXr89evX28PAB9//LH90KFDq9LS0iSXLl3KXLFihWNVVRULADIyMoyPHj2afe3atcyHfZZCobBRqVSioKBAvf3M8uXLCzds2NAmk7Js2bLi9PR0yc2bNzPq6+tZ8fHx5gAQGxtrl56eLs7KyhJ///33OQ/rH0Mqleqbm5srmGChdd87ayM1NbXXsWPHZBkZGRnHjh2zvHjxonFXP+NDhw7dYTJmx44du/3XX38ZHjp0yDIpKUkqlUrFLBaL/vrrr61ycnL0oqOjHRISEqTXrl2TZmVltai95u/vX/v777+3GVq8dOmSib+/P7NACrdu3TK8cOGCqa+vLz8wMNDrwoULxgDwxhtvlBsbG6tsbGz83NzcfCMjI4tsbW3VQdHMmTPLfvjhB4u6ujpKIpEYDxo0qEWdvICAgNpLly5pYVe8zmk1NUXT9Cmapnk0TbvTNL3+wbFVNE0fe/Dn4TRN29I03e/BI7zzFomeSJeTuhm6LnarK2SYqZv5rS9EqyFJUCzAb73WSi5ERESUA0BISEhtfn6+PgAMGjSoNiYmxn758uV2N2/e1DcxMWkRqF26dMlk/Pjx5Ww2G87Ozorg4OAWQ1/Tpk0rB4CgoKC6vLy8duf/tB4+Y4bEuFyuPCQkpB4A+vfvX3fnzp021//+++8m//jHPyrMzMxU5ubmqtdee638/PnzpgBgb2/fOGzYMPUXHpfLlQcFBdWz2WzweLz6V155pYrFYsHf378uPz/f4EF7Zp9//rk9n88XhoaGesnlckomk+kDQFhYWJXml+ujGj16dA0AnD17tsUX7unTp019fX35PB5PmJCQYJqenm4EAF5eXvXjxo1z27Jli6WeXnOA3Fn/GHl5eXqWlpYtdhXX7HtnbYSGhlbZ2dkpTUxM6Ndee638999/N3mUz1jTmTNnTNPT0439/PwEfD5fePnyZbNbt24ZXLx4sVdwcHC1g4ODwtDQkB4/fnyZ5nU2NjaKu3fv6rVur6ioSK9Pnz7qoS6lUkmVlZWxb9y4Id24cWPetGnT3FUqFS5cuGDMYrHooqKiVJlMlvbVV1/ZicVi9Wc0cODA+vz8fINvvvnGsr26a3Z2doqioiL91se1jRSEJZ4JK1cCO3c2/1mbWSKAqetmCEtTRxQa58Ky0gkf/Fs7dd2ehpWDV+Js9lmtZolemA0Ve7k0wXnifeQcsAbdRIHSo+EccR+9Hr+EBofDoZmhGQBoaGhoEXUZGhrSD86DUqmkAODtt98uCwsLq/3pp5/Mx4wZ4/nll1/mhIeHdzrnp4M2aYVC0WaX787o6+urAzA2m03X19c/0j+mjY2NVZrPNdtjsVjqvrHZbPX7pWkahw4dkvn5+bWYq3T58uVerdvrjFgs1mez2eByuYqUlBT18Y8++qhw3bp19hwOhwaAuro66v3333dJTEwUe3h4NC1ZssSB+bmcP3/+5unTp02PHj1qvmnTJvvMzMyMjvrX+n3L5XJW62PMnzt7j1Sr4f3Wz9u7V0ev0TRNRURElG7evLnF/7C7d+/u3VmbDQ0NLCMjozbtGhoaqjR/Z+3s7BonTpxYwWKxMHTo0LoHgRBn9+7dVq+++mqlgYEBzeVyFYGBgTUJCQm9hEKhOss5atSoitWrVzv9/PPPmcXFxS3ikfr6esrQ0LDLP+vu8kysPiMIXU3qBv7eAiDM0hMmlDFCLT20tgWArle6AboZZurJK93a0MwWdUOWyNHRUVFWVsYpKipi19fXU2fPnn3ovAmxWKwvEAjkK1asKH711Vcrbty40WKoIzQ0tObIkSMWSqUSeXl5nMTERK2tqGrP0KFDa06dOtW7urqaVVVVxTp16pTF0KFDuxy0tdNeVUxMjC0TPDLDRI/i7t27nLfeesvlzTffLGa1Gp8fP358VWVlJVsqlRoBQF1dHQtozk5UVlayjh8/bgE0z9XKzs7WHzt2bPXmzZsLampq2JWVleyu9M/Hx0deUFDQYaajszYuX75sdu/ePXZNTQ116tSp3kOGDKl5lM+Yw+HQcrmcAoBRo0ZVnThxwoIZQrx37x47KytLf/DgwbWJiYmmRUVFbLlcTv30008Wmm1kZWUZeHt717duWyAQNMhkMnW2cOzYsRW//fabKQCkpqYaNDU1sezs7BTOzs6N58+fNwOa52D99ddfvXx8fBo021qwYMH9pUuX3g0KCmpzn8zMTEORSNTmuLaRTBHxzFi5Ejh7VvvZmrffbq4fBxgCGIqMB8d//hkYO7Z77zVCYKuu6cbQ9ko3XVg5eCV23tjZ4lhPm8OkxmSL7uzr86RZIgAwMDCg33///cLAwECBra1tk4eHR8PDrtmzZ4/lgQMHrDgcDt2nT5+mqKioQs3XZ8+eXf7rr7+aenh4iOzt7RtFIlGd5qTWrmDmFDHPP/zww8KQkJB2h2RaCw0NrZs2bVqpv7+/AABmzpxZ8tJLL9Uzq40eVXR09N158+Y58/l8oUqlopycnOTnz5+XPew6ZoKxQqGg2Gw2PXny5NLVq1ffa+/cDz/8sHDGjBkeAGBtba2cPn16iUAgEPXp00fh5+dXCzTP75o2bZpbdXU1m6Zpau7cucXW1tbKrvTPzMxM5ezsLE9PTzfw9vZuk1HqrA1fX9/a8PBw96KiIv2JEyeWDh48uA4AuvoZP3gvQm9v77pjx47dXrFiRcGwYcN4KpUKenp6dGxsbO6wYcNqP/zww7vBwcECU1NTpbe3d51mG9euXTP55JNP2vwD4PXXX6+cNm2aejn9u+++e3/y5Mmunp6eIj09PdX27dtvs1gsfPDBB8VTpkxx9fDwENE0jWnTpt0fOHBgiyDH3d29acWKFe0usrp48aLpJ5980k6xKe0iBWGJF87ChcA33wAKja82DgeYNw/YvLl771Vc1YCwjechV/ydBTbksHDxw6HP/cTuhScXquu66bP1Mbf/XGx+rZs/wCfXPQVha3P08EuoF0ZckT5pUKQtlZWVLHNzc1VRURE7MDBQcOXKFamz87PZ1xdFXFxc76SkJOPY2NguZxdjY2OtkpKSesXFxeVqs2+duXLlitGnn35qd+TIkdvtvT5ixAj3zz77LN/Hx+ehWzE8jry8PM6kSZP6/vHHH1naaB94SgVhCeJZxMxfah0UaSNDxax0Y7JF2lzpNnDDr7hX1fbvKFszAyR+PLzb76eZLeqxWSJGL5cmvJ6X/rS70ZkRI0Z4VlVVsZuamqhly5YVkoDo6Zs1a1bF/fv3n7vv2eLiYr3OsjSbNm3Kz8/P19NWUHTr1i39mJiYvIef2f2eux8WQTwpZv4Sky3icIA5c7Q3j+ndYZ44eD0fAK3VlW66HqpjVrptu76NrHR7Bly9evWhS9QJ3VuyZMkjZSXffffdUgClWupOl4wbN66qs9f9/PzknU0yf1JDhgype/hZ2kEmWhMvpJUrm4MhQHtZIqB5pdsH7xrCstIRoKFe6aaNYrdPY1PKp7KhIkEQhJaQoIh4IWmudtNmlkiXK92exqaUT2VDRYIgCC0hQRHxwtJFCROm2C270RATDYeC3Wio1WK3PXVTSoIgCF0gc4qIFxZTwkSbBAKgoJ3piidPAvndvMdhT92UUtcTyAmCeHGRTBFBaJEui93qcqhOl0YIbNVDgoyesNdTd+jfvz+/O9o5ceKEqampaT/N2mdHjhzpdAPI5ORkQz6fLxQIBOo6aU8bm80ewOfzhR4eHiIvLy/h6tWrbZXK5u2aTpw4YUpR1IB9+/apN8scOnSox4kTJ7S20WVOTo7e0KFDn8t07e7du3tfv379kcfemSLFd+/e5YSFhXl2dF7rzyYxMdGoX79+fA8PDxGPxxPW1dVRALBt2zZLHo8n5PF4wrCwMM/CwkIOAEyYMMHVyMiof3l5uTqOmTNnjhNFUQMKCws5DQ0NVEBAgFdTU1Pbm3eCBEUEoUW6LHar66E6XXkaE8ifF8nJydLuaqt17bPXX3+90x2pDx482Ds8PLxcIpGo66R1RqVSgQlQukN7X3ZMIVSZTJZx7ty5rF9++cV86dKlDszrtra2TZ988kmbYrDasmHDBtt//etfbVafPeoX9dNw5MiR3qmpqY+8kzjDwcFBYWtr2/Tzzz/3au91zc+mqakJM2fOdNu6dWuOTCbLuHjxYqa+vj7d1NSEjz76yOnChQtZWVlZYpFIVP/pp5/aMG04OTnJ9+/f3xto3n388uXLpjY2Nk1Ac2mbIUOGVO3YscPyUfpNgiKC0CJdFrsVCIDt24HoaGDNmub/bt8O8Lsll9CSLsuXbPtf8+o9im4OjCiagmWlE7Z9of3NL2032fpSa6gBrR+2m2x9H7fNzMxMfU9PTxHzfNWqVbZLlixxAICgoCCvBQsWcH18fASurq7eZ86cMQGApKQkQx8fHwGfzxfyeDxhWlqaAfD3v8qVSiVmzJjh7ObmJgoJCfEcMmSIx86dOy0AgMvl+ixevNhBKBQKeDyeMDk5ucsfXGZmpn7fvn1FU6ZMcfHw8BC99NJLnjU1NdQPP/xgvn37dtvvv/++z8CBA3kA8J///MfW09NT5OnpKVq7dq0Nc72rq6v3uHHjXHk8nujMmTMmbm5uogkTJri6urp6h4eHux05csTU39+f7+Li4n3+/HljoLksREREhKuPj49AIBAI9+zZ0xto3tjwlVde8QgODuaFhIR4ddZ3Lper2LFjx52dO3faMKU0BAJBnampqfKnn34ya33+0qVL7b29vQWenp6iqVOnujDXrFu3zsbd3V3E4/GEY8aM6dtZ/1o7efKkxYQJEyrb63tHbSgUCsybN8/R09NTxOPxhOvXr7cBgKNHj5oKBAIhj8cTRkREuNbX11PMz/edd97h8vl8obe3t+Dy5cvGoaGhnk5OTt4bN27sAzRnyQICArxefvllD1dXV+9p06Y5MwEq8zsEADt37rSYMGGC6y+//NLr119/7b1ixQpHPp8vzMjIMMjIyDAICwvzFIlEggEDBngxv0dSqVS/X79+fB6PJ3z33XcdNN//66+/XhEXF2f1sM/m8OHD5gKBoH7QoEH1AGBnZ6fkcDhQqVQUTdOorq5mqVQqVFVVsRwcHNS108aPH1926NAhywftmQYGBtYw9ewAYOLEiRXx8fEkKCKIZ4lmtkibxW51OVSnyyEtPz/Are7vbBGLouBa56GTYcERfUdU6LH0Wmz7r8fSo0e6j6zQ1j0VCgWVlpYm+eSTT/LWrl3rAABffvlln4ULF96TSqXi1NRUiZubW6PmNXFxcRZ5eXn6MpksIz4+/nZycnKLCvDW1tYKsVgsmTNnTkl0dLRte/dlynwwD2ZILDc31/Ddd98tlslkGebm5sq4uDiLyZMnV86aNavk7bffvpeYmJh16dIl43379lldv35dkpSUJImLi+vD1PLKzc01iIyMLJHJZBkeHh6NeXl5hh9++OG97Ozs9OzsbMO9e/daJSUlSdevX5+/fv16ewD4+OOP7YcOHVqVlpYmuXTpUuaKFSscq6qqWACQkZFhfPTo0exr1649dF8moVDYqFQqwdT9AoDly5cXbtiwoU22aNmyZcXp6emSmzdvZtTX17Pi4+PNASA2NtYuPT1dnJWVJf7+++9zHtY/hlQq1Tc3N1cYGRmpf380+95RGzExMX1yc3P1xWJxRlZWlnju3LmldXV11Pz5891++OGH7KysLLFCocCnn37ah2nX2dm5USqVigcOHFgzZ84c1+PHj2cnJiZKP/nkE3WQkpaW1mvLli25Mpks/c6dOwZxcXEtap1pGjFiRO3w4cMr1q1bly+VSsUikUg+d+5cly1btuRmZGRIPv300/wFCxY4A8DChQud586dW5KVlSW2t7dvkQJ76aWXaq9evWrSuv3Wn01mZqYBRVEIDQ31FAqFghUrVtgCzSVxPvvss1x/f3+Rra2tb1ZWltGiRYvUmTcvLy95aWkpp6SkhL1v3z7LadOmlWneJzAwsD41NbXdTFVHSFBEEFqmq2K3uhyq0+WQ1tChgCnHEG5wBAC4wQmmHEOdDAuuf2V9IYtq+aGyKBbWv/JkRWE7ExERUQ4AISEhtfn5+foAMGjQoNqYmBj75cuX2928eVPfxMSkRaB26dIlk/Hjx5ez2Ww4OzsrgoODWwx9TZs2rRwAgoKC6vLy8tqd/9N6+IwZEuNyufKQkJB6AOjfv3/dnTt32lz/+++/m/zjH/+oMDMzU5mbm6tee+218vPnz5sCgL29feOwYcPUNdS4XK48KCions1mg8fj1b/yyitVLBYL/v7+dfn5+QYP2jP7/PPP7fl8vjA0NNRLLpdTMplMHwDCwsKqbG1tH3scbvTo0TUAcPbs2RZf1qdPnzb19fXl83g8YUJCgml6eroRAHh5edWPGzfObcuWLZZ6es0Bcmf9Y+Tl5elZWlq22FVcs+8dtXHu3Dmz+fPn39fT0wMA2NraKlNSUgwdHR3lvr6+cgB44403Si9fvqyeCzVp0qQKAPDx8anz9/evtbCwUDk4OCj0/7+9+w+Oujr3OP5+siH8FgRSRWIAgQABRDHIdAze5iKKToFqxaKMWi0qg97rT8Zbr/TWamfoFG5vtVjBAldura1iKcrlR52WaZVrU1MKihDbaLUQCD9EAgiE/HjuH/tduywBEtnvJrv5vGYYvvvd3e9zdrOQZ895zjk5OQ179+6NBPd9WlhYeCw7O5sbbrhh3+uvv35CsnIy1dXVWX/+85+7TJkyZcCQIUMKZ86c2Xf37t3tADZs2NDljjvu2Adw1113Hbfo5HnnnVe3e/fuE/ZmS3xv6urq7K233ury0ksv/a20tPS9lStXnr1ixYquNTU1tnDhwtzS0tItu3bteruwsPDII488clxCO3HixE8WL17cY8OGDZ0nTJhwKP6+7Oxs2rVr5/F1R6ej2WciKZCKzW5jydeiRXDsWLhDdancviQ2gy/SeRDnTNvLuucH8ptPw5nBl6hv97611xdev/fFd1/sVdtQa+2y2vmUYVP25nf7/FtoZGdne2xoBuDo0aPH/YfdoUMHDx5HfX29AcyYMWPf2LFjP12+fHm3L3/5y4OeeuqpjyZNmtTkXejjrul1dXUn7Ad3Kjk5OZ8lYJFIxI8cOdKsL9OdOnVqiL8df72srKzP2haJRD57ve7OsmXLKhJXTX7jjTc6J17vVLZs2ZITiUTo06dP3aZNmz47/81vfnPnE0880Ts21HL48GF78MEH+5aWlm4ZOHBg7QMPPHBe7Oeybt26v65evbrrihUrus2dO7f3e++99+7J2pf4umtqarISz8WOm3KNpoq9h1lZWSe8v7W1tQZgCV9iYrfjz8eG5BLV19fTtWvXuvLy8i2N3Z+VldXoJqqHDx+29u3bn/DzSnxv8vLyjo0ZM+Zg79696wDGjx9fXVZW1im2qXEsQb/xxhv3zZkz57j/0W655ZZPxowZM/T666//OBKJnNCG2tpa69SpU5M3eVVPkUgKxKb/h9VLFJOqoTpI3ZpIsWHB+k87sGNhCQ2fdghtWLAx8b1FyeglysvLq9u3b192VVVV5MiRI7Z27dpup3vOli1bcoYOHVrz6KOP7r7qqqv2b9y48bgC2OLi4kO/+tWvzq6vr2fbtm3ZpaWloc2oakxJScmhVatWdT948GDWgQMHslatWnV2SUlJk5O2Rq53YN68eefEksfYUFxz7NixI/uOO+7oe9ttt+3OSuhCve666w5UV1dHysvLOwIcPnw4C+Dcc8+tq66uznr11VfPhmgy8P777+dMnDjx4Pz58ysPHToUqa6ujjSlfSNGjKiprKw8oZfkdK9x3LhxBxYsWNArVoy9a9euyMiRI49WVlbmbN68uT3A0qVLe44dO7ZZ7+8777zTuby8PKe+vp5ly5b1iD2/Z8+etRs2bOhQX1/PihUrPhtS69KlS31sSLBHjx4NeXl5xxYvXnw2RIvm33zzzY4Ao0aNOvTss8/2AHj22WePqx/avHlzh4KCgiOne2+uvfbaA+Xl5R0PHjyYVVtby/r167sOGzbsaN++fWsrKio67NixIxtgzZo1ZxUUFByNv1ZBQcGxRx55pPK+++7bkxinqqoq0r1797r27dsrKRJpi1I1VJfK7UtSOSzYmFhvkWGcaS8RROskHnzwwZ2jR48eOnbs2IKBAwcePd1zfvrTn/YoKCgYNmTIkMKtW7d2TBymuPXWWz/p3bv3sYEDBw772te+1n/YsGGHY9+ymyqxpihWqN0UxcXFh2+66aaPR40aNfSSSy4ZevPNN++57LLLTvhl2FRz5szZUVdXZ7Hp9Y8++mifpjyvpqYmK/ackpKSgnHjxh2YO3duo0nsww8/vLOqqioHoFevXvXTpk3bM3To0GElJSUFI0eO/BSiwzo33XRT/4KCgsLhw4cXTp8+fXevXr3qm9K+s846qyE/P78mlsg09TXef//9e/Ly8o4NGTJk2ODBgwsXLVrUo1OnTv7MM898OGXKlAEFBQWFWVlZPPTQQyckAacyfPjwT2fMmJE/YMCA4fn5+TU333zzfoDHHnuscvLkyQNHjRo15JxzzvmsJmjatGn7nnzyyXNjSy688MILHyxZsqTX4MGDCwcNGjTs5Zdf7g7w9NNP/33hwoVfKCgoKKysrGwXH/O1117rOmHChOrTvTe5ubn199xzz66LL754aGFh4bALL7zw8NSpU6v79etXO2vWrJ3FxcWDgwkGnR5//PGdidebNWvW3sZmQK5evfqsK6644oT4p2LuTU6gWoWioiIvKytr6WaItFo7d0JxMaxfH15S9MorsHw55OYfZVXNm1zT/ovs+XsHrrsOJk5MfryZM48fFpw+HebPb9JTTxgO2LRp04cjR45s1iadH+3/qF3xkuLB629fX36mSVFYqqurs7p169ZQVVUVGT169ND169eX5+e3zra2FUuXLu1eVlbW6cknnwytBq0pVq5c2XXevHnnrFu3riKVcYuKigavXr26Ijc394QEPRXvzZVXXjlg7ty522O1WPE2bdrUa+TIkf0Sz2dmTdHyPDjSyDLCHfvAtSEXIYi0sFSs1F1SEq3pia2JVFNDqGsizZ4NS5ZEj1PZSxTTt3vf2m33b9uc2qjNM378+EEHDhyI1NbW2qxZs3YqIWp5t9xyy/69e/dm5u/Z09ixY0f2vffeu6uxhAjCf2+OHj1qkyZN2t9YQnQqmdlT9MeZ8MEiaIibtZqVAwOmw+imfb0UkZPLy2t8+5I+fcIrfp45ExYsgBkzmtxLBEnqKRKRzHKynqLMrCkaMRsSptFiERiexhtAibQiqVwTKSaJG/g2NDQ0NGsGlohkjuDff6OzGDMzKerYG/rfFu0dgujfF9wGHbVXkkgytETxcxJn8G3es2dPNyVGIm1PQ0OD7dmzpxvQ6HB45o51jpgNfwuKENRLJJJUqVwTKdnq6uqmV1VV/aSqqmo4mfrFUEROpgHYXFdXN72xO0OtKTKzCcAPgQjwE3efk3D/5cB/ARcCU9192emu2azZZ3+cCRULYNAM1RKJJNnOnXDBBXD0KHTsCB980CqTIvUGiUiThfYtycwiwHzgaqAQuNHMChMe9nfg68DPQmnEiNnQpZ96iURCkKo1kUREUiXMruNLgQp3/8DdjwE/B44rw3T3D939bU5S8HTGOvaGSe+rlkgkJEksfhYRaXFh1hT1AbbF3d4OjAkxXsvQmkjShqViTSQRkVRJiyJDM7vTzMrMrGzPnmatbB6+PpP+McstJisH8lK0MZOIiIgkRZhJUSVwftztvOBcs7n7Qncvcvei3NzcpDQuabQmkoiISEYIMyl6CxhkZv3NLAeYCrwSYryWoTWRREREMkJoSZG71wH3AGuBrcCL7v6umX3HzCYBmNloM9sOTAEWmNm7YbUnVPG9ReolEhERSUuhLt7o7quAVQnnvhV3/BbRYbX0FustqligXiIREZE0lRaF1mlBayKJiIiktczd5iPVYmsiiYiISFpSUpROtCaSiIhIaDR8lk60JpKIiEholBSlE62JJCIiEholRelEayKJiIiERjVF6WbEbPjbkuhxmL1Eql8SEZE2Rj1F6SbWW0RWuL1Eql8SEZE2RklROkrFmkiqXxIRkTZGSVE6iq2JFGYtkeqXRESkjVFSJCenPd1ERKQNUaG1nFyq9nRTUbeIiLQC6imSU0tF/ZKKukVEpBVQT5GcWir2dItfZiBGyw2IiEiKqadIWl6qi7rVMyUiIo1QUiStQyqLurXcgIiINEJJkbQOqVqUMj6WlhsQEZE4Soqk9UhFUXd8LC03ICIicZQUSeuRikUp42OlqmdKRETSgmafSds1YjZUrQ23l0gz3URE0oZ6iqTtSkXPlGa6iYikDSVFImHSTDcRkbShpEgkTJrpJiKSNlRTJBK2+BW71UvUfKrLEpEUCbWnyMwmmNl7ZlZhZv/WyP3tzewXwf2lZtYvzPaItAjNdDszqssSkRQJLSkyswgwH7gaKARuNLPChId9A/jE3QcCPwC+F1Z7RFpUKtdgyjSqyxKRFAmzp+hSoMLdP3D3Y8DPgcSvdpOB54LjZcA4M7MQ2yTSMlK5BlOmUV2WiKRImElRH2Bb3O3twblGH+PudUA10DPENolIOtIK5CKSAmlRaG1mdwJ3AuTn57dwa0RaqUwuSI71FlUsUC+RiIQmzJ6iSuD8uNt5wblGH2Nm2UA34OPEC7n7Qncvcvei3NzckJorkuYyvSBZdVkiErIwk6K3gEFm1t/McoCpwCsJj3kFuDU4vh74rbt7iG0SyVyZXpCsuiwRCVloSVFQI3QPsBbYCrzo7u+a2XfMbFLwsEVATzOrAB4ATpi2LyJNpIJkEZEzYunWMVNUVORlZWUt3QyR1unITnjlAqg/CpGOMOmDtp4UaTariDSZtvkQySRaKFJE5HNLi9lnItIMI2ZD1dpwa4kyeaabiLRZ6ikSyTSpKEjO9JluItImKSkSkebL9JluItImKSkSkebTTDcRyUBKikTk89HWGyKSYZQUicjno5luIpJhlBSJyOenrTdEJINoSr6IfH6xmW4iIhlAPUUiIiIiKCkSERERAZQUiYiIiABKikREREQAJUUiIiIigJIiEREREUBJkYiIiAigpEhEREQEAHP3lm5Ds5jZHuCjZjylF7A3pOYoVnrHSnU8xUp9rL3uPiGsxohIZkm7pKi5zKzM3YsUS7FaOp5ipVcsEWl7NHwmIiIigpIiEREREaBtJEULFUuxWkk8xUqvWCLSxmR8TZGIiIhIU7SFniIRERGR08rYpMjMOpjZH81sk5m9a2aPhRzvQzN7x8w2mllZiHEGBzFifw6Y2X0hxrvXzDYH72FS45jZYjPbbWab485NCWI1mFnSZhmdJNbjZvZ28D7+2szOCzHWt82sMu7ndk2IsX4RF+dDM9sYYqyRZvZm8Nl/1czOSlKs881snZltCT4P9wbnQ/l8iIhABg+fmZkBnd39kJm1A94A7nX3P4QU70OgyN1TtsaOmUWASmCMuzdn7aamXn848HPgUuAYsAaY4e4VSbr+5cAhYKm7Dw/ODQUagAXAQ+6elATzJLHOcvcDwfG/AoXuPiOkWN8GDrn73DO9/uliJdw/D6h29++EEcvM3iL6c/qdmd0O9Hf32UmI1Rvo7e4bzKwr8CfgK4ATwudDRAQyuKfIow4FN9sFfzItAxwHvB9GQhQYCpS6+2F3rwN+B1yXrIu7+++BfQnntrr7e8mKcZpYB+JudiZJn4/GYoXlVLGCLwY3AC+EGKsA+H1w/Brw1STF2unuG4Ljg8BWoE9Ynw8REcjgpAiiPSnB0MFu4DV3Lw0xnAO/NrM/mdmdIcaJN5Uk/cI7ic3AWDPraWadgGuA80OMl3Jm9l0z2wZMA74Vcrh7guG6xWZ2dsixAMYCu9z9ryHGeBeYHBxPIYTPh5n1Ay4Gwvz3KyKS2UmRu9e7+0VAHnBpMBwUlmJ3HwVcDdwdDDWExsxygEnAS2HFcPetwPeAXxMdOtsI1IcVryW4+7+7+/nA88A9IYb6MTAAuAjYCcwLMVbMjYSbNAPcDsw0sz8BXYkOsyaNmXUBXgbuS+jZExFJuoxOimLcfT+wDghtDyR3rwz+3g0sJ1qHE6argQ3uvivMIO6+yN0vcffLgU+Av4QZrwU9T5KGfhrj7ruCJL0BeJaQPx9mlk10qPMXYcZx93J3v9LdLyGagL2frGsHtYAvA8+7+y+TdV0RkZPJ2KTIzHLNrHtw3BEYD5SHFKtzUAyKmXUGriQ69BSmVPQCYGZfCP7OJ/pL9mdhx0wVMxsUd3MyIX0+gli9425eS/ifjyuAcnffHmaQuM9HFvAo8EySrmvAImCru/9nMq4pInI62S3dgBD1Bp4LZmhlAS+6+8qQYp0DLI/+P0428DN3XxNSrFjiNR64K6wYcV42s55ALXB30OuWFGb2AvAloJeZbQf+g2gh71NALvC/ZrbR3a8KKdY1ZjaY6Gymj4Aznnl2ilhfMrOLiNaefUiSfnaNxXL3RYRQb3aS19XFzO4OHvJLYEmSwl0G3Ay8E7ekwCNAe0L4fIiIQAZPyRcRERFpjowdPhMRERFpDiVFIiIiIigpEhEREQGUFImIiIgASopEREREACVF0oLM7Otm9qMzeP4aM9tvZisTzvc3s1Izqwh2jM8JzsfvVF9uZj8O1tfBzP7bzK5PuM4hRESkzVBSJOns+0TXskn0PeAH7j6Q6Crc34i77wfB1i+FwAjgn8JupIiIpAclRdIqmFk/M/ttsGHqb4IVtDGzAWb2BzN7x8yeiO+9cfffAAcTrmPAPwPLglPPAV9pJGQO0IFo0iQiIqKkSFqNp4Dn3P1CovuQPRmc/yHwQ3cfATRly4qewH53rwtubwf6xN1/f7BC8k7gL+6+Me6+7wdDaxvjVlEWEZE2QkmRtBZf5B/7qv0PUBx3/qXgOBn7rsWGz74AdDazqXH3zXL3i2J/khBLRETSiJIiSSkzuzuuJ+a8EEJ8DHQPdokHyAMqEx/k7rXAGuDyENogIiJpSEmRpJS7z4/ridkRd9f/Ed3EFGAa8Hpw/Afgq8FxfK/Oya7vwDogNpPsVmBF4uOC2qPLgPeb+RJERCRDKSmS1uJfgNvM7G2iM8ruDc7fBzwQnB8IVMeeYGavEx1aG2dm280stlv6w8FzKojWGC2KixOrKdoMRICnQ3tFIiKSViz6xVqkdTKzTsARd/eg/udGd5/c0u0SEZHMk336h4i0qEuAHwXDXfuB21u2OSIikqnUUyQiIiKCaopEREREACVFIiIiIoCSIhERERFASZGIiIgIoKRIREREBFBSJCIiIgLA/wPp6EvSqgk4ZwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(3,23,2).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase (reported) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(3,23,2).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase (reported) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(3,23,2).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase (recomputed) (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/eu_caqtls_pearsonr_compare_jsd.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enrichment plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import figure\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = [12, 10]\n",
+    "\n",
+    "def plot_enrichment_curves(df, compare_score, model_score, output_file, compare_score_ascending=False, model_score_ascending=True,\n",
+    "                           thresh_list=[0.5, 0.1, 0.05, 0.01, 0.005, 0.001], subsample=False, ymax='auto', debug=False):\n",
+    "    by_thresh_dict = {'score':[], 'ratio':[], 'thresh':[]}\n",
+    "    \n",
+    "    #df[model_score+\"_abs\"] = np.abs(df[model_score])\n",
+    "    #model_score = model_score+\"_abs\"\n",
+    "    print(model_score)\n",
+    "    #thresh_list = [df[model_score].quantile(1-0.5), df[model_score].quantile(1-0.1), df[model_score].quantile(1-0.05), df[model_score].quantile(1-0.01),\n",
+    "    #              df[model_score].quantile(1-0.005), df[model_score].quantile(1-0.001), df[model_score].quantile(1-0.0005), df[model_score].quantile(1-0.0001)]\n",
+    "    print(thresh_list)\n",
+    "    for thresh in thresh_list:\n",
+    "        df[compare_score].fillna(0, inplace=True)\n",
+    "        sample_df = df.sort_values(by=[compare_score, model_score], ascending=[False, True])\n",
+    "        #print(sample_df.head())\n",
+    "        #print(sample_df.tail())\n",
+    "        print(thresh)\n",
+    "        print(sum(sample_df[model_score] < thresh))\n",
+    "        ratio = len(sample_df.loc[sample_df[model_score] < thresh]) / len(sample_df)\n",
+    "        sample_df['sig'] = sample_df[model_score].apply(lambda x: x < thresh)\n",
+    "        sample_df['expected'] = [ratio] * len(sample_df)\n",
+    "        expected = sample_df['expected'].cumsum().tolist()\n",
+    "        observed = sample_df['sig'].cumsum().tolist()\n",
+    "        \n",
+    "        enrich_ratio = np.array(observed) / np.array(expected)\n",
+    "        compare_vals = sample_df[compare_score].tolist()\n",
+    "        enrich_ratio = enrich_ratio.tolist()\n",
+    "        assert len(compare_vals) == len(enrich_ratio)\n",
+    "        by_thresh_dict['ratio'].extend(enrich_ratio)\n",
+    "        by_thresh_dict['score'].extend(compare_vals)\n",
+    "        by_thresh_dict['thresh'].extend([thresh] * len(sample_df))\n",
+    "        \n",
+    "        if debug:\n",
+    "            print(thresh)\n",
+    "            print('Expected:', expected[:10])\n",
+    "            print('Observed:', observed[:10])\n",
+    "            print('Ratio:', enrich_ratio[:10])\n",
+    "            print('Compare Scores:', compare_vals[:10])\n",
+    "            print()\n",
+    "        \n",
+    "    by_thresh_df = pd.DataFrame(by_thresh_dict)\n",
+    "    by_thresh_df.sort_values(by=['score'], ascending=False, inplace=True)\n",
+    "    if subsample:\n",
+    "        by_thresh_df = pd.concat([by_thresh_df.iloc[:20000,].copy(), by_thresh_df.iloc[20000:,].copy().sample(30000)])\n",
+    "    return by_thresh_df\n",
+    "    #plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval\n",
+      "[0.5, 0.1, 0.05, 0.01, 0.005, 0.001]\n",
+      "0.5\n",
+      "116126\n",
+      "0.1\n",
+      "39073\n",
+      "0.05\n",
+      "21758\n",
+      "0.01\n",
+      "5856\n",
+      "0.005\n",
+      "3372\n",
+      "0.001\n",
+      "865\n"
+     ]
+    }
+   ],
+   "source": [
+    "by_thresh_df = plot_enrichment_curves(caqtls,\n",
+    "                       compare_score='Log10_BF', model_score='ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "                       compare_score_ascending=False, model_score_ascending=True, output_file=\"lcl_caqtls_latest.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_score='Log10_BF'\n",
+    "model_score='abs_logfc_x_jsd_x_max_percentile.mean.pval'\n",
+    "ax = sns.lineplot(data=by_thresh_df, x='score', y='ratio', linewidth=3, ci=None,\n",
+    "                  hue='thresh', palette=sns.color_palette('rocket', 6))\n",
+    "leg = plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
+    "for line in leg.get_lines():\n",
+    "    line.set_linewidth(4)\n",
+    "xmax=\"auto\"\n",
+    "ymax=\"auto\"\n",
+    "output_file=\"lcl_caqtls_latest.pdf\"\n",
+    "ax.set(title=compare_score + ' vs ' + model_score, xlabel=compare_score, ylabel='observed / expected')\n",
+    "ax.set(yscale='log')\n",
+    "ax.set_yticks([1,  3,  5, 7, 10, 20, 30, 40, 50, 200])\n",
+    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
+    "ax.axhline(y=1, color='grey', linestyle='-', alpha=0.2)\n",
+    "\n",
+    "ax.axhline(y=3, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=5, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=7, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=10, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=20, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=30, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=40, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=200, color='grey', linestyle='-', alpha=0.2)\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/eu_caqtls_enrichement_atac.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval\n",
+      "[0.5, 0.1, 0.05, 0.01, 0.005, 0.001]\n",
+      "0.5\n",
+      "111403\n",
+      "0.1\n",
+      "33654\n",
+      "0.05\n",
+      "18156\n",
+      "0.01\n",
+      "4469\n",
+      "0.005\n",
+      "2448\n",
+      "0.001\n",
+      "596\n"
+     ]
+    }
+   ],
+   "source": [
+    "by_thresh_df = plot_enrichment_curves(caqtls,\n",
+    "                       compare_score='Log10_BF', model_score='ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "                       compare_score_ascending=False, model_score_ascending=True, output_file=\"lcl_caqtls_latest.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_score='Log10_BF'\n",
+    "model_score='abs_logfc_x_jsd_x_max_percentile.mean.pval'\n",
+    "ax = sns.lineplot(data=by_thresh_df, x='score', y='ratio', linewidth=3, ci=None,\n",
+    "                  hue='thresh', palette=sns.color_palette('rocket', 6))\n",
+    "leg = plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
+    "for line in leg.get_lines():\n",
+    "    line.set_linewidth(4)\n",
+    "xmax=\"auto\"\n",
+    "ymax=\"auto\"\n",
+    "output_file=\"lcl_caqtls_latest.pdf\"\n",
+    "ax.set(title=compare_score + ' vs ' + model_score, xlabel=compare_score, ylabel='observed / expected')\n",
+    "ax.set(yscale='log')\n",
+    "ax.set_yticks([1,  3,  5, 7, 10, 20, 30, 40, 50, 200])\n",
+    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
+    "ax.axhline(y=1, color='grey', linestyle='-', alpha=0.2)\n",
+    "\n",
+    "ax.axhline(y=3, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=5, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=7, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=10, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=20, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=30, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=40, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=200, color='grey', linestyle='-', alpha=0.2)\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/eu_caqtls_enrichement_dnase.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/gm12878.dsqtls.scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/gm12878.dsqtls.scoring.ipynb
new file mode 100644
index 00000000..aef70041
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/gm12878.dsqtls.scoring.ipynb
@@ -0,0 +1,1379 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import scipy.stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"dsqtls.lcls.benchmarking.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>var.chrom</th>\n",
+       "      <th>var.pos</th>\n",
+       "      <th>var.allele1</th>\n",
+       "      <th>var.allele2</th>\n",
+       "      <th>var.rsid</th>\n",
+       "      <th>var.label</th>\n",
+       "      <th>var.isused</th>\n",
+       "      <th>obs.estimate</th>\n",
+       "      <th>pred.gkmsvm.encsr000emt.varscore.deltasvm</th>\n",
+       "      <th>pred.enformer.encsr000emt.varscore.sad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.logfc</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.jsd</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.crs</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_25m.varscore.pval</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.logfc</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.jsd</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.crs</th>\n",
+       "      <th>pred.chrombpnet.encsr637xsc_5m.varscore.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>856583</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs141671872</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.9301</td>\n",
+       "      <td>2.653531</td>\n",
+       "      <td>0.000342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.021877</td>\n",
+       "      <td>0.127298</td>\n",
+       "      <td>0.291935</td>\n",
+       "      <td>0.067401</td>\n",
+       "      <td>0.00484</td>\n",
+       "      <td>0.018692</td>\n",
+       "      <td>0.065098</td>\n",
+       "      <td>0.049902</td>\n",
+       "      <td>0.00136</td>\n",
+       "      <td>0.065265</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  var.chrom  var.pos var.allele1 var.allele2     var.rsid  var.label  \\\n",
+       "0      chr1   856583           A           G  rs141671872          1   \n",
+       "\n",
+       "   var.isused  obs.estimate  pred.gkmsvm.encsr000emt.varscore.deltasvm  \\\n",
+       "0        True        0.9301                                   2.653531   \n",
+       "\n",
+       "   pred.enformer.encsr000emt.varscore.sad  ...  \\\n",
+       "0                                0.000342  ...   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_50m.varscore.jsd_x_aaq  \\\n",
+       "0                                           0.021877    \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_50m.varscore.logfc_x_aaq  \\\n",
+       "0                                           0.127298      \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.logfc  \\\n",
+       "0                                        0.291935   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.jsd  \\\n",
+       "0                                      0.067401   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.crs  \\\n",
+       "0                                       0.00484   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_25m.varscore.pval  \\\n",
+       "0                                       0.018692   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.logfc  \\\n",
+       "0                                       0.065098   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.jsd  \\\n",
+       "0                                     0.049902   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.crs  \\\n",
+       "0                                      0.00136   \n",
+       "\n",
+       "   pred.chrombpnet.encsr637xsc_5m.varscore.pval  \n",
+       "0                                      0.065265  \n",
+       "\n",
+       "[1 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf = data[data[\"var.isused\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(27373, 52)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "560\n",
+      "26813\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sum(dataf[\"var.label\"]==1))\n",
+    "print(sum(dataf[\"var.label\"]==-1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUPRC Benchmarking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"pred.chrombpnet.encsr637xsc.varscore.crs\"\n",
+    "label=dataf[\"var.label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (572M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"pred.chrombpnet.encsr637xsc_50m.varscore.crs\"\n",
+    "label=dataf[\"var.label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"pred.chrombpnet.encsr000emt.varscore.crs\"\n",
+    "label=dataf[\"var.label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ DNASE (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"pred.enformer.encsr000emt.varscore.recomputed\"\n",
+    "label=dataf[\"var.label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ DNASE (recomputed) (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "enformer_auc_recomp = roc_auc\n",
+    "\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "# keyd=\"pred.enformer.encsr000emt.varscore.sad\"\n",
+    "# label=dataf[\"var.label\"].astype(int)\n",
+    "# score=abs(dataf[keyd])\n",
+    "# title=\"Enformer w/ DNASE SAD (50M)\"\n",
+    "\n",
+    "# fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "# roc_auc = metrics.average_precision_score(label, score)\n",
+    "# ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"pred.enformer.encsr000emt.varscore.sar\"\n",
+    "label=dataf[\"var.label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ DNASE (reported) (68M)\"\n",
+    "\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "enformer_auc = roc_auc\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"pred.gkmsvm.encsr000emt.varscore.deltasvm\"\n",
+    "label=dataf[\"var.label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"DeltaSVM w/ DNASE (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "deltasvm_roc=roc_auc\n",
+    "\n",
+    "roc_auc=sum(dataf[\"var.label\"]==1)/len(dataf[\"var.label\"])\n",
+    "ax.plot(fpr_chrombpnet, [sum(dataf[\"var.label\"]==1)/len(dataf[\"var.label\"])]*len(fpr_chrombpnet), linewidth=2, label=\"Random baseline, AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc='upper right')\n",
+    "ax.set_xlabel(\"Recall\")\n",
+    "ax.set_ylabel(\"Precision\")\n",
+    "\n",
+    "plt.savefig(\"subfigs/dsqtls_auprc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUPRC (best metric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = []\n",
+    "labels = []\n",
+    "keys_df = []\n",
+    "\n",
+    "keys = ['pred.chrombpnet.encsr637xsc.varscore.crs',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_jsd',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.jsd_x_aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc_x_aaq',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.jsd',     \n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.logfc',\n",
+    "       'pred.chrombpnet.encsr637xsc.varscore.aaq']\n",
+    "\n",
+    "\n",
+    "key_shorts = [\"CRS\", \n",
+    "              \"logfc_x_jsd\",\n",
+    "              \"jsd_x_max_percentile\",\n",
+    "              \"logfc_x_max_percentile\",\n",
+    "              \"jsd\",\n",
+    "              \"logfc\",\n",
+    "              \"max_percentile\"]\n",
+    "\n",
+    "names = [\"ATAC (572M)\", \"ATAC (50M)\", \"DNASE (50M)\"]\n",
+    "\n",
+    "j=0\n",
+    "for kd in keys:\n",
+    "    i = 0\n",
+    "    for title in [\"encsr637xsc\", \"encsr637xsc_50m\", \"encsr000emt\"]:\n",
+    "        label=data[\"var.label\"].astype(int)\n",
+    "        score=abs(data[kd.replace(\"encsr637xsc\", title)])\n",
+    "        roc_auc = metrics.average_precision_score(label, score)\n",
+    "        scores.append(roc_auc)\n",
+    "        labels.append(names[i])\n",
+    "        keys_df.append(key_shorts[j])\n",
+    "        i=i+1\n",
+    "    j=j+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "dictt={\"scores\":scores, \"labels\":labels, \"method\":keys_df}\n",
+    "df = pd.DataFrame(dictt)\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15,8))\n",
+    "\n",
+    "sns.barplot(data=df, x=\"labels\", y=\"scores\", hue=\"method\", ax=ax)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "plt.savefig(\"subfigs/dsqtls_auprc_score_compare.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1\n",
+       "1        1\n",
+       "2        1\n",
+       "3        1\n",
+       "4        1\n",
+       "        ..\n",
+       "28302   -1\n",
+       "28303   -1\n",
+       "28304   -1\n",
+       "28305   -1\n",
+       "28307   -1\n",
+       "Name: var.label, Length: 27373, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf[\"var.label\"].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "keyd=\"pred.chrombpnet.encsr000emt.varscore.crs\"\n",
+    "score=dataf[keyd]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([2.6739e+04, 6.0000e+01, 8.0000e+00, 1.0000e+00, 2.0000e+00,\n",
+       "        1.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
+       " array([2.90406164e-11, 2.06203065e-02, 4.12406129e-02, 6.18609193e-02,\n",
+       "        8.24812258e-02, 1.03101532e-01, 1.23721839e-01, 1.44342145e-01,\n",
+       "        1.64962451e-01, 1.85582758e-01, 2.06203064e-01]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(score[dataf[\"var.label\"].astype(int)==-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(score[dataf[\"var.label\"].astype(int)==-1]>0.025)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "119"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(score[dataf[\"var.label\"].astype(int)==1]>0.025)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([456.,  63.,  21.,  11.,   4.,   1.,   2.,   1.,   0.,   1.]),\n",
+       " array([1.07273470e-06, 2.82166130e-02, 5.64321533e-02, 8.46476936e-02,\n",
+       "        1.12863234e-01, 1.41078774e-01, 1.69294314e-01, 1.97509855e-01,\n",
+       "        2.25725395e-01, 2.53940935e-01, 2.82156476e-01]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAANcElEQVR4nO3dX4xc51nH8e+D3TggROzEqyiyTdZRjVAiQVNMCFT8i6maP1BbIq2CgFrBkiUIUlGQwCUXCMRFckOgEmpl4aoOQiQhRcRqi5BxEhAXSbtOXKdOFLJxE9lWGm8dxyWEBrl9uNjX0WS765mdmd2ZffL9SKN5z3veM+d99li/PT5nZjYyE0lSLT8w6glIkobPcJekggx3SSrIcJekggx3SSpo9agnALB+/fqcnJwc9TQkaUU5fPjwtzJzYr51YxHuk5OTTE1NjXoakrSiRMQrC63zsowkFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFTQWn1AdxOSeL41s3y/fe9vI9i1JF+OZuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQV1HO4R8SqiHgmIr7YljdHxFMRMR0RD0XEJa1/TVuebusnl2jukqQFLObM/ZPA8x3L9wH3Z+b7gbPArta/Czjb+u9v4yRJy6incI+IjcBtwN+25QBuAh5pQ/YDO1p7e1umrd/WxkuSlkmvZ+5/BfwR8L22fAXwRmaeb8sngQ2tvQE4AdDWn2vj3yUidkfEVERMzczM9Dd7SdK8uoZ7RPwqcDozDw9zx5m5NzO3ZubWiYmJYb60JL3nre5hzIeAj0bErcClwI8Afw2sjYjV7ex8I3CqjT8FbAJORsRq4DLgzNBnLklaUNcz98z8VGZuzMxJ4A7gscz8TeBx4PY2bCfwaGsfaMu09Y9lZg511pKkixrkfe5/DNwdEdPMXlPf1/r3AVe0/ruBPYNNUZK0WL1clnlHZj4BPNHax4Eb5hnzHeBjQ5ibJKlPfkJVkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpIMNdkgoy3CWpoK7hHhGXRsRXIuJrEXEsIv6s9W+OiKciYjoiHoqIS1r/mrY83dZPLnENkqQ5ejlzfxu4KTN/EvgAcHNE3AjcB9yfme8HzgK72vhdwNnWf38bJ0laRl3DPWe92Rbf1x4J3AQ80vr3Aztae3tbpq3fFhExrAlLkrrr6Zp7RKyKiCPAaeAg8BLwRmaeb0NOAhtaewNwAqCtPwdcMcQ5S5K66CncM/O7mfkBYCNwA/Djg+44InZHxFRETM3MzAz6cpKkDot6t0xmvgE8DvwssDYiVrdVG4FTrX0K2ATQ1l8GnJnntfZm5tbM3DoxMdHf7CVJ8+rl3TITEbG2tX8Q+DDwPLMhf3sbthN4tLUPtGXa+scyM4c4Z0lSF6u7D+EqYH9ErGL2l8HDmfnFiHgOeDAi/gJ4BtjXxu8D/i4ipoHXgTuWYN6SpIvoGu6ZeRS4fp7+48xef5/b/x3gY0OZnSSpL35CVZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIK6hruEbEpIh6PiOci4lhEfLL1Xx4RByPixfa8rvVHRHw6IqYj4mhEfHCpi5AkvVsvZ+7ngT/MzGuBG4G7IuJaYA9wKDO3AIfaMsAtwJb22A18ZuizliRdVNdwz8xXM/Pp1v5v4HlgA7Ad2N+G7Qd2tPZ24IGc9SSwNiKuGvbEJUkLW9Q194iYBK4HngKuzMxX26pvAle29gbgRMdmJ1vf3NfaHRFTETE1MzOz2HlLki6i53CPiB8GvgD8QWZ+u3NdZiaQi9lxZu7NzK2ZuXViYmIxm0qSuugp3CPifcwG+99n5j+17tcuXG5pz6db/ylgU8fmG1ufJGmZ9PJumQD2Ac9n5l92rDoA7GztncCjHf2faO+auRE413H5RpK0DFb3MOZDwG8Dz0bEkdb3J8C9wMMRsQt4Bfh4W/dl4FZgGngLuHOYE5Ykddc13DPzP4FYYPW2ecYncNeA85IkDcBPqEpSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBXUNdwj4nMRcToivt7Rd3lEHIyIF9vzutYfEfHpiJiOiKMR8cGlnLwkaX69nLl/Hrh5Tt8e4FBmbgEOtWWAW4At7bEb+MxwpilJWoyu4Z6Z/wG8Pqd7O7C/tfcDOzr6H8hZTwJrI+KqIc1VktSjfq+5X5mZr7b2N4ErW3sDcKJj3MnW930iYndETEXE1MzMTJ/TkCTNZ+AbqpmZQPax3d7M3JqZWycmJgadhiSpQ7/h/tqFyy3t+XTrPwVs6hi3sfVJkpZRv+F+ANjZ2juBRzv6P9HeNXMjcK7j8o0kaZms7jYgIv4B+CVgfUScBP4UuBd4OCJ2Aa8AH2/DvwzcCkwDbwF3LsGcJUlddA33zPyNBVZtm2dsAncNOilJ0mD8hKokFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFdT1u2W0sMk9XxrJfl++97aR7FfSyuGZuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkH+mb0VaFR/3g/8E3/SSuGZuyQVZLhLUkGGuyQVZLhLUkHeUNWijOpmrjdypcXxzF2SClqScI+ImyPihYiYjog9S7EPSdLChn5ZJiJWAX8DfBg4CXw1Ig5k5nPD3pfeO7wcJC3OUlxzvwGYzszjABHxILAdMNy14ozyA2PvRaP6ZVrxg4FLEe4bgBMdyyeBn5k7KCJ2A7vb4psR8UKf+1sPfKvPbceZda0s1jUEcd9y7Wl8jteANV+90IqRvVsmM/cCewd9nYiYysytQ5jSWLGulcW6VpaqdXVaihuqp4BNHcsbW58kaZksRbh/FdgSEZsj4hLgDuDAEuxHkrSAoV+WyczzEfH7wL8Cq4DPZeaxYe+nw8CXdsaUda0s1rWyVK3rHZGZo56DJGnI/ISqJBVkuEtSQWMd7t2+xiAi1kTEQ239UxEx2bHuU63/hYj4yLJOvIt+64qIyYj434g40h6fXfbJX0QPdf1CRDwdEecj4vY563ZGxIvtsXP5Zt3dgHV9t+N4jdUbC3qo6+6IeC4ijkbEoYi4umPdWB6vAWsa22PVl8wcywezN2NfAq4BLgG+Blw7Z8zvAZ9t7TuAh1r72jZ+DbC5vc6qUdc0hLomga+PuoYB6poEfgJ4ALi9o/9y4Hh7Xtfa60Zd06B1tXVvjrqGAer6ZeCHWvt3O/4djuXxGqSmcT5W/T7G+cz9na8xyMz/Ay58jUGn7cD+1n4E2BYR0fofzMy3M/MbwHR7vXEwSF3jrGtdmflyZh4Fvjdn248ABzPz9cw8CxwEbl6OSfdgkLrGWS91PZ6Zb7XFJ5n9zAqM7/EapKZyxjnc5/sagw0LjcnM88A54Ioetx2VQeoC2BwRz0TEv0fEzy/1ZBdhkJ/5Sj9eF3NpRExFxJMRsWOoMxvMYuvaBfxLn9sul0FqgvE9Vn3xj3WsLK8CP5qZZyLip4B/jojrMvPbo56YFnR1Zp6KiGuAxyLi2cx8adSTWoyI+C1gK/CLo57LsCxQ04o/Vp3G+cy9l68xeGdMRKwGLgPO9LjtqPRdV7vMdAYgMw8ze33xx5Z8xr0Z5Ge+0o/XgjLzVHs+DjwBXD/MyQ2gp7oi4leAe4CPZubbi9l2BAapaZyPVX9GfdF/oQez/6s4zuwN0Qs3R66bM+Yu3n3j8eHWvo5331A9zvjcUB2krokLdTB70+gUcPmoa+q1ro6xn+f7b6h+g9mbc+tau0Jd64A1rb0eeJE5N/jGuS5mw+0lYMuc/rE8XgPWNLbHqu+fx6gn0OVg3Qr8VzsY97S+P2f2Ny7ApcA/MnvD9CvANR3b3tO2ewG4ZdS1DKMu4NeBY8AR4Gng10ZdyyLr+mlmr4P+D7P/wzrWse3vtHqngTtHXcsw6gJ+Dni2hcyzwK5R17LIuv4NeK39ezsCHBj349VvTeN+rPp5+PUDklTQOF9zlyT1yXCXpIIMd0kqyHCXpIIMd0kqyHCXpIIMd0kq6P8Bg4Lb5xtkePUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(score[dataf[\"var.label\"].astype(int)==1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUPRC (subsampling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fetch_auprc_new(p):\n",
+    "    fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(merged_atac[key_label], abs(merged_atac[\"abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    roc_auc = metrics.average_precision_score(merged_atac[key_label], abs(merged_atac[\"abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    #roc_auc = metrics.average_precision_score(merged_atac[key_label], abs(merged_atac[\"jsd.mean\"]))\n",
+    "    print(roc_auc)\n",
+    "    return roc_auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5382377424514815\n",
+      "0.5212720206135839\n",
+      "0.5130607361102206\n",
+      "0.4573954341174485\n",
+      "0.4172765936392213\n",
+      "0.289267529781573\n",
+      "0.42936897087849046\n"
+     ]
+    }
+   ],
+   "source": [
+    "rocs = []\n",
+    "for title in [\"encsr637xsc\", \"encsr637xsc_250m\", \"encsr637xsc_100m\",\n",
+    "              \"encsr637xsc_50m\", \"encsr637xsc_25m\", \"encsr637xsc_5m\", \"encsr000emt\"]:\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"var.label\"].astype(int), abs(dataf[\"pred.chrombpnet.encsr637xsc.varscore.crs\".replace(\"encsr637xsc\", title)]))\n",
+    "    print(roc_auc)\n",
+    "    rocs.append(roc_auc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  del sys.path[0]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fix, ax = plt.subplots(figsize=(5,3))\n",
+    "ax.scatter([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\" ,\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],rocs[0:-1])\n",
+    "ax.plot([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\",\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],rocs[0:-1], alpha=0.5)\n",
+    "ax.scatter([\"DNASE\\n68M\"], [rocs[-1]], marker=\"*\")\n",
+    "ax.scatter([\"Enformer recomp\"], [enformer_auc_recomp], marker=\"*\")\n",
+    "ax.scatter([\"Enformer\"], [enformer_auc], marker=\"*\")\n",
+    "ax.scatter([\"DeltSVM\"], [deltasvm_roc], marker=\"*\")\n",
+    "\n",
+    "xtix=[\"ChromBPNet ATAC 572M\", \" ChromBPNet ATAC 250M\", \"ChromBPNet ATAC 100M\",\"ChromBPNet ATAC 50M\", \"ChromBPNet ATAC 25M\", \"ChromBPNet ATAC 5M\"]+[\"ChromBPNet DNASE 68M\"]+[\"Enformer DNase 68M\"]+[\"Enformer DNase 68M (reported)\"]+[\"SVM DNase 68m\"]\n",
+    "\n",
