{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bgee\n",
    "\n",
    "Extracting data from [Bgee](http://bgee.unil.ch/). See [this Thinklab discussion](http://thinklab.com/discussion/tissue-specific-gene-expression-resources/81#278) for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "import pandas\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import IPython\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_groupby_counts(df, columns):\n",
    "    \"\"\"Group datagrame by columns and return the number of rows for each grouping.\"\"\"\n",
    "    grouped = df.groupby(columns)\n",
    "    get_len = lambda df: pandas.Series({'count': len(df)})\n",
    "    df = grouped.apply(get_len)\n",
    "    df = df.sort('count', ascending=False)\n",
    "    df = df.reset_index()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and process presence of expression data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene ID</th>\n",
       "      <th>Gene name</th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>Developmental stage ID</th>\n",
       "      <th>Developmental stage name</th>\n",
       "      <th>Expression</th>\n",
       "      <th>Call quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000015</td>\n",
       "      <td>male germ cell</td>\n",
       "      <td>HsapDv:0000092</td>\n",
       "      <td>human middle aged stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>poor quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000019</td>\n",
       "      <td>sperm</td>\n",
       "      <td>HsapDv:0000088</td>\n",
       "      <td>human early adulthood stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>poor quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000023</td>\n",
       "      <td>oocyte</td>\n",
       "      <td>HsapDv:0000087</td>\n",
       "      <td>human adult stage (human)</td>\n",
       "      <td>absent</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000083</td>\n",
       "      <td>epithelial cell of pancreas</td>\n",
       "      <td>UBERON:0000104</td>\n",
       "      <td>life cycle</td>\n",
       "      <td>present</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000115</td>\n",
       "      <td>endothelial cell</td>\n",
       "      <td>HsapDv:0000092</td>\n",
       "      <td>human middle aged stage (human)</td>\n",
       "      <td>present</td>\n",
       "      <td>poor quality</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gene ID Gene name Anatomical entity ID  \\\n",
       "0  ENSG00000000003    TSPAN6           CL:0000015   \n",
       "1  ENSG00000000003    TSPAN6           CL:0000019   \n",
       "2  ENSG00000000003    TSPAN6           CL:0000023   \n",
       "3  ENSG00000000003    TSPAN6           CL:0000083   \n",
       "4  ENSG00000000003    TSPAN6           CL:0000115   \n",
       "\n",
       "        Anatomical entity name Developmental stage ID  \\\n",
       "0               male germ cell         HsapDv:0000092   \n",
       "1                        sperm         HsapDv:0000088   \n",
       "2                       oocyte         HsapDv:0000087   \n",
       "3  epithelial cell of pancreas         UBERON:0000104   \n",
       "4             endothelial cell         HsapDv:0000092   \n",
       "\n",
       "              Developmental stage name Expression  Call quality  \n",
       "0      human middle aged stage (human)    present  poor quality  \n",
       "1  human early adulthood stage (human)    present  poor quality  \n",
       "2            human adult stage (human)     absent  high quality  \n",
       "3                           life cycle    present  high quality  \n",
       "4      human middle aged stage (human)    present  poor quality  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read expression\n",
    "presence_df = pandas.read_table('download/Homo_sapiens_expr-simple.tsv.gz', compression='gzip')\n",
    "presence_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Apply filters for gene presence\n",
    "present_df = presence_df[\n",
    "    presence_df['Call quality'].isin({'high quality', 'low quality'}) &\n",
    "    presence_df['Expression'].isin({'present', 'low ambiguity'})\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Developmental stage ID</th>\n",
       "      <th>Developmental stage name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HsapDv:0000090</td>\n",
       "      <td>25-44 year-old human stage (human)</td>\n",
       "      <td>886385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000104</td>\n",
       "      <td>life cycle</td>\n",
       "      <td>795802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HsapDv:0000092</td>\n",
       "      <td>human middle aged stage (human)</td>\n",
       "      <td>767581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HsapDv:0000094</td>\n",
       "      <td>65-79 year-old human stage (human)</td>\n",
       "      <td>407378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HsapDv:0000087</td>\n",
       "      <td>human adult stage (human)</td>\n",
       "      <td>387225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HsapDv:0000089</td>\n",
       "      <td>young adult stage (human)</td>\n",
       "      <td>383819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HsapDv:0000095</td>\n",
       "      <td>80 year-old and over human stage (human)</td>\n",
       "      <td>160763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HsapDv:0000086</td>\n",
       "      <td>adolescent stage (human)</td>\n",
       "      <td>116353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HsapDv:0000085</td>\n",
       "      <td>6-12 year-old child stage (human)</td>\n",
       "      <td>75387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HsapDv:0000199</td>\n",
       "      <td>fifth LMP month human stage (human)</td>\n",
       "      <td>74310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HsapDv:0000083</td>\n",
       "      <td>infant stage (human)</td>\n",
       "      <td>71678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>69376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Developmental stage ID                  Developmental stage name   count\n",
       "0          HsapDv:0000090        25-44 year-old human stage (human)  886385\n",
       "1          UBERON:0000104                                life cycle  795802\n",
       "2          HsapDv:0000092           human middle aged stage (human)  767581\n",
       "3          HsapDv:0000094        65-79 year-old human stage (human)  407378\n",
       "4          HsapDv:0000087                 human adult stage (human)  387225\n",
       "5          HsapDv:0000089                 young adult stage (human)  383819\n",
       "6          HsapDv:0000095  80 year-old and over human stage (human)  160763\n",
       "7          HsapDv:0000086                  adolescent stage (human)  116353\n",
       "8          HsapDv:0000085         6-12 year-old child stage (human)   75387\n",
       "9          HsapDv:0000199       fifth LMP month human stage (human)   74310\n",
       "10         HsapDv:0000083                      infant stage (human)   71678\n",
       "11         UBERON:0000113                 post-juvenile adult stage   69376"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find genes present per developmental stage\n",
