example.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T01:50:21.976278Z",
     "start_time": "2018-05-03T01:50:21.205445Z"
    }
   },
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T01:50:22.536322Z",
     "start_time": "2018-05-03T01:50:22.123707Z"
    }
   },
   "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>date</th>\n",
       "      <th>var</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>min</td>\n",
       "      <td>38.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-10</td>\n",
       "      <td>min</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-17</td>\n",
       "      <td>min</td>\n",
       "      <td>39.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-24</td>\n",
       "      <td>min</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>min</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  var  temp\n",
       "0 2010-01-03  min  38.6\n",
       "1 2010-01-10  min  39.2\n",
       "2 2010-01-17  min  39.5\n",
       "3 2010-01-24  min  39.6\n",
       "4 2010-01-31  min  39.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import vega_datasets\n",
    "seattle_temps = vega_datasets.data.seattle_temps()\n",
    "\n",
    "seattle_temp_extrema = (seattle_temps\n",
    "                        .set_index('date')\n",
    "                        .resample('W')\n",
    "                        .apply(['min', 'max', 'mean'])\n",
    "                        .temp\n",
    "                        .reset_index()\n",
    "                        .melt(id_vars='date', var_name='var', value_name='temp')\n",
    "                       )\n",
    "seattle_temp_extrema.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize dataframes like this, Altair is very concise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T01:50:44.956917Z",
     "start_time": "2018-05-03T01:50:44.932728Z"
    }
   },
   "outputs": [
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       "<div class=\"vega-embed\" id=\"b1c697cd-29a3-43c7-87d1-41ef05b9f3da\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed .vega-actions > a {\n",
       "    transition: opacity 200ms ease-in;\n",
       "    opacity: 0.3;\n",
       "    margin-right: 0.6em;\n",
       "    color: #444;\n",
       "    text-decoration: none;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions > a:hover {\n",
       "    color: #000;\n",
       "    text-decoration: underline;\n",
       "}\n",
       "\n",
       ".vega-embed:hover .vega-actions > a {\n",
       "    opacity: 1;\n",
       "    transition: 0s;\n",
       "}\n",
       "\n",
       ".vega-embed .error p {\n",
       "    color: firebrick;\n",
       "    font-size: 1.2em;\n",
       "}\n",
       "</style>\n"
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jWnAegQqIcU32lHqVZtqC0Ummq46XqPltALwAJOGF/L2hq/qUUFBCwQ0v6IiNOjUJ5k+qOhgaphPgC2PZxsDdaS0Zc1O9F2Fnfznmlyw4CUTVBAxmxnek0bjaOYXLLMxjcYs6ZRedgbkBhxswROYMO4VCy4x+BwbJ+zHAdTlTGqKmmW6cmfsUQCM8HDgic+qGZNNMSzKopwD0DyeAqgu/O68HIBt1JQS74Hg9nBNCAjL5w3kgLgUQM1W8mzYzSo7tlyQbLQDJp10A4JVwatvnJGlGOGjKjK4yYCmhoITC7iwUwuGXpxFhXPiruPmMI3Kznn9vfd/uXGaI9qZ00wsh3gbTEf7He2nN3nlGoilGkInOrasofDGuAjBZYGfiwuQjJVzMTVzYKRS2zcwtJaI5xO1DsqdsjJpmumnmnkOZ0v/HQGmP8vpk0kxfDeBbACcAkM24bwEYASA9HMr5zHD45r8AOBqAZJMUTLMgkSND4Z4l9HPUy01CoSPAHAlIAeBsAL8Ip8u8GYD8u+TclvjWW6M9kRIKSijsjkJBYgQ0D9MlDvx1IEgo1zYGHmwPpM/401VHNsR7MSY8y1u40U0vBLN85JcueIhAlzFzI8Pzs2WVBf+y8Mgxi+6MXNjx3G4XkDYLBUnpfEyPKQ1dpplunJX7JQPv9ChvSCbNtCR9Em/CFYAR0Enel5PC79EjAAwDcEo4KZSc8hFxIL9vDOeJkARSIhp2KqFwdzj71d/C6TIlNaYIBxE2ouovAhB1t6gSCkoo7G5CYeTE6jzSgouI6KTwKP8rPJq/dlaB8RWjXkrbz32W+PD7tfyWA+sIdK7EmtA96Scsm3WBfLkldVnCkFRLXd/sBhxuwBAZJ7YLBaLDe5TXd5lmetvMvBUg/qcJodBVmukyANeGBUBauNcl+6skiRLvQQkASUMtZSSB1ANhoSCbmSXYmiSRun1nEgp7h5cgDgPQLDHnAUwH8G5YKIhKE1LEs7DDpYSCEgq708tRUivrrNcQ0J8Zn2hBLX/p3IKvOw4MN03EZr/kU/hutCycTvfPy8xpzvwHEf1UckWAgsfXVYzfnAxG1Sc/sucmLmwVCrNyywiYkd3u6RXZxNjZZtjfv0eTV/uOQWU9ptQnk2b6/HCWyAPDHgU5wSPL9LLMwADko1s89OJpkLwnknFSPAo7rVCQVJiiiO4Nk3o5AJn4xJMgubVlr8KJADZXVlZOY2bZjLHd5fNF9m8lM5TVvYoB9zIg5/de+Ggtlr+/WsIu46h9+mD86cOQ7tXcC3onRtbcGsStS/6NTd+3Ya/+ObhuzMHwaG5dud2JiXYBdLvE7LZZA3IB+pSARTnlDVHTTDfOypvH4PNz2nAATW+QzYaxrnhpps8CIJsWI14BER3yQX1k2Dsv9S4KexOOByDv2YhQkCWPPQH8cWfxKMjIkw0V8hCRHaDiJrkOwBnhdZZnAUS8Dcqj0IEBNylzuwZbovPGrszFOTdU7+nN0BdS6AtB9iLcUFdRJJuRol67MheJ2EeifIwuqf4JU/AdI+Q146narP9ekGjirEQxJPK8Xd3jBhxuwCAcCQ47PQpSV+Os3N8AJPsBOh2P7N+j0eu5nwi/AvjXOeUNdqSZliYl86s8h5wQjFzZ4dMPsq9PPsLl91CyE5OX2ySxbFb8AMBx4ZMP8hiCUUi8JPxMopA+jvV8aulBLT1EBr3TYiUVOEZNqv4pa/pi4+gesFoHj15eUfyh218GqeDC5By3Q7FkXkz5k6sOQ5DfIqIc2SxaV1FkhMG2eiWDwWpbbrcNN3Fht1AIi4XLGLjLCLhEvIIBLzEZaaaJcIONIsFO09iuLrcJha4edAAACVbRUSntUF4JBSUUdpWXUmfjzi9ZkA+ip8iIK8L/aG/1jDVz5NFNE/GuIN5GlD5xpgbtOaMfmEpqKwu7WluOOqepPvmRFjdxkQqhIHU2zew7mDXPGGY6BUxBgv6GCuGcMl0Tv2IlFJRQ2BWFghFiOND+EQh7MKOqLuu/4826vd00Ee8KQkHsK7+0ykdAtfxdwmG3B9ovf37uhJgBazrPXKpPdi+hEP/N5e4SO5NHwRSTSigoobCrCYUR1yzL8OR8J2vjR4Axr7ay6DJTgyFcSL2UtmfLLj5GlS4sA3h2WCysBWlldXMKHoWk0ohz2YUhXjvxfncDDjdgiMwZqfIoxOsHt/+uhILNPeSk0TvZdkca3YDDDRjsEiz5JQseI6KLwPhg7ebvTnz/wSvbrZjtrsSFleeOVdZOPkaVLBzNpN9FIAloI7vE3ge0K+sqCt7vCqudGJLhxA043IBBCYWurUgJhWRGWZR7nTR6J9tWQiG6ISXbJ6NKFlwOogeZscnDfPgztxavsWqyyWKw2l53vKCTwWQ3H8de8UBa3p57XEvM00DoyQwm4LG2YPrE5+eOjXrkzW4MifLhBhxuwJBqobC+qGhQKwd/aWRvZePUwesZgcDT/RcvVmmmEzXeZO5TSw9q6cGuL/lk7DBybzKTYH5p9bHg4JtE8OisnbGsslAirVm+ksFgubEubtjVcYwoq+6vsT6TwHIkTpOwzwTtltqsT+Z23k+yq3NhxW7cxIXgtnsfzSqf7xKQpHhHBhEZaaaZWeIiNBPxtUMWPmlHmmlJnCinBd8OH3+00gVxyyqPQlyKrBVw0uidbFt5FOz1KJx3/ZI92jNb/y3HIBPdWW+HWLFm/V2X3l3sc3TJE4fqpD1AwMkGI4zl27Kax77iv7RF9cmONuImu7BbKKwsvPAagmakmdaamq8atHSpkWZ6zejR2XpW1jwQCgh8zeCFNcmmmZZMkBJCQGI26HaOW6lLCQWbGXXS6J1sWwkFO4UC06jShRJYbDgzFtdVFknQsYQvZRfbU9ddfIwqWfB7Bt1LBGLm9wJtnnMix1m7C0M8o3EDDjdgEJ4Eh51CYbXPd6BORsyfuqHVNVHTTK8s9D0FxgiNccTgmppk0kyvDCeEEsEhERdNp5COZyNKKJhhyGIZJ43eybaVULBPKIwqXeAHaBoYn1JWzjFL/aONr5BEL2UXzggFaTW/dOHZxLw4vHfhS/amnSVJpVSf/NgnbuLCTqGwstBXSqA5rPOQoTU1UdNMry4qGsqs/48ZpUOrq5NJMy