+    "ax.set_ylim((0,0.55))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "ax.set_xlabel(\"Method\")\n",
+    "ax.set_ylabel(\"Average Precision\")\n",
+    "\n",
+    "plt.savefig(\"subfigs/dsqtls_auprc_subsampling.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.71\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "key_enf_1=\"pred.enformer.encsr000emt.varscore.recomputed\"\n",
+    "key_enf_2=\"pred.enformer.encsr000emt.varscore.sar\"\n",
+    "key_label=\"var.label\"\n",
+    "obs_estimate=\"obs.estimate\"\n",
+    "chrombpnet_logfc=\"pred.chrombpnet.encsr000emt.varscore.logfc\"\n",
+    "chrombpnet_atac_logfc=\"pred.chrombpnet.encsr637xsc.varscore.logfc\"\n",
+    "delta_svm_key=\"pred.gkmsvm.encsr000emt.varscore.deltasvm\"\n",
+    "chrombpnet_atac_50m_logfc=\"pred.chrombpnet.encsr637xsc_50m.varscore.logfc\"\n",
+    "\n",
+    "\n",
+    "# enformer_perf = np.max([scipy.stats.pearsonr(dataf[key_enf_1][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])[0],\n",
+    "#                        scipy.stats.pearsonr(dataf[key_enf_2][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])[0]])\n",
+    "enformer_perf = scipy.stats.pearsonr(dataf[key_enf_1][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])[0]\n",
+    "enformer_perf_old = scipy.stats.pearsonr(dataf[key_enf_2][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])[0]\n",
+    "\n",
+    "print(np.round(enformer_perf,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#enformer_perf = scipy.stats.pearsonr(abs(dataf[key_enf_1][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))[0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#print(np.round(enformer_perf,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.56\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.round(enformer_perf_old,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7124757538065036, 6.703532203865769e-88)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.pearsonr(dataf[key_enf_1][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.563128780419175, 3.625037841934576e-48)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.pearsonr(dataf[key_enf_2][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7393262959574392, 6.552289684724055e-98)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.pearsonr(dataf[chrombpnet_logfc][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7586179164294138, 6.689376032748603e-106)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.pearsonr(dataf[chrombpnet_atac_logfc][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7318756523100427, 5.1915505043409464e-95)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.pearsonr(dataf[delta_svm_key][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.19\n",
+      "(0.029602416955412617, 0.4844839205202049)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# enformer_perf_abs = np.max([scipy.stats.pearsonr(abs(dataf[key_enf_1][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))[0],\n",
+    "#                        scipy.stats.pearsonr(abs(dataf[key_enf_2][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))[0]])\n",
+    "enformer_perf_abs = scipy.stats.pearsonr(abs(dataf[key_enf_1][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))[0]\n",
+    "enformer_perf_abs_old = scipy.stats.pearsonr(abs(dataf[key_enf_2][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))[0]\n",
+    "\n",
+    "print(np.round(enformer_perf_abs,2))\n",
+    "print(scipy.stats.pearsonr(abs(dataf[key_enf_2][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1])))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.03\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.round(enformer_perf_abs_old,2))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.73\n"
+     ]
+    }
+   ],
+   "source": [
+    "deltasvm_perf = scipy.stats.pearsonr(dataf[delta_svm_key][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])[0]\n",
+    "print(np.round(deltasvm_perf,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.19\n",
+      "(0.19358920550964032, 3.937682048147411e-06)\n"
+     ]
+    }
+   ],
+   "source": [
+    "deltasvm_perf_abs = scipy.stats.pearsonr(abs(dataf[delta_svm_key][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))\n",
+    "print(np.round(deltasvm_perf_abs[0],2))\n",
+    "print(deltasvm_perf_abs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.74\n"
+     ]
+    }
+   ],
+   "source": [
+    "chrombpnet_perf = scipy.stats.pearsonr(dataf[chrombpnet_logfc][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])[0]\n",
+    "print(np.round(chrombpnet_perf,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.25\n",
+      "(0.24688242752352538, 3.19703213636387e-09)\n"
+     ]
+    }
+   ],
+   "source": [
+    "chrombpnet_perf_abs = scipy.stats.pearsonr(abs(dataf[chrombpnet_logfc][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))\n",
+    "print(np.round(chrombpnet_perf_abs[0],2))\n",
+    "print(chrombpnet_perf_abs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.73\n"
+     ]
+    }
+   ],
+   "source": [
+    "chrombpnet_perf = scipy.stats.pearsonr(dataf[chrombpnet_atac_50m_logfc][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])[0]\n",
+    "print(np.round(chrombpnet_perf,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.26\n",
+      "(0.2629603458940187, 2.608186148205023e-10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "chrombpnet_perf_abs = scipy.stats.pearsonr(abs(dataf[chrombpnet_atac_50m_logfc][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))\n",
+    "print(np.round(chrombpnet_perf_abs[0],2))\n",
+    "print(chrombpnet_perf_abs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.29208596203522214, 1.7776064659066986e-12)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scipy.stats.pearsonr(abs(dataf[chrombpnet_atac_logfc][dataf[key_label]==1]),abs(dataf[obs_estimate][dataf[key_label]==1]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(560, 2)\n",
+      "(0.7586179164294138, 6.689376032748603e-106)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = dataf[dataf[key_label]==1][chrombpnet_atac_logfc]\n",
+    "data[\"Beta\"] = dataf[dataf[key_label]==1][obs_estimate]\n",
+    "\n",
+    "print(data.shape)\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"Red\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#ffcccb\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(dataf[chrombpnet_atac_logfc][dataf[key_label]==1],dataf[obs_estimate][dataf[key_label]==1])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "plt.savefig(\"subfigs/dsqtls_pearsonr_plot.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonr (subsampling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.29208596203522214, 1.7776064659066986e-12)\n",
+      "(0.27736493794097455, 2.3813009032720084e-11)\n",
+      "(0.2816781946705184, 1.1309775172641475e-11)\n",
+      "(0.2629603458940187, 2.608186148205023e-10)\n",
+      "(0.22931197960366534, 4.072532181264645e-08)\n",
+      "(0.13358803525401117, 0.0015327079153868628)\n",
+      "(0.24688242752352538, 3.19703213636387e-09)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "pearsonrs_logfc_abs = []\n",
+    "for title in [\"encsr637xsc\", \"encsr637xsc_250m\", \"encsr637xsc_100m\",\n",
+    "              \"encsr637xsc_50m\", \"encsr637xsc_25m\", \"encsr637xsc_5m\", \"encsr000emt\"]:\n",
+    "  \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[obs_estimate][dataf[key_label]==1]), abs(dataf[\"pred.chrombpnet.encsr637xsc.varscore.logfc\".replace(\"encsr637xsc\", title)][dataf[key_label]==1]))\n",
+    "    print(pear)\n",
+    "    pearsonrs_logfc_abs.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pred.chrombpnet.encsr637xsc.varscore.logfc\n",
+      "(0.7586179164294138, 6.689376032748603e-106)\n",
+      "pred.chrombpnet.encsr637xsc_250m.varscore.logfc\n",
+      "(0.7526282890600602, 2.4287034191876065e-103)\n",
+      "pred.chrombpnet.encsr637xsc_100m.varscore.logfc\n",
+      "(0.7546672458842387, 3.328306684572512e-104)\n",
+      "pred.chrombpnet.encsr637xsc_50m.varscore.logfc\n",
+      "(0.7346524058027831, 4.429888240845516e-96)\n",
+      "pred.chrombpnet.encsr637xsc_25m.varscore.logfc\n",
+      "(0.7203103315262882, 1.0646610243625364e-90)\n",
+      "pred.chrombpnet.encsr637xsc_5m.varscore.logfc\n",
+      "(0.6681759905958633, 1.0998664422228002e-73)\n",
+      "pred.chrombpnet.encsr000emt.varscore.logfc\n",
+      "(0.7393262959574392, 6.552289684724055e-98)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pearsonrs_logfc = []\n",
+    "for title in [\"encsr637xsc\", \"encsr637xsc_250m\", \"encsr637xsc_100m\",\n",
+    "              \"encsr637xsc_50m\", \"encsr637xsc_25m\", \"encsr637xsc_5m\", \"encsr000emt\"]:\n",
+    " \n",
+    "    print(\"pred.chrombpnet.encsr637xsc.varscore.logfc\".replace(\"encsr637xsc\", title))\n",
+    "    pear = scipy.stats.pearsonr(dataf[obs_estimate][dataf[key_label]==1], dataf[\"pred.chrombpnet.encsr637xsc.varscore.logfc\".replace(\"encsr637xsc\", title)][dataf[key_label]==1])\n",
+    "    print(pear)\n",
+    "    pearsonrs_logfc.append(pear[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pred.chrombpnet.encsr637xsc.varscore.js\n",
+      "(0.3126615207076874, 3.6450264964081094e-14)\n",
+      "pred.chrombpnet.encsr637xsc_250m.varscore.js\n",
+      "(0.31167966683860004, 4.418936333384774e-14)\n",
+      "pred.chrombpnet.encsr637xsc_100m.varscore.js\n",
+      "(0.30728724129706153, 1.0365589041469113e-13)\n",
+      "pred.chrombpnet.encsr637xsc_50m.varscore.js\n",
+      "(0.29859614231417875, 5.372085406673485e-13)\n",
+      "pred.chrombpnet.encsr637xsc_25m.varscore.js\n",
+      "(0.2864012022361328, 4.930507413208403e-12)\n",
+      "pred.chrombpnet.encsr637xsc_5m.varscore.js\n",
+      "(0.250890979852558, 1.7392825202867793e-09)\n",
+      "pred.chrombpnet.encsr000emt.varscore.js\n",
+      "(0.31380363966877345, 2.911008818617905e-14)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "pearsonrs_jsd_abs = []\n",
+    "for title in [\"encsr637xsc\", \"encsr637xsc_250m\", \"encsr637xsc_100m\",\n",
+    "              \"encsr637xsc_50m\", \"encsr637xsc_25m\", \"encsr637xsc_5m\", \"encsr000emt\"]:\n",
+    "    \n",
+    "    print(\"pred.chrombpnet.encsr637xsc.varscore.js\".replace(\"encsr637xsc\", title))\n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[obs_estimate][dataf[key_label]==1]), abs(dataf[\"pred.chrombpnet.encsr637xsc.varscore.jsd\".replace(\"encsr637xsc\", title)][dataf[key_label]==1]))\n",
+    "    print(pear)\n",
+    "    pearsonrs_jsd_abs.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.7480524783614502, 1.959293501864504e-101)\n",
+      "(0.7523651296928697, 3.1344639726513044e-103)\n",
+      "(0.7470599652802675, 5.014684226524033e-101)\n",
+      "(0.736694138893397, 7.108672678159656e-97)\n",
+      "(0.7256926640794923, 1.1169288396490585e-92)\n",
+      "(0.6726748364294816, 5.1375454552778505e-75)\n",
+      "(0.7575962676005736, 1.8506194777967972e-105)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "pearsonrs_jsd = []\n",
+    "for title in [\"encsr637xsc\", \"encsr637xsc_250m\", \"encsr637xsc_100m\",\n",
+    "              \"encsr637xsc_50m\", \"encsr637xsc_25m\", \"encsr637xsc_5m\", \"encsr000emt\"]:\n",
+    "    pear = scipy.stats.pearsonr(dataf[obs_estimate][dataf[key_label]==1], dataf[\"pred.chrombpnet.encsr637xsc.varscore.jsd\".replace(\"encsr637xsc\", title)][dataf[key_label]==1])\n",
+    "    print(pear)\n",
+    "    pearsonrs_jsd.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:32: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(4,3))\n",
+    "ax.scatter([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\" ,\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_logfc[0:-1], c=\"orange\", marker=\"^\")\n",
+    "ax.plot([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\",\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_logfc[0:-1], alpha=0.1, c=\"orange\")\n",
+    "ax.scatter([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\" ,\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_jsd[0:-1], c=\"blue\", marker=\"^\")\n",
+    "ax.plot([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\",\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_jsd[0:-1], alpha=0.1, c=\"blue\")\n",
+    "\n",
+    "ax.scatter([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\" ,\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_logfc_abs[0:-1], c=\"orange\", marker=\"v\")\n",
+    "ax.plot([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\",\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_logfc_abs[0:-1], alpha=0.1,c=\"orange\")\n",
+    "ax.scatter([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\" ,\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_jsd_abs[0:-1], c=\"blue\", marker=\"v\")\n",
+    "ax.plot([\"ATAC\\n572M\", \"ATAC\\n250M\", \"ATAC\\n100M\",\"ATAC\\n50M\", \"ATAC\\n25M\", \"ATAC\\n5M\"],pearsonrs_jsd_abs[0:-1], alpha=0.1, c=\"blue\" )\n",
+    "\n",
+    "\n",
+    "ax.scatter([\"DNASE\\n68M\"], [pearsonrs_logfc[-1]],c=\"cyan\",  marker=\"^\")\n",
+    "ax.scatter([\"DNASE\\n68M\"], [pearsonrs_jsd[-1]],c=\"red\",  marker=\"^\")\n",
+    "ax.scatter([\"DNASE\\n68M\"], [pearsonrs_logfc_abs[-1]],c=\"cyan\",  marker=\"v\")\n",
+    "ax.scatter([\"DNASE\\n68M\"], [pearsonrs_jsd_abs[-1]],c=\"red\",  marker=\"v\")\n",
+    "\n",
+    "ax.scatter([\"Enformer DNase\\n68M\"], [enformer_perf], c=\"green\", marker=\"^\")\n",
+    "ax.scatter([\"Enformer DNase\\n68M\"], [enformer_perf_abs], c=\"green\", marker=\"v\")\n",
+    "\n",
+    "ax.scatter([\"Enformer DNase (reported)\\n68M\"], [enformer_perf_old], c=\"green\", marker=\"^\")\n",
+    "ax.scatter([\"Enformer DNase (reported) \\n68M\"], [enformer_perf_abs_old], c=\"green\", marker=\"v\")\n",
+    "\n",
+    "\n",
+    "ax.scatter([\"SVM DNase\\n68M\"], [deltasvm_perf], c=\"grey\", marker=\"^\")\n",
+    "ax.scatter([\"SVM DNase\\n68M\"], [deltasvm_perf_abs[0]], c=\"grey\",  marker=\"v\")\n",
+    "\n",
+    "\n",
+    "xtix=[\"ATAC 572M\", \"ATAC 250M\", \"ATAC 100M\",\"ATAC 50M\", \"ATAC 25M\", \"ATAC 5M\"]+[\"DNASE 68M\"]+[\"Enformer DNase 68M\"]+[\"Enformer DNase 68M (reported)\"]+[\"SVM DNase 68M\"]\n",
+    "ax.set_ylim((0,0.8))\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "plt.savefig(\"subfigs/dsqtls_subsample_pearsonr_performance.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/intersection_between_sets.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/intersection_between_sets.ipynb
new file mode 100644
index 00000000..0613c4ad
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/intersection_between_sets.ipynb
@@ -0,0 +1,2056 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(226346, 28)\n",
+      "(162381, 28)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "eus = pd.read_csv(\"Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n",
+    "#eus = eus[eus[\"IsUsed\"] & eus[\"Inside_Peak\"]==1]\n",
+    "\n",
+    "#eus = eus[eus[\"Inside_Peak\"]==2]\n",
+    "print(eus.shape)\n",
+    "eus = eus[(eus[\"Log10_BF\"]>3) | (eus[\"Log10_BF\"]<-1)]\n",
+    "print(eus.shape)\n",
+    "eus[\"labeln\"] = 1\n",
+    "eus.loc[eus[\"Log10_BF\"]>3,\"labeln\"]=1\n",
+    "eus.loc[eus[\"Log10_BF\"]<-1,\"labeln\"]=-1\n",
+    "eus = eus[['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'labeln']]\n",
+    "eus[\"my_rsid\"] = eus[\"chr\"]+\".\"+eus[\"Pos\"].astype(str)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sum(eus[\"Log10_BF\"]<0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(109646, 51)\n",
+      "(109646, 51)\n",
+      "   chr_hg19 pos_hg19 ref_hg19 alt_hg19   dbsnp_rsid  labeln      my_rsid\n",
+      "2      chr1   805556        T        A   rs72631880      -1  chr1.805556\n",
+      "4      chr1   840753        T        C    rs4970382      -1  chr1.840753\n",
+      "8      chr1   842250        A        G  rs148658266      -1  chr1.842250\n",
+      "9      chr1   842362        C        T   rs28540380       1  chr1.842362\n",
+      "12     chr1   856583        G        A  rs141671872       1  chr1.856583\n"
+     ]
+    }
+   ],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)\n",
+    "afrs = afrs[afrs[\"IsUsed\"]]\n",
+    "afrs[[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\"]] = afrs[\"id_hg19\"].str.split(\"_\", expand=True)\n",
+    "threshold = 1 / (10**(6))\n",
+    "print(afrs.shape)\n",
+    "#print(afrs.head())\n",
+    "#afrs = afrs[(afrs[\"label\"]==1) | (afrs[\"pval\"]>1e-3)]\n",
+    "print(afrs.shape)\n",
+    "afrs[\"labeln\"] = 1\n",
+    "afrs.loc[afrs[\"label\"]==1,\"labeln\"]=1\n",
+    "afrs.loc[afrs[\"pval\"]>1e-3,\"labeln\"]=-1\n",
+    "afrs = afrs[['chr_hg19', 'pos_hg19', 'ref_hg19', 'alt_hg19', 'dbsnp_rsid', 'labeln']]\n",
+    "afrs[\"my_rsid\"] = afrs[\"chr_hg19\"]+\".\"+afrs[\"pos_hg19\"].astype(str)\n",
+    "print(afrs.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n"
+     ]
+    }
+   ],
+   "source": [
+    "es = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/dsqtls_lcl/GSE31388_dsQtlTable.txt.gz\", sep=\"\\t\")\n",
+    "es[\"rsid\"] = es[\"Chr\"]+\".\"+es[\"SNP\"].astype(str)\n",
+    "gkm_svm = pd.read_csv(\"/mnt/lab_data3/anusri/histone_expts/all_qtl_analysis/gm12878_sequence_sets/test_set/deltasv,/41588_2015_BFng3331_MOESM26_ESM.csv\", header=0,sep=',')\n",
+    "# not the full dsqtls set\n",
+    "dsqtls = gkm_svm.merge(es, left_on=[\"SNPname1\"], right_on='rsid', how=\"left\").drop_duplicates('SNPname1')\n",
+    "dsqtls[dsqtls[\"SNPname1\"].isin(es[\"rsid\"])][\"label\"] = 1\n",
+    "dsqtls = dsqtls[[\"chrom_hg19\", \"pos_hg19\", \"allele1\", \"allele2\", \"label\"]]\n",
+    "dsqtls[\"my_rsid\"] = dsqtls[\"chrom_hg19\"]+\".\"+dsqtls[\"pos_hg19\"].astype(str)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(107841, 25)\n",
+      "(107841, 27)\n",
+      "(107841, 29)\n"
+     ]
+    }
+   ],
+   "source": [
+    "caqtls_ols=pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/rasqual_atac_1M\", sep='\\t', header=None)\n",
+    "print(caqtls_ols.shape)\n",
+    "qvals = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/Q.val.txt\", sep=\"\\t\", header=None, names=[\"peakid\", \"qval\"])\n",
+    "caqtls_ols = caqtls_ols.merge(qvals, right_on=[\"peakid\"], left_on=[0],how='left')\n",
+    "peaks=pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl/caqtls_peaks/peaks.bed.gz\", sep='\\t', header=None)\n",
+    "peaks['center'] = (peaks[2]+peaks[1])/2\n",
+    "print(caqtls_ols.shape)\n",
+    "caqtls_ols['peak_cntr'] = peaks['center']\n",
+    "# not intersected to peaks\n",
+    "# caqtls_ols = caqtls_ols[abs(caqtls_ols[3]-caqtls_ols['peak_cntr'])<100]\n",
+    "# print(caqtls_ols.shape)\n",
+    "#caqtls_ols = caqtls_ols[(caqtls_ols['qval']<0.1) | (caqtls_ols['qval']>0.4)]\n",
+    "caqtls_ols[\"labeln\"] = 1\n",
+    "caqtls_ols.loc[(caqtls_ols['qval']<0.1),\"labeln\"]=1\n",
+    "caqtls_ols.loc[(caqtls_ols['qval']>0.4),\"labeln\"]=-1\n",
+    "print(caqtls_ols.shape)\n",
+    "caqtls_ols[[\"Chr\", \"Pos\", \"Ref\", \"Alt\", \"rsid\"]] = caqtls_ols[[2,3,4,5,1]]\n",
+    "caqtls_ols = caqtls_ols[[\"Chr\", \"Pos\", \"Ref\", \"Alt\", \"rsid\", \"labeln\"]]\n",
+    "caqtls_ols[\"Chr\"] = \"chr\"+caqtls_ols[\"Chr\"].astype(str)\n",
+    "caqtls_ols[\"my_rsid\"] = caqtls_ols[\"Chr\"]+\".\"+caqtls_ols[\"Pos\"].astype(str)\n",
+    "#caqtls_ols\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#caqtls_ols,eus,afrs,dsqtls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_rsids = np.concatenate([caqtls_ols[\"my_rsid\"].values,eus[\"my_rsid\"].values,afrs[\"my_rsid\"].values,dsqtls[\"my_rsid\"].values])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(581878,)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_rsids.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_rsids = list(set(all_rsids))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "381026"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(all_rsids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "# all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[\"my_rsid\"]))\n",
+    "# all_rsids[\"Kumsaka (2018)\"] = list(set(eus[\"my_rsid\"]))\n",
+    "# all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[\"my_rsid\"]))\n",
+    "# all_rsids[\"Afr CaQTLs\"] = list(set(afrs[\"my_rsid\"]))\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==1][\"my_rsid\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 704x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==1][\"my_rsid\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==-1][\"my_rsid\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 704x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==-1][\"my_rsid\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 608x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==1][\"my_rsid\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.033875"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(203+68)/8000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08885017421602788"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(33+18)/574"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.14857142857142858"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1456/9800"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.03443740284586871"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "288/8363"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 704x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (2016)\"] = list(set(caqtls_ols[caqtls_ols[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"DsQTLs (deltasvm)\"] = list(set(dsqtls[dsqtls[\"label\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr CaQTLs\"] = list(set(afrs[afrs[\"labeln\"]==-1][\"my_rsid\"]))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78939"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(eus[\"labeln\"]==1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(226346, 7)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eus.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_sus = pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/susie_merged/QTD100018.credible_sets.tsv.gz\", header=0, sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>molecular_trait_id</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>cs_id</th>\n",
+       "      <th>variant</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>cs_size</th>\n",
+       "      <th>pip</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>z</th>\n",
+       "      <th>cs_min_r2</th>\n",
+       "      <th>region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100002782_A_AT</td>\n",
+       "      <td>rs531537001</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.185236</td>\n",
+       "      <td>7.604570e-10</td>\n",
+       "      <td>-0.614320</td>\n",
+       "      <td>0.087505</td>\n",
+       "      <td>-7.388146</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100002782_A_AT</td>\n",
+       "      <td>rs35227168</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.185236</td>\n",
+       "      <td>7.604570e-10</td>\n",
+       "      <td>-0.614320</td>\n",
+       "      <td>0.087505</td>\n",
+       "      <td>-7.388146</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Interval_461</td>\n",
+       "      <td>Interval_461</td>\n",
+       "      <td>Interval_461_L1</td>\n",
+       "      <td>chr1_10000458_T_C</td>\n",
+       "      <td>rs3003377</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.014039</td>\n",
+       "      <td>9.104810e-10</td>\n",
+       "      <td>0.619787</td>\n",
+       "      <td>0.088806</td>\n",
+       "      <td>7.545987</td>\n",
+       "      <td>0.937097</td>\n",
+       "      <td>chr1:9743190-10143190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046</td>\n",
+       "      <td>Interval_7046_L1</td>\n",
+       "      <td>chr1_100007048_A_G</td>\n",
+       "      <td>rs534252</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0.019619</td>\n",
+       "      <td>6.170090e-09</td>\n",
+       "      <td>-0.614765</td>\n",
+       "      <td>0.094044</td>\n",
+       "      <td>-6.877001</td>\n",
+       "      <td>0.930934</td>\n",
+       "      <td>chr1:99846814-100246814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047</td>\n",
+       "      <td>Interval_7047_L1</td>\n",
+       "      <td>chr1_100007048_A_G</td>\n",
+       "      <td>rs534252</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0.019667</td>\n",
+       "      <td>1.091410e-10</td>\n",
+       "      <td>-1.036960</td>\n",
+       "      <td>0.138943</td>\n",
+       "      <td>-8.093380</td>\n",
+       "      <td>0.824131</td>\n",
+       "      <td>chr1:99849456-100249456</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  molecular_trait_id        gene_id             cs_id              variant  \\\n",
+       "0      Interval_7046  Interval_7046  Interval_7046_L1  chr1_100002782_A_AT   \n",
+       "1      Interval_7046  Interval_7046  Interval_7046_L1  chr1_100002782_A_AT   \n",
+       "2       Interval_461   Interval_461   Interval_461_L1    chr1_10000458_T_C   \n",
+       "3      Interval_7046  Interval_7046  Interval_7046_L1   chr1_100007048_A_G   \n",
+       "4      Interval_7047  Interval_7047  Interval_7047_L1   chr1_100007048_A_G   \n",
+       "\n",
+       "          rsid  cs_size       pip        pvalue      beta        se         z  \\\n",
+       "0  rs531537001       66  0.185236  7.604570e-10 -0.614320  0.087505 -7.388146   \n",
+       "1   rs35227168       66  0.185236  7.604570e-10 -0.614320  0.087505 -7.388146   \n",
+       "2    rs3003377       30  0.014039  9.104810e-10  0.619787  0.088806  7.545987   \n",
+       "3     rs534252       66  0.019619  6.170090e-09 -0.614765  0.094044 -6.877001   \n",
+       "4     rs534252       80  0.019667  1.091410e-10 -1.036960  0.138943 -8.093380   \n",
+       "\n",
+       "   cs_min_r2                   region  \n",
+       "0   0.930934  chr1:99846814-100246814  \n",
+       "1   0.930934  chr1:99846814-100246814  \n",
+       "2   0.937097    chr1:9743190-10143190  \n",
+       "3   0.930934  chr1:99846814-100246814  \n",
+       "4   0.824131  chr1:99849456-100249456  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_sus[[\"chr\", \"pos\", \"ref\", \"alt\"]] = data_sus[\"variant\"].str.split(\"_\", expand=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.65849e-42"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus[\"pvalue\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26693"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(data_sus[\"pvalue\"]<1e-20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(446089, 13)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sus.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_sus[\"pip\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9295"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(data_sus[\"pip\"]>0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f4d13e1f750>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data_sus[\"pip\"],abs(data_sus[\"beta\"]), alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = eus.merge(data_sus, left_on=[\"Ref\", \"Alt\", \"RsID\"], right_on=[\"ref\", \"alt\", \"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(51662, 46)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_x</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>se</th>\n",
+       "      <th>z</th>\n",
+       "      <th>cs_min_r2</th>\n",
+       "      <th>region</th>\n",
+       "      <th>chr_y</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>917640</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs41285816</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.225</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.442401</td>\n",
+       "      <td>-0.380329</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.951030e-13</td>\n",
+       "      <td>-1.074320</td>\n",
+       "      <td>0.123041</td>\n",
+       "      <td>-9.408891</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>chr1:776050-1176050</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>982260</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>918238</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs28552711</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.225</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.442401</td>\n",
+       "      <td>-0.380329</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.951030e-13</td>\n",
+       "      <td>-1.074320</td>\n",
+       "      <td>0.123041</td>\n",
+       "      <td>-9.408891</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>chr1:776050-1176050</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>982858</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1004957</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs4073176</td>\n",
+       "      <td>62</td>\n",
+       "      <td>0.605</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303753</td>\n",
+       "      <td>-0.022021</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.388100e-05</td>\n",
+       "      <td>0.475413</td>\n",
+       "      <td>0.102395</td>\n",
+       "      <td>4.979371</td>\n",
+       "      <td>0.439644</td>\n",
+       "      <td>chr1:879854-1279854</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1069577</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.493991</td>\n",
+       "      <td>-0.046797</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.515830e-10</td>\n",
+       "      <td>-0.819723</td>\n",
+       "      <td>0.112704</td>\n",
+       "      <td>-7.785997</td>\n",
+       "      <td>0.834681</td>\n",
+       "      <td>chr1:1050703-1450703</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1251122</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1186502</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs6603785</td>\n",
+       "      <td>84</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.995510</td>\n",
+       "      <td>-0.406159</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.515830e-10</td>\n",
+       "      <td>-0.819723</td>\n",
+       "      <td>0.112704</td>\n",
+       "      <td>-7.785997</td>\n",
+       "      <td>0.834681</td>\n",
+       "      <td>chr1:1050703-1450703</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1251122</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51657</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50319351</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs9627800</td>\n",
+       "      <td>277040</td>\n",
+       "      <td>0.190</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999855</td>\n",
+       "      <td>-0.209689</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.478470e-07</td>\n",
+       "      <td>-0.721216</td>\n",
+       "      <td>0.129309</td>\n",
+       "      <td>-6.073618</td>\n",
+       "      <td>0.580440</td>\n",
+       "      <td>chr22:49725602-50125602</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>49925703</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51658</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50329830</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs79860705</td>\n",
+       "      <td>277044</td>\n",
+       "      <td>0.140</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.604522</td>\n",
+       "      <td>0.052395</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.364330e-21</td>\n",
+       "      <td>1.632570</td>\n",
+       "      <td>0.126964</td>\n",
+       "      <td>13.764463</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>chr22:49744954-50144954</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>49936182</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51659</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50455595</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs5771300</td>\n",
+       "      <td>277054</td>\n",
+       "      <td>0.190</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.917219</td>\n",
+       "      <td>-0.482022</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.191550e-12</td>\n",
+       "      <td>-0.851969</td>\n",
+       "      <td>0.104515</td>\n",
+       "      <td>-8.686298</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>chr22:49817306-50217306</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50017166</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51660</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50473396</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs5771312</td>\n",
+       "      <td>277056</td>\n",
+       "      <td>0.575</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.288326</td>\n",
+       "      <td>0.389836</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.087380e-14</td>\n",
+       "      <td>0.936656</td>\n",
+       "      <td>0.098161</td>\n",
+       "      <td>10.156039</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>chr22:49835319-50235319</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50034967</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51661</th>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50550808</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs138220</td>\n",
+       "      <td>277060</td>\n",
+       "      <td>0.295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999680</td>\n",
+       "      <td>0.192859</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.463220e-08</td>\n",
+       "      <td>0.824358</td>\n",
+       "      <td>0.137765</td>\n",
+       "      <td>6.358707</td>\n",
+       "      <td>0.601131</td>\n",
+       "      <td>chr22:49912524-50312524</td>\n",
+       "      <td>chr22</td>\n",
+       "      <td>50112379</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>51662 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       chr_x       Pos Ref Alt        RsID    Peak     AF  Inside_Peak  \\\n",
+       "0       chr1    917640   G   A  rs41285816      40  0.225            0   \n",
+       "1       chr1    918238   C   G  rs28552711      40  0.225            0   \n",
+       "2       chr1   1004957   G   A   rs4073176      62  0.605            1   \n",
+       "3       chr1   1186502   A   T   rs6603785      67  0.165            2   \n",
+       "4       chr1   1186502   A   T   rs6603785      84  0.165            1   \n",
+       "...      ...       ...  ..  ..         ...     ...    ...          ...   \n",
+       "51657  chr22  50319351   T   C   rs9627800  277040  0.190            1   \n",
+       "51658  chr22  50329830   G   A  rs79860705  277044  0.140            1   \n",
+       "51659  chr22  50455595   G   T   rs5771300  277054  0.190            1   \n",
+       "51660  chr22  50473396   G   C   rs5771312  277056  0.575            1   \n",
+       "51661  chr22  50550808   C   T    rs138220  277060  0.295            1   \n",
+       "\n",
+       "         P_Lead      Beta  ...        pvalue      beta        se          z  \\\n",
+       "0      0.442401 -0.380329  ...  3.951030e-13 -1.074320  0.123041  -9.408891   \n",
+       "1      0.442401 -0.380329  ...  3.951030e-13 -1.074320  0.123041  -9.408891   \n",
+       "2      0.303753 -0.022021  ...  1.388100e-05  0.475413  0.102395   4.979371   \n",
+       "3      0.493991 -0.046797  ...  2.515830e-10 -0.819723  0.112704  -7.785997   \n",
+       "4      0.995510 -0.406159  ...  2.515830e-10 -0.819723  0.112704  -7.785997   \n",
+       "...         ...       ...  ...           ...       ...       ...        ...   \n",
+       "51657  0.999855 -0.209689  ...  3.478470e-07 -0.721216  0.129309  -6.073618   \n",
+       "51658  0.604522  0.052395  ...  7.364330e-21  1.632570  0.126964  13.764463   \n",
+       "51659  0.917219 -0.482022  ...  5.191550e-12 -0.851969  0.104515  -8.686298   \n",
+       "51660  0.288326  0.389836  ...  1.087380e-14  0.936656  0.098161  10.156039   \n",
+       "51661  0.999680  0.192859  ...  6.463220e-08  0.824358  0.137765   6.358707   \n",
+       "\n",
+       "       cs_min_r2                   region  chr_y       pos  ref  alt  \n",
+       "0       1.000000      chr1:776050-1176050   chr1    982260    G    A  \n",
+       "1       1.000000      chr1:776050-1176050   chr1    982858    C    G  \n",
+       "2       0.439644      chr1:879854-1279854   chr1   1069577    G    A  \n",
+       "3       0.834681     chr1:1050703-1450703   chr1   1251122    A    T  \n",
+       "4       0.834681     chr1:1050703-1450703   chr1   1251122    A    T  \n",
+       "...          ...                      ...    ...       ...  ...  ...  \n",
+       "51657   0.580440  chr22:49725602-50125602  chr22  49925703    T    C  \n",
+       "51658   1.000000  chr22:49744954-50144954  chr22  49936182    G    A  \n",
+       "51659   1.000000  chr22:49817306-50217306  chr22  50017166    G    T  \n",
+       "51660   1.000000  chr22:49835319-50235319  chr22  50034967    G    C  \n",
+       "51661   0.601131  chr22:49912524-50312524  chr22  50112379    C    T  \n",
+       "\n",
+       "[51662 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f4d014e8450>"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1 = data[data[\"Inside_Peak\"]==2]\n",
+    "plt.scatter(data1.loc[data1[\"Log10_BF\"]>3, \"Beta\"], data1.loc[data1[\"Log10_BF\"]>3,\"beta\"],alpha=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f4cffd426d0>"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1 = data[data[\"Inside_Peak\"]==1]\n",
+    "plt.scatter(data1.loc[data1[\"Log10_BF\"]>3, \"Beta\"], data1.loc[data1[\"Log10_BF\"]>3,\"beta\"],alpha=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(109646, 51)\n",
+      "(109646, 51)\n"
+     ]
+    }
