plot_swe_cdfs_withtable_bargraph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "####### this makes the division in the pandas dataframe not result in whole numbers \n",
    "from __future__ import division\n",
    "from snowpack_functions import mesh_gridify,make_map\n",
    "# basins = [\"california\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving figure to '/raid9/gergel/agg_snowpack/swe_t_p_reg/plots/changeclass/RD_TR_SD_histogram.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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gM/3ulyRJUke1OofgfopfGof7cCIU27EH8PMm+t5Zq+Vt9Y0RMQl4EwM+JNng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\nIUmSJGmAlu9oS5IkSRpeZYJ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nM6sm8lMV1VGZkZGLgf0jYv70VFWSJElauWHpaO8EjAHn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\n/wEnKVZl1Y7CdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2afff46689d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basins = [\"cascades\",\"california\",\"northernrockies\",\"southernrockies\",\"whites\"]\n",
    "colors = ['r','g','b','c','y']\n",
    "count = 0\n",
    "# fig = plt.figure(figsize=(20,20))\n",
    "fig, axes = plt.subplots(figsize=(10,10),nrows=5, ncols=1)\n",
    "\n",
    "######################### set fontsize #####################\n",
    "fs = 25\n",
    "ls = 18\n",
    "############################################################\n",
    "\n",
    "lw = 2.0\n",
    "num = 0\n",
    "\n",
    "for basin in basins: \n",
    "    ## historical\n",
    "    file = '/raid9/gergel/agg_snowpack/swe_t_p_reg/proc_data/ensavg_%s_%s.npz' %(basin,\"historical\")\n",
    "    data = np.load(file)\n",
    "    temp_avg_hist = data['temp_avg']\n",
    "    precip_hist = data['precip']\n",
    "    swe_hist = data['swe']\n",
    "    ## rcp4.5\n",
    "    file = '/raid9/gergel/agg_snowpack/swe_t_p_reg/proc_data/ensavg_%s_%s.npz' %(basin,\"rcp45\")\n",
    "    data = np.load(file)\n",
    "    swe_2010_2039_rcp45 = data['swe_2010_2039']\n",
    "    swe_2040_2069_rcp45 = data['swe_2040_2069']\n",
    "    swe_2070_2099_rcp45 = data['swe_2070_2099']\n",
    "    temp_avg_2010_2039_rcp45 = data['temp_avg_2010_2039']\n",
    "    temp_avg_2040_2069_rcp45 = data['temp_avg_2040_2069']\n",
    "    temp_avg_2070_2099_rcp45 = data['temp_avg_2070_2099']\n",
    "    ## rcp8.5\n",
    "    file = '/raid9/gergel/agg_snowpack/swe_t_p_reg/proc_data/ensavg_%s_%s.npz' %(basin,\"rcp85\")\n",
    "    data = np.load(file)\n",
    "    swe_2010_2039_rcp85 = data['swe_2010_2039']\n",
    "    swe_2040_2069_rcp85 = data['swe_2040_2069']\n",
    "    swe_2070_2099_rcp85 = data['swe_2070_2099']\n",
    "    temp_avg_2010_2039_rcp85 = data['temp_avg_2010_2039']\n",
    "    temp_avg_2040_2069_rcp85 = data['temp_avg_2040_2069']\n",
    "    temp_avg_2070_2099_rcp85 = data['temp_avg_2070_2099']\n",
    "    ####################### bin by temperature and change classification in the future ############################\n",
    "    ## hist class\n",
    "    ## snow dominant\n",
    "    rd_cut = 278.15\n",
    "    sd_cut = 267.15\n",
    "    swe_sd_hist = swe_hist[temp_avg_hist <= sd_cut]\n",
    "    ## transient\n",
    "    swe_tr_hist = swe_hist[(temp_avg_hist > sd_cut) & (temp_avg_hist < rd_cut)]\n",
    "    ## rain dominant\n",
    "    swe_rd_hist = swe_hist[temp_avg_hist >= rd_cut]\n",
    "\n",
    "    ## rcp45\n",
    "    ## snow dominant\n",
    "    swe_sd_2010_2039_rcp45 = swe_2010_2039_rcp45[temp_avg_2010_2039_rcp45 <= sd_cut]\n",
    "    swe_sd_2040_2069_rcp45 = swe_2040_2069_rcp45[temp_avg_2040_2069_rcp45 <= sd_cut]\n",
    "    swe_sd_2070_2099_rcp45 = swe_2070_2099_rcp45[temp_avg_2070_2099_rcp45 <= sd_cut]\n",
    "\n",
    "    ## transient\n",
    "    swe_tr_2010_2039_rcp45 = swe_2010_2039_rcp45[(temp_avg_2010_2039_rcp45 > sd_cut) & (temp_avg_2010_2039_rcp45 < rd_cut)]\n",
    "    swe_tr_2040_2069_rcp45 = swe_2040_2069_rcp45[(temp_avg_2040_2069_rcp45 > sd_cut) & (temp_avg_2040_2069_rcp45 < rd_cut)]\n",
    "    swe_tr_2070_2099_rcp45 = swe_2070_2099_rcp45[(temp_avg_2070_2099_rcp45 > sd_cut) & (temp_avg_2070_2099_rcp45 < rd_cut)]\n",
    "\n",
    "\n",
    "    ## rain dominant\n",
    "    swe_rd_2010_2039_rcp45 = swe_2010_2039_rcp45[temp_avg_2010_2039_rcp45 >= rd_cut]\n",
    "    swe_rd_2040_2069_rcp45 = swe_2040_2069_rcp45[temp_avg_2040_2069_rcp45 >= rd_cut]\n",
    "    swe_rd_2070_2099_rcp45 = swe_2070_2099_rcp45[temp_avg_2070_2099_rcp45 >= rd_cut]\n",
    "\n",
    "\n",
    "    ## rcp85\n",
    "    ## snow dominant\n",
    "    swe_sd_2010_2039_rcp85 = swe_2010_2039_rcp85[temp_avg_2010_2039_rcp85 <= sd_cut]\n",
    "    swe_sd_2040_2069_rcp85 = swe_2040_2069_rcp85[temp_avg_2040_2069_rcp85 <= sd_cut]\n",
    "    swe_sd_2070_2099_rcp85 = swe_2070_2099_rcp85[temp_avg_2070_2099_rcp85 <= sd_cut]\n",
    "    ## transient\n",
    "    swe_tr_2010_2039_rcp85 = swe_2010_2039_rcp85[(temp_avg_2010_2039_rcp85 > sd_cut) & (temp_avg_2010_2039_rcp85 < rd_cut)]\n",
    "    swe_tr_2040_2069_rcp85 = swe_2040_2069_rcp85[(temp_avg_2040_2069_rcp85 > sd_cut) & (temp_avg_2040_2069_rcp85 < rd_cut)]\n",
    "    swe_tr_2070_2099_rcp85 = swe_2070_2099_rcp85[(temp_avg_2070_2099_rcp85 > sd_cut) & (temp_avg_2070_2099_rcp85 < rd_cut)]\n",
    "\n",
    "    ## rain dominant\n",
    "    swe_rd_2010_2039_rcp85 = swe_2010_2039_rcp85[temp_avg_2010_2039_rcp85 >= rd_cut]\n",
    "    swe_rd_2040_2069_rcp85 = swe_2040_2069_rcp85[temp_avg_2040_2069_rcp85 >= rd_cut]\n",