1ZI5eEU0s/biWFtJl5RXkUzLBkoYyTRu9k20oo2CMU8ssWXgyd5xGhMRjEUctvK/rSgvlFLarswjmhIC0bSxHQXiRCHgPrOagNv/e3x35o91p4InbiBttwA4ZUeBRWFfqMNNNDFtZ0mWZ6ZaHvSwK9M2RhdTJppiX/0UIxtw4Jn0ylkDZjN0oomGHJQhknjd7JtpVQSFwonFNavV8aeDyYLwZhH6mJmUfVVRbXWTC9mEVTbRfsh7fFO6CQSTuSmfcAaA8m3oOY+oB4D2bag4h76+m90qjt+xVgrg+liae1GvS1IKonoCEIfaOHvZszvS1bULJ5K5GxO9z2K9V8RAM8YtKiIR5qexZEknK46drRB2cPP/lIx+dfJ7jozI8bMKRKKDDj8KHVNV2mmV5VWLBCkh2aEApdpZmW/TASAEyEgqUU0mYGmOOGagaklTLq1IM69RAZ9G7+Yht+0+IBaZ62QoAvkvTFP9o4r9SZZi2rLJLBbsuVqomY/b32bPRmXUGEawAaZAvYSCXMOhN9R8xbANoCwtfE+uNZgXVLyW8toY1bXkxjJj3dM0jNtUQ4VRJPBhkFdRVFkg7YsStVtmHlgdyAISVCoeDCMoBmaM0tvSKbGDvzss7n69Gm0XfMetnQ6ieTSTOthIIVo1NCQQkFJ4WCHJHz6HwlSJfjSjj3mMHTnv1g9fQf3n8gLxhjCDgEBI/x74zvmbhGI8+jS+f4XjUT2c/KmLB7Im6d3fegAHuvB2gCgCzxfwBYxkw1hMB/vORpYU1v0YN6c5bW1oK07Bbs1dDy7WHvBwY+OfKIduKBIG0gCHkADwQwEEzy9z4E2kP+BFGPaM8ornswHkvT8FDG5Hr5ErN82c2HFQChmAu95xEwPtz319dWFt1tpQ47yzrJReQ53IAhFUKhobg4ty0Y+BSgRUOrq6OmmV5V6JvHjPPT2gMH5C1ZkkyaafEkPARgXCePQtwU0mbsSXkUzLBkoYyTRu9k2x0pcgOObsfg92v5zQddCeIKAvWKazIsH5R4kcCPbstqWdzx6Fzcey0WsIuLbTMGDCfSbgDh3DCEVoDne/TgrZlTN3S1Y9sobgWHLGcAPfdo9WT2Yc0zRGeWlNrFIDKWZkIvWX4HGj+c7dGfoJIN35ulxQoOs3VaLXd31T/4hY9+iLVzTm1F0fNW67CjvBu4cAOGVAgFqXNl4YW/IWhGmumOxyNDngTcD9CviPHrwdXVdqWZtsMsdqhDCQWbaXXS6J1se3cWCiPKqg/x6PpjIBwTisonqWRpERFzZ4+C8X7TaUu7nv5ErKh9NpukpRd0tLbZ37Nfkzd7PoiGG/iBzcT8p2yN7qLJ9evN4rXDPptm5v2UiS9ipmIi9Au33QxGNQH3Z0+pfyceHjtwxGsj3u+C4doH35lNRJMNLwmCB9ZVjN8c7z67f3cLF25ZJhR+7cayuqDgMgbfZQRcAlYwiVeRDwBRi8a4we0iQThRQsHmkefkwHOy7d1RKAy/6dGcNE+an0Dyle1hYAXp2qW1txb88LJyQ58kg6F5Zv9TgtAWEZGkeZeAANdlBTx/Jf8ay0c2k8HReZiKx6Eprf8vmLXxRCgAKC3kZcB7DNyX06u+iq5Fa7ThbSeORKePEIZhWn7JwldkzwIYS2sri2SzWrde7uHCHUumqRAKhmfB5xtMGsYwcIpsTwHTGyqEc7ea+vaNqT0K7hhwdqtyqyaV6gkwv2ThOQT9YRANklC9APzZX3nurKkpCO4Koon90JrS8qaC+WYQaQDe1Zh8WVPWfmu1LyLlU9UnPKd3n6Zg5lVMdD0B/cNej40a6w8Bgfuyp2ySYDQ/XKnCYYWXCIaRE6vzyBNcQaA+OnD5soqiv1qpJ9mybuIi2WdJ9n7hIlVCIVlsTt+vPAo294CTA8/Jtt06EdvcvfjZDdVZfTOC8wAqDH/BPqUx/z5WsiY39IlVDNtmDcgl1p4E4RQwBwCekX3AullUgO1EkFVureKwWj/fg4ym73IvgoZSgPYL9Q/rIHoKHJiTM2XDe/JPqcZhBndHDPklC/KJqNYQnKQfUVcx/iszddhRxm1c2PFMidahhEJs5pRQSNSqYtzn5MBzsu3dQSjIUkO6J/1FEE4EYwuDiuoqC5/ryoTc0CdWMMiGRZA2P7T+z19Bo6Kcsvp37RgmVnAk0x4zqGlObj4z3UTAaT/UxajxsF62etybX7jN45VfWiW5Ia6QVOLbsnJPeMV/RiAZDsze21194vYxEhGQyqMQvaeUUDA7okyWc3LgOdn2ri4URvgf76W1eP9OwLEMrAaCp5r58nNDn5jB0DKn/34BXZtOIDleJZ/iD2e3e65JZC9CrKFiBofJYWa6WOMt/Y9mTZtAhMsBygG4vXG/C9L6f/lYP5r8vUSuc+TqzMXp/nmZOS1Z/yTgAAbPqKsovrk7gDnRJ52fyw0YlFDo2tqUULB5NDpp9E62vSsLhbPKFvfN4ta/A3QEGP/TvWmnSrx+M6bjhj7pCoNsVtSh3QTCGEDOafAWAibkTGlYaub5rJRxkgtjOQUk8SwkBbSHwd8TUJmd1XAH3YhmK89hR9loXORPrjqMdLwPsDeo08nLby16y4623P4176RddJ63lEdBeRRSPeaM+p00eifb3lWFgkRQTPe0vgai/Rn8FXk8p9bOKoia4CWagbmhTzpjkI2Kzd4B5zO0UpCRw17W8rcx4SFCsDKnfMMPB/ztHDRu4KJ1Rt+DG4ectiJjzRuhxwY3EDA5p7yhW8+xx+Iiv2TBTUR0G8Ar2wLtBz8/d4JslE3Z5YY+cQMG5VFQHoWUDTK3vRjcNODctgaciBGMmbRgkE70mpF/gaFguIIAACAASURBVPGp5tFPe2b2uAYrdbmhTyIY2D8ou8mrS876SSDsFX6OVcy4NyfQ9mfyb9pq5dmslnUDF5EXwsCFPzuFme4D6KiwYHgppw3jaHpDV9HxrD5yzPKxuWCKHJlk8N/qKoqlv1J2uaFP3IBBCQUlFFI2yJRQiE6tGwZ+shhGl1T/hCn4GkBDwfwfCvDpS28ft8GqMSWLw2p70cp//d+Puf+iX0yGbO4j7Bn+lP5QI/2OzPZ1VcnmTjCL0Q1cRF4IImRl02Pz7NyrGZgj+xcY2Ag9eFGPqeufNftMiZbriovtj0zShcsqChcl2k68+9zQJ27AoISCEgrxxoqtvztp9E623ZFEN+BIBsPom6r3YU9QlhsGMeMjUPDMRKPmJYMjWcM0IiqmZV+ve3PKtUA4PhJjmUaYm1Ve/3Ky9Vu930kuurLPppl7DmVKWwCQJNaRBYkHstsbric/Wqw+o9ny8bgYVbrgfIAWA7yxhT37vlhZ8J3Zuq2Ui4fDSl2JlnUDBiUUlFBI1H4Tus9Jo3ey7V1FKIwqqToS4BdBJOGB3w1mBs5e7r8oYZe8E33SNLPvYB1eWeu+MpS0ydiDsMSrY1rGzQ3/SsiwbbjJCS6iwY6Gw/AuzBpwA0ObA0I6M3+epuOCVPFlhov8kqpniXAOA4/UVRRdakMX7FCFGRypaNdt84USCkoopNrOt6vfyYHnZNtuG/iJcDGqdEEhM/2NCBnMeLkxSxvzir9gWzIGlAiORNtrqtxzCAfSpgK41AhpbKRqxpObRj5R8JOjz3L8hFN3ctEVh13haJnR78AgeRaCSARjO4Om5kyuv5VIUlzYd5nhYsSkRUM81P4ZCFlE2ulL5xT8wz4EoZrM4LC7zc71uQGDEgpKKKTazpVQ6MSwGwa+FQw+X7WneZh+Kwg3Gh/fwPzGzNxL7Ah6YwVHMobaPLv/z4PsWUJAX4AlM+UCr6ZPzyxb/0V3YYiHf2fBEcojkVcO5ikg8jLjVZBe0KN8naWNrImKlY73/XAKgvF1sKnXwcvvHRk1f0U87mP97oY+cQMGJRSUUEh0DCV0n5NG72TbO6tH4bzrl+wRyGxZBNCZktcRTBNrK4vuSKjzo9zUHX3SNDP3WibcYcQHYLzqYb4ka2rD1xE43YHBDF87G45ttww4Epq2kAgHMmODh1Bg194Os1yIiG3aV/+IgMNSEYjJLA4z/ZtoGTdgUEJBCYVE7Teh+5w0eifb3hmFwrmTqg/0aMHnCCRHBbcx6Py6isIXE+r4GDelsk8kt0Hj1ty/EVGhscwAKusxpf5WN7t2nT42G3khmMVhHClN0/8I4BLZ6AFgdvb+DdO6M+9Ffmn1sWD9XRCCTDh02Zyiz+yy0VTap1mMbsCghMLOJxRkE5n8Vw9gSxi+FzDOfct6cZfuP5U9UmWPNPMyGFlWNYp0riIywvquJOLhS+eM+6/Zyc1suVRNgsZ+hPa0WmMtndEEhi9nav2yaLhShcEsB7uCZ6NpVl4BM/4GQiaY32Li85JZirDaJ/klVX8mwpVgvFVbWfQzq9zHKm8Vh13tuu3DQgmFnUsoHAPgFQC3S9peAMcDWAHgeQD/BHA6gBkAqmI9lhIKSih0LRSY8kurJKeBbPqT691mSh/x0pyxKYn7n4qJuNN+hLVeYHhGecO/3fwyMCPeUvESslM4yUbHAHmftmMpwqpdhJfIvgCoL5ivqK0s/osdfFnFYUebbvZ4CTaz3qZUcOHWOh3fCd2BmMywGDgFwHoAewPIlX4DcBAASZKSA0BSsO4PIOqRNSUUlFCI9VIa/Ycn+ulerZoIZ0gZBmqyM7WLavwFbakaoHZPxE0z865m4rtkPwIYH2a387nxognajSFRrnYFHHwHspqac+8B6DfhpYiK7P0bplpdikiEi1GlVeMBPM6M71q09H3tELeJ4Ei0/3cGIauEQvRecptQeB8wgpyIZ+ERAFcDmAJAEtRIcHbBK2vIFwGIGo9eCQUlFKIJhdEl1acwgotANABAE8B/qK0o/rPdk16qvpbY32vPJm/2HSBcbLTB/HR2oKHITFAgN7wMdgWPQse+DS9FzAMhG8D7Hj1YnDl1/edm7SnRPhlVWiWBsk5nRlVdZVGx2fbc/JJOlItknz3aWFVCYecQCv8BcD2AFwDIpizZjyDrcZL17d2wUJBlhzLxLFRWVk5jZlmi2O7y+Xx225CqbydlQGfGix/VY9n7q4ytaIP7ZuHSs/ZD/97iwNo5rsyVf0fPd2+Dp02caIRth0zAtiOu2DnA78IovdtWo/frU5G2+VMwedF46MVoPHSC8fdUXZu+b8PM6o8R1Bm/G3kgDhzcK1VN7bb1qqWHHbveTR4FWVaQzWQHhr74jKWHpwHIjmNZbhBPQmR54kQAm6NZsvIoKI9C5Ov1+vvf7v/jUgPrACq3ZebdbEd8BLOzaDJfSzwtd0BTOh4GKN9wIoD/Dei/6lG+/iOz7e9qX/JWnrs7vqKNmAveAZMhe15CMRc+IQQm5EzZ8F5XWJOxi1GlC6cB7GfmVZvaPAe8eWdBwmmyk8FhR1+4zT6VR8H9HgWR4bK8cJW48gBcB0D+7Zvw32VdeRgASdhyGBA9h7wSCkooAExPv/yB/pdnP1snSw0M/haMcXWVxaHcwt14JToRb5uRdyk0vpNAvcXWGZies3/9XKtr4W6biN3wtZZon3RlNh1jLoSOquKenOyGyXRj9HkqGQyn+1/29mhukIiNktV0Xm1l4a8BSihyZDI47BpGbsAQGSdKKLhfKAhC2ZsgIkEu8SKcBEDSvkqu+Ei61SMBfBzLSJVQ2L2FQn7ZgjFgmkPAIcZXOPPjjVmeq5INxZzopGh1EmyeOXCvIOnzCBTecMkve1i7NGvK2m+7C0Oi7cS7zyoX8epL9PdU4ZC4Fs1bB9zCoJtApIHxrUa4NFqQpmQxjJxYfRR5gv8gUC8wHq2tLLwkEbGQLI5E+6DjfW7AoIRC1z3ppqWHCNL0cCKbztnSwpvQjFgKMS8lFHZPoZBfsvAcgCuIcJQYR5+cdGzc1l64rLKw2o7JLNE6Yk2C4rJu9fTbN+jRDoZOhzDhYAIdDODYkMCRSID6DVnl6x5PtO3IfW6aiHdVj0LHPmqanXsCM8leKlk+lYCf03KmrLvF7pfjuZOfOMKr09/lyKRsbsz+SruopqYgaMVe3GAbbsCghMLOJxSs2PkOZZVQ2L2EQug0g34bCLJvRdbx1xJj1h2/Of6+A/bfz3EhHJkE+QGkNa4bcAYRXchEJxN4fyNxU+eLWQKN1WV7WiZS2XdR9+FYHSBumoh3B6Fg2OEdyGpsyv0LEcmRRrHLqpx+DRPoSrRHXkp2cDFq4vz9oWmSEl2W2JY0ZuYVWNmD4wbbcAMGJRSUULA6ryZV3kmjd7Jtu7+W4nWCEdYWegUBZ4cmYqwnRkWwqdcfJXGOG7jg23Jz1hw/a1vv18rnAxhNRB22qDMz4wsQPgTwEXT9w5yA9kG8mAjxeIn2uxu4sPPlmAgH3W2fkfZaZuSeHdCwmEA9wXgt29s6iko3f2dnn4y+qXof9gRFLAxi5rr6zVvPf//BKw1BEu+yE0e8tmL97gYMSigooZCo/SZ0n5NG72Tb3TUR+/zV6c0tQTkSWwKQxuDNBO02ysy+e6l/tJyWMS6nuDA8BxvyxhPzeSA6F0BGBJPsiBdvAUh/MaedXyf/+qRSWJs1UKe46Ixvd8XRekveoQGNXwRRHgOfeTytv1jje+9bOzwKEY5Hl1T/hCn4GkBDmfkFvan3aDOZJt3QJ27AoISCEgpm51Nbyjlp9E623R1CYVRJ1ZEAzwfRoWBsYeJ79czg3OX+i3aI0ukEF60z+h7cTt6FRHS4wQejpXXgCZmZa9++hkBLk9mQmIxxOsFFNLwdcTBAm8aP79na3NxHT0vbQ9P1nrqmfa/p+uaM9PQte86f/z0ZjiL7Lyf4kNwcenvas0R0qOw/2XTuQ/1+cly+rUtjYyYtGKQTvWachgBe2diqjYx3dNIJLtwsIAWbnQLOfut1pkZbDdWZR9i+VbVHYdfboyDHwXJa6ssAupkALwMvejT9omdmj4uZIKw7J0BmUPPs3OuZqQKEdGMrIuP87OyG57/65ZdNTk883cXFlnHj+jQFg3k6cx4BeUw8kAHj72Dk6YMH/4LWrP4KoD4EyLFPLeacwRxk4DsQthBoCxirQfw2mN7QmpvfHrR06Q/eI6vzTnfx0RkXV/br2djurSXCqaylg7h1dM7khlqr+LsqP/ymxQPSPa2yDLE/mF9bu3nrmV0tQzjFRXd8WFjlVbhQQiE6a0ooWLWmOOWdHHhOtp2qgS+poL2kLwThSAaL5+CGuopiOS7b5dVdXDTN7DtYR9oTMvmHAS3Obm+7lPybDC9Hd+HoioxUYuArrkhb892mi3SmyUS0X1c4WNMAXd9KzBKmvQVE8mczwC1gZIDQGjrxRJKhUYKrZYWDrPUA0Y9zFXMQhI+Z6Q2Q/maaTm/k1dR8Hc8mIr+nko94GIylqfW580KbHEVi8lU5k9c9EO8+K7+fVba4bya3vUdy6sKIs1B0Waz7neTCDf3Red5SQkEJBStjLeGyTg48J9u2XSj4/dqoloP+AGAmgHQGXm/3cPHzs4pXmumc7uCiaWZusQ782dikyLyNQVf3mFL/N9u5MPPAXZRJBRcrfb4saPwbYpoEoiHSPDPXE+FZMK1l0teSTmuJaK03GFw7oGfPNV9NndqcqHdlVWHhAYB+FhhnMtFZBPTZ7pGZ1zPoA434Q2b6kIPBD4csWvRFtOWLVPBhtYsalkziHiseNW4j8Pjs8oYnrNbRVfnRZU8cxLr2AQhZOuO3yyqLoooRN3DhBgwRUa+EghIKdo7DmHU5afROtm3ny3FEWfUhmh78GxEdJ+G8mVFaV1l4n5WAMqnkgv177NGUlvkQgLHGczM+1EDnR9uDkEocZg3aTgwbx4/v1RRo+x0xbgRR/9Dj8/MEbdqQhQvf6gqTXThkf8Nqn+9IEJ8FaGeCcFo4s+z2zTN/z4SPCPhAg/bAoIULZTOpa7w8A6tOvo2BiQC3E/Fp2ZPXvWm2T82Uy59UNZY0LGLmdk2nE5feVvRB5/vs6hMzeGKVcQMGJRS67kG19JCMhUe510mjd7JtO4TC6f7qHj2adUkAdh0IHoDfbIdnwnMVBV9Y7aZUcdEyp/9+Qd0jGfyGSKheALOzAw3TyY9ANIypwmGFDzswrCku7qcHg6VMuJwA8aAwE57RAvr0wYsWyRHPuJcdOKI1wj6fp17XDwp4PEcTcAyDj5E/QdSzQ3kd4EczyFO+ddas1Yl6NuI+pMkCwsWwYftqjbNyFxBRIQObvXrwBCsZKM00NbKkqlIjTALzmrZgxtHPzx0rkW5/uFLVJ2awRcq4AYMSCkooWLHZpMs6afROtp3s5DOydOEFGuv3hM+CN7KGycvmFN1rxYuQLIZ4nd96S+7h7R56mYC+YA4Q+LTsKeu6zB/hhj5JBIN8ta8tLDxaZ84HcT4YPw3vExBxVO3RecbAmpoV8ThLdZ/Eal/wr/L59iWiY0CYDEBCv8sxipbgz0/N7Pnmm737zp+/w2kZK8+TTNlIn4T2LOS9GN7j8k221nyMXYG2DHx+v5bffNDLRv2Mt7K+0k7pGL0xEdtI5rmj3esGDEooKKFgt113WZ+TRu9k24m+EIaXLxiaFqC/EOEcYyJnvKxz2oTlt16wKpmOs5uLptkDTtaZlocC53Btdq+GC+laY/Oda+3B6hfb+jFjerZkpf8CrOUTQTJW5kbqYOavCfQUM98/tKbGsocnMhE79SW/xuc7RSfqEMETmzXG7EHM91BNTVu8frT79472yf49ezWmpb9NwEHiRcvu2XCGGdsyi+mcG6r3TEsP/ktEOMD31FYUS8I947J7nJjFlOh8kUj9Zu8RLqSsUzZqFqcT5dTSg82sOznwnGzb6sD3+ao9TcOCN4Awg0CZzNgEjW6sm1O43WbARLvHTi62zRgwnDTtGQmeZITibW8YT37Il3Xcy04ccRuLUaArDGt9vv4BjX8J0FiAzibACCvNzK0EvAriZYBn2ZCFCz9LtH03vZRWFl44gnMHLtMafjhZu5J0Lh1UU7MgVXEbovHWuU+M0zPkfZ9AuWAsyplSf2GyfHe8f/TEqmN0DW8QIQPM42sri43Nk263Tzs5iFeXEgqxGVJCIZ71WPzdyYHnZNtWhMI5kxcNTAu2L47kZwDzkxTgq5bePm6DRbpjFreLi6bZA8ayTgtB5AXzX7LLG64kMh8IyC4cyfDSGcOqsWOHcJp2PoXEgRzrDMUzYN4G0HxiXkYtLS8mE6vAzMsxmWdK5t4vv/iCM8rKfCDMBNGB4Wd/X2NcOaimJpK9Npkm4t4bzS6MpS0Nr4VDfc/IKa+/OW5FFgqMKlkwDkTzmdHK0I5fVlnwLzfap4VHsrWoEgpKKNhqUF1V5uTAc7Jts0JhVGnVcPkqJ1AfMGRX+q/rKopq7O4gO7jYNjPvYiKeB4g04Jk5UxqmWsVpBw6rbXYuLxjSS0uziei3DC4iop/+UIa5iQlPgWn+EObnqKbGUvZBK9jcwEXHr2j2+7U1K1ZcwuA5klRJ3CgAPeptb5+Ut2TJdpv+rDynmbKxuGie2f9UnbQXJWFYKo5N5pdU3U+Eq8D4Xwu0Ix644tgtTrva3WQX0ndO82HGfrq7jPIo2My4k0bvZNvxhMKxVzyQNrBPb4lceGPo45XfIK+noHZWwWqbu8CoLlkummbmXc2Ee6UuYlyTPaX+vkRwJosjkTY73rPJ5+v9/bnnbNGee24TEe1p/GZEPaQXATyelpGxJO+xxxqTbcfM/U5zEcHYGcc6n69HK9FUEG4wll6YvyfCjEG9+9xFDz5oKrmSmeePN0YivzfNzC1kkjTVxrHG4VlT61+xWn+s8uFx+GrYm/fKnb85/vT993M2mqub7EIJheiWo4SCXSMwXI+TRu9k211NgsaGxSAtIeBYOdMte7Hrsj6tgN9vap0/kS5KlAuJkdCYlvkMAT+X448MuqxzECUreBLFYaWNaGWN44x64Howro0cE2TmTQT6o+bx3DNowQLblnnMYnWKi874YuFYecEF+5NHuxNEspFT4kN8RcRXDKl68iWzz2i2XDwuGmfllgM0E+BGYpydPaWhyxgVZtuVcpITIkh4m4iGHLlPH8y6criW6OkiK+3GKhuPCzvaMFOHWnqIzZISCmYsyEIZJ43eybZjCYX8kgX5xro3obdMvDpw4fKKYlPn7i3QvkPRRLgw3L7QZD9CnhEjQcMvk43FnwiOZJ57fVHRoFZdv4nBvyUiCYEM7tkT9P3WP7COPw2tqWlOpv5k7u1uLhJ9Ma0quvAsZnqQQMPCguF5r44brB4H7YorM1xsm5l7JxFdL2GuNfDorPIG2wRLODT6uyD0ZOa5dZXFE5Pp22TuNcNFMvWbvVcJBSUUzNpK0uWcNHon2+4sFK596N3DofNfCXSC8RvzXyirx/UdU0EnTXYXFVjhgv1Ib/TmzSLiP8h+BGZ8Cl0v7HHzun8mi9EKjmTaWjN6dLaelTWLCb+PnFwAsJIYla0zZtw37MADHf8o6C4u4vFoBkcoh8Xm6xk0zYj6KPsXiBayzlMTPR4aS0x3hbeDZ6EdDF/OlIan4z2f2d9Hl1Sfonn4/4I6y/i8uray+I9m77WznJn+sLO9rgSk/Kb2KOzIkOOTh90GoLJHOrveOGLSoiEnHthr5TufbWQiCWOPLQBNqK0sXGp3Xyf7xSb3t8zov3+AtEWh1NAcJGBuVnvDzeSHLWfru2MSXOPznR/U6D4CBoW/gL8CeM6Qhg2P0CuvBLoDg5m+3Rlx1J9//oBAWto0gK8EkSe8v+NvYL55aE1NwvtrrHDROHvAVWDtfvFyaeAJWVPWzTfDt5kyT7/8Pj/47OehdN6EwlRsLI6HwwoX8epK5nflUYjNnhIKyVhWlHudNHon2x7hf7yXp8VTypANYSSZ/9qYcX+Llj7zpTljN9pMc9zqzHDROHPAbwFZk0YmwF9ACxbllG2w9XicGRxxHyZGgdVFRUN1Xf8hWBWAD6DzxMGHHvoKddj/kUoMVrDvzDhW+3wH6kQVRDjPEGPMbQA9kNbePjORExJWuWiclSsZIP8aeqHz73Mmr/uTFe67+oq+7sF3rgbRfQwEdOjnLK8Y93c76jZbh1UuzNZrtZwSCkooWLWZhMs7afROtH26/2Vvj5aG3wLsB6gvM/i4/fakN7/Z+BOzmR4TJruLG7vionl23mm6zrNBdJKxYZHozpz2+inkh6Q9tvVKRZ+IW3z1lk03MWk3E5ApeQIIetnghU8+6NZsiUJqKrhIpLOSwbGy6IITwNp9BCNhWTgwFd3HzLOH1tRsMosnEQzNMweM14keM47rgqfklDfMMtteV0JBXO2RnBDM3KgTft4d+4gimBLhItnnjna/EgpKKKTCrqLW6aTRd2fb+aXz+zC0XxLTHCLkhSfNF+ChG+/59fH/cnqdLxoXjXP6HYugdyYI54Y6j78gwkXZkxveTpWB2Nknsg+BszN+wUyVRqCg0Lr5I2hrnzhkyZKYXhs7MSTD066EY1VBQQEIlQD2DpkSNwH0YJrHU5G7YMEPYR/jvaCt8tk0M88ncUhApDHzXT2mNNxgtY6O5Tv2SX5pVTUBPoA3BoN0wvLbir5Mpm6z97rJLgSz03OXWd66s5xaerCZbSeNPtVtjyypPhzQ8zVIoiCcBJAR0Y8ZH2madv3SOQX/cMuXY0cuWm/JOzTgwS2RtNDMvJUIt2a3N9yeCi9CrInYqqmxz5e+lujEIHAmgc9iohM6hFheAcalQ2tq3olXb6rtIl77bvxytONlYHh2vtv8GzAmg2hIWCy3EuFhtAdnD1m8OGa+kmT6pHFm7i8BPCnRQpnxWE6g/rJY2Uvj9U1HHCHvYP1zAJ0JcEMzZRzaHcuGyXAR7/ms/K48CsqjYMVekirrpNHb2bZMGpkt9UM