+   ],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)\n",
+    "afrs = afrs[afrs[\"IsUsed\"]]\n",
+    "afrs[[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\"]] = afrs[\"id_hg19\"].str.split(\"_\", expand=True)\n",
+    "threshold = 1 / (10**(6))\n",
+    "print(afrs.shape)\n",
+    "#print(afrs.head())\n",
+    "#afrs = afrs[(afrs[\"label\"]==1) | (afrs[\"pval\"]>1e-3)]\n",
+    "print(afrs.shape)\n",
+    "afrs[\"labeln\"] = 1\n",
+    "afrs.loc[afrs[\"label\"]==1,\"labeln\"]=1\n",
+    "afrs.loc[afrs[\"pval\"]>1e-3,\"labeln\"]=-1\n",
+    "\n",
+    "afr_sus = pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/AFGR_wgs/susie_merged/QTD100010.credible_sets.tsv.gz\", header=0, sep='\\t')\n",
+    "afr_sus[[\"chr\", \"pos\", \"ref\", \"alt\"]] = afr_sus[\"variant\"].str.split(\"_\", expand=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>dbsnp_rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs72631880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs4970382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs148658266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs28540380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs141671872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219372</th>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs112714656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219373</th>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3091400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219374</th>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs76733141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219375</th>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs75395540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>219378</th>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6151406</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>109646 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       allele1 allele2   dbsnp_rsid\n",
+       "2            T       A   rs72631880\n",
+       "4            C       T    rs4970382\n",
+       "8            A       G  rs148658266\n",
+       "9            C       T   rs28540380\n",
+       "12           G       A  rs141671872\n",
+       "...        ...     ...          ...\n",
+       "219372       C       G  rs112714656\n",
+       "219373       T       G    rs3091400\n",
+       "219374       G       A   rs76733141\n",
+       "219375       T       C   rs75395540\n",
+       "219378       C       G    rs6151406\n",
+       "\n",
+       "[109646 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs[[\"allele1\", \"allele2\", \"dbsnp_rsid\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ref</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>G</td>\n",
+       "      <td>GT</td>\n",
+       "      <td>rs146897901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs12085272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6675643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs6687448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs79230816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90545</th>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs417512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90546</th>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs696875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90547</th>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs988245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90548</th>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs323724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90549</th>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs701344</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>90550 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ref alt         rsid\n",
+       "0       G  GT  rs146897901\n",
+       "1       T   C   rs12085272\n",
+       "2       A   G    rs6675643\n",
+       "3       G   A    rs6687448\n",
+       "4       G   A   rs79230816\n",
+       "...    ..  ..          ...\n",
+       "90545   C   T     rs417512\n",
+       "90546   G   T     rs696875\n",
+       "90547   C   T     rs988245\n",
+       "90548   A   T     rs323724\n",
+       "90549   C   T     rs701344\n",
+       "\n",
+       "[90550 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afr_sus[[\"ref\", \"alt\", \"rsid\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1179, 69)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_afrs = afrs.merge(afr_sus, left_on=[\"allele2\", \"allele1\", \"dbsnp_rsid\"], right_on=[\"ref\", \"alt\", \"rsid\"], how=\"inner\").drop_duplicates()\n",
+    "print(data_afrs.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>labeln</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>565286</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs1578391</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.998561</td>\n",
+       "      <td>0.886884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.379356</td>\n",
+       "      <td>-0.045242</td>\n",
+       "      <td>-0.016605</td>\n",
+       "      <td>5.337650e-05</td>\n",
+       "      <td>0.505239</td>\n",
+       "      <td>-0.001945</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>748141</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs2427893</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.05005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988758</td>\n",
+       "      <td>0.347965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.159250</td>\n",
+       "      <td>-0.028731</td>\n",
+       "      <td>-0.013637</td>\n",
+       "      <td>1.321547e-04</td>\n",
+       "      <td>0.439836</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.000028</td>\n",
+       "      <td>-0.000037</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>726944</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131979</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.74175</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.953905</td>\n",
+       "      <td>-0.079065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.802258</td>\n",
+       "      <td>0.026410</td>\n",
+       "      <td>0.010944</td>\n",
+       "      <td>2.107834e-05</td>\n",
+       "      <td>0.580367</td>\n",
+       "      <td>-0.001038</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>752721</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs3131972</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.84500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.890193</td>\n",
+       "      <td>-0.011374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003650</td>\n",
+       "      <td>0.338401</td>\n",
+       "      <td>0.047837</td>\n",
+       "      <td>1.246855e-02</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>-0.344077</td>\n",
+       "      <td>0.002493</td>\n",
+       "      <td>0.003319</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>755940</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs3115857</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.59380</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.302008</td>\n",
+       "      <td>-0.077224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.674640</td>\n",
+       "      <td>-0.026630</td>\n",
+       "      <td>-0.011550</td>\n",
+       "      <td>2.854755e-07</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>-0.007762</td>\n",
+       "      <td>0.000022</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr     Pos Ref Alt       RsID  Peak       AF  Inside_Peak    P_Lead  \\\n",
+       "2  chr1  565286   C   T  rs1578391     7  0.59780            1  0.998561   \n",
+       "4  chr1  748141   G   A  rs2427893    10  0.05005            0  0.988758   \n",
+       "6  chr1  726944   C   G  rs3131979    14  0.74175            1  0.953905   \n",
+       "7  chr1  752721   A   G  rs3131972    17  0.84500            1  0.890193   \n",
+       "8  chr1  755940   C   T  rs3115857    18  0.59380            1  0.302008   \n",
+       "\n",
+       "       Beta  ...  \\\n",
+       "2  0.886884  ...   \n",
+       "4  0.347965  ...   \n",
+       "6 -0.079065  ...   \n",
+       "7 -0.011374  ...   \n",
+       "8 -0.077224  ...   \n",
+       "\n",
+       "   ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                           0.379356                \n",
+       "4                                           0.159250                \n",
+       "6                                           0.802258                \n",
+       "7                                           0.003650                \n",
+       "8                                           0.674640                \n",
+       "\n",
+       "   ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "2               -0.045242                  -0.016605   \n",
+       "4               -0.028731                  -0.013637   \n",
+       "6                0.026410                   0.010944   \n",
+       "7                0.338401                   0.047837   \n",
+       "8               -0.026630                  -0.011550   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "2                                       5.337650e-05   \n",
+       "4                                       1.321547e-04   \n",
+       "6                                       2.107834e-05   \n",
+       "7                                       1.246855e-02   \n",
+       "8                                       2.854755e-07   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \\\n",
+       "2                                           0.505239        \n",
+       "4                                           0.439836        \n",
+       "6                                           0.580367        \n",
+       "7                                           0.333333        \n",
+       "8                                           0.666667        \n",
+       "\n",
+       "   enformer_recomputed  DNASE_SAD  DNASE_SAR  IsUsed  labeln  \n",
+       "2            -0.001945   0.000008   0.000011    True       1  \n",
+       "4                  NaN  -0.000028  -0.000037    True       1  \n",
+       "6            -0.001038   0.000100   0.000133    True      -1  \n",
+       "7            -0.344077   0.002493   0.003319    True      -1  \n",
+       "8            -0.007762   0.000022   0.000029    True      -1  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eus.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/intersection_between_sets_2.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/intersection_between_sets_2.ipynb
new file mode 100644
index 00000000..13bc1d81
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/intersection_between_sets_2.ipynb
@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'Peak', 'AF', 'Inside_Peak',\n",
+      "       'P_Lead', 'Beta', 'SE', 'Log10_BF', 'ENCSR637XSC.logfc.mean',\n",
+      "       'ENCSR637XSC.jsd_sign.mean',\n",
+      "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+      "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+      "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+      "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+      "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+      "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+      "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+      "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+      "       'enformer_recomputed', 'DNASE_SAD', 'DNASE_SAR', 'IsUsed'],\n",
+      "      dtype='object')\n",
+      "(121766, 28)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "eus = pd.read_csv(\"Eu.new.CaQTLS.tsv\", sep=\"\\t\", header=0)\n",
+    "eus = eus[eus[\"IsUsed\"] & eus[\"Inside_Peak\"]==1]\n",
+    "print(eus.columns)\n",
+    "#eus = eus[eus[\"Inside_Peak\"]==2]\n",
+    "eus = eus[(eus[\"Log10_BF\"]>3) | (eus[\"Log10_BF\"]<-1)]\n",
+    "print(eus.shape)\n",
+    "eus[\"labeln\"] = 1\n",
+    "eus.loc[eus[\"Log10_BF\"]>3,\"labeln\"]=1\n",
+    "eus.loc[eus[\"Log10_BF\"]<-1,\"labeln\"]=-1\n",
+    "eus = eus[['chr', 'Pos', 'Ref', 'Alt', 'RsID', 'labeln']]\n",
+    "eus[\"my_rsid\"] = eus[\"chr\"]+\".\"+eus[\"RsID\"].astype(str)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(109646, 51)\n",
+      "(109646, 51)\n",
+      "   chr_hg19 pos_hg19 ref_hg19 alt_hg19   dbsnp_rsid  labeln           my_rsid\n",
+      "2      chr1   805556        T        A   rs72631880      -1   chr1.rs72631880\n",
+      "4      chr1   840753        T        C    rs4970382      -1    chr1.rs4970382\n",
+      "8      chr1   842250        A        G  rs148658266      -1  chr1.rs148658266\n",
+      "9      chr1   842362        C        T   rs28540380       1   chr1.rs28540380\n",
+      "12     chr1   856583        G        A  rs141671872       1  chr1.rs141671872\n"
+     ]
+    }
+   ],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)\n",
+    "afrs = afrs[afrs[\"IsUsed\"]]\n",
+    "afrs[[\"chr_hg19\", \"pos_hg19\", \"ref_hg19\", \"alt_hg19\"]] = afrs[\"id_hg19\"].str.split(\"_\", expand=True)\n",
+    "threshold = 1 / (10**(6))\n",
+    "print(afrs.shape)\n",
+    "#print(afrs.head())\n",
+    "#afrs = afrs[(afrs[\"label\"]==1) | (afrs[\"pval\"]>1e-3)]\n",
+    "print(afrs.shape)\n",
+    "afrs[\"labeln\"] = 1\n",
+    "afrs.loc[afrs[\"label\"]==1,\"labeln\"]=1\n",
+    "afrs.loc[afrs[\"pval\"]>1e-3,\"labeln\"]=-1\n",
+    "afrs = afrs[['chr_hg19', 'pos_hg19', 'ref_hg19', 'alt_hg19', 'dbsnp_rsid', 'labeln']]\n",
+    "afrs[\"my_rsid\"] = afrs[\"chr_hg19\"]+\".\"+afrs[\"dbsnp_rsid\"].astype(str)\n",
+    "print(afrs.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kaur_afr=pd.read_csv(\"../final_figures/afr.inpeaks.kaur.tsv\", sep=\"\\t\", header=0)\n",
+    "#print(kaur_afr.head(1))\n",
+    "kaur_afr[\"my_rsid\"] = kaur_afr[\"chromosome_x\"]+\".\"+kaur_afr[\"rsid\"].astype(str)\n",
+    "kaur_afr[\"log10p\"] = -1*np.log(kaur_afr[\"pvalue\"])\n",
+    "\n",
+    "#print(sum(np.isnan(kaur_afr[\"rsid\"].astype(str))))\n",
+    "#kaur_afr[\"\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kaur_eu=pd.read_csv(\"../final_figures/eu.inpeaks.kaur.tsv\", sep=\"\\t\", header=0)\n",
+    "#print(kaur_afr.head(1))\n",
+    "kaur_eu[\"my_rsid\"] = kaur_eu[\"chromosome_x\"]+\".\"+kaur_eu[\"rsid\"].astype(str)\n",
+    "kaur_eu[\"log10p\"] = -1*np.log(kaur_eu[\"pvalue\"])\n",
+    "\n",
+    "#print(sum(np.isnan(kaur_afr[\"rsid\"].astype(str))))\n",
+    "#kaur_afr[\"\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 672x320 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from upsetplot import from_contents\n",
+    "all_rsids={}\n",
+    "\n",
+    "all_rsids[\"Kumsaka (Kaur)\"] = list(set(kaur_eu[kaur_eu[\"log10p\"]>12][\"my_rsid\"]))\n",
+    "all_rsids[\"Kumsaka (2018)\"] = list(set(eus[eus[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr (Kaur)\"] = list(set(kaur_afr[kaur_afr[\"log10p\"]>12][\"my_rsid\"]))\n",
+    "all_rsids[\"Afr\"] = list(set(afrs[afrs[\"labeln\"]==1][\"my_rsid\"]))\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from upsetplot import plot, generate_counts\n",
+    "plot(from_contents(all_rsids), sort_by='cardinality',show_counts=\"{:,}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kaur_afr[\"log10p\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kaur_eu[\"log10p\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/k562.gwas_variants_scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/k562.gwas_variants_scoring.ipynb
new file mode 100644
index 00000000..4d668793
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/k562.gwas_variants_scoring.ipynb
@@ -0,0 +1,466 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: DtypeWarning: Columns (9,10,11) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"k562.gwas_variants.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'variant_id',\n",
+       "       'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.sign_jsd.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid',\n",
+       "       'traits', 'IDs', 'maxPIP', 'ENCSR000EMT.logfc.mean',\n",
+       "       'ENCSR000EMT.sign_jsd.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr</th>\n",
+       "      <th>pos</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>variant_id</th>\n",
+       "      <th>ENCSR637XSC.logfc.mean</th>\n",
+       "      <th>ENCSR637XSC.sign_jsd.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>traits</th>\n",
+       "      <th>IDs</th>\n",
+       "      <th>maxPIP</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.sign_jsd.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>10352</td>\n",
+       "      <td>T</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>rs150218945</td>\n",
+       "      <td>-0.036835</td>\n",
+       "      <td>-0.016749</td>\n",
+       "      <td>0.000497</td>\n",
+       "      <td>0.055084</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.004799</td>\n",
+       "      <td>0.013399</td>\n",
+       "      <td>0.000059</td>\n",
+       "      <td>0.219019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>11012</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs544419019</td>\n",
+       "      <td>-0.010156</td>\n",
+       "      <td>-0.022090</td>\n",
+       "      <td>0.000218</td>\n",
+       "      <td>0.116639</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.008337</td>\n",
+       "      <td>0.031942</td>\n",
+       "      <td>0.000489</td>\n",
+       "      <td>0.061075</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    chr    pos allele1 allele2   variant_id  ENCSR637XSC.logfc.mean  \\\n",
+       "0  chr1  10352       T      TA  rs150218945               -0.036835   \n",
+       "1  chr1  11012       C       G  rs544419019               -0.010156   \n",
+       "\n",
+       "   ENCSR637XSC.sign_jsd.mean  \\\n",
+       "0                  -0.016749   \n",
+       "1                  -0.022090   \n",
+       "\n",
+       "   ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.000497   \n",
+       "1                                           0.000218   \n",
+       "\n",
+       "   ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval rsid traits  IDs  \\\n",
+       "0                                           0.055084       NaN    NaN  NaN   \n",
+       "1                                           0.116639       NaN    NaN  NaN   \n",
+       "\n",
+       "   maxPIP  ENCSR000EMT.logfc.mean  ENCSR000EMT.sign_jsd.mean  \\\n",
+       "0     NaN                0.004799                   0.013399   \n",
+       "1     NaN                0.008337                   0.031942   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.000059   \n",
+       "1                                           0.000489   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval  \n",
+       "0                                           0.219019       \n",
+       "1                                           0.061075       "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import figure\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = [12, 10]\n",
+    "\n",
+    "def plot_enrichment_curves(df, compare_score, model_score, output_file, compare_score_ascending=False, model_score_ascending=True,\n",
+    "                           thresh_list=[0.5, 0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001], subsample=False, ymax='auto', debug=False):\n",
+    "    by_thresh_dict = {'score':[], 'ratio':[], 'thresh':[]}\n",
+    "    \n",
+    "    for thresh in thresh_list:\n",
+    "        df[compare_score].fillna(0, inplace=True)\n",
+    "        sample_df = df.sort_values(by=[compare_score, model_score], ascending=[False, True])\n",
+    "        #print(sample_df.head())\n",
+    "        #print(sample_df.tail())\n",
+    "        print(sum(sample_df[model_score] < thresh))\n",
+    "        ratio = len(sample_df.loc[sample_df[model_score] < thresh]) / len(sample_df)\n",
+    "        sample_df['sig'] = sample_df[model_score].apply(lambda x: x < thresh)\n",
+    "        sample_df['expected'] = [ratio] * len(sample_df)\n",
+    "        expected = sample_df['expected'].cumsum().tolist()\n",
+    "        observed = sample_df['sig'].cumsum().tolist()\n",
+    "        \n",
+    "        enrich_ratio = np.array(observed) / np.array(expected)\n",
+    "        compare_vals = sample_df[compare_score].tolist()\n",
+    "        enrich_ratio = enrich_ratio.tolist()\n",
+    "        assert len(compare_vals) == len(enrich_ratio)\n",
+    "        by_thresh_dict['ratio'].extend(enrich_ratio)\n",
+    "        by_thresh_dict['score'].extend(compare_vals)\n",
+    "        by_thresh_dict['thresh'].extend([thresh] * len(sample_df))\n",
+    "        \n",
+    "        if debug:\n",
+    "            print(thresh)\n",
+    "            print('Expected:', expected[:10])\n",
+    "            print('Observed:', observed[:10])\n",
+    "            print('Ratio:', enrich_ratio[:10])\n",
+    "            print('Compare Scores:', compare_vals[:10])\n",
+    "            print()\n",
+    "        \n",
+    "    by_thresh_df = pd.DataFrame(by_thresh_dict)\n",
+    "    by_thresh_df.sort_values(by=['score'], ascending=False, inplace=True)\n",
+    "    return by_thresh_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4986998\n",
+      "728384\n",
+      "336306\n",
+      "66207\n",
+      "33135\n",
+      "6280\n",
+      "3087\n",
+      "635\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "\n",
+    "data[np.isnan(data['maxPIP'])]['maxPIP'] = 0\n",
+    "\n",
+    "by_thresh_df = plot_enrichment_curves(data,\n",
+    "                       compare_score='maxPIP', model_score='ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "                       compare_score_ascending=False, model_score_ascending=True, output_file=\"bloodtraits_abs_logfc_x_jsd_x_max_percentile.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f7324f5c190>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "0.0001\n",
+    "n_by_thresh_df = by_thresh_df\n",
+    "by_thresh_df = n_by_thresh_df[~n_by_thresh_df['thresh'].isin([0.0001])]\n",
+    "\n",
+    "import matplotlib\n",
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "compare_score='maxPIP'\n",
+    "model_score='ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval'\n",
+    "\n",
+    "ax = sns.lineplot(data=by_thresh_df, x='score', y='ratio', linewidth=3, ci=None,\n",
+    "                  hue='thresh', palette=sns.color_palette('rocket', 7))\n",
+    "leg = plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
+    "for line in leg.get_lines():\n",
+    "    line.set_linewidth(4)\n",
+    "ax.set(title=compare_score + ' vs ' + model_score, xlabel=compare_score, ylabel='observed / expected')\n",
+    "\n",
+    "ax.set(yscale='log')\n",
+    "ax.set_yticks([1,  3,  5, 7, 10, 20, 40, 60, 80, 100, 120])\n",
+    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
+    "ax.axhline(y=1, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=3, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=5, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=7, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=10, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=20, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=40, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=60, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=80, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=100, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=120, color='grey', linestyle='-', alpha=0.2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5890175\n",
+      "1002134\n",
+      "487006\n",
+      "94517\n",
+      "46388\n",
+      "8834\n",
+      "4209\n",
+      "811\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "\n",
+    "data[np.isnan(data['maxPIP'])]['maxPIP'] = 0\n",
+    "\n",
+    "by_thresh_df = plot_enrichment_curves(data,\n",
+    "                       compare_score='maxPIP', model_score='ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+    "                       compare_score_ascending=False, model_score_ascending=True, output_file=\"bloodtraits_abs_logfc_x_jsd_x_max_percentile.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f719f1d0bd0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "0.0001\n",
+    "n_by_thresh_df = by_thresh_df\n",
+    "by_thresh_df = n_by_thresh_df[~n_by_thresh_df['thresh'].isin([0.0001])]\n",
+    "\n",
+    "import matplotlib\n",
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "compare_score='maxPIP'\n",
+    "model_score='ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval'\n",
+    "\n",
+    "ax = sns.lineplot(data=by_thresh_df, x='score', y='ratio', linewidth=3, ci=None,\n",
+    "                  hue='thresh', palette=sns.color_palette('rocket', 7))\n",
+    "leg = plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
+    "for line in leg.get_lines():\n",
+    "    line.set_linewidth(4)\n",
+    "ax.set(title=compare_score + ' vs ' + model_score, xlabel=compare_score, ylabel='observed / expected')\n",
+    "\n",
+    "ax.set(yscale='log')\n",
+    "ax.set_yticks([1,  3,  5, 7, 10, 20, 40, 60, 80, 100, 120])\n",
+    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
+    "ax.axhline(y=1, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=3, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=5, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=7, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=10, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=20, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=40, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=60, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=80, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=100, color='grey', linestyle='-', alpha=0.2)\n",
+    "ax.axhline(y=120, color='grey', linestyle='-', alpha=0.2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/kaur.caqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/kaur.caqtls.ipynb
new file mode 100644
index 00000000..d2b734ba
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/kaur.caqtls.ipynb
@@ -0,0 +1,836 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafn = pd.read_csv(\"/mnt/lab_data2/anusri/chrombpnet/src/evaluation/figure_notebooks/figure_6/final_figures/kaur.caqtls.tsv\", sep=\"\\t\", header=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos_x', 'allele1', 'allele2', 'rsid_x', 'ATAC.572M.logfc.mean',\n",
+       "       'ATAC.572M.jsd.mean', 'ATAC.572M.CRS', 'ATAC.50M.logfc.mean',\n",
+       "       'ATAC.50M.jsd.mean', 'ATAC.50M.CRS', 'DNASE.logfc.mean',\n",
+       "       'DNASE.jsd.mean', 'DNASE.CRS', 'enformer_recomputed', 'pvalue', 'beta',\n",
+       "       'dbsnp_rsdi', 'pos_y', 'ref', 'alt', 'rsid_y', 'DNASE_SAD',\n",
+       "       'DNASE_SAR'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafn.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafn[\"pos0\"] = datafn[\"pos_x\"] - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafn = datafn[['chr', \"pos0\", 'pos_x', 'allele1', 'allele2', 'rsid_x', 'ATAC.572M.logfc.mean',\n",
+    "       'ATAC.572M.jsd.mean', 'ATAC.572M.CRS', 'ATAC.50M.logfc.mean',\n",
+    "       'ATAC.50M.jsd.mean', 'ATAC.50M.CRS', 'DNASE.logfc.mean',\n",
+    "       'DNASE.jsd.mean', 'DNASE.CRS', 'enformer_recomputed', 'pvalue', 'beta',\n",
+    "       'dbsnp_rsdi', 'pos_y', 'ref', 'alt', 'rsid_y', 'DNASE_SAD',\n",
+    "       'DNASE_SAR']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(125682, 25)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafn.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8067"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(datafn[\"pvalue\"]<10**-7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pybedtools\n",
+    "# peaks_data_merged=pd.read_csv(\"/mnt/lab_data2/anusri/kumasaka/new_download/Kumasaka_2018_wgs/sumstats/merged.peaks.eu.top.20k.bed\", sep=\"\\t\", header=None)\n",
+    "# print(peaks_data_merged.head())\n",
+    "# beds_var = pybedtools.BedTool.from_dataframe(datafn)\n",
+    "# peak_bed = pybedtools.BedTool.from_dataframe(peaks_data_merged) # this is a merged peaks file\n",
+    "# vars_in_bed = beds_var.intersect(peak_bed, c=True)\n",
+    "# vars_in_bedn = vars_in_bed.to_dataframe(names=datafn.columns.tolist() + [\"count\"])\n",
+    "# datafn = vars_in_bedn[vars_in_bedn[\"count\"]>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# datafn.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sum(datafn[\"pvalue\"]<10**-7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos0', 'pos_x', 'allele1', 'allele2', 'rsid_x',\n",
+       "       'ATAC.572M.logfc.mean', 'ATAC.572M.jsd.mean', 'ATAC.572M.CRS',\n",
+       "       'ATAC.50M.logfc.mean', 'ATAC.50M.jsd.mean', 'ATAC.50M.CRS',\n",
+       "       'DNASE.logfc.mean', 'DNASE.jsd.mean', 'DNASE.CRS',\n",
+       "       'enformer_recomputed', 'pvalue', 'beta', 'dbsnp_rsdi', 'pos_y', 'ref',\n",
+       "       'alt', 'rsid_y', 'DNASE_SAD', 'DNASE_SAR'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datafn.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafn[\"log10p\"] = np.log10(datafn[\"pvalue\"])*-1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:58: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:62: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sig 9676\n",
+      "115236\n",
+      "0.084\n",
+      "sig 6735\n",
+      "112295\n",
+      "0.06\n",
+      "sig 4860\n",
+      "110420\n",
+      "0.044\n",
+      "sig 3582\n",
+      "109142\n",
+      "0.033\n",
+      "sig 2591\n",
+      "108151\n",
+      "0.024\n",
+      "sig 1859\n",
+      "107419\n",
+      "0.017\n",
+      "sig 1347\n",
+      "106907\n",
+      "0.013\n",
+      "sig 944\n",
+      "106504\n",
+      "0.009\n",
+      "sig 611\n",
+      "106171\n",
+      "0.006\n",
+      "sig 408\n",
+      "105968\n",
+      "0.004\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from scipy.stats import pearsonr\n",
+    "\n",
+    "random_bc = []\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_pear = {}\n",
+    "chrombpnet_pear1 = {}\n",
+    "chrombpnet_pear_abs = {}\n",
+    "chrombpnet_pear_abs1 = {}\n",
+    "enformer_dnase_roc = []\n",
+    "enformer_dnase_pear = []\n",
+    "enformer_dnase_pear_abs = []\n",
+    "sar_enformer_dnase_roc = []\n",
+    "sar_enformer_dnase_pear = []\n",
+    "sar_enformer_dnase_pear_abs = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_pear[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_pear[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_pear[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_pear1[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_pear1[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_pear1[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_pear_abs[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_pear_abs[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_pear_abs[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "chrombpnet_pear_abs1[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_pear_abs1[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_pear_abs1[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(6,25,2):\n",
+    "   \n",
+    "    dataf1 = datafn[datafn[\"log10p\"]>threshold]\n",
+    "    dataf1[\"label\"] = 1\n",
+    "\n",
+    "    \n",
+    "    dataf2 = datafn[datafn[\"log10p\"]<3]\n",
+    "    dataf2[\"label\"] = 0\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    dataf = pd.concat([dataf1, dataf2])\n",
+    "    \n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(\"sig\",sigs)\n",
+    "    print(total)\n",
+    "    print(np.round(sigs/total,3))\n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,3))\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"DNASE.CRS\"]))\n",
+    "  \n",
+    "    pear = pearsonr(dataf1[\"DNASE.logfc.mean\"], dataf1[\"beta\"])[0]\n",
+    "    chrombpnet_pear[\"ChromBPNet DNase (68M)\"].append(pear)\n",
+    "    pear = pearsonr(dataf1[\"DNASE.jsd.mean\"]*np.sign(dataf1[\"DNASE.logfc.mean\"]), dataf1[\"beta\"])[0]\n",
+    "    chrombpnet_pear1[\"ChromBPNet DNase (68M)\"].append(pear)\n",
+    "    \n",
+    "    pear = pearsonr(abs(dataf1[\"DNASE.logfc.mean\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    chrombpnet_pear_abs[\"ChromBPNet DNase (68M)\"].append(pear)\n",
+    "    pear = pearsonr(abs(dataf1[\"DNASE.jsd.mean\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    chrombpnet_pear_abs1[\"ChromBPNet DNase (68M)\"].append(pear)\n",
+    "\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ATAC.572M.CRS\"]))\n",
+    "\n",
+    "    pear = pearsonr(dataf1[\"ATAC.572M.logfc.mean\"], dataf1[\"beta\"])[0]\n",
+    "    chrombpnet_pear[\"ChromBPNet ATAC (572M)\"].append(pear)\n",
+    "    pear = pearsonr(abs(dataf1[\"ATAC.572M.logfc.mean\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    chrombpnet_pear_abs[\"ChromBPNet ATAC (572M)\"].append(pear)\n",
+    "\n",
+    "    pear = pearsonr(dataf1[\"ATAC.572M.jsd.mean\"]*np.sign(dataf1[\"ATAC.572M.logfc.mean\"]), dataf1[\"beta\"])[0]\n",
+    "    chrombpnet_pear1[\"ChromBPNet ATAC (572M)\"].append(pear)\n",
+    "    pear = pearsonr(abs(dataf1[\"ATAC.572M.jsd.mean\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    chrombpnet_pear_abs1[\"ChromBPNet ATAC (572M)\"].append(pear)\n",
+    "  \n",
+    "  \n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ATAC.50M.CRS\"]))\n",
+    "    pear = pearsonr(dataf1[\"ATAC.50M.logfc.mean\"], dataf1[\"beta\"])[0]\n",
+    "    chrombpnet_pear[\"ChromBPNet ATAC (50M)\"].append(pear)\n",
+    "    pear = pearsonr(abs(dataf1[\"ATAC.50M.logfc.mean\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    chrombpnet_pear_abs[\"ChromBPNet ATAC (50M)\"].append(pear)\n",
+    " \n",
+    "    pear = pearsonr(dataf1[\"ATAC.50M.jsd.mean\"]*np.sign(dataf1[\"ATAC.50M.logfc.mean\"]), dataf1[\"beta\"])[0]\n",
+    "    chrombpnet_pear1[\"ChromBPNet ATAC (50M)\"].append(pear)\n",
+    "    pear = pearsonr(abs(dataf1[\"ATAC.50M.jsd.mean\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    chrombpnet_pear_abs1[\"ChromBPNet ATAC (50M)\"].append(pear)\n",
+    "\n",
+    "  \n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer_recomputed\"]))\n",
+    "\n",
+    "    pear = pearsonr(dataf1[\"enformer_recomputed\"]*-1, dataf1[\"beta\"])[0]\n",
+    "    enformer_dnase_pear.append(pear)\n",
+    "    pear = pearsonr(abs(dataf1[\"enformer_recomputed\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    enformer_dnase_pear_abs.append(pear)\n",
+    "    \n",
+    "    enformer_dnase_roc.append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"DNASE_SAR\"]))\n",
+    "    pear = pearsonr(dataf1[\"DNASE_SAR\"], dataf1[\"beta\"])[0]\n",
+    "    sar_enformer_dnase_pear.append(pear)\n",
+    "    pear = pearsonr(abs(dataf1[\"DNASE_SAR\"]), abs(dataf1[\"beta\"]))[0]\n",
+    "    sar_enformer_dnase_pear_abs.append(pear)\n",
+    "    \n",
+    "    sar_enformer_dnase_roc.append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChromBPNet DNase (68M) [0.2692395185868811, 0.24315720442613264, 0.22615296810487026, 0.20780985036511346, 0.18949205864856145, 0.1668659313824714, 0.15272817321403684, 0.14630799107191056, 0.12530026794295016, 0.11802422815451459]\n",
+      "enf [0.3056518479514423, 0.2825133914506326, 0.26536386736172896, 0.24406406011192894, 0.2253705158983582, 0.20358446690139884, 0.18899678325344557, 0.17735446403746186, 0.1596878880775609, 0.14200417942823895]\n",
+      "enf [0.21941848726102864, 0.18788802680967356, 0.16808110134224757, 0.14581875929000016, 0.12892207288517493, 0.10642228213287598, 0.09500806794934488, 0.07653766511457176, 0.06133075121526359, 0.052189886626203205]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "colors = [\"blue\", \"blue\"]\n",
+    "alphas = [0.5, 1.0]\n",
+    "idx=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,2).astype(str), chrombpnet[key], label=key, c=\"blue\", alpha=alphas[idx])\n",
+    "    idx+=1\n",
+    "    \n",
+    "key=\"ChromBPNet DNase (68M)\"\n",
+    "ax.scatter(np.arange(6,25,2).astype(str), chrombpnet[key], label=key)\n",
+    "print(key, chrombpnet[key])\n",
+    "ax.scatter(np.arange(6,25,2).astype(str), enformer_dnase_roc, label=\"Enformer DNase (recomputed) (68M)\")\n",
+    "print(\"enf\", enformer_dnase_roc)\n",
+    "ax.scatter(np.arange(6,25,2).astype(str), sar_enformer_dnase_roc, label=\"Enformer DNase (reported) (68M)\")\n",
+    "print(\"enf\", sar_enformer_dnase_roc)\n",
+    "\n",
+    "ax.scatter(np.arange(6,25,2).astype(str), random_bc, label=\"random\")\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10 pval\")\n",
+    "ax.set_ylabel(\"Average precision\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/kaur_caqtls_auprc_compare.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "\n",
+    "threshold=6\n",
+    "dataf1 = datafn[datafn[\"log10p\"]>threshold]\n",
+    "dataf1[\"label\"] = 1\n",
+    "\n",
+    "\n",
+    "dataf2 = datafn[datafn[\"log10p\"]<3]\n",
+    "dataf2[\"label\"] = 0\n",
+    "\n",
+    "dataf = pd.concat([dataf1, dataf2])\n",
+    "\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ATAC.572M.CRS\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (572M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ATAC.50M.CRS\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"DNASE.CRS\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ DNASE (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"enformer_recomputed\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ DNASE (recomputed) (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "enformer_auc_recomp = roc_auc\n",