    "    swe_rd_2070_2099_rcp85 = swe_2070_2099_rcp85[temp_avg_2070_2099_rcp85 >= rd_cut]\n",
    "\n",
    "\n",
    "    ################################################### BAR GRAPH #####################################################\n",
    "    ## RD, TR, SD \n",
    "    # ax[num+1] = fig.add_subplot(5,2,num + 1)\n",
    "    ## totals: swe_hist, swe_2010_2039_rcp45\n",
    "    df1 = pd.DataFrame([[len(swe_rd_hist)/len(swe_hist),len(swe_tr_hist)/len(swe_hist),len(swe_sd_hist)/len(swe_hist)],\n",
    "                       [len(swe_rd_2010_2039_rcp45)/len(swe_2010_2039_rcp45),len(swe_tr_2010_2039_rcp45)/len(swe_2010_2039_rcp45),len(swe_sd_2010_2039_rcp45)/len(swe_2010_2039_rcp45)],\n",
    "                       [len(swe_rd_2040_2069_rcp45)/len(swe_2040_2069_rcp45),len(swe_tr_2040_2069_rcp45)/len(swe_2040_2069_rcp45),len(swe_sd_2040_2069_rcp45)/len(swe_2040_2069_rcp45)],\n",
    "                       [len(swe_rd_2070_2099_rcp45)/len(swe_2070_2099_rcp45),len(swe_tr_2070_2099_rcp45)/len(swe_2070_2099_rcp45),len(swe_sd_2070_2099_rcp45)/len(swe_2070_2099_rcp45)], \n",
    "                        [len(swe_rd_2010_2039_rcp85)/len(swe_2010_2039_rcp85),len(swe_tr_2010_2039_rcp85)/len(swe_2010_2039_rcp85),len(swe_sd_2010_2039_rcp85)/len(swe_2010_2039_rcp85)],\n",
    "                       [len(swe_rd_2040_2069_rcp85)/len(swe_2040_2069_rcp85),len(swe_tr_2040_2069_rcp85)/len(swe_2040_2069_rcp85),len(swe_sd_2040_2069_rcp85)/len(swe_2040_2069_rcp85)],\n",
    "                       [len(swe_rd_2070_2099_rcp85)/len(swe_2070_2099_rcp85),len(swe_tr_2070_2099_rcp85)/len(swe_2070_2099_rcp85),len(swe_sd_2070_2099_rcp85)/len(swe_2070_2099_rcp85)]], \n",
    "                       columns = ['RD','TR','SD'])\n",
    "    # df1.plot(ax=ax[num+1],kind = 'bar', stacked=True)\n",
    "    ax1 = df1.plot(kind = 'bar', stacked=True,ax=axes[num],color=['r', 'g', 'b'],legend=False if num != 0 else True,)\n",
    "    if num == 0:\n",
    "        ax1.legend(loc='center left',prop={'size':ls},bbox_to_anchor=(1, 0.5))\n",
    "    if (basin == \"whites\"):\n",
    "        x = [0,1,2,3,4,5,6]\n",
    "        xlabels = ['Historical','RCP 4.5 2010-2039','RCP 4.5 2040-2069','RCP 4.5 2070-2099',\n",
    "                  'RCP 8.5 2010-2039','RCP 8.5 2040-2069','RCP 8.5 2070-2099']\n",
    "        ax1.set_xticks(x)\n",
    "        ax1.set_xticklabels(xlabels, rotation=40,size=fs)\n",
    "    else: \n",
    "        ax1.axes.get_xaxis().set_visible(False)\n",
    "    ax1.set_yticks([0,0.5,1.0])\n",
    "    ax1.tick_params(labelsize=ls)\n",
    "    num += 1\n",
    "fig.text(0.045, 0.5, 'Fraction of Area', va='center', rotation='vertical',size = fs)\n",
    "# fig.text(0.5, 0.05, 'SWE (mm)', va='center', rotation='horizontal',size = 'x-large')\n",
    "#fig.text(0.075, 0.5, 'Probability', va='center', rotation='vertical',size = 'x-large')\n",
    "# plt.show()\n",
    "\n",
    "## adjust figure properties \n",
    "# plt.legend(loc='lower left',prop={'size':15},bbox_to_anchor=(1, 0.5))\n",
    "## save plot\n",
    "\n",
    "plotname = 'RD_TR_SD_histogram.png' \n",
    "direc = '/raid9/gergel/agg_snowpack/swe_t_p_reg/plots/changeclass/'\n",
    "savepath = os.path.join(direc,plotname)\n",
    "print (\"saving figure to '%s'\" %savepath)\n",
    "plt.savefig(savepath,dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# MAP OF RD/TR/SD GRID CELLS AND CHANGES # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "'''discm = {'red':   ((0, 0.15, 0.15),\n",
    "\n",
    "                   (0.25, 0.15, 0.2),\n",
    "\n",
    "                   (0.5, 0.2, 0.64),\n",
    "                   \n",
    "                   (0.75, .7, 1),\n",
    "\n",
    "                   (1, 1, 1)),\n",
    "         \n",
    "        'green': ((0, 0.1, 0.1),\n",
    "\n",
    "                   (0.25, 0.1, 0.4),\n",
    "\n",
    "                   (0.5, 0.4, 0.5),\n",
    "                  \n",
    "                  (0.75, 0.5, 0.15),\n",
    "\n",
    "                   (1, 0.15, 0.15)),\n",
    "         \n",
    "         'blue':  ((0, 1, 1),\n",
    "\n",
    "                   (0.25, 1, 0.7),\n",
    "                   \n",
    "                   (0.5, 0.7, 0.0),\n",
    "\n",
    "                   (0.75, 0.0, 0.0),\n",
    "\n",
    "                   (1, 0, 0))}'''\n",
    "\n",
    "discm = {'red':   ((0, 0.15, 0.15),\n",
    "\n",
    "                   (0.25, 0.15, 0.35),\n",
    "\n",
    "                   (0.5, 0.35, 0.64),\n",
    "                   \n",
    "                   (0.75, .7, 1),\n",
    "\n",
    "                   (1, 1, 1)),\n",
    "         \n",
    "        'green': ((0, 0.1, 0.1),\n",
    "\n",
    "                   (0.25, 0.1, 0.7),\n",
    "\n",
    "                   (0.5, 0.7, 0.5),\n",
    "                  \n",
    "                  (0.75, 0.5, 0.15),\n",
    "\n",
    "                   (1, 0.15, 0.15)),\n",
    "         \n",
    "         'blue':  ((0, 1, 1),\n",
    "\n",
    "                   (0.25, 1, 0.6),\n",
    "                   \n",
    "                   (0.5, 0.6, 0.0),\n",
    "\n",
    "                   (0.75, 0.0, 0.0),\n",
    "\n",
    "                   (1, 0, 0))}\n",
    "\n",
    "\n",
    "\n",
    "discmap = LinearSegmentedColormap('DiscMap', discm)\n",
    "plt.register_cmap(cmap=discmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving figure to '/raid9/gergel/agg_snowpack/swe_t_p_reg/plots/changeclass/RD_TR_SD_map.tiff'\n"