8oOMJfCaDRhMwOEIOg78lYDkzausqi5Z1PIdtJ45EO0MwDH7iZ/sFNPITMD7srpUz6fdmBVoryb9lS6J1W7nPChc8YkTG6h49jmHin4PoLDB+Hjni+GObLDvP/jhEx31moyhawWDl2ayW3VVxGCckNm+eoBPKiWgfQzAA7ZLWOk3HrLyamq87c5UsF40zc0fLKQwAWWB+K1ujMTS5fn2yfTL8pkdz0rzpbxFwGIPXElNpbWWhLHeENjym4EqWC7sgKaGghIJdthS3HieNPpG2R5RVH0IIHkuMvYlpHxZXKmEfYgwFwdPhgdvA+D8mWg6dl9XdWvRJLDISwRGXWAsFJMFO434XrMr+8ukgIDvV0cLgP+cEmmaR//tuDTQUi4st48b12RYIHAXgKBAfzaCjiflgY2f9D2qMmxh4VyO8CR1vciDweldLDG7tjwgup+0i1TjY5/OsIioGeBoR7We0xxwE4aE0zXtzxyUJO7gwPGUa6kDYC8z1rPO5Vo/0RsMxoqy6v6YH7yCii8Ki531Au7KuosDWjb6p7g8LU4ZRVAkFJRSs2kzC5e2YABJt3Erb+aXzhwHaTIJMbNtfsiERxP8jphVMWAHiV9vb2196fu4EU6F+reBI9Fmj3cfTcgc0pqGMiK6STI8SAhdMD4ECt+SUb1hrZ1tm6+rIhSwlrCKaQcSFAO21XR3MAQjXTB8S8C4RvTlQ1/9p1mvQFR6n+qMzpt0Fh+SQWL1ixYUgng7QQWEeGsG4w5uRUSkhs+3iYrtIokArMV+cPaVBPA2mrq5wjCxbcAIx/TEUUVWcJDw/4Emf9NzsC2wdS3ZxYeqBuyikhIISCsnakOn7nTR6M22P/sMT/TiNpjLjKiIKpxTGlwAvJKIVFMQnyMn5bzKBkczgME2oiYI8p3efJj1zIpiuAyFb4iE07z3Sk/PVsmFZUxt2cPuaqNK2IhEu1vh8x+qE+ZFshcwiBvhDgD/wwPNB3tatH9Py5a22Ndyhou7uj1jPsLvhkAxTawovLNQNQY59DV6Y1xHoltZZs+4btv/+tuwR42p4Gj/LnRuO4ihv9Ntz2usnmUmFHr9PmEaWVV0kgbsINFBORRC0imBTz9uW3zvSFnuNjyEVo2LHOpVQUEKheyzN4SNgXQ240f6l2XpL4w3EKJGwrcacBbzIOpctu7X4PTsJ6q6Bz/7+PZrS6AZmuimUmle+e+hJjyc4eXXBW5/bsWEtWV6++vRTzrh5SgWDJkk6Z2b+wsMo6q50xoK/u/ojHle7Kw5ZklgDXKITphORsc+H+/YFrd/gG1JT82Q83sz+biSTAj0KQjoz/z0n0HpBvL04ZvtE9i6ke9JKGTSRCBkMfNPu4VPtOAJtFoNZHhItp4TCziMU0o3NOT9e4uoOAPACEFftNgBdHj1SkRm3j8zo81V7mvbVf03gWwDKDX3V4C2CNnFpZcFriQ6qru5L9cBnP7zNabnX6ExTiBDJiFjr1TE54+aGf7nl5ShehOCA/u/Rhg3GgXsQ7hmclVNKjzxie7wGJ/vDrA2l2i7cjsNYetL4SmKaYqS1Dp0YepXa28cmsvck2vOGjgB7ao18JeAvPDqfmzl1Xcxjjlb7ZGT5or20QFsdiA4VsRDQ0k5KdinCKgaz/Wy1nBIKO49QkPXyuwBIClwRBrLWLEb+PACJu386gBkAqmI9khIKEaHAlF+ysBDATKKQ21OOMUqglrrK4jqrg8hK+VQO/OZZuWcFGX8iov1DmodfIg6W5kzZsJ1XJJUY4nEhpxfW9Ow5nQmSaEeOkErOheLB1dWvx7s3Fb87yUXH51E4QmxIPIy2889r9D73XISelVogOHzQokX/taP/eXZe/yadRSz8lBmbCIFzOo+PSDuJ9Ml51y/ZI5DR8pqIBTB/3t7mOfG5OwtMB5vq/IyJ6NpfhgAAIABJREFUYLCDp2g45N/c4IlMxfMlU6cta2TJAOh0ryRgqQGwosO/jwMgG4JuNpKzAF8BkJfE1mjtKqGwL+WXLRgDCYQEHGJwJEmOmKfU3lq0KJXHnJKZfOLZUMuc/vsFg9odIBodFj2fEAX/kFO+fnm0e52afFb5fEeBIAFxDhRcgeOOQ+bH/+ohG9jiPWOqfneKCze/EJx+GUifZJaWHqRrVGfsX2DexqDCodXVy+ywA/Yjs8mbWwOiUXLqB+CiaAmlErWN4TctHpDuaXtLTkgB/PG2TM/Jr/gLxONr+UoUg+WG4tygPAqxCXKbUJDdugVhuCIYLgdQCkASCr1hpC0JeRvk2E7Unbe7s1Co+8cHfP+yT98jCoWXlY1TTDQ9+0vtgZqagqDdAytWfXYOfK7s17O5XZvCRDcAlCbHwMC4OTvY8FBXm7XsxGCWt9U+3zid8DARZcgSGTEuapkz5wU3vJScxiAcOtEnbhKRHbFEuNg4fnyv5va2JSAJcgRd9hANrq6ea9bmuioni11Ns3IfANHlhj8xSo6IZPpkePmCoekBvAWiQbKcuS2r+YxX/JdaXlZLBoMdPHX8wJG/u2Gs2PlcdtTlJqEg+xAket4TAL4AcDsAccWdA2A6gHfDQkGWHcrCnoUdONgdhcKISVUneohvA9EpYUKawHxHW7C9wuyRRjuMqeOAS3awybTWOCtvAsC3krGey40Azc3Oqq+kG9EcD293Tj5GoKSePe8G4coQLn4DbYExsu7cnTi6Q7jF472r393AhVsES0cu5DjlqhUrJBS6bHiVTcbzhzSsu0QyfybDd+Teplm51zFwZyiFOt+VU95wI0leVxvE28iyqgM05jeMPC/Ai9mZWn6Nv8BS5lU32YUSCtEtzk1CQRDKZsaIkR0P4FcAZHOaHHETT4JkJZS9CicC2FxZWTmNWZIRbX/5fD47xpfr6/hmXSOe+2A1Vqz8zsAqqWJ+ekA/jD5+CHpmGScfd8rL+/1K9HrzFqRvWmFEiW7aJx+NR/4WwYw9XPc8tHUrvH97BNrakIMr8PNTETznHEAzolurSzFgmgHtP/+Gt6oKFAxC/8lPELj4EnBWx73dpqvaoWDGmtexx2tTQHobWgadgu9OmQHWZLpN/lq9sQl3P/MJWgM6DhnaG5cP3x+a5rZXi/nnTPYjx3xLO09JN/VmHwDfAhgGQKLnzQEg+d5lBr4OwBnh354FcBgQ/atyd/AojC6rPk1nfQoBZ0dMjcHPlvuOOPekYw91vE8T/UKQ0wyN3tybCDRdjniB8Z5HC4zPnLzhM6tDKlEMVtpZ6/OdFiAsJqI9wfw9ExcOXfjkdnsmugNHPMxuwCAYFY4feyoWF6sKC48E67IRUXJHfOMJBEcNXLToP/H62MzvciKCde8LBPQB+M3s9uZRX/3qw412vBjzSxacREQvhz72eGFtRVGx2f1QbrIL4dEOPsz0x85UxvGXSieyfg/gvvC/vQUgXzwHAB4GcEn4348E8HEskndloTCqtGo4GPISFY9K5Ho+qGPa8luL3nLTgLM62FpvyT283YMnCHQYWLxKPD070FBhJmhMNFtIJRcSSGd1QcEUEMSbJa6DT7w656cipr8dk0kqubCCT+GILxSkxKrzz+/L6d4lBPo5MzdLCvchTLfbEaWzZU7usKCOFwCSD7JVG895aMhPjsu35T0wsqxqlKbjKSP0O/MfayuLrzZjH26yCyUUoveYLQZixhgslMkOLzF0Pm4j546bwrEUYla3KwoFYwCyEQfh6A4ehGd0wN8xb7ybBpxZoSC7s5u9udNCRwnJw8z/SdOpMOPm+qS+olLFxebzzttjW3raAiIKpapmfpIZE4bW1ETdN5EqHBbGk/qS70TWztAnEqRpNXEZQNNCWSLZdLbQeLYhkUwb9cwaAp1lJIDVg7Oz+zf46UpJZJXcNbJ0YbEGln1mstniD7WVRXfEq9EN/SEY1amH2D3lRqEQz666/H1XEQrHXvFAWm7fXkWSve2HY45gnUGLdNL8y+cUdDxCanDipgFnRig0zcw9kSWssfF1w3IqoyK7X8N0OyasVHCxuqDgbCY8DiBXsgNq4BsHL6yJeMCi2mUqcFgdIG7AsDPap1WerZQ32ydrCgsPDkJ/lEDHSdAuJjzco7X9pj5PPZVU9lPZLNw8a8ANupZ+O3FAqrZFoAsH+aVV5QTMBFjXiX65bE5RbVfcmOXCCr+JlFVCQQmFROwmoXuSNfoR/sd7eVq8V4L5euPYkfHRyu0gqg5A8z9XUSAnQlz7UjLzQmB/z35NadmzALoi9CD8BbRgUU7ZBtuy0yXbDx0JXufz9WgnuoPJOK4r/fGFR9OLB1Utihv62k4cCRnkTiggE31Os/ftbH1inIr45D/XgDEnnHZ8A3SeOKSm5hGzzxyr3P8+fIn3rBv/CREOliU/JvbntDdUJrrkF2lnVEnVwyBcCkZzwKOf+OzscTGXi93QH5F5S/4085GTLO872/3Ko2BzjyVq9HImOS1I1xGMI3Y9jBeSsZmTH2gPZDzw/Nyx6+JBTbTtePVa/T0WDiP0sjf3dzpBslb2BLPOhHtz2htKyS9BYey77OJidUHByUxYAGConHMH4+7B2dmTzYZhtgtHMsy4AYMZAZnMM1q51w18JIJhzdixe+le7zyQsbFbFOubIO2SIQsXWt7sG+FLcAyr2zezcWtuRSSpFID3NQ0XZpXVf2OF145lJXR8877BlwA6jRn1Oqcdv/zWC1ZFqy8RLhLF1dV9yqMQmx0lFGy2OCtG/7MbqrP6ZgTPBegyMEYYm4BC1yvQ+Y/bsvOeesV/humz1Fbatvmxt6suGo7m2XmnBRl/plCUTRFB/0gL4vfJ7kWI9RzJcsGXXJK5uqlpNmB4dmScfMMULBpatehtK9wli8NKW6niwg4MSihsz2IydrGq8MIJDO0u4/QCcxCEhzw6pgysqVlvta864pDw6Drj8VCeCGOUPuMNYkokf4rVusU7qjV73xJvBTM+aQ+2HR8trksyXFjFpIRCYowpoZAYbzHvimf0YyY93TNIzfkEvgCgfFAoCZakbwXo0SBrdz97a8GnicCK13YidSZyz3aTz5y8vXXdCJ41Njz5fEXMN2VPWbckkbrN3pMMFyuLLjgBuvY4Ee1nrAuD7gfzxFgbFuNNPk67MpPhwizfZsopHD+ylCwXa32+/kGiWwC+HETygdHIjLlpGRm3WQkX3hkH+/fYo8mb8WgkVHo4I+tir4abMybX77AvKl6/j5i0aIhG7e8SIY+ZX6jL+vRc+P16x/uS5SIeBrO/K49CbKaUUDBrRSbLRTP60/3VPXJa+QLS+QKQEWkyFOmEEWTiF8Da/PZg65Jkoyi6Z8B9wblVJx1JoN8BJMdaMxj8PUCzcvrV32HHZsV43ZEIF/WFhYcFwOUAisL1r2TSJwytevKVeO3F+j0RHIm25WYMgs0NXLgFh11crLnggoOCXq2CQL8M938DGP7BzH8xc5wyFo5Q8jUjO6sk4gt9yjCqvRycljl1g6UPmXMnP3GEN6hJXogsZvyprrLod0oo2D3KU1ufEgo289tx4I0oW3iyR+dfgyBZHOXYZ1gf8Ntgmq8F9AVLbx8nwaVsueyafBIBYyRt0rUzGThLT+9d4GkL5+xi1kF4JLsNZTS9Ie4+i0TajnaPFS7WFBYeE5QAVkTnh/QbWojxZ29GxhQrX2fJ4rDr2TvXY4WLVGFwywvaLTjs7hNjLw34PhAdFe7Dz5hxQ7wkU/FwNM0e8DPWNYkZMjKsF2Rf0RNeTZ+eWbY+5sbqznYUPuL9tBFqlXF9bWXR3ZEy8TCk0iY7Cxb5f6c9gN31vFbaUULBClsmyn7070+5/PGPbiLwbwHs96M4wGowHtSC2mNL5xZISGrbr+4YcBL3oNXTb68AaB/y0D7MdDqBTgIgkeQ6Xu+CsdxLXJNR3vBv2x82ToVmuFjl8/0MRB0nwW1M+JNXx22JrPcqodB1p5