+    "\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "# keyd=\"pred.enformer.encsr000emt.varscore.sad\"\n",
+    "# label=dataf[\"var.label\"].astype(int)\n",
+    "# score=abs(dataf[keyd])\n",
+    "# title=\"Enformer w/ DNASE SAD (50M)\"\n",
+    "\n",
+    "# fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "# roc_auc = metrics.average_precision_score(label, score)\n",
+    "# ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "\n",
+    "keyd=\"DNASE_SAR\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ DNASE (reported) (68M)\"\n",
+    "\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "enformer_auc = roc_auc\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "roc_auc=sum(dataf[\"label\"]==1)/len(dataf[\"label\"])\n",
+    "ax.plot(fpr_chrombpnet, [sum(dataf[\"label\"]==1)/len(dataf[\"label\"])]*len(fpr_chrombpnet), linewidth=2, label=\"Random baseline, AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc='upper right')\n",
+    "ax.set_xlabel(\"Recall\")\n",
+    "ax.set_ylabel(\"Precision\")\n",
+    "\n",
+    "plt.savefig(\"subfigs/kaur_caqtls_auprc.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    #print(len(chrombpnet_pear[key]))\n",
+    "    #print(len(np.arange(7,25,2)))\n",
+    "    #print(key)\n",
+    "    #print(chrombpnet_pear[key])\n",
+    "    ax.scatter(np.arange(6,25,2).astype(str), chrombpnet_pear[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "ax.scatter(np.arange(6,25,2).astype(str), enformer_dnase_pear ,  marker=\"^\", label=\"Enformer DNase (recomputed) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,2).astype(str), sar_enformer_dnase_pear ,  marker=\"^\", label=\"Enformer DNase (reported) (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,2).astype(str), chrombpnet_pear_abs[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "ax.scatter(np.arange(6,25,2).astype(str), enformer_dnase_pear_abs, marker=\"v\", label=\"unsign \" +\"Enformer DNase (recomputed) (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(6,25,2).astype(str), sar_enformer_dnase_pear_abs, marker=\"v\", label=\"unsign \" +\"Enformer DNase (reported) (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10pval\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "#plt.ylim((0.4,0.6))\n",
+    "plt.savefig(\"subfigs/kaur_caqtls_pearsonr_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wAEBAQIDlT3/6U149z/cfMWJE0cXaqG3NmjUBkyZNOgsA4eHhFUOHDr0AAP7+/pZevXqVHD9+vDqIGT9+/NkNGzYEAMDmzZt9BwwYcM5gMFRHMRMnTixYu3YtgwcioivBp5/Cd9cutNu8Gb6Xe6zk5GSfPn36XGjo8bS0NJ9XX301MyMjI+X48eNen3/+eTvbYx07djSnpqamjRo16tyf//znkK+//vpQampqyt69e9u+++67HQCgpKREN2LEiKKMjIyUtm3bVj7zzDMh33777aH169dnLFiwoDrISEpKahcdHW3q2rVr/Lfffus7a9asM7X7kpqa6pmZmellMpnKAECn0+Ghhx46OX/+/BrZh5ycHMMLL7wQvH379kOpqalp/fr1u7BgwYKgZ5555nRgYGDFN998c2j37t2Hah8/IyPDIzc312P48OEXEhMT8+0DgvqsW7cu4I477jg7Y8aMsxs3bgwAgLNnz+rOnz+vN5lM5Rd7LgDs2rWrXUJCwnn7bfPnzw8xGo2m3//+92ElJSVi/1hxcbFu+/bt7e+444782sc6ePCgZ2pqaptrr722OosRFRVVlpeXZ8jNzdWvXr06YOrUqWftnzNgwICS5OTkto31szVg8EBEdBH5+dB98gk69OqFsk2b0MEZ2YeLiYuLO9+rV68KvV6P2NjYC4cPH67+Zjt9+vR8ANixY0fbwYMHF3ft2tXs4eGB22+//ew333zTDgA8PDzUxIkTiwAgNja2ZOjQocVeXl5q4MCBJdnZ2dXHsg1bnDx5Mnnq1Kl5s2bNCrU9tmnTJv/o6GjT5MmTe/79738/FhQUVGl77L777sv78ccf26Wnp1cf6+uvv257+PBh74EDB0ZHR0eb1q5d29H+G3lDVq5cGZCYmJgPAHfeeefZ//znPw0GD5mZmYZjx455jxo16lx8fHyZwWBQe/bs8W5o//qcOnXKIygoqDrD8te//jX7l19+ObBv3760/Px8/dy5c7vY77927dr2/fv3P2f/+gGgsLBQN378+F6LFi3KtGU7bMaNG5f/1ltvBfz4449tbdkkG4PBAA8PD2U/L6K14iJRREQX8emn8DWbAV9fWAoKoN+8Gb5Tp6LwUo8XFxdX8uGHH/o39LiXl1d1mluv18NsNld/G/b19bXU/6xfGQwGpdNZP5t0Ol318fR6PSorK6W+50yYMKFg0qRJvWz3x40bl79y5crj9e3r4eGBWbNmnXzuueeqP2iVUhg6dGjRpk2bjjTWP3v/+c9/AnJzcz1sWYTTp0977N+/3ysuLq6s9r4rV64MKCoq0oeFhcUBwLlz5/QrV67s+Morr2S3adPGkpqa6tlY9sHb29tSUlJS/cEdHh5eAQA+Pj7q7rvvzluyZEmQ/f7r1q0LuO2222pkD8rKyuSmm27qNWnSpLMzZswoqN3G9OnT8wcNGhQzceLEPL1eX6cPFRUV0qZNm1Z/qXCrj36IiLRiyzoEBsIMAIGBMF9u9mHcuHHF5eXlsnjx4k62bbt37/b57LPP2l3sefaGDRt2fvfu3b45OTkGs9mM9evXB1x33XXnGn9m/bZt29YuPDy8zgd2Q2bNmpW3Y8cOv7NnzxoA4LrrrjuflJTU7sCBA14AUFRUpEtOTvYCgLZt21YWFhbWOV/Jycle58+f158+fTo5Ozt7f3Z29v5Zs2adXLFiRb3Zhw0bNgR88MEHP9v23b17d6otCHv44Ydz7r///vCzZ8/qAGtmoL6rLaKiokoPHTpUPV/j2LFjHoB13sfGjRs7xMTElNgey8vL03///fe+U6dOLbBts1gsmDx5crjRaCz985//fKq+fhqNxvKnnnoq++GHH86t/djJkyf1HTp0MNsHiK0VgwciogbYsg5eXlCA9afZDFzO3AedToePP/748FdffeUXFhbWOyIiIvaJJ54ICQkJafI1fOHh4RXPPvts9rXXXmuMiYmJ7dOnz/k77rijwJF+2OY8REVFmdasWdPx73//e2ZTn+vt7a3uvffe07bgoWvXruZly5YdnTx5ck+j0WhKSEiI3r9/vzcAzJgx48zo0aPrTJhcsWJFwI033lhjLsHkyZPzbVkIewcPHvTMzs72/O1vf1s9XyE6Orrc19e38quvvmr7+OOP515zzTVF/fr1M0VGRsYOHjw4ur6FwBITEwu/+eab6r/d7bff3sNoNJqioqJi8/LyDC+++GKO7bFVq1Z1GDZsWJGfn191tufzzz9v9+GHH3bcsWOHr+1Szvfff7/OJNQ5c+aciY2NrROMbdmyxW/kyJGXnLVqSaS+y1lasoSEBJWUlNTc3SCi1qFOmn7fvn1H+/TpU2dyYH0eewxdjhxBnXH1Hj1QungxTjqjg+Ra/fv3j9q6dWtGp06dKhvf27lGjRrVa/HixVnx8fFNzvI0t3379nXq06dP99rbOeeBiKgBDBDcz8svv5x1+PBhz06dOpU0vrfzlJaWSmJiYkFrChwuhsEDERFdMeyHPlzJ29tbzZo1q87aE60V5zwQERGRQzQNHkRktIgcFJEMEfm/i+w3QUSUiCRo2R8iIiK6fJoFDyKiB/AqgDEATACmiIipnv18ATwEYLdWfSEiIiLn0TLzMBBAhlLqF6VUOYC1AG6uZ78FAP4CoFTDvhAREZGTaBk8hACwv244q2pbNRHpByBMKbVZw34QEbUoLMn9q5ZYkttiseB3v/tdWLdu3XobjUbTjh072gDW9SZEpP+DDz7YtXb/p0+f3g0AXnjhhc5///vf623bnTTbhEkR0QH4K4BHm7DvvSKSJCJJubl1Fu0iImo1WJK7ppZYknv9+vXtf/nlF++jR48eeO21147NnDmzm23/kJCQ8s8//7yDff8jIiKqM+d/+tOf8pYtW1ZjmWt3pGXwkA0gzO5+aNU2G18AvQF8LSJHAQwG8HF9kyaVUm8opRKUUgmdO3fWsMtERHUdOwaPsDD0Pn788i9vZ0numlpiSe6PPvqow7Rp0/J0Oh1GjBhxvqioyGBbytrHx8cSERFRsn379jaAtT7HLbfcUl3/wtfX1xIaGlq2bdu2No2105ppGTzsARApIj1ExBPAZAAf2x5UShUqpToppborpboD+A5AolKKy0cSUYvy9NMIzs6G19NPo2vje18cS3L/qqWW5M7JyfHo3r179fGCg4PLbcEDAEyePPnse++9F5CRkeGh1+tV165daywt3q9fv/Nff/31ZZdvb8k0Cx6UUmYAswBsBZAGYJ1SKkVEnhORRK3aJSJypmPH4LFhAzopBaxfj07OyD5cDEty1685SnI3ZMKECUXffPON34oVKwImTJhwtvbjgYGBZkeGoVojTf8RKKU+BfBprW3zGtj3Oi37QkR0KZ5+GsFVIwewWICnn0bXd99FveWqm4IluX/VUktyBwcHVxw9erQ6+MnJyfEMDw+vKC0tlarjqPj4+AuvvfZal5SUlAPvv/9+B/t2SktLdT4+Po3+rVozrjBJRNQAW9ahosJaYKuiAnK52QeW5LZqySW5ExMTC1atWtXRYrHgyy+/bOvr61tpCzRsnnjiiZPPPvtsln1WxubQoUNevXv3dmntDFdrNHgQEb2IrHJFZ4iIWhL7rIONLftwqcdkSW6rllyS+7bbbisMDw8vCw8P7/3AAw+Ev/rqq8dqHyshIaG0vkmZALBnz552iYmJjU7MbM2aVJJbRHYA+G3VYk/NiiW5icgBl1WSOygI8adPo87YdWAgKk6dQrIzOkiupXVJ7p07d/q8/PLLXT788EOHhnBaqsstyf0LgJ0i8jGA6shPKfVX53SPiKjlYYDgfrQuyX369GmPv/zlL9mN79m6NTV4OFx108G6PgMREVGro3VJ7ltvvdWthytsmhQ8KKXma90RIiIiah2aFDyIiBHAYwC62z9HKfVbbbpFRERELVVThy3WA3gdwHIAmkwyISIiotahqcGDWSn1mqY9ISIiolahqYtEbRKRmSISLCIBtpumPSMiclMsyW01cODAqO7du/c2Go2mHj16xE6fPr3bmTNn9LbHRaT/H/7wh+pls+fNmxc0e/bsy64v0hCLxYLBgwcbbYtNnTlzprpIWc+ePWO/+OKLtgCwa9cunz59+kTbyn/bimAtXbq0o4j0//DDD6svLHj33Xc7iEh/W0nxsWPH9ty/f3+dwmKtTVODhxkA5gDYBeCHqhsXWyAichBLcte0cuXKXw4dOpSalpaW6uXlZRkzZkx18OTp6ak+/fRT/5ycHE1LKdisW7eufWxsbImtOue9994bNmrUqKIjR46kpKampvbt27cUAObMmRP69NNPn0hPT0+dO3fuiSeeeKK6gnRkZGTJmjVrqr9cr127NiAqKqr6stAHHnjg9PPPP9+kGhotWZOCB6VUj3puPbXuHBFRS5CdX+Ix+MUve58oKGFJbieX5Lbx9vZWr732WtaJEyc8//e///kAgF6vV9OnT8994YUXgmrvv3r16vbx8fHRMTExpiFDhhhtAc7mzZvbRUdHm6Kjo00xMTGm/Px8HQDMnTs3qHfv3jFGo9H0yCOP1Ju9WLVqVcCtt95aAAB5eXn63bt3+z788MNnbP2zLSwlIigsLNQDQEFBgT4oKKh6AcVBgwad27t3b9uysjIpLCzUHT161Cs2Nra6iuro0aPPffvtt34VFU1eULRFalLwICIeIvKgiGyous0SEbeuGEZEZPPy1vTgU4WlXi9vPciS3E4syV2bwWBATEzMhQMHDlRXy5wzZ87pjRs3BuTl5ent973++uvP/fTTT+lpaWmpEydOPGsr1LVkyZIuS5cuPZaenp763Xffpbdr186yceNGv4yMDO/k5OS0tLS01J9++qnNli1b6tQS+eGHH9r95je/OQ9Yl8QOCAgwT5o0qXtMTIzp9ttvDy8qKtIBwNKlSzPnzZsX2qVLl/i5c+eGLlmypHpRKBHBNddcU7Rx40a/1atXdxg9enSBfRt6vR7h4eGl3333XZvGzkdL1tRhi9cA9Afwr6pb/6ptRERuLTu/xOPT/Sc7KQCb9+d0ckb24WKupJLc9aldMiEgIMAyadKkvEWLFgXabz9y5IjnsGHDIo1Go2np0qVd0tPTfQBg8ODB5x577LGwhQsXBp45c0bv4eGBzz77zG/79u1+JpPJFBsbazp8+LB3enp6nXLehYWFBn9/fwsAmM1mSUtLa/PHP/4xNy0tLbVNmzYWW8nupUuXdn7xxRczT548mfzCCy9k/u53v+tuf5xp06adXbt2bcD69esDZsyYUadkd6dOncyZmZmt+gt4U4OHAUqpGUqpr6pudwEYoGXHiIhagpe3pgdbqj7QLBaFy80+xMXFlezbt6/Bb53NVZJ79+7d1ZP8bHMekpOT06dPn15gv+/FSnKnp6enpqenpx4+fDhl3bp1dYpJNcZsNuPgwYNt4uPjS+23P/nkk6dWr17d6fz589WfWbNmzeo2c+bM04cOHUr95z//eaysrEwHAC+88MLJ5cuXHyspKdENGzYseu/evd5KKTz88MM5tv4dP378wCOPPFIn06LX61VlpTVO6t69e3lQUFC5bUXK22+/Pd/2d/vPf/7T0XZe7r777vzk5OS29scZPnz4hfT0dJ+zZ88a4uPj61QrLSsr07Vp06ZVl+xuavBQKSLVtd5FpCe43gMRuTlb1sFsUQIAZouSy80+sCR3/crKymTWrFmhwcHB5YMGDapRdyIoKKhy3Lhx+atXr64+Z8XFxfpu3bpVAMA777xTXX47JSXFa+DAgSXPP//8yfj4+PMHDhzwHjNmTNG7777bydaPI0eOeNiuLrHXo0eP0rS0NC8A6Natm7lLly7ltvkd//3vf/2ioqJKAaBz584Vn376qS8AbNq0yTc8PLy09rEWLFiQtWDBgnprXBw5csSrX79+rbpkd1P/AcwBsE1EfoG1Sl04gLs06xURUQtgn3WwsWUf/nZ73+OXckxbSe6ZM2eG/eMf/+ji5eWlQkNDy1555ZXMY8eONSnVb1+SWyklI0eOLLjUktxKKfj6+la+9dZbR5v6XFtJ7rlz54YBNUtyl5eXCwA8++yz2fHx8WW2ktxBQUHl9c17mD59ek9PT09LeXm5btiwYUVbtmzJqK/Np59++uSKFSs6290/MWXKlF7t27c3Dx06tPj48eNeAPDSSy8F7tq1y09EVFRUVMnEiRMLfXx8VEpKiveAAQOiAaBNmzaWVatWHQkJCakxSXTUqFGF//3vf3179+5dBgCvvPLK8WnTpvUsLy+Xbt26la1Zs+YoALz22mvHZs+eHfboo4+Kl5eX5fXXX6+TZbntttvqrXGRmZlp8PLyUt26dbv8S2aaUZNKcgOAiHgBiKq6e1Ap1eQo1ZlYkpuIHHBZJbn7L/g8Pu98eZ2x6Y5tPSt+mHs9K266mWPHjnlMmTKl+65du37Wqo358+cH+vn5WeobNmmJLqskt4hMAvCZUipZRJ4B0E9EFiqlfnRyP4mIWgwGCFeW8PDwirvvvvvM2bNndba1HpytQ4cOlTNnzszT4tiu1NQ5D3OVUsUiMhTACABvgldbEBGRm7nnnnvytQocAOChhx7K8/Bo1RdaAHBgwmTVz5sA/FsptRnAJV2GQ0RERK1bU4OHbBFZBuB2AJ9WzX9o6nOJiIjIjTQ1ALgNwFYANyilCgAEwHoFBhEREV1hGp0wKSJ6AD8qpaJt25RSOQBytOwYERERtUyNZh6UUpUADopINxf0h4iImuiqq66Kbnyvptm2bVubhISEqO7du/e21XIoLi7WzZ49u+u8efPqFKZyttmzZ3cNDAyMj46ONvXo0SN22rRp3WyrPU6YMKF7SEhIXHR0tMlkMsXYSmNPmDChe2BgYHxJSYkA1hobISEhcRdr58yZM/pFixZ1vtg+tjLae/fu9QaA+Pj46OjoaFNwcHCcrVx5dHS06eDBg54VFRXw9/fvM3PmzBD7YxQWFuqmTp0abiu7PnDgwKivvvqqbe22apcBDwkJiTMajSZbuW/bfjfddFNPW7u2cwEAH3zwgV9sbGyM0Wg0xcbGxnz88cfVK4WGhITE9e/fP8q+vejoaFNkZGQsAHz//fc+EyZM6H6xc9GQpg5b+ANIEZEvReRj2+1SGiQiIufYu3dvujOOk5mZaZg2bVqvRYsWZR09evRAWlpa6ujRo4sKCgqa9BlhsVhg+6C/HPfff/+p9PT01IyMjJT09HQf2yqOALBw4cKs9PT01IULF2bPnDkz3LZdr9erpUuXdqr/iHXl5eXp33zzzcCL7bN27dqAfv36nVu5cmUAACQnJ6enp6enPvnkkydsS3enp6enRkVFlX/wwQd+PXr0KNu0aZO/rQIqAEybNq27v7+/+ejRowdSUlLSVq5ceeT06dN1sv21y4ADwDfffHMoPT099cCBA2m2bZs3b/7F1u6NN96YP3bs2HwACAwMrNi8eXPGoUOHUt95550j99xzTw/7458/f16fkZHhAQA//vhjjXoeAwcOLMnJyfH8+eefHb4AosmXagIYC+A5AEvsbkREbuuxx9BlwgR0r3177DF0afzZ9Tt48KCn7ZsfAMybNy9o9uzZXQFg4MCBUQ888EBIXFxcTPfu3XvblqxOSkryjouLi4mOjjYZjUbT/v37vQBr+W0AqKysxB133NGtR48esUOGDIm89tprI95++21/wPrt85FHHulqMplijEajyfZt2t6SJUsCb7vttryRI0eet22766678sPCwsyAtdKnraz3woULA22vo3v37r1vvfXW7kajMfbw4cOe9913X2hkZGSs0Wg0/fvf//YHrCXIBwwYEDVixIheoaGhcTNnzgx57bXXAuLi4mKMRqMpJSWlTnnvsrIyKSsr03Xs2LHOKoyjR48uzszMrH7Offfdd/q1114Lqq/EdX1luB999NHQzMxMr+joaNN9990XWvs5hYWFuj179rR7++23j37wwQcBDfwZq61ZsyZg5syZp7p27Vr+5ZdftgWsS2Tv3bu37T/+8Y9svd5aDDQ6Orp88uTJhbWfb18GvCksFgs2bdpUXXDrN7/5TUn37t0rAKB///6lZWVlOlsmBgBuueWWs7YgaOXKlQETJkyoUahrzJgxBStWrPBvavs2TQoelFLf1HdztDEiotbkqqtQotNBhYej3HbT6aD69YNmdQnMZrPs378/7S9/+Uvmc8891xUAXnnllc4zZ848VVWsKq1Hjx7l9s9ZuXKlf2ZmpmdGRkbK2rVrj+zdu7dGnYxOnTqZU1NT0+6+++7cRYsW1RmCSE1N9UlISGiwTHhGRob3N998c2jPnj1pixcv7lpWViYAcPz4ca9Zs2blZmRkpOzatavN/v37fdLS0lK+/PLLQ/PmzQs9duyYBwCkp6f7vPXWW8d//vnnAxs2bOh46NAh7/3796fdeeedZ5YsWVKdBXj99deDoqOjTV26dOnTo0eP0iFDhtQ5z2vXru0QGRlZvT08PLx8wIAB5/71r391tN+voTLcS5YsyQoLCytLT09PXbZsWVbt469evbrDddddVxgfH1/m7+9v/vbbbxssYnbhwgXZuXOn3+TJkwsmTZp09r333gsAgJ9++snbZDJdMBgaX4fRvgy4zYgRIyJjY2Nj7Ouf2GzdurVdp06dKuLi4uqs8rxixQr/2NjYCz4+PtVLR0+ZMiV/06ZN/lXP7TB+/PgC++cMGjTo/K5du3zhoCYFDyIyWET2iMg5ESkXkUoRqXfdbiIid3HjjSg2GICyMusy12VlEIMBuOkmFGvV5qRJk/IBYMiQIeezsrI8AeDqq68+v2TJkuCnn366y88//+zZrl27GnUFvv3223bjx4/P1+v16Natm3nw4ME1+jd16tR8ABg4cOAF+2/tTTVq1KgCHx8fFRwcbA4ICKjIysoyAEBwcHD5iBEjzlf1wfe22247azAYEBYWZh40aNC5HTt2tAGsZcbDw8MrfHx8VLdu3crGjBlTCAB9+vQpsS/dbRu2yM3N3XfhwgXdG2+8Uf2N+JlnngmNjo42LV++vNObb7551L5/8+bNy1m6dGkX+2GDppbhrm3dunUBU6ZMyQeACRMmnH333XcbzD68//77HQYPHlzcrl07dccdd+Rv3brV32x2rGSFfRlwANixY0d6ampq2n//+9+f//3vfwdu2bKlRiD43nvv1ckeANbs1Lx580L+/e9/16izERgYWNm+fXvzG2+84R8REVHSrl27GgtgBQcHm0+dOuXwqlVNHbb4J4ApAH4G4APgHgCvOtoYEVFr4u8Py9ixKDh92npl2unTMIwbh4L27XHJKxAaDAZl/yFXWlpa4/9hb29vVbVfdQnt+++//+xHH32U4ePjYxk7dmyk/aS4prA7prIv8W0TExNTkpSU5HCZ8KaWlbZ/vk6nq+6PTqert0y4l5eXGjVqVNH27dvrzHnYtWvXzwMGDKhRxTIuLq7MZDJdsE+/N7UMt71Tp07pv/vuO98//vGP4SEhIXH//Oc/u9Sey2Bv7dq1ATt37vSrmphoKiws1G/atMmvb9++pWlpaW2aEkjYlwEHgB49elQAQEhIiPmmm24q+N///lc9ybKiogKfffaZ//Tp02sED4cPH/aYOHFixJtvvnkkNja2TkZi4sSJ+Y8//nj4lClT6gQdJSUlOm9vb4ffz01e6EkplQFAr5SqVEq9DWC0o40REbU2tuxDcTF0zsg6hIaGms+ePWs4efKkvqSkRLZu3dq+seekpqZ6xsTElD3zzDOnb7jhhoKffvrJx/7xoUOHnvvwww/9KysrkZmZadi9e7dDwcVjjz12et26dR3trwZYsWJFh8zMzCaXHr/mmmuKN2zYEGA2m3HixAnD999/327YsGHnG39mXRaLBbt27WrXq1evJhdgfPbZZ3NeffXV6rkoDZXhbt++feX58+fr/ex79913/W+99dazJ06c2J+dnb3/5MmTyaGhoeVbt26tUy797Nmzuj179rTLyspKzs7O3p+dnb1/0aJFx1evXh0QGxtbFh8ff3727NldbYHHwYMHPdeuXVvnb21fBryoqEiXn5+vs/2+bds2v/j4+Oohmo8++sivZ8+epb169aqe4HHmzBn9jTfeGDl//vysUaNG1Xu+p02blv/HP/7x5Pjx4+uMGKSmpnpFRUU5PAzX1ODhgoh4AvhJRF4SkUcceC4RUatlyz4cPgyvy806ANZv1Y8++mjOgAEDYoYNG2aMiIgobew57733XoDRaIyNjo42paWl+dx33301CivNmDEjPzg4uDwiIiL29ttv7xEbG3uhQ4cOTb78ISwszLxy5cpf5syZE9q9e/fePXv2jN26dWv7Dh06NPm13nnnnQWxsbElMTExsdddd51x/vz5WY6WnbbNeTAajbEWiwVz5sw53dTnJiQklMbGxlbP2xg/fnzRpEmTzg4YMCDaaDSabr311l4FBQX6Ll26VPbv3/9cZGRkbO0Jk+vXrw8YP358vv22m2++Od82l8HeqlWr/IcMGVJsP79g8uTJBV988UX7kpISee+9946ePn3aIzw8vHdkZGTsnXfe2SM4OLjOrE5bGXAAyMrKMgwePDg6KirK1K9fv5hRo0YVTJw4sfoDf82aNQGTJk2qkT146aWXAo8fP+714osvdrVdypmdnV0j6PP397c8//zzJ20ZH3tfffWV39ixY+tM5GxMk0pyi0g4gFOw1rN4BEB7AP+qyka4FEtyE5EDLqskt01+PnRLlqDznDnIvdzgQSuFhYW69u3bW06ePKkfMGBAzM6dO9Md/fAm13NFGfCGlJSUyODBg6OSkpLSGyrWdVkluZVSx0TEB0CwUmr+ZfWWiKiV8feHZeFCnGruflzM9ddfH1lUVKSvqKiQOXPm5DBwaB1cUQa8IRkZGZ7PP/989qVU+WxS8CAi4wAshjXz0ENE+gJ4TimV6HCLRETkdN9///3B5u4DXZp77rknv/G9nC8uLq6svks+m6Kp8xb+DGAggAIAUEr9BKBHw7sTERGRu2pq8FChlKo9oaLxyRJERETkdpp6GU6KiEwFoBeRSAAPAtilXbeIiIiopWpq5uFPAGIBlAFYDaAQwMMa9YmIiIhasIsGDyLiLSIPA3gJwHEAVyulBiilnlFKNXptMhERaYclua+cktynTp3SDxkyJDI8PLz3kCFDInNzc/UAsHTp0o4i0v/DDz+sXhjM1n9bcbSxY8f2tBVTc5bGMg8rACQA2A9gDKxXXBARUQvAktxXTknuZ599Nvi6664rPnbs2IHrrruueN68edWraUZGRpasWbOmeiGrtWvXBtivGvnAAw+cfv755y+5Emx9GntjmJRSdyillgGYCOAaZzZORNSSBQUhXgT9a9+CghB/qcdkSW6W5LbX1JLcn332WQfbyqL33Xdf3pYtW6rreAwaNOjc3r1725aVlUlhYaHu6NGjXvarbY4ePfrct99+61ffObpUjQUP1S0ppRxecERERovIQRHJEJH/q+fx+0Vkv4j8JCI7RMTkaBtERFq5/noUeHjUvLLMwwNq1CjrZetaYEluluSuryR3Xl6eITw8vAIAwsLCKvLy8qoPLiK45pprijZu3Oi3evXqDqNHjy6wP55er0d4eHjpd9991+BrcVRjwUMfESmquhUDiLf93lhJbhHRw1p5cwwAE4Ap9QQHq5VScUqpvrDOq/jrpb0MIiLne/555Ohq/S+p0wHPP48TWrXJktwsyX2xktyAtRqpSM2V16dNm3Z27dq1AevXrw+YMWNGneqZnTp1MmdmZjq+lGQDLho8KKX0Sim/qpuvUspg97tfI8ceCCBDKfWLUqocwFoAN9c6vn0A0hZcO4KIWpDwcFRMnIgztuyDhwfUpEk4060bLnnpZ5bkZklue00tyd2xY0ezLZNz7Ngxj4CAgBoHHz58+IX09HSfs2fPGuLj4+usGllWVqZr6t+rKbSsjBkCINPuflbVthpE5I8ichjWzMODGvaHiMhh9tkHZ2QdWJK7cSzJXbck9w033FCwbNmyjgCwbNmyjrWHJgBgwYIFWQsWLMiu77UdOXLEq1+/fg6X3m5Is5fVVkq9qpTqBeAJAM/Ut4+I3CsiSSKSlJub69oOEtEVzZZ9EAEuN+sAsCT3xbAkd8MluefPn5+zbds2v/Dw8N5ff/213/z583NqH+u2224rGjduXHHt7ZmZmQYvLy/lzGJpTSrJfUkHFrkawJ+VUjdU3X8SAJRSLzawvw5AvlLqolE4S3ITkQOcUpL72DF4DB2KqJ07kX65wYNWWJK7dXJFSe758+cH+vn5WRobtqnPZZXkvkR7AESKSA8A2QAmA5hqv4OIRCqlbCfsJgAur2dORNSY8HBUZGbiQHP342JYkrt1ckVJ7g4dOlTOnDkzr/E9m06z4EEpZRaRWQC2AtADeEsplSIizwFIUkp9DGCWiIyE9ZLQfAAztOoPEZE7Y0nu1kvrktwPPfSQUwMHQNvMA5RSnwL4tNa2eXa/P6Rl+0REROR8zT5hkoiIiFoXBg9ERETkEAYPRERE5BAGD0RErRRLcju/JLder+8fHR1tioiIiI2KijI9++yzQbZ+fPLJJ74i0n/16tXVSwoMHz484pNPPnFoUS5HHDt2zGP48OERtvu7d+/26du3b3RERESs0Wg0XbhwQQBg2bJlAUaj0WQ0Gk3Dhg2LzMnJMQDWc+Tj43OVbfEpALj77rvDRKR/Tk6OobS0VBISEqIcLZrF4IGIqJViSW7nl+T28vKy2Prw1VdfHfr888/bP/bYY11tjwcFBVX85S9/Cb6U13cpXnjhhaDf//73ZwCgoqICd955Z4/XXnvtWEZGRsr27dsPenp6qoqKCjz55JNh33zzzaFDhw6lxsbGlrz88svVrzEsLKxszZo1HQBrBdYdO3b4BgYGVgDWpcuvvfbaouXLlzdaQdQegwciogb0X/B5fPf/29y/9q3/gs9ZkttNS3LbCwkJMS9fvvzo22+/HWhbZjomJuaCr69v5QcffFCnvtNjjz0W3Lt375jIyMjYKVOmhNues3DhwsBevXrFGo1G09ixY3sC1uWnJ02a1D0uLi4mJibG9N5773Worw+bN2/2nzBhQiEAbNy4sX1MTEzJ1VdfXQIAXbp0qTQYDLBYLKKUQnFxsc5isaCoqEjXtWvX6sqr48ePP7thw4aAquP5Dhgw4JzBYKheIXLixIkFa9euZfBAROQMwyI7FRh0UmMZXoNO1DXGzgVatcmS3M1bkrs2k8lUXllZiezs7OqlDZ5++umcF154oU72Yc6cOacPHDiQ9vPPP6eUlJTobLUsli5d2uXAgQOphw4dSn3nnXeOAcBTTz0VPHz48KL9+/enffvttwefeeaZ0KKiohqfyenp6Z7t27c325bAPnjwoJeIYOjQoZEmkynmmWeeCQKsS57/9a9/Pd6vX7/YoKCg+EOHDvk8/PDD1atJRkVFleXl5Rlyc3P1q1evDpg6dWqNqpsDBgwoSU5ObgsHMHggImrAnBuic3S1Sh/rdII5N0SxJDfcsyR3U4wZM+YcANQumLVlyxbf+Pj4aKPRaNq1a5fvgQMHfAAgKiqq5NZbb+3xr3/9K8DDw0MBwNdff+33t7/9LTg6Oto0dOjQqLKyMsnIyPC0P15mZmaN6plms1n27NnTbv369Ud279598JNPPvH/6KOPfMvKyuSNN97ovHv37tRTp04lm0ymkqeeeqpGcDNu3Lj8t956K+DHH39sO3r06HP2jxkMBnh4eCj7eRGN0XSRqOYy6IUvcKqobjG2ID8v7H5qZDP0iIhaoxB/n4ob47qc+SQ5p5PZosSgE3VTXPCZrh18XF6Se9iwYec/+OCD9mPHjo185ZVXjiUmJtYpgNSQppbkvuOOOwrqe35zluS+99578wHrnIe77rqr3pUYL1aSe86cOTXqORw8eNCz7hEalpqa6qnX6xESEmLet29f9fYnn3wyZ+HChcG29P+FCxfk0UcfDd+9e3dqRERExezZs7va/rbbtm37ecuWLb4fffRR+8WLFwcfPHgwRSmFDRs2ZPTp06fByqFt2rSxlJWVVb8/QkNDywcNGlQcHBxsBoDrr7++MCkpqY2tCFpsbGwZAEyZMuXsokWLutgfa/r06fmDBg2KmThxYp5er6/TVkVFhbRp06bJxa7cMvPgfyEIomq+H0UJ/C90aeAZRET1s88+OCPrwJLcjWuukty1nThxwvCHP/wh/K677jqt09V8yvjx44sKCwv16enpPgBw4cIFHQB06dLFXFhYqNu0aZM/YJ2PcvjwYc9x48YVv/rqq9nnzp3TFxYW6ocPH160ZMmSIFsguXPnTh/UEhcXV5adnV0d7Nx6661F6enpPsXFxbqKigrs3LnTNzY2tjQ8PLwiIyPD+8SJEwbAmnUxGo01qrUajcbyp556Kvvhhx+uU5r65MmT+g4dOpjtg7zGuGXmYVrfSDy7JwsKv54HgeDOqyIu8iwiorps2YePfjrR+XKzDkDNktxBQUEVTS3JvW7duo4Gg0F17ty5YsGCBTXKMc+YMSP/iy++8I2IiIgNDg4uv5yS3Hl5eR46nU4NHjz43Pjx44uaeow777yzYNeuXe1iYmJiRUTZSnInJyc39RB4/fXXg9atW9fRbDZLTEzMhUspyZ2SktIGsH64p6SkeA8YMCAasH6LX7Vq1ZHY2NgyW0nu3/72t4W15z2UlZXpoqOjTWazWfR6vbr99tvznn322VP1tfnEE0/k3HHHHREA0KlTp8pp06blxsTExHbu3Nncp0+f84B1qGHq1Kk9iouL9Uopueeee0536tSpctGiRSfuvffebtHR0SaLxSJhYWFl27Zty7A/vp+fn6Vbt25lBw4c8Ordu3dZ586dK2fNmnXqqquuihERjBgxonDy5MmFADBnzpycoUOHRhkMBhUaGlq+evXqI7X7WzsLY7Nlyxa/kSNHFjb1XAMaluTWSlNKchcXAzfO3Y9s70xYoKCDIKS0G7Ys7I127S76VCJyL04pyZ2dX+Ix4fVdURsfGJJ+ucGDVliS2z2tXLmyQ1JSUpulS5dqNs9m1KhRvRYvXpwVHx9fJ9PTHCW5m42vL3DvUGv2AaIAJbh/WAQDByK6JCH+PhXfPTmCJbnJ5aZPn15w5swZzT6rS0tLJTExsaC+wOFi3DJ4AIBbbvDGGztCkel1HCFlYbj5BqdMsq2DkzOJqCVgSW73NXv2bIcyZY7w9vZWs2bNcrhkt1tOmAwNBfz8gP8tj0RFQRvsWh4BX1/rdme7PiYIHvqamVEPveB6EydnErVQFovFUmc4g4hqqvp3Uu8VNW4ZPCQmAp6eQOV5b5x4Yzgs573h6QncfLPz23pwRCRqXweuF8GDIzg5k6iFOpCbm9ueAQRRwywWi+Tm5rYHUO9wnVsOW8ydC7z9ds1ter11u7MF+nljUv9QvJ+UiYpKBQ+9YGJCGAJ9tRkmIaLLYzab7zl58uTykydP9oabfoEicgILgANms/me+h50y+AhOBi46y7gzTeB8nJrFuKuu4AuGowkLFgAHD4SicrO1smZlWbB4Y8jsGC/NsEKEV2e/v37nwaQ2Nz9IGrN3DbqnjsXsK3poVXWAQD69AG8lTciDdYJFZGGMHgrb/Ttq017REREzc1tgwdb9kGn0y7rAADDh1szGzGVkWiHNoipjICnp3U7ERGRO3LLRaJscnKAoUOBnTu1Cx5CQ4Hs7LrbQ0KArEbrtRGRxjgpkkgDbjnnwSY4GDh8WNs2EhN/nVtho9WVHVxTgoiIWgK3HbZwFfu5FTZazbFgwS8iImoJGDxcJtvcCs+qumdaXtkxrW8kpFYWlgW/iIjI1Rg8OIGrruy45QZvhJSFQlcVQOggmi69TUREVB8GD07gqis7bAW/YBu6YMEvIiJqBgwenGTuXKB7d+0XhrJlH6DArAMRETULBg9OYruyQ6usA+Dagl9EREQNYfDQiriy4BcREVFDGDy0Iq68LJSIiKghDB5aEVdeFkpERNQQBg+tjKsuCyUiImoIg4dWxlWXhRIRETWEwUMr5KrLQomIiOrj1oWx3JUrCn4RERE1hJkHIiIicggzD1Qvlv8mIqKGMPNA9bo+Jgge+poVPD30gutNnKFJRHSlY/BA9apMjkSluWbwUGkWVO5j+W8ioisdgweq19VXeaNLSc3y311KwjCkHwtxERFd6Rg8UL2GDweM5ZGQquBBIDCWR2D48GbuGBERNTsGD1QvX19g/I3eCLpgLdkZdCEME27yRrt2zdwxIiJqdppebSEiowH8A4AewHKl1KJaj88GcA8AM4BcAHcrpY5p2SdqmtBQIDsb0LeNRNC0M9i9KgKJrwAhIUBWlnPb4pUdRESti2aZBxHRA3gVwBgAJgBTRMRUa7e9ABKUUvEANgB4Sav+kGNcWf7b/0IQRNWcnClK4H+BV3YQEbVEWg5bDASQoZT6RSlVDmAtgBofPUqpbUqpC1V3vwMQqmF/yAGuLP89re+vcytsBII7r+KVHURELZGWwUMIgEy7+1lV2xryewBbNOwPOcCV5b9vucEbIWU1r+wIKQvDzTfwyg4iopaoRUyYFJE7ACQAeLmBx+8VkSQRScrNzXVt565grir/HRMD/G95JMwV1uDBXCHYtTwC0dHatEdERJdHywmT2QDC7O6HVm2rQURGAngawLVKqbqz5gAopd4A8AYAJCQkKOd3lepjyz4sW6Zt+e/ERODNN71xbn8ofPsex7n9YTBUeGsyvwIAFiyov7BYr16sVEpE1BSilDafxSJiAHAIwAhYg4Y9AKYqpVLs9rkK1omSo5VSPzfluAkJCSopKUmDHlN9cnKAoUOBnTu1Cx5ycoCePYEKfSmCpv0PJ1ddDS+LN375RZs2+8z7AoXldePU9p5e2Pccr+5wM9L4LkTkKM2GLZRSZgCzAGwFkAZgnVIqRUSeE5HEqt1eBtAOwHoR+UlEPtaqP3RpbOW/tQocbG3cdRegr7Be2WGo8NY003FD7/qv7hgdx6s7iIiaQrPMg1aYeXBPtuxDaSng4wPNsg4AcLqoFL95cRsqlKV6m4dOh51PDkegLydpuhlmHog00CImTBLZsg86nbbzKwAg0M8bt14VWp19ECUYf1UYAwcioiZi8EAtxty5QPfu2k9aDA0F/nr3r1VDK82CxXdFIJSrjBARNQmDB2oxXDG/ArBe3aGvsF7doSzQ/OoOIiJ3w+CBrji29SuKdkXCXNgGhbsiNF3HgojI3TB4oCuOq6/uICJyNwwe6IrkqtUziYjcEYMHuiK58uoOIiJ3w+CBrliuurqDiMjdaFnbgqhFs13dQUREjmHwQKQhFuEiInfE4IFIQ336WJfaDg//dduxY0Dfvs3WJSKiy8bggUhDC/d/gdNBZUCp3cYgICXZC+PGsYInEbVOnDBJpKFRpiDopWZtJr0IRsXy8g4iar2YeSDS0IMjIrH+hyxUmn+tXmvQCx4cEeH0tji/gohchcEDkYYC/bwxqX8o1nyfiUqloBfBpARtKni6cn4FAxWiKxuHLYg0FBoKLLozEhXl1qGLinLBi3doU8Fz+HDA0xMoK7PeLyuz3h8+3Plt9ekDiFjXybDdRDgRlOhKwcwDkYYSE4E337RW8PTte1zTCp4jX/kCp/zLAIVfJ2j6Az8t9cLup5w7OXP4cGDzZmuA4uWlbaBCRC0PgwciDc2dC7z9trWCp0+PMyjcFQEvjWppXB8ThPeTMlFR+ev8Cg+94HqT8ydnxsQA2dl1t2/eDGRlOb05ImphOGxBpCFXVvB85/FIlJXWvLKjrFTwzmPOn5yZmGjNNNjz9IQmGRUiankYPBBpzFUVPBOv90ZJaiiU2RpAKLOgJCUMN9/g/MmZ9q/JhtVJia4cDB6INOaqCp5z5wIXvo+EUlXBgxKUJEVo8oFue0227IOnJ6uTEl1JGDwQuYArKngGBwMzbq/KPliAkpQwzLjdW9NgxRUZFSJqeUQp1fheLUhCQoJKSkpq7m4QtUg5OUBE71J0mPg/FP7namQc0CZ4GPTCFzhVVFZne5Cf86/suEzS+C5E5ChmHojciC37cHL5cE2zDtfHBMFDX/NzWasrO4io5WHwQORmXDFE8uCISOjqqdmhxbLbRNTyMHggcjPBwdalo7WcvGhbdtuWffDQCyZqtOw2EbU8DB6I6JLYZx+YdSC6sjB4IKJLYss+iIBZB6IrDIMHIrpkD46IRJh/G2YdiK4wrG1BRJcs0M8b2x9nNSyiKw0zD0REROQQBg9ERETkEA5bEFGLtmCB9dLT2nr14pLYRM2FwQMRtWh9+gC//AKEh/+67dgxoG/fZusS0RWPtS2IqEXr2tVas6O+7dnZjT6dtS2INMA5D0TUot1yC2ColSM1GKzbiah5MHggohZt7tz6gwfOdyBqPgweiKhFCw4G7rrr1wDCYADuvlvb2h1EdHEMHoioxbPPPjDrQNT8eLUFEbVog174AqeKyhD00K/bBv8dCPLzwu6nRjZbv4iuZMw8EFGLdn1MUHXpbxsPveB6E8ctiJoLgwciatHsS3/bsAQ4UfPSNHgQkdEiclBEMkTk/+p5/BoR+VFEzCIyUcu+EFHrZCv9bcs+eOiFJcCJmplmwYOI6AG8CmAMABOAKSJiqrXbcQC/A7Baq34QUetnn31g1oGo+WmZeRgIIEMp9YtSqhzAWgA32++glDqqlEoGYNGwH0TUytmyDyJg1oGoBdAyeAgBkGl3P6tqGxGRwx4cEYkw/zbMOhC1AK3iUk0RuRfAvQDQrVu3Zu4NETWHQD9vbH98eHN3g4igbeYhG0CY3f3Qqm0OU0q9oZRKUEoldO7c2SmdIyIiokujZfCwB0CkiPQQEU8AkwF8rGF7RERE5AKaBQ9KKTOAWQC2AkgDsE4plSIiz4lIIgCIyAARyQIwCcAyEUnRqj9ERETkHKKUau4+OCQhIUElJSU1dzeIqHWQxnchIkdxhUkiIiJyCIMHIiIicgiDByIiInIIgwciIiJySKubMCkiuQCOOfCUTgDOaNQdttW623J1e2zL9W2dUUqN1qozRFeqVhc8OEpEkpRSCWyLbTV3e2yrdbVFRA3jsAURERE5hMEDEREROeRKCB7eYFtsq4W0x7ZaV1tE1AC3n/NAREREznUlZB6IiIjIidw6eBCRDiKyQUTSRSRNRK7WsK1HRCRFRA6IyBoR8Xbisd8SkdMicsBuW4CIfC4iP1f99NewrZerzmGyiHwgIh20asvusUdFRIlIJy3bEpE/Vb22FBF5Sau2RKSviHwnIj+JSJKIDHRSW2Eisk1EUqtew0NV253+/rhIW05/fzTUlt3jTn1/EJFj3Dp4APAPAJ8ppaIB9IG1uqfTiUgIgAcBJCilegPQw1qC3FneAVD7WvX/A/ClUioSwJdV97Vq63MAvZVS8QAOAXhSw7YgImEARgE47qR26m1LRIYDuBlAH6VULIDFWrUF4CUA85VSfQHMq7rvDGYAjyqlTAAGA/ijiJigzfujoba0eH801JZW7w8icoDbBg8i0h7ANQDeBAClVLlSqkDDJg0AfETEAKANgBPOOrBSajuAs7U23wxgRdXvKwDcolVbSqn/VpVYB4DvAIRq1VaVvwF4HIDTJuQ00NYDABYppcqq9jmtYVsKgF/V7+3hpPeHUipHKfVj1e/FsAbIIdDg/dFQW1q8Py7yugAN3h9E5Bi3DR4A9ACQC+BtEdkrIstFpK0WDSmlsmH91nocQA6AQqXUf7Voy06QUiqn6veTAII0bs/mbgBbtDq4iNwMIFsptU+rNuwYAQwTkd0i8o2IDNCwrYcBvCwimbC+V5yVvakmIt0BXAVgNzR+f9Rqy57T3x/2bbn4/UFEDXDn4MEAoB+A15RSVwE4D+el9muoGk++GdaApSuAtiJyhxZt1UdZL5nR/FuYiDwNazp5lUbHbwPgKVjT+q5gABAAa1p8DoB1IiIatfUAgEeUUmEAHkFVRsxZRKQdgP8AeFgpVWT/mLPfHw21pcX7w76tqmO78v1BRA1w5+AhC0CWUsr2zWgDrMGEFkYCOKKUylVKVQDYCGCIRm3ZnBKRYACo+umUlHtDROR3AMYCmKa0u763F6wB2D4ROQpr+vtHEemiUXtZADYqq+8BWGCtnaCFGbC+LwBgPQCnTJgEABHxgPUDdpVSytaGJu+PBtrS5P1RT1uufn8QUQPcNnhQSp0EkCkiUVWbRgBI1ai54wAGi0ibqm+uI6DR5Ew7H8P6gYSqnx9p1ZCIjIZ1jDlRKXVBq3aUUvuVUoFKqe5Kqe6wfrj3q/pbauFDAMMBQESMADyhXYGnEwCurfr9twB+dsZBq95vbwJIU0r91e4hp78/GmpLi/dHfW01w/uDiBqilHLbG4C+AJIAJMP6QeGvYVvzAaQDOADgXQBeTjz2GljnUlTA+h/m7wF0hHUW/c8AvgAQoGFbGQAyAfxUdXtdq7ZqPX4UQCcNX5cngPeq/mY/Avithm0NBfADgH2wzhPo76S2hsI6JJFs9/e5UYv3x0Xacvr7o6G2tHp/8MYbb47duMIkEREROcRthy2IiIhIGwweiIiIyCEMHoiIiMghDB6IiIjIIQweiIiIyCEMHshlROR3IvLPy3j+ZyJSICKf1Nreo2qJ6QwReV9EPC+/tzWOf13tNomIrmQMHqg1eRnAnfVs/wuAvymlIgDkw7quAhERaYTBAzULEekuIl+JSLKIfCki3aq29xKR70Rkv4gsFJFztucopb4EUFzrOALrio0bqjZVV5AUkXdE5HURSRKRQyIytmr7dyISa3eMr0UkQUQGisj/qgqp7bJbnZSIiOwweKDm8gqAFUqpeFgLKS2t2v4PAP9QSsXBujpjYzoCKFC/loTOwq+lmwGgO6x1JG4C8LqIeAN4H8BtQHXdh2ClVBKsK4QOU9ZCavMAvHDpL4+IyH0xeKDmcjWA1VW/vwvrcsS27eurfl9d+0mXYJ1SyqKU+hnALwCiAawDMLHq8dvwa9aiPYD1InIAwN8AxNY+GBERMXggjYnIH0XkJxH5CdZy5c6WB6CDiBiq7ocCyLZ7vPb660oplQ0gT0TiAdwOayYCABYA2KaU6g1gHABvDfpLRNTqMXggTSmlXlVK9VVK9YW1sqTNLgCTq36fBuDbqt+/AzCh6vfJaISyFmfZhl8zCbUrSE4SEZ2I9ALQE8DBqu3vw1oJsr1SKrlqW3v8Gnj8rtEXR0R0hWLwQM3lTwDuEpFkWK+geKhq+8MAZldtjwBQaHuCiHwL65DGCBHJEpEbqh56ouo5GbDOgXjTrp3jAL4HsAXA/Uqp0qrtG2ANTtbZ7fsSgBdFZC8AA4iIqF6sqkktioi0AVCilFIiMhnAFKXUzZd4rHcAfKKU2tDYvkRE1HT8dkUtTX8A/6y6BLMAwN3N2x0iIqqNmQciIiJyCOc8EBERkUMYPBAREZFDGDwQERGRQxg8EBERkUMYPBAREZFDGDwQERGRQ/4fBBU4D9aCAhkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    #print(len(chrombpnet_pear[key]))\n",
+    "    #print(len(np.arange(7,25,2)))\n",
+    "    #print(key)\n",
+    "    #print(chrombpnet_pear[key])\n",
+    "    ax.scatter(np.arange(6,25,2).astype(str), chrombpnet_pear1[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(6,25,2).astype(str), chrombpnet_pear_abs1[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10pval\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "#plt.ylim((0.4,0.6))\n",
+    "plt.savefig(\"subfigs/kaur_caqtls_jsd_pearsonr_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.5497003608149088, 0.0)\n",
+      "(0.2969767838519738, 3.0288812710935697e-196)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import scipy\n",
+    "\n",
+    "threshold=6\n",
+    "dataf1 = datafn[datafn[\"log10p\"]>threshold]\n",
+    "   \n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = dataf1[\"ATAC.572M.logfc.mean\"]\n",
+    "data[\"Beta\"] = dataf1[\"beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"#2B1C97\", rasterized=True)\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#2B1C97\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/kaur_caqtls_pearsonr.pdf\", transparent=True, dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/microglia.caqtls_scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/microglia.caqtls_scoring.ipynb
new file mode 100644
index 00000000..dec8acb1
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/microglia.caqtls_scoring.ipynb
@@ -0,0 +1,884 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micros = pd.read_csv(\"microglia.caqtls.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHROM</th>\n",
+       "      <th>POS0</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>non-effect_allele</th>\n",
+       "      <th>effect_allele</th>\n",
+       "      <th>Top_SNP_perPeak</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign.jsd.mean</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>199884842</td>\n",
+       "      <td>199884843</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs281785</td>\n",
+       "      <td>1.293180</td>\n",
+       "      <td>-0.032774</td>\n",
+       "      <td>-0.016354</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200357679</td>\n",
+       "      <td>200357680</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs2033804</td>\n",
+       "      <td>-0.396164</td>\n",
+       "      <td>0.008075</td>\n",
+       "      <td>0.009565</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>200656659</td>\n",
+       "      <td>200656660</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>rs2287012</td>\n",
+       "      <td>-0.498759</td>\n",
+       "      <td>-0.003838</td>\n",
+       "      <td>-0.004759</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172585460</td>\n",
+       "      <td>172585461</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs10752994</td>\n",
+       "      <td>-1.070820</td>\n",
+       "      <td>-0.005939</td>\n",
+       "      <td>-0.004529</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172625477</td>\n",
+       "      <td>172625478</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12123755</td>\n",
+       "      <td>0.816922</td>\n",
+       "      <td>0.441283</td>\n",
+       "      <td>0.047415</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  CHROM       POS0        POS non-effect_allele effect_allele Top_SNP_perPeak  \\\n",
+       "0  chr2  199884842  199884843                 A             G        rs281785   \n",
+       "1  chr2  200357679  200357680                 A             T       rs2033804   \n",
+       "2  chr2  200656659  200656660                 T             C       rs2287012   \n",
+       "3  chr1  172585460  172585461                 A             G      rs10752994   \n",
+       "4  chr1  172625477  172625478                 C             T      rs12123755   \n",
+       "\n",
+       "       Beta  logfc.mean  sign.jsd.mean  IsUsed  \n",
+       "0  1.293180   -0.032774      -0.016354   False  \n",
+       "1 -0.396164    0.008075       0.009565   False  \n",
+       "2 -0.498759   -0.003838      -0.004759   False  \n",
+       "3 -1.070820   -0.005939      -0.004529   False  \n",
+       "4  0.816922    0.441283       0.047415    True  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micros.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "micrs1 = micros[micros[\"IsUsed\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHROM</th>\n",
+       "      <th>POS0</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>non-effect_allele</th>\n",
+       "      <th>effect_allele</th>\n",
+       "      <th>Top_SNP_perPeak</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign.jsd.mean</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172625477</td>\n",
+       "      <td>172625478</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12123755</td>\n",
+       "      <td>0.816922</td>\n",
+       "      <td>0.441283</td>\n",
+       "      <td>0.047415</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>201661642</td>\n",
+       "      <td>201661643</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62193397</td>\n",
+       "      <td>0.913101</td>\n",
+       "      <td>0.428027</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>201661642</td>\n",
+       "      <td>201661643</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62193397</td>\n",
+       "      <td>1.123280</td>\n",
+       "      <td>0.428027</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>205546600</td>\n",
+       "      <td>205546601</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6435287</td>\n",
+       "      <td>0.572607</td>\n",
+       "      <td>0.077947</td>\n",
+       "      <td>0.009463</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>206246233</td>\n",
+       "      <td>206246234</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs12373575</td>\n",
+       "      <td>1.080930</td>\n",
+       "      <td>0.189114</td>\n",
+       "      <td>0.060282</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   CHROM       POS0        POS non-effect_allele effect_allele  \\\n",
+       "4   chr1  172625477  172625478                 C             T   \n",
+       "5   chr2  201661642  201661643                 G             A   \n",
+       "6   chr2  201661642  201661643                 G             A   \n",
+       "14  chr2  205546600  205546601                 C             G   \n",
+       "20  chr2  206246233  206246234                 T             G   \n",
+       "\n",
+       "   Top_SNP_perPeak      Beta  logfc.mean  sign.jsd.mean  IsUsed  \n",
+       "4       rs12123755  0.816922    0.441283       0.047415    True  \n",
+       "5       rs62193397  0.913101    0.428027       0.032660    True  \n",
+       "6       rs62193397  1.123280    0.428027       0.032660    True  \n",
+       "14       rs6435287  0.572607    0.077947       0.009463    True  \n",
+       "20      rs12373575  1.080930    0.189114       0.060282    True  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micrs1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "micrs1[\"rsid\"] = micrs1[\"CHROM\"] + \"_\" + micrs1[\"POS\"].astype(str) + \"_\" + micrs1[\"non-effect_allele\"] + \"_\" + micrs1[\"effect_allele\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "877"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(micrs1[\"rsid\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(956,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micrs1[\"rsid\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CHROM</th>\n",
+       "      <th>POS0</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>non-effect_allele</th>\n",
+       "      <th>effect_allele</th>\n",
+       "      <th>Top_SNP_perPeak</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>sign.jsd.mean</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>rsid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>172625477</td>\n",
+       "      <td>172625478</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>rs12123755</td>\n",
+       "      <td>0.816922</td>\n",
+       "      <td>0.441283</td>\n",
+       "      <td>0.047415</td>\n",
+       "      <td>True</td>\n",
+       "      <td>chr1_172625478_C_T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>201661642</td>\n",
+       "      <td>201661643</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62193397</td>\n",
+       "      <td>0.913101</td>\n",
+       "      <td>0.428027</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>True</td>\n",
+       "      <td>chr2_201661643_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>201661642</td>\n",
+       "      <td>201661643</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>rs62193397</td>\n",
+       "      <td>1.123280</td>\n",
+       "      <td>0.428027</td>\n",
+       "      <td>0.032660</td>\n",
+       "      <td>True</td>\n",
+       "      <td>chr2_201661643_G_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>205546600</td>\n",
+       "      <td>205546601</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs6435287</td>\n",
+       "      <td>0.572607</td>\n",
+       "      <td>0.077947</td>\n",
+       "      <td>0.009463</td>\n",
+       "      <td>True</td>\n",
+       "      <td>chr2_205546601_C_G</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>chr2</td>\n",
+       "      <td>206246233</td>\n",
+       "      <td>206246234</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>rs12373575</td>\n",
+       "      <td>1.080930</td>\n",
+       "      <td>0.189114</td>\n",
+       "      <td>0.060282</td>\n",
+       "      <td>True</td>\n",
+       "      <td>chr2_206246234_T_G</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   CHROM       POS0        POS non-effect_allele effect_allele  \\\n",
+       "4   chr1  172625477  172625478                 C             T   \n",
+       "5   chr2  201661642  201661643                 G             A   \n",
+       "6   chr2  201661642  201661643                 G             A   \n",
+       "14  chr2  205546600  205546601                 C             G   \n",
+       "20  chr2  206246233  206246234                 T             G   \n",
+       "\n",
+       "   Top_SNP_perPeak      Beta  logfc.mean  sign.jsd.mean  IsUsed  \\\n",
+       "4       rs12123755  0.816922    0.441283       0.047415    True   \n",
+       "5       rs62193397  0.913101    0.428027       0.032660    True   \n",
+       "6       rs62193397  1.123280    0.428027       0.032660    True   \n",
+       "14       rs6435287  0.572607    0.077947       0.009463    True   \n",
+       "20      rs12373575  1.080930    0.189114       0.060282    True   \n",
+       "\n",
+       "                  rsid  \n",
+       "4   chr1_172625478_C_T  \n",
+       "5   chr2_201661643_G_A  \n",
+       "6   chr2_201661643_G_A  \n",
+       "14  chr2_205546601_C_G  \n",
+       "20  chr2_206246234_T_G  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micrs1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "micrs1[\"abs_beta\"] = abs(micrs1[\"Beta\"])\n",
+    "micros = micrs1.sort_values('abs_beta', ascending=False).drop_duplicates('rsid', keep=\"first\").sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(877, 12)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micros.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6020040820648366, 1.3726945418075504e-87)\n",
+      "(0.2671265805109169, 8.546691741238511e-16)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats\n",
+    "import numpy as np\n",
+    "\n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = micros[micros[\"IsUsed\"]==True][\"logfc.mean\"]\n",
+    "data[\"Beta\"] = micros[micros[\"IsUsed\"]==True][\"Beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=5, color=\"#2da5ad\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#2da5ad\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "values = []\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "values.append(atc_pear[0])\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "values.append(atc_pear[0])\n",
+    "\n",
+    "print(atc_pear)\n",
+    "#ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "plt.savefig(\"subfigs/microglia_caqtls_scatter_plot.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6014727844621959, 2.1301181463727593e-87)\n",
+      "(0.2569352071468207, 1.0909585816693852e-14)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats\n",
+    "import numpy as np\n",
+    "\n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = micros[micros[\"IsUsed\"]==True][\"sign.jsd.mean\"]\n",
+    "data[\"Beta\"] = micros[micros[\"IsUsed\"]==True][\"Beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=5, color=\"#2da5ad\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#2da5ad\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "values.append(atc_pear[0])\n",
+    "\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "values.append(atc_pear[0])\n",
+    "\n",
+    "print(atc_pear)\n",
+    "#ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "#plt.savefig(\"subfigs/microglia_caqtls_scatter_plot.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "\n",
+    "\n",
+    "values = []\n",
+    "\n",
+    "atac_pear = scipy.stats.pearsonr(micros[micros[\"IsUsed\"]==True][\"logfc.mean\"],micros[micros[\"IsUsed\"]==True][\"Beta\"])\n",
+    "values.append(atac_pear[0])\n",
+    "atac_pear = scipy.stats.pearsonr(micros[micros[\"IsUsed\"]==True][\"sign.jsd.mean\"],micros[micros[\"IsUsed\"]==True][\"Beta\"])\n",
+    "values.append(atac_pear[0])\n",
+    "atac_pear = scipy.stats.pearsonr(abs(micros[micros[\"IsUsed\"]==True][\"logfc.mean\"]),abs(micros[micros[\"IsUsed\"]==True][\"Beta\"]))\n",
+    "values.append(atac_pear[0])\n",
+    "atac_pear = scipy.stats.pearsonr(abs(micros[micros[\"IsUsed\"]==True][\"sign.jsd.mean\"]),abs(micros[micros[\"IsUsed\"]==True][\"Beta\"]))\n",