     ]
    },
    {
     "data": {
      "image/png": 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QKGCBFr7/PLlqhTevP9fXLXHl+7nWjVz38j2rtykJmaEuWUlNS6Nnz5689dZb\n9O/fn7KyMr755hsOP/xwtmzZwqhRo5g8ebKfEpYVo/x3uJ6vaFru1113HQ/cdiG3XaIey4F8xQVF\n5ECA987Izb4o1035slKEd3eoYLEFVj6//BwtlkZmUa6bSUVOJunuOanExUYPdoa6JMBW7mEw+sRT\nyTlsEOedd57v3OTJk/ntt98YPnw4ixYt4sorr+TJJ5/0u85oBK4pJzNXvaxgIbTlvmHDBmpqanzH\n+h+5/lju7FatWkVD7T7Te+vbLrdfz6qHx5CaYhw4FWrrV31dDf/5cuMAOjnorsSVn9CWezIG1R19\n0gRyhh/od+6yyy6jrKyMESNG8MorrzBlyhTc7ubfmN5a18+1m81rywoWQlvurZGdFStWUGO+KMOw\nnWbbu87Z+Wc6H9DdsDzYKoFgwXbBlo361fH2N4ty3Rz/iSthM9TZyr0ZW7m3kszMTI488kgALrro\nIoYPH84dd9zBww8XAf6jditRp2bCqFnuVVVVvPPOOyxduhRFURBC0NTURKdOncjOzva7Rq8kzI5X\nN+zB09DI0WtqWDaks6/cz1o32ehGLWqOWj/+yUd5688/UOJ6P+QOcmapac02mdHQlL0WXKcn0S33\njRs3Mn36dEt1MzMzo9ya6NG1a1eOOOIIAC699FIOO+ww7rrrLjp43dqh1reDv+I026lNs9yrqqpY\nvHgx3333na+sqamJzp07k5WV5XeNVdnZv+VDaupga8/H6b1jWnNsAE5fXa19snWsn3NflOtmwO69\nrHj1fRbNDD2FF87yNxnZE+d7hrPI736NCWq5OxwOysrKLMtOt27dotyi2GIr9whz5JFHsn9/4FBe\nn7u9oREUBdK9A2RZqZeVlbFp0yaOP/546urqcLvd/PDDD6xbt47u3btz6KGH8vDDDwe4/1uKu2o1\n9bV1zJ39OH0cnehx4SkccEToDT+M5tvfPv9jOqWPszTlYJZ+VnPBN8+lh45u98UpFBYDw9hWu43H\nE9Ry79+/P/Pnz7dc3+l0Rq8xbcjo0aN5/vnnDctkJX7Gy2qCjsWXqOdlpb5u3Tq2bdvGscceS21t\nLU8++SSlpaU8+OCDZGdnM2LEiIjKTmXRh1Tvg7kPTKND96NYPfYHhpnszBwqoPabh56l15HDfHVC\nJqYxwS8g1kJ0uxanoD1t4+9fIm3lP0NeF284HA4GDBjQLmXHCFu5WySc5BspKSm8+eabHHzwwfRq\ngtQUGPcE2l2+AAAgAElEQVSpi5e+hR9/hY7pLkod4FEgNetIGhsb6f7p7dTU1FBXV0dWVhY5OTn8\n61//IjU1lQkTJlBVVcV5553HiBEjovL50jM6cvL9N1FbsYviux/j1If/zkWl04J2IkbL1zxKDcfe\nqbZx2dVXsanyRBxiMb27/ZPU1L6m99IPFNT87+O9UfGBAYbaNTKyp+GPnq6srd0ZML1h0/aEm7jm\nnXfeYfDgwYzzqDuqjXwebtoNRXOgs4C7ekPOVtj5xhHcla7Q9e9/Z9++fdTV1ZGdnU1WVhaFhYWk\npqZyxhlnUFVVRV5eHkOHhrEFbRh06QSzp8D2XT8w91/wYD786dXAOAEzd7sWYPfJvgMZ+sfzmVTi\n4rWRj+N69WDS33uMMddfRqfsrIBrzXBXrQbvfY0CDCHQcyhb8p7BL1BT/jmVRZ8D1vaut4k/bOUe\nBe677z4WLVrE+++/z5YtR+LxeOhYv5yjh0GHW57E4XBIwvUT5TmPkZqaykEHHUSHDsbLt0pLS9tk\nLrZjd7UTaaip8Sn2YEvY9PnkFeUpvr5rCRkdOlBTN4aszleT4ujDxsrxDO61xvS52vXNLvfAzV30\nwXPaEjsjFu5bjyMB5w3bO/fffz+LFi1i+fLlPNN5JB6Phw6Ucl0HOPZ6VdlPLITFMwBK2ZD1DzIz\nMxkwYICp7CxbtizqGeoAundVvXF19YGueP3f+oA3gMbn7+G7p1/ltsNOY9tLDzD0rBNxpKXy2d8f\n4cwn7w75fH3wqz6trNyOSQFXN1P17eWk2Zoh4bH/hVGgS5cuTJ482e+cLyOUt5PRZ4wKRVvllv96\njpth55ziZymYzZ3r88YDDDrgO/Y3rKJq36uc+8ZVOByCEtcfqay6k8bG7aSmHmD4XG1Zm9FzzFz8\nRnP+2vFPMy7GM/AVX10z698mvujatStTpkzxOzc+38niqc3HskU8atSokPdsi13hAB74F1wwXrXk\nNYyi3uUlcLKSP+OxO9m1fjOblnzPhEdm8qfvp7Io183K1z6kvrqW9C4Zps8O9dsOFnEvTxdMKnHx\nwWGzaPhkluXPbROf2Lnl25iWpm9si9zydbv3Ul9Ty0GnHOfXTn3e9mBBcbnuAo7755ectrAv30+9\nxnc+K+Mqtu+93vTZgS75wGV2Rm0xD9oT7CkdGrDTXLzmlJdJxmj51qIFqrUkiKwtcstv3wWbMw7l\nuBH+Uf5milwu18omlbi4uvIe7h76rq+9k0pcpO6azndPv2qpHXJmOrNsdvI5o41ohMPB1p6jfXW1\n3ePiNae8jTG25R5lAtxhOoGzOp/VFpb7t+5/kbLpfqCSRbluQyVutqNbqOPsLrBv59ds2XUZfbJf\n9Lsv6F2K4wNy2GuJa4zaYjxtoKBQ51cur423rfj4xyhaXpafcGQn2pb7sx/CoRefCvzM4jzjnO3h\nBshp5yYUwhf37WPpgpcYc92lAfWMclHIg4ZKXZKbUM9VPB6aGhsC7gPGSYJs4hNbubcB+kh5CN+C\nj7blvuHrEhDgfLEyqBI3UqTBlKxc1rvbK2zbPZk9NYVkdVZ7bK1jap5jH2/qhte35f0/LSPziKXs\nrnw/oI7HU0t902o2VZ7tO7dJmmhc/7vngcA1zK3Fyv06dOhguKICoLGxkdra2oi2KZHRstPJCl6z\njIud1u4Rbcu96Efo3An6jj7cL7OcjNnSNatL2sbOvIYl855h439/ZMDvRwLGSl3fz5jde8k/nmfN\nJtjyweMBZQ376qjeWkHxPY+z+yc19L/Yu9wXYNDHM4HIyo7Ve3Xs2JG6ujrDsvr6+oRd/hoNbOUe\nRYzcWC1dnxpty33DlyUccvoJQLNCNprLlo81rMyLA94Oton9jT/g8ZyLw5EREDhnlMXO7P77G1eS\ns/59TnlKnVv/6dY/+8oaGjdSWXUbvbMCv+sHn1jid2x10GRVQYS6X1NTk+kOVsuWLWP58uWWnpPM\nGO0M11KibbkvWQGTToAhOq8chJ5GCLbrm4zD4cDT2MTu3zbQ56jDSO2YbjjFFGwAIctxVfk2Rs6c\nSz+DZ1WVb2Ploo85epoqT6W3XAJlcMSDL3Npl4P96kZSdqzcy+PxmN6ruLiY8vJyS+1pD9jKPUqY\npZxsiWKH6FruXz/4NI60FDY/8Qi9LVrNGkZr3YNloOvV7Tm27bmKTZWnIUQ6A3r8x+86s2tLXPl4\nPB6278knNaU3PbrORpBGWmp/Vt12K4BfhK+CB4cj23D53RsdKuPWLd+lS5c2ieyOZ4xyy7eGaFru\nhfOg6xD48Ry3n3LX01K5lzn25iv536Mv8vld80ntkM6J91hL1rIo142nsZH6YcfSpU9P4CwcqSlk\n9Mg2rN+4v570zhlk5KhJXo559j1vSTdEyaS4dcvbsuOP/U20EUbBK+EISbQs9+LiYhypKRx305Wm\n1new83p3fajUsg5HOn2yX2Rgzy9JSxnA7uonAq7zeOqob1yLx+OhsXEbHk81+xtWUVF1Kx6lmvrG\n1eysnhfikzXPuVv5LPGCvStcM5pSN0pDa7TNqhnRstw/+ugjfhg7jmnnBk6z6affWpqQRiY1PZ3j\nb7mKUx+4mbSMjqz7bElAnca6eqq2bMfj8bBv5x4a6+rZs3ELJU+/hiPFwa61Zax5vyjoc7Q5d70s\nx/Ne7jaB2JZ7G2G4xjWMwK5oWO6VlZU888wz/P6RWyzVDxU8pyeUFd898242VZ5Ex/Tj2L5nOqCQ\n4uhOfeNq0lIPxuPZjUN0xaPsJsVxAI2ecrI6TaZrRj7lO8+m0VMR5OkCQUfDkni12sHetlJGm2s3\nyx9vdXAcDct9y5YtFBYW8uBlGwzLzZR9S9FPTR15yTkU37OArP59+P7Z1wHo0LULezZuIbNPT+pr\n9pGW0Yn66ho6Zneldscuhp51IgNPGMMX9zzG/qoas0chHA5SUtPIdRcETBnEq9UO9sBYj63cY4CV\nTR30RMNy37RpE4cdps7fmaF1KlY3fNGwYiGnpw4iu/ONbKw8gUE9S0h1HExD0yqE6EJ66qCg1/bv\n8SEbdjiD1Ai03OVoef0uc/GC3UH5Y7Y5TLher0hb7uvWreOoo44iLdVYuUPg9q7hEGqjpq79ezPk\nTCef3f4IZz5xDx27d2XPhi2kd+5E557dg977pNk38unMh0zLl98xiaaD3whoD+CLvI/HAbI9MPbH\ndstHkGBrQfUjeW2JilWiYbmPGDGC8vJyil13k/3hj5ZSvFol2Py5THaX6xjSZy/pqUNwOBwc98+v\nef619RQUFrfShW5suVttVyxpjx3U+HxnWHPt4cpOpC330aNHs3btWv5S0Jev9twYMsVruC5tK4OB\nQ885hQte+z8yenTD4XBwdeU9/PWgY1uteA+/dxEpqWkB7dCvk483HA6HvZ+7hK3cLWA1N3aOsyio\nRdGaOatoWO6pqaksWLCAGTNm8OGHHwLWvAlGyWXCIdj18vNdmcN8dfWv0ARa7vGu1CH5LHerslNc\nUBQwj67NtcsJbFq60iTSnX6HDh144oknuOGGG3jvPTXgzIo3QZ8cyuhvK+jruzKH+T3flTnM5zWQ\n431C9UElrny/de4a8azUbYyxlXsboe+YtGOrWdOiGS2fkpLCwIHNW1kFU/ChotqtYHRtQWFxWG7y\n0ArefM5dwyh/fayxXYuByPu4y8rJata0aK9zHzx4sO/vUAo+mKs9GEarVDSsWupWjItcdwErZ+Wx\n54dDTZ8NxGWmx2QbGLcWW7lHGKMOx8qoN5SwRHOde2FhIWeccYbfOVfmMN9LQ7/+vSUY7dseDH0b\nrGMcLR/vtOcOKlzXvEYoBR/Nde6vv/46Z555pt85zYMnK3p5/t2IUINmLbgtHEJ5Es2eNXxWIVlH\n/Wy4d0Q8Yw+M/bGVewSxOt9udi4Y0bTcq6urOeaYY8K+riVKXt4mVr8TnNXrrRHaco9H2mteebOk\nNfplcC0hmpZ7fX09I0eODFnPqtUcLNujUQpbqwNffY57M0pc+T7LXT8Qj/d4FXvO3R9buUcJK5mp\nwqGtdoULhSbgrRnNyx1SSxS7tedas9zz88b7XvFCPPyfY0lrk9boaatd4Yww2kTGqqI3Qj+vHi6h\n+p1cd4HPcjcqk9ulTSnGo4vexl4KFxKrAUF6zLZXNCOUoLbFrnBWsGqty5u76NPXwrCwlLrWechW\njXrPwiCWjr/lrrUjHpe/ySSTa7ElsqPlktf+tvScEPPcbbErnBlmm94Ew6jv8J1zFoWl1DVvopwO\nd1GuGxY9ZJjqOddd4Fvnrr8uHpe/ybTnKS0jbOUeRayO0q0QS8vdaIMKCK3oIzFHLz/TbM19qF3h\n5DrxvMYd7A4qXMVuhVha7trAQz9lp61jN+sfQs3RW8FoR0qjNfd6WdrfsIqmg99Qz0tVNVmMVyWf\nTAPjSGAr9yhhdUMIq8SD5e7KHBaWC661nYDZYEJT8hsmBnPRm69zB9UdH48Kvr13UJF2yUNsLXeN\nHGeRoYI3w5U5DFqRDc5sMKH9/SlD/Mpl2Tr83kWUvpzmO5bLIX63TG7vA2M9tnKPEuEqdlmRGQlO\nvMy5t5VQ5+eNJ9dtPpDQ3PLmhJ5zj0cFL4Rgz549rFixwlJ9s93lEpWWWO6yIjNy0cfScpdpq9St\n7qrVTDI4r/VJmlveiFx3AXs2Bq5zB/8UuPGq4Hfu3GlZdtLS0qLcmthiK/cIoh+dG237aHUrSD3x\nYLm3FepAJ3iAW667gF0zy2GnWQ1r0fLxpuCFEGzZsoV3333XUv0uXbpEuUVtQ3FBUUS3eZWJB8u9\nragsckKIvmVSiYsfq8zL5dzywYJY403BOxwONm3aZFl2srKyotyi2GIr9whitBTOSIkbnZNd3kZC\nEy+We7SwOp/v19l0N8+PHc4693hS8A6Hg6FDhzJjxgzL1yxatCiKLWob9IrdLKe8EfKg2mhDmXix\n3KOFVXe/37bTzzXX0cuanKHOKL4lXl30QghGjBjBzTffbPmaV199NYotii3tYzjbxsidi9XdofTK\nTb/EpD1Z7sGQO5aq1YF7tTcjGH1Mte8olPKOl6Vw7X2trpyC1uoad71y0yeSak+WezDkvmhHZvOc\nu94qFw4HQzs1bz6j3+dBXz9elsLZc+7+2L/4EFT2Hmq5rpwRSns3csmHi7tqNStqKpLyhxvuOlmr\na+vnzP+G+vp6v7S28WKdtxfCkR05t7z2HongusoiZ9Ja7lbT72pYnQr8U6dBNDQ0+GWHlPd5iFfa\nezCqHlu5txGtUewAKelpCCHiZpQcTcwUuLwULhQzpx9HenrgVrZmCj5eFL9tfUQOTfFplns4ijBR\nMVPgwZbW6eXptX1lhsFmmqLX148nt7wtO83Yc+4WqOw91HJCDm3OTx+xGmqePRRN9Q0sri7j9szT\nLdVv78yZ/w3z59cblsWLIjci2TqocGRnfL6T4oLAJWNG68GNlpaZUb3pE3Yt/YRD8oos1W/v/KnT\nIJ5uCIyWh/hR5EbYA2N/bMs9gshBPfqlcPKo2aqAyPVS0tOY2GVQ5BobJ1jZQ96VOSysVLcFhcU4\nHA5Dyz3eaa8dlBZQNz7fGRBMJyt2q8vJ5Hpd+k8ge8wzEWhlfBFqD3mtjl8gXQhcmcNwOBwJuUws\n2QbGrcVW7hFGv7WrrNSDCZeRkpOt+n07d0fth5uTkxOV+wKsXbs2ZJ2W7/xmjsfjob7e2HKPZ9pz\n0OTiPH+lPrHQWlCdkZKTrfptaz+J2px7rGUn1M5vMlanBj0eDw0mlns8055lxwjbLW+R119/na++\n+iponfKmIb7kEJ8yhL5vPsE9jKS8qRaAvm8+AcB0b/3p0rXT/e5TG3DvilW/4na7o/YDnj59euhK\nJnTp0oXq6mrDsrVr13LIIYdYvpf+s/dNyeCCplqqdqiR8btmlgNQtaOQhqZNpvdJVMs9Ga0PK7LT\nsB94Tv37DiDtzZHc0wkadv8Iz0FaN+/Oa29OB0Y2v8vn8NbXsXojPP7445H4KIbEh+yMDPjsad1G\n0vCc0xcZr61t37HoIWp37DK9k225Jwe2crfIBRdcwAUXXBC0jn4OUBtRyxa4Pg1kqFG3du2Hl96K\ny+VixIgR4TU8xkyfPp358+dbquuuWs1wAnPZD/eWl7jyyZ3T7LJ/UzLr9PPo0bDczdb0asvoIjGX\nn4xueSuyo7nlNcs9x6n+ZpplKlBph5Id7dop/8hi2rRpDB1qPXo/HghHdrTP6p/LXvvO1ujiFtZw\nzaLmpXD633Q0LHej/AMQ2Xz1ySg7rcF2y1uksvfQkEt79C6ycHJJh8LhcPDWvg0tvj4RCOWel/eU\n1tfTr1OPtOWudULB1sNHYgvMZOygrMiOthROc8VHMludw+Fgb0n87kMeCUK55+XUs/p6+t9rpC13\nrR8MJhfasr7WrGho7zki9NjKPUpo1rl+Z6aW7vbk8XhQ2sHv1izHfvM2ryqhks5Ew3KXd5aTMdqp\nTk84Sj/ZlHs4yPPukVLwHo+H9vCVmuXY18cBhfodRsNyN+oPoTkmKVg8krtqdbtYxhhpbOUeBXJ0\ney5rnb6RQpcj7INZfnV4EO30v2WmNIcN3w00K1f5e4vWnLu8cYZ+O1r5vaXWezJa7uGgt0BnFvh/\nF5plL1v34/OdQS0/h8NBe421MlOafVMygGblKn9v0Z5z1/o6rX36aP6Wejzbu+zosefcLWJ1ra6M\nZqXPyRdQop4zU/B6IdQrB0VR2oXlHmppnJzb+tOZtZyiWzanfW/VVZWsrd0ZME9uNj8ebO5PK9Nv\nOSuXGbW1JSRjB9US2Vmcpyrw1V2E3+Z/2nmN8fnOgEx2euXQuH9Xu7DcQy2Nk+fd79gd2Bf5DI0d\nUFMOlUWf+93TTEb08/1GZX470kllk8AvBskIrTyUircD6vxpp7ZgbNF+rNoaVKMkHfpjIURSrnMP\nl1AbymgIh8ARxPoId35cb5nrnycrfvAPCtQGGFaChuwOyhj9MrnigqKAc2Y4ICnXuYdLKOWpkeqA\ntCBmX7jz43qlbtYWfUyA9gyrspOMA+PWIOwvwx8hhPLGz+UB51tifYT68euD78xcaK7MYZx2ztmM\nu+1abh+TWBnqwon4NcNIAZe48qnoPJiUDmkBG8hkDiunsb6e2m076Tqgt+9835QMyptq6ZuSwfLS\nnXQf3jGgzOxYj/zMzGHlpufk+2lkZGRQW2t8b4/Hw+7du+nevbthuZ7MzEy++uorFEWJueM5krIT\nbL5d3lwGQsvZ5Afhzkvg6ATLUBcJ2THbqfK/V7vo1MF/AxmAnlVrqKuHyr3Qr0fz+bRu6lK7tG4j\n+aV8A4MzdgWUmR3LGD0v2HntfhrBZKexsZGqqiqys7MNy/V069aN4uLiuJCdaGC75aOI5j6UOyN3\n1WrfqFVW6JP0F+Nv/Qkh2u2cuxFqANtvUAPZPXWFO6G+cQP1dTeSvfMNP6tac+OXz3yI7J2/+HkC\nhutuIx/rLXW/Z+40P6fdP5ppO51OZ9TuHWtk2dEU1UQDV7we2fpzQLudczdiUokLnjYPYNu7eRul\nL7/DyFuvlhJwXev9+0fueA5GLpC9jXpF7n9sdcMaM2zZaRm2uogi8k5XMlb3eJdRFIVzOw2MVNPi\nEtlCD+UyD52Otnk/dznSXr7OSPG2piORd6DTTx+0dolce8NMdqAFu8WlZdE1N353M4sEsoUeymWu\n72s0+dBkY/kdk6hc1t9X16hv0hS77H20minPiGCu99YukWuvxL1yF0IMEULsE0K8KJ07WQixSghR\nLYT4jxBioFTWUwjxlRBimxDiaun880IIjxBitHTuYCFEyDC1lrgVzXBlDvMLLgkWCSoHsHROSbPT\nK4aFQNDRsKQl+1FrEfma0jab+9cC9rSgu3By4keaZJOdcJSHHOTlcDiSej/3YIrPSvpe/W/58HsX\nkXN0YPZHI0VvRemaLf21IoetyQ3S3kmEX/wCYKl2IIToAbwB3AZ0R41Df1WqP917zYHAFCGE1sMr\nQCUwW3f/mAYdWM03r67VTf74CL2Fq/0dvpJUyDriFz9lLFvWVav7+lks+qx4wQil2DXMgu/akKST\nHSvKCvwHAh6Pp10kN9FbuNpKAitucXngemrpnfTe8a1PKU8qcfks60klLnZkDvEpen0AXDA0Ra29\ny3JkJnetdem3Z+J6zl0IcRGwC1gJaEmWzwOWK4qy2FtnFlAhhBiqKMovgPC+UqS/NV4A8oQQ4xRF\n+dJqO8LZtrK1yCPVSpo7KXWtrm25g7FrXV6eBnBubTcef7y7qrQLi4FhftdsmNj6dli1zFu7PK4l\ntEfZAXw7ymnbxwJJb7mbbX9rNH2h39RKOyf3O7W/f4nOK/9JjvOuiLRPvr/8bkVx25Z7y4lb5S6E\n6ArcDZwIXC0VjUCK2FAUpVYIsdZ7/hdgPrAImAfMUhRln3RtLXC/9zUuqh8gCDnOIiZ5g+mMdozz\n+9F7l1G1F8sd/D0W2oi+oLAYd5V/PVlp6hVosAx1rsxhrBy+m/nzm9e65+eNJ9etftfuqtV+gwUj\nBR5rl3swkll2iguKfAF1mhUvKzHt78V5gDefeXux3MHfY6GtOMhxDqNSV09WmnoFGixDXY6ziL5v\nTg+Q0Uk0DzLkdevhbDdrE1niVrkD9wBPK4pSrrNYuwDbdXX3ApkAiqJsB04Ict8C4GYhxGmA4Z6K\nP3z5OTvKN9Ozbz/fe+3P39GrVy+2bdsWkfd130K5WMG/v4WeWbB2xxf8Yfcr/HsPHLLGxUfd/kzG\nATnUbq/krgNWULW/jlWrVvH8ym+4YNDIiLUj2u8VFRUsXbo0ZL3Xy370+7y12yvJOCCH0XvT6NWr\nFzeXPe0r7+NawapHRvDF5YPoe/FLvvPy+5xfN7B5zw7effddS+3aXbOa8u9W8O7eXyjv2kDWeZdQ\n/p16v5q6QTQ0lZGW0vz+n0sgLeXjgPNG73I73937S0S/37q6uriSndL/fsWmdWvof9AQ3/uu779m\n0KBBlJWVReR9xW7YsQ4+PAQu/hE+XKq+/9wAO/4Amyqgfw/v+zon9fWd+Pnnn1n6Vj7DT3kkYu2I\n9vuOHTv47rvvQtZb+elf/T6v9vnfPH26Wv6g01c+bJ2L9zIuZkH/Gzn4m380Xye9r/zscnZtgjff\nfNNSu9b8sozSWti1602yd0HnpS7694A5GReT+ePPVG3aSmb/3lRt2sqW166iz5+e8R2HepfbuWuX\ncXta+m62G1+yEJfKXQhxFHAKcJRBcTXQVXcuC6gyqBuAoij1Qoh7gXuBi43qbFj7Cxt+WcW+2hrf\n+7YVK9i3bx/r1q2LyPvqTXBg/f+xejv8r9ckun39ChsOgC+ajqVbr37s/nkd3er2s3v9ZrrV7Udp\nbKS8vJyc2lrWK+sj1o5ov9fV1bF+fej2Vqz2/7za+4rN1Xy2uyzgfF39Xjxp1VT8vI6ql35HfcNe\n0tMGkXn0MrrV7WfnrxtoqKphhcn/Td+uzqO/Z++Gfixev5luB/bze94B5z3Dr4Vd8KRVU99Q6vee\nefQiuh3Yz7S8vqGUfnn/puLn5s8Tye+3qsr/Zx9r2dm0bg2rv/sWwPe+fsUKAFZE6H3HPjj+I/hl\nHywcB7Vfws+d4F+9YFiFusUrNL831u9jy5Yt7Kg/hc5lZRFrR7Tf6+rqKLPQXv3n1d7Xl6+gZs38\ngPM7an71Hf8w8GJ21PxKz4EH02njQgB+3QLV+6y3q9PXC9k0AFZ/P59hA5qf02njQg4eAP+uGaM+\nd9Wv9PnTM+xY9avvONT7URsW+n2eSH6/O3d616smKXGZxEYIMR01eKcKdd6vC2rw3yrgKWCyoihj\nvXU7AzuAo7zzhmb3fA7YqCjKnUKIFO+9ngbmKoqSItUzTMShEcn5Q6PUmRqay15zZ7156c2Mu30q\nt41O7iQ2+sAazU0uo7nD9a5x2TVfs2Mnu5//kHvuuSdou1q63C6Yy96sLkRnza7T6fQl4mhPsgPN\n6WiDLY+b8iDccSmMvrgoYs9vC8KVHaMtp+W8GoChy1w71thSCW8sP5G77jKec9faFc5yOxmr8+36\ndrVmqZ0ZsuwkG3FpuQNuYKF0fDMwCLgGtaOaJ4Q4D/gAuAv4IVjnpEdRlCZvMNH/RazFLaC4oIgc\nAneOg8DAk/acWz4Y+rl2ba5c8Si+3PJ6Slz57Ope65tnl68zu3+wID4rg4A2pN3IDqgJbfTIEfWL\n88ADdm55A8yUfqMHaso/p7Lo84BrFuW6adj9o2+ePVRe+GDPlJW8mfzZtJy4DCFVFKVOUZTt2gvV\nnVinKMpORVEqgPNRA3t2AqOBi6zcVne8ENhicL7NkTsj/RaI2rudoc4fvTWsDQo0ZRsst3yuu4Cq\n1X0NI+3l5Wv6gD15uZD8rGC0ZE19a2hvsqMh7xbnF1QHdoY6HXprOMdZ5BfYGyy3vLYUziggT+6z\n5GV08ktGtt7NFLt+oxkb6ySEulAU5W5FUS6Tjj9TFOUwRVE6K4pykqIoGyzcY4qiKHdKx4qiKEco\nihKW96Ky99DwGm+B4oKigFG33nWlWe7tJctZqM9ppFRlBf/TjIvxNDSYKl8597tR1H0oK0K/v7wV\nwllLHynag+wYZbKT3fSa5d5elEOoz6n1K7JRoSn4SSUuPjhsFg2N5q51Oe+7kZIPZcWHEzmvz4TX\nXv6HkSAhlHu8EY1OCtQfsFmSjoa9nduN5a4pP6OUrZpFLaeOlXd40xToA4/9j0MyzDdf6ZuSETTl\npV5564/DcSG2do/3ZCJasiMreL0M1adlthvL3bdtq0HKVtmiBvU7k3d40xToxZkH07nviabPSOs2\n0mfxG6HPZKc/DmftunatrdTDp52oi8ShuMBYwXfptJUTVz7QLpI66BWudqy3lM0Us5YXoL6+3lAJ\n69foGil5eQChf64c0BcMOSOefnBgK/rIY5aLPrOqihM/tJ7dLpHRK1ztWG8tmylmLS9AQ0ODYV+j\n37NtPWsAACAASURBVMlSr+QnFvpPLRopebO027J86GXSds+HT7wG1LVriguKoMjp55pPTYG3sybz\ngHMy4J93vr1j9B04HA7S09OD1pM9BFq5Wbpbozn5cFCT8NgKPdoUFxQxPt/pm3ufWAh/EbBwDFRd\nVwT4551v7xh9Bw6HgzRdvIq+nuwh0Mori4zT3cpz8j65MfDMmw2WXZnDfAMJW7Fbx1bucYjRD3hI\nP/h58Sc8uvR5Os0wnrNKJoVvaL0XahnlQn8+zXIP97twZQ7zRtG3LnJXs9i1fPNaZL4dFRxd9PvA\nL86D382D176AjM+cjLze+LpkUvhG1rvPTrbw+TTLPdzvQr/0zsxC1wJR5YBUo+Wi8uB7kvRuYw1b\nubcQbe4wGnmz5eARTThuHnA8Y04by5plpTRMdXHYvQ+Q0aObbQ2aYGS5h0Jz0RfoctFD4KYwZmid\nVH6e//y86hVQj33WS2Gx/vJ2QTRlR3PNy9Z71cFw/bHwZSn8Zy5MuQ66dbHdu2YYWe6h0CxrV+Yw\n3wDCBeBV+BrBAla1vs6tm5/PcRb50udqXoHkn5xsPbZybyVtsTHGolw3TZ8/i+KBew77gGfG3UXx\nvQs4ZMJYhpzpDGyTFBzTXgmWWz4Yeu9Hc8dkTbnL6He9cmUOw22wh3x7pa02lWloVKPlL5sAvx0B\n974EZxwDJ/8u6o9OSILllg+G3vvhGzxJa9nVw8BYlhJXPpPyjfPda/3ZpCTyrrQFdkBdAjCpxEVK\nehrCoSr6boP6cto//k7lmvV8fuejNOzb71fXxprlHixavqUekVDBdsGeaRN5Jhaqa7a1aPnBfWCe\nC0p/g/tegf31/nVta96a5R4sWt4oKRdgmCdCfskBeEbL5YI90yYQW7nHMfIPud/Wr30Z6kpc+aoA\nrvqGw84/jX/fPJfNS38CApedtFfCsdxlhWsU0R6p3d+MlvbZRAc5cv6Adc0Z6hblunE4oP/9bv5w\nNMx4Bn7wboETLIVteyIcy11WuJGIaDdLeGMPusLHVu4RIFprd2XSUglY557rLqD3yMOY8MhM1n70\nBd/MewZPU1PU25IIWJ1zl9fIQ2S2ci1x5ePKHBa3W8LGE20hO52Ef4Y6LdArdyjcfyW8uwQWvAXt\nZFfYkFidcx+f7/S9tCVwVg0LM9lYlOsmx1lkeB9bwYeHrdwjhFknVdl7aKs6MG1U3NCIz3LXK6DU\n9HTG33kdFx17Eh/feD+7ftvY7i1EK5a72XcUqUj5gsJiw06svf9v9ERLdjTrfZ9inlu+Uwd1U5mj\nTryRWwpg43ZbiVix3LVlb3qsTgsGm7bS3m0F3zps5R5BtM5IfkWCHGcRaalw8i8P+M7JCl4TqA1j\nD8J59/Use6KQ0sJ32rUbuCXR8npakmLW6n3a6//FjGjJDqiW+ykfqX/r86BriW3GZv2DmRfDgrdh\n8ZfGGd7aC1Ytd+27W5wXOKURbHowHLkymodvr/+XcLGVe5SJVDSwbLlraApeFqJO2VlMmHcrDbV1\n/OfvD1Nfuy8iz080Whot31qFXqBb3qYd22vbwycSslNcUMQ+Rc0vryEreFkp5WTBA1dD5V64+wWQ\n4lTbFVbn3M0UejDXvD5CXsYsM6UdJNwybOXeRrTWEjGaczdTQpNKXLhvvIPD887m01seZOnSpe3O\nircaLa8nnH3a9dfpFbv+ntDcUdkR89Zprex0EtY7uomFMGNeEd2n3MC0wr788MMP7c6KtxotD/4K\nPlRKWQiMkNeusyIPhTf5byJjExxbuUcZbS1vztZfWtVJOXJOoeuoxwMESK84tKCWyiInmx57iJ77\n1/Bw4XPUPP0++V2iH7wUL1i13COhZLX/QbDBk9bJmWXtsgkkUrKzIdfJstsLLOWWX5ynBor1Oeow\nTnnwFp599AYWLp/UrhSKVcs93O9Er+jlqPhgsqN5ArR0wjbWsJV7G9IaN2N6ejrCG/KrCUSuu8BP\nUcwsUHyCkOMsItddwNFPv8xJ995AWfcUpk6dSkVFRSs+QeLQ2jl3/ZrcYGiu/BJXvmEmOy3rnTyQ\naG/WYGtprew4HGpXpymHSSUunyUI/su4igvU7U/TMzoxazKkVizi2muvZdeuXS1uQyIRToY6I2Ur\nK22j7ZQ19G58IwWvLVOVBxJahL5NcGzlniDU19ejKIrfj1y/f7IcZKcXlOHn/4Fbb72VW2+9lU8+\n+aRtGh1DrFruoaYq9G5EOX1muPPo2rPkDs1W8NGnvr4ej8cTdFtYGX0SlguccOONN3LTTTfx+eef\nR62d8YJVy90sYl5G7qOM9n63iiY7cvCereCDYyv3NiASkb+y5a7lcDbbzlF2f8lCNHjwYNxuN998\n8w133XUXjY2NrW5XvNISy12zrEMp7VDlVmIbtP+LttlGe4qHCIdIyY5muRcXqElX9NvDGm1RKiuk\nIUOG4Ha7+fTTT5k9ezaeJF4UH25u+YmFzS567fvS1r3L57S/gyn2UO75Rblu38BM3o/eJhA7t3yC\noFnurSU1NZVZs2bx8ccf43K5uOuuuxg4cGAEWmhMXV0dP/30U9Tub0ZlZSUVFRWUlpYaltfU1FBa\nWspx0rlSmo/frt1g6TkDp9/Czw+e4zs+9Ja32bESSjP8vQZv127g5wfP4dBb3sajKDzZ6WbOyRhI\neWkpO7zPms1anKvnkTXqMcufMzXVFuFQaJZ7a0lLS+O+++7jvffeIz8/n3vvvZc+ffpEoIXGxEp2\ntm7dakl2yFF/pwv+AkjHe77/C/eNBUKI0I5Oaw3P62Vnz/d/Ycewm3GunoeiwH1jUWWktJQ92jNe\ndOL8BL6/1brshLs5TqJh9wwJgmy5y2gbKmgWh+aaL3HlMydfqCNqg9HwaaedxlFHHcUdd9zBH//4\nR84666yotLu6upqPPvooKvcORm1tLbt37+aDDz4wLK+srDQtAzAS+9UNewzrpo/6moqvVat/3b/V\n7/offM2wtCy/6/bWVrDu36sRaakoDY18kLZcvUa6r6cC+O4vdOhzuuGzysvL2b9/P4MHDwagS5cu\npp/BRkW23GWKC4qYmN/sWpatd99uZAZW4VlnnUVubi533HEHf/rTn5gwYUJU2l1VVRUT2amurg4q\nH6FkBwJ/u/u3fBhY7bt5AKzv0TzEPrDiG94E3+9/dcMeDqyA9Ru+xlOhrhpqaIQOWz7w3rf5dr/u\nBW77C1uPN5adDRvUkYBmzGRlZQX5DImPiIQ1mEwIIZQ3fi6Pyr1bExQ0e/ZszjvvPEaMGGFYXlmk\npoDMe7h5ByZ9wJ1RdKvH42H27NkA3H777YadYGuYPn068+fPj+g9rbBlyxbmz5/P3LlzDctb2i4z\nt2GoDWO0Okb7VsuE2lf8/fffp6Kigssvv9x3zul0oihK4MivjYlX2Zk1axZ5eXkMHWrs4tfmbjUl\nrw2U5YA7vRsfVNmZNWsWHTt2ZMaMGUkjO2VlZTz99NO+fkFPS9tl5j7Xvm85fa0+2ZB8DozlQ/s/\nGv2vAF5//XUALrjgAt+5eJGdaGDPubchrcm8ZWa5y8iKHfzndRflug0Vk8Ph4M4772TUqFFcc801\n7Nixo0XtizcikaHOCLNd3fRKXasn1w01Vx9KsbdnWis7oRSvmWIvLihicZ6xYnI4HNxzzz0ceuih\nXHvttVRWVgbUSURasp+7Fazs6qatedfqWg28C6XY2yO2Wz5BCDXnrnfPg7o0LtddANpe4kGCVc4+\n+2xGjhzJjBkzuOSSSzjxxBMj2v62pqUZ6lpCqO9WDnDUFLzRAEH73wXrzsrLy1m5cqXvt5CRkdGC\nFrcvQs25a+55bXAM6kB5UokLvHuJBwvamjhxIkcddRQ333wzV155Jccff3wkm9/mtHQ/95Zg1G/J\nmG39qkcbnOUEeVZ5eTmbN2+mpqYGSP4pLdtyjwEtsUCsWO7ySLclWdEGDhyI2+3m3//+N/PmzQu7\njfFEtCx3I+Qd5YwoKCy2tHTOyl7vpaWlTJgwgV69etGrVy9ycoJ1Z8lHS2UnlOWurW0Hf5ewFpkd\nyuI86KCDeOqpp3jnnXd49NFHw25jPBEtyz0YZha61SRTVrwCa9as4cQTT/TJTo8ePcJtZkJhW+4J\ngtVo+UW5br+12No5F9bcvqmpqdx///28+uqrTJ06lTlz5tCtW7fWNr/NibblrrfWzTqhSC9xE0Jw\n+un+AUP33ntvRJ+RbIQTLa9ftrU4T7UGrbh909PTeeCBB3jxxRe57rrrmDNnDpmZma1oeWyItuUu\ne0IqvZ4RI7Q6kyL03JSUlADZSWZs5R4jNAvEaqCQFcsdmoNS9Hsrh7sW9MILL2TUqFH89a9/5Zpr\nrmHMmDFB63s8HubNm8fGjRsRQjBhwgS6d+8es4jUtrTcoVmJmyl5OZjOzivfOloiO1aC3SYWyilO\nm5V8uMlSLrvsMkaPHs3111/P9ddfz6hRo4LW93g8zJ07l/LycoQQnHXWWaSlpcXMK9PWlnsoo0NO\n2WzHo1jHVu4xxmpH1Zp17i0VjKFDh/Lkk09y2223UVJSwtSpU03rrlu3jp07d7JgwQLq6ur4+9//\nzp49e3jkkUda1ObW0hZz7vrodz2y1W4r9sgTjuy0dJ27nDAlHIYPH87jjz/uk52rrrrKtO7y5cup\nq6vzyc6tt95KdXU1CxYsaFGbW0tbzLmH2mNBNkZsxd4y7Dn3BMGq5S7Pu4fKBmWFDh068NBDD5Ga\nmspNN93E/v3G+2D26tXLF2nfsWNHHnnkEZ599tmkttytpJ+Vt5C1FXtssGq5Q2BaWqO9yq2SkZHB\nP/7xD/bv388tt9ximhFywIABlJerSwg7duzI/PnzefbZZ+nUqVPLHtxK2sJyt9IvyRkDbcUePrbl\nniC0xnKPhGBcffXVLFmyhGuvvZZZs2YxYMAAv/LMzMy4itxuy2h5Db3yDmXZ27QNVi334oIinwu+\npRa7EdOmTeOLL77A5XIxe/bsgKx22dnZdOzYsdXPiRRtGS2voe+j7N0TW4+t3OMEbXtLM6xa7noi\nOeI99thjOeSQQ5g5cyYXX3wxJ598sl+5oijU19e36Vy3GW1huYeyxG3F3jZYkZ2WJJiJ5JrpE044\ngSFDhnD77bdz+eWXc8IJJ/iVNzU14fF4Ip4IpyW0heVuZb27TeuwlXscEWwOMVK55VtLz549eeqp\np7j99ttZvnw506dP95XFQ/s0YmG567Hd8G1HKNmJh41e+vTp44thWbFiRdAYllgSC8tdj+2Gbz2x\nHybaWCIcy11b8xktAUlNTWXu3LlkZmZyww03+ObhTz31VF544YWoPDNc2jpa3iZ+CcdyLy4o8r2i\n1ZZ58+bhcDj429/+5huAjh07lldeeSUqzwyXWKxzt4k8tnJPEOLFcpeZMmUKeXl5TJs2jbKyMs49\n91yWL18e62YB8WG528QH8WK5y7hcLiZOnMi1117L5s2bmTRpEt99912smwXEh+Vu03pst3yCEO6c\ne7DkEJFkzJgxHHTQQcycOZMBAwbQq1evqD/TCrblbqMR7pz7+Hxnm+QoP+644xg8eDB33HEH/fr1\no1+/flF/phVsyz05sC33BCEeLXeNHj164Ha7Wbt2bUCgUKwIZbknal7pRG13LIlHy12jT58+PPXU\nU/z666+ccsopsW4OENpyT9TfYKK2u6XYyj1BCNdyb+uAFIfDwVVXXRWT/aeNCGW5V1dXt2FrIkei\ntjuWhGu5t/XOYqmpqVx22WW8/fbbbfpcM0JZ7on6G0zUdrcUW7knCPFsuWuccMIJDBgwgKeeeirW\nTbHn3G18xLPlrjFhwgSys7N5/vnnY90Ue849SbCVexxitHd1S9e5tzUul4tly5aZZrJrK+w59/aJ\nmezEw/rxUFx//fV88cUXppns2gp7zj05iP9ffDtG66gqew+lytGB3dn9Y90kS4wbN44ZM2ZQVVUV\n1edUFjl9Lz225d6+kWWn2tGBXdkDQl8UB4wZM4aZM2dSW1sb1eeMz3f6Xnpsyz05sJV7gpDWoSPC\nIQwtk3hj8uTJTJ48mWnTprWZFaJX8LLlHu6OeDbJRVqHDgiRGLJzzTXXcOGFF3Lttde22VSCXsHL\nlnu4O+LZxA+2ck8QGvbXoXia59xlyyTeqKur4/333ycnJyeq7tBgQYOa5a4pdnfV6ojvrW6TGDTs\n3+8XrxLPslNbW8sHH3xAz549oyo7wYIGNctdU+xm3jGb+MZe554gaJa7EUadlNW9riONx+Phhhtu\n4KKLLsLpdEb9eWYKXrbc5X3tNQVf3lQbcg92m+RAs9yNiDfZmT59Opdffjljx46N+vPMFLxsucs7\n4mkKvmF36D3YbWKPbbknCHrLPRSxskzmzp3LmDFj2kSxByOcOXfbok9u9JZ7KGIlO3fffTcnnXRS\nmyj2YIQz525b9PGLrdwThGCWezDaupOqra2NeecEzZZ7KMvClTnMttyTnGCWezBiITuxHhRDs+Ue\nar1/NPevsGk9tls+QQjXcpeRO6louxw9Hk/Ml/Jo7dAsd1fmMJ+Fobno+6bEz97zNtElXMtdpi1l\nB6CmpibqzwiF3nLX9rbXXPRp3UYCP7Z9w2zCwlbuCUJLLfe2pL6+ng0bNjB8+PBYNyVgnXuOsyjA\n/W5b7O2DllrubUldXR1bt27lkEMOiXVT/Obcc5xFFDsD3e+2xR7/2Mo9QWiN5S4TbN/r1lBbW8sl\nl1zCtGnTInrflmI05y4rc20XejswKPlpjeUuEy3Z2b17N1OmTOGmm26K6H1bitGcu598vKlKjxZN\n39bpem2sYc+5JwjxbrkrikLXrl05+eSTY90UwM5QZ9NMvFvuiqKQnZ3N8ccfH+umAHaGumTBttwT\nhEhZ7hqRtkI6d+5MRkb8zGNbjZa3LfbkJ1KWu0akZSc7O5sOHTpE5F6RwGq0vG2xxze25Z4gxLvl\nDpCVlcXUqVP58cfYB9skq+XesWPHWDch4Yh3yx3UwfHUqVNZtWpVrJuStJZ7e5MdW7knCJG23KPB\nnDlzuOKKK3jnnXdi3ZSkzS1fV1cX6yYkHJG23KPBvHnzuPDCC3nvvfdi3ZSkzS3f3mTHdssnCNGy\n3Ct7D22xe7GhoYG9e/eye/du9u7dy5tvvsmqVat48MEHI9zK8ElWy90mfKJlubdGdurr/7+9Mw/P\nqjgX+O+FQAQhCaho2EEjpYbIGkBQoNYNVG556q1drNa2Km1v6XVpr7ZWtFexj0tVbAXFuoC41IWq\nj1S8igIFwQWXgi1uBCwQJEACJKIm7/1j5guHjyzfF74t53t/z/M9yZnlzJx5Z+add2bOmc+pqqqi\nsrKSqqoqHn/8cdatW8ctt9yS4FzGT1gt92zDlHsrIZmWe8VRx9Kp7F0qKyvrO5vI36qqKmpraxuM\nl5OTQ15eHgUFBaxcuZJJkyZxySWX0L1796TkMx7Carkb8ZNMyz267QTbz65du5psO/n5+eTl5bFi\nxQqmTJlCYWFhxrSdMFru2YYp91ZC0iz3LZt44bGHKPhid31nk5+fT79+/eqvc3KariYvvfQSe/bs\nYceOHQwYkBnvjpvlbkRIluVe/skGFj/5aH3bibSX/v37k5eXF1PbWbRoETU1NVRUVFBUVJTwPLYE\ns9zDgSn3VkKyLPfDjurOuT+/osXTizU1NXz00UdMnz6djRs3smjRIiorK8nPz2fkyJH07t07LZuZ\nzHI3IiTLcj+yZ++Dajt79uxh48aNTJ8+nQ0bNvDcc8+xa9cuCgoKGD16ND179kxwjmPDLPdwYMq9\nlZDs3fItXT98+eWXOe200xARevfuTe/evQH3YY6VK1eyaNEi2rVrx+DBgykuLm7WkkkUZrkbEZK9\nW76lbefFF19k4sSJtGnThr59+9K3b18AduzYwauvvsrChQtp3749Q4YM4bjjjqNt27YJznnDmOUe\nDky5txJSsVu+JZ1UYWEhW7dupVevXvu5FxQUcNpppwFu89Bbb73FAw88QF1dHUVFRYwYMYJDDz00\nYXmPxix3I0Iqdsu3pO10796dbdu2UVhYuJ97ly5dOOOMMwDYu3cvq1ev5v7770dV69tOMr8pYZZ7\nODDl3krI1Pfcc3Nzm+1o2rdvT2lpKaWlpagqH3zwAQsWLKC6uprDDz+cMWPG0K1bt4TmK1WW+7Jl\ny5g1axaHHXZYzHE6duxIdXV1i9Kz2Yj4ydT33HNzc+natWuzYUaNGsWoUaNQVdatW8eTTz5JTU0N\n3bp144QTTuCII45IaL5SZbm/8MILzJ07ly5dusQc52DaTiZ9ZCsVmHJvJWTqe+4DBw6MK7yIUFRU\nVL95aOvWraxYsYLy8nLy8vIYPXo0ffr0Oeh8tdRyV1W2bNkSs6XXs2dP6urquO6668jPz487PSP5\nZOp77oMGDYorvIgwYMCA+k2r5eXlLF26lIqKCvLy8hgzZkxC1ulbarmrKps3b445fL9+/aitreX6\n66+nU6dOcadnNI0p91ZCqiz3lq4ftvRozG7dujF58mQAKisrWbFiBc8//zwdOnSgtLSUY489tkVW\nV0st982bN/Pwww9TXFwcc5yTTz6Zhx9+mMLCwvpnMTKHVFnuqW47Rx55JFOmTAHcHpfly5ezcOFC\nOnTowMiRIznmmGNa3HZaYrmXlZWxYMGCuAb8EyZMYN68efTs2ZMzzzwz7jSNxjHl3krIVMu9IYKd\nFcTeYeXn53P66acD7pS5VatWsWTJEnJychg6dCjFxcUxbyo6GMt90KBBnHrqqXHHffLJJykrK0vI\nzIORODLVcm+IlradgoICJk6cCLi2s3LlShYvXky7du0YNmxYXBvyDsZyHzx4MOPHj4877mOPPcam\nTZsy4j3/sGDKvZWQqWvuEQ7bsu6AjilCSzqsjh07Mn78eMaPH8/nn3/O6tWrue+++wAoLi5m6NCh\nTVrm6dgtf9ZZZzFz5kxGjRrF8OHDbX08Q8jUNfcIyWg7EyZMYMKECezdu5c333yzvu2UlJQwZMiQ\nJi3zdOyWnzx5MnfeeSdjxoxh2LBhtls/AZhybyWk0nJP1rnV0fePNY327dszcuRIRo4cSW1tLWvW\nrOGhhx6irq6ONm0aPh5h9+7drF+/vr5Ti0ZEuPfee6moqNhvQ1JNTQ2lpaVxPpGjXbt2TJs2jffe\ne4+ZM2dy3nnnJXyjoBE/qbTcM63t5ObmMnr0aEaPHs2XX37Ju+++y7x585psOzt37my27cyZM4ft\n27fv13aqq6sZO3ZsnE+0L5/Tpk1j7dq13HHHHVxwwQVxbVI1DsSUeysh0y33VNG2bVtKSkooKSlp\nMlx5eTmffvopP/jBDxoN89ZbbzFnzhx++ctfJjR/xcXFFBUVMWvWLKZMmXLAa4JGasl0yz1V5OTk\nMGTIEIYMGdJkuI0bN1JVVdVk21m1ahWPPPJIQttOTk4OJSUlFBUVMXv2bM455xx69OiRsPtnG3Yq\nXCuhNay5J8taaQmxrLkff/zxDBw4MClWXW5uLlOnTmXVqlUsXrw44fc3Yqc1rLlnWttpbs19xIgR\nHH300UlJv0OHDkydOpVly5axdOnSpKSRDUimV/pUIyJWIEarQ1XTbppa2zFaI5nQdpKBKXfDMAzD\nCBk2LW8YhmEYIcOUu2EYhmGEDFPuSUZEfioir4nIZyLy54D7SBFZJCIVIlIuIo+KyFFRcX8vIttE\n5FMRuTHKb7KIbBKRt0TkGO82S0T+FAiTIyK7ReSPDbi17H2vJNNEebUTkb+IyMciUiciJzUQN+vK\nK8xY24kPaztGEFPuyeffwO+Ae6PcuwCzgT7+txuof7FURC4GzgYGASXAWSJyUSD+r4HjgEuA6d5t\nCXBiIMxwYEOU2whAgTcO4pmSSWPlBbAU+C5wwAess7i8woy1nfiwtmPUY8o9yajqAlV9Gtge5f43\nVX1CVXer6mfAncAJgSDfB25R1c2quhm4Gbgg6vY5/hfZFbkEGCgikaOmTgQeAQ4NuI0FVqhqbWKe\nMLE0UV5fqOodqrocqGsgalaWV5ixthMf1naMIKbcM4dxwJrA9XHA24Hrt71bhBnA68BtwLUAqvoJ\n+4+eT8I1wuUNuIUNK6/sxdrOwWHlFUJMuWcAIlICXA1cHnDuBFQGrqu8GwCq+pSq9lHV4ar6QSDc\nK8BJ4j7JVQq8CiwLuI3xYcKGlVcWYm0nIVh5hRBT7mnGb1B5DvgvP20WYTeQF7jO927NsQQ3Yh4E\nfOinLZfhrJtBwCHAygRkPdOw8soyrO0kDCuvEGLKPY2ISB/gBeBaVZ0f5b0GOD5wPZj9px4bY4kP\nOwm3iQZVXQP08m6vqWr8Z6FmPlZeWYS1nYRi5RVCTLknGRFpKyKHAG2BHBHJ9W7dgReBmap6TwNR\nHwQuFZHuItIDuJTAjuDGUNUPgXJgGr7BeVZ6t4xeA2usvLxfe+8HkCsiuYGoWVleYcbaTnxY2zH2\nQ1Xtl8QfcA1uh2pt4Pdb/6vFrW9VAbuAqqi4NwIVwDZgRhxpzge+ALoE3K7w6Z2S7jJpSXl5v4+j\n3GuB3tlcXmH+WdtJTHl5P2s7Wfazb8sbhmEYRsiwaXnDMAzDCBmm3A3DMAwjZJhyNwzDMIyQYcrd\nMAzDMEKGKXfDMAzDCBmm3A3DMAwjZJhyNwzDMIyQYcrdMAzDMEJGTlOe7XL66pe1ZanKi2EYhmEY\n8VGmqn2jHZv8Qp2I6LHd7Qt2YWfY7LvTnQUjRUx842KkmTDN+ccS7mDTSOa9E3GPdD17rGHOij5K\nxwgtsg5U9YAqYdPyhmEYhhEyTLkbhmEYRsgw5W4YhmEYIcOUu2EYhmGEDFPuhmEYhhEyTLkbhmEY\nRsgw5W4YhmEYIcOUu2EYhmGEDFPuhmEYhhEyTLkbhmEYRsgw5W4YhmEYIcOUu2EYhmGEDFPuhmEY\nhhEyTLkbhmEYRsgw5W4YhmEYIcOUu2EYhmGEDFHVxj1F1gN9UpYbwzAMwzDioUxV+0Y7NqnczIH2\nnAAADtVJREFUDcMwDMNofdi0vGEYhmGEDFPuhmEYhhEyTLkbhmEYRsgw5Z5gRORKEbk73fkwGkZE\n2ojILhHpme68GMnFZJ06RKSXiFSJiKQ7L7Hg60XfdOcjmSRcuYvIWBH5u4jsFJFtIrJURIYlOp04\n8vOyiNSISKXP02si8isRaZ+M9FR1hqpelIx7BxGR80VkabLTSRS+MVX5X62IVAfcvp2qfKhqnap2\nVtVPDvZeIjJXRH4b5fZzEXldRPZm6yAvG2QtIrkicq+IlPm+5XUROfVg00knIrLey6pKRDaJyH0i\n0jGWuKq6UVXztAU7tEVknK8nkTqzQUQeFZHh8T9FbPh6sT5Z948gIh+LyNeSnU5DJFS5i0hn4Bng\ndqAL0AO4FtibyHTiRIGfqGo+UAhcBpwLPJfGPCUCwT1bq8A3pjxVzQPKgEkBt4ejw4tI29TnMiF8\ngqvz96U7I+kiS2TdHvgYGOP7luuAx0WkR3qzdVAoTlZ5wGBgCHBlitL+d6DOjAL+CSwVkQkpSj98\nqGrCfsAwYHsT/ucDS4GbgO3Ah8DpAf9C4K9ABbAO+JF3zwWqga7++tfAF0Anf30dcGsjaS4GLoxy\n6wXsASb66/bAbcC/cZ3zH4B23m8csBG4Aij3YSYDZwD/ArYBVwbufQ0w1//fB6gDvo/r5LYCVwXC\njgCWAzv8fWcCOQH/OuBiXxbbgTu9+1eAGl8Gu5oq80z84TrFr0W5/Q54BJgPVPoyGwWsCJTP7UBb\nH76tL5+LgPd9nbk9cL8i4BVgpy/3eVHxegfq1q3ABmAzcCfQ3vud7PN6hb/HJ8D3vN9U4HPgM6AK\neCLqeWYAd6e7rNP9ywZZB/KxBjgr3WWeKFkBvweeCVxPBN70MisDrgn4Rfq6Nv56Ma5fXubL7G/4\n/ruBdMcBGxpwnwmsClyfAKzydWQlMDrgt9jXq7/j+sS/Al2BeT6/KyP1wIevA/r7/+/zdeFZn9cV\nQL9A2Nt8nakEXgPGBvyuAR4FHvBx3wWGer8HgVqcrqkCLk+pPBNcOToDnwL3A6cDBVH+5+Os+Atx\nlucluBFbxH+JF2g74HjfyMZ7v5eBb/j/n8c18tP89SvA5EbydIByD8SZ4f+/DqdkD/O/vwPXBire\nF7gBRVvgRz5f84COwFdxA48+AWE/GFXhZ+MGECW4DmKA9x8KlPqy6I3rHH4eVQGf9uXay6d7aqAs\nl6SysiSwnjTW4X/GvgFXLm6wOMKXT1/caP4n3j/ScT8FdPJlXRG5L/AYcIX/vz2+I/DxatnX4c8E\nngDy/H2eDcj+ZC/73/h4ZwG72TeonAv8tpFnNOWeJbL2/oW4AffR6S7zRMgK6Am8Q8BoAk4CjvP/\nF+MGSGf76z6+rIPK/X3gaC/fxcANjaTbmHKfAHwJdMDNBG8HvoObcT7XX3cJpLfO153OuL70n/4e\nbXDK997AvWvZX7l/6utgG1zfPj8Q9jtAgff7b//ckUHhNbj+/zRfd28AVkSV6YR0yDOh0/KqugsY\ni2uIdwNbReSvInJEIFiZqv5Z3ZM/ABSKSDe/6WU08CtV/UJV3wbm4Eb14BT/OD+FVwLc4a9zcZ3C\nkjizuwk3sgMnvGtVtUJVK3DTqucFwn6Oq5i1OIvjcOA2Va1W1bXAWtxgpMFiAaar6ueq+g7wdiSs\nqr6pqqvUscGX2bio+DNUdZeqbsRV4MFxPmdrYpmqPgegqntV9Q1Vfc2Xz3rgHg4snxtUdbeqluEG\ngJHy+QLoKyLdfdmvCMQRAL/550fAL1S1SlV346yV4LpwDXC9qtaq6jO4wemxiXzoLCU0shaRdsBD\nwD2q+mE8hZCBLBCRKpylWg5Mj3io6hJVXeP//weuL4yWUZD7VPVDVd2LG4DF23dtwsmvAJgErFPV\n+er2UjyCU95nRaW33uuhhcCHqrpYVeuAv+CWGSJEb/x7ytfBOpws6/Pq09zp0/0DbrAyIBB3mao+\n73XaXJx+CpKWTYYJ31Cnqv9S1QtVtTdudNcdN60RYUsgbI3/t5MPt11VqwNhy3Dr9uAs7Qk4a/cd\n4AVgPG46731V3RFnVnvgRn74tDdEpds9cF3hBQeuAwBnRRNw69REWuWB/6sjYUWkSESeEZHNIrIT\nuB43cGg2bkjZGLwQkQEi8qwvn0rcoCvW8rkUZ8W9LiJvi8j3OZCjcA31bRHZLiLbcXtGgmlsC8g+\nOg2j5YRC1iLSBre8UAX8oqmwrYTJ6ta9x+GW/+rLR0RKReQlEdnq+6uLOVBGQbYE/m9Ju+mBM452\n4vrjsij/oH6A/etHTQPXTaXfaF5F5HIRWSsiO0RkB27m5/Am4h7i60VaSWoGVHUdboq+OIbgm4Cu\nInJowK03bv0N3LT5AOAbwCuq+k/vPxGn+GNGRHrhpmAi1v4m9v+Gfh/vlmzuAt7DTeUV4Kb+Yx3l\nafNBWh3RzzQbt4bVX92mpWuIsXxUtVxVf6yq3YGfAXeLSJ+oYOU462yAqnb1vwJV7XrADWPLrxE7\nrV7WfjbgPlxn/01v9bV2BEBVl+JmVm8J+M0HFgA9fH81m+RapVOAN70RuAk35R4kqB+SgoiciNuH\n8U1V7aKqXXADuYzvpxO9W36AiFwa2THqlei3cRsUmkTd6yrLgRn+NZMS4Ie4aY6Ilf8G8FP2KfPl\nuHX7mJS7iHQQkXG4Cvqqqi70Xg8DvxGRw0XkcODqSLoJoKlK0BmoUtVqEfkKbuNOrJQDPf2UYFjp\nDFSqao2IDMRZCjEhIueISGT2pRK3VFQbDOM74znA7V7uiEhPETklxmTKgf5R6bYVkUNw67Y5vi6n\nfRTfCmh1ssYto/UH/kNVv4w1v62I24BTRGSQv+4E7FDVL0SkFLecGeRgFH19XBHpLiLX4PZmRXbr\nPwcUici5vo19CxiIm31JJp1wyz4VItLevw7ZuZk4wXLYwoH1JiUkutPZBYwEVorILpzyfQe4vIk4\nwZHNt4F+uFHaE8DVqro44P8KrtNcFbjuRPPr7Xf6qb4tuN2yf8Htdo/wv8DrPq9v+/+vjzHPDV3H\nGvZy4Lt+jWs2bg0r1rgv4TaNbBGRrbQuYh3NXgZc4MvnLuIrn5HAa74ePo7bnPVJA+Euw03vrfJT\njX8Djokx73OAwSJSISKPebfpuKm5y3CbHquB/2nifmEnlLIWkf4442Mobm9R5D3+c5p90sxlvzJW\n1W046z3yfv9Pgd/5vvQ3uF3ijcWP12It9OW3C9e/HweMU9UXfV62A2fi+sxt/u+kwHJsvOnFGv55\n/1uH2xxXTdSSUjP3vhG42i8FXRpnHg8KOxXOMAzDMEKGTRcahmEYRsgw5W4YhmEYIcOUu2EYhmGE\nDFPuhmEYhhEyskq5i8h8ETk7gfe7WUQuSdT9jPhJtEybSKeb/5BFmF89zGhM1tlHMmQuIiv965ah\nJmuUu39Xs0RVn/bX7UTkFhHZ6F/B+EhEbg2Ejxx/WOlfY1gmIhf7D1dEuBm4SkRyUvw4BuC/hVCi\nqk+LyJWB15FqROTLyKs1IvKuD18XCLPRyz/4fu054o4r3iMiLwXTUtWtuNcPY37/2kgcSZD1TSKy\nzrfvtSJS/7lpk3Vm0ECffX5A1jtFZLWITAqE7+PlHjk6drOIPC0iX4+69U248w1CTdYod1xDfShw\nfRXuHdXh/nOL43EnHkVQ3HuU+bgv1t0I/Aq4tz6A6hbcF+aSbk0YDXIRXqaqOkP9saK4DxstV3eE\nZGdVjXyEQ3GdReTzmt/CfSgjQgXuRMAZjaQ3H+vw00WiZb2bfe37AtzHbUYF/E3W6Se6z4Z9si7A\nfw9BRPIC/grke7kfD/wf8JTs/0niZ4AJItItiXlPO9mk3M9g/y/ZDccdFlAOoKobVHVeVJzIpxh3\nqeqzuA7ifBH5aiDMK7hDDYzUEy3T5hD2yfQj3Ol/wQMiXlLVx3GnPjXESqC/uC8vGqkl0bK+VlXf\n9/+vwh1FPToQ32SdfpqT+VzgUNyRv0Eict+qqnfgPiz1+4inP8jmDdxJbqElK5S7iHTEffnuXwHn\nV4HLRGSqiMTy7XtU9TXcOc8nBpzfo/ET4Ywk0YhM44n/FZwc3481jrpTAT/A5J1Ski1rEemAO1ly\nTcTNZJ1empO5uNNBL8Sd2Bl9oEw0TwLdRCR4klvo++1sWSsuwE3X7Aq43cC+84FvFZEK4CpVfbCZ\newWPisXfsyCBeTVioyGZxsKbfo9ER9yZAnfFGd/knXqSLetZwGpVXRTlbrJOH43JfLS4E/0i33z/\nnv9MblNEjo6N7rePSlBeM5KssNxxRwZC4IP/6rhLVU/EVaQbgD9Hje4aInhUbOSeOxsJaySPA2Qa\nI0NUtRPwn7hvkh/aTPhoTN6pJ2myFpGbgK/iltyiMVmnj8ZkvsKf5FcAPA2cFMO9IsfCZlW/nRXK\n3Z8R/yFwbCP+e1X1T8AOXENvEBEZgTtXeFnAeSDusBkjhTQn0yaIrMc9jluauSbmiG4q8BhM3ikl\nWbIWkWtx666nqOruKD+TdRqJoc+uBn4CnCcizU2vTwHKVTU4xR/6fjsrlLvnOdyuWQBEZJqIjBOR\nQ8QdIXg+bqrnzeiIItJZRM7ETe3NVdU1Ae9xwMLoOEZK2E+mLeBG4MeRXbMi0kZEcoF2QFtxx7UG\nl65KgY9VtblToYzEk2hZX4k7hfLrqtqQBWeyTj9NytyfCHcP+w/a6jdS+u8V/Ax3hHf9yYy+jQ8D\nXkhCnjOGbFLu9wDfC1xXA7fgdkZ/ijtLfYqqBjdnPCPueMMNuHOFbybwOo2IFOJGgAuSm3WjEaJl\n2hzRR1r+A7cb9wrvdB5QA/wRGIurI3cHonwXtz5rpJ5Ey/p6oBfwQeB9+ODRvCbr9BOLzG8Hzghs\nilZgh7ijY98BTge+qaoPBOKcDSz2rzKHlqw68lVE5gGPRT6KkID73Qx8oKrWCaSJRMu0iXSOAF7G\nreN+nsy0jIYxWWcfyZC5iKwAfqiqaxN1z0wkq5S7YRiGYWQD2TQtbxiGYRhZgSl3wzAMwwgZptwN\nwzAMI2SYcjcMwzCMkGHK3TAMwzBChil3wzAMwwgZptwNwzAMI2T8P4c/QqemaJQlAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2afff834aad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basins = [\"cascades\",\"california\",\"northernrockies\",\"southernrockies\",\"whites\"]\n",
    "scenarios = ['historical','rcp85']\n",
    "titles = ['1970-1999', 'RCP 8.5 2070-2099']\n",
    "colors = ['r','g','b','c','y']\n",
    "'''lt = 10\n",
    "rt = 7'''\n",
    "lt = 6.8\n",
    "rt = 5\n",
    "# fig = plt.figure(figsize=(20,20))\n",
    "\n",
    "# fig = plt.subplots(figsize=(10,10),nrows=1, ncols=2)\n",
    "fig = plt.figure(figsize=(lt,rt))\n",
    "\n",
    "\n",
    "######################### set fontsize #####################\n",
    "fs = 12\n",
    "ls = 18\n",
    "############################################################\n",
    "\n",
    "lw = 2.0\n",
    "num = 0\n",
    "\n",
    "for scenario in scenarios:\n",
    "    ax = fig.add_subplot(1,2,num+1)\n",
    "    m = make_map(fs, label_parallels=True, label_meridians=True)\n",
    "    for basin in basins: \n",
    "        file = '/raid9/gergel/agg_snowpack/swe_t_p_reg/proc_data/ensavg_%s_%s.npz' %(basin, scenario)\n",
    "        data = np.load(file)\n",
    "        if scenario == \"historical\":\n",
    "            temp_avg_hist = data['temp_avg']\n",
    "        else:\n",
    "            temp_avg_hist = data['temp_avg_2070_2099']\n",
    "        lats = data['lats']\n",
    "        lons = data['lons']\n",
    "\n",
    "        # cutoffs \n",
    "        rd_cut = 278.15\n",
    "        tr_cut = 273.15\n",
    "        sd_cut = 267.15\n",
    "\n",
    "        ## 1 for SD, 2 for TR1, 3 for TR2, 4 for RD \n",
    "        cf = np.ndarray(shape=len(lats),dtype='float')\n",
    "        cf[temp_avg_hist <= sd_cut] = 0 ## SD\n",
    "        cf[(temp_avg_hist > sd_cut) & (temp_avg_hist <= tr_cut)] = 0.33 ## TR1\n",
    "        cf[(temp_avg_hist > tr_cut) & (temp_avg_hist < rd_cut)] = 0.66 ## TR2\n",
    "        cf[temp_avg_hist >= rd_cut] = 1 ## RD\n",
    "\n",
    "\n",
    "        lons_mesh, lats_mesh, data_mesh = mesh_gridify(lats.reshape(len(lats),1),\n",
    "                                                     lons.reshape(len(lons),1),\n",
    "                                                     cf.reshape(len(cf),1)) \n",
    "        cs = m.pcolormesh(lons_mesh, lats_mesh, data_mesh, shading='flat', cmap=discmap,\n",
    "                          latlon=True, zorder=1, vmin=0,vmax=1)\n",
    "    \n",
    "    \n",
    "\n",
    "        # fig.text(0.045, 0.5, 'Fraction of Area', va='center', rotation='vertical',size = fs)\n",
    "    ax.set_title(titles[num], size=fs)\n",
    "    \n",
    "    num += 1 \n",
    "\n",
    "from pylab import * \n",
    "# subplots_adjust(bottom=0.02, right=0.9, top=1.0)\n",
    "cax = axes([0.0, 0.2, 1.0, 0.05])\n",
    "cbar = plt.colorbar(cax=cax, orientation='horizontal')\n",
    "cbar.set_ticks([0.125, 0.37, 0.62, 0.87])\n",
    "\n",
    "cax.tick_params(axis=u'both', which=u'both',length=0)\n",
    "\n",
    "cbar.ax.set_xticklabels(('Snow Dominant \\n (SD)', 'Transient1 \\n (TR1)', \n",
    "                         'Transient2 \\n (TR2)' , 'Rain Dominant \\n (RD)'),\n",
    "                        size=fs)\n",
    "    \n",
    "plotname = 'RD_TR_SD_map.tiff' \n",
    "direc = '/raid9/gergel/agg_snowpack/swe_t_p_reg/plots/changeclass/'\n",
    "savepath = os.path.join(direc,plotname)\n",
    "print (\"saving figure to '%s'\" %savepath)\n",
    "plt.savefig(savepath, format='tiff', dpi=300)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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