jpk+6wEzfgSAUGCQS2xucrYI0qAOwd5nKe1tR89aClSyUGzQ6XWRyNs/odA3jLAT7fCBbPrGvQz8iasv5Vs32WX1J1FRHuD5XnsbUVxcbyo1kMZttJtJxaeojNnBIKiVpVh/uMmAd9eo/UCL8iwgVspFuRL1OWnfxLSMdDtbcW/d1sSNNEIdk54PgOZLU0556kS3Q48L4ESSdr/DcwlFVi+4uBjWB+HuDlG8Yuf3TvQ4511La64mJV0YVnQddKQaEQ2My8BYR7erS235ns+fTOvNjZJ26wi0QxuO2F4PRXYyrtgq+4Im3Nli3XMXgiiAYw+CstoF84eNGiD5O1z5YZ/Q4Mal7JuzMEjCZ4AqdaOdY8qqTqLhCuYwYT8Ii3NePG2685YrPT/RGxT/nTDViSGWepuNfRyTwVD9QtAZf8fi2/5ZCjifksBp9JhJ9vt7TA+IgID7Ww9tiLlQWhjE3dcCUz+bAf6S3p/U/Q2SPLB2cQ42dGvoUdFIEsJdAaBr4mI1kXf0aEF7PaGt6NnL1OBoddNHXGsH7MmJ5tWRkXM3AtQPuHlByvY9AdmS0t9/d/5pnv7Wq7Yz1u5CIVz2mmTjdw4RbB0h1crCku7qcHg9XGcUrmAEC3DGae3XHvQiI4Qpldc+cTUTGDvwP41B7l62LGSej0SUEjSxf+mhhzidAb4I3jTh3Wd9zIExx/FymPQuxR7HjnmJlgrJRJhVCQM8GNe+NwzaOfAsZZxovUMPIfLwZ/C1DdpPMP/d1pJxzuCK9WB73scG72pl+mg0YQ0clA6ARG5GLmzwn0BjT9TQ7iay/464zg+m/J/0OGz6hdYxWHlf41WzaCYa3Pd0iQ6PcgXAwgJ3Q/f0vgWwdl9XjYbDwEs+12LucmLhJ9BrvucwMXu5NQMCwdoFUFBZMIPDMUChrvegIB36DFiyUBX8Juf66Gp+mz3KdANIoZm7wcPDFz6vrPzdrK8JsWD0j3tN4JonEhzc5vBNlzWaInvsy221U5JRSUUDBtR6f752VmN2UeRhodA8YxAB8N0JFEyOhQSRsYHzDxm6TjDZ09ry27raA+mYFnGmAXBc1OxJJFDrrnd2AaBzJSd4ffn7waRC8x4++UFnghp2TDmkRwmcWRSN1m7pHJ8dtnntG9jz/2dxCd+eM9/DZ0zB186KGLqdMRLTP1JlLGaS6ctsmOnLmBC7fw0d1crPH5jtU1kj0BQxnYSjpfPaSm5rFkcIgXstGb91zoZASvJW4/IXvKppVWxokkuuvTI/25zdvajAi0BLp9W1bz9Ff8l9oagM0MJiUUlFCIycCIa5ZlUNaWU4m0kRRKZX1Yh8BHkLU0ED4XYQCi98D6m3pT7/eX3zuyNVqlyQw8M8YcTxHHWl/je5DRuHVAAcnXNeiEH7UBPiXwox4t+GQi6ZzdxsEOu79D8fGf8eiYO6im5rVkObZ6v5P2EMHqBgxueUG7BYcTfbLJ5+vdSPQIEc4THpjxauDii0/d+5FHMqimps2qbRt1+AdlN3mDb4BIsvp+k034KU2utxT46ZNPP+eJD78vGzAnRuZeZp5LRB8FSftwecaK/3aOvZAI1nj3KKGghMJ2DEi4ZG+ARhLxSALJhrYfjy4y/gXiD5jwAQEftre3f2AlvoETE0BXL4SmmXsOBdJ+p4OuIMKeYdeBJGBaqgH3ZZU3vBRvAFn93QkO5Dy57tEqQTTmB7yMx5l5+tCaGtPHuKw+a7zyTnDRGZMbMLjlBe0WHE72yapC31UMupuANONlD2wFow7AIk9z8/JYJyRi2TrP7tm3kXPeIOAAicCYE2g6hfxbO2f/jTlUIlyMLKk+nBB8mIiO264wo5mJPwbTRwx86Alqz6fi5JgSCkooYNSk6p+yFhwr0RBJvAbhi4H1BH4WQF0Le55NdvOhkxNAx7aNvPNBlAK4VNYmw18QGzTwX4H2+626COO9EDv+3p0crPP58tqI/AD/JhyhTmdwVfsfJo4b9tOfOrJXxCkuYvVRd/ZHV3aicPzIjtNcSGRHnagguNdP7tP+978ftTVzMwjLNaYnB33//WJavjyq57RzPzdW9huEdq8EVRrK4I9z2vWTyb9+m5l5ozMXI8sX7eVpDxytEx8N4qMIkOVfybViXMxoJdDMtZu3VL7//+2dC5hcRZn3/9Uzk9sQJEBIQsJKiEgQBLmsovgpiLsIuMqCEyNEF75VXC8r7gKSKEpYQBPvl3V3vSyfioGQgSgiCIooKqAiICjg6hLQQG4oiQiTSWam63v+PecknU531zk9Z1LvzPzP8/AAU6dP1Xlv9au36lR98e1DPhq7eqDF/9ZXDztrLXogzWJIee7ZtpiRXyZsOehYV3anA75r+0mMlc9y7vPO3+gdbrzpI/N/XuRnizEDAOueedVL5g6U2t8P+DOBytau/Ezz1/Du4527r1vu3oNMjp9H5jFGsOve/ObOga2953m491UWKQ5OMXyjVMb7Z3Z3/09MPQgU6luPdGIHFKo7x4kXXLD/QEfbmxzcWwAcXKW9P8Ljv8Z5//l9ursra7CaXRyc9A/gLufcPoC/Y9I4nOguWP9s6HdZ7OKERSv3mjiw5UXecbdX/L1zcMxeeO/fctNH3xQ8xTXUBpYro9BYSqMKFI5b/IP210z4Wd9PNj33v+BxGr8hrnr1u8vwV7eX/FXf+vAZ67MYTiv3ZDH6Vp4b+s0zl+9zWO9+J9w/8Q+38fPFUnL/LR7lT+z2gQ3fC/2+yPLhkkHl+/A/P3W896XTqjIIHGLc6D0W7dfd/avqAGhhZDBcssijLwttSAOxdDKoOas6Wd3V9cJSCWd4j3fBucmVgYb3W51zy33Zf7zax+rZ4JZ/m35IXwk/SqY575nU1/tqt3jTpmb2mlcWJ7/v6qNLDlfB8TNnX/befb5vYOuiPFPE9dojUBgjoHDSohUveOVzVj34k02Dm5J5j0cALOt3pStvWTJvl8xT5zX6PAG/9t7K9ELZnwlfeh0cBuf1fOXTxavbS/hoK4e4DKU9w9FJ8xTHNZufOdGjdJr3/vXOufSzVE4x3Nrmyh/Yd/l1O40odqUeigyCRci/9hmSxY4SsSAPC21oBiy+q2vc4yXf5Xzp3XA4ZrsE/Q9dGZ/at7v7BldZ3rDzlWzn/iPAzfDAQ51b/fHNtm9vRRZdi1eM69lc/gAc3u+Adu/94865f/z2kvnfbdWHBApjBBT4mpx6+PGm/f+9XMay73x0/k9bNZpWf9eK0eepyy+evHdPR+cbk6mFl6a/5e6CPS948x6THvjqjN0WPxlME+apM++9RchgTVfXy8vcwQ04xTlXvb/DvfDlq8Z5t6xZOrSINuR970ajlNijaMlCoNDIlrPYxpr5px9dLre928PPd84ln4n735RK7f9n36uvrntWDRdRe9fB3Wh5Auujpfatx01cuHH7YoiqBmVpQ6P2n/K+5QfD4SrnkJ5x8e2yx5dvWjr/+rz+K1AYY6CwcOHCaFMqQzH6RmriJ0ib2wde7x3OhMeJ6eJEgOsN/K0e7trOvtKKVW/+8bOxO6Vmo5QsjlvnNDwOWx5xvpJq/Pqsa675bZbnDIcestRrcTQvWQgUhgIK6W+50+NAuf+tDu4j/JuH/8Wsp595eaMFj/waoqfc+T04Lkb0a9rK5ePqbco0ZPtcvLh0cu9B55a8u5wnVFbaxq/XSu4Tz47fZ9kPFx/fn8V3f/HAw35K5zgtZqwjrFY61CMBcCTL1aZM5/MI3h3OF8+ilOG6Zzh2ZszT1iEbfVJZz2V7zfS+/WQ4nAK4k7Zvp+yfBdx3nPcrJ/aXb6heWVxU3Xnet6hRdJ2vF+jst6A0cPF+y6/7Wd42jWRZ5H3X0P2ShUChCFBIn8EvJgZK7h5u3gSP62atWPGGhoOcj03r7NmKWwB3bOU8GJRfVbvdc1H2ya8lXP9W7kA5n9MRgzCDJxzwmYEJ/V/4zuIFyZG2g6392/O/1tnR0XECvDsF8H83oaNtxpK3HIkDD3xeK/1iyA1HdHlegbwDSE//2vbeKwBwG05+mx/9Gqmg4BejtLl9n2M8aLQ4JdnApCLPyn7q3n3LlcorJ23dcLNbjLq7lhXlcENVYp528OuFvi1bLnAO56dbLBMQSgMDi+odYpO1bXnakPWZrdxnoR0W2kDZqR3bLWiky2Jwa3T8DM7tBvjLZ13TfVFDWPgsxvc8Pe3awe2e/dMl4MRJF63fNi1ctCxe+4EVM/1A+R3w4GmVlb1jPPzTgPsSnL8e5cpR3Kc4noGBwfNsPNB/4IzJ7f/wqjk44oVz8/aLrYSGEfWbPALhVr8c2T1R2UEL4ErWswFcCuAQAA9ZePORAgp+8e57bmmbeGS/c0c654+Fx6sGnW7w8t5vcMD13pdXdg48eatbjGD6rGiHa1WfWdrhjzuu/YlpU98Gj0vg3NTkpX8CV7pg1jXXDHltSZY2tPp+eX5noR0W2iBQ2NFqRoNO1sybd2IZ/jtwzrmyP3Nmd/dVDWFhMUo97dOvSM5cYYT7XHu5/z/Hf/BPDw+XLLgdf2fvhDMAd54DXlDbNu8r2dlbfMl/c2u57VtfOOeoytcZFqZv88SYXXFvXlDg2ePXAPhE0riZAB4H8FcAcu3xPVwvFxMUuPf5Y/Mf2DL1uldPb/NtE5zvm9DvSxNKJTcRpfIsXy7NBXh+hDsSDs/dSQYeqz38N9pKbuWEret+nJ7GmFVWw+VwWetP72vWDk4x9JVwTrlC+256AkU/L3lcNLO7u7DPOEeCLPLKtdX7JQt7nfRo0ckT8+ad7x0+5oE+V/avnNXdfVczO332smmfg3Pv3naP9/f/5Yh3H77b3Z8d1kXYf7doxSvLfuBcAMc4uBvLDtd3ji99t3vxvG1bV2sxY2PN5QEFPuUzAOYDeBeAtQAo+NcAeNNg9gb8TG1DqwGtiN8VCQr+C+jAmmlTtowf2KNcbtvTl7APPKbD++lAaQbgZwCYwc+A4Pg3V9kSNdM1+Bnjg4DnGRL3oYSfdy5ad3em3za4yXLweWL+G15WLlc+tXrD9q1j/S9K8JfNvOba3CuUQ3KyLItQ24sulywECo1sqgjbeHzevCvhsMADG9td6cgZy5c/1syGn7l86ovgS2cDbsG2beW9L3uH2x3w9UntA93uwj8Oy7HvzdolUCgGFAgVywHMayJsHlV8Z9GBLs/z8oBC8jXBYb7kj0DZHeGdmwvv93TAHoCbArf9DIhsbfB95fbODtf37AbnXK8H1xL4Xgds9sAzDnjIlwkFA/d17v3kg+7tlQWhhV1FOH0RjUnbsbqra2LJuTeV4d/rnHth5dneDwDum65U/uTM5dcOm61Yk0URcm31GZKFQGE4QYEboT2xadMP4HCs9/5/J3WMO2qvZct2WDhYr35mYDeP2+e1vTNeft34NXcwLlR2kh28/B2VI+59+a6Jfe6OZvswtOoXtb8TKBQDCnwK99uuPm65+slUMo8l3uUkWN2IRqDAxYK9bdNfMeDKR8O5I+DdEQ7+oKpdDOtIyQ94uE3O+02A2wjgj3BYVzlSlRkVV17ryljnvFs7vnPDGvev2BwzKBdd9+Dq5tIclMu9bW1tm8YPDGyc0t39dO1GKzzWeX1X1/59zh1cAl7Qf/RRH2v7xT3c9337Ri3e/wUOV5T6Bj6178qVvy/KuYczABbRxqJ10kqbLLSB7VY7tmtvtMli0xlnTPlLf989zrnZ3vvbZ3mc4Lq7My1wpyymXXnMDLS7BUDprc7hoDp2/pj3/q4S3J2lNn/ThEXrV7XiC81+I1AoDhSmAHg9gHTRHUfE/AzlKwCCe3oXrdh6z6sFhZ7Lpr+47PwZ8HjT4B7k1Zf3Hu53lfQ/HE+LfMijvLqtrW/TBNe2sZX0V8wA0ErdfvHi0oaHH57dVy7PhfNzvXMHO+/meviDnXPJaZM7CK3MlcsANjqHTR5uGryfUrMpUvUPVjuPz07o6PhillFGUTbSiiyKqrv6ORbaYaENAoUdrWs06uTxN77x+R7+bgfszr1P2sv+pTO6u4NHTtfKYvNHpu/vy/5l3uOlcO5lgD+8JtvQ5737XGdH/+JWYnSzwQXLtJhxZwnlWaPAe28F8KqaxzCDwD2TMx8rOhwBOX0mQeG9fZ94/kCp7Ux4/xY4N3tHNsBXHfy9DuV7J4wv3Zfl0JI87Y0ZAPLU/eTrXje5d8K4dzi487d9dVD7ot5vgMNmeGyEc3t4AgGwO1c57yQT7zd4uN844OH+157y9nHfvvE16O//zYyVK//QaKvXPHLNe28eWeR9dp77LbTDQhsECqMfFPiGj8+bdzzgv88Y4b1/Cs4v2O+aa78TGsk365z9x6Z19va6vy47/zLv8B7Hwcngojie/HvRpL71X8678Ltee5RRaKylPKDQCeABAP8N4D8BpL9lRoHUWHff7zxBtepePpPGwE8xefH/+ZUAjy1teKBT76VTD/x0x3m//eeBT29/lEePh7/eObdsUt+6W7J8Zthimys/ixmUs9S9uqtrT5S4CNW9t9LpVy5Pvf7Ae7eqBDza5tyj5Z6eVfXOpWcGYtMvf7n7lo6OKQPl8h7lDoybXBr/2z2uuopTM9FlYKkNse1BsqjvyVn8ZCgxIMtvLbRhuOxz9fzTX+LKpa/CucEpBI+vj+/tfefUb32r7rR0Xln0XD59ngeWJgNUfkr+YKnk3z7p/RvuyCL7RvcIFIoBBYIBlcOTCZfU7MbITqJIUFgEYAGAw5I1ETzo434AxyX7NnBR5U6X/yQmLt26pOefBz41AI/ve/ivd453K4vOGjQzxrxGPxTDrv1ts7qfPO20GVva28+Dwz8lxzJz8dDVzvvLeCzzrmpHkfVY1UN1u2Lag0BBoBDyt+GyT3/SSeOfmDz5YsBfwC3nPdevufKZ+y2/ljv57nC10gZ+kbb5yWnvKDv3IQfslTzpLOAcAAAgAElEQVTw2pJ350+8aG1La6AECsWAAp+yEEBln++qq+iph1cm20LfAODU5HNM7j/woWTnPi5iORBA3VW1nHq4cPOnpu2KVbL1xNqK0YecOWt5bd3c9bC/t/cwlNzbvPdn8EAX7/0WB/eVkvcf3re7u+4hLVnra0bmsef5YupBoGC3g2bLLNiGhTbsClk8/sY3Hu5RXubguCkfx5P/6cs4b7/u7s1FgKxfMuU5m/vHLfRw74UDNwVkhuGzrm3ga52L/shtpjNfAoViQCGdeuDRzddWZRC4BWZRixm5LwGJswvAvwOVLTYvA0Bo4Kd06ToJZhu4j8NOV57PIzNbUI4bd3UAWN3VNdO1+bneY/bAK47/UvuPbl8O+NnwmI0dF28+C/gvdJTaPzrt6qsbTt/keNWGt+5qGVgDNoGCQCHkRxZ8ZFeAQqXjPuecjjV/3rioDHfR9j1Uyu/h1LIru7V9F1545/4f/OCERodLhWTJ8p6le87y/eO4S/BZ6f0e+I3zflkJpSsbZRl6L937oAHXfgLX3vVNOfD0P73mK1rMWEfgedYocJ0Ad85bBuDLWZSX8x4+n5DwD0Blu+JvATx1rPLvSwBwM6J0LwdOTaxaunTpxd77xbX1dHWRM1q4ymW43l5g82a43s3A5vS/eyv/79va4fedAb/vTPhxlS3Cd83lPUobN8Jt2ABsWF/597Z/tm7bWGyHtvi2Nvi994afOhV+5iyUX/xi+InVpzXvmqarFklAEpAEKIHS+vVo616B0hp+Rb/z5SdMACZPhp+yJ8pzD0L5kEPhJ0/OJby2nnWY+OjNmPjIDeB/p9fWvQ5F7+wTsXXvw9Cx8XfoWHc3Jqy/G6XeHdfgb3j99Zh96Evy9Iu52jdSb84rkCuTzyN5EFR6YiR7n3cWsH/Cwcl5EVxYx7UJvFjP9wkFyRcXTC1xrQK/z9+2eK5a+M0yCjxfYP3Uqfv1A7Odc/t7YLZ3mO3gZ3u4/Z33M+qu6K/VrudnlXjEwd3rnb+vzbv7nPf38lOgokYKg1sd+5O8dyd75/52+8LDHRvjvV/n4B6Bw28HTjrp7PabvvO6svcPzzrkkFVu8eIop3oWJYOhOJWFNrD9FtphoQ1WZGGlHWNVJ1wMvebBB0/wzu3rgenO+RkDLzzs3NIDD3BXRm7pPnPbmTeVMOt+5pxf2Y7SymnXXMNsduar58P7HOu9Owse86vP0al+gPd4yjn/Iwf88MkTv/rp/inPw5w5Oj2yVsh5QeHzCRRUP4dbNnMNQd2OO7NWB0/x4j4N3JuB81nMWvA4a04/cKto/vsAADcDOBTAtjmuZqCw/vTTD9ja7k6BdyfzGZynr4+znl9u/IGf3Aw+2/U6h154bIbzvfCOizj3g8Pz4P0hXKBThx/W+TlzprtHVn2+5MoPeV96uKOt7aEsqX460BMPPvgSOMejpdlWHuddubz3mx3crXD+dw54tOzdKn6ZMFAuP1bUXF8OPQVvtRAELbRBndKOpiKdbJeHZNFYFqu7ul5ccu5M7yqnEu9dFQd/BYdvlMq4Ks8CbC5y39wz7VQPnOVROYTvJwSD8oD/YecHNzzg3OBCfK1RaBza84JC+iR26uw46x53HOxJwjc8D8CXAHDuiEq8omru6fDkM826T2FG4c333vs3Huxs/cnbPtHZ3unyfPKHHNzD3pUfKvnSwwPe/3q/7u7M+0BwRe/a3XY7dIAnPwJHenh26oc3ghDuge7g13tf/9NOBzd+h10MB+GAmYJvOu9X7gvclmWXMwUfBeJ6TiG7sAcs0knYV5kBXjt16qsHSm4BvD9t26ZulUwDrvMe/7Zfd/evwt1JtjsECsWBAumOWQWe98DP7Hh65E8A8PPF4b64q2JPspdC3bpWd3U9b9lRR/1uwX33bivnKn843A7vbuzw/obp3d2PDkdDfVdX2+Pl8gEDZ5/927avfW2hAw6u7G4IHAznMk20ee8fdXArHfCNfVesuDPvRkUKPuHgMxy6b/ZMCzqx0AYr2RUr7ZBO8vkqv+Dq27r5VAd3HuC4di0d+n275MqX7Lv8Oh5IOKRLoFAMKDD70A3g9ORxZ3O3SwDnFDT1MCQl88ec0Fq6ZEn5zHvveaIEdxNc+UbXs+V79TYOGnJlDR5QLwDwy4S2Nn/AAFB16EnNAwbcn4ZKxwo++YLPcNlA9XMt6MRCG6x00FbaIZ207qtrurpePuDcRc7hxO28gFvbvL9sRnf37a36tUChGFBIP48kIDwn2SmRixs3JWsJftqqgor83Vj7PNJap6RAvKM1W+gQLLTBil1YaYd00joopL9cM//0owfKbR9wrrLfTnJ5nt3zAV92388znZzaBf8dew+YIvvDop6VZ40CvzjgBhZcL8C1CZOStQLMMnDx4eqiGjWU5wgU5uTR6VBE3fC3FoKghTaoU7IHTdKJPZ0M1VfXnn76If3tpfc7uPnJ2rnKIq/KF3IOt3nvvt8xfvzt06+8ctvBhVw8vvZXv3q+L5WO9M5xjdmR5b32PL7v/AsECnUie95O5a3JIsPqR/04OSiKex9EvwQKAgUrnYGVdgw1EBfl1GrH0EfRRekifY4FnRTVhnVdXbP7nXsXnH8VPF60w6fu3vfD4eeV9/bOA/4IOMfB7vZcxG67oe/c9+KAww7L2y8WrRZzz2tFIC8HKnND3D+BGYbrANTf9SfC6woUBApWOmgr7SgqEA/VndUOgUI9GxoOu9h46ql79HR0vMI7dxycP64WHLz37LN+WYL7KVz5rpJr/2nP5ZdXFrpr6mFnLeUBBd7LTxa5v0G6HoGnD94FgPAw1H0UhhqHKr8XKAgUrHTQVtoxHIG4FWdVOwQKuwoUautJwaFcwuyS83fv++dn76ndMlqLGRt7dVZQeEPyxUO9JxW14VIrsWen3wgUBApWOmgr7VAHvWOYsCAPC22Qfe5sF8oo1O+Gs4ICNz56e3JY023cxCp5HL+E4BHFH07OZyiksx/KQwQKAgUrAdBKO9QpCRQaxVQLtmGhDamvChSGBgrpr3naEg9nemwonflw/lagIFCw0kFbaYelQGxh/teCPCy0QfapjELWvjhrRiHr86LfJ1AQKFgJgFbaoU5JGQVlFMJdk9YoNJaRQCFsP7nuiBmUY9ZdLSQL7bDQBoGCvQ5aOrGnE0u+qqmHYqYecnWaMW5WRkEZBSudgZV2WArEmnoYjIrSyfbewZIsBAoChV3CLTGNPmbdyijUNy8LOrHQBnWO9kbyVnRiyT4FCgIFgcIukYCN0ZKl4BN7FC1Z2OukpRNlFHZROC6kGq1RKESMNoxewceGHpRdsZtd0Sha0NQo5GsxY+POUKAgUChYAsooCBQECiGnEtTbhHpNPWjqIeS7hZTHDAAx61bnaLdzlF1oFN1sFK2psUHpKKOgjEIhEJDlITGDcsy6BQoChZB/yD5tjqIFCgKFkO9q6iEkoZzlMYNhzLoFCgKFkKvIPgUK9WzEkl1o6kFTD6E4Vkh5TKOPWbdAQaAQciDZp0BBoBDyEpvlyigUrJeYwTBm3QIFgULIlWSfAgWBQshLbJYLFArWS8xgGLNugYJAIeRKsk+BgkAh5CU2ywUKBeslZjCMWbdAQaAQciXZp0BBoBDyEpvlAoWC9RIzGMasW6AgUAi5kuxToCBQCHmJzXKBQsF6iRkMY9YtUBAohFxJ9ilQECiEvMRmuUChYL3EDIYx6xYoCBRCriT7FCgIFEJeYrNcoFCwXmIGw5h1CxQECiFXkn0KFAQKIS+xWS5QKFgvMYNhzLoFCgKFkCvJPgUKAoWQl9gstwgKswCMB/B7AP2J2NoBPBfAMwDWNxPlkiVL/MKFC6O9V8xgGLNugYJAIRTiZJ8CBYFCyEtslkfrUOuIg21ZBOBUAHcAOAPAUQCeAvBdAPcDOA7ApQCWNxKnQGFOdJ1a6BAstIE2aqEdFtpgRRZW2iGd2IQmtir22RcWUSF6p1IllE4APwdweJJJuDwBhJkA5gL4EADeswrAgQCeridQgYJAwUpnYKUd6pR2jBQW5GGhDbLPne1CoFAfUyyBQtrCKQD+HsB/AzgAwDkAbgBwJwC291YACwCsFSjYC4AKPvZ0ok5JOmk0SrVgGxbakMYtgcLIAYUZAN6ZAMKbAbwHwCUA7k5AgdMOnKJgZmGnSxkFZRSswIqVdlgKxBbSuhbkYaENsk9lFLJOc1jKKEwFcCyAbyaNfw2AIwD8EcCjSSZhQrJW4RgAG5cuXXqx935x7ct2dXVlfX/dJwlIApKAJCAJbJOABZi1pg5LoMCvHe5J1iNsBPAvAEoAHgNwLoDjk6mImwEcCmCzMgr2UqoapdjTiUav0ommHsJdL/1EUw/2px7Srx64iJEXpxaYYeDnkFcAOCv5Oxc7PtBI7Zp60NSDFVix0g6BgkBBoCBQCEug8R2WMgppK3cHwH0T+Flk9bUPgJ5kL4WGbyRQEChY6aCttEOgIFAQKIS7SWUURhYohDXa5A6BgkDBSgdtpR0CBYGCQCHcrQgUBAphKynojphBOWbd1eKz0A4LbRAo2OugpRN7OrHkq5SOFjPu3BlanHoYUpetjIIyClY6AyvtsBSILQRhC/Kw0AbZ587QJFCo3/0KFIaEJTv/OGYAiFm3Mgr1DcmCTiy0wUqnZKUd0sl2f7EkC4GCQKFgJLDXMVhyuNgjR8nCZiCObRcCBU09NOoItEZBaxR2CSTEDkLqHNU51jN02YU6x2adY2x4s2Sfyigoo7BLYCGm0cesW1MP9jJMaYtkFwIFgUI4/CujoIxC2EoKuiNmUI5Zt0BBoBByIdmnMl7WM17KKCijEIpjhZTHDIYx6xYoCBRCDiT7FCgIFEJeYrNcXz0UrJeYwTBm3QIFgULIlWSfAgWBQshLbJYLFArWS8xgGLNugYJAIeRKsk+BgkAh5CU2ywUKBeslZjCMWbdAQaAQciXZp0BBoBDyEpvlAoWC9RIzGMasW6AgUAi5kuxToCBQCHmJzXKBQsF6iRkMY9YtUBAohFxJ9ilQECiEvMRmuUChYL3EDIYx6xYoCBRCriT7FCgIFEJeYrNcoFCwXmIGw5h1CxQECiFXkn0KFAQKIS+xWS5QKFgvMYNhzLoFCgKFkCvJPgUKAoWQl9gsFygUrJeYwTBm3QIFgULIlWSfAgWBQshLbJYLFArWS8xgGLNugYJAIeRKsk+BgkAh5CU2ywUKBeslZjCMWbdAQaAQciXZp0BBoBDyEpvlAoWC9RIzGMasW6AgUAi5kuxToCBQCHmJzXKBQsF6iRkMY9YtUBAohFxJ9ilQECiEvMRmuUChYL3EDIYx6xYoCBRCriT7FCgIFEJeYrNcoFCwXmIGw5h1CxQECiFXkn0KFAQKIS+xWS5QKFgvMYNhzLoFCgKFkCvJPgUKAoWQl9gsFygUrJeYwTBm3QIFgULIlWSfAgWBQshLbJYLFArWS8xgGLNugYJAIeRKsk+BgkAh5CU2ywUKBeslZjCMWbdAQaAQciXZp0BBoBDyEpvlAoWC9RIzGMasW6AgUAi5kuxToCBQCHmJzXKLoDADwHMAPAHgL4nY2gE8F8AzANY3E+WSJUv8woULo71XzGAYs26BgkAhFOJknwIFgULIS2yWR+tQG4hjHoDPAbgCwEIARwN4GMB3AdwP4DgAlwJY3kicAoU50XVqoUOw0AbaqIV2WGiDFVlYaYd0YhOa2Ko5c+LHUGu4EL1TqRJIZ5Ix2AvAUwBeDYDg8EMAcwF8CADvWQXgQABP1xOmQCG+kVsIghbaoE5pRw+VTmx2jrE7Rkt2IVCojyiWQIEt3AfABgBs12cA/B7A3gBuAHBn8vdbASwAsFagoEDciLwtBR8F4kEtSScCBU09WMsVZGuPNVBgq2cD+CaA1QDOBLAMwCUA7k5AgdMOi5hZWLp06cXe+8W1r9rV1ZXt7XWXJCAJSAKSgCRQJYHYYG9RGdZA4XgAtwF4LYAbE4G9DcCjAJhJmJCsVTgGwEZlFJRRUEYhHFY0krfnJ9KJzeyKph7sTz0QAh4EcAaAewBMArAVwCkAzgVAiDgAwM0ADgWwWaBgLwBaSTErENsMxBZGaxZsw0Ib5Ks7x0+Bgn1QmAng3mSdQtradwP4j+QriLOSPx4O4IFG4ygtZtRiRisB0Eo71CnZA2rpxCbIChTsg0Ioh8qFjj3JlxEN7xUoCBSsdNBW2qFOSaBgeYrOkn0KFEY+KIRAolIuUBAoWOmgrbTDUiDW1MNgGJNOlFHI1KEZucnaYsYhi0WgIFBQILY3gpZOpBPLWY3UPpVRUEZhyBCS5QExRwox666WjYV2WGiDlc5RsrDXSUsnyihk6U+s3KOMQsGaiBkAYtYtUKhvSBZ0YqENVqDJSjukE4FCwV3PsD5OoFCweGMGgJh1CxQECiFXkn3a7BxjrxuxZBeaetDUQyiOFVIe0+hj1i1QECiEHEj2KVCoZyOW7EKgIFAIxbFCymMafcy6BQoChZADyT4FCgKFkJfYLNfUQ8F6iRkMY9YtUBAohFxJ9ilQECiEvMRmuUChYL3EDIYx6xYoCBRCriT7FCgIFEJeYrNcoFCwXmIGw5h1CxQECiFXkn0KFAQKIS+xWS5QKFgvMYNhzLoFCgKFkCvJPgUKAoWQl9gsFygUrJeYwTBm3QIFgULIlWSfAgWBQshLbJYLFArWS8xgGLNugYJAIeRKsk+BgkAh5CU2ywUKBeslZjCMWbdAQaAQciXZp0BBoBDyEpvlAoWC9RIzGMasW6AgUAi5kuxToCBQCHmJzXKBQsF6iRkMY9YtUBAohFxJ9ilQECiEvMRmuUChYL3EDIYx6xYoCBRCriT7FCgIFEJeYrNcoFCwXmIGw5h1CxQECiFXkn0KFAQKIS+xWS5QKFgvMYNhzLoFCgKFkCvJPgUKAoWQl9gsFygUrJeYwTBm3QIFgULIlWSfAgWBQshLbJYLFArWS8xgGLNugYJAIeRKsk+BgkAh5CU2ywUKBeslZjCMWbdAQaAQciXZp0BBoBDyEpvlAoWC9RIzGMasW6AgUAi5kuxToCBQCHmJzXKBQsF6iRkMY9YtUBAohFxJ9ilQECiEvMRmuUChYL3EDIYx6xYoCBRCriT7FCgIFEJeYrNcoFCwXmIGw5h1CxQECiFXkn0KFAQKIS+xWS5QKFgvMYNhzLoFCgKFkCvJPgUKAoWQl9gsFygUrJeYwTBm3QIFgULIlWSfAgWBQshLbJZbBYWpAP4EoJyIrR3AcwE8A2B9M1EuWbLEL1y4MNp7xQyGMesWKAgUQiFO9ilQECiEvMRmebQOtYE4xgHYD8BNAF4K4CkAkwB8F8D9AI4DcCmA5Y3EKVCYE12nFjoEC22gjVpoh4U2WJGFlXZIJzahia2aMyd+DLWGC9E7lRqBvDoBgQMAzAWwEcAZyX9/CEAngFUADgTwdD1hChTiG7mFIGihDeqUdvRQ6cRm5xi7Y7RkFwKF+ohiDRTYyg4AvwZwTAIKHwFwA4A7AbC9twJYAGCtQEGBuBF5Wwo+CsSDWpJOBAqaerCWK8jWHougMCGZZkhB4dsALgFwdwIKnHZYlGQWdnpLZRSUUVCnJIBsFv4sAIuFNljxE0uyUEZh5GQUakHhbQAeTTIJO5QtXbr0Yu/94tpX6+rqyoZJuksSkAQkAUlAEqiSQOwMoEVljISMwukAzgVwPACuXbgZwKEANmvqQSNHTT2Ew4qlEZuFIGxBHhbaoIzCzvFTGYWRlVH4WfKFAxczEmauAHBW8gqHA3igUXjU1IOmHqwEQCvtUKdkD6ilk+06sSQLgcLIAYVGDLAPgJ5kL4WGwyiBgkDBSgdtpR2WArEyCoOhSzoRKIRzgXbusDj1MCTpCBQECgrE9kbQ0ol0MhKmCZVRGPkZhUwAIVAQKKhTUqfULFhYGM1baIMVP7EkC4GCQCETaAz1pphGH7PuarlZaIeFNigQC1hGwig69nSQJV8VKAgUhsoAmX4f0+hj1i1QqG8eFnRioQ1WoMlKO6QTrVHI1KEYuUlrFApWRMwAELNugYJAIeRKsk+bnaMyCoN6oX0qo6CMQiiOFVIeMxjGrFugIFAIOZDsU6BQz0Ys2YVAQaAQimOFlMc0+ph1CxQECiEHkn0KFAQKIS+xWa6ph4L1EjMYxqxboCBQCLmS7FOgIFAIeYnNcoFCwXqJGQxj1i1QECiEXEn2KVAQKIS8xGa5QKFgvcQMhjHrFigIFEKuJPsUKAgUQl5is1ygULBeYgbDmHULFAQKIVeSfQoUBAohL7FZLlAoWC8xg2HMugUKAoWQK8k+BQoChZCX2CwXKBSsl5jBMGbdAgWBQsiVZJ8CBYFCyEtslgsUCtZLzGAYs26BgkAh5EqyT4GCQCHkJTbLBQoF6yVmMIxZt0BBoBByJdmnQEGgEPISm+UChYL1EjMYxqxboCBQCLmS7FOgIFAIeYnNcoFCwXqJGQxj1i1QECiEXEn2KVAQKIS8xGa5QKFgvcQMhjHrFigIFEKuJPsUKAgUQl5is1ygULBeYgbDmHULFAQKIVeSfQoUBAohL7FZLlAoWC8xg2HMugUKAoWQK8k+BQoChZCX2CwXKBSsl5jBMGbdAgWBQsiVZJ8CBYFCyEtslgsUCtZLzGAYs26BgkAh5EqyT4GCQCHkJTbLBQoF6yVmMIxZt0BBoBByJdmnQEGgEPISm+UChYL1EjMYxqxboCBQCLmS7FOgIFAIeYnNcoFCwXqJGQxj1i1QECiEXEn2KVAQKIS8xGa5QKFgvcQMhjHrFigIFEKuJPsUKAgUQl5is1ygULBeYgbDmHULFAQKIVeSfQoUBAohL7FZLlAoWC8xg2HMugUKAoWQK8k+BQoChZCX2CwXKBSsl5jBMGbdAgWBQsiVZJ8CBYFCyEtslo8UUGgH8FwAzwBY30yUS5Ys8QsXLoz2XjGDYcy6BQoChVCIk30KFAQKIS+xWR6tQ80hjkkAvgvgfgDHAbgUwPJGvxcozImuUwsdgoU20EYttMNCG6zIwko7pBOb0MRWzZkTP4bm6B93ya3RO5UMb3kGgLkAPgSgE8AqAAcCeLrebwUK8Y3cQhC00AZ1Sjt6qHRis3OM3TFasguBQv0eeSSAwkcA3ADgTgBs760AFgBYK1BQIG4EmpaCjwLxoJakE4GCph4yDI0N3jISQOHbAC4BcHcCCpx2WMTMwtKlSy/23i+uluukSZP6e3p6uKZBlyQgCUgCkoAkkFkCc+fO3XTqqadOyfyDMXLjSACFtwF4NMkkTEjWKhwDYKNFHcWc+ohZd7UuLLTDQhsoEwvtsNAGK7Kw0g7pZHvEkCws9mQ7tmkkgMLpAM4FcDyAAwDcDOBQAJstijem0cesW6BQ3xot6MRCG6x00FbaIZ0IFCz2X43aNBJAgW28AsBZyUscDuABq0KOGQBi1i1QECiEfFL2qc6xno3ILkKeE798JIBCKqV9APQkeynEl1yDFsQ0+ph1CxQECiGnlH0KFAQKIS+xWT6SQMGmBGtaxQWWF154IRdf7vIrZt3VL2uhHRbaQJlYaIeFNliRhZV2SCfbI4Zkscu7itwVChRyi0w/kAQkAUlAEpAExo4EBApjR9d6U0lAEpAEJAFJILcERjso8P1eAmBilWS4x8LtALbWkdb+ALhl9EMBSe4HoATg9zX3TQbQn+GLDLZhDoDemmdMBbB78jc+J714P//+VNXfWNe+ANYA+EtGzfMZRyabV9V7/4yPgYX3bySrrO9A23g5gJ81sIWsz8mjs0bP5Nc8tCfuOlpObmqmX9b5ZI19ZDoLpU4DZgGYmcgh6zs3u2+o8mjkG3nk0YpvpO/0CgCvTPTAreO5f0srVyMfqefL9Z6fN0bwGbV20Yoc0hhB/2AbuAMuZZDaZSuySNuWNbZZ9JNW33tU/G60g0K65fNnAPw52bCpA8AXATxbR4P8smJvAB9vol1u8MR9HP6UdNzvTe7dA8AXAHwy6YgbPYKbefwQwE+SAM3nnAPgRAD/D8BVAF4H4CgAmwA8B0AXgGkALk8eOjvpVD4K4H0Asn4JciaAryeB8EctWrCF9z+5gazyvBJtg4eM7VUDYHme0agd9XRW77n0v39PoJGQQBsg2DLA8/9r9UuIpa6/DOAwAAMJ2GY+C6VOI64GcErShmr4yCOH9N6hyqORb/xVDnm06hupLv462S6+LZH/dQAuBuDrCOQ0ADMAfL6mrJ6PUFdZ7SJvjKhnF63K4eAEGt+TwMGrARxbFY9i2IUFP2nlvUfNb0Y7KKQbNDGobqnRGp2RHTs74QsBfALA3yefYj6Y7NnAQHBH1e+eB+BGAC9InIjbSS9MRncEBP6egeYXTSyEG0hxtMGzKziKvA8AO/DrAbwUwAYAdFICDTuRzwHgb95dFZAY3P8ryYwcAeArAPjvZtTPuggo3OmS70E4GQ/gs0lHyfdgx8R2Mfhx7woGiHdWvY+F96dOqZ9aWVF/ea7qzbv+D4DHAdybyIaZBu4AStlwRHVe8s+nqjqMRu1opLN6bWNQ/iYA/pu643bl9wDg3iH19Esopa0yC3Jq8ptcZ6HUNIJ6pk0QNGjn6WFrtAX6Bjc1I7SmsnlXYpOUC0ebhNQUuIuQRz3foH9ycXBWebTiGxQLO0TaEDNu7NR50Qfpy28B8DsA1D/95r+5ThXAb5P7qkG9kY/wk+56vlzPLvLECLb5X+vYRaty4Dk6HEi9qsrW+c7MgNL2XpjYLLMXb0riIUGlWjaMHUXaRWw/yRNXRuW9YwEUGHivTdLz7BgZZL+f7PT4aQDfS0bZHDn0JQHgBADTk86bjp+m9k8C8KIkoNMgGCj5fD6PF52TwNAsXcn0GzttZgvolPw9O70VVaNEdtB/m4xk+Fx2HJwWYVBg8GJQ5yiQI8B09MEMRPV0Ra3B0tkuAnA2gF8ldRKefg5gWeLoVyYgweHwmIIAAApsSURBVOczCL4WwG1VUykW3p+p8luayCqro1aDAjtDdgQ8T+SQ5L2ZhSKQXJDYDOXAlPS6pIJQO6p11qhN4wAwE0U4pF38GMDbE0BppF/aAaEyBcNcZ6HUNIQZNNrAXUnAJ1ATWJjVIhRwqusfEtkwA0MIelkCKR9ODmtLd0gtQh71fIMdFOE8izyYBWjFNygWyoKZHHbm1ReBib5JkKBsmNUjKP00uZ+wxS3lU7gI+UgWu2glRlTbxVDkwJjEeFk98EgHI4QH7pLLrNcfExnwfRlTamXDrBevIuwitp9kjSmj9r6xAgoMcAxoHAX9L4CHk46Qo3ROSdDw2cn+Iel4eT+va5JpiLTjZzBhqjodvfL/OeJc2eD+RoZDuf8jgC8BeE1SLwMSj9Gmw7FTf0eSWeAzmHEguLBeBuzfJB0l34n/z2kETocQdBpdzBQw9UkH/iqAy6qO735xIgdCEdvEToKjKI4sqy8L70/ZNJNVVmdtBAp8PsGBNtAN4O+SToAQyE6EMMEr1I5qnYXaxF1HCSLvT+qgfbLTrqdfnqRKu0wDecOzUAKVElZpNxwJcp0L34udMjsC2gAhuBoU/jnpIAkVhNVfJzaXgkJR8qj1DU7RNbP3anlwLVIrvpH62J51QIF654ZvtId063gCBX2JmSiuE6rOZoV8JKtd5I0RRcmhHiikf/u/STaBGQaCEWMKBx/0x1rZMAs1Gvwk5LtjonwsgML9STqxek0CKZcj+X+pStcTIA4CUB0sONJm2jNNMTLtxlQvRxC8ajMItWBRz4iY1WAGgsGZMMA1Cuzsf5CM1ggsTOlVB6Dq4EKdsR6m93iCJjt3ZkOaTT3w+cwccKqEHSRHLPzbGwH8MpkuIfBUgwLny9mJVF8W3j8kq2aOS9lzxM4On/9N22CAIxj8TzJCSjMK7BjYQVDfDIq1ug21I2uHQFvilAIhkYAQ0m9tIG/1LBR2LLQJptEJDRwx/0fyD0GBmRRCQJpRIMSwU6C91AOFIuRRzzfyyIPrCPL6RmovnOJgx89pv/RKYYqpfWbbOK1IP+EIl1DCjFsK8OlvQj6SxS5aiRHVdjEUOdQDBWYMGI84Nce4Q/ugDfBihpbb6tfKhgMwXkXYBZ8Ty0/GBAiEXnKsgELtIVI0cgY8juZXAyApp9MBDBRMsXFRIzsSOg6DAy8uEGLKkZ0Jz5rgKOwNSWqW5bWdCUdZTFVXH2DF1COD7/zkmWwL078EF2YZ6HhMe3O0xxQgr9rgQoInvHAkw7lqLiIj5Te62AnQ2fkcXqyTIy++P+fI/ylJe9MZmVLkKI6dJ+dhq6+878+5W3Z+1V9YDPX9CTrNZNXM5tNsDNuVfmXArwVen8zHMtPCtDKnIQgJ1RmFWt2G2lGrM3Yo/M1jVQ1M5ZlmD9j5EGi5OK6RfmszCq2ehcIUOqdWOM3Ai5DIKR0+nwteCchcUEl7pF1yymE3AJxy+JvkHt6b2nZeeWT1DXY4zey9Vh55fSNVB/2dU3nMIDFLw4uZNcYD+jv9hPDITpHTPdQj/bY6w5glRtTaRVY5hGJEUXJgvOP7s+PnxcETgZI+wylYDjb4N8Iz/ePSZDqmVjbUA6+8dmHNT5rFkzFTNhZAgYGOo7Xa0yb5N47iebHzZ/AjIHD+N70YhNNphfRvhAqOwnixw/9a1f1M69NB0qkKOhyDSvWCSKbz+bf04vw0nZydBjtAXnw+O/50cSJhhB1amuLkQiIuQuOiSgbz9AuJRobLdnFExM9C04tt4OedTCnzObyYLjw6yaowYHJxY+2V9f0ZIAhTtV8VFPH+XHDWSFaNZJD+nSPj9OuRdIEoR4HV54fw71wgWp1RYPaI8q9eqNqsHbU6o63wM0iuoE8vAhzXyFRfnAZiJ9RIv+zQ2a50mqqVs1DS4M21OOmaC07LEXyZoWBHQDBKL3Ya/GSYtr0g+SPtrvZwtjzyyOMbbFtWeeT1jWrZE9g4OEgvxgVmDZj1Y4eefjad+hwzk1zvU7uAuZmP1NpFHjk0ixG1dtGqHPgcgnL1xbiYxkpmV9I4xPjAwQXtpVY2XIOVXnnswpKfhGLJmCkf7aAQUiRX63J0nabJ0vv5d8qm3ieUvCc9rzx01DXT1sxYcJFalov1ctRGeAhdXLDEaRK2odkixmbPSTsMLtLjqD/NnITqzvL+lB8zI/xUrHr/h2bPzvP+ee6trZNTL2xftd6ZTuY//GQyz5W1HYRBZmnqwVe9+vLqt+izUDgPT5tIvxYiwNBemOliGpqAQ8Cq/dImqzzy+kYeeeS5t1b2/C3hliNmAkL1xTLKoDou0Gbq7UmSxUf47LxyyCpfPnsocmjmA7QNTm9U+0o92VQ/I2u7R5qf5IkVI/besQ4Kw604BlKmd4e6WclwtZNBj6OHNPVdZD0cBXKkwffXNZgxYseTFcasySwdaX4j+QyY8/mcomr1su4brb5X3t9JDjtKbKT7SV79j4j7BQojQk3D2shGI6JhrVQPH5ES4KiRG38RdvJmXkbkC6vRkoAkMJh+1SUJSAKSgCQgCUgCkkBdCQgUZBiSgCQgCUgCkoAk0FACAgUZhyQgCUgCkoAkIAkIFGQDkoAkIAlIApKAJJBfAsoo5JeZfiEJSAKSgCQgCYwZCQgUxoyq9aJjSALcXY87O3IjLm67W+/i56vc8Innm6Sb5YwhEelVJQFJIKsEBApZJaX7JIGRIwGeJMjDnZodeZ5u483zPNJD0EbOG6qlkoAksMskIFDYZaJWRZLAsEqAh4LxsCtmE3iYGQ88Iyhwq+u3Avhislf/x5P7ePIfD6Pi/v08hIx7+PMgKO7dz+2sudU1j3fWJQlIAmNcAgKFMW4Aev1RIQEeaJTugEkA4BHRPFeCoDAzOdCIBxxxy2oe+MSDjnj416cB3Afg/OQMh3cmB0DxXAMeCsWDoHistC5JQBIYwxIQKIxh5evVR40EeFYHD016aXLAGTt4HkRGUHgkOWGUUw08AZAwwEOvmGHgAVfMQvDAK54QSOA4D8BfJQdnESDSA4BGjbD0IpKAJJBPAgKFfPLS3ZKARQmcBOAmAC9KTj9Mz2UgKHC9Ao8D5pHhhIHrElDgCaWcluAahRQUeNgRswxc6MitvbnIkSdK6pIEJIExLAGBwhhWvl591EhgBoA1CRB8EgCnHwgPBIW3ADgLwDHJUeY81phHSadwwHUIPHL8WgDPT35HuCBI/CMA3q9LEpAExrAEBApjWPl69VElgS4AK2re6GgA45NpCBbdBuDQ5L5/BcCTIE8BcBGAKwB8FcDfJM/4GIAPVh0zPaqEpZeRBCSB7BIQKGSXle6UBKxLYDKAUrJosbqtkwBwjQIXM9ZeBIl+AANJwZTkWPR691p/f7VPEpAEhkECAoVhEKoeKQlIApKAJCAJjBYJCBRGiyb1HpKAJCAJSAKSwDBIQKAwDELVIyUBSUASkAQkgdEiAYHCaNGk3kMSkAQkAUlAEhgGCQgUhkGoeqQkIAlIApKAJDBaJCBQGC2a1HtIApKAJCAJSALDIAGBwjAIVY+UBCQBSUASkARGiwT+PznS6PFnCL9EAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "jupyter-vega3": "#b1c697cd-29a3-43c7-87d1-41ef05b9f3da"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(seattle_temp_extrema).mark_line().encode(x='date', y='temp', color='var')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working in the notebook, I find myself frequently copy-pasting this cell around, modifying the encoding, mark type, and the name of the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`autovega` is a helper tool that speeds up simple plotting workflows like this using Jupyter widgets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T01:51:31.603081Z",
     "start_time": "2018-05-03T01:51:31.597369Z"
    }
   },
   "outputs": [],
   "source": [
    "import autovega"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wrap the `display_dataframe` function around a dataframe to render a GUI for choosing one of several plot types and encodings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-02T22:42:02.671317Z",
     "start_time": "2018-05-02T22:42:02.580596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02c9fe535c9b4e1dabed212eb36c0506",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "AutoVega(children=(ToggleButtons(options=('Table', 'Scatter', 'Line', 'Bar'), value='Table'), Output()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autovega.display_dataframe(seattle_temp_extrema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make this the default behavior, call `register_renderer`. Then autovega wil be the default display formatter for all Pandas dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T01:51:31.603081Z",
     "start_time": "2018-05-03T01:51:31.597369Z"
    }
   },
   "outputs": [],
   "source": [
    "autovega.register_renderer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T01:51:42.266614Z",
     "start_time": "2018-05-03T01:51:42.174001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f5f63faee3b4b17b145247aace10874",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "AutoVega(children=(ToggleButtons(options=('Table', 'Scatter', 'Line', 'Bar'), value='Table'), Output()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seattle_temp_extrema"
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}