+    "values.append(atac_pear[0])\n",
+    "\n",
+    "df[\"pearsonr\"] = values\n",
+    "df[\"method\"] = [\"logfc-signed\", \"jsd-signed\",  \"logfc-unsigned\", \"jsd-unsigned\"]\n",
+    "df[\"dtype\"] = [\"ATAC\"]*4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    0.602004\n",
+      "1    0.601473\n",
+      "2    0.267127\n",
+      "3    0.256935\n",
+      "Name: pearsonr, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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qq6uprq7mhhtuYI899mDjxo3Z/jVr1mx2fLt27QBo27Yt69evB+BrX/saDzzwAHvttRdDhw7lD3/4Q7MybO2cKSXuueceGhoaaGho4LXXXqN3794Ff507YkGQJElSq/OFL3yByZMns2HDBlasWMG0adOoq6vjyCOP5J577mHjxo289dZbPPHEE1t9/3HHHcett97KBx98AMA777xD9+7dsx/Czz//fDp37szbb7/NqlWr+PDDD3nwwQd3mGvx4sX07NmTCy+8kBNPPJEXXnhhs/355mvqhBNO4LrrriOlBMBzzz0HQH19PZMmTQLgpZde+si1CuUaBEmSJBUkn8eSlsqIESN4+umn6d+/PxHBNddcQ5cuXTjllFN4/PHHqaqqonv37gwYMICOHTt+5P1DhgyhoaGBmpoa9txzT4YOHcqVV1652TEVFRWMGzeOuro6unbtyqGHHrrDXHfddRe//vWvqaiooEuXLnz/+9/fbH+++Zq6/PLL+c53vkO/fv3YuHEjPXr04MEHH+Sf/umfOPvss+nduze9e/dm4MCBeXzndiw2NZHWoqamJs2ZM6fcMXZpAy++vdwR1Mrd12FiuSOolTtw3IvljiCpBBYsWFDSW1tayurVq6msrGTVqlXU1dUxc+ZMunTpUu5YmZ2dbxv/brGt451BkCRJ0m5l2LBhvPvuu6xdu5bLL798lyoHsOvnsyBIkiRpt5LPff3ltKvnc5GyJEmSpIwFQZIkSVLGgiBJkiQpY0GQJEmSlHGRsiRJkgryZP1RLXq+o6Y9mddxRxxxBE899dQOj7vtttuYM2cOP/vZz4qNtk1Dhw5l0qRJ7LvvviW7xujRoxk2bBgjR44s2TWasiBIkiSpVcmnHOwsU6dOLXeEFuctRpIkSWpVKisrWb58OfX19VRXV9OnTx+mT58OwK233kqvXr2yDyDb3jk2mTJlCqNHjwYaf1t/4YUXcsQRR9CzZ0+mTJkCsM3rHXTQQaxcuRKAH/3oRxxyyCF8/vOfZ9SoUVx77bUADB48mEsuuYS6ujp69eqVvXfDhg1cfPHF1NbW0q9fP2688UYAUkpccMEFHHLIIRx77LG8/fbbLfjd2zFnECRJktTqTJo0iRNOOIEf/OAHbNiwgQ8++IDly5dzxRVX8Oyzz9KxY0eOPvpoDjvssGafe/ny5cyYMYOFCxcyfPhwRo4cudXrNTV79mzuuecenn/+edatW8eAAQMYOHBgtn/9+vXMmjWLqVOn8sMf/pDHHnuMW265hY4dOzJ79mw+/PBDjjzySI4//niee+45Xn75ZebPn89bb71FVVUVY8aMKfp7li8LgiRJklqd2tpaxowZw7p16zjppJOorq7m8ccfZ/DgwXTq1AmA0047jVdeeaXZ5z7ppJNo06YNVVVVvPXWW9u8XlMzZ87kxBNPpH379rRv356vfOUrm+0/+eSTARg4cCBLliwB4NFHH+WFF17IZinee+89Xn31VaZNm8aoUaNo27YtBxxwAMccc0yzv4ZieIuRJEmSWp36+nqmTZtG165dGT16NLfffvs2j92wYQPV1dVUV1czbtw4ACIi279mzZrNjm/Xrl32OqXU7OttzaZztm3blvXr12fnvu6662hoaKChoYE//elPHH/88c06bylYECRJktTqLF26lM6dO3POOecwduxY5s6dy+GHH86TTz7JqlWrWLduHXfffTfQ+EP5ph/CJ0yYAEDnzp1ZsGABGzdu5L777ivoek0deeSR/O53v2PNmjWsXr2aBx98cIfnPOGEE/jFL37BunXrAHjllVd4//33qa+vZ/LkyWzYsIHly5fzxz/+sbnfnqJ4i5EkSZIKku9jSVtaRPDEE08wceJEKioqqKys5Pbbb+dTn/oU48ePZ9CgQey7774fuQ2oqauuuophw4bRqVMnampqWL169XavubXrNVVbW8vw4cPp168fnTt3pm/fvnTs2HG75xw7dixLlixhwIABpJTo1KkTv/3tbxkxYgR/+MMfqKqq4sADD2TQoEF5f29aQmyaNmktampq0pw5c8odY5c28OLmTXlJW7qvw8RyR1Ard+C4F8sdQVIJLFiwgN69e5c1w6pVqxgwYABLly4ta46tWb16NZWVlXzwwQfU19dz0003MWDAgHLH2ta/W2ztWHAGQZIkSa3Em2++yeDBg7nooovKHWWrzj33XObPn8+aNWs466yzdolyUIiSFoSIGAL8b6AtcHNK6aot9o8GJgJv5IZ+llK6uZSZJEmS1DodcMABBT2VaGeZNGlSuSO0iJIVhIhoC1wPHAcsA2ZHxAMppflbHDo5pXRBqXJIkiSp5aSUNnsCkHZthSwnKOVTjOqARSmlxSmltcCdwIklvJ4kSZJKqH379qxataqgHzq186WUWLVqFe3bt2/W+0p5i1FX4PUm28uAw7dy3CkRUQ+8Anw3pfT6Vo6RJElSmXXr1o1ly5axYsWKckdRntq3b0+3bt2a9Z5yL1L+HfAfKaUPI+I84FfARz4qLiLOBc4FOPDAA3duQkmSJAFQUVFBjx49yh1DJVbKW4zeALo32e7Gfy9GBiCltCql9GFu82Zg4NZOlFK6KaVUk1Kq2fTR2ZIkSZJaXikLwmzg4IjoERF7AqcDDzQ9ICI+1WRzOLCghHkkSZIk7UDJbjFKKa2PiAuAR2h8zOkvU0rzImICMCel9ABwYUQMB9YD7wCjS5VHkiRJ0o6VdA1CSmkqMHWLsXFNXl8GXFbKDJIkSZLyV8pbjCRJkiS1MhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZSwIkiRJkjIWBEmSJEkZC4IkSZKkjAVBkiRJUsaCIEmSJCljQZAkSZKUsSBIkiRJylgQJEmSJGUsCJIkSZIyFgRJkiRJGQuCJEmSpIwFQZIkSVLGgiBJkiQpY0GQJEmSlLEgSJIkScpYECRJkiRlLAiSJEmSMhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZSwIkiRJkjIWBEmSJEkZC4IkSZKkjAVBkiRJUsaCIEmSJCljQZAkSZKUsSBIkiRJylgQJEmSJGUsCJIkSZIyFgRJkiRJGQuCJEmSpExJC0JEDImIlyNiUURcup3jTomIFBE1pcwjSZIkaftKVhAioi1wPfAloAoYFRFVWzmuA/Bt4JlSZZEkSZKUn1LOINQBi1JKi1NKa4E7gRO3ctyPgKuBNSXMIkmSJCkPpSwIXYHXm2wvy41lImIA0D2l9PvtnSgizo2IORExZ8WKFS2fVJIkSRJQxkXKEdEG+Ffgf+7o2JTSTSmlmpRSTadOnUofTpIkSfo7VcqC8AbQvcl2t9zYJh2APsATEbEE+BzwgAuVJUmSpPIpZUGYDRwcET0iYk/gdOCBTTtTSu+llPZPKR2UUjoI+C9geEppTgkzSZIkSdqOkhWElNJ64ALgEWABcFdKaV5ETIiI4aW6riRJkqTC7VHKk6eUpgJTtxgbt41jB5cyiyRJkqQd85OUJUmSJGUsCJIkSZIyFgRJkiRJGQuCJEmSpIwFQZIkSVLGgiBJkiQpY0GQJEmSlLEgSJIkScpYECRJkiRlLAiSJEmSMhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZXZYECKiTUR8dWeEkSRJklReOywIKaWNwP/aCVkkSZIklVm+txg9FhEXRUT3iNhv05+SJpMkSZK00+2R53Gn5f7+ZpOxBPRs2TiSJEmSyimvgpBS6lHqIJIkSZLKL98ZBCLiCOCgpu9JKd1egkySJEmSyiSvghARvwY+AzQAG3LDCbAgSJIkSbuRfGcQaoCqlFIqZRhJkiRJ5ZXvU4xeArqUMogkSZKk8st3BmF/YH5EzAI+3DSYUhpeklSSJEmSyiLfgjC+lCEkSZIk7RryfczpkxHRGajNDc1KKb1duliSJEmSyiGvNQgR8VVgFnAq8FXgmYgYWcpgkiRJkna+fG8x+gFQu2nWICI6AY8BU0oVTJIkSdLOl+9TjNpscUvRqma8V5IkSVIrke8MwsMR8QjwH7nt04CppYkkSZIkqVzyXaR8cUScDHw+N3RTSum+0sWSJEmSVA55FYSI2Ae4P6V0b0QcAhwSERUppXWljSdJkiRpZ8p3HcE0oF1EdAUeBv4RuK1UoSRJkiSVR74FIVJKHwAnA79IKZ0KfLZ0sSRJkiSVQ94FISIGAWcAv8+NtS1NJEmSJEnlkm9B+DZwGXBfSmleRPQE/li6WJIkSZLKYYeLlCOiLTA8pTR801hKaTFwYSmDSZIkSdr5djiDkFLawH8/3lSSJEnSbizfD0p7LiIeAO4G3t80mFK6tySpJEmSJJVFvgWhPbAKOKbJWAIsCJIkSdJuJN9PUj67kJNHxBDgf9P4xKObU0pXbbH/fOCbwAZgNXBuSml+IdeSJEmSVLx8P0m5PfB1Gj/7oP2m8ZTSmO28py1wPXAcsAyYHREPbFEAJqWUbsgdPxz4V2BIc78ISZIkSS0j38ec/hroApwAPAl0A/66g/fUAYtSSotTSmuBO4ETmx6QUvpLk819aLxtSZIkSVKZ5FsQ/iGldDnwfkrpV8CXgcN38J6uwOtNtpflxjYTEd+MiP8DXMM2Hp0aEedGxJyImLNixYo8I0uSJElqrnwLwrrc3+9GRB+gI/DJlgiQUro+pfQZ4BLgn7dxzE0ppZqUUk2nTp1a4rKSJEmStiLfgnBTRHwcuBx4AJhP42/8t+cNoHuT7W65sW25EzgpzzySJEmSSiDfpxjdnHv5JNAzz3PPBg6OiB40FoPTga81PSAiDk4pvZrb/DLwKpIkSZLKJq8ZhIjoHBG3RMRDue2qiPj69t6TUloPXAA8AiwA7kopzYuICbknFgFcEBHzIqIB+B5wVqFfiCRJkqTi5ftBabcBtwI/yG2/AkwGbtnem1JKU4GpW4yNa/L62/kGlSRJklR6+a5B2D+ldBewEbLZgQ0lSyVJkiSpLPItCO9HxCfIfU5BRHwOeK9kqSRJkiSVRb63GH2PxqcX9YyImUAnYGTJUkmSJEkqi3wLwnzgPuADGj9B+bc0rkOQJEmStBvJ9xaj24FDgSuB64BewK9LFUqSJElSeeQ7g9AnpVTVZPuPETG/FIEkSZIklU++MwhzcwuTAYiIw4E5pYkkSZIkqVzynUEYCDwVEa/ltg8EXo6IF4GUUupXknSSJEmSdqp8C8KQkqaQJEmStEvIqyCklJaWOogkSZKk8st3DYIkSZKkvwP53mIkSVLejrzuyHJHUCs381szyx1B+rvlDIIkSZKkjAVBkiRJUsaCIEmSJCljQZAkSZKUcZGyJEna5TxZf1S5I6iVO2rak+WO0Go5gyBJkiQpY0GQJEmSlLEgSJIkScpYECRJkiRlLAiSJEmSMhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZSwIkiRJkjIWBEmSJEkZC4IkSZKkjAVBkiRJUsaCIEmSJCljQZAkSZKUsSBIkiRJylgQJEmSJGUsCJIkSZIyFgRJkiRJGQuCJEmSpIwFQZIkSVLGgiBJkiQpU9KCEBFDIuLliFgUEZduZf/3ImJ+RLwQEY9HxKdLmUeSJEnS9pWsIEREW+B64EtAFTAqIqq2OOw5oCal1A+YAlxTqjySJEmSdqyUMwh1wKKU0uKU0lrgTuDEpgeklP6YUvogt/lfQLcS5pEkSZK0A6UsCF2B15tsL8uNbcvXgYe2tiMizo2IORExZ8WKFS0YUZIkSVJTu8Qi5Yg4E6gBJm5tf0rpppRSTUqpplOnTjs3nCRJkvR3ZI8SnvsNoHuT7W65sc1ExLHAD4CjUkofljCPJEmSpB0o5QzCbODgiOgREXsCpwMPND0gIg4DbgSGp5TeLmEWSZIkSXkoWUFIKa0HLgAeARYAd6WU5kXEhIgYnjtsIlAJ3B0RDRHxwDZOJ0mSJGknKOUtRqSUpgJTtxgb1+T1saW8viRJkqTm2SUWKUuSJEnaNVgQJEmSJGUsCJIkSZIyFgRJkiRJGQuCJEmSpIwFQZIkSVLGgiBJkiQpY0GQJEmSlLEgSJIkScpYECRJkiRlLAiSJEmSMhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZSwIkiRJkjIWBEmSJEkZC4IkSZKkjAVBkiRJUsaCIEmSJCljQZAkSZKUsSBIkiRJylgQJEmSJGUsCJIkSZIyFgRJkiRJGQuCJEmSpIwFQZIkSVLGgiBJkiQpY0GQJEmSlLEgSJIkScpYECRJkiRlLAiSJEmSMhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZSwIkiRJkjIlLQgRMSQiXo6IRRFx6Vb210fE3IhYHxEjS5lFkiRJ0o6VrCBERFvgeuBLQBUwKiKqtjjsNWA0MKlUOSRJkiTlb48SnrsOWJRSWgwQEXcCJwLzNx2QUlqS27exhDkkSZIk5amUtxh1BV5vsr0sN9ZsEXFuRMyJiDkrVqxokXCSJEmSPqpVLFJOKd2UUqpJKdV06tSp3HEkSZKk3VYpC8IbQPcm291yY5IkSZJ2UaUsCLOBgyOiR0TsCZwOPFDC60mSJEkqUskKQkppPXAB8AiwALgrpTQvIiZExHCAiKiNiGXAqcCNETGvVHkkSZIk7Vgpn2JESmkqMHWLsXFNXs+m8dYjSZIkSbuAVrFIWZIkSdLOYUGQJEmSlLEgSJIkScpYECRJkiRlLAiSJEmSMhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZSwIkiRJkjIWBEmSJEkZC4IkSZKkjAVBkiRJUsaCIEmSJCljQZAkSZKUsSBIkiRJylgQJEmSJGUsCJIkSZIyFgRJkiRJGQuCJEmSpIwFQZIkSVLGgiBJkiQpY0GQJEmSlLEgSJIkScpYECRJkiRlLAiSJEmSMhYESZIkSRkLgiRJkqSMBUGSJElSxoIgSZIkKWNBkCRJkpSxIEiSJEnKWBAkSZIkZSwIkiRJkjIWBEmSJEkZC4IkSZKkjAVBkiRJUsaCIEmSJCljQZAkSZKUKWlBiIghEfFyRCyKiEu3sr9dREzO7X8mIg4qZR5JkiRJ21eyghARbYHrgS8BVcCoiKja4rCvA39OKf0D8FPg6lLlkSRJkrRjpZxBqAMWpZQWp5TWAncCJ25xzInAr3KvpwBfjIgoYSZJkiRJ21HKgtAVeL3J9rLc2FaPSSmtB94DPlHCTJIkSZK2Y49yB8hHRJwLnJvbXB0RL5czj7S7+3S5A2h3sD+wstwh1HoNLncAtX7elLIjD6eUhmxtRykLwhtA9ybb3XJjWztmWUTsAXQEVm15opTSTcBNJcopSWphETEnpVRT7hySpOYr5S1Gs4GDI6JHROwJnA48sMUxDwBn5V6PBP6QUkolzCRJkiRpO0o2g5BSWh8RFwCPAG2BX6aU5kXEBGBOSukB4Bbg1xGxCHiHxhIhSZIkqUzCX9hLklpaRJybuz1UktTKWBAkSZIkZUr6ScqSJEmSWhcLgiRJkqSMBUGSJElSxoIgSSpYRJwZEf+4lfF/jIivlSOTJKk4LlKWJBUsIp4BvphSWr3F+D7AtJTSwPIkkyQVyhkESVIxKrYsBwAppfeBijLkkSQVyYIgSSrGXrnZgs1ERAdgzzLkkSQVyYIgSSrGLcCUiPj0poGIOAi4M7dPktTK7FHuAJKk1iuldG1ErAamRURlbng1cFVK6RdljCZJKpCLlCVJLSJ3WxEppb/mtmtTSrPLm0qS1FwWBElSi4mIKmBU7s+7KaWaMkeSJDWTtxhJkoqSW3OwqRSsAz4N1KSUlpQxliSpQC5SliQVLCKeBn5P4y+cTsl97sFfLQeS1HpZECRJxXgL6AB0Bjrlxrx3VZJaMdcgSJKKEhEdgZNpvMXoYGBf4ISU0qxy5pIkFcaCIEkqWEScnFK6t8n2J4Gv0lgWDkwpdS9bOElSQSwIkqSCRcTclNKAbez7dEpp6c7OJEkqjmsQJEklYTmQpNbJGQRJUsEi4gNg0dZ2ASml1G8nR5IkFcnPQZAkFeNPwFfKHUKS1HIsCJKkYqz1ViJJ2r24BkGSVIyZWw5ExGci4vKImFeOQJKk4lgQJEkFSyldABARB0TEdyNiNjCPxv9fTi9rOElSQVykLEkqWEScS+NnHnQF7sr9uT+l1KOswSRJBbMgSJIKFhFrgaeB/5lSmpMbW5xS6lneZJKkQrlIWZJUjE8BpwL/X0R0oXEGoaK8kSRJxXAGQZLUIiKiG3Aajbcc7QPcl1L6fnlTSZKay4IgSWpxEdELOD2lNKHcWSRJzWNBkCRJkpTxMaeSJEmSMhYESZIkSRkLgiSpaBHxeD5jkqRdn485lSQVLCLaA3sD+0fEx4HI7foYjR+eJklqZSwIkqRinAd8BzgAmNtk/C/Az8oRSJJUHJ9iJEkqWkR8K6V0XblzSJKKZ0GQJBUtIvYBvgscmFI6NyIOBg5JKT1Y5miSpGZykbIkqSX8ElgLHJHbfgP4cfniSJIKZUGQJLWEz6SUrgHWAaSUPuC/FyxLkloRC4IkqSWsjYi9gAQQEZ8BPixvJElSIXyKkSSpJVwBPAx0j4g7gCOB0WVNJEkqiIuUJUktIiI+AXyOxluL/iultLLMkSRJBbAgSJIKFhEHbm9/Sum1nZVFktQyLAiSpIJFxIs0rjtouiA5AZ2AT6aU2pYlmCSpYK5BkCQVLKXUt+l2RBwEXAIcC1xZjkySpOL4FCNJUtEi4uCIuA14CHgWqPKTlSWpdfIWI0lSwSKiD/AD4LPANcB/pJQ2lDeVJKkYFgRJUsEiYgPwOvB74CPFIKV04U4PJUkqimsQJEnF+Dq5D0eTJO0enEGQJBUlIjoBnwYWpZTeLXMcSVKRXKQsSSpYRIwF5gHXAQsjYniZI0mSiuQMgiSpYBHxEnB0SmlFRPQE7kgpDSp3LklS4ZxBkCQVY21KaQVASmkx0K7MeSRJRXKRsiSpGN0i4t+2te1TjCSp9bEgSJKKcfEW28+WJYUkqcW4BkGSJElSxjUIkqSCRcT+EXFFRFwYEZUR8YuIeCki7o+Ifyh3PklS81kQJEnFmETjwuSDgVnAYmAk8CBwcxlzSZIK5C1GkqSCRcTzKaX+ERHA0pTSgU32NaSUqsuXTpJUCGcQJEnF2ACQGn/btHKLfRt3fhxJUrF8ipEkqRg9I+IBIJq8Jrfdo3yxJEmF8hYjSVLBIuKo7e1PKT25s7JIklqGBUGSJElSxjUIkqSiRcSwiHguIt6JiL9ExF8j4i/lziVJaj5nECRJRYuIRcDJwIvJ/1gkqVVzBkGS1BJeB16yHEhS6+cMgiSpaBFRC/wIeBL4cNN4SulfyxZKklQQH3MqSWoJPwFWA+2BPcucRZJUBAuCJKklHJBS6lPuEJKk4rkGQZLUEqZGxPHlDiFJKp5rECRJRYuIvwL7AGuBdbnhlFL6WPlSSZIKYUGQJEmSlHENgiSpRUTEcKA+t/lESunBcuaRJBXGGQRJUtEi4iqgFrgjNzQKmJNSuqx8qSRJhbAgSJKKFhEvANUppY257bbAcymlfuVNJklqLp9iJElqKfs2ed2xXCEkScVxDYIkqSVcCTwXEX8Egsa1CJeWN5IkqRAWBElSUSKiDbAR+ByN6xAALkkp/d/ypZIkFco1CJKkokXEnJRSTblzSJKKZ0GQJBUt9xSjlcBk4P1N4ymld8oWSpJUEAuCJKloEfGnrQynlFLPnR5GklQUC4IkSZKkjIuUJUktIiKOAA6iyf8tKaXbyxZIklQQC4IkqWgR8WvgM0ADsCE3nAALgiS1Mt5iJEkqWkQsAKqS/6lIUqvnJylLklrCS0CXcoeQJBXPW4wkSQWLiN/ReCtRB2B+RMwCPty0P6U0vFzZJEmFsSBIkopxbbkDSJJalgVBklSMN4DOKaWZTQcj4vPA8vJEkiQVwzUIkqRi/P/AX7Yy/l5unySplbEgSJKK0Tml9OKWg7mxg3Z+HElSsSwIkqRi7LudfXvtrBCSpJZjQZAkFWNORJyz5WBEjAWeLUMeSVKR/KA0SVLBIqIzcB+wlv8uBDXAnsCIlNL/LVc2SVJhLAiSpKJFxNFAn9zmvJTSH8qZR5JUOAuCJEmSpIxrECRJkiRlLAiSJEmSMn6SsiQpbxExHlgNrAQeTSm9Wd5EkqSW5gyCJKkQo4EDyh1CktTyLAiSpO2KiB9ExCsRMQM4JDdcA9wREQ0R8eWI+G2T44+LiPtyr1dHxE8jYl5EPB4RnXLjn4mIhyPi2YiYHhGH7uyvS5K0dRYESdI2RcRA4HSgGhgK1OZ2zQHOSClVA1OBQzf98A+cDfwy93ofYE5K6bPAk8AVufGbgG+llAYCFwE/L+1XIknKl2sQJEnb8wXgvpTSBwAR8cCWB6SUUkT8GjgzIm4FBgH/I7d7IzA59/o3wL0RUQkcAdwdEZtO0650X4IkqTksCJKklnAr8DtgDXB3Smn9No5LNM5ev5ubfZAk7WK8xUiStD3TgJMiYq+I6AB8JTf+V6DDpoNyTzN6E/hnGsvCJm2AkbnXXwNmpJT+AvwpIk4FiEb9S/tlSJLyZUGQJG1TSmkujbcIPQ88BMzO7boNuCG3SHmv3NgdwOsppQVNTvE+UBcRLwHHABNy42cAX4+I54F5wIkl/UIkSXmLlFK5M0iSdgMR8TPguZTSLU3GVqeUKssYS5LUTBYESVLRIuJZGmcLjkspfdhk3IIgSa2MBUGSJElSxjUIkiRJkjIWBEmSJEkZC4IkSZKkjAVBkiRJUsaCIEmSJCnz/wD5LHdVZ3796AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 936x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "plt.figure(figsize=(13,6))\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "print(df[\"pearsonr\"])\n",
+    "ax = sns.barplot(data=df, x=\"dtype\", y=\"pearsonr\", hue=\"method\")\n",
+    "xtix = [\"ChromBPNet ATAC\"]\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "\n",
+    "plt.savefig(\"subfigs/microglia_caqtls_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6020040820648366, 1.3726945418075504e-87)\n",
+      "(0.2671265805109169, 8.546691741238511e-16)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats\n",
+    "import numpy as np\n",
+    "\n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = micros[\"logfc.mean\"]\n",
+    "data[\"Beta\"] = micros[\"Beta\"]\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=15, color=\"green\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"green\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "print(atc_pear)\n",
+    "#ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/smc.caqtls_scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/smc.caqtls_scoring.ipynb
new file mode 100644
index 00000000..fdb6e4f5
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/smc.caqtls_scoring.ipynb
@@ -0,0 +1,406 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "smcs = pd.read_csv(\"smc.caqtls.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>Effect_size</th>\n",
+       "      <th>logfc.mean</th>\n",
+       "      <th>jsd.sign.mean</th>\n",
+       "      <th>IsUsed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.920961</td>\n",
+       "      <td>0.200715</td>\n",
+       "      <td>0.022735</td>\n",
+       "      <td>0.008771</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-4.118572</td>\n",
+       "      <td>0.228169</td>\n",
+       "      <td>0.021728</td>\n",
+       "      <td>0.009595</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108721447</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>-9.266900</td>\n",
+       "      <td>0.764114</td>\n",
+       "      <td>0.289341</td>\n",
+       "      <td>0.031146</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108842308</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>-4.721082</td>\n",
+       "      <td>0.242912</td>\n",
+       "      <td>-0.293381</td>\n",
+       "      <td>-0.041953</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>109272630</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-3.006430</td>\n",
+       "      <td>0.709092</td>\n",
+       "      <td>0.100375</td>\n",
+       "      <td>0.016716</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Chromosome  SNP_position Ref_allele Alt_allele  Log10_BH_Q  Effect_size  \\\n",
+       "0       chr1       1067007          A          G   -4.920961     0.200715   \n",
+       "1       chr1     108121219          C          G   -4.118572     0.228169   \n",
+       "2       chr1     108721447          C          T   -9.266900     0.764114   \n",
+       "3       chr1     108842308          G          C   -4.721082     0.242912   \n",
+       "4       chr1     109272630          A          G   -3.006430     0.709092   \n",
+       "\n",
+       "   logfc.mean  jsd.sign.mean  IsUsed  \n",
+       "0    0.022735       0.008771   False  \n",
+       "1    0.021728       0.009595   False  \n",
+       "2    0.289341       0.031146    True  \n",
+       "3   -0.293381      -0.041953   False  \n",
+       "4    0.100375       0.016716   False  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "smcs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6981379220575643, 1.1026216279404355e-57)\n",
+      "(0.3794467644777755, 1.1503131753550222e-14)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats\n",
+    "import numpy as np\n",
+    "\n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = smcs[smcs[\"IsUsed\"]==True][\"logfc.mean\"]\n",
+    "data[\"Beta\"] = smcs[smcs[\"IsUsed\"]==True][\"Effect_size\"]-0.5\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=5, color=\"#8f00b3\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#8f00b3\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "print(atc_pear)\n",
+    "#ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "plt.savefig(\"subfigs/smc_caqtls_scatter_plot.pdf\", transparent=True, dpi=300)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "\n",
+    "\n",
+    "values = []\n",
+    "\n",
+    "atac_pear = scipy.stats.pearsonr(smcs[smcs[\"IsUsed\"]==True][\"logfc.mean\"],smcs[smcs[\"IsUsed\"]==True][\"Effect_size\"])\n",
+    "values.append(atac_pear[0])\n",
+    "atac_pear = scipy.stats.pearsonr(smcs[smcs[\"IsUsed\"]==True][\"jsd.sign.mean\"],smcs[smcs[\"IsUsed\"]==True][\"Effect_size\"])\n",
+    "values.append(atac_pear[0])\n",
+    "atac_pear = scipy.stats.pearsonr(abs(smcs[smcs[\"IsUsed\"]==True][\"logfc.mean\"]),abs(smcs[smcs[\"IsUsed\"]==True][\"Effect_size\"]-0.5))\n",
+    "values.append(atac_pear[0])\n",
+    "atac_pear = scipy.stats.pearsonr(abs(smcs[smcs[\"IsUsed\"]==True][\"jsd.sign.mean\"]),abs(smcs[smcs[\"IsUsed\"]==True][\"Effect_size\"]-0.5))\n",
+    "values.append(atac_pear[0])\n",
+    "\n",
+    "df[\"pearsonr\"] = values\n",
+    "df[\"method\"] = [\"logfc-signed\", \"jsd-signed\",  \"logfc-unsigned\", \"jsd-unsigned\"]\n",
+    "df[\"dtype\"] = [\"ATAC\"]*4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    0.698138\n",
+      "1    0.684390\n",
+      "2    0.379447\n",
+      "3    0.374505\n",
+      "Name: pearsonr, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib\n",
+    "plt.figure(figsize=(13,6))\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "print(df[\"pearsonr\"])\n",
+    "ax = sns.barplot(data=df, x=\"dtype\", y=\"pearsonr\", hue=\"method\")\n",
+    "xtix = [\"ChromBPNet ATAC\"]\n",
+    "ax.set_xticklabels(labels=xtix, rotation=90)\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "\n",
+    "plt.savefig(\"subfigs/smc_caqtls_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.4073428461878613, 3.588308800985343e-80)\n",
+      "(0.26517398418227, 2.7971689609304756e-33)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats\n",
+    "import numpy as np\n",
+    "\n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = smcs[\"logfc.mean\"]\n",
+    "data[\"Beta\"] = smcs[\"Effect_size\"]-0.5\n",
+    "\n",
+    "plt.figure(figsize=(6,5))\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=5, color=\"#8f00b3\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#8f00b3\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(abs(data[\"logfc.mean\"]),abs(data[\"Beta\"]))\n",
+    "print(atc_pear)\n",
+    "#ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/final_figures_new/spi1.bqtls_scoring.ipynb b/src/evaluation/figure_notebooks/figure_6/final_figures_new/spi1.bqtls_scoring.ipynb
new file mode 100644
index 00000000..6749c22e
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/final_figures_new/spi1.bqtls_scoring.ipynb
@@ -0,0 +1,1866 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataf = pd.read_csv(\"spi1_bqtls.new.tsv\", sep=\"\\t\", header=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = pd.read_csv(\"Afr.CaQTLS.new.tsv\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "afrs = afrs[afrs[\"IsUsed\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>chr_hg38</th>\n",
+       "      <th>pos_hg38</th>\n",
+       "      <th>allele1</th>\n",
+       "      <th>allele2</th>\n",
+       "      <th>snp_id</th>\n",
+       "      <th>label</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>beta</th>\n",
+       "      <th>enformer.encsr000emt.variantscore.sad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.jsd</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.csr</th>\n",
+       "      <th>chrombpnet.encsr095qnb.variantscore.pval</th>\n",
+       "      <th>dbsnp_rsid</th>\n",
+       "      <th>region</th>\n",
+       "      <th>snp</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>se</th>\n",
+       "      <th>abs_beta</th>\n",
+       "      <th>id_hg19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>870176</td>\n",
+       "      <td>T</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.071532</td>\n",
+       "      <td>-0.250142</td>\n",
+       "      <td>-0.000139</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.019025</td>\n",
+       "      <td>0.001262</td>\n",
+       "      <td>0.048612</td>\n",
+       "      <td>rs72631880</td>\n",
+       "      <td>1:869800:870282</td>\n",
+       "      <td>1_870176_T_A</td>\n",
+       "      <td>375</td>\n",
+       "      <td>0.137133</td>\n",
+       "      <td>0.250142</td>\n",
+       "      <td>chr1_805556_T_A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>905373</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.188152</td>\n",
+       "      <td>-0.130176</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.025277</td>\n",
+       "      <td>0.003640</td>\n",
+       "      <td>0.018781</td>\n",
+       "      <td>rs4970382</td>\n",
+       "      <td>1:904938:905544</td>\n",
+       "      <td>1_905373_T_C</td>\n",
+       "      <td>434</td>\n",
+       "      <td>0.098145</td>\n",
+       "      <td>0.130176</td>\n",
+       "      <td>chr1_840753_T_C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  chr_hg38  pos_hg38 allele1 allele2        snp_id  label  IsUsed      pval  \\\n",
+       "2     chr1    870176       T       A  1_870176_T_A  False    True  0.071532   \n",
+       "4     chr1    905373       C       T  1_905373_T_C  False    True  0.188152   \n",
+       "\n",
+       "       beta  enformer.encsr000emt.variantscore.sad  ...  \\\n",
+       "2 -0.250142                              -0.000139  ...   \n",
+       "4 -0.130176                              -0.000071  ...   \n",
+       "\n",
+       "   chrombpnet.encsr095qnb.variantscore.jsd  \\\n",
+       "2                                 0.019025   \n",
+       "4                                 0.025277   \n",
+       "\n",
+       "   chrombpnet.encsr095qnb.variantscore.csr  \\\n",
+       "2                                 0.001262   \n",
+       "4                                 0.003640   \n",
+       "\n",
+       "   chrombpnet.encsr095qnb.variantscore.pval  dbsnp_rsid           region  \\\n",
+       "2                                  0.048612  rs72631880  1:869800:870282   \n",
+       "4                                  0.018781   rs4970382  1:904938:905544   \n",
+       "\n",
+       "            snp  distance        se  abs_beta          id_hg19  \n",
+       "2  1_870176_T_A       375  0.137133  0.250142  chr1_805556_T_A  \n",
+       "4  1_905373_T_C       434  0.098145  0.130176  chr1_840753_T_C  \n",
+       "\n",
+       "[2 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.head of           Chr   position  Depth  ALTdepth  REFDepth ALTallele  \\\n",
+       "0       chr17   18967176    538       418       120         G   \n",
+       "1        chr4  176935913    626       597        29         A   \n",
+       "2        chr1  144534083    618       368       250         T   \n",
+       "3       chr17   19015381    144       143         1         A   \n",
+       "4        chr1   17231724   2477      1321      1156         G   \n",
+       "...       ...        ...    ...       ...       ...       ...   \n",
+       "999794   chr7   72298789    214         4       210         T   \n",
+       "999795  chr18   46347288    109         3       106         G   \n",
+       "999796  chr18   55508743     65         2        63         G   \n",
+       "999797  chr19   16245697    109         3       106         G   \n",
+       "999798   chr5   96271116    154         3       151         C   \n",
+       "\n",
+       "        enformer_recomputed POSTallele  POSTfreq  prechipfreq  ...  \\\n",
+       "0                  0.001704          A  0.223048     0.866699  ...   \n",
+       "1                 -0.990719          C  0.046326     0.543949  ...   \n",
+       "2                  0.143367          C  0.404531     0.057295  ...   \n",
+       "3                 -0.016722          T  0.006944     0.872850  ...   \n",
+       "4                 -0.075683          A  0.466694     0.255195  ...   \n",
+       "...                     ...        ...       ...          ...  ...   \n",
+       "999794                  NaN          C  0.981308     0.979512  ...   \n",
+       "999795                  NaN          A  0.972477     0.972421  ...   \n",
+       "999796                  NaN          A  0.969231     0.975832  ...   \n",
+       "999797                  NaN          A  0.972477     0.976196  ...   \n",
+       "999798                  NaN          T  0.980519     0.981440  ...   \n",
+       "\n",
+       "        ENCSR000EMT.logfc.mean  ENCSR000EMT.jsd_sign.mean  \\\n",
+       "0                     0.360720                   0.052333   \n",
+       "1                     0.868729                   0.130124   \n",
+       "2                    -0.126218                  -0.026379   \n",
+       "3                    -0.024811                  -0.013973   \n",
+       "4                     0.010826                   0.014068   \n",
+       "...                        ...                        ...   \n",
+       "999794                0.050982                   0.021805   \n",
+       "999795               -0.012851                  -0.008375   \n",
+       "999796                0.046383                   0.013923   \n",
+       "999797                0.078009                   0.028060   \n",
+       "999798                0.028907                   0.016722   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                                0.017846   \n",
+       "1                                                0.027084   \n",
+       "2                                                0.003072   \n",
+       "3                                                0.000379   \n",
+       "4                                                0.000253   \n",
+       "...                                                   ...   \n",
+       "999794                                           0.001280   \n",
+       "999795                                           0.000073   \n",
+       "999796                                           0.000133   \n",
+       "999797                                           0.001760   \n",
+       "999798                                           0.000475   \n",
+       "\n",
+       "        ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval         rsid  \\\n",
+       "0                                                0.001838        rs78457033   \n",
+       "1                                                0.000827               NaN   \n",
+       "2                                                0.020925               NaN   \n",
+       "3                                                0.117985               NaN   \n",
+       "4                                                0.156003               NaN   \n",
+       "...                                                   ...               ...   \n",
+       "999794                                           0.055519         rs7794851   \n",
+       "999795                                           0.262099        rs75500680   \n",
+       "999796                                           0.194155         rs3915603   \n",
+       "999797                                           0.032229        rs55663102   \n",
+       "999798                                           0.093372       rs116023548   \n",
+       "\n",
+       "        DNASE_SAD  DNASE_SAR       pos0  IsUsed  InPeaks  \n",
+       "0       -0.000056  -0.000062   18967175    True    False  \n",
+       "1             NaN        NaN  176935912   False    False  \n",
+       "2             NaN        NaN  144534082   False    False  \n",
+       "3             NaN        NaN   19015380   False    False  \n",
+       "4             NaN        NaN   17231723   False    False  \n",
+       "...           ...        ...        ...     ...      ...  \n",
+       "999794   0.000777   0.000954   72298788    True    False  \n",
+       "999795   0.000094   0.000108   46347287    True    False  \n",
+       "999796   0.000035   0.000046   55508742    True    False  \n",
+       "999797  -0.000490  -0.000483   16245696    True    False  \n",
+       "999798   0.000170   0.000217   96271115    True    False  \n",
+       "\n",
+       "[999799 rows x 29 columns]>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(109646, 47)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "afrs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bqtls = dataf[dataf[\"IsUsed\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999799, 29)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(881752, 29)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>position</th>\n",
+       "      <th>Depth</th>\n",
+       "      <th>ALTdepth</th>\n",
+       "      <th>REFDepth</th>\n",
+       "      <th>ALTallele</th>\n",
+       "      <th>enformer_recomputed</th>\n",
+       "      <th>POSTallele</th>\n",
+       "      <th>POSTfreq</th>\n",
+       "      <th>prechipfreq</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ENCSR000EMT.logfc.mean</th>\n",
+       "      <th>ENCSR000EMT.jsd_sign.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean</th>\n",
+       "      <th>ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval</th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>DNASE_SAD</th>\n",
+       "      <th>DNASE_SAR</th>\n",
+       "      <th>pos0</th>\n",
+       "      <th>IsUsed</th>\n",
+       "      <th>InPeaks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>18967176</td>\n",
+       "      <td>538</td>\n",
+       "      <td>418</td>\n",
+       "      <td>120</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.001704</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.223048</td>\n",
+       "      <td>0.866699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.360720</td>\n",
+       "      <td>0.052333</td>\n",
+       "      <td>0.017846</td>\n",
+       "      <td>0.001838</td>\n",
+       "      <td>rs78457033</td>\n",
+       "      <td>-0.000056</td>\n",
+       "      <td>-0.000062</td>\n",
+       "      <td>18967175</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>chr16</td>\n",
+       "      <td>87705494</td>\n",
+       "      <td>115</td>\n",
+       "      <td>105</td>\n",
+       "      <td>10</td>\n",
+       "      <td>G</td>\n",
+       "      <td>-0.279317</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.934802</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.219415</td>\n",
+       "      <td>0.162647</td>\n",
+       "      <td>0.111883</td>\n",
+       "      <td>0.000023</td>\n",
+       "      <td>rs112728011</td>\n",
+       "      <td>0.004025</td>\n",
+       "      <td>0.005314</td>\n",
+       "      <td>87705493</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chr  position  Depth  ALTdepth  REFDepth ALTallele  enformer_recomputed  \\\n",
+       "0  chr17  18967176    538       418       120         G             0.001704   \n",
+       "5  chr16  87705494    115       105        10         G            -0.279317   \n",
+       "\n",
+       "  POSTallele  POSTfreq  prechipfreq  ...  ENCSR000EMT.logfc.mean  \\\n",
+       "0          A  0.223048     0.866699  ...                0.360720   \n",
+       "5          C  0.086957     0.934802  ...                1.219415   \n",
+       "\n",
+       "   ENCSR000EMT.jsd_sign.mean  \\\n",
+       "0                   0.052333   \n",
+       "5                   0.162647   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean  \\\n",
+       "0                                           0.017846   \n",
+       "5                                           0.111883   \n",
+       "\n",
+       "   ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval         rsid  \\\n",
+       "0                                           0.001838        rs78457033   \n",
+       "5                                           0.000023       rs112728011   \n",
+       "\n",
+       "   DNASE_SAD  DNASE_SAR      pos0  IsUsed  InPeaks  \n",
+       "0  -0.000056  -0.000062  18967175    True    False  \n",
+       "5   0.004025   0.005314  87705493    True    False  \n",
+       "\n",
+       "[2 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3818"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((bqtls[\"pvalue\"]<1e-4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Chr', 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+       "       'enformer_recomputed', 'POSTallele', 'POSTfreq', 'prechipfreq',\n",
+       "       'pvalue', 'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid',\n",
+       "       'DNASE_SAD', 'DNASE_SAR', 'pos0', 'IsUsed', 'InPeaks'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bqtls[\"pvalue\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.min(bqtls[\"pvalue\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(bqtls[\"pvalue\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AUPRC (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0001\n",
+      "3818\n",
+      "105101\n",
+      "0.036\n",
+      "1e-05\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:35: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:42: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:43: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:44: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:45: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:46: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1836\n",
+      "103119\n",
+      "0.018\n",
+      "1e-06\n",
+      "1082\n",
+      "102365\n",
+      "0.011\n",
+      "1e-07\n",
+      "756\n",
+      "102039\n",
+      "0.007\n",
+      "1e-08\n",
+      "588\n",
+      "101871\n",
+      "0.006\n",
+      "1e-09\n",
+      "447\n",
+      "101730\n",
+      "0.004\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42 \n",
+    "\n",
+    "import random\n",
+    "import os\n",
+    "import argparse\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "\n",
+    "random_bc = []\n",
+    "chrombpnet = {}\n",
+    "enformer_dnase_roc = []\n",
+    "enformer_dnase_roc_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(4,10,1):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "    print(threshold)\n",
+    "    dataf1 = bqtls[bqtls[\"pvalue\"]<threshold]\n",
+    "    dataf1[\"label\"] = 1\n",
+    "    dataf1 = dataf1[[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\", \n",
+    "                    \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "                     \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "                    \"DNASE_SAR\", \"enformer_recomputed\", \"label\"]]\n",
+    "    \n",
+    "    dataf2 = afrs[afrs[\"label\"]==0]\n",
+    "    dataf2[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr637xsc.variantscore.csr\"]\n",
+    "    dataf2[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr637xsc_50m.variantscore.csr\"]\n",
+    "    dataf2[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"] = dataf2[\"chrombpnet.encsr000emt.variantscore.csr\"]\n",
+    "    dataf2[\"DNASE_SAR\"] = dataf2[\"enformer.encsr000emt.variantscore.sar\"]\n",
+    "    dataf2[\"enformer_recomputed\"] = dataf2[\"enformer_recomputed\"]\n",
+    "\n",
+    "    dataf2 = dataf2[[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\", \n",
+    "                    \"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "                    \"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\",\n",
+    "                    \"DNASE_SAR\",  \"enformer_recomputed\", \"label\"]]\n",
+    "    \n",
+    "    dataf = pd.concat([dataf1, dataf2])\n",
+    "    \n",
+    "    total = dataf.shape[0]\n",
+    "    sigs = sum(dataf[\"label\"])\n",
+    "    print(sigs)\n",
+    "    print(total)\n",
+    "    print(np.round(sigs/total,3))\n",
+    "    \n",
+    "    random_bc.append(np.round(sigs/total,5))\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"]))\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(roc_auc)\n",
+    "    \n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"DNASE_SAR\"]))\n",
+    "    enformer_dnase_roc_sar.append(roc_auc)\n",
+    "\n",
+    "    roc_auc = metrics.average_precision_score(dataf[\"label\"], abs(dataf[\"enformer_recomputed\"]))\n",
+    "    enformer_dnase_roc.append(roc_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "colors = [\"blue\", \"blue\"]\n",
+    "alphas = [0.5, 1.0]\n",
+    "idx=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\"]:\n",
+    "    ax.scatter(np.arange(4,10,1).astype(str), chrombpnet[key], label=key, c=\"blue\", alpha=alphas[idx])\n",
+    "    idx+=1\n",
+    "    \n",
+    "key=\"ChromBPNet DNase (68M)\"\n",
+    "ax.scatter(np.arange(4,10,1).astype(str), chrombpnet[key], label=key)\n",
+    "ax.scatter(np.arange(4,10,1).astype(str), enformer_dnase_roc_sar, label=\"Enformer DNase (reported) (68M)\")\n",
+    "ax.scatter(np.arange(4,10,1).astype(str), enformer_dnase_roc, label=\"Enformer DNase (recomputed) (68M)\")\n",
+    "\n",
+    "ax.scatter(np.arange(4,10,1).astype(str), random_bc, label=\"random\")\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10 pval\")\n",
+    "ax.set_ylabel(\"Average precision\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_auprc_compare.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (572M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ ATAC (50M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "\n",
+    "title=\"ChromBPnet w/ DNASE (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"enformer_recomputed\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ (recomputed) (68M)\"\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "keyd=\"DNASE_SAR\"\n",
+    "label=dataf[\"label\"].astype(int)\n",
+    "score=abs(dataf[keyd])\n",
+    "title=\"Enformer w/ (reported) (68M)\"\n",
+    "\n",
+    "\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(label,score)\n",
+    "roc_auc = metrics.average_precision_score(label, score)\n",