    "stage_df = get_groupby_counts(present_df, ['Developmental stage ID', 'Developmental stage name'])\n",
    "stage_df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UBERON:0000473</td>\n",
       "      <td>testis</td>\n",
       "      <td>144453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000955</td>\n",
       "      <td>brain</td>\n",
       "      <td>114977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UBERON:0001870</td>\n",
       "      <td>frontal cortex</td>\n",
       "      <td>110212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBERON:0000178</td>\n",
       "      <td>blood</td>\n",
       "      <td>104919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UBERON:0002107</td>\n",
       "      <td>liver</td>\n",
       "      <td>97331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UBERON:0002037</td>\n",
       "      <td>cerebellum</td>\n",
       "      <td>92622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UBERON:0000992</td>\n",
       "      <td>female gonad</td>\n",
       "      <td>88936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>UBERON:0001987</td>\n",
       "      <td>placenta</td>\n",
       "      <td>83013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UBERON:0009834</td>\n",
       "      <td>dorsolateral prefrontal cortex</td>\n",
       "      <td>82020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>UBERON:0002048</td>\n",
       "      <td>lung</td>\n",
       "      <td>70698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Anatomical entity ID          Anatomical entity name   count\n",
       "0       UBERON:0000473                          testis  144453\n",
       "1       UBERON:0000955                           brain  114977\n",
       "2       UBERON:0001870                  frontal cortex  110212\n",
       "3       UBERON:0000178                           blood  104919\n",
       "4       UBERON:0002107                           liver   97331\n",
       "5       UBERON:0002037                      cerebellum   92622\n",
       "6       UBERON:0000992                    female gonad   88936\n",
       "7       UBERON:0001987                        placenta   83013\n",
       "8       UBERON:0009834  dorsolateral prefrontal cortex   82020\n",
       "9       UBERON:0002048                            lung   70698"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find genes present per anatomical entity\n",
    "anatomy_df = get_groupby_counts(present_df, ['Anatomical entity ID', 'Anatomical entity name'])\n",
    "anatomy_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1: Number of genes present by developmental stage and anatomical entity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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akVQjuwNvmdmDhAfwf8Xy5sCmZtaF4E3qLemnAm2cDwyWNMvMdgEmSJoHYGbT\nCAbTQ/HzysArQDNJZWZ2KcHtNx0YRHhI/xboCfwMDAPWBZoAD0vqF709qwKrAZ3jhr3E9jsAFxC8\nWNl2MonzxxFSHn4N/Bm4N894esXsICsDxwPfELxLO8Y2ngcOAXoAGwOrR12GAPsDmxLSJr5oZhfH\nuVwNeENSTzPrT1gjuiawPnCapCcSOi4P3BOvx4pAX4KXb1tgRPT6nJ+n3dWBO4CGgIBdJTUzszbA\nQOA34EOCV21BYrwdCHFzC8wMoJmZjYn6PSJpgJlNAI6V9F6cw7WA4VHPT+N47gK2InikHpPUN/Z9\nLuFHhZWAbsB84M4otzHwkqQToi73ACcSvtSO4ziO4zh1isxi/l4taQYhYQOSPiU86+fWuQm4Kafs\nF+CgPHVfJDh8UlP7d3oKrEF42D6AcksRgrX4T0ltgI8InoZFiPnXdyU8MAO8BexiZiuZ2WqEi7Ri\ntn40aJ4FOppZfaAj8CAhh/sJ0TM1FuhDMI6el9QRaBH1g2AdPyVppxwDKUMwqrpKagtMJOxLVRbP\nrwH0jm11juX5PEdTJe0GXAN0L1Anq8f/JHUibODaWdI+wCXAITEjyHeSdge2B1qaWdMoN1dSZ0KW\nkdNy2t2YYPzsDRwK/EnSGEImkSMJrtF87fYF7o9jH025IX9jYk6+iGNK0gZ4M/F5eaAL0JqQDjI7\nVvIcb0gwRPciGKenEeb36Hh+C+DweF3vBw6M8s2i3A5A53gfQbh/2uI4juM4jlMHqV8vU/RVG0gT\nk1QbmA28Gz0L75nZ3OiReCBhgDwAXGNm+1P+0Hy6pNcIxtWobPY+SdNjrM/jBE/Bi7GPJDcSlo7V\nA8ZLmm9mmwNDoyejASHzxnfA9mbWjhA4tlyiDQGY2YlRhzLCw/8PiZidyYQAtWwapU3iWOdH2Snk\nX172SnyfRcLAS5CUyaaCmgO8nTheHvgFWMvM7gB+InhSGsQ6r8X3z2Pd8oFJb5vZMIK3pQHBJZqk\nULubAdl0Qs/GMa5B8MKNjnO7AiFGLsnqQDIt2LREWsgFLEryB4KP4nrV+cAsSXNiv1lD6ktgsJn9\nBKyT1Qv4QNLPse7MxBzMJBiIjuM4juM4Ti0ktSfJzFapSkUWk2cJ3hyiN2JFgnHyuJltH+u0J3hX\n7kvEKGUf8ncjeH6IbawONJa0M2Gp2hbAC8kOJU0heEuOBrL5UqcDR0SPw9mEpBHdgTmSDgeuZGGD\nJWuUDYnOVLgbAAAgAElEQVT67Ap8BjQ2s2wyiTZEYyryPmGt5QrR67QD6WKQ5gJrmlk9M/sLwXuS\nS4ZFDa5OwLqSuhG8PCvkqbMIMa6rkaS9CHNwTTz1O1Cf4AXL1+40onuVsMQRgoH6GbBPnNtLgNy8\np18DyXs035zMBZrG479VUDfJDUB3ST0IBlP2e1NIbpWoj+M4juM4Tp2jfr16RV+1gQq1NLNtzWw6\n8KaZrWdmH5pZ86WgW2okPQa8ZmYvEVIEnijpd8LStqvM7BnCOsSBBZowwnK8bHuzAYvtjQP6SPox\nj9woYC1J78bPxwMjY7DYQMKyq6cIy/LGA/8GpkZDDvI8ZEdv1jHA/Wb2LGEZ4AXZ+lG3gQTD8AlC\nbEw+yhLvZTHAbTwh28cNlGf8W6RuzvFLwEZm9jQh3upFyg2NQsvXiO23NbOJhBidfrH8OWBE1CO3\n3SYEA2ifWP4PYH6ck1OBMdFzdizwTk5/EwhL5HLHkdRtMHCdmT1OuPfLcs4XOr4dmGxmjxIMtiYF\nxpylBYsacY7jOI7jOHWCTKb4qzaQZrndNYRdb0dJ+szMehEyR+xQpZqViKQz85S9AeycQnarPGXH\np+i2PmHZXVbmVfJn/ts2T1mPIvo8RTCukkyML2L69YIp2KPHI3s8jmDoIem4PNUHJOoOSxw/RExU\nQf5r/Vyi7nSCMZfU4VdC7E6ubv0oN5gWadfMOgHnSppqZu2J6dkljScYeYV4GjjHzBrEdOh/pESX\n1DS+jyXhMUyQDQycS2JzsYRcoQQMrRJ1k8GABxG8Y47jOI7jOHWOJbxPUrWQxt+1oqQ/frWX9CQL\nx9XUSWJ2uvaUJ4lwlgwfE+J/JhEMuD5phKK3aQBwQkV1qxIz25oQq/RJderhOI7jOI5TXWQq+Fcb\nSONJ+tbM/vCEmNlhhHifOk0tSY1e64heqVYVVswvO4Gw7K7akPQWYZml4ziO4zhOnSSzDHiSMmVl\nxWPWzWwTQgzJ9oSMZO8Dh0lSUUHHcYpRkzf8dRzHcRyndlIjrJMr97+g6HPO6ff1qxF6FiPNZrIf\nADuZ2Z+B+pJ+qHq1HMdxHMdxHMdxqocKjaSYGa6MaJma2e+EVMrvABdJ+m+Vaug4yyiPnT6kpPp7\nXnkiAN13TJNTJDD8+aEAnL3HWSX1ddG4iwF458a7UstsccwhADx34S2pZVr17QnAvScMKkE7OOC6\nUwH45ZsvUsussMY6AFxz8IUl9XXy3SEHx7wfvk0t07Bx2CbrlSsL5ldZhOanH1lyP8m+KqNfKdcK\nwvW6u9dVJckcPCzsM/3QKddUULOcLoNPBkAjRpfUlx0VcsU8etq1qWX2uipsm/e/Lz9OLbNi07CD\nwv9mfVqCdrDiWv8HwOP/ui61TMf/hDDLUu51KL/fv3gid0u5wqyze9jQvjL30tK8b2tqX1mZyt63\nL182PLXM9n26AzB76vPFK+aw+t9DjqHKjOvFS26toGY5Lf7do+R+kn1VRr8fZ5S2wKnRBgbA+DOH\nppbpcGn4//eTBx+toGY56++7F7B056ImUFs2jC1Gmpikd4F5wC0EQ6kbsC5hw8ybCZnvHMdxHMdx\nHMdxlomYpDRGUktJyY033zCzqZIOM7Mjqkoxx3Ecx3Ecx3FqH8uAjZQqBfifzOyPfYTicT0zWxFo\nWGWaLaOYWXczu7i69UiDmRXN0mZmM+P71mbWOmWbXc2sScU1S8fMWpvZKWbWxszurIo+UuiwlZmd\nWx19O47jOI7j1ATqZTJFX7WBNEbSKcAYM5tqZq8CjwAnA+dRZENTpyDLYlaz/YEtUtY9BWi8pBUw\nswzhnryOaszsImkasImZbVRhZcdxHMdxnGWQOrFPkqQJ8YFva+A34F1J883subiBp1M6Lc1sHLAG\nMFTSjWY2A9hU0jwzu4QQCzYDOJuQKGM94HpgV+CvwCBJ15vZAYQNVBsQDLCuhGt1JvArsBFwl6SL\nkgoUkPtv7GMb4DOiMRM3zr1T0jgz6wgcLKlHPNcU6A7MNbNXJE2N5csD98Q2VgT6xr62BUZEz9P5\nQHNgNeANST3NbHXgDoKXUsCukpqZWRtgIOEe/BDoJWlBYkgdgLclLTAzgGZmNgZYE3hE0gAzmwAc\nK+k9MzsOWAsYHvX8FNgAuAvYCtgOeExS39j3uYQfFVYixOXNB+6MchsDL0nKbmR7D3AicAaO4ziO\n4zh1jGUhcUOFniQz2wy4EjgJOA242cwmuYFUaTLAfEl7EAyT3rE8OZ/J43UIyTGOB84BDgc6Ab3i\n+WbAnpJaEzIO7hHl/y/KtQT65NEjn9y+wIqSWsb+Vk7oU5Y4/gNJXwK3AldmDaTIxgTjZ2/gUOBP\nksYArwNHAssD30nanbAHV8tocPUF7pfUFhhNuSF/I9A1ln9BMMyStAHeTHxeHugCtCbcu7m6J483\nBHoCewEXEO7zFsDR8fwWwOGS2gH3AwdG+WZRbgegs5mtGeu/BbTFcRzHcRzHqZWkWW53N8HDsB3w\nGuGX+bFVqdQyThnwajyeRfCy5JI0v6dJ+g34Hvgwek/mEIwAgG8InplbCB6gBrH8LUm/S/ofYRPg\nXPLJGfAygKTZBG9WLoXumYV+MpD0NjCM4G25Lo/cL8BaZnYHwXu1UtRhM+C5WOdZADNbA2gCjI4p\n6XcnGIFJVifMZ5ZpkuZL+gVYwKIk9flI0o+EOZ4laY6kXyk3pL4EBpvZrUA7yg23DyT9LOl3QrbH\n7DWZSTAQHcdxHMdx6hz16mWKvmoDaYykepLOA8YRHu67ELwOTuXJ54WbCzSNsTXbVlAXADNrDPQH\nDgaOIRge2TuvmNzKBeTeAVrFOqsAmyZ1i8fJTIdZfifnXooJPhpJ2ovg9bkmUbc+0BlYV1I3gvdo\nhajDtKwOBC8YwGzC8r99ojfnEuDJHB2+BlZJfC44x3nGUZFX9Aage1xi+GVirIXkVon6OI7jOI7j\n1DkymUzRV20gjZH0s5ktB7wHNI+/sK9etWot8+Rb9nUZMAZ4DPiO/MvbFjqW9AMwBXgeeIAQw9Ok\nAjkkfZ8j9x7QRNJDwEwze5GwL9ZXUeQm4DQzG08wMnJ1ewU4KcbuZHkfaGtmEwkxOv1i+XPACILH\naiMzexoYBLwYdb8E2CeW/4OwNLEMOJWQQGQKcCzBoEsygbBELqtXvvEPBq4zs8cJ936Fcxzfbwcm\nm9mjBIMt3xwnacGiRpzjOI7jOE6doF6m+Ks2kGafpNuBRwnB6i+YWSfCr+lOJZA0InE8l5BYAUm3\nEmJ7cpkYz08nJG1A0hxiNjlJBxfoamKin6a5JwvJSeqdp+wVQrKI3PKm8X0MwcBLnvuVELuTK9OP\ncoNph9zz8f46V9JUM2sPrB3lxgPj8+kceRo4x8waSJpInvFLGkv+paKt4vk/rkeOXKEEDK0SdXdM\nlB9E8I45juM4juPUOWqLt6gYFXqSJF0L7C/pG0Iw+g2EhAOOUxV8TIj/mQQMIH/SiUWI3qYBhIx9\n1YaZbU2IVfqkOvVwHMdxHMepLjKZ4q/aQIWeJDN7PvsruaTPzOxLQoayrataOafuET1mrSqsmF92\nAmHZXbUh6S1CdjvHcRzHcZw6Sf16aSJ6ajaZsrL8YRUxi1ibPKd+Ax6SdEBVKuY4yzieQt9xHMdx\nnCVNjfDTjOx5RdHnnCNuOaNG6FmMgp6kmEUMMxss6ZSlp5LjOI7jOI7jOLWV2pLmuxhpEjcMMLP2\nkp40s7MJ+yWdJyk3u5jjOCVwXufScjsMGHMhAOPPHJpapsOlxwMwbegdJfW11fHdAHjnprtTy2zx\nj5AL5Kmzrk8ts9vFxwHwxbhxJWgH6+wRdiH44I77U8ts0m0/AOb98G1JfTVsHLa8mtz/xtQyrfsf\nU3Jf2X5eunR4euWAHc7sDsDzF92SWmbHs3sC8PmYx0vqa93OHXl/1H0lyTQ7bH8AXr3qttQyfzvt\nSKDy1+qj0Q+lltnowC6Vlvn43vQyABseEORKmfd1O3cE4JOHHi2pr/W77AXA9FvvSS2zWY+DAHjt\n6pGpZbbrfQRQ+Ws1a/KE1DJrtW4LwAsX58txVJiWZ/UAKneNK/O9+nJ8sRxDi9K0QwcAZj7zVGqZ\nJu12Ayo/75X52/T99DcrqFnOypttA8B/up5fgnbwrwfOBeCZc4allmk3sBdQ+f9HKjMX066/M7XM\nVscdClRev8r8vagJLG7ckZm1AC6R1M7MtiVkKP4N+BU4UtLXZnYMIePxAmCgpMfMbAVC0rk1gB+B\noyTNNrOWwNWx7hOSKrw50ywYvBPYPGYaOwB4hLD5p+M4juM4juM4zkLUy2SKvophZn2AG4HlYtHV\nwElxldv9wJlmthZwMiGOfQ/gYjNrCBwPvCFpF+A24JzYxvXAoZJ2BlpEw6v4GFKMcxVJ1xA2kR0h\n6TZgxRRyjuM4juM4juPUMRYzu90HwH6Ux1cdIinrymwA/ELYRmaKpPlx39APgG2AnYCs++1xoL2Z\nNQIaSvo4lo8D2lekRBojKWNmzYF9gUej5ZVmmZ5TyzGz1WMCj0Ln25rZnfG4q5k1KVQ3R+6kJaVj\nnrbPMrPmZtbfzHpVVT8V6NDLzHatjr4dx3Ecx3Gqm0wmU/RVDEn3E5bFZT9/BWBmrYATgauAxsD3\nCbEfgZVj+Q9FypLlRUljJJ0J/Ae4QtKHwHXA6SnknLrFqYSbMA1VstGqma0HbB03v63O7HE3AX3N\nrPbnv3Qcx3EcxymRevUyRV+lYmYHA0OBzpK+JRg9jRJVGgFzcsrzlUF4Xp1TUZ8VeoQkPQU8lfhc\nqT1snPyY2ShglKQxZrY5wSDdFxgObAjUB66UdI+ZTQCOlfSemR0HrBXr3QV8CmwMvCTpBDNbHbgD\naAgI2FVSs5y+LwaaA6sR1m/2jGs8R8V+PyEaG2Y2A9hU0jwzuwR4F5gRz3UG/gqMMLPWkubH8k2B\nW4H5BIO8G3AUsKqZXQucBdxMsOabAkMkXW9mOwDXEiz9r4G5knqY2cnAoVGnu+Iy0CTHA6MTn7uY\n2YFxfP0kPWpmX0laO+p3F+ELtyGwN7A80AQYRFheuhXwT0kPR+9XV+DPwOx4fBjQGVghzv2lkkZI\n+s3MXgP2JMTwOY7jOI7jOJXAzA4nJGhoK+m/sfgl4EIzW47w/LY5MA2YQng2exnoBEyS9KOZzTOz\njYCPgd2B/hX16790Vz83EgwHgJ4EL8RxwCxJOxHWTA40s9VY2DuSPG4WZXcAOkdDpy9wv6S2BMNh\nIYM4rs/8TtLuwPZASzNrGuXujMFxoyhfD1qobySNIWwwfGTWQIq0B16I7+cBK0u6MPZ7ErBJ7GsP\nQtBd1kN5PSEbyW7Ah0BZNCAPIqw13QXYNxphSdoA2TWrGeBzSe2B3gQDqtA4yoCVJO0JXAocL2k/\nwheyh5llgFWB9pJaxrncPso1lrQ3sA/w70TbbwJtcRzHcRzHqWPUr5cp+kpJWVyVMwhYCbjfzJ4x\ns/MkzSJkvJtMcOacLelXwo/fW5rZZOAfwIDY1nGE59oXgVclvVxR5x5bVP1MBK6Jnp8OBO/K1cCT\nAJJ+MrN3CJ6KJEkD9wNJPwOY2UyCRb0ZwYsD8GyefucCa5rZHcBPhJuvAWAEQw3CjZePtHf3zYTl\nmo8T1o2enXP+a6C3me1HcIVm78cmkt5N6HAIwauzPvB0LP8Lwch6L9He6sCseFwGvBKPZ5E/2Uhy\nHK/F9+8JXjIIrtjlJZWZ2XzgTjP7CViXMFcQjEOAzwnznmUm4HFJjuM4juPUORY3BbikGYTMdRBW\nBOWrcxPlz6zZsl8IP6rn1n0R2LEUHdyTVM1IKgNGAtcA4yQtIDykt4Y/PD5bE9yDcwnL0gD+lmgm\nX/zNNMpvrpZ5zncC1pPUjeA9WoFgNLwD7JxHbi7QNHpV8qVN/J2wRC9JF2By9ObcSzCYoNw4OQN4\nXtIR8Xz2fvwseo6g/IYW8LakdtHLNZJyr1GWrwnGUzEamNmfY5rILRPlBWOYzGxroIukQ4BTop75\nPGxJVo36OI7jOI7j1CkWJ3FDTaGgJ8nMfi8iVyYp94HYqTzDgQsIxhDADcCN0VW4AtBf0jdmNhi4\nzsw+Bb5g4eViScqAS4CRZnYQ8CUhLijJi8A5ZvY08FX83CTqkZWbnmj7MmAMIQ7puzx9PwfcZmYd\nJGWD4aYS4pTmEQyo3rH8HTO7DbiF4EXrCrwN/GhmDYATgFui12YeYdncm2b2lJk9S/DYvBDHlWQC\nwbC7N8+8ZI+vjrIfxbHkni/LI/cB8LOZTSLEI71KubFaaBliC8pTUDqO4ziO49QZaokdVJSCRpIk\n9zItPeoTAsveA4hxPd1zK0kaC4zNI98qUWdHADPrBJwraWrcCHjtnLZmEWKY8rFnnr5vpXz5XpKJ\n8Xw/oF+OzEdEj1hOeXIZ2ta552Pihr3jDskXEHZXRtLlwOUFdIYQ33U5cK+k7BpUJE0nLn2TNBAY\nmG8M8fw4Qv58JL1OCP4D2K1Iv0iaC2wU9f8Twdt2RjEZx3Ecx3GcZZHKZLCraVQYkxSTABxGyOqV\nITzQbyjpyCrWrU4Q43H6A0t6T5+PCd6YBYRrdvISbr8qmQU8ET1JcyhPbFEUSZ+a2Ztm1jymAa8u\njgEuikspHcdxHMdx6hS1ZUldMdIkbrifsNxoR+ABQtq8fN4MpxLEDbPur4J2p5PwMNUmJN0H3FdJ\n2Vwv0VJH0tDq1sFxHMdxHMepPJmysuI/dpuZJJmZXUFIJf0uYTlTh6WhoOMso7iXyXEcx3GcJU2N\ncOGM/ed1RZ9zOl1+Qo3Qsxhp4o6+i+8CtpH0PSHVsuM4juM4juM4zkJkMsVftYE0y+2eNrPRwD8J\ncSLNiYH0juNUnm3Wb1NS/Tc/Cfklnu47LLXMrheGULcP7ihtRecm3fYDYN4P36aWadg4bGPw0eiH\nUstsdGAXAPp1yt1CqzgXjL0IqJx+pcgk5TRidGoZO+pAAL4YNy61zDp77AFUXr/Px6ZPprhup46V\n7quy+lXmWn016ZmS+lp7l3aV7mtp3ks1va//vpU+pHOVrZuX3E+yr29enJJaZo0WOwHw4wyV1Fej\nDQyo3Fx8eNcDqWU2PqRryf0k+6rMvH/2WGnRD+vt2QlYNr8jrw8aWVJf2556RKX7WppzMXvq86ll\nVv97SdsAVSklbBhbY6nQkySpL3CWpE+AbgSP0n5VrZjjOI7jOI7jOE51kCa73X2S9geIGcNeMbOn\nqCAlspnVI+yCuylho9FjYnDTJoR9gX4nbHh6YnVnATOzGcCmkuYlyv5O0K3HEurjWmC0pIkVVl78\nvroDJumsqu6rVMxsK2AVSZOroO3VgAslHWdmX0lau0KhJa/DCsBQSd2Xdt+O4ziO4zg1gWUhBXhB\nT5KZPWBmHwN7mdnHiddnhM08K2J34M+SdgbOBy6M5VcCZ0vahRBc1mXxhrBEWBpG2tI0BGtyUoAD\ngC2qqO2BwLXxuFrmQNIvwHNm5inyHcdxHMepk2TqZYq+agPFPEndgVWAwYQ9drIjWgB8laLtX4CV\nzSwDrAxkvTR/kzQpHo8lGFMPZoXMbBQwStIYM9sc+A+wLzAM2IRg2J0jaaKZHQCcADQgPBR3JWxO\neikhbmqYpFGJtv8C3A40imM/R9IzifMG3BJ1/xb4X3JA0Tt2A7Au0AR4WFI/MxsOzAU2iOXdJb1m\nZscBxwJfE/aZujenvQ1if/Wj/qcA6wNdJfWMdV4BOgJtgdOA34BnJZ1lZv0Jab7/DBwd035naWlm\n44A1CJ6NG5MeMzO7hJCpcAZwdtR/PeB6wsarfwUGSbq+yDyfGed5I+AuSRfljO/CqPefCCm9byfc\nV3PN7NU41tx2vwOGAM0J99mGwN4Ez+MwYIV4fY6V9Hmir8bA3yVNi0XLxXvp/wjX8gDgHGCmpGFm\ntlmcl3Zm9hZhQ9ltgOmEfZp2iWPrTNiI9zrCjwNNCPfNQ2b2JjAhypUBXST9ANwDPA7chuM4juM4\nTh2jtiRnKEZBT5Kk7yXNkLQPsBLhAfr/CA/EafbfmUJ4qJxOMCwGx/LktP1EMKCS3Ej55qE9CUv2\njgG+kdSGYDANieebAXtKag28A+xBeFhdTtIuSQMpcg4wLrZzIHBz4lyGYJCdK6k98GSeMa0HPC+p\nI9ACOC6WlwEzYvk1wLFmtgbQO9brHOvkejcuB66K+pwa9XkM2NHMVjSz7YEPCYZpf2DXONZ1zKx9\nbO9tSTvlGEgZYL6kPQiGR++EnuQ5XocQZ3Z8nKPDgU6Ub3BbaJ7/L8q1BPrkma9uwKFAa2COpC+B\nW4ErJb1coN19gFUltQCOjnOenavBktoBVwCX5PTVkhAvl2UlQixda8I9th2FvUsrEQzzXaKuU+I1\naQhsCRhwhaTdCUbviVGuEXCHpLbAF3HOkDQHWN3MGhXoz3Ecx3EcZ5klk8kUfdUG0sQkDSH8kv8R\nCz9ktqtAtA/hYbOvma1LyJK3NcEjkKURMCdHbiJwjZmtDnQAziIYWDubWYtYp36MP/kGGGFmPwGb\nAdkUIIq6b0IwugBGxjq3A0j60sx+MLM1k8MFXo7Hk1jUGPwvsL2ZtQN+AJZLnHstvn8G7ETwer0r\naX7UZQqL5q7fLPaDpDfMbD1Jv5vZvQTjY8eo/yYEj9DY4OyiEbBxbOM9FqUMeDUezwJWzFMnqcs0\nSb+Z2ffAh5IWmNkcypdVFprntyT9DvzPzH7J08dhBK/e2iy8AXG273zt/tG+pNlmljX+tgLONrMz\no/w8Fma1ONYs30n6NB5/lWcOcq9Fdr7mEAw2CNd7+Sjf18yOJsxt8nuTvO7JZaizgFWBH3Ecx3Ec\nx6lD1BI7qChp9knanZAEoK2kdtlXCrk/EwwJCA+bDQjLyl4zs2zu405EIyFLTOIwkuCRGSdpAWFZ\n2J2x3y6E5UzzCd6Vgwmepl8of/D9Pbb1QULnW2I7rQHMbB3gL4SlWFneAXaOx/nyKHYneEQOJ8RW\nFTM+3ge2NLMV4pLDHVjUk/EuYVkXZrYtMDOW3wwcCewgaTxhSdxnQPs4B9dRbqgkjc4k+bwmc4Gm\nUZ9tK6hL1Ksxhee5mFxD4EBJhxKW73U3s/+L+tYzs5ULtDuNOPdmtgoh8QcEj+SZcfwnAXfndPk1\n4XoWG9NcwnI5gL/lnCsWw3Q+cJukIwnL65Lfm0JyfyEYgY7jOI7jOHWKevUyRV+1gTRG0kcp6+Xy\nH0JczGTgKcLSp/8BZwADzOw5wi/y9+aRHU7wpGSXww0DNjOzCYSH1E9j7McUgrHwAMF7lH0ALvTg\nehGwq5lNjDLHSvqN8qVwpwF9Yva+dnnaeRLoaGbjgX8DU82saU6fZUCZpNmERALPAk8QjLpc/gmc\nHPW5jrC8DEkzYjsPxc/fEIyySWb2AsHD9n4FY823tO4yYAxhSd93OTrnlSthnhfSI2YK/C7q+zTB\n4P0UeIVg5GyX0+57QBNJjwGzo+ftJkJc2Lw4V+fFe+BmgjGV5AVCHFVefeLnu4HOZvYMxZff5cqN\nBi43s7GEJYarFqmbjX2bE+93x3Ecx3Ecp5aRZjPZ/wLvRKNmbiwryyYWKESMy+iap/x9QjB/MeoD\nkyS9F2XmUR6nlGzr4ALyedNsS/pvAZ02iocziukm6R0W9sBk6ZGoMw4YF49vo0jwftx7avcC5/bI\n+TwKyI2xGlBAdkTieC4hjgxJtxJignKZGM9PJ3h9stdvi3hc4TxLapp7UtIFwAU5ZWMIhhoEg3ch\nYvKMyZJOiksqpwGz47LFjgX0QNLPZvaymW0r6fWkPtGblWWHPLIbJo53TBxn75WXgLsSIgPyyCXT\nrXejPG7OcRzHcRynTpGpVxn/Ss0ijZH0eHxlf3XPkO4X+EphZvsRlmH1qqCqs2zyGXCpmfUmGMt9\nsnFdKTiXkGr+2KpSriLiPkmt4pJMx3Ecx3GcOseyEJNUoZEkabiZbUjI8jUOWE/SR1WlkKT7gfur\nqn2nZhOXqO1bSdlvqEYDKerwCyE7oOM4juM4Tp2ktuyFVIwKfWFmdgjwMDCIkEFsipkdUdWKOY7j\nOI7jOI5T+8hkir9qA5mysuIr58zsNaANMFHSdmbWBHhK0hZLQ0HHWUapsiWrjuM4juPUWWqECfLC\nxbcWfc5peVaPGqFnMdJEVf0WM5wBIGkm8FvVqeQ4juM4juM4Tm1lWUgBniZxw9tmdjLQMO7lcwLw\netWq5TjLPvN++LbiSgkaNl6tZLnKyCTlPnt0TAU1y1lvr84l95Xt55MHHy1BO1h/370q3Vdl5+Kl\nS4enltnhzO4l95Xt580huUksi7PNiYcBMO36O1PLbHVcSPj41aRnSupr7V3aVXr+3r7h/9k783jb\nxvqPv8+9cZGZZMgcH2UIkSFz5gb0o0yZFSFTkSkKlZJKhFDI2GBIGZI5ERnK+AlRZLzm6XLl/P74\nPttZdzt777X3Hc651/f9ep3X2Xut51nfZ6299trP9/lO53ZoOcDiX9wcGL7fkfG9l6YkWY0+D557\nQRejg