+    "ax.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, label=title+\", AP=\"+str(round(roc_auc,2)))\n",
+    "enformer_auc = roc_auc\n",
+    "\n",
+    "######################################\n",
+    "\n",
+    "roc_auc=sum(dataf[\"label\"]==1)/len(dataf[\"label\"])\n",
+    "ax.plot(fpr_chrombpnet, [sum(dataf[\"label\"]==1)/len(dataf[\"label\"])]*len(fpr_chrombpnet), linewidth=2, label=\"Random baseline, AP=\"+str(round(roc_auc,3)))\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.legend(loc='upper right')\n",
+    "ax.set_xlabel(\"Recall\")\n",
+    "ax.set_ylabel(\"Precision\")\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_auprc.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pearsonrs (benchmarking)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Chr', 'position', 'Depth', 'ALTdepth', 'REFDepth', 'ALTallele',\n",
+       "       'enformer_recomputed', 'POSTallele', 'POSTfreq', 'prechipfreq',\n",
+       "       'pvalue', 'ENCSR637XSC.logfc.mean', 'ENCSR637XSC.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR637XSC.sub.50M.logfc.mean', 'ENCSR637XSC.sub.50M.jsd_sign.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR637XSC.sub.50M.abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'ENCSR000EMT.logfc.mean', 'ENCSR000EMT.jsd_sign.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'ENCSR000EMT.abs_logfc_x_jsd_x_max_percentile.mean.pval', 'rsid',\n",
+       "       'DNASE_SAD', 'DNASE_SAR', 'pos0', 'IsUsed', 'InPeaks'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.5907530863032388, 2.126609788708331e-43)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Pearsonr = 0.59')"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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gBEGMGU3TEAwGEY1GIctyXfZxE42JyNwkk0nYbLYar6a+oMsZgiBKhnOOSCSCK1euIBaLweVyobOzEw6Ho2bi3XO0H9/59mvoOdpfk9cnyovFYoEkSYhGo7VeSt1BEThBECWhKAoCgQCSySSsVitaWlpgMtX+lFLrqmmivDDG4HQ6EQqFoCgKFUEaqP1fG0EQDQXnHKFQCJFIBIwxtLa2wm63V/x1C+1HrnXVNFF+nE4notEo/H4/Ojo6aGtmGBJwgiAKJplMwu/3I5VKweFwwOPxFFxYNNaBIIVG1s0worQZKOcAGEmS0NLSgqGhIfj9fni9XhJxkIATBFEAnHOEw2GEw2FIklRSdflYU9sUWTcW5d7KsNls8Hg8CAaD4JzD6/WO+6p0EnCCIEZEVVW9F9dut6OlpaWkE+dYBZgi68aiEhdcLpcLjDEEg0EMDg7C6/WO6z1xGuRCEERe4vE4/H4/AKClpaUqe90EMRqJRAI+nw+cc3g8HjidzpKP1QBmKTTIhSCIwhGFauFwWDeSqIcK83xompZxMwYmjDEwxiBJEmRZHvdp12bAarWis7MTgUAAgUAA8XgcXq8XsiwX9PxIOIlfHziLfTt6Mef6NvzBf729wiuuDPX7F0kQRE3QNA0+nw+JRAJOpxMej6duIhTOORRFQTKZRCqVgqIoUFW1qFnZjDHIsgyTyQSz2azfCj35j1dq7UqWjSzLaGtrQzQaRSAQQH9//4hZIiWp4tUXL2Dvjl68+Pw5xOMpAEDPsUH85z++FbLceBd2JOAEQegoioKhoSFomla19rDROPreJezb9RHuuf86TJ/l1qNrIcIWiwWyLOvRtYi4hYuVuInoXFVVpFIppFIpxONx/XVkWYbZbIbVaoXFYhnT3mq9iV05qNf+eofDAavVCp/Pp9dqiItOTeM4/OYl7NvRhwN7TyMYSOjPs1hl3L1qBtZv6UID7SRnQAJOEASA9H63z+eDJEno6OioaXGQoiiIxWKIxWLYu+sI9mzvBYeCR//zbbq4liNiFhG9iOqTyaQu6pIkwWKxwGq1wmq1FrWFUC2xq+aFQj13AciyjPb2doRCIYRCIRw70o9Xf/0pnttzGp9ejuiPkySGWz4zBRu3dmPV2llwexp7Tj8JOEEQ+pAMs9mMtra2mqSTNU1DLBZDNBqFoihgjMFqtWLthgVgcGHzAzfA4/GUTbSyjyMKoVRVRSKRQDKZRCKR0AXdZDLpYm61WkfcVqiW2FUzKs7VBVBPmYbzZwPYu6MPe7afxPkzwYz7Ft88Aeu3dmPtxjnomOCo0QrLDwk4QYxzwuEwgsEgbDYbWltby77fPdpJXlVVRCIRRKNRaJoGs9ms72VKkoS2tjbctGya/vixiJZxLfmOI8syHA4HHI70iT6VSiGRSCCRSCAajeoT6KxWK2w2G2w22zWFcdVqeat1VFzrtPqVyxE8u7sPe3f04diRzNn302a6sHrDTHz284swfVZL1ddWDUjACWIcEwgEEIlEYLfbKzbdKt9JXlVVhMNhRKNRcM5ht9vhdDpH9Q0fi2gZ11LocUwmE0wmE5xOJzjnepq95+gnePHgWdy1cjpuWDARDocDNputqgV/te6Nr8UFRDCQwPP7T2Pfjj689drFjP3riZOdWLe5Cxu2dmPaTBuCwSA8nuYtTiQBJ4hxihBvp9OJlpbKRShLb5mEC+eCWHrLJADpVHk4HEYkkt6btNvtcLlcBe0xjzVlaxScUsRPRN5WqxUv//oYnvrnM2CwoGtuh14/4HQ64XQ68dGxwbpJL1eKal1AxGMpvHTwHPbu6MUrh85DSV7tOmjxWrF6w2xs2NqNJbdMhiRdvYBSFAWhUAg2m62u2yBLpfneEUEQoyLE2+VywePxVPS13nnrMg4dOItpMzy4/a5JCIVCUFUVdrsdbre7KsVhRuH/xrfuKPo95MJ4MTBhQicSiQQikYhu9LLrmR48/tiJotc63hE/q/Wb5yDgT2Lvzl4cfPYMImFFf4zdbsLK1TOxfms3PnPXVJgtuaNsj8eDeDyOcDgMr9dbpXdQPUjACWKcEQwGqybeQFrgOFdxz32d8Pv9sFgsaG1tHTVVnu9Yxn8LRQj/hXNBPPq1pWUR1OzoU0TmIuq7c+UEcChYv3n2mF9rvMA5xz/94H3s33UKv3j8GOJxVb/PZJJwx91TsWFLN1aungmHc/QuCVHPEI1GizLeaRRIwAliHCEMSYSTWDWYPtuBhx7pAmMMHo9HLw4rlOy0eamp8wvngrh0IYTd23srGhGLSv6ltzgw5/p2AOnRn6dPBps+pV4qfR8NYe+OXuzb2YeLF0IAoIv30lsmY92WLqzZOButbcXPJbDb7YhEIkgkEnUx16CckIATxDghFoshGAzqhiTFUMres6qq8Pv9SCQSsNlsaGlpKag9Lfu1stPmpaxl3qJOPPq1pfrzinn9UrHZbOjs7MTQ0BCGhoaw65k+PP7YMX09452LF4LYv+sU9u7oRe+JoYz75i5ox/otXVi3uQuTr3OP6XUsFgskSdLNeJoJEnCCGAcIH2+LxVJStXmxe89Gswmv15s36s4lltmvlZ02L3UfvNDovZytUWLAyMDAAO66twPAwrochFIthgZjOLDnFPbu7MN7b1/OuG/qdA82bO3C+i1dmHN9W1lf12QyIZVKlfWY9QAJOEE0OalUCkNDQ/rs6FLanIrZexbTsAoxQcklltmvJYS352g/vvPt1/Rq9koJ4aZt3ZBkhnAwiZ6j/XlFvNBIXfSyq10qblgwEW1t5RWnesdoHPLayx9DVa/2fbV12LFu8xxs2NqNRTdNqFgLnhir22yQgBNEE8M5h8/nAwC0tbWVXMRTSPTKOYff70csFiu4rzzXhUG+1zKKfbkqyXMh0vZPP9UDh9OcN21fTKRuMpngcrkQCoWQTCZLKuBrJPIZhwCAy23BfWtnYd2WLtz6metgMlW+sKwBLENLggScIJoYv98PRVHQ3t5e0T5YVVUzjCRcLldBzzOKofHrXFRzaEghafti1+N0OhGJRPDBuxdw6MCnTVfMpqoa3nnzE+zbObJxyF33TofVVl3pUVW1KS+aSMAJokkJh8OIxWLweDywWitn2iBS9Kqqoq2tDTabreDn9hztx/e/9w4uDVcejyRoxYj9WMnOAhSTKciXWpckCXa7HQefex9PPHZGP0YjwznH8aMD2LejF/t39eHKp1H9vnzGIdWeny4c6GppzlMpSMAJogkxTqAqNBou9XUGBwcBAO3t7UVHObu39+LQgbNYuXpmQZFsrWZvF9O+NtIabTYbVqycBgZHQxeznTvjx76dfdi3ow9nTvkz7hvNOKTaP0NhRlPJi9haQQJOEE2Gpmn6WM9KTp9KJpMYGhoCY6zkFH32aNPRWLI8cyxrrRgpihwptW6xWDCrqxWLb54Ot3ts7VHVpv/TCJ7dnW77+vCDTOOQWV1ebNzajXWbu0Y1Dqn2/PRYLKZ7vTcbJOAE0WQEAgGoqor29vaKTZ4S4i0qrEvdXy92MMu7b18dy7pqbeETzsqdth0pihzpPTHGcP50EC8ePI4tD9xY9yn0gD+BF57NbxyyfksX1m/pxtwF7QUXiVXTgEU4yTXaxVKhkIATRBMRi8X0fe9KFe0oiqKLd3t7e8ne4aWI6lhHqQLlSduOJYp88eAF7PzlR5CYoy4FvFTjkHokHA6DMaZ7vTcbJOAE0SSoqopAIACLxVKxE1YqlcLg4KCeNi9VvIHCRDWVSuk3TdMweZoZD//HeWCMIRAIQJIkSJIEWZZhMpkgy3LOSLDcaduxRJGr1swGwOtqDzyV0vDmqxfHZBxSK/JdCCaTSUSjUbhcrqabgS6oqYAzxv4JwEYAVzjnC2u5FoJoZIz93pXy9VZVdVTxznUy7Tnaj727+hAOJvH5L80fsQ1L0zTE43HE43Ekk0lo2tXojzEGSZL0oRyc84z7xWPMZjPMZjOsVqs+RrPWvtlGuue2Y9pMJyZNKm495d4G4Jzjg3c/xRM/OooXnz87ZuOQWpHrQpBzjkAgAFmWK1rEWWtqHYH/GMD/BfDTGq+DIBqaSCSCZDI56uSzUtE0DYODg+Ccj1iwlutkunt7L9558xMcO9KvD0YR94v/CyvOEx9exsuHzuOeVTMxd+EkWCwWmM1mmEymnBclQsRVVUUqlYKiKFAUBdFoVPcbt1gssNlssNvtY8oYlItSL67KtQ3QeyJtHLJ/11XjEMGyWydj/dZu3L9+VknGIbUg14VgOByGoihobW1t2ugbqLGAc85fZozNrOUaCKLRES1jdru9ImYNnHO9z7u9vX3Eat5cJ1MxmnTewo5r0sbxeByhUAiKokCSJLxy6DL+5Z/PQGIe3HbnXAAjR56MMciyjJPHhzIewzmHoihIJBKIx+MIBoMIBoOwWq1wuVw1bSkqdiqYeP9jGSF76eMQ9u3sy2kcMnN2C9o67Hj4P9yMFffOKPrYxjXmyw5Usvc7O7uSTCYRDocr9vdQT9Q6AicIYgyI1LkkSUU7jBWK3+/Xo/vRCuNypapzfS+ZTCIYDCKZTMJkMsHr9cJut2PzAzIYs2WIVCGRZ/ZjGGOwWCywWCxwu91IpVKIxWKIRqMYHByExWLJW+hX6UEjxQp4qSNkq2kcMtrPqFq936KFUpbliv091BN1L+CMsUcAPAIA06dPr/FqCKK+CAQCSKVSFWsZe/etsziw/wRWr5+HKVPGHs1omoZgMIhoNApZlnXhFoKWS+wLKUAb7TEmkwlutxsulwvRaBThcBiDg4P694zkE5tihT3f44sV8GIK8EYyDmnvtGPtpvIahxSaHahG77fIFGmaho6OjqZOnQvqXsA55z8E8EMAWLZsWfPZyRBEicTjcb3KthIp4VgshgP7T+CZn52CxDxYsry09KogHo8jEAhA0zS4XC58fDaOn/zD+6MKYiEFaNmOZfmOKVqK7HY7/H4/gsEgThy7gl8/d1l/Tj6xKTaKzPd4TdOKEpfR3n8yoeLVF89j744+vPRCDuOQdbOwfksXbrmj/MYhhWYHqlFEGAgE9ExRMw5tyUVBAs4YewDA3wCYAIAN3zjn3FPBtREEkYdUKgW/3w+z2VyRIRWKosDv92Pl/bPB4B5T5CQqgqPRKM6fCeGlF65g8wNzsWdHX9nTqoWKrBhAEwwG8cKzR/CLJ07rz8knNsVGkfkezzkfc3QojEP27ujF8/tOIxhI6vdV0zik2lPV8hEKhRCNRuF2u5t+39tIoT/Z7wDYxDnvKeeLM8b+BcA9ADoYYx8D+DPO+Y/K+RoE0WxkW4SWu2VMVVV9UMuyW+fg1juuB1Da3nAqlYLP54OiKHC5XHjphTN4/LFjYEzOe/Ifyx50sYLi8Xhw7/1zADBs2DpnxMcWG0Xme7ymaSVFiKUYh1SaQjMflSQSiSAUCsHhcDTtxLV8FCrgn5ZbvAGAc/5vyn1Mgmh2jBah5W6LEhcHYh/RePxiU8ixWAx+v1/vG7dardj8wPVgjOkn+nIXPJWSqr1p2QxMnelES0t99gufPS2MQ3px9nQg477RjEMKoRxFe7UymYnFYggEArDZbOOiaC2bQgX8MGPs5wB2ANBNXjnnz1RiUQRB5CYYDFbUIjTfPmLP0X5EIwq+/NUbsWFz16jHCYVCCIVCsFgsaG1t1S8ECrHgLDSKLle1uMVigclkQiKRqNrIzb6PhvDr547nXfuVyxE8u7sPe3f04diRTOOQ2d2t2LC1K20cMnPsolUO8a1FKj0ej8Pv9+u/Y5UYXlTvFCrgHgBRAKsN3+MASMAJokqEw2GEw2E4nc6KTJcKh8N59xF3b+/F00/14KGHF494khcRfDweh8PhQEtLS0En1uxiqLEUiRkpVORNJhNUVc17f6nHzQVjDC88expPPNaXsXZhHPLLJ45f4/Y1aYoL6zanK8hvmF+4cUghlEN8qz3pLpFIwOfzwWw2V2QbqVEoSMA551+p9EIIgsiPiGjtdntFUoWxWAzBYBB2uz3nPmIhJ3lVVXH4jT4ceuEM1qyfh5uXewt67Z6j/ZBkhge/MA+btnUXPBSkkMEmhUaXYjxroYwlajWZTLjnvmmQmAOr18/Gc3tO5TQOsVhlbHnw+oobh9TTmNlCSCQSGBoagslkQltb27hoF8tHoVXoNgC/A2ABAJv4Puf8tyu0LoIgkI5og8EgIpGIHtGWm2Qyqaci8/mHj1asJBzKDr1wFj/7yWlIzIObC2w7E2Ioovv/+d9fxztvfpKe3jbKHnm+1qVCRN54oTDxutwmKPkYS9QqSSZ8ejmGoYEo/v1v7b3GOGTprZNhscr4d4/ejMVLJhZ9/GbGKN6VtMttFApNoT8O4ASANQC+DeCLAMpe1EYQxFXEVKlEIgGXywWPp/xdm0J4ZVkuKBUpxFOSmS5+s7rd+jS4DVsWQ2LFtZ1li2E4mMSxI/2Yt7AjZzS+ZPkkXDgX1MV5pHWK4+7e3ptxn/ieeMyXv9pV1Az5YqNWzjnef+dT7NvRi2f3nIJvMK7fZzJJ+Mw907BhSxfuWT0TDkdte5jHsj1QySl2woNe/K6Od/EGChfwLs755xhjWzjnP2GMPQXglUoujCDGM4lEAn6/H5qmwev1wuEorcJ4JBRFyXAXK+SEaBTZI+9+io8vDOLzvzUbN8yfiNbWVkycKGPRTZOLWofR9AQAPv+l+XA4zdi0rRs/f+I4nn6qB9GIgj//m7sBAO++fRmHDpzFtBkerFo7e8R1btrWjb27+vSIXlM5fvqPRxCNKHB5LHjwC/OwYescpFKpivQPj2Qcsujmdmzc1o0NW+fC22rLc4TqM5btgUpVoxsvNCvRfdGoFCrgIsfjZ4wtBHAZ6aEuBEGUEeOoUZPJhI6OjopMlRLRTLG+3iLyPH7kCnw+P9556yymTLXjjhXzx1RIlK+IzeWxYMHiTrg8V2eWZ0fsuaI+Y4T88yeO6xH95780HwAgyQw//sEHeOjhxei6wavPRy8HIxmHzF3QjvVb0hXkZmsCiqLAYtXyHKk2jGV7oBLV6NkXmiTeVylUwH/IGGsF8KcAdgFwAfhWxVZFEOMMzjmi0ShCoZA+atTtdlekulb0Z5cazaiqiglTJPzGl7ow5To3Nj+waNR1jpZa3bStG9GIAklm6Dnarz9mw+YuaCrPaF3LTl+PFvUZI3rjXr6mcmza1o1kMj3FLPtCqZh08OBA2jhk367cxiEbt3Vj3ZYuzOlu1b+vaU74fD7dLMbj8dRFWngsRW3lLogTQ4VIvHNTaBX6Y8P/fQlA7pwVQRBFI4Q7HA5DVVVYrVZ4PJ6KRN2cc4TDYb0/u5R9RGNqf8ny2fjMXQsKep4Q2Qvngnj0a0tzOpY5nGb8+AcfQFP5NZH0SJO+jFHfaNG44OLHIVw4F8SliyFMmCLBbDbrn4U4RjSi4OmnevRjZB87Ek7i4HNp45DXX7nWOGTd5i5s2NKFhXmMQ8Q4V9EeGI/H4fF4MsxdxjOFetCPZwqtQp8I4K8ATOGcr2OMzQdwO409JYjS0DQN0WgUkUgEqqrqFeCV8qlWVRV+vx+JRKKo/myBUfxLSe1v2tadFswLIX2vO1toR0q/jhRlGwX6O99+Td/jNkbd2ex8+iQOHTgLAJh/440ZNQbitb781Rvx0MOL9fWI758/G4DVasprHLJhazeW3z6lIOMQxhjcbjdsNhsCgQD8fj/C4TA8Hg9stvrZF682Yp5AIR7045lCL2l+DOCfAXxz+OuTAH4OgAScIIpAURREIhHEYjFwzmG1Wisq3MDVcZOc85IK4oRxSjKZhN1uh9frBWOsqBTzvEWdePRrS3NWgI8UKQsK3VsVqXhFUfHTf7waPWez9XPp+e6bPzsHnPMMgRCvsWFzF+Yt6oSqanjrtYu4eCEIi1XGi8+f0x9bLuMQs9mMjo4OxONxBINBDA0N6V7mlfzdqFeCwSASiURBHvTjmUJ/2zo4579gjP0xAHDOU4yxwscWEcQ4RtM0xGIxRKNRKIoCxhgcDgccDkdFI4tUKoVAIIBEIqFH+MWkIUV6PxgMgjGG1tbWjErtYiuOR/L6LuW5+R7ncJrx5qtXsHL1zLzHX7V2NlatnY1kMomBgYGMvdV5izoxd2EHjh8dwHe//VpVjUNsNhusVitisRg+ePc8Xjp4GKtWd2HJLbMafv+30Au+WCyGSCQCl8s1rpzFSqHQv+YIY6wd6fGpYIzdBiAw8lMIYvzCOUcikUAsFkM8HtejvJaWFtjt9ooWK6mqqo9FZYyhpaUFDoejqJS5oij6XHSr1YpPPlbw0x++p598881GL/QkXcnpX8ZofbTXyJ6+NpJxyKKbJmDDtrEZhxSCuMB7+eAgfvnkKQDA1JlOeDyeqs1qrwSFXPCpqopAIKBnH4iRKVTA/wvS1edzGGP/CqATwIMVWxVBNCjJZBKxWAyxWAyapkGSpKpE20D65BeJRBCJRAAAdru96MpmVVV1b2VJkvSo+0d//1rGyTffbPRqu1KNVrQ22gWFJEkY7I9j/46jOLD33DXGIbO6vNi4NV1BXg7jkGIQzm3rt8yGxWLWsyler7cuqtWLpZBtkGAwqG/1UCHf6BRahf4uY+xuADcAYAA+4pwrozyNIMYFqVQK0WgUsVgMqqqCMQabzQa73Q6r1VrxE1H2vrrD4YDL5SoqXa5pGsLhsC7+LpcLLpdLFwpx0l16yyR859uv5RxRWqxjWTkY7YIh3/3COGTf9l689folGAPxiZOdWL+lC+u3dGPugvIahxRDdpZCFBEODQ3h00sq9u44lXFhUskpaOVgtKyLuPh1u91UcV4ghVahywDWA5g5/JzVw8P//7aCayOIukVVVT3SFvvaIu1ns9kqHiFxzhGPxxGJRJBMJvW0q9PpLOrkJ6L2aDQKTdN0M5PsY4iTr6jyBq6dQ16oY1k5GS2qM94fiyl46YVz2LejD6+8mGkc4vaYsWbjHGx64AbcvHxS2YxDyimq4qLM5/Nh9/YPr3Ezq5Und7kIBoOQZbkiTnvNSqF/6bsBxAEcBVBfY4MIokqIYrRYLKYP/7BYLHrvbjWKjBRF0QviNE2DyWSCx+OBw+Eo6qIhO2q32+1wuVyjpvlH6rnOFlPOORRFQSqVgqqq+Oh4P/ZsPwmHy4xV98/GDQs6YbVax7S1MFpU1z2vHXcMxPDTHx3FwWfPIBrJNA65d81MrN08B11zrbDbLWhvb8eJDwfKJrojiWop4m6z2dDa2ooVK6cAkDIuXGrhyV0uFEXRh9lQ6rxwChXwqZzzxRVdCUHUISLSjcViSCQS4Jzrolkt0c4V7dtsNjgcjqJajMoRtefquRbfVzUVnCuIREPo70+flI288GwvTvZcgctjAUcKk6el1y7Lsp72L8fJ22gc8tyeUxgqwDgkFovB5/NhcHAQu575CI8/9qH+vophtIsaI6VGzDabDYtvno5ZXa1oa7ta6NVotqBGRMFlJWb+NzOFCvh+xthqzvmBiq6GIOqAXBXksizD6XTCbrdXZaiEpmkZFw5AOtovpYo9u7Cu1KhdIERqyfIJ4Hwe7l0zCVeuXMGeHe9jz/ZecMTx8O8ug9vthtlshslkgizL2PKAGRfOvI2XDp7HlCmTMXHiRCQSCcTjcb1wTlwYlcJIxiHLbp2M9Vu7cf/6WWhtu/b4YvqZz+fDXas6AMwvKZIVomwcJJPP8nQsEbPL5dI93KtRZ1Fp4vE4rFZrQxbn1ZJCBfwNANsZYxLSxiYMAOecl9/fkCBqhBCT7Apyu91elWES+aJ9t9sNu91e1N52KpXSRTuVSpUctWevT1EU7HrmQ2z/5TFovAsPPbwYjDGYTCasWT8PDE5s2nYD2tvbr3n+vEWd+N3fX44Zs73YtK1bj7wdDgeSySQCgQB8Ph+SySQunktgz46+UdPLFy8E021fO/uuMQ6Zt7AD64aNQyZPGX1f1Waz6fO2Z8z2wOEw678HhSLEWJiliPedi7FEzIwxeDweDA0NIR6PN3S/tNhiobax4in0jPC3AG4HcJRnN04SRAMj9pRrVUHOOdcj5Hg8Dk3TdGEr9sJBiHY8HtfT11arFS6Xq+TCOlVVkUgk9Jumabhz5QRwKLh/Xbc+UpUxhra2Nty8fAaA/Pu72aJlfNzchR0IhUIIh8PYtf0YnvxRZpGWQBiH7N3Zi/cPf5pxXz7jkEKxWCzo7OxEKBRCJBJBPB6Hy+WC0+ks6Hchl1lKpbDZbDCZTIhGow0t4MYME1EchQr4BQAfkngTzYAQ7Xg8rkenwkTEZrNVJR2Z3XomSZJ+4WCxWApeg6IoetSeSqXncotUu81mK3qPXtM0JJNJXbDFMWVZ1qeETbpz0qgmJoXu72Y/Thi53HXvdQA41m+eBQBjNg7JRb6LDBHd2u12BINB3d61paWl4OxFtfaj7Xa77mDXqOlnRVEgSRK1jpVAoZ/YaQAvMsb2A0iIb1IbGdEoJJNJxOPxDNG2WCxjik6LJbuKvdQLh+z3AqQjbafTWbRoiwxAIpFAMpnUq+vF2kTKPd++fz4RLGZ2efbj7HY7lt3ahakzWvD265fxo//3AV4+eAGJxNXpzaUYh2Qz2kVG3wk/dm/vxZqN0zF5KjA4OAiHw1E3tp9AOgoPhUJIJBING4Unk0mKvkukUAE/M3yzDN8Ioq4RkWQ8HkcikdDT4xaLRS9Gq9ZJOJlM6tG2GKlaTBGZ2HsWWQPxXkpJj4tjGQWbcw7GGMxmM9xuNywWy4hZAKNoP/6jI9j1q174h2L4q++t0h9TSASaS/xVVcPhNz7Bvp29OLD3NELBpP54i1XGPffNwLotXbhrZenGIYLRLjKMAv/1P71dH6SSTCbR1tZWUMRY6eEqJpMJjDEoiqILeL0PdDHCOUcqlWrYi49aU+gktr+o9EIIYixomqb3kiYSCSiKAs45JEmC1WqF1WqtWqQNpE9MwpShVAMT4964UbQ9Hk/BFbviBClS4kKwgbQDltPp1AW7kOP1HO3H97/3jm7FySSGBYs7wQocfGIUFyGQnHNoHNi7PV1B3n8l0zhk7gIvWlolfPnhm/GZe8o34S3XyNUlyyfh3bcvY9O2bmza1g1JZggHkzjx4QDmLUr3rQ8NDWFwcLAgj+pyDlfJJcyMMciyDFW9mp1opIEuIuNDdqGlUfIlLGPsEc75D8u5GIIYDU3ToKoqUqmUfhPDQgDokaTT6YTVai1qP7lc6xPzyEXLVjFmIvnGsopboccwCrampWcvmUwmPSVeqGBns3t7Ly5dCOlOX5cuhhAMJHHv6pl5n2MUnp8/cRxPP9WDaETBinum4YN3PsUL+8/g8ceOZjxn8c0TsH5r2jjkR//vPTzz8/fwykt9WLRkAjye8je/CNG7cC6oX5x841t36NPlHE4z5i3qhMWSHvYyODiIQCCQs9reSDmHq+QTZkmS9J9xuV+z0hgHIhHFM5YcVGM3HhI1RQixpmn6jXOu/2v8f/ZjjJhMJl2YzGYzzGZzTfYnjQ5gnHPYbDb9ImI0ck14s1qtBY9lFX3rog1ORGPGwjOLxTLmoTNi1vntd0/VvbLnLerEqrWzR3yeUXiYxDBxshOHnj+Lp5/qyXjc7O5WbNiabvsyGodsfiDt3b1q7WSEw2EAKEjEi0klG2e9T5vhuUYEjWIothref+csXj10DJsfmFeRVrF8a8wWZpFpqsRrVppkMlmzv9lmoGQB55z/QzkXQjQXInUrbiJqFqKdr6GBMabfJEnSU4Tij1yWZf0m9v9qiVG4AegjSQvZH1UUBdFoVBd9sTdeyIQ3VVX1QjbjPrbYF7darWWv6jXOOgfSk9gKMdNYed8M9J4YxDtvfoLjR/szjEMmTXENG4d04Yb5uY1DjIIUCAQQDof1i7bR1ltoKtn4GsYLknxiaLfb8cqhC/jlk6fAmKkqgplvLaqqNmQEK1oUqf+7dAo1M/krAN/hnPuHv24F8Puc8/9awbWNG0TEKYSr0RCFUcZbKpXKEGkhuiISlGUZkiRdc2uU95/t3iVMQAqJcsU400QiAcYY7HY7HA7HqCfhVCqlt4yJPm+TyVS17QJjBJhLHI3fm9nl1Y1DXj50Hinlaoq3xWvF6o1zsHFrd9HGIR6PB6lUCoFAAFardcTPu9Kp5BUrp4HBOuLxK11QlkqloGlaQ+4hx2IxAKACtjFQ6CX6Os75n4gvOOc+xth6ACTgRSAqo4XIiajUKHQi8jSZTDCbzTXZxx0NY6+weC/iPYhoWbQeiRR3Pa1/rESjUQSDwRHdu7IRU9bC4TAURYEsywVVoo9koCIGeVSLXBGgEK+eo2kf7TvvmYazp/xYceNPEI+l9MfZ7SasXD0TG7Z1444VU2G2lJbOZ4zB6/XiypUrCIfDaGnJ79E91lTySOJ75N2P8cqhC1i/eWTntXxZgHIJuxBBsVWTfdx6rUjnnCMSiVQkUzSeKPSTkxljVs55AgAYY3YApc1jHGeIE7Ax1Qlc3bsVxURC4MRer3CLCofDegWzy+WqinlGrvcgCqKMwz2MBWMWiwVms7km66sWiqIgEAjofastLS165DPSiTIejyMYDCKVSsFkMsHr9cJutw+7Xh3J+ZxkMqlPAis2vV4NhDhyzvHe4cv4b998BR8dH7zmce2ddnzpK4vwxd9ZpBuHGBGf29JbJuGdty4XJDSyLMNqteoTvHJRDuHKJ77HPriMHz92GG++egkS82DxzVPyHkNc4CxZPiljy6EcleKiYNLY+5993HqtSI9EIlBVFa2txU/LI65SqIA/CeAgY+yfh7/+CoCfVGZJzUEqlUI4HNZ7f0tJdYripHg8ru+VikESlYxojcM9RJQNQO+jFuleMUKz2eGcIxwO6xdTXq/3mv3XXCdKo+CbTCa0trbCbrcPi8v7iEYUvZBLiKGx9UzMYi+m9UzQc7QfP3/iOFwei15wVk5O9gxi784+7N/Zi0sfhzPuW3bbZCxZPgnHjw7g1RcvwOeLw+Ew5xTVXNXf4vsjie9ov3elCpdxjblS8JqmYfeOo+j/NIxlt80aNT2fy0d9NJeyQhET2Ix7yPmK7+qpIl1VVYRCIdhstobcu68nRhRwEXVzzv+GMfYBgPuG7/pLzvlzlV9e4yF+OY32eKWcgAFktBC5XC59zzWRSKC1tbWs+17G1iNhpGEc7iFS4uNBsI2kUin4/X4kk0nY7Xa0tLTkTHkbT5SapumztCVJ0iNu8dkJcfnyV2/EQw8vxsatXXq2RVVVmM3ma55TLLu39+LIu5+ipTWdKCuHgOvGITv60PvR6MYhPUf7Mbu7Vf9scomqsfp7xuwWhINJvdUs37pFRmikCn/jcbOL7UbCuMZvfOuOjOekUikMDQ3hrpXXgcGCzQ/ML/hzzRbSYtL7uS58xIWe0+nMOA9kH7feKtI55/D5fAAK6yQgRma0CPx1AEsYY49zzn8LwLNVWFPDEo1GEQgEAKTt/i6cieHxncfKsv9kTL36/X4MDAygra1tTM5SRsEWafFy9Ao3C/F4HH6/HwD06Dkf4ue765ke3BWYgBmzPXA6nXC73dd8huIkvmFzF2Z2uRAKhRAIBDJmmAvypYJHSxFv2taNc6f9eOngecyY5UXP0X7s3dWHof4o3B4rzFa5oMh8JOOQaTM86Qryrd05jUOyB6VIMsPmz6aHo/Qc7dfvF495563LePqpHvz2ozdi5eqZWHrLpGveJ+ccfr8fmqbB5crvMJYv8h2NfBFrJBJBMBgEYwzLbuvCHXfNH/VY+T6LYsm+8EkkEvD7/XotxFgZ63ZDMc8PBoNIJpNobW2lve8yMNonaGGMfQHAHYyxB7Lv5Jw/M5YXZ4ytBfC/AMgAHuOc//VYjlcrxEklFovBarXC6/VClmXs3flh2fefrFYrOjo6MDQ0hKGhoaJEXETZuVqPRHq/mf+oijnRvPvWWRzYfwIr75uFZbd1jbrvzDnH7p0f4viHF8HkOJbeck/e9OC8RZ3onteq22eePxPCSy9cxtJbpuHdt0/mTTE/+rWlOSu+cwn7kuWTMKu7FZ0Tnfj8l+Zj9/ZevPPmJzh2pB8LFqcfr6k85+dQqnHISPvZYr2fuWsqdv2qN+drC9EM+pM4dOAsps3w4B1c1t/nDQvaEQgEEI/HM+oPRmKkFHKu3wejm9h3vv0ablrajncPn8eKlVMwd8Ek/W+7mhjfQzweh8/ng8lkQltbW1kyYmPdJy/0+SKD6HK5qPK8TIx2tv4qgC8C8ALYlHUfB1CygDPGZAB/D+B+AB8DeJsxtotzfrzUY9YCkRKKx+PweDwZUUG59p+yTzSyLOvToHw+Hzo6OnIKr3EPXcwDB6rbelRPFHqiCQQCOLD/BJ752SkwuHHrZ24Y8biapsHv9yMajiAeYzjXp+DUR4Gcr9FztB+7njmGFfdOwOzuNrS0tOClF87g8cd6cP5MBC8dPA//UAzeNru+D3vhXBCXLoSwe3vvNaln4++WGHN66UJI31N+6OGrVdKSzDCn25sRgQuSCRWvHDqPfTv78OLzZ68xDrl//Wxs2NqF5bdPgSxfm5XpOdqPx//pKC5dCCHgj6Pn6EDGRceS5ZNw6qMhtHXasfnB6xGNKDj47OkMoRfr3LurD1/48kL4h2Jwe6z48ldvxJr1MzAwMIBUKgWPJ53dyH79QuxLjWT/PhiPseuZE3jm5x/g0qXJePv1S2AwX+PAVmzkWmqkO29RJ+Yu7EA4HMbQ0BAsFgva2trKlh0b63mqkOeHw2EEg0G9a4MoDyMKOOf8VQCvMsYOc85/VObXvgVAH+f8NAAwxn4GYAuAhhLwUCiEeDwOr9eLc6ciGVXF5dp/yiU8kiShtbUVAwMDGBoaQmdnp25qYCxAE1OahPPWeI6yCznR+P1+RKNR3L/2BkjMM+pJTdM0DA4OIpVKYcuDizHYfxwXzwfx/e+9kxExA+kLqp3PHMGe7cfBMRceTxt+8HeH4XSZce+amZjV1YKB/hiYxPSf96Zt3WjvsGPG7JYMwTVGtuLrnz9xHIcOnMW9a2Ziy4PXY9oMT0b18x988/aMtauqhrdeu4gn/+koXjl0AcnkVdG2WGXcvWoG1m/pwl335jcOEZ+3JDOcOunDsSP9mDLVheume/Qoet6iTrz79mX868sfY+XqmZg8xYUP3v0UiqJi5y9P6scSx/nxDz7AytUzcejAWSy4sR2TppgRDHkQPinj5YNXhiefuTJeP7sgMNds89F+H3Zv78VTP34fGo9gxcop4OjGjTdPxeTJk/RpcEZGuiAcqWAv1+NHwliH4XA40NLSol90l6PavtjzVPZrjvb8YDCIcDgMu90Or9c7bgKGalDomdyXI4UeAHCUc36lxNe+DmmfccHHAG7NfhBj7BEAjwDA9OnTS3ypyiAqzZ1OJxwOB3Zvf78iLRu5hEcMT5FlWXeqEq1nQLrVxuFw6JWetfijqUUP6kgnyUJONNFoFG63G1NunYKlt84a8bWEePee6MeBPZ/A5XFg6+eux45fnswQL/HYt9/oA5NTuG/NDdjywE36ZDOR0p402YU517ciHkthy+eux9JbJmHvrj4M9EcxNBgb9b26PBYsWNyJmbNb9Mg2+zGccxw/OoB9O9LGIVc+vWocwhhw251TsWFrF1atnQW3Z/StGXH8B78wD8vvmIJ5Czvw+S+l94ezR5JGIwpcHguUhIpjR/oxY1aLvtdtPM6DX5gHd4uMzZ+bBklSceaUH//68hX4BzVcuhCGbOrTP1dR8PbFryzEQw8v1i9YJJllVLdHIwocTnPOdLkYynPXqnZofDbuXjUVNqsDDC7MnDUB6zbn9j4f6YJwpIK9QiNd47AgxljOOoxatIkV+poiMxWPx+F0OivePTMS9doPP1YKFfDfAXA7gEPDX98D4B0Asxhj3+acP16BtQEAhg1TfggAy5Ytyz1/s0aIPlSRzqtUy4Y40QiBFhG2pmmQZRlut1sfjCD6xWsVZedymxLvoRqU+jOIRqP6xVghKT7OOYaGhtDb04+f/fQMfv3cBSxY3AlN5Xj0a0szxItzjsHBQRx6/jTeed2HSde16muMRhRYrTKsNhmnTvoQCSs4/OYnWLC4Ey1eG6IRBf2fRnHsSHoEqTGqz36vGzZ3QVM5goEknn6qBxfOBXH3qunY/OD1+PhcAH/xhy/h7Tcu4ezpQMZ7mTGrBRMmOfHI796M2++aVtTnZuxzfvfty/j8l+ZnCCRw9XcCAH78gw/w4Bfm4aGHF+vR9ozZLZBkhge/MBfbfmM29u85ge2/OIGN27px/9p5eOHZS4iGVJw6OYRjR/oxc45XP6bHm75oMVtl/ME3b9eL1sRriNnm4rXEusT2UiwWQ8/RT/DyofNYef9s/Kev3wW73Y7v/uXrePyxoxmdA8Wk53P9HubKmuSCc473D5/Hc/tOYMXKqZi/aAo8Hk/OvfdatIkV8pqKosDn80FV1Wu2FmtBvfbDj5VCz/JmAPM4558CAGNsIoCfIh0xvwygFAG/CMB4tpg6/L2GpZwtG2KYi0iJG12lxCAL0Y/NGNMnMlkslgzxrvaVp/EPpRYnl1J+BqJfW1h1FoKopn3lUD8uno/i3jUzMX1WS4bJh+DwG6dw8EAfbl46ExfPQ4/OxeditsoIBpJ46eB5bPnc9Zgy1YW2Tgc2bO7CpYshJBMptHfY9H1wAHqPd/bErSXLJ+H9dz/F3aum49xpPx4/G8DFC6GMqWgAcN00NyxWGeDAinun4xvfuqOoz0yQXe2dK9IVvxNf/MpCrFw9EytWTsOqtbP16W1n+gYw2B/CTcvb0TlZxt2rpoHBhI1b52PBjROx9NbZehX9vIUdWLFymr7Xv+jmCVh662R9e8H4OydeX7yWpnKs2zQTgUAAsVgMmqZBkiS8fOgynvrnM5CYB7fecf01xynl5J9PrHMdS/zsNmyZg+mzHYhEInhuXw92/KIPDA60tU7AP25/M+ffcC3axEZ6TRFIhEIhSJI0pk6ZclKP/fDloFABnyrEe5grAKZxzocYY0qJr/02gG7G2Cykhfs3AXyhxGPVBPGL6ff7C66KzYWqqhkzxI32mEC66EykwrMFWmC32/U2NuHKBVT2yjPXxUH2CbQRrnYDgYA+oKWQFJ+YkuZ0OrH5gflg7KpgvbD/NL7/vXew9XPXY9Xa2YhGozh4oA+/fOI0JObBo19bivYOOySZ6enfBYs7sfyOKXjo4cUZn2XP0X7s+OVJXLoQwpRpbkyZ5saS5ZPw/e+9g1RKw7kzfgCZE7dO9/nQ2zOIREKF35c5qczpMuO+tbPwW/9uMXY+/REef+yobgs6VsQxsiPd7PsOHTiLqdPdWHHvVEyZbkEqFcQH753FstumYNWaLrS3t2Py5Mm4/c65Gcc3/i5959uv4dCBs1i5emZGxJ/9OIGiKLhuhhVf/mqXbtcqLoCtViu2PCBDYrZrouV8e+aFUmgafdczPfjVz45C40GsWDkNrxz6BEuWzdJrMBolejTu1dtsNni93rppQ22Uc1GxFCrgLzLG9gD45fDXDw5/zwnAX8oLc85TjLHfBfAc0m1k/8Q5P1bKsWqFaOXw+/3o7+/Xp5OZTCbdnAO4alZitMU0unMZZ6GLGehi+IvJZCq4bcXr9WJgYAA+n08vaqvklWeuE0uj/aGIGePFtAcFAgF9lnnLIpbxfnc+fVKfKLZy9UwEg0Hce/8c/WQ8b1EnHE4zXnvxAuYt6sDmz3br0Xb257Z7ey8OHTiLu1dN14vYxPfuuGsq3n79E8yY5UUspqC93Y5pMzx4/eXMti8AaG2zYeYcL957+zLMFhm7fnUSy26drP9+lOPnZWy/0lR+jRjOXdiBI+9eRCoVxV33tmFgYACMMaxaPQcMNmzcNhcLFk8s6LVyRdnZJJNJ3a0tlUrpUwRdLtc1Fq2j/c4a7y/FojTXhUEymUQwGEQsFsOdK9vB0YX713bjhWcv4onH+iAxxzVZkXqNHjnn+uCifHv1RGVg+WwdMx6UDkseAHDn8Lf+FcCveCFPLiPLli3jhw8fruZLFoSYSSwGoohUdz6MtphCoI3CPxYSiQQGBwfhcDjg9XrHdKzRaIbCkP7+fnDO9Que0RCfb65xqgBw8NnT2PHLk9j6uetx24qJCAQC6OjoyOgJF+1eos3rG9+6I+OzBNIpco/XgqA/mRFlisfdtGQC9u7sg5LUcPjNTxCNXE2EWW0yJkx0Yu6CdlitMjomOjFzVgteOnges7pa8E/f/0B/3UoiLE+zOyKsVqvuU57r972U36tcnuhCtO12e0G+6oUgtgqK/fyEAZCxpVPMYBDrY4w13N9ULBZDMBiEqqr6mOd6ibqbiLwnpoIicM45Z4y9CiCJdP/3W9UW73pGkiS43W69+CmX57VwGau0ZabwgxaeyZUsHmm0aDsbsW1hbMsZjVgsBkmS8kYYq9bO1v2kBwcHYTabrxnoMm9R5zWFbsZsBgA9tb701smGSnaOWDyFZELFX37zVfiG4vrjmZSuYl+9fhaSCscDv3FDxs9GpJ3bO+bpqfpKIAos4/F4yZanxVQ5iyjbOP5XjB/Od4EwFgrNaAnBNt4A6Bcw4iIme32N8jeVSCQQDAahKArMZjNaW1tprnkNKNQP/DcAfBfAi0hfDfwfxtjXOedPV3BtDYuIrmuFx+OBqqr66MfsoRdEmng8LYDFpPvEDO5CBF9RlLzHzj5RG6u533/304zU+kjGIfMXdcDTYoVvKI6Pjg/i/LkQDh04CyWpZhSTbdqWHmEaDibLOjJTGN8IITVGvqVanmaLpPF159zQokfaQhRFy6QQxkpeIOcSWE3T9NqVZDKJj459ihcPnsWKldMwu7utqfwEkskkQqEQEokEZFnOm4kiqkOhf1nfBLBc9HwzxjoBvACABLxO8Xq94JwjEAiAc17zNo56REQPhUZpnHPdbKSQx4oq50LIruZ+4DfnoqXFij/5z4euMQ5pa7fBbJbx6eUIlt02BZu2dWPvrj4sumkCFt6UFherVc6IYkWh29NP9cDhNJck4MbIeM4NLbpPuaZpejrY7XYXna4eaTCIpmnY9cxx/OpnH0LjITz08CIA6W4L8VrlNPUZCfHzzy42NRacyrKMFw9+jF8+eQoMDtyxYn5DC7YgkUggHA4jkUhAkiS0tLTA4XA0xXtrZAoVcClrYMsgANroqGNEMYnf79e9qItJFY8HVFUtKlMiahsK+QwZY2CMoZidpsGBGCLhJDxeK5752YmM+6bN8GDD1m6s39qFRCyFvbv6MqJpUUAm2qtmzG7RU+XGGeXAtd7UhbJ+8yxwHscKQxGaSFeLPdxSMF4YzF3YcY2V7Z0rO8DRhVVrutDa2lqR1LgRIdTZIp1KpXIWnNrtdr3zQ5ZlbHlAgsQc2LStu6H/3jjniMfj+uwJUbjpdDob+n01E4UK+LOMsecA/Mvw158HsK8ySyLKhRBxk8mEUCikV1vTXlUaTdOKit6KPWmZTCZ9Hzgf4VASB587g307+vDGq5kV5Ha7CfeumYkv/c5iLLyxc9QiJ1GhvnL1zIyq9j//w5fw9FM9iEYU/Pnf3F2UQ5fwKI9Go2ibwPFbD8/VW7DGItpG1m2eBc4TuOf+Cbh8+XKGla3H48Ett3deM4e8XOSLqI1CLYpMxRhiUXCa773Xch+7HEVwmqYhGo0iEolAVVWYTCaKuOuUQovYvs4Y+yyAzwx/64ec8+2VWxZRTtxuNywWi25DKgwFmnkmeiEwxkbtGDAiChCNKdORsFqt+knQGOlfNQ7pxYvPn8swDjGZJcyY6YG7xYr3D3+K9k4HFt00Qb8/nzsZkL+9ymqVsWBxJ6xW+ZrH5SOVSiESiegpcpPJBI/HA7vdPub6DuHnLW7tEzh+6+EbYDKZ9H3sclvZcs51kTYKtvHnL7pCnE4nTCaTLtaNJFpj6RlXFAXRaBTRaBScc93ettJ1BUTpFHwG55z/CsCvKrgWooJYrVZ0dnbqs5Xj8ThsNhucTue4jchNJpPu0FYoZrNZL54aDYfDoX/eTqcLh9/4BHt39OKF/acRDFw9hsUq4577ZiCRUPHSC+cwfZYXly6Ecg5ZyedOBuSP/MzDwi3+zfc40YolWiJFilwUiJWKmNsvqsVFVsJYkW21Wsta+JlKpZBMJnXBVhRFj6pFdC/2z8vVwlkPlDJvPR6PIxqNIplMgjEGu90Op9NZtdoConRGFHDGWAjptrFr7kK6u2zsbvJE1ZAkSd/DCofDehGSyWTSe1HH0x+tyWTSZ8oXevK22Wx63+togiPLMs6fiWP/zmN4+eAn6L9y1ThEkhhuu/M6rN/ShfvWzYbLbUHP0bTBRy4/bSO53MlGQsxIz368PsJz6xzMmO1ENBpFKpXS5+s7nc6SRU0Ig4iyRaGbSIuLdHQ5Ijtjy5YQaxFZS5IEs9msC5LYp27WiLLQ9L2YAx+LxcA519Pkdru9KS5kxguj2YmScWsTIssyWlpa4Ha7EY/HEYvFEAqF9PnFIoUpxrY268nOarXqlbWFtpLZ7XYEg0FEIpG8c9PPnPJj385e7NvRh3NnMo1DFt88ARu2dWPNxjno6MxsvzGefEUvefaepqgkN/p8j0a+k/quZ07g6X85Ao0H8dDDi2GxWNDa2lrU3rZxfdfPb0MsFkM8Hs9o8RI92eUqPlNVNaO/WkT0jDH9YlT034/3bSIjot0vFotBVVV9noHwVCAaD/rtHsdIkgSHwwGHw5ERMSWTSd0cRURNxluziLrFYoEsy4jFYgULuCzL+tx5l8ulC9Knn4Tx7O5T2LujD8eHTToEs+a04O77J2PNxllYuHh6UZ9d9p5mvhSpsdJ8pOjdmCa/c2UbOObgvjXd6OzsLCn7suuZj/CLJ45A4xE81J4uNBN9z+XK6AjBFtG82PYQ0bWo8aiVbW49I6yGjSNlhWlPuYoQidpBAk4AyBRzABmDKURxS64WGuOt0VJvjDE4HA6EQiGkUqmCozWXy4VYLIZLF4fwxqv92LejF2+/fgnGjrFJU1xYv6UL67d04Yb57YjH4/D5fBgcHERraytOHh8qqFo4W7DzRdPG4jYxi118f9O2btywoF0vUBJp8sU3T8ftd84r+ucm2oui0SjuXNkKjtlYed/0kge35Dq+schNFA1KkqTPM2/27FCpjDRYJ9cceKKxIQEnciKqcI2RqbHVRoi7iNQB6EI+kmsaUF8z1EWhWTgcLmh2fCym4MXnz2HnL3vwxquXkEpdVW1vqw1rNs7G+i3duHn5JEjSVXERn6PoBNj1zEd4/LHj+v1j/TyWLJ+U9v++b7o+onXXMyfxLz/5ABqP4t92LADnHB+fjeDQ85ewads8TFxc3A6ZcPKKRqO6F/3im6fj1jtuGLNop1KpjAyQaCUzuoaRYOfGOHNe2A6PZbAO0TiQgBMFI0TdiCggEsVDIjIDrvqWWyyWjCrjerJHFGM4RUo8lxApioo3Xr2IvTt6cfDZM4hFr7aR2ewyVq2ZhQ3bunH7iqkwm/MXttntdphMJvh8vmEHqm7cvKxTH74iyN7zLuSzevfty7rP+Nf+aBlisRjuWtUGjtm4+97r4HQ64XA48MRjb+Hxx3rAmBnzF0/IezwjIuUej8czKtPHmrLOdgwDoBecFTo3fTwi/uayMxSi3kDUHNBn1/yQgBNjQpIk/aQhSKVSejRgFHTR57tm43Rwzgtudak0LpcL0WgUoVAIra2tANLGIe+/cxn7dvThuT2nMoxDTCYJd94zDfetn45FS9yYOLGt4FG1ZrMZnZ2dsNlsmN3dhp/88AO8+dop3PaZGdi4tSvvnvdI09NUVcWaDdOg8RhW3NuqT0m7Yf5E3LxsVsbJvNA2I5EmD4fDUBRFr0x3OBxjavcSe7KikEqkd51OJ2w2W009BOqVkYr2LBaLPlSHCvbGHwXZidYL9WonSoyMoigZBhQiPWqxWHTxr/WJW1Th+wYlHNh77hrjEMaApbdOxoat3bh//Wx4W9MXLENDQ0gkEujo6Ci6YEtVVbx/+Byef7YXK1ZOBWPp13a4bFizrgvzF0/UR7L+z//2Op78p6P44m8vwH/6xnJ9YpjY0gCu9lXbbDac7g1i745TRaflOeeIRqMIh8P63Hen0wm73V5URGfcJsnefxfpXfGzp/TuVUTPvBDqZDKpF+2JvxmR0Wq0ITNEyYzNTpQgxoLYG3e5XHqRjbCcDAQCCAQCGZFEtU/oH58PYv/OPuz81QmcPRXMuG/+og6s29yFdVu6MGnytVG21+tFf38/fD4fOjo6ilq7LMtYeutsLLllFuLxOP7Xd17DgX192PzZLnROltDff7WaPV0sNgt3rmyHz+cDcHVLQ6SzjSf0vTveK2qbIlu4xRQuY2alGHZv78WT//w+NB7Dv+2YnzHZi3qN02iads1kOOMYV1mW9Z9r9s+XIAAScKLKiOhLtLKI4qVoNAq/369XhotxlpVicCCGA3tOYe/OXrx/+NOM+6ZOd2PjtuuxfmsXZne1jngcSZLQ2tqKwcFB+P1+tLa2Fn2SFdOvtjywGBKzY8PWOejsbEMqlcJHxwbw3N5TWLNhNn7vG3frnvLGgq50tHtEtyINB5NYsXIagMJS5dnC7fV6S568JlLvd9/XAY3Pxl33TqnKz7OeEcYoxrnr4muBmLdunA5X66wUUf+Mz78oom4wmUxwuVxwuVxQFAWRSEQ3UnA4HHC73WU7kUXCSRx87iz27ejF669kGoe0d9qxbtMc3HFPB+YuaMOECYUVeAHQI0u/349AIFBQNXsuslvEzGYzDuy7gMcfOwHGLLhp2fSczxP75n5fHJ9cDCHoT+pe4Lu39+rHNqa15y7s0Af4lEO4AejHS6VSmNXlxcIbV4wLAwxN06CqasbNKNLGbUoxbMZojNKILZhEfUACTtQNZrMZXq8XHo9HnyEei8X0sZ6lCMFV45A+vPj82QzjELfHgvvWzcaGrV1YfvsUyLKEWCwGn89X1HAXIN2OlkqlEA6HIUkSLp5LlKVVzlh0lq/9TrSQ2e0y3n79E9y9arou3sY0evrrD8C5gvaJ85BKpfTPfCzCnUgkEAwGdX/1Yqe51RPCx138O9JNVVX9sUaESJtMJr2+Q3xNUTVRTkjAibrDOLM9GAwiGAwiHo/D6/UWlIZVVU03Dnl+32mEgtcah6zb0oW7Vk6H1ZZ5PFHNGw6HixJwAPB4PNA0DeFwGD9/8iiefvKMbuFZKsaoPJ8NqGghe/AL83QP8HmLOnHpYggXzgWx9JZJ4Jxj9YZp0HgYd67sAGMMbW1tJe9xA+nUcDAYRCwWgyzL8Hq9RRe7VRJjZJxLgLPFmnM+on87Y0zfwhAFg5IkQZZl/V9xI4hqQAJO1C2yLOvRXCAQwMDAANrb23NWe3POcexIP/bt6MP+XX2jGofkgzEGl8sFv9+vO7YVQ0tLS3pP2wXctKwFTnf+yvRiB9rkawHLZyP6zluXcejAGUydbsOiJS5MmCzjdx69GS6XC3a7ffj13y0pSxCLxRAIBMA5h9vthsvlqplwZxeDiVsuq1ijCDPGdGMT8bXxX+PjxP8Jop4gASfqHmFOMTg4iMHBQbS1tenmC6Uah4z2eqFQCJFIpGgBZ4yhpaUF96+ZC672YMXKdj21nE2xA23yjVHN9X1N03Df2inQeAh3ruzMGXGXMlBH0zQEAgHEYjF937zaxWli1KowTTH6s4t0tfAtFzahIkqul+wAQZQDEnCiITCZTOjo6MDg4CBOnriIt1/3Y//O09cYh8y5vnV4Bnk3ps0oze2WMaan7/OJ72gsWT4DCxZP0kenOp3ODPMToPChKoVG6sePXMHu7Sdw75opmDrDiUlTLXj4d5fB5XLl3OMu1jtaURT4fD6oqqpvcVRLEEV1eywWQyKRAOdcn43ucDioGIwYl5CAEw1BwBfH8/tPY8/2Xrzz5icZxiGTr0sbh6zb0oUb5rWXRVSEyUkkEim5qtxqtaKzsxPBYBDhcBjRaFQ3jDGZTAV7N48UKRvNK3Ztfw97tp8ERwz/39du04UtH4W+PgDdjEWSJLS3t1fNflLTNEQiEUQiEX3+upgXQKNWifEOCThRtwjjkH07+vDKi+eRUq7uaXpaLFi9YRY2PTD3GuOQciDc2aLRKDwezzWRXaFRsSRJ8Hq9cDqdumlKOBzWp2mJAR0jRY7GSNm43ytGawrzilWr54DBhk3b5qGlpaU8HwSASCSiD9tpbW2tSpEW51z/rDjnsNls+ox0giDSkIATdYXROOTXz51FNKLo99kdJty7eibWb+1G91wrzBYZnZ2VM0NxOp169Od2Zzp3Fbt/LNqrPB6P7s8cCl01MBHVy9nFUpxzTLxOxl2r2rHzmXexwjcVs4aHy4i9XjEYZ/JkhuW3d5XjreuIEbM2m62kITWlkEgk4Pf7oaoq7HY73G73uB0CQxAjQX8VRM3RNI73D1/Gvp05jEPMEu68exrWb+3GPffPgMORTgmLqLAYH+9iEX28kUjkmirrYvePBbIs64NrhKuUceiHmNYlELPQX3zhAn71L31gsGLZrV1j2u8tNHsgxNvhcOjV9ZVERN2hUEi/4KlWqp4gGhEScKImcM5x8sQQ9u3oxb6dffjkYqZxyLLbpmDD1i7cv242WlqvrQQXqdREIlHR6MzlcmFgYADhcDgjCi9m/zgfwsmtELY8cCMk5sCmbd0lpZGNol1I9kAIqcPhKLkGoBg45/D5fIjH41W7YCCIRocEnKgqH58PYt/OPuzb0Yu+k76M++Yv6sD6Ld1Yu3lOTuMQI6I9yNhCVAmEY5oY7Vro/m+hUa543JLlk/Du25fzPn6sFwxG0R4texCNRhEMBmG326su3i0tLXA6nRV/TYJoBkjAiYozOBDDc3tOYe+OXnzwTqZxyPSZHqzf0l2QcUg2sixnGEJUCo/Hg/7+fgQCAbS1tRX0nN3be/HGKx/jwrkgHv3a0rziK4T1wrkgDh04C6DwnmxBIRcL2cNe8j1OOMRZrdaqiDcAfWgOiTdBFAcJOFERwqEkDj53Bvt29OGNVzONQzomOLBu0xys39qNhTd2lpwqlSQp57StcmMymeB2uxEMBhGJRAoSmU3bunVRnjbDM6qwLr1lEqbN8BS9pw4UVlBXSASvKAr8fj9MJhPa2tqqksLOnndPEEThkIATZeOqcUgvXnz+3DXGIfevn40NW7ux7LbJkOX6HriRHdW6XC4kk0kEg0HIsjzq3vW8RZ149GtLRxVlo7CuWju7pLWWWlBnRNM0DA0N6RPbqiHeqVQKwWAQNpvtmip/giBGhwScGBOFGIes39KNFSunXWMcUs/kimq9Xi+Ghobg8/l0446RKEehWyGI1/j5E8fh8liwYXNXUa/LOcfQ0BA0TUNHR0fVzDiE/3s5e9YJYjxRkzMqY+xzAP4cwDwAt3DOD9diHURplMs4pBzrqFSkmCuqlSQJbW1tuogrigK3210X1dK7t/fi6ad6sGBxJzSVFyXgwWAQyWQSra2tJY2NLYVYLIZkMgmv10vuXQRRIrUKiT4E8ACAf6jR6xN5GKkg6swpP/bu6MX+ndcah9y4ZCI2bOvG6g2zizIOGQtiHnYlyBc9i1GigUAA4XAYiUQCXq83p/AV6zY2FjZt60Y0ougReKGIQTXCoawacM71Xm+Hozq/KwTRjNREwDnnPQDqInIhMslOHV/+JIxnd53Cvp29OH50IOOxc65vxYat3Vi3uatk45CxoGlaTSZ0Mcbg9Xp1m9P+/n44HA58fC6OfTtP64JdittXqcxb1HmN7/gL+09j59MnsfVz12PKde6MdrUlyyfhnbc+xoqVHZi7cBI8nur9/OLxOFKpVMEV/QRB5KZxNiWJqrBpWzcS8RTsdhO+8hu7cPiNSxU3DimVSkbghSAMNcLhMCKRCPbs+ADP/OwUOFcxb1FnUcVllYjWdz59Um9NmzbDk9GuduGcH+8ePgWOG3DbnXPL8nqFEg6HYTabi7ZqJQgik4oJOGPsBQCTctz1Tc75ziKO8wiARwBg+vTpZVodkc1IxiHeVhvWbJyN9Vu6K2IcUiqV3AMvFEmSdGvN+9deDwC4c2UbBgcHMbPLha//6e0FrbES0frWz12v/zvlunSV99JbJmHqdBfmLbLjuukyNm5dXNWLoHg8DkVRqtZjThDNDOPG8KraL87YiwD+oNAitmXLlvHDh6nerVwoiorXX/kY+3b24eCzZxCLXp1qZneYcO+aWdiwtQu3r5gKs7n+Co0uXboEt9tdVy1ImqYhGo0iEolAVVXIsgy73a5biOajWvvlol1MURR0dHRUrWhNIF57woQJNb/4IogGIe8fCqXQxxnCOGTvjl4c2Hv6GuOQFfdMx/qtXbjn/hmw26t7cm8GJEmCy+WC0+lEIpFANBrVbTGtVqtuiZktXtVoORPtYoqiVLXiXKBpGhKJBJxOJ4k3QZSBWrWRbQPwfwB0AtjLGHufc76mFmsZL5zsGcTePMYhS2+djI3buvMah9QrjDHUMoM0Eowx2Gw22Gw2qKqKaDSKaDSKoaEhyLIMp9MJh8NR1vT1SFG8EG/RLmaz2apaJQ+kW8c451WrdieIZqdWVejbAWyvxWuPJz4+H8T+XX3Yt6MPvR8NZdw3f1EH1m3uwtrNXZg8ZWTjkHqlngXciCzLcLvdcLlcSCQSiEQiCAaDCIfDcDqdcDqdZRHyfPvoIm0u+q6FgFazSh5IC7jZbK565E8QzQql0JuMgf4oDuw9nd84ZGs31m8p3jikHqnWLPRyYYzKk8mkbtkZiUTgdrvhcDjGlFrOVfWeSqUwNDQEVVXR1taWUfldjhGshZJKpZBMJqvarkYQzU5Ni9iKhYrYcjOaccjaTXOwYYzGIfXI4OAgOOfo6Oio9VJKRlEUBINB3de8paWlJL/vXMRiMQQC6YE7bW1tsFgqNxVvNMLhMILBICZOnEiT1wiiOKiIrdkYyTjE5RbGIV1YfvuUujcOKRWTyYRoNFoX7WSlYjab0d7ejng8jmAwiMHBQdjtdng8npKFTtM0BINBRKNRWCwWtLa21lw0Y7EYLBZLzddBEM0ECXgDMZJxiNUq4+4GNQ4pFYvFgkgkglQq1fD7qjabDVarVa9Yj8fjejV7ofvjx49cwe7tx3HXvRMxs8sLl8tVF7PaVVWFoiiUPieIMtP8Z/kGh3OODz/ox74dvXh296kM4xBZZrjtzqlYv6ULq9bOqqhxSD0iUs3xeLzhBRxI75GLvfBAIKDvjzscDtjt9pzvkXMORVEQj8exa/t72LP9JDgULLutq24+k0QiAQBl2xogCCINCXidIoxD9u3oxfmzwYz7blw6Eeu3dGHNxjlVMw6pRyRJgtVqRSwWq6thLmNFlmW0tbVBURQ9Ig+Hw5AkCSaTSY/INU1DKpXSC/lWrZ4DBjs2PzCvbsQbSF9gybJcV2siiGaABLyOqGfjkHrF4XDA5/MhHo839GztXD3ZZrMZra2t8Hg8SCaTSCQSUFUVqpqud5AkCXa7HWf7gnh2zzlsfuAG/OGf5XYiq3bPt4BzjkQiQa5jBFEBSMBrTMAXx4F9p7FvZ19e45D1W7tx/dy2mu9l1iM2mw2yLCMUCtW9gI8koiP1ZItxrPkGoDy39xgef+xDMCblFOeeo/34/vfe0Y1Nqing8XgcnPO6/9kQRCNCAl4DolFhHNKLV1+6kNM4ZMPWbty0rH6MQ+oVsW/s9/sRi8XqesrXSCI9lp7s0Z67e3svLl0IYeXqmVXp+TYSj8chSVJNW9gIolkhAa8Swjhk744+/Pq5a41DVq2dhQ1bu3HbndfVpXFIPWO32/XpZlartaYWoyMxktCOZRb6aM81vm610+fxeBx2u52yRwRRAWiQSwUxGoc8t+c0/L5M45A7756GDdu6yTikDCSTSQwMDMDlcjVUu1Kt9qarQTwex9DQENrb26kCnSBKhwa5VAvOOU6eGMK+PMYhy26bgg1buxrOOKTesVgscDgcetvVSNad9US155FXk1gsRulzgqggjXGWawA+Ph/Evp192LejF30nfRn3zV/UgfVburF28xxMmtyYxiGNgNvtRiwWQzgchtfrrfVyCqKa88iriaZpiMfjY57vThBEfkjAy8T//s5b2LezT/+62YxDGgFZluFwOBCNRuF2uxtibGclfcBrmZ4X1qHUPkYQlYMEvEys39qNt1+/hHWbu7BuS1fTGYc0Ck6nE5FIBPF4HE6ns9bLqSm1Ss9zzhGJRGCxWGh4C0FUEBLwMrFi5TS88NaXmtY4pFEwmUwwm80VF/BGKD7LTs9Xa82JRAKpVAqtrZR5IohKQgJeJki46wez2azP364U9VJ8NpIoZ6fnq7XmcDgMWZZpeAtBVBgScKLpkGUZqqpW1Ga0XorPihHlaqw5kUggmUyipaWFtpAIosKQgBNNhxCOSgp4vRSfFSPKlVyzIBQK6cWEBEFUFhJwgqgziomqqyHKhRKNRpFMJuH1ein6JogqQBu3RFXpOdqP73z7NfQc7a/4a5UqItVcYy42bevGQw8vxpLlk2q6jmLQNA2hUEgfqEMQROWhCJyoKtUopBLjgUsV8FoXqImo+jvffq0uCuUKIRQKQVVVqjwniCpCAk5UlWoUUo1177teCtTGuo5qtY0lk0lEIhE4nU4am0oQVYQEnKgq1diz1TRtTI5k9bKvPJZ1jOYBXi5x1zQNfr8fJpOpoUxkCKIZIAEniCZkNA/wcm0TBINBpFIpdHR0UOEaQVQZEnCi6ahk+1ijMJoHeDm2CaLRqD53nlLnBFF9SMAJogkZLf0+1m2CZDKJQCAAq9UKl4sc9giiFlAbGdF0MMb0SvRqUuv2s2qhqip8Ph8kSUJra+u4z3YQRK2gCJxoOiRJgqZpVX/dWrefVQPOOYaGhqBpGjo6OsZULEgQxNggASeaDlmWwTkfczV6sdRL+1mlEOItnMbIKpQgagsJONF0yLIMAEilUlUtrqqX9rNKwDmH3+9HIpGA1+slpzGCqAMo/0U0HSIyVBSlxitpDjjnCAQCiMVi8Hg8NCqVIOoEEnCi6ZBlGbIsI5lM1nopdUuhBXdCvEW7GFWcE0T9UJMUOmPsuwA2AUgCOAXgK5xzfy3WQjQnVqsV8XicesLzUEjBnUibx2IxuN1uuN3uai6RIIhRqFUE/jyAhZzzxQBOAvjjGq2DaFLsdjs0TUM8Hq/1UuoS4XiWr+BO0zQMDQ3paXMSb4KoP2oSgXPODxi+fAPAg7VYB9G8WCwWmEwmhMNh2O32Wi+n7hip4C6VSmFoaEh3F6PPjyDqk3rYA/9tAPtrvQiiuWCMweVyQVEURCKRWi+nYYjH4xgYGICmaWhrayPxJog6pmIROGPsBQCTctz1Tc75zuHHfBNACsCTIxznEQCPAMD06dMrsFKiWXE4HIjFYggGgzCZTLBarbVeUt3COUcoFEI4HIbZbEZbW5vejkcQRH3CajFyEgAYY18G8O8BrOKcRwt5zrJly/jhw4crui6iudA0DYODg0ilUnoLFBW1ZaIoCvx+PxRFgdPphMfjoc+IIOqHvH+MtapCXwvgGwDuLlS8CaIUJElCe3s7fD6f3g7lcrlgtVrH/RhQTdMQCoUQiUQgSRLa2tpoQAtBNBC1msT2fwFYATw/fKX/Buf8qzVaC9HkCBEX6XSfzwfGGCwWC8xmM8xms947LklSXUafPUf7sXt7b1570GLgnCMajSIUCkHTNDidTrjd7nF/QUMQjUatqtC7avG6xPjFKIBzbmhFLBZDMplEJBK5xrmMMZbzJsTd+H9JknThr+QFQDmMUjRNQzQaRSQSgaqqsFqt8Hg8NNOcIBoUmoVOjAuMAviNb92hz0jnnENVVf2maRo0TQPnPOOmaRpUVc34Olf9CGMMsizDZDLp0b3JZILJNLY/tVKNUjjnSCaTiEaj+mAbq9UKr9dLRX0E0eCQgBPjgnwCyBgrWWCFkAtxF7dUKoVUKpUxREaSJF3QzWYzLBZLUVXexRilqKqKZDKJeDyORCKhu7I5HA44HA6KuAmiSSABJ8YFlXAKE9G2LMs5RZFzDkVRkEqloCiK3pMuInch6uICQkTukiSNuh9tzAqIiwbxGqqq6se32WywWq2w2Wx1ubdPEETpkIATRIUQhXJGS1Mh6uKWSqUQi8Wgado1zzXuuYvnGlP42ZhMJr0wz2q1wmQykWgTRBNDAk4QVSSXqAPISMHn2osXQmwsohOFcyJ6J7EmiPEFCThB1AEiFU8QBFEo1PhJEOOYQn3BCYKoPygCJ4hxTDn6ywmCqA0k4AQxjim1v5wgiNpDAk4Q45hKtNcRBFEdaA+cIAiCIBoQEnCCIAiCaEBIwAmCIAiiASEBJwiCIIgGhAScIAiCIBoQEnCCIAiCaEBIwAmCIAiiASEBJwiCIIgGhAScIAiCIBoQEnCCIAiCaEBIwAmCIAiiASEBJwiCIIgGhAScIAiCIBoQEnCCIAiCaEBIwAmCIAiiASEBJwiCIIgGhAScIAiCIBoQEnCCIAiCaEBIwAmCIAiiASEBJwiCIIgGhAScIAiCIBoQxjmv9RoKhjHWD+BcrddRAB0ABmq9iCaGPt/KQp9vZaHPt7I02+c7wDlfm+uOhhLwRoExdphzvqzW62hW6POtLPT5Vhb6fCvLePp8KYVOEARBEA0ICThBEARBNCAk4JXhh7VeQJNDn29loc+3stDnW1nGzedLe+AEQRAE0YBQBE4QBEEQDQgJeIVhjP0+Y4wzxjpqvZZmgjH2XcbYCcbYEcbYdsaYt9ZragYYY2sZYx8xxvoYY39U6/U0E4yxaYyxQ4yx44yxY4yx36v1mpoRxpjMGHuPMban1mupNCTgFYQxNg3AagDna72WJuR5AAs554sBnATwxzVeT8PDGJMB/D2AdQDmA/g3jLH5tV1VU5EC8Puc8/kAbgPwH+jzrQi/B6Cn1ouoBiTgleXvAHwDABUalBnO+QHOeWr4yzcATK3lepqEWwD0cc5Pc86TAH4GYEuN19Q0cM4/4Zy/O/z/ENIic11tV9VcMMamAtgA4LFar6UakIBXCMbYFgAXOecf1Hot44DfBrC/1otoAq4DcMHw9ccggakIjLGZAG4G8GaNl9JsfA/poEmr8TqqgqnWC2hkGGMvAJiU465vAvgTpNPnRImM9PlyzncOP+abSKcmn6zm2giiVBhjLgC/AvA1znmw1utpFhhjGwFc4Zy/wxi7p8bLqQok4GOAc35fru8zxhYBmAXgA8YYkE7vvssYu4VzfrmKS2xo8n2+AsbYlwFsBLCKUz9kObgIYJrh66nD3yPKBGPMjLR4P8k5f6bW62kyPgNgM2NsPQAbAA9j7AnO+ZdqvK6KQX3gVYAxdhbAMs55Mw3YrymMsbUA/hbA3Zzz/lqvpxlgjJmQLghchbRwvw3gC5zzYzVdWJPA0lfzPwEwxDn/Wo2X09QMR+B/wDnfWOOlVBTaAycalf8LwA3gecbY+4yxH9R6QY3OcFHg7wJ4DukCq1+QeJeVzwD4LQD3Dv/Ovj8cLRJESVAEThAEQRANCEXgBEEQBNGAkIATBEEQRANCAk4QBEEQDQgJOEEQBEE0ICTgBEEQBNGAkIATxDiGMaYOtzN9wBh7lzF2xyiP9zLGHq3W+giCyA+1kRHEOIYxFuacu4b/vwbAn3DO7x7h8TMB7OGcL6zSEgmCyANF4ARBCDwAfOILxtjXGWNvD3uu/8Xwt/8awJzhqP27jDEXY+zgcPR+dNjEhyCIKkCz0AlifGNnjL2P9OzoyQDuBQDG2GoA3UhbjDIAuxhjdwH4I6R92G8afpwJwDbOeZAx1gHgDcbYLppNTxCVhwScIMY3MYMY3w7gp4yxhUg76a0G8N7w41xIC/r5rOczAH81LO4a0vajEwGQaQ9BVBgScIIgAACc89eHo+hOpIX5f3DO/8H4mOE9cCNfHH78Us65MmzcY6vCcgli3EN74ARBAAAYY3MByAAGkTY0+e1h72owxq5jjE0AEELaREbQgrQHs8IYWwlgRpWXTRDjForACWJ8I/bAgXTU/W855yqAA4yxeQBeH/a0DwP4Euf8FGPsXxljHwLYD+BvAOxmjB0FcBjAiaq/A4IYp1AbGUEQBEE0IJRCJwiCIIgGhAScIAiCIBoQEnCCIAiCaEBIwAmCIAiiASEBJwiCIIgGhAScIAiCIBoQEnCCIAiCaEBIwAmCIAiiAfn/AfMgEQxC/U3aAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats\n",
+    "\n",
+    "bqtls['logratio']=np.log2((bqtls['prechipfreq'].astype(float)+.01)/(bqtls['POSTfreq'].astype(float)+0.01))\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "data = pd.DataFrame()\n",
+    "data[\"logfc.mean\"] = bqtls[bqtls[\"pvalue\"] < 1e-9][\"ENCSR637XSC.logfc.mean\"]\n",
+    "data[\"Beta\"] = bqtls[bqtls[\"pvalue\"] < 1e-9][\"logratio\"]\n",
+    "\n",
+    "sns.kdeplot(\n",
+    "    data=data,\n",
+    "    x=\"Beta\",\n",
+    "    y=\"logfc.mean\",\n",
+    "    levels=5,\n",
+    "    fill=False,\n",
+    "    alpha=0.2,\n",
+    "    color=\"grey\",\n",
+    "    cut=2,\n",
+    "\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax = sns.scatterplot(x=\"Beta\", y=\"logfc.mean\", data=data, s=6, color=\"#2B1C97\")\n",
+    "sns.regplot(x=\"Beta\", y=\"logfc.mean\", data=data, scatter=False, ci=None, color=\"#2B1C97\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "\n",
+    "atc_pear = scipy.stats.pearsonr(data[\"logfc.mean\"],data[\"Beta\"])\n",
+    "print(atc_pear)\n",
+    "ax.set_title(\"Pearsonr = \"+str(np.round(atc_pear[0],2)))\n",
+    "\n",
+    "#plt.savefig(\"subfigs/bqtls_scatter_plot.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0001\n",
+      "(0.3456664008762522, 1.3461902564870874e-107)\n",
+      "(0.33825479358945226, 7.853922186470018e-103)\n",
+      "(0.09772657619245632, 1.4392000215477486e-09)\n",
+      "(0.29801135390237576, 3.705821829839822e-79)\n",
+      "(0.024670217302556304, 0.1274825574137993)\n",
+      "1e-05\n",
+      "(0.42329102700730575, 1.0235294326081794e-80)\n",
+      "(0.41689084290850426, 4.14130766524779e-78)\n",
+      "(0.125233422009284, 7.300037058382848e-08)\n",
+      "(0.3635434514270092, 1.8009184762082764e-58)\n",
+      "(0.04125415827793011, 0.07718968886737394)\n",
+      "1e-06\n",
+      "(0.50030692793228, 1.3235987654956608e-69)\n",
+      "(0.49575079374180037, 3.4757799582249244e-68)\n",
+      "(0.16968379172014564, 1.955875671048076e-08)\n",
+      "(0.43704013394810604, 1.0812892467843035e-51)\n",
+      "(0.06978802933595976, 0.021690905805218718)\n",
+      "1e-07\n",
+      "(0.5282917609125022, 1.4435264058065224e-55)\n",
+      "(0.5220637570429951, 4.400745286278192e-54)\n",
+      "(0.18866877284583086, 1.7326049125652955e-07)\n",
+      "(0.4783954363265271, 1.698913189034947e-44)\n",
+      "(0.09901833831729295, 0.006435060045045102)\n",
+      "1e-08\n",
+      "(0.5664134092166404, 3.4191444652453627e-51)\n",
+      "(0.5587969011930763, 1.3657457149373709e-49)\n",
+      "(0.23024701143461904, 1.6292883277695882e-08)\n",
+      "(0.50837006279763, 5.785964965339597e-40)\n",
+      "(0.1236010377032279, 0.0026794093754595075)\n",
+      "1e-09\n",
+      "(0.5907530863032388, 2.126609788708331e-43)\n",
+      "(0.5808846532506969, 1.0865445241226133e-41)\n",
+      "(0.2439772093528012, 1.76097732139845e-07)\n",
+      "(0.5339415174398064, 2.6186071361675365e-34)\n",
+      "(0.14111031626059437, 0.0027895171981299417)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "enformer = []\n",
+    "enformer_unsigned = []\n",
+    "enformer_sar = []\n",
+    "enformer_unsigned_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(4,10,1):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "\n",
+    "    print(threshold)\n",
+    "    dataf = bqtls[(bqtls[\"pvalue\"]<threshold)]\n",
+    "        \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR000EMT.logfc.mean\"], dataf[\"logratio\"])\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR000EMT.logfc.mean\"]), abs(dataf[\"logratio\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.logfc.mean\"], dataf[\"logratio\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    print(pear)\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.logfc.mean\"]), abs(dataf[\"logratio\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.sub.50M.logfc.mean\"], dataf[\"logratio\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    print(pear)\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.sub.50M.logfc.mean\"]), abs(dataf[\"logratio\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    print(pear)\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer_recomputed\"]*-1, dataf[\"logratio\"])\n",