4U3j+Shj11xRe0+c6+zDjD8r8WkHN+jl1zWlawPbLh+z7KG+7WYlLLuOuHs2n2W2HXLruVU\nZfUyvmTCUMeStBswD1Hs8+dEgdgvT8xBJUmSJEmSJEkyefKusCTZfpkonJoMAyRtCixue9AaSZIO\nAx63fdIkHNMswPq2Oy5lS1oCmMX29RNhHLMBR9reRdITtuec0DJqjGFa4ATb201q2UmSJEmSJMOC\nyb9MUmclqdSr+QYwc2Vzv+2RE21UyfgwFAkBPgJ8Bqjj77Mp8DgwwZUk4AjguPJ6SBIj2H5N0l8k\nbVOKCSdJkiRJkryr6BuPFHaSRgCnAIsCbwE7E/kQTivv7wJ2s90vaWei/MubwBG2/1AWrM8E3ge8\nBGxre3S346gTk7Q3sLTt/3R78KQ7JM0InEwopHMDx9s+UdLKwI+A54ExwN9K+32BzxM3xnW2v950\nvE3lnRoAACAASURBVO8AqxBFWY+x/RtJXwa2IW6yW2zvKWkR4macCngV2ByYFjip/H+NuAHfQyhC\n/wEWBm62/WXgIGApSTvZPqUi/0hgjdLvt8QNux0wRtJtwPyE6+ZUhFKzCfAscDzwUeAJYEHg02W8\n44zH9qNN124523eVTaMknQXMBzxDKGcHU6xskhYjLD5rSroTuBZYCrgPeBJYDXgd2BCYE/gpMA0w\nF3Cw7Ysk/QO4pvTrBzYqSU5+RRRgTiUpSZIkSZJ3HeOZ5ntd4L22V5G0NvBtYi55oO3rJJ0AbCTp\nJmAPYs44LfBnSVcAuwJ/t/0tSZ8n5n97dTuIOsawe4Cnuj1w0hMLA+faXg9YD9inbD8B2Mr2usCd\nQJ+kJYHNgJVsrwwsIumTjQNJ2gBYwPaqwFrAQZJmIpSU3UqfeyWNBI4m3NRWJuphLQN8HzjW9prA\nD4DvEorAIsAOwMeADSW9n7DgXFVVkApbAlsAqwLP234M+AWhsN1SjvXJMsZ7yjl/BpjV9grAjsC8\n5VhHDzKeKisCrryfHjigHHumck6trEvTA2fZXq2M9QbbqwNTE0WUBfygXP8vEnF6ADMAZ9teA/gv\nsAGA7eeB2SXN0EJekiRJkiTJFEtfX1/bvw68BswkqY+Yw70BfNT2dWX/pcDawPLEnG1sWaR+gFi4\n/jixWE35v3Yv51DHkvRj4M6irb1ZtvXb3qEXgUlbngL2kvRZIkFG4/OZ0/b95fV1hEIg4CbbjXTs\n1xMT+gZLAB+V1Ehf9R5gAWB74KuSFgRuJBTlRctrbF8MIOlHwIGS9idy7r9RjvOA7VdKm8eBUbTO\nyb8VcBRhibm0sr3R/mngdEkvA4uVMTT+Y3u0pPsq5zPYeBrMRliAGjxbsX4+AUzX1L55zLeV/88T\nChvAc4T16AlCydyRULSq35vby/9HStsGTwKzEmbeJEmSJEmSdw1945ec4QZiTnUfMb/7NOHh0+Al\nQnmaEXihxfYXm7Z1TR1L0k+AXwJXEi5Jjb9kwrMPcKPtLwC/YeDz+a+khgK0Uvl/H7CCpJFF014N\n+GflWPcBVxfLyzrAr4EHCb/OXYr1Y5lyvHsJyxCStpC0W9m2f+m/O3BeOW6zNaaP8BMd516SNDWw\nme0tCEvWdpLmI9zmRhSr1mGEu+DOxKpBH+FnulI5xiyEAtc4n8HG0+ApmuLmeCdjCHc5gGWb9rWL\nYfoWcIbtbQj3uuq5tuo3M6EEJkmSJEmSJPXZj7AQCViaCF+YqrJ/RmJR+0XCq6fBDINsb2zrmjpK\n0mu2v2X7tMrf6b0ISzpyMbCbpMsJrfklSVMBOwGnSvoT8CHCkncXEftyA/BX4CHbF5bj9BeL0MuS\nrgNuBt4qmQrvBK6XdCVh7bgJ+BpwQLE6bQWcVbYdKuka4FRCeYF3KgX9hPK1pKSvNDbafgN4tlgg\nrwIuL5adWwklZ5ky9huBCwgFby7bfwBGS7qBiJN6lbAafbXFeBrcRCSQqI6reZznES6CV9Pe/a65\n36+BoyVdSsQ4zdqmLZJmJtwLX61x/CRJkiRJkimKESP72v514L0MWIKeIzx4bpe0etm2AeFZdTOw\nqqRRZfH9Q8T88AYiprzatmvquNv9SdIPCHept12cKn6ByQTC9jXAkoPsuo1wsWtu/0Pgh03bvll5\nve8gfU4llIwqD/JOf83ngfUHGcvKlWOtVNn+4UFkHQ4c3rTtEqBRofSa5j6SBFxve/eS0vsuYLTt\nsS3G0zjuK5JukbS07Ttsz13Zt0Wl6ccG6bvgYOdke5Py8magWgnzm4P0O6Cyf0si+USSJEmSJMm7\njvHJbkfExf9C0vWEBekAYpH95OKpdA/wm5Ld7lgi5GQEkdjh9ZLY4fTS/3ViXtY1dZSkZYkV8mb3\npDV7EZgkHXgEOKqknh8J7FcUpDp8AziSSK4wJJS0kyvb3nqoxpAkSZIkSTKUjI+OVBJgbTLIrjUG\naXsK4XlU3fYa8LneRxDUKSa7BrxdqPN/ZeBJMlEoLmob99j3aYZQQSpjeA1IBSlJkiRJkncv45kD\nfDhQp5js0sDpwAeIgPt7iKJMD0zswSVJkiRJkiRJMnkxntnthgV9/f3tY9dL0c9v2P59eb8JsE+p\nP5MkSW/USRqRJEmSJEnSDcNCO7nnlPPaznM+vNPnh8U421Enux0NBam8voAovpkkSZIkSZIkSTLF\nUSdxw9WSvg6cQNTD2Qq4R9IcALafmojjS5IpljdefKar9lPPOFvX/Rp9bv/RL7uStcxeXwDAp/26\ndh9ttxkAT91QP/HlHB+P2nCPXnpZh5bj8oENItFhL9ei1+v+t+/Xr3yw3Ne27VpWQ45Pr3/NAbTt\nZj3L6uVaTMr7drjKGu7jm5Syxve+fe7u2zq0HGCWxSN/1DO33tSVrNk+Gslh877I78hQyBqK8Q0H\naqT5HvbUUZL+j3AN+lLT9r+W7QtN6EElSZIkSZIkSTJ5Mp4pwIcFdbLbLTAJxjHkSDqAKOA6FXBc\nq4K5klYAvmt7zfJ+aeBYwsr2OrDNUFvXJD0MLFoKuja2LQfsZnv7Dn3XAL7UVFsIST8EjgF2BB63\nfVLT/jttD1bjqfn4SwCz2L6+3tnUp2RgPNL2LpKesD3nhJZRYwzTAifY3m5Sy06SJEmSJBkWTP46\nUueYJElzSPqVpGckvSDpAknvnxSDm1QUxWAl2ysTOdgHtY5J2g84GRhV2fwjYPeiNJ0P7D9RB1uP\n8UkKMGhf23vbfmQ8jw2wKYMUnp1AHAEcV14PSWKEkgL8L5K2GQr5SZIkSZIkQ01fX1/bv8mBOu52\nJwE3ADsTeuEXgVOBT03EcU1q1gXulHQhMCPwtRbtHgA+C1QDPD5v+8nyeirgtWoHSWcBZ9m+RNKH\niCrCGxPX9YOEonqw7WslbQp8uRynnyiktSRwFGGlOsn2WZVjzwycCcxAfJYH2766sl/Az8uYngFe\nbRpbH/ATYHlgauBQ4AVgEUmXAHMAF9v+pqRrqLhclr4nAUsRBWBnbL5Yko4klM73AL8tY90OGFOy\nJs4/yPk+CxwPfBR4AliQsPC9VeRNW87ni7YfrciaEVjO9l1l06hy7ecr574pcDDFCiZpMcLis6ak\nO4Fry7ncBzwJrFau+YbAnMBPgWmAucp1vkjSP4BrSr9+YCPbLwK/Ai4Dzmi+JkmSJEmSJFM6k4si\n1I462e0Wsn207RdsP2/7e8ACE3lck5r3EZPyTYFdgLMGa2T7fODNpm1PAkhaGdgN+GFTt5OBbcvr\nHYiqwDsDT9tenVCYji/7FwE+WdKr3wOsR0y+R9leraogFQ4GLi/H2YxQXhv0EQrZN2yvDfxpkFPa\nGJjN9grAmsByZfs0wEbAqsDuZVuzZWYTYDrbKwK7AjMNcvwtgS3KcZ63/RjwC+AY27e0ON/PALOW\nMe0IzFuOdTRwbLHY/QD4bpOsFQFX3k8PHFCOPROwzCDnUG17lu3VylhvKNd0amBxQMAPbK9LLBLs\nVvrNAJxdCi7/F9gA3q4UPbukGVrIS5IkSZIkmWLpG9nX9m9yoI4l6S1J89n+D4Ck+YE3OvSZ3BgN\n3Gv7TeCfksZImpcBS8AVtr/dqrOkzwMHAhvabk5Dci3wE0mzA+sABxAxTKuU+CaAkSWe5mngdEkv\nA4sBN5b9LnI+SChdENasxQjrDLYfk/RiI+tgY2jALeX1dcDKTWNbtCGjTOy/UVwP77I9Fhgr6U0G\n5+1j2x4t6d5B2mxFWMHmBC6tbG98OwY737fPuxz3vtJ2CeBASfuX/s334GyEBajBs417lrBITdfU\nvvkb2kix9DyhsAE8RyiMTwAHSdqRULSq35vby/9HStsGTwKzAi+RJEmSJEmSTFbUUZIOIWIsbi7v\nVyRW06ck/gzsCRwjaW7gvcCjjeQM7ZC0NXE91rD9XPN+2/2Sfkm4tV1u+82iUDxi+zvFTWxfYCxw\nGGE5GQH8kYGJ/FvlWA8QFp+G7A8Rlo87JM0DzEy4ljW4B1gFuARYaZDh30tYoJA0E3AOodTUiee5\nh7AU/VjSLITCVb0uUwOb2d6iuObdLencci4jirzBzvcu4AuDHPc+4GjbN5bkDyswLk+V828w2DmM\nIdzlAJZt2tfunL8FnGz7MknbM2AZbNdvZkIJTJIkSZIkeVfRN2LysBa1o6O7XSkkuyzhyvULYOlq\ncdkpAdt/AG4viuDvgC/bbjdp7geQNBL4MeGudb6kqyUdNkj704hYpoY73EnAYiXO5xrgPyWW5QbC\ninIBYT1qTOhbjeXbwFqSri19vmj7f6V9P7A3sJ+kKwnlapzj2P4d8Jyk64kYmh8PIm8w2f22LwIe\nl/RXIu7piaZjvwE8K+km4CpCQfwPcCvhwrdM0/n+E5irfBajJd1AuCa+SliNvgocWq7ZqYQyVeUm\n4CNtxt0PnAdsKOlq2rvfNff7NXC0pEuJGKdZ27RtxIo9b/vVFu2SJEmSJEmmWPpG9LX9mxxoaUmS\ndGiLXctK6rf9rYk0piHBdq2sdLYfpritFYWkTuWukcB1tv9Z+r3BuNaIxrE/36L/tS3G8hwRG9S8\nvZGd72EicUJLbH+lnTzbc5f/DQvWNyv79upw7MOBw5u2XUJYtiAUxHEoySaut717cUG8Cxhd3P/W\nbyPrFUm3SFra9h2NcZd91XTmHxuk74KV1ytVXjeu7c3AuZUu3xyk3wGV/VsyEGeWJEmSJEny7mIK\nT9zQ6uz62uxLmpD0WeBy4BtDPZbJhEeALSTdSMQx7VcUpDp8g8iWN2SUOkkr2z57KMeRJEmSJEky\nVPT1tf+bHGhpSbJ9WOO1pOmBhYE7iYxmL0/8oU0ZlIx45w/1OCYXiovaxj32fZohjpcrdZK2Hsox\nJEmSJEmSDCXvihTgkj4B3AFcRMTIPCxpvYk9sCRJkiRJkiRJJj/6Ro5o+zc50Nff3z52vSQz2Ai4\nxPYykhYHzrG91KQYYJJModRJGpEkSZIkSdINw8KE8+8Lf992njP/xp8aFuNsRx1VboTtxxtvbN9N\nTvCSJEmSJEmSJBmEKTq7XYVHJX0a3k5tvBvwn/ZdkiTpxEVf+UlX7Tc6dg8A3nixuV5xa6aeMZIv\n3nrMGR1ajstH99mmZ1kTu0+138V7HVe7z6d/tPt4yRru12JSyRru45sUsob7+CalrOE+vkkpa7iP\nb1LKGu7jm5SyhmJ8w4HJRRFqRx1L0peArYiin/8i6stMacVkkyRJkiRJkiSZAPSNGNH2b3KgjiVp\nJWCbUttnyCkFXP8ETE0U+XzQ9sWSzgEWArYBVrF9ains+rjtk1ocawEivmqlpu2nEXFY72+ct6Rl\ngb8RdYdGAL8CGq6H0wJn2a6/rN0lkmYB1rd9ThnfObYvb9F2O0BNtXuGBZKWAGaxff1EOPZswJG2\nd5H0hO05J7SMGmOYFjjB9naTWnaSJEmSJEkyYaijym0NPCTpREmrTOwB1WAeYAbbH7f9I9sXl+2f\nsL0C8DqwU9k2PrFTjwEbVN5vBTxYOe6Vtte0vRawOrCvpBnHQ14nPgJ8piK/HcM5ZmxT4MMT6dhH\nAA1FdUiuQUkB/hdJ2wyF/CRJkiRJkqGmb2Rf27/JgY6WJNubSpoB2AT4uqQPAr+xffBEH93gnAgs\nIulE4HHgCWApYCZJFwJPAR+WdEhpv5GkzYDZgENs/76GjH7gXGAL4CJJIwg3w1sYPGvIjMD/gDcb\nG4qV6jwifmuBcrwlynH+YPsgScsAx5a+Y4CdgZHAOaXfwsDNtr8MHAQsJWnnIuJLkvYDZgJ2tX1L\n05hWlHQ58D7CsnGypIeBRW2/Iem7wL3Aw8CBRf68xPVdi1DKfmz7REmbEkVapyrXZhNgSWB/Qild\nCDjX9rerA5B0JGF5ew/wW+BMYDtgjKTbgPkHOe6zwPHAR4nPdkHg08BbwEmE1e414Iu2H63ImhFY\nzvZdZdMoSWcB8wHPEMrZwRTLoqTFynVZU9KdwLXEfXQf8CSwWjm3DYE5gZ8C0xBp8A+2fZGkfwDX\nlH79wEa2XySsjJcB3QUCJUmSJEmSTAm8G+okAdh+CbgBuBF4g3DBGyp2Be6xvUt53297N+BZ2xsD\nR5b9hxMKzaO21wb2Kn3rcjOwmKTpCKXhagYUpD5gLUlXS7qSmPzvXgqhVlkQ2AH4FHA4sDewArBj\n2X8ysJvtNYhJ+DHEZHuR0u9jwIaS3k9YSa6yfXLp+zfbnwB+QigeVfqAsbbXIxSPvRrXqtKm+noe\n4LPE9TmYsB5uQMSjUcbzSdurAvcA65X+85V+KwL7veMKwpaEorkq8Lztx4BfAMcUpW6w434GmLVY\nBXckFDeAo4Fjba8J/AD4bpOsFQFX3k8PHFCOPROhnLayLk1PuEuuVsZ6g+3VCZfOxQEBP7C9LhGP\nt1vpNwNwdvn8/luuGbafB2YviwtJkiRJkiTvKvr6+tr+TQ50tCRJ2hfYnFhFPxPYsLqCPwR0urLV\n/f3AreX1k8B0Xcq6CNgY+AShpDQsJf2EwrJFh/7/sv2SpLHAk2XyjKTGZH0u2/8or69nYOL/gO1X\nStvHgVG887zbnVc/cFub/TQd7y7b/5P0AhHj9aak54nPHOBp4HRJLwOLEcoywJ223wJelfTaIDK2\nAo4iLDGXDiJ7sOO+fXzboyXdV9ouARwoaf/SvzlGbrZyrg2etd3IwvjEINeg+Xo2rtfzhMIG8Bxx\nDZ4ADpK0I3Ftq9+b28v/Rxi4XpSxzAq8RJIkSZIkybuJCZDdTtIcxHz3E4RH0Wnl/12EkaG/eFh9\nkfDmOsL2H0p8+JmEN9VLwLa2R3d9CjXazAPsbHtJ20cNsYLUTB/vnOy+xcB5je8ndDaRCGJO2w/1\n0L9TXMxjkpYsr1dnwBLS3K+PcMnrJh3IYLLHAHNL6gOWrjPO4sZ2GPB5wh3wNQaua7t+UwObFUVy\nLWA7SfNRPh9JM7U47l0US2VJVrFoOeR9wP7FkrQ74cpY5Slg5g7nNIZwlwNYtmlfu8/qW8AZtrch\n3Ouqn0OrfjMTSmCSJEmSJMm7ivG1JEmaigizeIWYHx4DHFi8fvqIcJo5gT2AlQlvpO+U+eeuwN9L\n2zMIL6mu6Tjptr0PsLikIyVNP0wC0vsr//ubtj0FTF1ibqr7YfAJ7RKSbqn8rdZoa9vA7MDFlfbN\n8uqMs9XrnYHjJF1HfMh7Ex9887H7iaQRS0rac5BxDDaWweR9D7gE+AMR+zPYuYzzusTYNFwtLyAU\nubk69KNkBXxW0k3AVcDlxbJzK6HkLNN03H8SlrU/AKMl3QCcArxKWI2+Chwq6RrgVEKZqnITEUc1\n6HjK+/MI98Wrae9+19zv18DRki4lXAxnbdO2UU/s+UHcL5MkSZIkSaZ4JkAx2e8DJxD5BwCWtX1d\neX0psDawPBEiMbbMVx8g4sQ/TsSGU/6v3dM59Pe3nydKOgr4ALHyvjJwPnB7UZ6SZIIiScDSts8r\nKb3vAuazPbZG3xOAk2zfMbHH2WEcXyaUpLPbNOvPYrJZTHZ8+wyFrOE+vkkha7iPb1LKGu7jm5Sy\nhvv4JqWs4T6+SSlrCMY3LAJ+nrjmqrYKxpxrrNVynKWUzTy2jywL27sSWaXnKfvXJGL3LwOWtP31\nsv10wnL0dWAP2/eV5Gv/tj3vIKLaUsd9az3gC8AY288B6zBuauwkmZA8Amwh6UZipWC/OgpS4RtE\ntrwho/jBrtxBQUqSJEmSJJli6Rs5ou1fB7YH1ikK0tLA6UR8UYMZiRjyF4kkWg1mGGR7Y1vX1Ckm\n+7+m96MG2ZYkE4TiorZxj32fJoL3hoxSJ2nroRxDkiRJkiTJkDIeGexKhmEAiqK0C/B9SavbvpYw\n1lxJZKI+UtIoInnWhwgPpBuIEi63lLbX0QN1LEm/Jmr8zCppbyIL2zm9CEuSJEmSJEmSZMpmAqcA\n7wf2Bb4p6S+Ekec3tp8k6o1eTyhNB9p+nYhlWlzS9cBOwDd7OodOMUkAktYngp5GEKmv6xRkTZKk\nNXWSRiRJkiRJknTDsIhJeurG69vOc+ZYadVhMc521KmTtAThz3cNcHePqbCTJEmSJEmSJHkXMLkU\njG1HSyWpFHD6DVHE835i5VsloH7LRmHUJEl6o9dMN49eclmHlgN8YMP1x0vWHceeWbvP0l+JUKz7\nf/nb2n0W+cL/AcM3K1G131+O/HntPisftEPXshpy7jqxO2/mJXbZomdZmd1uyhzfpJTV6HPPKc2l\n69rz4Z0+D8BjV1xRu8/c66wDDP9rMVzHNyllDffxja+sfxx/Vu0+S+22VddyqrJ6Gd9woEZyhmFP\nuzM4Dvgz8H7bK9heEXg/8HfgR5NicEmSJEmSJEmSJJOadu52S9n+XHWD7TckHQQMaR2apDckrUfU\nHDq5xf5ZiMC3p22v18VxRwFb2z61Rtt5gY9MjLi2Mo6TgW2Bh4BFS1HbSYakPuAXwC62x0xK2UmS\nJEmSJMOBvhFTtiXptcE22n6LTAE+WWL78lYKUmFJ4F/dKEiFuYjsIXX4BFEJeWKwF3Ce7X7CPXSS\nO8QW2WcD+01q2UmSJEmSJMOCvr72f5MBdeokJVMIpYKxgBOJtO7/ARYm8szvSaRRnEvSYUTq92OA\nkcDswK62b5R0P+GGKeBJ4P+Ag4APSzrY9hEVeV8GtgHeInLV701UQZ62pHB8kSgAOwKYnoh1u1/S\nIUStpKeB6YBDCOvlqcCs5fBfsX1XRVYfUZ9o6copnyBpwfJ6k3JM2T5A0jTAvbYXlHRNOf4SwMtE\nKsn1gJmBdcv4TwFmAuYGjrd9Yul3e+k3I7CZ7f8Q1rhjgG91/FCSJEmSJEmmMPpGTB6KUDvaWZIW\nl/TQYH/AhyfVAJMJSjUd4yLADsDHiIJbsxCK0lW2DwMWB/a1vTZwFFH9GGBB4GDbKxPVj5cHjgDu\nqSpIhe2A3UrbewnLzneAs2xfTNxHW9teEzgf2EzSR4D1geUIpWaucqwDgT/ZXgv4EpEDv8oiwAu2\nq1bOU8qxHwbWoXXa7X7gr+VcRwGv2F4XuAdYnVAkzykWtvWAfZr6rQNcAWwBUMbwlKQlW8hLkiRJ\nkiSZcpnCLUmLTrJRJEPBA7ZfAZD0OFGpuHrXPgYcIuk1IgX8C2X7aNv/La8fIZSKVnf79sBXizXn\nxtKur9L+MeBYSS8D8xAVkhcDbi5ua2Mk/a20XQJYU9Lny/tZmmTNTli2qtxa/j9BWKSqNI/5tvL/\neUI5AniOuC5PAntJ+ixh/ap+b24v/x8B5qxsfxwYPmlmkiRJkiRJJhWTiSLUjpZKku2HJ+E4kklP\ns1Wl+W7+MbCV7fuK+90CLfpBxKgNZpXcmUhg8Lqky4CVm9r+DFjI9iuSTitjuBvYo7jPTQ0sU9re\nB5xp+xxJ8wBbNsl6knCPa3eOYxiwTC3boW2VfYEbi4vdmsAna/SbhXcqbUmSJEmSJFM8U3oK8GTK\npL/pf3V7f2X7mcCvJV1C3Cdz0ZqngKklfadp+53A9ZKuJBSGm8q2jYpF6Myy//fAaGCuEmd0SWl7\nPjAWeAM4EvicpKuB3xHue29j+0FgDkmNe3qw87sMWEDS9cBmDFjH2tEPXAzsJuly4NPAS5KmbtGW\nMoZ5bN87SJskSZIkSZJkmJOJG95F2D698nblyvaVysv/ANeWbT8EfjjIMeaqvN6ismuZQdqeSiRb\nqHIH4VIH8I7qg5LeBzxne4WS0vsu4BHbzxLJF9pxFvAZ4ELbC1XGcUClzRqDjHPNwc7J9t6VZoPF\nF1X7nVTZvj6DnFuSJEmSJMm7gb4RI4d6CONNWpKS4cZoYHlJNwPXASfbfrRm3x8RyR+GzBG2yN6C\nQRTMJEmSJEmSdwN9fX1t/yYH0pKUDCtKwoYdeuw7Bthqwo6o6zH0A18YyjEkSZIkSZIMKVNACvBU\nkpIkSZIkSZIkmWBMLtaidvT197dL6pUkyUQiv3hJkiRJkkxohoV28uIDd7ed58z4wcWHxTjbkZak\nJEmSJEmSJEkmGH0jJ//EDakkJckQ8caLz3TVfuoZZ+u6Xy99qv1uPuq02n0+tv92Xcsa3/H1Iuui\nr/ykK1kbHbtHz7KG+7XoRdZTN1zXVZ85Pr4aAE9cc1XtPnOusRYwfK/F5PJZTQpZw3181X63HnNG\n7T4f3WebrmU15Dx3920dWo7LLIsv27Os4Xrdh/v4qv3u/tm5tfss/sXNu5Y1FNcimTCkkpQkSZIk\nSZIkyQSjb8Tkn0B78j+DpCskfVHSO5RjSedImmoiyj1H0lSSTpO03gQ65uyluGyr/WtIOqe83kRS\nu4K41X67T4jxJUmSJEmSvCvpG9H+bzJg8hhlMiE5AHiHo6jtLWyPnVhCK8fvZ2iSFuwJzFiz7UET\ncyBJkiRJkiRTMn0j+tr+TQ6ku91khKTtgPWB2cvfYbYvlLQOcDgwBniGqDM0NXAekeVkGmAXYDlg\nTuAc4LNNx34IWAw4CTjH9uWS1gc+D5wPbGJ7h9L21jKONYC9gf8Bf7Z9gKTDgAWAOYD5gb1t/1HS\nw4Aq8t5TZH2QUNYPtn1t05i+A3wUmA34u+0dJL0fOItQ9P5NUbjK8Re1/Yak7wL3Ag+XfRsCHwFO\nl7RqQxmUtCjwC2BsGcOWwLbArJKOIxTKU4GZgLmB422fKOljwHHAS8BTwBjb20vagygk2w+ca7u7\n4JckSZIkSZIpgSkgBXhakiYv+oERttcmlJQfVZSNTWyvAVwLHAwsD4wGNgB2A95r+1TgCWDzDjL6\nK68B/gCsJGk6ScsDDwJvAocBa9leFZhH0tqlzxjbGxLWm72bjgWhuO0MPG17dWBj4PjqICTNADxr\ne91yLitKmpuw8pxje01CWWp8C6vHH8dSZfsS4A5gmyZr2drATeX/ocBMto8scncnFLhzbK8HrAfs\nU/qdCGxr+xPlWvRL+hDwOeDjwGrAxkUJS5IkSZIkeVfR19fX9m9yIC1Jkx9XAth+QtLzhGXosToi\nEQAAIABJREFURduPl/3XA0cC+wGLABcRlpIjKsfoK3E3/0coFFu3kDWiyHpL0m8I69NKwMmEAvE+\n4FJJADMAC5d+d5T/jxJWrMFYAlhV0grl/UhJs9p+trwfA8wh6WzgZWB6YCrCGnVK5VwHo+6371Rg\nf+Ay4AXgwKb9TwF7Sfos8CID35e5bN9bGcPm5XzmBxopvGYmrtE/a44lSZIkSZJkymAKSAGelqTJ\nj+UBitvZdMBjwIyS5iz7VwdMuMI9XqwgRwLfLvvfIqxRx9le0/Zath+rHH8M4VoGsGxl+6nANsDH\nbF9BuLI9AqxdrDo/BW4sbevEHN3HgEVoI+BXwHOV/RsA89rekrAeTUsoP/cAq5Q2KzaPW1IfsPQg\n8t7inbFYGwHXF8vcbwiFCQaUrH2BG21/oexvfF8eKZYjCKUR4prfXa7pmsAvgX+0vwRJkiRJkiTJ\ncCQtSZMfi0j6E5GEYNdi5dkZOF/SW8CzwHal7bmSdiU+52+WbdcT7nNrDXLsfsJK83NJWxFWkH4A\n2w9L6icsU9h+WtIxwHWSRgIPEbFOjePQ9Lp520nAyZKuKedyvO1qm78CB0u6inAR/CswFxF79UtJ\nnyMUrUaf7wGXEMrbs4PI/QtwhqR1bD9ftv2NiFN6g1Cg9irb75F0BvBz4CeSNgHuBl4qGQC/XK7R\ny8AbwKO2/yHpSkl/JqxnNxEKbJIkSZIkybuKvhGTvyUplaTJj9/Z/kF1g+0rKW54TazbvMH2di2O\n+x5grO1biSQH76BYparvzyLigqp8s7L/PooyZnuhsnn7StttW4wF208CH2ux+5ODtP8FkYShmWvL\n/kOAQ5r6/AtYdZBjVRXIJZv3l8QNn7Y9WtLhwOul39HA0S3GnCRJkiRJ8q5gfOKOJI0gPJSWIuZY\nO9l+cAINrTbpbjf5McHTZ5d4o5uaLDlJa54E/ijpOkKhPL5D+yRJkiRJkncP41cnaWNgatsrA18H\nftCh/UQhLUmTEbZPn0jH3XRiHHdKxfZvgd8O9TiSJEmSJEmGI+NZC+njRFItbP9V0nITZFBd0tff\nn8aDJBkC8ouXJEmSJMmEZljk1379+afaznNGzTxHy3FKOhn4re3Lyvt/AwvafmvCjrI9aUlKkqFh\nWDzEkiRJkiRJJjTtlKAavEiUlmkwYlIrSJAxSUmSJEmSJEmSDB9uADYEkLQiQ1RSJS1JSZIkSZIk\nSZIMFy4A1pF0Q3m/fbvGE4uMSUqSJEmSJEmSJKmQ7nZJkiRJkiRJkiQVUklKkiRJkiRJkiSpkEpS\nkiRJkiRJkiRJhVSSkiRJkiRJkiRJKmR2uyR5FyFpnFoDkmaw/dJQjml8kLR65W0/UX+qH8D2dRNJ\n5hRzDYfi+k0KJB3aYle/7W9NRLkzAtM03tt+qkP7zYALbL/Zg6xZgeds95R9SdJI2//r0Kane13S\nFsDFtl/uYjwfAGYC3gT2B461fUeHPn3AjMBbwCZF5nN1ZXZDr7IkTQ/MAowFvgicbvvfE2OMRV5X\n94WkpavXWdKnbP++Q5/3ANsB8wF/Au6xPbqmvEWBDxIpnR9rVftG0tStjmH7jRZ95mfcQu3V59l/\n2oxp2xa7+m2f0apfMuWTSlKSDBMkjSQe6isBf231Q1Bpvwzxo9uYlPXb3qGDmGskbW77MUkrAD8H\nFm8j487yciQwNfA0MDvwrO0VOoxvLdtXdRjPYP3eU8Y0ivIjZ/vmFs03I34Ely7/bwA+RkxI2k7y\nJU0FLE88B0cAc9k+p8YQu7qGRdahDCghlPH9BzjP9tg2/dapjO8nwCG2z+oga1vg64x7XyzUonlP\n169yX8xITGzvBRYFnrDd6VocCOwHvFYZ39wd+swIbNB0Tu0mL3eX/9sAdxLnshLw4XZyiqyDbR9R\nef8d2wfU6HcGsArwQmXzMh26LQccLOkK4FTb99aQszpwPPGd/JWk/9g+tUa/84AdbL8iaUHgLGDl\nDt26vtcLCwGXSHoBOB+4yPazHfqcDRwK7A78BvghsGaHPucCvyfOo49QXjbpNDhJnyFSClfvpw0n\nhiziXE4ANgXuAX4GrNdhfBsBuwFTFVmz2l6qQ5+e7gvgVEknAmcARwMfIs6zHScB/wXWBW4rfTtd\nPyTtAWwMzAr8krhPdm/R/J+Mq/BUWbDF9uPK/3mB6YFbiO/gaOK72Yr3FVnrlrbXASsSyu2gzxlJ\nHwWWAC4hrttywF3A19opZC2ONZftx2u06/Y5mIwnqSQlyTBA0o+Jieb8xEP9SaDV6laD04iJ86Pl\nfZ2Vw8OIycs1hILwf+0a216yjO8XwHdtW9LCwDdryPom0LWSRPzoTA1UV2kHnYzY3r2M7zJgQ9tv\nlRXfP9aQcwHxDPwAoYTcBtRRkg6ji2tYWIpQCq4nJuvzAo8Rk6UvtOl3JLAF8FPg48CviMltO/YH\nPs3AfdGSXq9f5b74NbCr7dGSZiEm0Z3YHJjb9qs12ja4iJiUPVKnse3flPF90fZBZfPlkv7Uqo+k\nHYGdgA9L+mTZPIK4FzsqSYDaKKOtxrl/URrXB46U9H7gZOCsNsrzEcDqxOT7B8A1QJ3J8GWE0nMW\noSDsWaPPYXR/r2P7SOJ8lieeUScR17EdbxHfj4NsnyNppxqi5rb9S0k72l6j3efbxNHEAtPzNduP\nj6zpgN8Be9r+gqRP1OhzRBnfLsTnO1/NPr3cF6sSisARwI9t71Gjz8K2d5S0qu0LJX2tRh+I7/5q\nwJ9sHyPpb60a2l6g5jGrfT4NIOliYDPbY4pF6sIO/Y4u/dazvVXZfFKHz/g44jM6HrgY2Is4tzOA\nNdrJk3Q48dmOIu6PvxFKWSe6eg4m408qSUkyPFje9p6Srik/wFfW6PO47VO6lHMP8BSwDjERfrBm\nv4VtG8D2g5IWqNGnX9KFgIkJUL/tA2v0G2V79c7NxmEOYgX1LWKVbdYafWa3vaKkU4CvAGfWlNXL\nNZzFdmOCeZKkK8qE6c8d+r1aZI21/bikQV1TmnjQ9gM12lXp5foBzNtws7H9nKS2FqHCv4AxXY6v\nz/bWXfYBmFnSIrbvl7Q4sbrcijOBK4EDCeW0j7geT9aUdbOkxWzfV3dwRSFdl7B4zUcowLMTk671\nW3R7y/YzkrD9oqQXa4o7D/gk8A3g+7avqdGnp+dFWfT5GGF5PovOCz4QVpOjgOskrUlnpQpgKkmf\nBe6W9D5ghjrjA+6qef4TQtbUhEJ6a7kH31ujz+O2b5S0q+1fSLq0Rp9e74utARGWu80lXWe703Np\npKTZIVwwie9JHRrfqQYtnwOSbmyxq992JwvoXLYbx36TeL7VYTZJs5Rn2RzAzG3ajrV9p6QZbf+y\nbLtI0v415HyGWCg7pvx9veb4en0OJj2SSlKSDA9GFPP9Q5JGUe8H+GFJXwduL+/7bXeyoFxHuDpd\nBOwL3AR8tIas0WX162+ERaOOT/3PqWfdescYJa1PTNCA9v7khZ8Bd0q6l3AHOqJDe4BXyiR1etuv\nNn7064yP7q/hTJLeZ/vpImemssI5XYd+LxIWgJMk7UZMWDvxWrEM3UFc/zrKaS/XD+Afks4k3FpW\nBlpNbKqMKrLurIxvyxpyViTu9UaMQVt31MJewG8kzUmswLa0Tth+nfhOLdhjzMgLhKL0Snnf0Y0Q\nuB/4MxF/06gsj6Ql2vR5QNJ3iQndAdT7LgLcDJxCWC6/L+ky260UsQa9Pi9GERPgR8r4/lujz/bA\n2oT1YyPqKVbfI6wT+wB7AIfX6AMxmb2JsN5DPVflXmXtS5zPkYRCUseCN6a4z72nPAvnrdGn1/ti\nbWAV2y9I+hWxWNBJCTkY+AswJ/BX6p0ThKX+OmD+ovi1s/BsUXndHGfUiT9Iug64FVgB+HXN8R1O\nKLMvEG7Eu7Zp+7CkrwKXKtypf0csQnR0myOU4DFFwXpAEUtVh16fg0mPpJKUJMODMwi/9e2J1dST\navSZhlgBVGVbJyXpE7YbpvqjixtNHbYm3AM2JJSXQ2r0OZMI7p2fEtxbU9b7iVXNqivMSu062D5R\n0m+JgOD7awYRX0Ccx9/LhOmVDu0b9HINDwVuKqu7MxB++PvQ2R1mM8KKd0+ZONexHF5GsdxRCVxu\nR4/XD+Ke2Ji4B8+2fVGNPt+teewqaxAuhA36iXiGttj+C/CRLmU9W2JCGhZQbP+zRr9PELEj3SRh\naCgcC0h6r+1Xirzt2vT5ErAz4Zr2cnldhy/Ybiyo7F7OsRM9PS9s7wJQ3O2+R0xSRw3WVtLytm8h\n4kweJFzGXiDuxX91kHM+EfMEYSGry57Ec7YRP1bnO9KVLEnzlms3mviezwFcUUcWMTlfjFCsvkW9\nRYue7gvbn5O0qKSPE8kU1qjR51pg0WJRG+2aSSJs/6S4sC0Rb/2PNm0fhrcTenyXuH7nEXE/D3eQ\nc6iki4jn0mm2/15zfBcWV705gKfcPrHJrsDXCEvw7ITl98+0WYip8Ghx7325KLbvqzM+3vkchNbx\nWckEIJWkJBkG2P4pEXcCsfrdEklTlXiFL/UgaiZJZxMBqaczsJLaiTeA14HGj0adH8VGcO86xIre\n6dQI7gUWs/2hmuMCQNJKhII5FdCnCIRtGxxNrI4/artf0h8It4w6dH0Nbf9e0iXEj+FTZVJxWQ1Z\nXweQ3taD+4lJUzu2sL1OjWO/jZqSgEiqs7IO4b42FRFfNbOkbVoFEkv6tO2LiclflX7g2nZCXILW\niwvMMx0mL1WZ3SSxaPB+3vkd7JRAACLQfE5qxIJVWAc4iPgt/rWkt1xJGtGCjxMLDo1FhxXpkKSk\n8HSx+s1BJCG4u0N76PF5IWlfYtI4HfAH4Mttmq9FWCI3H2Rf20UfSY1V+z7CRfRftpvvr8F43PZ5\nNdqNj6x9gL2J52Dz87LT/XS07YYlpVYcGD3eFxpIpjAbsVjXLplCo8/VTe/7ba9VQ9bChMInYnFq\nP9udrIw/I2KsDiGsVqcS1qF2cuYjvlvTEMrcRq6R1VLSusRnVn0ODnpeZUHjsPKHpI/UVcaI3+4P\nEDGm2wGdLOkNmY3n4GxE8qSeMlsm9UklKUmGEEm/tf1/kp5g3B/Sdq46ZxCuCG7uQ+fV9WOBHYgf\nnrMJF4FOmYwo7Z8nJi1rEBaNbTr06TW49x9F6bmN+i4FJxArw5sS2czapXtdEpi7tN+vKCAjge8Q\nWd460fU17ObHt4kniWswAliWerXterGEnEYE2D9CTetToZtA4kac01xdHB+AEqNyKuF+OLMiIUOd\n5By1k1g0sL1Gk+w6sTEQk9SHJD3DgBthJ3e7fQgr6aXAtwmXuE5K0q4MWAkXJ1bV6yhJ1cnmzdSY\nbNL782IskUmv431h+6jy8jnb+9Q4drXvXI3XxWXpsJpdx0i6nAG3pY4uqd3Ksr13+b9GzTFVmVrS\nRxj3O9zpGdjrfVE7mUKTLIqsZemcxbHBycD3CbfN1QiX7E6LWdPavlKRdfIuSa91aA9hubyC7hMc\n/JCwMnZ8Xkhaj4HnWB9wlKT9AGo8m95LKLHTEM+05ajhaaHeMxgmPZJKUpIMIZVg/uWrEwpJLVco\nGyuM7iH7T+l3fwnu/W8Xwb2L2F61vL6wTVBtlV6De1cnfLurdHIpGO3IiLWe7cOK1aYVMxNK5vsZ\n8Ht/iwFLXkd6uIa1f3yb5JxYfV9ijTrRiyWklyQg0EUgse3Ty8vf2r4Tog4Poch04ggibuIxSfMQ\nrpJ1lKSuk1hI2oVQXhqp118CluzUz/YHu5FT+F+JTcD2m5I61hWy/bbFpShwdeMtepls9vq8uAY4\nX9K8RIzGjrZv69DnwypB8zVlNI/z35LqWqAvprd4ydqyKpanZuooz+Kd8Tptn4HjcV/UTqZQkVVN\nTnKv6mUihDj3RhKKiyTViWV6rcRljSyLZ3WSvrxo++CaY6ryb9t1sxYeRVy3vxPXsPp70unZ1GuW\nul4zGCY9kkpSkgwhVatGYxWKmlYNDdTSaEzmOtbSIKwMuwDvVRR8rJsCd1QjZkLSdNSzaPQU3OuS\nXrpL/ldidqYtCmbLQGfb1wPXS1q2xsRtMHq5ht38+L6NovBig7mpkQrYkR1xNmBh4CHbT9cQ1UsS\nEOgtkPgUSVsSE4zTqRer9qbtx8rx/1t3gk9vSSx2I6ylBxGTkeYYgEFRpMmv1sKq47L4Z0nnAPNI\nOolwO+uGqagRm1XoZbLZ6/PiWGAn23+XtDSxANEpGcCHiAQxoxnIhtmpflY1Zf9cwBM1x/dQzXY9\ny6panrrFdrvEHXXo5r7oJpkCAJKqrt5z0SFjnyIpEcBzknYm3GtXpJ6S8CXC+jQbkQSjXTKFBndJ\n2pxxn0t14gqfUtSMqj4Hf9ai7cqEVefPtk+VdLXt7WvIgN6z1PWawTDpkVSSkmRoGR+rRi+1NHYg\nJn+jCRP/jjXH+WPgDkl3EwU5D63R5wXbjeDeZ9yisnozzf7uxA9VJ9e0fcu4fkKkHK5Tr2f2Mimo\nxqvUcYHr5Rp28+Nb5WcMrHiPIc6zLZI+R9wb9wBLSjrMAylqW9FLEhDoLZB4K8J1a1pgn5rK40sl\nduI6wk2nU2HSBpfQvcXgsWKxmtH21UV5rEMjxqXhgtQxHbrtAyRtQLiW3ldittrS5Jo7FfCjmuP7\nElEfaHbgq9SbbPb6vOhrxGfYvkNSy4LJDWzXzfBV5TyinlofUYesjqsYDO6a1jYurldZGoiXrBat\n7lRM9n7GnZ+9QSgU+7Va2BmP++JEIvX9EsB91FNcqi6zrwGf69B+j9L+JUK5aCjMdX4TXiMKLV+h\nyPBZx9K4DO9cZKwTV/hwGeecnRo66rxtL+mr5dk+VY3jN+g1S12vGQyTHkklKUmGkMGsGpJG1gxM\n76WWxldsv+3eJOk71CiUafuscvyFCOvEMzVkHVEsGj8nJsV1s8d17e9u+y4i6xHUS1EMPbrA0ds1\nfJiaP75VilVoJmABwnWsozsW4Sq2rO2Xi5vj1UR1+3Zytqu+V716R28HEtehafX5L0Rg/0IlvqiT\nwrg1YZk8kkgeUCepBITCvDwloQc1FBfgBUmbAG8VK0rda1F1hbxU0hWd+kiaEZiRSO0+q9okvqjI\n6eoeqrCe7c9XZH+FsPi0o6fnBWHZ/TQDSu3rnTpIWopwHerGRe9rtj9eYzzj0KNrWk+y6CJessJV\nZUx/JiwuOzEQNzjoGLq9LyTNRdx7pxPxpX8nlLjLiRpX7fhm6fsWUej7hXaNq8+XYvH/MJFF8/aW\nnQY4l1ikg1gcORP4VAd5a1Tfq2ZcYXHVnpsunhe2j1YUCK5bNwt6z1K3C3EvdJvZMumRVJKSZHjw\nYUki0uR+T9L3bX+/Q5/atTQU6UZ3KnIa8T4jiEKHHSc9zRMYRdX5thMY258qP8RbA1dIusd2R9/1\nXvzdJR1I1HNpuGHV8fvvygVuPK/hL+rKaZK5Kd1nP/tfQ5my/VId1zT1WAG+S5fP6urz88TKfFuX\npPKdaPQ5ubLrfdRbUb6gjO0DZXy3EQp7O3YiXBUPICx3e9SQ0xzIPTf1Clh2HZsgaR0GrvdPgENs\nn9Wm/RZE8cq1JK1FTP5GEHFWgypJ4/u8IJTYowm34XupN5nrxUXvmRLX0khiU9dNtEpd17ReZXUT\nL9lAlWfTNZK+YftPklqmHu/2viC+318hrMeNkhNvEUpSJ84lEnisTNxPm5S/thTFfEsiccNXJf26\nxu/cdA0La7mOX6whp6e4Qkk/J67L9ISl+690UMjKuK4krHG16GZxqYn3EplEG5b0jYkMeclEIpWk\nJBke7EmsrJ9HuM39kfDDbkc3tTTOJB7iB5b2jWDdOsVJobcJDMQEZBQRZ1U3bXNX/u6FzYG5iwtE\nXRoucNV4lXYWjfG5hueW/33EiuH9wCo1+vWS/ewhST8gVhtXJWrPdKLXCvC1XT5tHwagKOBbXYVu\nly1tsPTJDeq4z8xue0VJpxATwjNr9HmVsEbOR2Rzq1vfawvGdY2sY+3qJTbhyCLrp4RV4VeExawV\nlxGWmdmI69lHfBfb3Rfj+7z4iu1Na7Zt0LWLHvAM4VZVda3qqLj06JrWkyy6iJes8EaZ6P+FeM6O\nkbQc7edsXd0Xti8ALpC0oe06iluVuW3/siyWraGofVSHLYkELG9KmoooPt3pd26sIjvojYSFq87v\nSE9xhURNtSUIF8SDGLBgTRAkHW97N70z8VG/7Tq/p38knkfVBaJUkiYiqSQlyfCgsdr/oiPb1cga\nfWrX0rD9OhGcv6DtXvyYu57ASLqKiHU5FVjLpVBmDbr1d4coOlknEL3Kw0XO++s0Hp9raPvtYriS\nZiZijerQdfYzIv7hS8DaxCp+HYWn1wrwvbh81l6FrrrNqPtkFACvFKVsetuvqmRb7ECjvte6hOWp\nbX0vta5bVicWqpfYhFcJZWWs7ccltY3rcGSLu0bSlxxFQDsyAZ4XvWSq69pFj5gwnm67rvIG9Oyy\n2JMsYqFjcbqLl9ySmKRvRLgRf4FQENop3l3dFxUOIGL3umEqSZ8F7lbEnNZ2NXMptmx7rKQ6cTg7\nEVbJHxPPszr1AXuNK3zG9luSprf9tKReXVtb0ajVNFhNsDo83+wanUxcUklKkuHBg4Rpfy9JhxKV\nzzvRSy2NXmroQG8TmD1dUj3XoeJadU7Trjr+5KOAOyXdyYBVqG2BvuL6sjYx8b6RsO7Uoddr2ODF\nIrMOvWQ/ey9RX+ml8n4LorZWO3qtAF/b5bNC16vQ6i0ZBYS73SFE4cqbqBcX1219r0bdsvuattep\nW7YG3ccmvEhYh05SBLLXnbRPyudF15nq6M1F72Ui1fiTRO22y9ymwKakQ2wfrnEz1UGN50W3sirs\n6IH6T23jJSXN6ygFMSthzWgwmwdSZ7ei1/uiX9KFDHzGdTJAfo+Y6O9DuKMeXlPWDZJ+S1i5VwFu\n6NTB9v2EstgNPcUVAreW7/tjks4l3O7aoi4Sc9h+srw8jHEzYb4h6RHg+A4LC5eX83nbum27Ti2s\npEdSSUqSYYDt7cvq1cuS/ma7TipbETENsxHZp+pMynqpoQNdTGAkHWd7d+BnkqrF9jq5FIyPa9VR\nbfq2Gud3gHmIIOKxxIrqFm07BV1fw+Je0fhRnIModNgR95D9jFAMHqZ+OmQIl7l56bICPPBl4j6s\n4/LZoJdV6K6TUQDYPk5Sn+1+Sb8H6tRMqtb3argFtpPRuGcOsV3Hna/at5fYhM8BC9m+p7hx1a1v\n1Vx7Z2I+L7a3fVXNcQFg++GiqL+dbbJGnxOAEyQtTlhefibpVODHLSabvyv/z2PcdOYTQ1aDbqxq\n+xBFp6tZLRt0uu693hc/H0RWW2yfL+ki4nn2J2KBr06/fSV9inAT/4XtP3Qjtwt2BD5IuIs2FLk6\n4zugPF9eAzYg3Js70UtijmmIhdHrCXfq5Qml9nTC9bkVqxILgqtXtqWSNBFJJSlJhgHlR+0ESbMA\np0u613anyvb7EXUa/kUEmXZceXWXNXQ0kBXoMSJ1c4N2P6ojiwLycKfxNI+tm/ZN3E5kP1ucWBGt\ns7K5iu1VFfUtfl4nIBi6u4blOsC41+IOOtSbaYrLglglnlv1MsHhmvU6BpEDkW54FdrE4lSsfhDZ\nAfuJCUmdyVYvq9BdJaNothTEcKGMr5MCeDCxwj0XYWGsVd+LUDRrKUkaSHNfXU2GemnoRdQtWpGI\nU/s2MVFti0vtHUlzEG5FHWM7un1eVDiMyNBWG0lnEPddNVNa28yW5Xm5OeGO9jwRdzaScOd8Rxa4\nhsswPWSq61ZWhdpWNdt7l/9rdDO2xhDp4b4g7tkvMfDsPLF9c5DUcH2bn/iMngS2bdO++TnzEvCB\nus+zumjc5CmNBak/0sEbofKcbmYl4rnWjl4Sc8xRWVy5XNIVtg+R1Enhmd722jWOn0wgUklKkuHB\nsYS15mdE9q3f0T6gHaJW0Qq2n5I0DxGgulK7Dj24Lf2TcX906lit/lr2N7sftUXjV6H+50Sdk7OJ\nVbbTaL8iB6HMTVNkd5NYoptr2MiE1dW1YNy4rFoUhbaPSNywMnBr4xht3Kq6llPo2epn+3zg/PK2\nZbauJrpNRtFufJ1YlJjMvoewcv2MetnPRkm6g3HdllopZJuV/0cTcSrXE1m16sQqnEgEpn+LsGh8\njxqTYUlrEvGBLwIzlwlq28QD4+HmSA8uXLJdtwBqg5uJ67e57bdX8CUt26FfL5nqepLlHuo/SdqW\niCWs1nDrdG16ui+I+/t5QplYg8giuU2HPsvb3lPSNUWR7pTZrZfnWUPh6Wva1e6zqiZPaabd59u4\nD3qhl8QcM0j6kO17JX0ImL5Yrzu5992lyFZ5GwPP9m5cvZMuSSUpSYYJtu9XBOj/V/Uqab/cCCIu\nfepkduvKbcn2AgCSNqSm1cr2aTXGMVi/nivUEz77jXTGtytSZ3fih4Qi8T5iAnRMTVm1r+F4XIvD\nACR9EPiY7bPLaudJbbpVFdqqotJSoa3IWb1p11hJH7A9aA2p8bT69cKJhPK7DuFWtEG7xravAVDE\n91UZK2le4DxHsoXB2JXINPlki/2t2I93TuhajW90Gd/8thuul9dIOqxG9zGE0jKVI2nGmzXHdwRh\nPX2sLKpcQOfsbD25OdKDCxdws6TFPG4JgE6sS3GHlDQf4TY7uoZC9izdZ6rrSVbDSwCYhVi8ua+G\nl8D+RKxaNzXcer0vFrG9anl9od6ZeW0wRkj6KLF4MYoOLrON50yXdK3wuCQ10DsTz4zVQIKVwfqd\nVvo1W8PGSlrF9p/bjLOXQua7A2cpSmQ8Qrgtf45wW27H0kQGvip13F+THkklKUmGB8+UgMz3lpWi\nlu5YkvYtL/8n6UzCJ3lFIrC4E13X0Cl0bbXqlR4nFdNImsuR1WlOIoC2LbZ/rQjmnwt4oro63IFe\nr2EvnEH8CEOkAT8V+MRgDRsKbY8cTlyHvxHuM2OJa3qy7e+16iTpv0TcytPA7IQv/5Mmwl6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ewkLUEIRNRK+5pIihntGqhCi/odovHyBsT3+z7Exkc/AZHvAOeMk8rbr8dXmzrVJmIUE6Jjgw/i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\nDrAq3xlZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49bc865f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of present genes per development stage -- anatomical entity pair\n",