+    "    enformer.append(pear[0])\n",
+    "    print(pear)\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer_recomputed\"]), abs(dataf[\"logratio\"]))\n",
+    "    enformer_unsigned.append(pear[0])\n",
+    "    print(pear)\n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"DNASE_SAR\"], dataf[\"logratio\"])\n",
+    "    enformer_sar.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"DNASE_SAR\"]), abs(dataf[\"logratio\"]))\n",
+    "    enformer_unsigned_sar.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fe281e7c510>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(4,10,1).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "ax.scatter(np.arange(4,10,1).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(4,10,1).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(4,10,1).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "ax.scatter(np.arange(4,10,1).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "ax.scatter(np.arange(4,10,1).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "#plt.savefig(\"subfigs/bqtls_pearsonr_compare.pdf\", transparent=True, dpi=300)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0001\n",
+      "1e-05\n",
+      "1e-06\n",
+      "1e-07\n",
+      "1e-08\n",
+      "1e-09\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats\n",
+    "chrombpnet = {}\n",
+    "chrombpnet_unsigned = {}\n",
+    "enformer = []\n",
+    "enformer_unsigned = []\n",
+    "enformer_sar = []\n",
+    "enformer_unsigned_sar = []\n",
+    "\n",
+    "chrombpnet[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"] = []\n",
+    "chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"] = []\n",
+    "\n",
+    "for threshold in np.arange(4,10,1):\n",
+    "    threshold = 1 / (10**(threshold))\n",
+    "\n",
+    "    print(threshold)\n",
+    "    dataf = bqtls[(bqtls[\"pvalue\"]<threshold)]\n",
+    "        \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR000EMT.jsd_sign.mean\"], dataf[\"logratio\"])\n",
+    "    chrombpnet[\"ChromBPNet DNase (68M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR000EMT.jsd_sign.mean\"]), abs(dataf[\"logratio\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet DNase (68M)\"].append(pear[0])    \n",
+    " \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.jsd_sign.mean\"], dataf[\"logratio\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (572M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.jsd_sign.mean\"]), abs(dataf[\"logratio\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (572M)\"].append(pear[0])    \n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"ENCSR637XSC.sub.50M.jsd_sign.mean\"], dataf[\"logratio\"])\n",
+    "    chrombpnet[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"ENCSR637XSC.sub.50M.jsd_sign.mean\"]), abs(dataf[\"logratio\"]))\n",
+    "    chrombpnet_unsigned[\"ChromBPNet ATAC (50M)\"].append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(dataf[\"enformer_recomputed\"]*-1, dataf[\"logratio\"])\n",
+    "    enformer.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"enformer_recomputed\"]), abs(dataf[\"logratio\"]))\n",
+    "    enformer_unsigned.append(pear[0])\n",
+    "\n",
+    "    pear = scipy.stats.pearsonr(dataf[\"DNASE_SAR\"], dataf[\"logratio\"])\n",
+    "    enformer_sar.append(pear[0])\n",
+    "    \n",
+    "    pear = scipy.stats.pearsonr(abs(dataf[\"DNASE_SAR\"]), abs(dataf[\"logratio\"]))\n",
+    "    enformer_unsigned_sar.append(pear[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "colors = [\"blue\", \"blue\", \"C0\"]\n",
+    "alphas= [0.5, 1.0, 1.0, 1.0] \n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(4,10,1).astype(str), chrombpnet[key], marker=\"^\", label=key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "\n",
+    "\n",
+    "# ax.scatter(np.arange(4,10,1).astype(str), enformer_sar ,  marker=\"^\", label=\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "# ax.scatter(np.arange(4,10,1).astype(str), enformer ,  marker=\"^\", label=\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "i=0\n",
+    "for key in [\"ChromBPNet ATAC (572M)\", \"ChromBPNet ATAC (50M)\", \"ChromBPNet DNase (68M)\"]:\n",
+    "    ax.scatter(np.arange(4,10,1).astype(str), chrombpnet_unsigned[key], marker=\"v\", label=\"unsign \"+key, color=colors[i], alpha=alphas[i])\n",
+    "    i+=1\n",
+    "    \n",
+    "# ax.scatter(np.arange(4,10,1).astype(str), enformer_unsigned_sar, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAR (68M)\", color=\"orange\")\n",
+    "# ax.scatter(np.arange(4,10,1).astype(str), enformer_unsigned, marker=\"v\", label=\"unsign \" +\"Enformer DNase SAD (68M)\", color=\"green\")\n",
+    "\n",
+    "\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "ax.set_xlabel(\"-log10BH\")\n",
+    "ax.set_ylabel(\"Pearsonr\")\n",
+    "#plt.legend(loc='upper right')\n",
+    "\n",
+    "# Shrink current axis by 20%\n",
+    "box = ax.get_position()\n",
+    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
+    "\n",
+    "# Put a legend to the right of the current axis\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
+    "\n",
+    "#plt.show()\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_pearsonr_compare_jsd.pdf\", transparent=True, dpi=300)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n: 145', 'n: 302', 'n: 309', 'n: 1080', 'n: 8011', 'n: 117808', 'n: 182109', 'n: 284896', 'n: 287092']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = np.array(np.arange(0,5e-9,5e-8).tolist() + [1e-2,0.1,1.0])\n",
+    "bins = [-0.001,1e-11,1e-9, 1e-7, 1e-5,1e-3,1e-1,0.5,0.9,1.0 ]\n",
+    "bqtls['value_group'] = pd.cut(bqtls[\"pvalue\"].astype(np.float64), bins=bins)\n",
+    "bqtls['abs_logfc'] = abs(bqtls['ENCSR637XSC.logfc.mean'])\n",
+    "ax = sns.boxplot(x=\"value_group\", y=\"abs_logfc\", data=bqtls, showfliers=False)\n",
+    "plt.xticks(rotation=45)\n",
+    "medians = bqtls.groupby(['value_group'])['abs_logfc'].median().values\n",
+    "nobs= bqtls['value_group'].value_counts().values[::-1]\n",
+    "nobs = [str(x) for x in nobs.tolist()]\n",
+    "nobs = [\"n: \" + i for i in nobs] \n",
+    "print(nobs)\n",
+    "pos = range(len(nobs))\n",
+    "for tick,label in zip(pos,ax.get_yticklabels()):\n",
+    "    ax.text(tick+0.1,medians[tick]+0.9,nobs[tick],color='black', verticalalignment = \"bottom\",rotation=90)\n",
+    "sns.despine()\n",
+    "ax.set(xlabel='p-value intervals', ylabel='Predicted')\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_predicted_bins_atac.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n: 145', 'n: 302', 'n: 309', 'n: 1080', 'n: 8011', 'n: 117808', 'n: 182109', 'n: 284896', 'n: 287092']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = np.array(np.arange(0,5e-9,5e-8).tolist() + [1e-2,0.1,1.0])\n",
+    "bins = [-0.001,1e-11,1e-9, 1e-7, 1e-5,1e-3,1e-1,0.5,0.9,1.0 ]\n",
+    "bqtls['value_group'] = pd.cut(bqtls[\"pvalue\"].astype(np.float64), bins=bins)\n",
+    "bqtls['abs_logfc'] = abs(bqtls['ENCSR637XSC.jsd_sign.mean'])\n",
+    "ax = sns.boxplot(x=\"value_group\", y=\"abs_logfc\", data=bqtls, showfliers=False)\n",
+    "plt.xticks(rotation=45)\n",
+    "medians = bqtls.groupby(['value_group'])['abs_logfc'].median().values\n",
+    "nobs= bqtls['value_group'].value_counts().values[::-1]\n",
+    "nobs = [str(x) for x in nobs.tolist()]\n",
+    "nobs = [\"n: \" + i for i in nobs] \n",
+    "print(nobs)\n",
+    "pos = range(len(nobs))\n",
+    "for tick,label in zip(pos,ax.get_yticklabels()):\n",
+    "    ax.text(tick+0.1,medians[tick]+0.07,nobs[tick],color='black', verticalalignment = \"bottom\",rotation=90)\n",
+    "sns.despine()\n",
+    "ax.set(xlabel='p-value intervals', ylabel='Predicted')\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_predicted_bins_atac_jsd.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n: 145', 'n: 302', 'n: 309', 'n: 1080', 'n: 8011', 'n: 117808', 'n: 182109', 'n: 284896', 'n: 287092']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = np.array(np.arange(0,5e-9,5e-8).tolist() + [1e-2,0.1,1.0])\n",
+    "bins = [-0.001,1e-11,1e-9, 1e-7, 1e-5,1e-3,1e-1,0.5,0.9,1.0 ]\n",
+    "bqtls['value_group'] = pd.cut(bqtls[\"pvalue\"].astype(np.float64), bins=bins)\n",
+    "bqtls['abs_logfc'] = abs(bqtls['ENCSR000EMT.logfc.mean'])\n",
+    "ax = sns.boxplot(x=\"value_group\", y=\"abs_logfc\", data=bqtls, showfliers=False)\n",
+    "plt.xticks(rotation=45)\n",
+    "medians = bqtls.groupby(['value_group'])['abs_logfc'].median().values\n",
+    "nobs= bqtls['value_group'].value_counts().values[::-1]\n",
+    "nobs = [str(x) for x in nobs.tolist()]\n",
+    "nobs = [\"n: \" + i for i in nobs] \n",
+    "print(nobs)\n",
+    "pos = range(len(nobs))\n",
+    "for tick,label in zip(pos,ax.get_yticklabels()):\n",
+    "    ax.text(tick+0.1,medians[tick]+0.9,nobs[tick],color='black', verticalalignment = \"bottom\",rotation=90)\n",
+    "sns.despine()\n",
+    "ax.set(xlabel='p-value intervals', ylabel='Predicted')\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_predicted_bins_dnase.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n: 145', 'n: 302', 'n: 309', 'n: 1080', 'n: 8011', 'n: 117808', 'n: 182109', 'n: 284896', 'n: 287092']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = np.array(np.arange(0,5e-9,5e-8).tolist() + [1e-2,0.1,1.0])\n",
+    "bins = [-0.001,1e-11,1e-9, 1e-7, 1e-5,1e-3,1e-1,0.5,0.9,1.0 ]\n",
+    "bqtls['value_group'] = pd.cut(bqtls[\"pvalue\"].astype(np.float64), bins=bins)\n",
+    "bqtls['abs_logfc'] = abs(bqtls['ENCSR000EMT.jsd_sign.mean'])\n",
+    "ax = sns.boxplot(x=\"value_group\", y=\"abs_logfc\", data=bqtls, showfliers=False)\n",
+    "plt.xticks(rotation=45)\n",
+    "medians = bqtls.groupby(['value_group'])['abs_logfc'].median().values\n",
+    "nobs= bqtls['value_group'].value_counts().values[::-1]\n",
+    "nobs = [str(x) for x in nobs.tolist()]\n",
+    "nobs = [\"n: \" + i for i in nobs] \n",
+    "print(nobs)\n",
+    "pos = range(len(nobs))\n",
+    "for tick,label in zip(pos,ax.get_yticklabels()):\n",
+    "    ax.text(tick+0.1,medians[tick]+0.9,nobs[tick],color='black', verticalalignment = \"bottom\",rotation=90)\n",
+    "sns.despine()\n",
+    "ax.set(xlabel='p-value intervals', ylabel='Predicted')\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_predicted_bins_dnase_jsd.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['n: 145', 'n: 302', 'n: 309', 'n: 1080', 'n: 8011', 'n: 117808', 'n: 182109', 'n: 284896', 'n: 287092']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "bqtls[\"abs_logratio\"] = abs(bqtls[\"logratio\"])\n",
+    "bins = np.array(np.arange(0,5e-9,5e-8).tolist() + [1e-2,0.1,1.0])\n",
+    "bins = [-0.001,1e-11,1e-9, 1e-7, 1e-5,1e-3,1e-1,0.5,0.9,1.0 ]\n",
+    "bqtls['value_group'] = pd.cut(bqtls[\"pvalue\"].astype(np.float64), bins=bins)\n",
+    "ax = sns.boxplot(x=\"value_group\", y=\"abs_logratio\", data=bqtls, showfliers=False)\n",
+    "plt.xticks(rotation=45)\n",
+    "medians = bqtls.groupby(['value_group'])['abs_logratio'].median().values\n",
+    "nobs= bqtls['value_group'].value_counts().values[::-1]\n",
+    "nobs = [str(x) for x in nobs.tolist()]\n",
+    "nobs = [\"n: \" + i for i in nobs] \n",
+    "print(nobs)\n",
+    "pos = range(len(nobs))\n",
+    "for tick,label in zip(pos,ax.get_yticklabels()):\n",
+    "    ax.text(tick+0.1,medians[tick]+0.9,nobs[tick],color='black', verticalalignment = \"bottom\",rotation=90)\n",
+    "sns.despine()\n",
+    "ax.set(xlabel='p-value intervals', ylabel='Predicted')\n",
+    "\n",
+    "plt.savefig(\"subfigs/bqtls_obs_bins.pdf\", transparent=True, dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "447"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bqtls[\"pvalue\"]<1e-9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.1, 0.5]         287092\n",
+       "(0.5, 0.9]         284896\n",
+       "(0.9, 1.0]         182109\n",
+       "(0.001, 0.1]       117808\n",
+       "(1e-05, 0.001]       8011\n",
+       "(1e-07, 1e-05]       1080\n",
+       "(1e-09, 1e-07]        309\n",
+       "(-0.001, 1e-11]       302\n",
+       "(1e-11, 1e-09]        145\n",
+       "Name: value_group, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " bqtls['value_group'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(881752, 32)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         (0.0, 1e-11]\n",
+       "5         (0.0, 1e-11]\n",
+       "6         (0.0, 1e-11]\n",
+       "7         (0.0, 1e-11]\n",
+       "8                  NaN\n",
+       "              ...     \n",
+       "999794      (0.9, 1.0]\n",
+       "999795      (0.9, 1.0]\n",
+       "999796      (0.9, 1.0]\n",
+       "999797      (0.9, 1.0]\n",
+       "999798      (0.9, 1.0]\n",
+       "Name: pvalue, Length: 881752, dtype: category\n",
+       "Categories (9, interval[float64, right]): [(0.0, 1e-11] < (1e-11, 1e-09] < (1e-09, 1e-07] < (1e-07, 1e-05] ... (0.001, 0.1] < (0.1, 0.5] < (0.5, 0.9] < (0.9, 1.0]]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins = [0,1e-11,1e-9, 1e-7, 1e-5,1e-3,1e-1,0.5,0.9,1.0 ]\n",
+    "\n",
+    "pd.cut(bqtls[\"pvalue\"].astype(np.float64), bins=bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "447"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(bqtls[\"pvalue\"]<1e-9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         2.331853e-251\n",
+       "5         1.195059e-111\n",
+       "6         4.004184e-109\n",
+       "7         1.485270e-108\n",
+       "8          0.000000e+00\n",
+       "              ...      \n",
+       "999794     1.000000e+00\n",
+       "999795     1.000000e+00\n",
+       "999796     1.000000e+00\n",
+       "999797     1.000000e+00\n",
+       "999798     1.000000e+00\n",
+       "Name: pvalue, Length: 881752, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bqtls[\"pvalue\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/prepare_sig_nonsig_set.ipynb b/src/evaluation/figure_notebooks/figure_6/prepare_sig_nonsig_set.ipynb
new file mode 100644
index 00000000..79dac47e
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/prepare_sig_nonsig_set.ipynb
@@ -0,0 +1,434 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qvals = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/caqtls_lcl_latest/lead_caQTL_variants.tsv.gz\", sep=\"\\t\", header=0)\n",
+    "qvals[\"chr\"] = \"chr\"+qvals[\"Chr\"].astype(str) \n",
+    "contain_values = qvals[~qvals['Alt'].str.contains('<CN')]\n",
+    "contain_values = contain_values[~contain_values['Alt'].str.contains(',')]\n",
+    "contain_values = contain_values[contain_values['Alt'].str.len()==1]\n",
+    "contain_values = contain_values[contain_values['Ref'].str.len()==1]\n",
+    "contain_values = contain_values.sort_values('Log10_BF').drop_duplicates([\"chr\", \"Ref\", \"Alt\", \"Pos\"], keep='last')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>chr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10352</td>\n",
+       "      <td>rs555500075</td>\n",
+       "      <td>T</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>0.38240</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.565592</td>\n",
+       "      <td>0.091926</td>\n",
+       "      <td>0.055039</td>\n",
+       "      <td>-1.165323</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10177</td>\n",
+       "      <td>rs367896724</td>\n",
+       "      <td>A</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>0.32395</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.021318</td>\n",
+       "      <td>-0.141834</td>\n",
+       "      <td>0.062422</td>\n",
+       "      <td>-0.618151</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1142714</td>\n",
+       "      <td>esv3584990</td>\n",
+       "      <td>T</td>\n",
+       "      <td>&lt;CN0&gt;</td>\n",
+       "      <td>0.13485</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.202811</td>\n",
+       "      <td>-0.089320</td>\n",
+       "      <td>0.072935</td>\n",
+       "      <td>-1.315789</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Peak  Chr      Pos         RsID Ref    Alt       AF  Inside_Peak    P_Lead  \\\n",
+       "0     1    1    10352  rs555500075   T     TA  0.38240            1  0.565592   \n",
+       "1     2    1    10177  rs367896724   A     AC  0.32395            2  0.021318   \n",
+       "2     3    1   875770    rs4970379   A      G  0.62500            2  0.044994   \n",
+       "3     4    1  1142714   esv3584990   T  <CN0>  0.13485            0  0.202811   \n",
+       "4     5    1   930533    rs3128110   C      G  0.35435            0  0.099392   \n",
+       "\n",
+       "       Beta        SE  Log10_BF   chr  \n",
+       "0  0.091926  0.055039 -1.165323  chr1  \n",
+       "1 -0.141834  0.062422 -0.618151  chr1  \n",
+       "2  0.155446  0.057836 -0.232537  chr1  \n",
+       "3 -0.089320  0.072935 -1.315789  chr1  \n",
+       "4  0.174020  0.040562  1.751829  chr1  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2862"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(qvals[\"Log10_BF\"]>14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Peak</th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>RsID</th>\n",
+       "      <th>Ref</th>\n",
+       "      <th>Alt</th>\n",
+       "      <th>AF</th>\n",
+       "      <th>Inside_Peak</th>\n",
+       "      <th>P_Lead</th>\n",
+       "      <th>Beta</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>Log10_BF</th>\n",
+       "      <th>chr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10352</td>\n",
+       "      <td>rs555500075</td>\n",
+       "      <td>T</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>0.38240</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.565592</td>\n",
+       "      <td>0.091926</td>\n",
+       "      <td>0.055039</td>\n",
+       "      <td>-1.165323</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10177</td>\n",
+       "      <td>rs367896724</td>\n",
+       "      <td>A</td>\n",
+       "      <td>AC</td>\n",
+       "      <td>0.32395</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.021318</td>\n",
+       "      <td>-0.141834</td>\n",
+       "      <td>0.062422</td>\n",
+       "      <td>-0.618151</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>875770</td>\n",
+       "      <td>rs4970379</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.62500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.044994</td>\n",
+       "      <td>0.155446</td>\n",
+       "      <td>0.057836</td>\n",
+       "      <td>-0.232537</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1142714</td>\n",
+       "      <td>esv3584990</td>\n",
+       "      <td>T</td>\n",
+       "      <td>&lt;CN0&gt;</td>\n",
+       "      <td>0.13485</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.202811</td>\n",
+       "      <td>-0.089320</td>\n",
+       "      <td>0.072935</td>\n",
+       "      <td>-1.315789</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>930533</td>\n",
+       "      <td>rs3128110</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.35435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099392</td>\n",
+       "      <td>0.174020</td>\n",
+       "      <td>0.040562</td>\n",
+       "      <td>1.751829</td>\n",
+       "      <td>chr1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Peak  Chr      Pos         RsID Ref    Alt       AF  Inside_Peak    P_Lead  \\\n",
+       "0     1    1    10352  rs555500075   T     TA  0.38240            1  0.565592   \n",
+       "1     2    1    10177  rs367896724   A     AC  0.32395            2  0.021318   \n",
+       "2     3    1   875770    rs4970379   A      G  0.62500            2  0.044994   \n",
+       "3     4    1  1142714   esv3584990   T  <CN0>  0.13485            0  0.202811   \n",
+       "4     5    1   930533    rs3128110   C      G  0.35435            0  0.099392   \n",
+       "\n",
+       "       Beta        SE  Log10_BF   chr  \n",
+       "0  0.091926  0.055039 -1.165323  chr1  \n",
+       "1 -0.141834  0.062422 -0.618151  chr1  \n",
+       "2  0.155446  0.057836 -0.232537  chr1  \n",
+       "3 -0.089320  0.072935 -1.315789  chr1  \n",
+       "4  0.174020  0.040562  1.751829  chr1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qvals.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data_for_ap(data_df, sig_thresh, nonsig_thresh, namef):\n",
+    "    best_sig = data_df[data_df[\"Log10_BF\"]>sig_thresh]\n",
+    "    best_sig[\"label\"] = 1\n",
+    "    \n",
+    "    # non-significant set inside a peak\n",
+    "    nonsig_df = data_df[data_df[\"Log10_BF\"]<nonsig_thresh].reset_index()\n",
+    "    \n",
+    "    other_nonsigs = nonsig_df\n",
+    "    #other_nonsigs_inp = other_nonsigs[other_nonsigs[\"Inside_Peak\"]>0]\n",
+    "    other_nonsigs_inp = other_nonsigs\n",
+    "    final_set_nonsigs = other_nonsigs_inp.sample(best_sig.shape[0]*50, random_state=1).reset_index(drop=True)\n",
+    "    final_set_nonsigs[\"label\"] = 0\n",
+    "     \n",
+    "    alldf = pd.concat([final_set_nonsigs,best_sig])\n",
+    "    \n",
+    "    alldf = alldf[[\"chr\", \"Pos\", \"Ref\", \"Alt\", \"RsID\", \"label\", \"Beta\"]]\n",
+    "    \n",
+    "    alldf.to_csv(namef, sep=\"\\t\", header=True, index=False)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_data_for_ap(contain_values, 20, -1, \"/mnt/lab_data2/anusri/enformer/caqtls/set_sig20/source1.tsv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/prepare_sig_nonsig_set_bqtls.ipynb b/src/evaluation/figure_notebooks/figure_6/prepare_sig_nonsig_set_bqtls.ipynb
new file mode 100644
index 00000000..c1a9c9e2
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/prepare_sig_nonsig_set_bqtls.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps=pd.read_csv(\"/mnt/lab_data/kundaje/users/anusri/QTLs/bQTLs/src/pu1.txt\",header=0,sep='\\t')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chr</th>\n",
+       "      <th>position</th>\n",
+       "      <th>Depth</th>\n",
+       "      <th>ALTdepth</th>\n",
+       "      <th>REFDepth</th>\n",
+       "      <th>ALTallele</th>\n",
+       "      <th>POSTallele</th>\n",
+       "      <th>POSTfreq</th>\n",
+       "      <th>prechipfreq</th>\n",
+       "      <th>pvalue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>18967176</td>\n",
+       "      <td>538</td>\n",
+       "      <td>418</td>\n",
+       "      <td>120</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.223048</td>\n",
+       "      <td>0.866699</td>\n",
+       "      <td>2.331853e-251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr4</td>\n",
+       "      <td>176935913</td>\n",
+       "      <td>626</td>\n",
+       "      <td>597</td>\n",
+       "      <td>29</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.046326</td>\n",
+       "      <td>0.543949</td>\n",
+       "      <td>4.449643e-162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>144534083</td>\n",
+       "      <td>618</td>\n",
+       "      <td>368</td>\n",
+       "      <td>250</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.404531</td>\n",
+       "      <td>0.057295</td>\n",
+       "      <td>5.863065e-141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr17</td>\n",
+       "      <td>19015381</td>\n",
+       "      <td>144</td>\n",
+       "      <td>143</td>\n",
+       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.006944</td>\n",
+       "      <td>0.872850</td>\n",
+       "      <td>1.040250e-126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr1</td>\n",
+       "      <td>17231724</td>\n",
+       "      <td>2477</td>\n",
+       "      <td>1321</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.466694</td>\n",
+       "      <td>0.255195</td>\n",
+       "      <td>9.915948e-114</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Chr   position  Depth  ALTdepth  REFDepth ALTallele POSTallele  POSTfreq  \\\n",
+       "0  chr17   18967176    538       418       120         G          A  0.223048   \n",
+       "1   chr4  176935913    626       597        29         A          C  0.046326   \n",
+       "2   chr1  144534083    618       368       250         T          C  0.404531   \n",
+       "3  chr17   19015381    144       143         1         A          T  0.006944   \n",
+       "4   chr1   17231724   2477      1321      1156         G          A  0.466694   \n",
+       "\n",
+       "   prechipfreq         pvalue  \n",
+       "0     0.866699  2.331853e-251  \n",
+       "1     0.543949  4.449643e-162  \n",
+       "2     0.057295  5.863065e-141  \n",
+       "3     0.872850  1.040250e-126  \n",
+       "4     0.255195  9.915948e-114  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "254"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(snps[\"pvalue\"]<1e-20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7.24416915012e-08\n",
+      "0.0548097155473731\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "print(np.quantile(snps[\"pvalue\"],0.001))\n",
+    "print(np.quantile(snps[\"pvalue\"],0.1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data_for_ap(data_df, sig_thresh, nonsig_thresh, namef):\n",
+    "    best_sig = data_df[data_df[\"pvalue\"]<sig_thresh]\n",
+    "    best_sig[\"label\"] = 1\n",
+    "    print(best_sig.shape)\n",
+    "    \n",
+    "    # non-significant set inside a peak\n",
+    "    nonsig_df = data_df[data_df[\"pvalue\"]>nonsig_thresh].reset_index()\n",
+    "    \n",
+    "    other_nonsigs_inp = nonsig_df\n",
+    "    final_set_nonsigs = other_nonsigs_inp.sample(best_sig.shape[0]*25, random_state=1).reset_index(drop=True)\n",
+    "    final_set_nonsigs[\"label\"] = 0\n",
+    "    print(final_set_nonsigs.shape)\n",
+    "     \n",
+    "    alldf = pd.concat([final_set_nonsigs,best_sig])\n",
+    "    #print(alldf.head())\n",
+    "    \n",
+    "    alldf = alldf[[\"Chr\", \"position\", \"ALTallele\",  \"POSTallele\", \"label\", \"POSTfreq\", \"prechipfreq\"]]\n",
+    "    \n",
+    "    alldf.to_csv(namef, sep=\"\\t\", header=True, index=False)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(26432, 11)\n",
+      "(660800, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "namef=\"/mnt/lab_data2/anusri/enformer/bqtls/source1.tsv\"\n",
+    "#get_data_for_ap(snps, 1e-9, 0.1, namef)\n",
+    "get_data_for_ap(snps,  0.005, 0.1, namef)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "633"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(snps[\"pvalue\"]<1e-9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/evaluation/figure_notebooks/figure_6/smc_test.ipynb b/src/evaluation/figure_notebooks/figure_6/smc_test.ipynb
new file mode 100644
index 00000000..1614bbc7
--- /dev/null
+++ b/src/evaluation/figure_notebooks/figure_6/smc_test.ipynb
@@ -0,0 +1,2222 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps_sig=pd.read_csv(\"/oak/stanford/groups/akundaje/projects/cad/data/clint_data/rasqual_results/revisions/smc/SMC_rasqual_results_FDR_0.05_083121.txt\", sep=\"\\t\", header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Feature_ID</th>\n",
+       "      <th>rsID</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Freq</th>\n",
+       "      <th>HWE_Chisquare</th>\n",
+       "      <th>Imp_quality</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Distance to TSS</th>\n",
+       "      <th>Nearest PromoterID</th>\n",
+       "      <th>Entrez ID</th>\n",
+       "      <th>Nearest Unigene</th>\n",
+       "      <th>Nearest Refseq</th>\n",
+       "      <th>Nearest Ensembl</th>\n",
+       "      <th>Gene Name</th>\n",
+       "      <th>Gene Alias</th>\n",
+       "      <th>Gene Description</th>\n",
+       "      <th>Gene Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr1.1069225.1069626</td>\n",
+       "      <td>rs74048003</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>1067007</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.200</td>\n",
+       "      <td>0.156250</td>\n",
+       "      <td>0.999916</td>\n",
+       "      <td>-4.920961</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4881</td>\n",
+       "      <td>NM_001205252</td>\n",
+       "      <td>401934.0</td>\n",
+       "      <td>Hs.568137</td>\n",
+       "      <td>NM_001205252</td>\n",
+       "      <td>ENSG00000237330</td>\n",
+       "      <td>RNF223</td>\n",
+       "      <td>-</td>\n",
+       "      <td>ring finger protein 223</td>\n",
+       "      <td>protein-coding</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr1.108118484.108118885</td>\n",
+       "      <td>rs665659</td>\n",
+       "      <td>chr1</td>\n",
+       "      <td>108121219</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.725</td>\n",
+       "      <td>0.000393</td>\n",
+       "      <td>0.999935</td>\n",
+       "      <td>-4.118572</td>\n",
+       "      <td>...</td>\n",
+       "      <td>74124</td>\n",
+       "      <td>NM_213651</td>\n",
+       "      <td>29957.0</td>\n",
+       "      <td>Hs.656870</td>\n",
+       "      <td>NM_013386</td>\n",
+       "      <td>ENSG00000085491</td>\n",
+       "      <td>SLC25A24</td>\n",
+       "      <td>APC1|SCAMC-1|SCAMC1</td>\n",
+       "      <td>solute carrier family 25 member 24</td>\n",
+       "      <td>protein-coding</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Feature_ID        rsID Chromosome  SNP_position Ref_allele  \\\n",
+       "0      chr1.1069225.1069626  rs74048003       chr1       1067007          A   \n",
+       "1  chr1.108118484.108118885    rs665659       chr1     108121219          C   \n",
+       "\n",
+       "  Alt_allele   Freq  HWE_Chisquare  Imp_quality  Log10_BH_Q  ...  \\\n",
+       "0          G  0.200       0.156250     0.999916   -4.920961  ...   \n",
+       "1          G  0.725       0.000393     0.999935   -4.118572  ...   \n",
+       "\n",
+       "   Distance to TSS  Nearest PromoterID  Entrez ID  Nearest Unigene  \\\n",
+       "0             4881        NM_001205252   401934.0        Hs.568137   \n",
+       "1            74124           NM_213651    29957.0        Hs.656870   \n",
+       "\n",
+       "   Nearest Refseq  Nearest Ensembl  Gene Name           Gene Alias  \\\n",
+       "0    NM_001205252  ENSG00000237330     RNF223                    -   \n",
+       "1       NM_013386  ENSG00000085491   SLC25A24  APC1|SCAMC-1|SCAMC1   \n",
+       "\n",
+       "                     Gene Description       Gene Type  \n",
+       "0             ring finger protein 223  protein-coding  \n",
+       "1  solute carrier family 25 member 24  protein-coding  \n",
+       "\n",
+       "[2 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps_sig.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_sig=pd.read_csv(\"/oak/stanford/groups/akundaje/projects/cad/data/clint_data/rasqual_results/revisions/smc/smc_4_results_081921_2.txt\", header=None, sep=\"\\t\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>15</th>\n",
+       "      <th>16</th>\n",
+       "      <th>17</th>\n",
+       "      <th>18</th>\n",
+       "      <th>19</th>\n",
+       "      <th>20</th>\n",
+       "      <th>21</th>\n",
+       "      <th>22</th>\n",
+       "      <th>23</th>\n",
+       "      <th>24</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr10.100006344.100006745</td>\n",
+       "      <td>rs11592868</td>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.4625</td>\n",
+       "      <td>0.843225</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>-0.785133</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.275177</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>2.374082</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.982096</td>\n",
+       "      <td>0.993065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr10.100008240.100008641</td>\n",
+       "      <td>rs11190364</td>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100006780</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.3125</td>\n",
+       "      <td>0.647934</td>\n",
+       "      <td>0.999939</td>\n",
+       "      <td>-1.869783</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.589123</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100005358</td>\n",
+       "      <td>4.610132</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.989587</td>\n",
+       "      <td>0.992047</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          0           1      2          3  4  5       6   \\\n",
+       "0  chr10.100006344.100006745  rs11592868  chr10  100009635  T  G  0.4625   \n",
+       "1  chr10.100008240.100008641  rs11190364  chr10  100006780  C  T  0.3125   \n",
+       "\n",
+       "         7         8         9   ...        15  16  17  18  19         20  \\\n",
+       "0  0.843225  0.999948 -0.785133  ...  3.275177   2  50   4   3  100009635   \n",
+       "1  0.647934  0.999939 -1.869783  ...  2.589123   2  50   5   4  100005358   \n",
+       "\n",
+       "         21  22        23        24  \n",
+       "0  2.374082   0  0.982096  0.993065  \n",
+       "1  4.610132   0  0.989587  0.992047  \n",
+       "\n",
+       "[2 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_sig.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(all_sig.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "47"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(snps_sig.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_sig.columns = snps_sig.columns[0:25]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Feature_ID</th>\n",
+       "      <th>rsID</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Freq</th>\n",
+       "      <th>HWE_Chisquare</th>\n",
+       "      <th>Imp_quality</th>\n",
+       "      <th>Log10_BH_Q</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SNP_id_region</th>\n",
+       "      <th>Num_feature_SNPs</th>\n",
+       "      <th>Num_tested_SNPs</th>\n",
+       "      <th>Num_iterations_null</th>\n",
+       "      <th>Num_iterations_alt</th>\n",
+       "      <th>Random_ties</th>\n",
+       "      <th>Log_likelihood_null</th>\n",
+       "      <th>Convergence_status</th>\n",
+       "      <th>Sq_corr_fSNPs</th>\n",
+       "      <th>Sq_corr_rSNP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr10.100006344.100006745</td>\n",
+       "      <td>rs11592868</td>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.4625</td>\n",
+       "      <td>0.843225</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>-0.785133</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.275177</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>2.374082</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.982096</td>\n",
+       "      <td>0.993065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr10.100008240.100008641</td>\n",
+       "      <td>rs11190364</td>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100006780</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.3125</td>\n",
+       "      <td>0.647934</td>\n",
+       "      <td>0.999939</td>\n",
+       "      <td>-1.869783</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.589123</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100005358</td>\n",
+       "      <td>4.610132</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.989587</td>\n",
+       "      <td>0.992047</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Feature_ID        rsID Chromosome  SNP_position Ref_allele  \\\n",
+       "0  chr10.100006344.100006745  rs11592868      chr10     100009635          T   \n",
+       "1  chr10.100008240.100008641  rs11190364      chr10     100006780          C   \n",
+       "\n",
+       "  Alt_allele    Freq  HWE_Chisquare  Imp_quality  Log10_BH_Q  ...  \\\n",
+       "0          G  0.4625       0.843225     0.999948   -0.785133  ...   \n",
+       "1          T  0.3125       0.647934     0.999939   -1.869783  ...   \n",
+       "\n",
+       "   SNP_id_region  Num_feature_SNPs  Num_tested_SNPs  Num_iterations_null  \\\n",
+       "0       3.275177                 2               50                    4   \n",
+       "1       2.589123                 2               50                    5   \n",
+       "\n",
+       "   Num_iterations_alt  Random_ties  Log_likelihood_null  Convergence_status  \\\n",
+       "0                   3    100009635             2.374082                   0   \n",
+       "1                   4    100005358             4.610132                   0   \n",
+       "\n",
+       "   Sq_corr_fSNPs  Sq_corr_rSNP  \n",
+       "0       0.982096      0.993065  \n",
+       "1       0.989587      0.992047  \n",
+       "\n",
+       "[2 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_sig.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Feature_ID', 'rsID', 'Chromosome', 'SNP_position', 'Ref_allele',\n",
+       "       'Alt_allele', 'Freq', 'HWE_Chisquare', 'Imp_quality', 'Log10_BH_Q',\n",
+       "       'Chisquare', 'Effect_size', 'Delta', 'Phi', 'Overdispersion',\n",
+       "       'SNP_id_region', 'Num_feature_SNPs', 'Num_tested_SNPs',\n",
+       "       'Num_iterations_null', 'Num_iterations_alt', 'Random_ties',\n",
+       "       'Log_likelihood_null', 'Convergence_status', 'Sq_corr_fSNPs',\n",