    "pairwise_df = get_groupby_counts(present_df, ['Developmental stage name', 'Anatomical entity name'])\n",
    "rect_df = pairwise_df.pivot('Developmental stage name', 'Anatomical entity name', 'count').fillna(0)\n",
    "rect_df = rect_df.loc[stage_df['Developmental stage name'][:12], anatomy_df['Anatomical entity name'][:40]]\n",
    "IPython.core.pylabtools.figsize(12, 3.2)\n",
    "seaborn.heatmap(rect_df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and process differential expression data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene ID</th>\n",
       "      <th>Gene name</th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>Developmental stage ID</th>\n",
       "      <th>Developmental stage name</th>\n",
       "      <th>Differential expression</th>\n",
       "      <th>Call quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000015</td>\n",
       "      <td>male germ cell</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>low quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>CL:0000738</td>\n",
       "      <td>leukocyte</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>uterine cervix</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>UBERON:0000007</td>\n",
       "      <td>pituitary gland</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000000003</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>UBERON:0000029</td>\n",
       "      <td>lymph node</td>\n",
       "      <td>UBERON:0000113</td>\n",
       "      <td>post-juvenile adult stage</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>high quality</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gene ID Gene name Anatomical entity ID Anatomical entity name  \\\n",
       "0  ENSG00000000003    TSPAN6           CL:0000015         male germ cell   \n",
       "1  ENSG00000000003    TSPAN6           CL:0000738              leukocyte   \n",
       "2  ENSG00000000003    TSPAN6       UBERON:0000002         uterine cervix   \n",
       "3  ENSG00000000003    TSPAN6       UBERON:0000007        pituitary gland   \n",
       "4  ENSG00000000003    TSPAN6       UBERON:0000029             lymph node   \n",
       "\n",
       "  Developmental stage ID   Developmental stage name Differential expression  \\\n",
       "0         UBERON:0000113  post-juvenile adult stage         over-expression   \n",
       "1         UBERON:0000113  post-juvenile adult stage        under-expression   \n",
       "2         UBERON:0000113  post-juvenile adult stage         over-expression   \n",
       "3         UBERON:0000113  post-juvenile adult stage         over-expression   \n",
       "4         UBERON:0000113  post-juvenile adult stage        under-expression   \n",
       "\n",
       "   Call quality  \n",
       "0   low quality  \n",
       "1  high quality  \n",
       "2  high quality  \n",
       "3  high quality  \n",
       "4  high quality  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read simple differential expression by anatomy\n",
    "diffex_df = pandas.read_table('download/Homo_sapiens_diffexpr-anatomy-simple.tsv.gz', compression='gzip')\n",
    "diffex_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# filter differential expression\n",
    "diffex_df = diffex_df[\n",
    "    diffex_df['Differential expression'].isin({'over-expression', 'under-expression'}) &\n",
    "    diffex_df['Call quality'].isin({'low quality', 'high quality'})\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Anatomical entity ID</th>\n",
       "      <th>Anatomical entity name</th>\n",
       "      <th>Differential expression</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CL:0000738</td>\n",
       "      <td>leukocyte</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>12406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000473</td>\n",
       "      <td>testis</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>10267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UBERON:0002369</td>\n",
       "      <td>adrenal gland</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>9079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBERON:0000955</td>\n",
       "      <td>brain</td>\n",
       "      <td>over-expression</td>\n",
       "      <td>8485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UBERON:0002369</td>\n",
       "      <td>adrenal gland</td>\n",
       "      <td>under-expression</td>\n",
       "      <td>7989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Anatomical entity ID Anatomical entity name Differential expression  count\n",
       "0           CL:0000738              leukocyte        under-expression  12406\n",
       "1       UBERON:0000473                 testis         over-expression  10267\n",
       "2       UBERON:0002369          adrenal gland         over-expression   9079\n",
       "3       UBERON:0000955                  brain         over-expression   8485\n",
       "4       UBERON:0002369          adrenal gland        under-expression   7989"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate counts per anatomy--DE pair\n",
    "de_anatomy_df = get_groupby_counts(diffex_df, ['Anatomical entity ID', 'Anatomical entity name', 'Differential expression'])\n",
    "de_anatomy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 2: Number of differntially expressed genes present by anatomical entity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VdZyIrAdcjFWIvB0b6H0P2EpVVxWRbpgj3kyswNqLftwLKFB/2zUw12Niv47A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\n7UrTBoLeLfhaomfx5Q13JPaPv38WOA2P11RCqANfCLpgiK9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4Wmp8pY3myFEJAtv5VTO7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\nDDZStgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49bbcd7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot DE counts\n",
    "rect_df = de_anatomy_df.pivot('Anatomical entity name', 'Differential expression', 'count').fillna(0)\n",
    "rect_df['differential expression'] = rect_df['under-expression'] + rect_df['over-expression']\n",
    "rect_df = rect_df.sort('differential expression', ascending=False)\n",
    "IPython.core.pylabtools.figsize(1, 16)\n",
    "cmap = seaborn.cubehelix_palette(15, start=2.2, rot=1, gamma=1.6, hue=1, light=0.98, dark=0, as_cmap=True)\n",
    "seaborn.heatmap(rect_df.drop('differential expression', axis=1), cmap=cmap);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversion from ensembl to entrez gene\n",
    "\n",
    "Not yet implemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeneID</th>\n",
       "      <th>ensembl_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ENSG00000121410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>ENSG00000175899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>ENSG00000256069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>ENSG00000171428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>ENSG00000156006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GeneID       ensembl_id\n",
       "2        1  ENSG00000121410\n",
       "7        2  ENSG00000175899\n",
       "11       3  ENSG00000256069\n",
       "14       9  ENSG00000171428\n",
       "19      10  ENSG00000156006"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Entrez Genes\n",
    "url = 'https://raw.githubusercontent.com/dhimmel/entrez-gene/6e133f9ef8ce51a4c5387e58a6cc97564a66cec8/data/genes-human.tsv'\n",
    "entrez_df = pandas.read_table(url)\n",
    "coding_genes = set(entrez_df.GeneID[entrez_df.type_of_gene == 'protein-coding'])\n",
    "\n",
    "# Merge with entrez gene identifiers\n",
    "url = 'https://raw.githubusercontent.com/dhimmel/entrez-gene/6e133f9ef8ce51a4c5387e58a6cc97564a66cec8/data/xrefs-human.tsv'\n",
    "entrez_map_df = pandas.read_table(url)\n",
    "entrez_map_df = entrez_map_df[entrez_map_df.resource == 'Ensembl']\n",
    "entrez_map_df = entrez_map_df[['GeneID', 'identifier']].rename(columns={'identifier': 'ensembl_id'})\n",
    "entrez_map_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GNF Expression Atlas (BodyMap) expression counts\n",
    "\n",
    "Show the presence counts per tissue we observed in the [GNF Expression Atlas](https://dx.doi.org/10.1073/pnas.0400782101) data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import gzip\n",
    "import io\n",
    "import requests\n",
    "import numpy\n",
    "\n",
    "# Read BTO id and names\n",
    "url = 'https://gist.githubusercontent.com/dhimmel/1f252b674c0c75443cc1/raw/a97c3425792f2288b0369de70e1b46018832455b/bto-terms-in-gnf.tsv'\n",
    "bto_df = pandas.read_table(url)\n",
    "\n",
    "# Read expression\n",
    "url = 'http://het.io/disease-genes/downloads/files/expression.txt.gz'\n",
    "with gzip.open(io.BytesIO(requests.get(url).content)) as read_file:\n",
    "    gnf_df = pandas.read_table(read_file)\n",
    "gnf_df = numpy.log10(gnf_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bto_id</th>\n",
       "      <th>bto_name</th>\n",
       "      <th>cell_line</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>BTO:0003335</td>\n",
       "      <td>EBV-LCL cell</td>\n",
       "      <td>1</td>\n",
       "      <td>5138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BTO:0000725</td>\n",
       "      <td>hematopoietic stem cell</td>\n",
       "      <td>0</td>\n",
       "      <td>4996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BTO:0000914</td>\n",
       "      <td>natural killer cell</td>\n",
       "      <td>0</td>\n",
       "      <td>4883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>BTO:0004730</td>\n",
       "      <td>myeloid progenitor cell</td>\n",
       "      <td>0</td>\n",
       "      <td>4823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>BTO:0001067</td>\n",
       "      <td>pineal gland</td>\n",
       "      <td>0</td>\n",
       "      <td>4799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>BTO:0002042</td>\n",
       "      <td>dendritic cell</td>\n",
       "      <td>0</td>\n",
       "      <td>4782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>BTO:0001175</td>\n",
       "      <td>retina</td>\n",
       "      <td>0</td>\n",
       "      <td>4631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>BTO:0002807</td>\n",
       "      <td>prefrontal cortex</td>\n",
       "      <td>0</td>\n",
       "      <td>4566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>BTO:0001379</td>\n",
       "      <td>thyroid gland</td>\n",
       "      <td>0</td>\n",
       "      <td>4534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BTO:0000776</td>\n",
       "      <td>B-lymphocyte</td>\n",
       "      <td>0</td>\n",
       "      <td>4415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BTO:0000876</td>\n",
       "      <td>monocyte</td>\n",
       "      <td>0</td>\n",
       "      <td>4408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>BTO:0002417</td>\n",
       "      <td>helper T-lymphocyte</td>\n",
       "      <td>0</td>\n",
       "      <td>4347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BTO:0000289</td>\n",
       "      <td>cytotoxic T-lymphocyte</td>\n",
       "      <td>0</td>\n",
       "      <td>4298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>BTO:0001176</td>\n",
       "      <td>endothelial cell</td>\n",
       "      <td>0</td>\n",
       "      <td>4244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BTO:0000089</td>\n",
       "      <td>blood</td>\n",
       "      <td>0</td>\n",
       "      <td>4242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bto_id                 bto_name  cell_line  count\n",
       "72  BTO:0003335             EBV-LCL cell          1   5138\n",
       "19  BTO:0000725  hematopoietic stem cell          0   4996\n",
       "28  BTO:0000914      natural killer cell          0   4883\n",
       "75  BTO:0004730  myeloid progenitor cell          0   4823\n",
       "36  BTO:0001067             pineal gland          0   4799\n",
       "65  BTO:0002042           dendritic cell          0   4782\n",
       "43  BTO:0001175                   retina          0   4631\n",
       "70  BTO:0002807        prefrontal cortex          0   4566\n",
       "58  BTO:0001379            thyroid gland          0   4534\n",
       "24  BTO:0000776             B-lymphocyte          0   4415\n",
       "27  BTO:0000876                 monocyte          0   4408\n",
       "69  BTO:0002417      helper T-lymphocyte          0   4347\n",
       "11  BTO:0000289   cytotoxic T-lymphocyte          0   4298\n",
       "44  BTO:0001176         endothelial cell          0   4244\n",
       "5   BTO:0000089                    blood          0   4242"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute expressed genes per tissue\n",
    "gnf_summary_df = (gnf_df >= 1.4).sum().reset_index()\n",
    "gnf_summary_df.columns = ['bto_id', 'count']\n",
    "gnf_summary_df = bto_df.merge(gnf_summary_df, how='right')\n",
    "gnf_summary_df = gnf_summary_df.sort('count', ascending=False)\n",
    "gnf_summary_df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 3: Distibution of genes present per tissue in GNF Expression Atlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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twGriM9WlmLsp8s2hJHNnZjOBrxGvkAC4mHgvqxTzN0W+A+Q0f7oUTkQqSwuhRaSyVABF\npLJUAEWkslQARaSyVABFpLJUAEWkslQARaSyVABFpLL+D6piyqELf7VLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49e86dcf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of expressed genes per tissue\n",
    "IPython.core.pylabtools.figsize(5, 2)\n",
    "plt.hist(gnf_summary_df['count'], 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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   "file_extension": ".py",
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