+       "       'Sq_corr_rSNP'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_sig.columns \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "all_sig[\"Log10_BH_Q\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "snps_sig[\"Log10_BH_Q\"].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-40.136213949"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps_sig[\"Log10_BH_Q\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2.8759227842"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps_sig[\"Log10_BH_Q\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1984"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(all_sig[\"Log10_BH_Q\"]<=-2.8759227842)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1984, 47)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snps_sig.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "235557"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(all_sig[\"Log10_BH_Q\"]>=-2.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Feature_ID', 'rsID', 'Chromosome', 'SNP_position', 'Ref_allele',\n",
+       "       'Alt_allele', 'Freq', 'HWE_Chisquare', 'Imp_quality', 'Log10_BH_Q',\n",
+       "       'Chisquare', 'Effect_size', 'Delta', 'Phi', 'Overdispersion',\n",
+       "       'SNP_id_region', 'Num_feature_SNPs', 'Num_tested_SNPs',\n",
+       "       'Num_iterations_null', 'Num_iterations_alt', 'Random_ties',\n",
+       "       'Log_likelihood_null', 'Convergence_status', 'Sq_corr_fSNPs',\n",
+       "       'Sq_corr_rSNP'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_sig.columns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_sig[\"SNP_position1\"] =  all_sig[\"SNP_position\"] + 1\n",
+    "new_all_sig = all_sig[['Chromosome', 'SNP_position', \"SNP_position1\", 'rsID', 'Feature_ID', 'Ref_allele',\n",
+    "       'Alt_allele', 'Freq', 'HWE_Chisquare', 'Imp_quality', 'Log10_BH_Q',\n",
+    "       'Chisquare', 'Effect_size', 'Delta', 'Phi', 'Overdispersion',\n",
+    "       'SNP_id_region', 'Num_feature_SNPs', 'Num_tested_SNPs',\n",
+    "       'Num_iterations_null', 'Num_iterations_alt', 'Random_ties',\n",
+    "       'Log_likelihood_null', 'Convergence_status', 'Sq_corr_fSNPs',\n",
+    "       'Sq_corr_rSNP']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pathe=\"/mnt/lab_data2/anusri/variant-scorer/src/output/smc_caqtls/peaks_list/\"\n",
+    "new_all_sig = new_all_sig[new_all_sig['Chromosome'].notna()]\n",
+    "new_all_sig[new_all_sig[\"Log10_BH_Q\"]>-2].to_csv(pathe+\"nonsig.bed\",sep=\"\\t\", header=False, index=False)\n",
+    "new_all_sig[new_all_sig[\"Log10_BH_Q\"]<-2].to_csv(pathe+\"sig.bed\",sep=\"\\t\", header=False, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_non_sig = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/smc_caqtls/peaks_list/nonsig_peak_annot.bed\", sep=\"\\t\", header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
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+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>28</th>\n",
+       "      <th>29</th>\n",
+       "      <th>30</th>\n",
+       "      <th>31</th>\n",
+       "      <th>32</th>\n",
+       "      <th>33</th>\n",
+       "      <th>34</th>\n",
+       "      <th>35</th>\n",
+       "      <th>36</th>\n",
+       "      <th>37</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>100009636</td>\n",
+       "      <td>rs11592868</td>\n",
+       "      <td>chr10.100006344.100006745</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.4625</td>\n",
+       "      <td>0.843225</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>100010530</td>\n",
+       "      <td>SMC_m2PE_peak_14033</td>\n",
+       "      <td>6169</td>\n",
+       "      <td>.</td>\n",
+       "      <td>6.64567</td>\n",
+       "      <td>620.33539</td>\n",
+       "      <td>616.96069</td>\n",
+       "      <td>699</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100006780</td>\n",
+       "      <td>100006781</td>\n",
+       "      <td>rs11190364</td>\n",
+       "      <td>chr10.100008240.100008641</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.3125</td>\n",
+       "      <td>0.647934</td>\n",
+       "      <td>0.999939</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100015645</td>\n",
+       "      <td>100015646</td>\n",
+       "      <td>rs11593328</td>\n",
+       "      <td>chr10.100008812.100009213</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.156250</td>\n",
+       "      <td>0.999947</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100015400</td>\n",
+       "      <td>100015401</td>\n",
+       "      <td>rs12783181</td>\n",
+       "      <td>chr10.100009332.100009733</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.2875</td>\n",
+       "      <td>0.286748</td>\n",
+       "      <td>0.999936</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100001413</td>\n",
+       "      <td>100001414</td>\n",
+       "      <td>rs12261919</td>\n",
+       "      <td>chr10.100009818.100010219</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.5375</td>\n",
+       "      <td>0.079659</td>\n",
+       "      <td>0.975202</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 38 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0          1          2           3                          4  5  6   \\\n",
+       "0  chr10  100009635  100009636  rs11592868  chr10.100006344.100006745  T  G   \n",
+       "1  chr10  100006780  100006781  rs11190364  chr10.100008240.100008641  C  T   \n",
+       "2  chr10  100015645  100015646  rs11593328  chr10.100008812.100009213  T  C   \n",
+       "3  chr10  100015400  100015401  rs12783181  chr10.100009332.100009733  G  A   \n",
+       "4  chr10  100001413  100001414  rs12261919  chr10.100009818.100010219  G  T   \n",
+       "\n",
+       "       7         8         9   ...         28                   29    30  31  \\\n",
+       "0  0.4625  0.843225  0.999948  ...  100010530  SMC_m2PE_peak_14033  6169   .   \n",
+       "1  0.3125  0.647934  0.999939  ...         -1                    .     .   .   \n",
+       "2  0.4000  0.156250  0.999947  ...         -1                    .     .   .   \n",
+       "3  0.2875  0.286748  0.999936  ...         -1                    .     .   .   \n",
+       "4  0.5375  0.079659  0.975202  ...         -1                    .     .   .   \n",
+       "\n",
+       "        32         33         34   35  36  37  \n",
+       "0  6.64567  620.33539  616.96069  699   0   1  \n",
+       "1        .          .          .    .   .   0  \n",
+       "2        .          .          .    .   .   0  \n",
+       "3        .          .          .    .   .   0  \n",
+       "4        .          .          .    .   .   0  \n",
+       "\n",
+       "[5 rows x 38 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_non_sig.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_non_sig_no_peak = full_non_sig[full_non_sig[37]==0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_peaks_all = full_non_sig_no_peak[np.arange(27)]\n",
+    "#new_all_sig.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>100009636</td>\n",
+       "      <td>rs11592868</td>\n",
+       "      <td>chr10.100006344.100006745</td>\n",
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+       "      <td>0.843225</td>\n",
+       "      <td>0.999948</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100347494</td>\n",
+       "      <td>100347495</td>\n",
+       "      <td>rs1054411</td>\n",
+       "      <td>chr10.100346850.100347251</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.069444</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>100347494</td>\n",
+       "      <td>-0.130280</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.991341</td>\n",
+       "      <td>chr10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100536079</td>\n",
+       "      <td>100536080</td>\n",
+       "      <td>rs2295778</td>\n",
+       "      <td>chr10.100529316.100529717</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.2375</td>\n",
+       "      <td>2.317936</td>\n",
+       "      <td>0.999925</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100536079</td>\n",
+       "      <td>-0.049435</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.991203</td>\n",
+       "      <td>0.991137</td>\n",
+       "      <td>chr10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100535872</td>\n",
+       "      <td>100535873</td>\n",
+       "      <td>rs2295776</td>\n",
+       "      <td>chr10.100535657.100536058</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>1.202202</td>\n",
+       "      <td>0.999898</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>4</td>\n",
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+       "      <td>-0.892532</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.993748</td>\n",
+       "      <td>0.993748</td>\n",
+       "      <td>chr10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100827843</td>\n",
+       "      <td>100827844</td>\n",
+       "      <td>rs71496530</td>\n",
+       "      <td>chr10.100829265.100829666</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.004271</td>\n",
+       "      <td>0.999900</td>\n",
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+       "      <td>3</td>\n",
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+       "      <td>0.961682</td>\n",
+       "      <td>0.985755</td>\n",
+       "      <td>chr10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234202</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99075889</td>\n",
+       "      <td>99075890</td>\n",
+       "      <td>rs35783349</td>\n",
+       "      <td>chr9.99070264.99070665</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.7125</td>\n",
+       "      <td>0.286748</td>\n",
+       "      <td>0.999936</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>99075889</td>\n",
+       "      <td>-0.138820</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.991047</td>\n",
+       "      <td>chr9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234205</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99075889</td>\n",
+       "      <td>99075890</td>\n",
+       "      <td>rs35783349</td>\n",
+       "      <td>chr9.99075585.99075986</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.7125</td>\n",
+       "      <td>0.286748</td>\n",
+       "      <td>0.999936</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0.990468</td>\n",
+       "      <td>0.990468</td>\n",
+       "      <td>chr9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234206</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99075889</td>\n",
+       "      <td>99075890</td>\n",
+       "      <td>rs35783349</td>\n",
+       "      <td>chr9.99076609.99077010</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.7125</td>\n",
+       "      <td>0.286748</td>\n",
+       "      <td>0.999936</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>99075889</td>\n",
+       "      <td>1.222441</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.991192</td>\n",
+       "      <td>chr9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234264</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99821410</td>\n",
+       "      <td>99821411</td>\n",
+       "      <td>rs145592267</td>\n",
+       "      <td>chr9.99822579.99822980</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.0625</td>\n",
+       "      <td>0.177778</td>\n",
+       "      <td>0.999755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>99821410</td>\n",
+       "      <td>-0.273389</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.964523</td>\n",
+       "      <td>chr9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234271</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>99907337</td>\n",
+       "      <td>99907338</td>\n",
+       "      <td>rs12352899</td>\n",
+       "      <td>chr9.99906734.99907135</td>\n",
+       "      <td>G</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.3875</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.999947</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>99909971</td>\n",
+       "      <td>-1.231401</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.991607</td>\n",
+       "      <td>chr9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>28853 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0          1          2            3                          4   \\\n",
+       "0       chr10  100009635  100009636   rs11592868  chr10.100006344.100006745   \n",
+       "27      chr10  100347494  100347495    rs1054411  chr10.100346850.100347251   \n",
+       "46      chr10  100536079  100536080    rs2295778  chr10.100529316.100529717   \n",
+       "48      chr10  100535872  100535873    rs2295776  chr10.100535657.100536058   \n",
+       "62      chr10  100827843  100827844   rs71496530  chr10.100829265.100829666   \n",
+       "...       ...        ...        ...          ...                        ...   \n",
+       "234202   chr9   99075889   99075890   rs35783349     chr9.99070264.99070665   \n",
+       "234205   chr9   99075889   99075890   rs35783349     chr9.99075585.99075986   \n",
+       "234206   chr9   99075889   99075890   rs35783349     chr9.99076609.99077010   \n",
+       "234264   chr9   99821410   99821411  rs145592267     chr9.99822579.99822980   \n",
+       "234271   chr9   99907337   99907338   rs12352899     chr9.99906734.99907135   \n",
+       "\n",
+       "       5  6       7         8         9   ...  17  18  19  20         21  \\\n",
+       "0       T  G  0.4625  0.843225  0.999948  ...   2  50   4   3  100009635   \n",
+       "27      C  G  0.4000  0.069444  0.999948  ...   0  43   3   3  100347494   \n",
+       "46      C  G  0.2375  2.317936  0.999925  ...   1  43   3   3  100536079   \n",
+       "48      G  T  0.1625  1.202202  0.999898  ...   1  49   4   3  100535872   \n",
+       "62      G  A  0.1625  0.004271  0.999900  ...   3  34  11  10  100827843   \n",
+       "...    .. ..     ...       ...       ...  ...  ..  ..  ..  ..        ...   \n",
+       "234202  T  C  0.7125  0.286748  0.999936  ...   0  34   4   3   99075889   \n",
+       "234205  T  C  0.7125  0.286748  0.999936  ...   1  36   4   3   99075889   \n",
+       "234206  T  C  0.7125  0.286748  0.999936  ...   0  36   4   4   99075889   \n",
+       "234264  G  A  0.0625  0.177778  0.999755  ...   0  15   4   3   99821410   \n",
+       "234271  G  C  0.3875  0.000017  0.999947  ...   0  31   4   3   99909971   \n",
+       "\n",
+       "              22  23        24        25     26  \n",
+       "0       2.374082   0  0.982096  0.993065  chr10  \n",
+       "27     -0.130280   0       NaN  0.991341  chr10  \n",
+       "46     -0.049435   0  0.991203  0.991137  chr10  \n",
+       "48     -0.892532   0  0.993748  0.993748  chr10  \n",
+       "62      2.192811   0  0.961682  0.985755  chr10  \n",
+       "...          ...  ..       ...       ...    ...  \n",
+       "234202 -0.138820   0       NaN  0.991047   chr9  \n",
+       "234205  1.370013   0  0.990468  0.990468   chr9  \n",
+       "234206  1.222441   0       NaN  0.991192   chr9  \n",
+       "234264 -0.273389   0       NaN  0.964523   chr9  \n",
+       "234271 -1.231401   0       NaN  0.991607   chr9  \n",
+       "\n",
+       "[28853 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#nonsig_peaks = full_non_sig[(full_non_sig[37]>0) & (full_non_sig[36]==\"0\")].drop_duplicates(np.arange(27))[np.arange(27)]\n",
+    "nonsig_peaks = full_non_sig[full_non_sig[36]==\"0\"].drop_duplicates(np.arange(27))[np.arange(27)]\n",
+    "\n",
+    "nonsig_peaks.shape\n",
+    "nonsig_peaks\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pathe=\"/mnt/lab_data2/anusri/variant-scorer/src/output/smc_caqtls/peaks_list/\"\n",
+    "#nonsig_peaks.to_csv(pathe+\"final_nonsig.bed\", sep=\"\\t\", header=False, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_ids = new_all_sig[new_all_sig[\"Log10_BH_Q\"]<=-5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(606, 26)"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_ids.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>SNP_position1</th>\n",
+       "      <th>rsID</th>\n",
+       "      <th>Feature_ID</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Freq</th>\n",
+       "      <th>HWE_Chisquare</th>\n",
+       "      <th>Imp_quality</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SNP_id_region</th>\n",
+       "      <th>Num_feature_SNPs</th>\n",
+       "      <th>Num_tested_SNPs</th>\n",
+       "      <th>Num_iterations_null</th>\n",
+       "      <th>Num_iterations_alt</th>\n",
+       "      <th>Random_ties</th>\n",
+       "      <th>Log_likelihood_null</th>\n",
+       "      <th>Convergence_status</th>\n",
+       "      <th>Sq_corr_fSNPs</th>\n",
+       "      <th>Sq_corr_rSNP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1274</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>110683229</td>\n",
+       "      <td>110683230</td>\n",
+       "      <td>rs4917580</td>\n",
+       "      <td>chr10.110684725.110685126</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.7500</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.999932</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.308094</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45</td>\n",
+       "      <td>13</td>\n",
+       "      <td>14</td>\n",
+       "      <td>110680631</td>\n",
+       "      <td>22.947085</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.948810</td>\n",
+       "      <td>0.987099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1867</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>114875132</td>\n",
+       "      <td>114875133</td>\n",
+       "      <td>rs11818115</td>\n",
+       "      <td>chr10.114875019.114875420</td>\n",
+       "      <td>G</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.3000</td>\n",
+       "      <td>1.111111</td>\n",
+       "      <td>0.999937</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.648028</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>114875132</td>\n",
+       "      <td>11.134243</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.988630</td>\n",
+       "      <td>0.993286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2636</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>12257504</td>\n",
+       "      <td>12257505</td>\n",
+       "      <td>rs12098287</td>\n",
+       "      <td>chr10.12265735.12266136</td>\n",
+       "      <td>A</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.5500</td>\n",
+       "      <td>6.860524</td>\n",
+       "      <td>0.999954</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.573534</td>\n",
+       "      <td>3</td>\n",
+       "      <td>51</td>\n",
+       "      <td>12</td>\n",
+       "      <td>9</td>\n",
+       "      <td>12257504</td>\n",
+       "      <td>29.692450</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.978279</td>\n",
+       "      <td>0.986226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3450</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>129522932</td>\n",
+       "      <td>129522933</td>\n",
+       "      <td>rs7097774</td>\n",
+       "      <td>chr10.129525630.129526031</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.2375</td>\n",
+       "      <td>1.203054</td>\n",
+       "      <td>0.999929</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.375047</td>\n",
+       "      <td>3</td>\n",
+       "      <td>145</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>129522816</td>\n",
+       "      <td>15.877200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.979830</td>\n",
+       "      <td>0.985615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3615</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>132331784</td>\n",
+       "      <td>132331785</td>\n",
+       "      <td>rs12778987</td>\n",
+       "      <td>chr10.132331865.132332266</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>0.2625</td>\n",
+       "      <td>3.358198</td>\n",
+       "      <td>0.999930</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.427012</td>\n",
+       "      <td>2</td>\n",
+       "      <td>61</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>132331784</td>\n",
+       "      <td>13.262177</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.969459</td>\n",
+       "      <td>0.990729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238516</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>90020571</td>\n",
+       "      <td>90020572</td>\n",
+       "      <td>rs661741</td>\n",
+       "      <td>chr9.90020450.90020851</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.4875</td>\n",
+       "      <td>0.102644</td>\n",
+       "      <td>0.999950</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.473209</td>\n",
+       "      <td>2</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>90020571</td>\n",
+       "      <td>16.864893</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.992228</td>\n",
+       "      <td>0.992100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238545</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>90965076</td>\n",
+       "      <td>90965077</td>\n",
+       "      <td>rs386034</td>\n",
+       "      <td>chr9.90965256.90965657</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.4250</td>\n",
+       "      <td>0.251437</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.016518</td>\n",
+       "      <td>2</td>\n",
+       "      <td>65</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90963718</td>\n",
+       "      <td>14.941393</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.992694</td>\n",
+       "      <td>0.991096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238766</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>92729568</td>\n",
+       "      <td>92729569</td>\n",
+       "      <td>rs10821010</td>\n",
+       "      <td>chr9.92729232.92729633</td>\n",
+       "      <td>A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.3750</td>\n",
+       "      <td>5.184000</td>\n",
+       "      <td>0.999942</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.802748</td>\n",
+       "      <td>2</td>\n",
+       "      <td>47</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>92729568</td>\n",
+       "      <td>10.942855</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.993433</td>\n",
+       "      <td>0.992535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238860</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>93132604</td>\n",
+       "      <td>93132605</td>\n",
+       "      <td>rs7042420</td>\n",
+       "      <td>chr9.93132554.93132955</td>\n",
+       "      <td>C</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.4750</td>\n",
+       "      <td>1.568081</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.620640</td>\n",
+       "      <td>2</td>\n",
+       "      <td>55</td>\n",
+       "      <td>7</td>\n",
+       "      <td>22</td>\n",
+       "      <td>93132604</td>\n",
+       "      <td>15.634397</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.934826</td>\n",
+       "      <td>0.950595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239056</th>\n",
+       "      <td>chr9</td>\n",
+       "      <td>94885550</td>\n",
+       "      <td>94885551</td>\n",
+       "      <td>rs72748547</td>\n",
+       "      <td>chr9.94885416.94885817</td>\n",
+       "      <td>G</td>\n",
+       "      <td>T</td>\n",
+       "      <td>0.0625</td>\n",
+       "      <td>0.177778</td>\n",
+       "      <td>0.999755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.988053</td>\n",
+       "      <td>2</td>\n",
+       "      <td>57</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>94885550</td>\n",
+       "      <td>12.650735</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.996321</td>\n",
+       "      <td>0.980283</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>606 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Chromosome  SNP_position  SNP_position1        rsID  \\\n",
+       "1274        chr10     110683229      110683230   rs4917580   \n",
+       "1867        chr10     114875132      114875133  rs11818115   \n",
+       "2636        chr10      12257504       12257505  rs12098287   \n",
+       "3450        chr10     129522932      129522933   rs7097774   \n",
+       "3615        chr10     132331784      132331785  rs12778987   \n",
+       "...           ...           ...            ...         ...   \n",
+       "238516       chr9      90020571       90020572    rs661741   \n",
+       "238545       chr9      90965076       90965077    rs386034   \n",
+       "238766       chr9      92729568       92729569  rs10821010   \n",
+       "238860       chr9      93132604       93132605   rs7042420   \n",
+       "239056       chr9      94885550       94885551  rs72748547   \n",
+       "\n",
+       "                       Feature_ID Ref_allele Alt_allele    Freq  \\\n",
+       "1274    chr10.110684725.110685126          G          A  0.7500   \n",
+       "1867    chr10.114875019.114875420          G          A  0.3000   \n",
+       "2636      chr10.12265735.12266136          A          T  0.5500   \n",
+       "3450    chr10.129525630.129526031          C          T  0.2375   \n",
+       "3615    chr10.132331865.132332266          C          A  0.2625   \n",
+       "...                           ...        ...        ...     ...   \n",
+       "238516     chr9.90020450.90020851          G          T  0.4875   \n",
+       "238545     chr9.90965256.90965657          T          C  0.4250   \n",
+       "238766     chr9.92729232.92729633          A          G  0.3750   \n",
+       "238860     chr9.93132554.93132955          C          T  0.4750   \n",
+       "239056     chr9.94885416.94885817          G          T  0.0625   \n",
+       "\n",
+       "        HWE_Chisquare  Imp_quality  ...  SNP_id_region  Num_feature_SNPs  \\\n",
+       "1274         1.600000     0.999932  ...       4.308094                 3   \n",
+       "1867         1.111111     0.999937  ...       2.648028                 2   \n",
+       "2636         6.860524     0.999954  ...       3.573534                 3   \n",
+       "3450         1.203054     0.999929  ...       3.375047                 3   \n",
+       "3615         3.358198     0.999930  ...       5.427012                 2   \n",
+       "...               ...          ...  ...            ...               ...   \n",
+       "238516       0.102644     0.999950  ...       2.473209                 2   \n",
+       "238545       0.251437     0.999948  ...       4.016518                 2   \n",
+       "238766       5.184000     0.999942  ...       2.802748                 2   \n",
+       "238860       1.568081     0.999948  ...       4.620640                 2   \n",
+       "239056       0.177778     0.999755  ...       3.988053                 2   \n",
+       "\n",
+       "        Num_tested_SNPs  Num_iterations_null  Num_iterations_alt  Random_ties  \\\n",
+       "1274                 45                   13                  14    110680631   \n",
+       "1867                 50                    7                   7    114875132   \n",
+       "2636                 51                   12                   9     12257504   \n",
+       "3450                145                    6                  13    129522816   \n",
+       "3615                 61                    9                   9    132331784   \n",
+       "...                 ...                  ...                 ...          ...   \n",
+       "238516               57                    5                   5     90020571   \n",
+       "238545               65                    7                   4     90963718   \n",
+       "238766               47                    6                   5     92729568   \n",
+       "238860               55                    7                  22     93132604   \n",
+       "239056               57                    8                   4     94885550   \n",
+       "\n",
+       "        Log_likelihood_null  Convergence_status  Sq_corr_fSNPs  Sq_corr_rSNP  \n",
+       "1274              22.947085                   0       0.948810      0.987099  \n",
+       "1867              11.134243                   0       0.988630      0.993286  \n",
+       "2636              29.692450                   0       0.978279      0.986226  \n",
+       "3450              15.877200                   0       0.979830      0.985615  \n",
+       "3615              13.262177                   0       0.969459      0.990729  \n",
+       "...                     ...                 ...            ...           ...  \n",
+       "238516            16.864893                   0       0.992228      0.992100  \n",
+       "238545            14.941393                   0       0.992694      0.991096  \n",
+       "238766            10.942855                   0       0.993433      0.992535  \n",
+       "238860            15.634397                   0       0.934826      0.950595  \n",
+       "239056            12.650735                   0       0.996321      0.980283  \n",
+       "\n",
+       "[606 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sig_ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28853, 27)"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nonsig_peaks.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonsig_peaks.columns = sig_ids.columns.tolist() + [\"temp\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>SNP_position</th>\n",
+       "      <th>SNP_position1</th>\n",
+       "      <th>rsID</th>\n",
+       "      <th>Feature_ID</th>\n",
+       "      <th>Ref_allele</th>\n",
+       "      <th>Alt_allele</th>\n",
+       "      <th>Freq</th>\n",
+       "      <th>HWE_Chisquare</th>\n",
+       "      <th>Imp_quality</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Num_feature_SNPs</th>\n",
+       "      <th>Num_tested_SNPs</th>\n",
+       "      <th>Num_iterations_null</th>\n",
+       "      <th>Num_iterations_alt</th>\n",
+       "      <th>Random_ties</th>\n",
+       "      <th>Log_likelihood_null</th>\n",
+       "      <th>Convergence_status</th>\n",
+       "      <th>Sq_corr_fSNPs</th>\n",
+       "      <th>Sq_corr_rSNP</th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>100009636</td>\n",
+       "      <td>rs11592868</td>\n",
+       "      <td>chr10.100006344.100006745</td>\n",
+       "      <td>T</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.4625</td>\n",
+       "      <td>0.843225</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100009635</td>\n",
+       "      <td>2.374082</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.982096</td>\n",
+       "      <td>0.993065</td>\n",
+       "      <td>chr10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>chr10</td>\n",
+       "      <td>100347494</td>\n",
+       "      <td>100347495</td>\n",
+       "      <td>rs1054411</td>\n",
+       "      <td>chr10.100346850.100347251</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.069444</td>\n",
+       "      <td>0.999948</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100347494</td>\n",
+       "      <td>-0.130280</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.991341</td>\n",
+       "      <td>chr10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Chromosome  SNP_position  SNP_position1        rsID  \\\n",
+       "0       chr10     100009635      100009636  rs11592868   \n",
+       "27      chr10     100347494      100347495   rs1054411   \n",
+       "\n",
+       "                   Feature_ID Ref_allele Alt_allele    Freq  HWE_Chisquare  \\\n",
+       "0   chr10.100006344.100006745          T          G  0.4625       0.843225   \n",
+       "27  chr10.100346850.100347251          C          G  0.4000       0.069444   \n",
+       "\n",
+       "    Imp_quality  ...  Num_feature_SNPs  Num_tested_SNPs  Num_iterations_null  \\\n",
+       "0      0.999948  ...                 2               50                    4   \n",
+       "27     0.999948  ...                 0               43                    3   \n",
+       "\n",
+       "    Num_iterations_alt  Random_ties  Log_likelihood_null  Convergence_status  \\\n",
+       "0                    3    100009635             2.374082                   0   \n",
+       "27                   3    100347494            -0.130280                   0   \n",
+       "\n",
+       "    Sq_corr_fSNPs  Sq_corr_rSNP   temp  \n",
+       "0        0.982096      0.993065  chr10  \n",
+       "27            NaN      0.991341  chr10  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nonsig_peaks.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/users/anusri/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "sig_ids[\"ID\"]=sig_ids[\"rsID\"]+sig_ids[\"Ref_allele\"]+sig_ids[\"Alt_allele\"]+sig_ids[\"SNP_position\"].astype(str)\n",
+    "nonsig_peaks[\"ID\"]=nonsig_peaks[\"rsID\"]+nonsig_peaks[\"Ref_allele\"]+nonsig_peaks[\"Alt_allele\"]+nonsig_peaks[\"SNP_position\"].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "snps_all=pd.read_csv(\"/oak/stanford/groups/akundaje/projects/cad/data/clint_data/rasqual_results/revisions/smc/smc_4_results_081921_2.txt\", sep=\"\\t\", header=0)\n",
+    "snps2=pd.read_csv(\"/oak/stanford/groups/akundaje/projects/cad/data/clint_data/rasqual_results/revisions/smc/SMC_rasqual_results_FDR_0.05_083121.txt\", sep=\"\\t\", header=0)\n",
+    "\n",
+    "snps_all = snps_all.replace({\",\": 'comma'}, regex=True)\n",
+    "snps_all = snps_all.replace({\"_\": '--'}, regex=True)\n",
+    "snps_all.columns = snps2.columns[0:25]\n",
+    "\n",
+    "snps_all.columns\n",
+    "\n",
+    "preds = pd.read_csv(\"/mnt/lab_data2/anusri/variant-scorer/src/output/smc_caqtls/ATAC/summary.mean.variant_scores_new.tsv\", sep=\"\\t\", header=0)\n",
+    "preds[snps_all.columns] = preds[\"rsid\"].str.split(\",\", expand=True)\n",
+    "preds[snps_all.columns].head()\n",
+    "\n",
+    "preds[\"ID\"] = preds[\"rsID\"]+preds[\"Ref_allele\"]+preds[\"Alt_allele\"]+preds[\"SNP_position\"].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(23685, 49)\n",
+      "(601, 49)\n"
+     ]
+    }
+   ],
+   "source": [
+    "preds_sig = preds[preds['ID'].isin(set(sig_ids['ID']))].drop_duplicates([\"ID\"])\n",
+    "preds_nonsig = preds[preds['ID'].isin(set(nonsig_peaks['ID']))].drop_duplicates([\"ID\"])\n",
+    "#preds_sig = preds[preds[\"Log10_BH_Q\"].astype(float)<=-5].drop_duplicates([\"ID\"])\n",
+    "#preds_nonsig = preds[preds[\"Log10_BH_Q\"].astype(float)>=-0.5].drop_duplicates([\"ID\"]).sample(30000)\n",
+    "print(preds_nonsig.shape)\n",
+    "print(preds_sig.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        -0.785133\n",
+       "1        -1.869783\n",
+       "2        -0.340810\n",
+       "3        -0.471084\n",
+       "4        -0.525322\n",
+       "            ...   \n",
+       "238529   -0.962901\n",
+       "238530   -0.045387\n",
+       "238531   -0.052787\n",
+       "238532   -0.357810\n",
+       "238533   -0.138865\n",
+       "Name: Log10_BH_Q, Length: 238534, dtype: float64"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds_sig.shape\n",
+    "preds[\"Log10_BH_Q\"].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(23685, 49)"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds_nonsig.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['chr', 'pos', 'allele1', 'allele2', 'rsid', 'logfc.mean',\n",
+       "       'abs_logfc.mean', 'abs_logfc.mean.pval', 'jsd.mean', 'jsd.mean.pval',\n",
+       "       'logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean', 'abs_logfc_x_jsd.mean.pval',\n",
+       "       'max_percentile.mean', 'max_percentile.mean.pval',\n",
+       "       'logfc_x_max_percentile.mean', 'abs_logfc_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_max_percentile.mean.pval', 'jsd_x_max_percentile.mean',\n",
+       "       'jsd_x_max_percentile.mean.pval',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean',\n",
+       "       'abs_logfc_x_jsd_x_max_percentile.mean.pval',\n",
+       "       'logfc_x_jsd_x_max_percentile.mean', 'Feature_ID', 'rsID', 'Chromosome',\n",
+       "       'SNP_position', 'Ref_allele', 'Alt_allele', 'Freq', 'HWE_Chisquare',\n",
+       "       'Imp_quality', 'Log10_BH_Q', 'Chisquare', 'Effect_size', 'Delta', 'Phi',\n",
+       "       'Overdispersion', 'SNP_id_region', 'Num_feature_SNPs',\n",
+       "       'Num_tested_SNPs', 'Num_iterations_null', 'Num_iterations_alt',\n",
+       "       'Random_ties', 'Log_likelihood_null', 'Convergence_status',\n",
+       "       'Sq_corr_fSNPs', 'Sq_corr_rSNP', 'ID'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "601"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(sig_ids['ID']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds_sig[\"label\"] = 1\n",
+    "preds_nonsig[\"label\"] = 0\n",
+    "alldf = pd.concat([preds_sig,preds_nonsig])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import RocCurveDisplay\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "\n",
+    "\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.11659486385297094\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "keyd=\"abs_logfc_x_jsd_x_max_percentile.mean\"\n",
+    "#keyd=\"Beta\"\n",
+    "fpr_chrombpnet, tpr_chrombpnet, _ = precision_recall_curve(alldf[\"label\"].astype(int), abs(alldf[keyd]))\n",
+    "roc_auc = metrics.average_precision_score(alldf[\"label\"], abs(alldf[keyd]))\n",
+    "plt.plot(tpr_chrombpnet,fpr_chrombpnet,  linewidth=2, c='blue', linestyle='dashed', label=\"ChromBPnet w/ ATAC, AP=\"+str(round(roc_auc,2)))\n",
+    "print(roc_auc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}